🐍 Python Libraries: Data Science in Desi Andaz! 🐍

Salam Python Walon! 🙌 Aaj hum mil ke dekhenge 16 Python libraries jo har Data Science student aur learner ke liye laazmi hain. Toh bina waqt zaya kiye, chaliye shuru karte hain! 🎩✨

1️⃣ NumPy 🧮

Desi Example: Jaise aap tandoor wale se kehte ho ke 10 naan banaye, waise hi NumPy se keh sakte ho ke 10 lists banao. NumPy aapko math ke advanced functions bhi deti hai, jo ke aapko bazaar mein sabziyan lete waqt kaam aayengi!

2️⃣ Pandas 🐼

Desi Example: Pandas se aap data ko handle karte hain jaise aap apne dosto ko cricket khelte waqt handle karte hain! Jaise ek captain team ko handle karta hai, waise hi Pandas aapke data ko handle karta hai.

3️⃣ Matplotlib 📊

Desi Example: Matplotlib se graphs banate hain jaise mohallay ki aunty gossip banati hain! Yeh aapko har type ke graph banane ki sahulat deta hai.

4️⃣ Seaborn 🎨

  • WebLink: Seaborn Official Site
  • Use Case: Data visualization ke liye khoobsoorat aur informative statistical graphics.

Desi Example: Jaise eid ke din hum apne kapde aur jootay match karte hain, waise hi Seaborn aapko data visualizations ko khoobsoorat banane mein madad karta hai.

5️⃣ Scikit-Learn 🤖

Desi Example: Jaise aap apne dost se seekhte hain cricket khelna, waise hi Scikit-Learn se aap machine learning models ko train karte hain.

6️⃣ TensorFlow 🧠

Desi Example: TensorFlow se aap apne models ko train karte hain jaise abba ji aapko drive karna sikhate hain!

7️⃣ Keras 💡

Desi Example: Jaise ammi ke recipes se best biryani banti hai, waise hi Keras ke tools se best neural networks bante hain!

8️⃣ PyTorch 🔥

  • WebLink: PyTorch Official Site
  • Use Case: Deep learning aur artificial intelligence ke applications develop karna.

Desi Example: Jaise aap chai mein cheeni milate hain, waise hi PyTorch aapko models ke ingredients mix karne mein madad karta hai.

9️⃣ BeautifulSoup 🍲

Desi Example: BeautifulSoup se aap web pages ki maloomat nikalte hain jaise aap channe ke daane nikalte hain!

🔟 NLTK 📚

  • WebLink: NLTK Official Site
  • Use Case: Text analysis, processing aur natural language processing.

Desi Example: Jaise aap Urdu shayari samajhte hain, waise hi NLTK aapko natural languages ko process karne mein madad karta hai.

1️⃣1️⃣ Statsmodels 📈

Desi Example: Jaise aap apne mobile balance check karte hain, waise hi Statsmodels aapko statistical models check karne mein madad karta hai.

1️⃣2️⃣ Plotly 🌐

Desi Example: Jaise dawat mein dish ki presentation important hoti hai, waise hi Plotly aapke graphs aur charts ki presentation ko behetareen banata hai.

1️⃣3️⃣ SciPy 🔬

  • WebLink: SciPy Official Site
  • Use Case: Scientific computing, linear algebra, aur optimization ke tasks perform karna.

Desi Example: Jaise hakeem sahab desi nuskhe batate hain, waise hi SciPy aapko scientific computing ke desi tareeqe sikha deta hai.

1️⃣4️⃣ Openpyxl 📘

Desi Example: Openpyxl se aap Excel ki files ko handle karte hain jaise aap apne rishtedar ko handle karte hain eid ki dawaton mein!

1️⃣5️⃣ Scrapy 🕷

Desi Example: Jaise aap makhiyan bhaga dete hain, waise hi Scrapy se aap web crawling kar ke data extract kar lete hain!

