Data Science · मराठी आवृत्ती
26.1 API, JSON आणि zips का
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?
Live systems JSON देतात. Downloads zip येतात. pandas पर्यंतचा bridge शिकणे गरजेचे.
हे काय आहे?
API = HTTP ने data. JSON = nested structure. Zip = compressed files.
कसं करायचं?
Step 1. Source प्रकार ओळखा.
Step 2. Small sample आधी.
Step 3. DataFrame मध्ये नकाशा.
Step 4. Save clean copy.
उदाहरणं: copy करा आणि चालवा
| Source | You pull with | You analyse in |
|---|---|---|
| Drive CSV | pd.read_csv |
DataFrame |
Email .csv.zip |
pd.read_csv + compression / zip |
same |
| HTTP JSON | requests + json_normalize |
same |
उदाहरण A — When CSV is enough.
Raja downloads last month’s closed orders once. Plain read_csv (Chapter 5) is fine.
उदाहरण B — When JSON API is better (fictional).
At 4 pm IST Ruhi needs “open orders right now for Pune dark stores”. The CSV is from 9 am. A small GET to a practice status API returns fresh JSON.
उदाहरण C — Stack sketch.
| Source | You pull with | You analyse in |
|---|---|---|
| Drive CSV | pd.read_csv |
DataFrame |
Email .csv.zip |
pd.read_csv + compression / zip |
same |
| HTTP JSON | requests + json_normalize |
same |