Combining multiple cleaning steps in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to remove rows with missing values from the DataFrame.

Pandas

cleaned_df = df.[1]()

Drag options to blanks, or click blank then click option'

Aisnull

Bfillna

Creplace

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna() instead of dropna()

Using isnull() which only checks for missing values

The dropna() method removes rows with missing values.

2fill in blank

medium

Complete the code to convert the 'date' column to datetime format.

Pandas

df['date'] = pd.[1](df['date'])

Drag options to blanks, or click blank then click option'

Ato_numeric

Bto_datetime

Cto_string

Dto_timedelta

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_numeric() which converts to numbers

Using to_timedelta() which is for time differences

The pd.to_datetime() function converts strings to datetime objects.

3fill in blank

hard

Fix the error in the code to rename the column 'old_name' to 'new_name'.

Pandas

df = df.rename(columns=[1])

Drag options to blanks, or click blank then click option'

A{'old_name': 'new_name'}

B['old_name', 'new_name']

C('old_name', 'new_name')

D'old_name:new_name'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a list or tuple instead of a dictionary

Using a string with colon instead of a dictionary

The rename() method expects a dictionary mapping old names to new names.

4fill in blank

hard

Fill both blanks to create a new column 'length' with the length of strings in 'name' column, but only for names longer than 3 characters.

Pandas

df['length'] = df['name'].apply(lambda x: [1] if len(x) [2] 3 else 0)

Drag options to blanks, or click blank then click option'

Alen(x)

B>

C<

Dx

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'x' instead of 'len(x)'

Using '<' instead of '>' in the condition

We use len(x) to get string length and check if length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 4 characters.

Pandas

lengths = { [1]: [2] for [3] in words if len([3]) > 4 }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently

Using item instead of word

The dictionary comprehension uses word as key and len(word) as value, iterating over word in words.

Practice

1. What is the main benefit of combining multiple cleaning steps in pandas using method chaining?

easy

A. It creates multiple copies of the data.

B. It slows down the data processing.

C. It makes the code shorter and easier to read.

D. It hides errors in the data.

Solution

1. Step 1: Understand method chaining

   Method chaining lets you apply many cleaning steps one after another in one line or block.

2. Step 2: Benefits of chaining

   This approach reduces code length and improves readability by avoiding intermediate variables.

3. Final Answer:

   It makes the code shorter and easier to read. -> Option C

4. Quick Check:

   Method chaining = shorter, readable code [OK]

Hint: Chaining keeps code neat and easy to follow [OK]

Common Mistakes:

• Thinking chaining slows down processing
• Believing it creates multiple data copies
• Assuming it hides data errors

2. Which of the following is the correct syntax to chain pandas cleaning methods to drop missing values and then reset the index?

easy

A. df.dropna().reset_index(drop=True

B. df.dropna.reset_index(drop=True)

C. df.dropna().reset_index(drop=True))

D. df.dropna().reset_index(drop=True)

Solution

1. Step 1: Check method calls

   Each method must be called with parentheses: dropna() and reset_index().

2. Step 2: Verify parentheses balance

   df.dropna().reset_index(drop=True) has balanced parentheses and correct chaining syntax.

3. Final Answer:

   df.dropna().reset_index(drop=True) -> Option D

4. Quick Check:

   Method calls need parentheses [OK]

Hint: Always use () after pandas methods when chaining [OK]

Common Mistakes:

• Missing parentheses after methods
• Unbalanced parentheses causing syntax errors
• Using incorrect method names

3. Given the DataFrame df below, what will be the output after applying the chained cleaning steps?

import pandas as pd
df = pd.DataFrame({
  'A': [1, 2, None, 4],
  'B': ['x', None, 'y', 'z']
})
cleaned = df.dropna().reset_index(drop=True)

medium

A. {'A': [1.0, 4.0], 'B': ['x', 'y']}

B. {'A': [1.0, 4.0], 'B': ['x', 'z']}

C. {'A': [1.0, 2.0, 4.0], 'B': ['x', 'y', 'z']}

D. {'A': [1.0, 2.0, 4.0], 'B': ['x', None, 'z']}

Solution

1. Step 1: Apply dropna()

   Rows with any missing values are removed. Row 1 and 2 have missing values, so only rows 0 and 3 remain.

2. Step 2: Reset index

   Resetting index with drop=True gives new index 0 and 1 for the remaining rows.

3. Final Answer:

   {'A': [1.0, 4.0], 'B': ['x', 'z']} -> Option B

4. Quick Check:

   dropna removes rows with NaN, reset_index fixes index [OK]

Hint: dropna removes rows with NaN, reset_index fixes numbering [OK]

Common Mistakes:

• Forgetting dropna removes rows with any NaN
• Not resetting index after dropping rows
• Assuming missing values are filled automatically

4. The following code is intended to clean a DataFrame by filling missing values with 0 and then dropping duplicates. What is the error?

cleaned = df.fillna(0).drop_duplicates

medium

A. Missing parentheses after drop_duplicates method.

B. fillna should be called after drop_duplicates.

C. drop_duplicates does not exist in pandas.

D. fillna cannot be chained with drop_duplicates.

Solution

1. Step 1: Check method calls

   fillna(0) is called correctly with parentheses, but drop_duplicates is missing parentheses.

2. Step 2: Understand method chaining syntax

   All methods must be called with parentheses to execute them.

3. Final Answer:

   Missing parentheses after drop_duplicates method. -> Option A

4. Quick Check:

   Methods need () to run [OK]

Hint: Always add () after pandas methods when chaining [OK]

Common Mistakes:

• Forgetting parentheses after methods
• Changing method order unnecessarily
• Thinking drop_duplicates is not a pandas method

5. You have a DataFrame df with columns 'Name', 'Age', and 'Score'. Some 'Age' values are missing, and some rows are duplicates. You want to fill missing 'Age' with the average age, remove duplicates, and reset the index. Which chained code correctly does this?

hard

A. df.fillna({'Age': df['Age'].mean()}).drop_duplicates().reset_index(drop=True)

B. df.drop_duplicates().fillna(df['Age'].mean()).reset_index(drop=True)

C. df.fillna(df['Age'].mean()).drop_duplicates.reset_index(drop=True)

D. df.reset_index(drop=True).fillna({'Age': df['Age'].mean()}).drop_duplicates()

Solution

1. Step 1: Fill missing 'Age' with average

   Use fillna with a dictionary to fill only 'Age' column with its mean.

2. Step 2: Remove duplicates and reset index

   Then drop duplicates and reset index with drop=True to clean the DataFrame fully.

3. Final Answer:

   df.fillna({'Age': df['Age'].mean()}).drop_duplicates().reset_index(drop=True) -> Option A

4. Quick Check:

   Fill missing, drop duplicates, reset index in order [OK]

Hint: Fill missing with dict, then drop duplicates, reset index last [OK]

Common Mistakes:

• Filling all columns instead of only 'Age'
• Missing parentheses on drop_duplicates
• Resetting index before cleaning data