Pyecharts visualization

First, Pyecharts

Pyecharts is a library for generating Echarts diagrams

Echarts is an open source DATA visualization JS library, mainly used for data visualization, Pyecharts is a class library for generating Echarts charts. This is essentially Echarts docking with Python

Pyecharts can be used to generate standalone web pages, or integrated in Flask, Django

PIP install Pyecharts ==1.5

Pyecharts chart preparation

1. Global configuration items

Global configuration items can be set using the set_global_options method

The configuration is as follows:

2. Series configuration items

This can be set using the set_series_options method

This parameter is used to configure the specific parameters of font, color, and line lamp

3. Note: Data format

What Pyecharts is essentially doing is serializing Echarts configuration items into JSON format from the Python Dict, so what format of data types Pyecharts supports depends on what data types JSON supports

Pyecharts is a generic third party library, and it is impossible to care about all of the developers’ usage scenarios. This transformation requires us to introduce two third party libraries, numPY/Pandas, which are too important, so we leave it to the developers

Specific conversion mode:

You can use series.tolist () for quick conversions

4. More configurations

For more information about the configuration, see pyecharts.org/#/zh-cn/

Color comparison table lesson reference: www.fynas.com/system-draw…

3. Bar chart

Take the quantity of various goods sold by merchant A and merchant B as an example to draw A bar chart

Code implementation:

from pyecharts import options as opts  # Config module
from pyecharts.charts import Bar

Instantiate the bar object
bar = Bar()

# 2. Add data
# Horizontal data
bar.add_xaxis(
    xaxis_data=['shirt'.'sweaters'.'tie'.'pants'.'trench coat'.'High heels'.'socks'])Add vertical axis data
bar.add_yaxis(
    series_name=Merchants' A '.# Column name
    yaxis_data=[114.55.27.101.125.27.105].# Merchant A's data
)

Add vertical axis data
bar.add_yaxis(
    series_name=Merchants' B '.# Column name
    yaxis_data=[57.134.137.129.145.60.49].# Merchant A's data
)

# 3. Global configuration items
bar.set_global_opts(
    # set the title
    title_opts=opts.TitleOpts(
        title='Bar test'))# 4. Series configuration items
bar.set_series_opts(
    # set the tag
    label_opts=opts.LabelOpts(
        is_show=True,
        position='right'.# tag position))# Set direction
bar.reversal_axis()

# 5. Generate files
bar.render('./ bar graph.html ')
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Fourth, the pie chart

The pie chart is drawn based on the statistics of Python system classes in each branch school

Statistics on the number of branch schools in each region

Code implementation:

# guide package
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.globals import ThemeType  # Theme module
import pandas as pd

Instantiate the object
pie = Pie(
    Initial configuration
    init_opts=opts.InitOpts(
        width='1200px'.# canvas width
        height='500px'.# Canvas height
        # theme='white', # theme, set mode
        theme=ThemeType.WHITE,  # Theme, set mode
        page_title='Pie chart of Python branch schools'.# bg_color='#F0F8FF', # background color))Load branch data
data = pd.read_excel('./ Number of Python branch schools. XLSX ')
print('data:\n', data)
# for TMP in zip (data. Loc [: ', ']. Tolist (), and data. The loc [: 'number']. Tolist ()) :
# print(tmp)

data_pair = [(k, v) for k, v in zip(data.loc[:, '分校'].tolist(), data.loc[:, 'number'].tolist())]
print('data_pair:\n', data_pair)

# 2. Add data
pie.add(
    series_name=data.loc[:, '分校'].tolist(),  # legend name
    data_pair=data_pair,  # Data format: [(k1,v1),(k2,v2)... .
    radius=['30%'.'70%'].# Radius, the first is the inside diameter, the second is the outside diameter, usually set to a percentage
    is_clockwise=True.Arrange clockwise
)

# 3. Set global configuration items
pie.set_global_opts(
    # set the title
    title_opts=opts.TitleOpts(
        title='Pie chart of Python branch schools'.# Title name
        subtitle='Python0421 class of Guangzhou Branch'.# subtitle
        pos_left='left'.# position
    ),
    # Set legend
    legend_opts=opts.LegendOpts(
        is_show=True.# Show the legend
        pos_left='center'.# Position -- center))# 4. Set the series of configuration items
pie.set_series_opts(
    # set the tag
    label_opts=opts.LabelOpts(
        is_show=True.# Show tags
        font_size=12.# font size
        font_style='italic'.# font style, slant
        formatter='{b}:{d}%'.# Display style {a} series name {b} Data item name {C} Value {d} percentage))# 5. Generate files
pie.render('./ Pie chart of proportion of people in Python campuses. HTML ')
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Five, line chart

