如何計算 groupby 列的百分比並按降序排序？

Question

問題：如何計算 groupby 列的百分比並按降序排序？

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 文檔，在 Stackoverflow 上查看了其他問題，但沒有運氣。

我嘗試了以下方法：

#1 嘗試：

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 嘗試：

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

Output：

KeyError: 'show_id'

數據集樣本：

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

這並沒有解決“國家”字段中有多個國家的行，但下面的行應該適用於問題的其他部分：

創建初始 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']})

分組國家：

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

重命名 agg 列：

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

創建百分比列：

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

降序排序：

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

您的嘗試 #1 中的部分問題可能是您沒有在 groupby function 中包含“by”參數。

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