Python Pandas - Select a subset of rows and columns combined

To select a subset of rows and columns in Pandas, use the loc indexer. The loc method allows you to filter rows based on conditions and simultaneously select specific columns using boolean indexing.

Creating Sample Data

Let's create a DataFrame with car sales data to demonstrate the concept ?

import pandas as pd

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

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

Original DataFrame:
       Car  Reg_Price  Units
0      BMW       2500    100
1    Lexus       3500     80
2     Audi       2500    120
3   Jaguar       2000     70
4  Mustang       2500    110

Selecting Rows with Conditions

First, let's filter rows where Units are greater than 100 ?

import pandas as pd

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

dataFrame = pd.DataFrame(data)

# Select cars with Units more than 100
filtered_rows = dataFrame[dataFrame["Units"] > 100]
print("Cars with Units more than 100:")
print(filtered_rows)

Cars with Units more than 100:
       Car  Reg_Price  Units
2     Audi       2500    120
4  Mustang       2500    110

Selecting Specific Columns

You can select specific columns by passing a list of column names ?

import pandas as pd

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

dataFrame = pd.DataFrame(data)

# Display only two columns
selected_columns = dataFrame[['Reg_Price', 'Units']]
print("Displaying only Reg_Price and Units columns:")
print(selected_columns)

Displaying only Reg_Price and Units columns:
   Reg_Price  Units
0       2500    100
1       3500     80
2       2500    120
3       2000     70
4       2500    110

Combining Row and Column Selection with loc

The loc method allows you to filter rows and select columns simultaneously ?

import pandas as pd

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

dataFrame = pd.DataFrame(data)

# Select cars where Units > 100 and show only Car column
subset = dataFrame.loc[dataFrame["Units"] > 100, "Car"]
print("Car names with Units > 100:")
print(subset)

# Select multiple columns for filtered rows
subset_multiple = dataFrame.loc[dataFrame["Units"] > 100, ["Car", "Reg_Price"]]
print("\nCar and Price for Units > 100:")
print(subset_multiple)

Car names with Units > 100:
2       Audi
4    Mustang
Name: Car, dtype: object

Car and Price for Units > 100:
       Car  Reg_Price
2     Audi       2500
4  Mustang       2500

Syntax Summary

The loc method follows this syntax pattern ?

# Single column selection
dataFrame.loc[condition, 'column_name']

# Multiple columns selection
dataFrame.loc[condition, ['col1', 'col2']]

# All columns for filtered rows
dataFrame.loc[condition, :]

Conclusion

Use loc to combine row filtering and column selection in one operation. This method provides precise control over which data subsets to extract from your DataFrame.