Best Android Apps for Playing Matka Online

The Matka lovers are glad to have the Matka game back. In the 90s, it was played in every street and every gaming center, but as its popularity increased, it was banned in India.

As the internet made strong footings in India and online gaming became a trend, the Matka lovers got their favorite game back, but this time, it was an online version. Many websites offer the Matka lottery game and teach you how to play the game.

You will require a tablet or a PC for the web games, so the web owners made things furthermore accessible for everyone. There are great android apps for Matka lovers to play the games right from their homes with the devices in their hands.

Have a look at the best android apps for playing Matka online!

Lottoland

Lottoland is one of the most reliable and famous websites and apps for Matka game lovers. It offers a wide range of options for the Satta Matka game fans. Along with that it also gives a detailed insight of the game, the tips and tricks to win the game and the winning numbers to follow.

Similarly, you can also learn how to play Matka at Lottoland and enjoy the game’s perks in terms of monetary benefits. The payment mode is highly secure and the deposit and withdrawal are simple.

Satta Matka Online

It is the world’s first online Satta Matka website, widely known for providing the most reliable Indian Satta Matka games. It also offers free Kalyan Matka tips, the Matka results, the guessing chart of Satta Matka, Mumbai Matka, and Kapil Matka.

You can download the app and begin your journey towards the online Matka game and winning outstanding amounts as per your luck and experience. It is not a definite thing, and you might lose everything as well.

Expert Matka

Expert Matka allows you to contest for your chances to make a big win. It offers the players to play daily with the experts and make huge wins every day. All you have to do is download the app on your android phone and create your account.

Enter the details such as username, password, and then sign up for your account. Log in to your account and choose the type of game you would like to play. Once you have selected the game, you are all set to place a bet.

The games offered by Expert Matka are Star Morning, Sridevi, Madhur Morning, Time Bazar, Star Day, Madur Day, Milan Day, Supreme Day, Rajdhani Day, Kalyan, Supreme Day, Sridevi Night, and Star Evening.

B Matka

The B Matka android app could be easily downloaded from the play store for playing Matka online. This app allows you to play in the Satta Matka markets such as Kalyan, Milan, Mumbai, Raj, and many more.

You can also learn about the techniques to play Satta Matka in the Indian market. The B MAtka app also provides the Satta Matka numbers, the actual prices, and assistance in placing the bets. It asks you to make the deposits, and then you can see the fastest results and enjoy the lottery game to the fullest.

KheloLelo

KheloLelo allows Matka lovers to play the international lottery online in India and offers a chance to win real money. You can play from anywhere in the world and get payments the same day through bank transfers.

If you are lucky enough, you can make a minimum deposit of ₹500, and you might win ₹1000 on winning the bet. It gives fantastic rates for Jodi and Panna and single games.

The applications offer some great benefits such as the highest rates of Satta Matka, zero deposit and zero withdrawal fees, payments on the same day, referral bonuses such as 1% of their deposits every time. You can invite your friend and even earn money from that.

Online Matka Play

Online Matka Play is one of the biggest Satta Matka websites in India. It offers the following games: single, Jodi, Single Patti, Double Patti, Triple Patti, Half Sangam, Full Sangam, and many others.

It also provides accurate and high rates to all the players with less hassle and complications. The process of Online Matka Play is simple and allows you to win without any problems and risks.

The whole system is fully digital, and it transfers your winning amount directly to your bank accounts. All you have to do is download the app, submit your details and begin your Matka game.

Before you opt for any application, make sure you have all the information regarding it. There are many websites out there, and not all of them are genuine. Also, keep in mind that you don’t always need to win. There are equal chances of your win and losing, so be careful and well aware of all the rules and regulations.

The post Best Android Apps for Playing Matka Online appeared first on PythonBlog.

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Footy Predictor 2021-2022

Python Football Predictor 2021-2022 Season Download Options Download Footy Predictor V1.24 source code for Season 2021-2022 in a zip from MediaFire: 763kb Download Footy Predictor V1.24 source code And Windows Exe for Season 2021-2022 in a zip from MediaFire: 86.72Mb Footy Predictor is now back on GitHub too: https://github.com/steveshambles/Footy-Predictor Premier League Fixtures Were published 16th … Continue reading Footy Predictor 2021-2022

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Jupyter in Visual Studio Code – June 2021 Release

We are pleased to announce that the June 2021 release of the Jupyter Extension for Visual Studio Code is now available. If you are working with Python, we recommend downloading the Python extension from the Marketplace, or installing it directly from the extension gallery in Visual Studio Code. If you already have the Python extension installed, you can also get the latest update by restarting Visual Studio Code. You can learn more about  Python support in Visual Studio Code in the documentation.

