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.