info() for column types in Data Analysis Python - Mini Project: Build & Apply

Using info() to Check Column Types in a DataFrame

📖 Scenario: You work in a small shop and keep track of your sales data in a table. You want to understand what kind of information each column holds, like numbers or words, to help you analyze it better.

🎯 Goal: Learn how to use the info() method in pandas to see the types of columns in a sales data table.

📋 What You'll Learn

Create a pandas DataFrame with specific sales data

Use a variable to hold the DataFrame

Call the info() method on the DataFrame to display column types

Print the return value of info() to the screen

💡 Why This Matters

🌍 Real World

Checking column types helps you know if your data is ready for calculations or needs cleaning, like fixing text or missing numbers.

💼 Career

Data scientists and analysts often use <code>info()</code> to quickly understand the structure of new datasets before working on them.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and values: 'Product' with ['Apple', 'Banana', 'Carrot'], 'Quantity' with [10, 5, 7], and 'Price' with [0.5, 0.3, 0.2].

Data Analysis Python

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

Need a hint?

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

2

Prepare to check column types

Create a variable called info_output and assign it the result of calling sales_data.info(buf=None). This prints the DataFrame information including column types to the screen and returns None.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Quantity': [10, 5, 7],
    'Price': [0.5, 0.3, 0.2]
})
# Assign the info output of sales_data to info_output
# Your code here

Need a hint?

sales_data.info(buf=None) prints the DataFrame info to the screen and returns None, which is assigned to info_output.

3

Print the info output

Use print() to display the variable info_output to see what info() returns.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Quantity': [10, 5, 7],
    'Price': [0.5, 0.3, 0.2]
})
info_output = sales_data.info(buf=None)
# Print the info_output variable
# Your code here

Need a hint?

Just use print(info_output) to show the return value.

4

View the column types output

Run the program and observe the printed output showing the DataFrame info including the column names, non-null counts, and data types, followed by None.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Quantity': [10, 5, 7],
    'Price': [0.5, 0.3, 0.2]
})
info_output = sales_data.info(buf=None)
print(info_output)
# Run and check the output

Need a hint?

The output shows the DataFrame info including column types like object for text and int64 or float64 for numbers.