pandas.Series.pct_change#
- Series.pct_change(periods=1, fill_method=<no_default>, limit=<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:
- 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