2231 lines
71 KiB
Plaintext
2231 lines
71 KiB
Plaintext
{
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"cells": [
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{
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||
"cell_type": "markdown",
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"id": "c664c108-059f-402a-b216-5ba4caa2d98b",
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"metadata": {},
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"source": [
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"## 三大神器概述\n",
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"\n",
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"### 热身练习\n",
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"\n",
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"如下列表保存着本公司从2022年1月到12月五个销售区域（南京、无锡、苏州、徐州、南通）的销售额（以百万元为单位），请利用这些数据完成以下操作：\n",
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"\n",
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"```python\n",
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"sales_month = [f'{i:>2d}月' for i in range(1, 13)]\n",
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"sales_area = ['南京', '无锡', '苏州', '徐州', '南通']\n",
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"sales_data = [\n",
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" [32, 17, 12, 20, 28],\n",
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" [41, 30, 17, 15, 35],\n",
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" [35, 18, 13, 11, 24],\n",
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" [12, 42, 44, 21, 34],\n",
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" [29, 11, 42, 32, 50],\n",
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" [10, 15, 11, 12, 26],\n",
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" [16, 28, 48, 22, 28],\n",
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" [31, 40, 45, 30, 39],\n",
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" [25, 41, 47, 42, 47],\n",
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" [47, 21, 13, 49, 48],\n",
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" [41, 36, 17, 36, 22],\n",
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" [22, 25, 15, 20, 37]\n",
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"]\n",
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"```\n",
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"\n",
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"1. 统计本公司每个月的销售额。\n",
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"2. 统计本公司销售额的月环比。\n",
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"3. 统计每个销售区域全年的销售额。\n",
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"4. 按销售额从高到低排序销售区域及其销售额。\n",
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"5. 统计全年最高的销售额出现在哪个月哪个区域。\n",
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"6. 找出哪个销售区域的业绩最不稳定。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f9d87cfc-deb0-46eb-b98c-2799a4908bc8",
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"metadata": {},
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"outputs": [],
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"source": [
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"sales_month = [f'{i:>2d}月' for i in range(1, 13)]\n",
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"sales_area = ['南京', '无锡', '苏州', '徐州', '南通']\n",
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"sales_data = [\n",
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" [32, 17, 12, 20, 28],\n",
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" [41, 30, 17, 15, 35],\n",
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" [35, 18, 13, 11, 24],\n",
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" [12, 42, 44, 21, 34],\n",
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" [29, 11, 42, 32, 50],\n",
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" [10, 15, 11, 12, 26],\n",
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" [16, 28, 48, 22, 28],\n",
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" [31, 40, 45, 30, 39],\n",
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" [25, 41, 47, 42, 47],\n",
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" [47, 21, 13, 49, 48],\n",
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" [41, 36, 17, 36, 22],\n",
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" [22, 25, 15, 20, 37]\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 2,
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||
"id": "dc581dfc-9108-46fa-ace2-60ace650434e",
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||
"metadata": {},
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||
"outputs": [
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||
{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Variable Type Data/Info\n",
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"-------------------------------\n",
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"sales_area list n=5\n",
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"sales_data list n=12\n",
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"sales_month list n=12\n"
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]
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}
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],
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"source": [
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"# 魔法指令 - %whos - 查看变量\n",
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"%whos"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 3,
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||
"id": "a50e4c3e-6dc1-426f-977b-aef9a5c9a02f",
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||
"metadata": {},
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"outputs": [],
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"source": [
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"print = 100\n",
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"# TypeError: 'int' object is not callable\n",
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"# print('hello')"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 4,
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||
