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 bydefault. This is useful in comparing the fraction of change in a timeseries of elements.

Note

Despite the name of this method, it calculates fractional change(also known as per unit change or relative change) and notpercentage change. If you need the percentage change, multiplythese 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 NAsbefore computing percent changes.

Deprecated since version 2.1:All options offill_method are deprecated exceptfill_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 intoDataFrame.shift orSeries.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])>>>s0    901    912    85dtype: int64

>>>s.pct_change()0         NaN1    0.0111112   -0.065934dtype: float64

>>>s.pct_change(periods=2)0         NaN1         NaN2   -0.055556dtype: float64

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

>>>s=pd.Series([90,91,None,85])>>>s0    90.01    91.02     NaN3    85.0dtype: float64

>>>s.ffill().pct_change()0         NaN1    0.0111112    0.0000003   -0.065934dtype: float64

DataFrame

Percentage change in French franc, Deutsche Mark, and Italian lira from1980-01-01 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      IT1980-01-01  4.0405  1.7246  804.741980-02-01  4.0963  1.7482  810.011980-03-01  4.3149  1.8519  860.13

>>>df.pct_change()                  FR        GR        IT1980-01-01       NaN       NaN       NaN1980-02-01  0.013810  0.013684  0.0065491980-03-01  0.053365  0.059318  0.061876

Percentage of change in GOOG and APPL stock volume. Shows computingthe percentage change between columns.

>>>df=pd.DataFrame({...'2016':[1769950,30586265],...'2015':[1500923,40912316],...'2014':[1371819,41403351]},...index=['GOOG','APPL'])>>>df          2016      2015      2014GOOG   1769950   1500923   1371819APPL  30586265  40912316  41403351

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