Merge pull request #16 from gjbex/development

Development

Author: gjbex

environment.yml

@@ -40,8 +40,4 @@ dependencies:
   - sqlalchemy
   - plotly
   - beautifulsoup4
-  - lxml
-  - ca-certificates
-  - certifi
-  - openssl
 prefix: /home/gjb/mambaforge/envs/python_for_data_science

python_for_data_science.pptx

@@ -3,42 +3,24 @@
 This is source code that is either used in the presentation, or was developed
 to create it.  There is some material not covered in the presentation as well.
 
-## Requirements
-
-* Python version: at least 3.6
-* Packages (names listed that can be used with `pip` or `conda` to install):
-  * pandas
-  * xlrd
-  * seaborn
-  * holoviews
-  * sqlalchemy
-  * matplotlib
-  * scipy
-  * geopandas
-  * shapely
-  * beautifulsoup4
-  * networkx
-  * jupyter
-  * folium (with conda, use `-c conda-forge`)
-  * xarray
-  * 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.
+* [`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.
 * [`holoviews`](holoviews): illustrations of using HoloViews for convenient
   visualizations.
 * [`networkx`](networkx): illustration of using the networkx library for graph
   representation and algorithms.
 * [`pandas`](pandas): illustrations of using pandas and seaborn.
-* [`regexes`](regexes): illustrations of using regular expressions for validation
-  and information extraction from textual data.
+* [`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.
+* [`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.
+
+**Note:** material on dashboards has been moved to a [dedicated
+repository](https://github.com/gjbex/Python-dashboards).