"id": "4c0b54ca-1556-4a14-9a6a-b6bd6af5d822",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"# 魔法指令 - %xdel - 删除变量\n",
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"%xdel print"
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]
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||
},
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{
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||
"cell_type": "code",
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||
"execution_count": 5,
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||
"id": "fe8eb05f-f45b-491a-b98e-6f6c924997ff",
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"name": "stdout",
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"output_type": "stream",
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"text": [
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" 1月销售额: 109百万\n",
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" 2月销售额: 138百万\n",
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" 3月销售额: 101百万\n",
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" 4月销售额: 153百万\n",
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" 5月销售额: 164百万\n",
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" 6月销售额: 74百万\n",
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" 7月销售额: 142百万\n",
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" 8月销售额: 185百万\n",
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" 9月销售额: 202百万\n",
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"10月销售额: 178百万\n",
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"11月销售额: 152百万\n",
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"12月销售额: 119百万\n"
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]
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}
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],
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"source": [
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"# 1. 统计本公司每个月的销售额。\n",
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"monthly_sales = []\n",
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"for i, month in enumerate(sales_month):\n",
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" monthly_sales.append(sum(sales_data[i]))\n",
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" print(f'{month}销售额: {monthly_sales[i]}百万')"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 6,
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||
"id": "53e6bf88-e6a9-4ac9-a7fe-bd1d18ff88f5",
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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"text": [
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" 2月: 26.61%\n",
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" 3月: -26.81%\n",
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" 4月: 51.49%\n",
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" 5月: 7.19%\n",
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" 6月: -54.88%\n",
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" 7月: 91.89%\n",
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" 8月: 30.28%\n",
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" 9月: 9.19%\n",
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"10月: -11.88%\n",
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"11月: -14.61%\n",
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"12月: -21.71%\n"
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]
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||
}
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||
],
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||
"source": [
|
||
"# 2. 统计本公司销售额的月环比。\n",
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"for i in range(1, len(monthly_sales)):\n",
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" temp = (monthly_sales[i] - monthly_sales[i - 1]) / monthly_sales[i - 1]\n",
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" print(f'{sales_month[i]}: {temp:.2%}')"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "f5a130d6-b781-4ee3-a96b-d1fe5e3b4b90",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
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"南京: 341\n",
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"无锡: 324\n",
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"苏州: 324\n",
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"徐州: 310\n",
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"南通: 418\n"
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]
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||
}
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||
],
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||
"source": [
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||
"# 3. 统计每个销售区域全年的销售额。\n",
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||
"arealy_sales = {}\n",
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"for j, area in enumerate(sales_area):\n",
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" temp = [sales_data[i][j] for i in range(len(sales_month))]\n",
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" arealy_sales[area] = sum(temp)\n",
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||
" print(f'{area}: {arealy_sales[area]}')"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "a7bd0510-5e68-4e58-ac3b-6c531f7abccb",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
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||
"南通: 418\n",
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"南京: 341\n",
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"无锡: 324\n",
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"苏州: 324\n",
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"徐州: 310\n"
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||
]
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||
}
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||
],
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||
"source": [
|
||
"# 4. 按销售额从高到低排序销售区域及其销售额。\n",
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||
"sorted_keys = sorted(arealy_sales, key=lambda x: arealy_sales[x], reverse=True)\n",
|
||
"for key in sorted_keys:\n",
|
||
" print(f'{key}: {arealy_sales[key]}')"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "b4b2f3e8-c5c2-481e-b277-9623d30892ac",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
