Numeric types (int64, float64) in Pandas - Mini Project: Build & Apply

Working with Numeric Types in pandas

📖 Scenario: Imagine you are analyzing sales data for a small store. The data includes the number of items sold and the total sales amount. You want to make sure the numbers are stored correctly to do calculations later.

🎯 Goal: You will create a pandas DataFrame with sales data, check and set the numeric types for the columns to int64 and float64, and then display the data types to confirm.

📋 What You'll Learn

Create a pandas DataFrame with specific sales data

Check and set the data types of columns to int64 and float64

Print the data types of the DataFrame columns

💡 Why This Matters

🌍 Real World

Data scientists often need to ensure numeric data is stored in the correct format for calculations, memory efficiency, and compatibility with analysis tools.

💼 Career

Understanding and managing numeric types is essential for data cleaning, preprocessing, and preparing data for machine learning or reporting.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with two columns: 'items_sold' and 'total_sales'. Use these exact values: items_sold as [10, 15, 7, 20], and total_sales as [250.0, 375.5, 175.75, 500.0].

Pandas

import pandas as pd
# Create the sales_data DataFrame with the given values
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary containing the two columns and their values.

2

Check the current data types

Create a variable called current_types that stores the data types of the columns in sales_data using the dtypes attribute.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'items_sold': [10, 15, 7, 20],
    'total_sales': [250.0, 375.5, 175.75, 500.0]
})
# Store the data types of sales_data columns in current_types
# Your code here

Need a hint?

Use sales_data.dtypes to get the data types of each column.

3

Set the correct numeric types for columns

Use the astype method to convert the 'items_sold' column to int64 and the 'total_sales' column to float64. Update the sales_data DataFrame with these new types.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'items_sold': [10, 15, 7, 20],
    'total_sales': [250.0, 375.5, 175.75, 500.0]
})
current_types = sales_data.dtypes
# Convert 'items_sold' to int64 and 'total_sales' to float64
# Your code here

Need a hint?

Use sales_data.astype({'items_sold': 'int64', 'total_sales': 'float64'}) to set the types.

4

Print the data types to confirm

Print the data types of the sales_data DataFrame columns using the dtypes attribute.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'items_sold': [10, 15, 7, 20],
    'total_sales': [250.0, 375.5, 175.75, 500.0]
})
current_types = sales_data.dtypes
sales_data = sales_data.astype({'items_sold': 'int64', 'total_sales': 'float64'})
# Print the data types of sales_data columns
# Your code here

Need a hint?

Use print(sales_data.dtypes) to show the column types.