hosp_analysis_US_version.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H1 align='center'>HOSPITALIZATION ANALYSIS</H1>\n",
    "\n",
    "\n",
    "\n",
    "<H3  align='center'>HEALTH DEPARTMENT OF FEDERAL DISTRICT</H3>\n"
   ]
  },
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   "metadata": {},
   "source": [
    "  "
   ]
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/SES_DF.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: justify\">This notebook is about data analysis of patients hospitalizations in all health institutions of Health Department of Federal District from year 2010 to 2018.\n",
    "\n",
    "The data was obtained from DataSUS online platform (IT department of Unique Health System of Brazil). The data was collected from (.dbc) format, after that was decompressed to dBase III format and also transformed into Pandas Dataframes. Ultimately, was sent to (.csv) file.</div>\n",
    "\n",
    "**Goals**\n",
    "\n",
    "- Analyze the relations between hospitalizations and health institutions\n",
    "- Check the origin of patients in institutions\n",
    "- Relate the medical procedures costs and hospitalizations\n",
    "- Predict the amount of hospitalizations in 2019 (Machine Learning).\n",
    "\n",
    "The data visualization will be made on Microsoft Power BI. In this notebook, I'll just make data wrangling, the results will be visualized on Power BI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Source of Data**\n",
    "\n",
    "The data was collected from \"Share files\" link on Health Portal.\n",
    "\n",
    "Link: <http://www2.datasus.gov.br/DATASUS/index.php?area=0901&item=1&acao=25>\n",
    "\n",
    "The data is about 20 million of rows, after it'll be reduced."
   ]
  },
  {
   "attachments": {
    "INOVASES-DF.png": {
     "image/png": 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j9ax6F+ECm3PxBz/mYFiWlpDSqLzNWr/vuvgbUUfPOX45CNqL1q/87LNJm7MzZnN2+4WpR2pFyp58Mj43+Y8+sKKJ4la8kXg3/KMBeFK2URmkEk9+kzAA7SxasqapQnceCoVCoRYyPPlpjqE9RgBKDg5Gk24aqaWn9Vku17khl+/i78eVMVJGxK0YJD2v+lncdc7fEZJLpxkApevKE5U99qCfhQqQ/gGSe9q9vX1KgAJ1LFnrcwfeI3M78kd/aqCtNWLI6Hqdy14eY/ww0ArXF0IUCoVCoRYqQMEIvDFSKiNiSB3dryTOlVB2pVIwuRKC7DpgRXRASj2vw7/B133FncR38ffiUEfP5ljKxg/HEuJc+lriXbON7wn+Nu1d//eQKNNXhJdn72IVEGYGlCuDtyF1gWFwtMljvlyBQZdyf3Oxp6PzrlDZpJfQtLx+u7OL5TJriWLFmCcKhUKhUAsZoJiZsgEcnMsGiHPFpcdtzp6czeFKCVQCVhawsPjUkGTEusSqvUeod0pdRLhiB+1f5+u48FYCbr4CH53NJ1/IFX9fKBC7d4XD5uzqoq5ArpzqoKT0C78jGhAVVs70nPteMvfYJ8vQk3icFNKxhM21xFsxEJwBjI7OdSQ7c3d5fbOROUfPGkYMEytmTFovsDQP3IdEUeuQ8RkhCoVCoVCoGgCUn8KCCqCg0LDn/A+mBEBxU2vIsnLmceNpCXSTcSqTdcLnQl4BUJWASv6djevpcnDdRJ4nil1wOJ94IZbnDyuPfSd93Sxvj3XEeeqlxP70KlnwdvLp73m967caBCj9EjWF7FxWvlyBAU7aIFXI51uWVdCdh0KhUKiFJgCojcqZrtNvIF2XfYV0+Ne6mRYjlZWE5crTWoYo4IIw5rMArTJAmVV66tcs6xvTlec6/S+LMUzus97OS+fzR0dJPh1NMcHRpuW2tDOPze451V05Azwhmm7QXKqTcTwTCFEoFAqFQtVQtKiuKnDau+ZTvGftO3UgRmtUnV5guBYQSIHCbgKejIBVQeO78vzMS3crF4CUAEyIcp5+Rab4etqmAsRYSZV45PNuVSJRmz78QP1BqSuvBFGnO8wH45dBKjd3hGWJirbC9YbuPBQKhUItJI0oZ4AFiusNctpwZGQ+IdpxUITk5w7F+SN3+XLxp0khM0ts7l7S0b027VzxqpS9s6+nsnWpoANIpAJAlaXhyhulLk6VK08MRIe8UJ7AO9KJg1+cT6+QnbmP8FO/SnN9f+OqDFClKfP8uKzxIMjc0X3eMvY+i+CVTudnDyVzs8/MlVx/wr51BTodXavdNq7Llz3xkI9x4C1hiUKIQqFQKNSCELVC9SlBwbP2HdpwZKsETVquvNL3mWN3J5JP3OrN88/IO/oZqIlHXMkDxGXnVhNu5ZvTrtVvL6YJqARC+lBFJHXz1L+x4Mqb3x+u7/Wu9PN3zRcVBiUOft5FOjp57ozXcJUACvJIpZ75puz4nKe8hrCse4V8iucP/7LAP/9LVy4x6aL7YTRYfMp/9XRLWKLQnYdCoVCohaKQcoZnzftTbDgileGJOa88f27fMJmbvAkASnen4PtU5N9cM+NXuDJHxzLWAEkPuMrzdVx5Q8ovRFee9Pg6N3wirnLr7d/GJZ74JinkMmm9UYmJyVtdeV5WjpC4z3tPSpbtXXifjvw0PfPHa7jkwdsAoKwCc0ukOECIQqFQKNRCkcxdBWkMOpasdevDERuQ9JcHgPoMyRz9oamdK+ReEn73Tmfi8X83CUfGLFXUladcEFx5AaIItO/ovnTelSc9Xntnr89zzlZeuRL+yHfJ7L1vc6Ujo+kCPxOXufCO3Z+Yve8dJHPiN7LfcCveQuzuFe7iyEcbWJ/S/NzEJ0ny4Gdd0qzmFgTWxr3CcY00+4LDsi8oFAqFanvRsi57pPO8Z9+c5vqucZWBiFEQ2KacrwwKtyuWsZPE49sFqPiWah86uq8SoO2S+c+ZY38gucSDzP31nHNL2hV4s6sMQzQFQjEVQp6o5suWYc9PPvGfJP28CuyuJaXs3ttlbbPmU3w5TkwdFM9P7+HBAmX1fDi8/cR36R1pm6Psvpy99+1Ce7BDmYqxU53ryoiYnRGW3WdkU2DKCjbLvYcxUSgUCoVaCAqqOrjlF4MrjxGDZMaNJ1f25ERKACi3HABOIZ393453LL3IJ12H+7wPkQJ/LJ54YsSXOSqv/ZY8sM3lPP3KlN21zF05qLwAyTbj+eQL2WLiSl9fp73rTI/N3uEyMipPAEwVuThP20TYAFXad673Lzh7Z298bvKffGatRlCypvOCz8YFgJpPWprYdxsToLiV7yTuvmtjpSSn6pqCuZmnj6endy/LvPhzorEfYGGLAEQLx9rwYHO0RKFQKBSq7SV0ohD3I3Pn+a/6Xw1QEK1QWhYocXKoCg7P/PGvSZ4/JAOork2/TNndp7nVbsEyFGRP7I3PTbzbBy69eZBZcYMAG58k6gSaZStTfu7ZeOLxL/qyM/eorSDdlxP3OW9LdfjXuPOJ52Mzf34rK8HmsDAdkgHU0r8QoO+fJW1jV7wvB9QXctl08vGvuPijPzF0HpynXEO8FwzzNoebE9she3IyFX/oRhl4gqXKu+EzSUdXwFOOA5MmEFXP45/bzScPbOc0YKopFimMiUKhUCjUQpBf3kmvq2RDqPCd+nt++rdxKUCBOvv/I2V3rZAAQoGoLCoCJHQs2ehznzUkK84LMVWFXDqtFQ/FT/+On/nzdUyAKsLJzH0k/vAH3EU3nolRec4Vr4yrj5NVssZWTH3g3fBR0nPF7+LcypDQrhdowpPv4u+nOi/8N+E3HtlIvsQjn1JZ7nyX3sEXAao4Q78o87x17IzXcN1X/DQN9QIZ2kiPt6FCdx4KhUKhFpxsHV0W4En/+/TUd2WB2B3dQQGOLnKLoFT6qbScS4FIrSyuwN+5UodGiNQalZ89mHT417jkAAbxVH9OJJ78pNfIsTLioEDarrxTL3Mag8cyCNk4v8+79oOklJ4gk87HD88Kx9YhzM/bvStpxWGbm7UONXh+JSWAltyNWSyNoywLoy6NAzFWnRduI+lIfzoJ6RfkGoA0F3Q0IkIUCoVCoVA1wipSKYlmPvnCiXzixXzuxL5l0t/Yus46QfJ5LpeYkEGU57yt4Dryl4FJsYmCMsanQByd60l2ZrwMUakX0w6yRrYsJJ9MPPZpFUDBaEO7+wzhN88Rxig8pZij8sCVB9YlFijNf7bpZ2WHXFeObgA/m6KcjV0BojYKmwPzx9zR/Wqwyrnlu6qEJlaNPblcfW9wkcwMSR7+hvIrGLGHEIVCoVAoVL2Vn5uOpw7/ypc9vkcAk6mlGoup5ju8FxKH/2VlgBIBoKAEg3KQNAsKBHBLlZcrfc8f/pUqiNq75lae672SE2OE0gd/nE5Ofd2lB1GE5cpbfjmkL+C0rU46cKV8b5OWhNGuDeh7xTcIP31X0YXI9f6Vjw1IWtYnbaBynXMDyUQni25Nifogh1SjgswRolAoFAq1ECQLKM7O3K+7cC72dDT59B3+7My9Pisb41YO0mzfEoBSAYAUpNgj8BzdZ3fKLVYFkjk2LoMjbsUbCXfGVZw0Eafr7De78tnZVHr6e27G7mm68tLP/ozjn99NHJ1nk47ll8Scyy7qMWaFInJgYhZUtjPghx5D7+s04EnSZkWXnrnBbq6+NyWyj9yntNoBPDYEojCwHIVCoVALQapOM3N8IqacB6VJEk/eQWYfugkAyvLGnCs2pdRwxAgqV+R6krryitzQ4VMBlgAG85nEIX+S57x3JOSZzEvvudNeldLYvTDLlVeEx8TjRcCEOKq5fR/uiY1fS/ipX6aNWaFEC5RyNJ8UJ2w6EyF6hYbZ0FbhPCy/hBU3FmzURYeWKBQKVRddf/2N4OrQLc3wox/9YAxbakGd80qdV0Q455E6bR7cV9tkEPXCn3qcy8qXYCGXzsQf+Igrl3hUcyUw+szW0U2c/guLnXl27iDJJ6dlOY6cp/wNsXvP7GFbVQjRskDloo9JYqjo9orFefNKQHN2XvDlRD76pNe56pqUzeX3quOrdBUmDFceS4XcSZI4+AVXdvYg8W74CDHm4lO+t1cAJKV1jmW5sxk4NvYykOpB4dJrWEkY24nfroSNmU6dvuS100G8ZaBQi7rDhHtHgN6w+mnnAK89FlcJeV6gk4lIp3qBFt3/kTY/DWGhfcINhmLpefezrB0GFZNYj8bouYfPE8IxWcr1E93dC9eMrABx9yt/krK7lrhz8elk/KGtHoAGpexcH3Gvfg/vPO3VkuH5IhzQkiV8LJ45em+skJnLcquuO62UidtWwXoitx6ln/oWSR7eLoGxa0hn/21Enn2clam8wMwhlX7mp7Fk5GvK/xsU5w1QV56pc8OtfDvxrn0/0c6dZVflzVJbmuwaFiYtK51WNvY8YeWKYmVtTz55B0kf+b7sWIQ2sDXif9FB/wQD2CWgUKgKnWiAPt0G6dRT402IN/wBxXZFwJqg05jQydYi3mEh3PvG6nzOped7Y41X3yNpf+U5FwFrjJ5vo8cZVlqj5vZuc/su+X+5xKP/TwVQRVfZuR+Nc71XgU+NKwFAgQEAwieux8edcY1PnUxTy4JSUL1yfdcmBIjylqHl9VCM2MkGC8l6mEWJCySfPsnMDaXlyitZbS6bf5+d+bPsO6iP51r5F1FHzxq/MSuUFkBp53pSt49yMTHVQeURevMQ3OFt2h8Q3XkoFKqS9QE60qE6dKJmAQumLZJOdpR2sqNWLRco5jkHYArR897TpN0QAQumbRSkxyXnWwuiR+i1Or/fucRjZOaetzmUANXRvYl0XnQbZNauEFhe0IEmmwmQKhCba6m3+7JfxTNHfudzLH95qsO/3i135bGydRMNuBIgKDahBZIh1hfeNf9C6+WVYKdknfuQDC7Th3/h9244nw1LNlZcE9GwTFVqR2KwDQ2AVxOFgeUoFIoJT8I0TEoutR1NBiitTnYL3beTwr6G8KxVfc4HhQl65T20bXtabBeLQCVMe4X9jLAWoCU/hlVdrwKguBXXEt8ln4d8SZx+nFFlMFJDj36wud13ps913jtJCaBYy+fl2yqw1wUJLyFIXKEpOrRf9X/gVmwmXO+ArKCww3emx3PuR+IyMIs+wAAkLSsUA7R0lyPEbOB4qwstUSgUStmZDtGOqKeNdjuAZ87y+RZjw9rJtdmn9YUAESNQjJYo6ujNg4N3PfGs+yBNT0AqWEwkIGOzaVhQCNF2VWmkOCgo43y0oIxofs6+tC/H2Hlw5fUr28fmWEI8a9/Lg1NSua+O7tVg1Z23xuX5KTZA2bRgiAFQstF7tD0LeY0207MuGbE8FUgmug8hCoVCNb0zBdfdKMEYycUGzNsX4KGFSMn1p7Kges7/+7jNzvkMA5RuFm2bAnBYLikWQOWJtkVLAW8aIJZ58R5WIFCYMK1Qr6NWN6IGnkJOgwNYFiOWu06a5oBIijoTNUiZGoln3HWXm9uvnDWOEIVCoRptjYBOpwdbY9Gcc+hwtyzEYwO3XnR377Dw9k5Zh9d9Oenwn28guSYj5mneGsUCKcKGIKUFqcAYdafp/iO6nzMv/VG500VXHtSOU37hPvtN4LLzsYAoN3MwK51bKjCsn7cpc/z+WObofT35+P7idzZ3r9C256edK16Vsnee1aNuF7EpjYJRheUkcMlP7+ELuZOcYomJRl1rCFEoFHamCFAIUAtRKphwBd4ck13nsqLBhKgtUQqIKX7NygWlB1J6AEW0gUrPCnXsoYQADkpLFNOVV6yVx3X7tCxLHUvWyv733LJXqwGKHjP/3F186pmvc3n+kPxeMSPs01HiSh4gLju3mnAr35J2rX4HKaWBMBkMXjBuoYLEqalDO1hu2XCjLjIMLEehFndnGkCAQoBaDBAFxXudyy7sYbvOtOCHATEFlpUoT9i5jSgUFDS+k43Gk6yzwLJQlbeXefFe46683tcmlOAkhSl75xm+zg1fynR0v1KAnxBxnft2wgKuxL7PkcST/wgApdvoef4Zkor8q2tmfJMrc/SPCeNB5HrB/ezvko9/1cUoxDzeqLp5CFEoFGoUAWpRAdTgYgAoapGRXdfOU64gxkbhacQnaYKUHhdUctspQUpnm5LvtFx5SoiCgHLn8ou9coBSy7nicqfv5V+kiTbVwJXYdxvhj/6XqXNQyB0jc5Nv9fLPjfL64GQtbUHi0S8J+zTK+mq4kdcauvNQqMXbocLNZiO2xKI5337SQDdHkxVUzuBWvlpRcqVA9PMZ6RUWLn8s5NPp/OyhZPbkIzGYCQWFHV2r3Tan36ddgFjnc6GgC3I6rrxBNTi+WqN59BJiyuenIz9J80d/rHKZQekbh29tmtg6UqTA92SO7SG5xENq2HniHyCNBM+d8QbOGDyxE4vOp3ZIn0zM7fucV6Pu4e0CTI4hRKFQqEZ0qEN1Wr04MsbozUwsH1JNCZFGaryF9iViYtlhUh+r4xSRl+qpJGlNxf467ZMKohw957IBShYXVTDwWlI+fSyVeurbbgoYLqKoiWfnAsS9+n0813s1JwciHctUQS8HVUn89O+0XHmq/7PztCskMWAMaLLp5WyykUImFk9Fvi4LSLc5TiG+S76XdHSd4xGOEkrfFMvfuM/7kIBKL8UTT4z4Mke/J1tT8qnPcs7llyds3Cle8+BEP+XSPP/Mj7nU9I+9hdwJ1komBYAaavQfECEKhVqcGqph57WLlMtzVB2LoKjRJk4tA1fCMQbbFJpDNYSmUck5j9Zg/4ISuArWAK7kRX69LyPaQeM2nffszOTpyE/TyYO3ufV2IM9HSOLJj3Ppwz8i3g2fTjp8fR6l604Ag3TmhXsyhcSzvlzqReJwn0ps3pVx52mXczZ7B6eErUKOT2dO/F5pFWKOyiu68pb1awOUDjyJ3yWf/A9fIfcSA6BWe5i/5Jb5OjfeJoDepnji8ff7ygB0TGiLL3s7LxgmbLclKQOtEp74WDy5/5u+zEt/4DTgqQhQLHBGiEKhUPVStR0qPOFCgkYogBup5Y7RTnmMSCxZErASa7mhG9L8+a4WmsECN1yPgtCSdY5KzrkIVOJkZv/7ZZ17h08HnggDnLRzGyUe+Tzhj/63y+iO5BKTJP7guz2+S74lgNQqjwhP/DP/5UpN/5dLAAPlunzkScjtNEjc574tZXf53eXcUH/OEHWS0DBNLmrAlWfcjVfIp3n+6I9lqQPcZ9yUdnSd5al0zFzvNb4C/7l08sAn5vc1+9JvSDlcSSs+THFGcik+/tBHfLnEvkoPcSGarR4hCoVC1d0qoRoGbaEzHWxkvTolWNFRhUEKVZvxrNYVmgGYQ8I5GG3kDlOr5gSFdTEoXpwqAZXse7v7NEVnrYAjWUZym+ay/PRuASz+m1MhiGMpcXSuK1mgUs/SrN9SGDghgNR7PF2X/yAF65p7+GPuXOIR3QOAoGlhcnv63pd2nf2mIoxkjt7DynEVJhVdeYToW54Y2z/8y4L8GE8h3OobKzR7uc24VW8kAkRJ2uAlkos+GnX41/m1fye3QiWf+k9OB6Dg+IYhQ30z/1gIUSjU4tNgFb/dKXRuoWYfALV+QecRpkCF0obmamLNoKMK1sJNW4NzDhA3So8paOa3DvfyCh0/y40nBy2wiiSf/ndOCU/uwPvTrsC1MutQPn08lXx8xJ05cZcMpOYe/id3IRsjjGH5mkpOfcOVSz5PPOe/K5c58XuH4muIA4rou/KMg5PeMs5Tr6N5nyq1Iy22LCzb0X0lyc7smV8iG3vM4fCvVfxGe0Si5+wb4/yRsFZy1JFmAxRCFAq1OBW0+LvxVgAoDaBCaau/mmulFQCKcc7HzCyfjR+iPjAdYFJZo+QgBUHNypgc3yV3zLvopCBgdy11d174GZJ4pIvwR/9n/huW9akIPKcEBdBbQSAuKvPSuKpgMn90F8lG9zoYhxZmJtjUHJVnFKAI4Vb9NckcGxcg6E/E4e0nnjXv16g3WFCwqE5NPP6lLk1XXkEdWA5JQr1rbuWTB7az4qGGhGMfbWROKIQoFApVTacawqZbVNC8sxUByqCmpGBRyMZJpWBxbTceDSZ/4ReyDXhWfYg4fGd62PE8pXneDR8h+QeOCCByD3MnnUtfKyzzjxmbw+UsB51vJfz0HwRw+ConhSkN6xVY5oZV61W58oyqDFg2u8vle/lXJSP47C71MSrabb65jBUO1i93U5rHnXE15+g5Lxl/6P0eBUjB8Y2QJgWUi8JkmyjU4pOVAONdaPFpW/kt/m64jY9Zdq3m5p7S6chZ1hB5517gZ2aVcU5c3xvi2jXwyr93n/OuOGvLDu960tn/KSIHqNLvuN4g5133z4kKx1h05RGFe17tytNLY8AGKPOSpmlgZWanci0/rm2F0s7S7ug60+O7+OtJODaFBqK7e5v6cNdQS9TJ3xULQm4zsGhsyWun/SbXLR0aG6BTn+LJRPxjjZFSwOKYsJ1oq/376bFIJ6MxDeOK45sQji9Sw/2C9t1j5alQ2I9AA9otahEQLhT2b6KK7YrXm3iuxM/zf/QK50s8Z1F6zsbq1UZVxA+NElS7yorlcbLNoXlC+r8Di04+efSE3XPq0oq5oGRuvRJc5JPP5+SgspTWoytUBIuOJef7oOSM0pLUecEnaVHgPMMyUyDO5Rd5oe5d5sTvtTYQZiXY7Oi5iPAv3HuikHjBIWy0x8YtmXX416Qd3Wcvqx6clBY7ltWuvGxuTu6+7Og+r0N9rIQYKbrs8J3h8a77dGLukZu9DNgPLwqIMvOkDB1TJQCgnReMSggZ6Dz7JFA1IFkHdGRhYVvhZh6wsB9mRp5oaYBxfGJOl3A1oACCzp2uz+zIrj4Aw2q3X6H9QhbbbdzofgnbUOaxCZDqhtoPsN4L2yl2YvS8jda43axCFFqhFpeibb7/8CCyVTojc2S803X23xK2+05vtJ4Vi4wcBuzuM2QQBVYou7dXE6BEuc/626gAUX6dB5th5UyALmFaKpnVBVMp9mqAeNa8M17Mpm6zmTwuvTYjqs/5xFSskHtJPkqy62yPcSuUuj2ghE1H9+UkO3OfrH+BFA+NzlQ+f0wt/CcI6llEhAka7BD9o1ST/wQ6rx3C+iK0I24kOPnBOgfbFj7eSUo1rWqdvbePttFeaLMaHKNVi0S929bqiLOwgfMUFiYAGQhQAEscWFM3k/rmKtpItwPnbaLR1yZq0Wtsoe1/+vnfuNSwo1VomMhce3bPCllQN8Tm5NMvpfQ7//z8PKdffqvo8F9IKgFUqYfuSGscH9OVp4tBuZPFtAkz977dBzFXRgFQfzltV2bmxT/K+jKH90Jic3AuK1YoaSkc9zmheA3v/wsaovo1oCNMO7KBOsAGwNQotTjUG6BC9Ol+G6kuZ48VYByjLkMrsjqkdLCObekn1nIFxQxaILeQ5iZ33CgB/SD276gGKNDOO08TL+6UzgNLEH/kj3H9AsNskLJx3V3gkpNZfJ69y6aGh7z698LkWNafkv7WedqmlJa1RaZ8ViulANOVZxSmEvu3cfxzvzcAUpUgi2gce56kn/2ubGluWVCxbCUrFPscdfjX+JTngjQxuLxtLFG084iQ+lcgh854rF4gRUEQnpJ2kPrUjDIKU3tpjJopURerldphfdRlWQ9ZtdKESXsJYHuP0I4jBIVCiDL9/049E/ZB6RQ2wOiAlDC5z3qXDDpsHZ4XmABAl5dasjr8a92dG76YcK18G4FX+Gwka3cuul8rO/hopQdTh3cd4VZsJh3dlzG/T+z/NJc9+ViqMnVpWZ60QCov7Pdj0Tx/SN72098l6afugHxbaTVAacEsG9g6/BexHjQRorQaReg0IO5pTwOhY2M9QIpaf2QBj03WNmqV8ld7czKoVoOodoWRrRbPG6miZMcgQbWrrMQ39bf7QdMYGdkDH1ijUk9931XZqqIGBq73Ko5bcR0pjxAryIGpwCogXLbOOJdf4vWc/85iwLjRsieZk/tYlihdVx6AU8+rfhbvuuzLxLv+74nvkttIT/C3aU/f+1WuwcSjt7r1LU8aNe4MpCZQrS33EkkeHiGz97zJlT35aFy3ELN0PYx0CQ7PaapjofmyEKKU1ifqvtveJIgL1/BY4ASPkca57oxqwAIwwlNQrBU6Yjq4wMpTyHgtRy426byFG7g9hKj2lZWBCT3XX39jaAEc+7ByRvr5HxJ+eoyvbFUhqk7eu/5m0jPwC+K/6m7C9W3uY0NHQXcd2vOU0FEsNsx8iNVy5QFAATiVRg6WZbM7Xa6z3+zyrtnGK6EyP/dcvDbwVD4eKO3iXHE987igMHP8oRt8/PRdfDkuzOi6Kbj4zmQ9GPibcYG1ep4o6CS2NHH7m2sRg0IBZYw0z31nBBgNB4zTtBBWAsx76uDSG6ri2mp3bbbikiWlkX9m1Sd0qsME1Y6yOtJumJaMaVtRa9TtKgvM/s9y/PQ4z+68SQXoyRNl/I/8s/Q3eQPrZosWG9a6d6nuo2Ah86x9b1pvnVzvlRyMbpNt58gen5b1SDt/VkEHHEtT5wWfIt2X/zrOrXxXse6e6hw8+XHhHPyO111foWC63RCiFDfuFgG5atXKADVv2TDZIVttl1pDlJX1TTU7pUUNtY1a48woYnVbC8Q6sdhkNUUG3H/H2h2kSMkaNakGqVu59IH/IsZASstlpRUvlNcBMGMgkD78E1bNuJ00aF71cO/ufSMd/aYvp/8COURFJ0khfSKRjoymE498gcQfuJnM7f0Xkn46TPKJ6Zj2cVcGKXvnKp933VbS/eq7eG7FWxgg9c9c5tifE9oj8tjtnI8f9tfwYWFBQ1RLgFw11igKJhvb5Fi3GR21RxNCTlmBnlrFmtF9tQLa4QV2jZq1EI1Vsa0dQqc6arYALKp5qiIOjtB7VwTguV1hikIHwL8qBCF5+A4y++d/INmTT6QqB5krIUhvvh6EVQYo2J9c4nHmfz26u/jQpLrvcateHzfSHjanT3bfhpI0sbtf700e/DcX1PnLztxNMid+I7TNV8jMfX/TM/Onawl/ZHdcO8Bc75hL7WJzuDjvhk8Q7/n/phoRmHjs016AODas5pnglku+4GKc56aUKMLaecYUstLxSJKBVqsYkWRZV147pJwAshYB62ZqEcGyZuPVeqj1qBYg0+quvEmdpyOjWeiNaAsMvjCRfX+UVBdnCCNYNwudaoxekxFSfTLOCdpWE0Kn39KJHoXjHmuB3YB2MnP97yLW0oCI/9kdFKAn6bmutsOKiue8EfX5oIOFhIyE4RXIJR4j8b0fcju8LyOuM6/jnae/qgAxROxkkpUgSM9aZdwNlTr4fbeGFSpC46FkgpF4yjgozT3MxE09eEIMU+KJj/oyL76eeDdu420ODyc/Nla7KI61IJazea3w2wIPFqj5r3InSOLx7d7OC29RWKEKCpAqbzMb3avczalm/RHbHaJitEOASVXihFo8BikEVQMYVl1Qw6Q6Nx5cGMMGXE+jEmiDY91WxTbBrRcy6O6y2hnXCqKsnJddNQwol3YoIkhEzKyfWtNC9Fj6qmwLQ20K5TxoZ1gtxPXQ/1VNR5tSOBuj02gLlh8ZIO2n0SogSmmZ2lijdYnnG17GxYdE4XzXpcSQBKRGWf81gKnE/sc4sh+g5GXE4TuXONwripYb5+lXLLdxfq+xLRUqQ4WOIOg9O3M/p9GfEMIYOckt22Rgf0qwk0u9aKn9Mid+TeL3T3O+S78lAakKx8bIAwUgVUhMk+Thr0rW/VuSPfm3qY4l69zaAFp6n0+8EMvzh5X9atMebGwnfrsSLirT9dCEjsJ0tUITtfNqBRfSbYt/HqtQY6q+GgW4k1UcIySKG7JS2492zOEqOkkYuRY0uC2rN+cl1dQtpAHqd1r46bXCdkdNbqtAgWmClGsS1vRPS68X+H9stbgKgEPDUEljm3a0CQBM0us5XEsrFXVJ7iHtqXGhLYImj9dqbclmPRyP1MNKJYCUn15Ppu5brtNvJJ41b08RO+fWtzwRS/BUssrw/MzdN3KQEFOh2wUIHKL7r7rndl38jaij5xwddyt013ZSyGf4mT+9hbV+AgksO5ZdRRwdncXl+eN/FMByn2o559LXk84L/5Wu1qYDT4Toufxm/vQmIi3q3NH9SuJ7+ecV4KV2nyYe/Qrhj+5S3deF9mlKfc92i4mCP9dNUMzWbGAw7fSCVWzb7G9D1QCUsL8hq5BBYS9IrI3CEq1RRnNuWL1wqw0wt9K+U2YBSgJ8/fScjNSjQDCca2GCm+QtFldhKkeK0DmFSRNN4BasH2DxjOAIwarULnnRAPRgVPZecJ1WUTRbyyIVFSa4/1xr5j+Qfv4HJP3MT9zl0Xh5YrTmm1HFH/goC3CmiDzuUQVL+gAlYZt0VLV+gCffRXekuq/4MfGufR9xnfv24tR1+beF+d+NO7wbVRapbHRfSjNPViFP5KMW2XFk3vXbZIk+ITZLOzaq9LnAz8QZABVrFkC1G0RBywWqGVVF4cJqJ2X2j2wVosAKFKq2sSiADRJr+ZwM7z89Hw3NGVVFmZdwFW3ZEAnbGrYIN1ZcgUOkvQSdK4wQnBCmtk8G2SSImmqzfQbX6SHhfNf8WoWOV5jgvn4TMViFAQLRS0HoRNXBVzv8HiwsGsHkIRocz4QoRgkULYQids9yOXx515OuTWG+Y8nLmG60jiVrfb5Lv84rQSr19DfdrFIv5c95UmnEI7julPvOH/61Vw9GE49/zddqDwftAlFgfRqsUWdmtcEN37SrSABZDXyxOuQIMT9yywrkWIGTzVWM0qtbseF2thiYbU8ae7KrDWGgWFEA0y2YE3WFtmubbRfOd13+vwKghIUpKLw9iwLVTj3YnNv3L+5c/NlkLXMWabioQLfQXFfK67/cibtPN7Ut7+oPE8+q9xLveZ8mvlfcztvsHKeXxgG+d5/z7oR0LozgK+TTae28WSywVI9gdJ15gyyvFaRbKC8rhzqIFWMkH401+77eDhA1XsucPhTExuu8z1Y7+Z21zqIN7ierlg0T+Yesnh+rN/ShVmjbOspqHEi/xXMwSdpPxRFjCFKmQWqMgkI7aks9R0bCyDcKVGD5CQiTjYKVzNIO7rD4Qzd7SlnPq1Mhm8pAigUNgILReMO1Pk4u8AbiOucGwp3xGlICKOaeEalFyLn8Uq/NsVS2RH72mWTlVAeEyAoOK+Z3LH3ZrBzO7mWuIxd/LgkJUlkPnLQEDkJUG3RSZp7yrboa6mWWtLreoEFQm7DYEZvuAKuw8oUJSss6EWxTkCpe2+jaM33Ow20MUgP1skhpgRXrPgggBZ16/MFPklzsgCUPCUDYzD1vc2q48CZJTd3teqVclMuwQcjRua7CbyrFhqnfO7pXL1OtMTMbk/6Wn/pVevb/QqxCzJP1gEyEKGOyctGb6bit3NQnzYz+MymrQXdmjsMKqG20kG3biuVhqh7B4AsQpHa24e73VHF9L3aQupZYj5lspsAi1bBajjSJIxM6szP3k9mH3ucvWpOEzj6fPHpCb10wPB+Wm7n7HUUIY42SowAVVMRB1QGmlOVVCAOyyssXsjMay7DWr3TdaSQpZRQXzieeL87Mnnw8FX/wEyRx8EusLOwx0iKuaUy2WR9ZsZTUrZMHN5YAK+DSMxt8bAaioCOzMmR+0CSAWfnjjOAlaQikICv1KG2vvjba/WJdP+EYhvFMmjrno3TkG5zvLW22+2CBHGtUYlZw80V398K2woSRJgKsSYmDj7vIQVLs8Du6L2UCF6mcYqKY2qYCQEE4Su1zlQHQzGcsEBONll6hGHIusU8OMzZbVt+6VdB5ZQMUKPHEiL+QnYXiyG6NPY1RyJxohQsRIarGMpEaoGEQJVm/2RtlwASoRYVj32lhGyGjkNPqZV6oVS1A4dMvacNAhfMtZuuONDtuiwabj9JYoyHSPiWLhoR9Hmn1bOctDM8AoMP0oaYdckn1mbl31AikRmmyznCl/wUFJrO6xYp7Kjf3lBlSIupM7FJwksJOGaQyz4/LEkJBfJSj+5xlukWLmYCVLwOb9LMMSB/TO4CWAiiEqDr91yz+rt43fyuds1lgGbUAUUWXnkF4CFk4hp31SlFAoQ46nSAFJzOdz4DGOuGl6fFJ1N0TppYK8RiDLdzB9jS6U11gMBUR/1/UVTZIr+lWhuihep3vyy/fJP63I3SauO++e6O08+4XYCpEobMWFluwLA2ZAIMJ6f0DXIJgKapcfLhA5BYmxndFa5RNBlK52cPJ5IEvyWKSnKdcRYwlFmXEQ0kAKhd7Kmqiz4R2CjU7kBwhqkXVgJidiQYcw6hFt+EQMRZEaQWiwjUGJz/d1xCpn8urZTou2rmOiJ0VLUIrWtqqDegOkLLlrhZw1giIqnHAr2VF63jOxVJahJ7zIH3bX8VDoqggqV3dSHDj9tep9h4A1DZFBx6cf1K+err4kEFhCpa1krcOhuSNMFIYmL6XQ/yQc1m/y/wuqK1OIkgBPKUOfMeTOfE7VVC3+7ybINmmW3+dSogiimzkwn4fe8DI9VRMNkrbvOWEEFV7BVt0vyzddE1YiaTQYra0z2CljomWeTHb0U7WCk4l8DRE2sPdUa8OFq4jsU1rFtBNR9hB21YTm7MRIK/OLr0oTROwmM65eLxjNTzfYl3Taq05oQZB7cDll28aue++e2XbksCUn5Qt0kHFg0JEcg8WS0eNVRE4roKozAt/6hEgygQ0EQk8qYGqwEfj8Yc+4IPiwEp5Vn2A2F1L3cYKMSutT/LAdv74PXoPK3Cco83MRo4QhaqFpDeBekEU5KTqrzA60cponJpYJajbbpS0V7B1u3XUcO7F2JzRKiwVQYKj9doFxosAQs+51Zqq9UpvEWDM2yqA1JgAUqrriwLRaCOuPVpIWWbxz7z0R1LIvdeAS08PqspuvHziaJYFUNyKNxLXuW81sC7JeyZA5UkhMxPPJR5VZiDfRUvytI3s+HdG1VLUamUlmWlIB2L8FiwUsVrc0IRtw37tRYBqWOcaoSBktTwJ5oxqv3MOEGW1HNdAnXZriLDTP4QFkApUs2Lh935hGhKmYTpZWZ/s3gZxUfyhnxkEKO3adOJ7e9cqj7QkCwSSe87+p7R3w4eJui4eoz6etJYeszwMlHj5jbPScSFEoRarwhZ+M2jxO82bTLUB5QJAQWe+A09nU6wUIYSoRQdSlsC51gWKQRBETtihGT2kijhLAZjguoYHBSiovY1Oh+h8M1JZ2ZOHv13M7G18FVp17QRosjtdXZt2pAGcOi/4cqxn4OfE1fcGfUiTgRML1uTv08//yoUQhUIxZLEocR+FFpasdKhVufKo9QvdQs3rVMcsdqp+bL22ldX/bKAeOyOAFLiYb2Z8NWABegi1OI0QdkzlDuH7oOGLvDRCTZUcN/Ho5z2FHG+gHE3BEEi5+v7a5Vx2YY8stklrkq1bOcktVfzUL9N5/rByp3bWJ8EoQhSqubJ6UVsBkBADZuDGY9ZkX4vs71o3O1TjNIZNgOe7mRJACu4DrPCEEXDLmVzd/OCYyy86i3xtqDP6hqvOqeZhUeVyhKSf8Qc+yhkDKTk0VS7XYmTSyEwuWX8hz6eTke+wrFDhdrxoMbC8PjeCbS24X5ae0KuAEStZkAcNzqvXE60U3GqRwRlGmMAT44TOORHdT7Ua5r+QFLHwmwFstvYUDC64/vobW3HXAG4OKeb1UIgZtnIP/oe/fCLh97zov/GyY/Gf/8ErBlcHWD+isDZIvy8G5NOcVVGaYuFOFkh1bvhY3N7Z69MHKHlOKO1knFoJOrXWy3otvU/s+4KLUe5m3EKqB4QolKzjDtY5V1RDY0UAvizkjOqBVAaQb6qKp7NaBJRXMzokRiEubCX7OHVpBlsUxFGoRScBWCICyNzC+E8O0bQHpq31P7p/vfddV8ykdz+53knIM5oPDcL64V4UVjxgQTD6kLDdMM2ifrswb6sSpGYfHPJ5AqE013eNyzxIVQInGwOQ1MAkfw9uvF+nMyf+16UBqm0pdOfVXlbdX4E675eV9VdbnNSKRWhQAhRWMiaHa5Ch3CpEgeUJ8moNWy3fQkF6DP9GljWFTYCqg0YY90PRGmX43iS++fkfDpA33LLC9e2fPeNifU8BKkDYtfrg8w5xVJ8AUrAPqvgosPYkDm53QXHkzLGHEvogVQYdJfjI30vddizXHet3peX46TE+cfCLLIC6pdWykCNENVFVuL/qbSkKWvhNtXFF4SoBxsrTSS1yQ1lxCRVrOtWrxMwilZX/RASbrT0lyYzecqLWJta9JWRiHXBt3qTx9U7h+zHGuosAtWHtmeQz7z79RYilkmge4AQICbFACgRWqblHPuadufsdJPnEd0gudiAKZWLkwMWnM8cnYolHv0xm//whEn/wn0n6wI8YAKUFYXpxVBDw/mWS2H8rx3rwtFIvsJWE7rz6PQ2bzStUtxsIjfGxkueoqg6JFiWG0gZmSiKASy9ER/iZtQiNV1vAt4oC0sMIUC0BUSg83/XSCFFXLOgDlxsrAacGSEGeKbhH/UKYxHilDwvzt+u1x6eu3R/vdh879dSu816872FyKqu9AKSiu3vHiMagGBgNl37+hzCx4mNddJpXdubPxYzinjUfSHUsWecuwxHRgSn5Mtno/njyya/5NIoKx0jrVvgwLLRE1UdWLDgbKezUQ4MNPA6lwlb2l5Z56WvAtpSyOkR+DC/7mlslGg7+qKYqZOVHjSrDQ61R4Wr3m1qcfi+ZtbfS/feH91/gyOZdyYcP97r1rnVahgbgarwWxwzwE9/7QTdYpvgj43EYWVd5VB4pug/jD36CxB/+gC5AtWNKA6XQElUfwZ/ESkFKo4V4ray3KWBAixLHiLmRZ1baLkatV01RDVIqoOQatvg7hKj2hGZ4aLJS6ifW4F2Fe8zWGtyvJiS/C2rcax8U3+z6/QHPvZNrybHjz3gqPTTS+KJgdHfxQXSE1KDaAlilhMlHnhSgofsy4vRvIPauwIt297J561X2+MOeXPIFV6kEzUmvzuogdjQE5WsWwrWLlqj6QZSlJzGa5LFmomVLrPyJYjUEAytws7kB26hlO2OSx9p1qAD9Aw3+76Gad779Vfx/G3q+aQJO1eAFOoquZqJpDT4rnXfsuCwtACuGSglTULw3ILy9kpTipYwCZ6wSUCUP30HmHvvkqbMPvdcvTsmpb7r4o7sII32BVBDeEVwoAAVCS1SdrBIWhveDxJICNflD0o7d6hN9LbN1jzCe3mqtkSaf9v5268CFzguutaKLghYAboV9AujfbvX3jXLttCmsgMVjiP63R2l5nWbvU4Duj9Ucac0436OM+1l/re6ZFKDguKSWuf3C9AL9v45CbJXR9dH8S8V2iu4uxnzCdSDmqPPTdU7Q1zFa4LhmViwq6A+HAOwW2v8KIaqxfzQj2izAz5AAYiM12oe+Kn5bK6iMCMc0SayZ642o6oByiax2LMEa3tADDbpGYTtg8dkqdGZT9JyPCZ1rw290tDOFa35zFavZhbedyvcXOu0Q2nwXvWZHaeHnZgBztZUBmtEpjzHu7cEaPxBK75U3mYGmCkA1QQzEulLYGaXJPIequHfDfWWYxmotSCFE1dcyYtX6sl2ADmIVpKgFKkysu0SmFAkva9UeO+rY1rUCPrAiWvnpkPC7kWpG6Eksh1ubcL320e1upVmjx+nNFjqMSL0sVcK2+ulNepBUn7Edax1aA6rtFKLF8z1RL4sedd0N0uu8WivHZDPgTwNCzI4s9LPWR92CW+oBUBahC7YdFmAqQM/boIF+ZZJeR+GF5LZDiGqwqPVlJ7FePmQ7HaEWMmNloTFQ1d6g6uEaGyX1qUdXD+Cz6oqF4wtZhCdxUEGrlH4ZoNNW2vmJN8eI5KYvdrRRI5BFgUl0I4huhZq5C4R9aERnExCOY7iV7jXCcddif/rotFlyvqck51vq8iEUtKIGgEl6zoOktmV5muLCpxnMWf9/M5JCV5QClDI2bGc9AYom6xT/i/PlZDRgKkLbu9jmFKoCjOXGyCITQlR9NUyqq8EGN5xDQic7TiFkQlkahnbA/ZKnhGo7pakauRKVUAk5o0ZJbWrSSVWPm8yYxf3cQvNMDRkp4UMhebBGVphGaCOdRJfbNkmH2Qr/tUaoj7ReSZ7hOh5rnxb4NPmcNwqatTSubBcBSoKVgr01IGpCch57JA9yNR+pTS1dQY2+IkQMWtQoVEUICiGqnqLWKFVdoyqsAsSiq8mMhuq47nCbQFQ1sAeQsYcOLGDFH4hPfxvxH1IzTTa5Q0U1XqF23XEBZKTFxqfA+kOtQtJ+YshKTT6N7QXofT1U4WFto0kQRCFENewpsRYWokZoVx1cY1KoHLM4alFvfyN12E8rua20nuI341+groq1c4eKsqTb23wUpjQeaozx8DpuNAN6BXgK0v5nAC+Z+gnzRNVZNNB4kDQ+KZzpp/kGdUbhFl2XUiN49baFhlolPQOqMfcp4XwPtfMBUEsPPEyOS+4zIcWDd1XwJEywjT0mAWonWqHMCy1RjQGpCRrwfWcrP803qPYbgE8tYkqm6mw1G6YxS+h2a13dhG68xQVQpHVqrbHulRETIBVQzBJDCCatggwNTB8hxkMR5tOaCNMELZCMQohqWZACF9G1FCJaKYh4kgLURIPaIWKhKDFLjbAUhegNpgevYAQoVFMFVpvBVkgQSjVMgU68N+ysEkJgfWNWR+NR152RpKVTtA8KIzQhRLUrSIkXeyvESBVvTA2yQCmfuqqFqHADztcEPV/NAKkp0h5xdE2BfnThLSrdUqM0DjUTlH+RpAiI0nIw1awvYvWeJuyHkZyEcK8fRncdQtRCAKkJOgx+mDQnqSII3HfD9UhlYLANwpCYsgow2dko8JOAVKPAF87NCHUnhkj9EpS2m4rt0mqdKaruD3ktG/NGR881DUokeaU2Izw1TxhY3hyIiAoTBEeeRUqFIRvZEd0iTIFmAZRE1cQzhRt8viboE+ftDTo3wyJsCi831XGbw/T6m2pxeCq2CwJU1YLr+GZSsua1OjxdKZzvYLtbHAF0aKB3oA4ANaYDUPC/uVaAJ0xZUGehJaq5MBURXkInf9cLnUMxOzmpTyDzLgoto01w3WnJTACkVFNGElnWA3wJLe1CymVKamGZmqRQGGadG2q1g7c1t0jRYeLFtqTZxIOSqdlxYMVrFuOeanq+o/R/N0KziUvPd7MHUEzSe1S4SaVcag1PAaIovSXMi9H/W/FebDUPlEaBYuV/J1SrPFMohKh2ganizU3oMAP0piam4zeb40Ms1TBGnzzHWgicpMc8YbEo8UgLnKshClRS8AgYPJZJUq5RNmYkz1U9QUrSwYqJQUcoVAUk16BYuqNe+WZi0jZp8xxA7QRUo0RiERbOuXjfCUhe6+XCnlSc88gCa+JRxv0AHkzEeoUjNJZpxALsjOjca24W1ofpWRoo24nfrhTLhpjtTEzf6CggBEz+LFrrkWMW94M0wwIi2WexI9NSpB6JJ+t8TFbOw0QrQmGlc1WLa8fANdCQtqGdLaHnTnr+9O4lESIfAj5GO/Om/acktd3aHYjG6txO0vMcVHytd02OMc5/ZAECk0wCHIVMPPDExAdoIzAlrBse4LZrfN3UYsWLFqIKhQK2AgqFQqFQxkFJhMkJJfwI3w0T87nwAKaG9CCIuggPIUC1ltCdh0KhUCiUMXhSxSMJ8yAQPiyBGCvWYHD17aBWrJBGDqcQAlTrCUfnoVAoFAplTKx4pAEKQBEKQWNVrB/WdYi67ZRigdUuBKjmCt15KBQKhUIZkAA3YGWqNHIVLFP9pPoRrrAemVVK2D4AkziquVgGB0fhIUShUCgUCtUOENXoDjNGQWpUsg/FYH7M/4QQhUKhUCgUQlRlQVLcIbQ6tZ4wJgqFQqFQqNYWuPDGap35HIUQhUKhUChUo7SriduGgHYofDyIpwEhCoVCoVCodpNWzU+IXWpETUIIVr+TjgJEtYAwJgqFQqFQKIMSAAYqaLDKroj1/4ZI/WtPArQFMEaq+cJkmygUCoVCsYEJkmtCbiixPBBAS0QDosR5g0S/vp2ejNYT7aH7NIZnCSEKhUKhUKhWFFiWzBTe3kgBKkSnrSa3B7+/mZSsWX3Y/K0vdOehUCgUCqWQolYdWIjAjddPjFmKwN0GFimwZIWJOfee+FsAqc06yy1Bd17zhYHlKBQKhUKpFaCvUwKs9AsTJL0EiLqQlPI26QmgaQ9dR5CYCzqH34rWrFs0lrkJAQohCoVCoVCoVldE+kGAlwkAKuHtWaRyyoPtpGRRGjQAXlIV3YLCdoaF1yvpdsbp65VYL691hO48FAqFQqEYEjOUC9Bi01kmSEouO70YpkkKUoMUrIzqSizv0tpCSxQKhUKhUGztoqAU0lqAQg64+W7XWU8xUSYpWbXAshQzuP1hPAUIUSgUCoVCtaNGjMAMxCcJ0xAFpCmNxYqJMkkpRgomI3FSA1jqBSEKhUKhUKi2E7UygYWpT4CZYYPLg1VKL1ZqG4Uyo3FSCFEtLIyJQqFQKBRKRzRLOcCM4Szhwm/AMqUX/wSWqBBdb5iw0yDEhO358Qy0rtAShUKhUCiUvoKkFM8UNvoDAX7AFQjpELTcexAnNUbfA0hBOgNlrNQQNn1rCy1RKBQKhUIpBCVflFYnYR5AVNjMiDlaOgZ+p5c48yYxbQEd7QeKCPMieCYQolAoFAqFajeIAoABNx6UfhkVgYoFVwbXN0xK8VAVQQqFEIVCoVAoVDtDVEh42SGZtUsJVBbWCcHkAEo9CFIIUSgUCoVCLVSIAjdcRAN4dlGYCltYbz8FqY0IUghRKBQKhUItVJCC4PCtOotAILhonRo1CWiw/IDGOvsxHgohCoVCoVCodoaogPByyODipoGKBqpvYXx1O03eiUKIQqFQKBSqbUEKgGizyZ8ZBioNa9e48Lsgtj5CFAqFQqFQ7QxRADN7qlhFRaCiAecAU2IR42vNuAdRCFEoFKrBuv76G4cJe8g13PRhaDeMQBr90Y9+EFb8TtqpjAvfBxXfhUip9IU0cBYSDkaEaUxYflhYzuiNBxIQjul0YuK+wjIjwrqjjGODemZBoj+8fF7COmyS4wmQUsJDOJ4BxfHANsPC8mMG2trI8V4J6zK47Hy76yw/Tl9H6X5GFfs0RtgxOWa3I24LzsOwcjuS7SkDta8Vlh01cG1eaaSN6wxSEQngVCMRqJi5pmjQeRTjodpHmLEchUIp1UM7V3Bh7BA6tAlh6jcACiEKO1uIeuRRH13ntjrt6zZSzv5cK8gMUTDYyoCNPnqce4TlRlr0PA7QCUqPROjx1HNb0E4TFJZYGiTykW7tFPMzXMPrtXjdAJiBK4+CU1ECPE0gQLWXOrAJUCgUKRVCld68gxJwACAaBZDSsTIEiDynziR94hYF30utU7coViHCFVh4wpL5YwyLh3RePynHq2wES5iG1UI5LySxLNzCOJ5+xfHE6H5FJUAgHstWYfmIsF0jMKU8PqkiVSzLWr6fnsceOhWBWNjPCcZvb6liOwEJIPXRth0xACID9JqaaIP/xyg9pp4arrOPgudWAaSgTUNmMqGjEKJQKFTrSOWWoiAxZqBzFKFC1C5hXYM6sAUus2HFfBGiIozvglIYYnwvDcwNMoCJ0GMbU6yzj7UvjA4fgDCoAMhhhctpWKdtZFCisb1ql9VqO2XJkWHFuSI6bWBmOyEJdPoZ5z0ogdZxCaAP0euqpQXJNelIuq11WD20xzACVHsK3XkoFIopaiEY1AAlpaQdZ1hnnZE673at1i8djRVkWeAoSEzSjz0K2GuVcxilkBJjHFct5a/wvdR1B/sjFuXdouP+azXV2m0LbQCJNYMIUO2rDkWQKAqFal/osdVhnRDoLH7sNwgvw/Q3UYX1ouYAJQlkFzVWo3XOWwm0XJhU4OYR3XpBA9v3a8BWlOHW8uuA2USF/ZKBFA0i3yweH8PqqLWdiueNWiyHtc4BtT6K8AZWygi1Hm6XANZwq/+/IFbp8ss3gdt7S5WrAqAdEdY3jHetBQBR2AQoFKqCRPdLTwWYEGNGACruZHS2xbiPWoy0oq40aVwS6KY6gNpEld8rtVHjoXWcQpiRZUFXmgTGCaJvhdLazi1agCOcgyG6z9L13s44v1LIDUtet0u+bxegCNNjtjpSDyBsyGrtPVTrCd15KBSqkgJGrB0UaqZ0FoOOZ1SMi6pS2yQABdu8VpmKoUbCzk5b2yUABdaVm4VzwBpxNyQ5T2PU6tVPyikY+uo8crBmArebMMH1eyEAY4XrXQnJZwm/DSFALSyhJQqFQmmKxquIT92TFUAKLBAB6t6Rxrn4qaUBoKeHwla18SXS4OQxrXxDNQCnYIVl+02ue5Kwh/ZHTSwLMmsBkx5HxMR2IjrrlLq2mKMTKRyJFswwPfc7NEAr3A7/ieju3onffKbYLnDNDb/u031wDYSIOoXDfNtizNMChih647NhU6BQKIYGzXbcrCHrQmcKkLBHAlXVKkT3p5h3B2J+amWJgv2XxIHBMHy/TvyR2faJmnBnRmvk+gxIgDPGcHla3Y7ozgPIhvQSw4xRflI4A8jS2o5eeopW0wgFwWIetd98ZmoXBaohAaiC9JqAVxhxF8ZbyMIWuvNQKJRW59tP5Bajajq4YC33jYKAtMMeMZIQ1IR2KjpNVvsAyIkuxRipcbLPGp1DP5Hn6xqt4TkQR/6J2iYNUKfvxfbZSZcHqLpSMt2iAOOWl//q6TApj3YkIkwJ00kBqELCBEHjAQSoxSF056FQqGIHphihBdYL6SikyUqWHloWRJoME1776bSlRjAm7cRH6D5DJwYWqTAx717T0jApu2e20O3A+idIObmkNIv5iNHRciYUoAH0LEUY50O5vJ+CSY8E9IZrsB3pOYAYJ4gNEvMnFWPeGIAVFpdXrGKMBqiL7TzMsJSFGKMHI3WKgTMqAGtl9n2xBFEEbycIUSgUanFJb9h2MdmkwfUMSOCCVeJlZ41dNiFSrscGLqERjeBms4AWoZ27OOKwj2iXrNlpMlmlUeltc5yoY4j0locOflBj9KLZ7SjbakhideqhIBWSXFNTFc55WAJhIQbobbGyX3VWWNFmYLkc9l89jQC1yITuPBQKpdXpQkcFaQP6DVpZbifaweeTdF2hWu4kw+IBJVgGa7Ru6Cj7aQfJGoW1qx7HVGNBu4PLLFDneKOQAqS3Sz5XGkQg/b4t6ulRWNpJ/yMXCp9DCFCLU/9fgAEAIi7p7N13F3wAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**InovaSES**\n",
    "\n",
    "![](images/INOVASES-DF.png)\n",
    "\n",
    "InovaSES supports this project  <http://www.saude.df.gov.br/inovases-quem-somos/>\n",
    "\n",
    "Support to new ideas, innovative projects and entrepreneurship of Health Department Services."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1- Get Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import**\n",
    "\n",
    "Import the libraries and relate dBase III files on \"files\" list using (glob) function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from simpledbf import Dbf5\n",
    "import glob\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DBF_NAIH\\\\SPDF1001.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1002.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1003.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1004.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1005.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1006.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1007.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1008.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1009.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1010.dbf']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = glob.glob('DBF_NAIH\\\\*.dbf')\n",
    "files[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Transformation of Data**\n",
    "\n",
    "In order to manipulate (.dbf) files, the Project Simpledbf python library was chosen. This project is kept on PyPI Python repository: <https://pypi.org/project/simpledbf/>.\n",
    "\n",
    "Therefore, using the library all (.dbf) files turned into Pandas Dataframes using the function Dbf5().to_dataframe(), excluding all selected columns from the list (columns_excl) and removing using (drop). After the transformation, each Dataframe was inserted into variable (df_int) list.\n",
    "\n",
    "The codec \"latin-1\" was set due the information dBase III source format.\n",
    "\n",
    "**The Original Dataset SUS**\n",
    "\n",
    "The original dataset has 37 columns, but only 9 was selected to analysis.\n",
    "The 9 columns:\n",
    "\n",
    "- Hospitalization Year (SP_AA)\n",
    "- Hospitalization Month (SP_MM)\n",
    "- Health Institution Code (SP_CNES)\n",
    "- Hospitalization Unique Code (SP_NAIH)\n",
    "- Patient Admission Date (SP_DTINTER)\n",
    "- Patient Discharge Date (SP_DTSAIDA)\n",
    "- Amount of Professional Act (SP_QTD_ATO)\n",
    "- Value of Professional Act (SP_VALATO)\n",
    "- Origin of Patient (SP_DES_HOS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPDF1001.dbf processado.\n",
      "SPDF1002.dbf processado.\n",
      "SPDF1003.dbf processado.\n",
      "SPDF1004.dbf processado.\n",
      "SPDF1005.dbf processado.\n",
      "SPDF1006.dbf processado.\n",
      "SPDF1007.dbf processado.\n",
      "SPDF1008.dbf processado.\n",
      "SPDF1009.dbf processado.\n",
      "SPDF1010.dbf processado.\n",
      "SPDF1011.dbf processado.\n",
      "SPDF1012.dbf processado.\n",
      "SPDF1101.dbf processado.\n",
      "SPDF1102.dbf processado.\n",
      "SPDF1103.dbf processado.\n",
      "SPDF1104.dbf processado.\n",
      "SPDF1105.dbf processado.\n",
      "SPDF1106.dbf processado.\n",
      "SPDF1107.dbf processado.\n",
      "SPDF1108.dbf processado.\n",
      "SPDF1109.dbf processado.\n",
      "SPDF1110.dbf processado.\n",
      "SPDF1111.dbf processado.\n",
      "SPDF1112.dbf processado.\n",
      "SPDF1201.dbf processado.\n",
      "SPDF1202.dbf processado.\n",
      "SPDF1203.dbf processado.\n",
      "SPDF1204.dbf processado.\n",
      "SPDF1205.dbf processado.\n",
      "SPDF1206.dbf processado.\n",
      "SPDF1207.dbf processado.\n",
      "SPDF1208.dbf processado.\n",
      "SPDF1209.dbf processado.\n",
      "SPDF1210.dbf processado.\n",
      "SPDF1211.dbf processado.\n",
      "SPDF1212.dbf processado.\n",
      "SPDF1301.dbf processado.\n",
      "SPDF1302.dbf processado.\n",
      "SPDF1303.dbf processado.\n",
      "SPDF1304.dbf processado.\n",
      "SPDF1305.dbf processado.\n",
      "SPDF1306.dbf processado.\n",
      "SPDF1307.dbf processado.\n",
      "SPDF1308.dbf processado.\n",
      "SPDF1309.dbf processado.\n",
      "SPDF1310.dbf processado.\n",
      "SPDF1311.dbf processado.\n",
      "SPDF1312.dbf processado.\n",
      "SPDF1401.dbf processado.\n",
      "SPDF1402.dbf processado.\n",
      "SPDF1403.dbf processado.\n",
      "SPDF1404.dbf processado.\n",
      "SPDF1405.dbf processado.\n",
      "SPDF1406.dbf processado.\n",
      "SPDF1407.dbf processado.\n",
      "SPDF1408.dbf processado.\n",
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      "SPDF1709.dbf processado.\n",
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      "SPDF1711.dbf processado.\n",
      "SPDF1712.dbf processado.\n",
      "SPDF1801.dbf processado.\n",
      "SPDF1802.dbf processado.\n",
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      "SPDF1804.dbf processado.\n",
      "SPDF1805.dbf processado.\n",
      "SPDF1806.dbf processado.\n",
      "SPDF1807.dbf processado.\n",
      "SPDF1808.dbf processado.\n",
      "SPDF1809.dbf processado.\n",
      "SPDF1810.dbf processado.\n",
      "SPDF1811.dbf processado.\n",
      "SPDF1812.dbf processado.\n"
     ]
    }
   ],
   "source": [
    "df_int = []\n",
    "columns_excl=['SP_GESTOR', 'SP_UF', 'SP_PROCREA', 'SP_NUM_PR', 'SP_TIPO',\n",
    "       'SP_CPFCGC', 'SP_ATOPROF', 'SP_TP_ATO', 'SP_PTSP',\n",
    "       'SP_NF', 'SP_M_HOSP', 'SP_M_PAC', 'SP_DES_PAC', 'SP_COMPLEX',\n",
    "       'SP_FINANC', 'SP_CO_FAEC', 'SP_PF_CBO','SP_PF_DOC', 'SP_PJ_DOC', \n",
    "       'IN_TP_VAL', 'SEQUENCIA', 'REMESSA', 'SP_CIDPRI', \n",
    "       'SP_CIDSEC', 'SP_QT_PROC', 'SP_U_AIH']\n",