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||
"text": [
|
||
" 5月 南通\n"
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||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 5. 统计全年最高的销售额出现在哪个月哪个区域。\n",
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||
"max_value = sales_data[0][0]\n",
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||
"max_i, max_j = 0, 0\n",
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||
"for i in range(len(sales_month)):\n",
|
||
" for j in range(len(sales_area)):\n",
|
||
" temp = sales_data[i][j]\n",
|
||
" if temp > max_value:\n",
|
||
" max_value = temp\n",
|
||
" max_i, max_j = i, j\n",
|
||
"print(sales_month[max_i], sales_area[max_j])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "647d0a87-b672-4e0c-81cc-a3bbb76dca11",
|
||
"metadata": {},
|
||
"source": [
|
||
"总体方差：\n",
|
||
"$$\n",
|
||
"\\sigma^{2} = \\frac{1}{N} \\sum_{i=1}^{N}(x_{i} - \\mu)^{2}\n",
|
||
"$$\n",
|
||
"\n",
|
||
"样本方差：\n",
|
||
"$$\n",
|
||
"s^{2} = \\frac{1}{n - 1} \\sum_{i=1}^{n}(x_{i} - \\bar{x})^{2}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "b43fb247-32fc-4e10-a9ee-488fd1f56a9a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'苏州'"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
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||
],
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||
"source": [
|
||
"# 6. 找出哪个销售区域的业绩最不稳定。\n",
|
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"import statistics as stats\n",
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"\n",
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"arealy_vars = []\n",
|
||
"for j, area in enumerate(sales_area):\n",
|
||
" temp = [sales_data[i][j] for i in range(len(sales_month))]\n",
|
||
" arealy_vars.append(stats.pvariance(temp))\n",
|
||
"sales_area[arealy_vars.index(max(arealy_vars))]"
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||
]
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||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3ea677d0-7a33-43e5-b10b-ddfcb82f7f6a",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 三大神器\n",
|
||
"\n",
|
||
"1. numpy - Numerical Python - 核心是`ndarray`类型，可以用来表示N维数组，提供了一系列处理数据的运算、函数和方法。\n",
|
||
"2. pandas - Panel Data Set - 封装了和数据分析（加载、重塑、清洗、预处理、透视、呈现）相关的类型、函数和诸多的方法，为数据分析提供了一站式解决方案。它的核心有三个数据类型，分别是：`Series`、`DataFrame`、`Index`。\n",
|
||
"3. matplotlib - 封装了各种常用的统计图表，帮助我们实现数据呈现。\n",
|
||
"4. scipy - Scientific Python - 针对NumPy进行了很好的补充，提供了高级的数据运算的函数和方法。\n",
|
||
"5. scikit-learn - 封装了常用的机器学习（分类、聚类、回归等）算法，除此之外，还提供了数据预处理、特征工程、模型验证相关的函数和方法。\n",
|
||
"6. sympy - Symbolic Python - 封装了符号运算相关操作。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "0db758cc-d83c-47c4-9a0b-c7ef5abd6c18",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 魔法指令 - %pip - 调用包管理工具pip\n",
|
||
"# %pip install numpy pandas matplotlib openpyxl"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "8eb6970b-3907-4b84-af60-67cbf67f2e74",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
|
||
"plt.rcParams['axes.unicode_minus'] = False"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "5fb76dec-cd51-4e79-9bd2-3b210ae20522",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'1.26.4'"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.__version__"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "e6369df9-7577-496c-bfc1-2fce096c0162",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'2.2.3'"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd.__version__"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "eb5733cd-38f7-4afd-b45b-70c1439ab36b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[32, 17, 12, 20, 28],\n",
|
||
" [41, 30, 17, 15, 35],\n",
|
||
" [35, 18, 13, 11, 24],\n",
|
||
" [12, 42, 44, 21, 34],\n",
|
||
" [29, 11, 42, 32, 50],\n",
|
||
" [10, 15, 11, 12, 26],\n",
|
||
" [16, 28, 48, 22, 28],\n",
|
||
" [31, 40, 45, 30, 39],\n",
|
||
" [25, 41, 47, 42, 47],\n",
|
||
" [47, 21, 13, 49, 48],\n",
|
||
" [41, 36, 17, 36, 22],\n",
|
||
" [22, 25, 15, 20, 37]])"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 将嵌套列表处理成二维数组\n",
|
||
"data = np.array(sales_data)\n",
|
||
"data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "da304104-8cf0-4425-b3b4-dcb148ac4b3a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([109, 138, 101, 153, 164, 74, 142, 185, 202, 178, 152, 119])"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 沿着1轴求和（每个月的销售额）\n",
|
||
"data.sum(axis=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "1507ac63-f53b-4e36-a7fb-b9c636fd81ea",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([341, 324, 324, 310, 418])"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 沿着0轴求和（每个区域的销售）\n",
|
||
"data.sum(axis=0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "26be450d-44ba-4d83-9351-c52a13c2c338",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([128.4, 108.5, 241.3, 132.6, 85.6])"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 总体方差\n",
|
||
"data.var(axis=0).round(1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "81e5b2a0-c86e-4720-909f-ce8b1b6fdd58",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([140.1, 118.4, 263.3, 144.7, 93.4])"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 样本方差\n",
|
||
"data.var(axis=0, ddof=1).round(1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "ba4e0f0a-e711-4041-8834-1e3be86ce8a4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>南京</th>\n",
|
||
" <th>无锡</th>\n",
|
||
" <th>苏州</th>\n",
|
||
" <th>徐州</th>\n",
|
||
" <th>南通</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1月</th>\n",
|
||
" <td>32</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>28</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2月</th>\n",
|
||
" <td>41</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>35</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3月</th>\n",
|
||
" <td>35</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>24</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4月</th>\n",
|
||
" <td>12</td>\n",
|
||
" <td>42</td>\n",
|
||
" <td>44</td>\n",
|
||
" <td>21</td>\n",
|
||
" <td>34</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5月</th>\n",
|
||
" <td>29</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>42</td>\n",
|
||
" <td>32</td>\n",
|
||
