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Python 3, venv आणि Jupyter install (Data Science)

By , Data Science & Data Analytics trainer LinkedIn Published: Last updated:

Read this guide in: English · मराठी · हिंदी

Python 3 install करा, venv तयार करा, Jupyter आणि pandas pip install करा (sklearn नाही), मग notebook launch करा. हा Data Science course चा setup lap आहे.

चला मित्रांनो! तुटलेली installs weekend खाऊन टाकतात. आज clean path: Python → venv → packages → Jupyter. उभ्या साध्या steps. समजलं का? शेवटी check.

थोडक्यात उत्तर

उभा install path:

  1. Python 3 install → python3 --version confirm.
  2. Folder: ds-practice.
  3. venv: python3 -m venv ds-env.
  4. venv activate करा.
  5. pip install jupyter pandas openpyxl matplotlib seaborn.
  6. jupyter notebook किंवा jupyter lab → नवीन notebook → print(1+1).
Python3 → venv → pip install → Jupyter → first cell

Language: English · मराठी · हिंदी

या guide आधी काय हवं?

  • Software install करता येणारा computer (Windows, macOS किंवा Linux).
  • Terminal / command prompt वापरण्याची परवानगी.
  • Course: Install Python 3.

Before आणि after

Before install No venv and no Jupyter.

Before — no venv, Jupyter not found.

After install venv and Jupyter are ready.

After — ds-env venv and Jupyter running.

Before: venv नाही, Jupyter missing. After: ds-env active आणि Jupyter चालू.

venv का हवा?

  1. एका project साठी packages ची folder.
  2. Class work system Python शी भांडत नाही.
  3. ds-env delete करून पुन्हा बनवता येतो — OS reinstall नको.

Activate नंतर prompt वर बहुतेक (ds-env) दिसतं. which python / where python venv आत दाखवतो.

Step 1 — Python 3 install

  1. python.org installer किंवा OS package manager.
  2. Windows वर “Add Python to PATH” enable करा.
  3. नवीन terminal उघडा.
  4. python3 --version (काही Windows वर python --version).

हवं: 3.x version string, error नाही.

Step 2 — Project folder + venv

mkdir ds-practice
cd ds-practice
python3 -m venv ds-env

Activate:

  1. Linux/macOS: source ds-env/bin/activate
  2. Windows cmd: ds-env\Scripts\activate
  3. Windows PowerShell: ds-env\Scripts\Activate.ps1

Course: Project folder and venv.

Step 3 — Packages (sklearn नाही)

python -m pip install --upgrade pip
pip install jupyter pandas openpyxl matplotlib seaborn

का हे:

  1. jupyter — notebooks.
  2. pandas — DataFrames.
  3. openpyxl — Excel.
  4. matplotlib / seaborn — charts.

sklearn का नाही: हा course ML model training exclude करतो. Install packages (no sklearn).

Step 4 — Jupyter launch

jupyter notebook

किंवा jupyter lab.

उभी checks:

  1. Browser मध्ये Jupyter UI.
  2. New → Notebook → Python 3.
  3. Cell: print(1+1) → 2.
  4. Cell: import pandas as pd; print(pd.__version__).

Common problems

Problem Likely cause Fix
python3: command not found Install/PATH Reinstall; नवीन terminal
jupyter: command not found venv inactive Activate, किंवा python -​m jupyter notebook
pip चुकीच्या जागी Activate विसरले Activate; which python
Excel read fail openpyxl missing venv मध्ये pip install openpyxl
Port busy जुना Jupyter Session बंद / दुसरा port

Tiny first notebook

  1. shop_sales.csv — Mumbai 120, Pune 70.
  2. df = pd.read_csv("shop_sales.csv")
  3. df["sales"].sum() → 190
  4. एका sitting मध्ये install + pandas सिद्ध.

Ravindra Bagale's Tip

python --version आणि notebook cell मध्ये 190 चा screenshot ठेवा. तो pair लांब story पेक्षा setup सिद्ध करतो. समजलं का?

Practice task

  1. ds-env नावाचा venv.
  2. Quick answer ची package list (sklearn नाही).
  3. Jupyter → import pandas as pd.
  4. एक Markdown cell: “Setup done — next DataFrame”.

Learn it properly

Related: Python for data science beginners · pandas DataFrame beginners

समजलं का? Python 3, venv, sklearn नसलेल्या pip packages, Jupyter open, पहिला cell green. मग CSV load. आता पुढे जाऊया.

Frequently asked questions

What is a venv?

A virtual environment: an isolated folder of Python packages so class projects do not break your system Python.

Do I need Anaconda?

Optional. A plain Python + venv + pip path is enough for this course.

Why not install sklearn?

This Data Science course explicitly excludes machine learning model training. Stay with pandas, cleaning, EDA and charts.

jupyter: command not found?

Activate the venv first, or call python -m jupyter notebook.

Windows vs Linux/macOS?

Commands differ for activation paths; Python and pip ideas stay the same. Follow the course setup chapter for screenshots.

Next after install?

Learn Series vs DataFrame, then read_csv.