101 Polars Exercises for Data Analysis (with Solutions)
Master Polars with 101 hands-on exercises and solutions, covering DataFrames, groupby, joins, window functions, lazy eval, and more.
Python
Every Python tutorial on Machine Learning Plus in one place: Python, Pandas, Numpy, Data Manipulation and Plots. 92 guides with code you can read, copy and run.
Python 51
101 Pandas Exercises for Data Analysis (Interactive)
101 interactive pandas exercises with solutions. Edit and run every code block directly in your browser, no installation needed.
Build Your First AI App with Python, Step-by-Step Tutorial
Build your first AI app with Python and the OpenAI API. Step-by-step tutorial covering chat completions, streaming, error handling, and cost control...
Why Python Lacks Traditional OOP Encapsulation
Unlike Java Python treats encapsulation as a guideline, not a rule. Understand how this philosophy shapes OOP practices.
Memory Optimization in Python: How slots Works
Understand __slots__ in Python, a technique that replaces dynamic dictionaries with fixed attributes for faster, more efficient classes.
Understanding Python Descriptors and the __set_name__ Method
Learn how Python descriptors work with __set_name__. Control attribute access and understand descriptor protocols with clear examples.
Python’s Magic Methods: How to Overload Operators Properly
Discover how operator overloading is implemented using magic methods, allowing custom classes to integrate with Python's core operations.
Python @property: Getters, Setters, and Attribute Control Guide
Learn to use Python's @property decorator to implement getters and setters for safe attribute access and data validation in OOP.
Python Method Decorators- @classmethod vs @staticmethod vs @property
Understand Python's key method decorators: when to use @classmethod for class state, @staticmethod for pure functions, and @property for attribute...
Python ABCs- The Complete Guide to Abstract Base Classes
Explore Python's abc module, learn how Abstract Base Classes enforce interfaces, create robust hierarchies, and improve your OOP design.
Inheritance vs. Composition in Python- When to Use Which
Master Python's inheritance and composition, how they work, when to use each, with clear examples. Elevate your OOP skills effectively.
How Python Handles Multiple Inheritance- MRO Explained
Learn to design robust Python class hierarchies by mastering multiple inheritance and Method Resolution Order (MRO) behavior.
Python Exercises – Level 1
Python exercises to practice as a beginner. These are devised as mini tasks that you might need to apply when programming with Python.
How to convert Python code to Cython (and speed up 100x)?
Using Cython, you can speed up existing Python code by an order of 100x or more. This is possible because Cython converts some of the Python code to...
How to convert Python to Cython inside Jupyter Notebooks?
Lets see how to convert Python to Cython inside Jupyter Notebooks?
Install opencv python – A Comprehensive Guide to Installing “OpenCV-Python”
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV-Python is a Python...
Scrapy vs. Beautiful Soup: Which is better for web scraping?
Web scraping is the technique of extracting data from a specific website or web page. Let's understand how to use Scrapy for web scraping of Reddit...
install pip mac – How to install pip in MacOS?: A Comprehensive Guide
Pip is a widely used package manager for Python, allowing you to install and manage Python packages easily. In this blog post, we'll explore various...
add Python to PATH – How to add Python to the PATH environment variable in Windows?
Adding Python to the PATH environment variable in Windows allows you to run Python commands from any directory within the command prompt
Numpy.random.randint() in python
Numpy.random.randint is used to get random integers from low to high values. The low value is included while the high value is excluded while...
How to use numpy.random.uniform() in python.
The numpy.random.uniform() function is used to create an array with random samples from a uniform probability distribution of given low and high...
Numpy.sort() in python
The numpy.sort() function in python is used to sort the array along a specified axis.
numpy.median() – How to compute median in Python
numpy.median function is used to calculate the median of an array along a specific axis or multiple axes
How to deal with Big Data in Python for ML Projects (100+ GB)?
The size of the data you can load and train in your local computer is typically limited by the memory in your computer. Let's see how to deal with...
Python Module – What are modules and packages in python?
Any Python files is a Module in Python. A python package is a collection of such modules along with a `__init__.py` file. Let's understand how to...
Iterators in Python – What are Iterators and Iterables?
Iterable in Python is any object that can be looped over. In order for an iterable to be looped over, an iterable needs to be converted to an...
Generators in Python – How to lazily return values only when needed and save memory?
Generators in python provide an efficient way of generating numbers or objects as and when needed, without having to store all the values in memory...
Decorators in Python – How to enhance functions without changing the code?
Decorators in python allow you to dynamically change the functionality of another function, without altering it's code.
Object Oriented Programming (OOPS) in Python
Object oriented programming is an effective way of writing code. Classes represent meaningful entities which defines its own behaviour (via methods)...
Numpy Reshape – How to reshape arrays and what does -1 mean?
The numpy.reshape() is used to reshape a numpy array without changing the data in the array. Read on to know all about reshaping numpy arrays.