This release focused on:

Enhanced security measures
Additional Native Notebook layout settings
Improvements to our Data Viewer and Variable Explorer.

If you’re interested, you can check the full list of improvements in our changelog.

Workspace Trust

Visual Studio Code takes security seriously and the Workspace Trust feature allows you to determine which project folders and content you trust, and which ones you would like to keep in restricted mode.

What does this mean with respect to notebooks?

When opening a folder in VS Code, you will be asked if you trust the authors and content of the folders.

If you trust a folder, any notebooks and their outputs will be rendered and can run code. If you do not trust a folder with notebooks in them, you will be in restricted mode. If you would like to change restricted mode to trusted, click on the gear at the bottom left and select “Manage Workspace Trust”.

**Note: It is important to understand that notebooks may have harmful code embedded in their outputs and may run without you executing the cells. VS Code will suppress outputs until you have explicitly trusted the notebook. It is important to determine whether you trust notebooks that are downloaded or come from external sources.

For more information and details about Workspace Trust please see Visual Studio Code – Workspace Trust

Improved Filtering in Data Viewer

The Data Viewer now has improved capabilities for filtering string values with added support for the * wildcard character. In the example below, the column being filtered on contains both “male” and “female” entries. Searching for the values “male” will now only return exact matches. To search for all values that end in “male”, simply type “*male” into the filter box. The * wildcard character will match any number of characters and can be used anywhere in the string.

Sorting in Variable Explorer

By popular demand, variables in your notebook can now be sorted! Click on the “Name” or “Type” headers in Variable Explorer to sort. The direction of the arrow in the header will indicate which header the variables are being sorted on and whether the sort is in alphabetical or reverse alphabetical order.

These long-awaited changes to the Data Viewer and Variable Explorer have been implemented by our very own Microsoft Jupyter Extension Software Engineering Intern, Vandy Liu! Thank you Vandy for your hard work in shipping these great enhancements!

New Look for Native Notebooks!

To try out Native Notebooks today, download VS Code Insiders and the Jupyter extension. The Python extension is strongly recommended for working with Python notebooks.

The Notebook toolbar is now in the top left with all your favorite notebook-related actions and features.
The kernel picker has migrated back to the top right for easy switching between environments.
The cell indicator has gone bold! You can find this gutter indicator more helpful to identify where you are in the notebook, especially when working with long cells and outputs.

Customizable Native Notebooks

While the highlights above show what will come out-of-the-box for notebooks, you can always go ahead and customize the notebook to your liking! We have added a number of settings to truly make this your perfect notebook. To explore notebook layout settings, click on the “More actions” icon at the end of the toolbar and select “Customize Notebook Layout”.

This will bring you to the settings page with all notebook-related layout settings where you can create the ideal notebook layout that you prefer.

The full list of notebook layout settings are:

notebook.insertToolbarLocation: Controls whether the buttons to insert new cells (+Code, +Markdown) appear between cells, in the toolbar, both, or are hidden.
notebook.consolidatedRunButton: There are two new actions, Execute Above Cells and Execute Cell and Below. They will appear in the cell toolbar by default, but enabling this setting moves them to a new context menu next to the execute button.
notebook.cellFocusIndicator: Adds the option for cells to indicate their focused state with a colored bar on the side of the cell, similar to Jupyter.
notebook.cellToolbarVisibility: Determines whether the cell toolbar should appear when the cell is focused, or hovered. The default is to only show up when a cell is focused.
notebook.compactView: When enabled, cells are rendered in a more compact style with less empty space. It is enabled by default.
notebook.consolidatedOutputButton: The Clear Cell Outputs action and the button to pick a different output renderer or mimetype have been combined into a single  menu next to cell outputs. The new menu can be disabled with this setting.
notebook.dragAndDropEnabled: Disables drag and drop for cells. You can still rearrange cells using the commands Alt+Up/Alt+Down by default
notebook.globalToolbar: Adds a toolbar to the top of the notebook editor.
notebook.showCellStatusBar: This setting has a new option, visibleAfterExecute, which will hide the cell Status bar to save space until a cell is executed. Once it’s executed, it will become visible so the user can review the execution details.
notebook.showFoldingControls: Controls whether the folding chevron that appears on Markdown headers is always visible, or only visible on mouseover.
notebook.editorOptionsCustomizations: Lets the user customize the cell editor settings in the notebook.

