Python | Pandas Series.pct_change ()

First, the n (n = period) values are always NaN because there is no previous value to calculate the 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 a Python list using Pandas Series () . The series does not contain a null value, so pct_change () is called directly with a default value for the period parameter of 1.

# pandas module import

import pandas as pd

# importing the numpy module

import numpy as np

# create a list

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

# create a series

series = pd.Series ( list )

# calling method

result = series.pct_change ()


  
  # display  
  result

Exit:

 0 NaN 1 0.400000 2 0.428571 3 0.250000 4 -0.5000 00 5 0.040000 6 -1.000000 7 inf dtype: float64

As shown in the output, the first n values are always NaN. The remaining values are equal to the percentage change in the old values and are stored in the same position as a number of callers.
Note. Since the second last value was 0, the percentage change is inf. Inf means infinity.
Using the formula, pct_change = x-0/0 = Infinte

Example # 2: Handling null values

In this example, some null values are also created using Numpy`s np.nan method and passed to a list. & # 39; bfill & # 39; passed to fill_method . bfill stands for Back fill and will fill zero values with values in their next position.

# pandas module import

import pandas as pd

# numpy module import

import numpy as np

# create a list

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


  
  # create series  
  series   =   pd.Series (  list  ) 
  
  # calling method  
  result   =   series.pct_change (fill_method   =   `bfill`  ) 
   
  # display  
  result

Exit:

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As you can see from the output, the value at position 1 is 40 because the NaN has been replaced by 14. Therefore, (14-10 / 10) * 100 = 40. The next value is 0 because the percentage change in 14 and 14 is 0,

Python | Pandas Series.pct_change ()

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