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a/Day36-40/res/redis-replication.png and b/Day36-40/res/redis-replication.png differ diff --git a/Day36-40/res/redis-slow-logs.png b/Day36-40/res/redis-slow-logs.png index 6cc46ce..10816aa 100644 Binary files a/Day36-40/res/redis-slow-logs.png and b/Day36-40/res/redis-slow-logs.png differ diff --git a/Day41-55/49.RESTful架构和DRF入门.md b/Day41-55/49.RESTful架构和DRF入门.md index effe463..1c2d7a7 100644 --- a/Day41-55/49.RESTful架构和DRF入门.md +++ b/Day41-55/49.RESTful架构和DRF入门.md @@ -51,7 +51,7 @@ REST_FRAMEWORK = { # 配置默认页面大小 # 'PAGE_SIZE': 10, # 配置默认的分页类 - # 'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.PageNumberPagination', + # 'DEFAULT_PAGINATION_CLASS': '...', # 配置异常处理器 # 'EXCEPTION_HANDLER': '...', # 配置默认解析器 @@ -80,6 +80,9 @@ REST_FRAMEWORK = { 前后端分离的开发需要后端为前端、移动端提供API数据接口，而API接口通常情况下都是返回JSON格式的数据，这就需要对模型对象进行序列化处理。DRF中封装了`Serializer`类和`ModelSerializer`类用于实现序列化操作，通过继承`Serializer`类或`ModelSerializer`类，我们可以自定义序列化器，用于将对象处理成字典，代码如下所示。 ```Python +from rest_framework import serializers + + class SubjectSerializer(serializers.ModelSerializer): class Meta: @@ -94,6 +97,10 @@ class SubjectSerializer(serializers.ModelSerializer): DRF框架支持两种实现数据接口的方式，一种是FBV（基于函数的视图），另一种是CBV（基于类的视图）。我们先看看FBV的方式如何实现数据接口，代码如下所示。 ```Python +from rest_framework.decorators import api_view +from rest_framework.response import Response + + @api_view(('GET', )) def show_subjects(request: HttpRequest) -> HttpResponse: subjects = Subject.objects.all().order_by('no') @@ -343,4 +350,4 @@ except InvalidTokenError: raise AuthenticationFailed('无效的令牌或令牌已经过期') ``` -相信通过上面的讲解，大家已经可以自行完成对投票项目用户登录功能的修改，如果有什么疑惑，可以参考我的代码，点击[地址一](https://github.com/jackfrued/vote)或[地址二](https://gitee.com/jackfrued/vote)可以打开项目仓库的页面。 +如果不清楚JWT具体的使用方式，可以先看看第55天的内容，里面提供了完整的投票项目代码的地址。 \ No newline at end of file diff --git a/Day41-55/50.RESTful架构和DRF进阶.md b/Day41-55/50.RESTful架构和DRF进阶.md index 5ed148d..28083cc 100644 --- a/Day41-55/50.RESTful架构和DRF进阶.md +++ b/Day41-55/50.RESTful架构和DRF进阶.md @@ -1,18 +1,48 @@ ## RESTful架构和DRF进阶 -除了上一节讲到的方法，使用DRF创建REST风格的数据接口还有CBV（基于类的视图）的方式。使用CBV创建数据接口的特点是代码简单，开发效率高，但是没有FBV（基于函数的视图）灵活，因为使用FBV的方式，数据接口对应的视图函数执行什么样的代码以及返回什么的数据是高度可定制的。下面我们以定制学科的数据接口为例，讲解通过CBV方式定制数据接口的具体做法。 +除了上一节讲到的方法，使用DRF创建REST风格的数据接口也可以通过CBV（基于类的视图）的方式。使用CBV创建数据接口的特点是代码简单，开发效率高，但是没有FBV（基于函数的视图）灵活，因为使用FBV的方式，数据接口对应的视图函数执行什么样的代码以及返回什么的数据是高度可定制的。下面我们以定制学科的数据接口为例，讲解通过CBV方式定制数据接口的具体做法。 -### 使用ModelViewSet +### 使用CBV -修改之前项目中的`polls/views.py`，去掉`show_subjects`视图函数，添加一个名为`SubjectViewSet`的类。 +#### 继承APIView的子类 + +修改之前项目中的`polls/views.py`，去掉`show_subjects`视图函数，添加一个名为`SubjectView`的类，该类继承自`ListAPIView`，`ListAPIView`能接收GET请求，它封装了获取数据列表并返回JSON数据的`get`方法。`ListAPIView`是`APIView` 的子类，`APIView`还有很多的子类，例如`CreateAPIView`可以支持POST请求，`UpdateAPIView`可以支持PUT和PATCH请求，`DestoryAPIView`可以支持DELETE请求。`SubjectView` 的代码如下所示。 ```Python +from rest_framework.generics import ListAPIView + + +class SubjectView(ListAPIView): + # 通过queryset指定如何获取学科数据 + queryset = Subject.objects.all() + # 通过serializer_class指定如何序列化学科数据 + serializer_class = SubjectSerializer +``` + +刚才说过，由于`SubjectView`的父类`ListAPIView`已经实现了`get`方法来处理获取学科列表的GET请求，所以我们只需要声明如何获取学科数据以及如何序列化学科数据，前者用`queryset`属性指定，后者用`serializer_class`属性指定。要使用上面的`SubjectView`，需要修改`urls.py`文件，如下所示。 + +```Python +urlpatterns = [ + path('api/subjects/', SubjectView.as_view()), +] +``` + +很显然，上面的做法较之之前讲到的FBV要简单很多。 + +#### 继承ModelViewSet + +如果学科对应的数据接口需要支持GET、POST、PUT、PATCH、DELETE请求来支持对学科资源的获取、新增、更新、删除操作，更为简单的做法是继承`ModelViewSet`来编写学科视图类。再次修改`polls/views.py`文件，去掉`SubjectView`类，添加一个名为`SubjectViewSet`的类，代码如下所示。 + +```Python +from rest_framework.viewsets import ModelViewSet + + class SubjectViewSet(ModelViewSet): queryset = Subject.objects.all() serializer_class = SubjectSerializer ``` -通过查看`ModelViewSet`类的源代码可以发现，该类共有6个父类，其中前5个父类分别实现创建学科、获取指定学科、更新指定学科、删除指定学科和获取学科列表的数据接口，对应的方法分别是`create`、`retrieve`、`update`、`destroy`和`list`。由于ModelViewSet的父类中已经实现了这些方法，所以我们几乎没有编写任何代码就完成了学科数据接口的开发，我们要做的仅仅是指出如何获取到学科数据（通过`queryset`属性指定）以及如何序列化学科数据（通过`serializer_class`属性指定）。 +通过查看`ModelViewSet`类的源代码可以发现，该类共有6个父类，其中前5个父类分别实现对POST（新增学科）、GET（获取指定学科）、PUT/PATCH（更新学科）、DELETE（删除学科）和GET（获取学科列表）操作的支持，对应的方法分别是`create`、`retrieve`、`update`、`destroy`和`list`。由于`ModelViewSet`的父类中已经实现了这些方法，所以我们几乎没有编写任何代码就完成了学科数据全套接口的开发，我们要做的仅仅是指出如何获取到数据（通过`queryset`属性指定）以及如何序列化数据（通过`serializer_class`属性指定），这一点跟上面继承`APIView`的子类做法是一致的。 ```Python class ModelViewSet(mixins.CreateModelMixin, @@ -28,13 +58,72 @@ class ModelViewSet(mixins.CreateModelMixin, pass ``` -要使用上面的`SubjectViewSet`，需要在`urls.py`文件中进行URL映射。不同于之前的视图函数映射URL的方式，我们需要先创建一个路由器对象并通过该对象注册`SubjectViewSet`，然后将注册成功后生成的URL一并添加到`urlspattern`列表中，代码如下所示。 +要使用上面的`SubjectViewSet`，需要在`urls.py`文件中进行URL映射。由于`ModelViewSet`相当于是多个视图函数的汇总，所以不同于之前映射URL的方式，我们需要先创建一个路由器并通过它注册`SubjectViewSet`，然后将注册成功后生成的URL一并添加到`urlspattern`列表中，代码如下所示。 ```Python +from rest_framework.routers import DefaultRouter + router = DefaultRouter() router.register('api/subjects', SubjectViewSet) urlpatterns += router.urls ``` -### 使用APIView的子类 +除了`ModelViewSet`类外，DRF还提供了一个名为`ReadOnlyModelViewSet` 的类，从名字上就可以看出，该类是只读视图的集合，也就意味着，继承该类定制的数据接口只能支持GET请求，也就是获取单个资源和资源列表的请求。 +### 数据分页 + +在使用GET请求获取资源列表时，我们通常不会一次性的加载所有的数据，除非数据量真的很小。大多数获取资源列表的操作都支持数据分页展示，也就说我们可以通过指定页码（或类似于页码的标识）和页面大小（一次加载多少条数据）来获取不同的数据。我们可以通过对`QuerySet`对象的切片操作来实现分页，也可以利用Django框架封装的`Paginator`和`Page`对象来实现分页。使用DRF时，可以在Django配置文件中修改`REST_FRAMEWORK`并配置默认的分页类和页面大小来实现分页，如下所示。 + +```Python +REST_FRAMEWORK = { + 'PAGE_SIZE': 10, + 'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.PageNumberPagination' +} +``` + +除了上面配置的`PageNumberPagination`分页器之外，DRF还提供了`LimitOffsetPagination`和`CursorPagination`分页器，值得一提的是`CursorPagination`，它可以避免使用页码分页时暴露网站的数据体量，有兴趣的读者可以自行了解。如果不希望使用配置文件中默认的分页设定，可以在视图类中添加一个`pagination_class`属性来重新指定分页器，通常可以将该属性指定为自定义的分页器，如下所示。 + +```Python +from rest_framework.pagination import PageNumberPagination + + +class CustomizedPagination(PageNumberPagination): + # 默认页面大小 + page_size = 5 + # 页面大小对应的查询参数 + page_size_query_param = 'size' + # 页面大小的最大值 + max_page_size = 50 +``` + +```Python +class SubjectView(ListAPIView): + # 指定如何获取数据 + queryset = Subject.objects.all() + # 指定如何序列化数据 + serializer_class = SubjectSerializer + # 指定如何分页 + pagination_class = CustomizedPagination +``` + +如果不希望数据分页，可以将`pagination_class`属性设置为`None`来取消默认的分页器。 + +### 数据筛选 + +如果希望使用CBV定制获取老师信息的数据接口，也可以通过继承`ListAPIView`来实现。但是因为要通过指定的学科来获取对应的老师信息，因此需要对老师数据进行筛选而不是直接获取所有老师的数据。如果想从请求中获取学科编号并通过学科编号对老师进行筛选，可以通过重写`get_queryset`方法来做到，代码如下所示。 + +```Python +class TeacherView(ListAPIView): + serializer_class = TeacherSerializer + + def get_queryset(self): + queryset = Teacher.objects.defer('subject') + try: + sno = self.request.GET.get('sno', '') + queryset = queryset.filter(subject__no=sno) + return queryset + except ValueError: + raise Http404('No teachers found.') +``` + +除了上述方式之外，还可以使用三方库`django-filter`来配合DRF实现对数据的筛选，使用`django-filter`后，可以通过为视图类配置`filter-backends`属性并指定使用`DjangoFilterBackend`来支持数据筛选。在完成上述配置后，可以使用`filter_fields` 属性或`filterset_class`属性来指定如何筛选数据，有兴趣的读者可以自行研究。 \ No newline at end of file diff --git a/Day41-55/51.使用缓存.md b/Day41-55/51.使用缓存.md index ee55bf6..7e39e08 100644 --- a/Day41-55/51.使用缓存.md +++ b/Day41-55/51.使用缓存.md @@ -1,4 +1,147 @@ ## 使用缓存 +通常情况下，Web应用的性能瓶颈都会出现在关系型数据库上，当并发访问量较大时，如果所有的请求都需要通过关系型数据库完成数据持久化操作，那么数据库一定会不堪重负。优化Web应用性能最为重要的一点就是使用缓存，把那些数据体量不大但访问频率非常高的数据提前加载到缓存服务器中，这又是典型的空间换时间的方法。通常缓存服务器都是直接将数据置于内存中而且使用了非常高效的数据存取策略（哈希存储、键值对方式等），在读写性能上远远优于关系型数据库的，因此我们可以让Web应用接入缓存服务器来优化其性能，其中一个非常好的选择就是使用Redis。 + +Web应用的缓存架构大致如下图所示。 + +![](res/redis-cache-service.png) + +### Django项目接入Redis + +在此前的课程中，我们介绍过Redis的安装和使用，此处不再进行赘述。如果需要在Django项目中接入Redis，可以使用三方库`django-redis`，这个三方库又依赖了一个名为`redis` 的三方库，它封装了对Redis的各种操作。 + +安装`django-redis`。 + +```Bash +pip install django-redis +``` + +修改Django配置文件中关于缓存的配置。 + +```Python +CACHES = { + 'default': { + # 指定通过django-redis接入Redis服务 + 'BACKEND': 'django_redis.cache.RedisCache', + # Redis服务器的URL + 'LOCATION': ['redis://1.2.3.4:6379/0', ], + # Redis中键的前缀（解决命名冲突） + 'KEY_PREFIX': 'vote', + # 其他的配置选项 + 'OPTIONS': { + 'CLIENT_CLASS': 'django_redis.client.DefaultClient', + # 连接池（预置若干备用的Redis连接）参数 + 'CONNECTION_POOL_KWARGS': { + # 最大连接数 + 'max_connections': 512, + }, + # 连接Redis的用户口令 + 'PASSWORD': 'foobared', + } + }, +} +``` + +至此，我们的Django项目已经可以接入Redis，接下来我们修改项目代码，用Redis为之写的获取学科数据的接口提供缓存服务。 + +### 为视图提供缓存服务 + +#### 声明式缓存 + +所谓声明式缓存是指不修改原来的代码，通过Python中的装饰器（代理）为原有的代码增加缓存功能。对于FBV，代码如下所示。 + +```Python +from django.views.decorators.cache import cache_page +@api_view(('GET', )) +@cache_page(timeout=86400, cache='default') +def show_subjects(request): + """获取学科数据""" + queryset = Subject.objects.all() + data = SubjectSerializer(queryset, many=True).data + return Response({'code': 20000, 'subjects': data}) +``` + +上面的代码通过Django封装的`cache_page`装饰器缓存了视图函数的返回值（响应对象），`cache_page`的本意是缓存视图函数渲染的页面，对于返回JSON数据的视图函数，相当于是缓存了JSON数据。在使用`cache_page`装饰器时，可以传入`timeout`参数来指定缓存过期时间，还可以使用`cache`参数来指定需要使用哪一组缓存服务来缓存数据。Django项目允许在配置文件中配置多组缓存服务，上面的`cache='default'`指定了使用默认的缓存服务（因为之前的配置文件中我们也只配置了名为`default`的缓存服务）。视图函数的返回值会被序列化成字节串放到Redis中（Redis中的str类型可以接收字节串），缓存数据的序列化和反序列化也不需要我们自己处理，因为`cache_page`装饰器会调用`django-redis`库中的`RedisCache`来对接Redis，该类使用了`DefaultClient`来连接Redis并使用了[pickle序列化](https://python3-cookbook.readthedocs.io/zh_CN/latest/c05/p21_serializing_python_objects.html)，`django_redis.serializers.pickle.PickleSerializer`是默认的序列化类。 + +如果缓存中没有学科的数据，那么通过接口访问学科数据时，我们的视图函数会通过执行`Subject.objects.all()`向数据库发出SQL语句来获得数据，视图函数的返回值会被缓存，因此下次请求该视图函数如果缓存没有过期，可以直接从缓存中获取视图函数的返回值，无需再次查询数据库。如果想了解缓存的使用情况，可以配置数据库日志或者使用Django-Debug-Toolbar来查看，第一次访问学科数据接口时会看到查询学科数据的SQL语句，再次获取学科数据时，不会再向数据库发出SQL语句，因为可以直接从缓存中获取数据。 + +对于CBV，可以利用Django中名为`method_decorator`的装饰器将`cache_page`这个装饰函数的装饰器放到类中的方法上，效果跟上面的代码是一样的。需要提醒大家注意的是，`cache_page`装饰器不能直接放在类上，因为它是装饰函数的装饰器，所以Django框架才提供了`method_decorator`来解决这个问题，很显然，`method_decorator`是一个装饰类的装饰器。 + +```Python +from django.utils.decorators import method_decorator +from django.views.decorators.cache import cache_page + + +@method_decorator(decorator=cache_page(timeout=86400, cache='default'), name='get') +class SubjectView(ListAPIView): + """获取学科数据的视图类""" + queryset = Subject.objects.all() + serializer_class = SubjectSerializer +``` + +#### 编程式缓存 + +所谓编程式缓存是指通过自己编写的代码来使用缓存服务，这种方式虽然代码量会稍微大一些，但是相较于声明式缓存，它对缓存的操作和使用更加灵活，在实际开发中使用得更多。下面的代码去掉了之前使用的`cache_page`装饰器，通过`django-redis`提供的`get_redis_connection`函数直接获取Redis连接来操作Redis。 + +```Python +def show_subjects(request): + """获取学科数据""" + redis_cli = get_redis_connection() + # 先尝试从缓存中获取学科数据 + data = redis_cli.get('vote:polls:subjects') + if data: + # 如果获取到学科数据就进行反序列化操作 + data = json.loads(data) + else: + # 如果缓存中没有获取到学科数据就查询数据库 + queryset = Subject.objects.all() + data = SubjectSerializer(queryset, many=True).data + # 将查到的学科数据序列化后放到缓存中 + redis_cli.set('vote:polls:subjects', json.dumps(data), ex=86400) + return Response({'code': 20000, 'subjects': data}) +``` + +需要说明的是，Django框架提供了`cache`和`caches`两个现成的变量来支持缓存操作，前者访问的是默认的缓存（名为`default`的缓存），后者可以通过索引运算获取指定的缓存服务（例如：`caches['default']`）。向`cache`对象发送`get`和`set`消息就可以实现对缓存的读和写操作，但是这种方式能做的操作有限，不如上面代码中使用的方式灵活。还有一个值得注意的地方，由于可以通过`get_redis_connection`函数获得的Redis连接对象向Redis发起各种操作，包括`FLUSHDB`、`SHUTDOWN`等危险的操作，所以在实际商业项目开发中，一般都会对`django-redis`再做一次封装，例如封装一个工具类，其中只提供了项目需要用到的缓存操作的方法，从而避免了直接使用`get_redis_connection`的潜在风险。当然，自己封装对缓存的操作还可以使用“Read Through”和“Write Through”的方式实现对缓存的更新，这个在下面会介绍到。 + +### 缓存相关问题 + +#### 缓存数据的更新 + +在使用缓存时，一个必须搞清楚的问题就是，当数据改变时，如何更新缓存中的数据。通常更新缓存有如下几种套路，分别是： + +1. Cache Aside Pattern +2. Read/Write Through Pattern +3. Write Behind Caching Pattern + +第1种方式的具体做法就是，当数据更新时，先更新数据库，再删除缓存。注意，不能够使用先更新数据库再更新缓存的方式，也不能够使用先删除缓存再更新数据库的方式，大家可以自己想一想为什么（考虑一下有并发的读操作和写操作的场景）。当然，先更新数据库再删除缓存的做法在理论上也存在风险，但是发生问题的概率是极低的，所以不少的项目都使用了这种方式。 + +第1种方式相当于编写业务代码的开发者要自己负责对两套存储系统（缓存和关系型数据库）的操作，代码写起来非常的繁琐。第2种方式的主旨是将后端的存储系统变成一套代码，对缓存的维护封装在这套代码中。其中，Read Through指在查询操作中更新缓存，也就是说，当缓存失效的时候，由缓存服务自己负责对数据的加载，从而对应用方是透明的；而Write Through是指在更新数据时，如果没有命中缓存，直接更新数据库，然后返回。如果命中了缓存，则更新缓存，然后再由缓存服务自己更新数据库（同步更新）。刚才我们说过，如果自己对项目中的Redis操作再做一次封装，就可以实现“Read Through”和“Write Through”模式，这样做虽然会增加工作量，但无疑是一件“一劳永逸”且“功在千秋”的事情。 + +第3种方式是在更新数据的时候，只更新缓存，不更新数据库，而缓存服务这边会**异步的批量更新**数据库。这种做法会大幅度提升性能，但代价是牺牲数据的**强一致性**。第3种方式的实现逻辑比较复杂，因为他需要追踪有哪数据是被更新了的，然后再批量的刷新到持久层上。 + +#### 缓存穿透 + +缓存是为了缓解数据库压力而添加的一个中间层，如果恶意的访问者频繁的访问缓存中没有的数据，那么缓存就失去了存在的意义，瞬间所有请求的压力都落在了数据库上，这样会导致数据库承载着巨大的压力甚至连接异常，类似于分布式拒绝服务攻击（DDoS）的做法。解决缓存穿透的一个办法是约定如果查询返回为空值，把这个空值也缓存起来，但是需要为这个空值的缓存设置一个较短的超时时间，毕竟缓存这样的值就是对缓存空间的浪费。另一个解决缓存穿透的办法是使用布隆过滤器，具体的做法大家可以自行了解。 + +#### 缓存击穿 + +在实际的项目中，可能存在某个缓存的key某个时间点过期，但恰好在这个时间点对有对该key的大量的并发请求过来，这些请求没有从缓存中找到key对应的数据，就会直接从数据库中获取数据并写回到缓存，这个时候大并发的请求可能会瞬间把数据库压垮，这种现象称为缓存击穿。比较常见的解决缓存击穿的办法是使用互斥锁，简单的说就是在缓存失效的时候，不是立即去数据库加载数据，而是先设置互斥锁（例如：Redis中的setnx），只有设置互斥锁的操作成功的请求，才能执行查询从数据库中加载数据并写入缓存，其他设置互斥锁失败的请求，可以先执行一个短暂的休眠，然后尝试重新从缓存中获取数据，如果缓存还没有数据，则重复刚才的设置互斥锁的操作，大致的参考代码如下所示。 + +```Python +data = redis_cli.get(key) +while not data: + if redis_cli.setnx('mutex', 'x'): + redis.expire('mutex', timeout) + data = db.query(...) + redis.set(key, data) + redis.delete('mutex') + else: + time.sleep(0.1) + data = redis_cli.get(key) +``` + +#### 缓存雪崩 + +缓存雪崩是指在将数据放入缓存时采用了相同的过期时间，这样就导致缓存在某一时刻同时失效，请求全部转发到数据库，导致数据库瞬时压力过大而崩溃。解决缓存雪崩问题的方法也比较简单，可以在既定的缓存过期时间上加一个随机时间，这样可以从一定程度上避免不同的key在同一时间集体失效。还有一种办法就是使用多级缓存，每一级缓存的过期时间都不一样，这样的话即便某个级别的缓存集体失效，但是其他级别的缓存还能够提供数据，避免所有的请求都落到数据库上。 \ No newline at end of file diff --git a/Day41-55/52.接入三方平台.md b/Day41-55/52.接入三方平台.md new file mode 100644 index 0000000..b6094cf --- /dev/null +++ b/Day41-55/52.接入三方平台.md @@ -0,0 +1,201 @@ +## 接入三方平台 + +在Web应用的开发过程中，有一些任务并不是我们自己能够完成的。例如，我们的Web项目中需要做个人或企业的实名认证，很显然我们并没有能力判断用户提供的认证信息的真实性，这个时候我们就要借助三方平台提供的服务来完成该项操作。再比如说，我们的项目中需要提供在线支付功能，这类业务通常也是借助支付网关来完成而不是自己去实现，我们只需要接入像微信、支付宝、银联这样的三方平台即可。 + +在项目中接入三方平台基本上就两种方式：API接入和SDK接入。 + +1. API接入指的是通过访问三方提供的URL来完成操作或获取数据。国内有很多这样的平台提供了大量常用的服务，例如[聚合数据](https://www.juhe.cn/)上提供了生活服务类、金融科技类、交通地理类、充值缴费类等各种类型的API。我们可以通过Python程序发起网络请求，通过访问URL获取数据，这些API接口跟我们项目中提供的数据接口是一样的，只不过我们项目中的API是供自己使用的，而这类三方平台提供的API是开放的。当然开放并不代表免费，大多数能够提供有商业价值的数据的API都是需要付费才能使用的。 +2. SDK接入指的是通过安装三方库并使用三方库封装的类、函数来使用三方平台提供的服务的方式。例如我们刚才说到的接入支付宝，就需要先安装支付宝的SDK，然后通过支付宝封装的类和方法完成对支付服务的调用。 + +下面我们通过具体的例子来讲解如何接入三方平台。 + +### 接入短信网关 + +一个Web项目有很多地方都可以用到短信服务，例如：手机验证码登录、重要消息提醒、产品营销短信等。要实现发送短信的功能，可以通过接入短信网关来实现，国内比较有名的短信网关包括：云片短信、网易云信、螺丝帽、SendCloud等，这些短信网关一般都提供了免费试用功能。下面我们以[螺丝帽](https://luosimao.com/)平台为例，讲解如何在项目中接入短信网关，其他平台操作基本类似。 + +1. 注册账号，新用户可以免费试用。 + +2. 登录到管理后台，进入短信版块。 + +3. 点击“触发发送”可以找到自己专属的API Key（身份标识）。 + + ![](res/luosimao-sms-apikey.png) + +4. 点击“签名管理”可以添加短信签名，短信都必须携带签名，免费试用的短信要在短信中添加“【铁壳测试】”这个签名，否则短信无法发送。 + + ![](res/luosimao-sms-signature.png) + +5. 点击“IP白名单”将运行Django项目的服务器地址（公网IP地址，本地运行可以打开[xxx]()网站查看自己本机的公网IP地址）填写到白名单中，否则短信无法发送。 + + ![](res/luosimao-sms-whitelist.png) + +6. 如果没有剩余的短信条数，可以到“充值”页面选择“短信服务”进行充值。 + + ![](res/luosimao-pay-onlinebuy.png) + +接下来，我们可以通过调用螺丝帽短信网关实现发送短信验证码的功能，代码如下所示。 + +```Python +def send_mobile_code(tel, code): + """发送短信验证码""" + resp = requests.post( + url='http://sms-api.luosimao.com/v1/send.json', + auth=('api', 'key-自己的APIKey'), + data={ + 'mobile': tel, + 'message': f'您的短信验证码是{code}，打死也不能告诉别人哟。【Python小课】' + }, + verify=False + ) + return resp.json() +``` + +运行上面的代码需要先安装`requests`三方库，这个三方库封装了HTTP网络请求的相关功能，使用起来非常的简单，我们在之前的内容中也讲到过这个三方库。`send_mobile_code`函数有两个参数，第一个参数是手机号，第二个参数是短信验证码的内容，第5行代码需要提供自己的API Key，就是上面第2步中查看到的自己的API Key。请求螺丝帽的短信网关会返回JSON格式的数据，对于上面的代码如果返回`{'err': 0, 'msg': 'ok'}`，则表示短信发送成功，如果`err`字段的值不为`0`而是其他值，则表示短信发送失败，可以在螺丝帽官方的[开发文档](https://luosimao.com/docs/api/)页面上查看到不同的数值代表的含义，例如：`-20`表示余额不足，`-32`表示缺少短信签名。 + +可以在视图函数中调用上面的函数来完成发送短信验证码的功能，稍后我们可以把这个功能跟用户注册结合起来。 + +生成随机验证码和验证手机号的函数。 + +```Python +import random +import re + +TEL_PATTERN = re.compile(r'1[3-9]\d{9}') + + +def check_tel(tel): + """检查手机号""" + return TEL_PATTERN.fullmatch(tel) is not None + + +def random_code(length=6): + """生成随机短信验证码""" + return ''.join(random.choices('0123456789', k=length)) +``` + +发送短信验证码的视图函数。 + +```Python +@api_view(('GET', )) +def get_mobilecode(request, tel): + """获取短信验证码""" + if check_tel(tel): + redis_cli = get_redis_connection() + if redis_cli.exists(f'vote:block-mobile:{tel}'): + data = {'code': 30001, 'message': '请不要在60秒内重复发送短信验证码'} + else: + code = random_code() + send_mobile_code(tel, code) + # 通过Redis阻止60秒内容重复发送短信验证码 + redis_cli.set(f'vote:block-mobile:{tel}', 'x', ex=60) + # 将验证码在Redis中保留10分钟（有效期10分钟） + redis_cli.set(f'vote2:valid-mobile:{tel}', code, ex=600) + data = {'code': 30000, 'message': '短信验证码已发送，请注意查收'} + else: + data = {'code': 30002, 'message': '请输入有效的手机号'} + return Response(data) +``` + +> **说明**：上面的代码利用Redis实现了两个额外的功能，一个是阻止用户60秒内重复发送短信验证码，一个是将用户的短信验证码保留10分钟，也就是说这个短信验证码的有效期只有10分钟，我们可以要求用户在注册时提供该验证码来验证用户手机号的真实性。 + +### 接入云存储服务 + +当我们提到**云存储**这个词的时候，通常是指把数据存放在由第三方提供的虚拟服务器环境下，简单的说就是将某些数据或资源通过第三平台托管。一般情况下，提供云存储服务的公司都运营着大型的数据中心，需要云存储服务的个人或组织通过向其购买或租赁存储空间来满足数据存储的需求。在开发Web应用时，可以将静态资源，尤其是用户上传的静态资源直接置于云存储服务中，云存储通常会提供对应的URL使得用户可以访问该静态资源。国内外比较有名的云存储服务（如：亚马逊的S3、阿里的OSS2等）一般都物美价廉，相比自己架设静态资源服务器，云存储的代价更小，而且一般的云存储平台都提供了CDN服务，用于加速对静态资源的访问，所以不管从哪个角度出发，使用云存储的方式管理Web应用的数据和静态资源都是非常好的选择，除非这些资源涉及到个人或商业隐私，否则就可以托管到云存储中。 + +下面我们以接入[七牛云](https://www.qiniu.com/)为例，讲解如何实现将用户上传的文件保存到七牛云存储。七牛云是国内知名的云计算及数据服务提供商，七牛云在海量文件存储、CDN、视频点播、互动直播以及大规模异构数据的智能分析与处理等领域都有自己的产品，而且非付费用户也可以免费接入，使用其提供的服务。下面是接入七牛云的流程： + +1. 注册账号，登录管理控制台。 + + ![](res/qiniu-manage-console.png) + +2. 选择左侧菜单中的对象存储。 + + ![](res/qiniu-storage-service.png) + +3. 在空间管理中选择新建空间（例如：myvote），如果提示空间名称已被占用，更换一个再尝试即可。注意，创建空间后会提示绑定自定义域名，如果暂时还没有自己的域名，可以使用七牛云提供的临时域名，但是临时域名会在30天后被回收，所以最好准备自己的域名（域名需要备案，不清楚如何操作的请自行查阅相关资料）。 + + ![](res/qiniu-storage-create.png) + +4. 在网页的右上角点击个人头像中的“密钥管理”，查看自己的密钥，稍后在代码中需要使用AK（AccessKey）和SK（SecretKey）两个密钥来认证用户身份。 + + ![](res/qiniu-secretkey-management.png) + +5. 点击网页上方菜单中的“文档”，进入到[七牛开发者中心](https://developer.qiniu.com/)，选择导航菜单中的“SDK&工具”并点击“官方SDK”子菜单，找到Python（服务端）并点击“文档”查看官方文档。 + + ![](res/qiniu-document-python.png) + +接下来，只要安装官方文档提供的示例，就可以接入七牛云，使用七牛云提供的云存储以及其他服务。首先可以通过下面的命令安装七牛云的三方库。 + +```Bash +pip install qiniu +``` + +接下来可以通过`qiniu`模块中的`put_file`和`put_stream`两个函数实现文件上传，前者可以上传指定路径的文件，后者可以将内存中的二进制数据上传至七牛云，具体的代码如下所示。 + +```Python +import qiniu + +AUTH = qiniu.Auth('密钥管理中的AccessKey', '密钥管理中的SecretKey') +BUCKET_NAME = 'myvote' + + +def upload_file_to_qiniu(key, file_path): + """上传指定路径的文件到七牛云""" + token = AUTH.upload_token(BUCKET_NAME, key) + return qiniu.put_file(token, key, file_path) + + +def upload_stream_to_qiniu(key, stream, size): + """上传二进制数据流到七牛云""" + token = AUTH.upload_token(BUCKET_NAME, key) + return qiniu.put_stream(token, key, stream, None, size) +``` + +下面是一个文件上传的简单前端页。 + +```HTML + + + + + 上传文件 + + +
+
+ + +
+
+ + +``` + +> **说明**：前端如果使用表单实现文件上传，表单的method属性必须设置为post，enctype属性需要设置为multipart/form-data，表单中type属性为file的input标签，就是上传文件的文件选择器。 + +实现上传功能的视图函数如下所示。 + +```Python +from django.views.decorators.csrf import csrf_exempt + + +@csrf_exempt +def upload(request): + # 如果上传的文件小于2.5M，则photo对象的类型为InMemoryUploadedFile，文件在内存中 + # 如果上传的文件超过2.5M，则photo对象的类型为TemporaryUploadedFile，文件在临时路径下 + photo = request.FILES.get('photo') + _, ext = os.path.splitext(photo.name) + # 通过UUID和原来文件的扩展名生成独一无二的新的文件名 + filename = f'{uuid.uuid1().hex}{ext}' + # 对于内存中的文件，可以使用上面封装好的函数upload_stream_to_qiniu上传文件到七牛云 + # 如果文件保存在临时路径下，可以使用upload_file_to_qiniu实现文件上传 + upload_stream_to_qiniu(filename, photo.file, photo.size) + return redirect('/static/html/upload.html') +``` + +> **注意**：上面的视图函数使用了`csrf_exempt`装饰器，该装饰器能够让表单免除必须提供CSRF令牌的要求。此外，代码第11行使用了`uuid`模块的`uuid1`函数来生成全局唯一标识符。 + +运行项目尝试文件上传的功能，文件上传成功后，可以在七牛云“空间管理”中点击自己空间并进入“文件管理”界面，在这里可以看到我们刚才上传成功的文件，而且可以通过七牛云提供的域名获取该文件。 + +![](res/qiniu-file-management.png) + diff --git a/Day41-55/52.文件上传.md b/Day41-55/52.文件上传.md deleted file mode 100644 index 719c9ff..0000000 --- a/Day41-55/52.文件上传.md +++ /dev/null @@ -1,4 +0,0 @@ -## 文件上传 - - - diff --git a/Day41-55/53.异步任务和定时任务.md b/Day41-55/53.异步任务和定时任务.md index 6c69e70..61e320b 100644 --- a/Day41-55/53.异步任务和定时任务.md +++ b/Day41-55/53.异步任务和定时任务.md @@ -1,6 +1,14 @@ ## 异步任务和定时任务 +在Web应用中，如果一个请求执行了耗时间的操作或者该请求的执行时间无法确定，而且对于用户来说只需要知道服务器接收了他的请求，并不需要马上得到请求的执行结果，这样的操作我们就应该对其进行异步化处理。如果说**使用缓存是优化网站性能的第一要义**，那么将耗时间或执行时间不确定的任务**异步化则是网站性能优化的第二要义**，简单的说就是**能推迟做的事情都不要马上做**。 + +上一章节中讲到的发短信和上传文件到云存储为例，这两个操作前者属于时间不确定的操作（因为作为调用者，我们不能确定三方平台响应的时间），后者属于耗时间的操作（如果文件较大或者三方平台不稳定，都可能导致上传的时间较长），很显然，这两个操作都可以做异步化处理。 + +在Python项目中实现异步化处理可以使用多线程或借助三方库Celery来完成。 + +### 使用Celery实现异步化 - + +### 使用多线程实现异步化 diff --git a/Day41-55/54.单元测试.md b/Day41-55/54.单元测试.md index 7793bc1..d86a111 100644 --- a/Day41-55/54.单元测试.md +++ b/Day41-55/54.单元测试.md @@ -1,3 +1,4 @@ -## 单元测试和项目上线 +## 单元测试 +Python标准库中提供了名为`unittest` 的模块来支持我们对代码进行单元测试。所谓单元测试是指针对程序中最小的功能单元（在Python中指函数或类中的方法）进行的测试。 diff --git a/Day41-55/55.项目上线.md b/Day41-55/55.项目上线.md index 8f7d06e..eac5b54 100644 --- a/Day41-55/55.项目上线.md +++ b/Day41-55/55.项目上线.md @@ -1,12 +1,4 @@ ## 项目上线 -### 上线前的检查工作 - -### 同步代码到云服务器 - -### WSGI服务器的应用 - -### Nginx的相关配置 - - +请各位读者移步到[《项目部署上线和性能调优》](../Day91-100/98.项目部署上线和性能调优.md)一文。 diff --git a/Day41-55/res/asynchronous-web-request.png b/Day41-55/res/asynchronous-web-request.png index 3f8c2f6..41bf070 100644 Binary files a/Day41-55/res/asynchronous-web-request.png and b/Day41-55/res/asynchronous-web-request.png differ diff --git a/Day41-55/res/csrf-simple.png b/Day41-55/res/csrf-simple.png index 067ccd4..9ffae31 100644 Binary files a/Day41-55/res/csrf-simple.png and b/Day41-55/res/csrf-simple.png differ diff --git a/Day41-55/res/debug-toolbar.png b/Day41-55/res/debug-toolbar.png index 5d997e4..ffc1b15 100644 Binary files a/Day41-55/res/debug-toolbar.png and b/Day41-55/res/debug-toolbar.png differ diff --git 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多线程：Python中通过`threading`模块的`Thread`类并辅以`Lock`、`Condition`、`Event`、`Semaphore`和`Barrier`等类来支持多线程编程。Python解释器通过GIL（全局解释器锁）来防止多个线程同时执行本地字节码，这个锁对于CPython（Python解释器的官方实现）是必须的，因为CPython的内存管理并不是线程安全的。因为GIL的存在，Python的多线程并不能利用CPU的多核特性。 - -2. 多进程：使用多进程可以有效的解决GIL的问题，Python中的`multiprocessing`模块提供了`Process`类来实现多进程，其他的辅助类跟`threading`模块中的类类似，由于进程间的内存是相互隔离的（操作系统对进程的保护），进程间通信（共享数据）必须使用管道、套接字等方式，这一点从编程的角度来讲是比较麻烦的，为此，Python的`multiprocessing`模块提供了一个名为`Queue`的类，它基于管道和锁机制提供了多个进程共享的队列。 - - ```Python - """ - 用下面的命令运行程序并查看执行时间，例如： - time python3 example06.py - real 0m20.657s - user 1m17.749s - sys 0m0.158s - 使用多进程后实际执行时间为20.657秒，而用户时间1分17.749秒约为实际执行时间的4倍 - 这就证明我们的程序通过多进程使用了CPU的多核特性，而且这台计算机配置了4核的CPU - """ - import concurrent.futures - import math - - PRIMES = [ - 1116281, - 1297337, - 104395303, - 472882027, - 533000389, - 817504243, - 982451653, - 112272535095293, - 112582705942171, - 112272535095293, - 115280095190773, - 115797848077099, - 1099726899285419 - ] * 5 - - - def is_prime(num): - """判断素数""" - assert num > 0 - for i in range(2, int(math.sqrt(num)) + 1): - if num % i == 0: - return False - return num != 1 - - - def main(): - """主函数""" - with concurrent.futures.ProcessPoolExecutor() as executor: - for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)): - print('%d is prime: %s' % (number, prime)) - - - if __name__ == '__main__': - main() - ``` - -3. 异步编程（异步I/O）：所谓异步编程是通过调度程序从任务队列中挑选任务，调度程序以交叉的形式执行这些任务，我们并不能保证任务将以某种顺序去执行，因为执行顺序取决于队列中的一项任务是否愿意将CPU处理时间让位给另一项任务。异步编程通常通过多任务协作处理的方式来实现，由于执行时间和顺序的不确定，因此需要通过钩子函数（回调函数）或者`Future`对象来获取任务执行的结果。目前我们使用的Python 3通过`asyncio`模块以及`await`和`async`关键字（Python 3.5中引入，Python 3.7中正式成为关键字）提供了对异步I/O的支持。 - - ```Python - import asyncio - - - async def fetch(host): - """从指定的站点抓取信息(协程函数)""" - print(f'Start fetching {host}\n') - # 跟服务器建立连接 - reader, writer = await asyncio.open_connection(host, 80) - # 构造请求行和请求头 - writer.write(b'GET / HTTP/1.1\r\n') - writer.write(f'Host: {host}\r\n'.encode()) - writer.write(b'\r\n') - # 清空缓存区(发送请求) - await writer.drain() - # 接收服务器的响应(读取响应行和响应头) - line = await reader.readline() - while line != b'\r\n': - print(line.decode().rstrip()) - line = await reader.readline() - print('\n') - writer.close() - - - def main(): - """主函数""" - urls = ('www.sohu.com', 'www.douban.com', 'www.163.com') - # 获取系统默认的事件循环 - loop = asyncio.get_event_loop() - # 用生成式语法构造一个包含多个协程对象的列表 - tasks = [fetch(url) for url in urls] - # 通过asyncio模块的wait函数将协程列表包装成Task（Future子类）并等待其执行完成 - # 通过事件循环的run_until_complete方法运行任务直到Future完成并返回它的结果 - loop.run_until_complete(asyncio.wait(tasks)) - loop.close() - - - if __name__ == '__main__': - main() - ``` - - > 说明：目前大多数网站都要求基于HTTPS通信，因此上面例子中的网络请求不一定能收到正常的响应，也就是说响应状态码不一定是200，有可能是3xx或者4xx。当然我们这里的重点不在于获得网站响应的内容，而是帮助大家理解`asyncio`模块以及`async`和`await`两个关键字的使用。 - -我们对三种方式的使用场景做一个简单的总结。 - -以下情况需要使用多线程： - -1. 程序需要维护许多共享的状态（尤其是可变状态），Python中的列表、字典、集合都是线程安全的，所以使用线程而不是进程维护共享状态的代价相对较小。 -2. 程序会花费大量时间在I/O操作上，没有太多并行计算的需求且不需占用太多的内存。 - -以下情况需要使用多进程： - -1. 程序执行计算密集型任务（如：字节码操作、数据处理、科学计算）。 -2. 程序的输入可以并行的分成块，并且可以将运算结果合并。 -3. 程序在内存使用方面没有任何限制且不强依赖于I/O操作（如：读写文件、套接字等）。 - -最后，如果程序不需要真正的并发性或并行性，而是更多的依赖于异步处理和回调时，异步I/O就是一种很好的选择。另一方面，当程序中有大量的等待与休眠时，也应该考虑使用异步I/O。 - -> 扩展：关于进程，还需要做一些补充说明。首先，为了控制进程的执行，操作系统内核必须有能力挂起正在CPU上运行的进程，并恢复以前挂起的某个进程使之继续执行，这种行为被称为进程切换（也叫调度）。进程切换是比较耗费资源的操作，因为在进行切换时首先要保存当前进程的上下文（内核再次唤醒该进程时所需要的状态，包括：程序计数器、状态寄存器、数据栈等），然后还要恢复准备执行的进程的上下文。正在执行的进程由于期待的某些事件未发生，如请求系统资源失败、等待某个操作完成、新数据尚未到达等原因会主动由运行状态变为阻塞状态，当进程进入阻塞状态，是不占用CPU资源的。这些知识对于理解到底选择哪种方式进行并发编程也是很重要的。 - -### I/O模式和事件驱动 - -对于一次I/O操作（以读操作为例），数据会先被拷贝到操作系统内核的缓冲区中，然后从操作系统内核的缓冲区拷贝到应用程序的缓冲区（这种方式称为标准I/O或缓存I/O，大多数文件系统的默认I/O都是这种方式），最后交给进程。所以说，当一个读操作发生时（写操作与之类似），它会经历两个阶段：(1)等待数据准备就绪；(2)将数据从内核拷贝到进程中。 - -由于存在这两个阶段，因此产生了以下几种I/O模式： - -1. 阻塞 I/O（blocking I/O）：进程发起读操作，如果内核数据尚未就绪，进程会阻塞等待数据直到内核数据就绪并拷贝到进程的内存中。 -2. 非阻塞 I/O（non-blocking I/O）：进程发起读操作，如果内核数据尚未就绪，进程不阻塞而是收到内核返回的错误信息，进程收到错误信息可以再次发起读操作，一旦内核数据准备就绪，就立即将数据拷贝到了用户内存中，然后返回。 -3. 多路I/O复用（ I/O multiplexing）：监听多个I/O对象，当I/O对象有变化（数据就绪）的时候就通知用户进程。多路I/O复用的优势并不在于单个I/O操作能处理得更快，而是在于能处理更多的I/O操作。 -4. 异步 I/O（asynchronous I/O）：进程发起读操作后就可以去做别的事情了，内核收到异步读操作后会立即返回，所以用户进程不阻塞，当内核数据准备就绪时，内核发送一个信号给用户进程，告诉它读操作完成了。 - -通常，我们编写一个处理用户请求的服务器程序时，有以下三种方式可供选择： - -1. 每收到一个请求，创建一个新的进程，来处理该请求； -2. 每收到一个请求，创建一个新的线程，来处理该请求； -3. 每收到一个请求，放入一个事件列表，让主进程通过非阻塞I/O方式来处理请求 - -第1种方式实现比较简单，但由于创建进程开销比较大，会导致服务器性能比较差；第2种方式，由于要涉及到线程的同步，有可能会面临竞争、死锁等问题；第3种方式，就是所谓事件驱动的方式，它利用了多路I/O复用和异步I/O的优点，虽然代码逻辑比前面两种都复杂，但能达到最好的性能，这也是目前大多数网络服务器采用的方式。 diff --git a/Day61-65/62.Tornado入门.md b/Day61-65/62.Tornado入门.md deleted file mode 100644 index e1249f8..0000000 --- a/Day61-65/62.Tornado入门.md +++ /dev/null @@ -1,377 +0,0 @@ -## Tornado入门 - -### Tornado概述 - -Python的Web框架种类繁多（比Python语言的关键字还要多），但在众多优秀的Web框架中，Tornado框架最适合用来开发需要处理长连接和应对高并发的Web应用。Tornado框架在设计之初就考虑到性能问题，通过对非阻塞I/O和epoll（Linux 2.5.44内核引入的一种多路I/O复用方式，旨在实现高性能网络服务，在BSD和macOS中是kqueue）的运用，Tornado可以处理大量的并发连接，更轻松的应对C10K（万级并发）问题，是非常理想的实时通信Web框架。 - -> 扩展：基于线程的Web服务器产品（如：Apache）会维护一个线程池来处理用户请求，当用户请求到达时就为该请求分配一个线程，如果线程池中没有空闲线程了，那么可以通过创建新的线程来应付新的请求，但前提是系统尚有空闲的内存空间，显然这种方式很容易将服务器的空闲内存耗尽（大多数Linux发行版本中，默认的线程栈大小为8M）。想象一下，如果我们要开发一个社交类应用，这类应用中，通常需要显示实时更新的消息、对象状态的变化和各种类型的通知，那也就意味着客户端需要保持请求连接来接收服务器的各种响应，在这种情况下，服务器上的工作线程很容易被耗尽，这也就意味着新的请求很有可能无法得到响应。 - -Tornado框架源于FriendFeed网站，在FriendFeed网站被Facebook收购之后得以开源，正式发布的日期是2009年9月10日。Tornado能让你能够快速开发高速的Web应用，如果你想编写一个可扩展的社交应用、实时分析引擎，或RESTful API，那么Tornado框架就是很好的选择。Tornado其实不仅仅是一个Web开发的框架，它还是一个高性能的事件驱动网络访问引擎，内置了高性能的HTTP服务器和客户端（支持同步和异步请求），同时还对WebSocket提供了完美的支持。 - -了解和学习Tornado最好的资料就是它的官方文档，在[tornadoweb.org](http://www.tornadoweb.org)上面有很多不错的例子，你也可以在Github上找到Tornado的源代码和历史版本。 - -### 5分钟上手Tornado - -1. 创建并激活虚拟环境。 - - ```Shell - mkdir hello-tornado - cd hello-tornado - python3 -m venv venv - source venv/bin/activate - ``` - -2. 安装Tornado。 - - ```Shell - pip install tornado - ``` - -3. 编写Web应用。 - - ```Python - """ - example01.py - """ - import tornado.ioloop - import tornado.web - - - class MainHandler(tornado.web.RequestHandler): - - def get(self): - self.write('

