Python - Select multiple columns from a Pandas dataframe

Selecting multiple columns from a Pandas DataFrame is a common operation in data analysis. You can select specific columns using square brackets with column names to create a subset of your data.

Basic Syntax

To select multiple columns, use double square brackets with a list of column names ?

# Syntax: df[['column1', 'column2', 'column3']]

Creating Sample Data

Let's create a sample DataFrame to demonstrate column selection ?

import pandas as pd

# Create sample sales data
data = {
    'Car': ['BMW', 'Lexus', 'Audi', 'Jaguar', 'Mustang'],
    'Reg_Price': [2500, 3500, 2500, 2000, 2500],
    'Units': [100, 80, 120, 70, 110],
    'Discount': [5, 10, 8, 12, 6]
}

dataFrame = pd.DataFrame(data)
print("Complete DataFrame:")
print(dataFrame)

Complete DataFrame:
       Car  Reg_Price  Units  Discount
0      BMW       2500    100         5
1    Lexus       3500     80        10
2     Audi       2500    120         8
3   Jaguar       2000     70        12
4  Mustang       2500    110         6

Selecting Two Columns

Select specific columns by passing their names in a list ?

import pandas as pd

data = {
    'Car': ['BMW', 'Lexus', 'Audi', 'Jaguar', 'Mustang'],
    'Reg_Price': [2500, 3500, 2500, 2000, 2500],
    'Units': [100, 80, 120, 70, 110],
    'Discount': [5, 10, 8, 12, 6]
}

dataFrame = pd.DataFrame(data)

# Select Reg_Price and Units columns
selected_columns = dataFrame[['Reg_Price', 'Units']]
print("Selected columns (Reg_Price and Units):")
print(selected_columns)

Selected columns (Reg_Price and Units):
   Reg_Price  Units
0       2500    100
1       3500     80
2       2500    120
3       2000     70
4       2500    110

Selecting Multiple Columns

You can select any number of columns by including them in the list ?

import pandas as pd

data = {
    'Car': ['BMW', 'Lexus', 'Audi', 'Jaguar', 'Mustang'],
    'Reg_Price': [2500, 3500, 2500, 2000, 2500],
    'Units': [100, 80, 120, 70, 110],
    'Discount': [5, 10, 8, 12, 6]
}

dataFrame = pd.DataFrame(data)

# Select three columns
selected_columns = dataFrame[['Car', 'Reg_Price', 'Discount']]
print("Selected columns (Car, Reg_Price, and Discount):")
print(selected_columns)

Selected columns (Car, Reg_Price, and Discount):
       Car  Reg_Price  Discount
0      BMW       2500         5
1    Lexus       3500        10
2     Audi       2500         8
3   Jaguar       2000        12
4  Mustang       2500         6

Using Variables for Column Names

Store column names in a variable for reusability and cleaner code ?

import pandas as pd

data = {
    'Car': ['BMW', 'Lexus', 'Audi', 'Jaguar', 'Mustang'],
    'Reg_Price': [2500, 3500, 2500, 2000, 2500],
    'Units': [100, 80, 120, 70, 110],
    'Discount': [5, 10, 8, 12, 6]
}

dataFrame = pd.DataFrame(data)

# Define columns to select
columns_to_select = ['Car', 'Units']
result = dataFrame[columns_to_select]
print("Using variable for column selection:")
print(result)

Using variable for column selection:
       Car  Units
0      BMW    100
1    Lexus     80
2     Audi    120
3   Jaguar     70
4  Mustang    110

Conclusion

Use double square brackets df[['col1', 'col2']] to select multiple columns from a Pandas DataFrame. This creates a new DataFrame containing only the specified columns, which is useful for data analysis and visualization tasks.