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

Need a 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

Need a 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

Need a 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

Need a hint?

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