bigframes.pandas.DataFrame.update#

DataFrame.update(other, join: str = 'left', overwrite=True, filter_func=None)[source]#

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Examples:

>>> df = bpd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = bpd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

[3 rows x 2 columns]
Parameters:

  • other (DataFrame, or object coercible into a DataFrame) – Should have at least one matching index/column label with the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.

  • join ({'left'}, default 'left') – Only left join is implemented, keeping the index and columns of the original object.

  • overwrite (bool, default True) – How to handle non-NA values for overlapping keys: True: overwrite original DataFrame’s values with values from other. False: only update values that are NA in the original DataFrame.

  • filter_func (callable(1d-array) -> bool 1d-array, optional) – Can choose to replace values other than NA. Return True for values that should be updated.

Returns:

This method directly changes calling object.

Return type:

None

Raises:

ValueError – If a type of join other than left is provided as an argument.

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