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✅ Python for Data Science: Part-3

NumPy Pandas Basics 📊🐍
These two libraries form the foundation for handling and analyzing data in Python.

1️⃣ NumPy – Numerical Python
NumPy helps with fast numerical operations and array handling.

Importing NumPy

import numpy as np

Create Arrays

arr = np.array([1, 2, 3])
print(arr)

Array Operations

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)        # [5 7 9]
print(a * 2)        # [2 4 6]

Useful NumPy Functions

np.mean(a)          # Average
np.max(b)           # Max value
np.arange(0, 10, 2) # [0 2 4 6 8]

2️⃣ Pandas – Data Analysis Library
Pandas is used to work with data in table format (DataFrames).

Importing Pandas

import pandas as pd

Create a DataFrame

data = {
    "Name": ["Riya", "Aman"],
    "Age": [24, 30]
}
df = pd.DataFrame(data)
print(df)

Read CSV File

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

Basic DataFrame Operations

df.head()       # First 5 rows  
df.info()       # Column types  
df.describe()   # Stats summary  
df["Age"].mean()  # Average age

Filter Rows

df[df["Age"] > 25]

🎯 Why This Matters
• NumPy makes math faster and easier
• Pandas helps clean, explore, and transform data
• Essential for real-world data analysis

Practice Task:
• Create a NumPy array of 10 numbers
• Make a Pandas DataFrame with 2 columns (Name, Score)
• Filter all scores above 80

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