Update README and package info

.cache/v/cache/lastfailed

@@ -1 +1,3 @@
-{}
+{
+  "sci_analysis/test/test_groupcorrelation.py::MyTestCase::test_pearson_correlation_different_alpha": true
+}

README

@@ -1,35 +1,51 @@
+============
 sci-analysis
 ============
 
-An easy to use python based data exploration and analysis tool by Chris Morrow
+An easy to use and powerful python-based data exploration and analysis tool
 
+---------------
 Current Version
-===============
+---------------
 
-1.4 --- Released August 19, 2016
+2.0 --- Released December 27, 2017
 
 .. image:: https://img.shields.io/pypi/v/sci_analysis.svg   :target: https://pypi.python.org/pypi/sci_analysis
 .. image:: https://img.shields.io/pypi/format/sci_analysis.svg   :target: https://pypi.python.org/pypi/sci_analysis
 .. image:: https://img.shields.io/pypi/pyversions/sci_analysis.svg   :target: https://pypi.python.org/pypi/sci_analysis
-.. image:: https://travis-ci.org/cmmorrow/sci-analysis.svg?branch=master
-    :target: https://travis-ci.org/cmmorrow/sci-analysis
+.. image:: https://travis-ci.org/cmmorrow/sci-analysis.svg?branch=2.0.0   :target: https://travis-ci.org/cmmorrow/sci-analysis
+.. image:: https://coveralls.io/repos/github/cmmorrow/sci-analysis/badge.svg?branch=2.0.0   :target: https://coveralls.io/github/cmmorrow/sci-analysis?branch=2.0.0
 
 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.
 
-Currently, sci-analysis can only be used for analyzing numeric data. Categorical data analysis is planned for a future version. The three types of analysis that can be performed are histograms of single vectors, correlation between two vectors and one-way ANOVA.
+The types of analysis that can be performed are histograms of numeric or categorical data, bivariate analysis of two numeric vectors, and one-way analysis of variance.
 
-What's new in sci-analysis version 1.4?
-=======================================
+What's new in sci-analysis version 2.0?
+---------------------------------------
 
-In version 1.4, sci-analysis was re-written to be more pythonic and to support python 3. A ton of new graphing options have been added histograms, scatter plots and oneway plots. Histograms can now display an accompanying cumulative distribution plot and fit lines to a specified distribution can be displayed on both the histogram and cumulative distribution plot. Scatter plots can now overlay density contour lines and display boxplot borders. Boxplots have been revamped and are now overlayed on top of a kernel density estimation, which provides a much better representation of distribution density. New tests have been added for comparing two distributions -- Student's T Test for normally distributed data, the Mann Whitney U Test for non-parametric data and the two-sample Kolmogorov-Smirnov Test for small non-parametric samples. The Kolmogorov-Smirnov Test has been added for determining goodness-of-fit to a specified distribution as well. 
+* In version 2.0, the code base was re-factored to use pandas as the internal data structure instead of numpy. This change shouldn't have a noticeable effect, but should lead to faster releases moving forward. 
+* Categorical data is now supported by passing in a single array of string values to the ``analyze`` function. 
+* Multiple scatter plots can now be shown on the same graph by passing in a *groups* argument.
+* Oneway analysis can now be performed on stacked data by passing in all the data to the ``analyze`` function and an array of the corresponding groups using the *groups* argument.
+* The function ``analyse`` was added as an alias to ``analyze``.
 
 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/
 
+Requirements
+------------
+
+* Packages: pandas, numpy, scipy, matplotlib, six
+* Supports python 2.7, 3.5 and 3.6
+
+Bugs can be reported here:
+
+https://github.com/cmmorrow/sci-analysis/issues
+

README.md

@@ -1,26 +1,39 @@
 # sci-analysis
-An easy to use python based data exploration and analysis tool by Chris Morrow
+An easy to use and powerful python-based data exploration and analysis tool
 
 ## Current Version:
-1.4 --- Released August 19, 2016
+2.0 --- Released December 27, 2017
 
 [![PyPI](https://img.shields.io/pypi/v/sci_analysis.svg)](https://pypi.python.org/pypi/sci_analysis)
 [![PyPI](https://img.shields.io/pypi/format/sci_analysis.svg)](https://pypi.python.org/pypi/sci_analysis)
 [![PyPI](https://img.shields.io/pypi/pyversions/sci_analysis.svg)](https://pypi.python.org/pypi/sci_analysis)
-[![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=2.0.0)](https://coveralls.io/github/cmmorrow/sci-analysis?branch=2.0.0)
+[![Build Status](https://travis-ci.org/cmmorrow/sci-analysis.svg?branch=2.0.0)](https://travis-ci.org/cmmorrow/sci-analysis)
+[![Coverage Status](https://coveralls.io/repos/github/cmmorrow/sci-analysis/badge.svg?branch=2.0.0)](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.
 
-Currently, sci-analysis can only be used for analyzing numeric data. Categorical data analysis is planned for a future version. The three types of analysis that can be performed are histograms of single vectors, correlation between two vectors and one-way ANOVA.
+The types of analysis that can be performed are histograms of numeric or categorical data, bivariate analysis of two numeric vectors, and one-way analysis of variance.
 
-### What's new in sci-analysis version 1.4?
+### What's new in sci-analysis version 2.0?
 
-In version 1.4, sci-analysis was re-written to be more pythonic and to support python 3. A ton of new graphing options have been added histograms, scatter plots and oneway plots. Histograms can now display an accompanying cumulative distribution plot and fit lines to a specified distribution can be displayed on both the histogram and cumulative distribution plot. Scatter plots can now overlay density contour lines and display boxplot borders. Boxplots have been revamped and are now overlayed on top of a kernel density estimation, which provides a much better representation of distribution density. New tests have been added for comparing two distributions -- Student's T Test for normally distributed data, the Mann Whitney U Test for non-parametric data and the two-sample Kolmogorov-Smirnov Test for small non-parametric samples. The Kolmogorov-Smirnov Test has been added for determining goodness-of-fit to a specified distribution as well. 
+* In version 2.0, the code base was re-factored to use pandas as the internal data structure instead of numpy. This change shouldn't have a noticeable effect, but should lead to faster releases moving forward. 
+* Categorical data is now supported by passing in a single array of string values to the ```analyze``` function. 
+* Multiple scatter plots can now be shown on the same graph by passing in a *groups* argument.
+* Oneway analysis can now be performed on stacked data by passing in all the data to the ```analyze``` function and an array of the corresponding groups using the *groups* argument.
+* The function ```analyse``` was added as an alias to ```analyze```.
 
 ### 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/](http://sci-analysis.readthedocs.io/en/latest/)
+
+
+### Requirements
+* Packages: pandas, numpy, scipy, matplotlib, six
+* Supports python 2.7, 3.5 and 3.6
+
+Bugs can be reported here:
+
+[https://github.com/cmmorrow/sci-analysis/issues](https://github.com/cmmorrow/sci-analysis/issues)
 

docs/conf.py

@@ -54,17 +54,17 @@
 
 # General information about the project.
 project = u'sci_analysis'
-copyright = u'2015-2016, Chris Morrow'
+copyright = u'2015-2017, Chris Morrow'
 author = u'Chris Morrow'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
 # The short X.Y version.
-version = u'1.4'
+version = u'2.0'
 # The full version, including alpha/beta/rc tags.
-release = u'1.4.4'
+release = u'2.0.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/index.rst

@@ -11,17 +11,21 @@ sci_analysis is a python package for quickly performing statistical data analysi
 
 Currently, sci_analysis can only be used for analyzing numeric data. Categorical data analysis is planned for a future version. The three types of analysis that can be performed are histograms of single vectors, correlation between two vectors and a comparison of means between multiple vectors.
 
-What's new in sci_analysis version 1.4?
+What's new in sci_analysis version 2.0?
 =======================================
 
-In version 1.4, sci_analysis was re-written to be more pythonic and to support python 3. A ton of new graphing options have been added to histograms, scatter plots and oneway plots. Histograms can now display an accompanying cumulative distribution plot and fit lines to a specified distribution which are displayed on both the histogram and cumulative distribution plot. Scatter plots can now overlay density contour lines and display boxplot borders. Boxplots have been revamped and are now overlayed on top of a kernel density estimation, which provides a much better representation of distribution density. New tests have been added for comparing two distributions -- Student's T Test for normally distributed data, the Mann Whitney U Test for non-parametric data and the two-sample Kolmogorov-Smirnov Test for small non-parametric samples. The Kolmogorov-Smirnov Test has been added for determining goodness-of-fit to a specified distribution as well. 
+* In version 2.0, the code base was re-factored to use pandas as the internal data structure instead of numpy. This change shouldn't have a noticeable effect, but should lead to faster releases moving forward. 
+* Categorical data is now supported by passing in a single array of string values to the ``analyze`` function. 
+* Multiple scatter plots can now be shown on the same graph by passing in a *groups* argument.
+* Oneway analysis can now be performed on stacked data by passing in all the data to the ``analyze`` function and an array of the corresponding groups using the *groups* argument.
+* The function ``analyse`` was added as an alias to ``analyze``.
 
 Getting started with sci_analysis
 =================================
 
-sci_analysis requires python 2.7 or 3.5.
+sci_analysis requires python 2.7, 3.5, or 3.6.
 
-If you use OS X or Linux, python should already be installed. You can check by opening a terminal window and typing ``which python`` on the command line. To verify what version of python you have installed, type ``python --version`` at the command line. If the version is 2.7.x or 3.5.x, where x is any number, sci_analysis should work properly.
+If you use OS X or Linux, python should already be installed. You can check by opening a terminal window and typing ``which python`` on the command line. To verify what version of python you have installed, type ``python --version`` at the command line. If the version is 2.7.x, 3.5.x or 3.6.x, where x is any number, sci_analysis should work properly.
 
 If you are on Windows, you might need to install python. You can check to see if python is installed by clicking the Start button, typing ``cmd`` in the run text box, then type ``python.exe`` on the command line. If you receive an error message, you need to install python. The easiest way to install python is by installing Anaconda or Mini-conda from this page:
 

sci_analysis.egg-info/PKG-INFO

@@ -1,15 +1,15 @@
 Metadata-Version: 1.1
 Name: sci-analysis
-Version: 1.4.6
-Summary: A light weight python data exploration and analysis tool
+Version: 2.0.0
+Summary: An easy to use but powerful, python based data exploration and analysis tool
 Home-page: https://github.com/cmmorrow/sci-analysis
 Author: chris morrow
 Author-email: cmmorrow@gmail.com
 License: MIT License
 Description: UNKNOWN
 Keywords: statistics data EDA graphing visualization analysis scientific
 Platform: UNKNOWN
-Classifier: Development Status :: 4 - Beta
+Classifier: Development Status :: 5 - Production/Stable
 Classifier: Intended Audience :: Developers
 Classifier: Intended Audience :: Science/Research
 Classifier: Intended Audience :: Manufacturing
@@ -24,3 +24,4 @@ Classifier: Programming Language :: Python :: 2
 Classifier: Programming Language :: Python :: 2.7
 Classifier: Programming Language :: Python :: 3
 Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6

sci_analysis.egg-info/requires.txt

@@ -1,4 +1,5 @@
 numpy
 scipy
+pandas
 matplotlib
 six

setup.py

@@ -2,31 +2,31 @@
 
 setup(
     name='sci_analysis',
-    version='1.4.6',
+    version='2.0.0',
     packages=find_packages(),
     url='https://github.com/cmmorrow/sci-analysis',
     license='MIT License',
     author='chris morrow',
     author_email='cmmorrow@gmail.com',
     classifiers=[
-        'Development Status :: 4 - Beta',
+        'Development Status :: 5 - Production/Stable',
         'Intended Audience :: Developers',
-	'Intended Audience :: Science/Research',
-	'Intended Audience :: Manufacturing',
-	'Intended Audience :: Financial and Insurance Industry',
-	'Intended Audience :: Healthcare Industry',
-	'Intended Audience :: Education',
+	    'Intended Audience :: Science/Research',
+	    'Intended Audience :: Manufacturing',
+	    'Intended Audience :: Financial and Insurance Industry',
+	    'Intended Audience :: Healthcare Industry',
+	    'Intended Audience :: Education',
         'License :: OSI Approved :: MIT License',
-	'Natural Language :: English',
-	'Topic :: Scientific/Engineering :: Information Analysis',
-	'Topic :: Scientific/Engineering :: Visualization',
+	    'Natural Language :: English',
+	    'Topic :: Scientific/Engineering :: Information Analysis',
+	    'Topic :: Scientific/Engineering :: Visualization',
         'Programming Language :: Python :: 2',
         'Programming Language :: Python :: 2.7',
-	'Programming Language :: Python :: 3',
-	'Programming Language :: Python :: 3.5'
+	    'Programming Language :: Python :: 3',
+	    'Programming Language :: Python :: 3.5',
+        'Programming Language :: Python :: 3.6',
     ],
     keywords='statistics data EDA graphing visualization analysis scientific',
-    description='A light weight python data exploration and analysis tool',
+    description='An easy to use and powerful python-based data exploration and analysis tool',
     install_requires=['numpy', 'scipy', 'pandas', 'matplotlib', 'six'],
-    test_suite='nose.collector'
 )