Category: Python-Tut

Python is one of the best and well-reputed high-level programming languages since 1991. It is best for server-side development, AI, scripting, software development, and math. It works well on multiple platforms like Mac, Linux, Windows, and Raspberry Pi.

Developers use Python code editor’s software to program and debug the code easily. By using Python IDE, you can manage big codebases and achieve a better and quicker deployment.

Developers can use these IDEs to create web or desktop applications. DevOps engineers may also use it for continuous integration.

What’s an IDE and code editor?

IDE: The term IDE means Integrated Development Environment. It is the set of tools that enable programmers and coders to increase their productivity. It provides different ways of writing codes in any computer program.

Developers use text editors, testing platforms, code libraries, and compilers. They combine the most common activities like editing source code, debugging, compiler, and executable in a single application.

The IDE makes coders life easy because without an IDE, a developer needs to deploy, integrate, and manage all the tools individually. The IDE toolset simply brings all these tools together. It is more helpful to identify coding bugs and minimize coding mistakes and other errors.

Code Editor: Code editors are also known as lightweight editors. They are not as best as IDE’s, but still, they are handy, fast, and simple to use. They are great for editing files instantly.

This program is specifically designed for editing source code, and it might be a stand-alone application. Code editors may also have some plugins, directory level syntax analyzers, or autocompleters, so they are not much different from IDEs.

The code editor helps programmers in many ways like it makes editors more skilled at writing codes. Code editors are the personal assistant that will examine code for developers and show them all the results. It tells coders where they need to edit their code.

The most common examples of code editors are Atom, TextMate, notepad++, bluefish, NetBeans, vim, Geany, etc.

IDE and code editor resemble in many ways, but the main difference is that IDE works on a project-level where we need to analyze project structure and many other things. But, Code editor is great for one file mostly, and it is much faster than IDE.

There are multiple IDEs for Python, but here is the collection of some of the Best Python IDE for different environments.

Best Python IDE for Mac

Let’s discuss the best Python IDEs for the MacOS.

1. Pydev

Pydev is the feature-rich, and open-source Python IDE that is best to use for mac. It supports code completion, Django integration, code completion with auto-import, code analysis, and type hinting.

Pydev comes with a debugger, token browser, refactoring, code coverage, unit test integration, and Pylint integration. It can also be best for Jython, Python, and IronPython development.

Pros

• Maven Integration and Support
• Subversion/Git integration
• Simplified IDE makes it easy to write efficient code.
• Debugging is very easy in Pydev.

Cons

• Eclipse has a large footprint
• Updated versions require to build plugins and migrate projects
• Sometimes crashes on loading big projects.

2. GNU Emacs

GNU Emacs is one of the best Python editors since always. It is compatible with many operating systems, including mac. GNU is an extendable and self-documenting editor with an on to go display. The developers of GNU are always upgrading it for better results.

Furthermore, GNU Emacs adopts the lisp coding language and syntax coloring. It offers Unicode support as well.

Pros

• Enable developers to perform better.
• Compatible with many platforms.
• Enables customization of a variety of skills.

Cons

• Somehow complex than other IDEs
• Takes time to learn in the initial stages.

3. SciTE

It is the SCIntilla based text editor, and you can use it for mac. It is quite a useful editor for running and building different applications and programs. It is a powerful, flexible, and portable editor.

It is the free source code editor that comes with the source code and license that allows use in any free project or commercial project. Its features are useful for debugging and editing code.

Pros

• Flexible and light-weight.
• Built-in shell and powerful editor.
• Simple graphical user interface
• Syntax highlighting for many languages.

Cons

• Pretty hard to configure
• Missing file browser
• No extensions or themes.

Best Python IDE for Windows

These are the three top Python IDEs for Windows:

1. PyCharm

PyCharm is one of the most loved Python IDEs. PyCharm is JetBrains IDE, who is well known for making developers stuff. For IntelliJ products, it is one of the most comfortable IDEs.

It has excellent tools and plugins to work with. Its advanced commercial edition owns thousands of professional features. Moreover, it serves as a reliable support tool to develop any kind of python application from backend applications to web apps.

Pros

• Data science scripting
• Connectivity with several databases
• Quick and convenient to use and install
• Update and remove plugins
• Real-time verification of code
• Search files by code snippet or names

Cons

• The community edition is far behind the paid alternative.
• Process of upgrading IDEs can be better
• Full version is quite expensive
• Some new versions use a lot of machine memory like java.

2. Wing IDE

Wing IDE is a product of Wingware, and it is well-reputed when the main concern is a powerful debugger. It is a lightweight but full-featured IDE with editing, code inspection, and testing features.

It is available in different versions like free edition, personal edition, and professional edition. It is the perfect choice for professional developers because it provides auto-completion, refactoring, and auto-editing that speed up the process of development.

Pros

• Responsive support
• Very easy to learn
• Built-in debugger
• Check for errors in the source code
• Helps you to write readable and manageable code
• Customized plug-ins

Cons

• It provides less support to other programming languages.
• Its price is high for a single user.
• It has some kind of stability problems.

3. Eric Python IDE

It is the open-source, and full-featured editor and IDE. It is the best IDE for windows platform.

The best thing about Eric python IDE is that everyone can use it, whether they are students, beginners, or pro. Many lecturers and professors use it for teaching purposes as well. Moreover, it works on the cross-platform, and it has extensions and plug-ins for different IDE functionalities.

Pros

• Integrated class browser.
• Best project management.
• Best to use for everyone.

Cons

• The interface is somehow complex.
• Auto-completion is not so advanced.
• No tabbing for files.

Best Python IDE for Linux

These are the top Python IDEs for Linux:

1. IDLE

Idle is one of the best IDE for Linux as it is the standard Python development environment. IDLE stands for the integrated development environment, and it is the cross-platform that is best to use for both Windows and LINUX operating systems.

It is the best IDE that contains all the necessary functions and features you need to have python on your system. it is highly recommended for the Python beginner developers

Pros

• Best for beginners
• Written in Python/Tkinter
• Integrated debugger
• Easy to use
• Autocomplete feature
• Automatic identification for your code

Cons

• No tabbing for files.
• No shell instances.

2. PyDev For Eclipse

It is the feature-rich, and open-source Python IDE for the eclipse. It supports code completion, Django integration, code completion with auto-import, code analysis, and type hinting.

Pydev comes with a debugger, token browser, refactoring, code coverage, unit test integration, and Pylint integration. You can also find references using shortcut keys. Moreover, it can be great to use for Jython, Python, and IronPython development.

Pros

• Maven Integration and Support
• Subversion/Git integration
• Simplified IDE makes it easy to write efficient code.
• Debugging is very easy in Eclipse.

Cons

• Eclipse has a large footprint
• Updated versions require to build plugins and migrate projects
• Sometimes crashes on loading big projects.

3. VS Code

Visual studio is the cross-platform editor for the different OS including Linux. It is Microsoft IDE that supports multiple programming languages. You just need to install the extension of the programming language.

The best thing about VS is that you can customize it, create your shortcut keys, change the theme, and other preferences. Install Python extension to activate support for python development and use it in your VS code.

Pros

• Very active development with Microsoft backing.
• Offers all the plugins you need
• Stellar VSCode’s speed, almost comparable to ST3
• Manages any npm-based application, made by “npm install”.

Cons

• Sometimes the side preview plug-in doesn’t work properly.
• There is no visibility into conflicting extensions.

Best Python IDE for Ubuntu

These are the best Python IDEs for Ubuntu:

1. Vim

Developers use vim as a command-line interface and a standalone application. It is best to use for college projects. It makes programming more fun, easy, and enjoyable.

Vim is loved by the Linux and Ubuntu-users. It is because it is highly customizable and fast. Vim makes debugging a lot easier, and it also helps to support many plug-ins and tools.

Pros

• Vim is lightweight and fast.
• It has a powerful plug-in model.
• Free and open-source.
• CL interface.
• Provides great productivity

Cons

• Somehow difficult to learn.
• Poor support for internal tooling.
• Feature discovery is difficult.
• It needs a high effort to customize.