1️⃣6️⃣ Streamlit 💻

Desi Example: Streamlit se aap apne data science projects ko web apps mein easily show kar sakte hain, jaise hum sab dosto ko apni eid ki shopping dikhate hain!

Final Thoughts 🌟

Dosto, yeh the kuch behtareen Python libraries jo aapko aapke data science ke safar mein madadgar sabit hongi. Inhe try karna na bhulein aur coding ka maza lein! 💻🌟

🚀 Time to Take Action! 🚀

Ab jab aap in sab Python libraries ke bare mein jaan chuke hain, toh der kis baat ki? 🤔 Apne computer ko on karo, yeh libraries install karo, aur coding shuru karo! 💻✨ Jitna zyada practice karenge, utna hi aapko samajh aayega. 🔄

  • 🔍 Explore: Har library ki official website pe ja ke aur bhi zyada explore karo aur seekho! 🌐
  • 💬 Discuss: Apne teachers aur classmates se in libraries ke bare mein baat karo aur unke experiences suno! 🗣️
  • 👩‍💻 Code: Practical experience sab se behtar hota hai, toh jaldi se coding start karo! 🌟
  • 📚 Learn: Online tutorials aur courses se seekhne ka faida uthao. Aaj kal toh bohot saare free resources bhi available hain! 📘

🌐 Join the Community! 🌐

Aakhiri tor par, online forums aur communities join karo, jahan log aapas mein knowledge share karte hain. Aise platforms par aapko experienced log milenge jo aapki madad kar sakte hain aur aapke sawaalat ka jawab de sakte hain. 🙌

Remember, the journey of learning data science is challenging but also very rewarding. Toh abhi se tayyari shuru karo, seekho aur aagay barho! 💪🔥

💬 Let’s Discuss! 💬

Comment section mein apne thoughts share karo, aur agar koi sawaal ho toh poocho, hum mil ke solutions dhoondenge! 🤝

🔗 Share & Connect! 🔗

Agar aapko yeh article acha laga toh share zaroor karo apne dosto ke sath, aur humse connect karo Twitter/LinkedIn/Facebook par! 🌟


  1. I’m excited to discover this great site. I need to to thank you for ones time for
    this wonderful read!! I definitely really liked every part of it and
    I have you bookmarked to look at new stuff on your blog.

  2. It’s so helpful sir and it’s very easy to understand for all
    Thank you so much sir for providing this type of material
    All materials that you provide very helpful for me

  3. Very informative sir! Jo desi examples ap ne di hai sir uss se ik ik library demagh ma beth gai ha. Ab nhi bhoolti.

  4. each and every thing is just up-to-mark.we have got such a great opportunity to learn things in our own taste “Desi trka”.Keep it up!

  5. I just wanted to express how much I appreciate your blog, especially your articles on Python libraries for data science. Your insights and recommendations have been incredibly helpful in my data science journey. Your blog has become my go-to resource for staying updated on the latest developments in this field. Thank you for sharing your knowledge and expertise!

  6. Numpy, matplotlib and pandas are some what familiar. plotly, seaborn , scipy are yes, as they have been discussed in python ka chilla. how others can be practiced mean in what interface VS on else ? as a beginner in AI , I have no idea, In my opinion , if we get a video or videos that explain any dummy data /real world data with reference to above stated libraries, that may be more useful in understanding them (libraries) once one get an idea or say general road map to use them in practical manner then further advancement would be much easier. Any way this is just my opinion. jazakallah…

  7. Name Kry shine jiveen dhup Baliye..
    munh utny rkhde aan chup Baliye..
    Success rendi Germany shoor krdi
    Sada Top ty Codanics da Group Baliye………………………

  8. Sir,
    very helpful and informatics blog.
    mre system me Ubuntu installed hy or me Pycharm community pr kam krta hun tu koi issue tu ni hy ya VSCode ko install kro.

  9. Sir if a person undergraduate he can do these courses and if he complete these courses can he get job in date field or not or still he needs degree for that plz explain briefly about that thanks

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