Other Changes and Enhancements

We have also added small enhancements and fixed issues requested by users that should improve your experience working with Notebooks in Visual Studio Code. Some notable changes include:

Limit languages displayed in the Cell language picker to languages supported by the kernel. (#5580)
Add ABCMeta and type to variable explorer exclude list. (#5865)
Tweak variable view fit and finish to match VS Code. (#5955)
Hide kernels belonging to deleted Python environments from kernel picker. (#6164)

Be sure to download the Python extension and the Jupyter extension for Visual Studio Code now to try out the above improvements. If you run into any problems or have suggestions, please file an issue on the Jupyter VS Code GitHub page.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The post Jupyter in Visual Studio Code – June 2021 Release appeared first on Python.

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Python Error: No module named AnyQt.QtWinExtras Fixed

This Python Error: No module named AnyQt.QtWinExtras is a common type of Error in Python, NameError, ModuleNotFoundError, are also similar as there are a lot of Modules, and to use that module you need to install that module or library with Proper Spelling as new modules are uploaded every minute.

And to install that Run the Following Command line in your Terminal.

For Python 2 : To Solve this Error Run

pip install AnyQt

For Python 3+ : To Solve this Error Run pip3

pip3 install AnyQt

Everyone wants to learn Python and while learning errors are a common thing to encounter. There is a lot of error occur while running any specific module.

So we are here to help you with “importerror: no module named  AnyQt“. Terminal Also Says ModuleNotFoundError: No module named AnyQt ” Both are Same.

Python Error: No module named AnyQt.QtWinExtras

Know detail of why this error keeps occurring and Solve it.

https://usingpython.shodkk.com/python-error-importerror-no-module-named-why-how-command-line-solved2021/

Why Are You Getting this Error?

This error probably encounter because you have not install AnyQt

Or because you have not update AnyQt package owner update the module and added new function in the module that you are trying to access.

So the Next Obvious Question is How to Install this Module, Right?

How to Install?

You can install it in command line via pip: AnyQt

Whatever you are using Open Terminal

For Mac User:

Press the “Command” button and the space bar, simultaneously (this will open a search bar on your screen). Open Spotlight. …
Type “Terminal” (as you type, it should auto-fill). Search for Terminal and open it. …
Double click “Terminal” in the left sidebar to open your Mac’s Terminal.

For Linux: To Open Terminal Press Ctrl+t or CTRL+ALT+T

For Windows Computer: 

Open Command Prompt in Windows

Click Start and search for “Command Prompt.” Alternatively, you can also access the command prompt by pressing Ctrl + r on your keyboard, type “cmd” and then click OK.

pip install AnyQt

pip install AnyQt

If you don’t have Pip install in your Windows or Linux Or Mac you need to install it first using Terminal. Follow the Code to install Pip.

sudo apt install python3-pip

Upgrade Python  pip

python -m pip install –upgrade pip

If you want to install a Specific Version, you are free to do that too.

Also you can check Python version using command

python –version  or python -V

Install Specific Version:

pip install AnyQt
pip install “AnyQt==0.0.10”

Upgrade  AnyQt

sudo pip3 install –upgrade AnyQt

Getting Dependency Error in Window 10

Use code: easy_install instead of pip install

easy_install AnyQt 

Upgrade using easy install

sudo easy_install –upgrade AnyQt

On OSX System to install Module:

Use code: brew install instead of pip install

brew install AnyQt 

Without Using Pip :

sudo apt-get install -y AnyQt 

On CentOS7 or Linux Fedora:

yum -y install AnyQt 

Or on Fedora try

sudo dnf install AnyQt 

Command if Homebrew screws up your path on macOS:

python -m pip install AnyQt 

For Python3 MacOs Homebrew screws

python3 -m pip install AnyQt 

Verify module from list MacOs

pip freeze | grep AnyQt

For Execute on Anaconda as your python package manager

conda install -c anaconda AnyQt 

In Pycharm IDE to install a package:

Go to setting from File in menu.
Next Go to Python interpreter.
Click on PIP.

Search for AnyQt as above package and install it.

