modin.pandas.Series.pct_change¶

Series.pct_change(periods=1, fill_method=_NoDefault.no_default, limit=_NoDefault.no_default, freq=None, **kwargs)[source]¶

Fractional change between the current and a prior element.

Computes the fractional change from the immediately previous row by default. This is useful in comparing the fraction of change in a time series of elements.

Note

Despite the name of this method, it calculates fractional change (also known as per unit change or relative change) and not percentage change. If you need the percentage change, multiply these values by 100.

Parameters:

  • periods (int, default 1) – Periods to shift for forming percent change.

  • fill_method ({'backfill', 'bfill', 'pad', 'ffill', None}, default 'pad') –

    How to handle NAs before computing percent changes.

    Deprecated since version 2.1: All options of fill_method are deprecated except fill_method=None.

  • limit (int, default None) –

    The number of consecutive NAs to fill before stopping.

    Snowpark pandas does not yet support this parameter.

    Deprecated since version 2.1.

  • freq (DateOffset, timedelta, or str, optional) –

    Increment to use from time series API (e.g. ‘ME’ or BDay()).

    Snowpark pandas does not yet support this parameter.

  • **kwargs –

    Additional keyword arguments are passed into DataFrame.shift or Series.shift.

    Unlike pandas, Snowpark pandas does not use shift under the hood, and thus may not yet support the passed keyword arguments.

Returns:

The same type as the calling object.

Return type:

Series or DataFrame

See also

Series.diff

Compute the difference of two elements in a Series.

DataFrame.diff

Compute the difference of two elements in a DataFrame.

Series.shift

Shift the index by some number of periods.

DataFrame.shift

Shift the index by some number of periods.

Examples

Series

>>> s = pd.Series([90, 91, 85])
>>> s
0    90
1    91
2    85
dtype: int64
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>>> s.pct_change()
0         NaN
1    0.011111
2   -0.065934
dtype: float64
Copy
>>> s.pct_change(periods=2)
0         NaN
1         NaN
2   -0.055556
dtype: float64
Copy

See the percentage change in a Series where filling NAs with last valid observation forward to next valid.

>>> s = pd.Series([90, 91, None, 85])
>>> s
0    90.0
1    91.0
2     NaN
3    85.0
dtype: float64
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>>> s.ffill().pct_change()
0         NaN
1    0.011111
2    0.000000
3   -0.065934
dtype: float64
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DataFrame

Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.

>>> df = pd.DataFrame({
...     'FR': [4.0405, 4.0963, 4.3149],
...     'GR': [1.7246, 1.7482, 1.8519],
...     'IT': [804.74, 810.01, 860.13]},
...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
                FR      GR      IT
1980-01-01  4.0405  1.7246  804.74
1980-02-01  4.0963  1.7482  810.01
1980-03-01  4.3149  1.8519  860.13
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>>> df.pct_change()
                  FR        GR        IT
1980-01-01       NaN       NaN       NaN
1980-02-01  0.013810  0.013684  0.006549
1980-03-01  0.053365  0.059318  0.061876
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Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns.

>>> df = pd.DataFrame({
...     '2016': [1769950, 30586265],
...     '2015': [1500923, 40912316],
...     '2014': [1371819, 41403351]},
...     index=['GOOG', 'APPL'])
>>> df
          2016      2015      2014
GOOG   1769950   1500923   1371819
APPL  30586265  40912316  41403351
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>>> df.pct_change(axis='columns', periods=-1)
          2016      2015  2014
GOOG  0.179241  0.094112   NaN
APPL -0.252395 -0.011860   NaN
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