Data Science

Aggregation Functions

Explore the built-in and custom aggregation functions available after groupby, including agg() for applying multiple statistics at once.

Aggregations and Statistics

NumPy provides fast, axis-aware aggregation functions like sum, mean, std, and var, plus NaN-safe variants for handling missing values in numerical data.

apply, map, and applymap

A practical comparison of pandas' three element-wise transformation tools — Series.map, DataFrame.apply, and the deprecated DataFrame.applymap — and when to re…

Array Creation and Data Types

NumPy offers many ways to construct arrays — from literals to specialized constructors — and choosing the right dtype affects both correctness and memory effic…

Array Indexing and Slicing

NumPy supports basic slicing, fancy (array) indexing, and boolean masking, each with distinct rules about whether the result is a view or a copy.

Array Reshaping and Stacking

Reshaping changes an array's shape without altering its data, while stacking and splitting functions combine or divide arrays along chosen axes.

Binning and Discretization

Turning continuous numeric data into discrete categorical buckets using pandas' cut and qcut functions, and why bin edges and labels matter for downstream anal…

Boolean Filtering

Learn how to filter DataFrame rows using boolean masks built from comparison and logical operators, including combining conditions and using isin and query for…

Common Pandas & NumPy Pitfalls

A field guide to the mistakes that trip up even experienced users of pandas and NumPy — chained assignment, dtype surprises, mutable defaults, and silent misal…

Concatenating DataFrames

Using pandas' concat function to stack DataFrames or Series along rows or columns, and how it differs from merge in purpose and alignment behavior.

Crosstabs and Pivot Analysis

Use pd.crosstab() to build frequency and cross-tabulation tables between categorical variables, and compare it to pivot_table for richer aggregation.

Data Type Conversion

Master how to inspect and convert pandas column dtypes with astype, to_numeric, to_datetime, and category types to fix incorrect or inefficient typing.

DataFrame Basics

Understand the structure of a pandas DataFrame — rows, columns, index, and dtypes — and the core operations for creating, inspecting, and selecting from tabula…

Exporting and Reporting Results

Learn how to write pandas DataFrames back out to CSV, Excel, JSON, and other formats, and how to structure exports for downstream tools and stakeholder-facing…

GroupBy Basics

Understand pandas' split-apply-combine model via groupby(), including grouping keys, selecting columns, and iterating over groups.

Handling Missing Data in Pandas

Learn how pandas represents missing values with NaN and NA, and the core toolkit — isna, dropna, fillna, interpolate — for detecting and handling them.

Handling Outliers

Learn common statistical techniques — z-score, IQR, and percentile capping — for detecting and treating outliers in pandas and NumPy data.

Indexing with loc and iloc

Master the two primary pandas selection accessors — .loc for label-based indexing and .iloc for integer-position-based indexing — and the pitfalls of chained i…

Linear Algebra with NumPy

Learn how NumPy represents vectors and matrices and how to perform matrix multiplication, transposition, inversion, determinants, and eigen-decomposition using…

melt and Reshaping Data

Learn how pd.melt() converts wide-format DataFrames into long (tidy) format, and how it pairs with pivot to move data between shapes for analysis.

Merging and Joining DataFrames

How pandas' merge function combines two DataFrames on shared keys using inner, left, right, and outer join logic, and how it compares to the index-based join m…

MultiIndex Basics

Learn how pandas' hierarchical MultiIndex lets you represent and query higher-dimensional data using multiple index levels on a single axis.

The ndarray: NumPy Basics

The ndarray is NumPy's core data structure — understanding its shape, dtype, strides, and axis conventions is essential to using arrays correctly and efficient…

Pandas & NumPy Interview Questions

A curated set of frequently asked pandas and NumPy interview questions with precise answers, covering indexing, performance, missing data, and common gotchas.

Learn Pandas by Analyzing Virat Kohli's Career Stats

A comprehensive guide to learn pandas by analyzing virat kohli's career stats — written for learners at every level.

Data Analytics Roadmap for Beginners in 2026

Step-by-step roadmap to become a data analyst from scratch — no prior experience needed.

Data Science vs Data Analytics vs Data Engineering

A comprehensive guide to data science vs data analytics vs data engineering — written for learners at every level.

How to Become a Data Analyst From Scratch

A comprehensive guide to how to become a data analyst from scratch — written for learners at every level.

Top 10 Data Science Projects for Your Portfolio

A comprehensive guide to top 10 data science projects for your portfolio — written for learners at every level.

Pandas for Beginners: A Complete Tutorial

A comprehensive guide to pandas for beginners: a complete tutorial — written for learners at every level.

SQL Tutorial for Beginners with Examples

Master SQL with simple examples and real-world queries. Perfect for aspiring data analysts.

How a Teacher Switched to Data Analytics in 8 Months

A comprehensive guide to how a teacher switched to data analytics in 8 months — written for learners at every level.

Learn Python Through Cricket Statistics

Cricket generates rich data — runs, wickets, overs, strike rates, economy rates. This project uses real IPL-style match data to teach you pandas, matplotlib, a…

From Cricket Fan to Python Developer: An Illustrative Learning Journey

This is a composite illustrative journey — based on the real paths taken by many self- taught developers — showing how a passionate cricket fan used IPL data t…

From Teacher to Data Analyst: An Illustrative 8-Month Transition

This composite illustrative story follows how a secondary school maths teacher used her existing analytical skills to transition into a data analyst role — sta…

NumPy for Data Science: Arrays and Vectorisation

NumPy is the foundation of Python's scientific computing stack. This guide covers ndarrays, vectorised operations, broadcasting, linear algebra, and why NumPy…

Learn Data Science Through Bollywood Box Office Analytics

Bollywood produces hundreds of films a year and generates rich box office data. This project uses real film data to teach pandas groupby, matplotlib charting,…

SQL Tutorial for Beginners: With Real Examples

SQL is the language of data — used by data analysts, backend developers, and data scientists every day. This tutorial covers SELECT, WHERE, ORDER BY, GROUP BY,…

Matplotlib and Seaborn: Data Visualisation in Python

The best data insight is worthless if no one understands the chart. This guide covers matplotlib's core API, Seaborn's statistical plots, best practices for cl…

Scikit-Learn for Beginners: Machine Learning in Python

Scikit-learn is the most widely used Python library for classical machine learning. This guide covers the fit-predict workflow, train/test splits, classificati…

Pandas Cheat Sheet

Pandas data manipulation, filtering, grouping, and analysis techniques.

NumPy Cheat Sheet

NumPy arrays, operations, broadcasting, and linear algebra essentials.

TensorFlow Cheat Sheet

TensorFlow 2 model building, training loops, and deployment basics.

Scikit-learn Cheat Sheet

Core scikit-learn workflow covering train/test splitting, pipelines, preprocessing, common estimators, cross-validation, hyperparameter tuning, and evaluation…

PyTorch Cheat Sheet

Essential PyTorch syntax for tensors, autograd, building neural network modules, and writing a standard training loop for deep learning models.

Keras Cheat Sheet

High-level Keras API reference covering Sequential and Functional model building, compiling, training with callbacks, and common layer types.

Matplotlib Cheat Sheet

Matplotlib plotting reference covering the object-oriented API, subplots, common chart types, and styling options for publication-ready figures.

Seaborn Cheat Sheet

Seaborn statistical visualization cheat sheet covering distribution, relational, and categorical plots plus heatmaps and figure-level facet grids.

Plotly Cheat Sheet

Plotly reference for building interactive charts with the Express and Graph Objects APIs, subplots, and exporting to HTML or images.

SciPy Cheat Sheet

SciPy scientific computing reference covering statistics, optimization, linear algebra, and interpolation for numerical Python workflows.

Statsmodels Cheat Sheet

Statsmodels reference for OLS and logistic regression, the R-style formula API, and ARIMA time-series modeling with full statistical summaries.

XGBoost Cheat Sheet

XGBoost cheat sheet covering the scikit-learn and native training APIs, key hyperparameters, early stopping, and feature importance.

LightGBM Cheat Sheet

LightGBM reference covering the scikit-learn and native Dataset APIs, leaf-wise tree growth, and the hyperparameters that control speed and overfitting.

CatBoost Cheat Sheet

CatBoost reference covering native categorical feature handling, the Pool data structure, ordered boosting, and built-in cross-validation.

OpenCV Cheat Sheet

OpenCV computer vision reference covering image I/O, filtering, edge detection, contours, drawing, and video capture in Python.

NLTK Cheat Sheet

NLTK natural language toolkit reference covering tokenization, stopword removal, stemming, lemmatization, part-of-speech tagging, and named entity chunking.

spaCy Cheat Sheet

spaCy reference covering pretrained pipelines, tokenization, part-of-speech tagging, dependency parsing, named entity recognition, and similarity scoring.

Hugging Face Transformers Cheat Sheet

Hugging Face Transformers reference covering the pipeline API, AutoTokenizer/AutoModel classes, and fine-tuning with the Trainer API.

Apache Spark (PySpark) Cheat Sheet

PySpark reference covering SparkSession setup, DataFrame transformations and actions, Spark SQL queries, caching, and writing partitioned output.

Dask Cheat Sheet

Dask reference covering parallel DataFrame and Array APIs, delayed task graphs, lazy evaluation, and the distributed scheduler client.

Jupyter Notebook Cheat Sheet

Jupyter Notebook reference covering magic commands, shell integration, cell types, and essential keyboard shortcuts for efficient interactive computing.

Google Colab Cheat Sheet

A quick reference for running Python notebooks in Google Colab, covering GPU/TPU setup, mounting Drive, installing packages, and useful keyboard shortcuts.

MLflow Cheat Sheet

A reference for MLflow's experiment tracking, model registry, and CLI commands used to log, compare, and deploy machine learning models.

Weights & Biases Cheat Sheet

A guide to Weights & Biases for experiment tracking, hyperparameter sweeps, artifact versioning, and logging metrics and media during model training.