Table Of Contents

Search

Enter search terms or a module, class or function name.

pandas.DataFrame.assign

DataFrame.assign(**kwargs)[source]

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters:

kwargs : keyword, value pairs

The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.

Returns:

df : DataFrame

A new DataFrame with the new columns in addition to all the existing columns.

Notes

Assigning multiple columns within the same assign is possible. For Python 3.6 and above, later items in ‘**kwargs’ may refer to newly created or modified columns in ‘df’; items are computed and assigned into ‘df’ in order. For Python 3.5 and below, the order of keyword arguments is not specified, you cannot refer to newly created or modified columns. All items are computed first, and then assigned in alphabetical order.

Changed in version 0.23.0: Keyword argument order is maintained for Python 3.6 and later.

Examples

>>> df = pd.DataFrame({'A': range(1, 11), 'B': np.random.randn(10)})

Where the value is a callable, evaluated on df:

>>> df.assign(ln_A = lambda x: np.log(x.A))
    A         B      ln_A
0   1  0.426905  0.000000
1   2 -0.780949  0.693147
2   3 -0.418711  1.098612
3   4 -0.269708  1.386294
4   5 -0.274002  1.609438
5   6 -0.500792  1.791759
6   7  1.649697  1.945910
7   8 -1.495604  2.079442
8   9  0.549296  2.197225
9  10 -0.758542  2.302585

Where the value already exists and is inserted:

>>> newcol = np.log(df['A'])
>>> df.assign(ln_A=newcol)
    A         B      ln_A
0   1  0.426905  0.000000
1   2 -0.780949  0.693147
2   3 -0.418711  1.098612
3   4 -0.269708  1.386294
4   5 -0.274002  1.609438
5   6 -0.500792  1.791759
6   7  1.649697  1.945910
7   8 -1.495604  2.079442
8   9  0.549296  2.197225
9  10 -0.758542  2.302585

Where the keyword arguments depend on each other

>>> df = pd.DataFrame({'A': [1, 2, 3]})
>>> df.assign(B=df.A, C=lambda x:x['A']+ x['B'])
    A  B  C
 0  1  1  2
 1  2  2  4
 2  3  3  6
Scroll To Top