NumPy with Pandas integration - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert a Pandas Series to a NumPy array.

NumPy

import pandas as pd
s = pd.Series([1, 2, 3, 4])
arr = s.[1]()

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

Aarray

Basarray

Cvalues

Dto_numpy

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'values' which is deprecated in newer Pandas versions.

Trying to use 'array' which is not a Series method.

The to_numpy() method converts a Pandas Series to a NumPy array.

2fill in blank

medium

Complete the code to create a Pandas DataFrame from a NumPy array.

NumPy

import numpy as np
import pandas as pd
arr = np.array([[1, 2], [3, 4]])
df = pd.DataFrame([1])

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

Aarr

B[arr]

Carr.tolist()

Darr.values

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list containing the array instead of the array itself.

Using 'arr.values' which is invalid for a NumPy array.

You can pass a NumPy array directly to pd.DataFrame() to create a DataFrame.

3fill in blank

hard

Fix the error in the code to get the mean of a NumPy array column in a DataFrame.

NumPy

import numpy as np
import pandas as pd
data = {'A': np.array([1, 2, 3]), 'B': [4, 5, 6]}
df = pd.DataFrame(data)
mean_col = df['A'].[1]()

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

Amean

Baverage

Cmean()

Davg

Attempts:

3 left

💡 Hint

Common Mistakes

Including parentheses inside the blank causing syntax error.

Using 'average' or 'avg' which are not valid Pandas methods.

The correct method to get the mean of a Series is mean(), but in the blank you only put the method name without parentheses because parentheses are already in the code.

4fill in blank

hard

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

NumPy

words = ['data', 'science', 'is', 'fun']
lengths = { [1] : [2] for word in words if len(word) > 3 }

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

Aword

Blen(word)

Cword.upper()

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using transformations like upper or lower instead of the word itself.

Using the length as the key instead of the value.

The dictionary comprehension maps each word to its length using len(word).

5fill in blank

hard

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

NumPy

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

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Using wrong comparison operators like '<' or '==' in the condition.

Swapping keys and values.

The comprehension maps the uppercase key k.upper() to the value v only if v > 0.

Practice

1. What does the to_numpy() method do when used on a Pandas DataFrame column?

easy

A. Converts the column to a Pandas Series

B. Converts the column to a Python list

C. Converts the column to a NumPy array

D. Converts the column to a dictionary

Solution

1. Step 1: Understand the method purpose

   The to_numpy() method is designed to convert Pandas data structures into NumPy arrays.

2. Step 2: Apply to a DataFrame column

   When called on a DataFrame column, it returns the data as a NumPy array, which is useful for numerical operations.

3. Final Answer:

   Converts the column to a NumPy array -> Option C

4. Quick Check:

   to_numpy() = NumPy array [OK]

Hint: Remember: to_numpy() always returns a NumPy array [OK]

Common Mistakes:

• Confusing to_numpy() with to_list()
• Thinking it returns a Pandas Series
• Assuming it converts to a dictionary

2. Which of the following is the correct syntax to convert a Pandas DataFrame column named 'age' to a NumPy array?

easy

A. df['age'].to_numpy()

B. np.to_numpy(df['age'])

C. df.to_numpy('age')

D. df.age.numpy()

Solution

1. Step 1: Identify the correct method call

   The to_numpy() method is called on a Pandas Series or DataFrame column, so df['age'].to_numpy() is correct.

2. Step 2: Check other options for errors

   np.to_numpy() does not exist, df.to_numpy('age') is invalid syntax, and df.age.numpy() is not a valid method.

3. Final Answer:

   df['age'].to_numpy() -> Option A

4. Quick Check:

   Correct method call = df['age'].to_numpy() [OK]

Hint: Use to_numpy() directly on the DataFrame column [OK]

Common Mistakes:

• Trying to call to_numpy() from numpy module
• Passing column name as argument to to_numpy()
• Using .numpy() instead of .to_numpy()

3. Given the code below, what is the output of result?

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
arr = df['A'].to_numpy()
result = np.sum(arr * 2)

medium

A. 36

B. 12

C. 21

D. 18

Solution

1. Step 1: Convert column 'A' to NumPy array

   df['A'] is [1, 2, 3], so arr = [1, 2, 3].

2. Step 2: Multiply array by 2 and sum

   arr * 2 = [2, 4, 6]. Sum is 2 + 4 + 6 = 12.

3. Step 3: Match to options

   The calculated sum is 12, which matches 12.

4. Final Answer:

   12 -> Option B

5. Quick Check:

   Sum of doubled array = 12 [OK]

Hint: Multiply array first, then sum to avoid confusion [OK]

Common Mistakes:

• Summing before multiplying
• Confusing DataFrame with NumPy array
• Misreading the multiplication factor

4. Identify the error in the following code snippet:

import pandas as pd
import numpy as np
df = pd.DataFrame({'X': [10, 20, 30]})
arr = df.X.to_numpy
print(np.mean(arr))

medium

A. Missing parentheses after to_numpy

B. DataFrame column 'X' does not exist

C. np.mean cannot be applied to NumPy arrays

D. print statement syntax is incorrect

Solution

1. Step 1: Check method call syntax

   The code uses df.X.to_numpy without parentheses, so it assigns the method itself, not the array.

2. Step 2: Understand the impact

   Passing a method object to np.mean() causes an error because it's not an array.

3. Final Answer:

   Missing parentheses after to_numpy -> Option A

4. Quick Check:

   Method call needs () = Missing parentheses after to_numpy [OK]

Hint: Always add () to call methods like to_numpy() [OK]

Common Mistakes:

• Forgetting parentheses on method calls
• Assuming attribute access returns data
• Misunderstanding error messages

5. You have a Pandas DataFrame df with columns 'height' and 'weight'. You want to create a new NumPy array BMI using the formula weight / (height/100)**2. Which code correctly uses NumPy with Pandas to compute BMI?

hard

A. bmi = df['weight'].tolist() / np.array(df['height'] / 100) ** 2

B. bmi = df['weight'].divide((df['height'] / 100) ** 2)

C. bmi = df['weight'].to_numpy() / (df['height'].to_numpy() / 100) ** 2

D. bmi = df['weight'] / (df['height'] / 100) ** 2

Solution

1. Step 1: Understand Pandas operations

   bmi = df['weight'] / (df['height'] / 100) ** 2 uses Pandas Series operations which support element-wise division and return a Series, which can be used as a NumPy array if needed.

2. Step 2: Check other options

   bmi = df['weight'].divide((df['height'] / 100) ** 2) uses .divide(), which is similar to bmi = df['weight'] / (df['height'] / 100) ** 2 and returns a Series. bmi = df['weight'].to_numpy() / (df['height'].to_numpy() / 100) ** 2 converts to NumPy arrays explicitly, which is correct but not necessary. bmi = df['weight'].tolist() / np.array(df['height'] / 100) ** 2 uses .tolist() which returns a list and will cause a TypeError when dividing by a NumPy array.

3. Step 3: Choose the best option

   bmi = df['weight'] / (df['height'] / 100) ** 2 is the most straightforward and idiomatic Pandas way to compute BMI, returning a Series that behaves like a NumPy array for most purposes.

4. Final Answer:

   bmi = df['weight'] / (df['height'] / 100) ** 2 -> Option D

5. Quick Check:

   Use Pandas Series operations for element-wise math [OK]

Hint: Pandas Series support element-wise math without conversion [OK]

Common Mistakes:

• Mixing Pandas Series and NumPy arrays without conversion
• Applying np.array incorrectly on expressions
• Ignoring unit conversion (height/100)