2. PyCharm

PyCharm is one of the most loved IDEs for Ubuntu. It is JetBrains Product who is well known for making developers things. For IntelliJ products, it is one of the most comfortable IDEs.

It has handy tools and plugins to work. You can also use its advanced commercial edition with hundreds of professional features. It also serves as a reliable support tool to develop any kind of python application from backend applications to web apps.

Pros

• Data science scripting
• Connectivity with several databases
• Quick and convenient to use and install

Cons

• The community edition is far behind the paid alternative.
• Process of upgrading IDEs can be better

3. Atom

Atom is free and open-source with the same features as an integrated development environment. It supports almost all programming languages, including python. You can use the Atom editor on Ubuntu easily.

You can make Atom function like IDE by installing some plug-ins and extensions. It has a slick user interface. It provides many features like syntax highlighting in the code, diagnostics, auto-completion, etc.

Pros

• Git integration
• Plugin ecosystem
• C++ development
• Built-in package manager.
• Smart auto-completion
• Compatible and easy to use.

Cons

• No starter packages
• May need some improvements
• Performance needs some attention
• File extensions dictate editing functionality

Best Python IDE for Beginners

These are the top Python IDEs for beginner developers:

1. Spyder

It is the dedicated IDE for beginners, and it incorporates some very useful features that make it popular.

It facilitates coders and programmers to do programming easily and accurately. It’s code editor, debugger, and compiler make it the best choice for new learners. Moreover, the user doesn’t have to provide a compiler or interpreter; this IDE creates the environment itself.

Pros

• Light-weight and fast.
• Great for beginners
• Provide online help

Cons

• It is not very customizable.
• Basic than counterparts like PyCharm.

2. Eric Python IDE

It is the open-source, and full-featured editor and IDE and it will work fine for you if you are a newbie.

The best thing about Eric python IDE is that everyone can use it, whether they are students, beginners, or pro. Many lecturers and professors use it for teaching purposes as well. Moreover, it works on the cross-platform, and it has extensions and plug-ins for different IDE functionalities.

Pros

• Integrated class browser.
• Best project management.
• Best to use for beginners.

Cons

• The interface is somehow complex.
• Auto-completion is not so advanced.
• No tabbing for files.

3. Sublime

Sublime is the light-weight editor with the API and package system. It provides powerful features like python scripting, multiple panes, plug-ins, and much more.

It can customize everything by itself. So users can code with efficiency, speed, and accuracy.

Pros

• Text highlights are great for debugging.
• Better find-replace feature than others.
• Easily work with multiple projects without confusion.

Cons

• Less and complicated plugins.
• No auto-saving of documents.
• Formatting a heavy file can be confusing.

Best Python IDE for Machine Learning

Are there any special IDEs for machine learning and AI? You bet! Check out those top IDEs for ML and AI:

1. Visual Studio

Visual studio is the best python IDE for machine learning. It is a Microsoft product, and it provides support for many programming languages. It is the heavy-weight IDE that offers code refactoring, debugging, profiling, and many other tools.

It provides full support for python language, including scientific computing, web development, and data science. It is the best choice if you want to debug python and C/C++ side by side.

Pros

• Very active development with Microsoft backing.
• Syntax highlighter for every programming language.
• VSCode’s speed is awesome, almost comparable to ST3 which is natively built.
• Manages any npm-based application
• Highly pluggable architecture.

Cons

• Sometimes the side preview plug-in doesn’t work properly.
• The source code is a bit clunky.
• There is no visibility into conflicting extensions.

2. Geany

It has been a Python machine learning IDE since 2005. It is quite a light-weight IDE that is written in C and C++. It might be tiny, but it provides the same functionality as other IDEs in the market.

Along with this, it provides support to the coder by highlighting syntax and line numbering as well. It has features like code completion, auto HTML, XML tags, braces closing, etc. it also provides code folding and support code navigation.

Pros

• Light-weight and fast.
• Built-in plugin manager.
• Available on cross-platform.
• Build-in terminal.
• Native and actively free.
• Syntax parsing and code line numbering.

Cons

• It’s not very advanced.
• Not enough third-party plug-ins.

3. Atom

It is another free and open-source editor with the features the same as an integrated development environment. It is the best choice for supporting almost all programming languages, including Python. You can use the Atom IDE for machine learning.

You can make Atom function like IDE by installing some plug-ins and extensions. It has a slick user interface. It provides many features like syntax highlighting in the code, diagnostics, auto-completion, etc.

Pros

• Git integration
• Plugin ecosystem
• C++ development
• Built-in package manager.
• Smart auto-completion
• Compatible and easy to use.
• It is free even for commercial purposes.
• It is mature and has a dedicated community.

Cons

• No starter packages
• May need some improvements
• Performance needs some attention
• File extensions dictate editing functionality

Best Python IDE for Raspberry Pie

Now, that’s specific—but there are some IDEs that are much better for Raspberry Pies. Read on to learn which ones are the best!

1. Geany

Geany is a lightweight graphical user interface IDE. It uses a text editor that uses scintilla and GTK+ with IDE. It is independent of a special desktop environment, and it requires only a few dependencies on other packages. It supports tons of programming languages.

Along with this, it provides support to the coder by highlighting syntax and line numbering as well. It has features like code completion, auto HTML, XML tags, braces closing, etc. it also provides code folding and support code navigation.

Pros

• Light-weight and fast.
• Built-in plugin manager.
• Build-in terminal.
• Syntax parsing and code line numbering.

Cons

• Not enough third-party plug-ins.

2. Ninja

Ninja IDE is written purely in Python, and it supports multiple platforms like Linux, windows, mac for code execution. It is a cross-platform software that is exclusive to build Python applications and websites.

It is a very light-weight IDE that performs functionalities like file handling, code locating, going to lines and tabs, zoom editor, and automatic code identification. Moreover, it supports a few more languages other than python.

Pros

• Extensible and syntax highlighting
• Script runner
• Allows you to search one or more words
• Render HTML files
• Easily locate code
• Embedded Python console

Cons

• Sometimes bad performance.
• No new updations since 2013.

3. Code block

This IDE is also written in C++ using WX widgets as a graphical user interface in 2005. The code block is open-source, free and cross-platform IDE, and it supports multiple compilers like Visual C++, clang, and GCC.

This IDE is quite intelligent, and it also performs various functions like code folding, code completion, syntax highlighting, and much more. Code block also has several external plugins for different customizations. Moreover, it runs on various operating systems.

Pros

• It is open-source with many libraries.
• The debugger supports multi-threaded processes
• It let you choose the compiler
• You can use it on cross-platform

Cons

• Poor code completion
• Code is not easy to read

Best Python IDE for Data Science

What are the best IDEs for data science and data analytics? The difference between suitable and not-so-suitable editors is like 50 shades of grey. Start at the top!

1. PyCharm

PyCharm is a well-reputed python IDE by the data science developers. It has excellent tools and plugins to work with.

It is best for those who have experience of using different Jetbrains IDE. It is because the features and the interface can be similar. It also allows data science developers to integrate its tools and libraries such as Matplotib.

Pros

• Data science scripting
• Excellent tools and plugins.
• Connectivity with several databases
• Quick and convenient to use and install
• Update and remove plugins
• Real-time verification of code
• Search files by code snippet or names

Cons

• The community edition is far behind the paid alternative.
• Process of upgrading IDEs can be better
• Full version is quite expensive
• Some new versions use a lot of machine memory like java.

2. Spyder

It is the dedicated IDE for data science developers, and it incorporates some very useful features that make it popular among them.

Spyder is built specifically for data science. It facilitates coders and programmers to do programming easily and accurately. Its code editor, debugger, and compiler make it the best choice for new learners. Moreover, if you are a beginner, you will like features like online help that allows you to search for library information.

Pros

• Light-weight and fast.
• Great for beginners
• Provide online help

Cons

• It is not very customizable.
• Basic than counterparts like PyCharm.

3. Jupyter Notebook

Python notebooks have got a lot of attention in recent years as a tool showing code, and the results are amazing. It helps to lower the barrier to start with programming because the input is noticeable with output.

Jupyter lap is working to enable users to work with activities like Jupyter notebook and custom components in an interactive manner.

Pros

• Strong to visualization functionalities.
• Interaction with plots is convenient.
• Concise documentation with code
• The best tool for data science

Cons

• The variable inspector is missing
• No convenient file explorer view
• Opening and exploring file is bit clunky
• Absence of convenient visual debugging

Best Python IDE for Android

Programming Android? Then check out those awesome Python IDEs that belong to the best-in-class 100%!

1. IntelliJ IDEA

IntelliJ IDEA is one of the best python IDEs for android as it specializes in web and mobile app development. It uses java, groovy and other frameworks that are best for android app development.

The detailed documentation helps in integration that is also easy to understand. It has multiple plugins to perform different types of tasks. The assistance, unobtrusive intelligence, and inspections are also available with IDE. You can go in-depth coding, fast navigation, and error analysis by this IDE.

Pros

• Available free of cost in the Github Student Developer Pack.
• It has a lot of Configuration Options.
• Integrating hundreds of useful features and tweaks, which makes programming easier.
• Different plugins for even more customization.

Cons

• Indexing can be a bit boring
• It is not memory friendly.

2. Eclipse

Eclipse is the feature-rich, and open-source that is best for Python IDE for android. It supports code completion, Django integration, code completion with auto-import, code analysis, and type hinting.

It comes with a debugger, token browser, refactoring, code coverage, unit test integration, and Pylint integration to code the best python apps. It can also be best for Jython and IronPython development.

Pros

• Maven Integration and Support
• Subversion/Git integration
• Simplified IDE makes it easy to write efficient code.
• Debugging is very easy in eclipse.

Cons

• Eclipse has a large footprint
• Updated versions require to build plugins and migrate projects
• Sometimes crashes on loading big projects.

3. Visual Studio Python IDE for Android

Visual studio is the cross-platform editor for the different OS including Android. It is Microsoft IDE that supports multiple programming languages. You just need to install the extension of the programming language.

The best thing about VS is that you can customize it, create your shortcut keys, change the theme, and other preferences. Install Python extension to activate support for python development and use it in your VS code.

Pros

• Best Python Android IDE
• Very active development with Microsoft backing.
• Highly pluggable architecture for developers

Cons

• Sometimes the side preview plug-in doesn’t work properly.
• The source code is a bit clunky.
• There is no visibility into conflicting extensions.

Best Python IDE with Debugger

Code is more or less buggy. If your code leans towards the former—check out those IDEs with powerful debugging functionality.

1. Eclipse + Pydev

It is a Python IDE with a lot of plugins, extensions, and debugging features. You can use it with other programming languages like C, C++, Python, and PHP.

Pydev is a plugin that allows eclipse to use as a python IDE that also supports the Jython and IronPython. It also uses some advanced techniques to provide elements such as code completion and analysis. It also offers features like interactive console, basic syntax highlighting, and many more.

Pros

• Easy to learn
• Allow debugging feature
• Code completion and code analysis

Cons

• The user interface is not user-friendly

2. Vim

It is the text editor that allows the manipulation of the text file and debugging feature as well. This software is quite better by now as compared to its old version.

It differs from the other Python’s IDEs in the modal mode of operation. It has 3 modes: normal, command, and command-line mode. It is free software which means you can easily adapt extensions and modify its configuration files.

Pros

• Vim is lightweight and fast.
• It has a powerful plug-in model.
• Free and open-source.
• Command-line interface.
• Provides great productivity

Cons

• Somehow difficult to learn.
• Poor support for internal tooling.
• Feature discovery is difficult.
• It needs a high effort to customize.

3. GNU Emacs

GNU Emacs is one of the best Python IDEs with debugging features. It is compatible with many operating systems. GNU enables coders to perform better with a self-documenting editor with an on to go display. The developers of GNU are always upgrading it for better results.

Furthermore, GNU Emacs adopts the lisp coding language and syntax coloring. It offers Unicode support as well.

Pros

• Enable developers to perform better.
• Compatible with many platforms.
• Enables customization of a variety of skills.

Cons

• Somehow complex than other IDEs
• Takes time to learn in the initial stages.

Best Python IDE for Engineers

Are you an engineer? I mean a true engineer (not a mere software engineer)? Then check out these IDEs that may be just the ones you’ve been looking for:

1. Enthought Canopy

This Python IDE is one of the best for scientists and engineers. The canopy comes with many integrated tools that are best for data analysis, app development, and data visualization. You can use its free or commercial license.

Currently, it ships above 450 python packages for data science. It also offers a graphical package manager to install, update, and remove packages from the user environment.

Pros

• Providing scientific libraries, both open source, and Enthought’s libraries
• Providing course training in python for general use and data analysis.
• Canopy has a special debugging tool, especially for python.
• The Documentation Browser is useful
• The analytic Python package distribution is a win-win situation

Cons

• Some Python libraries are slow.
• Canopy does not support Python 3

2. Jupyter Notebook

Python notebooks have got a lot of attention in recent years as a tool showing code, and the results are amazing. It helps to lower the barrier to start with programming because the input is noticeable with output.

This IDE combines code, text, and images, thus providing an interstice data science environment for engineers. It has many integrated data science libraries. Jupyter lap is working to enable users to work with activities like Jupyter notebook and custom components in an interactive manner.

Pros

• Data cleaning and transformation
• Strong to visualization functionalities.
• Statistical modeling

Cons

• The variable inspector is missing
• No convenient file explorer view

3. Rodeo

Rodeo is another python IDE for data science and machine learning projects. It helps you to explore data and plots, that’s why it is the best IDE for engineers. It is much like the RStudio IDE for the R programming language.

Furthermore, it provides syntax highlighting and auto-completion features. It is also supportive of the other platforms. Rodeo IDE also helps to keep track of functions and variables.

Pros

• Highlight the bugs in the syntax.
• Auto-completion benefits.
• Supports Emacs and Vim
• Tutorials for beginners
• Keep track of variables
• Data pane for managing files, packages, photos, and settings

Cons

• It might load slowly sometimes.
• This IDE is somehow complex

Best Python IDE for Algorithmic Trading

Money, money, money. Make more money with those best Python IDEs for algorithmic trading:

1. Spyder

Spyder is a light-weight open-source IDE comes pre-installed with Anaconda distribution. It stands for Scientific Python Development Environment and was built mainly for data science practitioners. It offers a large set of data visualization models to simplify financial analysis. The most promising feature of Spyder is its HELP toolbar where you can search a plethora of information related to modules or libraries.

Pros

• Integrated with the essential data-centric libraries Such as Matplotlib, NumPy, Pandas, SciPy, and IPython.
• Contains features, including code completion, static code analysis, advanced editing, interactive testing, introspection, and debugging.
• A profiler determines the number of calls and runtime for every method and function called in a file.

Cons

• User-interface is not appealing as PyCharm or Visual Studio

2. Algorithm IDE

If you want to code trading strategies, the Algorithm Integrated Development Environment is best for you. It uses Algorithm API to streamline your work process.

Pros

• Smooth process of writing an algorithm
• Build an algorithm with the same backtest engine as running a complete backtest.
• Powerful debugger to inspect the details of backtesting
• Extensive syntax and validation checks

Cons

• The debugger is not available on the full backtest screens
• You can’t use edits in the debugger

3. Codenvy

It offers you a feature-rich cloud-based development environment where you code, develop and enhance effectiveness. It has everything a python developer can expect from an IDE.

Pros

• User-friendly and easy to set up
• Intelligent and fast development environment.
• Accessible on any OS.
• Offers flexibility to write anything from native Android apps to web apps.

Cons

• No keyboard shortcuts
• Particular Git commands don’t fully execute correctly

Best Python IDE For Tensor Flow

We’re getting kind of specific here. But, yes, there are some Python IDEs that are almost perfect for developing TensorFlow apps. Check them out if this is you!

1. Ninja IDE

It is a cross-platform IDE that allows developers to create several applications, including tensor flow. Ninja IDE is designed to make the task of coding more enjoyable and easier.

Pros

• Rich extensibility
• Powerful code editor with smart plugins
• Offer quick access to any function, file or class
• Identify and highlight PEP8 and static errors in the document

Cons

• Some sort of compatibility flaws with windows 10
• Irritating to work with multiple programming languages

2. Sublime Text

If you need to write tensor flow applications, you can test out ideas on sublime. It is one of the most widespread text editors for code and markup. The tricky part of every application is debugging, which you can easily handle with Sublime debugger.

Pros

• Accurate syntax suggestions.
• Slick user-interface.
• Easy to use for basic text manipulation.

Cons

• It cannot highlight a particular portion of the text.
• Often prompts to purchase a new version.

3. Jupyter Notebook

When you are dealing with complex tensorflow applications, Jupyter Notebook is best for code. It combines text, images, and code in an excellent way that will refresh a developer’s mind.

Pros

• Concise documentation with code
• The best tool for data science

Cons

• Opening and exploring file is bit clunky
• Absence of convenient visual debugging

Best Python IDE with Intellij

Intellij is very popular these days. These are the best Python IDEs with IntelliJ:

1. Jet Brains IDE (PyCharm)

JetBrains IDE PyCharm shares the same plugins and features as Intellij and offers all the necessary tools a python developer should want. You can keep control of the quality with testing assistance, inspections, smart refactorings, and PEP8 checks.

Pros

• Update and remove plugins
• Real-time verification of code
• Search files by code snippet or names

Cons

• Full version is quite expensive
• Some new versions use a lot of machine memory like java.

2. Intellij IDEA

Every aspect of this IDE is designed to enhance a developer’s productivity. The IDE uses Groovy, JavaScript, and other frameworks and provides detailed documentation.

Pros

• Multiple plugins to perform different types of tasks.
• Support in-depth coding, quick navigation, and error analysis
• Ergonomic programming environment with splendidly thought-out quick keys.

Cons

• The IDE is very slow to start.
• The complete package is a bit heavy.

3. WebStorm

It is a robust JavaScript IDE with all the features of IntelliJ. It is designed to maximize the performance of a modern developer by offering refactoring for TypeScript, JavaScript, style sheet languages, and many other popular web frameworks.

Pros

• It offers On-the-fly error detection, intelligent code finishing and useful navigation
• Seamless tool integration with linters, build tools and test runners options
• Debug all the apps easily

Cons

• Difficult to change the text color and size
• Less intuitive organization at the left sidebar
• Limited accessibility features.

Best Python IDE with Autocomplete

Auto… thi. sente…! Want to find the best Python IDEs that autocomplete your code, check out these ones:

1. IDLE

Every developer looks for the python IDE with autocomplete feature. IDLE is a popular Integrated Development Environment which is mainly used by newbie developers. It is a cross-platform developed purely in Python. It has a multi-window text editor with various features including smart indentation, call tips, python colorizing, and undo.

Pros

• Supports auto code completion, and syntax highlighting like other IDE’s.
• It has an integrated debugger with call stack visibility to boost the performance of developers.

Cons

• Usage issues.
• The very basic design of the interface that is numbering of line is missing in this IDE.

2. Wing

This IDE creates an intelligent development environment for a python developer. It has a smart debugger, auto code completion feature, and editor to make the development accurate and fun to complete. Wing supports the test-driven coding with Django testing, unit testing, pytesting framework.

Pros

• A source browser to display all the variables used in the script.
• An extra exception handling tab to debug the code.
• An extract function under the refactor panel to maximize performance.

Cons

• Does not support dark themes
• Interface can be daunting at the beginning
• The premium version is too expensive.

3. Geany

It is a lightweight graphical user interface IDE that has a text editor that uses scintilla and GTK+. It supports all types of programming languages and provides support to the coder by highlighting syntax and line numbering.

Pros

• Auto-completion of code.
• Available on cross-platform.
• Syntax parsing and code line numbering.

Cons

• Limited third-party plug-ins.

Best Python IDE for Data Visualization

More and more businesses rely on meaningful visualizations of their proprietary data. These IDEs help you produce them:

1. Visual Studio Code

It is a cross-platform, Microsoft IDE that supports multiple programming languages. You can customize it, create shortcut keys, and change the theme in this IDE. Just install Python extension to activate support for python development in VS Code.

Pros

• Syntax highlighter for every programming language.
• Highly pluggable architecture helps developers to configure their environment according to their choice.
• Excellent data visualization

Cons

• The source code is a bit clunky.

2. Rodeo

It is an open-source native python IDE for data science that was developed by Yhat. It has some amazing features, such as easy interaction with frames, plots, and data, syntax highlighting, auto-completion, and plots, built-in IPython support.

Pros

• Clear data visualization
• Good implementation of a scientific Python IDE

Cons

• Has many bugs and problems
• Overheating and memory issues

3. Angular IDE by CodeMix

Write your best code with the new source code editor. The Angular IDE has an excellent control panel where you can view your project in a particular browser, generate blocks like components, directives, and guards. With a quick drag and drop option, you can customize your control panel

Pros

• Well designed interfaces promote excellent design and data visualization
• It has smart open declaration commands
• Offer unified debugging support through an external Google Chrome browser
• Rich HTML template validation with auto-complete HTML element feature to keep coding moving.

Cons

• The commercial pricing plan is costly

Best Python IDE for Hackers

Believe it or not—many people want to know the best IDE for hackers. Well, I don’t think that a hacker would ever ask such a question… But I try to answer it anyway.

1. Jet Brains IDE

Hackers only use IDEs for debugging and code analysis. There is no specific IDE designed for a hacker. But yes, you can use those Integrated Development environments with all the features needed in the code analysis. Jet Brains Popular IDEs are ideal for this purpose as they have all the smart features a hacker should need. You can use any of the JetBrains IDEs including PyCharm, Webstorm and IntelliJ IDEA.

Pros

• Advanced Gradle build system
• Hassle-free, easy to use interface
• Intelligent code finishing and seamless tool integration

Cons

• Very expensive

2. Atom

Atom is an open-source editor with the same features as an IDE. It supports almost all programming languages, including python. Atom can work like an IDE if you install some specific plug-ins and extensions.

Pros

• Git integration
• Smart auto-completion
• It has a slick user interface and ideal for error diagnostics and auto code completion.

Cons

• Needs some improvement in its accessibility

3. Komodo

A hacker needs swift coding with excellent performance of the IDE, and Komodo is exactly what a hacker want. Now you can code faster in any of the modern programming languages including Python, PHP, Golang, Perl, Ruby and more.

Pros

• Visual debugger
• Great editor with code refactoring, autocomplete, and syntax highlighting
• Extensibility is ideal. You can add tons of add-ons to customize.

Cons

• The free version lacks functionality, and the licensed version is expensive.

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

Short answer: You can remove all empty lists from a list of lists by using the list comprehension statement [x for x in list if x] to filter the list.

In the following, you’ll learn about the two methods using list comprehension and the filter() function to remove all empty lists from a list of lists.

But before that, feel free to play with the code yourself:

Method 1: List Comprehension

How can you remove all empty lists from a list of lists? Say, you’ve got a list of lists

[[1, 2, 3], [1, 2], [], [], [], [1, 2, 3, 4], [], []]

and you want all empty lists removed to obtain the list of lists

[[1, 2, 3], [1, 2], [1, 2, 3, 4]].

Solution: Use list comprehension [x for x in list if x] to filter the list and remove all lists that are empty.

lst = [[1, 2, 3], [1, 2], [], [], [], [1, 2, 3, 4], [], []]
print([x for x in lst if x])
# [[1, 2, 3], [1, 2], [1, 2, 3, 4]]

The condition if x evaluates to False only if the list x is empty. In all other cases, it evaluates to True and the element is included in the new list.

You can visualize the execution flow here by clicking the “Next” button:

Method 2: filter()

An alternative is to use the filter() function to remove all empty lists from a list of lists:

lst = [[1, 2, 3], [1, 2], [], [], [], [1, 2, 3, 4], [], []]
print(list(filter(lambda x: x, lst)))

The filter() function takes two arguments:

• the filter decision function to check for each element whether it should be included in the filtered iterable (it returns a Boolean value), and
• the iterable to be filtered.

As filter decision function, you use the identity function that just passes the list through. Why does this work? Because only an empty list will be evaluated to False. All other lists will be evaluated to True (and, thus, pass the filtering test).

Related articles:

• How to filter a list in Python?
• Filter vs List Comprehension (Speed)
• List Comprehension
• Lists of Lists

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

Short answer: To print a list of lists in Python without brackets and aligned columns, use the ''.join() function and a generator expression to fill each string with enough whitespaces so that the columns align:

# Create the list of lists
lst = [['Alice', 'Data Scientist', '121000'], ['Bob', 'Java Dev', '99000'], ['Ann', 'Python Dev', '111000']] # Find maximal length of all elements in list
n = max(len(x) for l in lst for x in l) # Print the rows
for row in lst: print(''.join(x.ljust(n + 2) for x in row))

I’ll explain this code (and simpler variants) in the following video:

As an exercise, you can also try it yourself in our interactive shell:

Step-By-Step Problem Formulation

Do you want to print a list of lists in Python so that the format doesn’t look like a total mess? In this tutorial, I’ll show you how to orderly print a list of lists in Python—without using any external library.

Before you start to think about the solution, you should understand the problem. What happens if you print a list of lists? Well, let’s try:

lst = [['Alice', 'Data Scientist', 121000], ['Bob', 'Java Dev', 99000], ['Ann', 'Python Dev', 111000]]
print(lst)

The output is not very nice:

[['Alice', 'Data Scientist', 121000], ['Bob', 'Java Dev', 99000], ['Ann', 'Python Dev', 111000]]

Instead, you’d would at least want to see a beautiful output like this one where each row has a separate line and the rows are aligned:

[['Alice', 'Data Scientist', 121000], ['Bob', 'Java Dev', 99000], ['Ann', 'Python Dev', 111000]]

And maybe, you even want to see an output that aligns the three columns as well like this one:

Alice Data Scientist 121000 Bob Java Dev 99000 Ann Python Dev 111000 

I’ll show you all those different ways of printing a list of lists next. So let’s start with the easiest one:

Print List of Lists With Newline

Problem: Given a list of lists, print it one row per line.

Example: Consider the following example list:

lst = [[1, 2, 3], [4, 5, 6]]

You want to print the list of lists with a newline character after each inner list:

1 2 3
4 5 6

Solution: Use a for loop and a simple print statement:

lst = [[1, 2, 3], [4, 5, 6]] for x in lst: print(*x)

The output has the desired form:

1 2 3
4 5 6

Explanation: The asterisk operator “unpacks” all values in the inner list x into the print statement. You must know that the print statement also takes multiple inputs and prints them, whitespace-separated, to the shell.

Related articles:

• Unpacking Operator *
• How to Print a List Beautifully
• Python Lists of Lists

Print List of Lists Without Brackets

To print a list of lists without brackets, use the following code again.

Solution: Use a for loop and a simple print statement:

lst = [[1, 2, 3], [4, 5, 6]] for x in lst: print(*x)

The output has the desired form:

1 2 3
4 5 6

But how can you print a list of lists by aligning the columns?

Print List of Lists Align Columns

Problem: How to print a list of lists so that the columns are aligned?

Example: Say, you’re going to print the list of lists.

[['Alice', 'Data Scientist', 121000], ['Bob', 'Java Dev', 99000], ['Ann', 'Python Dev', 111000]]

How to align the columns?

Alice 'Data Scientist', 121000], Bob 'Java Dev', 99000], Ann 'Python Dev', 111000]]

Solution: Use the following code snippet to print the list of lists and align all columns (no matter how many characters each string in the list of lists occupies).

# Create the list of lists
lst = [['Alice', 'Data Scientist', '121000'], ['Bob', 'Java Dev', '99000'], ['Ann', 'Python Dev', '111000']] # Find maximal length of all elements in list
n = max(len(x) for l in lst for x in l) # Print the rows
for row in lst: print(''.join(x.ljust(n + 2) for x in row))

The output is the desired:

Alice Data Scientist 121000 Bob Java Dev 99000 Ann Python Dev 111000 

Explanation:

• First, you determine the length n (in characters) of the largest string in the list of lists using the statement max(len(x) for l in lst for x in l). The code uses a nested for loop in a generator expression to achieve this.
• Second, you iterate over each list in the list of lists (called row).
• Third, you create a string representation with columns aligned by ‘padding’ each row element so that it occupies n+2 characters of space. The missing characters are filled with empty spaces.

You can see the code in action in the following memory visualizer. Just click “Next” to see which objects are created in memory if you run the code in Python:

Related articles: You may need to refresh your understanding of the following Python features used in the code:

• List comprehension and generator expressions
• String join()
• Printing lists

Print List of Lists with Pandas

Last but not least, I’ll show you my simple favorite: simply import the pandas library (the excel of Python coders) and print the DataFrame. Pandas takes care of pretty formatting:

lst = [['Alice', 'Data Scientist', '121000'], ['Bob', 'Java Dev', '99000'], ['Ann', 'Python Dev', '111000']] import pandas as pd
df = pd.DataFrame(lst)
print(df)

The output looks beautiful—like a spreadsheet in your Python shell:

 0 1 2
0 Alice Data Scientist 121000
1 Bob Java Dev 99000
2 Ann Python Dev 111000

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

[20-SEC SUMMARY] Given a list of list stored in variable lst.

• To flatten a list of lists, use the list comprehension statement [x for l in lst for x in l].
• To modify all elements in a list of lists (e.g., increment them by one), use a list comprehension of list comprehensions [[x+1 for x in l] for l in lst].

List comprehension is a compact way of creating lists. The simple formula is [ expression + context ].

• Expression: What to do with each list element?
• Context: What list elements to select? It consists of an arbitrary number of for and if statements.

The example [x for x in range(3)] creates the list [0, 1, 2].

In this tutorial, you’ll learn three ways how to apply list comprehension to a list of lists:

• to flatten a list of lists
• to create a list of lists
• to iterate over a list of lists

Additionally, you’ll learn how to apply nested list comprehension. So let’s get started!

Python List Comprehension Flatten List of Lists

Problem: Given a list of lists. How to flatten the list of lists by getting rid of the inner lists—and keeping their elements?

Example: You want to transform a given list into a flat list like here:

lst = [[2, 2], [4], [1, 2, 3, 4], [1, 2, 3]] # ... Flatten the list here ... print(lst)
# [2, 2, 4, 1, 2, 3, 4, 1, 2, 3]

Solution: Use a nested list comprehension statement [x for l in lst for x in l] to flatten the list.

lst = [[2, 2], [4], [1, 2, 3, 4], [1, 2, 3]] # ... Flatten the list here ...
lst = [x for l in lst for x in l] print(lst)
# [2, 2, 4, 1, 2, 3, 4, 1, 2, 3]

Explanation: In the nested list comprehension statement [x for l in lst for x in l], you first iterate over all lists in the list of lists (for l in lst). Then, you iterate over all elements in the current list (for x in l). This element, you just place in the outer list, unchanged, by using it in the “expression” part of the list comprehension statement [x for l in lst for x in l].

Try It Yourself: You can execute this code snippet yourself in our interactive Python shell. Just click “Run” and test the output of this code.

Can you flatten a three-dimensional list (= a list of lists of lists)? Try it in the shell!

Python List Comprehension Create List of Lists

Problem: How to create a list of lists by modifying each element of an original list of lists?

Example: You’re given the list

[[1, 2, 3], [4, 5, 6], [7, 8, 9]]

You want to add one to each element and create a new list of lists:

[[2, 3, 4], [5, 6, 7], [8, 9, 10]]

Solution: Use two nested list comprehension statements, one to create the outer list of lists, and one to create the inner lists.

lst = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
new = [[x+1 for x in l] for l in lst]
print(new)
# [[2, 3, 4], [5, 6, 7], [8, 9, 10]]

Explanation: You’ll study more examples of two nested list comprehension statements later. The main idea is to use as “expression” of the outer list comprehension statement a list comprehension statement by itself. Remember, you can create any object you want in the expression part of your list comprehension statement.

Explore the code: You can play with the code in the interactive Python tutor that visualizes the execution step-by-step. Just click the “Next” button repeatedly to see what happens in each step of the code.

Let’s explore the third question: how to use list comprehension to iterate over a list of lists?

Python List Comprehension Over List of Lists

You’ve seen this in the previous example where you not only created a list of lists, you also iterated over each element in the list of lists. To summarize, you can iterate over a list of lists by using the statement [[modify(x) for x in l] for l in lst] using any statement or function modify(x) that returns an arbitrary object.

How Does Nested List Comprehension Work in Python?

After publishing the first version of this tutorial, many readers asked me to write a follow-up tutorial on nested list comprehension in Python. There are two interpretations of nested list comprehension:

• Coming from a computer science background, I was assuming that “nested list comprehension” refers to the creation of a list of lists. In other words: How to create a nested list with list comprehension?
• But after a bit of research, I learned that there is a second interpretation of nested list comprehension: How to use a nested for loop in the list comprehension?

How to create a nested list with list comprehension?

It is possible to create a nested list with list comprehension in Python. What is a nested list? It’s a list of lists. Here is an example:

## Nested List Comprehension
lst = [[x for x in range(5)] for y in range(3)]
print(lst)
# [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]

As you can see, we create a list with three elements. Each list element is a list by itself.

Everything becomes clear when we go back to our magic formula of list comprehension: [expression + context]. The expression part generates a new list consisting of 5 integers. The context part repeats this three times. Hence, each of the three nested lists has five elements.

If you are an advanced programmer (test your skills on the Finxter app), you may ask whether there is some aliasing going on here. Aliasing in this context means that the three list elements point to the same list [0, 1, 2, 3, 4]. This is not the case because each expression is evaluated separately, a new list is created for each of the three context executions. This is nicely demonstrated in this code snippet:

l[0].append(5)
print(l)
# [[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]
# ... and not [[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]]

How to use a nested for loop in the list comprehension?

To be frank, the latter one is super-simple stuff. Do you remember the formula of list comprehension (= ‘[‘ + expression + context + ‘]’)?

The context is an arbitrary complex restriction construct of for loops and if restrictions with the goal of specifying the data items on which the expression should be applied.

In the expression, you can use any variable you define within a for loop in the context. Let’s have a look at an example.

Suppose you want to use list comprehension to make this code more concise (for example, you want to find all possible pairs of users in your social network application):

# BEFORE
users = ["John", "Alice", "Ann", "Zach"]
pairs = []
for x in users: for y in users: if x != y: pairs.append((x,y))
print(pairs)
#[('John', 'Alice'), ('John', 'Ann'), ('John', 'Zach'), ('Alice', 'John'), ('Alice', 'Ann'), ('Alice', 'Zach'), ('Ann', 'John'), ('Ann', 'Alice'), ('Ann', 'Zach'), ('Zach', 'John'), ('Zach', 'Alice'), ('Zach', 'Ann')]

Now, this code is a mess! How can we fix it? Simply use nested list comprehension!

# AFTER
pairs = [(x,y) for x in users for y in users if x!=y]
print(pairs)
# [('John', 'Alice'), ('John', 'Ann'), ('John', 'Zach'), ('Alice', 'John'), ('Alice', 'Ann'), ('Alice', 'Zach'), ('Ann', 'John'), ('Ann', 'Alice'), ('Ann', 'Zach'), ('Zach', 'John'), ('Zach', 'Alice'), ('Zach', 'Ann')]

As you can see, we are doing exactly the same thing as with un-nested list comprehension. The only difference is to write the two for loops and the if statement in a single line within the list notation [].

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

You can convert a list of lists to a Pandas DataFrame that provides you with powerful capabilities such as the to_csv() method. This is the easiest method and it allows you to avoid importing yet another library (I use Pandas in many Python projects anyways).

salary = [['Alice', 'Data Scientist', 122000], ['Bob', 'Engineer', 77000], ['Ann', 'Manager', 119000]] # Method 2
import pandas as pd
df = pd.DataFrame(salary)
df.to_csv('file2.csv', index=False, header=False)

Output:

# file2.csv
Alice,Data Scientist,122000
Bob,Engineer,77000
Ann,Manager,119000

You create a Pandas DataFrame—which is Python’s default representation of tabular data. Think of it as an Excel spreadsheet within your code (with rows and columns).

The DataFrame is a very powerful data structure that allows you to perform various methods. One of those is the to_csv() method that allows you to write its contents into a CSV file.

You set the index and header arguments of the to_csv() method to False because Pandas, per default, adds integer row and column indices 0, 1, 2, …. Again, think of them as the row and column indices in your Excel spreadsheet. You don’t want them to appear in the CSV file so you set the arguments to False.

If you want to customize the CSV output, you’ve got a lot of special arguments to play with. Check out this article for a comprehensive list of all arguments.

Related article: Pandas Cheat Sheets to Pin to Your Wall

Feel free to play with alternative methods to convert a list of lists to a CSV file in our interactive code shell. Simply click the “Run” button and find the generated CSV files in the “Files” tab.

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

Pandas is an open-source Python library that is powerful and flexible for data analysis. If there is something you want to do with data, the chances are it will be possible in pandas. There are a vast number of possibilities within pandas, but most users find themselves using the same methods time after time. In this article, we compiled the best cheat sheets from across the web, which show you these core methods at a glance.

The primary data structure in pandas is the DataFrame used to store two-dimensional data, along with a label for each corresponding column and row. If you are familiar with Excel spreadsheets or SQL databases, you can think of the DataFrame as being the pandas equivalent. If we take a single column from a DataFrame, we have one-dimensional data. In pandas, this is called a Series. DataFrames can be created from scratch in your code, or loaded into Python from some external location, such as a CSV. This is often the first stage in any data analysis task. We can then do any number of things with our DataFrame in Pandas, including removing or editing values, filtering our data, or combining this DataFrame with another DataFrame. Each line of code in these cheat sheets lets you do something different with a DataFrame. Also, if you are coming from an Excel background, you will enjoy the performance pandas has to offer. After you get over the learning curve, you will be even more impressed with the functionality.

Whether you are already familiar with pandas and are looking for a handy reference you can print out, or you have never used pandas and are looking for a resource to help you get a feel for the library- there is a cheat sheet here for you!

1. The Most Comprehensive Cheat Sheet

https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

This one is from the pandas guys, so it makes sense that this is a comprehensive and inclusive cheat sheet. It covers the vast majority of what most pandas users will ever need to do to a DataFrame. Have you already used pandas for a little while? And are you looking to up your game? This is your cheat sheet! However, if you are newer to pandas and this cheat sheet is a bit overwhelming, don’t worry! You definitely don’t need to understand everything in this cheat sheet to get started. Instead, check out the next cheat sheet on this list.

2. The Beginner’s Cheat Sheet

https://www.dataquest.io/blog/pandas-cheat-sheet/

Dataquest is an online platform that teaches Data Science using interactive coding challenges. I love this cheat sheet they have put together. It has everything the pandas beginner needs to start using pandas right away in a friendly, neat list format. It covers the bare essentials of each stage in the data analysis process:

• Importing and exporting your data from an Excel file, CSV, HTML table or SQL database
• Cleaning your data of any empty rows, changing data formats to allow for further analysis or renaming columns
• Filtering your data or removing anomalous values
• Different ways to view the data and see it’s dimensions
• Selecting any combination of columns and rows within the DataFrame using loc and iloc
• Using the .apply method to apply a formula to a particular column in the DataFrame
• Creating summary statistics for columns in the DataFrame. This includes the median, mean and standard deviation
• Combining DataFrames

3. The Excel User’s Cheat Sheet

https://www.shanelynn.ie/using-pandas-dataframe-creating-editing-viewing-data-in-python/

Ok, this isn’t quite a cheat sheet, it’s more of an entire manifesto on the pandas DataFrame! If you have a little time on your hands, this will help you get your head around some of the theory behind DataFrames. It will take you all the way from loading in your trusty CSV from Microsoft Excel to viewing your data in Jupyter and handling the basics. The article finishes off by using the DataFrame to create a histogram and bar chart. For migrating your spreadsheet work from Excel to pandas, this is a fantastic guide. It will teach you how to perform many of the Excel basics in pandas. If you are also looking for how to perform the pandas equivalent of a VLOOKUP in Excel, check out Shane’s article on the merge method.

4. The Most Beautiful Cheat Sheet

https://www.enthought.com/wp-content/uploads/Enthought-Python-Pandas-Cheat-Sheets-1-8-v1.0.2.pdf

If you’re more of a visual learner, try this cheat sheet! Many common pandas tasks have intricate, color-coded illustrations showing how the operation works. On page 3, there is a fantastic section called ‘Computation with Series and DataFrames’, which provides an intuitive explanation for how DataFrames work and shows how the index is used to align data when DataFrames are combined and how element-wise operations work in contrast to operations which work on each row or column. At 8 pages long, it’s more of a booklet than a cheat sheet, but it can still make for a great resource! 

5. The Best Machine Learning Cheat Sheet

https://elitedatascience.com/python-cheat-sheet

Much like the other cheat sheets, there is comprehensive coverage of the pandas basic in here. So, that includes filtering, sorting, importing, exploring, and combining DataFrames. However, where this Cheat Sheet differs is that it finishes off with an excellent section on scikit-learn, Python’s machine learning library. In this section, the DataFrame is used to train a machine learning model. This cheat sheet will be perfect for anybody who is already familiar with machine learning and is transitioning from a different technology, such as R.

6. The Most Compact Cheat Sheet

http://datacamp-community-prod.s3.amazonaws.com/dbed353d-2757-4617-8206-8767ab379ab3

Data Camp is an online platform that teaches Data Science with videos and coding exercises. They have made cheat sheets on a bunch of the most popular Python libraries, which you can also check out here. This cheat sheet nicely introduces the DataFrame, and then gives a quick overview of the basics. Unfortunately, it doesn’t provide any information on the various ways you can combine DataFrames, but it does all fit on one page and looks great. So, if you are looking to stick a pandas cheat sheet on your bedroom wall and nail home the basics, this one might be for you! The cheat sheet finishes with a small section introducing NaN values, which come from NumPy. These indicate a null value and arise when the indices of two Series don’t quite match up in this case.

7. The Best Statistics Cheat Sheet

https://www.webpages.uidaho.edu/~stevel/504/pandas%20dataframe%20notes.pdf

While there aren’t any pictures to be found in this sheet, it is an incredibly detailed set of notes on the pandas DataFrame. This cheat shines with its complete section on time series and statistics. There are methods for calculating covariance, correlation, and regression here. So, if you are using pandas for some advanced statistics or any kind of scientific work, this is going to be your cheat sheet.

Where to go from here?

For just automating a few tedious tasks at work, or using pandas to replace your crashing Excel spreadsheet, everything covered in these cheat sheets should be entirely sufficient for your purposes. 

If you are looking to use pandas for Data Science, then you are only going to be limited by your knowledge of statistics and probability. This is the area that most people lack when they try to enter this field. I highly recommend checking out Think Stats by Allen B Downey, which provides an introduction to statistics using Python.

For those a little more advanced, looking to do some machine learning, you will want to start taking a look at the scikit-learn library. Data Camp has a great cheat sheet for this. You will also want to pick up a linear algebra textbook to understand the theory of machine learning. For something more practical, perhaps give the famous Kaggle Titanic machine learning competition.

Learning about pandas has many uses, and can be interesting simply for its own sake. However, Python is massively in demand right now, and for that reason, it is a high-income skill. At any given time, there are thousands of people searching for somebody to solve their problems with Python. So, if you are looking to use Python to work as a freelancer, then check out the Finxter Python Freelancer Course. This provides the step by step path to go from nothing to earning a full-time income with Python in a few months, and gives you the tools to become a six-figure developer!

Problem: You’re given a list of lists. Your goal is to convert it into a Pandas Dataframe.

Example: Say, you want to compare salary data of different companies and job descriptions. You’ve obtained the following salary data set as a list of list:

salary = [['Google', 'Machine Learning Engineer', 121000], ['Google', 'Data Scientist', 109000], ['Google', 'Tech Lead', 129000], ['Facebook', 'Data Scientist', 103000]]

How can you convert this into a Pandas Dataframe?

DataFrame()

Solution: The straight-forward solution is to use the pandas.DataFrame() constructor that creates a new Dataframe object from different input types such as NumPy arrays or lists.

Here’s how to do it for the given example:

import pandas as pd salary = [['Google', 'Machine Learning Engineer', 121000], ['Google', 'Data Scientist', 109000], ['Google', 'Tech Lead', 129000], ['Facebook', 'Data Scientist', 103000]] df = pd.DataFrame(salary)

This results in the following Dataframe:

print(df) ''' 0 1 2
0 Google Machine Learning Engineer 121000
1 Google Data Scientist 109000
2 Google Tech Lead 129000
3 Facebook Data Scientist 103000 '''

Try It Yourself: Run this code in our interactive Python shell by clicking the “Run” button.

DataFrame.from_records()

An alternative is the pandas.DataFrame.from_records() method that generates the same output:

import pandas as pd salary = [['Company', 'Job', 'Salary($)'], ['Google', 'Machine Learning Engineer', 121000], ['Google', 'Data Scientist', 109000], ['Google', 'Tech Lead', 129000], ['Facebook', 'Data Scientist', 103000]] df = pd.DataFrame.from_records(salary)
print(df) ''' 0 1 2
0 Google Machine Learning Engineer 121000
1 Google Data Scientist 109000
2 Google Tech Lead 129000
3 Facebook Data Scientist 103000 '''

Try It Yourself: Run this code in our interactive Python shell by clicking the “Run” button.

Column Names

If you want to add column names to make the output prettier, you can also pass those as a separate argument:

import pandas as pd salary = [['Google', 'Machine Learning Engineer', 121000], ['Google', 'Data Scientist', 109000], ['Google', 'Tech Lead', 129000], ['Facebook', 'Data Scientist', 103000]] df = pd.DataFrame(salary, columns=['Company', 'Job', 'Salary($)'])
print(df) ''' Company Job Salary($)
0 Google Machine Learning Engineer 121000
1 Google Data Scientist 109000
2 Google Tech Lead 129000
3 Facebook Data Scientist 103000 '''

Try It Yourself: Run this code in our interactive Python shell by clicking the “Run” button.

If the first list of the list of lists contains the column name, use slicing to separate the first list from the other lists:

import pandas as pd salary = [['Company', 'Job', 'Salary($)'], ['Google', 'Machine Learning Engineer', 121000], ['Google', 'Data Scientist', 109000], ['Google', 'Tech Lead', 129000], ['Facebook', 'Data Scientist', 103000]] df = pd.DataFrame(salary[1:], columns=salary[0])
print(df) ''' Company Job Salary($)
0 Google Machine Learning Engineer 121000
1 Google Data Scientist 109000
2 Google Tech Lead 129000
3 Facebook Data Scientist 103000 '''

Slicing is a powerful Python feature and before you can master Pandas, you need to master slicing. To refresh your Python slicing skills, download my ebook “Coffee Break Python Slicing” for free.

Summary: To convert a list of lists into a Pandas DataFrame, use the pd.DataFrame() constructor and pass the list of lists as an argument. An optional columns argument can help you structure the output.

