pandas.DataFrame.update#

DataFrame.update(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:

otherDataFrame, or object coercible into a DataFrame

Should have at least one matching index/column labelwith the original DataFrame. If a Series is passed,its name attribute must be set, and that will beused 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 theoriginal object.

overwritebool, default True

How to handle non-NA values for overlapping keys:

• True: overwrite original DataFrame’s valueswith values fromother.

• False: only update values that are NA inthe original DataFrame.

filter_funccallable(1d-array) -> bool 1d-array, optional

Can choose to replace values other than NA. Return True for valuesthat should be updated.

errors{‘raise’, ‘ignore’}, default ‘ignore’

If ‘raise’, will raise a ValueError if the DataFrame andotherboth contain non-NA data in the same place.

Returns:

None

This method directly changes calling object.

Raises:

ValueError

• Whenerrors=’raise’ and there’s overlapping non-NA data.

• Whenerrors is not either‘ignore’ or‘raise’

NotImplementedError

• Ifjoin != ‘left’

See also

dict.update

Similar method for dictionaries.

DataFrame.merge

For column(s)-on-column(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  B0  1  41  2  52  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  B0  a  d1  b  e2  c  f

>>>df=pd.DataFrame({'A':['a','b','c'],...'B':['x','y','z']})>>>new_df=pd.DataFrame({'B':['d','f']},index=[0,2])>>>df.update(new_df)>>>df   A  B0  a  d1  b  y2  c  f

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','f'],name='B')>>>df.update(new_column)>>>df   A  B0  a  d1  b  e2  c  f

Ifother contains NaNs the corresponding values are not updatedin 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      B0  1    4.01  2  500.02  3    6.0