Merge pull request #364 from jdebacker/auto_pub

Pandas bug fix, add auto publish

.github/workflows/create_pip_package.yml

@@ -0,0 +1,33 @@
+  name: Publish package to PyPI
+on:
+  push:
+    branches:
+      - master
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.9]
+
+  publish:
+     needs: build
+     name: Publish to PyPI
+     if: github.repository == 'PSLmodels/Cost-of-Capital-Calculator'
+     runs-on: ubuntu-latest
+     steps:
+       - name: Checkout repo
+         uses: actions/checkout@v2
+       - name: Setup Python
+         uses: actions/setup-python@v2
+         with:
+           python-version: 3.9
+       - name: Build package
+         run: make pip-package
+       - name: Publish a Python distribution to PyPI
+         uses: pypa/gh-action-pypi-publish@release/v1
+         with:
+           user: jdebacker
+           password: ${{ secrets.PYPI }}
+           skip_existing: true

Makefile

@@ -68,3 +68,7 @@ git-sync:
 .PHONY=git-pr
 git-pr:
 	@./gitpr $(N)
+
+pip-package:
+ 	pip install wheel
+ 	python setup.py sdist bdist_wheel

README.md

@@ -4,7 +4,7 @@
 [![codecov](https://codecov.io/gh/PSLmodels/Cost-of-Capital-Calculator/branch/master/graph/badge.svg?token=vOrtCdGu1c)](https://codecov.io/gh/PSLmodels/Cost-of-Capital-Calculator)
 
 # Cost-of-Capital-Calculator
-Cost-of-Capital-Calculator is a model that can be used to evaluate the effect of US federal taxes on the investment incentives of corporate and non-corporate businesses.  Specifically, Cost-of-Capital-Calculator uses data on the business assets and financial policy, as well as microdata on individual tax filers, to compute marginal effective tax rates on new investments.  In modeling the effects of changes to the individual income tax code, Cost-of-Capital-Calculator works with [Tax-Calculator](https://github.com/PSLmodels/tax-calculator), another open source model of US federal tax policy.  Cost-of-Capital-Calculator is written in Python, an interpreted language that can execute on Windows, Mac, or Linux.
+Cost-of-Capital-Calculator is a model that can be used to evaluate the effect of US federal taxes on the investment incentives of corporate and non-corporate businesses.  Specifically, Cost-of-Capital-Calculator uses data on the business assets and financial policy, as well as microdata on individual tax filers, to compute marginal effective tax rates on new investments.  In modeling the effects of changes to the individual income tax code, Cost-of-Capital-Calculator works with [Tax-Calculator](https://github.com/PSLmodels/tax-calculator), another open-source model of US federal tax policy.  Cost-of-Capital-Calculator is written in Python, an interpreted language that can execute on Windows, Mac, or Linux.
 
 ## Installation
 
@@ -16,15 +16,17 @@ or with PyPI via:
 
 ```pip install cost-of-capital-calculator ```
 
-## Disclaimer
-Results will change as the underlying models improve. A fundamental reason for adopting open source methods in this project is so that people from all backgrounds can contribute to the models that our society uses to assess economic policy; when community-contributed improvements are incorporated, the model will produce different results.
-
+## Web Application
 
-## Using/contributing to Cost-of-Capital-Calculator
+Cost-of-Capital-Calculator is available through a web
+application, [Cost of Capital Calculator](https://compute.studio/PSLmodels/Cost-of-Capital-Calculator/).  This app
+allows you to generate estimates of marginal effective tax rates and the cost of capital
+across production industries, type of asset, and separately for corporate and non-corporate
+entities and different forms of financing.  The web application is limited in that you cannot consider policy reforms to the individual income tax code.
 
-There are two common ways to get started with Cost-of-Capital-Calculator:
+## Contributing to Cost-of-Capital-Calculator
 
-The **first way** to use Cost-of-Capital-Calculator is to download the source code and install the model on your machine.  To do this, follow the following instructions:
+To contribute to Cost-of-Capital-Calculator, you'll want to clone the GitHub repository to your own machine, where you can work with and edit the source code. To do this, follow the following instructions:
 * Install the [Anaconda distribution](https://www.anaconda.com/distribution/) of Python
 * Clone this repository to a directory on your computer
 * From the terminal (or Conda command prompt), navigate to the directory to which you cloned this repository and run `conda env create -f environment.yml`
@@ -47,16 +49,10 @@ The **first way** to use Cost-of-Capital-Calculator is to download the source co
 
 The CSV output files can be compared to the `./example_output/*_expected.csv` files that are checked into the repository to confirm that you are generating the expected output.  The easiest way to do this is to use the `./example-diffs` command (or `example-diffs` on Windows).  If you run into errors running the example script, please open a new issue in the Cost-of-Capital-Calculator repo with a description of the issue and any relevant tracebacks you receive.
 
-As an alternative to the second and third steps above, one can download the `CCC` package directly from Anaconda with the command `conda install -c conda-forge ccc`.
 
+## Disclaimer
+Results will change as the underlying models improve. A fundamental reason for adopting open source methods in this project is so that people from all backgrounds can contribute to the models that our society uses to assess economic policy; when community-contributed improvements are incorporated, the model will produce different results.
 
-The **second way** to use Cost-of-Capital-Calculator is through a web
-application, [Cost of Capital Calculator](https://compute.studio/PSLmodels/Cost-of-Capital-Calculator/).  This way
-allows you to generate estimates of marginal effective tax rates and the cost of capital
-across production industries, type of asset, and separately for corporate and non-corporate
-entites and different forms of financing.  The web application is limited in that you cannot consider policy reforms to the individual income tax code.
-
-Of course, you can get started with Cost-of-Capital-Calculator both ways.
 
 ## Citing the Cost-of-Capital-Calculator Model
-Cost-of-Capital-Calculator (Version 1.2.9)[Source code], https://github.com/PSLmodels/Cost-of-Capital-Calculator
+Cost-of-Capital-Calculator (Version 1.2.10)[Source code], https://github.com/PSLmodels/Cost-of-Capital-Calculator

ccc/calculator.py

@@ -874,7 +874,7 @@ def grouped_bar(self, calc, output_variable='mettr',
         # Append dfs together so base policies in one
         base_df['policy'] = 'Baseline'
         reform_df['policy'] = 'Reform'
-        df = base_df.append(reform_df)
+        df = pd.concat([base_df, reform_df])
         # Drop corporate or non-corporate per arguments
         if corporate:
             df.drop(df[df.tax_treat == 'non-corporate'].index,

ccc/utils.py

@@ -6,6 +6,9 @@
 import json
 import pandas as pd
 
+PACKAGE_NAME = 'ccc'
+PYPI_PACKAGE_NAME = 'cost-of-capital-calculator'
+
 # Default year for model runs
 DEFAULT_START_YEAR = 2022
 
@@ -55,7 +58,7 @@ def str_modified(i):
 
 def diff_two_tables(df1, df2):
     '''
-    Create the difference betweeen two dataframes.
+    Create the difference between two dataframes.
 
     Args:
         df1 (Pandas DataFrame): first DataFrame in difference
@@ -116,11 +119,12 @@ def read_egg_csv(fname, index_col=None):
     Returns:
         vdf (Pandas DataFrame): data from csv file
     '''
+    # try:
+    path_in_egg = os.path.join(PACKAGE_NAME, fname)
     try:
-        path_in_egg = os.path.join('ccc', fname)
         vdf = pd.read_csv(
             pkg_resources.resource_stream(
-                pkg_resources.Requirement.parse('ccc'),
+                pkg_resources.Requirement.parse(PYPI_PACKAGE_NAME),
                 path_in_egg),
             index_col=index_col
         )
@@ -143,10 +147,10 @@ def read_egg_json(fname):
 
     '''
     try:
-        path_in_egg = os.path.join('ccc', fname)
+        path_in_egg = os.path.join(PACKAGE_NAME, fname)
         pdict = json.loads(
             pkg_resources.resource_stream(
-                pkg_resources.Requirement.parse('ccc'),
+                pkg_resources.Requirement.parse(PYPI_PACKAGE_NAME),
                 path_in_egg).read().decode('utf-8'),
             object_pairs_hook=OrderedDict
         )