Select DataFrame Rows Based on Conditions in Python Pandas

We can set conditions and fetch DataFrame rows. These conditions can be set using logical operators and even relational operators.

At first, import the required pandas libraries −

import pandas as pd

Let us create a DataFrame and read our CSV file −

dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesRecords.csv")

Fetching dataframe rows with registration price less than 1000. We are using relational operator for this −

dataFrame[dataFrame.Reg_Price < 1000]

Example

Following is the code −

import pandas as pd

# reading csv file
dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesRecords.csv")
print("DataFrame...\n",dataFrame)

# count the rows and columns in a DataFrame
print("\nNumber of rows and column in our DataFrame = ",dataFrame.shape)

# fetching dataframe rows with registration price less than 1000
resData = dataFrame[dataFrame.Reg_Price < 1000]

print("DataFrame...\n",resData)

Output

This will produce the following output −

DataFrame...
           Car   Date_of_Purchase   Reg_Price
0          BMW         10/10/2020        1000
1        Lexus         10/12/2020         750
2         Audi         10/17/2020         750
3       Jaguar         10/16/2020        1500
4      Mustang         10/19/2020        1100
5  Lamborghini         10/22/2020        1000

Number of rows and column in our DataFrame = (6, 3)
DataFrame...
     Car   Date_of_Purchase   Reg_Price
1  Lexus         10/12/2020         750
2   Audi         10/17/2020         750