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practical-python-data-viz-guide

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Resources for teaching & learning practical data visualization with python.

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- Acquisition_2017Q1.txt
- Acquisition_2017Q4.txt
- DatasaurusDozen.tsv
- jan_and_dec_17_acqs.csv
- - basic_seaborn_scatter_viz.jpg
  - ecdf_ks_test_viz.jpg
  - heatmap_plotly_viz.jpg
  - overlapping_pdf_viz.jpg
- anscombe_unstruct_quartet_comp.png
- basic_bokeh_fig.html
- basic_matplotlib_fig.png
- basic_plotly_fig.html
- basic_plotnine_fig.png
- basic_seaborn_fig.png
- bertins_components_of_image.jpg
- dti_origmonth_dist_plot.png
- fico_seller_dist_plot.png
- fico_seller_hist_plot.png
- heatmap_ltv_fico_upb_conct_fig.html
- inspecting_legend_param_px_figure.jpg
- inspecting_parameters_px_figure.jpg
- inspecting_top_of_px_figure.jpg
- jakes_bertin_levelsoforg.jpg
- leland_wilkinson_grammarofgraphics.jpg
- lender_fico_distplot.html
- lender_fico_hist.html
- purposefully_breaking_pxcode_layout.jpg
- px_error_layout_traceback.jpg
- python_viz_landscape.jpg
- tweet_at_plotly_devs_response.jpg
- data_prep_nb.ipynb
- part_1_main_nb.ipynb
- part_2_main_nb.ipynb
- part_3_main_nb.ipynb
- part_4_main_nb.ipynb
- participant_hw_nb.ipynb
.gitignore
LICENSE
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/pmaji/practical-python-data-viz-guide

Downloads the entire project code from GitHub to your computer.

cd practical-python-data-viz-guide

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

- additional information about this [can be found here](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)

Type this command into your terminal and run it.

- additional information about this [can be found here](https://pip.pypa.io/en/latest/user_guide/#requirements-files)

Type this command into your terminal and run it.

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# Practical Data Visualization with Python

## Overview

*All views expressed on this site are my own and do not represent the opinions of any entity with which I have been, am now, or will be affiliated.*

This repository contains all materials related to a lecture / seminar I teach on practical data visualization with python. What I mean by "practical" is that the materials herein do not focus on one particular library or data visualization method; rather, my goal is to empower the consumer of this content with the tools, heuristics, and methods needed to handle a wide variety of data visualization problems.

If you have questions, comments, or suggested alterations to these materials, please [open an issue](https://github.com/pmaji/practical-python-data-viz-guide/issues) here on GitHub. Also, don't hesitate to reach out via LinkedIn.

## Outline of Materials

Below you'll find a brief outline of the content contained in the four sections of this seminar, along with notebook links, and an example visualization from each section. For each section there is a separate notebook of python code containing all the materials for that section. Each notebook will start with a few setup steps--package imports and data prep mostly--that are almost identical between the notebooks, directly after which comes the content for each section. For information about the data used in these materials, check out the `data_prep_nb.ipynb` notebook, the easy-to-view version of which is [hosted here](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/data_prep_nb.ipynb).

### Section 1: Why We Visualize

[Here is the link](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_1_main_nb.ipynb) to the easy-to-view notebook for this section of material.
<br>
[Here is the link](https://github.com/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_1_main_nb.ipynb) to the GitHub-hosted notebook for this section of the material.

1. The power of visual data representation and storytelling. 
2. A few principles and heuristics of visualization.
3. The building blocks of visualization explored.

**Example Visualization from this Section:**

<p align="left">
  <img width="600" height="400" src="https://raw.githubusercontent.com/pmaji/practical-python-data-viz-guide/master/media/for_readme/overlapping_pdf_viz.jpg"></img>
</p>

### Section 2: Overview of Python Visualization Landscape

[Here is the link](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_2_main_nb.ipynb) to the easy-to-view notebook for this section of material.
<br>
[Here is the link](https://github.com/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_2_main_nb.ipynb) to the GitHub-hosted notebook for this section of the material.

1. Intro to the visualization ecosystem: python's Tower of Babel.
2. Smorgasbord of packages explored through a single example viz.
3. Quick & dirty (and subjective) heuristics for picking a visualization package.

**Example Visualization from this Section:**

<p align="left">
  <img width="600" height="400" src="https://raw.githubusercontent.com/pmaji/practical-python-data-viz-guide/master/media/for_readme/basic_seaborn_scatter_viz.jpg"></img>
</p>

### Section 3: Statistical Visualization in the Wild

[Here is the link](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_3_main_nb.ipynb) to the easy-to-view notebook for this section of material.
<br>
[Here is the link](https://github.com/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_3_main_nb.ipynb) to the GitHub-hosted notebook for this section of the material.

1. Example business use case of data visualization:
    1. Observational:
        - mean, median, and variance
        - distributions
    2. Inferential:
        - parametric tests
        - non-parametric tests

**Example Visualization from this Section:**

<p align="left">
  <img width="600" height="400" src="https://raw.githubusercontent.com/pmaji/practical-python-data-viz-guide/master/media/for_readme/ecdf_ks_test_viz.jpg"></img>
</p>

### Section 4: Library Deep-Dive (Plotly)

[Here is the link](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_4_main_nb.ipynb) to the easy-to-view notebook for this section of material.
<br>
[Here is the link](https://github.com/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_4_main_nb.ipynb) to the GitHub-hosted notebook for this section of the material.

1. Quick and simple data visualizations with Plotly Express.
2. Additional control and complexity with base Plotly.

**Example Visualization from this Section:**

<p align="left">
  <img width="600" height="400" src="https://raw.githubusercontent.com/pmaji/practical-python-data-viz-guide/master/media/for_readme/heatmap_plotly_viz.jpg"></img>
</p>

### Homework Exercises

There is a homework associated with these materials, for those interested. Given the open-ended nature of the homework, there is no answer key. That said, if you're working through it and would like some feedback, feel free to reach out to me via LinkedIn.

[Here is the link](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/participant_hw_nb.ipynb) to the easy-to-view homework notebook. 
<br>
[Here is the link](https://github.com/pmaji/practical-python-data-viz-guide/blob/master/notebooks/participant_hw_nb.ipynb) to the GitHub-hosted version of the homework notebook.
    
## Setup Instructions

- clone this repository
- create a virtual environment using `python3 -m venv env`
    - additional information about this [can be found here](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)
- activate that virtual environment using `source env/bin/activate`
- install needed packages using `pip install -r requirements.txt`
    - additional information about this [can be found here](https://pip.pypa.io/en/latest/user_guide/#requirements-files)
- run an instance of jupyter lab out of your virutal env using `env/bin/jupyter-lab`
- open and run the four main files of content for this course--one for each section:
    - `part_1_main_nb.ipynb`
    - `part_2_main_nb.ipynb`
    - `part_3_main_nb.ipynb`
    - `part_4_main_nb.ipynb`