Standardizing column names in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code after standardizing column names?

Given a DataFrame with columns having spaces and uppercase letters, what will be the column names after applying the standardization code?

Pandas

import pandas as pd

df = pd.DataFrame(columns=['First Name', 'Last Name', 'Age'])
df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')
print(list(df.columns))

A ['first name', 'last name', 'age']

B ['First_Name', 'Last_Name', 'Age']

C ['first_name', 'last_name', 'age']

D ['FIRST_NAME', 'LAST_NAME', 'AGE']

💡 Hint

Think about what each string method does: strip(), lower(), and replace().

The code removes spaces around column names, converts all letters to lowercase, and replaces spaces with underscores. So 'First Name' becomes 'first_name'.

❓ data_output

intermediate

How many columns remain after filtering standardized columns?

After standardizing column names, you want to keep only columns that start with 'user_'. How many columns remain?

Pandas

import pandas as pd

df = pd.DataFrame(columns=['User ID', 'User Name', 'Age', 'user_email'])
df.columns = df.columns.str.strip().str.lower().str.replace(' ', '_')
filtered_cols = [col for col in df.columns if col.startswith('user_')]
print(len(filtered_cols))

A 2

B 4

C 1

D 3

💡 Hint

Check which columns start exactly with 'user_' after standardization.

After standardization, columns are ['user_id', 'user_name', 'age', 'user_email']. 'user_id', 'user_name', and 'user_email' start with 'user_'. So 3 columns match.

🔧 Debug

advanced

What error does this code raise when standardizing columns?

Identify the error raised by this code snippet:

Pandas

import pandas as pd

df = pd.DataFrame(columns=['Name', 'Age'])
df.columns = df.columns.str.lower().replace(' ', '_')

A AttributeError: 'Index' object has no attribute 'replace'

B SyntaxError: invalid syntax

C No error, columns are standardized correctly

D TypeError: replace() missing 1 required positional argument

💡 Hint

Remember that str.lower() returns an Index object with string methods available, but replace is a string method, not an Index method.

The replace method is called on the Index object, which does not have it. The correct way is to use str.replace() to apply replace on each string in the Index.

🚀 Application

advanced

Which option correctly standardizes columns to lowercase with underscores?

Choose the code snippet that correctly standardizes DataFrame columns by making them lowercase and replacing spaces with underscores.

A df.columns = df.columns.str.replace(' ', '_').lower()

B df.columns = df.columns.str.lower().str.replace(' ', '_')

C df.columns = df.columns.replace(' ', '_').lower()

D df.columns = df.columns.str.lower().replace(' ', '_')

💡 Hint

Check which methods are available on the Index object and how to chain them.

Only str.lower() and str.replace() are valid methods on the Index's string accessor. The correct chain is df.columns.str.lower().str.replace(' ', '_').

🧠 Conceptual

expert

Why is standardizing column names important in data science projects?

Choose the best reason why standardizing column names is a crucial step in data science workflows.

A It ensures consistent column naming to avoid errors in code and makes data easier to understand and merge.

B It increases the size of the dataset for better model training.

C It automatically fixes missing values in the dataset.

D It encrypts the column names to protect sensitive data.

💡 Hint

Think about how consistent naming helps when writing code and sharing data.

Standardizing column names avoids bugs caused by inconsistent naming, improves readability, and helps when combining datasets from different sources.