info() for column types and nulls in Pandas - Mini Project: Build & Apply

Using info() to Check Column Types and Nulls in pandas

📖 Scenario: You work as a data analyst. You receive a small dataset about employees in a company. You want to quickly understand the data types of each column and check if there are any missing values.

🎯 Goal: Learn how to use the info() method in pandas to see column data types and count of non-null values.

📋 What You'll Learn

Create a pandas DataFrame with given employee data

Assign the DataFrame to a variable named df

Use the info() method on df to display column types and null counts

Print the output of df.info()

💡 Why This Matters

🌍 Real World

Data scientists and analysts often need to quickly understand the structure of new datasets, including data types and missing values, before starting analysis.

💼 Career

Knowing how to use pandas info() helps in data cleaning and preparation, which is a key skill for data science and analytics jobs.

1

Create the employee DataFrame

Create a pandas DataFrame called df with these exact columns and values:
'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Age': [25, 30, 35, None],
'Department': ['HR', 'IT', 'Finance', 'IT']

Pandas

import pandas as pd
# Create the DataFrame df 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 values.

2

Prepare to check data info

Assign the DataFrame df to a variable called data to prepare for analysis.

Pandas

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [25, 30, 35, None],
    'Department': ['HR', 'IT', 'Finance', 'IT']
})
# Assign df to data
# Your code here

Need a hint?

Just assign df to data with data = df.

3

Use info() to check column types and nulls

Call the info() method on the variable data to see the data types and count of non-null values for each column.

Pandas

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [25, 30, 35, None],
    'Department': ['HR', 'IT', 'Finance', 'IT']
})

data = df
# Use info() on data
# Your code here

Need a hint?

Use data.info() to display the summary of the DataFrame.

4

Print the info() output

Print the output of data.info() by calling print() with data.info() inside it.

Pandas

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [25, 30, 35, None],
    'Department': ['HR', 'IT', 'Finance', 'IT']
})

data = df

data.info()
# Print the info output
# Your code here

Need a hint?

Use print(data.info()) to show the info output in the console.