Is there a way to work in Pycharm in local system (mac) which connect and execute the code via jupyter installed on Hadoop edge nodes ?

We have a requirement from data scientist who would like to read data from HDFS using spark and plot some graphs or run some models in pycharm (mac) snippet by snippet.

Really interested to know if this model is possible or how generally data scientist work in big data eco system from local machine to read data from HDFS to run some models ?

1. First, you should create a new project.
2. In that project, create a new ipynb file by going to File > New… > Jupyter Notebook. This should open up a new notebook file.
3. If you don’t have the Jupyter Notebook package installed, an error will appear above the newly opened ipynb file. The error reads “Jupyter package is not installed” and you will have the option to “Install jupyter package” next to it.
   https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-3-300x169.png 300w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-3-768x432.png 768w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-3-810x456.png 810w" alt="" width="1024" height="576" data-lazy-srcset="https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-3.png 1024w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-3-300x169.png 300w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-3-768x432.png 768w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-3-810x456.png 810w" data-lazy-sizes="(max-width: 1024px) 100vw, 1024px" data-lazy-src="https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-3.png" data-ll-status="loaded">
4. Click on “Install jupyter package”. This will start the installation process, which you can view by clicking on the running processes in the bottom right corner of the PyCharm window.
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5. To start exploring Jupyter Notebook in PyCharm, create code cells and execute them.
   https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-5-300x236.png 300w" alt="" width="433" height="341" data-lazy-srcset="https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-5.png 433w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-5-300x236.png 300w" data-lazy-sizes="(max-width: 433px) 100vw, 433px" data-lazy-src="https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-5.png" data-ll-status="loaded">
6. Execute the code cell to launch the Jupyter server. By default, the Jupyter server uses the 8888 port by default on the localhost. These configurations are available in the server’s tool window. Once launched, you can view the server above your source code window, and next to it, you can view the kernel created as “Python 2” or “Python 3”.
7. You can now access the variables tab in PyCharm to view how the values of your variables change as you execute code cells. This helps in debugging. You can also set breakpoints at lines of code and then click on the Run icon and select “Debug Cell” (or use the shortcut Alt + Shift + Enter) to begin debugging.
8. The following tabs at the bottom of the PyCharm window are essential to using Jupyter Notebook:
   https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-6-300x24.png 300w" alt="" width="355" height="28" data-lazy-srcset="https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-6.png 355w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-6-300x24.png 300w" data-lazy-sizes="(max-width: 355px) 100vw, 355px" data-lazy-src="https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-6.png" data-ll-status="loaded">

Getting along with the User Interface

Out of the many user interface components, let us begin exploring the ones you can work with without facing any difficulties.

Viewing Modes

PyCharm offers three viewing modes to edit your Jupyter notebook files:

1. Editor Only Mode
   This allows adding and editing notebook cells.
   https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-7-300x163.png 300w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-7-768x418.png 768w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-7-810x441.png 810w" alt="" width="1024" height="557" data-lazy-srcset="https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-7.png 1024w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-7-300x163.png 300w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-7-768x418.png 768w, https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-7-810x441.png 810w" data-lazy-sizes="(max-width: 1024px) 100vw, 1024px" data-lazy-src="https://linuxhint.com/wp-content/uploads/2019/09/How-to-work-with-Jupyter-Notebooks-in-PyCharm-7.png" data-ll-status="loaded">
2. Split View Mode
   The split view mode lets you add cells and preview their output. This is also the default-viewing mode for all Jupyter notebooks in PyCharm.
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3. Preview Only Mode
   Here you can preview your code execution results, raw cells, and code markdown.
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PyCharm allows you to make changes to your source document in a number of ways:

• Editing and making previews
• Use notebook as source code with definitions in form of texts
• Live previews along with debugging
• Options for Auto-saving your code
• Highlighting of all types of Error and syntax mistakes
• Ability to add line comments
• Ability to execute and preview results simultaneously
• Allows using the dedicated Jupyter Notebook Debugger