Data Science · मराठी आवृत्ती
25.7 Practice, tip आणि close
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?
एक city leaderboard + एक running total — दोन्ही काढून वाक्यात सांगा.
हे काय आहे?
Practice + quiz.
कसं करायचं?
Step 1. rank top 5.
Step 2. cumsum by date.
Step 3. group-wise एक उदाहरण.
Step 4. Quiz.
उदाहरणं: copy करा आणि चालवा
हा code / output:
from pathlib import Path
import pandas as pd
Path("outputs").mkdir(exist_ok=True)
orders = pd.DataFrame({
"order_id": [f"BLK-{i}" for i in range(1, 13)],
"city": ["Pune", "Pune", "Nashik", "Kolhapur", "Solapur", "Pune",
"Nashik", "Pune", "Nagpur", "Kolhapur", "Solapur", "Pune"],
"order_date": pd.to_datetime(
["2026-10-20"] * 3 + ["2026-10-21"] * 3 + ["2026-10-22"] * 2
+ ["2026-10-23"] * 2 + ["2026-10-24"] * 2
),
"amount_inr": [90, 210, 95, 180, 70, 220, 110, 150, 200, 340, 80, 210],
})
orders["rank_global"] = orders["amount_inr"].rank(ascending=False, method="dense")
orders["rank_city"] = (
orders.groupby("city")["amount_inr"].rank(ascending=False, method="dense")
)
orders.to_csv("outputs/rank_cumsum_lab.csv", index=False)
daily = (
orders.groupby("order_date", as_index=False)["amount_inr"]
.sum()
.rename(columns={"amount_inr": "gmv"})
.sort_values("order_date")
)
daily["gmv_running"] = daily["gmv"].cumsum()
daily["gmv_roll3"] = daily["gmv"].rolling(3).mean()
print(orders.head())
print(daily)