    "\n",
    "for file in files:\n",
    "    df_int.append(Dbf5(file, codec='latin-1').to_dataframe().drop(columns_excl,axis=1))\n",
    "    \n",
    "    print(file[-12:]+' processado.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**General view of first Dataframe on list (df_int[0])**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>SP_AA</th>\n",
       "      <th>SP_MM</th>\n",
       "      <th>SP_CNES</th>\n",
       "      <th>SP_NAIH</th>\n",
       "      <th>SP_DTINTER</th>\n",
       "      <th>SP_DTSAIDA</th>\n",
       "      <th>SP_QTD_ATO</th>\n",
       "      <th>SP_VALATO</th>\n",
       "      <th>SP_DES_HOS</th>\n",
       "      <th>SERV_CLA\u0000L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>239.23</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>46.66</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>13.99</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>9.33</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>42.00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>214.20</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>15</td>\n",
       "      <td>93.34</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>15</td>\n",
       "      <td>180.00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>11</td>\n",
       "      <td>267.13</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309644</td>\n",
       "      <td>20091203</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>263.01</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>74.71</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>22.41</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>14.94</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>61.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>6</td>\n",
       "      <td>29.89</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309666</td>\n",
       "      <td>20091217</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>2672197</td>\n",
       "      <td>5309101894790</td>\n",
       "      <td>20091221</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>122.58</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>2672197</td>\n",
       "      <td>5309101894790</td>\n",
       "      <td>20091221</td>\n",
       "      <td>20091223</td>\n",
       "      <td>2</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>2672197</td>\n",
       "      <td>5309101894790</td>\n",
       "      <td>20091221</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>26.10</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>2672197</td>\n",
       "      <td>5309101894790</td>\n",
       "      <td>20091221</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>2672197</td>\n",
       "      <td>5309101894790</td>\n",
       "      <td>20091221</td>\n",
       "      <td>20091223</td>\n",
       "      <td>1</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>2</td>\n",
       "      <td>285.60</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>562.78</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>95.35</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>28.60</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>19.07</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>77.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>5</td>\n",
       "      <td>38.14</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309677</td>\n",
       "      <td>20091213</td>\n",
       "      <td>20091218</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309688</td>\n",
       "      <td>20091220</td>\n",
       "      <td>20091225</td>\n",
       "      <td>1</td>\n",
       "      <td>252.08</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309688</td>\n",
       "      <td>20091220</td>\n",
       "      <td>20091225</td>\n",
       "      <td>1</td>\n",
       "      <td>53.19</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309688</td>\n",
       "      <td>20091220</td>\n",
       "      <td>20091225</td>\n",
       "      <td>1</td>\n",
       "      <td>15.95</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309688</td>\n",
       "      <td>20091220</td>\n",
       "      <td>20091225</td>\n",
       "      <td>1</td>\n",
       "      <td>10.63</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309688</td>\n",
       "      <td>20091220</td>\n",
       "      <td>20091225</td>\n",
       "      <td>1</td>\n",
       "      <td>44.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309688</td>\n",
       "      <td>20091220</td>\n",
       "      <td>20091225</td>\n",
       "      <td>5</td>\n",
       "      <td>21.30</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309688</td>\n",
       "      <td>20091220</td>\n",
       "      <td>20091225</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309688</td>\n",
       "      <td>20091220</td>\n",
       "      <td>20091225</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309688</td>\n",
       "      <td>20091220</td>\n",
       "      <td>20091225</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>0010456</td>\n",
       "      <td>5309101309688</td>\n",
       "      <td>20091220</td>\n",
       "      <td>20091225</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SP_AA SP_MM  SP_CNES        SP_NAIH SP_DTINTER SP_DTSAIDA  SP_QTD_ATO  \\\n",
       "0   2010    01  0010456  5309101309644   20091203   20091218           1   \n",
       "1   2010    01  0010456  5309101309644   20091203   20091218           1   \n",
       "2   2010    01  0010456  5309101309644   20091203   20091218           1   \n",
       "3   2010    01  0010456  5309101309644   20091203   20091218           1   \n",
       "4   2010    01  0010456  5309101309644   20091203   20091218           1   \n",
       "5   2010    01  0010456  5309101309644   20091203   20091218           1   \n",
       "6   2010    01  0010456  5309101309644   20091203   20091218          15   \n",
       "7   2010    01  0010456  5309101309644   20091203   20091218          15   \n",
       "8   2010    01  0010456  5309101309644   20091203   20091218          11   \n",
       "9   2010    01  0010456  5309101309644   20091203   20091218           1   \n",
       "10  2010    01  0010456  5309101309644   20091203   20091218           1   \n",
       "11  2010    01  0010456  5309101309644   20091203   20091218           1   \n",
       "12  2010    01  0010456  5309101309644   20091203   20091218           1   \n",
       "13  2010    01  0010456  5309101309666   20091217   20091223           1   \n",
       "14  2010    01  0010456  5309101309666   20091217   20091223           1   \n",
       "15  2010    01  0010456  5309101309666   20091217   20091223           1   \n",
       "16  2010    01  0010456  5309101309666   20091217   20091223           1   \n",
       "17  2010    01  0010456  5309101309666   20091217   20091223           1   \n",
       "18  2010    01  0010456  5309101309666   20091217   20091223           6   \n",
       "19  2010    01  0010456  5309101309666   20091217   20091223           1   \n",
       "20  2010    01  0010456  5309101309666   20091217   20091223           1   \n",
       "21  2010    01  0010456  5309101309666   20091217   20091223           1   \n",
       "22  2010    01  0010456  5309101309666   20091217   20091223           1   \n",
       "23  2010    01  0010456  5309101309666   20091217   20091223           1   \n",
       "24  2010    01  2672197  5309101894790   20091221   20091223           1   \n",
       "25  2010    01  2672197  5309101894790   20091221   20091223           2   \n",
       "26  2010    01  2672197  5309101894790   20091221   20091223           1   \n",
       "27  2010    01  2672197  5309101894790   20091221   20091223           1   \n",
       "28  2010    01  2672197  5309101894790   20091221   20091223           1   \n",
       "29  2010    01  0010456  5309101309677   20091213   20091218           2   \n",
       "30  2010    01  0010456  5309101309677   20091213   20091218           1   \n",
       "31  2010    01  0010456  5309101309677   20091213   20091218           1   \n",
       "32  2010    01  0010456  5309101309677   20091213   20091218           1   \n",
       "33  2010    01  0010456  5309101309677   20091213   20091218           1   \n",
       "34  2010    01  0010456  5309101309677   20091213   20091218           1   \n",
       "35  2010    01  0010456  5309101309677   20091213   20091218           5   \n",
       "36  2010    01  0010456  5309101309677   20091213   20091218           2   \n",
       "37  2010    01  0010456  5309101309677   20091213   20091218           1   \n",
       "38  2010    01  0010456  5309101309677   20091213   20091218           1   \n",
       "39  2010    01  0010456  5309101309677   20091213   20091218           1   \n",
       "40  2010    01  0010456  5309101309688   20091220   20091225           1   \n",
       "41  2010    01  0010456  5309101309688   20091220   20091225           1   \n",
       "42  2010    01  0010456  5309101309688   20091220   20091225           1   \n",
       "43  2010    01  0010456  5309101309688   20091220   20091225           1   \n",
       "44  2010    01  0010456  5309101309688   20091220   20091225           1   \n",
       "45  2010    01  0010456  5309101309688   20091220   20091225           5   \n",
       "46  2010    01  0010456  5309101309688   20091220   20091225           1   \n",
       "47  2010    01  0010456  5309101309688   20091220   20091225           1   \n",
       "48  2010    01  0010456  5309101309688   20091220   20091225           1   \n",
       "49  2010    01  0010456  5309101309688   20091220   20091225           1   \n",
       "\n",
       "    SP_VALATO SP_DES_HOS  SERV_CLA\u0000L  \n",
       "0      239.23          1         NaN  \n",
       "1       46.66          1         NaN  \n",
       "2       13.99          1         NaN  \n",
       "3        9.33          1         NaN  \n",
       "4       42.00          1         NaN  \n",
       "5      214.20          1         NaN  \n",
       "6       93.34          1         NaN  \n",
       "7      180.00          1         NaN  \n",
       "8      267.13          1         NaN  \n",
       "9        0.00          1         NaN  \n",
       "10       0.00          1         NaN  \n",
       "11       0.00          1         NaN  \n",
       "12       0.00          1         NaN  \n",
       "13     263.01          0         NaN  \n",
       "14      74.71          0         NaN  \n",
       "15      22.41          0         NaN  \n",
       "16      14.94          0         NaN  \n",
       "17      61.00          0         NaN  \n",
       "18      29.89          0         NaN  \n",
       "19       0.00          0         NaN  \n",
       "20       0.00          0         NaN  \n",
       "21       0.00          0         NaN  \n",
       "22       0.00          0         NaN  \n",
       "23       0.00          0         NaN  \n",
       "24     122.58          0         NaN  \n",
       "25      16.00          0         NaN  \n",
       "26      26.10          0         NaN  \n",
       "27       7.83          0         NaN  \n",
       "28      15.00          0         NaN  \n",
       "29     285.60          0         NaN  \n",
       "30     562.78          0         NaN  \n",
       "31      95.35          0         NaN  \n",
       "32      28.60          0         NaN  \n",
       "33      19.07          0         NaN  \n",
       "34      77.00          0         NaN  \n",
       "35      38.14          0         NaN  \n",
       "36       0.00          0         NaN  \n",
       "37       0.00          0         NaN  \n",
       "38       0.00          0         NaN  \n",
       "39       0.00          0         NaN  \n",
       "40     252.08          0         NaN  \n",
       "41      53.19          0         NaN  \n",
       "42      15.95          0         NaN  \n",
       "43      10.63          0         NaN  \n",
       "44      44.00          0         NaN  \n",
       "45      21.30          0         NaN  \n",
       "46       0.00          0         NaN  \n",
       "47       0.00          0         NaN  \n",
       "48       0.00          0         NaN  \n",
       "49       0.00          0         NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int[0].head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Concatenation of Data**\n",
    "\n",
    "For the best analysis, it's necessary to join all 108 Dataframes in just one, for this purpose the pandas function (concat) was chosen. For analysis the (SERV_CLAL) column was removed.\n",
    "\n",
    "After de concatenation, at the moment there were 20379185 rows of data on only one Dataframe (df_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int = pd.concat(df_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int.drop(df_int.columns[9],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20376185 entries, 0 to 199618\n",
      "Data columns (total 9 columns):\n",
      "SP_AA         object\n",
      "SP_MM         object\n",
      "SP_CNES       object\n",
      "SP_NAIH       object\n",
      "SP_DTINTER    object\n",
      "SP_DTSAIDA    object\n",
      "SP_QTD_ATO    int64\n",
      "SP_VALATO     float64\n",
      "SP_DES_HOS    object\n",
      "dtypes: float64(1), int64(1), object(7)\n",
      "memory usage: 1.5+ GB\n"
     ]
    }
   ],
   "source": [
    "df_int.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conversion** \n",
    "\n",
    "Conversion of (SP_CNES), (SP_NAIH), (SP_DES_HOS) to numeric format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['SP_CNES','SP_NAIH','SP_DES_HOS']:\n",
    "    df_int[col] = df_int[col].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SP_AA         0\n",
       "SP_MM         0\n",
       "SP_CNES       0\n",
       "SP_NAIH       0\n",
       "SP_DTINTER    0\n",
       "SP_DTSAIDA    0\n",
       "SP_QTD_ATO    0\n",
       "SP_VALATO     0\n",
       "SP_DES_HOS    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2- Monthly Value of Hospitalizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Monthly Value file**\n",
    "\n",
    "For future analysis, was created another file with all values grouped by month and year.\n",
    "\n",
    "It first group all data by year (SP_AA) and month (SP_MM) and aggregate the hospitalizations costs (SP_VALATO) applying the (sum) function. Therefore, make a (DATE) column getting information from (SP_AA) and (SP_MM)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_value_month = df_int.groupby(['SP_AA','SP_MM'],as_index = False)['SP_VALATO'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_value_month['DATE'] = pd.to_datetime(df_value_month['SP_AA'] + df_value_month['SP_MM'].apply(lambda x: x.zfill(2)), format='%Y%m')\n",
    "df_value_month.drop(columns = ['SP_AA','SP_MM'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_value_month.columns = ['VALOR_MENSAL','DATE']\n",
    "df_value_month.to_csv('INT_VALOR_MENSAL.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3- Data Manipulation\n",
    "\n",
    "2 new Datasets will be created from rows and column manipulation.\n",
    "\n",
    "- **Int_2010_to_2018.csv**: Dataset with all hospitalizations informations.\n",
    "- **INT_VALOR_MENSAL.csv**: Dataset with all Monthly and Yearly values of hospitalizations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Grouping Rows**\n",
