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Using Python

Python Tutorial - Python for Beginners [Full Course]
Learn Python programming for a career in machine learning, data science & web development. Get My FREE Python Cheat Sheet:

What Does It Take To Be An Expert At Python?
If you want to become an expert in Python, you should definitely watch this PyData talk from James Powell.

Introduction to Python | SciPy 2019 Tutorial | Matt Davis
This tutorial is a gentle introduction to Python for folks who are completely new to it and may not have much experience programming. We’ll work in a Jupyter Notebook, one of the most popular tools in scientific Python. You’ll learn how to write beautiful Python while practicing loops, if’s, functions, and usage of Python’s built-in features in a series of fun, interactive exercises. By the end of the tutorial we think you’ll be ready to write your own basic Python -- but most importantly, we want you to learn the form and vocabulary of Python so that you can understand Python documentation and interpret code written by others. To continue learning more about Python as it’s applied to data and science, join our companion tutorial titled Introduction to Scientific Python.

Top 5 Python Libraries For Data Science | Python Libraries Explained | Python Tutorial | Simplilearn
Python is the most widely used programming language today. When it comes to solving Data Science tasks and challenges, Python never ceases to surprise its audience. Most data scientists are already leveraging the power of Python programming every day. Python is easy to learn, easier to debug, widely used, object-oriented, open source, high-performance language and there are many more benefits of using Python programming. Python has been built with extraordinary libraries which are used by programmers everyday in solving the problems. So, now let us talk about the Top 5 Python libraries for Data Science. 1. Tensorflow 2. Numpy 3. Scipy 4. Pandas 5. Matplotlib

Python For Deep Learning - I | Python Basics | Python Tutorial | Python Training | Edureka
In this Edureka tutorial on Code-Basics For Deep Learning, we will be covering all the fundamentals of Python required for implementing deep learning models. This is the first video in the series, 1. What is Python? 2. Variables, Data Types, Operators 3. Conditional Statements, Loops, Functions 4. Pandas, Numpy and Matplotlib

Practical Machine Learning Tutorial with Python Intro p.1
The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are. In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues.

Learn Python Programming - Full Course for Beginners [2019]
Watch this Python tutorial for absolute beginners to learn Python programming from scratch. Includes full exercises and projects.

Best feature of Python | List Comprehensions
Python has lots of amazing features, but to me, list comprehensions are #1.

Python Data Science Handbook

Keynote Jake VanderPlas
Jul 24, 2017 PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States


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SentenceTransformers is a Python framework for state-of-the-art sentence, text, and image embeddings. It is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. You can use this framework to compute sentence/text embeddings for more than 100 languages. These embeddings can then be compared, for example, with cosine similarity to find sentences with a similar meaning. This can be useful for semantic textual similarity, semantic search, or paraphrase mining. You can install the Sentence Transformers library using pip: pip install -U sentence-transformers


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  • NumPy -manipulation of numerical arrays. NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
  • Python Numpy Tutorial | Justin Johnson


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  • Python Data Analysis library - data structures and data analysis tools for the Python programming language. Pandas is a newer package built on top of NumPy, and provides an efficient implementation of a Pandas DataFrame. Pandas DataFrames are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data. As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs.
  • Python for Data Analysis | Wes McKinney
  • Modin accelerates Pandas by automatically distributing the computation across all of the system’s available CPU cores

Pandas DataFrame

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  • SciPy library - one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines such as routines for numerical integration, interpolation, optimization, linear algebra and statistics.


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  • SymPy library - a Python library for symbolic mathematics aiming to become a full-featured computer algebra system (CAS)
  • mpmath | Fredrik Johansson library for real and complex floating-point arithmetic with arbitrary precision


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  • mpmath | Fredrik Johansson library for real and complex floating-point arithmetic with arbitrary precision. can be used as a library, interactively via the Python interpreter, or from within the SymPy or Sage computer algebra systems which include mpmath as standard component. CoCalc lets you use mpmath directly in the browser. Cocalc or "Collaborative Calculation in the Cloud" enables programming online without the need to install any software.


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  • Scikit-learn library for machine learning in Python built on NumPy, SciPy, and matplotlib. A toolkit implement a wide variety of algorithms for un/supervised machine learning tasks, including regressions, clustering, manifold learning, principal components, density estimation, and much more. It also provides many useful tools to help build “pipelines” for managing modeling tasks such as data processing /normalization, feature engineering, cross-validation, fitting, and prediction. The package scikit-learn is recommended to be installed using pip install scikit-learn but in your code imported using import sklearn.


