rename files to make room for vega-altair episode

content/index.rst

@@ -10,13 +10,13 @@ Python for Scientific Computing
    Whether you are or aren't, the course material is below.  Videos
    will appear in `this playlist <https://www.youtube.com/playlist?list=PLZLVmS9rf3nNI3oQEqSJW6yXltOAZnkpa>`__.
 
-   
+
 Python is a modern, object-oriented programming language, which has
 become popular in several areas of software development. This course
 discusses how Python can be utilized in scientific computing. The
 course starts by introducing some of the main Python tools for
 computing: Jupyter for interactive analysis, NumPy and SciPy for
-numerical analysis, matplotlib for visualization, and so on.  In
+numerical analysis, Matplotlib for visualization, and so on.  In
 addition, it talks about *how* python is used:
 related scientific libraries, reproducibility, and the broader
 ecosystem of science in Python, because your work is more than the raw
@@ -71,7 +71,7 @@ to learn yourself as you need to.
    60 min   ; :doc:`numpy` or :doc:`numpy-advanced`
    60 min   ; :doc:`pandas`
    30 min   ; :doc:`xarray`
-   60 min   ; :doc:`data-visualization`
+   60 min   ; :doc:`plotting-matplotlib`
    30 min   ; :doc:`data-formats`
    60 min   ; :doc:`scripts`
    40 min   ; :doc:`profiling`
@@ -96,7 +96,7 @@ to learn yourself as you need to.
    numpy-advanced
    pandas
    xarray
-   data-visualization
+   plotting-matplotlib
    data-formats
    scripts
    profiling

content/data-visualization.md → content/plotting-matplotlib.md

@@ -1,4 +1,4 @@
-# Data visualization with Matplotlib
+# Plotting with Matplotlib
 
 ```{questions}
 - What happens if you can't automatically produce plots?
@@ -101,7 +101,7 @@ ax.set_title("some title")
 # fig.savefig("my-first-plot.png")
 ```
 
-```{figure} data-visualization/first-plot/getting-started.png
+```{figure} plotting-matplotlib/first-plot/getting-started.png
 :alt: Result of our first plot
 :width: 80%
 
@@ -142,7 +142,7 @@ matplotlib.use("Agg")
   [quick start guide](https://matplotlib.org/stable/users/explain/quick_start.html).
 
 - At the end it should look like this one:
-  ```{figure} data-visualization/first-plot/exercise.png
+  ```{figure} plotting-matplotlib/first-plot/exercise.png
   :alt: Result of the exercise
   ```
 
@@ -321,12 +321,12 @@ In this exercise we will learn how to use log scales.
   ```
 
   This is the result but we realize that a linear scale is not ideal here:
-  ```{figure} data-visualization/customizing/gapminder-linear.png
+  ```{figure} plotting-matplotlib/customizing/gapminder-linear.png
   :alt: Gapminder data plotted using a linear scale
   ```
 
 - Your task is to switch to a log scale and arrive at this result:
-  ```{figure} data-visualization/customizing/gapminder-log.png
+  ```{figure} plotting-matplotlib/customizing/gapminder-log.png
   :alt: Gapminder data plotted using log scale
   ```
 
@@ -365,7 +365,7 @@ Your task is to make the tickmarks and the axis label font larger, using
 [Matplotlib parts of a figure](https://matplotlib.org/stable/users/explain/quick_start.html#parts-of-a-figure)
 and web search, and to arrive at this:
 
-```{figure} data-visualization/customizing/gapminder-larger-font.png
+```{figure} plotting-matplotlib/customizing/gapminder-larger-font.png
 :alt: Gapminder data plotted with larger font and larger ticks
 ```
 ````