pandas.DataFrame.diff

DataFrame.diff(periods=1, axis=0)[source]

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