combine_first 和 fillna 有什么区别？

Question

These two functions seem equivalent to me. You can see that they accomplish the same goal in the code below, as columns c and d are equal. So when should I use one over the other?

Here is an example:

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randint(0, 10, size=(10, 2)), columns=list('ab'))
df.loc[::2, 'a'] = np.nan

Returns:

     a  b
0  NaN  4
1  2.0  6
2  NaN  8
3  0.0  4
4  NaN  4
5  0.0  8
6  NaN  7
7  2.0  2
8  NaN  9
9  7.0  2

This is my starting point. Now I will add two columns, one using combine_first and one using fillna, and they will produce the same result:

df['c'] = df.a.combine_first(df.b)
df['d'] = df['a'].fillna(df['b'])

Returns:

     a  b    c    d
0  NaN  4  4.0  4.0
1  8.0  7  8.0  8.0
2  NaN  2  2.0  2.0
3  3.0  0  3.0  3.0
4  NaN  0  0.0  0.0
5  2.0  4  2.0  2.0
6  NaN  0  0.0  0.0
7  2.0  6  2.0  2.0
8  NaN  4  4.0  4.0
9  4.0  6  4.0  4.0

Credit to this question for the data set: Combine Pandas data frame column values into new column

Answer 1

combine_first is intended to be used when there is exists non-overlapping indices. It will effectively fill in nulls as well as supply values for indices and columns that didn't exist in the first.

dfa = pd.DataFrame([[1, 2, 3], [4, np.nan, 5]], ['a', 'b'], ['w', 'x', 'y'])

     w    x    y  
a  1.0  2.0  3.0  
b  4.0  NaN  5.0  

dfb = pd.DataFrame([[1, 2, 3], [3, 4, 5]], ['b', 'c'], ['x', 'y', 'z'])

     x    y    z
b  1.0  2.0  3.0
c  3.0  4.0  5.0

dfa.combine_first(dfb)

     w    x    y    z
a  1.0  2.0  3.0  NaN
b  4.0  1.0  5.0  3.0  # 1.0 filled from `dfb`; 5.0 was in `dfa`; 3.0 new column
c  NaN  3.0  4.0  5.0  # whole new index

Notice that all indices and columns are included in the results

Now if we fillna

dfa.fillna(dfb)

   w    x  y
a  1  2.0  3
b  4  1.0  5  # 1.0 filled in from `dfb`

Notice no new columns or indices from dfb are included. We only filled in the null value where dfa shared index and column information.

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In your case, you use fillna and combine_first on one column with the same index. These translate to effectively the same thing.