pe_ratio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import datetime\n",
    "\n",
    "api_key = 'REGTU7YTI3S3D5UM'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'fiscalDateEnding': '2022-12-31', 'reportedDate': '2023-01-24', 'reportedEPS': '2.32', 'estimatedEPS': '2.3', 'surprise': '0.02', 'surprisePercentage': '0.8696'}, {'fiscalDateEnding': '2022-09-30', 'reportedDate': '2022-10-25', 'reportedEPS': '2.35', 'estimatedEPS': '2.3', 'surprise': '0.05', 'surprisePercentage': '2.1739'}, {'fiscalDateEnding': '2022-06-30', 'reportedDate': '2022-07-26', 'reportedEPS': '2.23', 'estimatedEPS': '2.29', 'surprise': '-0.06', 'surprisePercentage': '-2.6201'}, {'fiscalDateEnding': '2022-03-31', 'reportedDate': '2022-04-26', 'reportedEPS': '2.22', 'estimatedEPS': '2.19', 'surprise': '0.03', 'surprisePercentage': '1.3699'}, {'fiscalDateEnding': '2021-12-31', 'reportedDate': '2022-01-25', 'reportedEPS': '2.48', 'estimatedEPS': '2.31', 'surprise': '0.17', 'surprisePercentage': '7.3593'}, {'fiscalDateEnding': '2021-09-30', 'reportedDate': '2021-10-26', 'reportedEPS': '2.27', 'estimatedEPS': '2.08', 'surprise': '0.19', 'surprisePercentage': '9.1346'}, {'fiscalDateEnding': '2021-06-30', 'reportedDate': '2021-07-27', 'reportedEPS': '2.17', 'estimatedEPS': '1.92', 'surprise': '0.25', 'surprisePercentage': '13.0208'}, {'fiscalDateEnding': '2021-03-31', 'reportedDate': '2021-04-27', 'reportedEPS': '2.03', 'estimatedEPS': '1.78', 'surprise': '0.25', 'surprisePercentage': '14.0449'}, {'fiscalDateEnding': '2020-12-31', 'reportedDate': '2021-01-26', 'reportedEPS': '2.03', 'estimatedEPS': '1.64', 'surprise': '0.39', 'surprisePercentage': '23.7805'}, {'fiscalDateEnding': '2020-09-30', 'reportedDate': '2020-10-27', 'reportedEPS': '1.82', 'estimatedEPS': '1.54', 'surprise': '0.28', 'surprisePercentage': '18.1818'}, {'fiscalDateEnding': '2020-06-30', 'reportedDate': '2020-07-22', 'reportedEPS': '1.46', 'estimatedEPS': '1.34', 'surprise': '0.12', 'surprisePercentage': '8.9552'}, {'fiscalDateEnding': '2020-03-31', 'reportedDate': '2020-04-29', 'reportedEPS': '1.4', 'estimatedEPS': '1.26', 'surprise': '0.14', 'surprisePercentage': '11.1111'}, {'fiscalDateEnding': '2019-12-31', 'reportedDate': '2020-01-29', 'reportedEPS': '1.51', 'estimatedEPS': '1.32', 'surprise': '0.19', 'surprisePercentage': '14.3939'}, {'fiscalDateEnding': '2019-09-30', 'reportedDate': '2019-10-23', 'reportedEPS': '1.38', 'estimatedEPS': '1.25', 'surprise': '0.13', 'surprisePercentage': '10.4'}, {'fiscalDateEnding': '2019-06-30', 'reportedDate': '2019-07-18', 'reportedEPS': '1.37', 'estimatedEPS': '1.21', 'surprise': '0.16', 'surprisePercentage': '13.2231'}, {'fiscalDateEnding': '2019-03-31', 'reportedDate': '2019-04-24', 'reportedEPS': '1.14', 'estimatedEPS': '1', 'surprise': '0.14', 'surprisePercentage': '14'}, {'fiscalDateEnding': '2018-12-31', 'reportedDate': '2019-01-30', 'reportedEPS': '1.1', 'estimatedEPS': '1.09', 'surprise': '0.01', 'surprisePercentage': '0.9174'}, {'fiscalDateEnding': '2018-09-30', 'reportedDate': '2018-10-24', 'reportedEPS': '1.14', 'estimatedEPS': '0.96', 'surprise': '0.18', 'surprisePercentage': '18.75'}, {'fiscalDateEnding': '2018-06-30', 'reportedDate': '2018-07-19', 'reportedEPS': '1.13', 'estimatedEPS': '1.08', 'surprise': '0.05', 'surprisePercentage': '4.6296'}, {'fiscalDateEnding': '2018-03-31', 'reportedDate': '2018-04-26', 'reportedEPS': '0.95', 'estimatedEPS': '0.85', 'surprise': '0.1', 'surprisePercentage': '11.7647'}, {'fiscalDateEnding': '2017-12-31', 'reportedDate': '2018-01-31', 'reportedEPS': '0.96', 'estimatedEPS': '0.87', 'surprise': '0.09', 'surprisePercentage': '10.3448'}, {'fiscalDateEnding': '2017-09-30', 'reportedDate': '2017-10-26', 'reportedEPS': '0.84', 'estimatedEPS': '0.72', 'surprise': '0.12', 'surprisePercentage': '16.6667'}, {'fiscalDateEnding': '2017-06-30', 'reportedDate': '2017-07-20', 'reportedEPS': '0.98', 'estimatedEPS': '0.71', 'surprise': '0.27', 'surprisePercentage': '38.0282'}, {'fiscalDateEnding': '2017-03-31', 'reportedDate': '2017-04-27', 'reportedEPS': '0.73', 'estimatedEPS': '0.7', 'surprise': '0.03', 'surprisePercentage': '4.2857'}, {'fiscalDateEnding': '2016-12-31', 'reportedDate': '2017-01-26', 'reportedEPS': '0.83', 'estimatedEPS': '0.79', 'surprise': '0.04', 'surprisePercentage': '5.0633'}, {'fiscalDateEnding': '2016-09-30', 'reportedDate': '2016-10-20', 'reportedEPS': '0.76', 'estimatedEPS': '0.68', 'surprise': '0.08', 'surprisePercentage': '11.7647'}, {'fiscalDateEnding': '2016-06-30', 'reportedDate': '2016-07-19', 'reportedEPS': '0.69', 'estimatedEPS': '0.58', 'surprise': '0.11', 'surprisePercentage': '18.9655'}, {'fiscalDateEnding': '2016-03-31', 'reportedDate': '2016-04-21', 'reportedEPS': '0.62', 'estimatedEPS': '0.64', 'surprise': '-0.02', 'surprisePercentage': '-3.125'}, {'fiscalDateEnding': '2015-12-31', 'reportedDate': '2016-01-28', 'reportedEPS': '0.78', 'estimatedEPS': '0.71', 'surprise': '0.07', 'surprisePercentage': '9.8592'}, {'fiscalDateEnding': '2015-09-30', 'reportedDate': '2015-10-22', 'reportedEPS': '0.67', 'estimatedEPS': '0.59', 'surprise': '0.08', 'surprisePercentage': '13.5593'}, {'fiscalDateEnding': '2015-06-30', 'reportedDate': '2015-07-21', 'reportedEPS': '0.6', 'estimatedEPS': '0.56', 'surprise': '0.04', 'surprisePercentage': '7.1429'}, {'fiscalDateEnding': '2015-03-31', 'reportedDate': '2015-04-23', 'reportedEPS': '0.61', 'estimatedEPS': '0.51', 'surprise': '0.1', 'surprisePercentage': '19.6078'}, {'fiscalDateEnding': '2014-12-31', 'reportedDate': '2015-01-26', 'reportedEPS': '0.71', 'estimatedEPS': '0.71', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '2014-09-30', 'reportedDate': '2014-10-23', 'reportedEPS': '0.54', 'estimatedEPS': '0.49', 'surprise': '0.05', 'surprisePercentage': '10.2041'}, {'fiscalDateEnding': '2014-06-30', 'reportedDate': '2014-07-22', 'reportedEPS': '0.55', 'estimatedEPS': '0.6', 'surprise': '-0.05', 'surprisePercentage': '-8.3333'}, {'fiscalDateEnding': '2014-03-31', 'reportedDate': '2014-04-24', 'reportedEPS': '0.68', 'estimatedEPS': '0.63', 'surprise': '0.05', 'surprisePercentage': '7.9365'}, {'fiscalDateEnding': '2013-12-31', 'reportedDate': '2014-01-23', 'reportedEPS': '0.78', 'estimatedEPS': '0.68', 'surprise': '0.1', 'surprisePercentage': '14.7059'}, {'fiscalDateEnding': '2013-09-30', 'reportedDate': '2013-10-24', 'reportedEPS': '0.62', 'estimatedEPS': '0.54', 'surprise': '0.08', 