Changing data types (astype) in Data Analysis Python - Mini Project: Build & Apply

Changing Data Types with astype

📖 Scenario: You work in a small store and keep track of sales data. Sometimes the numbers are saved as text, but you need them as numbers to do math.

🎯 Goal: You will change the data types of sales data from text to numbers using astype so you can analyze it easily.

📋 What You'll Learn

Create a pandas DataFrame with sales data as strings

Create a list of columns to convert

Use astype to change the data types of those columns to integers

Print the DataFrame to see the changes

💡 Why This Matters

🌍 Real World

Stores and businesses often get data as text but need numbers to calculate totals, averages, or do other analysis.

💼 Career

Data analysts and scientists frequently convert data types to prepare data for calculations and visualizations.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales with these columns and string values: 'Product' with values 'Pen', 'Notebook', 'Eraser'; 'Quantity' with values '10', '5', '8'; and 'Price' with values '1.5', '3.0', '0.5'.

Data Analysis Python

import pandas as pd
# Create the sales DataFrame with string values
# Your code here

Hint

Use pd.DataFrame with a dictionary where each key is a column name and each value is a list of strings.

2

Create a list of columns to convert

Create a list called cols_to_convert containing the column names 'Quantity' and 'Price' that you want to change from strings to numbers.

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Quantity': ['10', '5', '8'],
    'Price': ['1.5', '3.0', '0.5']
})
# Create a list called cols_to_convert with 'Quantity' and 'Price'
# Your code here

Hint

Make a list with the exact column names as strings.

3

Convert the columns to numeric types

Use astype on the sales DataFrame to convert the columns in cols_to_convert to numeric types. Convert 'Quantity' to integers and 'Price' to floats. Assign the result back to sales[cols_to_convert].

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Quantity': ['10', '5', '8'],
    'Price': ['1.5', '3.0', '0.5']
})
cols_to_convert = ['Quantity', 'Price']
# Convert 'Quantity' to int and 'Price' to float using astype
# Your code here

Hint

Use sales['Quantity'].astype(int) and sales['Price'].astype(float) to convert each column separately.

4

Print the updated DataFrame

Print the sales DataFrame to see the columns 'Quantity' and 'Price' as numbers.

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Quantity': ['10', '5', '8'],
    'Price': ['1.5', '3.0', '0.5']
})
cols_to_convert = ['Quantity', 'Price']
sales['Quantity'] = sales['Quantity'].astype(int)
sales['Price'] = sales['Price'].astype(float)
# Print the sales DataFrame
# Your code here

Hint

Use print(sales) to show the DataFrame.