Question

鉴于以下代码连接三个数据帧，我需要展平结果：

import pandas as pd

raw_data = {
        'type_1': [1, 1, 0, 0, 1],
        'subject_id_1': ['1', '2', '3', '4', '5'],
        'first_name_1': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung']}
df_a = pd.DataFrame(raw_data, columns = ['type_1', 'subject_id_1', 'first_name_1'])

raw_datab = {
        'type_2': [1, 1, 0, 0, 0],
        'subject_id_2': ['4', '5', '6', '7', '8'],
        'first_name_2': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty']}
df_b = pd.DataFrame(raw_datab, columns = ['type_2', 'subject_id_2', 'first_name_2'])

raw_datac = {
        'type_3': [1, 1],
        'subject_id_3': ['4', '5'],
        'first_name_3': ['Joe', 'Paul']}
df_c = pd.DataFrame(raw_datac, columns = ['type_3', 'subject_id_3', 'first_name_3'])

dfs = [df_a.set_index(['type_1','subject_id_1']),
       df_b.set_index(['type_2','subject_id_2']),
       df_c.set_index(['type_3','subject_id_3'])]

df = pd.concat(dfs, axis=1)
print (df)

代码打印：

    first_name_1 first_name_2 first_name_3
0 3        Allen          NaN          NaN
  4        Alice          NaN          NaN
  6          NaN         Bran          NaN
  7          NaN        Bryce          NaN
  8          NaN        Betty          NaN
1 1         Alex          NaN          NaN
  2          Amy          NaN          NaN
  4          NaN        Billy          Joe
  5       Ayoung        Brian         Paul

但我需要展平它，结果应该是一个包含以下内容的列表，类似于SQL SELECT结果（不能包含所有数据，但你明白了）：

type_1   subject_id_1  first_name_1 type_2   subject_id_2  first_name_2  ...
 0            3          Allen       0            3          NaN   ...
 0            4          Alice       0            4          NaN   ...            
 0            6          NaN         0            6          Bran  ...
 0            7          NaN         0            7          Bryce ...
 0            8          NaN         0            8          Betty ...
 1            1          Alex        1            1          NaN   ...
 1            2          Amy         1            2          NaN   ...    
 1            4          NaN         1            4          Billy ...   
 1            5          Ayoung      1            5          Brian ...

Pandas可以吗？

Answer 1

在drop=False中添加set_index，然后使用str.contains + fillna我们实现预期的输出

dfs = [df_a.set_index(['type_1','subject_id_1'],drop=False),
       df_b.set_index(['type_2','subject_id_2'],drop=False),
       df_c.set_index(['type_3','subject_id_3'],drop=False)]

df = pd.concat(dfs, axis=1)

df.loc[:,df.columns.str.contains('type')]=df.loc[:,df.columns.str.contains('type')].apply(lambda x : x.fillna(df.index.to_frame()[0]).astype(int))
df.loc[:,df.columns.str.contains('subject_id')]=df.loc[:,df.columns.str.contains('subject_id')].apply(lambda x :x.fillna(df.index.to_frame()[1]).astype(int))

展平Pandas数据帧concat结果

1 个答案: