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        "author": "Ran Aroussi",
        "author_email": "ran@aroussi.com",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Office/Business :: Financial",
            "Topic :: Office/Business :: Financial :: Investment",
            "Topic :: Scientific/Engineering :: Interface Engine/Protocol Translator",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Python Monthly Returns Heatmap\n==============================\n\n.. image:: https://img.shields.io/pypi/pyversions/monthly-returns-heatmap.svg?maxAge=60\n    :target: https://pypi.python.org/pypi/monthly-returns-heatmap\n    :alt: Python version\n\n.. image:: https://img.shields.io/travis/ranaroussi/monthly-returns-heatmap/master.svg?\n    :target: https://travis-ci.org/ranaroussi/monthly-returns-heatmap\n    :alt: Travis-CI build status\n\n.. image:: https://img.shields.io/pypi/v/monthly-returns-heatmap.svg?maxAge=60\n    :target: https://pypi.python.org/pypi/monthly-returns-heatmap\n    :alt: PyPi version\n\n.. image:: https://img.shields.io/pypi/status/monthly-returns-heatmap.svg?maxAge=60\n    :target: https://pypi.python.org/pypi/monthly-returns-heatmap\n    :alt: PyPi status\n\n.. image:: https://img.shields.io/github/stars/ranaroussi/monthly-returns-heatmap.svg?style=social&label=Star&maxAge=60\n    :target: https://github.com/ranaroussi/monthly-returns-heatmap\n    :alt: Star this repo\n\n.. image:: https://img.shields.io/twitter/follow/aroussi.svg?style=social&label=Follow%20Me&maxAge=60\n    :target: https://twitter.com/aroussi\n    :alt: Follow me on twitter\n\n\\\n\n**monthly-returns-heatmap** is a simple Python library for creating\nMonthly Returns Heatmap from Pandas series with ease.\n\n`Changelog \u00bb <./CHANGELOG.rst>`__\n\n-----\n\nQuick Start\n-----------\n\nLet's create a returns heatmap for `SPY <https://finance.yahoo.com/quote/SPY>`_\n(S&P 500 Spider ETF).\n\nFirst, let's download SPY's daily close prices from Google finance.\n\n.. code:: python\n\n    from pandas_datareader import data\n    prices = data.get_data_google(\"SPY\")['Close']\n    returns = prices.pct_change()\n\nNext, we'll import ``monthly_returns_heatmap`` and plot the monthly return heatmap:\n\n.. code:: python\n\n    import monthly_returns_heatmap as mrh\n\n    returns.plot_monthly_returns_heatmap()\n    # mrh.plot(returns) # <== or using direct call\n\n\n.. image:: https://raw.githubusercontent.com/ranaroussi/monthly-returns-heatmap/master/demo.png?\n    :width: 720\n    :height: 318\n    :alt: demo\n\n\n**Getting heatmap data only (no plotting)**\n\n.. code:: python\n\n    heatmap = prices.get_monthly_returns_heatmap()\n    # heatmap = mrh.get(returns) # <== or using direct call\n\n    print(heatmap)\n\n    # prints:\n\n    Month       Jan        Feb        Mar        Apr  ...        Dec\n    Year\n    2010   0.000000   0.031195   0.056529   0.015470  ...   0.061271\n    2011   0.023300   0.034737  -0.004807   0.030413  ...   0.003117\n    2012   0.045498   0.043137   0.028129  -0.006751  ...   0.001759\n    2013   0.051190   0.012759   0.033375   0.019212  ...   0.020387\n    2014  -0.035248   0.045516   0.003865   0.006951  ...  -0.008012\n    2015  -0.029629   0.056205  -0.020080   0.009834  ...  -0.023096\n    2016  -0.049787  -0.001910   0.062943   0.003941  ...   0.014293\n    2017   0.017895   0.039292  -0.003087   0.009926  ...   0.000000\n\n\nGet Parameters (optional)\n--------------------------\n- ``is_prices`` - set to ``True`` if the data used is price data instead of returns data\n- ``compounded`` - set to ``False`` if the you don't want the calculation to use compounded returns\n- ``eoy`` - set to ``True`` to add a **End Of Year** column with total yearly returns\n\nPlot Parameters (optional)\n--------------------------\n- ``title`` - Heatmap title (defaults to ``\"Monthly Returns (%)\"``)\n- ``title_color`` - Heatmap title color (defaults to ``\"black\"``)\n- ``title_size`` - Heatmap title font size (defaults to ``12``)\n- ``annot_size`` - Returns boxes font size (defaults to ``10``)\n- ``figsize`` - Heatmap figure size (defaults to ``None``)\n- ``cmap`` - Color map (defaults to ``\"RdYlGn\"``)\n- ``cbar`` - Show color bar? (defaults to ``True``)\n- ``square`` - Force squere returns boxes? (defaults to ``False``)\n- ``is_prices`` - set to ``True`` if the data used is price data instead of returns data\n- ``compounded`` - set to ``False`` if the you don't want the calculation to use compounded returns\n- ``eoy`` - set to ``True`` to add a **End Of Year** column with total yearly returns\n\nInstallation\n------------\n\nInstall ``monthly_returns_heatmap`` using ``pip``:\n\n.. code:: bash\n\n    $ pip install monthly_returns_heatmap --upgrade --no-cache-dir\n\nRequirements\n------------\n\n* `Python <https://www.python.org>`_ >=3.4\n* `Pandas <https://github.com/pydata/pandas>`_ (tested to work with >=0.18.1)\n* `Matplotlib <https://matplotlib.org>`_ (tested to work with >=1.5.3)\n* `Seaborn <https://seaborn.pydata.org/>`_ (tested to work with >=0.7)\n\n\nLegal Stuff\n------------\n\n**monthly-returns-heatmap** is distributed under the **GNU Lesser General Public License v3.0**. See the `LICENSE.txt <./LICENSE.txt>`_ file in the release for details.\n\n\nP.S.\n------------\n\nPlease drop me an note with any feedback you have.\n\n**Ran Aroussi**\n",
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