To select a subset of rows and columns, use the loc. Use the index operator i.e. the square bracket and set conditions in the loc.
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")
Select a subset of rows and columns combined. Right column displays the column you want to display i.e. Cars column here −
dataFrame.loc[dataFrame["Units"] > 100, "Car"]
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) # selecting a subset of rows print("\nSelect cars with Units more than 100: \n",dataFrame[dataFrame["Units"] > 100]) # displaying only two columns res = dataFrame[['Reg_Price','Units']]; print("\nDisplaying only two columns : \n",res) # Select a subset of rows and columns combined # Right column displays the column you want to display i.e. Cars column here res2 = dataFrame.loc[dataFrame["Units"] > 100, "Car"] # display subset print("\nSubset...\n",res2)
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 Select cars with Units more than 100: Car Reg_Price Units 2 Audi 2500 120 4 Mustang 2500 110 Displaying only two columns : Reg_Price Units 0 2500 100 1 3500 80 2 2500 120 3 2000 70 4 2500 110 Subset... 2 Audi 4 Mustang Name: Car, dtype: object