1.爬取廣惠河深2022-2024年的天氣數(shù)據(jù)?
import requests # 發(fā)送請求要用的模塊 需要額外安裝的
import parsel
import csv
f = open('廣-惠-河-深天氣.csv', mode='a', encoding='utf-8', newline='')
csv_writer = csv.writer(f)
csv_writer.writerow(['日期', '最高溫度', '最低溫度', '天氣', '風(fēng)向', '城市'])
city_list = [72049, 59287,59293,59493]
for city in city_list:
city_name = ''
if city == 72049:
city_name = '惠州'
elif city == 59287:
city_name = '廣州'
elif city == 59293:
city_name = '河源'
elif city == 59493:
city_name = '深圳'
for year in range(2022, 2024):
for month in range(1, 13):
url = f'https://tianqi.2345.com/Pc/GetHistory?areaInfo%5BareaId%5D={city}&areaInfo%5BareaType%5D=2&date%5Byear%5D={year}&date%5Bmonth%5D={month}'
# 1. 發(fā)送請求
response = requests.get(url=url)
# 2. 獲取數(shù)據(jù)
html_data = response.json()['data']
# 3. 解析數(shù)據(jù)
select = parsel.Selector(html_data)
trs = select.css('.history-table tr') # 拿到31個tr
for tr in trs[1:]: # 第一個表頭不要
tds = tr.css('td::text').getall() # 針對每個tr進(jìn)行提取 取出所有的td里面的內(nèi)容
tds.append(city_name) # 把城市追加到列表里面
print(tds)
# 4. 保存數(shù)據(jù)
csv_writer.writerow(tds)爬取的數(shù)據(jù)如下圖所示?
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?2.讀取csv文件
import pandas as pd
data = pd.read_csv('廣-惠-河-深天氣.csv')
data
3.去除多余字符
#去除多余字符
data[['最高溫度','最低溫度']] = data[['最高溫度','最低溫度']].apply(lambda x: x.str.replace('°','').replace('', '0'))
data.head()
4.分割星期和日期
#分割日期與星期
data[['日期','星期']] = data['日期'].str.split(' ',expand=True,n=1)
data
5.篩選出城市數(shù)據(jù)子集。其中包含了四個城市在不同天氣下的天數(shù)統(tǒng)計結(jié)果。
# 按城市和天氣分組，并計算每組的天數(shù)
grouped = data.groupby(['城市', '天氣']).size().reset_index(name='天數(shù)')
# 將結(jié)果按城市分為4個DataFrame
gz_weather = grouped[grouped['城市'] == '廣州']
hy_weather = grouped[grouped['城市'] == '河源']
hz_weather = grouped[grouped['城市'] == '惠州']
sz_weather = grouped[grouped['城市'] == '深圳']
gz_weatherhy_weatherhz_weathersz_weather
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?6.將原有的天氣類型按照關(guān)鍵字劃分
# 定義一個函數(shù)，將原有的天氣類型按照關(guān)鍵字劃分
def classify_weather(weather):
if '多云' in weather:
return '多云'
elif '晴' in weather:
return '晴'
elif '陰' in weather:
return '陰'
elif '大雨' in weather:
return '雨'
elif '中雨' in weather:
return '雨'
elif '小雨' in weather:
return '雨'
elif '雷陣雨' in weather:
return '雨'
elif '霧' in weather:
return '霧'
else:
return '其他'
# 將原有的天氣類型按照關(guān)鍵字劃分，并存進(jìn)新的 DataFrame 中
new_data = data[['城市', '天氣']].copy()
new_data['新天氣'] = new_data['天氣'].apply(classify_weather)
new_data
7.對城市的天氣數(shù)據(jù)按照新天氣列分組后，計算每一種天氣的天數(shù)，然后將“天氣”列名改為“天數(shù)”得到的數(shù)據(jù)框。
# 按照城市和新天氣列進(jìn)行分組，并計算每一種天氣的天數(shù)
count_data = new_data.groupby(['城市', '新天氣'])['天氣'].count().reset_index()
