# pandas.core.groupby.DataFrameGroupBy.diff

property DataFrameGroupBy.diff

  First discrete difference of element.

  Calculates the difference of a Dataframe element compared with another element in the Dataframe (default is element in previous row).

  Parameters
    periods int, default 1

      Periods to shift for calculating difference, accepts negative values.

    axis{0 or ‘index’, 1 or ‘columns’}, default 0

      Take difference over rows (0) or columns (1).

  Returns
    Dataframe

      First differences of the Series.

  See also

  `Dataframe.pct_change`

    Percent change over given number of periods.

  `Dataframe.shift`

    Shift index by desired number of periods with an optional time freq.

  `Series.diff`

    First discrete difference of object.

  Notes

  For boolean dtypes, this uses `operator.xor()` rather than `operator.sub()`. The result is calculated according to current dtype in Dataframe, however dtype of the result is always float64.

  Examples

  Difference with previous row

      >>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
      ...                    'b': [1, 1, 2, 3, 5, 8],
      ...                    'c': [1, 4, 9, 16, 25, 36]})
      >>> df
         a  b   c
      0  1  1   1
      1  2  1   4
      2  3  2   9
      3  4  3  16
      4  5  5  25
      5  6  8  36

      >>> df.diff()
           a    b     c
      0  NaN  NaN   NaN
      1  1.0  0.0   3.0
      2  1.0  1.0   5.0
      3  1.0  1.0   7.0
      4  1.0  2.0   9.0
      5  1.0  3.0  11.0

  Difference with previous column

      >>> df.diff(axis=1)
          a  b   c
      0 NaN  0   0
      1 NaN -1   3
      2 NaN -1   7
      3 NaN -1  13
      4 NaN  0  20
      5 NaN  2  28

  Difference with 3rd previous row

      >>> df.diff(periods=3)
           a    b     c
      0  NaN  NaN   NaN
      1  NaN  NaN   NaN
      2  NaN  NaN   NaN
      3  3.0  2.0  15.0
      4  3.0  4.0  21.0
      5  3.0  6.0  27.0

  Difference with following row

      >>> df.diff(periods=-1)
           a    b     c
      0 -1.0  0.0  -3.0
      1 -1.0 -1.0  -5.0
      2 -1.0 -1.0  -7.0
      3 -1.0 -2.0  -9.0
      4 -1.0 -3.0 -11.0
      5  NaN  NaN   NaN

  Overflow in input dtype

      >>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)
      >>> df.diff()
             a
      0    NaN
      1  255.0