Add Altair and streamlit examples

Author: gjbex

source-code/README.md

@@ -24,6 +24,8 @@ to create it.  There is some material not covered in the presentation as well.
   * dash
 
 ## What is it?
+* [`altair`](altair): illustration of an interactive plot using Altair.
+* [`dash`](dash): illustration of creating a simple dashboard using dash.
 * [`db-access`](db-access): illustration of accessing SQLite databases and using
   SQLAlchemy, including object-relational mapping.
 * [`gis`](gis): illustrations of working with geospatial data, including geopandas.
@@ -35,8 +37,8 @@ to create it.  There is some material not covered in the presentation as well.
 * [`regexes`](regexes): illustrations of using regular expressions for validation
   and information extraction from textual data.
 * [`seaborn`](seaborn): illustrations of using Seaborn to create plots.
+* [`streamlit`](streamlit): illustration of a simple dashboard created with streamlit.
 * [`web-scraping`](web-scraping): illustration of web scraping using beautiful soup
   and graph representation using networkx.
 * [`xarray`](xarray): illustrates the xarray library for pandas-like operations
   on multi-dimensional arrays.
-* [`dash`](dash): illustration of creating a simple dashboard using dash.

source-code/altair/README.md

@@ -0,0 +1,10 @@
+# Altair
+
+Altair is a sophisticated plotting library that lets you make highly customizalble
+and interactive plots.
+
+
+## What is it?
+
+1. [`altair_interaction.ipynb`](altair/altair_interaction.ipynb): create a plot with
+   hover values and an interactive legend.

source-code/altair/altair_interaction.ipynb

@@ -0,0 +1,9 @@
+# streamlit
+
+streamlit is a framework for creating dashboards.
+
+
+## What is it?
+
+1. [`distributions.py`](distributions.py): dashboard that displays a sample from a
+   user-selected probability distribution.

source-code/streamlit/README.md

@@ -0,0 +1,37 @@
+#!/usr/bin/env python
+
+import matplotlib.pyplot as plt
+import numpy as np
+import streamlit as st
+
+# This is markdown that will be displayed automatically (magic commands)
+'''
+# Distribution tester
+
+Pick a probability distribution from the list and we shall draw the a
+histogram for a random sample from the selected distribution.
+'''
+
+# Make some choices for a user to select
+keys = ['Normal','Uniform']
+dist_key = st.selectbox('Which Distribution do you want to plot?',keys)
+
+# Logic of our program
+if dist_key == 'Normal':
+    '''
+    Select the mean value $\mu$ and the standard deviation $\sigma$ using the
+    sliders.
+    '''
+    mu = st.slider('mu', min_value=-2.0, max_value=2.0, value=0.0, step=0.25)
+    sigma = st.slider('sigma', min_value=0.0, max_value=5.0, value=1.0, step=0.25)
+    nums = np.random.normal(loc=mu, scale=sigma, size=1000)
+elif dist_key == 'Uniform':
+    nums = np.random.uniform(0.0, 2.0, size=1000)
+
+# Display data
+figure, axes = plt.subplots()
+axes.hist(nums, bins=20)
+axes.set_xlabel('$x$')
+axes.set_ylabel('$P(x)$')
+
+st.pyplot(figure)