# 根據(jù)條件篩選出符合要求的行
df1 = count_data.loc[count_data['城市'] == '廣州']
df2 = count_data.loc[count_data['城市'] == '河源']
df3 = count_data.loc[count_data['城市'] == '惠州']
df4 = count_data.loc[count_data['城市'] == '深圳']
# 將“天氣”列名改為“天數(shù)”
df5 = df1.rename(columns={'天氣': '天數(shù)'})
df6 = df2.rename(columns={'天氣': '天數(shù)'})
df7 = df3.rename(columns={'天氣': '天數(shù)'})
df8 = df4.rename(columns={'天氣': '天數(shù)'})
# 輸出結(jié)果
df5.to_csv('df5.csv',index=False)上面輸出結(jié)果保存到csv文件中,如果要查看可以輸出df5 查看數(shù)據(jù)
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8.篩選出每個城市平均溫度等于最高溫度和最低溫度平均值的數(shù)據(jù)
# 篩選出平均溫度等于最高溫度和最低溫度平均值的數(shù)據(jù)
data1 = data[(data['平均溫度'] == (data['最高溫度'] + data['最低溫度']) / 2)]
data_AB = data1[(data1['城市'] == '廣州') | (data1['城市'] == '深圳') | (data1['城市'] == '河源') | (data1['城市'] == '惠州')]
#將日期轉(zhuǎn)換為月份并賦值給新的列
data_AB['月份'] = pd.to_datetime(data_AB['日期']).dt.month
#按照城市和月份分組，計算每組的平均氣溫
grouped_AB = data_AB.groupby(['城市', '月份'])['平均溫度'].mean().reset_index()
#按照城市和月份排序
grouped_AB = grouped_AB.sort_values(['城市', '月份'])
#打印結(jié)果
grouped_AB
9.繪制廣州、河源、惠州和深圳每日平均溫度的折線圖
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
# 篩選出廣州和湛江的數(shù)據(jù)
gz_data = data[data['城市'] == '廣州']
hy_data = data[data['城市'] == '河源']
hz_data = data[data['城市'] == '惠州']
sz_data = data[data['城市'] == '深圳']
# 提取日期和平均溫度數(shù)據(jù)
x = gz_data['日期']
y1 = gz_data['平均溫度']
y2 = hy_data['平均溫度']
y3 = hz_data['平均溫度']
y4 = sz_data['平均溫度']
# 繪制折線圖
plt.figure(dpi=500, figsize=(10, 5))
plt.title("廣河惠深每日平均溫度折線圖")
plt.plot(x, y1, color='red', label='廣州')
plt.plot(x, y2, color='blue', label='河源')
plt.plot(x,y3,color='green',label='惠州')
plt.plot(x,y4,color='yellow',label='深圳')
# 獲取圖的坐標(biāo)信息
coordinates = plt.gca()
# 設(shè)置x軸每個刻度的間隔天數(shù)
xLocator = mpl.ticker.MultipleLocator(30)
coordinates.xaxis.set_major_locator(xLocator)
# 將日期旋轉(zhuǎn)30°
plt.xticks(rotation=30)
plt.xticks(fontsize=8)
plt.ylabel("溫度(℃)")
plt.xlabel("日期")
plt.legend()
plt.savefig("廣河惠深每日平均溫度折線圖.png")
plt.show()
10.繪制城市為廣州、惠州、深圳、河源的月平均氣溫數(shù)據(jù)
data_GZ_HZ_SZ_HY = grouped_AB[(grouped_AB['城市'] == '廣州') | (grouped_AB['城市'] == '惠州') | (grouped_AB['城市'] == '深圳') | (grouped_AB['城市'] == '河源')]
#繪制折線圖
fig, ax = plt.subplots()
for city in ['廣州', '惠州', '深圳', '河源']:
ax.plot(data_GZ_HZ_SZ_HY[data_GZ_HZ_SZ_HY['城市'] == city]['月份'], data_GZ_HZ_SZ_HY[data_GZ_HZ_SZ_HY['城市'] == city]['平均溫度'], label=city)
#設(shè)置圖例和標(biāo)題
ax.legend()
ax.set_title('廣州、惠州、深圳、河源每月氣溫折線圖')
#顯示圖形
plt.show()
11.繪制四個城市數(shù)據(jù)對比
import matplotlib.pyplot as plt
#創(chuàng)建一個畫布
fig, ax = plt.subplots(figsize=(10, 5))
