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 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.

overwritebool, 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_funccallable(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

This 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-column(s) operations.

Notes

  1. Duplicate indices on other are not supported and raises ValueError.

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

>>> 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  B
0  a  d
1  b  y
2  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  B
0  a  d
1  b  e
2  c  f

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

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [400.0, 500.0, 600.0]})
>>> 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