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.

Practice

1. Which step in the data analysis workflow involves removing errors and fixing missing values in the data?

easy

A. Collect

B. Clean

C. Visualize

D. Conclude

Solution

1. Step 1: Understand the meaning of each workflow step

   Cleaning data means fixing errors and handling missing values to prepare data for analysis.

2. Step 2: Match the step to the description

   Removing errors and fixing missing values is exactly what cleaning does, not collecting or visualizing.

3. Final Answer:

   Clean -> Option B

4. Quick Check:

   Cleaning = Fix errors and missing values [OK]

Hint: Cleaning fixes errors and missing data [OK]

Common Mistakes:

• Confusing 'collect' with cleaning
• Thinking visualization fixes data
• Mixing 'conclude' with cleaning step

2. Which Python library is commonly used for data visualization in the data analysis workflow?

easy

A. matplotlib

B. scikit-learn

C. numpy

D. pandas

Solution

1. Step 1: Identify libraries and their main uses

   pandas is for data manipulation, numpy for numerical operations, scikit-learn for machine learning, matplotlib for plotting graphs.

2. Step 2: Choose the library for visualization

   matplotlib is the standard Python library for creating charts and graphs.

3. Final Answer:

   matplotlib -> Option A

4. Quick Check:

   Visualization = matplotlib [OK]

Hint: matplotlib is for plotting graphs [OK]

Common Mistakes:

• Choosing pandas for visualization
• Confusing numpy with plotting
• Selecting scikit-learn for charts

3. What will be the output of this Python code snippet?

import pandas as pd

data = {'age': [25, 30, None, 22], 'score': [88, 92, 85, None]}
df = pd.DataFrame(data)
clean_df = df.dropna()
print(clean_df)

medium

A. DataFrame with all rows including those with None values

B. DataFrame with rows containing None values replaced by 0

C. DataFrame with rows containing None values removed

D. Error because dropna() is not a valid method

Solution

1. Step 1: Understand what dropna() does in pandas

   dropna() removes rows that have any missing (None/NaN) values.

2. Step 2: Apply dropna() to the DataFrame

   Rows with None in 'age' or 'score' are removed, leaving only rows with complete data.

3. Final Answer:

   DataFrame with rows containing None values removed -> Option C

4. Quick Check:

   dropna() removes rows with missing data [OK]

Hint: dropna() removes rows with missing values [OK]

Common Mistakes:

• Thinking dropna() replaces missing values
• Expecting an error from dropna()
• Assuming dropna() keeps all rows

4. Identify the error in this data exploration code and choose the correct fix:

import pandas as pd

data = {'height': [170, 165, 180], 'weight': [65, 70, 75]}
df = pd.DataFrame(data)
print(df.mean(axis=1))

medium

A. No error, code runs correctly

B. Use df.mean() without axis parameter

C. Replace df.mean(axis=1) with df.sum(axis=1)

D. Change axis=1 to axis=0 to get mean of columns

Solution

1. Step 1: Understand what df.mean(axis=1) does

   axis=1 calculates the mean across columns for each row, which is valid here.

2. Step 2: Check if code runs without error

   The code runs correctly and prints the mean height and weight per row.

3. Final Answer:

   No error, code runs correctly -> Option A

4. Quick Check:

   df.mean(axis=1) calculates row means [OK]

Hint: axis=1 means row-wise operation [OK]

Common Mistakes:

• Thinking axis=1 causes error
• Confusing axis=0 and axis=1
• Replacing mean with sum unnecessarily

5. You have a dataset with sales data including missing values. Which sequence of steps correctly follows the data analysis workflow to find average sales per region?

Options:
A) Collect data -> Visualize data -> Clean data -> Explore data -> Conclude
B) Collect data -> Clean data -> Explore data -> Visualize data -> Conclude
C) Clean data -> Collect data -> Explore data -> Visualize data -> Conclude
D) Collect data -> Explore data -> Clean data -> Visualize data -> Conclude

hard

A. Collect data -> Visualize data -> Clean data -> Explore data -> Conclude

B. Collect data -> Explore data -> Clean data -> Visualize data -> Conclude

C. Clean data -> Collect data -> Explore data -> Visualize data -> Conclude

D. Collect data -> Clean data -> Explore data -> Visualize data -> Conclude

Solution

1. Step 1: Recall the correct order of data analysis workflow

   The standard order is: collect data, clean data, explore data, visualize data, then conclude.

2. Step 2: Match the options to this order

   Collect data -> Clean data -> Explore data -> Visualize data -> Conclude matches the correct sequence exactly.

3. Final Answer:

   Collect data -> Clean data -> Explore data -> Visualize data -> Conclude -> Option D

4. Quick Check:

   Workflow order = B [OK]

Hint: Follow collect -> clean -> explore -> visualize -> conclude [OK]

Common Mistakes:

• Visualizing before cleaning data
• Cleaning data before collecting it
• Exploring data before cleaning