Hello, world!

') - - - def main(): - app = tornado.web.Application(handlers=[(r'/', MainHandler), ]) - app.listen(8888) - tornado.ioloop.IOLoop.current().start() - - - if __name__ == '__main__': - main() - ``` - -4. 运行并访问应用。 - - ```Shell - python example01.py - ``` - - ![](./res/run-hello-world-app.png) - -在上面的例子中，代码example01.py通过定义一个继承自`RequestHandler`的类（`MainHandler`）来处理用户请求，当请求到达时，Tornado会实例化这个类（创建`MainHandler`对象），并调用与HTTP请求方法（GET、POST等）对应的方法，显然上面的`MainHandler`只能处理GET请求，在收到GET请求时，它会将一段HTML的内容写入到HTTP响应中。`main`函数的第1行代码创建了Tornado框架中`Application`类的实例，它代表了我们的Web应用，而创建该实例最为重要的参数就是`handlers`，该参数告知`Application`对象，当收到一个请求时应该通过哪个类的对象来处理这个请求。在上面的例子中，当通过HTTP的GET请求访问站点根路径时，就会调用`MainHandler`的`get`方法。 `main`函数的第2行代码通过`Application`对象的`listen`方法指定了监听HTTP请求的端口。`main`函数的第3行代码用于获取Tornado框架的`IOLoop`实例并启动它，该实例代表一个条件触发的I/O循环，用于持续的接收来自于客户端的请求。 - -> 扩展：在Python 3中，`IOLoop`实例的本质就是`asyncio`的事件循环，该事件循环在非Windows系统中就是`SelectorEventLoop`对象，它基于`selectors`模块（高级I/O复用模块），会使用当前操作系统最高效的I/O复用选择器，例如在Linux环境下它使用`EpollSelector`，而在macOS和BSD环境下它使用的是`KqueueSelector`；在Python 2中，`IOLoop`直接使用`select`模块（低级I/O复用模块）的`epoll`或`kqueue`函数，如果这两种方式都不可用，则调用`select`函数实现多路I/O复用。当然，如果要支持高并发，你的系统最好能够支持epoll或者kqueue这两种多路I/O复用方式中的一种。 - -如果希望通过命令行参数来指定Web应用的监听端口，可以对上面的代码稍作修改。 - -```Python -""" -example01.py -""" -import tornado.ioloop -import tornado.web - -from tornado.options import define, options, parse_command_line - - -# 定义默认端口 -define('port', default=8000, type=int) - - -class MainHandler(tornado.web.RequestHandler): - - def get(self): - self.write('

Hello, world!

') - - -def main(): - # python example01.py --port=8000 - parse_command_line() - app = tornado.web.Application(handlers=[(r'/', MainHandler), ]) - app.listen(options.port) - tornado.ioloop.IOLoop.current().start() - - -if __name__ == '__main__': - main() -``` - -在启动Web应用时，如果没有指定端口，将使用`define`函数中设置的默认端口8000，如果要指定端口，可以使用下面的方式来启动Web应用。 - -```Shell -python example01.py --port=8000 -``` - -### 路由解析 - -上面我们曾经提到过创建`Application`实例时需要指定`handlers`参数，这个参数非常重要，它应该是一个元组的列表，元组中的第一个元素是正则表达式，它用于匹配用户请求的资源路径；第二个元素是`RequestHandler`的子类。在刚才的例子中，我们只在`handlers`列表中放置了一个元组，事实上我们可以放置多个元组来匹配不同的请求（资源路径），而且可以使用正则表达式的捕获组来获取匹配的内容并将其作为参数传入到`get`、`post`这些方法中。 - -```Python -""" -example02.py -""" -import os -import random - -import tornado.ioloop -import tornado.web - -from tornado.options import define, options, parse_command_line - - -# 定义默认端口 -define('port', default=8000, type=int) - - -class SayingHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def get(self): - sayings = [ - '世上没有绝望的处境，只有对处境绝望的人', - '人生的道路在态度的岔口一分为二，从此通向成功或失败', - '所谓措手不及，不是说没有时间准备，而是有时间的时候没有准备', - '那些你认为不靠谱的人生里，充满你没有勇气做的事', - '在自己喜欢的时间里，按照自己喜欢的方式，去做自己喜欢做的事，这便是自由', - '有些人不属于自己，但是遇见了也弥足珍贵' - ] - # 渲染index.html模板页 - self.render('index.html', message=random.choice(sayings)) - - -class WeatherHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def get(self, city): - # Tornado框架会自动处理百分号编码的问题 - weathers = { - '北京': {'temperature': '-4~4', 'pollution': '195 中度污染'}, - '成都': {'temperature': '3~9', 'pollution': '53 良'}, - '深圳': {'temperature': '20~25', 'pollution': '25 优'}, - '广州': {'temperature': '18~23', 'pollution': '56 良'}, - '上海': {'temperature': '6~8', 'pollution': '65 良'} - } - if city in weathers: - self.render('weather.html', city=city, weather=weathers[city]) - else: - self.render('index.html', message=f'没有{city}的天气信息') - - -class ErrorHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def get(self): - # 重定向到指定的路径 - self.redirect('/saying') - - -def main(): - """主函数""" - parse_command_line() - app = tornado.web.Application( - # handlers是按列表中的顺序依次进行匹配的 - handlers=[ - (r'/saying/?', SayingHandler), - (r'/weather/([^/]{2,})/?', WeatherHandler), - (r'/.+', ErrorHandler), - ], - # 通过template_path参数设置模板页的路径 - template_path=os.path.join(os.path.dirname(__file__), 'templates') - ) - app.listen(options.port) - tornado.ioloop.IOLoop.current().start() - - -if __name__ == '__main__': - main() -``` - -模板页index.html。 - -```HTML - - - - - - Tornado基础 - - -

{{message}}

- - -``` - -模板页weather.html。 - -```HTML - - - - - - Tornado基础 - - -

{{city}}

-
-

温度：{{weather['temperature']}}摄氏度

-

污染指数：{{weather['pollution']}}

- - -``` - -Tornado的模板语法与其他的Web框架中使用的模板语法并没有什么实质性的区别，而且目前的Web应用开发更倡导使用前端渲染的方式来减轻服务器的负担，所以这里我们并不对模板语法和后端渲染进行深入的讲解。 - -### 请求处理器 - -通过上面的代码可以看出，`RequestHandler`是处理用户请求的核心类，通过重写`get`、`post`、`put`、`delete`等方法可以处理不同类型的HTTP请求，除了这些方法之外，`RequestHandler`还实现了很多重要的方法，下面是部分方法的列表： - -1. `get_argument` / `get_arguments` / `get_body_argument` / `get_body_arguments` / `get_query_arugment` / `get_query_arguments`：获取请求参数。 -2. `set_status` / `send_error` / `set_header` / `add_header` / `clear_header` / `clear`：操作状态码和响应头。 -3. `write` / `flush` / `finish` / `write_error`：和输出相关的方法。 -4. `render` / `render_string`：渲染模板。 -5. `redirect`：请求重定向。 -6. `get_cookie` / `set_cookie` / `get_secure_cookie` / `set_secure_cookie` / `create_signed_value` / `clear_cookie` / `clear_all_cookies`：操作Cookie。 - -我们用上面讲到的这些方法来完成下面的需求，访问页面时，如果Cookie中没有读取到用户信息则要求用户填写个人信息，如果从Cookie中读取到用户信息则直接显示用户信息。 - -```Python -""" -example03.py -""" -import os -import re - -import tornado.ioloop -import tornado.web - -from tornado.options import define, options, parse_command_line - - -# 定义默认端口 -define('port', default=8000, type=int) - -users = {} - - -class User(object): - """用户""" - - def __init__(self, nickname, gender, birthday): - self.nickname = nickname - self.gender = gender - self.birthday = birthday - - -class MainHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def get(self): - # 从Cookie中读取用户昵称 - nickname = self.get_cookie('nickname') - if nickname in users: - self.render('userinfo.html', user=users[nickname]) - else: - self.render('userform.html', hint='请填写个人信息') - - -class UserHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def post(self): - # 从表单参数中读取用户昵称、性别和生日信息 - nickname = self.get_body_argument('nickname').strip() - gender = self.get_body_argument('gender') - birthday = self.get_body_argument('birthday') - # 检查用户昵称是否有效 - if not re.fullmatch(r'\w{6,20}', nickname): - self.render('userform.html', hint='请输入有效的昵称') - elif nickname in users: - self.render('userform.html', hint='昵称已经被使用过') - else: - users[nickname] = User(nickname, gender, birthday) - # 将用户昵称写入Cookie并设置有效期为7天 - self.set_cookie('nickname', nickname, expires_days=7) - self.render('userinfo.html', user=users[nickname]) - - -def main(): - """主函数""" - parse_command_line() - app = tornado.web.Application( - handlers=[ - (r'/', MainHandler), (r'/register', UserHandler) - ], - template_path=os.path.join(os.path.dirname(__file__), 'templates') - ) - app.listen(options.port) - tornado.ioloop.IOLoop.current().start() - - -if __name__ == '__main__': - main() -``` - -模板页userform.html。 - -```HTML - - - - - - Tornado基础 - - - -

填写用户信息

-
-

{{hint}}

-
-

- - - （字母数字下划线，6-20个字符） -

-

- - 男 - 女 -

-

- - -

-

- -

-
- - -``` - -模板页userinfo.html。 - -```HTML - - - - - - Tornado基础 - - -

用户信息

-
-

昵称：{{user.nickname}}

-

性别：{{user.gender}}

-

出生日期：{{user.birthday}}

- - -``` diff --git a/Day66-75/67.数据采集和解析.md b/Day61-65/62.数据采集和解析.md similarity index 100% rename from Day66-75/67.数据采集和解析.md rename to Day61-65/62.数据采集和解析.md diff --git a/Day61-65/63.Tornado中的异步化.md b/Day61-65/63.Tornado中的异步化.md deleted file mode 100644 index a450534..0000000 --- a/Day61-65/63.Tornado中的异步化.md +++ /dev/null @@ -1,152 +0,0 @@ -## Tornado中的异步化 - -在前面的例子中，我们并没有对`RequestHandler`中的`get`或`post`方法进行异步处理，这就意味着，一旦在`get`或`post`方法中出现了耗时间的操作，不仅仅是当前请求被阻塞，按照Tornado框架的工作模式，其他的请求也会被阻塞，所以我们需要对耗时间的操作进行异步化处理。 - -在Tornado稍早一些的版本中，可以用装饰器实现请求方法的异步化或协程化来解决这个问题。 - -- 给`RequestHandler`的请求处理函数添加`@tornado.web.asynchronous`装饰器，如下所示： - - ```Python - class AsyncReqHandler(RequestHandler): - - @tornado.web.asynchronous - def get(self): - http = httpclient.AsyncHTTPClient() - http.fetch("http://example.com/", self._on_download) - - def _on_download(self, response): - do_something_with_response(response) - self.render("template.html") - ``` - -- 给`RequestHandler`的请求处理函数添加`@tornado.gen.coroutine`装饰器，如下所示： - - ```Python - class GenAsyncHandler(RequestHandler): - - @tornado.gen.coroutine - def get(self): - http_client = AsyncHTTPClient() - response = yield http_client.fetch("http://example.com") - do_something_with_response(response) - self.render("template.html") - ``` - -- 使用`@return_future`装饰器，如下所示： - - ```Python - @return_future - def future_func(arg1, arg2, callback): - # Do stuff (possibly asynchronous) - callback(result) - - async def caller(): - await future_func(arg1, arg2) - ``` - -在Tornado 5.x版本中，这几个装饰器都被标记为**deprcated**（过时），我们可以通过Python 3.5中引入的`async`和`await`（在Python 3.7中已经成为正式的关键字）来达到同样的效果。当然，要实现异步化还得靠其他的支持异步操作的三方库来支持，如果请求处理函数中用到了不支持异步操作的三方库，就需要靠自己写包装类来支持异步化。 - -下面的代码演示了在读写数据库时如何实现请求处理的异步化。我们用到的数据库建表语句如下所示： - -```SQL -create database hrs default charset utf8; - -use hrs; - -/* 创建部门表 */ -create table tb_dept -( - dno int not null comment '部门编号', - dname varchar(10) not null comment '部门名称', - dloc varchar(20) not null comment '部门所在地', - primary key (dno) -); - -insert into tb_dept values - (10, '会计部', '北京'), - (20, '研发部', '成都'), - (30, '销售部', '重庆'), - (40, '运维部', '深圳'); -``` - -我们通过下面的代码实现了查询和新增部门两个操作。 - -```Python -import json - -import aiomysql -import tornado -import tornado.web - -from tornado.ioloop import IOLoop -from tornado.options import define, parse_command_line, options - -define('port', default=8000, type=int) - - -async def connect_mysql(): - return await aiomysql.connect( - host='120.77.222.217', - port=3306, - db='hrs', - user='root', - password='123456', - ) - - -class HomeHandler(tornado.web.RequestHandler): - - async def get(self, no): - async with self.settings['mysql'].cursor(aiomysql.DictCursor) as cursor: - await cursor.execute("select * from tb_dept where dno=%s", (no, )) - if cursor.rowcount == 0: - self.finish(json.dumps({ - 'code': 20001, - 'mesg': f'没有编号为{no}的部门' - })) - return - row = await cursor.fetchone() - self.finish(json.dumps(row)) - - async def post(self, *args, **kwargs): - no = self.get_argument('no') - name = self.get_argument('name') - loc = self.get_argument('loc') - conn = self.settings['mysql'] - try: - async with conn.cursor() as cursor: - await cursor.execute('insert into tb_dept values (%s, %s, %s)', - (no, name, loc)) - await conn.commit() - except aiomysql.MySQLError: - self.finish(json.dumps({ - 'code': 20002, - 'mesg': '添加部门失败请确认部门信息' - })) - else: - self.set_status(201) - self.finish() - - -def make_app(config): - return tornado.web.Application( - handlers=[(r'/api/depts/(.*)', HomeHandler), ], - **config - ) - - -def main(): - parse_command_line() - app = make_app({ - 'debug': True, - 'mysql': IOLoop.current().run_sync(connect_mysql) - }) - app.listen(options.port) - IOLoop.current().start() - - -if __name__ == '__main__': - main() -``` - -上面的代码中，我们用到了`aiomysql`这个三方库，它基于`pymysql`封装，实现了对MySQL操作的异步化。操作Redis可以使用`aioredis`，访问MongoDB可以使用`motor`，这些都是支持异步操作的三方库。 \ No newline at end of file diff --git a/Day66-75/68.存储数据.md b/Day61-65/63.存储数据.md similarity index 100% rename from Day66-75/68.存储数据.md rename to Day61-65/63.存储数据.md diff --git a/Day61-65/64.WebSocket的应用.md b/Day61-65/64.WebSocket的应用.md deleted file mode 100644 index ef25c9e..0000000 --- a/Day61-65/64.WebSocket的应用.md +++ /dev/null @@ -1,228 +0,0 @@ -## WebSocket的应用 - -Tornado的异步特性使其非常适合处理高并发的业务，同时也适合那些需要在客户端和服务器之间维持长连接的业务。传统的基于HTTP协议的Web应用，服务器和客户端（浏览器）的通信只能由客户端发起，这种单向请求注定了如果服务器有连续的状态变化，客户端（浏览器）是很难得知的。事实上，今天的很多Web应用都需要服务器主动向客户端（浏览器）发送数据，我们将这种通信方式称之为“推送”。过去很长一段时间，程序员都是用定时轮询（Polling）或长轮询（Long Polling）等方式来实现“推送”，但是这些都不是真正意义上的“推送”，而且浪费资源且效率低下。在HTML5时代，可以通过一种名为WebSocket的技术在服务器和客户端（浏览器）之间维持传输数据的长连接，这种方式可以实现真正的“推送”服务。 - -### WebSocket简介 - -WebSocket 协议在2008年诞生，2011年成为国际标准（[RFC 6455](https://tools.ietf.org/html/rfc6455)），现在的浏览器都能够支持它，它可以实现浏览器和服务器之间的全双工通信。我们之前学习或了解过Python的Socket编程，通过Socket编程，可以基于TCP或UDP进行数据传输；而WebSocket与之类似，只不过它是基于HTTP来实现通信握手，使用TCP来进行数据传输。WebSocket的出现打破了HTTP请求和响应只能一对一通信的模式，也改变了服务器只能被动接受客户端请求的状况。目前有很多Web应用是需要服务器主动向客户端发送信息的，例如股票信息的网站可能需要向浏览器发送股票涨停通知，社交网站可能需要向用户发送好友上线提醒或聊天信息。 - -![](./res/websocket.png) - -WebSocket的特点如下所示： - -1. 建立在TCP协议之上，服务器端的实现比较容易。 -2. 与HTTP协议有着良好的兼容性，默认端口是80（WS）和443（WSS），通信握手阶段采用HTTP协议，能通过各种 HTTP 代理服务器（不容易被防火墙阻拦）。 -3. 数据格式比较轻量，性能开销小，通信高效。 -4. 可以发送文本，也可以发送二进制数据。 -5. 没有同源策略的限制，客户端（浏览器）可以与任意服务器通信。 - -![](./res/ws_wss.png) - -### WebSocket服务器端编程 - -Tornado框架中有一个`tornado.websocket.WebSocketHandler`类专门用于处理来自WebSocket的请求，通过继承该类并重写`open`、`on_message`、`on_close` 等方法来处理WebSocket通信，下面我们对`WebSocketHandler`的核心方法做一个简单的介绍。 - -1. `open(*args, **kwargs)`方法：建立新的WebSocket连接后，Tornado框架会调用该方法，该方法的参数与`RequestHandler`的`get`方法的参数类似，这也就意味着在`open`方法中可以执行获取请求参数、读取Cookie信息这样的操作。 - -2. `on_message(message)`方法：建立WebSocket之后，当收到来自客户端的消息时，Tornado框架会调用该方法，这样就可以对收到的消息进行对应的处理，必须重写这个方法。 - -3. `on_close()`方法：当WebSocket被关闭时，Tornado框架会调用该方法，在该方法中可以通过`close_code`和`close_reason`了解关闭的原因。 - -4. `write_message(message, binary=False)`方法：将指定的消息通过WebSocket发送给客户端，可以传递utf-8字符序列或者字节序列，如果message是一个字典，将会执行JSON序列化。正常情况下，该方法会返回一个`Future`对象；如果WebSocket被关闭了，将引发`WebSocketClosedError`。 - -5. `set_nodelay(value)`方法：默认情况下，因为TCP的Nagle算法会导致短小的消息被延迟发送，在考虑到交互性的情况下就要通过将该方法的参数设置为`True`来避免延迟。 - -6. `close(code=None, reason=None)`方法：主动关闭WebSocket，可以指定状态码（详见[RFC 6455 7.4.1节](https://tools.ietf.org/html/rfc6455#section-7.4.1)）和原因。 - -### WebSocket客户端编程 - -1. 创建WebSocket对象。 - - ```JavaScript - var webSocket = new WebSocket('ws://localhost:8000/ws'); - ``` - - >说明：webSocket对象的readyState属性表示该对象当前状态，取值为CONNECTING-正在连接，OPEN-连接成功可以通信，CLOSING-正在关闭，CLOSED-已经关闭。 - -2. 编写回调函数。 - - ```JavaScript - webSocket.onopen = function(evt) { webSocket.send('...'); }; - webSocket.onmessage = function(evt) { console.log(evt.data); }; - webSocket.onclose = function(evt) {}; - webSocket.onerror = function(evt) {}; - ``` - - > 说明：如果要绑定多个事件回调函数，可以用addEventListener方法。另外，通过事件对象的data属性获得的数据可能是字符串，也有可能是二进制数据，可以通过webSocket对象的binaryType属性（blob、arraybuffer）或者通过typeof、instanceof运算符检查类型进行判定。 - -### 项目：Web聊天室 - -```Python -""" -handlers.py - 用户登录和聊天的处理器 -""" -import tornado.web -import tornado.websocket - -nicknames = set() -connections = {} - - -class LoginHandler(tornado.web.RequestHandler): - - def get(self): - self.render('login.html', hint='') - - def post(self): - nickname = self.get_argument('nickname') - if nickname in nicknames: - self.render('login.html', hint='昵称已被使用，请更换昵称') - self.set_secure_cookie('nickname', nickname) - self.render('chat.html') - - -class ChatHandler(tornado.websocket.WebSocketHandler): - - def open(self): - nickname = self.get_secure_cookie('nickname').decode() - nicknames.add(nickname) - for conn in connections.values(): - conn.write_message(f'~~~{nickname}进入了聊天室~~~') - connections[nickname] = self - - def on_message(self, message): - nickname = self.get_secure_cookie('nickname').decode() - for conn in connections.values(): - if conn is not self: - conn.write_message(f'{nickname}说：{message}') - - def on_close(self): - nickname = self.get_secure_cookie('nickname').decode() - del connections[nickname] - nicknames.remove(nickname) - for conn in connections.values(): - conn.write_message(f'~~~{nickname}离开了聊天室~~~') - -``` - -```Python -""" -run_chat_server.py - 聊天服务器 -""" -import os - -import tornado.web -import tornado.ioloop - -from handlers import LoginHandler, ChatHandler - - -if __name__ == '__main__': - app = tornado.web.Application( - handlers=[(r'/login', LoginHandler), (r'/chat', ChatHandler)], - template_path=os.path.join(os.path.dirname(__file__), 'templates'), - static_path=os.path.join(os.path.dirname(__file__), 'static'), - cookie_secret='MWM2MzEyOWFlOWRiOWM2MGMzZThhYTk0ZDNlMDA0OTU=', - ) - app.listen(8888) - tornado.ioloop.IOLoop.current().start() -``` - -```HTML - - - - - - Tornado聊天室 - - - -
-
-

进入聊天室

-
-

{{hint}}

-
- - - -
-
-
- - -``` - -```HTML - - - - - - Tornado聊天室 - - -

聊天室

-
-
- -
-
- - -
-

- 退出聊天室 -

- - - - -``` - diff --git a/Day66-75/69.并发下载.md b/Day61-65/64.并发下载.md similarity index 100% rename from Day66-75/69.并发下载.md rename to Day61-65/64.并发下载.md diff --git a/Day66-75/70.解析动态内容.md b/Day61-65/65.解析动态内容.md similarity index 100% rename from Day66-75/70.解析动态内容.md rename to Day61-65/65.解析动态内容.md diff --git a/Day61-65/65.项目实战.md b/Day61-65/65.项目实战.md deleted file mode 100644 index dbbae84..0000000 --- a/Day61-65/65.项目实战.md +++ /dev/null @@ -1,2 +0,0 @@ -## 项目实战 - diff --git a/Day61-65/code/.gitkeep b/Day61-65/code/.gitkeep deleted file mode 100644 index e69de29..0000000 diff --git a/Day66-75/code/asyncio01.py b/Day61-65/code/asyncio01.py similarity index 100% rename from Day66-75/code/asyncio01.py rename to Day61-65/code/asyncio01.py diff --git a/Day66-75/code/asyncio02.py b/Day61-65/code/asyncio02.py similarity index 100% rename from Day66-75/code/asyncio02.py rename to Day61-65/code/asyncio02.py diff --git a/Day66-75/code/coroutine01.py b/Day61-65/code/coroutine01.py similarity 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a/Day66-75/code/example11.py b/Day61-65/code/example11.py similarity index 100% rename from Day66-75/code/example11.py rename to Day61-65/code/example11.py diff --git a/Day66-75/code/example11a.py b/Day61-65/code/example11a.py similarity index 100% rename from Day66-75/code/example11a.py rename to Day61-65/code/example11a.py diff --git a/Day66-75/code/example12.py b/Day61-65/code/example12.py similarity index 100% rename from Day66-75/code/example12.py rename to Day61-65/code/example12.py diff --git a/Day66-75/code/generator01.py b/Day61-65/code/generator01.py similarity index 100% rename from Day66-75/code/generator01.py rename to Day61-65/code/generator01.py diff --git a/Day66-75/code/generator02.py b/Day61-65/code/generator02.py similarity index 100% rename from Day66-75/code/generator02.py rename to Day61-65/code/generator02.py diff --git a/Day61-65/code/guido.jpg b/Day61-65/code/guido.jpg new file mode 100644 index 0000000..a8f217c Binary files /dev/null and b/Day61-65/code/guido.jpg differ diff --git a/Day61-65/code/hello-tornado/chat_handlers.py b/Day61-65/code/hello-tornado/chat_handlers.py deleted file mode 100644 index 528d8a1..0000000 --- a/Day61-65/code/hello-tornado/chat_handlers.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -handlers.py - 用户登录和聊天的处理器 -""" -import tornado.web -import tornado.websocket - -nicknames = set() -connections = {} - - -class LoginHandler(tornado.web.RequestHandler): - - def get(self): - self.render('login.html', hint='') - - def post(self): - nickname = self.get_argument('nickname') - if nickname in nicknames: - self.render('login.html', hint='昵称已被使用，请更换昵称') - self.set_secure_cookie('nickname', nickname) - self.render('chat.html') - - -class ChatHandler(tornado.websocket.WebSocketHandler): - - def open(self): - nickname = self.get_secure_cookie('nickname').decode() - nicknames.add(nickname) - for conn in connections.values(): - conn.write_message(f'~~~{nickname}进入了聊天室~~~') - connections[nickname] = self - - def on_message(self, message): - nickname = self.get_secure_cookie('nickname').decode() - for conn in connections.values(): - if conn is not self: - conn.write_message(f'{nickname}说：{message}') - - def on_close(self): - nickname = self.get_secure_cookie('nickname').decode() - del connections[nickname] - nicknames.remove(nickname) - for conn in connections.values(): - conn.write_message(f'~~~{nickname}离开了聊天室~~~') diff --git a/Day61-65/code/hello-tornado/chat_server.py b/Day61-65/code/hello-tornado/chat_server.py deleted file mode 100644 index 4985acd..0000000 --- a/Day61-65/code/hello-tornado/chat_server.py +++ /dev/null @@ -1,24 +0,0 @@ -""" -chat_server.py - 聊天服务器 -""" -import os - -import tornado.web -import tornado.ioloop - -from chat_handlers import LoginHandler, ChatHandler - - -def main(): - app = tornado.web.Application( - handlers=[(r'/login', LoginHandler), (r'/chat', ChatHandler)], - template_path=os.path.join(os.path.dirname(__file__), 'templates'), - static_path=os.path.join(os.path.dirname(__file__), 'static'), - cookie_secret='MWM2MzEyOWFlOWRiOWM2MGMzZThhYTk0ZDNlMDA0OTU=', - ) - app.listen(8888) - tornado.ioloop.IOLoop.current().start() - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example01.py b/Day61-65/code/hello-tornado/example01.py deleted file mode 100644 index 6005256..0000000 --- a/Day61-65/code/hello-tornado/example01.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -example01.py - 五分钟上手Tornado -""" -import tornado.ioloop -import tornado.web - -from tornado.options import define, options, parse_command_line - -# 定义默认端口 -define('port', default=8000, type=int) - - -class MainHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def get(self): - # 向客户端（浏览器）写入内容 - self.write('

Hello, world!

