- The cleaning and transformation of data
- Numerical simulation
- Statistical modelling
- Automatic learning
- And much more
Who uses it?
And so , Jupyter Notebooks are used in academic environments (UC Berkeley, Stanford, UW, NYU, Cal Poly etc.), public sector investigations (NASA, JPL, KBase), and also in the private sector (IBM, Facebook, Microsoft, Bloomberg, JP Morgan, WhatsApp, Quantopain, GraphLab, Enthought, etc.) In terms of an architect of open modules, they’re are widely used to create all types of solutions and services, equally in commerce and non-profit.
How do we access it?
|Figure 2: Project web page. We can try it from the browser or install it locally.|
Accessing the environment.
|Figure 3: Access from the Windows menu|
|Figure 4: Jupyter Interface|
We create a notebook and name it.
|Figure 5: From the menu”New” we create a new Python notebook.|
|Figure 6: It´s created as ´Untitled´|
|Figure 7: We re-name the notebook.|
|Figure 8: The new notebook appears at the end of the list|
|Figure 9: Command mode cell (blue).|
|Figure 10: We try the print command.|
|Figure 11: Result of carrying out the cell|
Creating a “Checkpoint”
To create a checkpoint, you only need to select the option ´Save and Checkpoint´ from the ´File´ menu. To return to a previous checkpoint, you just have to select what you want from the menu ´File/revert to checkpoint´.
|Figure 12: How to get back to the previous checkpoint|
Exporting a Notebook
In the next post we will talk about the libraries and we will finish preparing the environment for our experiment of Machine Learning. Until them, we recommend you explore the help menus in Jupyter Notebooks and test out some of the simple commands to start getting to know the environment a little. In this video by CodingtheSmartWay you will find some examples to practice with.
All the posts in this tutorial here:
- Dare with Python: An experiment for all (intro)
- Python for all (1): Installation of the Anaconda environment.
- Python for all (2): What are the Jupiter Notebook ?. We created our first notebook and practiced some easy commands.
- Python for all (3): What are bookstores ?. We prepare the environment.
- Python for all (4): We started the experiment properly. Data loading, exploratory analysis (dimensions of the dataset, statistics, visualization, etc.)
- Python for all (5) Final: Creation of the models and estimation of their accuracy