dask_expr._collection.DataFrame.diff

dask_expr._collection.DataFrame.diff

DataFrame.diff(periods=1, axis=0)

First discrete difference of element.

This docstring was copied from pandas.core.frame.DataFrame.diff.

Some inconsistencies with the Dask version may exist.

Note

Pandas currently uses an object-dtype column to represent boolean data with missing values. This can cause issues for boolean-specific operations, like |. To enable boolean- specific operations, at the cost of metadata that doesn’t match pandas, use .astype(bool) after the shift.

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

Parameters
periodsint, 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

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