Why DataFrame is the core data structure 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 Series instead of DataFrame

Trying to create DataFrame with list directly

The DataFrame constructor creates a table-like structure from a dictionary.

2fill in blank

medium

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

Data Analysis Python

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

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

A.at

B.loc

C[

D.iloc

Attempts:

3 left

💡 Hint

Common Mistakes

Using .iloc for label selection

Using .at which is for single values

Using square brackets [ ] selects a column by name from a DataFrame.

3fill in blank

hard

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

Data Analysis Python

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 22]}
df = pd.DataFrame(data)
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 < instead of >

Using == which checks equality

The operator > selects rows where Age is greater than 25.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps names to ages for people older than 23.

Data Analysis Python

data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 22]}
result = {data['Name'][i]: data['Age'][i] for i in range(len(data['Name'])) if data['Age'][i] [1] 23}
print(result)

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

A<

B>

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using < which selects ages less than 23

Using == which selects only age 23

The operator > filters ages greater than 23.

5fill in blank

hard

Fill all three blanks to create a DataFrame from a list of dictionaries and select rows where score is above 80.

Data Analysis Python

import pandas as pd
data = [{'name': 'Alice', 'score': 85}, {'name': 'Bob', 'score': 75}, {'name': 'Cara', 'score': 90}]
df = pd.[1](data)
high_scores = df[df['[2]'] [3] 80]
print(high_scores)

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

ADataFrame

Bscore

C>

DSeries

Attempts:

3 left

💡 Hint

Common Mistakes

Using Series instead of DataFrame

Selecting wrong column name

Using < instead of >

Use DataFrame to create the table, select the 'score' column, and filter with > to get scores above 80.