'surprisePercentage': '14.8148'}, {'fiscalDateEnding': '2013-06-30', 'reportedDate': '2013-07-18', 'reportedEPS': '0.66', 'estimatedEPS': '0.75', 'surprise': '-0.09', 'surprisePercentage': '-12'}, {'fiscalDateEnding': '2013-03-31', 'reportedDate': '2013-04-18', 'reportedEPS': '0.72', 'estimatedEPS': '0.68', 'surprise': '0.04', 'surprisePercentage': '5.8824'}, {'fiscalDateEnding': '2012-12-31', 'reportedDate': '2013-01-24', 'reportedEPS': '0.76', 'estimatedEPS': '0.75', 'surprise': '0.01', 'surprisePercentage': '1.3333'}, {'fiscalDateEnding': '2012-09-30', 'reportedDate': '2012-10-18', 'reportedEPS': '0.53', 'estimatedEPS': '0.56', 'surprise': '-0.03', 'surprisePercentage': '-5.3571'}, {'fiscalDateEnding': '2012-06-30', 'reportedDate': '2012-07-19', 'reportedEPS': '0.67', 'estimatedEPS': '0.62', 'surprise': '0.05', 'surprisePercentage': '8.0645'}, {'fiscalDateEnding': '2012-03-31', 'reportedDate': '2012-04-19', 'reportedEPS': '0.6', 'estimatedEPS': '0.57', 'surprise': '0.03', 'surprisePercentage': '5.2632'}, {'fiscalDateEnding': '2011-12-31', 'reportedDate': '2012-01-19', 'reportedEPS': '0.78', 'estimatedEPS': '0.76', 'surprise': '0.02', 'surprisePercentage': '2.6316'}, {'fiscalDateEnding': '2011-09-30', 'reportedDate': '2011-10-20', 'reportedEPS': '0.68', 'estimatedEPS': '0.68', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '2011-06-30', 'reportedDate': '2011-07-21', 'reportedEPS': '0.69', 'estimatedEPS': '0.58', 'surprise': '0.11', 'surprisePercentage': '18.9655'}, {'fiscalDateEnding': '2011-03-31', 'reportedDate': '2011-04-28', 'reportedEPS': '0.61', 'estimatedEPS': '0.56', 'surprise': '0.05', 'surprisePercentage': '8.9286'}, {'fiscalDateEnding': '2010-12-31', 'reportedDate': '2011-01-27', 'reportedEPS': '0.77', 'estimatedEPS': '0.68', 'surprise': '0.09', 'surprisePercentage': '13.2353'}, {'fiscalDateEnding': '2010-09-30', 'reportedDate': '2010-10-28', 'reportedEPS': '0.62', 'estimatedEPS': '0.55', 'surprise': '0.07', 'surprisePercentage': '12.7273'}, {'fiscalDateEnding': '2010-06-30', 'reportedDate': '2010-07-22', 'reportedEPS': '0.51', 'estimatedEPS': '0.46', 'surprise': '0.05', 'surprisePercentage': '10.8696'}, {'fiscalDateEnding': '2010-03-31', 'reportedDate': '2010-04-22', 'reportedEPS': '0.45', 'estimatedEPS': '0.42', 'surprise': '0.03', 'surprisePercentage': '7.1429'}, {'fiscalDateEnding': '2009-12-31', 'reportedDate': '2010-01-28', 'reportedEPS': '0.74', 'estimatedEPS': '0.59', 'surprise': '0.15', 'surprisePercentage': '25.4237'}, {'fiscalDateEnding': '2009-09-30', 'reportedDate': '2009-10-23', 'reportedEPS': '0.4', 'estimatedEPS': '0.32', 'surprise': '0.08', 'surprisePercentage': '25'}, {'fiscalDateEnding': '2009-06-30', 'reportedDate': '2009-07-23', 'reportedEPS': '0.36', 'estimatedEPS': '0.36', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '2009-03-31', 'reportedDate': '2009-04-23', 'reportedEPS': '0.39', 'estimatedEPS': '0.39', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '2008-12-31', 'reportedDate': '2009-01-22', 'reportedEPS': '0.47', 'estimatedEPS': '0.49', 'surprise': '-0.02', 'surprisePercentage': '-4.0816'}, {'fiscalDateEnding': '2008-09-30', 'reportedDate': '2008-10-23', 'reportedEPS': '0.48', 'estimatedEPS': '0.47', 