To remove  AnyQt  module:

sudo apt-get remove AnyQt

Python has vast application in every field from machine learning, web development, game making, medical science, to finance. And as Python is use by millions of programmer new modules are coded, everyday and to know detail and documentation of module one should visit https://devdocs.io/   or read below

For more Detail Visit Pypi.org where the Module is Uploaded by Owner

https://pypi.org/project/AnyQt/

Email of the Module Owner: mailto:[email protected]

Maintainers: ansible, badger, deric.crago, dmsimard, gmainwaring, jimi, mattclay, nitzmahone, relrod, ansible, badger, deric.crago, dmsimard, gmainwaring, jimi, mattclay, nitzmahone, relrod

Maintainers Bio : https://pypi.org/user/ansible/

Module License: GNU General Public License v3 or later (GPLv3+) (GPLv3+)

Module GitHub / HomePage Link:        Documentation : https://devdocs.io/AnyQt

Release Date:  Dec 14, 2020

Follow Our Instagram Page: https://www.instagram.com/pypi_repo/

Reference:

https://en.wikipedia.org/wiki/Python_Package_Index
https://www.python.org/
https://en.wikipedia.org/wiki/Python_(programming_language)

 

The post Python Error: No module named AnyQt.QtWinExtras Fixed appeared first on Using Python Power.

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Python Error: No module named AnyQt.QtPrintSupport Fixed

This Python Error: No module named AnyQt.QtPrintSupport is a common type of Error in Python, NameError, ModuleNotFoundError, are also similar as there are a lot of Modules, and to use that module you need to install that module or library with Proper Spelling as new modules are uploaded every minute.

And to install that Run the Following Command line in your Terminal.

For Python 2 : To Solve this Error Run

pip install AnyQt

For Python 3+ : To Solve this Error Run pip3

pip3 install AnyQt

Everyone wants to learn Python and while learning errors are a common thing to encounter. There is a lot of error occur while running any specific module.

So we are here to help you with “importerror: no module named  AnyQt“. Terminal Also Says ModuleNotFoundError: No module named AnyQt ” Both are Same.

Python Error: No module named AnyQt.QtPrintSupport

Know detail of why this error keeps occurring and Solve it.

https://usingpython.shodkk.com/python-error-importerror-no-module-named-why-how-command-line-solved2021/

Why Are You Getting this Error?

This error probably encounter because you have not install AnyQt

Or because you have not update AnyQt package owner update the module and added new function in the module that you are trying to access.

So the Next Obvious Question is How to Install this Module, Right?

How to Install?

You can install it in command line via pip: AnyQt

Whatever you are using Open Terminal

For Mac User:

Press the “Command” button and the space bar, simultaneously (this will open a search bar on your screen). Open Spotlight. …
Type “Terminal” (as you type, it should auto-fill). Search for Terminal and open it. …
Double click “Terminal” in the left sidebar to open your Mac’s Terminal.

For Linux: To Open Terminal Press Ctrl+t or CTRL+ALT+T

For Windows Computer: 

Open Command Prompt in Windows

Click Start and search for “Command Prompt.” Alternatively, you can also access the command prompt by pressing Ctrl + r on your keyboard, type “cmd” and then click OK.

pip install AnyQt

pip install AnyQt

If you don’t have Pip install in your Windows or Linux Or Mac you need to install it first using Terminal. Follow the Code to install Pip.

sudo apt install python3-pip

Upgrade Python  pip

python -m pip install –upgrade pip

If you want to install a Specific Version, you are free to do that too.

Also you can check Python version using command

python –version  or python -V

Install Specific Version:

pip install AnyQt
pip install “AnyQt==0.0.10”

Upgrade  AnyQt

sudo pip3 install –upgrade AnyQt

Getting Dependency Error in Window 10

Use code: easy_install instead of pip install

easy_install AnyQt 

Upgrade using easy install

sudo easy_install –upgrade AnyQt

On OSX System to install Module:

Use code: brew install instead of pip install

brew install AnyQt 

Without Using Pip :

sudo apt-get install -y AnyQt 

On CentOS7 or Linux Fedora:

yum -y install AnyQt 

Or on Fedora try

sudo dnf install AnyQt 

Command if Homebrew screws up your path on macOS:

python -m pip install AnyQt 

For Python3 MacOs Homebrew screws

python3 -m pip install AnyQt 

Verify module from list MacOs

pip freeze | grep AnyQt

For Execute on Anaconda as your python package manager

conda install -c anaconda AnyQt 

In Pycharm IDE to install a package:

Go to setting from File in menu.
Next Go to Python interpreter.
Click on PIP.

Search for AnyQt as above package and install it.

