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Функция подсказки и дополнения кода Jupyter Notebook
1. Функция завершения кода
Нажмите Tab для завершения
2. Функция подсказки кода
1. Откройте Anaconda Navigator, щелкните Среды -> база (корень) -> Открыть терминал: 
2. Введите следующие 4 команды во всплывающем окне команд:
Процесс установки выглядит следующим образом: 
После завершения установки откройте Jupyter Notebook и щелкните вкладку Nbextensions на панели вкладок страницы корневого каталога: 
Проверьте Hinterland:
Проверка: 
Реализована функция подсказки кода Jupyter Notebook! ! !
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How to Enable and Use Autocomplete in Jupyter Notebook
The code autocomplete feature by default included in most of the source code editors through which you can view IntelliSense menu or code suggestions inside the drop-down in a program file. The code autocomplete functionality saves you time for writing complex programs, receives fewer bugs, and you can quickly include methods, attributes, and classes in your code using drop-down suggestions.
In this article, we will show how to enable and use the autocomplete in the Jupyter notebook using different methods. Before starting the demo, the Jupyter notebook environment should be running on your system.
Autocomplete Feature in Jupyter Notebook
Most programmers who use the Jupyter environment for the first time wonder how to use code auto-completion in an IPython notebook. For those users, it is surprising and the good news is that you do not need to install any external tool for using the autocomplete feature in the Jupyter notebook. The autocomplete feature comes with the standard Jupyter notebook setup.
The auto-completion in the Jupyter notebook helps you to choose an appropriate object, modules, classes, and variables inside the notebook. In this way, you can complete your code efficiently and in an easier way.
Autocomplete in Jupyter Notebook Using Tab Control
To use the code auto completion, you have to type the module, object, or class name and then hit the ‘Tab’ button from your keyboard. After pressing the Tab key, the completed suggestions appear in the drop-down box that is shown in the following example:
For example, we import a pandas library for solving data science problems. When you create a new object, the autocomplete option also helps you in object creation, which is shown in the following screenshot:
You can also use the auto-completion in the Jupyter notebook when you already have an object and you want to assign it to a variable. Using the autocomplete, you can access the list of parameters and methods of the typed prefix object. For example, the DataFrame object contains various methods and parameters that you can easily access after the ‘.’ dot. Type the first letter of the method and press ‘Tab’ as follows:
The auto-completion used to import the names of the modules in your Jupyter notebook. The autocomplete features will help you because in most cases you do not remember the exact name of a particular library. However, you know this library provides cool algorithms. To import this module, just type the first one or two letters and then press the ‘Tab’ key from your keyboard. The related suggestions would display under the box as follows:
Choose the desired module, which you want to add to an IPython notebook.
You can also use the autocomplete in the Jupyter notebook while you want to reuse variables, classes, or attributes inside a program. For example, we have initialized two test variables in our program. When we type the prefix of a variable that also start starts with the same letter and press the ‘Tab’ key, it shows us the relevant variable name suggestions in the drop-down box as follows:
Only one variable will show in suggestions instead of many choices. In this case, it will type the full variable name for us.
Auto-completion is also helpful, when you press the ‘Shift+Tab’ keys, it shows the function help, type, and tooltip information in a box as follows:
The use of ‘?’ after the function name without using ‘()’ shows the object documentation and methods documentation in the IPython notebook that you can see in the following screenshot:
All auto-completion shortcuts ‘Tab, Shift+Tab, and ?’ by default are enabled in Jupyter notebook without using any extension or a plugin.
Add IntelliSense or Auto-completion in Jupyter Notebook Using the Hinterland Extension
To add the code IntelliSense or auto-completion in the Jupyter notebook, you can use a Jupyter ‘hinterland’ extension. Most users unable to find how to install this extension through the pip package manager directly. Therefore, you can enable this extension in your Jupyter notebook by using the following steps:
Step 1: First, install the jupyter contrib nbextensions by executing the following command in the terminal:
Step 2: Install the CSS and js files for the jupyter environment by using the following command:
Step 3: In the last step, you need to enable the ‘hinterland’ extension
The following output shows in the terminal:
Now, again restart the Jupyter notebook kernel online. Import the modules and create objects. You will notice that auto-completion or IntelliSense automatically shows in the drop-down without using the ‘Tab’ control in the Jupyter notebook as follows:
There are more extensions used for auto-completion but users prefer to use the hinterland and Tab option for auto-completion in the Jupyter notebook.
Conclusion
We have discussed in this tutorial how to enable and use auto-completion or IntelliSense functionality in the Jupyter notebook. We have seen how the ‘Tab, Shift+Tab, and ?’ help us in writing code in a Jupyter notebook. We have also demonstrated how to install and enable hinterland extension in Jupyter notebook for using the code autocomplete feature.
About the author
Samreena Aslam
Samreena Aslam holds a master’s degree in Software Engineering. Currently, she’s working as a Freelancer & Technical writer. She’s a Linux enthusiast and has written various articles on Computer programming, different Linux flavors including Ubuntu, Debian, CentOS, and Mint.