') - - -def main(): - """主函数""" - # 解析命令行参数，例如： - # python example01.py --port 8888 - parse_command_line() - # 创建了Tornado框架中Application类的实例并指定handlers参数 - # Application实例代表了我们的Web应用，handlers代表了路由解析 - app = tornado.web.Application(handlers=[(r'/', MainHandler), ]) - # 指定了监听HTTP请求的TCP端口（默认8000，也可以通过命令行参数指定） - app.listen(options.port) - # 获取Tornado框架的IOLoop实例并启动它（默认启动asyncio的事件循环） - tornado.ioloop.IOLoop.current().start() - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example02.py b/Day61-65/code/hello-tornado/example02.py deleted file mode 100644 index c9ff9c0..0000000 --- a/Day61-65/code/hello-tornado/example02.py +++ /dev/null @@ -1,77 +0,0 @@ -""" -example02.py - 路由解析 -""" -import os -import random - -import tornado.ioloop -import tornado.web - -from tornado.options import define, options, parse_command_line - - -# 定义默认端口 -define('port', default=8000, type=int) - - -class SayingHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def get(self): - sayings = [ - '世上没有绝望的处境，只有对处境绝望的人', - '人生的道路在态度的岔口一分为二，从此通向成功或失败', - '所谓措手不及，不是说没有时间准备，而是有时间的时候没有准备', - '那些你认为不靠谱的人生里，充满你没有勇气做的事', - '在自己喜欢的时间里，按照自己喜欢的方式，去做自己喜欢做的事，这便是自由', - '有些人不属于自己，但是遇见了也弥足珍贵' - ] - # 渲染index.html模板页 - self.render('index.html', message=random.choice(sayings)) - - -class WeatherHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def get(self, city): - # Tornado框架会自动处理百分号编码的问题 - weathers = { - '北京': {'temperature': '-4~4', 'pollution': '195 中度污染'}, - '成都': {'temperature': '3~9', 'pollution': '53 良'}, - '深圳': {'temperature': '20~25', 'pollution': '25 优'}, - '广州': {'temperature': '18~23', 'pollution': '56 良'}, - '上海': {'temperature': '6~8', 'pollution': '65 良'} - } - if city in weathers: - self.render('weather.html', city=city, weather=weathers[city]) - else: - self.render('index.html', message=f'没有{city}的天气信息') - - -class ErrorHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def get(self): - # 重定向到指定的路径 - self.redirect('/saying') - - -def main(): - """主函数""" - parse_command_line() - app = tornado.web.Application( - # handlers是按列表中的顺序依次进行匹配的 - handlers=[ - (r'/saying/?', SayingHandler), - (r'/weather/([^/]{2,})/?', WeatherHandler), - (r'/.+', ErrorHandler), - ], - # 通过template_path参数设置模板页的路径 - template_path=os.path.join(os.path.dirname(__file__), 'templates') - ) - app.listen(options.port) - tornado.ioloop.IOLoop.current().start() - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example03.py b/Day61-65/code/hello-tornado/example03.py deleted file mode 100644 index 16ff8e5..0000000 --- a/Day61-65/code/hello-tornado/example03.py +++ /dev/null @@ -1,75 +0,0 @@ -""" -example03.py - RequestHandler解析 -""" -import os -import re - -import tornado.ioloop -import tornado.web - -from tornado.options import define, options, parse_command_line - - -# 定义默认端口 -define('port', default=8000, type=int) - -users = {} - - -class User(object): - """用户""" - - def __init__(self, nickname, gender, birthday): - self.nickname = nickname - self.gender = gender - self.birthday = birthday - - -class MainHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def get(self): - # 从Cookie中读取用户昵称 - nickname = self.get_cookie('nickname') - if nickname in users: - self.render('userinfo.html', user=users[nickname]) - else: - self.render('userform.html', hint='请填写个人信息') - - -class UserHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def post(self): - # 从表单参数中读取用户昵称、性别和生日信息 - nickname = self.get_body_argument('nickname').strip() - gender = self.get_body_argument('gender') - birthday = self.get_body_argument('birthday') - # 检查用户昵称是否有效 - if not re.fullmatch(r'\w{6,20}', nickname): - self.render('userform.html', hint='请输入有效的昵称') - elif nickname in users: - self.render('userform.html', hint='昵称已经被使用过') - else: - users[nickname] = User(nickname, gender, birthday) - # 将用户昵称写入Cookie并设置有效期为7天 - self.set_cookie('nickname', nickname, expires_days=7) - self.render('userinfo.html', user=users[nickname]) - - -def main(): - """主函数""" - parse_command_line() - app = tornado.web.Application( - handlers=[ - (r'/', MainHandler), - (r'/register', UserHandler), - ], - template_path=os.path.join(os.path.dirname(__file__), 'templates'), - ) - app.listen(options.port) - tornado.ioloop.IOLoop.current().start() - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example04.py b/Day61-65/code/hello-tornado/example04.py deleted file mode 100644 index 405b00c..0000000 --- a/Day61-65/code/hello-tornado/example04.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -example04.py - 同步请求的例子 -""" -import json -import os - -import requests -import tornado.gen -import tornado.ioloop -import tornado.web -import tornado.websocket -import tornado.httpclient -from tornado.options import define, options, parse_command_line - -define('port', default=8888, type=int) - -# 请求天行数据提供的API数据接口 -REQ_URL = 'http://api.tianapi.com/guonei/' -# 在天行数据网站注册后可以获得API_KEY -API_KEY = 'your_personal_api_key' - - -class MainHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - def get(self): - resp = requests.get(f'{REQ_URL}?key={API_KEY}') - newslist = json.loads(resp.text)['newslist'] - self.render('news.html', newslist=newslist) - - -def main(): - """主函数""" - parse_command_line() - app = tornado.web.Application( - handlers=[(r'/', MainHandler), ], - template_path=os.path.join(os.path.dirname(__file__), 'templates'), - ) - app.listen(options.port) - tornado.ioloop.IOLoop.current().start() - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example05.py b/Day61-65/code/hello-tornado/example05.py deleted file mode 100644 index 5ae1ced..0000000 --- a/Day61-65/code/hello-tornado/example05.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -example05.py - 异步请求的例子 -""" -import aiohttp -import json -import os - -import tornado.gen -import tornado.ioloop -import tornado.web -import tornado.websocket -import tornado.httpclient -from tornado.options import define, options, parse_command_line - -define('port', default=8888, type=int) - -# 请求天行数据提供的API数据接口 -REQ_URL = 'http://api.tianapi.com/guonei/' -# 在天行数据网站注册后可以获得API_KEY -API_KEY = 'your_personal_api_key' - - -class MainHandler(tornado.web.RequestHandler): - """自定义请求处理器""" - - async def get(self): - async with aiohttp.ClientSession() as session: - resp = await session.get(f'{REQ_URL}?key={API_KEY}') - json_str = await resp.text() - print(json_str) - newslist = json.loads(json_str)['newslist'] - self.render('news.html', newslist=newslist) - - -def main(): - """主函数""" - parse_command_line() - app = tornado.web.Application( - handlers=[(r'/', MainHandler), ], - template_path=os.path.join(os.path.dirname(__file__), 'templates'), - ) - app.listen(options.port) - tornado.ioloop.IOLoop.current().start() - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example06.py b/Day61-65/code/hello-tornado/example06.py deleted file mode 100644 index bcb02bf..0000000 --- a/Day61-65/code/hello-tornado/example06.py +++ /dev/null @@ -1,80 +0,0 @@ -""" -example06.py - 异步操作MySQL -""" -import json - -import aiomysql -import tornado -import tornado.web - -from tornado.ioloop import IOLoop -from tornado.options import define, parse_command_line, options - -define('port', default=8888, type=int) - - -async def connect_mysql(): - return await aiomysql.connect( - host='1.2.3.4', - port=3306, - db='hrs', - charset='utf8', - use_unicode=True, - user='yourname', - password='yourpass', - ) - - -class HomeHandler(tornado.web.RequestHandler): - - async def get(self, no): - async with self.settings['mysql'].cursor(aiomysql.DictCursor) as cursor: - await cursor.execute("select * from tb_dept where dno=%s", (no, )) - if cursor.rowcount == 0: - self.finish(json.dumps({ - 'code': 20001, - 'mesg': f'没有编号为{no}的部门' - })) - return - row = await cursor.fetchone() - self.finish(json.dumps(row)) - - async def post(self, *args, **kwargs): - no = self.get_argument('no') - name = self.get_argument('name') - loc = self.get_argument('loc') - conn = self.settings['mysql'] - try: - async with conn.cursor() as cursor: - await cursor.execute('insert into tb_dept values (%s, %s, %s)', - (no, name, loc)) - await conn.commit() - except aiomysql.MySQLError: - self.finish(json.dumps({ - 'code': 20002, - 'mesg': '添加部门失败请确认部门信息' - })) - else: - self.set_status(201) - self.finish() - - -def make_app(config): - return tornado.web.Application( - handlers=[(r'/api/depts/(.*)', HomeHandler), ], - **config - ) - - -def main(): - parse_command_line() - app = make_app({ - 'debug': True, - 'mysql': IOLoop.current().run_sync(connect_mysql) - }) - app.listen(options.port) - IOLoop.current().start() - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example07.py b/Day61-65/code/hello-tornado/example07.py deleted file mode 100644 index df38980..0000000 --- a/Day61-65/code/hello-tornado/example07.py +++ /dev/null @@ -1,92 +0,0 @@ -""" -example07.py - 将非异步的三方库封装为异步调用 -""" -import asyncio -import concurrent -import json - -import tornado -import tornado.web -import pymysql - -from pymysql import connect -from pymysql.cursors import DictCursor - -from tornado.ioloop import IOLoop -from tornado.options import define, parse_command_line, options -from tornado.platform.asyncio import AnyThreadEventLoopPolicy - -define('port', default=8888, type=int) - - -def get_mysql_connection(): - return connect( - host='1.2.3.4', - port=3306, - db='hrs', - charset='utf8', - use_unicode=True, - user='yourname', - password='yourpass', - ) - - -class HomeHandler(tornado.web.RequestHandler): - executor = concurrent.futures.ThreadPoolExecutor(max_workers=10) - - async def get(self, no): - return await self._get(no) - - @tornado.concurrent.run_on_executor - def _get(self, no): - con = get_mysql_connection() - try: - with con.cursor(DictCursor) as cursor: - cursor.execute("select * from tb_dept where dno=%s", (no, )) - if cursor.rowcount == 0: - self.finish(json.dumps({ - 'code': 20001, - 'mesg': f'没有编号为{no}的部门' - })) - return - row = cursor.fetchone() - self.finish(json.dumps(row)) - finally: - con.close() - - async def post(self, *args, **kwargs): - return await self._post(*args, **kwargs) - - @tornado.concurrent.run_on_executor - def _post(self, *args, **kwargs): - no = self.get_argument('no') - name = self.get_argument('name') - loc = self.get_argument('loc') - conn = get_mysql_connection() - try: - with conn.cursor() as cursor: - cursor.execute('insert into tb_dept values (%s, %s, %s)', - (no, name, loc)) - conn.commit() - except pymysql.MySQLError: - self.finish(json.dumps({ - 'code': 20002, - 'mesg': '添加部门失败请确认部门信息' - })) - else: - self.set_status(201) - self.finish() - - -def main(): - asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy()) - parse_command_line() - app = tornado.web.Application( - handlers=[(r'/api/depts/(.*)', HomeHandler), ] - ) - app.listen(options.port) - IOLoop.current().start() - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example_of_aiohttp.py b/Day61-65/code/hello-tornado/example_of_aiohttp.py deleted file mode 100644 index 362f6ff..0000000 --- a/Day61-65/code/hello-tornado/example_of_aiohttp.py +++ /dev/null @@ -1,30 +0,0 @@ -import asyncio -import re - -import aiohttp - -PATTERN = re.compile(r'\(?P.*)\<\/title\>') - - -async def show_title(url): - async with aiohttp.ClientSession() as session: - resp = await session.get(url, ssl=False) - html = await resp.text() - print(PATTERN.search(html).group('title')) - - -def main(): - urls = ('https://www.python.org/', - 'https://git-scm.com/', - 'https://www.jd.com/', - 'https://www.taobao.com/', - 'https://www.douban.com/') - # asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) - # 获取事件循环（） - loop = asyncio.get_event_loop() - tasks = [show_title(url) for url in urls] - loop.run_until_complete(asyncio.wait(tasks)) - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example_of_asyncio.py b/Day61-65/code/hello-tornado/example_of_asyncio.py deleted file mode 100644 index 0851f38..0000000 --- a/Day61-65/code/hello-tornado/example_of_asyncio.py +++ /dev/null @@ -1,40 +0,0 @@ -import asyncio - - -async def fetch(host): - """从指定的站点抓取信息(协程函数)""" - print(f'Start fetching {host}\n') - # 跟服务器建立连接 - reader, writer = await asyncio.open_connection(host, 80) - # 构造请求行和请求头 - writer.write(b'GET / HTTP/1.1\r\n') - writer.write(f'Host: {host}\r\n'.encode()) - writer.write(b'\r\n') - # 清空缓存区(发送请求) - await writer.drain() - # 接收服务器的响应(读取响应行和响应头) - line = await reader.readline() - while line != b'\r\n': - print(line.decode().rstrip()) - line = await reader.readline() - print('\n') - writer.close() - - -def main(): - """主函数""" - urls = ('www.sohu.com', 'www.douban.com', 'www.163.com') - # 获取系统默认的事件循环 - loop = asyncio.get_event_loop() - # 用生成式语法构造一个包含多个协程对象的列表 - tasks = [fetch(url) for url in urls] - # 通过asyncio模块的wait函数将协程列表包装成Task（Future子类）并等待其执行完成 - # 通过事件循环的run_until_complete方法运行任务直到Future完成并返回它的结果 - futures = asyncio.wait(tasks) - print(futures, type(futures)) - loop.run_until_complete(futures) - loop.close() - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example_of_coroutine.py b/Day61-65/code/hello-tornado/example_of_coroutine.py deleted file mode 100644 index 070dad2..0000000 --- a/Day61-65/code/hello-tornado/example_of_coroutine.py +++ /dev/null @@ -1,50 +0,0 @@ -""" -协程（coroutine）- 可以在需要时进行切换的相互协作的子程序 -""" -import asyncio - -from example_of_multiprocess import is_prime - - -def num_generator(m, n): - """指定范围的数字生成器""" - for num in range(m, n + 1): - print(f'generate number: {num}') - yield num - - -async def prime_filter(m, n): - """素数过滤器""" - primes = [] - for i in num_generator(m, n): - if is_prime(i): - print('Prime =>', i) - primes.append(i) - - await asyncio.sleep(0.001) - return tuple(primes) - - -async def square_mapper(m, n): - """平方映射器""" - squares = [] - for i in num_generator(m, n): - print('Square =>', i * i) - squares.append(i * i) - - await asyncio.sleep(0.001) - return squares - - -def main(): - """主函数""" - loop = asyncio.get_event_loop() - start, end = 1, 100 - futures = asyncio.gather(prime_filter(start, end), square_mapper(start, end)) - futures.add_done_callback(lambda x: print(x.result())) - loop.run_until_complete(futures) - loop.close() - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/example_of_multiprocess.py b/Day61-65/code/hello-tornado/example_of_multiprocess.py deleted file mode 100644 index bef4c0f..0000000 --- a/Day61-65/code/hello-tornado/example_of_multiprocess.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -用下面的命令运行程序并查看执行时间，例如： -time python3 example05.py -real 0m20.657s -user 1m17.749s -sys 0m0.158s -使用多进程后实际执行时间为20.657秒，而用户时间1分17.749秒约为实际执行时间的4倍 -这就证明我们的程序通过多进程使用了CPU的多核特性，而且这台计算机配置了4核的CPU -""" -import concurrent.futures -import math - -PRIMES = [ - 1116281, - 1297337, - 104395303, - 472882027, - 533000389, - 817504243, - 982451653, - 112272535095293, - 112582705942171, - 112272535095293, - 115280095190773, - 115797848077099, - 1099726899285419 -] * 5 - - -def is_prime(num): - """判断素数""" - assert num > 0 - if num % 2 == 0: - return False - for i in range(3, int(math.sqrt(num)) + 1, 2): - if num % i == 0: - return False - return num != 1 - - -def main(): - """主函数""" - with concurrent.futures.ProcessPoolExecutor() as executor: - for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)): - print('%d is prime: %s' % (number, prime)) - - -if __name__ == '__main__': - main() diff --git a/Day61-65/code/hello-tornado/requirements.txt b/Day61-65/code/hello-tornado/requirements.txt deleted file mode 100644 index 619660c..0000000 --- a/Day61-65/code/hello-tornado/requirements.txt +++ /dev/null @@ -1,18 +0,0 @@ -aiohttp==3.5.4 -aiomysql==0.0.20 -asn1crypto==0.24.0 -async-timeout==3.0.1 -attrs==19.1.0 -certifi==2019.3.9 -cffi==1.12.2 -chardet==3.0.4 -cryptography==2.6.1 -idna==2.8 -multidict==4.5.2 -pycparser==2.19 -PyMySQL==0.9.2 -requests==2.21.0 -six==1.12.0 -tornado==5.1.1 -urllib3==1.24.1 -yarl==1.3.0 diff --git a/Day61-65/code/hello-tornado/templates/chat.html b/Day61-65/code/hello-tornado/templates/chat.html deleted file mode 100644 index d83fd5c..0000000 --- a/Day61-65/code/hello-tornado/templates/chat.html +++ /dev/null @@ -1,67 +0,0 @@ -<!-- chat.html --> -<!DOCTYPE html> -<html lang="en"> -<head> - <meta charset="UTF-8"> - <title>Tornado聊天室 - - -

聊天室

-
-
- -
-
- - -
-

- 退出聊天室 -

- - - - diff --git a/Day61-65/code/hello-tornado/templates/login.html b/Day61-65/code/hello-tornado/templates/login.html deleted file mode 100644 index 69d6c55..0000000 --- a/Day61-65/code/hello-tornado/templates/login.html +++ /dev/null @@ -1,25 +0,0 @@ - - - - - - Tornado聊天室 - - - -
-
-

进入聊天室

-
-

{{hint}}

-
- - - -
-
-
- - diff --git a/Day61-65/code/hello-tornado/templates/news.html b/Day61-65/code/hello-tornado/templates/news.html deleted file mode 100644 index 9665d6b..0000000 --- a/Day61-65/code/hello-tornado/templates/news.html +++ /dev/null @@ -1,17 +0,0 @@ - - - - - 新闻列表 - - -

