To select a subset of rows, use conditions and fetch data.
Let’s say the following are the contents of our CSV file opened in Microsoft Excel −
At first, load data from a CSV file into a Pandas DataFrame −
dataFrame = pd.read_csv("C:\\Users\\amit_\\Desktop\\SalesData.csv")
Let’s say we want the Car records with “Units” more than 100 i.e. subset of rows. For this, use −
dataFrame[dataFrame["Units"] > 100]
Now, let’s say we want the Car records with “Reg_Price” less than 100 i.e. subset of rows. For this, use −
dataFrame[dataFrame["Reg_Price"] < 3000]
Example
Following is the code −
import pandas as pd # Load data from a CSV file into a Pandas DataFrame dataFrame = pd.read_csv("C:\\Users\\amit_\\Desktop\\SalesData.csv") print("\nReading the CSV file...\n",dataFrame) # displaying two columns res2 = dataFrame[['Reg_Price','Units']]; print("\nDisplaying two columns : \n",res2) # selecting a subset of rows print("\nSelect cars with Units more than 100: \n",dataFrame[dataFrame["Units"] > 100]) # selecting a subset of rows print("\nSelect cars with Reg_Price less than 3000: \n",dataFrame[dataFrame["Reg_Price"] < 3000])
Output
This will produce the following output −
Reading the CSV file... Car Reg_Price Units 0 BMW 2500 100 1 Lexus 3500 80 2 Audi 2500 120 3 Jaguar 2000 70 4 Mustang 2500 110 Displaying only one column Car : Reg_Price Units 0 2500 100 1 3500 80 2 2500 120 3 2000 70 4 2500 110 Name: Car, dtype: object Select cars with Units more than 100: Car Reg_Price Units 2 Audi 2500 120 4 Mustang 2500 110 Select cars with Reg_Price less than 3000: Car Reg_Price Units 0 BMW 2500 100 2 Audi 2500 120 3 Jaguar 2000 70 4 Mustang 2500 110