'surprise': '0.01', 'surprisePercentage': '2.1277'}, {'fiscalDateEnding': '2008-06-30', 'reportedDate': '2008-07-17', 'reportedEPS': '0.46', 'estimatedEPS': '0.47', 'surprise': '-0.01', 'surprisePercentage': '-2.1277'}, {'fiscalDateEnding': '2008-03-31', 'reportedDate': '2008-04-24', 'reportedEPS': '0.47', 'estimatedEPS': '0.44', 'surprise': '0.03', 'surprisePercentage': '6.8182'}, {'fiscalDateEnding': '2007-12-31', 'reportedDate': '2008-01-24', 'reportedEPS': '0.5', 'estimatedEPS': '0.46', 'surprise': '0.04', 'surprisePercentage': '8.6957'}, {'fiscalDateEnding': '2007-09-30', 'reportedDate': '2007-10-25', 'reportedEPS': '0.45', 'estimatedEPS': '0.39', 'surprise': '0.06', 'surprisePercentage': '15.3846'}, {'fiscalDateEnding': '2007-06-30', 'reportedDate': '2007-07-19', 'reportedEPS': '0.39', 'estimatedEPS': '0.39', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '2007-03-31', 'reportedDate': '2007-04-26', 'reportedEPS': '0.49', 'estimatedEPS': '0.46', 'surprise': '0.03', 'surprisePercentage': '6.5217'}, {'fiscalDateEnding': '2006-12-31', 'reportedDate': '2007-01-25', 'reportedEPS': '0.26', 'estimatedEPS': '0.23', 'surprise': '0.03', 'surprisePercentage': '13.0435'}, {'fiscalDateEnding': '2006-09-30', 'reportedDate': '2006-10-26', 'reportedEPS': '0.35', 'estimatedEPS': '0.31', 'surprise': '0.04', 'surprisePercentage': '12.9032'}, {'fiscalDateEnding': '2006-06-30', 'reportedDate': '2006-07-20', 'reportedEPS': '0.31', 'estimatedEPS': '0.3', 'surprise': '0.01', 'surprisePercentage': '3.3333'}, {'fiscalDateEnding': '2006-03-31', 'reportedDate': '2006-04-27', 'reportedEPS': '0.32', 'estimatedEPS': '0.33', 'surprise': '-0.01', 'surprisePercentage': '-3.0303'}, {'fiscalDateEnding': '2005-12-31', 'reportedDate': '2006-01-26', 'reportedEPS': '0.33', 'estimatedEPS': '0.33', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '2005-09-30', 'reportedDate': '2005-10-27', 'reportedEPS': '0.31', 'estimatedEPS': '0.3', 'surprise': '0.01', 'surprisePercentage': '3.3333'}, {'fiscalDateEnding': '2005-06-30', 'reportedDate': '2005-07-21', 'reportedEPS': '0.32', 'estimatedEPS': '0.31', 'surprise': '0.01', 'surprisePercentage': '3.2258'}, {'fiscalDateEnding': '2005-03-31', 'reportedDate': '2005-04-28', 'reportedEPS': '0.32', 'estimatedEPS': '0.32', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '2004-12-31', 'reportedDate': '2005-01-27', 'reportedEPS': '0.35', 'estimatedEPS': '0.33', 'surprise': '0.02', 'surprisePercentage': '6.0606'}, {'fiscalDateEnding': '2004-09-30', 'reportedDate': '2004-10-21', 'reportedEPS': '0.31', 'estimatedEPS': '0.3', 'surprise': '0.01', 'surprisePercentage': '3.3333'}, {'fiscalDateEnding': '2004-06-30', 'reportedDate': '2004-07-22', 'reportedEPS': '0.28', 'estimatedEPS': '0.29', 'surprise': '-0.01', 'surprisePercentage': '-3.4483'}, {'fiscalDateEnding': '2004-03-31', 'reportedDate': '2004-04-22', 'reportedEPS': '0.34', 'estimatedEPS': '0.29', 'surprise': '0.05', 'surprisePercentage': '17.2414'}, {'fiscalDateEnding': '2003-12-31', 'reportedDate': '2004-01-22', 'reportedEPS': '0.34', 'estimatedEPS': '0.3', 'surprise': '0.04', 'surprisePercentage': '13.3333'}, {'fiscalDateEnding': '2003-09-30', 'reportedDate': '2003-10-23', 