Dropping columns and rows in Pandas - Mini Project: Build & Apply

Dropping columns and rows

📖 Scenario: You work as a data analyst for a small online store. You have a table of sales data, but some columns and rows are not needed for your analysis.

🎯 Goal: You will learn how to remove unwanted columns and rows from a table using pandas. This helps clean your data and focus on what matters.

📋 What You'll Learn

Create a pandas DataFrame with given sales data

Create a list of columns to drop

Drop specified columns from the DataFrame

Drop specified rows from the DataFrame

Print the final cleaned DataFrame

💡 Why This Matters

🌍 Real World

Cleaning data by removing unnecessary columns and rows is a common step before analysis or visualization.

💼 Career

Data analysts and data scientists often need to clean datasets to focus on relevant information and improve model accuracy.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales with these exact columns and rows:
'OrderID': [101, 102, 103, 104, 105],
'Product': ['Shirt', 'Pants', 'Hat', 'Shoes', 'Socks'],
'Quantity': [2, 1, 4, 1, 3],
'Price': [20, 40, 15, 60, 5],
'Discount': [0, 5, 0, 10, 0]

Pandas

import pandas as pd
# Create the DataFrame called sales with the given data
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary of lists for each column.

2

Create a list of columns to drop

Create a list called drop_columns containing the column names 'Discount' and 'Price' that you want to remove from the DataFrame.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'OrderID': [101, 102, 103, 104, 105],
    'Product': ['Shirt', 'Pants', 'Hat', 'Shoes', 'Socks'],
    'Quantity': [2, 1, 4, 1, 3],
    'Price': [20, 40, 15, 60, 5],
    'Discount': [0, 5, 0, 10, 0]
})
# Create a list called drop_columns with 'Discount' and 'Price'
# Your code here

Need a hint?

Use a list with the exact column names to drop.

3

Drop the specified columns and rows

Create a new DataFrame called cleaned_sales by dropping the columns in drop_columns from sales. Then drop the rows with index 1 and 3 from cleaned_sales.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'OrderID': [101, 102, 103, 104, 105],
    'Product': ['Shirt', 'Pants', 'Hat', 'Shoes', 'Socks'],
    'Quantity': [2, 1, 4, 1, 3],
    'Price': [20, 40, 15, 60, 5],
    'Discount': [0, 5, 0, 10, 0]
})
drop_columns = ['Discount', 'Price']
# Drop the columns in drop_columns from sales and assign to cleaned_sales
# Then drop rows with index 1 and 3 from cleaned_sales
# Your code here

Need a hint?

Use drop(columns=...) to remove columns and drop(index=[...]) to remove rows.

4

Print the cleaned DataFrame

Print the cleaned_sales DataFrame to see the final table after dropping columns and rows.

Pandas

import pandas as pd
sales = pd.DataFrame({
    'OrderID': [101, 102, 103, 104, 105],
    'Product': ['Shirt', 'Pants', 'Hat', 'Shoes', 'Socks'],
    'Quantity': [2, 1, 4, 1, 3],
    'Price': [20, 40, 15, 60, 5],
    'Discount': [0, 5, 0, 10, 0]
})
drop_columns = ['Discount', 'Price']
cleaned_sales = sales.drop(columns=drop_columns)
cleaned_sales = cleaned_sales.drop(index=[1, 3])
# Print cleaned_sales
# Your code here

Need a hint?

Use print(cleaned_sales) to display the DataFrame.