assign(self, **kwargs) -> 'DataFrame'
Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.
Assigning multiple columns within the same assign
is possible. Later items in '\*\*kwargs' may refer to newly created or modified columns in 'df'; items are computed and assigned into 'df' in order.
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.
A new DataFrame with the new columns in addition to all the existing columns.
Assign new columns to a DataFrame.
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
... index=['Portland', 'Berkeley'])
... df temp_c Portland 17.0 Berkeley 25.0
Where the value is a callable, evaluated on :None:None:`df`:
>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32) temp_c temp_f Portland 17.0 62.6 Berkeley 25.0 77.0
Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:
This example is valid syntax, but we were not able to check execution>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32) temp_c temp_f Portland 17.0 62.6 Berkeley 25.0 77.0
You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:
This example is valid syntax, but we were not able to check execution>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,See :
... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9) temp_c temp_f temp_k Portland 17.0 62.6 290.15 Berkeley 25.0 77.0 298.15
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.frame.DataFrame.eval
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