To remove  AnyQt  module:

sudo apt-get remove AnyQt

Python has vast application in every field from machine learning, web development, game making, medical science, to finance. And as Python is use by millions of programmer new modules are coded, everyday and to know detail and documentation of module one should visit https://devdocs.io/   or read below

For more Detail Visit Pypi.org where the Module is Uploaded by Owner

https://pypi.org/project/AnyQt/

Email of the Module Owner: mailto:[email protected]

Maintainers: ansible, badger, deric.crago, dmsimard, gmainwaring, jimi, mattclay, nitzmahone, relrod, ansible, badger, deric.crago, dmsimard, gmainwaring, jimi, mattclay, nitzmahone, relrod

Maintainers Bio : https://pypi.org/user/ansible/

Module License: GNU General Public License v3 or later (GPLv3+) (GPLv3+)

Module GitHub / HomePage Link:        Documentation : https://devdocs.io/AnyQt

Release Date:  Dec 14, 2020

Follow Our Instagram Page: https://www.instagram.com/pypi_repo/

Reference:

https://en.wikipedia.org/wiki/Python_Package_Index
https://www.python.org/
https://en.wikipedia.org/wiki/Python_(programming_language)

 

The post Python Error: No module named AnyQt.QtPrintSupport Fixed appeared first on Using Python Power.

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which is best online compiler for Unix

We know that most of the student looking for compile there code in online mode so in this article we will see which is best online compiler/ terminal/ editor for unix. The Unix command for compiling C code is gcc. This is a compiler from Gnu for Linux.

List of best online compiler/ terminal for Unix

Tutorialspoint
Onlinegdb
Cocalc

How to use online compiler for Unix

which is best online compiler for Unix

Step 1: if you want to Run your code in this compiler then click on Fork this Button

Step 2: After the press this button Editor window is open for you

Step 3: if you want to share this code then simply click on embedded button and share this code to your friends

Summary

In this article we saw How to compile online code in Unix so about this section you have any query then free to ask me.

Also read:

Online compiler for Matlab
Online compiler for machine learning

The post which is best online compiler for Unix appeared first on pythonslearning.

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PyCharm 2021.2 EAP 2: Python Packages Installation From Custom Locations

The second EAP of PyCharm 2021.2 brings a lot of improvements, both platform-wide and Python-specific. Take a look at what we have new in store for Python 3.10 support, easy package installation, work with JavaScript and TypeScript, and the debugger.

Starting with this EAP, you can join the program in PyCharm Professional only if you have an active JetBrains account. If you have already logged in with your credentials, you can access the EAP builds automatically. If you have not yet logged in, the IDE will redirect you to account.jetbrains.com where you can enter your login and password or create a new JetBrains account.

Read the blog post for more information.

As usual, our main goal for the EAP is to give you a sense of what to expect in the upcoming PyCharm 2021.2 release. We are hoping to get your active feedback on the overall performance of the PyCharm 2021.2 EAP versions, and in particular, on the feel, look, and performance of the new features we will be highlighting throughout the EAP blog posts.

You can do this on Twitter (mentioning @pycharm), or on our issue tracker.

Important! PyCharm EAP builds are not fully tested and might be unstable.

The Toolbox App is the easiest way to get the EAP builds and keep both your stable and EAP versions up to date. You can also manually download the EAP builds from our website.

DOWNLOAD PYCHARM 2021.2 EAP

Python 3.10

New syntax for union types

In this release, we added support for PEP 604 “Allow writing union types as X | Y”. This PEP was aimed to tackle the syntax verbosity of the current union type. It added a special operator type.__or__() that allowed writing int | str instead of Union[int, str].

This functionality is already available in earlier versions of Python with from __future__ import annotations. Mypy recently added support for this style of annotation in their v0.800 release, so it is a good time to try it out with the newly added PyCharm code assistance.

So what exactly is now supported in PyCharm?

Overall code insight for new syntax, including intention actions. The new syntax is supported in PyCharm Quick Documentation. See quick information by placing the caret at the symbol, and press F1 (View | Quick Documentation). Type inference for union types for isinstance and issubclass arguments, using the new syntax. The from __future__ import annotation is recognized so that you can use the new syntax even with earlier versions of Python. Please note that this import works only for annotations, not on runtime inside isinstance/issubclass. Inspection and a quick fix to switch to the old-style syntax in earlier Python versions.

Structural Pattern Matching

In the previous EAP, we announced initial support for structural pattern matching (PEP 634, PEP 635, PEP 636). We are still working on making it fully-fledged. Now, inspections like Unused local symbols and Unreachable code work for pattern matching cases.

For all PEPs and Python versions, visit the dedicated page.

Python Packages Tool Window: install packages from custom locations

In PyCharm 2021.1 we implemented the Python Packages tool window to provide you with the quickest and neatest way to preview and install packages for the currently selected Python interpreter. This window is enabled by default, and you can find it in the lower group of the tool windows. At any time you can open it using the main menu: View | Tool Windows | Python Packages.

You can preview installed packages, update them, install new ones or uninstall them via the Python Packages tool window. Note that all this will be performed for the currently selected Python interpreter. To manage the packages across various Python interpreters go to the Python interpreter settings (Preferences | Settings / Python Interpreter).