" <td>50</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6月</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>26</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7月</th>\n",
|
||
" <td>16</td>\n",
|
||
" <td>28</td>\n",
|
||
" <td>48</td>\n",
|
||
" <td>22</td>\n",
|
||
" <td>28</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8月</th>\n",
|
||
" <td>31</td>\n",
|
||
" <td>40</td>\n",
|
||
" <td>45</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>39</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9月</th>\n",
|
||
" <td>25</td>\n",
|
||
" <td>41</td>\n",
|
||
" <td>47</td>\n",
|
||
" <td>42</td>\n",
|
||
" <td>47</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10月</th>\n",
|
||
" <td>47</td>\n",
|
||
" <td>21</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>49</td>\n",
|
||
" <td>48</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11月</th>\n",
|
||
" <td>41</td>\n",
|
||
" <td>36</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>36</td>\n",
|
||
" <td>22</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12月</th>\n",
|
||
" <td>22</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>37</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 南京 无锡 苏州 徐州 南通\n",
|
||
" 1月 32 17 12 20 28\n",
|
||
" 2月 41 30 17 15 35\n",
|
||
" 3月 35 18 13 11 24\n",
|
||
" 4月 12 42 44 21 34\n",
|
||
" 5月 29 11 42 32 50\n",
|
||
" 6月 10 15 11 12 26\n",
|
||
" 7月 16 28 48 22 28\n",
|
||
" 8月 31 40 45 30 39\n",
|
||
" 9月 25 41 47 42 47\n",
|
||
"10月 47 21 13 49 48\n",
|
||
"11月 41 36 17 36 22\n",
|
||
"12月 22 25 15 20 37"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 构造DataFrame对象（处理二维数据）\n",
|
||
"df = pd.DataFrame(data, columns=sales_area, index=sales_month)\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "9d1a6a43-6dfc-41e3-98c8-be2681e0d547",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"南京 341\n",
|
||
"无锡 324\n",
|
||
"苏州 324\n",
|
||
"徐州 310\n",
|
||
"南通 418\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 求和（默认沿着0轴）\n",
|
||
"df.sum()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "a478ec0e-499f-4e31-b8c2-ba45e691b834",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"南通 418\n",
|
||
"南京 341\n",
|
||
"无锡 324\n",
|
||
"苏州 324\n",
|
||
"徐州 310\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 排序\n",
|
||
"df.sum().sort_values(ascending=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "6f221833-855c-45ad-91b2-e3f4da627704",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" 1月 109\n",
|
||
" 2月 138\n",
|
||
" 3月 101\n",
|
||
" 4月 153\n",
|
||
" 5月 164\n",
|
||
" 6月 74\n",
|
||
" 7月 142\n",
|
||
" 8月 185\n",
|
||
" 9月 202\n",
|
||
"10月 178\n",
|
||
"11月 152\n",
|
||
"12月 119\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 求和（指定沿着1轴）\n",
|
||
"df.sum(axis=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "80df8865-4ea0-4c72-a581-215cd953cfbe",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" 1月 NaN\n",
|
||
" 2月 0.266055\n",
|
||
" 3月 -0.268116\n",
|
||
" 4月 0.514851\n",
|
||
" 5月 0.071895\n",
|
||
" 6月 -0.548780\n",
|
||
" 7月 0.918919\n",
|
||
" 8月 0.302817\n",
|
||
" 9月 0.091892\n",
|
||
"10月 -0.118812\n",
|
||
"11月 -0.146067\n",
|
||
"12月 -0.217105\n",
|
||
"dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 计算月环比\n",
|
||
"df.sum(axis=1).pct_change()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "ea4579c3-11cd-4179-9c96-8dbe9a033da2",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>南京</th>\n",
|
||
" <th>无锡</th>\n",
|
||
" <th>苏州</th>\n",
|
||
" <th>徐州</th>\n",
|
||
" <th>南通</th>\n",
|
||
" <th>合计</th>\n",
|
||
" <th>月环比</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1月</th>\n",
|
||
" <td>32</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>28</td>\n",
|
||
" <td>109</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2月</th>\n",
|
||
" <td>41</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>35</td>\n",
|
||
" <td>138</td>\n",
|
||
" <td>0.266055</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3月</th>\n",
|
||
" <td>35</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>24</td>\n",
|
||
" <td>101</td>\n",
|
||
" <td>-0.268116</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4月</th>\n",
|
||
" <td>12</td>\n",
|
||
" <td>42</td>\n",
|
||
" <td>44</td>\n",
|
||
" <td>21</td>\n",
|
||
" <td>34</td>\n",
|
||
" <td>153</td>\n",
|
||
" <td>0.514851</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5月</th>\n",
|
||
" <td>29</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>42</td>\n",
|
||
" <td>32</td>\n",
|
||
" <td>50</td>\n",
|
||
" <td>164</td>\n",
|
||
" <td>0.071895</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6月</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>26</td>\n",
|
||
" <td>74</td>\n",
|
||
" <td>-0.548780</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7月</th>\n",
|
||
" <td>16</td>\n",
|
||
" <td>28</td>\n",
|
||
" <td>48</td>\n",
|
||
" <td>22</td>\n",
|
||
" <td>28</td>\n",
|
||
" <td>142</td>\n",
|
||
" <td>0.918919</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8月</th>\n",
|
||
" <td>31</td>\n",
|
||
" <td>40</td>\n",
|
||
" <td>45</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>39</td>\n",
|
||
" <td>185</td>\n",
|
||
" <td>0.302817</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9月</th>\n",
|
||
" <td>25</td>\n",
|
||
" <td>41</td>\n",
|
||
" <td>47</td>\n",
|
||
" <td>42</td>\n",
|
||
" <td>47</td>\n",
|
||
" <td>202</td>\n",
|
||
" <td>0.091892</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10月</th>\n",
|
||
" <td>47</td>\n",
|
||
" <td>21</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>49</td>\n",
|
||
" <td>48</td>\n",
|
||
" <td>178</td>\n",
|
||
" <td>-0.118812</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11月</th>\n",
|
||
" <td>41</td>\n",
|
||
" <td>36</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>36</td>\n",
|
||
" <td>22</td>\n",
|
||
" <td>152</td>\n",
|
||
" <td>-0.146067</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12月</th>\n",
|
||
" <td>22</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>37</td>\n",
|
||
" <td>119</td>\n",
|
||
" <td>-0.217105</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 南京 无锡 苏州 徐州 南通 合计 月环比\n",
|
||
" 1月 32 17 12 20 28 109 NaN\n",
|
||
" 2月 41 30 17 15 35 138 0.266055\n",
|
||
" 3月 35 18 13 11 24 101 -0.268116\n",
|
||
" 4月 12 42 44 21 34 153 0.514851\n",
|
||
" 5月 29 11 42 32 50 164 0.071895\n",
|
||
" 6月 10 15 11 12 26 74 -0.548780\n",
|
||
" 7月 16 28 48 22 28 142 0.918919\n",
|
||
" 8月 31 40 45 30 39 185 0.302817\n",
|
||
" 9月 25 41 47 42 47 202 0.091892\n",
|
||
"10月 47 21 13 49 48 178 -0.118812\n",
|
||
"11月 41 36 17 36 22 152 -0.146067\n",
|
||
"12月 22 25 15 20 37 119 -0.217105"
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df['合计'] = df.sum(axis=1)\n",
|
||
"df['月环比'] = df['合计'].pct_change()\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "3c660052-dded-4a0a-8b72-7747d3cae816",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
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|
||
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|
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|
||
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|
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|
||
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|
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|
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|
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|
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|
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|
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|
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" width: 10em;\n",
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|
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|
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" width: 10em;\n",
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" background: linear-gradient(90deg, #d65f5f 58.9%, transparent 58.9%);\n",
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|
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" background-color: #f67c4a;\n",
|
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" color: #f1f1f1;\n",
|
||
"}\n",
|
||
"</style>\n",
|
||
"<table id=\"T_59145\">\n",
|
||
" <thead>\n",
|
||
" <tr>\n",
|
||
" <th class=\"blank level0\" > </th>\n",
|
||
" <th id=\"T_59145_level0_col0\" class=\"col_heading level0 col0\" >南京</th>\n",
|
||
" <th id=\"T_59145_level0_col1\" class=\"col_heading level0 col1\" >无锡</th>\n",
|
||
" <th id=\"T_59145_level0_col2\" class=\"col_heading level0 col2\" >苏州</th>\n",
|
||
" <th id=\"T_59145_level0_col3\" class=\"col_heading level0 col3\" >徐州</th>\n",
|
||
" <th id=\"T_59145_level0_col4\" class=\"col_heading level0 col4\" >南通</th>\n",
|
||
" <th id=\"T_59145_level0_col5\" class=\"col_heading level0 col5\" >合计</th>\n",
|
||
" <th id=\"T_59145_level0_col6\" class=\"col_heading level0 col6\" >月环比</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row0\" class=\"row_heading level0 row0\" > 1月</th>\n",
|
||
" <td id=\"T_59145_row0_col0\" class=\"data row0 col0\" >32</td>\n",
|
||
" <td id=\"T_59145_row0_col1\" class=\"data row0 col1\" >17</td>\n",
|
||
" <td id=\"T_59145_row0_col2\" class=\"data row0 col2\" >12</td>\n",
|
||
" <td id=\"T_59145_row0_col3\" class=\"data row0 col3\" >20</td>\n",
|
||
" <td id=\"T_59145_row0_col4\" class=\"data row0 col4\" >28</td>\n",
|
||
" <td id=\"T_59145_row0_col5\" class=\"data row0 col5\" >109</td>\n",
|
||
" <td id=\"T_59145_row0_col6\" class=\"data row0 col6\" >------</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row1\" class=\"row_heading level0 row1\" > 2月</th>\n",
|
||
" <td id=\"T_59145_row1_col0\" class=\"data row1 col0\" >41</td>\n",
|
||
" <td id=\"T_59145_row1_col1\" class=\"data row1 col1\" >30</td>\n",
|
||
" <td id=\"T_59145_row1_col2\" class=\"data row1 col2\" >17</td>\n",
|
||
" <td id=\"T_59145_row1_col3\" class=\"data row1 col3\" >15</td>\n",
|
||
" <td id=\"T_59145_row1_col4\" class=\"data row1 col4\" >35</td>\n",
|
||
" <td id=\"T_59145_row1_col5\" class=\"data row1 col5\" >138</td>\n",
|
||
" <td id=\"T_59145_row1_col6\" class=\"data row1 col6\" >26.61%</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row2\" class=\"row_heading level0 row2\" > 3月</th>\n",
|
||
" <td id=\"T_59145_row2_col0\" class=\"data row2 col0\" >35</td>\n",
|
||
" <td id=\"T_59145_row2_col1\" class=\"data row2 col1\" >18</td>\n",
|
||
" <td id=\"T_59145_row2_col2\" class=\"data row2 col2\" >13</td>\n",
|
||
" <td id=\"T_59145_row2_col3\" class=\"data row2 col3\" >11</td>\n",
|
||
" <td id=\"T_59145_row2_col4\" class=\"data row2 col4\" >24</td>\n",
|
||
" <td id=\"T_59145_row2_col5\" class=\"data row2 col5\" >101</td>\n",
|
||
" <td id=\"T_59145_row2_col6\" class=\"data row2 col6\" >-26.81%</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row3\" class=\"row_heading level0 row3\" > 4月</th>\n",
|
||
" <td id=\"T_59145_row3_col0\" class=\"data row3 col0\" >12</td>\n",
|
||
" <td id=\"T_59145_row3_col1\" class=\"data row3 col1\" >42</td>\n",
|
||
" <td id=\"T_59145_row3_col2\" class=\"data row3 col2\" >44</td>\n",
|
||
" <td id=\"T_59145_row3_col3\" class=\"data row3 col3\" >21</td>\n",
|
||
" <td id=\"T_59145_row3_col4\" class=\"data row3 col4\" >34</td>\n",
|
||
" <td id=\"T_59145_row3_col5\" class=\"data row3 col5\" >153</td>\n",
|
||
" <td id=\"T_59145_row3_col6\" class=\"data row3 col6\" >51.49%</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row4\" class=\"row_heading level0 row4\" > 5月</th>\n",
|
||
" <td id=\"T_59145_row4_col0\" class=\"data row4 col0\" >29</td>\n",
|
||
" <td id=\"T_59145_row4_col1\" class=\"data row4 col1\" >11</td>\n",
|
||
" <td id=\"T_59145_row4_col2\" class=\"data row4 col2\" >42</td>\n",
|
||
" <td id=\"T_59145_row4_col3\" class=\"data row4 col3\" >32</td>\n",
|
||
" <td id=\"T_59145_row4_col4\" class=\"data row4 col4\" >50</td>\n",
|
||
" <td id=\"T_59145_row4_col5\" class=\"data row4 col5\" >164</td>\n",
|
||
" <td id=\"T_59145_row4_col6\" class=\"data row4 col6\" >7.19%</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row5\" class=\"row_heading level0 row5\" > 6月</th>\n",
|
||
" <td id=\"T_59145_row5_col0\" class=\"data row5 col0\" >10</td>\n",
|
||
" <td id=\"T_59145_row5_col1\" class=\"data row5 col1\" >15</td>\n",
|
||
" <td id=\"T_59145_row5_col2\" class=\"data row5 col2\" >11</td>\n",
|
||
" <td id=\"T_59145_row5_col3\" class=\"data row5 col3\" >12</td>\n",
|
||
" <td id=\"T_59145_row5_col4\" class=\"data row5 col4\" >26</td>\n",
|
||
" <td id=\"T_59145_row5_col5\" class=\"data row5 col5\" >74</td>\n",
|
||