COVID-19 (Corona Virus Disease 2019, COVID-19 for short) stands for pneumonia caused by 2019-Novel coronavirus infection. Since December 2019, A number of pneumonia cases of unknown cause with a history of exposure to seafood markets in South China have been detected in some hospitals in Wuhan, Hubei Province, which have been confirmed as acute respiratory infections caused by 2019 Novel Coronavirus infection

To this end, the Hubei government, provinces and even the country have attached great importance to the development of the novel coronavirus outbreak, and have taken prompt measures to isolate and treat those who are sick

In this case, statistical analysis of epidemic data was conducted with the help of national release data (part)

A broken line chart and a bar chart are drawn based on the national epidemic statistics as of 10:49 p.m. on February 6, 2020

Statistics of daily changes in China’s epidemic data

Code implementation:

# Drawing module
from pyecharts.charts import *
# Config module
from pyecharts import options as opts
# Theme module
from pyecharts.globals import ThemeType

import pandas as pd

Instantiate the object
bar = Bar(
    # init_opts=opts.InitOpts(
    # width='2000px',
    # # height='700px'
    #)
)

# load data
history_data = pd.read_excel('./ Epidemic history data.xls', index_col=0)
print('history_data:\n', history_data)

Get horizontal axis data
# (1) Get the time data and change its type to STR
x_data = history_data.loc[:, 'time'].astype('str')
# PI (2) the other way around
x_data = x_data[::-1].tolist()

print('x_data:\n', x_data)

Prepare the vertical axis data
# Death data
death_data = history_data.loc[:, 'Deaths'] [: : -1].tolist()
print('death_data:\n', death_data)
# Confirmed data
ensure_data = history_data.loc[:, 'Confirmed number'] [: : -1].tolist()
print('ensure_data:\n', ensure_data)
# Suspected data
suspected_data = history_data.loc[:, 'Suspected number'] [: : -1].tolist()
print('suspected_data:\n', suspected_data)
# Cure data
cure_data = history_data.loc[:, 'Cure number'] [: : -1].tolist()
print('cure_data:\n', cure_data)

# 2. Add data
# Horizontal data
bar.add_xaxis(
    xaxis_data=x_data,  # Horizontal data
)

# Vertical axis data
bar.add_yaxis(
    series_name='Deaths'.# legend name
    yaxis_data=death_data,  # data
    yaxis_index=1.# For multiple vertical axes, select the first vertical axis as the number of deaths reference
)

bar.add_yaxis(
    series_name='Cure number'.# legend name
    yaxis_data=cure_data,  # data
    yaxis_index=2.# when multiple vertical axes are represented, the second vertical axis is selected as the reference for healing number
)

# Increase the vertical axis
bar.extend_axis(
    Add a vertical axis
    yaxis=opts.AxisOpts(
        type_='value'.Type of coordinate axes
        name='Deaths'.The name of the coordinate axis
        min_=0.# the minimum
        max_=1200.# maximum value,
        position='right'.# Position, right
        offset=0.# Coordinate line setup
        axisline_opts=opts.AxisLineOpts(
            is_show=True.# display coordinate line,
            # Coordinate line style
            linestyle_opts=opts.LineStyleOpts(
                color='#a5a391'.# Color of the line)),# coordinate line label Settings
        axislabel_opts=opts.LabelOpts(
            is_show=True.Display axis label
            position='top'.# tag position
            formatter='{value}'  # label value
        )
    )
)
bar.extend_axis(
    Add a vertical axis
    yaxis=opts.AxisOpts(
        type_='value'.Type of coordinate axes
        name='Cure number'.The name of the coordinate axis
        min_=0.# the minimum
        max_=1200.# maximum value,
        position='right'.# Position, right
        offset=60.# Coordinate line setup
        axisline_opts=opts.AxisLineOpts(
            is_show=True.# display coordinate line,
            # Coordinate line style
            linestyle_opts=opts.LineStyleOpts(
                color='#fd5956'.# Color of the line)),# coordinate line label Settings
        axislabel_opts=opts.LabelOpts(
            is_show=True.Display axis label
            position='top'.# tag position
            formatter='{value}'  # label value
        )
    )
)