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Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

LazyPredict is a low-code machine learning library that allows you to run up to 40 baseline models with two lines of code. LazyPredict uses Sklearn, which allows you to get the models, see what works best for you, and hypertune it as you would usually do. To install, you can type pip install lazypredict in your Terminal. 3 Awesome Python Libraries That You Should Know About | Ismael Araujo - Towards Data Science

Graphical User Interface (GUI)


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HiPlot is Facebook’s interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data


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  • TkInter ... comes with Python already. Tkinter is a Python binding to the Tk GUI toolkit. It is the standard Python interface to the Tk GUI toolkit, and is Python's de facto standard GUI. Tkinter is included with standard Linux, Microsoft Windows and Mac OS X installs of Python. The name Tkinter comes from Tk interface.
  • flatplanet/Intro-To-TKinter-Youtube-Course


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  • Kivy ... open source Python library for rapid development of applications that make use of innovative user interfaces, such as multi-touch apps. Kivy bassed on OpenGL, draw in 2D, 3D, meshes, and shaders, on runs on Linux, Windows, OS X, Android, iOS, and Raspberry Pi. You can run the same code on all supported platforms. Kivy is 100% free to use, under an MIT license (starting from 1.7.2) and LGPL 3 for the previous versions.


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  • Kivy ... Qt is set of cross-platform C++ libraries that implement high-level APIs for accessing many aspects of modern desktop and mobile systems. Library implements the QT application development framework and has QTDesigner: drag and drop interface. These include location and positioning services, multimedia, NFC and Bluetooth connectivity, a Chromium based web browser, as well as traditional UI development. PyQt5 is a comprehensive set of Python bindings for Qt v5. It is implemented as more than 35 extension modules and enables Python to be used as an alternative application development language to C++ on all supported platforms including iOS and Android. PyQt5 is released under the GPL v3 license and under a commercial license that allows for the development of proprietary applications.


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  • wxPython a cross-platform GUI toolkit for the Python programming language. It allows Python programmers to create programs with a robust, highly functional graphical user interface, simply and easily. It is implemented as a set of Python extension modules that wrap the GUI components of the popular wxWidgets cross platform library, which is written in C++.Like Python and wxWidgets, wxPython is Open Source, which means that it is free for anyone to use and the source code is available for anyone to look at and modify. And anyone can contribute fixes or enhancements to the project.wxPython is a cross-platform toolkit. This means that the same program will run on multiple platforms without modification. Currently Supported platforms are Microsoft Windows, Mac OS X and macOS, and Linux or other unix-like systems with GTK2 or GTK3 libraries. In most cases the native widgets are used on each platform to provide a 100% native look and feel for the application.


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  • Pyside2 ...a Python binding of the cross-platform GUI toolkit Qt, currently developed by The Qt Company under the Qt for Python project on porting PySide to work with Qt 5 instead of Qt 4. It is one of the alternatives to the standard library package Tkinter. Like Qt, PySide2 is free software.


Python & Google Sheets


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  • gsheets - small wrapper around the Google Sheets API to provide more convenient access to Google Sheets from Python scripts.

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  • gsheets - self-containd script to dump all worksheets of a Google Spreadsheet to CSV or convert any subsheet to a pandas DataFrame


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  • df2gspread ...transfer data between Google Sheets and Pandas


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  • pygsheets ...Google Sheets Python API v4 (v4 port of gspread providing further extensions)


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  • gspread-pandas ...Interact with Google Sheet through Pandas DataFrames


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  • pgsheets ...manipulate Google Sheets Using Pandas DataFrames (independent bidirectional transfer library, using the legacy v3 API, Python 3 only)

Python & Excel

What is the best library out there for working with Excel through Python? You can just export to CSV if it's just a table of data that doesn't need any formatting. Pandas works great for this. You don't need anything else

  • pyxll
  • xlwings
  • openpyxl
  • xlsxWriter


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PyXLL is an Excel Add-In that enables developers to extend Excel’s capabilities with Python code. For organizations that want to provide their end users with functionality within Excel, PyXLL makes Python a productive, flexible back-end for Excel worksheets. With PyXLL, your own Python code runs in Excel using any Python distribution you like (e.g. Anaconda, Enthought’s Canopy or any other CPython distribution from 2.3 to 3.7). Because PyXLL runs your own full Python distribution you have access to all 3rd party Python packages such as NumPy, Pandas and SciPy and can call them from Excel.