" <td id=\"T_59145_row5_col6\" class=\"data row5 col6\" >-54.88%</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row6\" class=\"row_heading level0 row6\" > 7月</th>\n",
|
||
" <td id=\"T_59145_row6_col0\" class=\"data row6 col0\" >16</td>\n",
|
||
" <td id=\"T_59145_row6_col1\" class=\"data row6 col1\" >28</td>\n",
|
||
" <td id=\"T_59145_row6_col2\" class=\"data row6 col2\" >48</td>\n",
|
||
" <td id=\"T_59145_row6_col3\" class=\"data row6 col3\" >22</td>\n",
|
||
" <td id=\"T_59145_row6_col4\" class=\"data row6 col4\" >28</td>\n",
|
||
" <td id=\"T_59145_row6_col5\" class=\"data row6 col5\" >142</td>\n",
|
||
" <td id=\"T_59145_row6_col6\" class=\"data row6 col6\" >91.89%</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row7\" class=\"row_heading level0 row7\" > 8月</th>\n",
|
||
" <td id=\"T_59145_row7_col0\" class=\"data row7 col0\" >31</td>\n",
|
||
" <td id=\"T_59145_row7_col1\" class=\"data row7 col1\" >40</td>\n",
|
||
" <td id=\"T_59145_row7_col2\" class=\"data row7 col2\" >45</td>\n",
|
||
" <td id=\"T_59145_row7_col3\" class=\"data row7 col3\" >30</td>\n",
|
||
" <td id=\"T_59145_row7_col4\" class=\"data row7 col4\" >39</td>\n",
|
||
" <td id=\"T_59145_row7_col5\" class=\"data row7 col5\" >185</td>\n",
|
||
" <td id=\"T_59145_row7_col6\" class=\"data row7 col6\" >30.28%</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row8\" class=\"row_heading level0 row8\" > 9月</th>\n",
|
||
" <td id=\"T_59145_row8_col0\" class=\"data row8 col0\" >25</td>\n",
|
||
" <td id=\"T_59145_row8_col1\" class=\"data row8 col1\" >41</td>\n",
|
||
" <td id=\"T_59145_row8_col2\" class=\"data row8 col2\" >47</td>\n",
|
||
" <td id=\"T_59145_row8_col3\" class=\"data row8 col3\" >42</td>\n",
|
||
" <td id=\"T_59145_row8_col4\" class=\"data row8 col4\" >47</td>\n",
|
||
" <td id=\"T_59145_row8_col5\" class=\"data row8 col5\" >202</td>\n",
|
||
" <td id=\"T_59145_row8_col6\" class=\"data row8 col6\" >9.19%</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row9\" class=\"row_heading level0 row9\" >10月</th>\n",
|
||
" <td id=\"T_59145_row9_col0\" class=\"data row9 col0\" >47</td>\n",
|
||
" <td id=\"T_59145_row9_col1\" class=\"data row9 col1\" >21</td>\n",
|
||
" <td id=\"T_59145_row9_col2\" class=\"data row9 col2\" >13</td>\n",
|
||
" <td id=\"T_59145_row9_col3\" class=\"data row9 col3\" >49</td>\n",
|
||
" <td id=\"T_59145_row9_col4\" class=\"data row9 col4\" >48</td>\n",
|
||
" <td id=\"T_59145_row9_col5\" class=\"data row9 col5\" >178</td>\n",
|
||
" <td id=\"T_59145_row9_col6\" class=\"data row9 col6\" >-11.88%</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row10\" class=\"row_heading level0 row10\" >11月</th>\n",
|
||
" <td id=\"T_59145_row10_col0\" class=\"data row10 col0\" >41</td>\n",
|
||
" <td id=\"T_59145_row10_col1\" class=\"data row10 col1\" >36</td>\n",
|
||
" <td id=\"T_59145_row10_col2\" class=\"data row10 col2\" >17</td>\n",
|
||
" <td id=\"T_59145_row10_col3\" class=\"data row10 col3\" >36</td>\n",
|
||
" <td id=\"T_59145_row10_col4\" class=\"data row10 col4\" >22</td>\n",
|
||
" <td id=\"T_59145_row10_col5\" class=\"data row10 col5\" >152</td>\n",
|
||
" <td id=\"T_59145_row10_col6\" class=\"data row10 col6\" >-14.61%</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th id=\"T_59145_level0_row11\" class=\"row_heading level0 row11\" >12月</th>\n",
|
||
" <td id=\"T_59145_row11_col0\" class=\"data row11 col0\" >22</td>\n",
|
||
" <td id=\"T_59145_row11_col1\" class=\"data row11 col1\" >25</td>\n",
|
||
" <td id=\"T_59145_row11_col2\" class=\"data row11 col2\" >15</td>\n",
|
||
" <td id=\"T_59145_row11_col3\" class=\"data row11 col3\" >20</td>\n",
|
||
" <td id=\"T_59145_row11_col4\" class=\"data row11 col4\" >37</td>\n",
|
||
" <td id=\"T_59145_row11_col5\" class=\"data row11 col5\" >119</td>\n",
|
||
" <td id=\"T_59145_row11_col6\" class=\"data row11 col6\" >-21.71%</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n"
|
||
],
|
||
"text/plain": [
|
||
"<pandas.io.formats.style.Styler at 0x105dddbb0>"
|
||
]
|
||
},
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 渲染DataFrame\n",
|
||
"df.style.format(\n",
|
||
" formatter={'月环比': '{:.2%}'},\n",
|
||
" na_rep='------'\n",
|
||
").bar(\n",
|
||
" subset='合计'\n",
|
||
").background_gradient(\n",
|
||
" 'RdYlBu', subset='月环比'\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "a092c12c-dab6-4272-b1cd-5218998fcd90",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 将DataFrame输出到Excel文件\n",
|
||
"df.to_excel('sales.xlsx', sheet_name='data')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "54c3f505-e866-4c4e-a3f8-f55a71a95c3f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 魔法指令 - %config - 修改配置\n",
|
||
"# %config InlineBackend.figure_format = 'svg'\n",
|
||
"get_ipython().run_line_magic('config', 'InlineBackend.figure_format = \"svg\"')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "3951055d-d5d2-4e4e-bbe7-a1b40a6731e0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/svg+xml": [
|
||
"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
|
||
"<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
|
||
" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
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|
||
"Q 950 3175 925 2375 \n",
|
||
"Q 1100 2700 1300 2825 \n",
|
||
"Q 1500 2950 1775 2950 \n",
|
||
"Q 2250 2950 2550 2575 \n",
|
||
"Q 2850 2200 2850 1550 \n",
|
||
"z\n",
|
||
"M 2250 1550 \n",
|
||
"Q 2250 2000 2100 2250 \n",
|
||
"Q 1950 2500 1675 2500 \n",
|
||
"Q 1350 2500 1162 2250 \n",
|
||
"Q 975 2000 975 1650 \n",
|
||
"Q 975 1100 1162 800 \n",
|
||
"Q 1350 500 1675 500 \n",
|
||
"Q 1900 500 2075 725 \n",
|
||
"Q 2250 950 2250 1550 \n",
|
||
"z\n",
|
||
"\" transform=\"scale(0.015625)\"/>\n",
|
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" </defs>\n",
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" <use xlink:href=\"#SimHei-20\"/>\n",
|
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" <use xlink:href=\"#SimHei-36\" x=\"50\"/>\n",
|
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" <use xlink:href=\"#SimHei-6708\" x=\"100\"/>\n",
|
||
" </g>\n",
|
||
" </g>\n",
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||
" </g>\n",
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||
" <g id=\"xtick_7\">\n",
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" <g id=\"line2d_7\">\n",
