Essential libraries overview (Pandas, NumPy, Matplotlib) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this NumPy array operation?

Consider the following code that uses NumPy to create and manipulate arrays. What will be printed?

Data Analysis Python

import numpy as np
arr = np.array([1, 2, 3, 4])
result = arr * 2 + 1
print(result)

A [3 5 7 9]

B [2 4 6 8]

C [1 2 3 4]

D [4 6 8 10]

💡 Hint

Remember that NumPy applies operations element-wise on arrays.

Each element in the array is multiplied by 2, then 1 is added. So 1*2+1=3, 2*2+1=5, etc.

❓ data_output

intermediate

What does this Pandas DataFrame look like after filtering?

Given this DataFrame, what rows remain after filtering for values greater than 50 in column 'B'?

Data Analysis Python

import pandas as pd
df = pd.DataFrame({'A': [10, 20, 30, 40], 'B': [45, 55, 65, 35]})
filtered_df = df[df['B'] > 50]
print(filtered_df)

A

   A   B
1  20  55
2  30  65

B

   A   B
0  10  45
3  40  35

C

   A   B
1  20  55
3  40  35

D

   A   B
0  10  45
2  30  65

💡 Hint

Look for rows where column 'B' has values strictly greater than 50.

Only rows with 'B' values 55 and 65 meet the condition, which are rows with index 1 and 2.

❓ visualization

advanced

What kind of plot does this Matplotlib code produce?

Look at this Matplotlib code. What type of plot will it create?

Data Analysis Python

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
plt.bar(x, y)
plt.show()

A A pie chart showing proportions of values 10, 20, 25, 30

B A line plot connecting points (1,10), (2,20), (3,25), (4,30)

C A bar chart with bars at positions 1 to 4 and heights 10, 20, 25, 30

D A scatter plot with points at (1,10), (2,20), (3,25), (4,30)

💡 Hint

The function plt.bar creates vertical bars.

plt.bar creates a bar chart with bars at the x positions with heights from y.

🧠 Conceptual

advanced

Which library is best suited for fast numerical operations on arrays?

You want to perform fast mathematical operations on large arrays of numbers. Which library is designed specifically for this?

A Seaborn

B Matplotlib

C Pandas

D NumPy

💡 Hint

Think about which library provides efficient array objects and math functions.

NumPy provides fast array structures and mathematical functions optimized for numerical data.

🔧 Debug

expert

What error does this Pandas code raise?

This code tries to select a column from a DataFrame but causes an error. What error is raised?

Data Analysis Python

import pandas as pd
df = pd.DataFrame({'X': [1, 2], 'Y': [3, 4]})
print(df['Z'])

A TypeError

B KeyError

C ValueError

D AttributeError

💡 Hint

The column 'Z' does not exist in the DataFrame.

Accessing a non-existent column in a DataFrame raises a KeyError.