新闻列表

-
- {% for news in newslist %} -
- -

{{news['title']}}

-
- {% end %} - - \ No newline at end of file diff --git a/Day61-65/code/project_of_tornado/service/handlers/__init__.py b/Day61-65/code/image360/image360/__init__.py similarity index 100% rename from Day61-65/code/project_of_tornado/service/handlers/__init__.py rename to Day61-65/code/image360/image360/__init__.py diff --git a/Day66-75/code/image360/image360/items.py b/Day61-65/code/image360/image360/items.py similarity index 100% rename from Day66-75/code/image360/image360/items.py rename to Day61-65/code/image360/image360/items.py diff --git a/Day66-75/code/image360/image360/middlewares.py b/Day61-65/code/image360/image360/middlewares.py similarity index 100% rename from Day66-75/code/image360/image360/middlewares.py rename to Day61-65/code/image360/image360/middlewares.py diff --git a/Day66-75/code/image360/image360/pipelines.py b/Day61-65/code/image360/image360/pipelines.py similarity index 100% rename from Day66-75/code/image360/image360/pipelines.py rename to Day61-65/code/image360/image360/pipelines.py diff --git a/Day66-75/code/image360/image360/settings.py b/Day61-65/code/image360/image360/settings.py similarity index 100% rename from Day66-75/code/image360/image360/settings.py rename to Day61-65/code/image360/image360/settings.py diff --git a/Day66-75/code/image360/image360/spiders/__init__.py b/Day61-65/code/image360/image360/spiders/__init__.py similarity index 100% rename from Day66-75/code/image360/image360/spiders/__init__.py rename to Day61-65/code/image360/image360/spiders/__init__.py diff --git a/Day66-75/code/image360/image360/spiders/image.py b/Day61-65/code/image360/image360/spiders/image.py similarity index 100% rename from Day66-75/code/image360/image360/spiders/image.py rename to Day61-65/code/image360/image360/spiders/image.py diff --git a/Day66-75/code/image360/image360/spiders/taobao.py b/Day61-65/code/image360/image360/spiders/taobao.py similarity index 100% rename from Day66-75/code/image360/image360/spiders/taobao.py 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right; - text-align: right; - color: rgba(255, 255, 255, 0.7); - font-size: 11px; - width: 50px; - margin-left: 10px; -} -ul.tpl-dropdown-content .tpl-dropdown-menu-notifications:last-child .tpl-dropdown-menu-notifications-item { - text-align: center; - border: none; - font-size: 12px; -} -ul.tpl-dropdown-content .tpl-dropdown-menu-notifications:last-child .tpl-dropdown-menu-notifications-item i { - margin-left: -6px; -} -ul.tpl-dropdown-content .tpl-dropdown-menu-messages:last-child .tpl-dropdown-menu-messages-item { - text-align: center; - border: none; - font-size: 12px; -} -ul.tpl-dropdown-content .tpl-dropdown-menu-messages:last-child .tpl-dropdown-menu-messages-item i { - margin-left: -6px; -} -ul.tpl-dropdown-content .tpl-dropdown-menu-notifications-item, -ul.tpl-dropdown-content .tpl-dropdown-menu-messages-item { - padding: 12px; - color: #fff; - line-height: 20px; - border-bottom: 1px solid rgba(255, 255, 255, 0.15); -} -ul.tpl-dropdown-content .tpl-dropdown-menu-notifications-item:hover, -ul.tpl-dropdown-content .tpl-dropdown-menu-messages-item:hover, -ul.tpl-dropdown-content .tpl-dropdown-menu-notifications-item:focus, -ul.tpl-dropdown-content .tpl-dropdown-menu-messages-item:focus { - background-color: #465154; - color: #fff; -} -ul.tpl-dropdown-content .tpl-dropdown-menu-notifications-item .menu-messages-ico, -ul.tpl-dropdown-content .tpl-dropdown-menu-messages-item .menu-messages-ico { - line-height: initial; - float: left; - width: 35px; - height: 35px; - border-radius: 50%; - margin-right: 10px; - margin-top: 6px; - overflow: hidden; -} -ul.tpl-dropdown-content .tpl-dropdown-menu-notifications-item .menu-messages-ico img, -ul.tpl-dropdown-content .tpl-dropdown-menu-messages-item .menu-messages-ico img { - width: 100%; - height: auto; - vertical-align: middle; -} -ul.tpl-dropdown-content .tpl-dropdown-menu-notifications-item .menu-messages-time, -ul.tpl-dropdown-content .tpl-dropdown-menu-messages-item 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background: #fff; - border: 2px solid #eee; -} -.tpl-skiner-content-bar .skiner-black { - background: #000; - border: 2px solid #222; -} -.sub-active { - color: #fff!important; -} -.left-sidebar { - transition: all 0.4s ease-in-out; - width: 240px; - min-height: 100%; - padding-top: 57px; - position: absolute; - z-index: 1104; - top: 0; - left: 0px; -} -.left-sidebar.xs-active { - left: 0px; -} -.left-sidebar.active { - left: -240px; -} -.tpl-sidebar-user-panel { - padding: 22px; - padding-top: 28px; -} -.tpl-user-panel-profile-picture { - border-radius: 50%; - width: 82px; - height: 82px; - margin-bottom: 10px; - overflow: hidden; -} -.tpl-user-panel-profile-picture img { - width: auto; - height: 82px; - vertical-align: middle; -} -.tpl-user-panel-status-icon { - margin-right: 2px; -} -.user-panel-logged-in-text { - display: block; - color: #cfcfcf; - font-size: 14px; -} -.tpl-user-panel-action-link { - color: #6d787c; - font-size: 12px; -} -.tpl-user-panel-action-link:hover { - 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border-left: #282d2f 3px solid; - font-size: 14px; - cursor: pointer; -} -.sidebar-nav-link a.active { - cursor: pointer; - border-left: #1CA2CE 3px solid; - color: #fff; -} -.sidebar-nav-link a:hover { - color: #fff; -} -.tpl-content-wrapper { - transition: all 0.4s ease-in-out; - position: relative; - margin-left: 240px; - z-index: 1101; - min-height: 922px; - border-bottom-left-radius: 3px; -} -.tpl-content-wrapper.xs-active { - margin-left: 240px; -} -.tpl-content-wrapper.active { - margin-left: 0; -} -.page-header { - background: #424b4f; - margin-top: 0; - margin-bottom: 0; - padding: 40px 0; - border-bottom: 0; -} -.container-fluid { - margin-top: 0; - margin-bottom: 0; - padding: 40px 0; - border-bottom: 0; - padding-left: 20px; - padding-right: 20px; -} -.row { - margin-right: -10px; - margin-left: -10px; -} -.page-header-description { - margin-top: 4px; - margin-bottom: 0; - font-size: 14px; - color: #e6e6e6; -} -.page-header-heading { - font-size: 20px; - font-weight: 400; 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0); -} -.tpl-switch input[type="checkbox"].green.ios-switch:checked + div > div { - box-shadow: 0px 2px 5px rgba(0, 0, 0, 0.3), 0 0 0 1px #00a23f; -} -.tpl-page-state { - width: 100%; -} -.tpl-page-state-title { - font-size: 40px; - font-weight: bold; -} -.tpl-page-state-content { - padding: 10px 0; -} -.tpl-login { - width: 100%; -} -.tpl-login-logo { - max-width: 159px; - height: 205px; - margin: 0 auto; - margin-bottom: 20px; -} -.tpl-login-title { - width: 100%; - font-size: 24px; -} -.tpl-login-content { - width: 300px; - margin: 12% auto 0; -} -.tpl-login-remember-me { - color: #B3B3B3; - font-size: 14px; -} -.tpl-login-remember-me label { - position: relative; - top: -2px; -} -.tpl-login-content-info { - color: #B3B3B3; - font-size: 14px; -} -.cl-p { - padding: 0!important; -} -.tpl-table-line-img { - max-width: 100px; - padding: 2px; -} -.tpl-table-list-select { - text-align: right; -} -.fc-button-group, -.fc button { - display: block; -} -.theme-white { - background: #e9ecf3; 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input[type=search], -.theme-black .tpl-form-line-form input[type=text], -.theme-black .tpl-form-line-form input[type=password], -.theme-black .tpl-form-line-form input[type=datetime], -.theme-black .tpl-form-line-form input[type=datetime-local], -.theme-black .tpl-form-line-form input[type=date], -.theme-black .tpl-form-line-form input[type=month], -.theme-black .tpl-form-line-form input[type=time], -.theme-black .tpl-form-line-form input[type=week], -.theme-black .tpl-form-line-form input[type=email], -.theme-black .tpl-form-line-form input[type=url], -.theme-black .tpl-form-line-form input[type=tel], -.theme-black .tpl-form-line-form input[type=color], -.theme-black .tpl-form-line-form select, -.theme-black .tpl-form-line-form textarea, -.theme-black .am-form-field { - display: block; - width: 100%; - padding: 6px 12px; - line-height: 1.42857; - color: #4d6b8a; - background-color: #fff; - background-image: none; - border: 1px solid #c2cad8; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - -webkit-transition: border-color ease-in-out 0.15s, box-shadow ease-in-out 0.15s; - -o-transition: border-color ease-in-out 0.15s, box-shadow ease-in-out 0.15s; - transition: border-color ease-in-out 0.15s, box-shadow ease-in-out 0.15s; - background: 0 0; - border: 0; - border-bottom: 1px solid rgba(255, 255, 255, 0.2); - -webkit-border-radius: 0; - -moz-border-radius: 0; - -ms-border-radius: 0; - -o-border-radius: 0; - border-radius: 0; - color: #fff; - box-shadow: none; - padding-left: 0; - padding-right: 0; - font-size: 14px; -} -.theme-black .tpl-form-line-form .am-checkbox, -.theme-black .tpl-form-line-form .am-checkbox-inline, -.theme-black .tpl-form-line-form .am-form-label, -.theme-black .tpl-form-line-form .am-radio, -.theme-black .tpl-form-line-form .am-radio-inline { - margin-top: 0; - margin-bottom: 0; -} -.theme-black .tpl-form-line-form .am-form-group:after { - clear: both; -} -.theme-black .tpl-form-line-form .am-form-group:after, -.theme-black .tpl-form-line-form .am-form-group:before { - content: " "; - display: table; -} -.theme-black .tpl-form-line-form .am-form-label { - padding-top: 5px; - font-size: 16px; - color: #fff; - font-weight: inherit; - text-align: right; -} -.theme-black .tpl-form-line-form .am-form-group { - /*padding: 20px 0;*/ -} -.theme-black .tpl-form-line-form .am-form-label .tpl-form-line-small-title { - color: #999; - font-size: 12px; -} -.theme-black .tpl-table-black-operation a { - border: 1px solid #7b878d; - color: #7b878d; -} -.theme-black .tpl-table-black-operation a:hover { - background: #7b878d; - color: #fff; -} -.theme-black .tpl-table-black-operation a.tpl-table-black-operation-del { - border: 1px solid #f35842; - color: #f35842; -} -.theme-black .tpl-table-black-operation a.tpl-table-black-operation-del:hover { - background: #f35842; - color: #fff; -} -.theme-black .am-table-bordered { - border: 1px solid #666d70; -} -.theme-black .am-table-bordered > tbody > tr > td, -.theme-black .am-table-bordered > tbody > tr > th, -.theme-black .am-table-bordered > tfoot > tr > td, -.theme-black .am-table-bordered > tfoot > tr > th, -.theme-black .am-table-bordered > thead > tr > td, -.theme-black .am-table-bordered > thead > tr > th { - border: 1px solid #666d70; -} -.theme-black .am-table-bordered > thead + tbody > tr:first-child > td, -.theme-black .am-table-bordered > thead + tbody > tr:first-child > th { - border: 1px solid #666d70; -} -.theme-black .am-table-striped > tbody > tr:nth-child(odd) > td, -.theme-black .am-table-striped > tbody > tr:nth-child(odd) > th { - background-color: #5d6468; -} -.theme-black .tpl-table-black { - color: #fff; -} -.theme-black .tpl-table-black thead > tr > th { - font-size: 14px; - padding: 6px; - border-bottom: 1px solid #666d70; -} -.theme-black .tpl-table-black tbody > tr > td { - font-size: 14px; - padding: 7px 6px; - border-top: 1px solid #666d70; -} -.theme-black .tpl-table-black tfoot > tr > th { - font-size: 14px; - padding: 6px 0; -} -.theme-black .tpl-user-card { - border: 1px solid #11627d; - border-top: 2px solid #105f79; - background: #1786aa; - color: #ffffff; -} -.theme-black .tpl-user-card-title { - font-size: 26px; - margin-top: 0; - font-weight: 300; - margin-top: 25px; - margin-bottom: 10px; -} -.theme-black .achievement-subheading { - font-size: 12px; - margin-top: 0; - margin-bottom: 15px; -} -.theme-black .achievement-image { - border-radius: 50%; - margin-bottom: 22px; -} -.theme-black .achievement-description { - margin: 0; - font-size: 12px; -} -.theme-black .am-progress { - height: 12px; - margin-bottom: 14px; - background: rgba(0, 0, 0, 0.15); -} -.theme-black .am-progress-title { - font-size: 14px; - margin-bottom: 8px; -} -.theme-black .am-progress-title-more { - color: #a1a8ab; -} -.theme-black .widget-fluctuation-tpl-btn { - margin-top: 6px; - display: block; - color: #fff; - font-size: 12px; - padding: 5px 10px; - outline: none; - background-color: rgba(255, 255, 255, 0); - border: 1px solid #fff; -} -.theme-black .widget-fluctuation-tpl-btn:hover { - background: #fff; - color: #4b5357; -} -.theme-black .widget-fluctuation-description-text { - color: #c5cacd; -} -.theme-black .text-success { - color: #08ed72; -} -.theme-black .widget-fluctuation-period-text { - color: #fff; -} -.theme-black .widget-head { - border-bottom: 1px solid #3f4649; -} -.theme-black .widget-function a { - color: #7b878d; -} -.theme-black .widget-function a:hover { - color: #fff; -} -.theme-black .widget { - border: 1px solid #33393c; - border-top: 2px solid #313639; - background: #4b5357; - color: #ffffff; -} -.theme-black .widget-primary { - border: 1px solid #11627d; - border-top: 2px solid #105f79; - background: #1786aa; - color: #ffffff; - padding: 12px 17px; -} -.theme-black .widget-statistic-icon { - position: absolute; - z-index: 30; - right: 30px; - top: 0px; - font-size: 70px; - color: #1b9eca; -} -.theme-black .widget-statistic-description { - position: relative; - z-index: 35; - display: block; - font-size: 14px; - line-height: 14px; - padding-top: 8px; - color: #9cdcf2; -} -.theme-black .widget-statistic-value { - position: relative; - z-index: 35; - font-weight: 300; - display: block; - color: #fff; - font-size: 46px; - line-height: 46px; - margin-bottom: 8px; -} -.theme-black .widget-statistic-header { - color: #9cdcf2; -} -.theme-black .widget-purple { - padding: 12px 17px; - border: 1px solid #5e4578; - border-top: 2px solid #5c4375; - background: #785799; - color: #ffffff; -} -.theme-black .widget-purple .widget-statistic-icon { - color: #8a6aaa; -} -.theme-black .widget-purple .widget-statistic-header { - color: #ded5e7; -} -.theme-black .widget-purple .widget-statistic-description { - color: #ded5e7; -} -.theme-black .page-header-description { - color: #e6e6e6; -} -.theme-black .page-header-heading { - color: #666; -} -.theme-black .container-fluid { - background: #424b4f; -} -.theme-black .page-header-heading { - color: #fff; -} -.theme-black .sidebar-nav-heading { - color: #fff; -} -.theme-black .tpl-sidebar-user-panel { - background: #1f2224; - border-bottom: 1px solid #1f2224; -} -.theme-black .tpl-content-wrapper { - background: #3a4144; -} -.theme-black .tpl-header-fluid { - background: #2f3638; -} -.theme-black .sidebar-nav-link a.active { - background: #232829; -} -.theme-black .sidebar-nav-link a:hover { - background: #232829; -} -.theme-black .tpl-header-switch-button { - background: #2f3638; - border-right: 1px solid #282d2f; -} -.theme-black .tpl-header-switch-button:hover { - background: #282d2f; - color: #fff; -} -.theme-black .tpl-header-navbar a { - color: #cfcfcf; -} -.theme-black .tpl-header-navbar a:hover { - color: #fff; -} -.theme-black .left-sidebar { - padding-top: 56px; - background: #282d2f; -} -.theme-black .widget-color-green { - border: 1px solid #11627d; - border-top: 2px solid #105f79; - background: #1786aa; - color: #ffffff; -} -.theme-black .widget-color-green .widget-head { - border-bottom: 1px solid #147494; -} -.theme-black .widget-color-green .widget-fluctuation-description-text { - color: #bbe7f6; -} -.theme-black .widget-color-green .widget-function a { - color: #42bde5; -} -.theme-black .widget-color-green .widget-function a:hover { - color: #fff; -} -@media screen and (max-width: 1024px) { - .tpl-index-settings-button { - display: none; - } - .theme-black .left-sidebar { - padding-top: 111px; - } - .left-sidebar { - padding-top: 111px; - } - .tpl-content-wrapper { - margin-left: 0; - } - .tpl-header-logo { - float: none; - width: 100%; - } - .tpl-header-navbar-welcome { - display: none; - } - .tpl-sidebar-user-panel { - border-top: 1px solid #eee; - } - .tpl-header-fluid { - border-top: none; - margin-left: 0; - } - .theme-white .tpl-header-fluid { - border-top: none; - } - .theme-black .tpl-sidebar-user-panel { - border-top: 1px solid #1f2224; - } -} -@media screen and (min-width: 641px) { - [class*=am-u-] { - padding-left: 10px; - padding-right: 10px; - } -} -@media screen and (max-width: 641px) { - .theme-white .tpl-error-title, - .theme-black .tpl-error-title { - font-size: 130px; - line-height: 140px; - } - .theme-white .tpl-login-title { - font-size: 20px; - } - .theme-white .tpl-login-content { - width: 86%; - padding: 22px 30px 25px; - } - .tpl-header-search { - display: none; - } - ul.tpl-dropdown-content { - position: fixed; - width: 100%; - left: 0; - top: 112px; - right: 0; - } -} diff --git a/Day61-65/code/project_of_tornado/assets/css/app.less b/Day61-65/code/project_of_tornado/assets/css/app.less deleted file mode 100644 index 1b8de9a..0000000 --- a/Day61-65/code/project_of_tornado/assets/css/app.less +++ /dev/null @@ -1,2056 +0,0 @@ -ul,li { - list-style: none; - padding: 0; - margin: 0; -} - -a { - -} - -header { - z-index: 1200; - position: relative; -} -.tpl-header-logo { - width: 240px; - height: 57px; - display: table; - text-align:center; - position: relative; - z-index: 1300; - - a { - display:table-cell; - vertical-align:middle; - } - - img { - width:170px; - } -} - - -.tpl-header-fluid { - margin-left: 240px; - height: 56px; - - padding-left: 20px; - padding-right: 20px; -} - - -.tpl-header-switch-button { - - margin-top: 0px; - margin-bottom: 0px; - float: left; - color: #cfcfcf; - margin-left: -20px; - margin-right: 0; - border: 0; - border-radius: 0; - padding: 0px 22px; - font-size: 22px; - line-height: 55px; - - &:hover { - - outline: none; - } -} - - -.tpl-header-search-form { - height: 54px; - line-height: 52px; - margin-left: 10px; - -} -.tpl-header-search-box , .tpl-header-search-btn { - transition: all 0.4s ease-in-out; - color: #848c90; - background: none; - border: none; - outline: none; -} - -.tpl-header-search-box { - font-size: 14px; - - &:hover,&:active { - color: #fff; - } -} - -.tpl-header-search-btn { - font-size: 15px; - - &:hover,&:active { - color: #fff; - } -} - -.tpl-header-navbar { - color: #fff; - li { - float: left; - } - a { - line-height: 56px; - display: block; - padding: 0 16px; - position: relative; - - - &:hover { - - } - - .item-feed-badge { - position: absolute; - top: 9px; - left: 25px; - } - } -} - -ul.tpl-dropdown-content { - padding: 10px; - margin-top: 0; - width: 300px; - background-color: #2f3638; - border: 1px solid #525e62; - border-radius: 0; - - li { - float:none; - } - - &:before , &:after { - display: none; - } -} - - -ul.tpl-dropdown-content { - - - .tpl-dropdown-menu-notifications { - - } - - .tpl-dropdown-menu-notifications-title { - font-size: 12px; - float: left; - color: rgba(255, 255, 255, 0.7); - } - - .tpl-dropdown-menu-notifications-time { - float: right; - text-align: right; - color: rgba(255, 255, 255, 0.7); - font-size: 11px; - width: 50px; - margin-left: 10px; - } - - .tpl-dropdown-menu-notifications:last-child .tpl-dropdown-menu-notifications-item { - text-align: center; - border: none; - font-size: 12px; - i { - margin-left: -6px; - } - } - - .tpl-dropdown-menu-messages:last-child .tpl-dropdown-menu-messages-item { - text-align: center; - border: none; - font-size: 12px; - i { - margin-left: -6px; - } - } - .tpl-dropdown-menu-notifications-item , .tpl-dropdown-menu-messages-item { - padding: 12px; - color: #fff; - line-height: 20px; - border-bottom: 1px solid rgba(255, 255, 255, 0.15); - - &:hover , &:focus { - background-color: #465154; - color: #fff; - } - - - - .menu-messages-ico { - line-height: initial; - float: left; - width: 35px; - height: 35px; - border-radius: 50%; - margin-right: 10px; - margin-top: 6px; - overflow: hidden; - - img { - width: 100%; - height: auto; - vertical-align: middle; - } - } - - .menu-messages-time { - float: right; - text-align: right; - color: rgba(255, 255, 255, 0.7); - font-size: 11px; - width: 40px; - margin-left: 10px; - - - } - - .menu-messages-content { - display: block; - font-size: 13px; - margin-left: 45px; - margin-right: 50px; - - .menu-messages-content-title { - - } - - .menu-messages-content-time { - margin-top: 3px; - color: rgba(255, 255, 255, 0.7); - font-size: 11px; - } - } - - - } -} - -.am-dimmer { - z-index: 1200; -} -.am-modal { - z-index: 1300; -} -.am-datepicker-dropdown { - z-index: 1400; -} - -.tpl-skiner { - transition: all 0.4s ease-in-out; - position: fixed; - z-index: 10000; - right: -130px; - top: 65px; -} - -.tpl-skiner.active { - right: 0px; -} -.tpl-skiner-content { - background: rgba(0, 0, 0, 0.7); - width: 130px; - padding: 15px; - border-radius: 4px 0 0 4px; - overflow: hidden; -} - -.fc-content .am-icon-close { - position: absolute; - right: 0; - top: 0px; -} -.tpl-skiner-toggle { - position: absolute; - top: 5px; - left: -40px; - width: 40px; - color:#969a9b; - font-size: 20px; - height: 40px; - line-height: 40px; - text-align: center; - background: rgba(0, 0, 0, 0.7); - cursor: pointer; - border-top-left-radius: 4px; - border-bottom-left-radius: 4px; - -} -.tpl-skiner-content-title { - margin: 0; - margin-bottom: 4px; - padding-bottom: 4px; - font-size: 16px; - text-transform: uppercase; - color:#fff; - border-bottom: 1px solid rgba(255, 255, 255, 0.3); -} - -.tpl-skiner-content-bar { - padding-top: 10px; - .skiner-color { - transition: all 0.4s ease-in-out; - float: left; - width: 25px; - height: 25px; - margin-right: 10px; - cursor: pointer; - } - .skiner-white { - background: #fff; - border: 2px solid #eee; - } - - .skiner-black { - background: #000; - border: 2px solid #222; - } -} - -.sub-active { - color:#fff!important; -} -.left-sidebar { - transition: all 0.4s ease-in-out; - width: 240px; - min-height: 100%; - padding-top: 57px; - position: absolute; - z-index: 1104; - top: 0; - left: 0px; - &.xs-active { - left:0px; - } - &.active { - left:-240px; - } - -} -.tpl-sidebar-user-panel { - padding: 22px; - padding-top: 28px; -} - -.tpl-user-panel-slide-toggleable { - -} - -.tpl-user-panel-profile-picture { - border-radius: 50%; - width: 82px; - height: 82px; - margin-bottom: 10px; - overflow: hidden; - - img { - width: auto; - height: 82px; - vertical-align: middle; - } -} -.tpl-user-panel-status-icon { - margin-right: 2px; -} -.user-panel-logged-in-text { - display: block; - - color:#cfcfcf; - font-size: 14px; -} -.tpl-user-panel-action-link { - color: #6d787c; - font-size: 12px; - &:hover { - color: #a2aaad; - } -} - -.sidebar-nav { - list-style-type: none; - padding: 0; - margin: 0; -} - -.sidebar-nav-sub { - display: none; - .sidebar-nav-link { - font-size: 12px; - padding-left: 30px; - a { - font-size: 12px; - padding-left: 0; - } - } - - .sidebar-nav-link-logo { - margin-right: 8px; - width: 20px; - font-size: 16px; - } -} - -.sidebar-nav-sub-ico-rotate{ - -webkit-transform: rotate(180deg); - transform: rotate(180deg); - -webkit-transition: all 300ms; - transition: all 300ms; -} -.sidebar-nav-link-logo-ico { - margin-top: 5px; -} -.sidebar-nav-heading { - padding: 24px 17px; - font-size: 15px; - font-weight: 500; -} -.sidebar-nav-heading-info { - font-size: 12px; - color:#868E8E; - padding-left: 10px; -} -.sidebar-nav-link-logo { - margin-right: 8px; - width: 20px; - font-size: 16px; -} -.sidebar-nav-link { - - color: #fff; - - a { - display: block; - color: #868E8E; - padding: 10px 17px; - border-left: #282d2f 3px solid; - font-size: 14px; - cursor: pointer; - - &.active { - cursor: pointer; - border-left: #1CA2CE 3px solid; - color: #fff; - - } - - &:hover { - color: #fff; - } - } -} - -.tpl-content-wrapper { - transition: all 0.4s ease-in-out; - position: relative; - margin-left: 240px; - z-index: 1101; - min-height: 922px; - border-bottom-left-radius: 3px; - &.xs-active { - margin-left: 240px; - } - &.active { - margin-left: 0; - } -} - -.page-header { - background: #424b4f; - margin-top: 0; - margin-bottom: 0; - padding: 40px 0; - border-bottom: 0; -} - -.container-fluid { - margin-top: 0; - margin-bottom: 0; - padding: 40px 0; - border-bottom: 0; - padding-left: 20px; - padding-right: 20px; -} - -.row { - margin-right: -10px; - margin-left: -10px; -} - -.page-header-description { - margin-top: 4px; - margin-bottom: 0; - font-size: 14px; - color: #e6e6e6; -} -.page-header-heading { - font-size: 20px; - font-weight: 400; - .page-header-heading-ico { - font-size: 28px; - position: relative; - top: 3px; - } - small { - font-weight: normal; - line-height: 1; - color: #B3B3B3; - } -} - -.page-header-button { - transition: all 0.4s ease-in-out; - opacity: 0.3; - font-weight: 500; - border-radius: 0; - float: right; - outline: none; - border: 1px solid #fff; - padding: 16px 36px; - font-size: 23px; - line-height: 23px; - border-radius: 0; - padding-top: 14px; - color: #fff; - background-color: rgba(0, 0, 0, 0); - font-weight: 500; - - &:hover { - background-color: #ffffff; - color: #333; - opacity: 1; - } -} -.widget { - width: 100%; - min-height: 148px; - margin-bottom: 20px; - border-radius: 0; - position: relative; -} - -.widget-head { - width: 100%; - padding: 15px; -} - -.widget-title { - font-size: 14px; -} -.widget-function { - -} -.widget-fluctuation-period-text { - display: inline-block; - font-size: 16px; - line-height: 20px; - margin-bottom: 9px; -} -.widget-body { - padding: 13px 15px; - width: 100%; -} -.row-content { - padding: 20px; -} - -.widget-fluctuation-description-text{ -margin-top: 4px; - display: block; - font-size: 12px; - line-height: 13px; - } - -.text-success { - -} -.widget-fluctuation-tpl-btn { - -} -.widget-fluctuation-description-amount { - display: block; - font-size: 20px; - line-height: 22px; -} - -.widget-primary { - -} - -.widget-statistic-header { - position: relative; - z-index: 35; - display: block; - font-size: 14px; - text-transform: uppercase; - margin-bottom: 8px; -} - .widget-body-md { - height: 200px; - } -.widget-body-lg { - min-height: 330px; - // height: 330px; -} -.widget-margin-bottom-lg { - margin-bottom: 20px; -} - -.tpl-table-black-operation { - -} - - -.tpl-table-black-operation { - a { - display: inline-block; - padding: 5px 6px; - font-size: 12px; - line-height: 12px; - } -} -.tpl-switch input[type="checkbox"] { - position: absolute; - opacity: 0; - width: 50px; - height: 20px; - } - - - .tpl-switch input[type="checkbox"].ios-switch + div { - vertical-align: middle; - width: 40px; - height: 20px; - - border-radius: 999px; - background-color: rgba(0, 0, 0, 0.1); - -webkit-transition-duration: .4s; - -webkit-transition-property: background-color, box-shadow; - - margin-top: 6px; - } - - - .tpl-switch input[type="checkbox"].ios-switch:checked + div { - width: 40px; - background-position: 0 0; - background-color: #36c6d3; - - - } - - - .tpl-switch input[type="checkbox"].tinyswitch.ios-switch + div { - width: 34px; - height: 18px; - } - - - .tpl-switch input[type="checkbox"].bigswitch.ios-switch + div { - width: 50px; - height: 25px; - } - - - .tpl-switch input[type="checkbox"].green.ios-switch:checked + div { - background-color: #00e359; - border: 1px solid rgba(0, 162, 63, 1); - box-shadow: inset 0 0 0 10px rgba(0, 227, 89, 1); - } - - - .tpl-switch input[type="checkbox"].ios-switch + div > div { - float: left; - width: 18px; - height: 18px; - border-radius: inherit; - background: #ffffff; - -webkit-transition-timing-function: cubic-bezier(.54, 1.85, .5, 1); - -webkit-transition-duration: 0.4s; - -webkit-transition-property: transform, background-color, box-shadow; - -moz-transition-timing-function: cubic-bezier(.54, 1.85, .5, 1); - -moz-transition-duration: 0.4s; - -moz-transition-property: transform, background-color; - - pointer-events: none; - margin-top: 1px; - margin-left: 1px; - } - - - .tpl-switch input[type="checkbox"].ios-switch:checked + div > div { - -webkit-transform: translate3d(20px, 0, 0); - -moz-transform: translate3d(20px, 0, 0); - background-color: #ffffff; - - } - - - .tpl-switch input[type="checkbox"].tinyswitch.ios-switch + div > div { - width: 16px; - height: 16px; - margin-top: 1px; - } - - - .tpl-switch input[type="checkbox"].tinyswitch.ios-switch:checked + div > div { - -webkit-transform: translate3d(16px, 0, 0); - -moz-transform: translate3d(16px, 0, 0); - box-shadow: 0px 2px 5px rgba(0, 0, 0, 0.3), 0px 0px 0 1px rgba(8, 80, 172, 1); - } - - - .tpl-switch input[type="checkbox"].bigswitch.ios-switch + div > div { - width: 23px; - height: 23px; - margin-top: 1px; - } - - - .tpl-switch input[type="checkbox"].bigswitch.ios-switch:checked + div > div { - -webkit-transform: translate3d(25px, 0, 0); - -moz-transform: translate3d(16px, 0, 0); - - } - - - .tpl-switch input[type="checkbox"].green.ios-switch:checked + div > div { - box-shadow: 0px 2px 5px rgba(0, 0, 0, 0.3), 0 0 0 1px rgba(0, 162, 63, 1); - } - - - -.tpl-page-state { - width: 100%; -} - -.tpl-page-state-title { - font-size: 40px; - font-weight: bold; -} - -.tpl-page-state-content { - padding: 10px 0; -} - -.tpl-login { - width: 100%; -} - -.tpl-login-logo { - max-width: 159px; - height: 205px; - margin: 0 auto; - margin-bottom: 20px; -} -.tpl-login-title { - width: 100%; - font-size: 24px; -} -.tpl-login-content { - width: 300px; - margin: 12% auto 0; -} -.tpl-login-remember-me { - color: #B3B3B3; - font-size: 14px; - - label { - position: relative; - top: -2px; - } -} -.tpl-login-content-info { - color: #B3B3B3; - font-size: 14px; -} - -.tpl-pagination { - -} - -.cl-p { - padding: 0!important; -} -.tpl-table-line-img { - max-width: 100px; - padding: 2px; -} -.tpl-table-list-select { - text-align:right; - } -.fc-button-group, .fc button { - display: block; -} - -.theme-white { - - .sidebar-nav-sub { - .sidebar-nav-link-logo { - margin-left: 10px; - } - } - .tpl-header-search-box:hover, .tpl-header-search-box:active - .tpl-error-title { - - color: #848c90; - } - .tpl-error-title-info { - line-height: 30px; - font-size: 21px; - margin-top: 20px; - text-align: center; - color: #dce2ec; - } - .tpl-error-btn { - background: #03a9f3; - border: 1px solid #03a9f3; - border-radius: 30px; - padding: 6px 20px 8px; - } - .tpl-error-content { - margin-top: 20px; - margin-bottom: 20px; - font-size: 16px; - text-align: center; - color: #96a2b4; - } -.tpl-calendar-box { - background: #fff; - border-radius: 4px; - padding: 20px; - .fc-event { - border-radius: 0; - background: #03a9f3; - border: 1px solid #14b0f6; - } - .fc-axis { - color: #868E8E; - } - .fc-unthemed .fc-today { - background: #eee; - } - .fc-more { - color: #868E8E; - } - - .fc th.fc-widget-header { - background: #32c5d2!important; - - color: #ffffff; - font-size: 14px; - line-height: 20px; - padding: 7px 0px; - text-transform: uppercase; - border:none!important; - a { - color: #fff; - } - } - - .fc-center { - h2 { - color:#868E8E; - } - } - .fc-state-default { - background-image: none; - background: #fff; - font-size: 14px; - color: #868E8E; -} - .fc th, .fc td, .fc hr, .fc thead, .fc tbody, .fc-row { - // background: rgba(0, 0, 0, 0)!important; - border-color: #eee!important; - } - .fc-day-number { - color: #868E8E; - padding-right: 6px; - } - .fc th { - color: #868E8E; - font-weight: normal; - font-size: 14px; - padding: 6px 0; - } - } - - .tpl-login-logo { - background: url(../img/logoa.png) center no-repeat; - - } - .sub-active { - - color:#23abf0!important; - } -.tpl-table-line-img { - border: 1px solid #ddd; -} -.tpl-pagination .am-disabled a , .tpl-pagination li a { - color: #23abf0; - border-radius: 3px; - padding: 6px 12px; -} - -.tpl-pagination .am-active a{ - background: #23abf0;color: #fff; - border: 1px solid #23abf0; - padding: 6px 12px; -} - - -.tpl-login-btn { - background-color:#32c5d2; - border: none; - padding: 10px 16px; - font-size: 14px; - line-height: 14px; - outline: none; - - &:hover,&:active { - background: #22b2e1; - color:#fff; - } - -} -.tpl-login-title { - color: #697882; - strong { - color: #39bae4; - } -} - .tpl-login-content{ - width: 500px; - padding: 40px 40px 25px; - background-color: #fff; - border-radius: 4px; - } - - .tpl-form-line-form , .tpl-form-border-form { - padding-top: 20px; - } - - -.tpl-form-border-form input[type=number]:focus, .tpl-form-border-form input[type=search]:focus, .tpl-form-border-form input[type=text]:focus, .tpl-form-border-form input[type=password]:focus, .tpl-form-border-form input[type=datetime]:focus, .tpl-form-border-form input[type=datetime-local]:focus, .tpl-form-border-form input[type=date]:focus, .tpl-form-border-form input[type=month]:focus, .tpl-form-border-form input[type=time]:focus, .tpl-form-border-form input[type=week]:focus, .tpl-form-border-form input[type=email]:focus, .tpl-form-border-form input[type=url]:focus, .tpl-form-border-form input[type=tel]:focus, .tpl-form-border-form input[type=color]:focus, .tpl-form-border-form select:focus, .tpl-form-border-form textarea:focus, .am-form-field:focus{ - -webkit-box-shadow: none; - box-shadow: none; - } -.tpl-form-border-form input[type=number], .tpl-form-border-form input[type=search], .tpl-form-border-form input[type=text], .tpl-form-border-form input[type=password], .tpl-form-border-form input[type=datetime], .tpl-form-border-form input[type=datetime-local], .tpl-form-border-form input[type=date], .tpl-form-border-form input[type=month], .tpl-form-border-form input[type=time], .tpl-form-border-form input[type=week], .tpl-form-border-form input[type=email], .tpl-form-border-form input[type=url], .tpl-form-border-form input[type=tel], .tpl-form-border-form input[type=color], .tpl-form-border-form select, .tpl-form-border-form textarea, .am-form-field { - display: block; - width: 100%; - - padding: 6px 12px; - font-size: 14px; - line-height: 1.42857; - color: #4d6b8a; - background-color: #fff; - background-image: none; - border: 1px solid #c2cad8; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,0.075); - box-shadow: inset 0 1px 1px rgba(0,0,0,0.075); - -webkit-transition: border-color ease-in-out 0.15s,box-shadow ease-in-out 0.15s; - -o-transition: border-color ease-in-out 0.15s,box-shadow ease-in-out 0.15s; - transition: border-color ease-in-out 0.15s,box-shadow ease-in-out 0.15s; - background: 0 0; - border: 0; - border: 1px solid #c2cad8; - -webkit-border-radius: 0; - -moz-border-radius: 0; - -ms-border-radius: 0; - text-indent: .5em; - -o-border-radius: 0; - border-radius: 0; - color: #555; - box-shadow: none; - padding-left: 0; - padding-right: 0; - font-size: 14px; -} - -.tpl-form-border-form .am-checkbox, .tpl-form-border-form .am-checkbox-inline, .tpl-form-border-form .am-form-label, .tpl-form-border-form .am-radio, .tpl-form-border-form .am-radio-inline{ - margin-top: 0; - margin-bottom: 0; - -} - -.tpl-form-border-form .am-form-group:after { - clear: both; -} -.tpl-form-border-form .am-form-group:after, .tpl-form-border-form .am-form-group:before { -content: " "; - display: table; - -} -.tpl-form-border-form .am-form-label{ - padding-top: 5px; -font-size: 16px; -color: #888; -font-weight: inherit; -text-align: right; -} -.tpl-form-border-form .am-form-group { - /*padding: 20px 0;*/ - -} -.tpl-form-border-form .am-form-label .tpl-form-line-small-title { - color: #999; - font-size: 12px; -} - - .tpl-form-line-form input[type=number]:focus, .tpl-form-line-form input[type=search]:focus, .tpl-form-line-form input[type=text]:focus, .tpl-form-line-form input[type=password]:focus, .tpl-form-line-form input[type=datetime]:focus, .tpl-form-line-form input[type=datetime-local]:focus, .tpl-form-line-form input[type=date]:focus, .tpl-form-line-form input[type=month]:focus, .tpl-form-line-form input[type=time]:focus, .tpl-form-line-form input[type=week]:focus, .tpl-form-line-form input[type=email]:focus, .tpl-form-line-form input[type=url]:focus, .tpl-form-line-form input[type=tel]:focus, .tpl-form-line-form input[type=color]:focus, .tpl-form-line-form select:focus, .tpl-form-line-form textarea:focus, .am-form-field:focus{ - -webkit-box-shadow: none; - box-shadow: none; - } -.tpl-form-line-form input[type=number], .tpl-form-line-form input[type=search], .tpl-form-line-form input[type=text], .tpl-form-line-form input[type=password], .tpl-form-line-form input[type=datetime], .tpl-form-line-form input[type=datetime-local], .tpl-form-line-form input[type=date], .tpl-form-line-form input[type=month], .tpl-form-line-form input[type=time], .tpl-form-line-form input[type=week], .tpl-form-line-form input[type=email], .tpl-form-line-form input[type=url], .tpl-form-line-form input[type=tel], .tpl-form-line-form input[type=color], .tpl-form-line-form select, .tpl-form-line-form textarea, .am-form-field { - display: block; - width: 100%; - - padding: 6px 12px; - font-size: 14px; - line-height: 1.42857; - color: #4d6b8a; - background-color: #fff; - background-image: none; - border: 1px solid #c2cad8; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,0.075); - box-shadow: inset 0 1px 1px rgba(0,0,0,0.075); - -webkit-transition: border-color ease-in-out 0.15s,box-shadow ease-in-out 0.15s; - -o-transition: border-color ease-in-out 0.15s,box-shadow ease-in-out 0.15s; - transition: border-color ease-in-out 0.15s,box-shadow ease-in-out 0.15s; - background: 0 0; - border: 0; - border-bottom: 1px solid #c2cad8; - -webkit-border-radius: 0; - -moz-border-radius: 0; - -ms-border-radius: 0; - -o-border-radius: 0; - border-radius: 0; - color: #555; - box-shadow: none; - padding-left: 0; - padding-right: 0; - font-size: 14px; -} - -.tpl-form-line-form .am-checkbox, .tpl-form-line-form .am-checkbox-inline, .tpl-form-line-form .am-form-label, .tpl-form-line-form .am-radio, .tpl-form-line-form .am-radio-inline{ - margin-top: 0; - margin-bottom: 0; - -} - -.tpl-form-line-form .am-form-group:after { - clear: both; -} -.tpl-form-line-form .am-form-group:after, .tpl-form-line-form .am-form-group:before { -content: " "; - display: table; - -} -.tpl-form-line-form .am-form-label{ - padding-top: 5px; -font-size: 16px; -color: #888; -font-weight: inherit; -text-align: right; -} -.tpl-form-line-form .am-form-group { - /*padding: 20px 0;*/ - -} -.tpl-form-line-form .am-form-label .tpl-form-line-small-title { - color: #999; - font-size: 12px; -} - - .tpl-table-black-operation { - a { - border: 1px solid #36c6d3; - color:#36c6d3; - &:hover { - background: #36c6d3; - color:#fff; - } - } - a.tpl-table-black-operation-del { - border: 1px solid #e7505a; - color:#e7505a; - &:hover { - background: #e7505a; - color:#fff; - } - } -} - .tpl-amendment-echarts { - left: -17px; - } - .tpl-user-card { - border: 1px solid #3598dc; - border-top: 2px solid #3598dc; - background: #3598dc; - color: #ffffff; - border-radius: 4px; - } - .tpl-user-card-title { - font-size: 26px; - margin-top: 0; - font-weight: 300; - margin-top: 25px; - margin-bottom: 10px; - } - .achievement-subheading { - font-size: 12px; - margin-top: 0; - margin-bottom: 15px; - } - .achievement-image { - border-radius: 50%; - margin-bottom: 22px; - } - .achievement-description { - margin: 0; - font-size: 12px; - } - - .tpl-table-black { - color: #838FA1; - - thead>tr>th { - font-size: 14px; - padding: 6px; - } - tbody>tr>td { - font-size: 14px; - padding: 7px 6px; - - } - tfoot>tr>th { - font-size: 14px; - padding: 6px 0; - } - } - - - .am-progress { - height: 12px; - } - .am-progress-title { - font-size: 14px; - margin-bottom: 8px; - } - .am-progress-title-more { - - } - .widget-fluctuation-tpl-btn { - margin-top: 6px; - display: block; - color: #fff; - font-size: 12px; - padding: 8px 14px; - outline: none; - background-color: #e7505a; - border: 1px solid #e7505a; - &:hover { - background:transparent; - color:#e7505a; - } - - } - .widget-fluctuation-description-text{ -color: #c5cacd; - } - background: #e9ecf3; - .widget-fluctuation-period-text { - color:#838FA1; - } -.text-success { - color: #5eb95e; -} - .widget-head { - border-bottom: 1px solid #eef1f5; -} - .widget-function { - a { - color: #838FA1; - &:hover { - color:#a7bdcd; - } - } - - } - .widget { - padding: 10px 20px 13px; - background-color: #fff; - border-radius: 4px; - color: #838FA1; - - } - .widget-title { - font-size: 16px; - } - - .widget-primary { - - min-height: 174px; - border: 1px solid #32c5d2; - border-top: 2px solid #32c5d2; - background: #32c5d2; - color: #ffffff; - padding: 12px 17px; - padding-left: 22px; -} -.widget-statistic-body { - -} -.widget-statistic-icon { - position: absolute; - z-index: 30; - right: 30px; - top: 24px; - font-size: 70px; - color: #46cad6; -} -.widget-statistic-description { - position: relative; - z-index: 35; - display: block; - font-size: 14px; - line-height: 14px; - padding-top: 8px; - color: #fff; -} -.widget-statistic-value { - position: relative; - z-index: 35; - font-weight: 300; - display: block; - color: #fff; - font-size: 46px; - line-height: 46px; - margin-bottom: 8px; -} -.widget-statistic-header { - padding-top: 18px; - color: #fff; -} -.widget-purple { - padding: 12px 17px; - border: 1px solid #8E44AD; - border-top: 2px solid #8E44AD; - background: #8E44AD; - color: #ffffff; - min-height: 174px; - .widget-statistic-icon { - color: #9956b5; - } - .widget-statistic-header { - color: #ded5e7; - } - .widget-statistic-description { - color: #ded5e7; - } -} - .page-header-button { - opacity: .8; - border: 1px solid #32c5d2; - background: #32c5d2; - color:#fff; - &:hover { - opacity: 1; - } - } - .page-header-description { - color: #666; - } - .page-header-heading { - color: #666; - } - .container-fluid { - - } - ul.tpl-dropdown-content .tpl-dropdown-menu-messages-item .menu-messages-content .menu-messages-content-time { - color: #96a5aa; - } - ul.tpl-dropdown-content { - background: #fff; - border: 1px solid #ddd; - .tpl-dropdown-menu-notifications-item , .tpl-dropdown-menu-messages-item { - border-bottom: 1px solid #eee; - color:#999; - - &:hover{ - background-color: #f5f5f5; - } - .tpl-dropdown-menu-notifications-time { - color: #999; - } - } - .tpl-dropdown-menu-messages-item:hover { - background-color: #f5f5f5; - } - - .tpl-dropdown-menu-notifications-title { - color:#999; - } - - - } - .sidebar-nav-link { - a { - border-left: #fff 3px solid; - } - a:hover { - - background: #f2f6f9; - color: #868E8E; - border-left: #3bb4f2 3px solid; - } - } - - .sidebar-nav-link a.active { - background: #f2f6f9; - color: #868E8E; - border-left: #3bb4f2 3px solid; - } - .sidebar-nav-heading { - color: #999; - border-bottom: 1px solid #eee; - } - .tpl-sidebar-user-panel { - background: #fff; - border-bottom: 1px solid #eee; - } -.tpl-content-wrapper { - background: #e9ecf3; - } - .tpl-header-fluid { - background: #fff; - border-top: 1px solid #eee; -} - .tpl-header-logo { - background: #fff; - border-bottom: 1px solid #eee; -} - -.tpl-header-switch-button { - background: #fff; - border-right: 1px solid #eee; - border-left: 1px solid #eee; - &:hover { - background: #fff; - color: #999; - } -} - .tpl-header-navbar { - a { - color:#999; - - &:hover { - color: #999; - } - } - } - .left-sidebar { - background: #fff; - } - - .widget-color-green { - border: 1px solid #32c5d2; - border-top: 2px solid #32c5d2; - background: #32c5d2; - color: #ffffff; - .widget-fluctuation-period-text { - color:#fff; - } - .widget-head { - border-bottom: 1px solid #2bb8c4; - } - .widget-fluctuation-description-text { - color:#bbe7f6; - } - .widget-function { - a { - color:#42bde5; - &:hover { - color: #fff; - } - } - } - } - - - -} - - -.theme-black { - - .tpl-am-model-bd { - background: #424b4f; - } - .tpl-model-dialog { - background: #424b4f; - } - .tpl-error-title { - font-size: 210px; - line-height: 220px; - color: #868E8E; - } - .tpl-error-title-info { - line-height: 30px; - font-size: 21px; - margin-top: 20px; - text-align: center; - color: #868E8E; - } - .tpl-error-btn { - background: #03a9f3; - border: 1px solid #03a9f3; - border-radius: 30px; - padding: 6px 20px 8px; - } - .tpl-error-content { - margin-top: 20px; - margin-bottom: 20px; - font-size: 16px; - text-align: center; - color: #cfcfcf; - } - .tpl-calendar-box { - background: #424b4f; - padding: 20px; - .fc-button { - border-radius: 0; - box-shadow:0; - } - .fc-event { - border-radius: 0; - background: #03a9f3; - } - .fc-axis { - color: #fff; - } - .fc-unthemed .fc-today { - background: #3a4144; - } - .fc-more { - color: #fff; - } - .fc th.fc-widget-header { - background: #9675ce!important; - color: #ffffff; - font-size: 14px; - line-height: 20px; - padding: 7px 0px; - text-transform: uppercase; - border:none!important; - a { - color: #fff; - } - } - - .fc-center { - h2 { - color:#fff; - } - } - .fc-state-default { - background-image: none; - background: #fff; - font-size: 14px; -} - .fc th, .fc td, .fc hr, .fc thead, .fc tbody, .fc-row { - // background: rgba(0, 0, 0, 0)!important; - border-color: rgba(120, 130, 140, 0.4) !important; - } - .fc-day-number { - color: #868E8E; - padding-right: 6px; - } - .fc th { - color: #868E8E; - font-weight: normal; - font-size: 14px; - padding: 6px 0; - } - } - .tpl-login-logo { - background: url(../img/logob.png) center no-repeat; - - } - .tpl-table-line-img { - max-width: 100px; - padding: 2px; -border: none; -} - .tpl-table-list-field { - border: none; - } - .tpl-table-list-select { - - .am-dropdown-content { - color:#888; - } - .am-selected-btn { - border:1px solid rgba(255, 255, 255, 0.2); - color:#fff; - } - - .am-btn-default.am-active, .am-btn-default:active, .am-dropdown.am-active .am-btn-default.am-dropdown-toggle { - border:1px solid rgba(255, 255, 255, 0.2); - color:#fff; - background: #5d6468; - } - } -.tpl-pagination .am-disabled a , .tpl-pagination li a { - color: #fff; - padding: 6px 12px; - background: #3f4649; - border: none; -} - -.tpl-pagination .am-active a{ - background: #167fa1;color: #fff; - border: 1px solid #167fa1; - padding: 6px 12px; -} - -.tpl-login-btn { - border: 1px solid #b5b5b5; - background-color: rgba(0, 0, 0, 0); - padding: 10px 16px; - font-size: 14px; - line-height: 14px; - color:#b5b5b5; - - &:hover,&:active { - background: #b5b5b5; - color:#fff; - } - -} -.tpl-login-title { - color:#fff; - strong { - color: #39bae4; - } -} - - - - - .tpl-form-line-form , .tpl-form-border-form { - padding-top: 20px; - - .am-btn-default { - color:#fff; - border: 1px solid rgba(255, 255, 255, 0.2); - - } - .am-selected-text { - color:#888; - } - } - .tpl-form-border-form input[type=number]:focus, .tpl-form-border-form input[type=search]:focus, .tpl-form-border-form input[type=text]:focus, .tpl-form-border-form input[type=password]:focus, .tpl-form-border-form input[type=datetime]:focus, .tpl-form-border-form input[type=datetime-local]:focus, .tpl-form-border-form input[type=date]:focus, .tpl-form-border-form input[type=month]:focus, .tpl-form-border-form input[type=time]:focus, .tpl-form-border-form input[type=week]:focus, .tpl-form-border-form input[type=email]:focus, .tpl-form-border-form input[type=url]:focus, .tpl-form-border-form input[type=tel]:focus, .tpl-form-border-form input[type=color]:focus, .tpl-form-border-form select:focus, .tpl-form-border-form textarea:focus, .am-form-field:focus{ - -webkit-box-shadow: none; - box-shadow: none; - } 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-.fc { - max-width: 100% !important; -} - - -/* Global Event Restyling ---------------------------------------------------------------------------------------------------*/ - -.fc-event { - background: #fff !important; - color: #000 !important; - page-break-inside: avoid; -} - -.fc-event .fc-resizer { - display: none; -} - - -/* Table & Day-Row Restyling ---------------------------------------------------------------------------------------------------*/ - -.fc th, -.fc td, -.fc hr, -.fc thead, -.fc tbody, -.fc-row { - border-color: #ccc !important; - background: #fff !important; -} - -/* kill the overlaid, absolutely-positioned components */ -/* common... */ -.fc-bg, -.fc-bgevent-skeleton, -.fc-highlight-skeleton, -.fc-helper-skeleton, -/* for timegrid. within cells within table skeletons... */ -.fc-bgevent-container, -.fc-business-container, -.fc-highlight-container, -.fc-helper-container { - display: none; -} - -/* don't force a min-height on rows (for DayGrid) */ -.fc tbody .fc-row { - height: auto !important; /* undo height that JS set in distributeHeight */ - min-height: 0 !important; /* undo the min-height from each view's specific stylesheet */ -} - -.fc tbody .fc-row .fc-content-skeleton { - position: static; /* undo .fc-rigid */ - padding-bottom: 0 !important; /* use a more border-friendly method for this... */ -} - -.fc tbody .fc-row .fc-content-skeleton tbody tr:last-child td { /* only works in newer browsers */ - padding-bottom: 1em; /* ...gives space within the skeleton. also ensures min height in a way */ -} - -.fc tbody .fc-row .fc-content-skeleton table { - /* provides a min-height for the row, but only effective for IE, which exaggerates this value, - making it look more like 3em. for other browers, it will already be this tall */ - height: 1em; -} - - -/* Undo month-view event limiting. 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-} - -/* in case there are no events, we still want some height */ -.fc-time-grid .fc-content-skeleton table { - height: 4em; -} - -/* kill the horizontal spacing made by the event container. event margins will be done below */ -.fc-time-grid .fc-event-container { - margin: 0 !important; -} - - -/* TimeGrid *Event* Restyling ---------------------------------------------------------------------------------------------------*/ - -/* naturally position events, vertically stacking them */ -.fc-time-grid .fc-event { - position: static !important; - margin: 3px 2px !important; -} - -/* for events that continue to a future day, give the bottom border back */ -.fc-time-grid .fc-event.fc-not-end { - border-bottom-width: 1px !important; -} - -/* indicate the event continues via "..." text */ -.fc-time-grid .fc-event.fc-not-end:after { - content: "..."; -} - -/* for events that are continuations from previous days, give the top border back */ -.fc-time-grid .fc-event.fc-not-start { - border-top-width: 1px !important; -} - -/* indicate the event is a continuation via "..." text */ -.fc-time-grid .fc-event.fc-not-start:before { - content: "..."; -} - -/* time */ - -/* undo a previous declaration and let the time text span to a second line */ -.fc-time-grid .fc-event .fc-time { - white-space: normal !important; -} - -/* hide the the time that is normally displayed... */ -.fc-time-grid .fc-event .fc-time span { - display: none; -} - -/* ...replace it with a more verbose version (includes AM/PM) stored in an html attribute */ -.fc-time-grid .fc-event .fc-time:after { - content: attr(data-full); -} - - -/* Vertical Scroller & Containers ---------------------------------------------------------------------------------------------------*/ - -/* kill the scrollbars and allow natural height */ -.fc-scroller, -.fc-day-grid-container, /* these divs might be assigned height, which we need to cleared */ -.fc-time-grid-container { /* */ - overflow: visible !important; - height: auto !important; -} - -/* kill the horizontal border/padding used to compensate for scrollbars */ -.fc-row { - border: 0 !important; - margin: 0 !important; -} - - -/* Button Controls ---------------------------------------------------------------------------------------------------*/ - -.fc-button-group, -.fc button { - display: none; /* don't display any button-related controls */ -} diff --git a/Day61-65/code/project_of_tornado/assets/fonts/FontAwesome.otf b/Day61-65/code/project_of_tornado/assets/fonts/FontAwesome.otf deleted file mode 100644 index d4de13e..0000000 Binary files a/Day61-65/code/project_of_tornado/assets/fonts/FontAwesome.otf and /dev/null differ diff --git a/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.eot b/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.eot deleted file mode 100644 index c7b00d2..0000000 Binary files a/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.eot and /dev/null differ diff --git a/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.ttf b/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.ttf deleted file mode 100644 index f221e50..0000000 Binary files a/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.ttf and /dev/null differ diff --git a/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.woff b/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.woff deleted file mode 100644 index 6e7483c..0000000 Binary files a/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.woff and /dev/null differ diff --git a/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.woff2 b/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.woff2 deleted file mode 100644 index 7eb74fd..0000000 Binary files a/Day61-65/code/project_of_tornado/assets/fonts/fontawesome-webfont.woff2 and /dev/null differ diff --git a/Day61-65/code/project_of_tornado/assets/html/404.html b/Day61-65/code/project_of_tornado/assets/html/404.html deleted file mode 100644 index 48f614c..0000000 --- a/Day61-65/code/project_of_tornado/assets/html/404.html +++ /dev/null @@ -1,294 +0,0 @@ - - - - - - - Amaze UI Admin index Examples - - - - - - - - - - - - - - - - - - -
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this.session.added||(this.session.added=!0,this.updateSession(),this.options.onAdd&&s.hasLocalStorage&&this.options.onAdd.call(this)),void this.doLog("Add to homescreen: not displaying callout because in standalone mode");if(this.options.detectHomescreen){if(s.hasToken)return a(),this.session.added||(this.session.added=!0,this.updateSession(),this.options.onAdd&&s.hasLocalStorage&&this.options.onAdd.call(this)),void this.doLog("Add to homescreen: not displaying callout because URL has token, so we are likely coming from homescreen");"hash"==this.options.detectHomescreen?history.replaceState("",window.document.title,document.location.href+"#ath"):"smartURL"==this.options.detectHomescreen?history.replaceState("",window.document.title,document.location.href.replace(/(\/)?$/,"/ath$1")):history.replaceState("",window.document.title,document.location.href+(document.location.search?"&":"?")+"ath=")}if(!this.session.returningVisitor&&(this.session.returningVisitor=!0,this.updateSession(),this.options.skipFirstVisit))return void this.doLog("Add to homescreen: not displaying callout because skipping first visit");if(!this.options.privateModeOverride&&!s.hasLocalStorage)return void this.doLog("Add to homescreen: not displaying callout because browser is in private mode");this.ready=!0,this.options.onInit&&this.options.onInit.call(this),this.options.autostart&&(this.doLog("Add to homescreen: autostart displaying callout"),this.show())}},s.Class.prototype={events:{load:"_delayedShow",error:"_delayedShow",orientationchange:"resize",resize:"resize",scroll:"resize",click:"remove",touchmove:"_preventDefault",transitionend:"_removeElements",webkitTransitionEnd:"_removeElements",MSTransitionEnd:"_removeElements"},handleEvent:function(t){var e=this.events[t.type];e&&this[e](t)},show:function(t){if(this.options.autostart&&!c)return void setTimeout(this.show.bind(this),50);if(this.shown)return void this.doLog("Add to homescreen: not displaying callout because already shown on screen");var e=Date.now(),i=this.session.lastDisplayTime;if(t!==!0){if(!this.ready)return void this.doLog("Add to homescreen: not displaying callout because not ready");if(e-i<6e4*this.options.displayPace)return void this.doLog("Add to homescreen: not displaying callout because displayed recently");if(this.options.maxDisplayCount&&this.session.displayCount>=this.options.maxDisplayCount)return void this.doLog("Add to homescreen: not displaying callout because displayed too many times already")}this.shown=!0,this.session.lastDisplayTime=e,this.session.displayCount++,this.updateSession(),this.applicationIcon||("ios"==s.OS?this.applicationIcon=document.querySelector('head link[rel^=apple-touch-icon][sizes="152x152"],head link[rel^=apple-touch-icon][sizes="144x144"],head link[rel^=apple-touch-icon][sizes="120x120"],head link[rel^=apple-touch-icon][sizes="114x114"],head link[rel^=apple-touch-icon]'):this.applicationIcon=document.querySelector('head link[rel^="shortcut icon"][sizes="196x196"],head link[rel^=apple-touch-icon]'));var n="";"object"==typeof this.options.message&&s.language in this.options.message?n=this.options.message[s.language][s.OS]:"object"==typeof this.options.message&&s.OS in this.options.message?n=this.options.message[s.OS]:this.options.message in s.intl?n=s.intl[this.options.message][s.OS]:""!==this.options.message?n=this.options.message:s.OS in s.intl[s.language]&&(n=s.intl[s.language][s.OS]),n="

    "+n.replace("%icon",'icon')+"

