NumPy with Pandas integration - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of NumPy array used in a Pandas DataFrame

What is the output of the following code snippet?

NumPy

import numpy as np
import pandas as pd

arr = np.array([10, 20, 30, 40])
df = pd.DataFrame({'values': arr})
print(df['values'] * 2)

A

0    20
1    40
2    60
3    80
Name: values, dtype: int64

B [20 40 60 80]

C [10 20 30 40]

D TypeError: unsupported operand type(s) for *: 'DataFrame' and 'int'

💡 Hint

Remember that multiplying a Pandas Series by a number multiplies each element.

The DataFrame column 'values' is a Pandas Series. Multiplying it by 2 doubles each element and preserves the index and name.

❓ data_output

intermediate

Shape of NumPy array extracted from DataFrame

Given the code below, what is the shape of the NumPy array 'arr'?

NumPy

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': range(3), 'B': range(3, 6)})
arr = df.to_numpy()

A (3, 2)

B (2, 3)

C (3,)

D (2,)

💡 Hint

Check how many rows and columns the DataFrame has.

The DataFrame has 3 rows and 2 columns, so the NumPy array has shape (3, 2).

❓ visualization

advanced

Visualizing correlation matrix using NumPy and Pandas

Which option correctly produces a heatmap visualization of the correlation matrix of the DataFrame 'df' using NumPy and Pandas?

NumPy

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'X': np.random.randn(100),
    'Y': np.random.randn(100),
    'Z': np.random.randn(100)
})

A

corr = df.corr()
plt.plot(corr)
plt.show()

B

corr = np.corrcoef(df)
plt.imshow(corr, cmap='coolwarm')
plt.colorbar()
plt.show()

C

corr = df.corr().values
plt.scatter(corr)
plt.show()

D

corr = df.corr().to_numpy()
plt.imshow(corr, cmap='coolwarm')
plt.colorbar()
plt.show()

💡 Hint

Use the DataFrame's built-in correlation method and a heatmap for visualization.

Option D correctly computes the correlation matrix as a NumPy array and visualizes it as a heatmap with colorbar.

🔧 Debug

advanced

Identify the error in NumPy array assignment from DataFrame

What error will the following code produce?

NumPy

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
arr = np.array(df['A', 'B'])

A IndexError: too many indices for array

B KeyError: ('A', 'B')

C No error, arr is a NumPy array with columns A and B

D TypeError: unhashable type: 'slice'

💡 Hint

Check how columns are accessed in a DataFrame.

df['A', 'B'] tries to access a single key that is a tuple, which does not exist, causing a KeyError.

🚀 Application

expert

Efficiently replace NaN values in DataFrame using NumPy

You have a DataFrame 'df' with some NaN values. Which option efficiently replaces all NaNs with the mean of their respective columns using NumPy?

NumPy

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, np.nan, 3],
    'B': [4, 5, np.nan]
})

A

for col in df.columns:
    df[col].fillna(df[col].mean(), inplace=True)

B df.fillna(df.mean(), inplace=True)

C

arr = df.to_numpy()
col_means = np.nanmean(arr, axis=0)
inds = np.where(np.isnan(arr))
arr[inds] = np.take(col_means, inds[1])
df.loc[:, :] = arr

D

arr = df.values
arr[np.isnan(arr)] = 0
df[:] = arr

💡 Hint

Use NumPy functions to compute means and replace NaNs in the array, then assign back.

Option C uses NumPy's nanmean and indexing to replace NaNs efficiently in the underlying array, then updates the DataFrame.

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)