Ordered categories in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an ordered categorical type with levels 'low', 'medium', 'high'.

Pandas

import pandas as pd
levels = ['low', 'medium', 'high']
cat_type = pd.CategoricalDtype(categories=levels, ordered=[1])

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

ATrue

B0

CNone

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Setting ordered=False when order matters

Using None instead of True for ordered

Setting ordered=True creates an ordered categorical type.

2fill in blank

medium

Complete the code to convert a column 'priority' in DataFrame df to the ordered categorical type cat_type.

Pandas

df['priority'] = df['priority'].astype([1])

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Alist

Bstr

Cint

Dcat_type

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'str' instead of the categorical type

Using 'int' or 'list' which are not categorical types

Use astype(cat_type) to convert the column to the ordered categorical type.

3fill in blank

hard

Fix the error in the code to compare if 'priority' is greater than 'medium'.

Pandas

result = df['priority'] [1] 'medium'

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A>

B!=

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using equality instead of comparison

Using less than or equal which checks the wrong condition

Ordered categorical types support comparison operators like >.

4fill in blank

hard

Fill both blanks to create a dictionary of counts for categories greater than 'low'.

Pandas

counts = {cat: sum(df['priority'] [1] cat) for cat in cat_type.categories if cat [2] 'low'}

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A>

B==

C<

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using equality or not equal operators instead of greater than

Mixing different comparison operators in the two blanks

We count how many rows have priority greater than each category, and only for categories greater than 'low'.

5fill in blank

hard

Fill both blanks to create a filtered DataFrame with priority between 'medium' and 'high' inclusive.

Pandas

filtered = df[(df['priority'] [1]= 'medium') & (df['priority'] [2]= 'high')]

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A>=

B<=

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators that exclude boundaries

Using != which excludes values

Use >= for the lower bound and <= for the upper bound.