    ",this.viewport=document.createElement("div"),this.viewport.className="ath-viewport",this.options.modal&&(this.viewport.className+=" ath-modal"),this.options.mandatory&&(this.viewport.className+=" ath-mandatory"),this.viewport.style.position="absolute",this.element=document.createElement("div"),this.element.className="ath-container ath-"+s.OS+" ath-"+s.OS+(s.OSVersion+"").substr(0,1)+" ath-"+(s.isTablet?"tablet":"phone"),this.element.style.cssText="-webkit-transition-property:-webkit-transform,opacity;-webkit-transition-duration:0s;-webkit-transition-timing-function:ease-out;transition-property:transform,opacity;transition-duration:0s;transition-timing-function:ease-out;",this.element.style.webkitTransform="translate3d(0,-"+window.innerHeight+"px,0)",this.element.style.transform="translate3d(0,-"+window.innerHeight+"px,0)",this.options.icon&&this.applicationIcon&&(this.element.className+=" ath-icon",this.img=document.createElement("img"),this.img.className="ath-application-icon",this.img.addEventListener("load",this,!1),this.img.addEventListener("error",this,!1),this.img.src=this.applicationIcon.href,this.element.appendChild(this.img)),this.element.innerHTML+=n,this.viewport.style.left="-99999em",this.viewport.appendChild(this.element),this.container.appendChild(this.viewport),this.img?this.doLog("Add to homescreen: not displaying callout because waiting for img to load"):this._delayedShow()},_delayedShow:function(t){setTimeout(this._show.bind(this),1e3*this.options.startDelay+500)},_show:function(){var t=this;this.updateViewport(),window.addEventListener("resize",this,!1),window.addEventListener("scroll",this,!1),window.addEventListener("orientationchange",this,!1),this.options.modal&&document.addEventListener("touchmove",this,!0),this.options.mandatory||setTimeout(function(){t.element.addEventListener("click",t,!0)},1e3),setTimeout(function(){t.element.style.webkitTransitionDuration="1.2s",t.element.style.transitionDuration="1.2s",t.element.style.webkitTransform="translate3d(0,0,0)",t.element.style.transform="translate3d(0,0,0)"},0),this.options.lifespan&&(this.removeTimer=setTimeout(this.remove.bind(this),1e3*this.options.lifespan)),this.options.onShow&&this.options.onShow.call(this)},remove:function(){clearTimeout(this.removeTimer),this.img&&(this.img.removeEventListener("load",this,!1),this.img.removeEventListener("error",this,!1)),window.removeEventListener("resize",this,!1),window.removeEventListener("scroll",this,!1),window.removeEventListener("orientationchange",this,!1),document.removeEventListener("touchmove",this,!0),this.element.removeEventListener("click",this,!0),this.element.addEventListener("transitionend",this,!1),this.element.addEventListener("webkitTransitionEnd",this,!1),this.element.addEventListener("MSTransitionEnd",this,!1),this.element.style.webkitTransitionDuration="0.3s",this.element.style.opacity="0"},_removeElements:function(){this.element.removeEventListener("transitionend",this,!1),this.element.removeEventListener("webkitTransitionEnd",this,!1),this.element.removeEventListener("MSTransitionEnd",this,!1),this.container.removeChild(this.viewport),this.shown=!1,this.options.onRemove&&this.options.onRemove.call(this)},updateViewport:function(){if(this.shown){this.viewport.style.width=window.innerWidth+"px",this.viewport.style.height=window.innerHeight+"px",this.viewport.style.left=window.scrollX+"px",this.viewport.style.top=window.scrollY+"px";var t=document.documentElement.clientWidth;this.orientation=t>document.documentElement.clientHeight?"landscape":"portrait";var e="ios"==s.OS?"portrait"==this.orientation?screen.width:screen.height:screen.width;this.scale=screen.width>t?1:e/window.innerWidth,this.element.style.fontSize=this.options.fontSize/this.scale+"px"}},resize:function(){clearTimeout(this.resizeTimer),this.resizeTimer=setTimeout(this.updateViewport.bind(this),100)},updateSession:function(){s.hasLocalStorage!==!1&&localStorage&&localStorage.setItem(this.options.appID,JSON.stringify(this.session))},clearSession:function(){this.session=v,this.updateSession()},getItem:function(t){try{if(!localStorage)throw new Error("localStorage is not defined");return localStorage.getItem(t)}catch(e){s.hasLocalStorage=!1}},optOut:function(){this.session.optedout=!0,this.updateSession()},optIn:function(){this.session.optedout=!1,this.updateSession()},clearDisplayCount:function(){this.session.displayCount=0,this.updateSession()},_preventDefault:function(t){t.preventDefault(),t.stopPropagation()}},s.VERSION="3.2.2",t.exports=r.addToHomescreen=s},function(t,e,i){"use strict";var n=i(1),s=i(2),o=function(t,e){var i=this;this.options=n.extend({},o.DEFAULTS,e),this.$element=n(t),this.$element.addClass("am-fade am-in").on("click.alert.amui",".am-close",function(){i.close()})};o.DEFAULTS={removeElement:!0},o.prototype.close=function(){function t(){e.trigger("closed.alert.amui").remove()}var e=this.$element;e.trigger("close.alert.amui").removeClass("am-in"),s.support.transition&&e.hasClass("am-fade")?e.one(s.support.transition.end,t).emulateTransitionEnd(200):t()},s.plugin("alert",o),n(document).on("click.alert.amui.data-api","[data-am-alert]",function(t){var e=n(t.target);e.is(".am-close")&&n(this).alert("close")}),t.exports=o},function(t,e,i){"use strict";var n=i(1),s=i(2),o=function(t,e){this.$element=n(t),this.options=n.extend({},o.DEFAULTS,e),this.isLoading=!1,this.hasSpinner=!1};o.DEFAULTS={loadingText:"loading...",disabledClassName:"am-disabled",activeClassName:"am-active",spinner:void 0},o.prototype.setState=function(t,e){var i=this.$element,o="disabled",a=i.data(),r=this.options,l=i.is("input")?"val":"html",c="am-btn-"+t+" "+r.disabledClassName;t+="Text",r.resetText||(r.resetText=i[l]()),s.support.animation&&r.spinner&&"html"===l&&!this.hasSpinner&&(r.loadingText=''+r.loadingText,this.hasSpinner=!0),e=e||(void 0===a[t]?r[t]:a[t]),i[l](e),setTimeout(n.proxy(function(){"loadingText"===t?(i.addClass(c).attr(o,o),this.isLoading=!0):this.isLoading&&(i.removeClass(c).removeAttr(o),this.isLoading=!1)},this),0)},o.prototype.toggle=function(){var t=!0,e=this.$element,i=this.$element.parent('[class*="am-btn-group"]'),n=o.DEFAULTS.activeClassName;if(i.length){var s=this.$element.find("input");"radio"==s.prop("type")&&(s.prop("checked")&&e.hasClass(n)?t=!1:i.find("."+n).removeClass(n)),t&&s.prop("checked",!e.hasClass(n)).trigger("change")}t&&(e.toggleClass(n),e.hasClass(n)||e.blur())},s.plugin("button",o,{dataOptions:"data-am-loading",methodCall:function(t,e){"toggle"===t[0]?e.toggle():"string"==typeof t[0]&&e.setState.apply(e,t)}}),n(document).on("click.button.amui.data-api","[data-am-button]",function(t){t.preventDefault();var e=n(t.target);e.hasClass("am-btn")||(e=e.closest(".am-btn")),e.button("toggle")}),s.ready(function(t){n("[data-am-loading]",t).button(),n("[data-am-button]",t).find("input:checked").each(function(){n(this).parent("label").addClass(o.DEFAULTS.activeClassName)})}),t.exports=s.button=o},function(t,e,i){"use strict";function n(t){return this.each(function(){var e=s(this),i=e.data("amui.collapse"),n=s.extend({},a.DEFAULTS,o.utils.options(e.attr("data-am-collapse")),"object"==typeof t&&t);!i&&n.toggle&&"open"===t&&(t=!t),i||e.data("amui.collapse",i=new a(this,n)),"string"==typeof t&&i[t]()})}var s=i(1),o=i(2),a=function(t,e){this.$element=s(t),this.options=s.extend({},a.DEFAULTS,e),this.transitioning=null,this.options.parent&&(this.$parent=s(this.options.parent)),this.options.toggle&&this.toggle()};a.DEFAULTS={toggle:!0},a.prototype.open=function(){if(!this.transitioning&&!this.$element.hasClass("am-in")){var t=s.Event("open.collapse.amui");if(this.$element.trigger(t),!t.isDefaultPrevented()){var e=this.$parent&&this.$parent.find("> .am-panel > .am-in");if(e&&e.length){var i=e.data("amui.collapse");if(i&&i.transitioning)return;n.call(e,"close"),i||e.data("amui.collapse",null)}this.$element.removeClass("am-collapse").addClass("am-collapsing").height(0),this.transitioning=1;var a=function(){this.$element.removeClass("am-collapsing").addClass("am-collapse am-in").height("").trigger("opened.collapse.amui"),this.transitioning=0};if(!o.support.transition)return a.call(this);var r=this.$element[0].scrollHeight;this.$element.one(o.support.transition.end,s.proxy(a,this)).emulateTransitionEnd(300).css({height:r})}}},a.prototype.close=function(){if(!this.transitioning&&this.$element.hasClass("am-in")){var t=s.Event("close.collapse.amui");if(this.$element.trigger(t),!t.isDefaultPrevented()){this.$element.height(this.$element.height()).redraw(),this.$element.addClass("am-collapsing").removeClass("am-collapse am-in"),this.transitioning=1;var e=function(){this.transitioning=0,this.$element.trigger("closed.collapse.amui").removeClass("am-collapsing").addClass("am-collapse")};return o.support.transition?void this.$element.height(0).one(o.support.transition.end,s.proxy(e,this)).emulateTransitionEnd(300):e.call(this)}}},a.prototype.toggle=function(){this[this.$element.hasClass("am-in")?"close":"open"]()},s.fn.collapse=n,s(document).on("click.collapse.amui.data-api","[data-am-collapse]",function(t){var e,i=s(this),a=o.utils.options(i.attr("data-am-collapse")),r=a.target||t.preventDefault()||(e=i.attr("href"))&&e.replace(/.*(?=#[^\s]+$)/,""),l=s(r),c=l.data("amui.collapse"),u=c?"toggle":a,h=a.parent,d=h&&s(h);c&&c.transitioning||(d&&d.find("[data-am-collapse]").not(i).addClass("am-collapsed"),i[l.hasClass("am-in")?"addClass":"removeClass"]("am-collapsed")),n.call(l,u)}),t.exports=o.collapse=a},function(t,e,i){"use strict";var n=i(1),s=i(2),o=n(document),a=function(t,e){if(this.$element=n(t),this.options=n.extend({},a.DEFAULTS,e),this.format=r.parseFormat(this.options.format),this.$element.data("date",this.options.date),this.language=this.getLocale(this.options.locale),this.theme=this.options.theme,this.$picker=n(r.template).appendTo("body").on({click:n.proxy(this.click,this)}),this.isInput=this.$element.is("input"),this.component=!!this.$element.is(".am-datepicker-date")&&this.$element.find(".am-datepicker-add-on"),this.isInput?this.$element.on({"click.datepicker.amui":n.proxy(this.open,this),"keyup.datepicker.amui":n.proxy(this.update,this)}):this.component?this.component.on("click.datepicker.amui",n.proxy(this.open,this)):this.$element.on("click.datepicker.amui",n.proxy(this.open,this)),this.minViewMode=this.options.minViewMode,"string"==typeof this.minViewMode)switch(this.minViewMode){case"months":this.minViewMode=1;break;case"years":this.minViewMode=2;break;default:this.minViewMode=0}if(this.viewMode=this.options.viewMode,"string"==typeof this.viewMode)switch(this.viewMode){case"months":this.viewMode=1;break;case"years":this.viewMode=2;break;default:this.viewMode=0}this.startViewMode=this.viewMode,this.weekStart=(this.options.weekStart||a.locales[this.language].weekStart||0)%7,this.weekEnd=(this.weekStart+6)%7,this.onRender=this.options.onRender,this.setTheme(),this.fillDow(),this.fillMonths(),this.update(),this.showMode()};a.DEFAULTS={locale:"zh_CN",format:"yyyy-mm-dd",weekStart:void 0,viewMode:0,minViewMode:0,date:"",theme:"",autoClose:1,onRender:function(t){return""}},a.prototype.open=function(t){this.$picker.show(),this.height=this.component?this.component.outerHeight():this.$element.outerHeight(),this.place(),n(window).on("resize.datepicker.amui",n.proxy(this.place,this)),t&&(t.stopPropagation(),t.preventDefault());var e=this;o.on("mousedown.datapicker.amui touchstart.datepicker.amui",function(t){0===n(t.target).closest(".am-datepicker").length&&e.close()}),this.$element.trigger({type:"open.datepicker.amui",date:this.date})},a.prototype.close=function(){this.$picker.hide(),n(window).off("resize.datepicker.amui",this.place),this.viewMode=this.startViewMode,this.showMode(),this.isInput||n(document).off("mousedown.datapicker.amui touchstart.datepicker.amui",this.close),this.$element.trigger({type:"close.datepicker.amui",date:this.date})},a.prototype.set=function(){var t,e=r.formatDate(this.date,this.format);this.isInput?t=this.$element.attr("value",e):(this.component&&(t=this.$element.find("input").attr("value",e)),this.$element.data("date",e)),t&&t.trigger("change")},a.prototype.setValue=function(t){"string"==typeof t?this.date=r.parseDate(t,this.format):this.date=new Date(t),this.set(),this.viewDate=new Date(this.date.getFullYear(),this.date.getMonth(),1,0,0,0,0),this.fill()},a.prototype.place=function(){var t=this.component?this.component.offset():this.$element.offset(),e=this.component?this.component.width():this.$element.width(),i=t.top+this.height,n=t.left,s=o.width()-t.left-e,a=this.isOutView();if(this.$picker.removeClass("am-datepicker-right"),this.$picker.removeClass("am-datepicker-up"),o.width()>640){if(a.outRight)return this.$picker.addClass("am-datepicker-right"),void this.$picker.css({top:i,left:"auto",right:s});a.outBottom&&(this.$picker.addClass("am-datepicker-up"),i=t.top-this.$picker.outerHeight(!0))}else n=0;this.$picker.css({top:i,left:n})},a.prototype.update=function(t){this.date=r.parseDate("string"==typeof t?t:this.isInput?this.$element.prop("value"):this.$element.data("date"),this.format),this.viewDate=new Date(this.date.getFullYear(),this.date.getMonth(),1,0,0,0,0),this.fill()},a.prototype.fillDow=function(){for(var t=this.weekStart,e="";t'+a.locales[this.language].daysMin[t++%7]+"";e+="",this.$picker.find(".am-datepicker-days thead").append(e)},a.prototype.fillMonths=function(){for(var t="",e=0;e<12;)t+=''+a.locales[this.language].monthsShort[e++]+"";this.$picker.find(".am-datepicker-months td").append(t)},a.prototype.fill=function(){var t=new Date(this.viewDate),e=t.getFullYear(),i=t.getMonth(),n=this.date.valueOf(),s=new Date(e,i-1,28,0,0,0,0),o=r.getDaysInMonth(s.getFullYear(),s.getMonth()),l=this.$picker.find(".am-datepicker-days .am-datepicker-select");"zh_CN"===this.language?l.text(e+a.locales[this.language].year[0]+" "+a.locales[this.language].months[i]):l.text(a.locales[this.language].months[i]+" "+e),s.setDate(o),s.setDate(o-(s.getDay()-this.weekStart+7)%7);var c=new Date(s);c.setDate(c.getDate()+42),c=c.valueOf();for(var u,h,d,p=[];s.valueOf()"),u=this.onRender(s,0),h=s.getFullYear(),d=s.getMonth(),di&&h===e||h>e)&&(u+=" am-datepicker-new"),s.valueOf()===n&&(u+=" am-active"),p.push(''+s.getDate()+""),s.getDay()===this.weekEnd&&p.push(""),s.setDate(s.getDate()+1);this.$picker.find(".am-datepicker-days tbody").empty().append(p.join(""));var m=this.date.getFullYear(),f=this.$picker.find(".am-datepicker-months").find(".am-datepicker-select").text(e);f=f.end().find("span").removeClass("am-active").removeClass("am-disabled");for(var v=0;v<12;)this.onRender(t.setFullYear(e,v),1)&&f.eq(v).addClass("am-disabled"),v++;m===e&&f.eq(this.date.getMonth()).removeClass("am-disabled").addClass("am-active"),p="",e=10*parseInt(e/10,10);var g,y=this.$picker.find(".am-datepicker-years").find(".am-datepicker-select").text(e+"-"+(e+9)).end().find("td"),w=new Date(this.viewDate);e-=1;for(var b=-1;b<11;b++)g=this.onRender(w.setFullYear(e),2),p+=''+e+"",e+=1;y.html(p)},a.prototype.click=function(t){t.stopPropagation(),t.preventDefault();var e,i,s=this.$picker.find(".am-datepicker-days").find(".am-active"),o=this.$picker.find(".am-datepicker-months"),a=o.find(".am-active").index(),l=n(t.target).closest("span, td, th");if(1===l.length)switch(l[0].nodeName.toLowerCase()){case"th":switch(l[0].className){case"am-datepicker-switch":this.showMode(1);break;case"am-datepicker-prev":case"am-datepicker-next":this.viewDate["set"+r.modes[this.viewMode].navFnc].call(this.viewDate,this.viewDate["get"+r.modes[this.viewMode].navFnc].call(this.viewDate)+r.modes[this.viewMode].navStep*("am-datepicker-prev"===l[0].className?-1:1)),this.fill(),this.set()}break;case"span":if(l.is(".am-disabled"))return;l.is(".am-datepicker-month")?(e=l.parent().find("span").index(l),l.is(".am-active")?this.viewDate.setMonth(e,s.text()):this.viewDate.setMonth(e)):(i=parseInt(l.text(),10)||0,l.is(".am-active")?this.viewDate.setFullYear(i,a,s.text()):this.viewDate.setFullYear(i)),0!==this.viewMode&&(this.date=new Date(this.viewDate),this.$element.trigger({type:"changeDate.datepicker.amui",date:this.date,viewMode:r.modes[this.viewMode].clsName})),this.showMode(-1),this.fill(),this.set();break;case"td":if(l.is(".am-datepicker-day")&&!l.is(".am-disabled")){var c=parseInt(l.text(),10)||1;e=this.viewDate.getMonth(),l.is(".am-datepicker-old")?e-=1:l.is(".am-datepicker-new")&&(e+=1),i=this.viewDate.getFullYear(),this.date=new Date(i,e,c,0,0,0,0),this.viewDate=new Date(i,e,Math.min(28,c),0,0,0,0),this.fill(),this.set(),this.$element.trigger({type:"changeDate.datepicker.amui",date:this.date,viewMode:r.modes[this.viewMode].clsName}),this.options.autoClose&&this.close()}}},a.prototype.mousedown=function(t){t.stopPropagation(),t.preventDefault()},a.prototype.showMode=function(t){t&&(this.viewMode=Math.max(this.minViewMode,Math.min(2,this.viewMode+t))),this.$picker.find(">div").hide().filter(".am-datepicker-"+r.modes[this.viewMode].clsName).show()},a.prototype.isOutView=function(){var t=this.component?this.component.offset():this.$element.offset(),e={outRight:!1,outBottom:!1},i=this.$picker,n=t.left+i.outerWidth(!0),s=t.top+i.outerHeight(!0)+this.$element.innerHeight();return n>o.width()&&(e.outRight=!0),s>o.height()&&(e.outBottom=!0),e},a.prototype.getLocale=function(t){return t||(t=navigator.language&&navigator.language.split("-"),t[1]=t[1].toUpperCase(),t=t.join("_")),a.locales[t]||(t="en_US"),t},a.prototype.setTheme=function(){this.theme&&this.$picker.addClass("am-datepicker-"+this.theme)},a.locales={en_US:{days:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],daysShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],daysMin:["Su","Mo","Tu","We","Th","Fr","Sa"],months:["January","February","March","April","May","June","July","August","September","October","November","December"],monthsShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],weekStart:0},zh_CN:{days:["\u661f\u671f\u65e5","\u661f\u671f\u4e00","\u661f\u671f\u4e8c","\u661f\u671f\u4e09","\u661f\u671f\u56db","\u661f\u671f\u4e94","\u661f\u671f\u516d"],daysShort:["\u5468\u65e5","\u5468\u4e00","\u5468\u4e8c","\u5468\u4e09","\u5468\u56db","\u5468\u4e94","\u5468\u516d"],daysMin:["\u65e5","\u4e00","\u4e8c","\u4e09","\u56db","\u4e94","\u516d"],months:["\u4e00\u6708","\u4e8c\u6708","\u4e09\u6708","\u56db\u6708","\u4e94\u6708","\u516d\u6708","\u4e03\u6708","\u516b\u6708","\u4e5d\u6708","\u5341\u6708","\u5341\u4e00\u6708","\u5341\u4e8c\u6708"],monthsShort:["\u4e00\u6708","\u4e8c\u6708","\u4e09\u6708","\u56db\u6708","\u4e94\u6708","\u516d\u6708","\u4e03\u6708","\u516b\u6708","\u4e5d\u6708","\u5341\u6708","\u5341\u4e00\u6708","\u5341\u4e8c\u6708"],weekStart:1,year:["\u5e74"]}};var 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s=i(1),o=i(2);s(n),t.exports=o.header={VERSION:"2.0.0",init:n}},function(t,e,i){"use strict";var n=i(2);t.exports=n.intro={VERSION:"4.0.2",init:function(){}}},function(t,e,i){"use strict";var n=i(2);t.exports=n.listNews={VERSION:"4.0.0",init:function(){}}},function(t,e,i){function n(t){var e=o(" - - - - - - - -
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        部件首页 Amaze UI
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        Amaze UI 含近 20 个 CSS 组件、20 余 JS 组件，更有多个包含不同主题的 Web 组件。

