Data Science · मराठी आवृत्ती
24.1 SQL मधून pandas मध्ये का आणायचं
या course चे सर्व chapters (py + ch)
- How to Use This Free Data Science Course
- How to Install Python, Jupyter Notebook and VS Code for Data Science (Windows and Mac)
- Python Variables and Data Types Explained for Beginners (int, float, str, bool)
- Python Strings Tutorial: Slicing, f-strings and String Methods with Examples
- Python Lists, Tuples, Dictionaries and Sets Explained with Examples
- Python if, elif and else Statements with Real Examples for Beginners
- Python for Loops and while Loops Explained with Examples (range, break, continue)
- Python Functions Tutorial: def, return, Arguments and lambda with Examples
- Python Errors and Exceptions: How to Use try, except, else and finally
- How to Read and Write CSV, JSON and Text Files in Python (with open and with)
- Python Modules, pip and Virtual Environments (venv) Explained for Beginners
- Python Classes and Objects for Beginners: OOP Basics with Simple Examples
- NumPy Tutorial for Beginners: Arrays, Maths and Filtering in Python
- Python Mini Projects for Beginners: 5 Practice Projects Before Learning pandas
- Data Science vs Data Analytics Tutorial
- Python Jupyter Setup for Data Science
- Python Basics for Data Science Tutorial
- NumPy Essentials for Data Science Tutorial
- pandas DataFrames and Series Tutorial
- Filter and Sort Data in pandas Tutorial
- pandas GroupBy and Aggregations Tutorial
- Merge and Concat Data in pandas Tutorial
- Data Cleaning in pandas A–Z Tutorial
- Dates and Text in pandas – Free Tutorial
- Reshape Data: melt and pivot_table
- Exploratory Data Analysis in Python
- matplotlib Visualization Tutorial – Free
- seaborn Visualization Tutorial – Free
- Chart Choice and Data Storytelling
- Data Science Project: Blinkit Maharashtra
- Data Science Practice Exercises
- Data Science Interview Questions
- pandas matplotlib seaborn Cheat Sheet
- Data Science Glossary – Free Tutorial
- Stats for Data Work Mean Median IQR
- Export CSV Excel PNG from pandas Tutorial
- Common pandas Mistakes and Fixes Tutorial
- Load SQL Query Results into pandas DataFrame – Free Tutorial
- How to Rank and Running Totals in pandas
- How to Read APIs, JSON and Zipped CSVs into pandas
- How to Use Git with Jupyter Notebooks for Data Projects
- How to Merge and Join Tables in pandas for Blinkit Data
- What Is Machine Learning Simple Explanation
- Machine Learning Workflow Step by Step
- Train Test Split in Machine Learning sklearn
- Features and Labels in Machine Learning
- Linear Regression in Python with sklearn
- Classification with Logistic Regression in Python
- Decision Trees and Random Forests in Python
- K-Means Clustering in Python for Beginners
- How to Check If Your ML Model Is Good
- Overfitting and Underfitting in Machine Learning
- End-to-End ML Project India Dataset
- Data Preprocessing for ML: Impute Scale Encode
- What Is a Neural Network Simple Explanation
- Activation Loss Gradient Descent Plain English
- Backpropagation Intuition No Heavy Maths
- Install PyTorch and First Tensors
- Your First Neural Network in PyTorch
- Deep Learning vs Classical ML When to Use Which
- Convolutional Neural Networks CNNs for Images
- Small CNN Image Project in PyTorch
- What Transformers Are Text and Sequences
- Tiny Text Classifier Transformer Intro PyTorch
- What Is Natural Language Processing NLP
- Text Cleaning and TF-IDF in Python sklearn
- Sentiment Analysis Project in Python TF-IDF
- NLP vs ChatGPT Classical NLP and Generative AI
हे का शिकायचं?
Database सत्य स्रोत. pandas मध्ये cleaning, groupby, charts सोपे. दोन्हीची ताकद वापरा.
हे काय आहे?
SQL = filter/join जवळ data. pandas = exploratory analysis. थोडे rows आणा, सर्व database RAM मध्ये ओढू नका.
कसं करायचं?
Step 1. प्रश्न लिहा.
Step 2. SQL मध्ये आधी filter.
Step 3. pandas मध्ये आणा.
Step 4. shape आणि head तपासा.
उदाहरणं: copy करा आणि चालवा
| Store of truth | You pull with | You analyse in |
|---|---|---|
| MySQL / Postgres / SQLite | SQL SELECT |
pandas DataFrame |
| Shared drive CSV | pd.read_csv |
same DataFrame tools |
उदाहरण A — When CSV is enough.
Raja downloads last month’s closed orders once for a training notebook. CSV is fine. No live connection needed.
उदाहरण B — When SQL is better (fictional).
Amir’s manager asks at 4 pm IST: “Delivered GMV today for Pune dark stores only.” The morning CSV missed the afternoon rush. A small SELECT … WHERE order_date = CURRENT_DATE AND city = 'Pune' into pandas answers in one cell.
उदाहरण C — Stack sketch.
| Store of truth | You pull with | You analyse in |
|---|---|---|
| MySQL / Postgres / SQLite | SQL SELECT |
pandas DataFrame |
| Shared drive CSV | pd.read_csv |
same DataFrame tools |