Data Science · मराठी आवृत्ती
21.1 Charts आधी stats का महत्त्वाच्या
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?
फक्त सुंदर chart पुरेसं नाही. Manager ला सरासरी, मधली किंमत आणि outlier ची गोष्ट हवी असते. Stats ही त्याची भाषा आहे.
हे काय आहे?
Summary numbers charts ला अर्थ देतात. Blinkit-style order amounts वर विचार करू.
कसं करायचं?
Step 1. छोटा amount column तयार करा.
Step 2. Mean आणि median काढा.
Step 3. एक मोठी outlier order टाकून फरक पाहा.
Step 4. Chart आधी हे आकडे लिहून ठेवा.
उदाहरणं: copy करा आणि चालवा
उदाहरण A — Blinkit Monday vs Diwali Friday (fictional).
import pandas as pd
monday = pd.Series(
[64, 72, 80, 90, 95, 110, 120, 130, 140, 150],
name="amount_inr",
)
diwali_fri = pd.Series(
[64, 80, 90, 110, 130, 150, 180, 220, 890, 1200],
name="amount_inr",
)
def kit(s, label):
print(label)
print(" count", s.count())
print(" sum ", f"₹{s.sum():,}")
print(" mean ", f"₹{s.mean():.0f}")
print(" med ", f"₹{s.median():.0f}")
print(" min ", f"₹{s.min():,}", "max", f"₹{s.max():,}")
kit(monday, "Normal Monday — Pune dark store PUN-01")
kit(diwali_fri, "Diwali Friday — same store")
What you should see. Monday mean ≈ median (calm week). Diwali Friday mean jumps because of two big gift hampers (₹890, ₹1,200); median stays closer to everyday baskets. If Shraddha quotes only the mean on WhatsApp, the city manager over-orders gift stock next week.
उदाहरण B — Amazon Now Kolhapur returns.
Rani pulls 8 fictional return amounts: ₹40, ₹55, ₹60, ₹60, ₹70, ₹85, ₹90, ₹2,499 (one wrong fridge-line item tagged as grocery). Mean ≈ ₹370. Median = ₹65. The mean screams “crisis”. The median says “normal returns plus one data error”. Stats first; chart second.