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differ diff --git a/Day61-65/res/ws_wss.png b/Day61-65/res/ws_wss.png deleted file mode 100644 index c71d386..0000000 Binary files a/Day61-65/res/ws_wss.png and /dev/null differ diff --git a/Day66-70/66.数据分析概述.md b/Day66-70/66.数据分析概述.md new file mode 100644 index 0000000..a051cce --- /dev/null +++ b/Day66-70/66.数据分析概述.md @@ -0,0 +1,2 @@ +## NumPy的应用 + diff --git a/Day66-70/67.NumPy的应用.md b/Day66-70/67.NumPy的应用.md new file mode 100644 index 0000000..a051cce --- /dev/null +++ b/Day66-70/67.NumPy的应用.md @@ -0,0 +1,2 @@ +## NumPy的应用 + diff --git a/Day76-90/77.Pandas的应用.md b/Day66-70/68.Pandas的应用.md similarity index 100% rename from Day76-90/77.Pandas的应用.md rename to Day66-70/68.Pandas的应用.md diff --git a/Day76-90/79.Matplotlib和数据可视化.md b/Day66-70/69.数据可视化.md similarity index 99% rename from Day76-90/79.Matplotlib和数据可视化.md rename to Day66-70/69.数据可视化.md index c451975..9f0cbf4 100644 --- a/Day76-90/79.Matplotlib和数据可视化.md +++ b/Day66-70/69.数据可视化.md @@ -1,4 +1,4 @@ -## Matplotlib和数据可视化 +## 数据可视化 数据的处理、分析和可视化已经成为Python近年来最为重要的应用领域之一，其中数据的可视化指的是将数据呈现为漂亮的统计图表，然后进一步发现数据中包含的规律以及隐藏的信息。数据可视化又跟数据挖掘和大数据分析紧密相关，而这些领域以及当下被热议的“深度学习”其最终的目标都是为了实现从过去的数据去对未来的状况进行预测。Python在实现数据可视化方面是非常棒的，即便是使用个人电脑也能够实现对百万级甚至更大体量的数据进行探索的工作，而这些工作都可以在现有的第三方库的基础上来完成（无需“重复的发明轮子”）。[Matplotlib](https://matplotlib.org/)就是Python绘图库中的佼佼者，它包含了大量的工具，你可以使用这些工具创建各种图形（包括散点图、折线图、直方图、饼图、雷达图等），Python科学计算社区也经常使用它来完成数据可视化的工作。 diff --git a/Day66-70/70.数据分析项目实战.md b/Day66-70/70.数据分析项目实战.md new file mode 100644 index 0000000..09158a7 --- /dev/null +++ b/Day66-70/70.数据分析项目实战.md @@ -0,0 +1,2 @@ +## 数据分析项目实战 + diff --git a/Day76-90/res/result-in-jupyter.png b/Day66-70/res/result-in-jupyter.png similarity index 100% rename from Day76-90/res/result-in-jupyter.png rename to Day66-70/res/result-in-jupyter.png diff --git a/Day76-90/res/result1.png b/Day66-70/res/result1.png similarity index 100% rename from Day76-90/res/result1.png rename to Day66-70/res/result1.png diff --git a/Day76-90/res/result2.png b/Day66-70/res/result2.png similarity 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Scrapy引擎（Engine）：Scrapy引擎是用来控制整个系统的数据处理流程。 -2. 调度器（Scheduler）：调度器从Scrapy引擎接受请求并排序列入队列，并在Scrapy引擎发出请求后返还给它们。 -3. 下载器（Downloader）：下载器的主要职责是抓取网页并将网页内容返还给蜘蛛（Spiders）。 -4. 蜘蛛（Spiders）：蜘蛛是有Scrapy用户自定义的用来解析网页并抓取特定URL返回的内容的类，每个蜘蛛都能处理一个域名或一组域名，简单的说就是用来定义特定网站的抓取和解析规则。 -5. 条目管道（Item Pipeline）：条目管道的主要责任是负责处理有蜘蛛从网页中抽取的数据条目，它的主要任务是清理、验证和存储数据。当页面被蜘蛛解析后，将被发送到条目管道，并经过几个特定的次序处理数据。每个条目管道组件都是一个Python类，它们获取了数据条目并执行对数据条目进行处理的方法，同时还需要确定是否需要在条目管道中继续执行下一步或是直接丢弃掉不处理。条目管道通常执行的任务有：清理HTML数据、验证解析到的数据（检查条目是否包含必要的字段）、检查是不是重复数据（如果重复就丢弃）、将解析到的数据存储到数据库（关系型数据库或NoSQL数据库）中。 -6. 中间件（Middlewares）：中间件是介于Scrapy引擎和其他组件之间的一个钩子框架，主要是为了提供自定义的代码来拓展Scrapy的功能，包括下载器中间件和蜘蛛中间件。 - -#### 数据处理流程 - -Scrapy的整个数据处理流程由Scrapy引擎进行控制，通常的运转流程包括以下的步骤： - -1. 引擎询问蜘蛛需要处理哪个网站，并让蜘蛛将第一个需要处理的URL交给它。 - -2. 引擎让调度器将需要处理的URL放在队列中。 - -3. 引擎从调度那获取接下来进行爬取的页面。 - -4. 调度将下一个爬取的URL返回给引擎，引擎将它通过下载中间件发送到下载器。 - -5. 当网页被下载器下载完成以后，响应内容通过下载中间件被发送到引擎；如果下载失败了，引擎会通知调度器记录这个URL，待会再重新下载。 - -6. 引擎收到下载器的响应并将它通过蜘蛛中间件发送到蜘蛛进行处理。 - -7. 蜘蛛处理响应并返回爬取到的数据条目，此外还要将需要跟进的新的URL发送给引擎。 - -8. 引擎将抓取到的数据条目送入条目管道，把新的URL发送给调度器放入队列中。 - -上述操作中的2-8步会一直重复直到调度器中没有需要请求的URL，爬虫停止工作。 - -### 安装和使用Scrapy - -可以先创建虚拟环境并在虚拟环境下使用pip安装scrapy。 - -```Shell - -``` - -项目的目录结构如下图所示。 - -```Shell -(venv) $ tree -. -|____ scrapy.cfg -|____ douban -| |____ spiders -| | |____ __init__.py -| | |____ __pycache__ -| |____ __init__.py -| |____ __pycache__ -| |____ middlewares.py -| |____ settings.py -| |____ items.py -| |____ pipelines.py -``` - -> 说明：Windows系统的命令行提示符下有tree命令，但是Linux和MacOS的终端是没有tree命令的，可以用下面给出的命令来定义tree命令，其实是对find命令进行了定制并别名为tree。 -> -> `alias tree="find . -print | sed -e 's;[^/]*/;|____;g;s;____|; |;g'"` -> -> Linux系统也可以通过yum或其他的包管理工具来安装tree。 -> -> `yum install tree` - -根据刚才描述的数据处理流程，基本上需要我们做的有以下几件事情： - -1. 在items.py文件中定义字段，这些字段用来保存数据，方便后续的操作。 - - ```Python - # -*- coding: utf-8 -*- - - # Define here the models for your scraped items - # - # See documentation in: - # https://doc.scrapy.org/en/latest/topics/items.html - - import scrapy - - - class DoubanItem(scrapy.Item): - - name = scrapy.Field() - year = scrapy.Field() - score = scrapy.Field() - director = scrapy.Field() - classification = scrapy.Field() - actor = scrapy.Field() - ``` - -2. 在spiders文件夹中编写自己的爬虫。 - - ```Shell - (venv) $ scrapy genspider movie movie.douban.com --template=crawl - ``` - - ```Python - # -*- coding: utf-8 -*- - import scrapy - from scrapy.selector import Selector - from scrapy.linkextractors import LinkExtractor - from scrapy.spiders import CrawlSpider, Rule - - from douban.items import DoubanItem - - - class MovieSpider(CrawlSpider): - name = 'movie' - allowed_domains = ['movie.douban.com'] - start_urls = ['https://movie.douban.com/top250'] - rules = ( - Rule(LinkExtractor(allow=(r'https://movie.douban.com/top250\?start=\d+.*'))), - Rule(LinkExtractor(allow=(r'https://movie.douban.com/subject/\d+')), callback='parse_item'), - ) - - def parse_item(self, response): - sel = Selector(response) - item = DoubanItem() - item['name']=sel.xpath('//*[@id="content"]/h1/span[1]/text()').extract() - item['year']=sel.xpath('//*[@id="content"]/h1/span[2]/text()').re(r'\((\d+)\)') - item['score']=sel.xpath('//*[@id="interest_sectl"]/div/p[1]/strong/text()').extract() - item['director']=sel.xpath('//*[@id="info"]/span[1]/a/text()').extract() - item['classification']= sel.xpath('//span[@property="v:genre"]/text()').extract() - item['actor']= sel.xpath('//*[@id="info"]/span[3]/a[1]/text()').extract() - return item - ``` - > 说明：上面我们通过Scrapy提供的爬虫模板创建了Spider，其中的rules中的LinkExtractor对象会自动完成对新的链接的解析，该对象中有一个名为extract_link的回调方法。Scrapy支持用XPath语法和CSS选择器进行数据解析，对应的方法分别是xpath和css，上面我们使用了XPath语法对页面进行解析，如果不熟悉XPath语法可以看看后面的补充说明。 - - 到这里，我们已经可以通过下面的命令让爬虫运转起来。 - - ```Shell - (venv)$ scrapy crawl movie - ``` - - 可以在控制台看到爬取到的数据，如果想将这些数据保存到文件中，可以通过`-o`参数来指定文件名，Scrapy支持我们将爬取到的数据导出成JSON、CSV、XML、pickle、marshal等格式。 - - ```Shell - (venv)$ scrapy crawl moive -o result.json - ``` - -3. 在pipelines.py中完成对数据进行持久化的操作。 - - ```Python - # -*- coding: utf-8 -*- - - # Define your item pipelines here - # - # Don't forget to add your pipeline to the ITEM_PIPELINES setting - # See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html - import pymongo - - from scrapy.exceptions import DropItem - from scrapy.conf import settings - from scrapy import log - - - class DoubanPipeline(object): - - def __init__(self): - connection = pymongo.MongoClient(settings['MONGODB_SERVER'], settings['MONGODB_PORT']) - db = connection[settings['MONGODB_DB']] - self.collection = db[settings['MONGODB_COLLECTION']] - - def process_item(self, item, spider): - #Remove invalid data - valid = True - for data in item: - if not data: - valid = False - raise DropItem("Missing %s of blogpost from %s" %(data, item['url'])) - if valid: - #Insert data into database - new_moive=[{ - "name":item['name'][0], - "year":item['year'][0], - "score":item['score'], - "director":item['director'], - "classification":item['classification'], - "actor":item['actor'] - }] - self.collection.insert(new_moive) - log.msg("Item wrote to MongoDB database %s/%s" % - (settings['MONGODB_DB'], settings['MONGODB_COLLECTION']), - level=log.DEBUG, spider=spider) - return item - - ``` - 利用Pipeline我们可以完成以下操作： - - - 清理HTML数据，验证爬取的数据。 - - 丢弃重复的不必要的内容。 - - 将爬取的结果进行持久化操作。 - -4. 修改settings.py文件对项目进行配置。 - - ```Python - # -*- coding: utf-8 -*- - - # Scrapy settings for douban project - # - # For simplicity, this file contains only settings considered important or - # commonly used. You can find more settings consulting the documentation: - # - # https://doc.scrapy.org/en/latest/topics/settings.html - # https://doc.scrapy.org/en/latest/topics/downloader-middleware.html - # https://doc.scrapy.org/en/latest/topics/spider-middleware.html - - BOT_NAME = 'douban' - - SPIDER_MODULES = ['douban.spiders'] - NEWSPIDER_MODULE = 'douban.spiders' - - - # Crawl responsibly by identifying yourself (and your website) on the user-agent - USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_3) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.54 Safari/536.5' - - # Obey robots.txt rules - ROBOTSTXT_OBEY = True - - # Configure maximum concurrent requests performed by Scrapy (default: 16) - # CONCURRENT_REQUESTS = 32 - - # Configure a delay for requests for the same website (default: 0) - # See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay - # See also autothrottle settings and docs - DOWNLOAD_DELAY = 3 - RANDOMIZE_DOWNLOAD_DELAY = True - # The download delay setting will honor only one of: - # CONCURRENT_REQUESTS_PER_DOMAIN = 16 - # CONCURRENT_REQUESTS_PER_IP = 16 - - # Disable cookies (enabled by default) - COOKIES_ENABLED = True - - MONGODB_SERVER = '120.77.222.217' - MONGODB_PORT = 27017 - MONGODB_DB = 'douban' - MONGODB_COLLECTION = 'movie' - - # Disable Telnet Console (enabled by default) - # TELNETCONSOLE_ENABLED = False - - # Override the default request headers: - # DEFAULT_REQUEST_HEADERS = { - # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', - # 'Accept-Language': 'en', - # } - - # Enable or disable spider middlewares - # See https://doc.scrapy.org/en/latest/topics/spider-middleware.html - # SPIDER_MIDDLEWARES = { - # 'douban.middlewares.DoubanSpiderMiddleware': 543, - # } - - # Enable or disable downloader middlewares - # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html - # DOWNLOADER_MIDDLEWARES = { - # 'douban.middlewares.DoubanDownloaderMiddleware': 543, - # } - - # Enable or disable extensions - # See https://doc.scrapy.org/en/latest/topics/extensions.html - # EXTENSIONS = { - # 'scrapy.extensions.telnet.TelnetConsole': None, - # } - - # Configure item pipelines - # See https://doc.scrapy.org/en/latest/topics/item-pipeline.html - ITEM_PIPELINES = { - 'douban.pipelines.DoubanPipeline': 400, - } - - LOG_LEVEL = 'DEBUG' - - # Enable and configure the AutoThrottle extension (disabled by default) - # See https://doc.scrapy.org/en/latest/topics/autothrottle.html - #AUTOTHROTTLE_ENABLED = True - # The initial download delay - #AUTOTHROTTLE_START_DELAY = 5 - # The maximum download delay to be set in case of high latencies - #AUTOTHROTTLE_MAX_DELAY = 60 - # The average number of requests Scrapy should be sending in parallel to - # each remote server - #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 - # Enable showing throttling stats for every response received: - #AUTOTHROTTLE_DEBUG = False - - # Enable and configure HTTP caching (disabled by default) - # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings - HTTPCACHE_ENABLED = True - HTTPCACHE_EXPIRATION_SECS = 0 - HTTPCACHE_DIR = 'httpcache' - HTTPCACHE_IGNORE_HTTP_CODES = [] - HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage' - ``` - diff --git a/Day66-75/73.Scrapy高级应用.md b/Day66-75/73.Scrapy高级应用.md deleted file mode 100644 index 264c3c2..0000000 --- a/Day66-75/73.Scrapy高级应用.md +++ /dev/null @@ -1,32 +0,0 @@ -## Scrapy爬虫框架高级应用 - -### Spider的用法 - -在Scrapy框架中，我们自定义的蜘蛛都继承自scrapy.spiders.Spider，这个类有一系列的属性和方法，具体如下所示： - -1. name：爬虫的名字。 -2. allowed_domains：允许爬取的域名，不在此范围的链接不会被跟进爬取。 -3. start_urls：起始URL列表，当我们没有重写start_requests()方法时，就会从这个列表开始爬取。 -4. custom_settings：用来存放蜘蛛专属配置的字典，这里的设置会覆盖全局的设置。 -5. crawler：由from_crawler()方法设置的和蜘蛛对应的Crawler对象，Crawler对象包含了很多项目组件，利用它我们可以获取项目的配置信息，如调用crawler.settings.get()方法。 -6. settings：用来获取爬虫全局设置的变量。 -7. start_requests()：此方法用于生成初始请求，它返回一个可迭代对象。该方法默认是使用GET请求访问起始URL，如果起始URL需要使用POST请求来访问就必须重写这个方法。 -8. parse()：当Response没有指定回调函数时，该方法就会被调用，它负责处理Response对象并返回结果，从中提取出需要的数据和后续的请求，该方法需要返回类型为Request或Item的可迭代对象（生成器当前也包含在其中，因此根据实际需要可以用return或yield来产生返回值）。 -9. closed()：当蜘蛛关闭时，该方法会被调用，通常用来做一些释放资源的善后操作。 - -### 中间件的应用 - -#### 下载中间件 - - - -#### 蜘蛛中间件 - - - -### Scrapy对接Selenium - - - -### Scrapy部署到Docker - diff --git a/Day66-75/74.Scrapy分布式实现.md b/Day66-75/74.Scrapy分布式实现.md deleted file mode 100644 index 9fe53d1..0000000 --- a/Day66-75/74.Scrapy分布式实现.md +++ /dev/null @@ -1,30 +0,0 @@ -## Scrapy爬虫框架分布式实现 - -### 分布式爬虫原理 - - - -### Scrapy分布式实现 - -1. 安装Scrapy-Redis。 -2. 配置Redis服务器。 -3. 修改配置文件。 - - SCHEDULER = 'scrapy_redis.scheduler.Scheduler' - - DUPEFILTER_CLASS = 'scrapy_redis.dupefilter.RFPDupeFilter' - - REDIS_HOST = '1.2.3.4' - - REDIS_PORT = 6379 - - REDIS_PASSWORD = '1qaz2wsx' - - SCHEDULER_QUEUE_CLASS = 'scrapy_redis.queue.FifoQueue' - - SCHEDULER_PERSIST = True（通过持久化支持接续爬取） - - SCHEDULER_FLUSH_ON_START = True（每次启动时重新爬取） - -### Scrapyd分布式部署 - -1. 安装Scrapyd -2. 修改配置文件 - - mkdir /etc/scrapyd - - vim /etc/scrapyd/scrapyd.conf -3. 安装Scrapyd-Client - - 将项目打包成Egg文件。 - - 将打包的Egg文件通过addversion.json接口部署到Scrapyd上。 - diff --git a/Day66-75/code/douban/douban/__init__.py b/Day66-75/code/douban/douban/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/Day66-75/code/guido.jpg b/Day66-75/code/guido.jpg deleted file mode 100644 index 78f71ae..0000000 Binary files a/Day66-75/code/guido.jpg and /dev/null differ diff --git a/Day66-75/code/image360/image360/__init__.py b/Day66-75/code/image360/image360/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/Day66-75/code/tesseract.png b/Day66-75/code/tesseract.png deleted file mode 100644 index 315cf94..0000000 Binary files a/Day66-75/code/tesseract.png and /dev/null differ diff --git a/Day66-75/res/api-image360.png b/Day66-75/res/api-image360.png deleted file mode 100644 index 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@@ -0,0 +1,130 @@ +## 人工智能和机器学习概述 + +所谓“人工智能”通常是泛指让机器具有像人一样的智慧的技术，其目的是让机器像人一样能够感知、思考和解决问题；而“机器学习”通常是指让计算机通过学习现有的数据，实现认知的更新和进步。显然，机器学习是实现人工智能的一种途径，这也是我们的课程要讨论的内容。现如今，“机器学习”和“大数据”可以说是最时髦的两个词汇，而在弱人工智能阶段，无论是“机器学习”还是“大数据”最终要解决的问题本质上是一样的，就是让计算机将纷繁复杂的数据处理成有用的信息，这样就可以发掘出数据带来的意义以及隐藏在数据背后的规律，简单的说就是用现有的数据对将来的状况做出预测和判断。 + +在讨论机器学习相关内容之前，我们先按照问题的“输入”和“输出”对用计算机求解的问题进行一个分类，如下所示： + +1. 输入的信息是精确的，要求输出最优解。 +2. 输入的信息是精确的，无法找到最优解，只能获得满意解。 +3. 输入的信息是模糊的，要求输出最优解。 +4. 输入的信息是模糊的，无法找到最优解，只能获得满意解。 + +在上面的四大类问题中，第1类问题是计算机最擅长解决的，这类问题其实就是“数值计算”和“逻辑推理”方面的问题，而传统意义上的人工智能也就是利用逻辑推理来解决问题（如早期的“人机对弈”）。一直以来，我们都习惯于将计算机称为“电脑”，而基于“冯诺依曼”体系结构的“电脑”实际上只是实现了“人脑”理性思维这部分的功能，而且在这一点上“电脑”的表现通常是优于“人脑”的；但是“人脑”在处理模糊输入信息时表现出来的强大处理能力，在很多场景下“电脑”是难以企及的。所以我们研究机器学习的算法，就是要解决在输入模糊信息时让计算机给出满意解甚至是最优解的问题。 + +人类通过记忆和归纳这两种方式进行学习，通过记忆可以积累单个事实，使用归纳可以从旧的事实推导出新的事实。所以机器学习其实是一种训练，让计算机通过这种训练能够学会根据数据隐含模式进行合理推断的能力，其基本流程如下所示： + +1. 观察一组实例，通常称为训练数据，它们可以表示某种统计现象的不完整信息; +2. 对观测到的实例进行扩展，并使用推断技术对扩展过程建模; +3. 使用这个模型对未知实例进行预测。 + +### 基本概念 + +#### 监督学习和非监督学习 + +监督学习是从给定的训练数据集中学习得到一个函数，当新的数据到来时，可以根据这个函数预测结果，监督学习的训练集包括输入和输出，也可以说是特征和目标。监督学习的目标是由人来标注的，而非监督学习的数据没有类别信息，训练集也没有人为标注结果，通过无监督学习可以减少数据特征的维度，以便我们可以使用二维或三维图形更加直观地展示数据中的信息 。 + +#### 特征向量和特征工程 + + + +#### 距离度量 + + + +1. 欧氏距离 + +$$ +d = \sqrt{\sum_{k=1}^n(x_{1k}-x_{2k})^2} +$$ + +2. 曼哈顿距离 + +$$ +d = \sum_{k=1}^n \mid {x_{1k}-x_{2k}} \mid +$$ + +3. 切比雪夫距离 + +$$ +d = max(\mid x_{1k}-x_{2k} \mid) +$$ + +4. 闵可夫斯基距离 + - 当$p=1$时，就是曼哈顿距离 + - 当$p=2$时，就是欧式距离 + - 当$p \to \infty$时，就是切比雪夫距离 + +$$ +d = \sqrt[p]{\sum_{k=1}^n \mid x_{1k}-x_{2k} \mid ^p} +$$ + +5. 余弦距离 + $$ + cos(\theta) = \frac{\sum_{k=1}^n x_{1k}x_{2k}}{\sqrt{\sum_{k=1}^n x_{1k}^2} \sqrt{\sum_{k=1}^n x_{2k}^2}} + $$ + +### 机器学习的定义和应用领域 + +根据上面的论述，我们可以给“机器学习”下一个正式的定义：**机器学习是一门专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身性能的学科**。即使对于机器学习这个概念不那么熟悉，但是机器学习的成果已经广泛渗透到了生产生活的各个领域，下面的这些场景对于你来说一定不陌生。 + +场景1：搜索引擎会根据搜索和使用习惯，优化下一次搜索的结果。 + +场景2：电商网站会根据你的访问历史自动推荐你可能感兴趣的商品。 + +场景3：金融类产品会通过你最近的金融活动信息综合评定你的贷款申请。 + +场景4：视频和直播平台会自动识别图片和视频中有没有不和谐的内容。 + +场景5：智能家电和智能汽车会根据你的语音指令做出相应的动作。 + +简单的总结一下，机器学习可以应用到但不限于以下领域： + +1. 计算机视觉。计算机视觉是指机器感知环境的能力，目前在[**物体检测**](https://pjreddie.com/darknet/yolo/)和**人脸识别**这两个领域已经非常成熟且产生了大量的应用。 + + - 刷脸支付 + + ![](res/face_paying.png) + + - [涂鸦识别](https://quickdraw.withgoogle.com/) + + ![](res/quickdraw.png) + +2. 自然语言处理（NLP）。自然语言处理是目前机器学习中一个非常热门的分支，具体的又可以分为三类应用场景。其中文本挖掘主要是对文本进行分类，包括句法分析、情绪分析和垃圾信息检测等；而机器翻译和语音识别相信不用太多的解释大家也都清楚。 + + - 文本挖掘 + - 机器翻译 + + - 语音识别 + + ![](res/xiaomi_ai_voice_box.png) + +3. 机器人。机器人可以分为固定机器人和移动机器人两大类。固定机器人通常被用于工业生产，例如用于装配流水线。常见的移动机器人应用有货运机器人、空中机器人和自动载具。机器人需要软硬件的协作才能实现最优的作业，其中硬件包含传感器、反应器和控制器等，而软件主要是实现感知能力，包括定位、测绘、目标检测和识别等。 + + - 机甲大师 + + ![](res/dajiang_robomaster.png) + + - 扫地机器人 + + ![](res/sweep_robot.jpg) + +### 机器学习实施步骤 + +实现机器学习的一般步骤： + +1. 数据收集 +2. 数据准备 +3. 数据分析 +4. 训练算法 +5. 测试算法 +6. 应用算法 + +### Scikit-learn介绍 + +![](res/scikit-learn-logo.png) + +Scikit-learn源于Google Summer of Code项目，由David Cournapeau在2007年发起，它提供了机器学习可能用到的工具，包括数据预处理、监督学习（分类、回归）、非监督学习（聚类）、模型选择、降维等。 + +官网地址： + +安装方法：`pip install scikit-learn` \ No newline at end of file diff --git a/Day71-85/72.k最近邻分类.md b/Day71-85/72.k最近邻分类.md new file mode 100644 index 0000000..1568098 --- /dev/null +++ b/Day71-85/72.k最近邻分类.md @@ -0,0 +1,35 @@ +## k最近邻分类 + +$k$最近邻（简称kNN，k-Nearest Neighbor）是Cover和Hart在1968年提出的一种简单的监督学习算法，可用于字符识别、文本分类、图像识别等领域。kNN的工作机制非常简单：给定测试样本，基于某种距离度量（如：欧式距离、曼哈顿距离等）找出训练集中与其最接近的$k$个训练样本，然后基于这$k$个“最近邻居”的信息来进行预测。对于分类任务，可以在$k$个最近邻居中选择出现次数最多的类别标签作为预测的结果；对于回归任务，可以使用$k$个最近邻居实际输出（目标值）的平均值作为预测的结果，当然也可以根据距离的远近进行加权平均，距离越近的样本权重值就越大。 + +### 案例：电影分类预测 + + + +### k值的选择和交叉检验 + +k值的选择对于kNN算法的结果有非常显著的影响。下面用李航博士的《统计学习方法》一书中的叙述，来对k值的选择加以说明。 + +如果选择较小的$k$值，就相当于用较小的邻域中的训练实例进行预测，“学习”的近似误差会减小，只有与输入实例较近（相似的）训练实例才会对预测结果起作用；但缺点是“学习”的估计误差会增大，预测结果会对近邻的实例点非常敏感，如果近邻的实例点刚好是噪声，预测就会出错。换句话说，$k$值的减小就意味着整体模型变得复杂，容易发生**过拟合**。 + +如果选择较大的$k$值，就相当于用较大的邻域中的训练实例进行预测，其优点是可以减少学习的估计误差，但缺点是学习的近似误差会增大。这时候，与输入实例较远（不相似的）训练实例也会对预测起作用，使预测发生错误。对于$k=N$的极端情况（其中$N$代表所有的训练实例的数量），那么无论输入实例是什么，都会预测它属于训练实例中最多的类，很显然，这样的模型完全忽略了训练实例中大量的有用信息，是不可取的。 + +实际应用中，$k$的取值通常都比较小，可以通过交叉检验的方式来选择较好的$k$值。 + + + +### 算法优缺点 + +优点： + +1. 简单有效 +2. 重新训练代价低 +3. 适合类域交叉样本 +4. 适合大样本分类 + +缺点： + +1. 惰性学习 +2. 输出的可解释性不强 +3. 不擅长处理不均衡样本 +4. 计算量比较大 \ No newline at end of file diff --git a/Day76-90/81.决策树.md b/Day71-85/73.决策树.md similarity index 100% rename from Day76-90/81.决策树.md rename to Day71-85/73.决策树.md diff --git a/Day76-90/82.贝叶斯分类.md b/Day71-85/74.贝叶斯分类.md similarity index 100% rename from Day76-90/82.贝叶斯分类.md rename to Day71-85/74.贝叶斯分类.md diff --git a/Day76-90/83.支持向量机.md b/Day71-85/75.支持向量机.md similarity index 100% rename from Day76-90/83.支持向量机.md rename to Day71-85/75.支持向量机.md diff --git a/Day76-90/84.K-均值聚类.md b/Day71-85/76.K-均值聚类.md similarity index 100% rename from Day76-90/84.K-均值聚类.md rename to Day71-85/76.K-均值聚类.md diff --git a/Day76-90/85.回归分析.md b/Day71-85/77.回归分析.md similarity index 100% rename from Day76-90/85.回归分析.md rename to Day71-85/77.回归分析.md diff --git a/Day76-90/88.Tensorflow入门.md b/Day71-85/78.深度学习入门.md similarity index 100% rename from Day76-90/88.Tensorflow入门.md rename to Day71-85/78.深度学习入门.md diff --git a/Day71-85/79.Tensorflow概述.md b/Day71-85/79.Tensorflow概述.md new file mode 100644 index 0000000..ade2f52 --- /dev/null +++ b/Day71-85/79.Tensorflow概述.md @@ -0,0 +1,2 @@ +## Tensorflow入门 + diff --git a/Day76-90/89.Tensorflow实战.md b/Day71-85/80.Tensorflow实战.md similarity index 100% rename from Day76-90/89.Tensorflow实战.md rename to Day71-85/80.Tensorflow实战.md diff --git a/Day71-85/81.Kaggle项目实战.md b/Day71-85/81.Kaggle项目实战.md new file mode 100644 index 0000000..3b2fe56 --- /dev/null +++ b/Day71-85/81.Kaggle项目实战.md @@ -0,0 +1,2 @@ +## Kaggle项目实战 + diff --git a/Day71-85/82.天池大数据项目实战.md b/Day71-85/82.天池大数据项目实战.md new file mode 100644 index 0000000..883b98e --- /dev/null +++ b/Day71-85/82.天池大数据项目实战.md @@ -0,0 +1,2 @@ +## 天池大数据项目实战 + diff --git a/Day71-85/83.推荐系统实战-1.md b/Day71-85/83.推荐系统实战-1.md new file mode 100644 index 0000000..55ca245 --- /dev/null +++ b/Day71-85/83.推荐系统实战-1.md @@ -0,0 +1,2 @@ +## 推荐系统实战(1) + diff --git a/Day71-85/84.推荐系统实战-2.md b/Day71-85/84.推荐系统实战-2.md new file mode 100644 index 0000000..47b5ac0 --- /dev/null +++ b/Day71-85/84.推荐系统实战-2.md @@ -0,0 +1,2 @@ +## 推荐系统实战(2) + diff --git a/Day71-85/85.推荐系统实战-3.md b/Day71-85/85.推荐系统实战-3.md new file mode 100644 index 0000000..3d28af5 --- /dev/null +++ b/Day71-85/85.推荐系统实战-3.md @@ -0,0 +1,2 @@ +## 推荐系统实战(3) + diff --git a/Day71-85/res/dajiang_robomaster.png b/Day71-85/res/dajiang_robomaster.png new file mode 100644 index 0000000..d65809c Binary files /dev/null and b/Day71-85/res/dajiang_robomaster.png differ diff --git a/Day71-85/res/face_paying.png b/Day71-85/res/face_paying.png new file mode 100644 index 0000000..66d619a Binary files /dev/null and b/Day71-85/res/face_paying.png differ diff --git a/Day71-85/res/quickdraw.png b/Day71-85/res/quickdraw.png new file mode 100644 index 0000000..463023f Binary files /dev/null and b/Day71-85/res/quickdraw.png differ diff --git a/Day71-85/res/scikit-learn-logo.png b/Day71-85/res/scikit-learn-logo.png new file mode 100644 index 0000000..c06a838 Binary files /dev/null and b/Day71-85/res/scikit-learn-logo.png differ diff --git a/Day71-85/res/sweep_robot.jpg b/Day71-85/res/sweep_robot.jpg new file mode 100644 index 0000000..bc2593f Binary files /dev/null and b/Day71-85/res/sweep_robot.jpg differ diff --git a/Day71-85/res/xiaomi_ai_voice_box.png b/Day71-85/res/xiaomi_ai_voice_box.png new file mode 100644 index 0000000..7f20777 Binary files /dev/null and b/Day71-85/res/xiaomi_ai_voice_box.png differ diff --git a/Day76-90/76.机器学习基础.md b/Day76-90/76.机器学习基础.md deleted file mode 100644 index b7a5941..0000000 --- a/Day76-90/76.机器学习基础.md +++ /dev/null @@ -1,33 +0,0 @@ -## 机器学习基础 - -所谓“机器学习”就是利用计算机将纷繁复杂的数据处理成有用的信息，这样就可以发掘出数据带来的意义以及隐藏在数据背后的规律。现如今，“机器学习”和“大数据”可以说是IT行业中最热点的两个词汇，而无论是“机器学习”还是“大数据”最终要解决的问题本质上是一样的，用最为直白的话来说就是用现有的数据去预测将来的状况。 - -按照问题的“输入”和“输出”，我们可以将用计算机解决的问题分为四大类： - -1. 输入的信息是精确的，要求输出最优解。 -2. 输入的信息是精确的，无法找到最优解。 -3. 输入的信息是模糊的，要求输出最优解。 -4. 输入的信息是模糊的，无法找到最优解。 - -在上面的四大类问题中，第1类问题是计算机最擅长解决的，这类问题其实就是“数值计算”和“逻辑推理”方面的问题，而传统意义上的人工智能也就是利用逻辑推理来解决问题（如早期的“人机对弈”）。一直以来，我们都习惯于将计算机称为“电脑”，而基于“冯诺依曼”体系结构的“电脑”实际上只是实现了“人脑”理性思维这部分的功能，而且在这一点上“电脑”通常是优于“人脑”的，而“人脑”在处理输入模糊信息时表现出来的强大的处理能力，在今天看来也不是“电脑”可以完全企及的。所以我们研究人工智能也好，研究机器学习也好，是希望输入模糊信息时，计算机能够给出满意的甚至是最优的答案。 - -至此，我们可以给“机器学习”下一个定义：机器学习是一门专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身性能的学科。机器学习目前已经广泛的应用到生产生活的各个领域，以下列举了一些经典的场景： - -1. 搜索引擎：根据搜索和使用习惯，优化下一次搜索的结果。 -2. 电商网站：自动推荐你可能感兴趣的商品。 -3. 贷款申请：通过你最近的金融活动信息进行综合评定。 -4. 图像识别：自动识别图片中有没有不和谐的内容。 - -机器学习可以分为监督学习和非监督学习。监督学习是从给定的训练数据集中学习得到一个函数，当新的数据到来时，可以根据这个函数预测结果，监督学习的训练集包括输入和输出，也可以说是特征和目标。监督学习的目标是由人来标注的，而非监督学习的数据没有类别信息，训练集也没有人为标注结果，通过无监督学习可以减少数据特征的维度，以便我们可以使用二维或三维图形更加直观地展示数据信息 。 - -实现机器学习的一般步骤： - -1. 数据收集 -2. 数据准备 -3. 数据分析 -4. 训练算法 -5. 测试算法 -6. 应用算法 - - - diff --git a/Day76-90/78.NumPy和SciPy的应用.md b/Day76-90/78.NumPy和SciPy的应用.md deleted file mode 100644 index 6bc102f..0000000 --- a/Day76-90/78.NumPy和SciPy的应用.md +++ /dev/null @@ -1,2 +0,0 @@ -## NumPy和SciPy的应用 - diff --git a/Day76-90/80.k最近邻分类.md b/Day76-90/80.k最近邻分类.md deleted file mode 100644 index 918cf9c..0000000 --- a/Day76-90/80.k最近邻分类.md +++ /dev/null @@ -1,2 +0,0 @@ -## k最近邻分类 - diff --git a/Day76-90/86.大数据分析入门.md b/Day76-90/86.大数据分析入门.md deleted file mode 100644 index 988f860..0000000 --- a/Day76-90/86.大数据分析入门.md +++ /dev/null @@ -1,2 +0,0 @@ -## 大数据分析入门 - diff --git a/Day76-90/87.大数据分析进阶.md b/Day76-90/87.大数据分析进阶.md deleted file mode 100644 index e2b83b1..0000000 --- a/Day76-90/87.大数据分析进阶.md +++ /dev/null @@ -1,2 +0,0 @@ -## 大数据分析进阶 - diff --git a/Day76-90/90.推荐系统实战.md b/Day76-90/90.推荐系统实战.md deleted file mode 100644 index 77172df..0000000 --- a/Day76-90/90.推荐系统实战.md +++ /dev/null @@ -1,2 +0,0 @@ -## 推荐系统实战 - diff --git a/Day76-90/code/.ipynb_checkpoints/1-pandas入门-checkpoint.ipynb b/Day76-90/code/.ipynb_checkpoints/1-pandas入门-checkpoint.ipynb deleted file mode 100644 index d10293f..0000000 --- a/Day76-90/code/.ipynb_checkpoints/1-pandas入门-checkpoint.ipynb +++ /dev/null @@ -1,628 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pandas import Series,DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Math 120\n", - "Python 136\n", - "En 128\n", - "Chinese 99\n", - "dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 创建\n", - "# Series是一维的数据\n", - "s = Series(data=[120,136,128,99], index=['Math','Python','En','Chinese'])\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(4,)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([120, 136, 128, 99])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "v = s.values\n", - "v" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(v)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "120.75" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "136" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "15.903353943953666" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.std()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Math 122\n", - "Python 138\n", - "En 130\n", - "Chinese 101\n", - "dtype: int64" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.add(1)\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"k 55.666667\n", - "dtype: float64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.mean(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day76-90/code/.ipynb_checkpoints/2-pandas-索引-checkpoint.ipynb b/Day76-90/code/.ipynb_checkpoints/2-pandas-索引-checkpoint.ipynb deleted file mode 100644 index 98c1704..0000000 --- a/Day76-90/code/.ipynb_checkpoints/2-pandas-索引-checkpoint.ipynb +++ /dev/null @@ -1,372 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import pandas as pd\n", - "\n", - "from pandas import Series, DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s = Series(np.random.randint(0,150,size = 100),index = np.arange(10,110),dtype=np.int16,name = 'Python')\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s[10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s[[10,20]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 切片操作\n", - "s[10:20]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s[::2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s[::-2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 可以使用pandas为开发者提供方法，去进行检索\n", - "s.loc[10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "s.loc[[10,20]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.loc[10:20]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.loc[::2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.loc[::-2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# iloc 索引从0开始，数字化自然索引\n", - "s.iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.iloc[[0,10]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.iloc[0:20]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.iloc[::-2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# DataFrame是二维，索引大同小异，\n", - "df = DataFrame(data = np.random.randint(0,150,size= (10,3)),index=list('ABCDEFHIJK'),columns=['Python','En','Math'])\n", - "\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['A']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['Python']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df[['Python','En']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df['Python':'Math']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['A':'D']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc['Python']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df.loc['A']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc[['A','H']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc['A':'E']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc[::2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc[::-2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.iloc['A']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df.iloc[[0,5]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.iloc[0:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df.iloc[::-2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.iloc[::2,1:]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day76-90/code/.ipynb_checkpoints/3-pandas数据清洗之空数据-checkpoint.ipynb b/Day76-90/code/.ipynb_checkpoints/3-pandas数据清洗之空数据-checkpoint.ipynb deleted file mode 100644 index 17346ba..0000000 --- a/Day76-90/code/.ipynb_checkpoints/3-pandas数据清洗之空数据-checkpoint.ipynb +++ /dev/null @@ -1,5834 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import pandas as pd\n", - 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" pad / ffill: propagate last valid observation forward to next valid\n", - " backfill / bfill: use NEXT valid observation to fill gap'''\n", - "df3.fillna(method='bfill',axis = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2000, 5)" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#数据量足够大，空数据比较少，直接删除\n", - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.dro" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day76-90/code/.ipynb_checkpoints/4-pandas多层索引-checkpoint.ipynb b/Day76-90/code/.ipynb_checkpoints/4-pandas多层索引-checkpoint.ipynb deleted file mode 100644 index d8e0d1e..0000000 --- a/Day76-90/code/.ipynb_checkpoints/4-pandas多层索引-checkpoint.ipynb +++ /dev/null @@ -1,494 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import pandas as pd\n", - "# 数据分析BI-------->人工智能AI\n", - "# 数据分析和数据挖掘一个意思，\n", - "# 工具和软件：Excel 免费版\n", - "# SPSS（一人一年10000）、SAS（一人一年5000）、Matlab 收费\n", - "# R、Python（全方位语言，流行） 免费\n", - "# Python + numpy + scipy + pandas + matplotlib + seaborn + pyEcharts + sklearn + kereas（Tensorflow）+…… \n", - "# 代码，自动化（数据输入----输出结果）\n", - "from pandas import Series,DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "a 63\n", - "b 107\n", - "c 16\n", - "d 35\n", - "e 140\n", - "f 83\n", - "dtype: int32" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 多层索引，行列\n", - "# 单层索引\n", - "s = Series(np.random.randint(0,150,size = 6),index=list('abcdef'))\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "张三 期中 114\n", - " 期末 131\n", - "李四 期中 3\n", - " 期末 63\n", - "王五 期中 107\n", - " 期末 34\n", - "dtype: int32" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 多层索引，两层，三层以上（规则一样）\n", - "s2 = Series(np.random.randint(0,150,size = 6),index = pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末']]))\n", - "s2" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'DataFrame' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'En'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Math'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_product\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'张三'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'李四'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'王五'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'期中'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'期末'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'DataFrame' is not defined" - ] - } - ], - "source": [ - "df = DataFrame(np.random.randint(0,150,size = (6,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末']]) )\n", - "\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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        期末A14278
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        " - ], - "text/plain": [ - " Python En Math\n", - "张三 期中 A 15 31 17\n", - " B 82 56 123\n", - " 期末 A 14 2 78\n", - " B 69 50 17\n", - "李四 期中 A 91 87 143\n", - " B 120 118 39\n", - " 期末 A 56 76 55\n", - " B 11 105 121\n", - "王五 期中 A 147 78 1\n", - " B 128 126 146\n", - " 期末 A 49 45 114\n", - " B 121 26 77" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 三层索引\n", - "df3 = DataFrame(np.random.randint(0,150,size = (12,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末'],['A','B']]) )\n", - "\n", - "df3" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "73" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 先获取列后获取行\n", - "df['Python']['张三']['期中']" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " Python En Math\n", - "张三 期中 73 5 25\n", - " 期末 37 36 56\n", - "李四 期中 149 81 142\n", - " 期末 71 138 0\n", - "王五 期中 11 94 103\n", - " 期末 25 121 83" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df2.sort_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "73" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 先获取行，后获取列\n", - "df.loc['张三'].loc['期中']['Python']" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        PythonEnMath
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"k 55.666667\n", - "dtype: float64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.mean(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day76-90/code/2-pandas-索引.ipynb b/Day76-90/code/2-pandas-索引.ipynb deleted file mode 100644 index 98c1704..0000000 --- a/Day76-90/code/2-pandas-索引.ipynb +++ /dev/null @@ -1,372 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import pandas as pd\n", - "\n", - "from pandas import Series, DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s = Series(np.random.randint(0,150,size = 100),index = np.arange(10,110),dtype=np.int16,name = 'Python')\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s[10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s[[10,20]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 切片操作\n", - "s[10:20]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s[::2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s[::-2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 可以使用pandas为开发者提供方法，去进行检索\n", - "s.loc[10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "s.loc[[10,20]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.loc[10:20]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.loc[::2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.loc[::-2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# iloc 索引从0开始，数字化自然索引\n", - "s.iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.iloc[[0,10]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.iloc[0:20]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.iloc[::-2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# DataFrame是二维，索引大同小异，\n", - "df = DataFrame(data = np.random.randint(0,150,size= (10,3)),index=list('ABCDEFHIJK'),columns=['Python','En','Math'])\n", - "\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['A']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['Python']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df[['Python','En']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df['Python':'Math']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['A':'D']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc['Python']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df.loc['A']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc[['A','H']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc['A':'E']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc[::2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc[::-2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.iloc['A']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df.iloc[[0,5]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.iloc[0:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df.iloc[::-2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.iloc[::2,1:]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day76-90/code/3-pandas数据清洗之空数据.ipynb b/Day76-90/code/3-pandas数据清洗之空数据.ipynb deleted file mode 100644 index 2c6fb40..0000000 --- a/Day76-90/code/3-pandas数据清洗之空数据.ipynb +++ /dev/null @@ -1,452 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import pandas as pd\n", - "\n", - "from pandas import Series,DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = DataFrame(np.random.randint(0,150,size = (100,5)),index = np.arange(100,200),columns=['Python','En','Math','Physic','Chem'])\n", - "df.loc[100, 'En'] = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# 判断DataFrame是否存在空数据\n", - "df.isnull().any()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df.notnull().all()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(50):\n", - " # 行索引\n", - " index = np.random.randint(100,200,size =1)[0]\n", - "\n", - " cols = df.columns\n", - "\n", - " # 列索引\n", - " col = np.random.choice(cols)\n", - "\n", - " df.loc[index,col] = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(20):\n", - " # 行索引\n", - " index = np.random.randint(100,200,size =1)[0]\n", - "\n", - " cols = df.columns\n", - "\n", - " # 列索引\n", - " col = np.random.choice(cols)\n", - "\n", - "# not a number 不是一个数\n", - " df.loc[index,col] = np.NAN" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df.isnull().any()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "df.isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df2.isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 固定值填充\n", - "df.fillna(value=100)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df2.mean()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 均值\n", - "df3 = df2.fillna(value=df2.mean())\n", - "df3.astype(np.int16)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "nd = np.random.randint(0,20,size = 10)\n", - "nd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nd.sort()\n", - "nd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(13 + 16)/2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.median(nd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 中位数填充\n", - "df2.median()\n", - "df4 = df2.fillna(df2.median())\n", - "df4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 众数填充，数量最多的那个数\n", - "df2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = DataFrame(np.random.randint(0,150,size = (2000,5)),index = np.arange(100,2100),columns=['Python','En','Math','Physic','Chem'])\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(1000):\n", - " # 行索引\n", - " index = np.random.randint(100,2100,size =1)[0]\n", - "\n", - " cols = df.columns\n", - "\n", - " # 列索引\n", - " col = np.random.choice(cols)\n", - "\n", - " df.loc[index,col] = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# 去重之后的数据\n", - "df['Python'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df['Python'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "en = df['En'].value_counts()\n", - "en" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "en.index[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "s = df.median()\n", - "print(s,type(s))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "zhongshu = []\n", - "for col in df.columns:\n", - " zhongshu.append(df[col].value_counts().index[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s = Series(zhongshu,index = df.columns)\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = df.fillna(s)\n", - "df2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df2.isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df3 = df.iloc[:20]\n", - "df3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None\n", - " Method to use for filling holes in reindexed Series\n", - " pad / ffill: propagate last valid observation forward to next valid\n", - " backfill / bfill: use NEXT valid observation to fill gap'''\n", - "df3.fillna(method='bfill',axis = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#数据量足够大，空数据比较少，直接删除\n", - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.dro" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day76-90/code/4-pandas多层索引.ipynb b/Day76-90/code/4-pandas多层索引.ipynb deleted file mode 100644 index d8e0d1e..0000000 --- a/Day76-90/code/4-pandas多层索引.ipynb +++ /dev/null @@ -1,494 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import pandas as pd\n", - "# 数据分析BI-------->人工智能AI\n", - "# 数据分析和数据挖掘一个意思，\n", - "# 工具和软件：Excel 免费版\n", - "# SPSS（一人一年10000）、SAS（一人一年5000）、Matlab 收费\n", - "# R、Python（全方位语言，流行） 免费\n", - "# Python + numpy + scipy + pandas + matplotlib + seaborn + pyEcharts + sklearn + kereas（Tensorflow）+…… \n", - "# 代码，自动化（数据输入----输出结果）\n", - "from pandas import Series,DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "a 63\n", - "b 107\n", - "c 16\n", - "d 35\n", - "e 140\n", - "f 83\n", - "dtype: int32" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 多层索引，行列\n", - "# 单层索引\n", - "s = Series(np.random.randint(0,150,size = 6),index=list('abcdef'))\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "张三 期中 114\n", - " 期末 131\n", - "李四 期中 3\n", - " 期末 63\n", - "王五 期中 107\n", - " 期末 34\n", - "dtype: int32" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 多层索引，两层，三层以上（规则一样）\n", - "s2 = Series(np.random.randint(0,150,size = 6),index = pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末']]))\n", - "s2" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'DataFrame' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - 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"\u001b[0;31mNameError\u001b[0m: name 'DataFrame' is not defined" - ] - } - ], - "source": [ - "df = DataFrame(np.random.randint(0,150,size = (6,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末']]) )\n", - "\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " Python En Math\n", - "张三 期中 A 15 31 17\n", - " B 82 56 123\n", - " 期末 A 14 2 78\n", - " B 69 50 17\n", - "李四 期中 A 91 87 143\n", - " B 120 118 39\n", - " 期末 A 56 76 55\n", - " B 11 105 121\n", - "王五 期中 A 147 78 1\n", - " B 128 126 146\n", - " 期末 A 49 45 114\n", - " B 121 26 77" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 三层索引\n", - "df3 = DataFrame(np.random.randint(0,150,size = (12,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末'],['A','B']]) )\n", - "\n", - "df3" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "73" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 先获取列后获取行\n", - "df['Python']['张三']['期中']" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "df2 = df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " Python En Math\n", - "张三 期中 73 5 25\n", - " 期末 37 36 56\n", - "李四 期中 149 81 142\n", - " 期末 71 138 0\n", - "王五 期中 11 94 103\n", - " 期末 25 121 83" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df2.sort_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "73" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 先获取行，后获取列\n", - "df.loc['张三'].loc['期中']['Python']" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        statearea (sq. mi)
        0Alabama52423
        1Alaska656425
        2Arizona114006
        3Arkansas53182
        4California163707
        5Colorado104100
        6Connecticut5544
        7Delaware1954
        8Florida65758
        9Georgia59441
        10Hawaii10932
        11Idaho83574
        12Illinois57918
        13Indiana36420
        14Iowa56276
        15Kansas82282
        16Kentucky40411
        17Louisiana51843
        18Maine35387
        19Maryland12407
        20Massachusetts10555
        21Michigan96810
        22Minnesota86943
        23Mississippi48434
        24Missouri69709
        25Montana147046
        26Nebraska77358
        27Nevada110567
        28New Hampshire9351
        29New Jersey8722
        30New Mexico121593
        31New York54475
        32North Carolina53821
        33North Dakota70704
        34Ohio44828
        35Oklahoma69903
        36Oregon98386
        37Pennsylvania46058
        38Rhode Island1545
        39South Carolina32007
        40South Dakota77121
        41Tennessee42146
        42Texas268601
        43Utah84904
        44Vermont9615
        45Virginia42769
        46Washington71303
        47West Virginia24231
        48Wisconsin65503
        49Wyoming97818
        50District of Columbia68
        51Puerto Rico3515
        \n", - "
        " - ], - "text/plain": [ - " state area (sq. mi)\n", - "0 Alabama 52423\n", - "1 Alaska 656425\n", - "2 Arizona 114006\n", - "3 Arkansas 53182\n", - "4 California 163707\n", - "5 Colorado 104100\n", - "6 Connecticut 5544\n", - "7 Delaware 1954\n", - "8 Florida 65758\n", - "9 Georgia 59441\n", - "10 Hawaii 10932\n", - "11 Idaho 83574\n", - "12 Illinois 57918\n", - "13 Indiana 36420\n", - "14 Iowa 56276\n", - "15 Kansas 82282\n", - "16 Kentucky 40411\n", - "17 Louisiana 51843\n", - "18 Maine 35387\n", - "19 Maryland 12407\n", - "20 Massachusetts 10555\n", - "21 Michigan 96810\n", - "22 Minnesota 86943\n", - "23 Mississippi 48434\n", - "24 Missouri 69709\n", - "25 Montana 147046\n", - "26 Nebraska 77358\n", - "27 Nevada 110567\n", - "28 New Hampshire 9351\n", - "29 New Jersey 8722\n", - "30 New Mexico 121593\n", - "31 New York 54475\n", - "32 North Carolina 53821\n", - "33 North Dakota 70704\n", - "34 Ohio 44828\n", - "35 Oklahoma 69903\n", - "36 Oregon 98386\n", - "37 Pennsylvania 46058\n", - "38 Rhode Island 1545\n", - "39 South Carolina 32007\n", - "40 South Dakota 77121\n", - "41 Tennessee 42146\n", - "42 Texas 268601\n", - "43 Utah 84904\n", - "44 Vermont 9615\n", - "45 Virginia 42769\n", - "46 Washington 71303\n", - "47 West Virginia 24231\n", - "48 Wisconsin 65503\n", - "49 Wyoming 97818\n", - "50 District of Columbia 68\n", - "51 Puerto Rico 3515" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 美国各州的面积\n", - "areas = pd.read_csv('./state-areas.csv')\n", - "areas" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(52, 2)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "areas.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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        stateabbreviation
        0AlabamaAL
        1AlaskaAK
        2ArizonaAZ
        3ArkansasAR
        4CaliforniaCA
        5ColoradoCO
        6ConnecticutCT
        7DelawareDE
        8District of ColumbiaDC
        9FloridaFL
        10GeorgiaGA
        11HawaiiHI
        12IdahoID
        13IllinoisIL
        14IndianaIN
        15IowaIA
        16KansasKS
        17KentuckyKY
        18LouisianaLA
        19MaineME
        20MontanaMT
        21NebraskaNE
        22NevadaNV
        23New HampshireNH
        24New JerseyNJ
        25New MexicoNM
        26New YorkNY
        27North CarolinaNC
        28North DakotaND
        29OhioOH
        30OklahomaOK
        31OregonOR
        32MarylandMD
        33MassachusettsMA
        34MichiganMI
        35MinnesotaMN
        36MississippiMS
        37MissouriMO
        38PennsylvaniaPA
        39Rhode IslandRI
        40South CarolinaSC
        41South DakotaSD
        42TennesseeTN
        43TexasTX
        44UtahUT
        45VermontVT
        46VirginiaVA
        47WashingtonWA
        48West VirginiaWV
        49WisconsinWI
        50WyomingWY
        \n", - "
        " - ], - "text/plain": [ - " state abbreviation\n", - "0 Alabama AL\n", - "1 Alaska AK\n", - "2 Arizona AZ\n", - "3 Arkansas AR\n", - "4 California CA\n", - "5 Colorado CO\n", - "6 Connecticut CT\n", - "7 Delaware DE\n", - "8 District of Columbia DC\n", - "9 Florida FL\n", - "10 Georgia GA\n", - "11 Hawaii HI\n", - "12 Idaho ID\n", - "13 Illinois IL\n", - "14 Indiana IN\n", - "15 Iowa IA\n", - "16 Kansas KS\n", - "17 Kentucky KY\n", - "18 Louisiana LA\n", - "19 Maine ME\n", - "20 Montana MT\n", - "21 Nebraska NE\n", - "22 Nevada NV\n", - "23 New Hampshire NH\n", - "24 New Jersey NJ\n", - "25 New Mexico NM\n", - "26 New York NY\n", - "27 North Carolina NC\n", - "28 North Dakota ND\n", - "29 Ohio OH\n", - "30 Oklahoma OK\n", - "31 Oregon OR\n", - "32 Maryland MD\n", - "33 Massachusetts MA\n", - "34 Michigan MI\n", - "35 Minnesota MN\n", - "36 Mississippi MS\n", - "37 Missouri MO\n", - "38 Pennsylvania PA\n", - "39 Rhode Island RI\n", - "40 South Carolina SC\n", - "41 South Dakota SD\n", - "42 Tennessee TN\n", - "43 Texas TX\n", - "44 Utah UT\n", - "45 Vermont VT\n", - "46 Virginia VA\n", - "47 Washington WA\n", - "48 West Virginia WV\n", - "49 Wisconsin WI\n", - "50 Wyoming WY" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 美国各州 缩写\n", - "abbrevs = pd.read_csv('./state-abbrevs.csv')\n", - "abbrevs" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(51, 2)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "abbrevs.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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        state/regionagesyearpopulation
        0ALunder1820121117489.0
        1ALtotal20124817528.0
        2ALunder1820101130966.0
        3ALtotal20104785570.0
        4ALunder1820111125763.0
        5ALtotal20114801627.0
        6ALtotal20094757938.0
        7ALunder1820091134192.0
        8ALunder1820131111481.0
        9ALtotal20134833722.0
        10ALtotal20074672840.0
        11ALunder1820071132296.0
        12ALtotal20084718206.0
        13ALunder1820081134927.0
        14ALtotal20054569805.0
        15ALunder1820051117229.0
        16ALtotal20064628981.0
        17ALunder1820061126798.0
        18ALtotal20044530729.0
        19ALunder1820041113662.0
        20ALtotal20034503491.0
        21ALunder1820031113083.0
        22ALtotal20014467634.0
        23ALunder1820011120409.0
        24ALtotal20024480089.0
        25ALunder1820021116590.0
        26ALunder1819991121287.0
        27ALtotal19994430141.0
        28ALtotal20004452173.0
        29ALunder1820001122273.0
        ...............
        2514USAunder18199971946051.0
        2515USAtotal2000282162411.0
        2516USAunder18200072376189.0
        2517USAtotal1999279040181.0
        2518USAtotal2001284968955.0
        2519USAunder18200172671175.0
        2520USAtotal2002287625193.0
        2521USAunder18200272936457.0
        2522USAtotal2003290107933.0
        2523USAunder18200373100758.0
        2524USAtotal2004292805298.0
        2525USAunder18200473297735.0
        2526USAtotal2005295516599.0
        2527USAunder18200573523669.0
        2528USAtotal2006298379912.0
        2529USAunder18200673757714.0
        2530USAtotal2007301231207.0
        2531USAunder18200774019405.0
        2532USAtotal2008304093966.0
        2533USAunder18200874104602.0
        2534USAunder18201373585872.0
        2535USAtotal2013316128839.0
        2536USAtotal2009306771529.0
        2537USAunder18200974134167.0
        2538USAunder18201074119556.0
        2539USAtotal2010309326295.0
        2540USAunder18201173902222.0
        2541USAtotal2011311582564.0
        2542USAunder18201273708179.0
        2543USAtotal2012313873685.0
        \n", - "

