astype() for type conversion in Pandas - Mini Project: Build & Apply

Using astype() for Type Conversion in pandas

📖 Scenario: You work in a small store and have a list of products with their prices and quantities. The data was entered as text, but you need to convert the prices and quantities to numbers to do calculations.

🎯 Goal: You will create a pandas DataFrame with product data, then convert the price and quantity columns from text to the correct number types using astype(). Finally, you will print the updated DataFrame.

📋 What You'll Learn

Create a pandas DataFrame with product names, prices, and quantities as strings

Create a variable to hold the list of columns to convert

Use astype() to convert the price column to float and quantity column to integer

Print the final DataFrame to see the changes

💡 Why This Matters

🌍 Real World

Stores and businesses often get data as text but need to convert it to numbers for calculations like totals and averages.

💼 Career

Data analysts and scientists frequently convert data types to prepare data for analysis and visualization.

1

Create the initial DataFrame with product data

Import pandas as pd. Create a DataFrame called products with these exact columns and values:
'Product': ['Apple', 'Banana', 'Carrot'],
'Price': ['0.50', '0.30', '0.20'],
'Quantity': ['10', '20', '30']. All values should be strings.

Pandas

import pandas as pd
# Create the products DataFrame with the exact data
# Your code here

Need a hint?

Use pd.DataFrame() with a dictionary where keys are column names and values are lists of strings.

2

Create a list of columns to convert

Create a list called cols_to_convert that contains the exact column names 'Price' and 'Quantity'.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': ['0.50', '0.30', '0.20'],
    'Quantity': ['10', '20', '30']
})
# Create the list cols_to_convert with 'Price' and 'Quantity'
# Your code here

Need a hint?

Just assign the list ['Price', 'Quantity'] to the variable cols_to_convert.

3

Convert the columns to correct types using astype()

Use astype() to convert the 'Price' column to float and the 'Quantity' column to int. Update the products DataFrame with these conversions.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': ['0.50', '0.30', '0.20'],
    'Quantity': ['10', '20', '30']
})
cols_to_convert = ['Price', 'Quantity']
# Convert 'Price' to float and 'Quantity' to int using astype()
# Your code here

Need a hint?

Use products['Price'] = products['Price'].astype(float) and similarly for 'Quantity' with int.

4

Print the updated DataFrame

Print the products DataFrame to see the updated data types and values.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': ['0.50', '0.30', '0.20'],
    'Quantity': ['10', '20', '30']
})
cols_to_convert = ['Price', 'Quantity']
products['Price'] = products['Price'].astype(float)
products['Quantity'] = products['Quantity'].astype(int)
# Print the products DataFrame
# Your code here

Need a hint?

Use print(products) to show the DataFrame.