Standardizing column names in Pandas - Mini Project: Build & Apply

Standardizing column names

📖 Scenario: You have a dataset with messy column names from different sources. Some columns have spaces, uppercase letters, or special characters. You want to clean these column names so they are all lowercase and use underscores instead of spaces. This makes it easier to work with the data later.

🎯 Goal: Clean and standardize the column names of a pandas DataFrame by making all letters lowercase and replacing spaces with underscores.

📋 What You'll Learn

Create a pandas DataFrame with specific columns

Create a variable to hold the cleaned column names

Use a list comprehension to clean each column name

Assign the cleaned column names back to the DataFrame

Print the updated DataFrame columns

💡 Why This Matters

🌍 Real World

Data scientists often get data from many sources with inconsistent column names. Cleaning column names helps avoid errors and makes data easier to analyze.

💼 Career

Standardizing column names is a common task in data cleaning, which is a key skill for data analysts and data scientists.

1

Create the initial DataFrame

Create a pandas DataFrame called df with columns exactly named 'First Name', 'Last Name', and 'Age'. The data can be any three rows you like.

Pandas

import pandas as pd
# Create the DataFrame df with columns 'First Name', 'Last Name', and 'Age'
# Your code here

Need a hint?

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

2

Create a variable for cleaned column names

Create a variable called clean_columns and set it to an empty list. This will hold the cleaned column names.

Pandas

import pandas as pd

df = pd.DataFrame({
    'First Name': ['Alice', 'Bob', 'Charlie'],
    'Last Name': ['Smith', 'Jones', 'Brown'],
    'Age': [25, 30, 35]
})

# Create an empty list called clean_columns
# Your code here

Need a hint?

Just create an empty list named clean_columns.

3

Clean the column names using list comprehension

Use a list comprehension to fill clean_columns with the column names from df.columns, but make each name lowercase and replace spaces with underscores. Use clean_columns = [col.lower().replace(' ', '_') for col in df.columns].

Pandas

import pandas as pd

df = pd.DataFrame({
    'First Name': ['Alice', 'Bob', 'Charlie'],
    'Last Name': ['Smith', 'Jones', 'Brown'],
    'Age': [25, 30, 35]
})

clean_columns = []

# Use list comprehension to fill clean_columns with lowercase column names with underscores instead of spaces
# Your code here

Need a hint?

Use a list comprehension with col.lower().replace(' ', '_') for each col in df.columns.

4

Assign cleaned names and print the result

Assign clean_columns back to df.columns. Then print df.columns to show the updated column names.

Pandas

import pandas as pd

df = pd.DataFrame({
    'First Name': ['Alice', 'Bob', 'Charlie'],
    'Last Name': ['Smith', 'Jones', 'Brown'],
    'Age': [25, 30, 35]
})

clean_columns = [col.lower().replace(' ', '_') for col in df.columns]

# Assign clean_columns to df.columns
# Print df.columns
# Your code here

Need a hint?

Set df.columns = clean_columns and then print df.columns.