        2544 rows × 4 columns

        \n", - "
        " - ], - "text/plain": [ - " state/region ages year population\n", - "0 AL under18 2012 1117489.0\n", - "1 AL total 2012 4817528.0\n", - "2 AL under18 2010 1130966.0\n", - "3 AL total 2010 4785570.0\n", - "4 AL under18 2011 1125763.0\n", - "5 AL total 2011 4801627.0\n", - "6 AL total 2009 4757938.0\n", - "7 AL under18 2009 1134192.0\n", - "8 AL under18 2013 1111481.0\n", - "9 AL total 2013 4833722.0\n", - "10 AL total 2007 4672840.0\n", - "11 AL under18 2007 1132296.0\n", - "12 AL total 2008 4718206.0\n", - "13 AL under18 2008 1134927.0\n", - "14 AL total 2005 4569805.0\n", - "15 AL under18 2005 1117229.0\n", - "16 AL total 2006 4628981.0\n", - "17 AL under18 2006 1126798.0\n", - "18 AL total 2004 4530729.0\n", - "19 AL under18 2004 1113662.0\n", - "20 AL total 2003 4503491.0\n", - "21 AL under18 2003 1113083.0\n", - "22 AL total 2001 4467634.0\n", - "23 AL under18 2001 1120409.0\n", - "24 AL total 2002 4480089.0\n", - "25 AL under18 2002 1116590.0\n", - "26 AL under18 1999 1121287.0\n", - "27 AL total 1999 4430141.0\n", - "28 AL total 2000 4452173.0\n", - "29 AL under18 2000 1122273.0\n", - "... ... ... ... ...\n", - "2514 USA under18 1999 71946051.0\n", - "2515 USA total 2000 282162411.0\n", - "2516 USA under18 2000 72376189.0\n", - "2517 USA total 1999 279040181.0\n", - "2518 USA total 2001 284968955.0\n", - "2519 USA under18 2001 72671175.0\n", - "2520 USA total 2002 287625193.0\n", - "2521 USA under18 2002 72936457.0\n", - "2522 USA total 2003 290107933.0\n", - "2523 USA under18 2003 73100758.0\n", - "2524 USA total 2004 292805298.0\n", - "2525 USA under18 2004 73297735.0\n", - "2526 USA total 2005 295516599.0\n", - "2527 USA under18 2005 73523669.0\n", - "2528 USA total 2006 298379912.0\n", - "2529 USA under18 2006 73757714.0\n", - "2530 USA total 2007 301231207.0\n", - "2531 USA under18 2007 74019405.0\n", - "2532 USA total 2008 304093966.0\n", - "2533 USA under18 2008 74104602.0\n", - "2534 USA under18 2013 73585872.0\n", - "2535 USA total 2013 316128839.0\n", - "2536 USA total 2009 306771529.0\n", - "2537 USA under18 2009 74134167.0\n", - "2538 USA under18 2010 74119556.0\n", - "2539 USA total 2010 309326295.0\n", - "2540 USA under18 2011 73902222.0\n", - "2541 USA total 2011 311582564.0\n", - "2542 USA under18 2012 73708179.0\n", - "2543 USA total 2012 313873685.0\n", - "\n", - "[2544 rows x 4 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 美国的人口数据\n", - "pop = pd.read_csv('./state-population.csv')\n", - "pop" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2544, 4)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
        \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
        state/regionagesyearpopulation
        0ALunder1820121117489.0
        1ALtotal20124817528.0
        2ALunder1820101130966.0
        3ALtotal20104785570.0
        4ALunder1820111125763.0
        \n", - "
        " - ], - "text/plain": [ - " state/region ages year population\n", - "0 AL under18 2012 1117489.0\n", - "1 AL total 2012 4817528.0\n", - "2 AL under18 2010 1130966.0\n", - "3 AL total 2010 4785570.0\n", - "4 AL under18 2011 1125763.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
        \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
        stateabbreviation
        0AlabamaAL
        1AlaskaAK
        2ArizonaAZ
        3ArkansasAR
        4CaliforniaCA
        \n", - "
        " - ], - "text/plain": [ - " state abbreviation\n", - "0 Alabama AL\n", - "1 Alaska AK\n", - "2 Arizona AZ\n", - "3 Arkansas AR\n", - "4 California CA" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "abbrevs.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2544, 4)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(51, 2)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(pop.shape,abbrevs.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(2544, 6)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 级联时，数据变少了96个，哪些数据变少\n", - "# inner内连接，outer叫做外连接\n", - "pop2 = pop.merge(abbrevs,how = 'outer',left_on='state/region',right_on='abbreviation')\n", - "pop2.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "state/region False\n", - "ages False\n", - "year False\n", - "population True\n", - "state True\n", - "abbreviation True\n", - "dtype: bool" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 前三列没有空值\n", - "pop2.isnull().any()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
        \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
        state/regionagesyearpopulationstateabbreviation
        0ALunder1820121117489.0AlabamaAL
        1ALtotal20124817528.0AlabamaAL
        2ALunder1820101130966.0AlabamaAL
        3ALtotal20104785570.0AlabamaAL
        4ALunder1820111125763.0AlabamaAL
        \n", - "
        " - ], - "text/plain": [ - " state/region ages year population state abbreviation\n", - "0 AL under18 2012 1117489.0 Alabama AL\n", - "1 AL total 2012 4817528.0 Alabama AL\n", - "2 AL under18 2010 1130966.0 Alabama AL\n", - "3 AL total 2010 4785570.0 Alabama AL\n", - "4 AL under18 2011 1125763.0 Alabama AL" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop2.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# 删除一列\n", - "pop2.drop(labels = 'abbreviation',axis = 1,inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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        state/regionagesyearpopulationstate
        0ALunder1820121117489.0Alabama
        1ALtotal20124817528.0Alabama
        2ALunder1820101130966.0Alabama
        3ALtotal20104785570.0Alabama
        4ALunder1820111125763.0Alabama
        \n", - "
        " - ], - "text/plain": [ - " state/region ages year population state\n", - "0 AL under18 2012 1117489.0 Alabama\n", - "1 AL total 2012 4817528.0 Alabama\n", - "2 AL under18 2010 1130966.0 Alabama\n", - "3 AL total 2010 4785570.0 Alabama\n", - "4 AL under18 2011 1125763.0 Alabama" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop2.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "state/region False\n", - "ages False\n", - "year False\n", - "population True\n", - "state True\n", - "dtype: bool" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop2.isnull().any()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 False\n", - "1 False\n", - "2 False\n", - "3 False\n", - "4 False\n", - "5 False\n", - "6 False\n", - "7 False\n", - "8 False\n", - "9 False\n", - "10 False\n", - "11 False\n", - "12 False\n", - "13 False\n", - "14 False\n", - "15 False\n", - "16 False\n", - "17 False\n", - "18 False\n", - "19 False\n", - "20 False\n", - "21 False\n", - "22 False\n", - "23 False\n", - "24 False\n", - "25 False\n", - "26 False\n", - "27 False\n", - "28 False\n", - "29 False\n", - " ... \n", - "2514 True\n", - "2515 True\n", - "2516 True\n", - "2517 True\n", - "2518 True\n", - "2519 True\n", - "2520 True\n", - "2521 True\n", - "2522 True\n", - "2523 True\n", - "2524 True\n", - "2525 True\n", - "2526 True\n", - "2527 True\n", - "2528 True\n", - "2529 True\n", - "2530 True\n", - "2531 True\n", - "2532 True\n", - "2533 True\n", - "2534 True\n", - "2535 True\n", - "2536 True\n", - "2537 True\n", - "2538 True\n", - "2539 True\n", - "2540 True\n", - "2541 True\n", - "2542 True\n", - "2543 True\n", - "Name: state, Length: 2544, dtype: bool" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 定位为空的数据\n", - "cond = pop2['state'].isnull()\n", - "cond" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['PR', 'USA'], dtype=object)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 只有当state为空，返回，为空时True\n", - "# 去重操作，非重复值\n", - "pop2[cond]['state/region'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(51, 2)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "abbrevs.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(52, 2)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "areas.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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        1Alaska656425
        2Arizona114006
        3Arkansas53182
        4California163707
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        21Michigan96810
        22Minnesota86943
        23Mississippi48434
        24Missouri69709
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        26Nebraska77358
        27Nevada110567
        28New Hampshire9351
        29New Jersey8722
        30New Mexico121593
        31New York54475
        32North Carolina53821
        33North Dakota70704
        34Ohio44828
        35Oklahoma69903
        36Oregon98386
        37Pennsylvania46058
        38Rhode Island1545
        39South Carolina32007
        40South Dakota77121
        41Tennessee42146
        42Texas268601
        43Utah84904
        44Vermont9615
        45Virginia42769
        46Washington71303
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        50District of Columbia68
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"38 Rhode Island 1545\n", - "39 South Carolina 32007\n", - "40 South Dakota 77121\n", - "41 Tennessee 42146\n", - "42 Texas 268601\n", - "43 Utah 84904\n", - "44 Vermont 9615\n", - "45 Virginia 42769\n", - "46 Washington 71303\n", - "47 West Virginia 24231\n", - "48 Wisconsin 65503\n", - "49 Wyoming 97818\n", - "50 District of Columbia 68\n", - "51 Puerto Rico 3515" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "areas" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 False\n", - "1 False\n", - "2 False\n", - "3 False\n", - "4 False\n", - "5 False\n", - "6 False\n", - "7 False\n", - "8 False\n", - "9 False\n", - "10 False\n", - "11 False\n", - "12 False\n", - "13 False\n", - "14 False\n", - "15 False\n", - "16 False\n", - "17 False\n", - "18 False\n", - "19 False\n", - "20 False\n", - "21 False\n", - "22 False\n", - "23 False\n", - "24 False\n", - "25 False\n", - "26 False\n", - "27 False\n", - "28 False\n", - "29 False\n", - " ... \n", - "2514 False\n", - "2515 False\n", - "2516 False\n", - "2517 False\n", - "2518 False\n", - "2519 False\n", - "2520 False\n", - "2521 False\n", - "2522 False\n", - "2523 False\n", - "2524 False\n", - "2525 False\n", - "2526 False\n", - "2527 False\n", - "2528 False\n", - "2529 False\n", - "2530 False\n", - "2531 False\n", - "2532 False\n", - "2533 False\n", - "2534 False\n", - "2535 False\n", - "2536 False\n", - "2537 False\n", - "2538 False\n", - "2539 False\n", - "2540 False\n", - "2541 False\n", - "2542 False\n", - "2543 False\n", - "Name: state/region, Length: 2544, dtype: bool" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cond = pop2['state/region'] == 'PR'\n", - "cond" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n" - ] - } - ], - "source": [ - "pop2['state'][cond] = 'Puerto Rico'" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\python36\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " \n" - ] - } - ], - "source": [ - "cond = pop2['state/region'] == 'USA'\n", - "pop2['state'][cond] = 'United State'" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "state/region False\n", - "ages False\n", - "year False\n", - "population True\n", - "state False\n", - "dtype: bool" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop2.isnull().any()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(20, 5)" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cond = pop2['population'].isnull()\n", - "pop2[cond].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2544, 5)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop2.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# 将难于进行补全的空数据进行删除\n", - "pop2.dropna(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2524, 5)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop2.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "state/region False\n", - "ages False\n", - "year False\n", - "population False\n", - "state False\n", - "dtype: bool" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop2.isnull().any()" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "state/region True\n", - "ages True\n", - "year True\n", - "population True\n", - "state True\n", - "dtype: bool" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop2.notnull().all()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        " - ], - "text/plain": [ - " state/region ages year population state\n", - "0 AL under18 2012 1117489.0 Alabama\n", - "1 AL total 2012 4817528.0 Alabama\n", - "2 AL under18 2010 1130966.0 Alabama\n", - "3 AL total 2010 4785570.0 Alabama\n", - "4 AL under18 2011 1125763.0 Alabama" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop2.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2524, 6)" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop3 = pop2.merge(areas,how = 'outer')\n", - "pop3.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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        2ALunder1820101130966.0Alabama52423.0
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        4ALunder1820111125763.0Alabama52423.0
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        state/regionagesyearpopulationstatearea (sq. mi)pop_density
        1ALtotal20124817528.0Alabama52423.091.9
        95AKtotal2012730307.0Alaska656425.01.1
        97AZtotal20126551149.0Arizona114006.057.5
        191ARtotal20122949828.0Arkansas53182.055.5
        193CAtotal201237999878.0California163707.0232.1
        287COtotal20125189458.0Colorado104100.049.9
        289CTtotal20123591765.0Connecticut5544.0647.9
        383DEtotal2012917053.0Delaware1954.0469.3
        385DCtotal2012633427.0District of Columbia68.09315.1
        479FLtotal201219320749.0Florida65758.0293.8
        480GAtotal20129915646.0Georgia59441.0166.8
        575HItotal20121390090.0Hawaii10932.0127.2
        576IDtotal20121595590.0Idaho83574.019.1
        671ILtotal201212868192.0Illinois57918.0222.2
        672INtotal20126537782.0Indiana36420.0179.5
        767IAtotal20123075039.0Iowa56276.054.6
        768KStotal20122885398.0Kansas82282.035.1
        863KYtotal20124379730.0Kentucky40411.0108.4
        864LAtotal20124602134.0Louisiana51843.088.8
        959MEtotal20121328501.0Maine35387.037.5
        960MDtotal20125884868.0Maryland12407.0474.3
        1055MAtotal20126645303.0Massachusetts10555.0629.6
        1056MItotal20129882519.0Michigan96810.0102.1
        1151MNtotal20125379646.0Minnesota86943.061.9
        1152MStotal20122986450.0Mississippi48434.061.7
        1247MOtotal20126024522.0Missouri69709.086.4
        1248MTtotal20121005494.0Montana147046.06.8
        1343NEtotal20121855350.0Nebraska77358.024.0
        1344NVtotal20122754354.0Nevada110567.024.9
        1439NHtotal20121321617.0New Hampshire9351.0141.3
        1440NJtotal20128867749.0New Jersey8722.01016.7
        1535NMtotal20122083540.0New Mexico121593.017.1
        1536NYtotal201219576125.0New York54475.0359.4
        1631NCtotal20129748364.0North Carolina53821.0181.1
        1632NDtotal2012701345.0North Dakota70704.09.9
        1727OHtotal201211553031.0Ohio44828.0257.7
        1728OKtotal20123815780.0Oklahoma69903.054.6
        1823ORtotal20123899801.0Oregon98386.039.6
        1824PAtotal201212764475.0Pennsylvania46058.0277.1
        1919RItotal20121050304.0Rhode Island1545.0679.8
        1920SCtotal20124723417.0South Carolina32007.0147.6
        2015SDtotal2012834047.0South Dakota77121.010.8
        2016TNtotal20126454914.0Tennessee42146.0153.2
        2111TXtotal201226060796.0Texas268601.097.0
        2112UTtotal20122854871.0Utah84904.033.6
        2207VTtotal2012625953.0Vermont9615.065.1
        2208VAtotal20128186628.0Virginia42769.0191.4
        2303WAtotal20126895318.0Washington71303.096.7
        2304WVtotal20121856680.0West Virginia24231.076.6
        2399WItotal20125724554.0Wisconsin65503.087.4
        2400WYtotal2012576626.0Wyoming97818.05.9
        2475PRtotal20123651545.0Puerto Rico3515.01038.8
        2523USAtotal2012313873685.0United State3790399.082.8
        \n", - "
        " - ], - "text/plain": [ - " state/region ages year population state area (sq. mi) pop_density\n", - "1 AL total 2012 4817528.0 Alabama 52423.0 91.9\n", - "95 AK total 2012 730307.0 Alaska 656425.0 1.1\n", - "97 AZ total 2012 6551149.0 Arizona 114006.0 57.5\n", - "191 AR total 2012 2949828.0 Arkansas 53182.0 55.5\n", - "193 CA total 2012 37999878.0 California 163707.0 232.1\n", - "287 CO total 2012 5189458.0 Colorado 104100.0 49.9\n", - "289 CT total 2012 3591765.0 Connecticut 5544.0 647.9\n", - "383 DE total 2012 917053.0 Delaware 1954.0 469.3\n", - "385 DC total 2012 633427.0 District of Columbia 68.0 9315.1\n", - "479 FL total 2012 19320749.0 Florida 65758.0 293.8\n", - "480 GA total 2012 9915646.0 Georgia 59441.0 166.8\n", - "575 HI total 2012 1390090.0 Hawaii 10932.0 127.2\n", - "576 ID total 2012 1595590.0 Idaho 83574.0 19.1\n", - "671 IL total 2012 12868192.0 Illinois 57918.0 222.2\n", - "672 IN total 2012 6537782.0 Indiana 36420.0 179.5\n", - "767 IA total 2012 3075039.0 Iowa 56276.0 54.6\n", - "768 KS total 2012 2885398.0 Kansas 82282.0 35.1\n", - "863 KY total 2012 4379730.0 Kentucky 40411.0 108.4\n", - "864 LA total 2012 4602134.0 Louisiana 51843.0 88.8\n", - "959 ME total 2012 1328501.0 Maine 35387.0 37.5\n", - "960 MD total 2012 5884868.0 Maryland 12407.0 474.3\n", - "1055 MA total 2012 6645303.0 Massachusetts 10555.0 629.6\n", - "1056 MI total 2012 9882519.0 Michigan 96810.0 102.1\n", - "1151 MN total 2012 5379646.0 Minnesota 86943.0 61.9\n", - "1152 MS total 2012 2986450.0 Mississippi 48434.0 61.7\n", - "1247 MO total 2012 6024522.0 Missouri 69709.0 86.4\n", - "1248 MT total 2012 1005494.0 Montana 147046.0 6.8\n", - "1343 NE total 2012 1855350.0 Nebraska 77358.0 24.0\n", - "1344 NV total 2012 2754354.0 Nevada 110567.0 24.9\n", - "1439 NH total 2012 1321617.0 New Hampshire 9351.0 141.3\n", - "1440 NJ total 2012 8867749.0 New Jersey 8722.0 1016.7\n", - "1535 NM total 2012 2083540.0 New Mexico 121593.0 17.1\n", - "1536 NY total 2012 19576125.0 New York 54475.0 359.4\n", - "1631 NC total 2012 9748364.0 North Carolina 53821.0 181.1\n", - "1632 ND total 2012 701345.0 North Dakota 70704.0 9.9\n", - "1727 OH total 2012 11553031.0 Ohio 44828.0 257.7\n", - "1728 OK total 2012 3815780.0 Oklahoma 69903.0 54.6\n", - "1823 OR total 2012 3899801.0 Oregon 98386.0 39.6\n", - "1824 PA total 2012 12764475.0 Pennsylvania 46058.0 277.1\n", - "1919 RI total 2012 1050304.0 Rhode Island 1545.0 679.8\n", - "1920 SC total 2012 4723417.0 South Carolina 32007.0 147.6\n", - "2015 SD total 2012 834047.0 South Dakota 77121.0 10.8\n", - "2016 TN total 2012 6454914.0 Tennessee 42146.0 153.2\n", - "2111 TX total 2012 26060796.0 Texas 268601.0 97.0\n", - "2112 UT total 2012 2854871.0 Utah 84904.0 33.6\n", - "2207 VT total 2012 625953.0 Vermont 9615.0 65.1\n", - "2208 VA total 2012 8186628.0 Virginia 42769.0 191.4\n", - "2303 WA total 2012 6895318.0 Washington 71303.0 96.7\n", - "2304 WV total 2012 1856680.0 West Virginia 24231.0 76.6\n", - "2399 WI total 2012 5724554.0 Wisconsin 65503.0 87.4\n", - "2400 WY total 2012 576626.0 Wyoming 97818.0 5.9\n", - "2475 PR total 2012 3651545.0 Puerto Rico 3515.0 1038.8\n", - "2523 USA total 2012 313873685.0 United State 3790399.0 82.8" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 查找2012年美国各州的全民人口数据\n", - "\n", - "# pandas非常强大的，可以像查询数据库一样进行数据查询\n", - "\n", - "pop5 = pop4.query(\"year == 2012 and ages == 'total'\")\n", - "pop5" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "pop5.set_index(keys = 'state/region',inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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        agesyearpopulationstatearea (sq. mi)pop_density
        state/region
        AKtotal2012730307.0Alaska656425.01.1
        WYtotal2012576626.0Wyoming97818.05.9
        MTtotal20121005494.0Montana147046.06.8
        NDtotal2012701345.0North Dakota70704.09.9
        SDtotal2012834047.0South Dakota77121.010.8
        NMtotal20122083540.0New Mexico121593.017.1
        IDtotal20121595590.0Idaho83574.019.1
        NEtotal20121855350.0Nebraska77358.024.0
        NVtotal20122754354.0Nevada110567.024.9
        UTtotal20122854871.0Utah84904.033.6
        KStotal20122885398.0Kansas82282.035.1
        MEtotal20121328501.0Maine35387.037.5
        ORtotal20123899801.0Oregon98386.039.6
        COtotal20125189458.0Colorado104100.049.9
        IAtotal20123075039.0Iowa56276.054.6
        OKtotal20123815780.0Oklahoma69903.054.6
        ARtotal20122949828.0Arkansas53182.055.5
        AZtotal20126551149.0Arizona114006.057.5
        MStotal20122986450.0Mississippi48434.061.7
        MNtotal20125379646.0Minnesota86943.061.9
        VTtotal2012625953.0Vermont9615.065.1
        WVtotal20121856680.0West Virginia24231.076.6
        USAtotal2012313873685.0United State3790399.082.8
        MOtotal20126024522.0Missouri69709.086.4
        WItotal20125724554.0Wisconsin65503.087.4
        LAtotal20124602134.0Louisiana51843.088.8
        ALtotal20124817528.0Alabama52423.091.9
        WAtotal20126895318.0Washington71303.096.7
        TXtotal201226060796.0Texas268601.097.0
        MItotal20129882519.0Michigan96810.0102.1
        KYtotal20124379730.0Kentucky40411.0108.4
        HItotal20121390090.0Hawaii10932.0127.2
        NHtotal20121321617.0New Hampshire9351.0141.3
        SCtotal20124723417.0South Carolina32007.0147.6
        TNtotal20126454914.0Tennessee42146.0153.2
        GAtotal20129915646.0Georgia59441.0166.8
        INtotal20126537782.0Indiana36420.0179.5
        NCtotal20129748364.0North Carolina53821.0181.1
        VAtotal20128186628.0Virginia42769.0191.4
        ILtotal201212868192.0Illinois57918.0222.2
        CAtotal201237999878.0California163707.0232.1
        OHtotal201211553031.0Ohio44828.0257.7
        PAtotal201212764475.0Pennsylvania46058.0277.1
        FLtotal201219320749.0Florida65758.0293.8
        NYtotal201219576125.0New York54475.0359.4
        DEtotal2012917053.0Delaware1954.0469.3
        MDtotal20125884868.0Maryland12407.0474.3
        MAtotal20126645303.0Massachusetts10555.0629.6
        CTtotal20123591765.0Connecticut5544.0647.9
        RItotal20121050304.0Rhode Island1545.0679.8
        NJtotal20128867749.0New Jersey8722.01016.7
        PRtotal20123651545.0Puerto Rico3515.01038.8
        DCtotal2012633427.0District of Columbia68.09315.1
        \n", - "
        " - ], - "text/plain": [ - " ages year population state area (sq. mi) pop_density\n", - "state/region \n", - "AK total 2012 730307.0 Alaska 656425.0 1.1\n", - "WY total 2012 576626.0 Wyoming 97818.0 5.9\n", - "MT total 2012 1005494.0 Montana 147046.0 6.8\n", - "ND total 2012 701345.0 North Dakota 70704.0 9.9\n", - "SD total 2012 834047.0 South Dakota 77121.0 10.8\n", - "NM total 2012 2083540.0 New Mexico 121593.0 17.1\n", - "ID total 2012 1595590.0 Idaho 83574.0 19.1\n", - "NE total 2012 1855350.0 Nebraska 77358.0 24.0\n", - "NV total 2012 2754354.0 Nevada 110567.0 24.9\n", - "UT total 2012 2854871.0 Utah 84904.0 33.6\n", - "KS total 2012 2885398.0 Kansas 82282.0 35.1\n", - "ME total 2012 1328501.0 Maine 35387.0 37.5\n", - "OR total 2012 3899801.0 Oregon 98386.0 39.6\n", - "CO total 2012 5189458.0 Colorado 104100.0 49.9\n", - "IA total 2012 3075039.0 Iowa 56276.0 54.6\n", - "OK total 2012 3815780.0 Oklahoma 69903.0 54.6\n", - "AR total 2012 2949828.0 Arkansas 53182.0 55.5\n", - "AZ total 2012 6551149.0 Arizona 114006.0 57.5\n", - "MS total 2012 2986450.0 Mississippi 48434.0 61.7\n", - "MN total 2012 5379646.0 Minnesota 86943.0 61.9\n", - "VT total 2012 625953.0 Vermont 9615.0 65.1\n", - "WV total 2012 1856680.0 West Virginia 24231.0 76.6\n", - "USA total 2012 313873685.0 United State 3790399.0 82.8\n", - "MO total 2012 6024522.0 Missouri 69709.0 86.4\n", - "WI total 2012 5724554.0 Wisconsin 65503.0 87.4\n", - "LA total 2012 4602134.0 Louisiana 51843.0 88.8\n", - "AL total 2012 4817528.0 Alabama 52423.0 91.9\n", - "WA total 2012 6895318.0 Washington 71303.0 96.7\n", - "TX total 2012 26060796.0 Texas 268601.0 97.0\n", - "MI total 2012 9882519.0 Michigan 96810.0 102.1\n", - "KY total 2012 4379730.0 Kentucky 40411.0 108.4\n", - "HI total 2012 1390090.0 Hawaii 10932.0 127.2\n", - "NH total 2012 1321617.0 New Hampshire 9351.0 141.3\n", - "SC total 2012 4723417.0 South Carolina 32007.0 147.6\n", - "TN total 2012 6454914.0 Tennessee 42146.0 153.2\n", - "GA total 2012 9915646.0 Georgia 59441.0 166.8\n", - "IN total 2012 6537782.0 Indiana 36420.0 179.5\n", - "NC total 2012 9748364.0 North Carolina 53821.0 181.1\n", - "VA total 2012 8186628.0 Virginia 42769.0 191.4\n", - "IL total 2012 12868192.0 Illinois 57918.0 222.2\n", - "CA total 2012 37999878.0 California 163707.0 232.1\n", - "OH total 2012 11553031.0 Ohio 44828.0 257.7\n", - "PA total 2012 12764475.0 Pennsylvania 46058.0 277.1\n", - "FL total 2012 19320749.0 Florida 65758.0 293.8\n", - "NY total 2012 19576125.0 New York 54475.0 359.4\n", - "DE total 2012 917053.0 Delaware 1954.0 469.3\n", - "MD total 2012 5884868.0 Maryland 12407.0 474.3\n", - "MA total 2012 6645303.0 Massachusetts 10555.0 629.6\n", - "CT total 2012 3591765.0 Connecticut 5544.0 647.9\n", - "RI total 2012 1050304.0 Rhode Island 1545.0 679.8\n", - "NJ total 2012 8867749.0 New Jersey 8722.0 1016.7\n", - "PR total 2012 3651545.0 Puerto Rico 3515.0 1038.8\n", - "DC total 2012 633427.0 District of Columbia 68.0 9315.1" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop5.sort_values(by = 'pop_density')" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        agesyearpopulationstatearea (sq. mi)pop_density
        state/region
        DCtotal2012633427.0District of Columbia68.09315.1
        PRtotal20123651545.0Puerto Rico3515.01038.8
        NJtotal20128867749.0New Jersey8722.01016.7
        RItotal20121050304.0Rhode Island1545.0679.8
        CTtotal20123591765.0Connecticut5544.0647.9
        MAtotal20126645303.0Massachusetts10555.0629.6
        MDtotal20125884868.0Maryland12407.0474.3
        DEtotal2012917053.0Delaware1954.0469.3
        NYtotal201219576125.0New York54475.0359.4
        FLtotal201219320749.0Florida65758.0293.8
        PAtotal201212764475.0Pennsylvania46058.0277.1
        OHtotal201211553031.0Ohio44828.0257.7
        CAtotal201237999878.0California163707.0232.1
        ILtotal201212868192.0Illinois57918.0222.2
        VAtotal20128186628.0Virginia42769.0191.4
        NCtotal20129748364.0North Carolina53821.0181.1
        INtotal20126537782.0Indiana36420.0179.5
        GAtotal20129915646.0Georgia59441.0166.8
        TNtotal20126454914.0Tennessee42146.0153.2
        SCtotal20124723417.0South Carolina32007.0147.6
        NHtotal20121321617.0New Hampshire9351.0141.3
        HItotal20121390090.0Hawaii10932.0127.2
        KYtotal20124379730.0Kentucky40411.0108.4
        MItotal20129882519.0Michigan96810.0102.1
        TXtotal201226060796.0Texas268601.097.0
        WAtotal20126895318.0Washington71303.096.7
        ALtotal20124817528.0Alabama52423.091.9
        LAtotal20124602134.0Louisiana51843.088.8
        WItotal20125724554.0Wisconsin65503.087.4
        MOtotal20126024522.0Missouri69709.086.4
        USAtotal2012313873685.0United State3790399.082.8
        WVtotal20121856680.0West Virginia24231.076.6
        VTtotal2012625953.0Vermont9615.065.1
        MNtotal20125379646.0Minnesota86943.061.9
        MStotal20122986450.0Mississippi48434.061.7
        AZtotal20126551149.0Arizona114006.057.5
        ARtotal20122949828.0Arkansas53182.055.5
        OKtotal20123815780.0Oklahoma69903.054.6
        IAtotal20123075039.0Iowa56276.054.6
        COtotal20125189458.0Colorado104100.049.9
        ORtotal20123899801.0Oregon98386.039.6
        MEtotal20121328501.0Maine35387.037.5
        KStotal20122885398.0Kansas82282.035.1
        UTtotal20122854871.0Utah84904.033.6
        NVtotal20122754354.0Nevada110567.024.9
        NEtotal20121855350.0Nebraska77358.024.0
        IDtotal20121595590.0Idaho83574.019.1
        NMtotal20122083540.0New Mexico121593.017.1
        SDtotal2012834047.0South Dakota77121.010.8
        NDtotal2012701345.0North Dakota70704.09.9
        MTtotal20121005494.0Montana147046.06.8
        WYtotal2012576626.0Wyoming97818.05.9
        AKtotal2012730307.0Alaska656425.01.1
        \n", - "
