Exploratory data analysis workflow in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

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

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

A"data.csv"

Bdataframe

Cread_csv

Dpd

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to put the file name in quotes

Using a variable name instead of a string

To load data from a CSV file, you need to provide the file name as a string to pd.read_csv().

2fill in blank

medium

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

Pandas

print(df.[1]())

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

Ahead

Btail

Cshow

Ddisplay

Attempts:

3 left

💡 Hint

Common Mistakes

Using tail() which shows the last rows

Using show() which is not a pandas method

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

3fill in blank

hard

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

Pandas

summary = df.[1]()

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

Asummary

Bdescribe

Cinfo

Dstats

Attempts:

3 left

💡 Hint

Common Mistakes

Using info() which shows data types but not statistics

Using non-existent methods like summary()

The describe() method returns summary statistics like mean, min, max, and quartiles.

4fill in blank

hard

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

Pandas

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 word instead of len(word) for the value

Using < instead of > in the condition

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

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

Pandas

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.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper()

Using < instead of > in the condition

This dictionary comprehension creates a new dictionary with keys in uppercase and includes only items where the value is greater than zero.

Practice

1. Which step is usually done first in an exploratory data analysis workflow?

easy

A. Viewing the first few rows of the dataset

B. Creating complex machine learning models

C. Removing all missing values immediately

D. Generating final reports without checking data

Solution

1. Step 1: Understand the initial data

   Exploratory data analysis starts by looking at the first few rows to get a feel for the data structure and values.

2. Step 2: Avoid premature actions

   Jumping to modeling or removing missing data without understanding can cause mistakes.

3. Final Answer:

   Viewing the first few rows of the dataset -> Option A

4. Quick Check:

   First step = View data sample [OK]

Hint: Always start by looking at data samples [OK]

Common Mistakes:

• Starting with modeling before understanding data
• Removing missing values without checking their impact
• Ignoring data overview and jumping to conclusions

2. Which pandas command correctly shows summary statistics of numeric columns in a DataFrame df?

easy

A. df.head()

B. df.info()

C. df.describe()

D. df.sample()

Solution

1. Step 1: Identify summary statistics function

   df.describe() returns count, mean, std, min, max, and quartiles for numeric columns.

2. Step 2: Differentiate other commands

   df.head() shows first rows, df.info() shows data types, and df.sample() shows random rows.

3. Final Answer:

   df.describe() -> Option C

4. Quick Check:

   Summary stats = df.describe() [OK]

Hint: Use df.describe() for quick numeric summaries [OK]

Common Mistakes:

• Using df.head() to get statistics
• Confusing df.info() with statistical summary
• Expecting df.sample() to summarize data

3. Given the DataFrame df below, what is the output of df.isnull().sum()?

import pandas as pd
data = {'A': [1, 2, None], 'B': [None, 2, 3], 'C': [1, 2, 3]}
df = pd.DataFrame(data)

medium

A. {'A': 1, 'B': 1, 'C': 0}

B. {'A': 0, 'B': 0, 'C': 0}

C. {'A': 1, 'B': 2, 'C': 1}

D. {'A': 2, 'B': 1, 'C': 0}

Solution

1. Step 1: Check missing values per column

   Column 'A' has one None, 'B' has one None, 'C' has none.

2. Step 2: Use isnull().sum() to count missing

   This method counts missing values per column, so output is {'A': 1, 'B': 1, 'C': 0}.

3. Final Answer:

   {'A': 1, 'B': 1, 'C': 0} -> Option A

4. Quick Check:

   Missing counts = {'A':1,'B':1,'C':0} [OK]

Hint: Count missing with df.isnull().sum() [OK]

Common Mistakes:

• Counting total missing instead of per column
• Confusing None with zero or empty string
• Misreading which columns have missing values

4. You run df.info() but get an error: AttributeError: 'DataFrame' object has no attribute 'infoo'. What is the likely cause?

medium

A. The DataFrame is empty

B. You typed the method name incorrectly

C. The pandas library is not imported

D. The DataFrame has no columns

Solution

1. Step 1: Check the error message

   The error says 'infoo' attribute not found, which suggests a typo in method name.

2. Step 2: Confirm correct method name

   The correct method is df.info() with one 'o'.

3. Final Answer:

   You typed the method name incorrectly -> Option B

4. Quick Check:

   Typo in method name = error [OK]

Hint: Check spelling of method names carefully [OK]

Common Mistakes:

• Assuming empty DataFrame causes this error
• Forgetting to import pandas
• Thinking DataFrame must have columns to use info()

5. You have a DataFrame df with columns ['Age', 'Salary', 'Department']. You want to find the average salary per department but only for employees older than 30. Which pandas workflow is correct?

hard

A. df.groupby('Department')['Salary'].mean()

B. df['Salary'].mean().groupby('Department')[df['Age'] > 30]

C. df.groupby('Department').mean()['Salary'][df['Age'] > 30]

D. df[df['Age'] > 30].groupby('Department')['Salary'].mean()

Solution

1. Step 1: Filter rows where Age > 30

   Use df[df['Age'] > 30] to keep only employees older than 30.

2. Step 2: Group filtered data by Department and calculate mean Salary

   Apply .groupby('Department')['Salary'].mean() on filtered data to get average salary per department.

3. Final Answer:

   df[df['Age'] > 30].groupby('Department')['Salary'].mean() -> Option D

4. Quick Check:

   Filter then group and mean = df[df['Age'] > 30].groupby('Department')['Salary'].mean() [OK]

Hint: Filter first, then group and aggregate [OK]

Common Mistakes:

• Grouping before filtering data
• Trying to filter after aggregation
• Using mean() without grouping