bar.extend_axis(
    Add a vertical axis
    yaxis=opts.AxisOpts(
        type_='value'.Type of coordinate axes
        name='Suspected number'.The name of the coordinate axis
        min_=0.# the minimum
        max_=30000.# maximum value,
        position='right'.# Position, right
        offset=120.# Coordinate line setup
        axisline_opts=opts.AxisLineOpts(
            is_show=True.# display coordinate line,
            # Coordinate line style
            linestyle_opts=opts.LineStyleOpts(
                color='#ac7e04'.# Color of the line)),# coordinate line label Settings
        axislabel_opts=opts.LabelOpts(
            is_show=True.Display axis label
            position='top'.# tag position
            formatter='{value}'  # label value)))# Draw a line chart
Instantiate the object
line = Line()
# add data
Add horizontal axis data
line.add_xaxis(
    xaxis_data=x_data
)
Add vertical axis data
line.add_yaxis(
    series_name='Confirmed number'.# legend name
    y_axis=ensure_data,  # Confirmed data
    yaxis_index=0.# select the 0 axis as a reference
    # tag Settings
    label_opts=opts.LabelOpts(
        is_show=False
    )
)
line.add_yaxis(
    series_name='Suspected number',
    y_axis=suspected_data,  # Suspected data
    yaxis_index=3.# tag Settings
    label_opts=opts.LabelOpts(
        is_show=False),# 3. Global configuration
bar.set_global_opts(
    # header configuration
    title_opts=opts.TitleOpts(
        title='Epidemic Trends in China'.# titles
        subtitle='Python0421 class of Guangzhou Branch'.# subtitle
        pos_left='3%'.# the position of the title
        pos_top='5%'.# the position of the title
        # Set the main heading style
        title_textstyle_opts=opts.TextStyleOpts(
            # color='#000000', # text color
            font_style='normal'.# font style
            # font_size=12, # font size
            font_weight='normal'.The size of the # font
        ),
        # Subtitle style Settings
        subtitle_textstyle_opts=opts.TextStyleOpts(
            # color='#000000', # text color
            font_style='normal'.# font style
            # font_size=7, # font size
            font_weight='normal'.The size of the # font)),# legend setup
    legend_opts=opts.LegendOpts(
        is_show=True.# display legend
        pos_left='25%'.# legend position
        pos_top='7%'.# legend position
        # Legend Style Settings - Do not set them yet
        # textstyle_opts=opts.TextStyleOpts()
    ),
    # Vertical axis Settings
    yaxis_opts=opts.AxisOpts(
        type_='value'.Type of coordinate axes
        name='Confirmed number'.The name of the coordinate axis
        min_=0.# the minimum
        max_=30000.# maximum value,
        position='left'.# Position, left
        # Coordinate line setup
        axisline_opts=opts.AxisLineOpts(
            is_show=True.# display coordinate line,
            # Coordinate line style
            linestyle_opts=opts.LineStyleOpts(
                color='#FFF5EE'.# Color of the line)),# coordinate line label Settings
        axislabel_opts=opts.LabelOpts(
            is_show=True.Display axis label
            position='top',
            formatter='{value}')),# Prompt box Settings
    tooltip_opts=opts.TooltipOpts(
        trigger='axis'.# 'axis': Axis trigger, mainly used in bar charts, line charts, etc., which use category axes
        axis_pointer_type='cross'.# 'cross' : Crosshair indicator. This is a shorthand for enabling axisPointer for two orthogonal axes.
    ),
    # Text box Settings
    graphic_opts=opts.GraphicGroup(
        # Configuration items for graphics
        # Position of text relative to graph
        graphic_item=opts.GraphicItem(
            # Control the overall position
            left="75%",
            top="45%",
        ),
        children=[
            # # opts.GraphicRect controls the display of the box
            # # If you don't need the box, remove the paragraph
            # opts.GraphicRect(
            # # Text box position
            # graphic_item=opts.GraphicItem(
            # z=100, the display position of the # z axis
            # left="center", # left
            # top="middle", # bottom position
            #).
            # # textbox shape
            # graphic_shape_opts=opts.GraphicShapeOpts(
            # width=420, # width
            # height=180, # height
            #).
            # # text box style
            # graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
            # fill="# FFF ", # text box fill color
            # stroke="#00008B", # border color
            # line_width=10, # border width
            # shadow_blur=8, # shadow correlation
            # shadow_offset_x=3
            # shadow_offset_y=3
            # shadow_color="rgba(0,0,0,0.3)", # shadow color
            #).
            #).
            # config text
            opts.GraphicText(
                Position of text --> Position of text relative to text box
                graphic_item=opts.GraphicItem(
                    left="center",
                    top='middle',
                    z=100.# display position
                    scale=[1.5.1.5].# scaling
                ),
                graphic_textstyle_opts=opts.GraphicTextStyleOpts(
                    # Text message
                    text="Up to {},\n the relevant information of the COVID-19 virus across the country is :\n death cases are {} cases,\n cured cases are {} cases,\ N confirmed cases are {} cases,\n suspected cases are {} cases,\n experts call for :\n the majority of the national policy allocation for epidemic prevention.".format(
                        history_data.loc[0.'time'], history_data.loc[0.'Deaths'], history_data.loc[0.'Cure number'],
                        history_data.loc[0.'Confirmed number'], history_data.loc[0.'Suspected number']
                    ),