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" <g>\n",
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" <use xlink:href=\"#m31a22ed532\" x=\"271\" y=\"228.96\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" <g id=\"text_7\">\n",
|
||
" <!-- 7月 -->\n",
|
||
" <g transform=\"translate(261 243.108437) scale(0.1 -0.1)\">\n",
|
||
" <defs>\n",
|
||
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|
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"L 1600 100 \n",
|
||
"L 1025 100 \n",
|
||
"L 2225 3900 \n",
|
||
"L 400 3900 \n",
|
||
"L 400 4375 \n",
|
||
"L 2775 4375 \n",
|
||
"L 2775 3850 \n",
|
||
"z\n",
|
||
"\" transform=\"scale(0.015625)\"/>\n",
|
||
" </defs>\n",
|
||
" <use xlink:href=\"#SimHei-20\"/>\n",
|
||
" <use xlink:href=\"#SimHei-37\" x=\"50\"/>\n",
|
||
" <use xlink:href=\"#SimHei-6708\" x=\"100\"/>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" <g id=\"xtick_8\">\n",
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||
" <g id=\"line2d_8\">\n",
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" <use xlink:href=\"#m31a22ed532\" x=\"308.2\" y=\"228.96\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" <g id=\"text_8\">\n",
|
||
" <!-- 8月 -->\n",
|
||
" <g transform=\"translate(298.2 243.108437) scale(0.1 -0.1)\">\n",
|
||
" <defs>\n",
|
||
" <path id=\"SimHei-38\" d=\"M 2875 1325 \n",
|
||
"Q 2875 700 2525 375 \n",
|
||
"Q 2175 50 1575 50 \n",
|
||
"Q 975 50 625 375 \n",
|
||
"Q 275 700 275 1325 \n",
|
||
"Q 275 1650 475 1912 \n",
|
||
"Q 675 2175 1025 2300 \n",
|
||
"Q 725 2425 562 2650 \n",
|
||
"Q 400 2875 400 3225 \n",
|
||
"Q 400 3775 750 4100 \n",
|
||
"Q 1100 4425 1575 4425 \n",
|
||
"Q 2050 4425 2400 4100 \n",
|
||
"Q 2750 3775 2750 3225 \n",
|
||
"Q 2750 2875 2587 2650 \n",
|
||
"Q 2425 2425 2125 2300 \n",
|
||
"Q 2475 2175 2675 1912 \n",
|
||
"Q 2875 1650 2875 1325 \n",
|
||
"z\n",
|
||
"M 2200 3225 \n",
|
||
"Q 2200 3625 2025 3800 \n",
|
||
"Q 1850 3975 1575 3975 \n",
|
||
"Q 1300 3975 1125 3800 \n",
|
||
"Q 950 3625 950 3225 \n",
|
||
"Q 950 2825 1137 2662 \n",
|
||
"Q 1325 2500 1575 2500 \n",
|
||
"Q 1825 2500 2012 2662 \n",
|
||
"Q 2200 2825 2200 3225 \n",
|
||
"z\n",
|
||
"M 2300 1325 \n",
|
||
"Q 2300 1675 2112 1875 \n",
|
||
"Q 1925 2075 1575 2075 \n",
|
||
"Q 1225 2075 1037 1875 \n",
|
||
"Q 850 1675 850 1325 \n",
|
||
"Q 850 925 1050 712 \n",
|
||
"Q 1250 500 1575 500 \n",
|
||
"Q 1900 500 2100 712 \n",
|
||
"Q 2300 925 2300 1325 \n",
|
||
"z\n",
|
||
"\" transform=\"scale(0.015625)\"/>\n",
|
||
" </defs>\n",
|
||
" <use xlink:href=\"#SimHei-20\"/>\n",
|
||
" <use xlink:href=\"#SimHei-38\" x=\"50\"/>\n",
|
||
" <use xlink:href=\"#SimHei-6708\" x=\"100\"/>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" <g id=\"xtick_9\">\n",
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" <g>\n",
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|
||
" </g>\n",
|
||
" </g>\n",
|
||
" <g id=\"text_9\">\n",
|
||
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|
||
" <g transform=\"translate(335.4 243.108437) scale(0.1 -0.1)\">\n",
|
||
" <defs>\n",
|
||
" <path id=\"SimHei-39\" d=\"M 2825 2300 \n",
|
||
"Q 2825 1275 2462 662 \n",
|
||
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|
||
"Q 950 50 662 400 \n",
|
||
"Q 375 750 375 1175 \n",
|
||
"L 950 1175 \n",
|
||
"Q 950 925 1087 725 \n",
|
||
"Q 1225 525 1450 525 \n",
|
||
"Q 1825 525 2012 950 \n",
|
||
"Q 2200 1375 2250 2200 \n",
|
||
"Q 2125 1925 1900 1775 \n",
|
||
"Q 1675 1625 1400 1625 \n",
|
||
"Q 925 1625 625 1975 \n",
|
||
"Q 325 2325 325 2975 \n",
|
||
"Q 325 3625 625 4025 \n",
|
||
"Q 925 4425 1525 4425 \n",
|
||
"Q 2125 4425 2475 3925 \n",
|
||
"Q 2825 3425 2825 2300 \n",
|
||
"z\n",
|
||
"M 2200 2875 \n",
|
||
"Q 2200 3425 2012 3700 \n",
|
||
"Q 1825 3975 1500 3975 \n",
|
||
"Q 1275 3975 1100 3762 \n",
|
||
"Q 925 3550 925 2975 \n",
|
||
"Q 925 2550 1062 2312 \n",
|
||
"Q 1200 2075 1500 2075 \n",
|
||
"Q 1825 2075 2012 2300 \n",
|
||
"Q 2200 2525 2200 2875 \n",
|
||
"z\n",
|
||
"\" transform=\"scale(0.015625)\"/>\n",
|
||
" </defs>\n",
|
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" <use xlink:href=\"#SimHei-20\"/>\n",
|
||
" <use xlink:href=\"#SimHei-39\" x=\"50\"/>\n",
|
||
" <use xlink:href=\"#SimHei-6708\" x=\"100\"/>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" <g id=\"xtick_10\">\n",
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||
" <g id=\"line2d_10\">\n",
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" <g>\n",
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" <use xlink:href=\"#m31a22ed532\" x=\"382.6\" y=\"228.96\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" <g id=\"text_10\">\n",
|
||
" <!-- 10月 -->\n",
|
||
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" <defs>\n",
|
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|
||
"Q 2975 1350 2650 700 \n",
|
||
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|
||
"Q 875 50 537 700 \n",
|
||
"Q 200 1350 200 2250 \n",
|
||
"Q 200 3150 537 3787 \n",
|
||
"Q 875 4425 1600 4425 \n",
|
||
"Q 2325 4425 2650 3787 \n",
|
||
"Q 2975 3150 2975 2250 \n",
|
||
"z\n",
|
||
"M 2375 2250 \n",
|
||
"Q 2375 3050 2187 3500 \n",
|
||
"Q 2000 3950 1600 3950 \n",
|
||
"Q 1200 3950 1000 3500 \n",
|
||
"Q 800 3050 800 2250 \n",
|
||
"Q 800 1450 1000 987 \n",
|
||
"Q 1200 525 1600 525 \n",
|
||
"Q 2000 525 2187 987 \n",
|
||
"Q 2375 1450 2375 2250 \n",
|
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"z\n",
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" </defs>\n",
|
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" <use xlink:href=\"#SimHei-31\"/>\n",
|
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" <use xlink:href=\"#SimHei-30\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-6708\" x=\"100\"/>\n",
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" </g>\n",
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||
" </g>\n",
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" </g>\n",
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" <g id=\"xtick_11\">\n",
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" <g id=\"line2d_11\">\n",
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" <g>\n",
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" <use xlink:href=\"#m31a22ed532\" x=\"419.8\" y=\"228.96\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" <g id=\"text_11\">\n",
