Why reshaping data matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

long_df = df.melt(id_vars=[1])

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

A'variable'

B'id_vars'

C'value'

D['Name', 'Date']

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single string instead of a list for id_vars

Confusing id_vars with value_vars

Passing column names that should be reshaped instead of kept fixed

To reshape data from wide to long format, you specify the columns to keep fixed using id_vars. Here, ['Name', 'Date'] are the identifier columns.

2fill in blank

medium

Complete the code to pivot the DataFrame to wide format using pandas.

Pandas

wide_df = df.pivot(index=[1], columns='Month', values='Sales')

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

A'Month'

B'Store'

C'Sales'

D'Date'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the column for columns or values as the index

Confusing index and columns parameters

Passing a list instead of a single column name for index

The index parameter specifies the column to use as new row labels. Here, 'Store' is used as the index.

3fill in blank

hard

Fix the error in the code to correctly stack the DataFrame columns into a Series.

Pandas

stacked = df.[1]()

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

Astack

Bmelt

Cunstack

Dpivot

Attempts:

3 left

💡 Hint

Common Mistakes

Using unstack instead of stack

Using melt which reshapes differently

Using pivot which requires different parameters

The stack() method compresses columns into a single Series with a MultiIndex, which is the correct way to stack columns.

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.

Pandas

{word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length

Using the wrong condition syntax

Confusing the order of key and value in the comprehension

The dictionary comprehension maps each word to its length using len(word). The condition keeps only words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts if count is greater than 0.

Pandas

result = [1]: [2] for [3] in data.items() if [2] > 0}

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

Ak.upper()

Bv

Ck, v

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong variable name in the loop

Not converting the key to uppercase

Using the value as the loop variable

The comprehension maps each key converted to uppercase (k.upper()) to its value v, iterating over keys k and values v from data.items(). It includes only items where the value is greater than 0.

Practice

1. Why is reshaping data important in pandas?

easy

A. It organizes data to make analysis and visualization easier.

B. It deletes unnecessary rows automatically.

C. It changes data types of columns.

D. It encrypts data for security.

Solution

1. Step 1: Understand the purpose of reshaping

   Reshaping changes the layout of data to make it easier to work with.

2. Step 2: Identify the benefit for analysis and visualization

   Organized data helps tools and people understand patterns and trends better.

3. Final Answer:

   It organizes data to make analysis and visualization easier. -> Option A

4. Quick Check:

   Reshaping = Organizing data [OK]

Hint: Think: reshaping makes data easier to read and use [OK]

Common Mistakes:

• Confusing reshaping with deleting data
• Thinking reshaping changes data types
• Believing reshaping encrypts data

2. Which pandas function would you use to make a DataFrame longer by turning columns into rows?

easy

A. groupby()

B. pivot()

C. melt()

D. concat()

Solution

1. Step 1: Recall the function to make data longer

   The melt() function converts columns into rows, making data longer.

2. Step 2: Differentiate from pivot()

   pivot() does the opposite by making data wider, so it is not correct here.

3. Final Answer:

   melt() -> Option C

4. Quick Check:

   Longer data = melt() [OK]

Hint: Remember: melt makes data longer, pivot makes it wider [OK]

Common Mistakes:

• Choosing pivot() instead of melt()
• Confusing groupby() with reshaping
• Using concat() which combines dataframes, not reshapes

3. What is the output of this code?

import pandas as pd
df = pd.DataFrame({
  'Name': ['Anna', 'Bob'],
  'Math': [90, 80],
  'Science': [85, 95]
})
melted = df.melt(id_vars='Name', var_name='Subject', value_name='Score')
print(melted)

medium

A. An error because melt() needs a list for id_vars.

B. A DataFrame with columns Name, Math, Science unchanged.

C. A DataFrame with columns Name, Subject, Score but only 2 rows.

D. A DataFrame with columns Name, Subject, Score and 4 rows showing each subject score per student.

Solution

1. Step 1: Understand melt() with id_vars

   The melt() keeps 'Name' fixed and turns 'Math' and 'Science' columns into rows.

2. Step 2: Count resulting rows and columns

   Each student has 2 subjects, so 2 students x 2 subjects = 4 rows with columns Name, Subject, Score.

3. Final Answer:

   A DataFrame with columns Name, Subject, Score and 4 rows showing each subject score per student. -> Option D

4. Quick Check:

   melt() expands columns to rows [OK]

Hint: melt with id_vars keeps those columns fixed [OK]

Common Mistakes:

• Thinking melt() keeps original shape
• Believing id_vars must be a list, not a string
• Expecting fewer rows than subjects x students

4. You try to reshape data using pivot() but get an error: "ValueError: Index contains duplicate entries, cannot reshape". What is the likely cause?

medium

A. pivot() requires numeric columns only.

B. The DataFrame has duplicate index values preventing pivot.

C. pivot() needs a list for columns parameter.

D. pivot() cannot be used on DataFrames with missing values.

Solution

1. Step 1: Understand pivot() requirements

   pivot() needs unique index/column pairs to reshape data without duplicates.

2. Step 2: Identify cause of duplicate entries error

   If duplicates exist in the index or columns used, pivot() cannot reshape and throws this error.

3. Final Answer:

   The DataFrame has duplicate index values preventing pivot. -> Option B

4. Quick Check:

   Duplicate entries block pivot() [OK]

Hint: Check for duplicates before pivoting [OK]

Common Mistakes:

• Assuming pivot() only works with numeric data
• Thinking pivot() needs list for columns always
• Believing missing values cause this error

5. You have sales data with columns: Store, Month, and Sales. You want a table showing each store as a row and each month as a column with sales values. Which steps correctly reshape the data?

hard

A. Use pivot(index='Store', columns='Month', values='Sales') to make data wider.

B. Use melt() to make data longer, then groupby() to sum sales.

C. Use concat() to join Store and Month columns.

D. Use drop_duplicates() to remove repeated sales before pivoting.

Solution

1. Step 1: Identify desired shape

   You want stores as rows and months as columns, which means making data wider.

2. Step 2: Choose correct function for widening

   pivot() reshapes data from long to wide format using specified index, columns, and values.

3. Final Answer:

   Use pivot(index='Store', columns='Month', values='Sales') to make data wider. -> Option A

4. Quick Check:

   Wide table = pivot() [OK]

Hint: pivot() makes wide tables from long data [OK]

Common Mistakes:

• Using melt() when wide format is needed
• Trying concat() to reshape data
• Dropping duplicates unnecessarily before pivot