Python - Grouping columns in Pandas Dataframe

To group columns in Pandas dataframe, use the groupby(). At first, let us create Pandas dataframe −

dataFrame = pd.DataFrame(
   {
      "Car": ["Audi", "Lexus", "Audi", "Mercedes", "Audi", "Lexus", "Mercedes", "Lexus", "Mercedes"],
      "Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]
   }
)

Let us now group according to Car column −

res = dataFrame.groupby("Car")

After grouping, we will use functions to find the means Registration prices (Reg_Price) of grouped car names −

res.mean()

This calculates mean of the Registration price according to column Car.

Example

Following is the code −

import pandas as pd

# dataframe with one of the columns as Reg_Price
dataFrame = pd.DataFrame(
   {
      "Car": ["Audi", "Lexus", "Audi", "Mercedes", "Audi", "Lexus", "Mercedes", "Lexus", "Mercedes"],
      "Reg_Price": [1000, 1400, 1100, 900, 1700, 1800, 1300, 1150, 1350]
   }
)

print"DataFrame...\n",dataFrame

# grouped according to Car
res = dataFrame.groupby("Car")

print"\nMean of Registration Price grouped according to Car names...\n",res.mean()

Output

This will produce the following output −

DataFrame...
      Car    Reg_Price
0     Audi        1000
1    Lexus        1400
2     Audi        1100
3 Mercedes         900
4     Audi        1700
5    Lexus        1800
6 Mercedes        1300
7    Lexus        1150
8 Mercedes        1350
Mean of Registration Price grouped according to Car names...
           Reg_Price
    Car
    Audi   1266.666667
   Lexus   1450.000000
Mercedes   1183.333333