根据列的重复值折叠数据并删除NaN值

Question

我正在使用一个具有多个实验室值的病人数据库，每个实验室都有自己的行，即使在同一天也是如此。我想根据每个病人重复的日期折叠行，以便每个日期有一行，其中包含当天所有实验室的结果。

我尝试过各种groupby()和pd.merge()函数，但都没有效果。

玩具例子：

import pandas as pd
import numpy as np
PID = [1, 1, 1, 2, 2, 2]
ALC = [200, np.nan, np.nan, 300, np.nan, np.nan]
WBC = [np.nan, 1000, np.nan, np.nan, 2000, np.nan]
per_neut = [np.nan, np.nan, 0.64, np.nan, np.nan, 0.77]
date = ['11/1/18', '11/2/18', '11/2/18', '1/11/04', 
        '1/11/04','1/11/04']

prac_dict = {'PID':PID, 'date':date, 'ALC':ALC, 'WBC':WBC,
             'per_neut':per_neut}
pract_df = pd.DataFrame(prac_dict)

这就是我所拥有的

print(pract_df)
   PID     date    ALC     WBC  per_neut
0    1  11/1/18  200.0     NaN       NaN
1    1  11/2/18    NaN  1000.0       NaN
2    1  11/2/18    NaN     NaN      0.64
3    2  1/11/04  300.0     NaN       NaN
4    2  1/11/04    NaN  2000.0       NaN
5    2  1/11/04    NaN     NaN      0.77

这就是我想要的

   PID     date    ALC     WBC  per_neut
0    1  11/1/18  200.0     NaN       NaN
1    1  11/2/18    NaN  1000.0      0.64
2    2  1/11/04  300.0  2000.0      0.77

非常欢迎你的建议！

Answer 1

如果需要，每个列的每个组首先不缺少值，请使用GroupBy.first

df = pract_df.groupby(['PID','date'], as_index=False).first()
print (df)
   PID     date    ALC     WBC  per_neut
0    1  11/1/18  200.0     NaN       NaN
1    1  11/2/18    NaN  1000.0      0.64
2    2  1/11/04  300.0  2000.0      0.77

但是，如果每个组重复的值(如50在ALC列中的最后一个组中的值)是必需的，则指定聚合函数(如sum、mean )，如果使用first第二个值丢失：

PID = [1, 1, 1, 2, 2, 2]
ALC = [200, np.nan, np.nan, 300, np.nan, 50]
WBC = [np.nan, 1000, np.nan, np.nan, 2000, np.nan]
per_neut = [np.nan, np.nan, 0.64, np.nan, np.nan, 0.77]
date = ['11/1/18', '11/2/18', '11/2/18', '1/11/04', 
        '1/11/04','1/11/04']

prac_dict = {'PID':PID, 'date':date, 'ALC':ALC, 'WBC':WBC,
             'per_neut':per_neut}
pract_df = pd.DataFrame(prac_dict)
print (pract_df)
   PID     date    ALC     WBC  per_neut
0    1  11/1/18  200.0     NaN       NaN
1    1  11/2/18    NaN  1000.0       NaN
2    1  11/2/18    NaN     NaN      0.64
3    2  1/11/04  300.0     NaN       NaN
4    2  1/11/04    NaN  2000.0       NaN
5    2  1/11/04   50.0     NaN      0.77

df1 = pract_df.groupby(['PID','date'], as_index=False).sum(min_count=1)
print (df1)
   PID     date    ALC     WBC  per_neut
0    1  11/1/18  200.0     NaN       NaN
1    1  11/2/18    NaN  1000.0      0.64
2    2  1/11/04  350.0  2000.0      0.77

df2 = pract_df.groupby(['PID','date'], as_index=False).mean()
print (df2)
   PID     date    ALC     WBC  per_neut
0    1  11/1/18  200.0     NaN       NaN
1    1  11/2/18    NaN  1000.0      0.64
2    2  1/11/04  175.0  2000.0      0.77

df3 = pract_df.groupby(['PID','date'], as_index=False).first()
print (df3)
   PID     date    ALC     WBC  per_neut
0    1  11/1/18  200.0     NaN       NaN
1    1  11/2/18    NaN  1000.0      0.64
2    2  1/11/04  300.0  2000.0      0.77