                    font="13px Microsoft YaHei".# font and font size
                    text_align='left'.Align horizontally to the left
                    text_vertical_align='middle'.# Vertical alignment: Default None,
                    Color and width of the # font
                    graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
                        fill='#ffdf22'.# color
                        line_width=1.2.# width)))]))# 4. Add a series of configuration items
bar.set_series_opts(
    # tag Settings
    label_opts=opts.LabelOpts(
        is_show=True.# Show tags
        position='top'.# tag position),# Stack multiple graphics
all_chart = bar.overlap(line)

# combination
grid = Grid(
    Initial configuration
    init_opts=opts.InitOpts(
        width="1750px".# width
        height='650px'.# height
        theme=ThemeType.PURPLE_PASSION,  Topic #))# add chart
grid.add(
    # add chart
    chart=all_chart,
    # Set the combined configuration
    grid_opts=opts.GridOpts(pos_top="20%".# position
                            pos_left="5%".# position
                            pos_right="40%"  # position
                            ),
    is_control_axis_index=True.# control index
)

# 5. Generate files
grid.render('./ combination of bar chart and line chart. HTML ')
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6. Map making

(1) Take the maps of popular migration routes before wuhan was closed down (0120, 0121, 0122) as examples to draw provincial maps

Before the lockdown of Wuhan, people migrated to the city

Code implementation:

# Import flow map
from pyecharts.charts import Geo
# Theme module
from pyecharts.globals import ThemeType
# Config module
from pyecharts import options as opts
#
from pyecharts.globals import GeoType

Instantiate the object
geo = Geo(
    Initial configuration
    init_opts=opts.InitOpts(
        width='900px',
        height='500px',
        theme=ThemeType.CHALK,  Topic #
        page_title='Flow map'.# page name))# 2. Add a map type
geo.add_schema(
    maptype='hubei'.# Map type, China map is China, Guangdong map is Guangdong, world map is World
    # Change the map style
    itemstyle_opts=opts.ItemStyleOpts(
        # Map color
        color='#FF8C00'.# Map edge color
        border_color='#ffdf22',
        area_color='#FF8C00'))# Build the access data of Wuhan before lockdown (data within Hubei Province)
# out -- represents wuhan --> the city
# enter -- represents the city --> Wuhan
data_0120_ru = 'Xiaogan, Huanggang, Ezhou, Jingzhou, Huangshi, Xiangyang'
data_0120_chu = 'Xiaogan, Huanggang, Jingzhou, Xiangyang, Huangshi, Jingmen, Ezhou, Suizhou, Xiantao'
data_0121_ru = 'Xiaogan, Huanggang, Jingzhou, Ezhou, Huangshi'
data_0121_chu = 'Xiaogan, Huanggang, Jingzhou, Xiangyang, Jingmen, Huangshi, Suizhou, Ezhou, Xiantao'
data_0122_ru = 'Xiaogan, Huanggang, Ezhou, Jingzhou, Xianning, Huangshi'
data_0122_chu = Xiaogan, Huanggang, Jingzhou, Xiangyang, Jingzhou, Suizhou, Yichang, Huangshi, Ezhou

# data_pair can be exactly [(out, in),(out, in),...
# [(Xiaogan, Wuhan),(Huanggang, Wuhan),(Wuhan, Huangshi),...]
Build a List to store the data into Wuhan
data_ru = []
# building data_pair
for city_str in [data_0120_ru, data_0121_ru, data_0122_ru]:
    # city_str: city string of Wuhan
    # split
    city_list = city_str.split(', ')

    # print('city_list:\n', city_list)

    Add city_list to data_ru
    data_ru.extend(city_list)

    Decrement data_ru
    data_ru = list(set(data_ru))

    # building data_pair_ru
    data_pair_ru = [(city, 'wuhan') for city in data_ru]

Build a List to store wuhan data
data_chu = []

# Build wuhan data
for city_str in [data_0120_chu, data_0121_chu, data_0122_chu]:
    # city_str: string of the city out of Wuhan
    # split
    city_list = city_str.split("、")
    Add # city_list to data_chu
    data_chu.extend(city_list)
    Delete data_chu
    data_chu = list(set(data_chu))
    # building data_pair_chu
    data_pair_chu = [('wuhan', city) for city in data_chu]

# print('data_pair_ru:\n', data_pair_ru)
# print('data_pair_chu:\n', data_pair_chu)

# Combine the data out of Wuhan and the data into Wuhan
data_pair = data_pair_ru + data_pair_chu
print('data_pair:\n', data_pair)

# 3. Add data
geo.add(
    series_name=' '.# Set to null
    data_pair=data_pair,  # data
    type_=GeoType.LINES,  # linear
    # Ripple effect Settings
    effect_opts=opts.EffectOpts(
        # Ripple draw style
        brush_type='stroke'.# Type of effect point
        symbol='arrow',
        symbol_size=5,
        color='yellow'
    ),
    # Line style
    linestyle_opts=opts.LineStyleOpts(
        # Degree of bending
        curve=0.2.# Line style
        type_='solid',
        color='#7FFF00'))# 4. Global configuration items
geo.set_global_opts(
    # header configuration
    title_opts=opts.TitleOpts(
        title='Map of The Flow of Wuhan before lockdown',
        pos_left='center'
    ),
    legend_opts=opts.LegendOpts(
        is_show=False
    ),
    # Text box Settings
    graphic_opts=opts.GraphicGroup(
        # Configuration items for graphics
        # Position of text relative to graph
        graphic_item=opts.GraphicItem(
            # Control the overall position
            left="85%",
            top="45%",
        ),
        children=[
            # # opts.GraphicRect controls the display of the box
            # # If you don't need the box, remove the paragraph
            # opts.GraphicRect(
            # # Text box position
            # graphic_item=opts.GraphicItem(
            # z=100, the display position of the # z axis
            # left="center", # left
            # top="middle", # bottom position
            #).
            # # textbox shape
            # graphic_shape_opts=opts.GraphicShapeOpts(
            # width=420, # width
            # height=180, # height
            #).
            # # text box style
            # graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
            # fill="# FFF ", # text box fill color
            # stroke="#00008B", # border color
            # line_width=10, # border width
            # shadow_blur=8, # shadow correlation
            # shadow_offset_x=3
            # shadow_offset_y=3
            # shadow_color="rgba(0,0,0,0.3)", # shadow color
            #).
            #).
            # config text
            opts.GraphicText(
                Position of text --> Position of text relative to text box
                graphic_item=opts.GraphicItem(
                    left="center",
                    top='middle',
                    z=100.# display position
                    scale=[1.1].# scaling
                ),
                graphic_textstyle_opts=opts.GraphicTextStyleOpts(
                    # Text message
                    text='I love Tiananmen In Beijing',

                    font="13px Microsoft YaHei".# font and font size
                    text_align='left'.Align horizontally to the left
                    text_vertical_align='middle'.# Vertical alignment: Default None,
                    Color and width of the # font
                    graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
                        fill='#ffdf22'.# color
                        line_width=1.2.# width)))]))# 5 series configuration items
geo.set_series_opts(
    # tag configuration
    label_opts=opts.LabelOpts(
        is_show=False))# 6. Generate files
geo.render('./ Flow map.html ')
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(2) Map the whole country using the national epidemic data as of 22:37 on 17 February 2020 as an example

National epidemic data table as of 22:37, 17 February 2020 (part)

Code implementation:

# Import draw module
from pyecharts.charts import Map
# Import configuration module
from pyecharts import options as opts
Import the theme module
from pyecharts.globals import ThemeType

import pandas as pd

Instantiate the object
map = Map(
    Initial configuration
    init_opts=opts.InitOpts(
        width='900px',
        height='500px',
        theme=ThemeType.CHALK,  Topic #
        page_title='Map of China'.# page name))# Prepare data -- number of confirmed cases by province
data = pd.read_excel('./ Epidemic data as of 22:37, 17 February.)
print('data:\n', data)
# Get the data for each province in China first
Determine the bool array -- province data in the province = city line
mask = data.loc[:, 'province'] == data.loc[:, 'city']
# filter data
data = data.loc[mask, ['province'.'Confirmed number']]
print('data:\n', data)
# prepared data_pair
data_pair = [(k, v) for k, v in zip(data.loc[:, 'province'].tolist(), data.loc[:, 'Confirmed number'].tolist())]

print('data_pair:\n', data_pair)
# 2. Add data
map.add(
    series_name='Epidemic data as of 22:37, 17 February'.# legend name
    data_pair=data_pair,  # data format: [(k1,v1),(k2,v2)...
    maptype='china'.# Map type
    is_map_symbol_show=False.# Cancel the checkmark
)