    "\n",
    "The Dataframe has about 20 million of rows. In order to reduce that, the all data was grouped by (SP_NAIH) because it has unique numbers codes. In fact, the whole data was reduced to 1685512 million, for reach this goal the functions (groupby) and\n",
    "(agg) was used to aggregate data by first parameter (first) and (sum)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int_new = df_int.groupby('SP_NAIH',as_index = False).agg({\n",
    "                                 'SP_AA': 'first',\n",
    "                                 'SP_MM': 'first',\n",
    "                                 'SP_CNES':'first', \n",
    "                                 'SP_DTINTER': 'first',\n",
    "                                 'SP_DTSAIDA': 'first',\n",
    "                                 'SP_QTD_ATO': 'sum',\n",
    "                                 'SP_VALATO': 'sum',\n",
    "                                 'SP_DES_HOS': 'first'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SP_NAIH</th>\n",
       "      <th>SP_AA</th>\n",
       "      <th>SP_MM</th>\n",
       "      <th>SP_CNES</th>\n",
       "      <th>SP_DTINTER</th>\n",
       "      <th>SP_DTSAIDA</th>\n",
       "      <th>SP_QTD_ATO</th>\n",
       "      <th>SP_VALATO</th>\n",
       "      <th>SP_DES_HOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5309100004120</td>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>10464</td>\n",
       "      <td>20091202</td>\n",
       "      <td>20100108</td>\n",
       "      <td>112</td>\n",
       "      <td>1199.71</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5309100026318</td>\n",
       "      <td>2010</td>\n",
       "      <td>05</td>\n",
       "      <td>10464</td>\n",
       "      <td>20100410</td>\n",
       "      <td>20100422</td>\n",
       "      <td>35</td>\n",
       "      <td>447.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5309100040783</td>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>10464</td>\n",
       "      <td>20091117</td>\n",
       "      <td>20091119</td>\n",
       "      <td>4</td>\n",
       "      <td>262.54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5309100040838</td>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>10464</td>\n",
       "      <td>20091116</td>\n",
       "      <td>20091123</td>\n",
       "      <td>20</td>\n",
       "      <td>834.02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5309100040849</td>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>10464</td>\n",
       "      <td>20091109</td>\n",
       "      <td>20091116</td>\n",
       "      <td>29</td>\n",
       "      <td>538.45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SP_NAIH SP_AA SP_MM  SP_CNES SP_DTINTER SP_DTSAIDA  SP_QTD_ATO  \\\n",
       "0  5309100004120  2010    01    10464   20091202   20100108         112   \n",
       "1  5309100026318  2010    05    10464   20100410   20100422          35   \n",
       "2  5309100040783  2010    01    10464   20091117   20091119           4   \n",
       "3  5309100040838  2010    01    10464   20091116   20091123          20   \n",
       "4  5309100040849  2010    01    10464   20091109   20091116          29   \n",
       "\n",
       "   SP_VALATO  SP_DES_HOS  \n",
       "0    1199.71           0  \n",
       "1     447.68           0  \n",
       "2     262.54           0  \n",
       "3     834.02           0  \n",
       "4     538.45           0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename the columns\n",
    "df_int_new.columns = ['NAIH','ANO','MES','CNES','DTINTER','DTSAIDA','QTD_PROD','VALOR_AIH','DF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>20091117</td>\n",
       "      <td>20091119</td>\n",
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       "      <th>3</th>\n",
       "      <td>5309100040838</td>\n",
       "      <td>2010</td>\n",
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       "      <td>10464</td>\n",
       "      <td>20091116</td>\n",
       "      <td>20091123</td>\n",
       "      <td>20</td>\n",
       "      <td>834.02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5309100040849</td>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>10464</td>\n",
       "      <td>20091109</td>\n",
       "      <td>20091116</td>\n",
       "      <td>29</td>\n",
       "      <td>538.45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            NAIH   ANO MES   CNES   DTINTER   DTSAIDA  QTD_PROD  VALOR_AIH  DF\n",
       "0  5309100004120  2010  01  10464  20091202  20100108       112    1199.71   0\n",
       "1  5309100026318  2010  05  10464  20100410  20100422        35     447.68   0\n",
       "2  5309100040783  2010  01  10464  20091117  20091119         4     262.54   0\n",
       "3  5309100040838  2010  01  10464  20091116  20091123        20     834.02   0\n",
       "4  5309100040849  2010  01  10464  20091109  20091116        29     538.45   0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Date Field**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int_new['DTSAIDA'] = pd.to_datetime(df_int_new['DTSAIDA'], format='%Y%m%d')\n",
    "df_int_new['DTINTER'] = pd.to_datetime(df_int_new['DTINTER'], format='%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#zfill function to fill with 0 the only one character months like January 1 or July 7\n",
    "df_int_new['DATE'] = pd.to_datetime(df_int_new['ANO'] + df_int_new['MES'].apply(lambda x: x.zfill(2)) + '01', format='%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>NAIH</th>\n",
       "      <th>ANO</th>\n",
       "      <th>MES</th>\n",
       "      <th>CNES</th>\n",
       "      <th>DTINTER</th>\n",
       "      <th>DTSAIDA</th>\n",
       "      <th>QTD_PROD</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5309100004120</td>\n",
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       "      <td>01</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-12-02</td>\n",
       "      <td>2010-01-08</td>\n",
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       "      <td>2010-01-01</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5309100026318</td>\n",
       "      <td>2010</td>\n",
       "      <td>05</td>\n",
       "      <td>10464</td>\n",
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       "      <td>2010-04-22</td>\n",
       "      <td>35</td>\n",
       "      <td>447.68</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-05-01</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5309100040783</td>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-11-17</td>\n",
       "      <td>2009-11-19</td>\n",
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       "      <td>262.54</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5309100040838</td>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-11-16</td>\n",
       "      <td>2009-11-23</td>\n",
       "      <td>20</td>\n",
       "      <td>834.02</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5309100040849</td>\n",
       "      <td>2010</td>\n",
       "      <td>01</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-11-09</td>\n",
       "      <td>2009-11-16</td>\n",
       "      <td>29</td>\n",
       "      <td>538.45</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            NAIH   ANO MES   CNES    DTINTER    DTSAIDA  QTD_PROD  VALOR_AIH  \\\n",
       "0  5309100004120  2010  01  10464 2009-12-02 2010-01-08       112    1199.71   \n",
       "1  5309100026318  2010  05  10464 2010-04-10 2010-04-22        35     447.68   \n",
       "2  5309100040783  2010  01  10464 2009-11-17 2009-11-19         4     262.54   \n",
       "3  5309100040838  2010  01  10464 2009-11-16 2009-11-23        20     834.02   \n",
       "4  5309100040849  2010  01  10464 2009-11-09 2009-11-16        29     538.45   \n",
       "\n",
       "   DF       DATE  \n",
       "0   0 2010-01-01  \n",
       "1   0 2010-05-01  \n",
       "2   0 2010-01-01  \n",
       "3   0 2010-01-01  \n",
       "4   0 2010-01-01  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove the column ANO (Year) and MES (Month)\n",
    "df_int_new.drop(columns = ['ANO','MES'],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>2010-04-10</td>\n",
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       "      <td>35</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>10464</td>\n",
       "      <td>2009-11-17</td>\n",
       "      <td>2009-11-19</td>\n",
       "      <td>4</td>\n",
       "      <td>262.54</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5309100040838</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-11-16</td>\n",
       "      <td>2009-11-23</td>\n",
       "      <td>20</td>\n",
       "      <td>834.02</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5309100040849</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-11-09</td>\n",
       "      <td>2009-11-16</td>\n",
       "      <td>29</td>\n",
       "      <td>538.45</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5309100040904</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-11-27</td>\n",
       "      <td>2009-12-09</td>\n",
       "      <td>22</td>\n",
       "      <td>482.45</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5309100040970</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-12-08</td>\n",
       "      <td>2009-12-09</td>\n",
       "      <td>7</td>\n",
       "      <td>786.02</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5309100041069</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-12-04</td>\n",
       "      <td>2009-12-15</td>\n",
       "      <td>70</td>\n",
       "      <td>723.84</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5309100041135</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-12-13</td>\n",
       "      <td>2009-12-14</td>\n",
       "      <td>8</td>\n",
       "      <td>1040.19</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5309100041146</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-11-24</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>24</td>\n",
       "      <td>834.02</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5309100041157</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-12-23</td>\n",
       "      <td>2009-12-24</td>\n",
       "      <td>9</td>\n",
       "      <td>786.02</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5309100041179</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>2010-01-11</td>\n",
       "      <td>12</td>\n",
       "      <td>294.54</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5309100041223</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-12-21</td>\n",
       "      <td>2010-01-10</td>\n",
       "      <td>83</td>\n",
       "      <td>671.65</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5309100041234</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-01-17</td>\n",
       "      <td>2010-01-19</td>\n",
       "      <td>9</td>\n",
       "      <td>498.45</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5309100041245</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-12-25</td>\n",
       "      <td>2010-01-21</td>\n",
       "      <td>82</td>\n",
       "      <td>1142.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5309100041256</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>2010-01-18</td>\n",
       "      <td>36</td>\n",
       "      <td>890.02</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5309100041267</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-01-30</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>5</td>\n",
       "      <td>786.02</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5309100041289</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>2010-02-04</td>\n",
       "      <td>103</td>\n",
       "      <td>1255.07</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5309100041300</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-12-27</td>\n",
       "      <td>2010-02-11</td>\n",
       "      <td>192</td>\n",
       "      <td>2071.06</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5309100041311</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>2010-02-17</td>\n",
       "      <td>33</td>\n",
       "      <td>302.54</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5309100041322</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-02-02</td>\n",
       "      <td>2010-02-10</td>\n",
       "      <td>24</td>\n",
       "      <td>288.97</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5309100041333</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-01-29</td>\n",
       "      <td>2010-02-13</td>\n",
       "      <td>32</td>\n",
       "      <td>898.02</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5309100041344</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-01-17</td>\n",
       "      <td>2010-02-11</td>\n",
       "      <td>89</td>\n",
       "      <td>1174.52</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5309100041355</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>2010-02-21</td>\n",
       "      <td>46</td>\n",
       "      <td>906.02</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5309100041366</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-02-02</td>\n",
       "      <td>2010-02-11</td>\n",
       "      <td>28</td>\n",
       "      <td>842.02</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5309100041377</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-02-15</td>\n",
       "      <td>2010-02-20</td>\n",
       "      <td>26</td>\n",
       "      <td>818.02</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5309100041388</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-01-26</td>\n",
       "      <td>2010-02-10</td>\n",
       "      <td>49</td>\n",
       "      <td>485.39</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5309100041400</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>2010-02-11</td>\n",
       "      <td>149</td>\n",
       "      <td>1591.04</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5309100041410</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-02-18</td>\n",
       "      <td>2010-02-21</td>\n",
       "      <td>8</td>\n",
       "      <td>606.42</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5309100041421</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-02-15</td>\n",
       "      <td>2010-02-23</td>\n",
       "      <td>20</td>\n",
       "      <td>328.91</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685482</th>\n",
       "      <td>9911300006256</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-04-27</td>\n",
       "      <td>2011-05-06</td>\n",
       "      <td>202</td>\n",
       "      <td>20767.82</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685483</th>\n",
       "      <td>9911300007213</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-05-06</td>\n",
       "      <td>2011-06-17</td>\n",
       "      <td>621</td>\n",
       "      <td>33933.72</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685484</th>\n",
       "      <td>9911300007895</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-04-26</td>\n",
       "      <td>2011-05-05</td>\n",
       "      <td>101</td>\n",
       "      <td>13387.89</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685485</th>\n",
