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# pandas.DataFrame.pct_change

DataFrame.pct_change(periods=1, fill_method=<no_default>, limit=<no_default>, freq=None, **kwargs)

  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:
    periodsint, 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.

    limitint, default None

      The number of consecutive NAs to fill before stopping.

      Deprecated since version 2.1.

    freqDateOffset, timedelta, or str, optional

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

    **kwargs

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

  Returns:
    Series or DataFrame

      The same type as the calling object.

  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

      >>> s.pct_change()
      0         NaN
      1    0.011111
      2   -0.065934
      dtype: float64

      >>> s.pct_change(periods=2)
      0         NaN
      1         NaN
      2   -0.055556
      dtype: float64

  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

      >>> s.ffill().pct_change()
      0         NaN
      1    0.011111
      2    0.000000
      3   -0.065934
      dtype: float64

  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

      >>> 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

  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

      >>> df.pct_change(axis='columns', periods=-1)
                2016      2015  2014
      GOOG  0.179241  0.094112   NaN
      APPL -0.252395 -0.011860   NaN