# 3 Global configuration
map.set_global_opts(
    # title Settings
    title_opts=opts.TitleOpts(
        title='Epidemic Data in China',
        subtitle='Python0421 class of Guangzhou Branch',
        pos_top='3%'
    ),
    # legend
    legend_opts=opts.LegendOpts(
        is_show=True,
        pos_top='3%'
    ),
    # set VisualMap
    visualmap_opts=opts.VisualMapOpts(
        is_show=True.# show
        type_='color'.# use color to distinguish different values,
        min_=0.# the minimum
        max_=60000.# maximum value,
        is_piecewise=True.# Enable segmented display
        pieces=[
            {"min": 10001."label": "10000" >."color": "#4b0101"},
            {"max": 10000."min": 5001."label": "5000" >."color": "#4a0100"},
            {"max": 5000."min": 1001."label": "1000" >."color": "#8A0808"},
            {"max": 1000."min": 500."label": "500-1000"."color": "#B40404"},
            {"max": 499."min": 108."label": "100-499"."color": "#DF0101"},
            {"max": 99."min": 10."label": '10-99'."color": "#F78181"},
            {"max": 9."min": 1."label": "1-9"."color": "#F5A9A9"},
            {"max": 0."min": 0."label": "0"."color": "#FFFFFF"},
        ],
        textstyle_opts=opts.TextStyleOpts(
            color='white')))# 4 series configuration
map.set_series_opts(
    label_opts=opts.LabelOpts(
        is_show=True)),# 5. Generate files
map.render('./ Epidemic data as of 2237 hours, 17 February HTML ')
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(3) Map the world using global epidemic data as of 22:37, 17 February 2020 as an example

Table 11-15 Global epidemic data as of 22:37 utc, 17 February 2020 (partial)

Code implementation:

# guide package
from pyecharts import options as opts
from pyecharts.globals import ThemeType
from pyecharts.charts import Map
import pandas as pd
from translate import Translator

Instantiate the object
map = Map(
    Initial configuration
    init_opts=opts.InitOpts(
        width='900px',
        height='500px',
        theme=ThemeType.CHALK,  Topic #
        page_title='World Map'.# page name))# # Prepare data
data = pd.read_excel('./ Epidemic data as of 22:37, 17 February.)
# print('data:\n', data)

# Get data from countries around the world
Determine the bool array
mask = data.loc[:, 'province'].str.contains('states')
# filter data
data = data.loc[mask, ['city'.'Confirmed number']]
print('data:\n', data)


# If you draw a world map, it will not support Chinese by default
# Translate Chinese into English -->translate
PIP install translate

def translate_country_name(country_name) :
    Param country_name: Chinese country name: return: English country name """
    Instantiate the object
    translator = Translator(from_lang='chinese', to_lang='english')
    # 2. Translate
    english_country_nam = translator.translate(country_name)
    # 3. Return
    return english_country_nam


# transform calls custom functions
data.loc[:, 'Country name _English'] = data.loc[:, 'city'].transform(translate_country_name)

print('data:\n',data)

Build data_pair data
data_pair = [(k, v) for k, v in zip(data.loc[:, 'Country name _English'].tolist(), data.loc[:, 'Confirmed number'].tolist())]
#
print('data_pair:\n', data_pair)

data_pair = [('China'.70642), ('Singapore'.77), ('Japan'.519), ('South Korea'.30), ('Thailand'.35),
             ('Malaysia'.22), ('Germany'.16), ('US'.15), ('Vietnam'.16), ('France'.12), ('Canada'.8),
             ('Australia'.15), ('United Arab Emirates'.9), ('Italy'.3), ('Spain'.2), ('Nepal'.1), ('Sri Lanka'.1),
             ('UK'.9), ('Swedish'.1), ('Philippines'.3), ('Egypt'.1), ('Cambodia'.1), ('Finland'.1), ('India'.3),
             ('Russia'.2), ('Belgium'.1)]
#
# 2. Add data
map.add(
    series_name='World epidemic data as of 2237 hours, 17 February',
    data_pair=data_pair,
    maptype='world',
    is_map_symbol_show=False
)