Autocomplete your code in Jupyter Notebook
Jupyter Notebook is often used as a Python development environment, but code completion is not enabled by default. That’s why it’s a bit of a problem when importing packages. This time, I introduce some tips for code completion in Jupyter Notebook.
Autocomplete the code
Code completion in the IDE is an important feature for smooth implementation of the program. Some people call it autocomplete , and others call it intelli sense, but the functions are the same.
I investigated how to enable autocompletion when coding in Jupyter Notebook, so I’ll leave it as a reminder.
Method 1: Use IPCompleter.greedy
Here’s the easiest way to do it first. Just open any Notebook file and run the following magic command.
Press the Tab key to display the input completion.
However, there is a one-tempo delay until the candidate is displayed after the typing key. In my environment, I had to wait about 1-2 seconds.
Method 2: Use autocompletion of Nbextensions
Next, I’ll show you how to use the library to introduce Jupyter Notebook’s extensions at once.
The following command assumes that Jupyter Notebook is already installed.
After that, restart Jupyter Notebook and you will see that a new tab labeled “Nbextentions” has been added.
Activate Hinterland for code completion .
You can also press the Tab key to display the input suggestions. I recommend Hinterland because the time to display candidates is faster than IPCompleter.greedy .
Conclusion
I showed you how to use code completion on Jupyter Notebook. I recommend you to use the autocompletion of Nbextensions in Method 2. It’s quick to complete, and it’s very helpful because it will show you candidates even when you’re typing.
How to Get the Best Autocomplete in Jupyter Notebooks and More
Writing code in a “plain text”-looking Jupyter notebook, without any coding assistance, can be overwhelming and can definitely slow data scientists down. In this blog post we’ll explain 2 ways to avoid this pitfall and access autocompletion and other coding assistance features for your Jupyter notebooks.
Enable autocomplete feature
To enable code autocomplete in Jupyter Notebook or JupyterLab, you just need to hit the Tab key while writing code. Jupyter will suggest a few completion options. Navigate to the one you want with the arrow keys, and hit Enter to choose the suggestion.
Press the Tab key to enable code autocomplete in Jupyter Notebook
Unfortunately, Jupyter doesn’t offer automatic invocation for code completion options, which means you will be pressing the Tab key all the time.
To avoid this, you can consider trying Datalore – a collaborative data science platform, which is available online for free.
Code completion in Datalore
Datalore will automatically invoke code completion options, and it will give you contextual help when specifying method parameters. It will also take care of small but important things like putting your caret inside the parentheses, making your code less error-prone.
A Notebook environment will already be preconfigured for you with the top data science packages preinstalled, so you can start writing code in Datalore right away.
Get proper code indentation for Python
In Python, indentation is important. Let’s take a look at how indenting works for a simple if clause.
In the example below, JupyterLab uses the wrong indentation, which leads to an error when executing the cell:
Importance of indentation in Python
Datalore corrects the indentation automatically, ensuring your code will be executed without errors.
Automatic Python indentation in Datalore
Get contextual help
In JupyterLab you can get contextual help by selecting a function and using the context menu. Datalore gives you contextual help when you hover over any function/method, and it also suggests method parameters on the fly while you’re typing code.
Contextual documentation in Jupyter vs Datalore
Other code editing features in Datalore
Quick-fixes
Datalore offers a wide variety of quick-fixes, such as import optimization, which helps your code look more professional and clean.
Import optimization in Datalore
Refactorings
Datalore lets you rename variables and functions using the context menu. It will rename only the variables or functions, leaving text inputs with the same name unchanged. In JupyterLab, classic Find and Replace acts differently: it will replace all entries just like in a text file.
Rename refactoring in Datalore
Highlighting errors before you execute the code
Nobody likes to see a red error message in a notebook. Datalore will highlight the errors before the execution of the cell, giving you a heads up about any potential problems.
Error-highlighting in Datalore
SQL, R, Scala, and Kotlin code autocomplete
In addition to Python, Datalore also offers code completion and syntax highlighting for SQL, R, Scala, and Kotlin.
Summary
| JupyterLab | Datalore | |
| Python code autocomplete | Invoked manually by pressing tab; auto-invocation available via Jupyter plugins and extensions | Invoked automatically out of the box |
| SQL, R, Scala, and Kotlin code autocomplete | ⛔️ | ✅ |
| Contextual help | In a separate tab | On hover |
| Quick-fixes (e.g. removing unused imports) | ⛔️ | ✅ |
| Refactorings (e.g. renaming variables) | ⛔️ | ✅ |
| Method specifications when typing code | ⛔️ | ✅ |
| Error highlighting | ⛔️ | ✅ |
| Code indentation | Sometimes inaccurate (see examples above) | ✅ |
We hope these tips will help you enjoy a better code editing experience and make you more confident when sharing your work.
How to try Datalore
If you’d like to use Datalore for yourself, register online for Datalore Community or Datalore Professional. You can also try it for your team by hosting Datalore in your private cloud or on-premises with the Enterprise plans.
That’s all for now! Keep an eye on our blog for useful tips and follow us on Twitter for the latest updates!