Pandas+Pyecharts | Visualization of historical weather data in Beijing in the past five years-Python Tutorial-php.cn

Pandas+Pyecharts | Visualization of historical weather data in Beijing in the past five years

Release： 2023-08-09 15:29:26

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##This issue uses python’s pyecharts visualization library Plot Beijing’s historical weather data to see historical high temperature and historical low temperature distribution, as well as wind power and wind direction distribution during the day and night,Hope it is helpful to everyone. If you have any questions or areas that need improvement, please contact the editor.

Involved libraries:
Pandas — Data processing
Pyecharts — Data visualization

1. Import module

import pandas as pd
from pyecharts.charts import Line
from pyecharts.charts import Bar
from pyecharts.charts import Scatter
from pyecharts.charts import Pie
from pyecharts.charts import EffectScatter
from pyecharts.charts import Calendar
from pyecharts.charts import Polar
from pyecharts import options as opts
import warnings
warnings.filterwarnings('ignore')

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##2. Pandas data processing

##2.1 Read data

df_weather = pd.read_excel('./2018-2022年天气数据.xlsx')

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Pandas+Pyecharts | Visualization of historical weather data in Beijing in the past five years

There are a total of

1839 pieces of historical weather data from 2018 to 2022. 2.2 Processing minimum and maximum temperature data

df_weather_1 = df_weather.copy()
df_weather_1[['最低气温','最高气温']] = df_weather_1['最低气温/最高气温'].str.split(' / ',expand=True) 
df_weather_1['最低气温'] = df_weather_1['最低气温'].str[:-2]
df_weather_1['最高气温'] = df_weather_1['最高气温'].str[:-1]
df_weather_1['最低气温'] = df_weather_1['最低气温'].astype('int')
df_weather_1['最高气温'] = df_weather_1['最高气温'].astype('int')

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Pandas+Pyecharts | Visualization of historical weather data in Beijing in the past five years

2.3 处理日期数据

df_weather_1['日期'] = pd.to_datetime(df_weather_1['日期'],format='%Y年%m月%d日')
df_weather_1['日期s'] = df_weather_1['日期'].dt.strftime('%Y/%m/%d')

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2.4 处理风力风向数据

Pandas+Pyecharts | Visualization of historical weather data in Beijing in the past five years

3. Pyecharts数据可视化

3.1 2018-2022年历史温度分布

def get_scatter():
    scatter = (
        Scatter()
        .add_xaxis(x_data)
        .add_yaxis("最低气温", y_data1)
        .add_yaxis("最高气温", y_data2)
        .set_global_opts(
            legend_opts=opts.LegendOpts(is_show=False),
            visualmap_opts=opts.VisualMapOpts(
                is_show=False,
                range_color=range_color
            ),
            title_opts=opts.TitleOpts(
                title='1-2018-2022年历史温度分布',
                subtitle='-- 制图@公众号：Python当打之年 --',
                pos_top='1%',
                pos_left="1%",
            )
        )
    )

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Pandas+Pyecharts | Visualization of historical weather data in Beijing in the past five years

3.2 2022年历史温度分布

Pandas+Pyecharts | Visualization of historical weather data in Beijing in the past five years

历史最高温度39℃，历史最低温度-12℃。

3.3 2021年历史温度分布

def get_calendar():
    calendar = (
        Calendar()
        .add('',
             data_21,
             calendar_opts=opts.CalendarOpts(
                 pos_right='5%',
                 range_='2021',
                daylabel_opts=opts.CalendarDayLabelOpts(name_map='cn'),
                monthlabel_opts=opts.CalendarMonthLabelOpts(name_map='cn')
             ),
        )
        .set_global_opts(
            title_opts=opts.TitleOpts(
                title='3-2021年历史温度分布',
                subtitle='-- 制图@公众号：Python当打之年 --',
                pos_top='1%',
                pos_left="1%",
            ),
            visualmap_opts=opts.VisualMapOpts(
                range_color=range_color,
            )
        )
    )

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Pandas+Pyecharts | Visualization of historical weather data in Beijing in the past five years

3.4 2019年历史温度分布

3.5 2022年夜间_白天风力分布

def get_pie():
    pie = (
        Pie()
        .add(
            "",
            [list(z) for z in zip(x_data, y_data1)],
            radius=["30%", "50%"],
            center=["30%", "55%"],
        )
        .add(
            "",
            [list(z) for z in zip(x_data, y_data2)],
            radius=["30%", "50%"],
            center=["70%", "55%"],
        )
        .set_global_opts(
            title_opts=opts.TitleOpts(
                title='5-2022年夜间_白天风力分布',
                subtitle='-- 制图@公众号：Python当打之年 --',
                pos_top='1%',
                pos_left="1%",
            ),
            legend_opts=opts.LegendOpts(pos_top='10%'),
            visualmap_opts=opts.VisualMapOpts(
                is_show=False,
                range_color=range_color
            ),
        )
    )

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Pandas+Pyecharts | Visualization of historical weather data in Beijing in the past five years

3.6 2022年夜间风向分布

def get_polor():
    polor = (
        Polar()
        .add("", values,type_="bar")
        .set_global_opts(
            legend_opts=opts.LegendOpts(is_show=False),
            visualmap_opts=opts.VisualMapOpts(
                is_show=False,
                range_color=range_color
            ),
            title_opts=opts.TitleOpts(
                title='6-2022年夜间风向分布',
                subtitle='-- 制图@公众号：Python当打之年 --',
                pos_top='1%',
                pos_left="1%",
            ),
        )
    )

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3.7 2022年白天风向分布

3.8 2018-2022年各类型天气数量

def get_bar():
    bar = (
        Bar()
        .add_xaxis(x_data)
        .add_yaxis("",y_data)
        .set_global_opts(
            visualmap_opts=opts.VisualMapOpts(
                is_show=False,
                range_color=range_color
            ),
            legend_opts=opts.LegendOpts(is_show=False),
            title_opts=opts.TitleOpts(
                title='8-2018-2022年各类型天气数量',
                subtitle='-- 制图@公众号：Python当打之年 --',
                pos_top='1%',
                pos_left="1%",
            ),
        )
    )

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3.9 2018-2022年每月平均最高温度

Pandas+Pyecharts | Visualization of historical weather data in Beijing in the past five years

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