Reshaping and transposing in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to reshape the array into 2 rows and 3 columns.

Data Analysis Python

import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
reshaped = arr.[1]((2, 3))
print(reshaped)

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

Atranspose

Bresize

Cflatten

Dreshape

Attempts:

3 left

💡 Hint

Common Mistakes

Using flatten instead of reshape.

Using transpose which only swaps axes.

The reshape method changes the shape of the array without changing its data.

2fill in blank

medium

Complete the code to transpose the 2D array.

Data Analysis Python

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
transposed = arr.[1]()
print(transposed)

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

Atranspose

BT

Creshape

Dflatten

Attempts:

3 left

💡 Hint

Common Mistakes

Using reshape instead of transpose.

Using T without parentheses (T is an attribute, not a method).

The transpose() method swaps the rows and columns of the array.

3fill in blank

hard

Fix the error in reshaping the array to 3 rows and 2 columns.

Data Analysis Python

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
reshaped = np.[1](arr, (3, 2))
print(reshaped)

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

Aflatten

Bresize

Creshape

Dtranspose

Attempts:

3 left

💡 Hint

Common Mistakes

Using reshape which causes an error due to size mismatch.

Using transpose which does not change shape like this.

The resize method can change the size of the array and fill with repeated values if needed, allowing reshaping incompatible with original size.

4fill in blank

hard

Fill both blanks to create a dictionary with word lengths for words longer than 3 characters.

Data Analysis Python

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

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > in the condition.

Using the word itself instead of its length.

The dictionary comprehension uses len(word) to get length and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

Data Analysis Python

data = {'a': 1, 'b': -2, 'c': 3}
result = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}
print(result)

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys instead of uppercase.

Using < instead of > in the condition.

The dictionary comprehension uses k.upper() for keys, v for values, and filters values greater than 0 with >.

Practice

1. What does the transpose() function do to a DataFrame in pandas?

easy

A. It swaps the rows and columns of the DataFrame.

B. It sorts the DataFrame by index.

C. It removes duplicate rows from the DataFrame.

D. It fills missing values with zeros.

Solution

1. Step 1: Understand transpose operation

   The transpose() function flips the DataFrame so rows become columns and columns become rows.

2. Step 2: Compare with other options

   Sorting, removing duplicates, or filling missing values are different operations not related to transposing.

3. Final Answer:

   It swaps the rows and columns of the DataFrame. -> Option A

4. Quick Check:

   transpose() = swap rows and columns [OK]

Hint: Remember transpose flips rows and columns [OK]

Common Mistakes:

• Confusing transpose with sorting
• Thinking transpose removes duplicates
• Assuming transpose fills missing values

2. Which of the following is the correct syntax to reshape a DataFrame df from wide to long format using pandas?

easy

A. df.melt(id_vars=['id'], var_name='variable', value_name='value')

B. df.stack(level=0)

C. df.transpose()

D. df.pivot(index='id', columns='variable', values='value')

Solution

1. Step 1: Identify function for wide to long

   The melt() function converts wide format to long format by unpivoting columns.

2. Step 2: Check syntax correctness

   df.melt(id_vars=['id'], var_name='variable', value_name='value') is the correct syntax to keep 'id' fixed and melt other columns.

3. Final Answer:

   df.melt(id_vars=['id'], var_name='variable', value_name='value') -> Option A

4. Quick Check:

   melt() = wide to long reshape [OK]

Hint: Use melt() to go from wide to long format [OK]

Common Mistakes:

• Using pivot() instead of melt() for wide to long
• Confusing transpose() with reshaping
• Using stack() without understanding its effect

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({
  'A': [1, 2],
  'B': [3, 4]
})

What is the output of df.T?

medium

A. An error because T is not a valid attribute.

B. A DataFrame sorted by column 'A'.

C. A DataFrame with rows and columns swapped, showing columns as rows.

D. A DataFrame with duplicated rows removed.

Solution

1. Step 1: Understand df.T usage

   T is a shortcut for transpose() in pandas, swapping rows and columns.

2. Step 2: Visualize the transposed DataFrame

   Original df has columns 'A' and 'B' with 2 rows. After transpose, columns become rows and rows become columns.

3. Final Answer:

   A DataFrame with rows and columns swapped, showing columns as rows. -> Option C

4. Quick Check:

   df.T = transpose DataFrame [OK]

Hint: Use df.T to quickly transpose DataFrame [OK]

Common Mistakes:

• Thinking df.T sorts data
• Believing df.T removes duplicates
• Assuming df.T causes error

4. What is wrong with this code snippet that tries to reshape a DataFrame df using pivot()?

df.pivot(index='id', columns='variable')

medium

A. Pivot cannot be used on DataFrames with numeric columns.

B. Missing the values parameter required for pivot.

C. The index parameter should be a list, not a string.

D. The columns parameter should be omitted.

Solution

1. Step 1: Check pivot() required parameters

   The pivot() function needs index, columns, and values to reshape data properly.

2. Step 2: Identify missing argument

   The code misses the values argument, which tells pivot what data to fill in the new table.

3. Final Answer:

   Missing the values parameter required for pivot. -> Option B

4. Quick Check:

   pivot() needs values parameter [OK]

Hint: Always include values= when using pivot() [OK]

Common Mistakes:

• Forgetting values parameter in pivot()
• Using string instead of list for index incorrectly
• Thinking pivot() can't handle numeric columns

5. You have a DataFrame df with columns ['id', 'year', 'sales_Q1', 'sales_Q2']. You want to reshape it to a long format with columns ['id', 'year', 'quarter', 'sales']. Which code correctly achieves this?

hard

A. df.stack(level=['quarter', 'sales'])

B. df.pivot(index=['id', 'year'], columns='quarter', values='sales')

C. df.transpose()

D. df.melt(id_vars=['id', 'year'], var_name='quarter', value_name='sales')

Solution

1. Step 1: Identify wide to long reshape method

   To convert columns like 'sales_Q1', 'sales_Q2' into rows, use melt().

2. Step 2: Use melt() with correct parameters

   Keep 'id' and 'year' fixed with id_vars, set var_name='quarter' and value_name='sales' to rename columns.

3. Step 3: Check other options

   pivot() is for long to wide, transpose() swaps rows and columns, stack() is for multi-index reshaping but not here.

4. Final Answer:

   df.melt(id_vars=['id', 'year'], var_name='quarter', value_name='sales') -> Option D

5. Quick Check:

   melt() with id_vars = long format reshape [OK]

Hint: Use melt() with id_vars to reshape wide to long [OK]

Common Mistakes:

• Using pivot() instead of melt() for wide to long
• Trying transpose() for reshaping columns
• Misusing stack() without multi-index