init

README.md

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+# crawl_electricity
+
+全球电力可视化分析。
+
+crawl_electricity.py    爬取数据
+
+electricity_analysis.ipynb     分析数据
+
+
+
+```
+pip install requiremets.txt
+
+
+```

crawl_electricity.py

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+#!/usr/bin/env python
+# -*- encoding: utf-8 -*-
+'''
+@Contact :   liuyuqi.gov@msn.cn
+@Time    :   2021/10/10 07:24:07
+@License :   Copyright © 2017-2020 liuyuqi. All Rights Reserved.
+@Desc    :   全球电力数据爬虫
+https://www.iea.org/fuels-and-technologies/electricity
+'''
+import re
+import sys
+import os
+import time
+import requests
+import pandas as pd
+import numpy as np
+
+headers = {
+    'user-agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'}
+res = requests.get(r'https://api.iea.org/stats/countries', headers=headers)
+
+# 获取所有国家
+countries = pd.DataFrame(res.json())
+countries = countries.dropna(subset=["region"])
+
+dataList = []
+for i, country in enumerate(countries["stats"]):
+    params = {
+        "countries": country,
+        "startYear": 1990, }
+
+    res = requests.get(
+        r'https://api.iea.org/stats/indicator/TotElecCons', headers=headers, params=params)
+    df = pd.DataFrame(res.json())
+    try:
+        data = df[["year"]].copy()
+        data["country"] = df["value"]
+        data.set_index("year", inplace=True)
+        dataList.append(data)
+        print(f"{i}")
+    except:
+        continue
+
+results = pd.concat(dataList, axis=1).fillna(0).loc[:"2018"]
+results.to_csv("results.csv")

electricity_analysis.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 中国限电舆情数据分析\r\n",
+    "\r\n",
+    "国庆的到来，各地区都出台各种限电措施，各种“专家”纷纷揣度圣意。\r\n",
+    "\r\n",
+    "1、有专家大言不惭，美国大量印钞，而中国没有同步跟上，导致贬值的美元可以大量买走中国商品，限电是吾皇为了和美国贸易战下的一盘大棋，目的就是提高商品价格，争夺定价权。可笑之极。。。\r\n",
+    "\r\n",
+    "2、有些专家迎合大众口味，面对即将过年的能耗达标，北京中央出台文件点名批评各耗电大省。限电是各地方政府为了碳排放达标，而在年终前的赶作业行为。\r\n",
+    "\r\n",
+    "3、当然还有很多其他“专家”大言不惭，中国电价太便宜，限电是为了即将电价大涨的前兆。\r\n",
+    "\r\n",
+    "**限电的原因其实很简单，就是产电不足。而产电不足的核心原因就是煤炭供应不够，北京中央已经下发文件，内蒙古优质煤矿开采迅速审批，发电厂能发尽发。**\r\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import os,sys,re\r\n",
+    "import numpy as np\r\n",
+    "import pandas as pd\r\n",
+    "import matplotlib.pyplot as plt"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "df=pd.read_csv(\"result.csv\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

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+requests
+pandas