'reportedEPS': '0.3', 'estimatedEPS': '0.29', 'surprise': '0.01', 'surprisePercentage': '3.4483'}, {'fiscalDateEnding': '2003-06-30', 'reportedDate': '2003-07-17', 'reportedEPS': '0.23', 'estimatedEPS': '0.24', 'surprise': '-0.01', 'surprisePercentage': '-4.1667'}, {'fiscalDateEnding': '2003-03-31', 'reportedDate': '2003-04-15', 'reportedEPS': '0.27', 'estimatedEPS': '0.24', 'surprise': '0.03', 'surprisePercentage': '12.5'}, {'fiscalDateEnding': '2002-12-31', 'reportedDate': '2003-01-16', 'reportedEPS': '0.27', 'estimatedEPS': '0.23', 'surprise': '0.04', 'surprisePercentage': '17.3913'}, {'fiscalDateEnding': '2002-09-30', 'reportedDate': '2002-10-17', 'reportedEPS': '0.28', 'estimatedEPS': '0.21', 'surprise': '0.07', 'surprisePercentage': '33.3333'}, {'fiscalDateEnding': '2002-06-30', 'reportedDate': '2002-07-18', 'reportedEPS': '0.21', 'estimatedEPS': '0.21', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '2002-03-31', 'reportedDate': '2002-04-18', 'reportedEPS': '0.25', 'estimatedEPS': '0.25', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '2001-12-31', 'reportedDate': '2002-01-17', 'reportedEPS': '0.25', 'estimatedEPS': '0.21', 'surprise': '0.04', 'surprisePercentage': '19.0476'}, {'fiscalDateEnding': '2001-09-30', 'reportedDate': '2001-10-18', 'reportedEPS': '0.22', 'estimatedEPS': '0.2', 'surprise': '0.02', 'surprisePercentage': '10'}, {'fiscalDateEnding': '2001-06-30', 'reportedDate': '2001-07-19', 'reportedEPS': '0.22', 'estimatedEPS': '0.21', 'surprise': '0.01', 'surprisePercentage': '4.7619'}, {'fiscalDateEnding': '2001-03-31', 'reportedDate': '2001-04-19', 'reportedEPS': '0.22', 'estimatedEPS': '0.21', 'surprise': '0.01', 'surprisePercentage': '4.7619'}, {'fiscalDateEnding': '2000-12-31', 'reportedDate': '2001-01-18', 'reportedEPS': '0.24', 'estimatedEPS': '0.23', 'surprise': '0.01', 'surprisePercentage': '4.3478'}, {'fiscalDateEnding': '2000-09-30', 'reportedDate': '2000-10-18', 'reportedEPS': '0.23', 'estimatedEPS': '0.21', 'surprise': '0.02', 'surprisePercentage': '9.5238'}, {'fiscalDateEnding': '2000-06-30', 'reportedDate': '2000-07-18', 'reportedEPS': '0.22', 'estimatedEPS': '0.21', 'surprise': '0.01', 'surprisePercentage': '4.7619'}, {'fiscalDateEnding': '2000-03-31', 'reportedDate': '2000-04-20', 'reportedEPS': '0.22', 'estimatedEPS': '0.2', 'surprise': '0.02', 'surprisePercentage': '10'}, {'fiscalDateEnding': '1999-12-31', 'reportedDate': '2000-01-18', 'reportedEPS': '0.24', 'estimatedEPS': '0.21', 'surprise': '0.03', 'surprisePercentage': '14.2857'}, {'fiscalDateEnding': '1999-09-30', 'reportedDate': '1999-10-19', 'reportedEPS': '0.19', 'estimatedEPS': '0.17', 'surprise': '0.02', 'surprisePercentage': '11.7647'}, {'fiscalDateEnding': '1999-06-30', 'reportedDate': '1999-07-19', 'reportedEPS': '0.2', 'estimatedEPS': '0.18', 'surprise': '0.02', 'surprisePercentage': '11.1111'}, {'fiscalDateEnding': '1999-03-31', 'reportedDate': '1999-04-20', 'reportedEPS': '0.19', 'estimatedEPS': '0.16', 'surprise': '0.03', 'surprisePercentage': '18.75'}, {'fiscalDateEnding': '1998-12-31', 'reportedDate': '1999-01-19', 'reportedEPS': '0.18', 'estimatedEPS': '0.15', 'surprise': '0.03', 'surprisePercentage': '20'}, {'fiscalDateEnding': '1998-09-30', 