With this EAP you are no longer limited to the PyPI repository. You can install packages from VCS or a local machine. Click the Add Package link on the Python Packages toolbar and select From Version Control / From Disc.

For the PyPI repository, we implemented a new sorting order. Now when browsing the PyPI repository, the most downloaded packages are shown first in the list of results. We hope that this will help you to find the packages you need faster.

Debugger

In this EAP release, we worked through a list of issues related to the debugger. Most of them are bug fixes (see the list at the bottom of the page), but some of them in particular are worth giving a special mention.

Preview Tab now works in Debugger

The preview tab that used to work only for files in the Project view now also works for files that open during debugging.

It helps not to pollute the editor with multiple files that open in separate tabs when you stop at a breakpoint, step through the code, navigate between frames, or use the prev/next frame actions.

If you enable the preview, these files will all appear successively in one tab. You can turn this feature on in Preferences / Settings | Editor | General | Editor Tabs.

“Step into my code” dedicated shortcut

We added a dedicated shortcut for the Step into my code action to the standard keymap. It can be found (and customized) in Keymap settings: Preferences / Settings | Keymap | Main Menu | Run | Debugging Actions.

Python Console: changed behaviour

To enable you to do REPL work in the console while scripts are running in the background, we changed the behavior of the Python Console:

If the Python Console tool window is visible, it will be used for executing line / selection instead of the Debug console. If the Python Console tool window is hidden or not initialized yet, a Debug console will be used.

Terminal

New Terminal options

We’ve improved our in-built terminal with two new options. First, you can now select the cursor shape. Choose whichever you like best!

Second, we now support Use Option as Meta key, similar to the same-name option in the native Terminal on macOS. This allows the Option (⌥) key on the keyboard to act as a meta modifier that can be used in combination with other keys instead of just as an Escape key. For example, you can now use the following shortcuts:

⌥+F – go to the next word ⌥+B – go back a word ⌥+D – delete the next word

You can tick the checkboxes for these options in Preferences/ Settings | Tools | Terminal.

Testing

Pytest: managing the test output

It was a regular occurance to get duplicated error messages in the console when running pytest in PyCharm. In PyCharm 2021.1, we introduced special flags –no-header –no-summary -q to suppress the extra error messages. In this EAP, we added the option to disable extra flags to enable some specific use cases.

This new option will be useful for those who use special hooks in pytest (for instance, pytest_report_header or pytest_terminal_summary) that were suppressed by those new flags.

If you are using one of the popular testing libraries such as Hypothesis and cannot see their feedback on your failing tests and test statistics, when running your test in PyCharm, try disabling –no-header –no-summary -q by going to Preferences | Settings / Advanced Settings / Python and tick the checkbox “Pytest: don’t add “–no-header –no-summary -q”.

Test Sources Root folder

You can now mark a folder as a Test Sources Root. PyCharm will use it as a working directory to run tests under the test root.

To mark a directory as a test root, open the context menu in the project tree by right-clicking the mouse, find the option Mark Directory as and choose Test Sources Root.

Once it’s configured, the Test Sources Root is marked with the light-green folder icon in your project structure in the Project tool window.

You can learn more about configuration of the project structure in PyCharm in the PyCharm Help.

Frontend development

Support for TypeScript types in JSDoc

PyCharm now properly supports the TypeScript syntax used within JSDoc comments in your .js files. Support for some syntactic constructs, such as union types, has already been available for a while. With this release, we’ve reworked and expanded the existing support, making PyCharm recognize more syntactic constructs. We’ve also fixed a lot of known issues. For example, optional properties in @typedef declarations are now supported.

You can learn about other improvements that are underway by looking at the list of related issues here.

Auto-imports for CommonJS modules

The next improvement in this release is for Node.js users. As you may know, PyCharm adds missing import statements as you complete ES6 symbols. It will now do the same for CommonJS modules – require imports will be inserted on code completion.

Whenever the IDE isn’t sure what syntax should be used in a file, it will show you a popup allowing you to choose between using the ES6 and CommonJS syntax.

Notable bug fixes:

Testing: Doctest: Arguments with keywords are now passed to DocTestRunner. [PY-47625] Debugger: PyCharm recognizes and hits the breakpoints correctly when attached to the Autodesk Maya process. [PY-44778] Debugger: Attach to the process functionality is available while PyCharm is indexing the project. [PY-48865] Debugger: When attaching the debugger to a process in the Python Console, both command execution and its results are displayed in the Debug Console interactive prompt. [PY-40000] Debugger: Debugger in Jupyter Notebook is not disconnected after session interruption in external function. [PY-47721] Scientific View: For pandas DataFrame column width is now resized automatically adjusting to the length of the column name. [PY-45634] Python Console: pasting text to the current cursor position is available again. [PY-38421] Testing: Rerunning failed Django tests containing mixins runs original test cases, not mixin classes/methods. [PY-30788]

Those are the most significant changes we have for week four. You can check out all the fixed and improved issues included in this build in the release notes. Stay tuned for more updates next week! For now, you can test the exciting new features and share your feedback in the comments to this post or report bugs, if any, to our issue tracker.

Ready to join the EAP?

Some ground rules

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which is best bash online tester/ editor/ compiler

We know that most of the student looking for compile there code in online mode so in this article we will see which is best bash online tester. so let’s see:

List of the best bash online tester :

Rextester
Jdoodle
Tutorialspoint
Onlinegb
Replit

How to use Bash online compiler

Step 1st: Here we use onlinegb compiler

Step 2nd : so you need to write your program logic in editor windows

Step3rd: After click on Run Button remember that Bug option not run in Bash compiler

Step 4th: Finally your code know start to debug- run

Brash online compiler

Summary:

In this article we saw which is best bash online tester/ editor/ compiler so about this article you have any question then free to ask me

Also read :

Online HTML editor
Online Kotlin compiler
Online Angularjs compiler

The post which is best bash online tester/ editor/ compiler appeared first on pythonslearning.

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Python NumPy to list with examples

In this Python NumPy tutorial, we will discuss Python NumPy to list and also cover the below examples:

Python numpy list to array
Python numpy list to matrix
Python numpy list to ndarray
Python numpy list to string
Python numpy list to CSV
Python numpy array to list of strings
Python numpy array to list of tuples
Python numpy tuple to list
Python numpy array to list of lists

Python numpy to list

In this section, we will learn about python numpy to list.
Python stores data in several different ways but the most popular methods are lists and dictionaries.
Arrays also a data structure that is used to store the data.
These arrays are similar to the list.
Both lists and arrays are used for storing data.
The LIst is mutable as well as array is mutable. So we can add and remove an element from the list and array.
Both can be indexed and iterated.
Both can be slicing operations.
List are containers for elements having different datatype but arrays as containers for elements of the same datatype.
We can store int, float, string in a single list but in arrays, we need to store the same type of data.

Syntax:

Here is the Syntax of numpy array to list

numpy.ndarray.tolist()

It returns a copy of the array data as a python list and the possible nested list of array elements.

Example:

import numpy as np

arr1= np.array([2, 4, 5])
list1 = arr1.tolist()
print(list1)

Here is the Screenshot of following given code

Python numpy to list

The above code we can use to convert NumPy to list in Python.

Read: Python concatenate list with examples

Python numpy list to array

In this section, we will learn about python numpy list to an array.
In python, the list can be converted to arrays by using two methods from the numpy library.
In this method, we can easily use the function using numpy. array().
First, we import a numpy library and then initialize a list, and converting the list to an array.

Example:

import numpy

list = [2, 4, 8, 9, 2, 5, 6]
arr1 = numpy.array(list)
print(arr1)

Here is the Screenshot of following given code

Python numpy list to array

Python numpy list to matrix

In this section, we will learn about python’s numpy list to a matrix.
Use the np. array(list) function to convert a list into a matrix form.
The np. array function takes an iterable and returns a numpy array.

Example:

import numpy

list = [2, 4, 8],[9, 2, 5]
arr1 = numpy.array(list)
print(arr1)

Here is the Screenshot of following given code

Python numpy list to matrix

This is how to convert numpy list to matrix in Python.

Read: Python NumPy matrix and How to make a matrix in Python

Python numpy list to ndarray

In this section, we will learn about python numpy list to ndarray.
ndarray is a (usually fixed-size) multidimensional container of items of the same type and size.
First, we have to import a package and initialize the nested 4-dimensional list, and then use numpy.array() function to convert the list to the array and store it in a different object.
It returns in the matrix form and displays the result in a numpy array.

Example:

import numpy

list = [2, 4, 8,4],[9, 2, 5,4],[1,3,4,5],[3,4,5,6]
arr1 = numpy.array(list)
print(arr1)

Here is the Screenshot of following given code

Python numpy list to ndarray

This is an example of how to covert numpy list to ndarray in Python.

Python numpy list to string

In this section, we will learn about python numpy list to string.
In this method, we are using the join() function.
First, we have to create a function list to string and initialize an empty string.
And it will return the string.
The list will contain both string and integer as its element.

