pandas.DataFrame.update

DataFrame.update(self, other, join='left', overwrite=True, filter_func=None, errors='ignore') [source]

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

Aligns on indices. There is no return value.

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. errors : {‘raise’, ‘ignore’}, default ‘ignore’ If ‘raise’, will raise a ValueError if the DataFrame and other both contain non-NA data in the same place. Changed in version 0.24.0: Changed from raise_conflict=False|True to errors=’ignore’|’raise’.
Returns:	None : method directly changes calling object
Raises:	ValueError When errors=’raise’ and there’s overlapping non-NA data. When errors is not either ‘ignore’ or ‘raise’ NotImplementedError If join != ‘left’

See also

dict.update

Similar method for dictionaries.

DataFrame.merge

For column(s)-on-columns(s) operations.

Examples

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.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

The DataFrame’s length does not increase as a result of the update, only values at matching index/column labels are updated.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

For Series, it’s name attribute must be set.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  y
2  c  e
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  x
1  b  d
2  c  e

If other contains NaNs the corresponding values are not updated in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0