Vaex – Faster Pandas Alternate in Python
Vaex Python is an alternative to the Pandas library that take less time to do computations on huge data using Out of Core Dataframe. It has fast...
Modin – How to speedup pandas by changing one line of code
Modin is a python library that can be used to handle large datasets using parallelisation. The syntax is similar to pandas and its astounding...
Dask – How to handle large dataframes in python using parallel computing
Learn how to use Dask to handle large datasets in Python using parallel computing. Covers Dask DataFrames, delayed execution, and integration with...
What does Python Global Interpreter Lock – (GIL) do?
Global Interpreter Lock (GIL) in python restricts multithreading. This post walks you behind the neccesity of GIL and how it it works.
Lambda Function in Python – How and When to use?
Learn how Python lambda functions work, when to use them, and how they differ from regular functions. Includes practical examples with map, filter...
Python Yield – What does the yield keyword do?
Adding yield keyword to a function will make the function return a generator object that can be iterated upon.
cProfile – How to profile your python code
Python Profilers, like cProfile helps to find which part of the program or code takes more time to run. This article will walk you through the...
Python Collections – An Introductory Guide
Collections is a built-in python module that provides useful container types. They allow us to store and access values in a convenient way...
Python JSON – Guide
JSON (JavaScript Object Notation) is a lightweight data format for exchanging data between languages. Learn how to serialize and deserialize JSON in...
Requests in Python Tutorial – How to send HTTP requests in Python?
Learn how to send HTTP requests in Python using the Requests library. Covers GET, POST, PUT, DELETE methods, headers, parameters, and response...
datetime in Python – Simplified Guide with Clear Examples
datetime is the standard module for working with dates in python. It provides 4 main objects for date and time operations: datetime, date, time and...
Python Logging – Simplest Guide with Full Code and Examples
The logging module lets you track events while your code runs, so you can find what caused a crash from the logs. This post is a simple, clear guide...
List Comprehensions in Python – My Simplified Guide
List comprehensions is a pythonic way of expressing a 'For Loop' that appends to a list in a single line of code. It is an intuitive, easy-to-read...
Python @Property Explained – How to Use and When? (Full Examples)
A python @property decorator lets a method to be accessed as an attribute instead of as a method with a '()'. Today, you will gain an understanding...
Parallel Processing in Python – A Practical Guide with Examples
Parallel processing is when the task is executed simultaneously in multiple processors. In this tutorial, you'll understand the procedure to...
101 Pandas Exercises for Data Analysis
Master pandas with 101 hands-on exercises across 3 difficulty levels. Practice data manipulation, filtering, grouping, and more to sharpen your...
pdb – How to use Python debugger
pdb, short for python debugger is a standard built-in module used to debug python code interactively. You can set breakpoints, see variable values...
101 NumPy Exercises for Data Analysis (Python)
The goal of the numpy exercises is to serve as a reference as well as to get you to apply numpy beyond the basics. The questions are of 4 levels of...
Numpy Tutorial Part 2 – Vital Functions for Data Analysis
This is part 2 of the mega numpy tutorial. In this part, I go into the details of the advanced features of numpy arrays that are essential for data...
Numpy Tutorial – Your first numpy guide to build python coding foundations
This is part 1 of the numpy tutorial covering all the core aspects of performing data manipulation and analysis with numpy's ndarrays. Numpy is the...
Python Regular Expressions Tutorial and Examples: A Simplified Guide
Regular expressions, also called regex, is a syntax or rather a language to search, extract and manipulate specific string patterns from a larger...
Pandas 24
Pandas Sample – Randomly Sample Rows From Dataframe
Use the pandas.DataFrame.sample() method from pandas library to randomly select rows from a Dataframe
Pandas Iterate Over Rows
How to iterate over rows in a pandas dataframe using diffferent methods like loc(),iloc(),iterrows(), iteritems etc, with practical examples
Pandas Head – How to get the first few rows?
pandas.head() function is used to access the first n rows of a dataframe or series. It returns a smaller version of the caller object with the first...
Pandas Dataframe.duplicated()
The pandas.DataFrame.duplicated() method is used to find duplicate rows in a DataFrame. It returns a boolean which tells whether a row is duplicate...
Pandas Describe
The pandas describe function is used to get a descriptive statistics like mean, median, min-max values of different data columns.
Creating pandas series from dictionary
Pandas Series is a 1-dimensional array like object which can hold data of any type. You can create a pandas series from a dictionary by passing the...
RegEx Replace values using Pandas
You can manipulate the data using regular expressions (Regex). This article explains different ways of using pandas replace function with regex
Pandas Histogram
This article guides you on different methods to plot a histogram in a pandas dataframe along with various features available.
Pandas Select Columns
You can select columns in a pandas dataframe using multiple methods including loc, iloc, filter functions, etc. Read on to know more
Pandas Line Plot | Python
You can create quick line plot on a pandas dataframe in python to understand the relationship between features. Learn how with practical examples.