'reportedDate': '1998-10-20', 'reportedEPS': '0.14', 'estimatedEPS': '0.12', 'surprise': '0.02', 'surprisePercentage': '16.6667'}, {'fiscalDateEnding': '1998-06-30', 'reportedDate': '1998-07-16', 'reportedEPS': '0.13', 'estimatedEPS': '0.12', 'surprise': '0.01', 'surprisePercentage': '8.3333'}, {'fiscalDateEnding': '1998-03-31', 'reportedDate': '1998-04-22', 'reportedEPS': '0.13', 'estimatedEPS': '0.12', 'surprise': '0.01', 'surprisePercentage': '8.3333'}, {'fiscalDateEnding': '1997-12-31', 'reportedDate': '1998-01-21', 'reportedEPS': '0.11', 'estimatedEPS': '0.1', 'surprise': '0.01', 'surprisePercentage': '10'}, {'fiscalDateEnding': '1997-09-30', 'reportedDate': '1997-10-20', 'reportedEPS': '0.09', 'estimatedEPS': '0.09', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '1997-06-30', 'reportedDate': '1997-07-17', 'reportedEPS': '0.1', 'estimatedEPS': '0.1', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '1997-03-31', 'reportedDate': '1997-04-17', 'reportedEPS': '0.1', 'estimatedEPS': '0.08', 'surprise': '0.02', 'surprisePercentage': '25'}, {'fiscalDateEnding': '1996-12-31', 'reportedDate': '1997-01-17', 'reportedEPS': '0.07', 'estimatedEPS': '0.06', 'surprise': '0.01', 'surprisePercentage': '16.6667'}, {'fiscalDateEnding': '1996-09-30', 'reportedDate': '1996-10-21', 'reportedEPS': '0.06', 'estimatedEPS': '0.06', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '1996-06-30', 'reportedDate': '1996-07-22', 'reportedEPS': '0.05', 'estimatedEPS': '0.05', 'surprise': '0', 'surprisePercentage': '0'}, {'fiscalDateEnding': '1996-03-31', 'reportedDate': '1996-04-18', 'reportedEPS': '0.06', 'estimatedEPS': '0.05', 'surprise': '0.01', 'surprisePercentage': '20'}]\n"
     ]
    }
   ],
   "source": [
    "url = 'https://www.alphavantage.co/query?function=EARNINGS&symbol={}&apikey={}'.format('MSFT', api_key)\n",
    "r = requests.get(url)\n",
    "data = r.json()\n",
    "\n",
    "quarterly_earnings = data['quarterlyEarnings']\n",
    "print(quarterly_earnings)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1996-03-31 2022-12-31\n"
     ]
    }
   ],
   "source": [
    "dates = []\n",
    "eps = []\n",
    "for item in quarterly_earnings:\n",
    "    dates.append(item['fiscalDateEnding'])\n",
    "    eps.append(item['reportedEPS'])\n",
    "    # print(item['fiscalDateEnding'], item['reportedEPS'])\n",
    "\n",
    "end_date = dates[0]\n",
    "start_date = dates[-1]\n",
    "\n",
    "print(start_date, end_date)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Date</th>\n",
       "      <th>EPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-09-30</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>2.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-03-31</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   EPS\n",
       "0  2022-12-31  2.32\n",
       "1  2022-09-30  2.35\n",
       "2  2022-06-30  2.23\n",
       "3  2022-03-31  2.22\n",
       "4  2021-12-31  2.48"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qe_df = pd.DataFrame(list(zip(dates, eps)), columns=['Date', 'EPS'])\n",