#繪制廣州各類天氣條形圖
ax.bar(df5['新天氣'], df5['天數(shù)'], width=0.2, label='廣州')
#繪制惠州各類天氣條形圖
ax.bar(df7['新天氣'], df7['天數(shù)'], width=0.2, label='惠州', alpha=0.7)
#繪制河源各類天氣條形圖
ax.bar(df6['新天氣'], df6['天數(shù)'], width=0.2, label='河源', alpha=0.7)
#繪制深圳各類天氣條形圖
ax.bar(df8['新天氣'], df8['天數(shù)'], width=0.2, label='深圳', alpha=0.7)
#設(shè)置圖例
ax.legend()
#設(shè)置 x 軸標(biāo)簽和標(biāo)題
ax.set_xlabel('天氣類型')
ax.set_ylabel('天數(shù)')
ax.set_title('廣州、惠州、河源、深圳各類天氣天數(shù)對比')
#顯示圖表
plt.show()?
12.各個城市天氣占比
import pandas as pd
from pyecharts.charts import Pie
from pyecharts import options as opts
# 讀取csv文件并轉(zhuǎn)換為列表格式
df = pd.read_csv('df5.csv')
data_list = df[['新天氣', '天數(shù)']].values.tolist()
# 生成餅圖
pie = Pie()
pie.add("", data_list)
pie.set_global_opts(title_opts=opts.TitleOpts(title="廣州各類天氣天數(shù)占比"))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter=""))
pie.render_notebook()
import pandas as pd
from pyecharts.charts import Pie
from pyecharts import options as opts
# 讀取csv文件并轉(zhuǎn)換為列表格式
df = pd.read_csv('df6.csv')
data_list = df[['新天氣', '天數(shù)']].values.tolist()
# 生成餅圖
pie = Pie()
pie.add("", data_list)
pie.set_global_opts(title_opts=opts.TitleOpts(title="河源各類天氣天數(shù)占比"))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter=""))
pie.render_notebook()
import pandas as pd
from pyecharts.charts import Pie
from pyecharts import options as opts
# 讀取csv文件并轉(zhuǎn)換為列表格式
df = pd.read_csv('df7.csv')
data_list = df[['新天氣', '天數(shù)']].values.tolist()
# 生成餅圖
pie = Pie()
pie.add("", data_list)
pie.set_global_opts(title_opts=opts.TitleOpts(title="惠州各類天氣天數(shù)占比"))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter=""))
pie.render_notebook()
import pandas as pd
from pyecharts.charts import Pie
from pyecharts import options as opts
# 讀取csv文件并轉(zhuǎn)換為列表格式
df = pd.read_csv('df8.csv')
data_list = df[['新天氣', '天數(shù)']].values.tolist()
# 生成餅圖
pie = Pie()
pie.add("", data_list)
pie.set_global_opts(title_opts=opts.TitleOpts(title="深圳各類天氣天數(shù)占比"))
pie.set_series_opts(label_opts=opts.LabelOpts(formatter=""))
pie.render_notebook()
13.統(tǒng)計四個城市每日氣溫折線圖
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
# 篩選出廣州和湛江的數(shù)據(jù)
gz_data = data[data['城市'] == '廣州']
hy_data = data[data['城市'] == '河源']
hz_data = data[data['城市'] == '惠州']
sz_data = data[data['城市'] == '深圳']
# 提取日期和平均溫度數(shù)據(jù)
x = gz_data['日期']
y1 = gz_data['平均溫度']
y2 = hy_data['平均溫度']
y3 = hz_data['平均溫度']
y4 = sz_data['平均溫度']
# 繪制面積圖
plt.figure(dpi=500, figsize=(10, 5))
plt.title("廣州惠州河源深圳折線圖")
plt.fill_between(x, y1, color='red', alpha=0.5, label='廣州')
