Essential libraries overview (Pandas, NumPy, Matplotlib) in Data Analysis Python - Mini Project: Build & Apply

Essential libraries overview (Pandas, NumPy, Matplotlib)

📖 Scenario: You are starting a new data science project. You want to get familiar with three important Python libraries: Pandas, NumPy, and Matplotlib. These libraries help you work with data, do math, and make charts.

🎯 Goal: You will create a small dataset, set a threshold value, filter data using NumPy, and then plot the filtered data using Matplotlib.

📋 What You'll Learn

Create a Pandas DataFrame with specific data

Create a threshold variable

Use NumPy to filter data based on the threshold

Use Matplotlib to plot the filtered data

Print the filtered data

💡 Why This Matters

🌍 Real World

Data scientists often use Pandas to organize data, NumPy to perform fast calculations, and Matplotlib to visualize results. This combination helps in understanding and communicating data insights clearly.

💼 Career

Knowing these libraries is essential for data analyst and data scientist roles, as they form the foundation for data manipulation, analysis, and visualization in Python.

1

Create a Pandas DataFrame

Create a Pandas DataFrame called df with two columns: 'Name' and 'Score'. Use these exact entries: 'Alice' with score 85, 'Bob' with score 92, 'Charlie' with score 78, 'Diana' with score 90, and 'Evan' with score 72.

Data Analysis Python

import pandas as pd
# Create the DataFrame df with the given names and scores
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Set a threshold value

Create a variable called threshold and set it to the integer 80.

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Evan'],
    'Score': [85, 92, 78, 90, 72]
})
# Create the threshold variable and set it to 80
# Your code here

Hint

Just create a variable named threshold and assign the number 80 to it.

3

Filter scores using NumPy

Import NumPy as np. Use NumPy to create a variable called filtered_scores that contains only the scores from df['Score'] that are greater than the threshold.

Data Analysis Python

import pandas as pd

threshold = 80

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Evan'],
    'Score': [85, 92, 78, 90, 72]
})
# Import numpy as np and filter scores greater than threshold
# Your code here

Hint

Use NumPy arrays and boolean indexing to select scores above the threshold.

4

Plot filtered scores and print them

Import Matplotlib's pyplot as plt. Use plt.bar to create a bar chart of filtered_scores with labels as the corresponding names from df['Name'] who have scores above the threshold. Then print the filtered_scores array.

Data Analysis Python

import pandas as pd
import numpy as np

threshold = 80

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Evan'],
    'Score': [85, 92, 78, 90, 72]
})

filtered_scores = np.array(df['Score'])[np.array(df['Score']) > threshold]
# Import matplotlib.pyplot as plt
# Plot a bar chart of filtered_scores with matching names
# Print filtered_scores
# Your code here

Hint

Use plt.bar with names and scores filtered by the threshold. Then use print(filtered_scores).

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