Exploratory data analysis in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a CSV file into a DataFrame using pandas.

ML Python

import pandas as pd
df = pd.read_csv([1])

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

Adata.csv

B"data.csv"

Cread.csv

Dcsv.read

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the file name.

Using wrong function names like read.csv.

The file path must be a string, so it needs quotes around it.

2fill in blank

medium

Complete the code to show the first 5 rows of the DataFrame.

ML Python

print(df.[1]())

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

Atop

Btail

Chead

Dshow

Attempts:

3 left

💡 Hint

Common Mistakes

Using tail() which shows last rows.

Using non-existent methods like show() or top().

The head() method shows the first 5 rows by default.

3fill in blank

hard

Fix the error in the code to get summary statistics of the DataFrame.

ML Python

summary = df.[1]()

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

Adescribe

Bsummary

Cstats

Dinfo

Attempts:

3 left

💡 Hint

Common Mistakes

Using info() which shows data types, not statistics.

Using non-existent methods like summary() or stats().

The describe() method returns summary statistics like mean and std.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

ML Python

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

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using 'word' instead of 'len(word)' for the dictionary value.

We want the length of each word (len(word)) and only words longer than 3 (>) are included.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.

ML Python

result = [1]: [2] for k, v in data.items() if v [3] 0 }

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys.

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

Using k instead of v for values.

Keys are converted to uppercase with k.upper(), values are v, and we filter values greater than 0 with '>'.

Practice

1. What is the main goal of exploratory data analysis (EDA) in machine learning?

easy

A. To train the machine learning model

B. To understand the main features and quality of the data

C. To deploy the model to production

D. To write the final report without looking at data

Solution

1. Step 1: Understand the purpose of EDA

   EDA is used to explore and understand the data before modeling.

2. Step 2: Differentiate EDA from other steps

   Training and deployment happen after EDA, so they are not the main goal of EDA.

3. Final Answer:

   To understand the main features and quality of the data -> Option B

4. Quick Check:

   EDA = Understand data [OK]

Hint: EDA means exploring data, not modeling or deploying [OK]

Common Mistakes:

• Confusing EDA with model training
• Thinking EDA is for deployment
• Skipping data understanding step

2. Which Python library is commonly used for basic exploratory data analysis tasks like viewing data samples and summary statistics?

easy

A. matplotlib

B. tensorflow

C. pandas

D. scikit-learn

Solution

1. Step 1: Identify libraries for EDA

   Pandas provides functions like head() and describe() for data samples and summary stats.

2. Step 2: Exclude other libraries

   Matplotlib is for plotting, scikit-learn for modeling, tensorflow for deep learning, not basic EDA.

3. Final Answer:

   pandas -> Option C

4. Quick Check:

   pandas = data samples and stats [OK]

Hint: Use pandas for quick data overview and stats [OK]

Common Mistakes:

• Using matplotlib for data samples
• Confusing modeling libraries with EDA
• Trying tensorflow for simple stats

3. Given the following Python code using pandas, what will print(df.describe()) output?

import pandas as pd
data = {'age': [25, 30, 22, 40, 28], 'score': [88, 92, 85, 95, 90]}
df = pd.DataFrame(data)
print(df.describe())

medium

A. Summary statistics including count, mean, std, min, max for 'age' and 'score'

B. The first 5 rows of the dataframe

C. A plot showing distribution of 'age' and 'score'

D. An error because describe() needs parameters

Solution

1. Step 1: Understand what describe() does

   It returns summary statistics like count, mean, std, min, max for numeric columns.

2. Step 2: Check the code output

   Since 'age' and 'score' are numeric, describe() will show stats for both columns.

3. Final Answer:

   Summary statistics including count, mean, std, min, max for 'age' and 'score' -> Option A

4. Quick Check:

   describe() = summary stats [OK]

Hint: describe() shows stats, head() shows rows [OK]

Common Mistakes:

• Thinking describe() shows data rows
• Expecting a plot from describe()
• Assuming describe() needs arguments

4. You run this code but get an error:

import pandas as pd
data = {'age': [25, 30, None, 40], 'score': [88, 92, 85, 95]}
df = pd.DataFrame(data)
print(df.mean())

What is the likely cause and fix?

medium

A. Error due to None value; fix by using df.mean(skipna=True)

B. Error due to missing import; fix by importing numpy

C. Error due to wrong data type; fix by converting all to strings

D. No error; code runs fine as is

Solution

1. Step 1: Check how pandas handles None in numeric columns

   Pandas treats None as NaN and by default mean() skips NaN values.

2. Step 2: Verify if code runs without error

   The code runs fine and calculates mean ignoring None.

3. Final Answer:

   No error; code runs fine as is -> Option D

4. Quick Check:

   pandas mean() skips None by default [OK]

Hint: pandas mean() ignores None/NaN by default [OK]

Common Mistakes:

• Assuming None causes error in mean()
• Thinking numpy import is needed here
• Trying to convert numbers to strings

5. You have a dataset with columns age, income, and purchased (Yes/No). Which EDA step helps you find if income affects purchased?

hard

A. Plotting boxplots of income grouped by purchased categories

B. Calculating mean age of all customers

C. Checking for missing values in age column

D. Sorting data by income in ascending order

Solution

1. Step 1: Identify relationship to explore

   We want to see if income differs between those who purchased and those who did not.

2. Step 2: Choose appropriate EDA method

   Boxplots grouped by purchased categories show income distribution differences clearly.

3. Final Answer:

   Plotting boxplots of income grouped by purchased categories -> Option A

4. Quick Check:

   Boxplots show group differences [OK]

Hint: Use grouped boxplots to compare numeric by category [OK]

Common Mistakes:

• Ignoring grouping by purchased
• Checking unrelated columns
• Just sorting without visualization