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# pandas.Series.pct_change¶

Series.pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, **kwargs)[source]

Percentage change between the current and a prior element.

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

Parameters: periods : int, default 1 Periods to shift for forming percent change. fill_method : str, default ‘pad’ How to handle NAs before computing percent changes. limit : int, default None The number of consecutive NAs to fill before stopping. freq : DateOffset, timedelta, or offset alias string, optional Increment to use from time series API (e.g. ‘M’ or BDay()). **kwargs Additional keyword arguments are passed into DataFrame.shift or Series.shift. chg : Series or DataFrame The same type as the calling object.

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.pct_change(fill_method='ffill')
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')
2016      2015      2014
GOOG   NaN -0.151997 -0.086016
APPL   NaN  0.337604  0.012002

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