feat: Add final content

[max/plotting_with_matplotlib.git] / slides.rst

diff --git a/slides.rst b/slides.rst
index 79c4685e3b5297ce84f14f204d14a4069a5d57e3..d15eab4df6114df2f7d4983f5bb730fa10ea04bf 100644 (file)
--- a/slides.rst
+++ b/slides.rst
@@ -57,6 +57,20 @@ Numpy
 ``numpy.loadtxt`` is generally faster at runtime if your data is well formated 
 (no missing values, only numerical data or constant length strings)
 
+Loading Data from Disk
+----------------------
+Numpy NB.
+=========
+**Remind me to look at some actual numpy usage at the end**
+
+- I think numpy does some type coercion when creating arrays.
+- Arrays created by ``numpy.genfromtxt`` can not in general be indexed like
+  ``data[xstart:xend, ystart:yend]``.
+- Data of unequal types are problematic!  Pandas *may* be a better choice in
+  that case.
+- Specifying some value for ``dtype`` is probably necessary in most cases in
+  practice: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
+
 Loading Data from Disk
 ----------------------
 Pandas
@@ -130,8 +144,23 @@ The keyword arguments are optional here.  Other options exist.
 Error Bars
 ----------
 
+.. code-block:: python
+   :include: error_bars.py
+
 Stacked Bar Graph
 -----------------
 
 .. code-block:: python
    :include: stacked_bars.py
+
+Resources
+---------
+NumPy User Guide: https://docs.scipy.org/doc/numpy/user/index.html
+
+NumPy Reference: https://docs.scipy.org/doc/numpy/reference/index.html#reference
+
+Matplotlib example gallery: https://matplotlib.org/gallery/index.html
+
+Pandas: It probably exists.  Good luck.
+
+This presentation: https://git.friedersdorff.com/max/plotting_with_matplotlib.git