plt.fill_between(x, y2, color='blue', alpha=0.5, label='河源')
plt.fill_between(x, y3, color='yellow', alpha=0.5, label='惠州')
plt.fill_between(x, y4, color='green', alpha=0.5, label='深圳')
# 獲取圖的坐標(biāo)信息
coordinates = plt.gca()
# 設(shè)置x軸每個刻度的間隔天數(shù)
xLocator = mpl.ticker.MultipleLocator(30)
coordinates.xaxis.set_major_locator(xLocator)
# 將日期旋轉(zhuǎn)30°
plt.xticks(rotation=30)
plt.xticks(fontsize=8)
plt.ylabel("溫度(℃)")
plt.xlabel("日期")
plt.legend()
plt.savefig("廣-惠-河-深折線圖")
plt.show()
14.讀取四個城市各類天氣天數(shù)對比圖
import matplotlib.pyplot as plt
from pyecharts.charts import Bar
# 讀取廣州數(shù)據(jù)并生成柱狀圖
df1 = pd.read_csv('df5.csv')
data_list1 = df1[['新天氣', '天數(shù)']].values.tolist()
# 讀取河源數(shù)據(jù)并生成柱狀圖
df2 = pd.read_csv('df6.csv')
data_list2 = df2[['新天氣', '天數(shù)']].values.tolist()
# 讀取惠州數(shù)據(jù)并生成柱狀圖
df3 = pd.read_csv('df7.csv')
data_list3 = df3[['新天氣', '天數(shù)']].values.tolist()
# 讀取深圳數(shù)據(jù)并生成柱狀圖
df4 = pd.read_csv('df8.csv')
data_list4 = df4[['新天氣', '天數(shù)']].values.tolist()
# 合并 x 軸數(shù)據(jù)
x_data = list(set([i[0] for i in data_list1] + [i[0] for i in data_list2] + [i[0] for i in data_list3] + [i[0] for i in data_list4]))
# 生成柱狀圖
bar = Bar()
bar.add_xaxis(x_data)
bar.add_yaxis("廣州", [df1.loc[df1['新天氣']==x, '天數(shù)'].tolist()[0] if x in df1['新天氣'].tolist() else 0 for x in x_data])
bar.add_yaxis("河源", [df2.loc[df2['新天氣']==x, '天數(shù)'].tolist()[0] if x in df2['新天氣'].tolist() else 0 for x in x_data])
bar.add_yaxis("惠州", [df3.loc[df3['新天氣']==x, '天數(shù)'].tolist()[0] if x in df3['新天氣'].tolist() else 0 for x in x_data])
bar.add_yaxis("深圳", [df4.loc[df4['新天氣']==x, '天數(shù)'].tolist()[0] if x in df4['新天氣'].tolist() else 0 for x in x_data])
bar.set_global_opts(title_opts=opts.TitleOpts(title="廣州惠州河源深圳各類天氣天數(shù)對比圖"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45)),
legend_opts=opts.LegendOpts(pos_left="center"))
bar.render_notebook()
?15.讀取四個城市當(dāng)中一個城市的天氣天數(shù)對比圖
import matplotlib.pyplot as plt
from pyecharts.charts import Bar
# 讀取惠州數(shù)據(jù)并生成柱狀圖
df3 = pd.read_csv('df7.csv')
data_list3 = df3[['新天氣', '天數(shù)']].values.tolist()
# 生成柱狀圖
bar = Bar()
bar.add_xaxis(x_data)
bar.add_yaxis("惠州", [df3.loc[df3['新天氣']==x, '天數(shù)'].tolist()[0] if x in df3['新天氣'].tolist() else 0 for x in x_data])
bar.set_global_opts(title_opts=opts.TitleOpts(title="廣州惠州河源深圳各類天氣天數(shù)對比圖"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45)),
legend_opts=opts.LegendOpts(pos_left="center"))
bar.render_notebook()
當(dāng)今社會，數(shù)據(jù)越來越重要，因此，儀表盤成為了一種非常流行的數(shù)據(jù)可視化方式。通過使用儀表盤，用戶可以更輕松地理解數(shù)據(jù)，從而做出更好的決策。在本文中，我們將介紹如何使用前端網(wǎng)頁來實(shí)現(xiàn)儀表盤文字。