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" <!-- 11月 -->\n",
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|
||
" <use xlink:href=\"#SimHei-31\"/>\n",
|
||
" <use xlink:href=\"#SimHei-31\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-6708\" x=\"100\"/>\n",
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" </g>\n",
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" </g>\n",
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" </g>\n",
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" <g id=\"xtick_12\">\n",
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" <g id=\"line2d_12\">\n",
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" <g>\n",
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" <use xlink:href=\"#m31a22ed532\" x=\"457\" y=\"228.96\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
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" </g>\n",
|
||
" </g>\n",
|
||
" <g id=\"text_12\">\n",
|
||
" <!-- 12月 -->\n",
|
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" <g transform=\"translate(447 243.108437) scale(0.1 -0.1)\">\n",
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||
" <use xlink:href=\"#SimHei-31\"/>\n",
|
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" <use xlink:href=\"#SimHei-32\" x=\"50\"/>\n",
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||
" <use xlink:href=\"#SimHei-6708\" x=\"100\"/>\n",
|
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" </g>\n",
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||
" </g>\n",
|
||
" </g>\n",
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||
" </g>\n",
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" <g id=\"matplotlib.axis_2\">\n",
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" <g id=\"ytick_1\">\n",
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" <g id=\"line2d_13\">\n",
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" <path id=\"m23726a4c75\" d=\"M 0 0 \n",
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"L -3.5 0 \n",
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" <use xlink:href=\"#m23726a4c75\" x=\"29.2\" y=\"228.96\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
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" <use xlink:href=\"#m23726a4c75\" x=\"29.2\" y=\"176.682772\" style=\"stroke: #000000; stroke-width: 0.8\"/>\n",
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" </g>\n",
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" </g>\n",
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" </g>\n",
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" <g id=\"ytick_4\">\n",
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" <g id=\"ytick_5\">\n",
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" <use xlink:href=\"#SimHei-31\"/>\n",
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" <use xlink:href=\"#SimHei-30\" x=\"50\"/>\n",
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" <use xlink:href=\"#SimHei-30\" x=\"100\"/>\n",
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" </g>\n",
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" </g>\n",
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|
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" <path d=\"M 29.2 7.2 \n",
|
||
"L 475.6 7.2 \n",
|
||
"\" style=\"fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\"/>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" </g>\n",
|
||
" <defs>\n",
|
||
" <clipPath id=\"pc95f63a0a0\">\n",
|
||
" <rect x=\"29.2\" y=\"7.2\" width=\"446.4\" height=\"221.76\"/>\n",
|
||
" </clipPath>\n",
|
||
" </defs>\n",
|
||
"</svg>\n"
|
||
],
|
||
"text/plain": [
|
||
"<Figure size 1600x800 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 绘制柱状图\n",
|
||
"plt.figure(figsize=(8, 4), dpi=200)\n",
|
||
"df.plot(ax=plt.gca(), kind='bar', y='合计', legend=False)\n",
|
||
"plt.xticks(rotation=0)\n",
|
||
"plt.savefig('aa.png')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8a5236f7-072b-466c-9be3-afbab394f5cb",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 魔法指令"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d5c6a18b-2863-4855-8ef7-2c0aa99b7d5c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 查看当前工作路径 - print working directory\n",
|
||
"%pwd"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "80a9f9e0-1528-40cf-910c-f3c8e5e7e3b9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 查看指定路径文件列表 - list directory contents\n",
|
||
"%ls"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "620a54ed-9c29-4058-9d20-c4df72ba4c62",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 执行系统命令\n",
|
||
"%system date"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "659215ed-113a-4d8f-9036-0fcf47c96021",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 保存运行过的代码\n",
|
||
"%save temp.py"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "8fc9c4e4-1423-40f3-b4ee-db2ba2e5d125",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 加载文件内容到单元格\n",
|
||
"%load temp.py"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "58a08283-561c-43d4-8db6-74cde401b8a9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 统计代码执行时间\n",
|
||
"%timeit (1, 2, 3, 4, 5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "22a271ab-3f5c-4167-b89e-66a31e891cbd",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 查看历史输入\n",
|
||
"%hist"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d4ffa792-f1a0-4be9-b2aa-642ee0b9a1ae",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 查看魔法指令\n",
|
||
"%lsmagic"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a15db907-c068-41d7-a24c-8f1c5c20d4ec",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 获取帮助"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "5e037694-9357-46b9-864a-c5f93e1aa8c8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"np.random?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "11a97abd-d73d-493e-b727-9c4ded3e5060",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"np.random.normal?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "66503921-cd69-4394-80ea-7fecf6ecdc33",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"np.random.r*?"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.9.13"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|