Pandas read_csv() – How to read a csv file in Python
Explains different ways pandas read_csv function can be used to read csv files into pandas dataframe, along with examples
Pandas DateTime
This article walks you through the different ways through which you can create a pandas datetime series along with it's features.
Pandas Add Column
This article explains how to add new column in an existing pandas dataframe using list, dictionary, insert, assign and so on with examples
Pandas Groupby Examples
This article introduces pandas groupby method, and explains different ways of using it along with practical code examples.
Pandas Dateframe Index
Learn about pandas index and how to use it to access rows, how to define custom row labels and more with practical examples
Pandas Series to List
You can convert a pandas series to list using the tolist() or typecasting method. This article explains both with practical examples.
Pandas Dropna – How to drop missing values?
The pandas dropna function allows you to drop rows & columns that contain missing values. You can drop rows that have certain columns missing
Pandas iloc – How to select rows using index in DataFrames?
Pandas iloc is a method for integer-based indexing, which is used for selecting specific rows and subsetting pandas DataFrames and Series. The...
Pandas drop column : Different methods
To drop a single column or multiple columns from pandas dataframe in Python, you can use `df.drop` and other different methods.
Pandas reset index – How to reset the index and convert the index to a column?
pandas.reset_index in Python is used to reset the current index of a dataframe to default indexing (0 to number of rows minus 1) or to reset multi...
How to rename column in Pandas
There are several methods to rename column in Pandas or change column name of Pandas Dataframe in Python. In this article, you will see some of these...
How to create a Pandas Dataframe in Python
In Pandas, DataFrame is the primary data structures to hold tabular data. You can create it using the DataFrame constructor pandas.DataFrame() or by...
Pandas Series
pandas Series is a one-dimensional labelled data structure which can hold data such as strings, integers and even other Python objects.
Pandas Pivot Table in Python
Pivot table in pandas is an excellent tool to summarize one or more numeric variable based on two other categorical variables.
Numpy 2
How to Use Numpy Where Function?
The numpy.where() function is used to filter data based on the conditions provided. These conditions can vary.
How to use Numpy Random Function in Python
The numpy.random.rand() function is used to generate random float values from a uniform distribution over [0,1). These values can be extracted as a...
Data Manipulation 4
How to reduce the memory size of Pandas Data frame
Let's see how to reduce the memory size of a pandas dataframe.
data.table in R – The Complete Beginners Guide
data.table package is used for working with tabular data in R. It is super fast and has intuitive and terse syntax. This tutorial guide shows how to...
101 R data.table Exercises
Sharpen your R data.table skills with 101 practical exercises. Cover data manipulation, aggregation, joins, and reshaping with one of R's fastest...
101 Python datatable Exercises (pydatatable)
Python datatable is the newest package for data manipulation and analysis in Python. It carries the spirit of R's `data.table` with similar syntax...
Plots 11
Matplotlib Tutorial – Principles of creating any plot with the Matplotlib library
Matplotlib is the most popular Python library to plot beautiful graphs. Grasp fundamental plotting through reproducible examples.
Matplotlib Subplots – How to create multiple plots in same figure in Python?
Subplots mean groups of axes that can exist in a single matplotlib figure. subplots() function in the matplotlib library, helps in creating multiple...
Waterfall Plot in Python
A waterfall chart shows the effect of adding positive and negative values over time, steps or categories, and is common in financial analysis. Learn...
Matplotlib Line Plot – How to create a line plot to visualize the trend?
Learn how to create line plots in Matplotlib to visualize trends. Covers multiple lines, secondary axes, time series plots, styling, and highlighting...
Python Scatter Plot – How to visualize relationship between two numeric features
Learn how to create scatter plots in Python to visualize relationships between variables. Covers color groups, linear fits, bubble plots, and Seaborn...
Matplotlib Pyplot – How to import matplotlib in Python and create different plots
A comprehensive guide to Matplotlib Pyplot, Python's core plotting interface. Learn to create line, scatter, bar, pie, histogram, and box plots with...
Python Boxplot – How to create and interpret boxplots (also find outliers and summarize distributions)
Learn how to create and interpret boxplots in Python. Understand quartiles, detect outliers, and summarize distributions using Matplotlib and Seaborn.
Bar Plot in Python – How to compare Groups visually
Learn how to create bar plots in Python using Matplotlib and Seaborn. Covers simple, grouped, stacked, and horizontal bar charts with customization...
Matplotlib Histogram – How to Visualize Distributions in Python
Matplotlib histogram is used to visualize the frequency distribution of numeric array. In this article, we explore practical techniques like...
Matplotlib Tutorial – A Complete Guide to Python Plot with Examples
This tutorial explains matplotlib's way of making python plot, like scatterplots, bar charts and customize th components like figure, subplots...
Top 50 matplotlib Visualizations – The Master Plots (with full python code)
A compilation of the Top 50 matplotlib plots most useful in data analysis and visualization. This list helps you to choose what visualization to show...