This article introduces Pyecharts geographic data visualization, which is shared as follows:
I. Introduction and Installation of Pyecharts
1. Introduction
Echarts is an open source data visualization by Baidu, which has been recognized by many developers for its good interactivity and sophisticated chart design. And Python is an expressive language, very suitable for data processing. When data analysis meets data visualization, pyecharts is born.
- Simple API design, silky smooth use, support for chained calls
- Includes 30+ common charts and everything in between
- Supports major Notebook environments, Jupyter Notebook and JupyterLab.
- Easily integrates with major web frameworks such as Flask, Sanic, Django, etc.
- Highly flexible configuration items that can be easily paired with beautiful charts
- Detailed documentation and examples to help developers get started with their projects faster
- Up to 400+ map files and native Baidu map support, providing strong support for geographic data visualization
Incompatibility between pyecharts versions v0. and v1.The syntax of v1 is quite different.
2. Installation
Install pyecharts
pip install pyecharts -i /simple --trusted-host
import pyecharts print(pyecharts.__version__) # View currentpyechartsreleases
Installation of relevant map expansion packs
pip install -i /simple echarts-countries-pypkg # Global country maps pip install -i /simple echarts-china-provinces-pypkg # Provincial Maps of China pip install -i /simple echarts-china-cities-pypkg # China Municipal Maps pip install -i /simple echarts-china-counties-pypkg # County and district level map of China
II. Map visualization
1. World map
Using the data in this study, we first calculate the number of stores corresponding to each country, and then use a world map to visualize the distribution of Starbucks stores around the world.
# -*- coding: UTF-8 -*-
"""
@File :
@Author: Tingyun Ye
@CSDN ：/
"""
import pandas as pd
from import Map
from pyecharts import options as opts
from import ThemeType, CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# pandas reads data from a csv file
df = pd.read_csv("")['Country']
# of Starbucks stores by region
data = df.value_counts()
datas = [(i, int(j)) for i, j in zip(, )]
# Instantiate a Map object
map_ = Map(init_opts=(theme=ThemeType.PURPLE_PASSION))
# Map of the world
map_.add("Number of stores", data_pair=datas, maptype="world")
map_.set_series_opts(label_opts=(is_show=False)) # Do not show the label
map_.set_global_opts(
title_opts=(title="Global Distribution of Starbucks Store Numbers.", pos_left='40%', pos_top='10'), # Adjust title position
legend_opts=(is_show=False),
visualmap_opts=(max_=13608, min_=1, is_piecewise=True,
pieces=[{"max": 9, "min": 1, "label": "1-9", "color": "#00FFFF"}, # Segmentation Add legend comments and colors
{"max": 99, "min": 10, "label": "10-99", "color": "#A52A2A"},
{"max": 499, "min": 100, "label": "100-499", "color": "#0000FF "},
{"max": 999, "min": 500, "label": "500-999", "color": "#FF00FF"},
{"max": 2000, "min": 1000, "label": "1000-2000", "color": "#228B22"},
{"max": 3000, "min": 2000, "label": "2000-3000", "color": "#FF0000"},
{"max": 20000, "min": 10000, "label": ">=10000", "color": "#FFD700"}
])
)
# Rendered on the web
map_.render('Starbucks stores worldwide.html')
The running effect is as follows:
2. Country maps
Ripple Scatterplot
Using the data in pyecharts, we first calculate the number of stores in each city, and then use the Geo module in the pyecharts package to create a ripple scatter map of the number of Starbucks stores in each city in China.
