One-hot encoding in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the pandas library.

Data Analysis Python

import [1] as pd

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Anumpy

Bseaborn

Cmatplotlib

Dpandas

Attempts:

3 left

💡 Hint

Common Mistakes

Importing numpy instead of pandas

Forgetting to use 'as pd'

We use import pandas as pd to work with dataframes easily.

2fill in blank

medium

Complete the code to create a DataFrame from a dictionary.

Data Analysis Python

df = pd.DataFrame({'color': ['red', 'blue', 'green']})
print(df.[1]())

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Ahead

Btail

Cinfo

Ddescribe

Attempts:

3 left

💡 Hint

Common Mistakes

Using tail() to see the first rows

Using info() which shows summary info

head() shows the first rows of the DataFrame.

3fill in blank

hard

Fix the error in the code to apply one-hot encoding using pandas.

Data Analysis Python

one_hot = pd.get_dummies(df['color'], [1]=True)
print(one_hot)

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Aremove

Bdrop

Cdrop_first

Dexclude

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names like 'drop' or 'remove'

Not using any parameter and causing redundant columns

drop_first=True avoids dummy variable trap by dropping the first category.

4fill in blank

hard

Fill both blanks to create a one-hot encoded DataFrame and join it with the original.

Data Analysis Python

one_hot = pd.get_dummies(df['color'], [1]=False)
df_new = df.[2](one_hot, axis=1)
print(df_new)

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Adrop_first

Bconcat

Cappend

Dmerge

Attempts:

3 left

💡 Hint

Common Mistakes

Using append which adds rows, not columns

Using merge which requires keys

Use drop_first=False to keep all dummies, then concat to join columns.

5fill in blank

hard

Fill all three blanks to create a one-hot encoded DataFrame, drop the original column, and display the result.

Data Analysis Python

one_hot = pd.get_dummies(df['color'], [1]=True)
df = df.[2]('color', axis=[3])
df_final = pd.concat([df, one_hot], axis=1)
print(df_final)

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

Adrop_first

Bdrop

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 which drops rows instead of columns

Not dropping the original column before concatenation

We drop the first dummy column, remove the original 'color' column with drop along columns (axis=1), then join the dummies.

Practice

1. What does one-hot encoding do in data science?

easy

A. It groups data into clusters automatically.

B. It normalizes numerical data to a 0-1 range.

C. It removes missing values from the dataset.

D. It converts categorical data into multiple binary columns.

Solution

1. Step 1: Understand categorical data representation

   One-hot encoding changes categories into separate columns with 1 or 0 to represent presence or absence.

2. Step 2: Differentiate from other data processing methods

   Normalization scales numbers, missing value removal cleans data, clustering groups data; none create binary columns for categories.

3. Final Answer:

   It converts categorical data into multiple binary columns. -> Option D

4. Quick Check:

   One-hot encoding = binary columns for categories [OK]

Hint: One-hot encoding = categories to binary columns [OK]

Common Mistakes:

• Confusing one-hot encoding with normalization
• Thinking it removes missing data
• Assuming it groups data into clusters

2. Which Python code correctly applies one-hot encoding using pandas?

easy

A. pd.get_dummies(df['color'])

B. pd.one_hot(df['color'])

C. df.encode_onehot('color')

D. pd.to_dummies(df['color'])

Solution

1. Step 1: Recall pandas function for one-hot encoding

   The correct function is pd.get_dummies() to convert categorical columns into dummy variables.

2. Step 2: Check the syntax correctness

   Only pd.get_dummies(df['color']) is valid pandas syntax; others are incorrect or non-existent functions.

3. Final Answer:

   pd.get_dummies(df['color']) -> Option A

4. Quick Check:

   Use pd.get_dummies() for one-hot encoding [OK]

Hint: Use pd.get_dummies() to one-hot encode columns [OK]

Common Mistakes:

• Using non-existent pandas functions
• Calling methods on DataFrame incorrectly
• Confusing function names like to_dummies

3. What is the output of this code?

import pandas as pd
df = pd.DataFrame({'fruit': ['apple', 'banana', 'apple']})
dummies = pd.get_dummies(df['fruit'])
print(dummies)

medium

A. A DataFrame with a single column 'fruit' containing numeric codes.

B. A DataFrame with columns 'apple' and 'banana' showing 1 or 0 for each row.

C. An error because get_dummies requires a DataFrame, not a Series.

D. A DataFrame with original 'fruit' column unchanged.

Solution

1. Step 1: Apply pd.get_dummies on the 'fruit' column

   This creates new columns for each unique fruit: 'apple' and 'banana', with 1 where the fruit matches and 0 otherwise.

2. Step 2: Understand the printed output

   The printed DataFrame shows rows with 1 or 0 under 'apple' and 'banana' columns corresponding to each original row.

3. Final Answer:

   A DataFrame with columns 'apple' and 'banana' showing 1 or 0 for each row. -> Option B

4. Quick Check:

   get_dummies creates binary columns for unique categories [OK]

Hint: get_dummies creates one column per category with 1/0 [OK]

Common Mistakes:

• Expecting numeric codes instead of binary columns
• Thinking get_dummies needs a DataFrame, not Series
• Assuming original column stays unchanged

4. Identify the error in this code snippet:

import pandas as pd
df = pd.DataFrame({'color': ['red', 'blue', 'green']})
dummies = pd.get_dummies(df.color, drop_first=True)
print(dummies)

medium

A. Error because get_dummies cannot be applied on a Series.

B. Error because drop_first is not a valid parameter for get_dummies.

C. No error; code correctly drops the first category column.

D. Error because df.color should be df['color'].

Solution

1. Step 1: Check get_dummies parameters

   The parameter drop_first=True is valid and drops the first category column to avoid multicollinearity.

2. Step 2: Verify syntax and input type

   Using df.color is valid shorthand for df['color'], and get_dummies works on Series.

3. Final Answer:

   No error; code correctly drops the first category column. -> Option C

4. Quick Check:

   drop_first=True is valid in get_dummies [OK]

Hint: drop_first=True drops one dummy column to avoid redundancy [OK]

Common Mistakes:

• Thinking drop_first is invalid parameter
• Confusing df.color with incorrect syntax
• Believing get_dummies only works on DataFrames

5. You have a DataFrame with a column 'city' containing many categories, including some rare ones. How can you apply one-hot encoding but group rare categories into a single 'Other' category before encoding?

hard

A. Replace rare categories with 'Other' in 'city' column, then apply pd.get_dummies().

B. Apply pd.get_dummies() directly; it automatically groups rare categories.

C. Use pd.get_dummies() with parameter group_rare=True.

D. Remove rare categories before applying pd.get_dummies().

Solution

1. Step 1: Identify rare categories and replace them

   Manually replace rare categories in 'city' column with a common label like 'Other' to group them.

2. Step 2: Apply one-hot encoding on the modified column

   Use pd.get_dummies() on the updated 'city' column to create binary columns including 'Other'.

3. Final Answer:

   Replace rare categories with 'Other' in 'city' column, then apply pd.get_dummies(). -> Option A

4. Quick Check:

   Group rare categories manually before encoding [OK]

Hint: Group rare categories manually before one-hot encoding [OK]

Common Mistakes:

• Expecting get_dummies to group rare categories automatically
• Using non-existent parameters like group_rare
• Removing rare categories instead of grouping