pandas.DataFrame.combine#

DataFrame.combine(other,func,fill_value=None,overwrite=True)[source]#

Perform column-wise combine with another DataFrame.

Combines a DataFrame withother DataFrame usingfuncto element-wise combine columns. The row and column indexes of theresulting DataFrame will be the union of the two.

Parameters:

otherDataFrame

The DataFrame to merge column-wise.

funcfunction

Function that takes two series as inputs and return a Series or ascalar. Used to merge the two dataframes column by columns.

fill_valuescalar value, default None

The value to fill NaNs with prior to passing any column to themerge func.

overwritebool, default True

If True, columns inself that do not exist inother will beoverwritten with NaNs.

Returns:

DataFrame

Combination of the provided DataFrames.

See also

DataFrame.combine_first

Combine two DataFrame objects and default to non-null values in frame calling the method.

Examples

Combine using a simple function that chooses the smaller column.

>>>df1=pd.DataFrame({'A':[0,0],'B':[4,4]})>>>df2=pd.DataFrame({'A':[1,1],'B':[3,3]})>>>take_smaller=lambdas1,s2:s1ifs1.sum()<s2.sum()elses2>>>df1.combine(df2,take_smaller)   A  B0  0  31  0  3

Example using a true element-wise combine function.

>>>df1=pd.DataFrame({'A':[5,0],'B':[2,4]})>>>df2=pd.DataFrame({'A':[1,1],'B':[3,3]})>>>df1.combine(df2,np.minimum)   A  B0  1  21  0  3

Usingfill_value fills Nones prior to passing the column to themerge function.

>>>df1=pd.DataFrame({'A':[0,0],'B':[None,4]})>>>df2=pd.DataFrame({'A':[1,1],'B':[3,3]})>>>df1.combine(df2,take_smaller,fill_value=-5)   A    B0  0 -5.01  0  4.0

However, if the same element in both dataframes is None, that Noneis preserved

>>>df1=pd.DataFrame({'A':[0,0],'B':[None,4]})>>>df2=pd.DataFrame({'A':[1,1],'B':[None,3]})>>>df1.combine(df2,take_smaller,fill_value=-5)    A    B0  0 -5.01  0  3.0

Example that demonstrates the use ofoverwrite and behavior whenthe axis differ between the dataframes.

>>>df1=pd.DataFrame({'A':[0,0],'B':[4,4]})>>>df2=pd.DataFrame({'B':[3,3],'C':[-10,1],},index=[1,2])>>>df1.combine(df2,take_smaller)     A    B     C0  NaN  NaN   NaN1  NaN  3.0 -10.02  NaN  3.0   1.0

>>>df1.combine(df2,take_smaller,overwrite=False)     A    B     C0  0.0  NaN   NaN1  0.0  3.0 -10.02  NaN  3.0   1.0

Demonstrating the preference of the passed in dataframe.

>>>df2=pd.DataFrame({'B':[3,3],'C':[1,1],},index=[1,2])>>>df2.combine(df1,take_smaller)   A    B   C0  0.0  NaN NaN1  0.0  3.0 NaN2  NaN  3.0 NaN

>>>df2.combine(df1,take_smaller,overwrite=False)     A    B   C0  0.0  NaN NaN1  0.0  3.0 1.02  NaN  3.0 1.0