import pandas as pd
from import ThemeType, CurrentConfig, GeoType
from pyecharts import options as opts
from import Geo
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# pandas reads csv file data
df = pd.read_csv("")['City']
data = df.value_counts()
datas = [(i, int(j)) for i, j in zip(, )]
print(datas)
geo = Geo(init_opts=(width='1000px', height='600px', theme=))
geo.add_schema(maptype='china', label_opts=(is_show=True)) # show label Province name
('Number of stores', data_pair=datas, type_=GeoType.EFFECT_SCATTER, symbol_size=8)
geo.set_series_opts(label_opts=(is_show=False))
geo.set_global_opts(title_opts=(title='Distribution of Starbucks stores in China'),
visualmap_opts=(max_=550, is_piecewise=True,
pieces=[{"max": 50, "min": 0, "label": "0-50", "color": "#708090"}, # Segmentation Add legend comments and colors
{"max": 100, "min": 51, "label": "51-100", "color": "#00FFFF"},
{"max": 200, "min": 101, "label": "101-200", "color": "#00008B"},
{"max": 300, "min": 201, "label": "201-300", "color": "#8B008B"},
{"max": 600, "min": 500, "label": "500-600", "color": "#FF0000"},
])
)
("Distribution of Starbucks stores in China.html")
The running effect is as follows:
Dynamic trajectory diagram
# -*- coding: UTF-8 -*-
"""
@File :
@Author: Tingyun Ye
@CSDN ：/
"""
from pyecharts import options as opts
from import Geo
from import ChartType, SymbolType, CurrentConfig, ThemeType
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# Chained calls
c = (
Geo()
.add_schema(
maptype="china",
itemstyle_opts=(color="#323c48", border_color="#111"),
label_opts=(is_show=True)
)
.add(
"",
[("Guangzhou.", 55), ("Beijing.", 66), ("Hangzhou", 77), ("Chongqing.", 88), ('Chengdu', 100), ('Haikou', 80)],
type_=ChartType.EFFECT_SCATTER,
color="white",
)
.add(
"",
[("Guangzhou.", "Shanghai."), ("Guangzhou.", "Beijing."), ("Guangzhou.", "Hangzhou"), ("Guangzhou.", "Chongqing."),
('Chengdu', 'Haikou'), ('Haikou', 'Beijing'), ('Haikou', 'Chongqing'), ('Chongqing', 'Shanghai')
],
type_=,
effect_opts=(
symbol=, symbol_size=6, color="blue" # Trajectory line blue
),
linestyle_opts=(curve=0.2), # Trajectory line curvature
)
.set_series_opts(label_opts=(is_show=False))
.set_global_opts(title_opts=(title="Dynamic trajectory map"))
.render("geo_lines_background.html")
)
The running effect is as follows:
3. Provincial and municipal maps
heat map
# -*- coding: UTF-8 -*-
"""
@File :
@Author: Tingyun Ye
@CSDN ：/
"""
from pyecharts import options as opts
from import Geo
from import Faker
from import GeoType, CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
c = (
Geo()
.add_schema(maptype="Guangdong", label_opts=(is_show=True))
.add(
"Heat map.",
[list(z) for z in zip(Faker.guangdong_city, ())],
type_=,
)
.set_series_opts(label_opts=(is_show=True))
.set_global_opts(
visualmap_opts=(), title_opts=(title="Geo-Guangdong map")
)
.render("geo_guangdong.html")
)
The running effect is as follows:
Batch add latitude and longitude data on map
Data from Meituan.com Chengdu area hotel information, using which the hotel's latitude and longitude data, batch add on the map visualization.
# -*- coding: UTF-8 -*-
"""
@File :
@Author: Tingyun Ye
@CSDN ：/
"""
import pandas as pd
from import Geo
from pyecharts import options as opts
from import GeoType, CurrentConfig, ThemeType
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# Read Excel data Data Source Meituan.com Hotel Information
df = pd.read_excel("")
# Get location and latitude and longitude information #
geo_sight_coord = {[i]['Hotel address']: [[i]['Longitude'], [i]['Latitude']] for i in range(len(df))}
data = [(df['Hotel address'][j], f"{int(df['Lowest price'][j])}unit of money (in PRC: Chinese yuan, in USA: dollar, etc)(lowest price)") for j in range(len(df))]
# print(data)
# print(geo_sight_coord)
# Instantiate Geo objects Import Chengdu map
g = Geo(init_opts=(theme=ThemeType.PURPLE_PASSION, width="1000px", height="600px"))
g.add_schema(maptype="Chengdu.")
for k, v in list(geo_sight_coord.items()):
# Add address, latitude and longitude data
g.add_coordinate(k, v[0], v[1])
# Generate ripple scatterplots
("", data_pair=data, type_=GeoType.EFFECT_SCATTER, symbol_size=6)
g.set_series_opts(label_opts=(is_show=False))
g.set_global_opts(title_opts=(title="Chengdu - Hotel Address Distribution"))
("HotelAddressDistribution.html")
The running effect is as follows:
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