Why Pandas performance matters - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a DataFrame from a dictionary.

Pandas

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'

ASeries

BDataFrame

Carray

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using pd.Series instead of pd.DataFrame

Trying to create a list or array instead of a DataFrame

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.

Pandas

ages = df[1]['age']
print(ages)

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A.loc

B.iloc

C.select

D[

Attempts:

3 left

💡 Hint

Common Mistakes

Using .loc without brackets

Using .select which is not a pandas method

Use square brackets [] to select a column by name in pandas.

3fill in blank

hard

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

Pandas

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

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A>

B<

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of >

Using == which checks equality

Use the greater than operator > to filter ages above 25.

4fill in blank

hard

Complete the code to create a dictionary comprehension that maps names to ages for people older than 25.

Pandas

result = {person: age for person, age in zip(df['name'], df['age']) if age [1] 25}
print(result)

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A:

B>

C<

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using = instead of : in dictionary comprehension

Using < instead of > in the condition

Use : to separate key and value in a dictionary, and > to filter ages greater than 25.

5fill in blank

hard

Fill all three blanks to create a DataFrame from a dictionary, select the 'score' column, and filter scores above 80.

Pandas

data = {'name': ['Ann', 'Ben'], 'score': [85, 75]}
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

B'score'

C>

DSeries

Attempts:

3 left

💡 Hint

Common Mistakes

Using pd.Series instead of pd.DataFrame

Not quoting the column name

Using < instead of > for filtering

Create a DataFrame with pd.DataFrame, select the 'score' column with 'score', and filter with > for scores above 80.