如何在不使用 df.iterrows() 的情况下将 Pandas DataFrame 的列转换为最内层索引？

Question

原始 .csv 文件 -

#,Name,Type 1,Type 2,HP,Attack,Defense,Sp. Atk,Sp. Def,Speed,Generation,Legendary
1,Bulbasaur,Grass,Poison,45,49,49,65,65,45,1,FALSE
2,Ivysaur,Grass,Poison,60,62,63,80,80,60,1,FALSE
3,Venusaur,Grass,Poison,80,82,83,100,100,80,1,FALSE

我的 Python 代码使用 df.iterrows() -

import pandas as pd
import os

df = pd.read_csv('pokemon_data.csv')
with open('output.txt', 'w') as f:
    for index, row in df.iterrows():
        row_i = str(index) + str(row)
        f.write(row_i)

我了解到我们应该避免使用 df.iterrow()，因为它在处理大数据时会变得非常慢。

如何不使用 df.iterrows() 将 Pandas DataFrame 的列转入最内层索引，并获得如下结果？

0 #                     1
Name          Bulbasaur
Type 1            Grass
Type 2           Poison
HP                   45
Attack               49
Defense              49
Sp. Atk              65
Sp. Def              65
Speed                45
Generation            1
Legendary         False

1 #                   2
Name          Ivysaur
Type 1          Grass
Type 2         Poison
HP                 60
Attack             62
Defense            63
Sp. Atk            80
Sp. Def            80
Speed              60
Generation          1
Legendary       False

2 #                    3
Name          Venusaur
Type 1           Grass
Type 2          Poison
HP                  80
Attack              82
Defense             83
Sp. Atk            100
Sp. Def            100
Speed               80
Generation           1
Legendary        False

Answer 1

使用str()，您可以获得每一行的字符串表示，然后将它们与.str.cat 连接在一起：

>>> print(df.agg(str, axis='columns').str.cat(sep='\n\n'))
#                     1
Name          Bulbasaur
Type 1            Grass
Type 2           Poison
HP                   45
Attack               49
Defense              49
Sp. Atk              65
Sp. Def              65
Speed                45
Generation            1
Legendary         False
Name: 0, dtype: object

#                   2
Name          Ivysaur
Type 1          Grass
Type 2         Poison
HP                 60
Attack             62
Defense            63
Sp. Atk            80
Sp. Def            80
Speed              60
Generation          1
Legendary       False
Name: 1, dtype: object

#                    3
Name          Venusaur
Type 1           Grass
Type 2          Poison
HP                  80
Attack              82
Defense             83
Sp. Atk            100
Sp. Def            100
Speed               80
Generation           1
Legendary        False
Name: 2, dtype: object

如果你想保留索引号，你可以使用reset_index()，然后调整字符串表示

>>> print(df.reset_index().agg(str, axis='columns').str.replace(r'^index\s*', '', regex=True).str.cat(sep='\n\n'))
0
#                     1
Name          Bulbasaur
Type 1            Grass
Type 2           Poison
HP                   45
Attack               49
Defense              49
Sp. Atk              65
Sp. Def              65
Speed                45
Generation            1
Legendary         False

Answer 2

我们可以试试stack + to_string:

df.stack().to_string('output.txt')

output.txt:

0  #                     1
   Name          Bulbasaur
   Type 1            Grass
   Type 2           Poison
   HP                   45
   Attack               49
   Defense              49
   Sp. Atk              65
   Sp. Def              65
   Speed                45
   Generation            1
   Legendary         False
1  #                     2
   Name            Ivysaur
   Type 1            Grass
   Type 2           Poison
   HP                   60
   Attack               62
   Defense              63
   Sp. Atk              80
   Sp. Def              80
   Speed                60
   Generation            1
   Legendary         False
2  #                     3
   Name           Venusaur
   Type 1            Grass
   Type 2           Poison
   HP                   80
   Attack               82
   Defense              83
   Sp. Atk             100
   Sp. Def             100
   Speed                80
   Generation            1
   Legendary         False

Answer 3

你可以使用df.apply(axis=1):

import pandas as pd
import os

df = pd.read_csv('pokemon_data.csv')
with open('output.txt', 'w') as f:
    def write_pokemon(pokemon):
        f.write('\n\n')
        f.write(pokemon.to_string())

    df.apply(write_pokemon, axis=1)