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If you want a user to enter some data in Microsoft Excel, hand it off to Python, and then show the results to your user in Microsoft Excel, xlwings is great.

Machine Learning Algorithms in Microsoft Excel | Custom Excel Functions (Part 1 of 2)
SATSifaction I this video learn how to add your own custom functions to Microsoft Excel using Python. Integrate both worlds seamlessly.

Machine Learning Algorithms in Microsoft Excel | Machine Learning Functions (Part 2 of 2)
SATSifaction In this video learn how to add your own Machine Learning Algorithms in Excel and call them for some simple front end and complex backend calculations

Automate Microsoft Excel with Python and xlwings Part 1: Install xlwings and the basic
Jie Jenn Buy Me a Coffee? Your donation will support me to continue to make more tutorial videos! xlwings is an open-source library created by Zoomer Analytics to manipulate Microsoft Excel with Python. In my opinion, xlwings is probably one of the best libraries out there to interact with Excel application beside win32com. Since you can achieve quiet many things with #xlwings, I construct the tutorial into multiple series. In part 1 of the tutorial, I will be covering just the basic: 1) How to install xlwings, 2) How to create new Excel file & link existing Excel file, 3) Write/Read data

Part 2: Run Python Function in Excel

Part 3: Options and Default Converters

Part 4: Built-in Converters

Part 5: Access the Missing Features

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The new dynamic arrays in Microsoft Excel with Python and xlwings
The days of "Ctrl-Shift-Enter" and "You can't change part of an array" are finally over! Say hello to the new dynamic arrays. Dynamic arrays are easily the most revolutionary feature since the introduction of the ribbon and the xlsx format, so make sure to check them out. They also work great with xlwings UDFs (user defined functions).


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I am just pulling data from an SQL Server, manipulating the results, and then dumping the results into an Excel spreadsheet. Just working with Excel cells, and ranges.


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  • StatsModels A module for fitting and estimating many different types of statistical models as well as performing hypothesis testing and exploratory data analysis. It features tools for fitting generalized linear models, survival analyses, and multi-variate statistics.


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  • OpenCV - Open Computer Vision - work with images and/or videos and wish to add a variety of classical and state-of-the-art vision algorithms to their toolbox.


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  • LibROSA - audio and voice processing which can extract various kinds of features from audio segments, such as the rhythm, beats and tempo.


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  • DASK provides advanced parallelism for analytics, enabling performance at scale for the tools you love - it is developed in coordination with other community projects like NumPy, Pandas, and scikit-learn


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  • Joblib provide lightweight pipelining


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  • Tornado is a web framework and asynchronous networking library. By using non-blocking network I/O, Tornado can scale to tens of thousands of open connections, making it ideal for long polling, WebSockets, and other applications that require a long-lived connection to each user.


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  • Numba JIT compiler that translates a subset of Python and NumPy code into fast machine code.


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  • xarray working with labelled multi-dimensional arrays simple, and efficient. Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays, which allows for a more intuitive, more concise, and less error-prone developer experience. The package includes a large and growing library of domain-agnostic functions for advanced analytics and visualization with these data structures. Xarray was inspired by and borrows heavily from Pandas, the popular data analysis package focused on labelled tabular data. It is particularly tailored to working with netCDF files, which were the source of xarray’s data model, and integrates tightly with DASK for parallel computing.

IPython Blocks

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  • IPython Blocks a tool for practicing Python in the Jupyter giving learners a grid of colors to manipulate while practicing for loops, if statements, and other aspects of Python.


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  • Metaflow, Netflix and AWS open source Python library

Web Automation with Python - Data Gathering

Write a Python crawler to extract information from websites to identify patterns, both in terms of the URL patterns and XPath patterns. Once these patterns are figured out, these tools can automatically extract the needed information and organize data a usable structure.


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Beautiful Soup - bs4

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Allows you to import its functions and use them in-line. Therefore, you could even use it in your Jupyter notebooks.


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  • Scrapy webscraping .. open source and collaborative framework for extracting the data you need from websites


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Initialises a web browser such as Chrome and then simulates all the actions defined in the code; JavaScript functions to e.g. register an account, then log in and get the content after clicking some buttons and links.