       "      <td>9911300008962</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-07-06</td>\n",
       "      <td>2011-07-19</td>\n",
       "      <td>204</td>\n",
       "      <td>15460.05</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685486</th>\n",
       "      <td>9911300009809</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-07-15</td>\n",
       "      <td>2011-08-11</td>\n",
       "      <td>276</td>\n",
       "      <td>21438.07</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685487</th>\n",
       "      <td>9911300010788</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-08-22</td>\n",
       "      <td>2011-08-30</td>\n",
       "      <td>130</td>\n",
       "      <td>9679.99</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685488</th>\n",
       "      <td>9911300010909</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-08-23</td>\n",
       "      <td>2011-08-31</td>\n",
       "      <td>86</td>\n",
       "      <td>17142.03</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685489</th>\n",
       "      <td>9911300011239</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-08-02</td>\n",
       "      <td>2011-08-17</td>\n",
       "      <td>153</td>\n",
       "      <td>34346.56</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685490</th>\n",
       "      <td>9911300011569</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-08-22</td>\n",
       "      <td>2011-09-14</td>\n",
       "      <td>361</td>\n",
       "      <td>22590.66</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685491</th>\n",
       "      <td>9911300011811</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-09-11</td>\n",
       "      <td>2011-09-22</td>\n",
       "      <td>158</td>\n",
       "      <td>27122.04</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685492</th>\n",
       "      <td>9911300012636</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-09-29</td>\n",
       "      <td>2011-10-09</td>\n",
       "      <td>181</td>\n",
       "      <td>24869.80</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685493</th>\n",
       "      <td>9911300012955</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-09-05</td>\n",
       "      <td>2011-11-16</td>\n",
       "      <td>848</td>\n",
       "      <td>66292.53</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685494</th>\n",
       "      <td>9911300013285</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-09-21</td>\n",
       "      <td>2011-10-22</td>\n",
       "      <td>246</td>\n",
       "      <td>28714.73</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685495</th>\n",
       "      <td>9911300014528</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-10-05</td>\n",
       "      <td>2011-11-02</td>\n",
       "      <td>333</td>\n",
       "      <td>39178.43</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685496</th>\n",
       "      <td>9911300014704</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-11-14</td>\n",
       "      <td>2011-11-23</td>\n",
       "      <td>104</td>\n",
       "      <td>11220.02</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685497</th>\n",
       "      <td>9911300014726</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-11-01</td>\n",
       "      <td>2011-11-11</td>\n",
       "      <td>140</td>\n",
       "      <td>37865.12</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685498</th>\n",
       "      <td>9911300015320</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-11-11</td>\n",
       "      <td>2011-11-22</td>\n",
       "      <td>119</td>\n",
       "      <td>4530.99</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685499</th>\n",
       "      <td>9911300016190</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-11-11</td>\n",
       "      <td>2011-12-07</td>\n",
       "      <td>397</td>\n",
       "      <td>26508.92</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685500</th>\n",
       "      <td>9911300016200</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-12-20</td>\n",
       "      <td>2011-12-28</td>\n",
       "      <td>95</td>\n",
       "      <td>11254.18</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685501</th>\n",
       "      <td>9911300016310</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-11-16</td>\n",
       "      <td>2011-11-30</td>\n",
       "      <td>166</td>\n",
       "      <td>9305.90</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685502</th>\n",
       "      <td>9912300000306</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-12-20</td>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>215</td>\n",
       "      <td>17482.95</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685503</th>\n",
       "      <td>9912300000328</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>111</td>\n",
       "      <td>10775.46</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685504</th>\n",
       "      <td>9912300000405</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-12-23</td>\n",
       "      <td>2012-01-11</td>\n",
       "      <td>224</td>\n",
       "      <td>11878.57</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685505</th>\n",
       "      <td>9912300000438</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-12-05</td>\n",
       "      <td>2012-01-16</td>\n",
       "      <td>569</td>\n",
       "      <td>23130.08</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685506</th>\n",
       "      <td>9912300000449</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>2012-01-16</td>\n",
       "      <td>173</td>\n",
       "      <td>23152.97</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685507</th>\n",
       "      <td>9912300000625</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2012-01-11</td>\n",
       "      <td>2012-01-23</td>\n",
       "      <td>117</td>\n",
       "      <td>9358.08</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685508</th>\n",
       "      <td>9912300001076</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2012-01-23</td>\n",
       "      <td>2012-02-04</td>\n",
       "      <td>148</td>\n",
       "      <td>16026.87</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685509</th>\n",
       "      <td>9912300001208</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-12-27</td>\n",
       "      <td>2012-02-07</td>\n",
       "      <td>421</td>\n",
       "      <td>17568.14</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685510</th>\n",
       "      <td>9912300001461</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2011-11-28</td>\n",
       "      <td>2012-02-14</td>\n",
       "      <td>779</td>\n",
       "      <td>57540.86</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685511</th>\n",
       "      <td>9912300002792</td>\n",
       "      <td>3276678</td>\n",
       "      <td>2012-02-28</td>\n",
       "      <td>2012-03-19</td>\n",
       "      <td>260</td>\n",
       "      <td>18652.36</td>\n",
       "      <td>0</td>\n",
       "      <td>2012-03-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1685512 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  NAIH     CNES    DTINTER    DTSAIDA  QTD_PROD  VALOR_AIH  \\\n",
       "0        5309100004120    10464 2009-12-02 2010-01-08       112    1199.71   \n",
       "1        5309100026318    10464 2010-04-10 2010-04-22        35     447.68   \n",
       "2        5309100040783    10464 2009-11-17 2009-11-19         4     262.54   \n",
       "3        5309100040838    10464 2009-11-16 2009-11-23        20     834.02   \n",
       "4        5309100040849    10464 2009-11-09 2009-11-16        29     538.45   \n",
       "5        5309100040904    10464 2009-11-27 2009-12-09        22     482.45   \n",
       "6        5309100040970    10464 2009-12-08 2009-12-09         7     786.02   \n",
       "7        5309100041069    10464 2009-12-04 2009-12-15        70     723.84   \n",
       "8        5309100041135    10464 2009-12-13 2009-12-14         8    1040.19   \n",
       "9        5309100041146    10464 2009-11-24 2009-12-01        24     834.02   \n",
       "10       5309100041157    10464 2009-12-23 2009-12-24         9     786.02   \n",
       "11       5309100041179    10464 2010-01-07 2010-01-11        12     294.54   \n",
       "12       5309100041223    10464 2009-12-21 2010-01-10        83     671.65   \n",
       "13       5309100041234    10464 2010-01-17 2010-01-19         9     498.45   \n",
       "14       5309100041245    10464 2009-12-25 2010-01-21        82    1142.83   \n",
       "15       5309100041256    10464 2010-01-04 2010-01-18        36     890.02   \n",
       "16       5309100041267    10464 2010-01-30 2010-01-31         5     786.02   \n",
       "17       5309100041289    10464 2010-01-04 2010-02-04       103    1255.07   \n",
       "18       5309100041300    10464 2009-12-27 2010-02-11       192    2071.06   \n",
       "19       5309100041311    10464 2010-02-12 2010-02-17        33     302.54   \n",
       "20       5309100041322    10464 2010-02-02 2010-02-10        24     288.97   \n",
       "21       5309100041333    10464 2010-01-29 2010-02-13        32     898.02   \n",
       "22       5309100041344    10464 2010-01-17 2010-02-11        89    1174.52   \n",
       "23       5309100041355    10464 2010-02-05 2010-02-21        46     906.02   \n",
       "24       5309100041366    10464 2010-02-02 2010-02-11        28     842.02   \n",
       "25       5309100041377    10464 2010-02-15 2010-02-20        26     818.02   \n",
       "26       5309100041388    10464 2010-01-26 2010-02-10        49     485.39   \n",
       "27       5309100041400    10464 2010-01-04 2010-02-11       149    1591.04   \n",
       "28       5309100041410    10464 2010-02-18 2010-02-21         8     606.42   \n",
       "29       5309100041421    10464 2010-02-15 2010-02-23        20     328.91   \n",
       "...                ...      ...        ...        ...       ...        ...   \n",
       "1685482  9911300006256  3276678 2011-04-27 2011-05-06       202   20767.82   \n",
       "1685483  9911300007213  3276678 2011-05-06 2011-06-17       621   33933.72   \n",
       "1685484  9911300007895  3276678 2011-04-26 2011-05-05       101   13387.89   \n",
       "1685485  9911300008962  3276678 2011-07-06 2011-07-19       204   15460.05   \n",
       "1685486  9911300009809  3276678 2011-07-15 2011-08-11       276   21438.07   \n",
       "1685487  9911300010788  3276678 2011-08-22 2011-08-30       130    9679.99   \n",
       "1685488  9911300010909  3276678 2011-08-23 2011-08-31        86   17142.03   \n",
       "1685489  9911300011239  3276678 2011-08-02 2011-08-17       153   34346.56   \n",
       "1685490  9911300011569  3276678 2011-08-22 2011-09-14       361   22590.66   \n",
       "1685491  9911300011811  3276678 2011-09-11 2011-09-22       158   27122.04   \n",
       "1685492  9911300012636  3276678 2011-09-29 2011-10-09       181   24869.80   \n",
       "1685493  9911300012955  3276678 2011-09-05 2011-11-16       848   66292.53   \n",
       "1685494  9911300013285  3276678 2011-09-21 2011-10-22       246   28714.73   \n",
       "1685495  9911300014528  3276678 2011-10-05 2011-11-02       333   39178.43   \n",
       "1685496  9911300014704  3276678 2011-11-14 2011-11-23       104   11220.02   \n",
       "1685497  9911300014726  3276678 2011-11-01 2011-11-11       140   37865.12   \n",
       "1685498  9911300015320  3276678 2011-11-11 2011-11-22       119    4530.99   \n",
       "1685499  9911300016190  3276678 2011-11-11 2011-12-07       397   26508.92   \n",
       "1685500  9911300016200  3276678 2011-12-20 2011-12-28        95   11254.18   \n",
       "1685501  9911300016310  3276678 2011-11-16 2011-11-30       166    9305.90   \n",
       "1685502  9912300000306  3276678 2011-12-20 2012-01-09       215   17482.95   \n",
       "1685503  9912300000328  3276678 2012-01-02 2012-01-09       111   10775.46   \n",
       "1685504  9912300000405  3276678 2011-12-23 2012-01-11       224   11878.57   \n",
       "1685505  9912300000438  3276678 2011-12-05 2012-01-16       569   23130.08   \n",
       "1685506  9912300000449  3276678 2012-01-02 2012-01-16       173   23152.97   \n",
       "1685507  9912300000625  3276678 2012-01-11 2012-01-23       117    9358.08   \n",
       "1685508  9912300001076  3276678 2012-01-23 2012-02-04       148   16026.87   \n",
       "1685509  9912300001208  3276678 2011-12-27 2012-02-07       421   17568.14   \n",
       "1685510  9912300001461  3276678 2011-11-28 2012-02-14       779   57540.86   \n",
       "1685511  9912300002792  3276678 2012-02-28 2012-03-19       260   18652.36   \n",
       "\n",
       "         DF       DATE  \n",
       "0         0 2010-01-01  \n",
       "1         0 2010-05-01  \n",
       "2         0 2010-01-01  \n",
       "3         0 2010-01-01  \n",
       "4         0 2010-01-01  \n",
       "5         0 2010-01-01  \n",
       "6         0 2010-01-01  \n",
       "7         1 2010-01-01  \n",
       "8         0 2010-01-01  \n",
       "9         1 2010-01-01  \n",
       "10        0 2010-02-01  \n",
       "11        1 2010-01-01  \n",
       "12        0 2010-01-01  \n",
       "13        0 2010-01-01  \n",
       "14        0 2010-01-01  \n",
       "15        1 2010-01-01  \n",
       "16        1 2010-01-01  \n",
       "17        0 2010-02-01  \n",
       "18        0 2010-02-01  \n",
       "19        0 2010-02-01  \n",
       "20        0 2010-02-01  \n",
       "21        1 2010-02-01  \n",
       "22        0 2010-02-01  \n",
       "23        0 2010-02-01  \n",
       "24        0 2010-02-01  \n",
       "25        0 2010-02-01  \n",
       "26        0 2010-02-01  \n",
       "27        1 2010-02-01  \n",
       "28        0 2010-03-01  \n",
       "29        0 2010-03-01  \n",
       "...      ..        ...  \n",
       "1685482   1 2011-06-01  \n",
       "1685483   1 2011-06-01  \n",
       "1685484   0 2011-06-01  \n",
       "1685485   1 2011-07-01  \n",
       "1685486   0 2011-10-01  \n",
       "1685487   1 2011-08-01  \n",
       "1685488   0 2011-08-01  \n",
       "1685489   1 2011-10-01  \n",
       "1685490   0 2011-10-01  \n",
       "1685491   1 2011-10-01  \n",
       "1685492   0 2011-10-01  \n",
       "1685493   1 2011-12-01  \n",
       "1685494   0 2011-11-01  \n",
       "1685495   1 2011-11-01  \n",
       "1685496   1 2011-12-01  \n",
       "1685497   1 2011-12-01  \n",
       "1685498   1 2012-01-01  \n",
       "1685499   0 2011-12-01  \n",
       "1685500   1 2012-01-01  \n",
       "1685501   1 2012-01-01  \n",
       "1685502   1 2012-01-01  \n",
       "1685503   0 2012-01-01  \n",
       "1685504   1 2012-01-01  \n",
       "1685505   1 2012-01-01  \n",
       "1685506   1 2012-01-01  \n",
       "1685507   1 2012-01-01  \n",
       "1685508   1 2012-02-01  \n",
       "1685509   1 2012-02-01  \n",
       "1685510   1 2012-02-01  \n",
       "1685511   0 2012-03-01  \n",
       "\n",
       "[1685512 rows x 8 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Temporary save\n",
    "df_int_new.to_csv(\"INT_2010_to_2018.csv\", index=False )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**QTD_NAIH Field on INT_VALOR_MENSAL.csv**\n",
    "\n",
