*53*

You can use the **pct_change()** function to calculate the percent change between values in pandas:

#calculate percent change between values in pandas Series s.pct_change() #calculate percent change between rows in pandas DataFrame df['column_name'].pct_change()

The following examples show how to use this function in practice.

**Example 1: Percent Change in pandas Series**

The following code shows how to calculate percent change between values in a pandas Series:

import pandas as pd #create pandas Series s = pd.Series([6, 14, 12, 18, 19]) #calculate percent change between consecutive values s.pct_change() 0 NaN 1 1.333333 2 -0.142857 3 0.500000 4 0.055556 dtype: float64

Hereâ€™s how these values were calculated:

- Index 1: (14 â€“ 6) / 6 =
**1.333333** - Index 2: (12 â€“ 14) / 14 =
**-.142857** - Index 3: (18 â€“ 12) / 12 =
**0.5** - Index 4: (19 â€“ 18) / 18 =
**.055556**

Note that you can also use the **periods** argument to calculate the percent change between values at different intervals:

import pandas as pd #create pandas Series s = pd.Series([6, 14, 12, 18, 19]) #calculate percent change between values 2 positions apart s.pct_change(periods=2) 0 NaN 1 NaN 2 1.000000 3 0.285714 4 0.583333 dtype: float64

Hereâ€™s how these values were calculated:

- Index 2: (12 â€“ 6) / 6 =
**1.000000** - Index 3: (18 â€“ 14) / 14 =
**0.285714** - Index 4: (19 â€“ 12) / 12 =
**.583333**

**Example 2: Percent Change in pandas DataFrame**

The following code shows how to calculate the percent change between consecutive rows in a pandasÂ DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'period': [1, 2, 3, 4, 5], 'sales': [6, 7, 7, 9, 12]}) #view DataFrame df period sales 0 1 6 1 2 7 2 3 7 3 4 9 4 5 12 #calculate percent change between consecutive values in 'sales' column df['sales_pct_change'] = df['sales'].pct_change() #view updated DataFrame df period sales sales_pct_change 0 1 6 NaN 1 2 7 0.166667 2 3 7 0.000000 3 4 9 0.285714 4 5 12 0.333333

Here is how these values were calculated:

- Index 1: (7 â€“ 6) / 6 =
**.166667** - Index 2: (7 â€“ 7) / 7 =
**0.000000** - Index 3: (9 â€“ 7) / 7 =
**.285714** - Index 4: (12 â€“ 9) / 9 =
**.333333**

You can find the complete documentation for the **pct_change()** function here.

**Additional Resources**

How to Calculate the Mean of Columns in Pandas

How to Calculate the Median in Pandas

How to Calculate a Rolling Mean in Pandas

How to Calculate Rolling Correlation in Pandas