首先，我們需要一個基本的 HTML 文件。在這個文件中，我們將創(chuàng)建一個儀表盤的基本框架，并使用 CSS 來設(shè)置樣式。在這個基本框架中，我們將包含一個 div 元素，用于顯示儀表盤的文本。我們將使用 JavaScript 來動態(tài)地更新這個文本。
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Awesome-pyecharts</title>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/echarts.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/jquery.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/jquery-ui.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/ResizeSensor.js"></script>
<link rel="stylesheet" >
</head>
<body >
<style>.box { } </style>
<button onclick="downloadCfg()">Save Config</button>
<div class="box">
<div id="c48b9e2d9b7440e1b2f458d54f3b383c" class="chart-container" style="width:900px; height:500px; "></div>
<script>
var chart_c48b9e2d9b7440e1b2f458d54f3b383c = echarts.init(
document.getElementById('c48b9e2d9b7440e1b2f458d54f3b383c'), 'white', {renderer: 'canvas'});
var option_c48b9e2d9b7440e1b2f458d54f3b383c = {
"animation": true,
"animationThreshold": 2000,
"animationDuration": 1000,
"animationEasing": "cubicOut",
"animationDelay": 0,
"animationDurationUpdate": 300,
"animationEasingUpdate": "cubicOut",
"animationDelayUpdate": 0,
"aria": {
"enabled": false
},
"color": [
"#5470c6",
"#91cc75",
"#fac858",
"#ee6666",
"#73c0de",
"#3ba272",
"#fc8452",
"#9a60b4",
"#ea7ccc"
],
"series": [
{
"type": "bar",
"name": "廣州",
"legendHoverLink": true,
"data": [
247,
0,
1,
14,
61,
42
],
"realtimeSort": false,
"showBackground": false,
"stackStrategy": "samesign",
"cursor": "pointer",
"barMinHeight": 0,
"barCategoryGap": "20%",
"barGap": "30%",
"large": false,
"largeThreshold": 400,
"seriesLayoutBy": "column",
"datasetIndex": 0,
"clip": true,
"zlevel": 0,
"z": 2,
"label": {
"show": true,
"margin": 8
}
},
{
"type": "bar",
"name": "河源",
"legendHoverLink": true,
"data": [
249,
0,
6,
19,
62,
29
],
"realtimeSort": false,
"showBackground": false,
"stackStrategy": "samesign",
"cursor": "pointer",
"barMinHeight": 0,
"barCategoryGap": "20%",
"barGap": "30%",
"large": false,
"largeThreshold": 400,
"seriesLayoutBy": "column",
"datasetIndex": 0,
"clip": true,
"zlevel": 0,
"z": 2,
"label": {
"show": true,
"margin": 8
}
},
{ "type": "bar",
"name": "惠州",
"legendHoverLink": true,
"data": [
243,
5,
7,
16,
68,
26,
],
"realtimeSort": false,
"showBackground": false,
"stackStrategy": "samesign",
"cursor": "pointer",
"barMinHeight": 0,
"barCategoryGap": "20%",
"barGap": "30%",
"large": false,
"largeThreshold": 400,
"seriesLayoutBy": "column",
"datasetIndex": 0,
"clip": true,
"zlevel": 0,
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