# 3. Set the global configuration
map.set_global_opts(
    # title Settings
    title_opts=opts.TitleOpts(
        title='World Epidemic Data',
        subtitle='Python0421 class of Guangzhou Branch',
        pos_top='3%'
    ),
    # legend
    legend_opts=opts.LegendOpts(
        is_show=True,
        pos_top='3%'
    ),
    # set VisualMap
    visualmap_opts=opts.VisualMapOpts(
        is_show=True.# show
        type_='color'.# use color to distinguish different values,
        min_=0.# the minimum
        max_=80000.# maximum value,
        is_piecewise=True.# Enable segmented display
        pieces=[
            {"min": 10001."label": "10000" >."color": "#4b0101"},
            {"max": 10000."min": 5001."label": "5000" >."color": "#4a0100"},
            {"max": 5000."min": 1001."label": "1000" >."color": "#8A0808"},
            {"max": 1000."min": 500."label": "500-1000"."color": "#B40404"},
            {"max": 499."min": 108."label": "100-499"."color": "#DF0101"},
            {"max": 99."min": 10."label": '10-99'."color": "#F78181"},
            {"max": 9."min": 1."label": "1-9"."color": "#F5A9A9"},
            {"max": 0."min": 0."label": "0"."color": "#FFFFFF"},
        ],
        textstyle_opts=opts.TextStyleOpts(
            color='white')))# 4. Set the series configuration
map.set_series_opts(
    label_opts=opts.LabelOpts(
        is_show=False)),# 5. Generate files
map.render('./ World map of epidemic data. HTML ')
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7. Combine China map and world map

Code implementation:

# Import draw module
from pyecharts.charts import Map
# Import configuration module
from pyecharts import options as opts
Import the theme module
from pyecharts.globals import ThemeType

import pandas as pd

from pyecharts.charts import Grid, Page

# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = Chinese map = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

Instantiate the object
map_china = Map(
    Initial configuration
    init_opts=opts.InitOpts(
        width='900px',
        height='500px',
        theme=ThemeType.CHALK,  Topic #
        page_title='Map of China'.# page name))# Prepare data -- number of confirmed cases by province
data = pd.read_excel('./ Epidemic data as of 22:37, 17 February.)
print('data:\n', data)
# Get the data for each province in China first
Determine the bool array -- province data in the province = city line
mask = data.loc[:, 'province'] == data.loc[:, 'city']
# filter data
data_china = data.loc[mask, ['province'.'Confirmed number']]
print('data_china:\n', data_china)
# prepared data_pair
data_pair_china = [(k, v) for k, v in zip(data_china.loc[:, 'province'].tolist(), data_china.loc[:, 'Confirmed number'].tolist())]

print('data_pair_china:\n', data_pair_china)
# 2. Add data
map_china.add(
    series_name='Epidemic data as of 22:37, 17 February'.# legend name
    data_pair=data_pair_china,  # data format: [(k1,v1),(k2,v2)...
    maptype='china'.# Map type
    is_map_symbol_show=False.# Cancel the checkmark
)

# 3 Global configuration
map_china.set_global_opts(
    # title Settings
    title_opts=opts.TitleOpts(
        title='Epidemic Data in China',
        subtitle='Python0421 class of Guangzhou Branch',
        pos_top='3%'
    ),
    # legend
    legend_opts=opts.LegendOpts(
        is_show=True,
        pos_top='3%'
    ),
    # set VisualMap
    visualmap_opts=opts.VisualMapOpts(
        is_show=True.# show
        type_='color'.# use color to distinguish different values,
        min_=0.# the minimum
        max_=60000.# maximum value,
        is_piecewise=True.# Enable segmented display
        pieces=[
            {"min": 10001."label": "10000" >."color": "#4b0101"},
            {"max": 10000."min": 5001."label": "5000" >."color": "#4a0100"},
            {"max": 5000."min": 1001."label": "1000" >."color": "#8A0808"},
            {"max": 1000."min": 500."label": "500-1000"."color": "#B40404"},
            {"max": 499."min": 108."label": "100-499"."color": "#DF0101"},
            {"max": 99."min": 10."label": '10-99'."color": "#F78181"},
            {"max": 9."min": 1."label": "1-9"."color": "#F5A9A9"},
            {"max": 0."min": 0."label": "0"."color": "#FFFFFF"},
        ],
        textstyle_opts=opts.TextStyleOpts(
            color='white')))# 4 series configuration
map_china.set_series_opts(
    label_opts=opts.LabelOpts(
        is_show=True)),# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = world map = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

from translate import Translator

Instantiate the object
map_world = Map(
    Initial configuration
    init_opts=opts.InitOpts(
        width='900px',
        height='500px',
        theme=ThemeType.CHALK,  Topic #
        page_title='World Map'.# page name))# Get data from countries around the world
Determine the bool array
mask = data.loc[:, 'province'].str.contains('states')
# filter data
data_world = data.loc[mask, ['city'.'Confirmed number']]
print('data_world:\n', data_world)


