假设我有一个数据框,其中的行包含缺少的数据,但有一组列充当键:
import pandas as pd
import numpy as np
data = {"id": [1, 1, 2, 2, 3, 3, 4 ,4], "name": ["John", "John", "Paul", "Paul", "Ringo", "Ringo", "George", "George"], "height": [178, np.nan, 182, np.nan, 175, np.nan, 188, np.nan], "weight": [np.nan, np.NaN, np.nan, 72, np.nan, 68, np.nan, 70]}
df = pd.DataFrame.from_dict(data)
print(df)
id name height weight
0 1 John 178.0 NaN
1 1 John NaN NaN
2 2 Paul 182.0 NaN
3 2 Paul NaN 72.0
4 3 Ringo 175.0 NaN
5 3 Ringo NaN 68.0
6 4 George 188.0 NaN
7 4 George NaN 70.0
我将如何使用重复键“挤压”这些行,以拾取非nan值(如果存在)
desired output:
id name height weight
0 1 John 178.0 NaN
2 2 Paul 182.0 72.0
4 3 Ringo 175.0 68.0
6 4 George 188.0 70.0
索引并不重要,并且总是最多有一行包含非NaN数据。我想我需要使用groupby(['id', 'name'])
,但我不确定从那里开始
如果每个组始终只有一个非
NaN
值,则可以通过多种方式聚合:或:
或:
或:
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