Python3 - plotly, graph_objs, 炫酷的数据可视化

最新推荐文章于 2024-04-13 10:55:05 发布

CHNMSCS

最新推荐文章于 2024-04-13 10:55:05 发布

阅读量3.2k

点赞数 4

CC 4.0 BY-SA版权

分类专栏： Python3 文章标签： plotly pandas graph_objs 数据可视化地球

本文链接：https://blog.youkuaiyun.com/BSCHN123/article/details/112003431

Python3 专栏收录该内容

65 篇文章

订阅专栏

博主展示了如何使用Plotly库在Jupyter Notebooks中创建不同投影类型的全球GDP地图，通过`choropleth`图表展示各国GDP数据。从数据加载到代码实现，详细步骤清晰可见。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

博主是在Jupyter Notebooks上进行练习的，如果想知道如何创建Jupyter Notebooks，请点击这里

在coding 之前，得安装graph_objs

pip install graph_objs

这次实验使用的数据只是用来练习

先看要使用的数据：

import chart_studio.plotly as py
from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot
import plotly.graph_objs as go
import pandas as pd
init_notebook_mode(connected=True)

df = pd.read_csv('2014_World_GDP')
df.head()

结果如下：
在这里插入图片描述

data = dict(type='choropleth',
           locations = df['CODE'],
           z = df['GDP (BILLIONS)'],
           text = df['COUNTRY'],
           colorbar = {'title': 'GDP in Billions USD'})

layout = dict(title = '2014 Global GDP',
             geo = dict(showframe = False,
                       projection = {'type':'mercator'}))

choromap = go.Figure(data=[data], layout = layout)
iplot(choromap)

结果如下：
在这里插入图片描述

data = dict(type='choropleth',
           locations = df['CODE'],
           z = df['GDP (BILLIONS)'],
           text = df['COUNTRY'],
           colorbar = {'title': 'GDP in Billions USD'})

layout = dict(title = '2014 Global GDP',
             geo = dict(showframe = False,
                       projection = {'type':'stereographic'}))

choromap = go.Figure(data=[data], layout = layout)
iplot(choromap)

结果如下：
在这里插入图片描述

data = dict(type='choropleth',
           locations = df['CODE'],
           z = df['GDP (BILLIONS)'],
           text = df['COUNTRY'],
           colorbar = {'title': 'GDP in Billions USD'})

layout = dict(title = '2014 Global GDP',
             geo = dict(showframe = False,
                       projection = {'type':'natural earth'}))

choromap = go.Figure(data=[data], layout = layout)
iplot(choromap)