    "qe_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>EPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1997-03-31</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1996-12-31</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1996-09-30</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1996-06-30</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1996-03-31</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   EPS\n",
       "103  1997-03-31   0.1\n",
       "104  1996-12-31  0.07\n",
       "105  1996-09-30  0.06\n",
       "106  1996-06-30  0.05\n",
       "107  1996-03-31  0.06"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qe_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>2.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             EPS\n",
       "2022-12-31  2.32\n",
       "2022-09-30  2.35\n",
       "2022-06-30  2.23\n",
       "2022-03-31  2.22\n",
       "2021-12-31  2.48"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qe_df_2 = pd.DataFrame(eps, index=dates, columns=['EPS'])\n",
    "qe_df_2.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas_datareader.data as pdr\n",
    "\n",
    "msft = pdr.DataReader(\"MSFT\", \"av-monthly\", start=start_date, end=end_date, api_key='REGTU7YTI3S3D5UM')\n",
    "# print(list(msft.index))\n",
    "# print(msft.loc['2022-12-30']['close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "share_prices_final = []\n",
    "PE_ratios = []\n",
    "dates_final = []\n",
    "\n",
    "stock_price_dates = list(msft.index)\n",
    "for date in dates:\n",
    "    for stock_price_date in stock_price_dates:\n",
    "        if date[:7] == stock_price_date[:7]:\n",
    "            PE_ratio = float(msft.loc[stock_price_date]['close']) / float(qe_df_2.loc[date]['EPS'])\n",
    "            PE_ratios.append(PE_ratio)\n",
    "            dates_final.append(date)\n",
    "            share_prices_final.append(msft.loc[stock_price_date]['close'])\n",
    "\n",
    "            # print(stock_price_date[:7], msft.loc[stock_price_date]['close'], PE_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Share Price</th>\n",
       "      <th>PE Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>239.82</td>\n",
       "      <td>103.370690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>232.90</td>\n",
       "      <td>99.106383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>256.83</td>\n",
       "      <td>115.170404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>308.31</td>\n",
       "      <td>138.878378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>336.32</td>\n",
       "      <td>135.612903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Share Price    PE Ratio\n",
       "2022-12-31       239.82  103.370690\n",
       "2022-09-30       232.90   99.106383\n",
       "2022-06-30       256.83  115.170404\n",
       "2022-03-31       308.31  138.878378\n",
       "2021-12-31       336.32  135.612903"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pd.DataFrame(list(zip(share_prices_final, PE_ratios)), index=dates_final, columns=['Share Price', 'PE Ratio'])\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plot a line graph of the PE ratio and the share price\n",
    "df_final.plot()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}