Merge pull request #1 from gjbex/development

Environment & hands-on material

Author: gjbex

README.md

@@ -9,8 +9,14 @@ For information on the training, see the website
 
 1. [`python_for_data_science.pptx`](python_for_data_science.pptx): PowerPoint
    presentation used for the training.
-1. [`source-code`](source-code): directory that contains sample code written to
-   develop the slides and illustrate concepts.
+1. [`hands-on`](hands-on): Jupyter notebooks for hands-on sessions.
+1. [`source-code`](source-code): sample code written to develop the slides and
+   illustrate concepts.
+1. [`environment.yml`](environment.yml): conda environment file intended to be
+   cross-platform.
+1. [`python_for_data_science_linux64_conda_specs.txt`](python_for_data_science_linux64_conda_specs.txt):
+   conda environment specification file specific for 64-bit Linux to precisely
+   reproduce the environment on which the code was developed.
 1. [License](LICENSE): license information for the material in this repository.
 1. [Contributing](CONTRIBUTING.md): information on how to contribute to this
    repository.

environment.yml

@@ -0,0 +1,19 @@
+name: python_for_data_science
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - beautifulsoup4
+  - folium
+  - geopandas
+  - holoviews
+  - jupyter
+  - matplotlib
+  - networkx
+  - pandas
+  - python=3.7
+  - scipy
+  - seaborn
+  - shapely
+  - sqlalchemy
+  - xlrd

hands-on/README.md

@@ -0,0 +1,8 @@
+# Hands-on
+
+Here you find some notebooks that can be used during the training session
+to follow along, or for hands-on sessions.
+
+## What is it?
+  1.  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gjbex/Python-for-data-science/blob/master/hands-on/patient_data.ipynb) [`patient_data.ipynb`](patient_data.ipynb):      
+       illustrates using pandas for data analysis and seaborn for visualizations.

source-code/pandas/patient_data_tmpl.ipynb renamed to hands-on/patient_data.ipynb

@@ -46,13 +46,31 @@
     "## Experiment data"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Read the data set as a pandas `DataFrame` from an Excel spreadsheet."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_data = pd.read_excel('https://github.com/gjbex/Python-for-data-science/blob/master/source-code/pandas/data/patient_experiment.xlsx?raw=true')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -249,6 +267,15 @@
     "There is a second file with metadata on the patients, let's read that as well. The file is `data/patient_metadata.xlsx`."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metadata = pd.read_excel('https://github.com/gjbex/Python-for-data-science/blob/master/source-code/pandas/data/patient_metadata.xlsx?raw=true')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -444,7 +471,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,

python_for_data_science_linux64_conda_specs.txt

@@ -0,0 +1,184 @@
+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: linux-64
+@EXPLICIT
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+https://repo.anaconda.com/pkgs/main/noarch/holoviews-1.12.6-py_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/mkl_fft-1.0.15-py37ha843d7b_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/mkl_random-1.1.0-py37hd6b4f25_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.17.3-py37hd14ec0e_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/pandas-0.25.2-py37he6710b0_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/scipy-1.3.1-py37h7c811a0_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/shapely-1.6.4-py37hc5e8c75_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/altair-3.2.0-py37_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/fiona-1.8.11-py37h41e4f33_0.conda
+https://repo.anaconda.com/pkgs/main/noarch/mapclassify-2.0.1-py_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/patsy-0.5.1-py37_0.conda
+https://conda.anaconda.org/conda-forge/noarch/folium-0.10.0-py_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/noarch/geopandas-0.4.1-py_0.tar.bz2
+https://repo.anaconda.com/pkgs/main/linux-64/statsmodels-0.10.1-py37hdd07704_0.conda
+https://repo.anaconda.com/pkgs/main/linux-64/seaborn-0.9.0-py37_0.conda

source-code/README.md

@@ -6,23 +6,20 @@ to create it.  There is some material not covered in the presentation as well.
 ## Requirements
 
 * Python version: at least 3.6
-* Packages:
+* Packages (names listed that can be used with `pip` or `conda` to install):
   * pandas
+  * xlrd
   * seaborn
   * holoviews
   * sqlalchemy
-  * folium
   * matplotlib
-  * numpy
   * scipy
   * geopandas
   * shapely
-  * bs4
+  * beautifulsoup4
   * networkx
-  * Jupyter notebook
-* Optional packages
-  * gdal
-  * fiona
+  * jupyter
+  * folium (with conda, use `-c conda-forge`)
 
 ## What is it?
 * [`db-access`](db-access): illustration of accessing SQLite databases and using