How To Update A DataFrame ?
Table Of Contents:
- Syntax ‘update( )’ Method In Pandas.
- Examples ‘update( )’ Method.
(1) Syntax:
DataFrame.update(other, join='left', overwrite=True, filter_func=None, errors='ignore')Description:
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.
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’
(2) Examples Of update() Method:
Example-1:
df = pd.DataFrame({'A': [1, 2, 3],
'B': [400, 500, 600]})
new_df = pd.DataFrame({'B': [4, 5, 6],
'C': [7, 8, 9]})Output:
df.update(new_df)
dfOutput:
df = pd.DataFrame({'A': ['a', 'b', 'c'],
'B': ['x', 'y', 'z']})
new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})Output:
df.update(new_df)
dfOutput:
# For Series, its 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])Output:
df.update(new_column)
dfOutput:
df = pd.DataFrame({'A': [1, 2, 3],
'B': [400, 500, 600]})
new_df = pd.DataFrame({'B': [4, np.nan, 6]})Output:
df.update(new_df)
dfOutput:
