Combining multiple cleaning steps in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of combined cleaning steps on a DataFrame

What is the output of the following code after applying multiple cleaning steps?

Pandas

import pandas as pd

data = {'Name': ['Alice', 'Bob', None, 'David'],
        'Age': [25, None, 30, 22],
        'City': ['New York', 'Los Angeles', 'New York', None]}
df = pd.DataFrame(data)

# Cleaning steps
result = (df.dropna(subset=['Name'])
            .fillna({'Age': df['Age'].mean(), 'City': 'Unknown'})
            .reset_index(drop=True))
print(result)

A

    Name   Age         City
0  Alice  25.0     New York
1    Bob  27.0       Los Angeles
2  David  22.0      Unknown

B

    Name   Age         City
0  Alice  25.0     New York
1    Bob  25.666667  Los Angeles
2  David  22.0      New York

C

    Name   Age         City
0  Alice  25.0     New York
1    Bob  NaN       Los Angeles
2  David  22.0      Unknown

D

    Name   Age         City
0  Alice  25.0     New York
1    Bob  25.666667  Los Angeles
2  David  22.0      Unknown

💡 Hint

Look carefully at how missing values are handled and what the mean age is.

The code first removes rows where 'Name' is missing. Then it fills missing 'Age' with the mean of the 'Age' column (which is (25 + 30 + 22)/3 = 25.6667). Missing 'City' values are replaced with 'Unknown'. The index is reset.

❓ data_output

intermediate

Number of rows after multiple cleaning steps

After applying the cleaning steps below, how many rows remain in the DataFrame?

Pandas

import pandas as pd

data = {'Product': ['A', 'B', 'C', 'D', None],
        'Price': [10, None, 15, 20, 25],
        'Stock': [100, 200, None, 150, 300]}
df = pd.DataFrame(data)

cleaned = (df.dropna(subset=['Product'])
             .fillna({'Price': 0, 'Stock': 0})
             .query('Price > 0 and Stock > 0'))
print(len(cleaned))

A 3

B 2

C 4

D 5

💡 Hint

Check which rows are dropped and which remain after filtering.

Row with Product None is dropped. Missing Price and Stock are filled with 0. Then rows with Price > 0 and Stock > 0 remain. Rows B (Price NaN replaced with 0) and C (Stock NaN replaced with 0) are filtered out. Remaining are A, D. So 2 rows remain.

🔧 Debug

advanced

Identify the error in combined cleaning steps

What error does the following code raise?

Pandas

import pandas as pd

data = {'A': [1, 2, None], 'B': [4, None, 6]}
df = pd.DataFrame(data)

result = df.bfill().dropna(subset=['C'])

A KeyError: 'C'

B TypeError: fillna() got an unexpected keyword argument 'method'

C AttributeError: 'DataFrame' object has no attribute 'backfill'

D No error, returns DataFrame

💡 Hint

Check if the column 'C' exists before dropping rows based on it.

The DataFrame has columns 'A' and 'B' only. Trying to drop rows with missing values in column 'C' raises a KeyError because 'C' does not exist.

❓ visualization

advanced

Resulting plot after cleaning and grouping

After cleaning and grouping the data, what does the bar plot show?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Category': ['X', 'Y', 'X', 'Z', None],
        'Value': [10, 20, None, 15, 5]}
df = pd.DataFrame(data)

cleaned = df.dropna(subset=['Category']).fillna({'Value': 0})
grouped = cleaned.groupby('Category').sum()

ax = grouped.plot(kind='bar', legend=False)
plt.ylabel('Sum of Values')
plt.title('Sum of Values by Category')
plt.close()  # Prevent actual plot display in test

print(grouped)

A {'Value': {'X': 10.0, 'Y': 20.0, 'Z': 15.0}}

B {'Value': {'X': 10.0, 'Y': 20.0, 'Z': 0.0}}

C {'Value': {'X': 0.0, 'Y': 20.0, 'Z': 15.0}}

D {'Value': {'X': 10.0, 'Y': 0.0, 'Z': 15.0}}

💡 Hint

Look at how missing values in 'Value' are replaced and how grouping sums values.

Rows with missing 'Category' are dropped. Missing 'Value' is replaced with 0. Grouping by 'Category' sums the 'Value' column. For 'X', values are 10 and NaN replaced by 0, sum 10. For 'Y', value 20. For 'Z', value 15.

🚀 Application

expert

Combining multiple cleaning steps for a real dataset

You have a DataFrame with columns 'ID', 'Score', and 'Grade'. You want to:

• Remove rows where 'ID' is missing.
• Fill missing 'Score' with the median score.
• Replace missing 'Grade' with 'Incomplete'.
• Drop rows where 'Score' is less than 50 after filling.

Which code snippet correctly performs all these steps?

A df.fillna({'Score': df['Score'].median(), 'Grade': 'Incomplete'}).dropna(subset=['ID']).query('Score >= 50')

B df.query('Score >= 50').dropna(subset=['ID']).fillna({'Score': df['Score'].median(), 'Grade': 'Incomplete'})

C df.dropna(subset=['ID']).fillna({'Score': df['Score'].median(), 'Grade': 'Incomplete'}).query('Score >= 50')

D df.dropna(subset=['ID']).query('Score >= 50').fillna({'Score': df['Score'].median(), 'Grade': 'Incomplete'})

💡 Hint

Think about the order of operations: drop missing IDs first, then fill missing values, then filter by score.

Option C first removes rows missing 'ID', then fills missing 'Score' and 'Grade', then filters rows with 'Score' >= 50. Other options either fill before dropping missing IDs or filter before filling, which can cause incorrect results.