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

Short answer: Convert a list of lists—let’s call it l—to a NumPy array by using the standard np.array(l) function. This works even if the inner lists have a different number of elements.

Convert List of Lists to 2D Array

Problem: Given a list of lists in Python. How to convert it to a 2D NumPy array?

Example: Convert the following list of lists

[[1, 2, 3], [4, 5, 6]]

into a NumPy array

[[1 2 3] [4 5 6]]

Solution: Use the np.array(list) function to convert a list of lists into a two-dimensional NumPy array. Here’s the code:

# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5, 6]] # Convert it to a NumPy array
a = np.array(lst) # Print the resulting array
print(a) '''
[[1 2 3] [4 5 6]] '''

Try It Yourself: Here’s the same code in our interactive code interpreter:

<iframe height="700px" width="100%" src="https://repl.it/@finxter/numpylistoflists?lite=true" scrolling="no" frameborder="no" allowtransparency="true" allowfullscreen="true" sandbox="allow-forms allow-pointer-lock allow-popups allow-same-origin allow-scripts allow-modals"></iframe>

Hint: The NumPy method np.array() takes an iterable as input and converts it into a NumPy array.

Convert a List of Lists With Different Number of Elements

Problem: Given a list of lists. The inner lists have a varying number of elements. How to convert them to a NumPy array?

Example: Say, you’ve got the following list of lists:

[[1, 2, 3], [4, 5], [6, 7, 8]]

What are the different approaches to convert this list of lists into a NumPy array?

Solution: There are three different strategies you can use. (source)

(1) Use the standard np.array() function.

# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5], [6, 7, 8]] # Convert it to a NumPy array
a = np.array(lst) # Print the resulting array
print(a) '''
[list([1, 2, 3]) list([4, 5]) list([6, 7, 8])] '''

This creates a NumPy array with three elements—each element is a list type. You can check the type of the output by using the built-in type() function:

>>> type(a)
<class 'numpy.ndarray'>

(2) Make an array of arrays.

# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5], [6, 7, 8]] # Convert it to a NumPy array
a = np.array([np.array(x) for x in lst]) # Print the resulting array
print(a) '''
[array([1, 2, 3]) array([4, 5]) array([6, 7, 8])] '''

This is more logical than the previous version because it creates a NumPy array of 1D NumPy arrays (rather than 1D Python lists).

(3) Make the lists equal in length.

# Import the NumPy library
import numpy as np # Create the list of lists
lst = [[1, 2, 3], [4, 5], [6, 7, 8, 9]] # Calculate length of maximal list
n = len(max(lst, key=len)) # Make the lists equal in length
lst_2 = [x + [None]*(n-len(x)) for x in lst]
print(lst_2)
# [[1, 2, 3, None], [4, 5, None, None], [6, 7, 8, 9]] # Convert it to a NumPy array
a = np.array(lst_2) # Print the resulting array
print(a) '''
[[1 2 3 None] [4 5 None None] [6 7 8 9]] '''

You use list comprehension to “pad” None values to each inner list with smaller than maximal length.

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

For some applications, it’s quite useful to convert a list of lists into a dictionary.

• Databases: List of list is table where the inner lists are the database rows and you want to assign each row to a primary key in a new dictionary.
• Spreadsheet: List of list is two-dimensional spreadsheet data and you want to assign each row to a key (=row name).
• Data Analytics: You’ve got a two-dimensional matrix (=NumPy array) that’s initially represented as a list of list and you want to obtain a dictionary to ease data access.

There are three main ways to convert a list of lists into a dictionary in Python (source):

1. Dictionary Comprehension
2. Generator Expression
3. For Loop

Let’s dive into each of those.

1. Dictionary Comprehension

Problem: Say, you’ve got a list of lists where each list represents a person and consists of three values for the person’s name, age, and hair color. For convenience, you want to create a dictionary where you use a person’s name as a dictionary key and the sublist consisting of the age and the hair color as the dictionary value.

Solution: You can achieve this by using the beautiful (but, surprisingly, little-known) feature of dictionary comprehension in Python.

persons = [['Alice', 25, 'blonde'], ['Bob', 33, 'black'], ['Ann', 18, 'purple']] persons_dict = {x[0]: x[1:] for x in persons}
print(persons_dict)
# {'Alice': [25, 'blonde'],
# 'Bob': [33, 'black'],
# 'Ann': [18, 'purple']}

Explanation: The dictionary comprehension statement consists of the expression x[0]: x[1:] that assigns a person’s name x[0] to the list x[1:] of the person’s age and hair color. Further, it consists of the context for x in persons that iterates over all “data rows”.

Exercise: Can you modify the code in our interactive code shell so that each hair color is used as a key and the name and age are used as the values?

Modify the code and click the “run” button to see if you were right!

2. Generator Expression

A similar way of achieving the same thing is to use a generator expression in combination with the dict() constructor to create the dictionary.

persons = [['Alice', 25, 'blonde'], ['Bob', 33, 'black'], ['Ann', 18, 'purple']] persons_dict = dict((x[0], x[1:]) for x in persons)
print(persons_dict)
# {'Alice': [25, 'blonde'],
# 'Bob': [33, 'black'],
# 'Ann': [18, 'purple']}

This code snippet is almost identical to the one used in the “list comprehension” part. The only difference is that you use tuples rather than direct mappings to fill the dictionary.

3. For Loop

Of course, there’s no need to get fancy here. You can also use a regular for loop and define the dictionary elements one by one within a simple for loop. Here’s the alternative code:

persons = [['Alice', 25, 'blonde'], ['Bob', 33, 'black'], ['Ann', 18, 'purple']] persons_dict = {}
for x in persons: persons_dict[x[0]] = x[1:] print(persons_dict)
# {'Alice': [25, 'blonde'],
# 'Bob': [33, 'black'],
# 'Ann': [18, 'purple']}

Again, you map each person’s name to the list consisting of its age and hair color.

Where to Go From Here?

Enough theory, let’s get some practice!

To become successful in coding, you need to get out there and solve real problems for real people. That’s how you can become a six-figure earner easily. And that’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

Practice projects is how you sharpen your saw in coding!

Do you want to become a code master by focusing on practical code projects that actually earn you money and solve problems for people?

Then become a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

Join my free webinar “How to Build Your High-Income Skill Python” and watch how I grew my coding business online and how you can, too—from the comfort of your own home.

Join the free webinar now!

Problem: Given a Python tuple with n elements. How to convert it into a list with the same n elements?

Examples:

• Convert tuple (1, 2, 3, 4, 5) into list [1, 2, 3, 4, 5].
• Convert tuple ('Alice', 'Bob', 'Ann') into list ['Alice', 'Bob', 'Ann'].
• Convert tuple (1,) into list [1].

Note Tuple: Tuples are similar to lists—with the difference that you cannot change the tuple values (tuples are immutable) and you use parentheses rather than square brackets.

Solution: Use the built-in Python list() function to convert a list into a tuple. You don’t need to import any external library.

Code: The following code converts the three given tuples into lists.

tuple_1 = (1, 2, 3, 4, 5)
print(list(tuple_1))
# [1, 2, 3, 4, 5] tuple_2 = ('Alice', 'Bob', 'Ann')
print(list(tuple_2))
# ['Alice', 'Bob', 'Ann'] tuple_3 = (1,)
print(list(tuple_3))
# [1]

Try It Yourself: With our interactive code shell, you can try it yourself. As a small exercise, try to convert the empty tuple () into a list and see what happens.

Explanation: You can see that converting a tuple with one element leads to a list with one element. The list() function is the easiest way to convert a tuple into a list. Note that the values in the tuple are not copied—only a new reference to the same element is created:

The graphic also shows how to convert a tuple back to a list by using the tuple() function (that’s also a Python built-in function). Thus, calling list(tuple(lst)) on a list lst will result in a new list with the same elements.

Related articles:

• List copy
• List complete guide
• Convert list to tuple
• Convert list to dict

Try to execute this code with the interactive Python tutor: