Merge d3de341 into c7efe3a

.travis.yml

@@ -3,7 +3,6 @@ os: linux
 dist: xenial
 language: python
 python:
-- '2.7'
 - '3.5'
 - '3.6'
 - '3.7'
@@ -14,11 +13,12 @@ before_install:
 services:
   - xvfb
 install:
-- pip install scipy
-- pip install pandas
-- pip install matplotlib
-- pip install six
-- pip install pytest
+- pip install -r requirements.txt
+- pip install scipy==1.2.2
+- pip install pandas==0.24.2
+- pip install matplotlib==3.0.3
+- pip install six==1.12.0
+- pip install pytest==5.0.1
 - pip install pytest-pep8
 - pip install pytest-cov
 - pip install coveralls

README.md

@@ -1,7 +1,9 @@
 # sci-analysis
+
 An easy to use and powerful python-based data exploration and analysis tool
 
 ## Current Version:
+
 2.2 --- Released January 5, 2019
 
 [![PyPI](https://img.shields.io/pypi/v/sci_analysis.svg)](https://pypi.python.org/pypi/sci_analysis)
@@ -10,29 +12,40 @@ An easy to use and powerful python-based data exploration and analysis tool
 [![Build Status](https://travis-ci.org/cmmorrow/sci-analysis.svg?branch=master)](https://travis-ci.org/cmmorrow/sci-analysis)
 [![Coverage Status](https://coveralls.io/repos/github/cmmorrow/sci-analysis/badge.svg?branch=master)](https://coveralls.io/github/cmmorrow/sci-analysis?branch=master)
 
-### What is sci-analysis?
-sci-analysis is a python package for quickly performing statistical data analysis. It provides a graphical representation of the supplied data as well as the statistical analysis. sci-analysis is smart enough to determine the correct analysis and tests to perform based on the shape of the data you provide, as well as how the data is distributed.
+## What is sci-analysis?
+
+sci-analysis is a python package for quickly performing exploratory data analysis (EDA). It aims to make performing EDA easier for newcomers and experienced data analysts alike by abstracting away the specific SciPy, NumPy, and Matplotlib commands. This is accomplished by using sci-analysis's ``analyze()`` function.
 
-The types of analysis that can be performed are histograms of numeric or categorical data, bi-variate analysis of two numeric data vectors, and one-way analysis of variance.
+The main features of sci-analysis are:
+* Fast EDA with the analyze() function.
+* Great looking graphs without writing several lines of matplotlib code.
+* Automatic use of the most appropriate hypothesis test for the supplied data.
+* Automatic handling of missing values.
 
-### What's new in sci-analysis version 2.2?
+Currently, sci-analysis is capable of performing four common statistical analysis techniques:
+* Histograms and summary of numeric data
+* Histograms and frequency of categorical data
+* Bivariate and linear regression analysis
+* Location testing
+
+## What's new in sci-analysis version 2.2?
 
 * Version 2.2 adds the ability to add data labels to scatter plots.
 * The default behavior of the histogram and statistics was changed from assuming a sample, to assuming a population.
 * Fixed a bug involving the Mann Whitney U test, where the minimum size was set incorrectly.
 * Verified compatibility with python 3.7.
 
-### Getting started with sci-analysis
-The documentation on how to install and use sci-analysis can be found here:
-
-[http://sci-analysis.readthedocs.io/en/latest/](http://sci-analysis.readthedocs.io/en/latest/)
-
+## Requirements
 
-### Requirements
 * Packages: pandas, numpy, scipy, matplotlib, six
 * Supports python 2.7, 3.5, 3.6, and 3.7
 
 Bugs can be reported here:
 
 [https://github.com/cmmorrow/sci-analysis/issues](https://github.com/cmmorrow/sci-analysis/issues)
 
+## Documentation
+
+The documentation on how to install and use sci-analysis can be found here:
+
+[http://sci-analysis.readthedocs.io/en/latest/](http://sci-analysis.readthedocs.io/en/latest/)

build_docs.sh

@@ -1,19 +1,51 @@
 #!/bin/bash
-com=$HOME/sci_analysis_qa_env/bin/jupyter
+
+# The path to the jupyter executable.
+com=jupyter
+
+# The path to the docs directory.
 docs=./docs
-images=./img
-index=sci_analysis_main
-notebook=${docs}/${index}.ipynb
-output=${docs}/index.md
-output_backup=${output}.bak
-if [ -f ${output} ]; then
-    mv ${output} ${output_backup}
-fi
-${com} nbconvert --to markdown NbConvertApp.output_base=${docs} --NbConvertApp.output_files_dir=${images} ${notebook}
-mv ${docs}/${index}.md ${output}
-sed -i -e "s/> Note:/.. note::/g" ${output}
-sed -i -e "s/.*warnings.*//" ${output}
+
+# The path to the documentation image directory under docs.
+images=img
+
+# The path to the toc file. This file is combined with the welcome page.
+toc=${docs}/toc.rst
+
+# The path to the README file.
+readme=./README
+
+# The filename of the welcome page. This will be a combination of the README and toc file.
+index=${docs}/index.rst
+
+#index=sci_analysis_main
+
+# The list of notebooks to be converted to md for documentation.
+notebooks=(
+    getting_started
+    using_sci_analysis
+    pandas
+    distribution
+    frequency
+    bivariate
+    location_test
+)
+for nb in ${notebooks[*]}; do
+    notebook=${docs}/${nb}.ipynb
+    output=${docs}/${nb}.md
+    output_backup=${output}.bak
+    if [[ -f ${output} ]]; then
+        mv ${output} ${output_backup}
+    fi
+    ${com} nbconvert --to markdown NbConvertApp.output_base=${docs} --NbConvertApp.output_files_dir=${images} ${notebook}
+    sed -i -e "s/> Note:/.. note::/g" ${output}
+    sed -i -e "s/.*warnings.*//" ${output}
+done
+
+m2r ${readme}.md
+cat ${readme}.rst ${toc} > ${index}
 make -C ${docs} html
+rm ${readme}.rst
 rm ${docs}/*-e
 
-exit 0
+exit 0