如何根據dataframe python中列中的列表值進行分組

Question

我有像這樣的熊貓電影的數據幀

id, name,     genre, release_year 
1    A    [a,b,c]     2017
2    B    [b,c]       2017
3    C    [a,c]       2010
4    D    [d,c]       2010
....

我想根據流派列表中的值來分組電影。 我的預期輸出是：

year, genre, number_of_movies
2017  a       1
2017  b       2
2017  c       2
2010  a       1
2010  c       2 
...

有人可以幫我實現這個目標嗎？

Answer 1

您可以創建新的DataFrame由構造器，通過重塑stack和計數使用groupby與size ：

df1 = (pd.DataFrame(df['genre'].values.tolist(), index=df['release_year'].values)
         .stack()
         .reset_index(name='genre')
         .groupby(['release_year','genre'])
         .size()
         .reset_index(name='number_of_movies'))

print (df1)
   release_year genre  number_of_movies
0          2010     a                 1
1          2010     c                 2
2          2010     d                 1
3          2017     a                 1
4          2017     b                 2
5          2017     c                 2

Answer 2

為了提高性能，請使用itertools.chain展平genre列：

from itertools import chain

df = pd.DataFrame({
      'genre' : list(
           chain.from_iterable(df.genre.tolist())
       ), 
      'release_year' : df.release_year.repeat(df.genre.str.len())
})

df
  genre  release_year
0     a          2017
0     b          2017
0     c          2017
1     b          2017
1     c          2017
2     a          2010
2     c          2010
3     d          2010
3     c          2010

現在，對genre和release_year分組，找到每個組的size ：

df.groupby(
     ['genre', 'release_year'], sort=False
 ).size()\
  .reset_index(name='number_of_movies')

  genre  release_year  number_of_movies
0     a          2017                 1
1     b          2017                 2
2     c          2017                 2
3     a          2010                 1
4     c          2010                 2
5     d          2010                 1

Answer 3

另一種很酷的方法是使用Counter ie

from collections import Counter

ndf = df.groupby('release_year')['genre'].apply(lambda x : Counter(np.concatenate(x.values))).reset_index()

ndf = ndf.set_axis('release_year,genre,number_of_movies'.split(','),inplace=False,axis=1)

輸出：

   release_year genre  number_of_movies
0          2010     a               1.0
1          2010     c               2.0
2          2010     d               1.0
3          2017     a               1.0
4          2017     b               2.0
5          2017     c               2.0

Answer 4

這是一個collections.Counter方法，它具有O（n）復雜度，並且不需要df.groupby / df.apply ：

from collections import Counter
from itertools import product, chain
import pandas as pd

df = pd.DataFrame({'id': [1, 2, 3, 4],
                   'name': ['A', 'B', 'C', 'D'],
                   'genre': [['a', 'b', 'c'], ['b', 'c'], ['a', 'c'], ['d', 'c']],
                   'year': [2017, 2017, 2010, 2010]})

c = Counter(chain.from_iterable([list(product([x['year']], x['genre'])) \
                                 for idx, x in df.iterrows()]))

# Counter({(2010, 'a'): 1,
#          (2010, 'c'): 2,
#          (2010, 'd'): 1,
#          (2017, 'a'): 1,
#          (2017, 'b'): 2,
#          (2017, 'c'): 2})

df = pd.DataFrame.from_dict(c, orient='index')

#            0
# (2017, a)  1
# (2017, b)  2
# (2017, c)  2
# (2010, a)  1
# (2010, c)  2
# (2010, d)  1