如何计算 groupby 列的百分比并按降序排序？

Question

问题：如何计算 groupby 列的百分比并按降序排序？

所需的 output：

country            count     percentage
United States      2555        45%
India               923        12%
United Kingdom      397        4%
Japan               226        3%
South Korea         183        2% 

我做了一些研究，查看了 Pandas 文档，在 Stackoverflow 上查看了其他问题，但没有运气。

我尝试了以下方法：

#1 尝试：

Df2 = df.groupby('country')['show_id'].count().nlargest()
df3 = df2.groupby(level=0).apply(lambda x: x/x.sum() * 100)

Output：

director
A. L. Vijay            100.0
A. Raajdheep           100.0
A. Salaam              100.0
A.R. Murugadoss        100.0
Aadish Keluskar        100.0
...
Çagan Irmak            100.0
Ísold Uggadóttir       100.0
Óskar Thór Axelsson    100.0
Ömer Faruk Sorak       100.0
Şenol Sönmez           100.0

Name: show_id, Length: 4049, dtype: float64

#2 尝试：

df2 = df.groupby('country')['show_id'].count()
df2['percentage'] = df2['show_id']/6000

Output：

KeyError: 'show_id'

数据集样本：

import pandas as pd
df = pd.DataFrame({
'show_id':['81145628','80117401','70234439'],
'type':['Movie','Movie','TV Show'],
'title':['Norm of the North: King Sized Adventure',
'Jandino: Whatever it Takes',
'Transformers Prime'],
'director':['Richard Finn, Tim Maltby',NaN,NaN],
'cast':['Alan Marriott, Andrew Toth, Brian Dobson',
'Jandino Asporaat','Peter Cullen, Sumalee Montano, Frank Welker'], 
'country':['United States, India, South Korea, China',
'United Kingdom','United States'], 
'date_added':['September 9, 2019',
'September 9, 2016',
'September 8, 2018'],
'release_year':['2019','2016','2013'],
'rating':['TV-PG','TV-MA','TV-Y7-FV'],
'duration':['90 min','94 min','1 Season'],
'listed_in':['Children & Family Movies, Comedies',
'Stand-Up Comedy','Kids TV'],
'description':['Before planning an awesome wedding for his',
'Jandino Asporaat riffs on the challenges of ra',
'With the help of three human allies, the Autob']})

Answer 1

这并没有解决“国家”字段中有多个国家的行，但下面的行应该适用于问题的其他部分：

创建初始 dataframe：

df = pd.DataFrame({
'show_id':['81145628','80117401','70234439'],
'type':['Movie','Movie','TV Show'],
'title':['Norm of the North: King Sized Adventure',
'Jandino: Whatever it Takes',
'Transformers Prime'],
'director':['Richard Finn, Tim Maltby',0,0],
'cast':['Alan Marriott, Andrew Toth, Brian Dobson',
'Jandino Asporaat','Peter Cullen, Sumalee Montano, Frank Welker'], 
'country':['United States, India, South Korea, China',
'United Kingdom','United States'], 
'date_added':['September 9, 2019',
'September 9, 2016',
'September 8, 2018'],
'release_year':['2019','2016','2013'],
'rating':['TV-PG','TV-MA','TV-Y7-FV'],
'duration':['90 min','94 min','1 Season'],
'listed_in':['Children & Family Movies, Comedies',
'Stand-Up Comedy','Kids TV'],
'description':['Before planning an awesome wedding for his',
'Jandino Asporaat riffs on the challenges of ra',
'With the help of three human allies, the Autob']})

分组国家：

df2 = df.groupby(by="country", as_index=False)['show_id']\
    .agg('count')

重命名 agg 列：

df2 = df2.rename(columns={'show_id':'count'})

创建百分比列：

df2['percent'] = (df2['count']*100)/df2['count'].sum()

降序排序：

df2 = df2.sort_values(by='percent', ascending=False)

您的尝试 #1 中的部分问题可能是您没有在 groupby function 中包含“by”参数。

Answer 2

    newDF = pd.DataFrame(DF.Country.value_counts())
    newDF['percentage'] = round(pd.DataFrame(DF.Country.value_counts(normalize =  \
         True).mul(100)),2)
    newDF.columns = ['count', 'percentage']

    newDF