Python pandas 用模式（同一列 -A）相对于 Pandas 数据帧中的另一列替换一列（A）的 NaN 值

Question

Here is the dataframe with some NaN values,

data = {'Number':[100,None,None,200,150,None,100,120,110,210,120],
    'Street':['A','B','C','D','C','D','A','B','B','D','B']}
df =pd.DataFrame(data)
df

Output:

    Number  Street
0   100.0   A
1   NaN     B
2   NaN     C
3   200.0   D
4   150.0   C
5   NaN     D
6   100.0   A
7   120.0   B
8   110.0   B
9   210.0   D
10  120.0   B

I want to replace the NaN values of the column 'Number' by the mode of the same column with respect to the column 'Street' .

The output I need is:

    Number  Street
0   100       A
1   120       B
2   150       C
3   200       D
4   150       C
5   200       D
6   100       A
7   120       B
8   110       B
9   210       D
10  120       B

Explanation:

For example, consider row 1 which has a NaN value in the column Number and the corresponding Street is B . The NaN value of Number should be replaced by 120.0 which is the mode of the column Number with respect to Street . Because, the other values for the column Number for Street B are 120.0, 110.0 and 120.0 (look at row nos 7,8,10), and the mode for this is 120.0 .

Answer 1

Use GroupBy.transform with lambda function for return first mode and replace missing values by Series.fillna :

f = lambda x: x.mode().iat[0]
df['Number'] = df['Number'].fillna(df.groupby('Street')['Number'].transform(f))

Or:

f = lambda x: fillna(x.mode().iat[0])
df['Number'] = df.groupby('Street')['Number'].transform(f)

---

print (df)
    Number Street
0    100.0      A
1    120.0      B
2    150.0      C
3    200.0      D
4    150.0      C
5    200.0      D
6    100.0      A
7    120.0      B
8    110.0      B
9    210.0      D
10   120.0      B

Here is possible error if some group has only NaN/None s:

IndexError: index 0 is out of bounds for axis 0 with size 0

Then solution is:

data = {'Number':[None,None,None,200,150,None,None,120,110,210,120],
    'Street':['A','B','C','D','C','D','A','B','B','D','B']}
df =pd.DataFrame(data)
print (df)
    Number Street
0      NaN      A
1      NaN      B
2      NaN      C
3    200.0      D
4    150.0      C
5      NaN      D
6      NaN      A
7    120.0      B
8    110.0      B
9    210.0      D
10   120.0      B

f = lambda x: x.mode().iat[0] if x.notna().any() else np.nan
df['Number'] = df['Number'].fillna(df.groupby('Street')['Number'].transform(f))
print (df)
    Number Street
0      NaN      A
1    120.0      B
2    150.0      C
3    200.0      D
4    150.0      C
5    200.0      D
6      NaN      A
7    120.0      B
8    110.0      B
9    210.0      D
10   120.0      B

Answer 2

Maybe a bit simpler, as mode returns an array So, you can impute Nan values within Number by grabbing the first element of it to get the results.

Solution 1:

>>> df['Number'] = df.groupby('Street')['Number'].apply(lambda x: x.fillna(x.mode()[0]))
    # df['Number'] = df.groupby('Street').transform(lambda x: x.fillna(x.mode()[0]))
>>> df
    Number Street
0    100.0      A
1    120.0      B
2    150.0      C
3    200.0      D
4    150.0      C
5    200.0      D
6    100.0      A
7    120.0      B
8    110.0      B
9    210.0      D
10   120.0      B

Solution 2:

You can see the another solution here based on the loc and first_valid_index

df['Number'] = df.groupby('Street')['Number'].transform(lambda s: s.loc[s.first_valid_index()])

OR

df.assign(Number=df.groupby(['Street']).Number.apply(lambda x: x.fillna(x.mode()[0])))

  or

df.assign(Number=df.groupby(['Street']).transform(lambda x: x.fillna(x.mode()[0])))