# If you draw a world map, it will not support Chinese by default
# Translate Chinese into English -->translate
PIP install translate

def translate_country_name(country_name) :
    Param country_name: Chinese country name: return: English country name """
    Instantiate the object
    translator = Translator(from_lang='chinese', to_lang='english')
    # 2. Translate
    english_country_nam = translator.translate(country_name)
    # 3. Return
    return english_country_nam


# transform calls custom functions
data_world.loc[:, 'Country name _English'] = data_world.loc[:, 'city'].transform(translate_country_name)

print('data_world:\n', data_world)

Build data_pair data
data_pair_world = [(k, v) for k, v in
                   zip(data_world.loc[:, 'Country name _English'].tolist(), data_world.loc[:, 'Confirmed number'].tolist())]
#
print('data_pair_world:\n', data_pair_world)

data_pair_world = [('China'.70642), ('Singapore'.77), ('Japan'.519), ('South Korea'.30), ('Thailand'.35),
                   ('Malaysia'.22), ('Germany'.16), ('US'.15), ('Vietnam'.16), ('France'.12), ('Canada'.8),
                   ('Australia'.15), ('United Arab Emirates'.9), ('Italy'.3), ('Spain'.2), ('Nepal'.1),
                   ('Sri Lanka'.1),
                   ('UK'.9), ('Swedish'.1), ('Philippines'.3), ('Egypt'.1), ('Cambodia'.1), ('Finland'.1),
                   ('India'.3),
                   ('Russia'.2), ('Belgium'.1)]
#
# 2. Add data
map_world.add(
    series_name='World epidemic data as of 2237 hours, 17 February',
    data_pair=data_pair_world,
    maptype='world',
    is_map_symbol_show=False
)

# 3. Set the global configuration
map_world.set_global_opts(
    # title Settings
    title_opts=opts.TitleOpts(
        title='World Epidemic Data',
        subtitle='Python0421 class of Guangzhou Branch',
        pos_top='3%'
    ),
    # legend
    legend_opts=opts.LegendOpts(
        is_show=True,
        pos_top='3%'
    ),
    # set VisualMap
    visualmap_opts=opts.VisualMapOpts(
        is_show=True.# show
        type_='color'.# use color to distinguish different values,
        min_=0.# the minimum
        max_=80000.# maximum value,
        is_piecewise=True.# Enable segmented display
        pieces=[
            {"min": 10001."label": "10000" >."color": "#4b0101"},
            {"max": 10000."min": 5001."label": "5000" >."color": "#4a0100"},
            {"max": 5000."min": 1001."label": "1000" >."color": "#8A0808"},
            {"max": 1000."min": 500."label": "500-1000"."color": "#B40404"},
            {"max": 499."min": 108."label": "100-499"."color": "#DF0101"},
            {"max": 99."min": 10."label": '10-99'."color": "#F78181"},
            {"max": 9."min": 1."label": "1-9"."color": "#F5A9A9"},
            {"max": 0."min": 0."label": "0"."color": "#FFFFFF"},
        ],
        textstyle_opts=opts.TextStyleOpts(
            color='white')))# 4. Set the series configuration
map_world.set_series_opts(
    label_opts=opts.LabelOpts(
        is_show=False)),# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = map of China, the world map = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =


Instantiate the object
grid = Grid(
    init_opts=opts.InitOpts(
        width='1200px',
        height='1600px',
        theme=ThemeType.PURPLE_PASSION
    )
)
# 2. Add data
grid.add(
    chart=map_china,  # chart
    grid_opts=opts.GridOpts(
        pos_top='3%',
        height='30%',
    ),
    grid_index=0,
    is_control_axis_index=True
)

grid.add(
    chart=map_world,
    grid_opts=opts.GridOpts(
        pos_top='40%',
        height='50%',
    ),
    grid_index=1,
    is_control_axis_index=True
)

# 3. Generate files
grid.render('./ Combine China map and world map.html ')

# Sequence multigraph
Instantiate the object
page = Page(
    page_title='Combine China map and World Map'.# page name
    interval=0.# legend interval
    layout=Page.DraggablePageLayout
)
# 2, t add data
page.add(map_china)
page.add(map_world)
#
# # 3. Generate files
page.render("./ Combine China map and world map.html")
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