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  • Twisted an event-driven networking engine

Twisted has been around a long time in the Python world. Pioneering the Deferred abstraction, which later turned into Promises and found their way into JavaScript, it is a fertile ground for asynchronous I/O experimentation. Through its groundbreaking protocol/transport design that some of us might take for granted these days, and a strict adherence to unit testing and representing things through abstract interfaces, it lets you talk a lot of different network protocols without really having to know everything about them.


Python is one of the most crucial orchestration and infrastructure automation components of AIOps/MLOps to reduce or almost eliminates disconnect between developers and system admins. AIOps/MLOps is centered on enabling AI pipelines for continuous integration and continuous deployment (CI/CD) with no downtime.


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  • ELI5 "Explain it like I'm 5" helps to...
    • debug machine learning classifiers and explain their predictions.
      • scikit-learn - Currently ELI5 allows to explain weights and predictions of scikit-learn linear classifiers and regressors, print decision trees as text or as SVG, show feature importances and explain predictions of decision trees and tree-based ensembles. ELI5 understands text processing utilities from scikit-learn and can highlight text data accordingly. Pipeline and FeatureUnion are supported. It also allows to debug scikit-learn pipelines which contain HashingVectorizer, by undoing hashing.
      • xgboost - show feature importances and explain predictions of XGBClassifier, XGBRegressor and xgboost.Booster.
      • LightGBM ...Microsoft's gradient boosting framework that uses tree based learning algorithms ... LightGBM - show feature importances and explain predictions of LGBMClassifier and LGBMRegressor. A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the Microsoft Distributed Machine Learning Toolkit (DMTK) project of Microsoft.
      • CatBoost - show feature importances of CatBoostClassifier, CatBoostRegressor and catboost.CatBoost. A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU
      • lightning - explain weights and predictions of lightning classifiers and regressors. Large-scale linear classification, regression and ranking in Python
      • sklearn-crfsuite ELI5 allows to check weights of sklearn_crfsuite.CRF models. CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data.
    • ELI5 also implements several algorithms for inspecting black-box models (see Inspecting Black-Box Estimators):
      • TextExplainer allows to explain predictions of any text classifier using LIME algorithm. There are utilities for using LIME with non-text data and arbitrary black-box classifiers as well, but this feature is currently experimental.
      • Permutation importance method can be used to compute feature importances for black box estimators.

Explanation and formatting are separated; you can get text-based explanation to display in console, HTML version embeddable in an IPython notebook or web dashboards, a Pandas DataFrame object if you want to process results further, or JSON version which allows to implement custom rendering and formatting on a client.


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This library is essentially an extension of the scikit-learn library and provides some really useful and pretty looking visualisations for machine learning models. The visualiser objects, the core interface, are scikit-learn estimators and so if you are used to working with scikit-learn the workflow should be quite familiar.


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This library contains a host of helper functions for machine learning. This covers things like stacking and voting classifiers, model evaluation, feature extraction and engineering and plotting.


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Python Stack

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Flask is considered more Pythonic than the Django web framework because in common situations the equivalent Flask web application is more explicit. Flask is also easy to get started with as a beginner because there is little boilerplate code for getting a simple app up and running. Flask | Full Stack Python


Flask & React

Flask & Docker

Flask, React, & Docker



  • Django - a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source.
  • News Aggregation

Django is a widely-used Python web application framework with a "batteries-included" philosophy. The principle behind batteries-included is that the common functionality for building web applications should come with the framework instead of as separate libraries. Django | Full Stack Python

Other Web Frameworks supporting Python

Time Series

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  • tsfresh ...python package that automatically calculates a large number of time series characteristics, the so called features. Further the package contains methods to evaluate the explaining power and importance of such characteristics for regression or classification tasks.


