diff --git a/docs/.doctrees/Module3_IntroducingNumpy/IntroducingTheNDarray.doctree b/docs/.doctrees/Module3_IntroducingNumpy/IntroducingTheNDarray.doctree index 5b74d7f8..05a3bf20 100644 Binary files a/docs/.doctrees/Module3_IntroducingNumpy/IntroducingTheNDarray.doctree and b/docs/.doctrees/Module3_IntroducingNumpy/IntroducingTheNDarray.doctree differ diff --git a/docs/.doctrees/environment.pickle b/docs/.doctrees/environment.pickle index 6adf0b07..216f2032 100644 Binary files a/docs/.doctrees/environment.pickle and b/docs/.doctrees/environment.pickle differ diff --git a/docs/Module3_IntroducingNumpy/IntroducingTheNDarray.html b/docs/Module3_IntroducingNumpy/IntroducingTheNDarray.html index 8cdc5a03..2e3894f4 100644 --- a/docs/Module3_IntroducingNumpy/IntroducingTheNDarray.html +++ b/docs/Module3_IntroducingNumpy/IntroducingTheNDarray.html @@ -247,7 +247,7 @@
Let’s take the mean value over the three distinct columns in our data:
+Let’s take the mean value along the three distinct rows of our data:
>>> np.mean(x, axis=1)
array([ 1., 4., 7.])
Visual Studio Code with the Python extension: A lightweight, highly customizable IDE.
Pros
Cons
Takeaway:
Integrated Development Environments (IDEs) provide powerful tools for helping you write well-formatted and typo-free code. We recommend using PyCharm Community Edition or Visual Studio Code (with the Python extension installed) for your Python IDE.
Jupyter Lab:
-Jupyter Lab is a new IDE that is being developed by the same team that develops the Jupyter Notebook. It aims to mix the polish and power of a traditional IDE, along with the convenience and great utility of the notebook environment. As of writing this, Jupyter Lab is still in the beta release phase. Given the massive popularity of Jupyter Notebook, Jupyter Lab will likely become a widely used IDE, quickly.
-Jupyter lab is a new web interface from Project Jupyter that provides a rich web-based interface for managing and running Jupyter notebooks, console terminals, and text editors, all within your browser. Among its useful features and polished user interface - compared to that a Jupyter notebook server - Jupyter lab provides moveable panes for viewing data, images, and code output apart from the rest of the notebook. This is facilitates effective data +science work flows.
+It is recommended that you peruse the Jupyter lab documentation to get a feel for all of its added capabilities.
+The following instructions are laid out for running a Jupyter notebook server. That being said, the process for running a Jupyter lab server and working with notebooks therein is nearly identical. Both Jupyter notebook and Jupyter lab should already be installed via Anaconda.
+Enough gushing about Jupyter notebooks. Let’s start using them!
In your terminal, navigate to a directory (a.k.a folder) that you are okay creating files in. If you don’t know how to do this, Google it!
Once you are in the desired directory, execute in your terminal (type the following, and then hit <ENTER>
): jupyter notebook
Alternatively, if you want to work in Jupyter lab, run: jupyter lab
You should see some text appear in your terminal:
This is a “notebook server” that is running on your machine - it basically handles all of the communication between your browser and your machine. A new window or tab should open in your web browser, which looks like a file explorer.
@@ -514,7 +525,7 @@Notice that this notebook interface is great for making adjustments to this plot. You can easily change the color or line-style of the plot and redraw it without having to recompute the functions. You simply re-execute the cell containing the plot code. This is especially nice when the numerical computations required to generate the curves are costly.
@@ -562,6 +573,10 @@Native Jupyter notebook support was recently added to Visual Studio Code. This means that you can now edit Jupyter notebooks within the Visual Studio Code IDE, and that you will benefit from added features like code-completion, debugging, and variable inspection.
+This object is belongs to the NumPy-defined type numpy.ndarray
.
This object belongs to the NumPy-defined type numpy.ndarray
.
# An ND-array belongs to the type `numpy.ndarray`
>>> type(x)
numpy.ndarray
diff --git a/docs_backup/Module3_IntroducingNumpy/Problems/Approximating_pi.html b/docs_backup/Module3_IntroducingNumpy/Problems/Approximating_pi.html
index 6180442e..0de96ed8 100644
--- a/docs_backup/Module3_IntroducingNumpy/Problems/Approximating_pi.html
+++ b/docs_backup/Module3_IntroducingNumpy/Problems/Approximating_pi.html
@@ -112,6 +112,7 @@
This is a record of all past mygrad releases and what went into them, +in reverse chronological order. All previous releases should still be available +on pip.
+We’re finally keeping a formal changelog! This update includes our first discussion of features that were introduced in Python 3.8. Also includes various typo/grammar fixes.
+typing.Literal
, which was introduced in Python 3.8, to the discussion of type-hints .pyright
is now listed alongside mypy
as a tool for doing static type analysis.