Overview of Python Libraries for Data Science

Python is powerful because of its rich ecosystem of libraries.
Among them, four libraries are absolutely essential for Data Science:

NumPy – Numerical computing
Pandas – Data manipulation
Matplotlib – Data visualization
Seaborn – Statistical visualizations

NumPy (Numerical Python)

NumPy is the foundation of scientific computing in Python.
It introduces the powerful NumPy array, which is faster and more efficient than Python lists.

What NumPy Is Used For

Mathematical operations
Matrix algebra
Statistical calculations
Working with multi-dimensional data
Foundation for Pandas & ML libraries

Example: Creating a NumPy Array

import numpy as np

arr = np.array([10, 20, 30])
print(arr)

Example: Basic Math with NumPy

arr = np.array([1, 2, 3])
print(arr * 5)     # Multiply each element
print(np.mean(arr))
print(np.std(arr))

Why NumPy is Important

Faster than Python lists
Essential for Machine Learning
Backbone of Pandas & SciPy

Pandas (Python Data Analysis Library)

Pandas is the most important library for Data Science.
It helps you work with:

Tables
CSV files
Excel files
Databases

Pandas introduces the DataFrame: a table-like structure (rows + columns).

What Pandas Is Used For

Data cleaning
Data wrangling
Data analysis
Importing/exporting datasets
Handling missing values

Example: Creating a DataFrame

import pandas as pd

df = pd.DataFrame({
    "Name": ["Asha", "Rahul", "John"],
    "Score": [90, 85, 88]
})
print(df)

Example: Reading a CSV File

df = pd.read_csv("students.csv")

Example: DataFrame Operations

df.head()         # View first rows
df.info()         # Display column info
df.describe()     # Summary stats
df["Score"] > 85  # Filter

Why Pandas is Important

Matplotlib is the most basic and powerful plotting library in Python.

It helps create:

Line charts
Bar charts
Scatter plots
Histograms

It is highly customizable but sometimes looks less modern — which is why Seaborn exists.

What Matplotlib Is Used For

Custom data visualizations
Plotting trends
Creating publication-level charts

Example: Simple Line Plot

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [10, 20, 15])
plt.title("Sales Growth")
plt.xlabel("Month")
plt.ylabel("Sales")
plt.show()

Example: Bar Chart

plt.bar(["A", "B", "C"], [50, 70, 40])
plt.show()

Seaborn (Statistical Visualization Library)

Seaborn is built on top of Matplotlib.
It makes your visualizations more beautiful, modern, and simplified.

What Seaborn Is Used For

Statistical visualizations
Correlation heatmaps
Distribution plots
Category plots

Example: Line Plot with Seaborn

import seaborn as sns

sns.lineplot(x=[1,2,3], y=[5,7,9])

Example: Histogram

sns.histplot([10,20,30,20,10,40])

Example: Correlation Heatmap

import seaborn as sns
import pandas as pd

df = pd.DataFrame({
    "A": [1,2,3],
    "B": [4,5,6],
    "C": [7,8,9]
})

sns.heatmap(df.corr(), annot=True)

Comparison Table: NumPy vs Pandas vs Matplotlib vs Seaborn

Library	Main Use	Best For	Output
NumPy	Numerical computing	Arrays, math, ML preprocessing	nD arrays
Pandas	Data analysis	Cleaning & manipulating datasets	DataFrames
Matplotlib	Basic visualization	Custom, detailed plots	Graphs & charts
Seaborn	Advanced visualization	Statistical & modern visuals	Pretty charts

Mini Project Example Using All 4 Libraries

Here’s a small beginner-friendly project.

Step 1: Import Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Step 2: Create Data

scores = np.array([85, 90, 78, 92, 88])
df = pd.DataFrame({"Scores": scores})

Step 3: Visualize

sns.histplot(df["Scores"])
plt.title("Student Score Distribution")
plt.show()

This simple project teaches you all the basics.