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Import data into Jupyter Notebook

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From a Jupyter Notebook, you retrieve the data that was generated in Quix in the previous part, and which was persisted into the Quix data store.

Run Jupyter Notebook

Make sure you have reviewed the prerequisites, and have Jupyter Notebook already installed.

1. Now run Jupyter Notebook by entering the following command into your terminal:

jupyter notebook

1. Navigate to http://localhost:8888 with your web browser.

2. Select New and then Python 3 (ipykernel) from the menu, as shown here:

   

Tip

If you don’t see Python 3 in the New menu, run the following commands in your Python environment:

pip install ipykernel
python -m ipykernel install --user

Obtain the training data

In the previous part you generated some real-time data in Quix.

Quix has a code generator that can generate code to connect your Jupyter Notebook to Quix. To use the code generator to retrieve data from Quix and import it into your Notebook:

1. Make sure you are logged into Quix.

2. Select your Workspace (you probably only have one currently).

3. Click Data Explorer in the left-hand sidebar.

4. Click Add Query to add a query to visualize some data.

5. Select the F1 Game stream in the Add Query wizard, and click Next.

6. In the Select parameters and events step of the wizard, select the Brake, WorldPositionX, Steer, Speed, and Gear parameters.

7. Click Done.

8. Turn off aggregation using the slider button, as illustrated in the following screenshot:

   

9. Use the time slider to select about ten minutes of data, as shown in the following screenshot:

   

   This is a precaution, as if your try to import too much data into Jupyter Notebook you may get a IOPub data rate exceeded. error. Alternatively, you can increase your capacity by setting the config variable --NotebookApp.iopub_data_rate_limit in Jupyter.

10. Select the Code tab.

11. Select Python from the the LANGUAGE dropdown.

    

12. Copy all the code in the Code tab to your clipboard.

13. Paste the Python code from your clipboard to your Jupyter Notebook:

    

14. Click Run.

    The code prints out the pandas data frame containing the retrieved data, as shown in the following screenshot:

    

Tip

If you want to use this generated code for more than 30 days, replace the temporary token with a Personal Access Token.

Train your ML model