Why transformation reshapes data for analysis in Data Analysis Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to reshape the data using pandas melt function.

Data Analysis Python

import pandas as pd

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

reshaped = pd.melt(data, id_vars=['Name'], value_vars=[1])
print(reshaped)

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

A['Math', 'Science']

B['Name']

C['Math']

D['Science']

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Name' in value_vars instead of id_vars

Not passing a list for value_vars

The value_vars parameter specifies which columns to reshape into rows. Here, we want to reshape the 'Math' and 'Science' columns.

2fill in blank

medium

Complete the code to pivot the reshaped data back to wide format.

Data Analysis Python

reshaped.pivot(index='Name', columns=[1], values='value')

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

A'score'

B'Name'

C'variable'

D'value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Name' or 'value' as columns parameter

Confusing index and columns parameters

The pivot function uses the 'variable' column created by melt as the new columns.

3fill in blank

hard

Fix the error in the code to correctly filter rows where score is greater than 85.

Data Analysis Python

filtered = reshaped[reshaped['value'] [1] 85]
print(filtered)

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

A>

B<

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' or '<=' instead of '>'

Using '==' which checks for equality

To filter rows where the score is greater than 85, use the '>' operator.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length if length is greater than 3.

Data Analysis Python

words = ['data', 'is', 'fun', 'science']
lengths = {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 word in the condition instead of its length

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

5fill in blank

hard

Fill all three blanks to create a dictionary from data items where the value is greater than 50, with keys uppercased.

Data Analysis Python

data = {'a': 40, 'b': 60, 'c': 70}
result = [1]: [2] for k, v in data.items() if v [3] 50}}

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys without uppercasing

Using wrong comparison operator

Swapping keys and values

The dictionary comprehension creates keys as uppercase versions of original keys using k.upper(), values as v, and filters items where v > 50.

Practice

1. Why do we transform data before analysis?

easy

A. To reshape data for easier comparison and visualization

B. To delete important information

C. To make data harder to understand

D. To increase the file size unnecessarily

Solution

1. Step 1: Understand the purpose of data transformation

   Data transformation changes the shape or format of data to make it easier to analyze.

2. Step 2: Identify the benefit of reshaping data

   Reshaping helps in comparing values side-by-side and creating clear visualizations.

3. Final Answer:

   To reshape data for easier comparison and visualization -> Option A

4. Quick Check:

   Transformation = Easier analysis [OK]

Hint: Think: transformation makes data easier to use [OK]

Common Mistakes:

• Confusing transformation with deleting data
• Believing transformation makes data more complex
• Thinking transformation increases file size

2. Which of the following is the correct way to use pandas melt to reshape data?

easy

A. pd.melt(df, id_vars=['score'], value_vars=['id'])

B. pd.melt(df, id_vars=['id'], value_vars=['score'])

C. pd.melt(df, id_vars='score', value_vars='id')

D. pd.melt(df, id_vars=['score', 'id'])

Solution

1. Step 1: Recall pandas melt syntax

   pandas melt requires id_vars as columns to keep and value_vars as columns to unpivot.

2. Step 2: Check pd.melt(df, id_vars=['id'], value_vars=['score'])

   pd.melt(df, id_vars=['id'], value_vars=['score']) correctly uses id_vars=['id'] and value_vars=['score'], matching the syntax.

3. Final Answer:

   pd.melt(df, id_vars=['id'], value_vars=['score']) -> Option B

4. Quick Check:

   Correct melt syntax = pd.melt(df, id_vars=['id'], value_vars=['score']) [OK]

Hint: id_vars are columns to keep, value_vars to unpivot [OK]

Common Mistakes:

• Swapping id_vars and value_vars
• Passing strings instead of lists
• Omitting value_vars parameter

3. Given this DataFrame:

import pandas as pd
df = pd.DataFrame({
  'id': [1, 2],
  'math': [90, 80],
  'english': [85, 88]
})
reshaped = pd.melt(df, id_vars=['id'], value_vars=['math', 'english'])
print(reshaped)

What is the output?

medium

A. A DataFrame with columns: id, math, english and 2 rows

B. A DataFrame with columns: variable, value and 2 rows

C. A DataFrame with columns: id, variable, value and 4 rows

D. An error because of wrong melt usage

Solution

1. Step 1: Understand the original DataFrame

   It has 2 rows and 3 columns: id, math, english.

2. Step 2: Apply pd.melt with id_vars=['id'] and value_vars=['math', 'english']

   This reshapes math and english columns into two rows per id, creating 4 rows total with columns id, variable, value.

3. Final Answer:

   A DataFrame with columns: id, variable, value and 4 rows -> Option C

4. Quick Check:

   melt reshapes wide to long with 4 rows [OK]

Hint: Melt stacks columns into rows, increasing row count [OK]

Common Mistakes:

• Expecting same number of rows after melt
• Confusing columns after melt
• Thinking melt causes error here

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'id':[1,2],'score':[10,20]})
reshaped = pd.melt(df, id_vars='id', value_vars='score')
print(reshaped)

medium

A. print statement is missing parentheses

B. DataFrame creation syntax is incorrect

C. pd.melt does not exist in pandas

D. id_vars and value_vars should be lists, not strings

Solution

1. Step 1: Check pandas melt parameters

   id_vars and value_vars expect list of column names, not single strings.

2. Step 2: Identify error cause

   Passing 'id' and 'score' as strings causes pandas to treat each character as a column, leading to error or wrong output.

3. Final Answer:

   id_vars and value_vars should be lists, not strings -> Option D

4. Quick Check:

   Use lists for id_vars and value_vars [OK]

Hint: Always pass lists for id_vars and value_vars [OK]

Common Mistakes:

• Passing single strings instead of lists
• Misunderstanding melt parameters
• Assuming melt is not in pandas

5. You have sales data in wide format:

import pandas as pd
df = pd.DataFrame({
  'store': ['A', 'B'],
  'Jan': [100, 150],
  'Feb': [120, 160],
  'Mar': [130, 170]
})

You want to analyze monthly sales trends easily. Which transformation helps and why?

hard

A. Use pd.melt to convert months into one column for easier trend analysis

B. Use df.sort_values() to reorder rows by store

C. Use df.drop() to remove month columns

D. Use df.groupby('store') without reshaping

Solution

1. Step 1: Identify the data format and goal

   Data is wide with months as columns; goal is to analyze trends over months.

2. Step 2: Choose transformation to reshape data

   Using pd.melt converts month columns into a single 'month' column and a 'sales' column, making trend analysis easier.

3. Step 3: Explain why other options don't fit

   Sorting or dropping columns doesn't reshape data for trend analysis; grouping without reshaping keeps data wide.

4. Final Answer:

   Use pd.melt to convert months into one column for easier trend analysis -> Option A

5. Quick Check:

   Wide to long format helps trend analysis [OK]

Hint: Melt wide months into one column for trends [OK]

Common Mistakes:

• Trying to analyze trends without reshaping
• Dropping useful columns
• Confusing sorting with reshaping