Data Science · मराठी आवृत्ती
23.4 Timezone: naive vs IST चुका
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?
UTC आणि IST मिसळले की “हा order कालचा की आजचा?” चुकतं. Naive datetime = timezone माहिती नाही.
हे काय आहे?
Parse → tz_localize/tz_convert स्पष्ट करा. Course मध्ये IST (Asia/Kolkata) लक्ष्य.
कसं करायचं?
Step 1. Column dtype datetime का ते पाहा.
Step 2. जर UTC असेल तर Asia/Kolkata मध्ये convert.
Step 3. Date filter IST नुसार.
Step 4. Clock change / DST India मध्ये नाही — तरी timezone label ठेवा.
उदाहरणं: copy करा आणि चालवा
उदाहरण A — Localise fictional dispatch times.
import pandas as pd
ts = pd.DataFrame({
"order_id": ["BLK-30", "BLK-31"],
"city": ["Pune", "Kolhapur"],
"dispatched_at": ["22-10-2026 21:05:00", "22-10-2026 21:20:00"],
"delivered_at": ["22-10-2026 21:18:00", "22-10-2026 21:31:00"],
})
for c in ["dispatched_at", "delivered_at"]:
ts[c] = pd.to_datetime(ts[c], dayfirst=True)
ts[c] = ts[c].dt.tz_localize("Asia/Kolkata")
ts["mins"] = (ts["delivered_at"] - ts["dispatched_at"]).dt.total_seconds() / 60
print(ts[["order_id", "mins"]])
उदाहरण B — UTC feed mistaken as local.
utc_feed = pd.Series(["2026-10-22 15:35:00"]) # actually UTC
aware_utc = pd.to_datetime(utc_feed, utc=True)
ist = aware_utc.dt.tz_convert("Asia/Kolkata")
print("UTC", aware_utc.iloc[0])
print("IST", ist.iloc[0]) # evening in India
If Salman treats 15:35 UTC as 15:35 IST, the “evening rush” chart shifts by 5 hours 30 minutes.
उदाहरण C — Date-only filters stay simple.
days = pd.to_datetime(
pd.Series(["15-10-2026", "20-10-2026", "25-10-2026"]), dayfirst=True
)
diwali_window = days.to_series().between("2026-10-15", "2026-10-22")
print(list(zip(days.dt.strftime("%d-%m-%Y"), diwali_window)))
For day-level EDA you often do not need tz — but say so in the README.