Data Science Setup: Python, Anaconda aur Jupyter Notebook

Lesson 3 of 26

Data Science ke liye sabse popular language Python hai — iski libraries (NumPy, Pandas, scikit-learn) ne isko is field ka standard bana diya hai. Is lesson me hum apna setup ready karenge.

Option 1: Anaconda (beginners ke liye recommended)

Anaconda ek package hai jisme Python, Jupyter Notebook aur 250+ data science libraries pehle se aati hain. Ek install me sab ready.

  1. anaconda.com/download se apne OS ka installer download kariye.
  2. Installer chalaiye aur default options ke saath install kariye.
  3. Start menu se Anaconda Navigator ya Jupyter Notebook kholiye.

Option 2: Python + pip

Agar Python pehle se installed hai, to libraries khud install kar sakte hain. Ek alag virtual environment banana acchi aadat hai, taaki har project ki libraries alag rahein:

Terminal

Bash 
# virtual environment banaiye aur activate kariye
python -m venv ds-env
ds-env\Scripts\activate        # Windows
source ds-env/bin/activate     # Mac / Linux

# zaroori libraries install kariye
pip install numpy pandas matplotlib seaborn scipy scikit-learn jupyter

# Jupyter Notebook start kariye
jupyter notebook

Option 3: Google Colab (bina install ke)

Google Colab browser me chalne wala free Jupyter Notebook hai. Bas Google account se login kariye aur code likhna shuru. Saari popular libraries pehle se installed hoti hain. Practice ke liye ye sabse aasaan raasta hai.

Jupyter Notebook kaise use karein

Jupyter me code cells me likha jaata hai. Har cell alag se run hota hai aur uska output turant neeche dikhta hai — isliye data explore karne ke liye ye perfect hai.

Shortcut

Kaam

Shift + Enter

Cell run karo aur agle cell pe jao

Ctrl + Enter

Cell run karo, usi cell pe raho

A / B

Upar / neeche naya cell (command mode me)

M / Y

Cell ko Markdown / Code banao

D D

Cell delete karo

Setup check kariye

Ek naya notebook kholiye aur ye code chalaiye. Agar koi error nahi aaya to aapka setup ready hai:

Example

Python 
import numpy
import pandas
import matplotlib
import sklearn

print("NumPy:", numpy.__version__)
print("Pandas:", pandas.__version__)
print("Matplotlib:", matplotlib.__version__)
print("scikit-learn:", sklearn.__version__)

Output

Plain Text 
NumPy: 2.3.5
Pandas: 2.3.3
Matplotlib: 3.10.6
scikit-learn: 1.7.2

Aapke versions thode alag ho sakte hain — koi baat nahi. Is course ka code NumPy 2.x, Pandas 2.x aur scikit-learn 1.x pe test kiya gaya hai.

Data Science ki main libraries

Library

Kaam

Import kaise karte hain

NumPy

Fast numerical arrays aur math

import numpy as np

Pandas

Tables (DataFrame) me data handle karna

import pandas as pd

Matplotlib

Charts aur graphs

import matplotlib.pyplot as plt

Seaborn

Sundar statistical charts

import seaborn as sns

SciPy

Statistics aur scientific functions

from scipy import stats

scikit-learn

Machine Learning models

from sklearn... import ...

np, pd, plt, sns — ye short names poori duniya me same use hote hain. Aap bhi yahi use kariye, taaki dusron ka code padhna aasaan rahe.

Summary

  • Beginners ke liye Anaconda ya Google Colab sabse aasaan hai.
  • Jupyter Notebook me code cells me chalta hai aur output turant dikhta hai.
  • NumPy, Pandas, Matplotlib, Seaborn, SciPy aur scikit-learn — ye hamari main toolkit hai.

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