14 📊 Data Science

Data Science involves analyzing, processing, and visualizing data to derive insights. Python is widely used in Data Science due to its rich ecosystem of libraries. This chapter covers NumPy, Pandas, and Matplotlib, the three core libraries for data manipulation, analysis, and visualization.

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14.1 🔢 NumPy – Numerical Computing

NumPy (Numerical Python) provides fast, efficient array operations and is the foundation for scientific computing in Python.

✅ Installing NumPy

pip install numpy

🔹 Creating NumPy Arrays

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
print(arr)  # Output: [1 2 3 4 5]

🔹 NumPy Array Operations

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(a + b)  # Output: [5 7 9]
print(a * b)  # Output: [4 10 18]
print(np.dot(a, b))  # Dot product: 1*4 + 2*5 + 3*6 = 32

🔹 Generating Random Data

random_numbers = np.random.rand(5)  # 5 random numbers
print(random_numbers)

✅ Use Case: Data preprocessing, handling large numerical datasets efficiently.

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14.2 🗃️ Pandas – Data Manipulation

Pandas simplifies working with structured data (tables, CSVs, JSONs, databases).

✅ Installing Pandas

pip install pandas

🔹 Creating a DataFrame

import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "Salary": [50000, 60000, 70000]
}

df = pd.DataFrame(data)
print(df)

🔹 Reading Data from CSV

df = pd.read_csv("data.csv")
print(df.head())  # Display first 5 rows

🔹 Filtering Data

young_employees = df[df["Age"] < 30]
print(young_employees)

🔹 Adding a New Column

df["Bonus"] = df["Salary"] * 0.10  # 10% bonus
print(df)

✅ Use Case: Handling datasets, cleaning and preparing data for ML models.

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14.3 📊 Matplotlib – Data Visualization

Matplotlib is the primary library for plotting graphs and visualizing data.

✅ Installing Matplotlib

pip install matplotlib

🔹 Plotting a Line Graph

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 15, 20, 25, 30]

plt.plot(x, y, marker="o", linestyle="-")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Simple Line Plot")
plt.show()

🔹 Creating a Bar Chart

categories = ["A", "B", "C", "D"]
values = [10, 20, 15, 25]

plt.bar(categories, values, color="blue")
plt.title("Bar Chart Example")
plt.show()

🔹 Creating a Histogram

data = np.random.randn(1000)

plt.hist(data, bins=30, color="green")
plt.title("Histogram of Random Data")
plt.show()

✅ Use Case: Exploratory Data Analysis (EDA), understanding dataset distributions.

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🚀 Summary

Library	Purpose	Best For
NumPy	Fast numerical computing	Arrays, linear algebra, statistics
Pandas	Data manipulation	CSVs, databases, data wrangling
Matplotlib	Data visualization	Graphs, charts, and plots

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🔚 Final Thoughts

Mastering NumPy, Pandas, and Matplotlib is essential for data science, machine learning, and AI applications.
Would you like practical projects to apply these concepts? 🚀