🐍 Python for Data Science

📘 Why Python for Data Science

Python is one of the most popular programming languages for Data Science due to its:

• Simplicity and readability
• Large collection of powerful libraries
• Strong community support
• Flexibility for data analysis, visualization, and machine learning

Python allows you to go from data collection to model building to visualization — all within one language.

🧩 Python Syntax & Data Types

Python uses a clean, easy-to-read syntax.
Here are some basic concepts and data types used in almost every program.

Basic Syntax

# This is a comment
print("Hello, Data Science!")

Variables

Variables are used to store data.

name = "Alice"
age = 25
height = 1.68

Data Types

📋 Lists, Dictionaries, Loops, and Functions

Lists

Lists are used to store multiple items in one variable.

numbers = [10, 20, 30]
print(numbers[0])  # Access the first element
numbers.append(40)  # Add a new element

Dictionaries

Dictionaries store data as key-value pairs.

person = {"name": "Alice", "age": 25}
print(person["name"])  # Access value by key
person["city"] = "London"  # Add a new key-value pair

Loops

Loops help repeat actions efficiently.

For loop

for i in range(5):
    print(i)

While loop

count = 0
while count < 3:
    print("Count:", count)
    count += 1

Functions

Functions group reusable blocks of code.

def greet(name):
    return f"Hello, {name}!"

print(greet("Data Scientist"))

💻 Using Jupyter Notebook and VS Code

Jupyter Notebook

Jupyter Notebook is an interactive environment for data analysis, visualization, and experimentation.

You can organize code in cells and display charts or tables inline.
It’s commonly used for:

• Exploratory Data Analysis (EDA)
• Machine learning experiments
• Tutorials and reports

To run Jupyter:

pip install jupyter
jupyter notebook

VS Code

VS Code is a general-purpose code editor with extensions for Python and Data Science.

It provides:

• Syntax highlighting and autocompletion
• Debugging tools
• Integration with Jupyter Notebooks
• Git version control

Recommended Extensions:

• Python
• Jupyter
• Pylance (for IntelliSense)
• GitLens

📚 Popular Python Libraries for Data Science

1. NumPy

Used for numerical and matrix operations.

import numpy as np

arr = np.array([1, 2, 3, 4])
print(arr.mean())  # Average
print(arr * 2)     # Vectorized operations

Key features:

• Fast mathematical operations
• Multidimensional arrays
• Foundation for many other libraries (like pandas and TensorFlow)

2. pandas

Used for data manipulation and analysis.

import pandas as pd

data = {"Name": ["Alice", "Bob"], "Age": [25, 30]}
df = pd.DataFrame(data)

print(df)
print(df["Age"].mean())  # Average age

Key features:

• Works with tabular data (rows and columns)
• Tools for filtering, grouping, and aggregating
• Can read/write CSV, Excel, and SQL files

3. Matplotlib

Used for data visualization.

import matplotlib.pyplot as plt

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

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

Key features:

• Create line, bar, scatter, and pie charts
• Highly customizable
• Often used with pandas and NumPy

🧠 Summary

With these foundations, you’re ready to start working with real datasets and exploring the world of Data Science!