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.

Practice

1. Which library is mainly used to organize data in tables for easy analysis?

easy

A. Seaborn

B. NumPy

C. Matplotlib

D. Pandas

Solution

1. Step 1: Understand library purposes

   Pandas is designed to organize data in tables called DataFrames.

2. Step 2: Match library to task

   Since the question asks about organizing data in tables, Pandas fits best.

3. Final Answer:

   Pandas -> Option D

4. Quick Check:

   Tables = Pandas [OK]

Hint: Tables mean Pandas, numbers mean NumPy, charts mean Matplotlib [OK]

Common Mistakes:

• Confusing NumPy with Pandas for table data
• Thinking Matplotlib organizes data
• Choosing Seaborn which is for visualization, not tables

2. Which of the following is the correct way to import NumPy in Python?

easy

A. import matplotlib.pyplot as plt

B. import pandas as pd

C. import numpy as np

D. import numpy as pd

Solution

1. Step 1: Recall standard import conventions

   NumPy is commonly imported as 'np' to shorten code.

2. Step 2: Check each option

   import numpy as np uses 'import numpy as np' which is correct; others are for different libraries or wrong aliases.

3. Final Answer:

   import numpy as np -> Option C

4. Quick Check:

   NumPy alias = np [OK]

Hint: NumPy is always imported as np [OK]

Common Mistakes:

• Using wrong alias like pd for NumPy
• Mixing import statements of different libraries
• Forgetting 'as' keyword

3. What will be the output of this code?

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

medium

A. [2 4 6]

B. [1 2 3 1 2 3]

C. [3 4 5]

D. Error

Solution

1. Step 1: Understand NumPy array multiplication

   Multiplying a NumPy array by a number multiplies each element by that number.

2. Step 2: Calculate element-wise multiplication

   Each element in [1, 2, 3] multiplied by 2 gives [2, 4, 6].

3. Final Answer:

   [2 4 6] -> Option A

4. Quick Check:

   Array * 2 doubles each element [OK]

Hint: NumPy * number multiplies each element [OK]

Common Mistakes:

• Thinking * duplicates the array
• Expecting element-wise addition instead of multiplication
• Assuming syntax error

4. Find the error in this code snippet:

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5])
plt.show()

medium

A. plt.show() is missing

B. The lists have different lengths causing an error

C. Wrong import statement for matplotlib

D. No error, code runs fine

Solution

1. Step 1: Check input lists length

   Plotting requires x and y lists to have the same length.

2. Step 2: Identify mismatch

   Here, x has 3 elements, y has 2, causing a ValueError.

3. Final Answer:

   The lists have different lengths causing an error -> Option B

4. Quick Check:

   Plot lists length must match [OK]

Hint: x and y lists must be same length for plot [OK]

Common Mistakes:

• Ignoring length mismatch
• Thinking plt.show() is missing
• Assuming import is wrong

5. You have a Pandas DataFrame with a column 'Age'. Which library and method would you use to quickly calculate the average age?

hard

A. Pandas' mean() method on the 'Age' column

B. Matplotlib's plot() on the 'Age' column

C. NumPy's np.mean() on the 'Age' column

D. NumPy's array() to convert 'Age' column

Solution

1. Step 1: Identify the data structure

   The 'Age' column is part of a Pandas DataFrame.

2. Step 2: Choose the best method for average

   Pandas columns have a built-in mean() method to calculate average quickly.

3. Step 3: Compare options

   NumPy's np.mean() works but requires extra step; Matplotlib is for plotting, not calculation.

4. Final Answer:

   Pandas' mean() method on the 'Age' column -> Option A

5. Quick Check:

   DataFrame column average = mean() [OK]

Hint: Use Pandas mean() directly on DataFrame columns [OK]

Common Mistakes:

• Using Matplotlib for calculations
• Converting column unnecessarily with NumPy
• Not using built-in Pandas methods