# pandas.DataFrame.assign

`DataFrame.``assign`(**kwargs)

  Assign new columns to a DataFrame, returning a new object (a copy) with all the original columns in addition to the new ones.

  Parameters:

  kwargs : keyword, value pairs

  > keywords are the column names. 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

  For python 3.6 and above, the columns are inserted in the order of **kwargs. For python 3.5 and earlier, since **kwargs is unordered, the columns are inserted in alphabetical order at the end of your DataFrame. Assigning multiple columns within the same `assign` is possible, but you cannot reference other columns created within the same `assign` call.

  Examples

      >>> df = 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

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