Example:

import numpy as np
def lToS(list1):

str2 = ” ”

return (str2.join(list1))
list1 = [‘John’, ‘GEORGE’, ‘MICHEAL’]
print(lToS(list1))

Here is the Screenshot of following given code

Python numpy list to string

Another method from numpy list to string by using map() method.
Use map() method for converting elements in the list to string.

Example:

import numpy as np

str1 = [‘Mango’,’Onion’,’Apple’]
listToStr = ‘ ‘.join(map(str, str1))
print(listToStr)

Here is the Screenshot of following given code

Python numpy list to string map method

This is how to convert numpy list to string in Python.

Read: Python remove substring from a String

Python numpy list to CSV

In this section, we will learn about the numpy list to CSV.
The CSV file is opened as a text file with Python’s built-in open() function, which returns a file object.
In this method, we use the dataframe function to store the list in a dataframe and we simply export it to a CSV file using to_csv.

Example:

import numpy as np
import pandas as pd

list = [2,3,4,5,6,7,8]
print(list)
DF = pd.DataFrame(list)
DF.to_csv(‘/home/arvind/Documents/test.csv’)

Here is the Screenshot of following given code

Python numpy list to csv
numpy list to csv

Read: Python NumPy read CSV

Python numpy array to list of strings

In this section, we will learn about the numpy array to list strings.
In this method, we can easily use the function to list().it converts the array into a list.
In the list, we can only use the string characters.
It returns a copy of the array data as a Python list.

Example:

import numpy as np

a = np.array([[‘john’,’George’, ‘Micheal’], [‘orange’, ‘apple’, ‘grapes’]])
list1 = a.tolist()
print(list1)

Here is the Screenshot of following given code

Python numpy array to list of strings

This is how to convert numpy array to list of strings in Python.

Read: Python sort NumPy array + Examples

Python numpy array to list of tuples

In this section, we will learn about the numpy array to list tuples.
In this method, we can easily use map and tuple functions.
Map function returns a map object of the results after applying the given function to each item of a given iterable.
A tuple is an object in Python that has items separated by commas and enclosed in round brackets.

Example:

import numpy as np

arr = np.array([[‘mango’, ‘apple’], [‘grapes’, ‘orange’]])
res = tuple(map(tuple, arr))
print(res)

Here is the Screenshot of following given code

Python numpy array to list of tuples

Read: Python concatenate tuples with examples

Python numpy tuple to list

In this section, we will learn about the numpy tuple to list
Tuple cannot be changed but in list we can add new values and elements.
Tuple uses parenthesis whereas list uses square brackets.
Python list method list() takes sequence types and converts them to lists. This is used to convert a given tuple into list.
The output will be in the form of list.

Example:

import numpy as np
Tup = (456, ‘tuv’, ‘mno’, ‘klm’)
List1 = list(Tup)
print(List1)

In the above example first we create a tuple and then use the function list() and converts them into the list.

Here is the Screenshot of following given code

Python numpy tuple to list

This is how to convert numpy array to list of tuples in Python.

Read: Create a tuple in Python

Python numpy array to list of lists

In this section, we will learn about the numpy array to list of lists.
In Python’s numpy module, the ndarray class provides a member function tolist(), which returns a list containing the copy of elements in the numpy array. If the numpy array is 2D, then it returns a list of lists.
List are containers for elements having different datatype but arrays as containers for elements of the same datatype.
The output will be in the form of lists into the list.

Example:

import numpy as np

arr1 = np.array([[4, 5, 6, 7],
[5, 4, 4, 9],
[3, 4, 5, 6]])

listoflists = arr1.tolist()
print(listoflists)

Here is the Screenshot of following given code

Python numpy array to list of lists

You may like the following Python tutorials:

Python NumPy log
Python NumPy where with examples
Python NumPy linspace
Python NumPy concatenate
Python sort NumPy array
Python NumPy square with examples

In this Python numpy tutorial, we will discuss Python NumPy to list and also cover the below examples:

Python numpy list to array
Python numpy list to matrix
Python numpy list to ndarray
Python numpy list to string
Python numpy list to CSV
Python numpy array to list of strings
Python numpy array to list of tuples
Python numpy tuple to list
Python numpy array to list of lists

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7 Efficient Ways to Replace Item in List in Python

The post 7 Efficient Ways to Replace Item in List in Python appeared first on Python Pool.

Lists in python are data structures used to store items of multiple types together under the same list. Using a list, we can store several …

Read more

The post 7 Efficient Ways to Replace Item in List in Python appeared first on Python Pool.

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