  # Python | Pandas Series.pct_change ()

NumPy | Python Methods and Functions

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As you can see from the output, the value at position 1 is 40 because the NaN has been replaced by 14.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,