Why project-based learning cements skills in Data Analysis Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a DataFrame from a dictionary.

Data Analysis Python

import pandas as pd
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.[1](data)
print(df)

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

ADataFrame

BSeries

Carray

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using pd.Series instead of pd.DataFrame

Trying to use list or array which are not pandas constructors

Use pd.DataFrame() to create a DataFrame from a dictionary.

2fill in blank

medium

Complete the code to select the 'Age' column from the DataFrame.

Data Analysis Python

ages = df[1]
print(ages)

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

A.select('Age')

B['Age']

C.Age()

D[['Age']]

Attempts:

3 left

💡 Hint

Common Mistakes

Using double square brackets returns a DataFrame, not a Series

Using dot notation with parentheses is invalid

Use df['Age'] to select a single column as a Series.

3fill in blank

hard

Fix the error in the code to filter rows where Age is greater than 25.

Data Analysis Python

filtered = df[df['Age'] [1] 25]
print(filtered)

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

A<

B==

C<=

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using equality operator instead of greater than

Using less than or less than or equal operators

Use > to filter rows where Age is greater than 25.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths for words longer than 3 letters.

Data Analysis Python

words = ['data', 'science', 'is', 'fun']
lengths = {word: [1] for word in words if [2]
print(lengths)

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

Alen(word)

Bword > 3

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing the word string directly to a number

Using the word itself as the value instead of its length

Use len(word) to get length and filter words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values filtered by value > 20.

Data Analysis Python

data = {'a': 10, 'b': 25, 'c': 30}
result = { [1]: [2] for k, v in data.items() if v [3] 20 }
print(result)

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase

Filtering with wrong comparison operator

Swapping keys and values

Keys are uppercase using k.upper(), values are v, filtered by v > 20.

Practice

1. Why does project-based learning help you remember data science steps better?

easy

A. Because you memorize code without running it

B. Because you only read theory without doing anything

C. Because you practice loading, cleaning, analyzing, and visualizing data

D. Because you watch videos without practicing

Solution

1. Step 1: Understand project-based learning

   Project-based learning means doing real tasks, not just reading or watching.

2. Step 2: Connect practice with memory

   Practicing steps like loading and cleaning data helps you remember them better.

3. Final Answer:

   Because you practice loading, cleaning, analyzing, and visualizing data -> Option C

4. Quick Check:

   Practice improves memory = C [OK]

Hint: Practice all steps to remember better [OK]

Common Mistakes:

• Thinking watching videos is enough
• Believing memorizing code without running helps
• Ignoring the importance of hands-on practice

2. Which of the following is the correct way to load a CSV file using pandas in Python?

easy

A. df = pandas.read_csv('data.csv')

B. df = pd.read_csv('data.csv')

C. df = pd.load_csv('data.csv')

D. df = pandas.load('data.csv')

Solution

1. Step 1: Recognize pandas import alias

   Usually pandas is imported as import pandas as pd, so pd is used.

2. Step 2: Identify correct function name

   The correct function to load CSV is read_csv, so pd.read_csv('data.csv') is correct.

3. Final Answer:

   df = pd.read_csv('data.csv') -> Option B

4. Quick Check:

   pandas alias + read_csv = A [OK]

Hint: Use pd.read_csv() after importing pandas as pd [OK]

Common Mistakes:

• Using pandas without alias when alias is used
• Using wrong function name like load_csv
• Confusing read_csv with load

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

import pandas as pd
data = {'A': [1, 2], 'B': [3, 4]}
df = pd.DataFrame(data)
result = df['A'] + df['B']
print(result.tolist())

medium

A. [4, 6]

B. [3, 6]

C. [1, 2, 3, 4]

D. [1, 3]

Solution

1. Step 1: Understand DataFrame creation

   DataFrame has columns 'A' = [1, 2] and 'B' = [3, 4].

2. Step 2: Add columns element-wise

   Adding df['A'] + df['B'] gives [1+3, 2+4] = [4, 6].

3. Final Answer:

   [4, 6] -> Option A

4. Quick Check:

   1+3=4 and 2+4=6 [OK]

Hint: Add columns element-wise to get sums [OK]

Common Mistakes:

• Concatenating lists instead of adding
• Mixing up column names
• Printing DataFrame instead of list

4. Find the error in this code that tries to filter rows where column 'Age' is greater than 30:

import pandas as pd
df = pd.DataFrame({'Age': [25, 35, 45]})
filtered = df[df.Age > 30]
print(filtered)

medium

A. The print statement should be print(df.filtered)

B. The DataFrame creation is missing column names

C. The syntax df.Age > 30 is incorrect

D. There is no error; the code works correctly

Solution

1. Step 1: Check DataFrame creation

   DataFrame is created with column 'Age' and values [25, 35, 45], which is correct.

2. Step 2: Check filtering syntax

   Using df[df.Age > 30] is valid pandas syntax to filter rows.

3. Final Answer:

   There is no error; the code works correctly -> Option D

4. Quick Check:

   Filtering with df.Age > 30 works = A [OK]

Hint: Use df[df.Column > value] to filter rows [OK]

Common Mistakes:

• Thinking df.Age > 30 is invalid syntax
• Trying to access filtered as attribute
• Missing column names in DataFrame

5. You have a project where you must clean a dataset by removing rows with missing values and then calculate the average of column 'Score'. Which sequence of pandas commands correctly does this?

hard

A. df_clean = df.dropna()
avg_score = df_clean['Score'].mean()

B. df_clean = df.fillna(0)
avg_score = df_clean['Score'].mean()

C. df_clean = df.drop_duplicates()
avg_score = df_clean['Score'].sum()

D. df_clean = df.dropna()
avg_score = df_clean['Score'].sum()

Solution

1. Step 1: Remove rows with missing values

   Use df.dropna() to remove rows with any missing data.

2. Step 2: Calculate average of 'Score'

   Use mean() on the 'Score' column to get average.

3. Final Answer:

   df_clean = df.dropna() avg_score = df_clean['Score'].mean() -> Option A

4. Quick Check:

   dropna + mean() = D [OK]

Hint: Drop missing rows then use mean() for average [OK]

Common Mistakes:

• Using fillna(0) changes data meaning
• Using sum() instead of mean() for average
• Dropping duplicates instead of missing values