combine(self, other: 'DataFrame', func, fill_value=None, overwrite: 'bool' = True) -> 'DataFrame'
Combines a DataFrame with other
DataFrame using func
to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.
The DataFrame to merge column-wise.
Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.
The value to fill NaNs with prior to passing any column to the merge func.
If True, columns in :None:None:`self` that do not exist in other
will be overwritten with NaNs.
Combination of the provided DataFrames.
Perform column-wise combine with another DataFrame.
DataFrame.combine_first
Combine two DataFrame objects and default to non-null values in frame calling the method.
Combine using a simple function that chooses the smaller column.
This example is valid syntax, but we were not able to check execution>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
... df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
... take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
... df1.combine(df2, take_smaller) A B 0 0 3 1 0 3
Example using a true element-wise combine function.
This example is valid syntax, but we were not able to check execution>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
... df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
... df1.combine(df2, np.minimum) A B 0 1 2 1 0 3
Using :None:None:`fill_value` fills Nones prior to passing the column to the merge 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 B 0 0 -5.0 1 0 4.0
However, if the same element in both dataframes is None, that None is preserved
This example is valid syntax, but we were not able to check execution>>> 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 B 0 0 -5.0 1 0 3.0
Example that demonstrates the use of :None:None:`overwrite` and behavior when the axis differ between the dataframes.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})This example is valid syntax, but we were not able to check execution
... df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
... df1.combine(df2, take_smaller) A B C 0 NaN NaN NaN 1 NaN 3.0 -10.0 2 NaN 3.0 1.0
>>> df1.combine(df2, take_smaller, overwrite=False) A B C 0 0.0 NaN NaN 1 0.0 3.0 -10.0 2 NaN 3.0 1.0
Demonstrating the preference of the passed in dataframe.
This example is valid syntax, but we were not able to check execution>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])This example is valid syntax, but we were not able to check execution
... df2.combine(df1, take_smaller) A B C 0 0.0 NaN NaN 1 0.0 3.0 NaN 2 NaN 3.0 NaN
>>> df2.combine(df1, take_smaller, overwrite=False) A B C 0 0.0 NaN NaN 1 0.0 3.0 1.0 2 NaN 3.0 1.0See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.frame.DataFrame.combine_first
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them