问题
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
回答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'])
dfb = pd.DataFrame([[1, 2, 3], [3, 4, 5]], ['b', 'c'], ['x', 'y', 'z'])
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.
In your case, you use fillna and combine_first on one column with the same index. These translate to effectively the same thing.
来源：https://stackoverflow.com/questions/46676134/what-is-the-difference-between-combine-first-and-fillna