Numpy and Pandas Quiz

What does the following NumPy code output? import numpy as np arr = np.array([1, 2, 3, 4]) print(arr.shape)

• (4,)
• (1, 4)
• (4, 1)
• 4

Answer explanation

A 1D NumPy array has a shape of (n,) where n is the number of elements.

What is the output of this NumPy operation? import numpy as np arr = np.array([10, 20, 30]) print(arr * 2)

• [10, 20, 30, 10, 20, 30]
• [20, 40, 60]
• [102, 202, 302]
• Error

Answer explanation

NumPy broadcasts 2 across the entire array and multiplies each element.

What will the following code output? import numpy as np arr = np.array([[1, 2], [3, 4]]) print(arr.T)

• [[1, 3], [2, 4]]
• [[1, 2], [3, 4]]
• [[1, 2, 3, 4]]
• Error

Answer explanation

arr.T transposes the matrix, swapping rows and columns.

What does np.zeros((2,3)) create?

• A 2D array filled with random values
• A 2D array of shape (2,3) filled with 0s
• A 1D array of length 6 filled with 0s
• An error because NumPy does not support zero arrays

Answer explanation

np.zeros((2,3)) creates a 2-row, 3-column matrix filled with zeros.

import numpy as np

arr = np.array(['A', 'B', 'C', 'D'])

print(arr.dtype)

What will be the output of the following code?

• int32

• float64

• U1

• Error

Answer explanation

np.arange(start, stop, step) generates numbers starting from 3 up to 10 (exclusive), incrementing by 2.

What does the following pandas code output? import pandas as pd data = {'A': [1, 2, 3], 'B': [4, 5, 6]} df = pd.DataFrame(data) print(df.shape)

• (3, 2)
• (2, 3)
• (3, 3)
• (6,)

Answer explanation

The DataFrame has 3 rows and 2 columns, so df.shape returns (3, 2).

What will df.head(2) return? import pandas as pd df = pd.DataFrame({'A': [10, 20, 30], 'B': [40, 50, 60]}) print(df.head(2))

• The first 2 rows of the DataFrame
• The last 2 rows of the DataFrame
• The entire DataFrame
• An error

Answer explanation

df.head(n) returns the first n rows of the DataFrame.

How do you get summary statistics of a pandas DataFrame?

• df.summary()
• df.describe()
• df.info()
• df.stats()

Answer explanation

df.describe() provides statistics like mean, min, max, and percentiles for numerical columns.

What does df['A'].mean() do? import pandas as pd df = pd.DataFrame({'A': [5, 10, 15], 'B': [20, 25, 30]}) print(df['A'].mean())

• Returns the sum of column 'A'
• Returns the mean (average) of column 'A'
• Returns the maximum value in column 'A'
• Returns an error

Answer explanation

df['A'].mean() calculates the average value of column 'A'.

How do you select rows where column 'A' is greater than 10?

• df[df.A > 10]
• df[df['A'] > 10]
• Both A and B
• An error

Answer explanation

Both df[df.A > 10] and df[df['A'] > 10] correctly filter rows where column 'A' is greater than 10.