    "*Reading the Datasets again*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int_new = pd.read_csv('INT_2010_to_2018.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_value_month_new = pd.read_csv('INT_VALOR_MENSAL.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Arranging Date field on datasets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int_new['DATE']= df_int_new['DATE'].apply(lambda x: x.split(\"-\")[0]+\"-\"+x.split(\"-\")[1])\n",
    "df_value_month_new['DATE']= df_value_month_new['DATE'].apply(lambda x: x.split(\"-\")[0]+\"-\"+x.split(\"-\")[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>NAIH</th>\n",
       "      <th>CNES</th>\n",
       "      <th>DTINTER</th>\n",
       "      <th>DTSAIDA</th>\n",
       "      <th>QTD_PROD</th>\n",
       "      <th>VALOR_AIH</th>\n",
       "      <th>DF</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5309100004120</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-12-02</td>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>112</td>\n",
       "      <td>1199.71</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5309100026318</td>\n",
       "      <td>10464</td>\n",
       "      <td>2010-04-10</td>\n",
       "      <td>2010-04-22</td>\n",
       "      <td>35</td>\n",
       "      <td>447.68</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5309100040783</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-11-17</td>\n",
       "      <td>2009-11-19</td>\n",
       "      <td>4</td>\n",
       "      <td>262.54</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5309100040838</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-11-16</td>\n",
       "      <td>2009-11-23</td>\n",
       "      <td>20</td>\n",
       "      <td>834.02</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5309100040849</td>\n",
       "      <td>10464</td>\n",
       "      <td>2009-11-09</td>\n",
       "      <td>2009-11-16</td>\n",
       "      <td>29</td>\n",
       "      <td>538.45</td>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            NAIH   CNES    DTINTER    DTSAIDA  QTD_PROD  VALOR_AIH  DF  \\\n",
       "0  5309100004120  10464 2009-12-02 2010-01-08       112    1199.71   0   \n",
       "1  5309100026318  10464 2010-04-10 2010-04-22        35     447.68   0   \n",
       "2  5309100040783  10464 2009-11-17 2009-11-19         4     262.54   0   \n",
       "3  5309100040838  10464 2009-11-16 2009-11-23        20     834.02   0   \n",
       "4  5309100040849  10464 2009-11-09 2009-11-16        29     538.45   0   \n",
       "\n",
       "        DATE  \n",
       "0 2010-01-01  \n",
       "1 2010-05-01  \n",
       "2 2010-01-01  \n",
       "3 2010-01-01  \n",
       "4 2010-01-01  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Variable (df_value_month_temp)**\n",
    "\n",
    "New temporary variable to get grouped data by Date and aggregated by sum of hospitalizations (NAIH)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_value_month_temp = df_int_new.groupby('DATE', as_index = False)['NAIH'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>NAIH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>16302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-02-01</td>\n",
       "      <td>13506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-03-01</td>\n",
       "      <td>16704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-04-01</td>\n",
       "      <td>16422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-05-01</td>\n",
       "      <td>17032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE   NAIH\n",
       "0 2010-01-01  16302\n",
       "1 2010-02-01  13506\n",
       "2 2010-03-01  16704\n",
       "3 2010-04-01  16422\n",
       "4 2010-05-01  17032"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_value_month_temp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(QTD_NAIH) field**\n",
    "\n",
    "Take the values from temporary variable to (INT_VALOR_MENSAL.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_value_month_new['QTD_NAIH'] = df_value_month_temp['NAIH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove \"unnamed\" column\n",
    "df_value_month_new.drop(df_value_month_new.columns[df_value_month_new.columns.str.contains('unnamed',case = False)],axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int_new.to_csv(\"INT_2010_to_2018.csv\", index=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_value_month_new.to_csv('INT_VALOR_MENSAL.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4- Medical Procedures Groups\n",
    "\n",
    "For visualize the medical procedures data, was created a dataset just to relate the raw (.dbf) dataset field (SP_ATOPROF) to new medical procedures group (GP_PROCED) and subgroup (SB_PROCED). (SP_ATOPROF) contains the Main medical procedure, the subprocedure and de specific procedure.\n",
    "\n",
    "In order to create this column, was extracted the 10 characters of each row (SP_ATOPROF).\n",
    "The meaning of these characters is:\n",
    "\n",
    "- 2 first digits: Medical procedures Main Group - EG: 04 (Surgery Procedure)\n",
    "- 4 first digits: Medical procedures Subgroup - EG: **04**03 (Surgery of the Central and Peripheral Nervous System)\n",
    "- All 10 digits: Specific medical procedure - EG: **0403**010195 (Surgical Treatment of Intracranial Abscess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Original Dataset**\n",
    "\n",
    "Read all (.dbf) files again but containing only 3 columns:\n",
    "\n",
    "- Hospitalization Unique Code (SP_NAIH)\n",
    "- Professional Act code (SP_ATOPROF)\n",
    "- Value of Professional Act (SP_VALATO)\n",
    "\n",
    "Ultimately, the **INT_PROCEDS.csv** will be created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DBF_NAIH\\\\SPDF1001.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1002.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1003.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1004.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1005.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1006.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1007.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1008.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1009.dbf',\n",
       " 'DBF_NAIH\\\\SPDF1010.dbf']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = glob.glob('DBF_NAIH\\\\*.dbf')\n",
    "files[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPDF1001.dbf processado.\n",
      "SPDF1002.dbf processado.\n",
      "SPDF1003.dbf processado.\n",
      "SPDF1004.dbf processado.\n",
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      "SPDF1805.dbf processado.\n",
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      "SPDF1807.dbf processado.\n",
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      "SPDF1812.dbf processado.\n"
     ]
    }
   ],
   "source": [
    "df_int_prd = []\n",
    "columns_excl_prd=['SP_GESTOR', 'SP_UF', 'SP_PROCREA', 'SP_NUM_PR', 'SP_TIPO',\n",
    "       'SP_CPFCGC', 'SP_TP_ATO', 'SP_PTSP',\n",
    "       'SP_NF', 'SP_M_HOSP', 'SP_M_PAC', 'SP_DES_PAC', 'SP_COMPLEX',\n",
    "       'SP_FINANC', 'SP_CO_FAEC', 'SP_PF_CBO','SP_PF_DOC', 'SP_PJ_DOC', \n",
    "       'IN_TP_VAL', 'SEQUENCIA', 'REMESSA', 'SP_CIDPRI', \n",
    "       'SP_CIDSEC', 'SP_QT_PROC', 'SP_U_AIH', 'SP_AA', 'SP_MM', 'SP_CNES', \n",
    "       'SP_DTINTER', 'SP_DTSAIDA', 'SP_QTD_ATO', 'SP_DES_HOS']\n",
    "\n",
    "for file in files:\n",
    "    df_int_prd.append(Dbf5(file, codec='latin-1').to_dataframe().drop(columns_excl_prd,axis=1))\n",
    "    \n",
    "    print(file[-12:]+' processado.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>SP_NAIH</th>\n",
       "      <th>SP_ATOPROF</th>\n",
       "      <th>SP_VALATO</th>\n",
       "      <th>SERV_CLA\u0000L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0408020407</td>\n",
       "      <td>239.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0408020407</td>\n",
       "      <td>46.66</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0408020407</td>\n",
       "      <td>13.99</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0408020407</td>\n",
       "      <td>9.33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0408020407</td>\n",
       "      <td>42.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0702030996</td>\n",
       "      <td>214.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0301010170</td>\n",
       "      <td>93.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0802010024</td>\n",
       "      <td>180.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0802010199</td>\n",
       "      <td>267.13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0202020380</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0202010473</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0202010694</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0202010317</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>0408050543</td>\n",
       "      <td>263.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>0408050543</td>\n",
       "      <td>74.71</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>0408050543</td>\n",
       "      <td>22.41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>0408050543</td>\n",
       "      <td>14.94</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>0408050543</td>\n",
       "      <td>61.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>0301010170</td>\n",
       "      <td>29.89</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>0202020380</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SP_NAIH  SP_ATOPROF  SP_VALATO  SERV_CLA\u0000L\n",
       "0   5309101309644  0408020407     239.23         NaN\n",
       "1   5309101309644  0408020407      46.66         NaN\n",
       "2   5309101309644  0408020407      13.99         NaN\n",
       "3   5309101309644  0408020407       9.33         NaN\n",
       "4   5309101309644  0408020407      42.00         NaN\n",
       "5   5309101309644  0702030996     214.20         NaN\n",
       "6   5309101309644  0301010170      93.34         NaN\n",
       "7   5309101309644  0802010024     180.00         NaN\n",
       "8   5309101309644  0802010199     267.13         NaN\n",
       "9   5309101309644  0202020380       0.00         NaN\n",
       "10  5309101309644  0202010473       0.00         NaN\n",
       "11  5309101309644  0202010694       0.00         NaN\n",
       "12  5309101309644  0202010317       0.00         NaN\n",
       "13  5309101309666  0408050543     263.01         NaN\n",
       "14  5309101309666  0408050543      74.71         NaN\n",
       "15  5309101309666  0408050543      22.41         NaN\n",
       "16  5309101309666  0408050543      14.94         NaN\n",
       "17  5309101309666  0408050543      61.00         NaN\n",
       "18  5309101309666  0301010170      29.89         NaN\n",
       "19  5309101309666  0202020380       0.00         NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int_prd[0].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int_prd = pd.concat(df_int_prd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove (SERV_CLAL) column\n",
    "df_int_prd.drop(df_int_prd.columns[3],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Association**\n",
    "\n",
    "Association of the Professional Act (SP_ATOPROF) to the Group (GP_PROCED) and Subgroup (SB_PROCED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int_prd['GP_PROCED'] = df_int_prd['SP_ATOPROF'].str[0:2]\n",
    "df_int_prd['SB_PROCED'] = df_int_prd['SP_ATOPROF'].str[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove column (SP_ATOPROF)\n",
    "df_int_prd.drop(columns=['SP_ATOPROF'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>SP_NAIH</th>\n",
       "      <th>SP_VALATO</th>\n",
       "      <th>GP_PROCED</th>\n",
       "      <th>SB_PROCED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>239.23</td>\n",
       "      <td>04</td>\n",
       "      <td>0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>46.66</td>\n",
       "      <td>04</td>\n",
       "      <td>0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>13.99</td>\n",
       "      <td>04</td>\n",
       "      <td>0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>9.33</td>\n",
       "      <td>04</td>\n",
       "      <td>0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>42.00</td>\n",
       "      <td>04</td>\n",
       "      <td>0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>214.20</td>\n",
       "      <td>07</td>\n",
       "      <td>0702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>93.34</td>\n",
       "      <td>03</td>\n",
       "      <td>0301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>180.00</td>\n",
       "      <td>08</td>\n",
       "      <td>0802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>267.13</td>\n",
       "      <td>08</td>\n",
       "      <td>0802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0.00</td>\n",
       "      <td>02</td>\n",
       "      <td>0202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0.00</td>\n",
       "      <td>02</td>\n",
       "      <td>0202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0.00</td>\n",
       "      <td>02</td>\n",
       "      <td>0202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5309101309644</td>\n",
       "      <td>0.00</td>\n",
       "      <td>02</td>\n",
       "      <td>0202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>263.01</td>\n",
       "      <td>04</td>\n",
       "      <td>0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>74.71</td>\n",
       "      <td>04</td>\n",
       "      <td>0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>22.41</td>\n",
       "      <td>04</td>\n",
       "      <td>0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>14.94</td>\n",
       "      <td>04</td>\n",
       "      <td>0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>61.00</td>\n",
       "      <td>04</td>\n",
       "      <td>0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>29.89</td>\n",
       "      <td>03</td>\n",
       "      <td>0301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5309101309666</td>\n",
       "      <td>0.00</td>\n",
       "      <td>02</td>\n",
       "      <td>0202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SP_NAIH  SP_VALATO GP_PROCED SB_PROCED\n",
       "0   5309101309644     239.23        04      0408\n",
       "1   5309101309644      46.66        04      0408\n",
       "2   5309101309644      13.99        04      0408\n",
       "3   5309101309644       9.33        04      0408\n",
       "4   5309101309644      42.00        04      0408\n",
       "5   5309101309644     214.20        07      0702\n",
       "6   5309101309644      93.34        03      0301\n",
       "7   5309101309644     180.00        08      0802\n",
       "8   5309101309644     267.13        08      0802\n",
       "9   5309101309644       0.00        02      0202\n",
       "10  5309101309644       0.00        02      0202\n",
       "11  5309101309644       0.00        02      0202\n",
       "12  5309101309644       0.00        02      0202\n",
       "13  5309101309666     263.01        04      0408\n",
       "14  5309101309666      74.71        04      0408\n",
       "15  5309101309666      22.41        04      0408\n",
       "16  5309101309666      14.94        04      0408\n",
