Melt for wide-to-long reshaping in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to reshape the DataFrame from wide to long format using pandas melt.

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Math': [90, 80],
    'Science': [85, 88]
})

long_df = pd.melt(df, id_vars=[1], var_name='Subject', value_name='Score')
print(long_df)

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

A'Name'

B'Math'

C'Science'

D'Score'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a subject column like 'Math' or 'Science' as id_vars instead of 'Name'.

Passing the value column name to id_vars.

The id_vars parameter specifies the column(s) to keep fixed. Here, 'Name' is the identifier column, so it should be passed to id_vars.

2fill in blank

medium

Complete the code to specify the variable name for the melted columns.

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'City': ['NY', 'LA'],
    '2019': [100, 200],
    '2020': [110, 210]
})

long_df = pd.melt(df, id_vars='City', var_name=[1], value_name='Population')
print(long_df)

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

A'Year'

B'Population'

C'City'

D'Value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the value column name as var_name.

Using the id_vars column name as var_name.

The var_name parameter sets the name of the new column that holds the original column headers. Here, it should be 'Year' because the columns represent years.

3fill in blank

hard

Fix the error in the code to melt the DataFrame correctly.

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Product': ['A', 'B'],
    'Q1': [10, 20],
    'Q2': [15, 25]
})

long_df = pd.melt(df, id_vars='Product', var_name='Quarter', value_name=[1])
print(long_df)

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

A'Quarter'

B'Sales'

C'Product'

D'Q1'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the var_name or id_vars as value_name.

Using a column name that does not represent the values.

The value_name parameter should be the name of the column that will hold the values from the melted columns. Here, it should be 'Sales' to represent the sales numbers.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Data Analysis Python

words = ['apple', 'bat', 'car', 'door']
lengths = { [1] : [2] for word in words if len(word) > 3 }
print(lengths)

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

Aword

Blen(word)

Cword.upper()

Dlen

Attempts:

3 left

💡 Hint

Common Mistakes

Using the length as key and word as value.

Using word.upper() instead of word as key.

The dictionary comprehension keys should be the word itself, and the values should be the length of the word using len(word).

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is greater than 3.

Data Analysis Python

words = ['apple', 'bat', 'car', 'door']
lengths = { [1] : [2] for word in words if [3] }
print(lengths)

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

Aword.upper()

Blen(word)

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Not converting words to uppercase for keys.

Using the wrong condition in the if clause.

The keys are uppercase words using word.upper(), the values are their lengths using len(word), and the condition filters words longer than 3 characters.