pandas.DataFrame.update¶
-
DataFrame.
update
(other, join='left', overwrite=True, filter_func=None, raise_conflict=False)[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) -> boolean 1d-array, optional
Can choose to replace values other than NA. Return True for values that should be updated.
raise_conflict : bool, default False
If True, will raise a ValueError if the DataFrame and other both contain non-NA data in the same place.
Raises: ValueError
When raise_conflict is True and there’s overlapping non-NA data.
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