[英]Pandas: replace a NaN with another DataFrame
我正在嘗試解決這個問題,所以請幫助我,我有這個數據集:
df1= pd.DataFrame(data={'col1': ['a','b','c','d'],
'col2': [1,2,np.nan,4]})
df2=pd.DataFrame(data={'col1': ['a','b','b','a','f','c','e','d','e','a'],
'col2':[1,3,2,3,6,4,1,2,5,2]})
df1
col1 col2
0 a 1.0
1 b 2.0
2 c NaN
3 d 4.0
df2
col1 col2
0 a 1
1 b 3
2 b 2
3 a 3
4 f 6
5 c 4
6 e 1
7 d 2
8 e 5
9 a 2
我試過這個
df1[df1['col2'].isna()] = pd.merge(df1, df2, on=['col1'], how='left')
我期待這個
col1 col2
0 a 1.0
1 b 2.0
2 c 4
3 d 4.0
但相反,我得到了這個
col1 col2
0 a 1.0
1 b 2.0
2 a NaN
3 d 4.0
然后我嘗試了這個
for x in zip(df1,df2):
if x in df1['col2'] == x in df2['col2']:
df1['col1'][df1['col1'].isna()] = df2['col1'].where(df1['col2'][x] == df2['col2'][x])
但得到了這個
col1 col2
0 a 1.0
1 b 2.0
2 c NaN
3 d 4.0
我也試過這個答案
但仍然沒有
使用Series.map
將col1
col1
Series
匹配DataFrame.drop_duplicates
並僅用Series.fillna
替換缺失值:
s = df2.drop_duplicates('col1').set_index('col1')['col2']
print (s)
col1
a 1
b 3
f 6
c 4
e 1
d 2
Name: col2, dtype: int64
print (df1['col1'].map(s))
0 1
1 3
2 4
3 2
Name: col1, dtype: int64
df1['col2'] = df1['col2'].fillna(df1['col1'].map(s))
print (df1)
col1 col2
0 a 1.0
1 b 2.0
2 c 4.0
3 d 4.0
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