        " - ], - "text/plain": [ - " ages year population state area (sq. mi) pop_density\n", - "state/region \n", - "DC total 2012 633427.0 District of Columbia 68.0 9315.1\n", - "PR total 2012 3651545.0 Puerto Rico 3515.0 1038.8\n", - "NJ total 2012 8867749.0 New Jersey 8722.0 1016.7\n", - "RI total 2012 1050304.0 Rhode Island 1545.0 679.8\n", - "CT total 2012 3591765.0 Connecticut 5544.0 647.9\n", - "MA total 2012 6645303.0 Massachusetts 10555.0 629.6\n", - "MD total 2012 5884868.0 Maryland 12407.0 474.3\n", - "DE total 2012 917053.0 Delaware 1954.0 469.3\n", - "NY total 2012 19576125.0 New York 54475.0 359.4\n", - "FL total 2012 19320749.0 Florida 65758.0 293.8\n", - "PA total 2012 12764475.0 Pennsylvania 46058.0 277.1\n", - "OH total 2012 11553031.0 Ohio 44828.0 257.7\n", - "CA total 2012 37999878.0 California 163707.0 232.1\n", - "IL total 2012 12868192.0 Illinois 57918.0 222.2\n", - "VA total 2012 8186628.0 Virginia 42769.0 191.4\n", - "NC total 2012 9748364.0 North Carolina 53821.0 181.1\n", - "IN total 2012 6537782.0 Indiana 36420.0 179.5\n", - "GA total 2012 9915646.0 Georgia 59441.0 166.8\n", - "TN total 2012 6454914.0 Tennessee 42146.0 153.2\n", - "SC total 2012 4723417.0 South Carolina 32007.0 147.6\n", - "NH total 2012 1321617.0 New Hampshire 9351.0 141.3\n", - "HI total 2012 1390090.0 Hawaii 10932.0 127.2\n", - "KY total 2012 4379730.0 Kentucky 40411.0 108.4\n", - "MI total 2012 9882519.0 Michigan 96810.0 102.1\n", - "TX total 2012 26060796.0 Texas 268601.0 97.0\n", - "WA total 2012 6895318.0 Washington 71303.0 96.7\n", - "AL total 2012 4817528.0 Alabama 52423.0 91.9\n", - "LA total 2012 4602134.0 Louisiana 51843.0 88.8\n", - "WI total 2012 5724554.0 Wisconsin 65503.0 87.4\n", - "MO total 2012 6024522.0 Missouri 69709.0 86.4\n", - "USA total 2012 313873685.0 United State 3790399.0 82.8\n", - "WV total 2012 1856680.0 West Virginia 24231.0 76.6\n", - "VT total 2012 625953.0 Vermont 9615.0 65.1\n", - "MN total 2012 5379646.0 Minnesota 86943.0 61.9\n", - "MS total 2012 2986450.0 Mississippi 48434.0 61.7\n", - "AZ total 2012 6551149.0 Arizona 114006.0 57.5\n", - "AR total 2012 2949828.0 Arkansas 53182.0 55.5\n", - "OK total 2012 3815780.0 Oklahoma 69903.0 54.6\n", - "IA total 2012 3075039.0 Iowa 56276.0 54.6\n", - "CO total 2012 5189458.0 Colorado 104100.0 49.9\n", - "OR total 2012 3899801.0 Oregon 98386.0 39.6\n", - "ME total 2012 1328501.0 Maine 35387.0 37.5\n", - "KS total 2012 2885398.0 Kansas 82282.0 35.1\n", - "UT total 2012 2854871.0 Utah 84904.0 33.6\n", - "NV total 2012 2754354.0 Nevada 110567.0 24.9\n", - "NE total 2012 1855350.0 Nebraska 77358.0 24.0\n", - "ID total 2012 1595590.0 Idaho 83574.0 19.1\n", - "NM total 2012 2083540.0 New Mexico 121593.0 17.1\n", - "SD total 2012 834047.0 South Dakota 77121.0 10.8\n", - "ND total 2012 701345.0 North Dakota 70704.0 9.9\n", - "MT total 2012 1005494.0 Montana 147046.0 6.8\n", - "WY total 2012 576626.0 Wyoming 97818.0 5.9\n", - "AK total 2012 730307.0 Alaska 656425.0 1.1" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop5.sort_values(by='pop_density',ascending=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Day76-90/code/cancer_predict.npy b/Day76-90/code/cancer_predict.npy deleted file mode 100644 index a6bf034..0000000 Binary files a/Day76-90/code/cancer_predict.npy and /dev/null differ diff --git a/Day76-90/code/cancer_true.npy b/Day76-90/code/cancer_true.npy deleted file mode 100644 index 97e4b66..0000000 Binary files a/Day76-90/code/cancer_true.npy and /dev/null differ diff --git a/Day76-90/code/state-abbrevs.csv b/Day76-90/code/state-abbrevs.csv deleted file mode 100644 index 6d4db36..0000000 --- a/Day76-90/code/state-abbrevs.csv +++ /dev/null @@ -1,52 +0,0 @@ -"state","abbreviation" -"Alabama","AL" -"Alaska","AK" -"Arizona","AZ" -"Arkansas","AR" -"California","CA" -"Colorado","CO" -"Connecticut","CT" -"Delaware","DE" -"District of Columbia","DC" -"Florida","FL" -"Georgia","GA" -"Hawaii","HI" -"Idaho","ID" -"Illinois","IL" -"Indiana","IN" -"Iowa","IA" -"Kansas","KS" -"Kentucky","KY" -"Louisiana","LA" -"Maine","ME" -"Montana","MT" -"Nebraska","NE" -"Nevada","NV" -"New Hampshire","NH" -"New Jersey","NJ" -"New Mexico","NM" -"New York","NY" -"North Carolina","NC" -"North Dakota","ND" -"Ohio","OH" -"Oklahoma","OK" -"Oregon","OR" -"Maryland","MD" -"Massachusetts","MA" -"Michigan","MI" -"Minnesota","MN" -"Mississippi","MS" -"Missouri","MO" -"Pennsylvania","PA" -"Rhode Island","RI" -"South Carolina","SC" -"South Dakota","SD" -"Tennessee","TN" -"Texas","TX" -"Utah","UT" -"Vermont","VT" -"Virginia","VA" -"Washington","WA" -"West Virginia","WV" -"Wisconsin","WI" -"Wyoming","WY" \ No newline at end of file diff --git a/Day76-90/code/state-areas.csv b/Day76-90/code/state-areas.csv deleted file mode 100644 index 322345c..0000000 --- a/Day76-90/code/state-areas.csv +++ /dev/null @@ -1,53 +0,0 @@ -state,area (sq. mi) -Alabama,52423 -Alaska,656425 -Arizona,114006 -Arkansas,53182 -California,163707 -Colorado,104100 -Connecticut,5544 -Delaware,1954 -Florida,65758 -Georgia,59441 -Hawaii,10932 -Idaho,83574 -Illinois,57918 -Indiana,36420 -Iowa,56276 -Kansas,82282 -Kentucky,40411 -Louisiana,51843 -Maine,35387 -Maryland,12407 -Massachusetts,10555 -Michigan,96810 -Minnesota,86943 -Mississippi,48434 -Missouri,69709 -Montana,147046 -Nebraska,77358 -Nevada,110567 -New Hampshire,9351 -New Jersey,8722 -New Mexico,121593 -New York,54475 -North Carolina,53821 -North Dakota,70704 -Ohio,44828 -Oklahoma,69903 -Oregon,98386 -Pennsylvania,46058 -Rhode Island,1545 -South Carolina,32007 -South Dakota,77121 -Tennessee,42146 -Texas,268601 -Utah,84904 -Vermont,9615 -Virginia,42769 -Washington,71303 -West Virginia,24231 -Wisconsin,65503 -Wyoming,97818 -District of Columbia,68 -Puerto Rico,3515 diff --git a/Day76-90/code/state-population.csv b/Day76-90/code/state-population.csv deleted file mode 100644 index c76110e..0000000 --- a/Day76-90/code/state-population.csv +++ /dev/null @@ -1,2545 +0,0 @@ -state/region,ages,year,population -AL,under18,2012,1117489 -AL,total,2012,4817528 -AL,under18,2010,1130966 -AL,total,2010,4785570 -AL,under18,2011,1125763 -AL,total,2011,4801627 -AL,total,2009,4757938 -AL,under18,2009,1134192 -AL,under18,2013,1111481 -AL,total,2013,4833722 -AL,total,2007,4672840 -AL,under18,2007,1132296 -AL,total,2008,4718206 -AL,under18,2008,1134927 -AL,total,2005,4569805 -AL,under18,2005,1117229 -AL,total,2006,4628981 -AL,under18,2006,1126798 -AL,total,2004,4530729 -AL,under18,2004,1113662 -AL,total,2003,4503491 -AL,under18,2003,1113083 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-TX,under18,2013,7041986 -TX,total,2013,26448193 -TX,total,2009,24801761 -TX,under18,2009,6792907 -TX,total,2010,25245178 -TX,under18,2010,6879014 -TX,under18,2011,6931758 -TX,total,2011,25640909 -TX,under18,2012,6985807 -TX,total,2012,26060796 -UT,total,2012,2854871 -UT,under18,2012,888578 -UT,total,2011,2814784 -UT,under18,2011,881350 -UT,under18,2010,873019 -UT,total,2010,2774424 -UT,total,2009,2723421 -UT,under18,2009,857853 -UT,total,2013,2900872 -UT,under18,2013,896589 -UT,total,2007,2597746 -UT,under18,2007,815496 -UT,total,2008,2663029 -UT,under18,2008,837258 -UT,total,2006,2525507 -UT,under18,2006,789957 -UT,total,2005,2457719 -UT,under18,2005,767888 -UT,total,2003,2360137 -UT,under18,2003,740483 -UT,total,2004,2401580 -UT,under18,2004,751771 -UT,total,2002,2324815 -UT,under18,2002,733517 -UT,total,2001,2283715 -UT,under18,2001,726819 -UT,total,1999,2203482 -UT,under18,1999,715398 -UT,total,2000,2244502 -UT,under18,2000,721686 -UT,total,1997,2119784 -UT,under18,1997,699528 -UT,under18,1998,709386 -UT,total,1998,2165961 -UT,under18,1996,687078 -UT,total,1996,2067976 -UT,total,1995,2014179 -UT,under18,1995,679636 -UT,under18,1994,673935 -UT,total,1994,1960446 -UT,under18,1992,648725 -UT,total,1992,1836799 -UT,total,1993,1898404 -UT,under18,1993,662968 -UT,total,1991,1779780 -UT,under18,1991,637216 -UT,under18,1990,627122 -UT,total,1990,1731223 -VT,under18,1990,143296 -VT,total,1990,564798 -VT,total,1991,568606 -VT,under18,1991,145219 -VT,total,1993,577748 -VT,under18,1993,148705 -VT,under18,1992,146983 -VT,total,1992,572751 -VT,under18,1994,150794 -VT,total,1994,583836 -VT,total,1995,589003 -VT,under18,1995,151439 -VT,under18,1996,151490 -VT,total,1996,593701 -VT,under18,1998,148467 -VT,total,1998,600416 -VT,total,1997,597239 -VT,under18,1997,150040 -VT,total,2000,609618 -VT,under18,2000,147549 -VT,total,1999,604683 -VT,under18,1999,147859 -VT,total,2001,612223 -VT,under18,2001,146040 -VT,total,2002,615442 -VT,under18,2002,144441 -VT,total,2004,619920 -VT,under18,2004,141068 -VT,total,2003,617858 -VT,under18,2003,142718 -VT,total,2005,621215 -VT,under18,2005,138933 -VT,total,2006,622892 -VT,under18,2006,136731 -VT,total,2008,624151 -VT,under18,2008,132600 -VT,total,2007,623481 -VT,under18,2007,134695 -VT,under18,2013,122701 -VT,total,2013,626630 -VT,total,2009,624817 -VT,under18,2009,130450 -VT,total,2010,625793 -VT,under18,2010,128601 -VT,under18,2011,126500 -VT,total,2011,626320 -VT,under18,2012,124555 -VT,total,2012,625953 -VA,total,2012,8186628 -VA,under18,2012,1861323 -VA,total,2011,8105850 -VA,under18,2011,1857585 -VA,under18,2010,1855025 -VA,total,2010,8024417 -VA,total,2009,7925937 -VA,under18,2009,1845132 -VA,total,2013,8260405 -VA,under18,2013,1864535 -VA,total,2007,7751000 -VA,under18,2007,1834386 -VA,total,2008,7833496 -VA,under18,2008,1838361 -VA,total,2005,7577105 -VA,under18,2005,1816270 -VA,total,2006,7673725 -VA,under18,2006,1826368 -VA,total,2003,7366977 -VA,under18,2003,1782254 -VA,total,2004,7475575 -VA,under18,2004,1801958 -VA,total,2002,7286873 -VA,under18,2002,1771247 -VA,total,2001,7198362 -VA,under18,2001,1754549 -VA,total,1999,7000174 -VA,under18,1999,1723125 -VA,total,2000,7105817 -VA,under18,2000,1741420 -VA,total,1997,6829183 -VA,under18,1997,1683766 -VA,under18,1998,1706261 -VA,total,1998,6900918 -VA,under18,1996,1664147 -VA,total,1996,6750884 -VA,total,1995,6670693 -VA,under18,1995,1649005 -VA,under18,1994,1628711 -VA,total,1994,6593139 -VA,under18,1992,1581544 -VA,total,1992,6414307 -VA,total,1993,6509630 -VA,under18,1993,1604758 -VA,total,1991,6301217 -VA,under18,1991,1548258 -VA,under18,1990,1520670 -VA,total,1990,6216884 -WA,under18,1990,1301545 -WA,total,1990,4903043 -WA,total,1991,5025624 -WA,under18,1991,1326527 -WA,total,1993,5278842 -WA,under18,1993,1387716 -WA,under18,1992,1365480 -WA,total,1992,5160757 -WA,under18,1994,1409922 -WA,total,1994,5375161 -WA,total,1995,5481027 -WA,under18,1995,1429397 -WA,under18,1996,1449613 -WA,total,1996,5569753 -WA,under18,1998,1494784 -WA,total,1998,5769562 -WA,total,1997,5674747 -WA,under18,1997,1473646 -WA,total,2000,5910512 -WA,under18,2000,1516361 -WA,total,1999,5842564 -WA,under18,1999,1507824 -WA,total,2001,5985722 -WA,under18,2001,1517527 -WA,total,2002,6052349 -WA,under18,2002,1517655 -WA,total,2004,6178645 -WA,under18,2004,1520751 -WA,total,2003,6104115 -WA,under18,2003,1514877 -WA,total,2005,6257305 -WA,under18,2005,1523890 -WA,total,2006,6370753 -WA,under18,2006,1536926 -WA,total,2008,6562231 -WA,under18,2008,1560302 -WA,total,2007,6461587 -WA,under18,2007,1549582 -WA,under18,2013,1595795 -WA,total,2013,6971406 -WA,total,2009,6667426 -WA,under18,2009,1574403 -WA,total,2010,6742256 -WA,under18,2010,1581436 -WA,under18,2011,1584709 -WA,total,2011,6821481 -WA,under18,2012,1588451 -WA,total,2012,6895318 -WV,total,2012,1856680 -WV,under18,2012,384030 -WV,total,2011,1855184 -WV,under18,2011,385283 -WV,under18,2010,387224 -WV,total,2010,1854146 -WV,total,2009,1847775 -WV,under18,2009,389036 -WV,total,2013,1854304 -WV,under18,2013,381678 -WV,total,2007,1834052 -WV,under18,2007,390661 -WV,total,2008,1840310 -WV,under18,2008,390210 -WV,total,2006,1827912 -WV,under18,2006,390637 -WV,total,2005,1820492 -WV,under18,2005,390431 -WV,total,2003,1812295 -WV,under18,2003,392460 -WV,total,2004,1816438 -WV,under18,2004,391856 -WV,total,2002,1805414 -WV,under18,2002,393569 -WV,total,2001,1801481 -WV,under18,2001,395307 -WV,total,1999,1811799 -WV,under18,1999,406784 -WV,total,2000,1807021 -WV,under18,2000,401062 -WV,total,1997,1819113 -WV,under18,1997,418037 -WV,under18,1998,412793 -WV,total,1998,1815609 -WV,under18,1996,422831 -WV,total,1996,1822808 -WV,total,1995,1823700 -WV,under18,1995,428790 -WV,under18,1994,429128 -WV,total,1994,1820421 -WV,under18,1992,433116 -WV,total,1992,1806451 -WV,total,1993,1817539 -WV,under18,1993,432364 -WV,total,1991,1798735 -WV,under18,1991,433918 -WV,under18,1990,436797 -WV,total,1990,1792548 -WI,under18,1990,1302869 -WI,total,1990,4904562 -WI,total,1991,4964343 -WI,under18,1991,1314855 -WI,total,1993,5084889 -WI,under18,1993,1337334 -WI,under18,1992,1330555 -WI,total,1992,5025398 -WI,under18,1994,1348110 -WI,total,1994,5133678 -WI,total,1995,5184836 -WI,under18,1995,1351343 -WI,under18,1996,1352877 -WI,total,1996,5229986 -WI,under18,1998,1362907 -WI,total,1998,5297673 -WI,total,1997,5266213 -WI,under18,1997,1359712 -WI,total,1999,5332666 -WI,under18,1999,1367019 -WI,total,2000,5373999 -WI,under18,2000,1370440 -WI,total,2001,5406835 -WI,under18,2001,1367593 -WI,total,2002,5445162 -WI,under18,2002,1365315 -WI,total,2004,5514026 -WI,under18,2004,1354643 -WI,total,2003,5479203 -WI,under18,2003,1358505 -WI,total,2005,5546166 -WI,under18,2005,1349866 -WI,total,2006,5577655 -WI,under18,2006,1348785 -WI,total,2008,5640996 -WI,under18,2008,1345573 -WI,total,2007,5610775 -WI,under18,2007,1348901 -WI,under18,2013,1307776 -WI,total,2013,5742713 -WI,total,2009,5669264 -WI,under18,2009,1342411 -WI,total,2010,5689060 -WI,under18,2010,1336094 -WI,under18,2011,1325870 -WI,total,2011,5708785 -WI,under18,2012,1316113 -WI,total,2012,5724554 -WY,total,2012,576626 -WY,under18,2012,136526 -WY,total,2011,567329 -WY,under18,2011,135407 -WY,under18,2010,135351 -WY,total,2010,564222 -WY,total,2009,559851 -WY,under18,2009,134960 -WY,total,2013,582658 -WY,under18,2013,137679 -WY,total,2007,534876 -WY,under18,2007,128760 -WY,total,2008,546043 -WY,under18,2008,131511 -WY,total,2006,522667 -WY,under18,2006,125525 -WY,total,2005,514157 -WY,under18,2005,124022 -WY,total,2003,503453 -WY,under18,2003,124182 -WY,total,2004,509106 -WY,under18,2004,123974 -WY,total,2002,500017 -WY,under18,2002,125495 -WY,total,2001,494657 -WY,under18,2001,126212 -WY,total,2000,494300 -WY,under18,2000,128774 -WY,total,1999,491780 -WY,under18,1999,130793 -WY,total,1997,489452 -WY,under18,1997,134328 -WY,under18,1998,132602 -WY,total,1998,490787 -WY,under18,1996,135698 -WY,total,1996,488167 -WY,total,1995,485160 -WY,under18,1995,136785 -WY,under18,1994,137733 -WY,total,1994,480283 -WY,under18,1992,137308 -WY,total,1992,466251 -WY,total,1993,473081 -WY,under18,1993,137458 -WY,total,1991,459260 -WY,under18,1991,136720 -WY,under18,1990,136078 -WY,total,1990,453690 -PR,under18,1990,NaN -PR,total,1990,NaN -PR,total,1991,NaN -PR,under18,1991,NaN -PR,total,1993,NaN -PR,under18,1993,NaN -PR,under18,1992,NaN -PR,total,1992,NaN -PR,under18,1994,NaN -PR,total,1994,NaN -PR,total,1995,NaN -PR,under18,1995,NaN -PR,under18,1996,NaN -PR,total,1996,NaN -PR,under18,1998,NaN -PR,total,1998,NaN -PR,total,1997,NaN -PR,under18,1997,NaN -PR,total,1999,NaN -PR,under18,1999,NaN -PR,total,2000,3810605 -PR,under18,2000,1089063 -PR,total,2001,3818774 -PR,under18,2001,1077566 -PR,total,2002,3823701 -PR,under18,2002,1065051 -PR,total,2004,3826878 -PR,under18,2004,1035919 -PR,total,2003,3826095 -PR,under18,2003,1050615 -PR,total,2005,3821362 -PR,under18,2005,1019447 -PR,total,2006,3805214 -PR,under18,2006,998543 -PR,total,2007,3782995 -PR,under18,2007,973613 -PR,total,2008,3760866 -PR,under18,2008,945705 -PR,under18,2013,814068 -PR,total,2013,3615086 -PR,total,2009,3740410 -PR,under18,2009,920794 -PR,total,2010,3721208 -PR,under18,2010,896945 -PR,under18,2011,869327 -PR,total,2011,3686580 -PR,under18,2012,841740 -PR,total,2012,3651545 -USA,under18,1990,64218512 -USA,total,1990,249622814 -USA,total,1991,252980942 -USA,under18,1991,65313018 -USA,under18,1992,66509177 -USA,total,1992,256514231 -USA,total,1993,259918595 -USA,under18,1993,67594938 -USA,under18,1994,68640936 -USA,total,1994,263125826 -USA,under18,1995,69473140 -USA,under18,1996,70233512 -USA,total,1995,266278403 -USA,total,1996,269394291 -USA,total,1997,272646932 -USA,under18,1997,70920738 -USA,under18,1998,71431406 -USA,total,1998,275854116 -USA,under18,1999,71946051 -USA,total,2000,282162411 -USA,under18,2000,72376189 -USA,total,1999,279040181 -USA,total,2001,284968955 -USA,under18,2001,72671175 -USA,total,2002,287625193 -USA,under18,2002,72936457 -USA,total,2003,290107933 -USA,under18,2003,73100758 -USA,total,2004,292805298 -USA,under18,2004,73297735 -USA,total,2005,295516599 -USA,under18,2005,73523669 -USA,total,2006,298379912 -USA,under18,2006,73757714 -USA,total,2007,301231207 -USA,under18,2007,74019405 -USA,total,2008,304093966 -USA,under18,2008,74104602 -USA,under18,2013,73585872 -USA,total,2013,316128839 -USA,total,2009,306771529 -USA,under18,2009,74134167 -USA,under18,2010,74119556 -USA,total,2010,309326295 -USA,under18,2011,73902222 -USA,total,2011,311582564 -USA,under18,2012,73708179 -USA,total,2012,313873685 diff --git a/Day91-100/100.Python面试题实录.md b/Day91-100/100.Python面试题实录.md new file mode 100644 index 0000000..1421ea8 --- /dev/null +++ b/Day91-100/100.Python面试题实录.md @@ -0,0 +1,4 @@ +## Python面试题实录 + +> **温馨提示**：请访问我的另一个项目[“Python面试宝典”](https://github.com/jackfrued/Python-Interview-Bible)。 + diff --git a/Day91-100/100.Python面试题集.md b/Day91-100/100.Python面试题集.md deleted file mode 100644 index 9e12dba..0000000 --- a/Day91-100/100.Python面试题集.md +++ /dev/null @@ -1,318 +0,0 @@ -## Python面试题 - -1. 说一说Python中的新式类和旧式类有什么区别。 - - 答： - -2. Python中`is`运算符和`==`运算符有什么区别？ - - 答：请参考[《那些年我们踩过的那些坑》](../番外篇/那些年我们踩过的那些坑.md)。 - -3. Python中如何动态设置和获取对象属性？ - - 答：`setattr(object, name, value)`和`getattr(object, name[, default])`内置函数，其中`object`是对象，`name`是对象的属性名，`value`是属性值。这两个函数会调用对象的`__getattr__`和`__setattr__`魔术方法。 - -4. Python如何实现内存管理？有没有可能出现内存泄露的问题？ - - 答： - -5. 阐述列表和集合的底层实现原理。 - - 答： - -6. 现有字典`d = {'a': 24, 'g': 52, 'i': 12, 'k': 33}`，如何按字典中的值对字典进行排序得到排序后的字典。 - - 答： - - ```Python - - ``` - -7. 实现将字符串`k1:v1|k2:v2|k3:v3`处理成字典`{'k1': 'v1', 'k2': 'v2', 'k3': 'v3'}`。 - - 答： - - ```Python - {key: value for key, value in ( - item.split(':') for item in 'k1:v1|k2:v2|k3:v3'.split('|') - )} - ``` - -8. 写出生成从`m`到`n`公差为`k`的等差数列的生成器。 - - 答: - - ```Python - (value for value in range(m, n + 1, k)) - ``` - - 或 - - ```Python - def generate(m, n, k): - for value in range(m, n + 1, k): - yield value - ``` - - 或 - - ```Python - def generate(m, n, k): - yield from range(m, n + 1, k) - ``` - -9. 请写出你能想到的反转一个字符串的方式。 - - 答： - - ```Python - ''.join(reversed('hello')) - ``` - - ```Python - 'hello'[::-1] - ``` - - ```Python - def reverse(content): - return ''.join(content[i] for i in range(len(content) - 1, -1, -1)) - - reverse('hello') - ``` - - ```Python - def reverse(content): - return reverse(content[1:]) + content[0] if len(content) > 1 else content - - reverse('hello') - ``` - -10. 不使用任何内置函数，将字符串`'123'`转换成整数`123`。 - - 答： - - ```Python - nums = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9} - total = 0 - for ch in '123': - total *= 10 - total += nums[ch] - print(total) - ``` - -11. 写一个返回bool值的函数，判断给定的非负整数是不是回文数。 - - 答： - - ```Python - - ``` - -12. 用一行代码实现求任意非负整数的阶乘。 - - 答： - - ```Python - from functools import reduce - - (lambda num: reduce(int.__mul__, range(2, num + 1), 1))(5) - ``` - -13. 写一个函数返回传入的整数列表中第二大的元素。 - - 答： - - ```Python - - ``` - -14. 删除列表中的重复元素并保留原有的顺序。 - - 答： - - ```Python - - ``` - -15. 找出两个列表中的相同元素和不同元素。 - - 答： - -16. 列表中的某个元素出现次数占列表元素总数的半数以上，找出这个元素。 - - 答： - - ```Python - - ``` - -17. 实现对有序列表进行二分查找的算法。 - - 答： - - ```Python - - ``` - -18. 输入年月日，输出这一天是这一年的第几天。 - - 答： - - ```Python - - ``` - -19. 统计一个字符串中各个字符出现的次数。 - - 答： - - ```Python - - ``` - -20. 在Python中如何实现单例模式？ - - 答： - - ```Python - - ``` - -21. 下面的代码会输出什么。 - - ```Python - class A: - - def __init__(self, value): - self.__value = value - - @property - def value(self): - return self.__value - - - a = A(1) - a.__value = 2 - print(a.__value) - print(a.value) - ``` - -22. 实现一个记录函数执行时间的装饰器。 - - 答： - - ```Python - - ``` - -23. 写一个遍历指定目录下指定后缀名的文件的函数。 - - 答： - - ```Python - - ``` - -24. 有如下所示的字典，请将其转换为CSV格式。 - - 转换前： - - ```Python - dict_corp = { - 'cn': {'id': 1, 'name': '土豆', 'desc': '土豆', 'price': {'gold': 20, 'kcoin': 20}}, - 'en': {'id': 1, 'name': 'potato', 'desc': 'potato', 'price': {'gold': 20, 'kcoin': 20}}, - 'kr': {'id': 1, 'name': '감자', 'desc':'감자', 'price': {'gold': 20, 'kcoin': 20}}, - 'jp': {'id': 1, 'name': 'ジャガイモ', 'desc': 'ジャガイモ', 'price': {'gold': 20, 'kcoin': 20}}, - } - ``` - - 转换后： - - ```CSV - ,id,name,desc,gold,kcoin - cn,1,土豆,土豆,20,20 - en,1,potato,potato,20,20 - kr,1,감자,감자,20,20 - jp,1,ジャガイモ,ジャガイモ,20,20 - ``` - -25. 有如下所示的日志文件，请用Python程序或Linux命令打印出独立IP并统计数量。 - - ``` - 221.228.143.52 - - [23/May/2019:08:57:42 +0800] ""GET /about.html HTTP/1.1"" 206 719996 - 218.79.251.215 - - [23/May/2019:08:57:44 +0800] ""GET /index.html HTTP/1.1"" 206 2350253 - 220.178.150.3 - - [23/May/2019:08:57:45 +0800] ""GET /index.html HTTP/1.1"" 200 2350253 - 218.79.251.215 - - [23/May/2019:08:57:52 +0800] ""GET /index.html HTTP/1.1"" 200 2350253 - 219.140.190.130 - - [23/May/2019:08:57:59 +0800] ""GET /index.html HTTP/1.1"" 200 2350253 - 221.228.143.52 - - [23/May/2019:08:58:08 +0800] ""GET /about.html HTTP/1.1"" 206 719996 - 221.228.143.52 - - [23/May/2019:08:58:08 +0800] ""GET /news.html HTTP/1.1"" 206 713242 - 221.228.143.52 - - [23/May/2019:08:58:09 +0800] ""GET /products.html HTTP/1.1"" 206 1200250 - ``` - -26. 请写出从HTML页面源代码中获取a标签href属性的正则表达式。 - - 答： - - ```Python - - ``` - -27. 正则表达式对象的`search`和`match`方法有什么区别？ - - 答： - -28. 当做个线程竞争一个对象且该对象并非线程安全的时候应该怎么办？ - - 答： - -29. 说一下死锁产生的条件以及如何避免死锁的发生。 - - 答： - -30. 请阐述TCP的优缺点。 - - 答： - -31. HTTP请求的GET和POST有什么区别？ - - 答： - -32. 说一些你知道的HTTP响应状态码。 - - 答： - -33. 简单阐述HTTPS的工作原理。 - - 答： - -34. 阐述Django项目中一个请求的生命周期。 - - 答： - -35. Django项目中实现数据接口时如何解决跨域问题。 - - 答： - -36. Django项目中如何对接Redis高速缓存服务。 - - 答： - -37. 请说明Cookie和Session之间的关系。 - - 答： - -38. 说一下索引的原理和作用。 - - 答： - -39. 是否使用过Nginx实现负载均衡？用过哪些负载均衡算法？ - - 答： - -40. 一个保存整数（int）的数组，除了一个元素出现过1次外，其他元素都出现过两次，请找出这个元素。 - - 答： - -41. 有12个外观相同的篮球，其中1个的重要和其他11个的重量不同（有可能轻有可能重），现在有一个天平可以使用，怎样才能通过最少的称重次数找出这颗与众不同的球。 - - 答： \ No newline at end of file diff --git a/Day91-100/93.MySQL性能优化.md b/Day91-100/93.MySQL性能优化.md index 6cccabb..80caeca 100644 --- a/Day91-100/93.MySQL性能优化.md +++ b/Day91-100/93.MySQL性能优化.md @@ -79,22 +79,32 @@ MySQL支持做数据分区，通过分区可以存储更多的数据、优化查 +-----------------+-----------+ ``` + - 创建慢查询日志文件并修改所有者。 + + ```Bash + touch /var/log/mysqld-slow.log + chown mysql /var/log/mysqld-slow.log + ``` + - 修改全局慢查询日志配置。 ```SQL + mysql> set global slow_query_log_file='/var/log/mysqld-slow.log' mysql> set global slow_query_log='ON'; mysql> set global long_query_time=1; - ``` - - 或者直接修改MySQL配置文件启用慢查询日志。 - + ``` + + 或者直接修改MySQL配置文件启用慢查询日志。 + ```INI [mysqld] slow_query_log=ON - slow_query_log_file=/usr/local/mysql/data/slow.log + slow_query_log_file=/var/log/mysqld-slow.log long_query_time=1 ``` + > **注意**：修改了配置文件需要重启MySQL，CentOS上对应的命令是`systemctl restart mysqld`。 + 2. 通过`explain`了解SQL的执行计划。例如： ```SQL diff --git a/Day91-100/res/aliyun-certificate.png b/Day91-100/res/aliyun-certificate.png index dcf9ba5..8f86eb9 100644 Binary files a/Day91-100/res/aliyun-certificate.png and b/Day91-100/res/aliyun-certificate.png differ diff --git a/Day91-100/res/builtin-middlewares.png b/Day91-100/res/builtin-middlewares.png index d2eb9dc..8a15281 100644 Binary files a/Day91-100/res/builtin-middlewares.png and b/Day91-100/res/builtin-middlewares.png differ diff --git a/Day91-100/res/click-jacking.png b/Day91-100/res/click-jacking.png index 7cc3ef6..7223d99 100644 Binary files a/Day91-100/res/click-jacking.png and b/Day91-100/res/click-jacking.png differ diff --git a/Day91-100/res/django-middleware.png b/Day91-100/res/django-middleware.png index 2281851..3cab159 100644 Binary files a/Day91-100/res/django-middleware.png and b/Day91-100/res/django-middleware.png differ diff --git a/Day91-100/res/gitlab-about.png b/Day91-100/res/gitlab-about.png index 107e5e1..362cdad 100644 Binary files a/Day91-100/res/gitlab-about.png and b/Day91-100/res/gitlab-about.png differ diff --git a/Day91-100/res/mvc.png b/Day91-100/res/mvc.png index b12ee5a..0481ccb 100644 Binary files a/Day91-100/res/mvc.png and b/Day91-100/res/mvc.png differ diff --git a/Day91-100/res/pylint.png b/Day91-100/res/pylint.png index f793a72..78576ae 100644 Binary files a/Day91-100/res/pylint.png and b/Day91-100/res/pylint.png differ diff --git a/Day91-100/res/python_jobs_chengdu.png b/Day91-100/res/python_jobs_chengdu.png index 43eb46b..355280b 100644 Binary files a/Day91-100/res/python_jobs_chengdu.png and b/Day91-100/res/python_jobs_chengdu.png differ diff --git a/Day91-100/res/rbac-full.png b/Day91-100/res/rbac-full.png index 2dfbe0d..4e8d5b6 100644 Binary files a/Day91-100/res/rbac-full.png and b/Day91-100/res/rbac-full.png differ diff --git a/Day91-100/res/shopping-pdm.png b/Day91-100/res/shopping-pdm.png index 2516ba3..0e87815 100644 Binary files a/Day91-100/res/shopping-pdm.png and b/Day91-100/res/shopping-pdm.png differ diff --git a/Day91-100/res/zentao-index.png b/Day91-100/res/zentao-index.png index 34195a7..785cc55 100644 Binary files a/Day91-100/res/zentao-index.png and b/Day91-100/res/zentao-index.png differ diff --git a/Day91-100/res/zentao-login.png b/Day91-100/res/zentao-login.png index 206f126..22cd3ee 100644 Binary files a/Day91-100/res/zentao-login.png and b/Day91-100/res/zentao-login.png differ diff --git a/README.md b/README.md index 4c6a788..7dd69c5 100644 --- a/README.md +++ b/README.md @@ -8,40 +8,36 @@ ![](./res/python-qq-group.png) -### Python应用领域和就业形势分析 +### Python应用领域和职业发展分析 简单的说，Python是一个“优雅”、“明确”、“简单”的编程语言。 - 学习曲线低，非专业人士也能上手 - 开源系统，拥有强大的生态圈 - 解释型语言，完美的平台可移植性 - - 支持面向对象和函数式编程 - - 能够通过调用C/C++代码扩展功能 + - 动态类型语言，支持面向对象和函数式编程 - 代码规范程度高，可读性强 -目前几个比较流行的领域，Python都有用武之地。 +Python在以下领域都有用武之地。 - - 云基础设施 - Python / Java / Go - - DevOps - Python / Shell / Ruby / Go - - 网络爬虫 - Python / PHP / C++ - - 数据分析挖掘 - Python / R / Scala / Matlab - - 机器学习 - Python / R / Java / Lisp + - 后端开发 - Python / Java / Go / PHP + - DevOps - Python / Shell / Ruby + - 数据采集 - Python / C++ / Java + - 量化交易 - Python / C++ / R + - 数据科学 - Python / R / Julia / Matlab + - 机器学习 - Python / R / C++ / Julia + - 自动化测试 - Python / Shell -作为一名Python开发者，主要的就业领域包括： +作为一名Python开发者，根据个人的喜好和职业规划，可以选择的就业领域也非常多。 -- Python服务器后台开发 / 游戏服务器开发 / 数据接口开发工程师 -- Python自动化运维工程师 -- Python数据分析 / 数据可视化 / 大数据工程师 +- Python后端开发工程师（服务器、云平台、数据接口） +- Python运维工程师（自动化运维、SRE、DevOps） +- Python数据分析师（数据分析、商业智能、数字化运营） +- Python数据挖掘工程师（机器学习、深度学习、算法专家） - Python爬虫工程师 -- Python聊天机器人开发 / 图像识别和视觉算法 / 深度学习工程师 +- Python测试工程师（自动化测试、测试开发） -下图显示了主要城市Python招聘需求量及薪资待遇排行榜（截止到2018年5月）。 - -![Python招聘需求及薪资待遇Top 10](./res/python-top-10.png) - -![](./res/python-bj-salary.png) - -![](./res/python-salary-chengdu.png) +> **说明**：目前，**数据分析是一个非常热门的方向**，因为不管是互联网行业还是传统行业都已经积累了大量的数据，现在需要的就是从这些数据中提取有价值的信息，以便打造更好的产品或者为将来的决策提供支持。 给初学者的几个建议： @@ -168,7 +164,6 @@ - 用Pillow处理图片 - 图片读写 / 图片合成 / 几何变换 / 色彩转换 / 滤镜效果 - 读写Word文档 - 文本内容的处理 / 段落 / 页眉和页脚 / 样式的处理 - 读写Excel文件 - xlrd模块 / xlwt模块 -- 生成PDF文件 - pypdf2模块 / reportlab模块 ### Day16~Day20 - [Python语言进阶 ](./Day16-20/16-20.Python语言进阶.md) @@ -287,7 +282,7 @@ - 使用装饰器实现页面缓存 - 为数据接口提供缓存服务 -#### Day52 - [文件上传](./Day41-55/52.文件上传.md) +#### Day52 - [接入三方平台](./Day41-55/52.接入三方平台.md) - 文件上传表单控件和图片文件预览 - 服务器端如何处理上传的文件 @@ -313,136 +308,101 @@ ### Day56~60 - [用FastAPI开发数据接口](./Day56-60/56-60.用FastAPI开发数据接口.md) -- +- FastAPI五分钟上手 +- 请求和响应 +- 接入关系型数据库 +- 依赖注入 +- 中间件 +- 异步化 +- 虚拟化部署（Docker） +- 项目实战：车辆违章查询项目 -- 项目实战：车辆违章查询系统的开发和虚拟化部署 +### Day61~65 - [爬虫开发](./Day61-65) -### Day61~65 - [实战Tornado](./Day61-65) - -#### Day61 - [预备知识](./Day61-65/61.预备知识.md) - -- 并发编程 -- I/O模式和事件驱动 - -#### Day62 - [Tornado入门](./Day61-65/62.Tornado入门.md) - -- Tornado概述 -- 5分钟上手Tornado -- 路由解析 -- 请求处理器 - -#### Day63 - [Tornado中的异步化](./Day61-65/63.Tornado中的异步化.md) - -- aiomysql和aioredis的使用 - -#### Day64 - [WebSocket的应用](./Day61-65/64.WebSocket的应用.md) - -- WebSocket简介 -- WebSocket服务器端编程 -- WebSocket客户端编程 -- 项目：Web聊天室 - -#### Day65 - [项目实战](./Day61-65/65.项目实战.md) - -- 前后端分离开发和接口文档的撰写 -- 使用Vue.js实现前端渲染 -- 使用ECharts实现报表功能 -- 使用WebSocket实现推送服务 - -### Day66~75 - [爬虫开发](./Day66-75) - -#### Day66 - [网络爬虫和相关工具](./Day66-75/66.网络爬虫和相关工具.md) +#### Day61 - [网络爬虫和相关工具](./Day61-65/61.网络爬虫和相关工具.md) - 网络爬虫的概念及其应用领域 - 网络爬虫的合法性探讨 - 开发网络爬虫的相关工具 - 一个爬虫程序的构成 -#### Day67 - [数据采集和解析](./Day66-75/67.数据采集和解析.md) +#### Day62 - [数据采集和解析](./Day61-65/62.数据采集和解析.md) - 数据采集的标准和三方库 - 页面解析的三种方式：正则表达式解析 / XPath解析 / CSS选择器解析 -#### Day68 - [存储数据](./Day66-75/68.存储数据.md) +#### Day63 - [存储数据](./Day61-65/63.存储数据.md) - 如何存储海量数据 - 实现数据的缓存 -#### Day69 - [并发下载](./Day66-75/69.并发下载.md) +#### Day64 - [并发下载](./Day61-65/64.并发下载.md) - 多线程和多进程 - 异步I/O和协程 - async和await关键字的使用 - 三方库aiohttp的应用 -#### Day70 - [解析动态内容](./Day66-75/70.解析动态内容.md) +#### Day65 - [解析动态内容](./Day61-65/65.解析动态内容.md) - JavaScript逆向工程 - 使用Selenium获取动态内容 -#### Day71 - [表单交互和验证码处理](./Day66-75/71.表单交互和验证码处理.md) +### Day66~70 - [数据分析](./Day66-70) -- 自动提交表单 -- Cookie池的应用 -- 验证码处理 +#### Day66 - [数据分析概述](./Day66-70/66.数据分析概述.md) -#### Day72 - [Scrapy入门](./Day66-75/72.Scrapy入门.md) +#### Day67 - [NumPy的应用](./Day66-70/67.NumPy的应用.md) -- Scrapy爬虫框架概述 -- 安装和使用Scrapy +#### Day68 - [Pandas的应用](./Day66-70/68.Pandas的应用.md) -#### Day73 - [Scrapy高级应用](./Day66-75/73.Scrapy高级应用.md) +#### Day69 - [数据可视化](./Day66-70/69.数据可视化.md) -- Spider的用法 -- 中间件的应用：下载中间件 / 蜘蛛中间件 -- Scrapy对接Selenium抓取动态内容 -- Scrapy部署到Docker +#### Day70 - [数据分析项目实战](./Day66-70/70.数据分析项目实战.md) -#### Day74 - [Scrapy分布式实现](./Day66-75/74.Scrapy分布式实现.md) +### Day71~85 - [机器学习和深度学习](./Day71-85) -- 分布式爬虫的原理 -- Scrapy分布式实现 -- 使用Scrapyd实现分布式部署 +#### Day71 - [机器学习基础](./Day71-85/71.机器学习基础.md) -#### Day75 - [爬虫项目实战](./Day66-75/75.爬虫项目实战.md) +#### Day72 - [k最近邻分类](./Day71-85/72.k最近邻分类.md) -- 爬取招聘网站数据 -- 爬取房地产行业数据 -- 爬取二手车交易平台数据 +#### Day73 - [决策树](./Day71-85/73.决策树.md) -### Day76~90 - [数据分析和机器学习](./Day76-90) +#### Day74 - [贝叶斯分类](./Day71-85/74.贝叶斯分类.md) -> **温馨提示**：数据分析和机器学习的内容在code文件夹中，是用jupyter notebook书写的代码和笔记，需要先启动jupyter notebook再打开对应的文件进行学习。2020年会持续补充相关文档，希望大家持续关注。 +#### Day75 - [支持向量机](./Day71-85/75.支持向量机.md) -#### Day76 - [机器学习基础](./Day76-90/76.机器学习基础.md) +#### Day76 - [K-均值聚类](./Day71-85/76.K-均值聚类.md) -#### Day77 - [Pandas的应用](./Day76-90/77.Pandas的应用.md) +#### Day77 - [回归分析](./Day71-85/77.回归分析.md) -#### Day78 - [NumPy和SciPy的应用](./Day76-90/78.NumPy和SciPy的应用) +#### Day78 - [深度学习入门](./Day71-85/78.深度学习入门.md) -#### Day79 - [Matplotlib和数据可视化](./Day76-90/79.Matplotlib和数据可视化) +#### Day79 - [Tensorflow概述](./Day71-85/79.Tensorflow概述.md) -#### Day80 - [k最近邻(KNN)分类](./Day76-90/80.k最近邻分类.md) +#### Day80 - [Tensorflow实战](./Day71-85/79.Tensorflow实战.md) -#### Day81 - [决策树](./Day76-90/81.决策树.md) +#### Day81 - [Kaggle项目实战](./Day71-85/81.Kaggle项目实战.md) -#### Day82 - [贝叶斯分类](./Day76-90/82.贝叶斯分类.md) +#### Day82 - [天池大数据项目实战](./Day71-85/82.天池大数据项目实战.md) -#### Day83 - [支持向量机(SVM)](./Day76-90/83.支持向量机.md) +#### Day83 - [推荐系统实战-1](./Day71-85/83.推荐系统实战-1.md) -#### Day84 - [K-均值聚类](./Day76-90/84.K-均值聚类.md) +#### Day84 - [推荐系统实战-2](./Day71-85/84.推荐系统实战-2.md) -#### Day85 - [回归分析](./Day76-90/85.回归分析.md) +#### Day85 - [推荐系统实战-3](./Day71-85/85.推荐系统实战-3.md) -#### Day86 - [大数据分析入门](./Day76-90/86.大数据分析入门.md) +### Day86~90 - [大数据分析概述](./Day86-90) -#### Day87 - [大数据分析进阶](./Day76-90/87.大数据分析进阶.md) +####Day86 - [大数据概述]() -#### Day88 - [Tensorflow入门](./Day76-90/88.Tensorflow入门.md) +#### Day87 - [Hive查询]() -#### Day89 - [Tensorflow实战](./Day76-90/89.Tensorflow实战.md) +#### Day88 - [PySpark和离线数据处理]() -#### Day90 - [推荐系统实战](./Day76-90/90.推荐系统实战.md) +#### Day89 - [Flink和流式数据处理]() + +#### Day90 - [大数据分析项目实战]() ### Day91~100 - [团队项目开发](./Day91-100) @@ -656,5 +616,5 @@ #### 第99天：[面试中的公共问题](./Day91-100/99.面试中的公共问题.md) -#### 第100天：[Python面试题集](./Day91-100/100.Python面试题集.md) +#### 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- ua.google - # Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_4) AppleWebKit/537.13 (KHTML, like Gecko) Chrome/24.0.1290.1 Safari/537.13 - ua['google chrome'] - # Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11 - ua.firefox - # Mozilla/5.0 (Windows NT 6.2; Win64; x64; rv:16.0.1) Gecko/20121011 Firefox/16.0.1 - ua.ff - # Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:15.0) Gecko/20100101 Firefox/15.0.1 - ua.safari - # Mozilla/5.0 (iPad; CPU OS 6_0 like Mac OS X) AppleWebKit/536.26 (KHTML, like Gecko) Version/6.0 Mobile/10A5355d Safari/8536.25 - - # and the best one, random via real world browser usage statistic - ua.random - ``` + - User-Agent - Referer - + - Accept-Encoding - + - Accept-Language 2. 检查网站生成的Cookie。 - 有用的插件：[EditThisCookie](http://www.editthiscookie.com/) diff --git a/番外篇/租房网项目接口文档.md b/番外篇/接口文档参考示例.md similarity index 99% rename from 番外篇/租房网项目接口文档.md rename to 番外篇/接口文档参考示例.md index 2b3f575..1d88ba7 100644 --- a/番外篇/租房网项目接口文档.md +++ b/番外篇/接口文档参考示例.md @@ -1,4 +1,4 @@ -## 租房网项目接口文档 +## 接口文档参考示例 0. 用户登录 - **POST** `/api/login/` diff --git a/玩转PyCharm.md b/番外篇/玩转PyCharm.md similarity index 100% rename from 玩转PyCharm.md rename to 番外篇/玩转PyCharm.md