Data analysis workflow (collect, clean, explore, visualize, conclude) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this data cleaning step?

Given a DataFrame with missing values, what will be the result after running this code?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, None, 4],
    'B': [None, 2, 3, 4]
})

cleaned = df.dropna()
print(cleaned)

A

     A    B
0  1.0  NaN
1  2.0  2.0
2  NaN  3.0
3  4.0  4.0

B

     A    B
1  2.0  2.0
3  4.0  4.0

C

Empty DataFrame
Columns: [A, B]
Index: []

D

     A    B
0  1.0  NaN
3  4.0  4.0

💡 Hint

dropna() removes rows with any missing values.

The dropna() function removes rows where any column has NaN. Only rows 1 and 3 have no missing values.

❓ data_output

intermediate

How many unique categories are in this dataset after cleaning?

After removing duplicates, how many unique 'Category' values remain?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Category': ['A', 'B', 'A', 'C', 'B', 'D', 'D', 'E']
})

cleaned = df.drop_duplicates()
unique_count = cleaned['Category'].nunique()
print(unique_count)

A 5

B 8

C 4

D 6

💡 Hint

drop_duplicates() removes repeated rows, then count unique categories.

After removing duplicates, categories are A, B, C, D, E, total 5 unique values.

❓ visualization

advanced

Which plot correctly shows the distribution of 'Age' after cleaning?

Given this cleaned DataFrame, which plot code will produce a histogram of 'Age'?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Age': [22, 25, 29, 35, 40, 40, 22, 30]
})

A

plt.hist(df['Age'])
plt.show()

B

plt.plot(df['Age'])
plt.show()

C

plt.scatter(df.index, df['Age'])
plt.show()

D

plt.boxplot(df['Age'])
plt.show()

💡 Hint

Histogram shows frequency distribution of a single variable.

plt.hist() creates a histogram showing how ages are distributed in bins.

🧠 Conceptual

advanced

What is the correct order of steps in a data analysis workflow?

Arrange these steps in the correct order for a typical data analysis workflow.

A 4,2,1,3,5

B 1,2,4,3,5

C 2,4,1,3,5

D 2,1,4,3,5

💡 Hint

Think about what you do first: get data, then clean it, then explore.

First collect data, then clean it, explore patterns, visualize findings, and conclude.

🚀 Application

expert

What is the mean value of 'Score' after cleaning and filtering?

Given this DataFrame, after removing rows with missing 'Score' and filtering scores >= 70, what is the mean?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Name': ['Anna', 'Ben', 'Cara', 'Dan', 'Eva'],
    'Score': [85, None, 70, 65, 90]
})

cleaned = df.dropna(subset=['Score'])
filtered = cleaned[cleaned['Score'] >= 70]
mean_score = filtered['Score'].mean()
print(round(mean_score, 2))

A 75.00

B 80.00

C 81.67

D 70.00

💡 Hint

Calculate mean after filtering scores 70 and above.

Scores after cleaning and filtering are 85, 70, 90. Mean is (85+70+90)/3 = 81.67.