       "17  5309101309666      61.00        04      0408\n",
       "18  5309101309666      29.89        03      0301\n",
       "19  5309101309666       0.00        02      0202"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int_prd.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20376185, 4)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_int_prd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_int_prd.to_csv('INT_PROCEDS.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5- MACHINE LEARNING (Time Series)\n",
    "\n",
    "In order to make predictions about the amount of hospitalizations in Health Department in 2019, the amount of hospitalizations (QTD_NAIH) and the Monthly hospitalization value (VALOR_MENSAL) were collected from **INT_VALOR_MENSAL.csv**.\n",
    "\n",
    "For making the prediction, the SARIMAX was used. This model is based on ARIMA (AR) auto-regressive and (MA) move averages used in time series.\n",
    "\n",
    "The \"S\" on SARIMAX is the Seasonal component, to give periodicity to ARIMA where there is seasonality paterns on ranges of time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from itertools import product\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ts = pd.read_csv('INT_VALOR_MENSAL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 108 entries, 0 to 107\n",
      "Data columns (total 3 columns):\n",
      "VALOR_MENSAL    108 non-null float64\n",
      "DATE            108 non-null object\n",
      "QTD_NAIH        108 non-null int64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 2.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_ts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transformation of DATA into Timestamp\n",
    "df_ts['DATE'] = pd.to_datetime(df_ts['DATE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>VALOR_MENSAL</th>\n",
       "      <th>DATE</th>\n",
       "      <th>QTD_NAIH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.415236e+07</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>16302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.279796e+07</td>\n",
       "      <td>2010-02-01</td>\n",
       "      <td>13506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.476611e+07</td>\n",
       "      <td>2010-03-01</td>\n",
       "      <td>16704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.425150e+07</td>\n",
       "      <td>2010-04-01</td>\n",
       "      <td>16422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.399232e+07</td>\n",
       "      <td>2010-05-01</td>\n",
       "      <td>17032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.302971e+07</td>\n",
       "      <td>2010-06-01</td>\n",
       "      <td>15195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.373143e+07</td>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>15558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.449289e+07</td>\n",
       "      <td>2010-08-01</td>\n",
       "      <td>15850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.370897e+07</td>\n",
       "      <td>2010-09-01</td>\n",
       "      <td>15067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.315430e+07</td>\n",
       "      <td>2010-10-01</td>\n",
       "      <td>14737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.401719e+07</td>\n",
       "      <td>2010-11-01</td>\n",
       "      <td>14643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.389286e+07</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>13775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.368519e+07</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>14453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.259623e+07</td>\n",
       "      <td>2011-02-01</td>\n",
       "      <td>14410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.377661e+07</td>\n",
       "      <td>2011-03-01</td>\n",
       "      <td>16339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.634514e+07</td>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>17135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.608791e+07</td>\n",
       "      <td>2011-05-01</td>\n",
       "      <td>17709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.397273e+07</td>\n",
       "      <td>2011-06-01</td>\n",
       "      <td>16457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.489228e+07</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>16115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.327849e+07</td>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>14982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    VALOR_MENSAL       DATE  QTD_NAIH\n",
       "0   1.415236e+07 2010-01-01     16302\n",
       "1   1.279796e+07 2010-02-01     13506\n",
       "2   1.476611e+07 2010-03-01     16704\n",
       "3   1.425150e+07 2010-04-01     16422\n",
       "4   1.399232e+07 2010-05-01     17032\n",
       "5   1.302971e+07 2010-06-01     15195\n",
       "6   1.373143e+07 2010-07-01     15558\n",
       "7   1.449289e+07 2010-08-01     15850\n",
       "8   1.370897e+07 2010-09-01     15067\n",
       "9   1.315430e+07 2010-10-01     14737\n",
       "10  1.401719e+07 2010-11-01     14643\n",
       "11  1.389286e+07 2010-12-01     13775\n",
       "12  1.368519e+07 2011-01-01     14453\n",
       "13  1.259623e+07 2011-02-01     14410\n",
       "14  1.377661e+07 2011-03-01     16339\n",
       "15  1.634514e+07 2011-04-01     17135\n",
       "16  1.608791e+07 2011-05-01     17709\n",
       "17  1.397273e+07 2011-06-01     16457\n",
       "18  1.489228e+07 2011-07-01     16115\n",
       "19  1.327849e+07 2011-08-01     14982"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ts.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting Date as index\n",
    "df_ts = df_ts.set_index('DATE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Outliers**\n",
    "\n",
    "Despite there are few outliers, there weren't reasons to remove because:\n",
    "\n",
    "- There weren't many available data to machine learning model\n",
    "- The distance of data isn't so far from the range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x201674fe2e8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ts.boxplot(column='QTD_NAIH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20167605ac8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ts.boxplot(column='VALOR_MENSAL')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Trend**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chart has all data of hospitalizations split on months. Here, you can see a trend of 12 months (1 year) throughout data. The year start increasing the hospitalizations and after august this trend goes decreasing until December."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2016765c5c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M6Nlw11RTiQgd2oTRoU0Yo/s6puAur6ph0/6S011aneNb89uR3d36vS3l59d34dbXV/DOir08cFWnZvmOmlrD5CW5/P3z7cS2Dua9+wcwqJNn3ex2RUJkCLf0acfMrDyeuK6LbXOJqZajRcOHzcs+QHx4MJmpMc3+XSGB/mSmxrTIdzW3fh1iuKpLHP/6Zhd3Dezg9okAC0pO8cSM9azMLeamngn85Qc9bX/24kLcf0VHPlq7n/e+28tDV3e2Ox3VzLznOlidlxMV1Xy1vYibeia2yGR2vmbSsC4cLavi7W/dO6Prgo0FDH9xKdn5Jfzf2F689qMMry4YAN3bRTAkPZapuo74RaHRoiEib4lIoYhsamDbkyJiRCTW+ny1iJSIyHrr9TuntsNFZLuI5IjIU07xNBFZJSI7RWSGiARZ8WDrc461PdUdJ3yxWLz1EJXVtYw8y6gpdW69U6IY1r0tk5fmUlJWdcHHO1lRzS8+3MBD760ltU0oCx4dwm2Znj066nzcP6QjhccrmOuhU7Eo93HlSmMqMLx+UERSgGHAvnqblhpj+livP1lt/YHXgBuB7sAdIlLX4f1X4AVjTDpwFJhgxScAR40xnYEXrHbKRfOyC0iICNHx8xdg0rAuHC+v5s1vL2y1uvV5xxjx8lJmrc3nkWs6M+vBQaTGeu6Djk1xZXosXduG8+bSXK9d1Eq5ptGiYYxZAhQ3sOkF4JeAK39C+gM5xphcY0wlMB0YJY5fs4YCs6x204DR1vtR1mes7deKr/xa1sxKy6v4xuqa8tOuqSbrlhjBiJ6JvPXtbopPVp73/jW1hte+ymHs68uprK5l+k8G8uQNXQn0wKlULpSIcP+QNLYdPM5SH1hCV51dk/70isgtwH5jzIYGNl8uIhtEZKGI1I3BSwLynNrkW7E2wDFjTHW9+Bn7WNtLrPYN5TNRRLJEJKuoqKgpp+RTvthyiMqa2rM+0Kdc98SwdE5V1fCvb3ad1377j53ijjdW8txn27mhRwILH7uSAR1de1bGW93Spx1x4cG8oeuI+7TzLhoiEgo8Dfyugc1rgQ7GmN7AK8Ccut0aaGvOET/XPt8PGjPZGJNpjMmMi4s7V/oXhfnZBSRFtSKjfZTdqXi9zvHhjOqTxLQVe1xere6TDQcY/uISNu8v4e8/7M2rd/S9KIaiBgf4c8+gVJbuPMzWglK701HNpClXGp2ANGCDiOwBkoG1IpJgjCk1xpwAMMYsAAKtm+T5gPPkQ8nAAeAwECUiAfXiOO9jbY+k4W4y5aTkVBVLdhZxU88En7nJarfHrk2nqsbw+tfnvto4UVHNpJnr+dkH6+gc35oFjw3h1n7JF9X/hzsHtKdVoL+uI+7DzrtoGGM2GmPijTGpxphUHP+4ZxhjDopIQt19BxHpbx3/CLAaSLdGSgUB44C5xnHH7CtgrHX48cDH1vu51mes7V8avcPWqEVbDlFVYxjRq53dqfiM1NgwxmYk897KfRSUnGqwzdp9R7nppaXMWbefR4d2ZuZPL/foWX2bS1RoELdlJjN3w34Oleo64r7IlSG3HwArgK4iki8iE87RfCywSUQ2AC8D44xDNfAI8BmwFZhpjNls7fMrYJKI5OC4ZzHFik8B2ljxScBTqEbNyz5AcnQreid7zup4vuBn13bGYHj1y5wz4jW1hpcX7+SH/1xBTa1hxk8vZ9L1vnmz21X3XZFGda1h2vI9dqeimkGjj7oaY+5oZHuq0/tXgVfP0m4BsKCBeC6O0VX14+XADxvLT/3XsbJKvt15mAlDPGOxHl+SHB3K7ZelMGN1Hg9c1YmUmFDyisuYNHM9q/cc5Zbe7XhmdI/zmjreV3VoE8bwSxN4d+VeHr6mM2FufqJe2evi/XXIB32++RDVtYaRPbVrqjk8ck06IsIrX+7k4/X7uemlpWwtOM6Lt/fh5Tv6asFwcv+QjpSWV/NhVl7jjZVX0aLhQ+ZtLKB9TCg9kiLsTsUnJUSGcNeADszMyuex6evpkhDOwseGnJ60Uf1Xvw7R9OsQzZRluo64r9Gi4SOKT1ayLOcwI3olatdUM3rw6k50T4zg8evSmTFxoK4hcQ4/GZJGXvEpPrOWFFa+QTsbfcRnmw9SU2vcPg26OlNceDALHhtidxpeYVj3BDq0CWXyklxu7KFDwH2FXmn4iPnZBaTFhnFpO+2aUp7B30+YcEUa6/OOsWbvUbvTUW6iRcMHHDlRwfJdhxnRU7umlGcZ2y+ZyFaBOrWID9Gi4QMWbjpIrUHnmlIeJzQogLsGtufzLYfYc/ik3ekoN9Ci4QPmZxfQMS6MSxLC7U5Fqe8Zf3kqgX5+THHzglbKHlo0vFzh8XJW7T7CyF7ttGtKeaT4iBBG9WnHh2vyONqEKeaVZ9Gi4eU+s7qmdIU+5cnuH9KR8qpa3lu11+5U1AXSouHlPskuID2+NV3aateU8lxdE8K5qkscU5fvpbyqxu501AXQouHFDpWWs3pPsd4AV17hJ0M6cvhEBXPX6zri3kyLhhdbuLEAo11TyksM7tyGbokRvKHriHs1LRpebP7GAi5JCKdzvHZNKc8nIvxkSBo7C0/w9Q5dltlbadHwUgdLylm956hOG6K8yshe7WgbEcyb+rCf19Ki4aXmbywA9IE+5V2CAvy4d3Aay3KOsPlAid3pqCbQouGl5mcfoHtiBB3jWtudilLn5Y7+7QkL0nXEvZUWDS+0/9gp1u47plcZyitFtgrktstS+GTDgbOuua48lxYNL7SwrmtK72coL3Xf4DRqjWHqsj12p6LOkxYNL/RJdgE9kiJIjQ2zOxWlmiQlJpQbeyby/nf7OFFRbXc66jxo0fAyecVlbMg7xsheug648m4Th3TkeHk1P5mWpd1UXsSloiEib4lIoYhsamDbkyJiRCTW+iwi8rKI5IhItohkOLUdLyI7rdd4p3g/Edlo7fOyWDPviUiMiCyy2i8SkegLP2XvtkC7ppSP6J0Sxf+N7cWG/GMMf3Hp6T/byrO5eqUxFRhePygiKcAwYJ9T+EYg3XpNBF632sYAvwcGAP2B3zsVgdettnX71X3XU8BiY0w6sNj6fFGbv7GA3smRuja18gm3ZaYw/9EhpLYJ5aH31vKLDzdod5WHc6loGGOWAMUNbHoB+CXgPCfAKOAd47ASiBKRROAGYJExptgYcxRYBAy3tkUYY1YYx9wC7wCjnY41zXo/zSl+Udp75CTZ+SU6akr5lLTYMGY9OIhHrunMf9bmM+Llpazdp8vDeqom39MQkVuA/caYDfU2JQF5Tp/zrdi54vkNxAHaGmMKAKyf8U3N1xfUPdB3k3ZNKR8T6O/Hkzd0ZfrEy6muMfzwnyt4efFOqmtq7U5N1dOkoiEiocDTwO8a2txAzDQhfj75TBSRLBHJKiry3Tlt5mcX0Ld9FMnR2jWlfFP/tBgWPj6Em3sl8vyiHYybvJK84jK701JOmnql0QlIAzaIyB4gGVgrIgk4rhRSnNomAwcaiSc3EAc4ZHVfYf0sbCgZY8xkY0ymMSYzLi6uiafk2XYfPsnmA6V6A1z5vIiQQF4c15eXxvVh+8Hj3PjSUmavy9eZcT1Ek4qGMWajMSbeGJNqjEnF8Q9/hjHmIDAXuNsaRTUQKLG6lj4DrheRaOsG+PXAZ9a24yIy0Bo1dTfwsfVVc4G6UVbjneIXnfnZjjqqXVPqYjGqTxILHhtCt8RwnpixgUenr6fkVJXdaV30XB1y+wGwAugqIvkiMuEczRcAuUAO8AbwEIAxphh4Blhtvf5kxQAeBN609tkFLLTizwLDRGQnjlFaz7p+ar5lXnYBmR2iaRfVyu5UlGoxKTGhTJ94OU9e34UFGwu46aWlrMo9YndaFzXxtUu+zMxMk5WVZXcabpVTeILrnv+G39/cnXsHp9mdjlK2WJ93jMenr2NvcRkPXd2Jx6/rQqC/Pp/sLiKyxhiT2Vg7/S/uBRZsLEAEbuyhXVPq4tUnJYr5jw7htn4pvPbVLm59fTm5RSfsTuuio0XDC8zLPsBlHWJIiAyxOxWlbBUWHMBfx/bin3dlsK+4jBEvf8sH3+3Tm+QtSIuGh9tx6Dg7Dp1gZG+9ylCqzvAeiXz62JX06xDNrz/ayE//vYbik5V2p3VR0KLh4eZnO7qmhvdIsDsVpTxKQmQI79zXn9+M6MbX24sY/uISluja481Oi4YHM8Ywf2MBA9JiiA/Xriml6vPzE+4f0pE5Dw8mslUgd7/1Hc/M20J5VY3dqfksLRoebMehE+QUnmCEToOu1Dl1bxfBJz+7gvGXd2DKt7sZ/doydhw6bndaPkmLhgebl30AP4EbtWtKqUaFBPrzx1E9ePueyzh8ooKRr3zL1GW79Sa5m2nR8FDGGOZnF3B5pzbEtg62Ox2lvMY1l8Tz6eNXMqRzLH/4ZAv3vL2awuPldqflM7RoeKitBcfJPXySET21a0qp8xXbOpg3x2fyzOgerMw9wvAXl/LFlkN2p+UTtGh4qPkbD+DvJzpqSqkmEhF+PLAD8x+9goSIEO5/J4vfztmk061fIC0aHsgYw7zsAgZ1akNMWJDd6Sjl1TrHhzP74UH8ZEga/165l9/M2aT3OS5AgN0JqO/bfKCUvUcc8+sopS5ccIA/T4/oTkigP698mUNMWBC/HH6J3Wl5JS0aHmhedgEBfsL13bVrSil3mjSsC0dOVvKPr3cRExbE/UM62p2S19Gi4WEcD/QdYHDnWKK1a0optxIRnhnVg2Nllfx5/lbatA7iB32TG99Rnab3NDxMdn4JecWnGNlL55pSqjn4+wkv3N6HQZ3a8IsPs/lqW4MLgqqz0KLhYeZvLCDQX7umlGpOwQH+TL47k26JETz43hrW7C1ufCcFaNHwKHUP9A1JjyMyNNDudJTyaa2DA3j73stoF9mKe99ezfaDOu2IK7RoeJD1ecfYf+wUI3QdcKVaRGzrYKbd159WQf7c/dYq8orL7E7J42nR8CDzsgsI8vdj2KVt7U5FqYtGSkwo79w3gFOVNdz91nccPlFhd0oeTYuGh6itNSzYWMCVXeKICNGuKaVaUteEcN6+9zIKSk5x79urOVFRbXdKHkuLhodYl3eUgpJyHTWllE36dYjh9Tv7saWglInvZFFRrWtyNESLhoeYl11AUIAf13aLtzsVpS5a11wSz99+2Ivlu47wxIz11NTqdCP1NVo0ROQtESkUkU1OsWdEJFtE1ovI5yLSzopfLSIlVny9iPzOaZ/hIrJdRHJE5CmneJqIrBKRnSIyQ0SCrHiw9TnH2p7qzhP3JHVdU9d0jSNcu6aUstUP+ibz25HdWbDxIL/9WOepqs+VK42pwPB6seeMMb2MMX2AecDvnLYtNcb0sV5/AhARf+A14EagO3CHiHS32v8VeMEYkw4cBSZY8QnAUWNMZ+AFq51Pytp7lEOlFbpCn1IeYsIVaTx0dSfeX7WPFxbtsDsdj9Jo0TDGLAGK68VKnT6GAY2V4v5AjjEm1xhTCUwHRomIAEOBWVa7acBo6/0o6zPW9mut9j5nfvYBggP8uPYS7ZpSylP84oaujLsshZe/zGHqst12p+Mxmjz3lIj8D3A3UAJc47TpchHZABwAnjTGbAaSgDynNvnAAKANcMwYU+0UT7Len97HGFMtIiVW+8MN5DIRmAjQvn37pp6SLWpqDQs2HWToJfGEBetUYEp5ChHhz6N7cLSskj98soXosCBG9UlqfEcf1+Qb4caYp40xKcB7wCNWeC3QwRjTG3gFmGPFG7pCMOeIn2ufhnKZbIzJNMZkxsXFuXoKHmH1nmKKjlcwQkdNKeVxAvz9eGlcXwakxfDzmRv4ervOU+WO0VPvA7eCo9vKGHPCer8ACBSRWBxXEClO+yTjuBI5DESJSEC9OM77WNsjqddN5gvmZR+gVaA/Q7VrSimPFBLozxvjM+nSNpwH313Lun1H7U7JVk0qGiKS7vTxFmCbFU+ou+8gIv2t4x8BVgPp1kipIGAcMNc4hiV8BYy1jjUe+Nh6P9f6jLX9S+Njwxiqa2r5dNNBhnaLJzRIu6aU8lQRIYFMu68/8RHB3Dt1NTsPXbzzVLky5PYDYAXQVUTyRWQC8KyIbBKRbOB64DGr+Vhgk3VP42V/iWDUAAATGUlEQVRgnHGoxtGF9RmwFZhp3esA+BUwSURycNyzmGLFpwBtrPgk4PQwXV/x3e5iDp+oZKTONaWUx4sLD+bf9w0g0N+Pu9/6jv3HTtmdki3Ex355JzMz02RlZZ33fst3HearbYUkRLYiISKEhEjHKz48mED/5nkG8v/N3sicdftZ85thtAryb5bvUEq519aCUm771wriw4P58IFBxPjIYmkissYYk9lYO+0TsWwrOM47K/ZSUV17RlwE4loHO4qIUzE5/d76eb7dS3VdU9d1a6sFQykv0i0xginjL+PHU1Zx79TVvH//gItq5OPFc6aNuO+KNO4dnMqxsioOlpZzsKScg6XlFJSUc6iknILScvYeKWNl7hFKy78/mVlESACJka1oGxlCYkSI46dTgUmMDCGyVSB1j5qsyD1C8clKHTWllBfqnxbDaz/K4KfvruGBd9cwZfxlBAVcHLMyadFwIiJEhwURHRZEt8SIs7Yrq6w+XVScfxaUlHOotJytBaUcPlFB/Z6/4AA/EiNDaBsRwpGTlYQF+XNVF+8aIqyUcriue1v+emsvnvxwA5NmruelcX3x9/PJ54/PoEWjCUKDAugY15qOca3P2qaqppbC4xWOonK6sJziYGkFB0tOUVFdwz2DUwkJ1K4ppbzV2H7JFJ+s4C8LthETFsQfb7kUH5244jQtGs0k0N+PpKhWJEW1sjsVpVQzmnhlJ46cqORfS3JpExbMY9elN76TF9OioZRSF+ipGy+h+GQlL3yxg5jWQfx4YAe7U2o2WjSUUuoCiQj/O6YnR8uq+N3Hm4gODWSkj85afXHc7ldKqWYW4O/Hqz/qy2UdYnhixnq+3fm9uVV9ghYNpZRyk7p5qjrFtWbiv7PYkHfM7pTcTouGUkq5UWSrQN65rz/RoUE89dFGu9NxOy0aSinlZvERIfz0qo5sLShla0Fp4zt4ES0aSinVDEb2akegvzB73X67U3ErLRpKKdUMYsKCuKZrPLPX7ae6prbxHbyEFg2llGomYzKSKTpewbJdR+xOxW20aCilVDO55pI4IlsF8tHafLtTcRstGkop1UyCA/y5uXcin20+yPHyKrvTcQstGkop1YzGZCRTXlXLwk0H7U7FLbRoKKVUM+qbEkVabBiz1/rGKCotGkop1YxEhDF9k1iRe4T8o2V2p3PBtGgopVQzG903CYCP1x+wOZMLp0VDKaWaWUpMKP3TYvjP2nxM/SU9vYxLRUNE3hKRQhHZ5BR7RkSyRWS9iHwuIu2suIjIyyKSY23PcNpnvIjstF7jneL9RGSjtc/LYi19JSIxIrLIar9IRKLdd+pKKdVybs1IIrfoJBvyS+xO5YK4eqUxFRheL/acMaaXMaYPMA/4nRW/EUi3XhOB18FRAIDfAwOA/sDvnYrA61bbuv3qvuspYLExJh1YbH1WSimvc2PPRIID/Jjt5c9suFQ0jDFLgOJ6MedZuMKAumuuUcA7xmElECUiicANwCJjTLEx5iiwCBhubYswxqwwjuu2d4DRTseaZr2f5hRXSimvEhESyPWXJjB3wwEqq713WpELuqchIv8jInnAnfz3SiMJyHNqlm/FzhXPbyAO0NYYUwBg/Yy/kHyVUspOYzKSOFpWxdfbC+1OpckuqGgYY542xqQA7wGPWGFpqGkT4i4TkYkikiUiWUVFReezq1JKtZghnWOJbR3MR178zIa7Rk+9D9xqvc8HUpy2JQMHGoknNxAHOGR1X2H9bLA8G2MmG2MyjTGZcXFxF3gqSinVPAL8/RjVpx2Ltx3iWFml3ek0SZOLhoikO328BdhmvZ8L3G2NohoIlFhdS58B14tItHUD/HrgM2vbcREZaI2auhv42OlYdaOsxjvFlVLKK43JSKKqxvBJdoHdqTRJgCuNROQD4GogVkTycYyCuklEugK1wF7gAav5AuAmIAcoA+4FMMYUi8gzwGqr3Z+MMXU31x/EMUKrFbDQegE8C8wUkQnAPuCHTTpLpZTyEN0TI7gkIZzZa/P58cAOdqdz3sTbHzSpLzMz02RlZdmdhlJKndXkJbv4y4JtfPXk1aTFhtmdDgAissYYk9lYO30iXCmlWtioPkn4CV75zIYWDaWUamFtI0IY3DmWj9btp7bWu3p7tGgopZQNbs1IJv/oKVbvKW68sQfRoqGUUja4/tK2hAX5e90zG1o0lFLKBqFBAdzYM5EFGwsor6qxOx2XadFQSimbjMlI4nhFNYu2HLI7FZdp0VBKKZsMTGtDu8gQPvKiUVRaNJRSyiZ+fsLovkks2XmYwuPldqfjEi0aSillozEZSdTUGuZ6yVKwWjSUUspGnePD6Z0cyex13jGKSouGUkrZbExGMpsPlLLtYGnjjW2mRUMppWx2c+92BPgJs73gmQ0tGkopZbOYsCCu7hrP7HX7qfHwaUW0aCillAe4NSOJwuMVLMs5bHcq56RFQymlPMDQbvFEhAR4/DMbWjSUUsoDBAf4c3Pvdny2+RAnKqrtTuestGgopZSHGJORzKmqGj7ddNDuVM5Ki4ZSSnmIjPZRpLYJ9eguKi0aSinlIUSEH/RNZkXuEfYfO2V3Og3SoqGUUh7kB32TMAbmeOgT4lo0lFLKg7RvE0r/1Bg+WpuPMZ73zIYWDaWU8jBjMpLYVXSSjftL7E7lexotGiLylogUisgmp9hzIrJNRLJFZLaIRFnxVBE5JSLrrdc/nfbpJyIbRSRHRF4WEbHiMSKySER2Wj+jrbhY7XKs78lw/+krpZTnualXIkEBfh65FKwrVxpTgeH1YouAHsaYXsAO4NdO23YZY/pYrwec4q8DE4F061V3zKeAxcaYdGCx9RngRqe2E639lVLK50WEBHJ997bM3XCAyupau9M5Q6NFwxizBCiuF/vcGFP39MlKIPlcxxCRRCDCGLPCODrp3gFGW5tHAdOs99Pqxd8xDiuBKOs4Sinl88ZkJFF8spJvdhTZncoZ3HFP4z5godPnNBFZJyLfiMgQK5YEOA88zrdiAG2NMQUA1s94p33yzrLPGURkoohkiUhWUZFn/QdWSqmmGJIeR2zrII97ZuOCioaIPA1UA+9ZoQKgvTGmLzAJeF9EIgBpYPfGhgW4vI8xZrIxJtMYkxkXF+da8kop5cEC/f24pXcSi7cWUlJWZXc6pzW5aIjIeGAkcKfV5YQxpsIYc8R6vwbYBXTBcZXg3IWVDNStbXiortvJ+lloxfOBlLPso5RSPm9MRhKVNbXM2+g5//Q1qWiIyHDgV8Atxpgyp3iciPhb7zviuImda3U7HReRgdaoqbuBj63d5gLjrffj68XvtkZRDQRK6rqxlFLqYnBpuwi6tg33qFFUrgy5/QBYAXQVkXwRmQC8CoQDi+oNrb0SyBaRDcAs4AFjTN1N9AeBN4EcHFcgdfdBngWGichOYJj1GWABkGu1fwN46ILOVCmlvIyI8IOMJNbsPcqewyftTgcA8cQnDi9EZmamycrKsjsNpZRyi4Ml5Vz+7GJ+NjSdScO6NNv3iMgaY0xmY+30iXCllPJgCZEhXNE5ltnrPGNaES0aSinl4cZkJJFXfIqsvUftTkWLhlJKebobLk0gNMjfI57Z0KKhlFIeLjQogBt7JDIvu4Dyqhpbc9GioZRSXmBMRhLHy6v5YushW/PQoqGUUl5gYMc2JEaG2P7MhhYNpZTyAv5+wui+SXyzo4ii4xW25aFFQymlvMSYvknU1Bo+2WDftCJaNJRSykuktw2nV3IkH62zbxSVFg2llPIiY/omsWl/KdsPHrfl+7VoKKWUF7m5dzsC/MS2qw0tGkop5UXatA7m6q5xzFm3n5ralp9WRIuGUkp5mTEZyRwqrWD5rsMt/t1aNJRSyssMvSSeiJAAZtvwzIYWDaWU8jIhgf6M7N2OhZsOcrKiukW/W4uGUkp5oTF9kzhVVcOnmw626Pdq0VBKKS/Ur0M07WNCW3wUlRYNpZTyQiLCmIwklu86QkHJqRb7Xi0aSinlpcb0TcYYmLOu5aYV0aKhlFJeqn2bUC5LjeajtS23FKwWDaWU8mJjMpLZWXiCTftLW+T7Gi0aIvKWiBSKyCan2HMisk1EskVktohEOW37tYjkiMh2EbnBKT7ciuWIyFNO8TQRWSUiO0VkhogEWfFg63OOtT3VXSetlFK+4qaeiQQF+PGfFloK1pUrjanA8HqxRUAPY0wvYAfwawAR6Q6MAy619vmHiPiLiD/wGnAj0B24w2oL8FfgBWNMOnAUmGDFJwBHjTGdgResdkoppZxEtgpkWLe2zN1wgKqa2mb/vkaLhjFmCVBcL/a5MabuiZKVQLL1fhQw3RhTYYzZDeQA/a1XjjEm1xhTCUwHRomIAEOBWdb+04DRTseaZr2fBVxrtVdKKeVkTEYSxScr+WZ7UbN/lzvuadwHLLTeJwF5TtvyrdjZ4m2AY04FqC5+xrGs7SVWe6WUUk6u7BLH0EviCQ5s/tvUAReys4g8DVQD79WFGmhmaLg4mXO0P9exGspjIjARoH379ufIWCmlfE+gvx9v3XNZi3xXk8uSiIwHRgJ3mv+O9coHUpyaJQMHzhE/DESJSEC9+BnHsrZHUq+brI4xZrIxJtMYkxkXF9fUU1JKKdWIJhUNERkO/Aq4xRhT5rRpLjDOGvmUBqQD3wGrgXRrpFQQjpvlc61i8xUw1tp/PPCx07HGW+/HAl+alhqIrJRSqkGNdk+JyAfA1UCsiOQDv8cxWioYWGTdm15pjHnAGLNZRGYCW3B0Wz1sjKmxjvMI8BngD7xljNlsfcWvgOki8mdgHTDFik8B/i0iOTiuMMa54XyVUkpdAPG1X94zMzNNVlaW3WkopZRXEZE1xpjMxtrpE+FKKaVcpkVDKaWUy7RoKKWUcpkWDaWUUi7zuRvhIlIE7LU7DxfF4nhWxRf58rmBb5+fnpv3upDz62CMafRBN58rGt5ERLJcGa3gjXz53MC3z0/PzXu1xPlp95RSSimXadFQSinlMi0a9ppsdwLNyJfPDXz7/PTcvFezn5/e01BKKeUyvdJQSinlMi0aNhCRFBH5SkS2ishmEXnM7pzczVrmd52IzLM7F3cSkSgRmSUi26z/f5fbnZO7iMgT1p/HTSLygYiE2J3ThRCRt0SkUEQ2OcViRGSRiOy0fkbbmWNTneXcnrP+XGaLyGwRiWqO79aiYY9q4OfGmG7AQOBhpzXTfcVjwFa7k2gGLwGfGmMuAXrjI+coIknAo0CmMaYHjtmovX1m6anA8Hqxp4DFxph0YLH12RtN5fvntgjoYYzpBezAMRu522nRsIExpsAYs9Z6fxzHPzxJ597Le4hIMjACeNPuXNxJRCKAK7Gm7zfGVBpjjtmblVsFAK2sRc9C+e+CaF7JGLOE7y/cNgqYZr2fBoxu0aTcpKFzM8Z87rR09koci9q5nRYNm4lIKtAXWGVvJm71IvBLoNbuRNysI1AEvG11vb0pImF2J+UOxpj9wN+AfUABUGKM+dzerJpFW2NMATh+eQPibc6nudwHLGyOA2vRsJGItAb+AzxujCm1Ox93EJGRQKExZo3duTSDACADeN0Y0xc4ifd2b5zB6tsfBaQB7YAwEbnL3qxUU4jI0zi6wN9rjuNr0bCJiATiKBjvGWM+sjsfNxoM3CIie4DpwFARedfelNwmH8g3xtRdFc7CUUR8wXXAbmNMkTGmCvgIGGRzTs3hkIgkAlg/C23Ox61EZDwwErizuZbH1qJhA3GskTsF2GqMed7ufNzJGPNrY0yyMSYVx43UL40xPvEbqzHmIJAnIl2t0LU4ljb2BfuAgSISav35vBYfuclfz1xgvPV+PPCxjbm4lYgMx7F89i3GmLLm+h4tGvYYDPwYx2/h663XTXYnpVzyM+A9EckG+gB/sTkft7CunmYBa4GNOP5t8Oqnp0XkA2AF0FVE8kVkAvAsMExEdgLDrM9e5yzn9ioQDiyy/k35Z7N8tz4RrpRSylV6paGUUsplWjSUUkq5TIuGUkopl2nRUEop5TItGkoppVymRUOpJhKRGmto42YR2SAik0TEr16bl0Rkf11cRO51GmZdKSIbrffPisg9IlLktH29D05kqbycDrlVqolE5IQxprX1Ph54H1hmjPm9FfMD9uCY+O8pY8zX9fbfg2NW2cPW53usz4+00Ckodd70SkMpNzDGFAITgUesJ6oBrgE2Aa8Dd9iVm1LupEVDKTcxxuTi+DtVN3PqHcAHwGxgpDXfWGNur9c91aqZ0lWqSbRoKOVeAiAiQcBNwBxrBuNVwPUu7D/DGNPH6XWqGXNV6rwF2J2AUr5CRDoCNThmTr0ZiAQ2Wr1VoUAZMN+2BJVyAy0aSrmBiMQB/wReNcYYEbkDuN8Y84G1PQzYLSKhzTkDqVLNTbunlGq6VnVDboEvgM+BP4pIKHADTlcVxpiTwLc4rkDOpf49DV9c00J5MR1yq5RSymV6paGUUsplWjSUUkq5TIuGUkopl2nRUEop5TItGkoppVymRUMppZTLtGgopZRymRYNpZRSLvv/XgMaIm2LgxgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ts.groupby(df_ts.index.month)['QTD_NAIH'].sum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**SARIMAX Machine Learning Model**\n",
    "\n",
    "The SARIMAX model is specified by:\n",
    "$(p,d,q)-(P,D,Q)s$\n",
    "\n",
    "- $(p,d,q)$ -> Trend Components Configuration (ARIMA)\n",
    "- $(P,D,Q)$ -> Seasonality Components Configuration (SARIMA)\n",
    "- $s$ -> Period configuration (Eg: 12 months, 30 days)\n",
    "\n",
    "The model has s = 12 (12 months)\n",
    "\n",
    "In order to find the best specification, an automatized method was used to test all combination of components (p,d,q,P,D,Q,s) on a 0, 1 range of values.\n",
    "\n",
    "**AIC (results.aic)**\n",
    "\n",
    "Each specification was scored with AIC score, that shows us how much the model fits on data (Lower is better)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = d = q = range(0, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdq = list(product(p, d, q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(product(p, d, q))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0)x(0, 0, 0, 12)12 - AIC:2373.0627427670593\n",
      "ARIMA(0, 0, 0)x(0, 0, 1, 12)12 - AIC:2048.47696085024\n",
      "ARIMA(0, 0, 0)x(0, 1, 0, 12)12 - AIC:1693.0980430797858\n",
      "ARIMA(0, 0, 0)x(0, 1, 1, 12)12 - AIC:1481.9345473162273\n",
      "ARIMA(0, 0, 0)x(1, 0, 0, 12)12 - AIC:1711.5674742664999\n",
      "ARIMA(0, 0, 0)x(1, 0, 1, 12)12 - AIC:1693.465464599374\n",
      "ARIMA(0, 0, 0)x(1, 1, 0, 12)12 - AIC:1494.4830163401966\n",
      "ARIMA(0, 0, 0)x(1, 1, 1, 12)12 - AIC:1481.1573216789177\n",
      "ARIMA(0, 0, 1)x(0, 0, 0, 12)12 - AIC:2223.587708164753\n",
      "ARIMA(0, 0, 1)x(0, 0, 1, 12)12 - AIC:2005.7873379988866\n",
      "ARIMA(0, 0, 1)x(0, 1, 0, 12)12 - AIC:1643.280599005199\n",
      "ARIMA(0, 0, 1)x(0, 1, 1, 12)12 - AIC:1431.459321818493\n",
      "ARIMA(0, 0, 1)x(1, 0, 0, 12)12 - AIC:1690.8383982871746\n",
      "ARIMA(0, 0, 1)x(1, 0, 1, 12)12 - AIC:1639.536949733573\n",
      "ARIMA(0, 0, 1)x(1, 1, 0, 12)12 - AIC:1467.7274365462988\n",
      "ARIMA(0, 0, 1)x(1, 1, 1, 12)12 - AIC:1433.0544771219757\n",
      "ARIMA(0, 1, 0)x(0, 0, 0, 12)12 - AIC:1825.85063424484\n",
      "ARIMA(0, 1, 0)x(0, 0, 1, 12)12 - AIC:1608.8783471569827\n",
      "ARIMA(0, 1, 0)x(0, 1, 0, 12)12 - AIC:1629.9665470564207\n",
      "ARIMA(0, 1, 0)x(0, 1, 1, 12)12 - AIC:1402.9714489427604\n",
      "ARIMA(0, 1, 0)x(1, 0, 0, 12)12 - AIC:1619.6774854705995\n",
      "ARIMA(0, 1, 0)x(1, 0, 1, 12)12 - AIC:1595.795218734469\n",
      "ARIMA(0, 1, 0)x(1, 1, 0, 12)12 - AIC:1436.809826317406\n",
      "ARIMA(0, 1, 0)x(1, 1, 1, 12)12 - AIC:1404.3988691757688\n",
      "ARIMA(0, 1, 1)x(0, 0, 0, 12)12 - AIC:1802.5002406575177\n",
      "ARIMA(0, 1, 1)x(0, 0, 1, 12)12 - AIC:1591.204494842465\n",
      "ARIMA(0, 1, 1)x(0, 1, 0, 12)12 - AIC:1612.083188076535\n",
      "ARIMA(0, 1, 1)x(0, 1, 1, 12)12 - AIC:1380.1187977754555\n",
      "ARIMA(0, 1, 1)x(1, 0, 0, 12)12 - AIC:1619.315258689917\n",
      "ARIMA(0, 1, 1)x(1, 0, 1, 12)12 - AIC:1575.6325590759682\n",
      "ARIMA(0, 1, 1)x(1, 1, 0, 12)12 - AIC:1434.695997540431\n",
      "ARIMA(0, 1, 1)x(1, 1, 1, 12)12 - AIC:1381.8846562864207\n",
      "ARIMA(1, 0, 0)x(0, 0, 0, 12)12 - AIC:1848.1283497190436\n",
      "ARIMA(1, 0, 0)x(0, 0, 1, 12)12 - AIC:1627.4291483644636\n",
      "ARIMA(1, 0, 0)x(0, 1, 0, 12)12 - AIC:1635.5053954593898\n",
      "ARIMA(1, 0, 0)x(0, 1, 1, 12)12 - AIC:1421.1077091258492\n",
      "ARIMA(1, 0, 0)x(1, 0, 0, 12)12 - AIC:1621.2532314004595\n",
      "ARIMA(1, 0, 0)x(1, 0, 1, 12)12 - AIC:1620.6533418541092\n",
      "ARIMA(1, 0, 0)x(1, 1, 0, 12)12 - AIC:1425.6244569292269\n",
      "ARIMA(1, 0, 0)x(1, 1, 1, 12)12 - AIC:1419.796132660358\n",
      "ARIMA(1, 0, 1)x(0, 0, 0, 12)12 - AIC:1825.6206947668018\n",
      "ARIMA(1, 0, 1)x(0, 0, 1, 12)12 - AIC:1610.3679615703527\n",
      "ARIMA(1, 0, 1)x(0, 1, 0, 12)12 - AIC:1618.4132841575008\n",
      "ARIMA(1, 0, 1)x(0, 1, 1, 12)12 - AIC:1400.6936901765168\n",
      "ARIMA(1, 0, 1)x(1, 0, 0, 12)12 - AIC:1621.898923233076\n",
      "ARIMA(1, 0, 1)x(1, 0, 1, 12)12 - AIC:1597.2080551349204\n",
      "ARIMA(1, 0, 1)x(1, 1, 0, 12)12 - AIC:1427.016671742788\n",
      "ARIMA(1, 0, 1)x(1, 1, 1, 12)12 - AIC:1404.807301152245\n",
      "ARIMA(1, 1, 0)x(0, 0, 0, 12)12 - AIC:1822.487613627399\n",
      "ARIMA(1, 1, 0)x(0, 0, 1, 12)12 - AIC:1608.67645613499\n",
      "ARIMA(1, 1, 0)x(0, 1, 0, 12)12 - AIC:1628.575764692792\n",
      "ARIMA(1, 1, 0)x(0, 1, 1, 12)12 - AIC:1400.071061112234\n",
      "ARIMA(1, 1, 0)x(1, 0, 0, 12)12 - AIC:1602.7398780437456\n",
      "ARIMA(1, 1, 0)x(1, 0, 1, 12)12 - AIC:1593.96350304494\n",
      "ARIMA(1, 1, 0)x(1, 1, 0, 12)12 - AIC:1417.8736499188215\n",
      "ARIMA(1, 1, 0)x(1, 1, 1, 12)12 - AIC:1401.922945690836\n",
      "ARIMA(1, 1, 1)x(0, 0, 0, 12)12 - AIC:1803.7398585311544\n",
      "ARIMA(1, 1, 1)x(0, 0, 1, 12)12 - AIC:1593.1259106560015\n",
      "ARIMA(1, 1, 1)x(0, 1, 0, 12)12 - AIC:1613.2545172525113\n",
      "ARIMA(1, 1, 1)x(0, 1, 1, 12)12 - AIC:1377.3912836470115\n",
      "ARIMA(1, 1, 1)x(1, 0, 0, 12)12 - AIC:1604.5818094221365\n",
      "ARIMA(1, 1, 1)x(1, 0, 1, 12)12 - AIC:1570.6544495421297\n",
      "ARIMA(1, 1, 1)x(1, 1, 0, 12)12 - AIC:1418.4262489363466\n",
      "ARIMA(1, 1, 1)x(1, 1, 1, 12)12 - AIC:1379.3591414691218\n",
      "The best model is:  (1, 1, 1) x (0, 1, 1, 12)   - AIC:  1377.3912836470115\n"
     ]
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "lower_aic = None\n",
    "\n",
    "for param in pdq:\n",
    "\n",
    "    for param_seasonal in seasonal_pdq:\n",
    "            \n",
    "        mod = sm.tsa.statespace.SARIMAX(df_ts['QTD_NAIH'],order=param,seasonal_order=param_seasonal,enforce_stationarity=False,enforce_invertibility=False)\n",
    "        results = mod.fit()\n",
    "        print('ARIMA{}x{}12 - AIC:{}'.format(param, param_seasonal, results.aic))\n",
    "        \n",
    "        if lower_aic == None:\n",
    "            lower_aic = results.aic\n",
    "            \n",
    "        if lower_aic > results.aic:\n",
    "                lower_aic = results.aic\n",
    "                opt_pdq = param\n",
    "                opt_pdq_seasonal = param_seasonal\n",
    "            \n",
    "print('The best model is: ',opt_pdq,'x',opt_pdq_seasonal,'  - AIC: ',lower_aic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After test all possibly combinations, the best configuration was:\n",
    "\n",
    "$(p,d,q)-(P,D,Q)s$\n",
    "\n",
    "(1, 1, 1) x (0, 1, 1, 12) - AIC: 1377\n",
    "\n",
    "For the best visualization of model, plot_diagnostics function was used to analyze the charts (QQ Plot, KDE). The normal distribution fitting was confirmed from that. The (Standardized residual) show us a uniform variancy lying around 0. The (Correlogram) show us the residual erros aren't autocorrelates, this implies that there aren't error patterns without understanding inside the model. This model seems good to proceed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.6097      0.140      4.353      0.000       0.335       0.884\n",
      "ma.L1         -0.9047      0.083    -10.952      0.000      -1.067      -0.743\n",
      "ma.S.L12      -0.7421      0.133     -5.580      0.000      -1.003      -0.481\n",
      "sigma2      1.171e+06   2.14e+05      5.486      0.000    7.53e+05    1.59e+06\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(df_ts['QTD_NAIH'],\n",
    "                                order=opt_pdq,\n",
    "                                seasonal_order=opt_pdq_seasonal,\n",
    "                                enforce_stationarity=False,\n",
    "                                enforce_invertibility=False)\n",
    "\n",
    "results = mod.fit()\n",
    "\n",
    "print(results.summary().tables[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot_diagnostics(figsize=(15, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = results.get_prediction(start=1, dynamic=False)\n",
    "pred_ci = pred.conf_int()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comparing the Models**\n",
    "\n",
    "Here, is compared the machine learning model to real data.\n",
    "\n",
    "The blue line is the real hospitalizations amount and green line is the machine learning prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove (VALOR_MENSAL) column\n",
    "df_naih = df_ts.drop('VALOR_MENSAL', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>QTD_NAIH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>16302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>13506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>16704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>16422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>17032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            QTD_NAIH\n",
       "DATE                \n",
       "2010-01-01     16302\n",
       "2010-02-01     13506\n",
       "2010-03-01     16704\n",
       "2010-04-01     16422\n",
       "2010-05-01     17032"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_naih.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_naih['2010':].plot(label='Observed')\n",
    "\n",
    "pred.predicted_mean.plot(ax=ax, label='Forecast', alpha=.7)\n",
    "\n",
    "ax.fill_between(pred_ci.index,\n",
    "                pred_ci.iloc[:, 0],\n",
    "                pred_ci.iloc[:, 1], color='w', alpha=.2)\n",
    "\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Hospitalizations')\n",
    "plt.legend([\"HOSPITALIZATIONS\", \"FORECAST\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prediction**\n",
    "\n",
    "Here, show us the real prediction after 2018 until December 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_uc = results.get_forecast(steps=12)\n",
    "pred_ci = pred_uc.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_naih.plot(label='Internações', figsize=(20, 15), fontsize=26, title='HOSPITALIZATIONS PREDICTION',legend=True)\n",
    "\n",
    "pred_uc.predicted_mean.plot(ax=ax, label='Previsão')\n",
    "ax.fill_between(pred_ci.index,\n",
    "                pred_ci.iloc[:, 0],\n",
    "                pred_ci.iloc[:, 1], color='r', alpha=.25)\n",
    "\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Hospitalizations')\n",
    "\n",
    "ax.legend([\"HOSPITALIZATIONS\", \"FORECAST\"]);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_uc.predicted_mean.plot(label='PREDICTION', figsize=(12, 10), fontsize=20, title='HOSPITALIZATION 2019 FORECAST',legend=True)\n",
    "ax.fill_between(pred_ci.index,\n",
    "                pred_ci.iloc[:, 0],\n",
    "                pred_ci.iloc[:, 1], color='k', alpha=.25)\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Hospitalizations')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-01-01    15812.790745\n",
       "2019-02-01    16277.215300\n",
       "2019-03-01    18021.247600\n",
       "2019-04-01    17805.616444\n",
       "2019-05-01    18585.949763\n",
       "2019-06-01    18102.462898\n",
       "2019-07-01    18294.819157\n",
       "2019-08-01    18943.571679\n",
       "2019-09-01    17692.120357\n",
       "2019-10-01    17066.199199\n",
       "2019-11-01    16589.551988\n",
       "2019-12-01    15207.611193\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_uc.predicted_mean"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}