Pandas fill missing values in dataframe from another dataframe

As I just learned, there is a DataFrame.combine_first() method, which does precisely this, with the additional property that if your updating data frame d2 is bigger than your original df, the additional rows and columns are added, as well.

df = df.combine_first(d2)

DataFrame.combine_first() answers this question exactly.

However, sometimes you want to fill/replace/overwrite some of the non-missing (non-NaN) values of DataFrame A with values from DataFrame B. That question brought me to this page, and the solution is DataFrame.mask()

A = B.mask(condition, A)

When condition is true, the values from A will be used, otherwise B’s values will be used.

For example, you could solve the OP’s original question with mask such that when an element from A is non-NaN, use it, otherwise use the corresponding element from B.

But using DataFrame.mask() you could replace the values of A that fail to meet arbitrary criteria (less than zero? more than 100?) with values from B. So mask is more flexible, and overkill for this problem, but I thought it was worthy of mention (I needed it to solve my problem).

It’s also important to note that B could be a numpy array instead of a DataFrame. DataFrame.combine_first() requires that B be a DataFrame, but DataFrame.mask() just requires that B’s is an NDFrame and its dimensions match A’s dimensions.

This should be as simple as

df.fillna(d2)

A dedicated method for this is DataFrame.update:

Quoted from the documentation:

Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.

Important to note is that this method will modify your data inplace. So it will overwrite your updated dataframe.

Example:

print(df1)
       A    B     C
aaa  NaN  1.0   NaN
bbb  NaN  NaN  10.0
ccc  3.0  NaN   6.0
ddd  NaN  NaN   NaN
eee  NaN  NaN   NaN

print(df2)
         A    B     C
index                
aaa    1.0  1.0   NaN
bbb    NaN  NaN  10.0
eee    NaN  1.0   NaN

# update df1 NaN where there are values in df2
df1.update(df2)
print(df1)
       A    B     C
aaa  1.0  1.0   NaN
bbb  NaN  NaN  10.0
ccc  3.0  NaN   6.0
ddd  NaN  NaN   NaN
eee  NaN  1.0   NaN

Notice the updated NaN values at intersect aaa, A and eee, B

One important info missing from the other answers is that both combine_first and fillna match on index, so you have to make the indices of match across the DataFrames for these methods to work.

Oftentimes, there’s a need to match on some other column(s) to fill in missing values. In that case, you need to use set_index first to make the columns to be matched, the index.

df1 = df1.set_index(cols_to_be_matched).fillna(df2.set_index(cols_to_be_matched)).reset_index()

or

df1 = df1.set_index(cols_to_be_matched).combine_first(df2.set_index(cols_to_be_matched)).reset_index()

Another option is to use merge:

df1 = (df1.merge(df2, on=cols_to_be_matched, how='left', suffixes=('','x00'))
       .sort_index(axis=1).bfill(axis=1)[df.columns])

The idea here is to left-merge and by sorting the columns (we use 'x00' as the suffix for columns from df2 since it’s the character with the lowest Unicode value), we make sure the same column values end up next to each other. Then use bfill horizontally to update df1 with values from df2.

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Example:

Suppose you had df1:

   C1 C2   C3  C4
0   1  a  1.0   0
1   1  b  NaN   1
2   2  b  NaN   2
3   2  b  NaN   3

and df2

   C1 C2  C3
0   1  b   2
1   2  b   3

and you want to fill in the missing values in df1 with values in df2 for each pair of C1–C2 value pair. Then

cols_to_be_matched = ['C1', 'C2']

and all of the codes above produce the following output (where the values are indeed filled as required):

   C1 C2   C3  C4
0   1  a  1.0   0
1   1  b  2.0   1
2   2  b  3.0   2
3   2  b  3.0   3