Pandas Series: pct_change() function

Last update on September 15 2022 12:54:15 (UTC/GMT +8 hours)

Percentage change between the current and a prior element

The pct_change() function is used to get 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.

Syntax:

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

Parameters:

Name	Description	Type/Default Value	Required / Optional
periods	Periods to shift for forming percent change.	int Default Value: 1	Required
fill_method	How to handle NAs before computing percent changes.	str Default Value: ‘pad’	Required
limit	The number of consecutive NAs to fill before stopping.	int Default Value: None	Required
freq	Increment to use from time series API (e.g. ‘M’ or BDay()).	DateOffset, timedelta, or offset alias string	Optional
**kwargs	Additional keyword arguments are passed into DataFrame.shift or Series.shift.		Required

Returns: chg - Series or DataFrame
The same type as the calling object.

Example - Series:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([80, 81, 75])
s

Output:

0    80
1    81
2    75
dtype: int64

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([80, 81, 75])
s.pct_change()

Output:

0         NaN
1    0.012500
2   -0.074074
dtype: float64

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([80, 81, 75])
s.pct_change(periods=2)

Output:

0       NaN
1       NaN
2   -0.0625
dtype: float64

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

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([80, 81, None, 75])
s

Output:

0    80.0
1    81.0
2     NaN
3    75.0
dtype: float64

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([80, 81, None, 75])
s.pct_change(fill_method='ffill')

Output:

0         NaN
1    0.012500
2    0.000000
3   -0.074074
dtype: float64

Example - DataFrame:

Percentage change in French franc, Deutsche Mark, and Italian lira from 2000-01-01 to 2000-03-01

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({
     'FR': [5.0505, 5.0963, 5.3149],
     'GR': [2.7246, 2.7482, 2.8519],
     'IT': [904.74, 910.01, 960.13]},
     index=['2000-01-01', '2000-02-01', '2000-03-01'])
df

Output:

               FR	     GR	    IT
2000-01-01	5.0505	2.7246	904.74
2000-02-01	5.0963	2.7482	910.01
2000-03-01	5.3149	2.8519	960.13

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({
     'FR': [5.0505, 5.0963, 5.3149],
     'GR': [2.7246, 2.7482, 2.8519],
     'IT': [904.74, 910.01, 960.13]},
     index=['2000-01-01', '2000-02-01', '2000-03-01'])
df.pct_change()

Output:

               FR	         GR	          IT
2000-01-01	NaN	           NaN	       NaN
2000-02-01	0.009068	0.008662	0.005825
2000-03-01	0.042894	0.037734	0.055076

Example - Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({
     '2019': [1869950, 32586265],
     '2018': [1600923, 42912316],
     '2017': [1471819, 42403351]},
     index=['GOOG', 'APPL'])
df

Output:

          2019	 2018	       2017
GOOG	  1869950	1600923	    1471819
APPL	 32586265	42912316	42403351

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({
     '2019': [1869950, 32586265],
     '2018': [1600923, 42912316],
     '2017': [1471819, 42403351]},
     index=['GOOG', 'APPL'])
df.pct_change(axis='columns')

Output:

          2019	2018	  2017
GOOG	    NaN	-0.143869	-0.080643
APPL	    NaN	 0.316884	-0.011861

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