如何在 pandas 中使用 groupby 按 bin 对数据进行排序?
How can I sort data by bins using groupby in pandas?
问题:如何在 pandas 中使用 groupby 按 bin 对数据进行排序?
我想要的是:
release_year listed_in
1920 Documentaries
1930 TV Shows
1940 TV Shows
1950 Classic Movies, Documentaries
1960 Documentaries
1970 Classic Movies, Documentaries
1980 Classic Movies, Documentaries
1990 Classic Movies, Documentaries
2000 Classic Movies, Documentaries
2010 Children & Family Movies, Classic Movies, Comedies
2020 Classic Movies, Dramas
为了达到这个结果,我尝试了以下公式:
bins = [1925,1950,1960,1970,1990,2000,2010,2020]
groups = df.groupby(['listed_in', pd.cut(df.release_year, bins)])
groups.size().unstack()
显示如下结果:
release_year (1925,1950] (1950,1960] (1960,1970] (1970,1990] (1990,2000] (2000,2010] (2010, 2020]
listed_in
Action & Adventure 0 0 0 0 9 16 43
Action & Adventure, Anime Features, Children & Family Movies 0 0 0 0 0 0 1
Action & Adventure, Anime Features, Classic Movies 0 0 0 1 0 0 0
...
461 rows x 7 columns
我也试过下面的公式:
df['release_year'] = df['release_year'].astype(str).str[0:2] + '0'
df.groupby('release_year')['listed_in'].apply(lambda x: x.mode().iloc[0])
结果如下:
release_year
190 Dramas
200 Documentaries
Name: listed_in, dtype:object
这是数据集的示例:
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']})
执行此操作的最简单方法是使用代码的第一部分,并将 release_year
的最后一位设为 0
。然后你可以 .groupby
十年并获得每个十年最流行的类型,即 mode
:
输入:
import pandas as pd
import numpy as np
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',np.nan,np.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']})
代码:
df['release_year'] = df['release_year'].astype(str).str[0:3] + '0'
df = df.groupby('release_year', as_index=False)['listed_in'].apply(lambda x: x.mode().iloc[0])
df
输出:
release_year listed_in
0 2010 Children & Family Movies, Comedies
问题:如何在 pandas 中使用 groupby 按 bin 对数据进行排序?
我想要的是:
release_year listed_in
1920 Documentaries
1930 TV Shows
1940 TV Shows
1950 Classic Movies, Documentaries
1960 Documentaries
1970 Classic Movies, Documentaries
1980 Classic Movies, Documentaries
1990 Classic Movies, Documentaries
2000 Classic Movies, Documentaries
2010 Children & Family Movies, Classic Movies, Comedies
2020 Classic Movies, Dramas
为了达到这个结果,我尝试了以下公式:
bins = [1925,1950,1960,1970,1990,2000,2010,2020]
groups = df.groupby(['listed_in', pd.cut(df.release_year, bins)])
groups.size().unstack()
显示如下结果:
release_year (1925,1950] (1950,1960] (1960,1970] (1970,1990] (1990,2000] (2000,2010] (2010, 2020]
listed_in
Action & Adventure 0 0 0 0 9 16 43
Action & Adventure, Anime Features, Children & Family Movies 0 0 0 0 0 0 1
Action & Adventure, Anime Features, Classic Movies 0 0 0 1 0 0 0
...
461 rows x 7 columns
我也试过下面的公式:
df['release_year'] = df['release_year'].astype(str).str[0:2] + '0'
df.groupby('release_year')['listed_in'].apply(lambda x: x.mode().iloc[0])
结果如下:
release_year
190 Dramas
200 Documentaries
Name: listed_in, dtype:object
这是数据集的示例:
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']})
执行此操作的最简单方法是使用代码的第一部分,并将 release_year
的最后一位设为 0
。然后你可以 .groupby
十年并获得每个十年最流行的类型,即 mode
:
输入:
import pandas as pd
import numpy as np
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',np.nan,np.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']})
代码:
df['release_year'] = df['release_year'].astype(str).str[0:3] + '0'
df = df.groupby('release_year', as_index=False)['listed_in'].apply(lambda x: x.mode().iloc[0])
df
输出:
release_year listed_in
0 2010 Children & Family Movies, Comedies