Introduction to NumPy, Pandas & Matplotlib (Drawing Plots)

Python is the #1 programming language in the world for Data Science, Analytics, and Machine Learning because of three foundational libraries: NumPy, Pandas, and Matplotlib.

1. Introduction to NumPy (Numerical Python)

NumPy (import numpy as np) is the core library for scientific and numerical computing in Python. Its main object is the homogeneous multidimensional array called ndarray (N-dimensional array).

Why Use NumPy Arrays Instead of Python Lists?

Creating & Using NumPy Arrays:

import numpy as np

# 1. Creating a 1D NumPy Array
arr1 = np.array([10, 20, 30, 40])
print("1D Array:", arr1)
print("Add 5 to each element:", arr1 + 5)  # Output: [15 25 35 45]

# 2. Creating a 2D Matrix (Rows x Columns)
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print("Shape (rows, cols):", matrix.shape) # Output: (2, 3)
print("Dimensions (ndim):", matrix.ndim)   # Output: 2

# 3. Built-in Array Generators & Statistical Functions
zeros_arr = np.zeros((2, 2))               # 2x2 array of 0.0
range_arr = np.arange(1, 10, 2)            # [1, 3, 5, 7, 9]
print("Mean:", np.mean(arr1), "Max:", np.max(arr1), "Sum:", np.sum(arr1))

2. Introduction to Pandas (Data Analysis Library)

Pandas (import pandas as pd) is built on top of NumPy and is used for cleaning, analyzing, and manipulating structured tabular data (like CSV files, Excel spreadsheets, and SQL tables).

The Two Core Data Structures of Pandas:

1. Series (1-Dimensional): A single labeled column of data (like a 1D array with custom index labels).
2. DataFrame (2-Dimensional): A 2D table consisting of Rows and Columns (just like an Excel sheet or SQL table).

import pandas as pd

# Creating a 2D DataFrame from a Python Dictionary
data = {
    "Roll_No": [101, 102, 103, 104],
    "Name": ["Aarav", "Diya", "Karan", "Sneha"],
    "Marks": [85, 92, 76, 89]
}

df = pd.DataFrame(data)
print("--- Full Student Table ---")
print(df)

# Useful Pandas Inspection & Filtering Operations:
print("\nFirst 2 rows:\n", df.head(2))               # Top 2 rows
print("\nAverage Marks:", df["Marks"].mean())        # Mean of Marks column
print("\nToppers (>85):\n", df[df["Marks"] > 85])    # Filter rows where Marks > 85

# Reading & Writing CSV files in 1 line:
# df = pd.read_csv("students.csv")
# df.to_csv("output.csv", index=False)

3. Introduction to Matplotlib & Drawing Plots

Matplotlib is Python’s most popular 2D Data Visualization library. Its primary sub-module is matplotlib.pyplot (imported as plt), which allows us to draw publication-quality graphs and charts in just a few lines of code.

import matplotlib.pyplot as plt

Common Plotting Functions in matplotlib.pyplot:

Example 1: Drawing a Line Plot (plt.plot())

import matplotlib.pyplot as plt

# Data coordinates (X-axis and Y-axis)
semesters = [1, 2, 3, 4, 5]
sgpa = [7.8, 8.1, 8.5, 8.4, 9.1]

# Draw Line Graph
plt.plot(semesters, sgpa, color="blue", marker="o", linestyle="--")

# Add Title and Axis Labels
plt.title("BCA Student SGPA Progress")
plt.xlabel("Semester")
plt.ylabel("SGPA Scored")
plt.grid(True)

# Display the Plot Window
plt.show()

Example 2: Drawing a Bar Chart (plt.bar()) & Pie Chart (plt.pie())

import matplotlib.pyplot as plt

subjects = ["Python", "Java", "OS", "CyberSec"]
marks = [92, 85, 78, 88]

# 1. Drawing a Bar Chart
plt.bar(subjects, marks, color=["#3b82f6", "#10b981", "#f59e0b", "#ec4899"])
plt.title("Subject-wise Marks in BCA")
plt.xlabel("Subjects")
plt.ylabel("Marks Obtained")
plt.show()

# 2. Drawing a Pie Chart
plt.pie(marks, labels=subjects, autopct="%1.1f%%", startangle=90)
plt.title("Marks Distribution")
plt.show()