本文介绍了Seaborn 中的堆积条形图的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我有以下数据:
countries2012 = ['玻利维亚','巴西','斯里兰卡','多明尼加共和国','印度尼西亚','肯尼亚','洪都拉斯',莫桑比克'秘鲁','菲律宾','印度','越南','泰国','美国','世界']百分比2012 = [0.042780099,0.16599952,0.012373058,0.019171717,0.011868674,0.019239173,0.00000332,0.014455196,0.016006654,0.132970981,0.077940824,0.411752517,0.017986798,0.017361808,0.058076027]国家 2013 = ['玻利维亚','巴西','斯里兰卡','多明尼加共和国','印度尼西亚','洪都拉斯','莫桑比克','秘鲁','菲律宾','印度','越南','泰国','美国','世界']2013 年百分比 = [0.02736294,0.117160272,0.015815952,0.018831589,0.020409103 ,0.00000000285,0.018876854,0.018998639,0.117221146,0.067991687,0.496110972,0.019309486,0.026880553,0.03503080414999993]
我想制作一个堆积条形图,以便有一个 2012 年的堆积条形图和另一个 2013 年的堆积条形图.
由于 2012 年和 2013 年的国家/地区不同,我该怎么做?
解决方案
IIUC,您可以创建
I have the following data:
countries2012 = [
'Bolivia',
'Brazil',
'Sri Lanka',
'Dominican Republic',
'Indonesia',
'Kenya',
'Honduras',
'Mozambique',
'Peru',
'Philipines',
'India',
'Vietnam',
'Thailand',
'USA',
'World'
]
percentage2012 = [
0.042780099,
0.16599952,
0.012373058,
0.019171717,
0.011868674,
0.019239173,
0.00000332,
0.014455196,
0.016006654,
0.132970981,
0.077940824,
0.411752517,
0.017986798,
0.017361808,
0.058076027
]
countries2013 = [
'Bolivia',
'Brazil',
'Sri Lanka',
'Dominican Republic',
'Indonesia',
'Honduras',
'Mozambique',
'Peru',
'Philippines',
'India',
'Vietnam',
'Thailand',
'USA',
'World'
]
percentage2013 = [
0.02736294,
0.117160272,
0.015815952 ,
0.018831589,
0.020409103 ,
0.00000000285,
0.018876854,
0.018998639,
0.117221146,
0.067991687,
0.496110972,
0.019309486,
0.026880553,
0.03503080414999993
]
I want to make a stacked bar plot so that there's a stacked bar for 2012 and another for 2013.
How can I go about this since the countries in 2012 and 2013 are different?
解决方案
IIUC, you can create a Pandas dataframe and use its plot function:
import pandas as pd
df = pd.concat([pd.DataFrame({2012:percentage2012}, index=countries2012),
pd.DataFrame({2013:percentage2013}, index=countries2013)],
axis=1, sort=False)
df.T.plot.bar(stacked=True, figsize=(12,6))
Output:
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