Python | Pandas Series.pct_change()

Pandas pct_change() method is applied on series with numeric data to calculate Percentage change after n number of elements. By default, it calculates percentage change of current element from the previous element. (Current-Previous/Previous) * 100.
First, n(n=period) values are always NaN, since there is no previous value to calculate change.

Syntax: Series.pct_change(periods=1, fill_method=’pad’, limit=None)
Parameters: 
periods: Defines gap between current and previous value. Default is 1 
fill_method: Defines method used to handle null values 
limit: Number of consecutive NaN values to fill before stopping.
Return type: Numeric series with percentage change 

Example #1: 
In this method, a Series is created from Python list using Pandas Series(). The series doesn’t contain any null value and hence pct_change() method is called directly with default value of period parameter, that is 1. 

Python3

# importing pandas module
import pandas as pd

# importing numpy module
import numpy as np

# creating list
list = [10, 14, 20, 25, 12.5, 13, 0, 50]

# creating series
series = pd.Series(list)

# calling method
result = series.pct_change()

# display
result

Output:

0         NaN
1    0.400000
2    0.428571
3    0.250000
4   -0.500000
5    0.040000
6   -1.000000
7         inf
dtype: float64

As shown in output, first n values are always equal to NaN. Rest of the values are equal to the percentage change in Old values and are stored at the same position as caller series. 
Note: Since second last value was 0, the Percentage change is inf. inf stands for infinite. 
Using the formula, pct_change= x-0/0 = Infinite

Example #2: Handling Null values
In this example, some null values are also created using Numpy’s np.nan method and passed to the list. ‘bfill‘ is passed to fill_method. bfill stands for Back fill and will fill Null values with values at their very next position.

Python3

# importing pandas module
import pandas as pd

# importing numpy module
import numpy as np

# creating list
list =[10, np.nan, 14, 20, 25, 12.5, 13, 0, 50]

# creating series
series = pd.Series(list)

# calling method
result = series.pct_change(fill_method ='bfill')

# display
result

Output:

0         NaN
1    0.400000
2    0.000000
3    0.428571
4    0.250000
5   -0.500000
6    0.040000
7   -1.000000
8         inf
dtype: float64

As it can be seen in output, value at position 1 is 40 because NaN was replaced by 14. Hence, (14-10/10) *100 = 40. The very next value is 0 because percentage change in 14 and 14 is 0.