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an open source scientific Python library that implements a novel yet intuitive approach for discovering patterns, anomalies, and other insights from any time series data. STUMPY is a powerful and scalable library that efficiently computes something called the matrix profile, which can be used for a variety of time series data mining tasks such as pattern/motif (approximately repeated subsequences within a longer time series) discovery, anomaly/novelty (discord) discovery, shapelet discovery, semantic segmentation, streaming (on-line) data fast approximate matrix profiles, time series chains (temporally ordered set of subsequence patterns). STUMPY

Visualization with Python

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  • Plotly | Plotly - graphing library makes interactive, publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts. Plotly is a web-based service by default, but you can use the library offline in Python and upload plots to Plotly's free, public server or paid, private server. From there, you can embed your plots in a web page. All Plotly graphs include tooltips, and you can build custom controls (like sliders and filters) on top of a chart once it's embedded using Plotly's JavaScript API. Plotly.js is based on D3.js and WebGL.js. Another way to work in Plotly and share plots is in Mode Notebooks. You can pull data with SQL, use the Plotly offline library in the Python Notebook to plot the results of your query, and then add the interactive chart to a report. Get pricing
  • Learn Plotly | SuperDataScience
  • Driving your graphic via ipyWidgets
  • It’s 2019 — Make Your Data Visualizations Interactive with Plotly | Jeff Hale - Towards Data Science Find the path to make awesome figures quickly with Express and Cufflinks



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  • Dash a framework for building analytical web applications. No Javascript required; sits on top of Flask
    • Dash Bio a web application framework that provides pure Python abstraction around HTML, CSS, and JavaScript. Dash Bio is a suite of bioinformatics components that make it simpler to analyze and visualize bioinformatics data and interact with them in a Dash application.


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         Cufflinks --> Plotly  -->  ployly.js  -->  D3.js


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Plotly Chart Studio

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  • Plotly Chart Studio - editor for creating d3.js and WebGL charts. Chart Studio is built on top of Plotly React, Plotly React Editor, the Plotly Image Server, Sheet.js, Handsontable and many other top-quality, open-source projects.


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  • mpld3 | Jake VanderPlas - brings together Matplotlib, the popular Python-based graphing library, and D3js, the popular Javascript library for creating interactive data visualizations for the web. The result is a simple API for exporting your Matplotlib graphics to HTML code which can be used within the browser, within standard web pages, blogs, or tools such as the IPython notebook.


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  • lux-org/lux Python API for Intelligent Visual Data Discovery
  • How to Create Data Visualizations In Python With One Click | Ismael Araujo Lux is a low-code library that allows us to do a fast and easy data exploration by creating data visualizations with one click. The coolest part is that Lux decides what visualizations are recommended for your dataset without you having to decide. You can install it by typing pip install lux-api in your Terminal.


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  • Bokeh | Continuum Analytics an interactive visualization library that targets modern web browsers for presentation - inspired by the concepts outlined in The Grammar of Graphics. Interactive plotting in web browsers, running JavaScript but controlled by Python. You can layer components on top of one another to create a finished plot—for example, you can start with the axes and then add points, lines, labels, etc. Plots can be output as JSON objects, HTML documents, or interactive web applications. Bokeh does a good job of allowing users to manipulate data in the browser, with sliders and dropdown menus for filtering. Like in mpld3, you can zoom and pan to navigate plots, but you can also focus in on a set of data points with a box or lasso select.


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  • Pygal | Florian Mounier for producing beautiful out-of-the-box charts with very few lines of code. Each chart type is packaged into a method (e.g. pygal.Histogram() makes a histogram, pygal.Box() makes a box plot), and there's a variety of colorful default styles. If you want more control, you can configure almost every element of a plot—including sizing, titles, labels, and rendering. You can output charts as SVGs and add them to a web page with an embed tag or by inserting the code directly into the HTML.


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  • scikit-image An image processing library featuring many common operations including convolutional mapping, filtering, edge detection, and image segmentation.


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  • Shapely - a spatial analysis library which extends Python to work as a fully-featured GIS environmental comparable to commercial software such as ArcGIS.

Satellite Imagery

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  • Satellite Imagery
  • Open Street Map a map of the world, created by people
  • Geospatial Data Abstraction Library (GDAL) a translator library for raster and vector geospatial data formats
  • Pyresample - re-projecting earth observing satellite data, capable of handling both swath data from polar-orbiting satellites and gridded data from geostationary satellites.
  • Fiona - handle vector data
  • rasterio - handle raster data
  • pyproj - transforming spatial reference systems - python interface to PROJ (cartographic projections and coordinate transformations library).
  • Folium - creating maps
  • GeoPandas - geospatial analysis; extends the datatypes used by pandas to allow spatial operations on geometric types. Geometric operations are performed by shapely. Geopandas further depends on fiona for file access and descartes and matplotlib for plotting.
  • GeoViews - visualizable geographic data that that can be mixed and matched with HoloViews objects