US Birth Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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56RAr95yyWWy1vtfTWq8Darwl0VqfAuKsFZQjVVU+d+jQoQwdOtT+wQjnkLULjqwx3sZ7elvvuF3ugrB4mDMOFj9sjIjx8LTe8Z3Zwe/h+7/AiL9Amxuu71haw/Kn4dwxY0x5YLg1IqxS6sl8Nh05w4tj2xPf2LJkfslfbulIetZ5nvsshdZRQbSOCrJ6fDJTVIjabHzHqNHSY7L1j92sL4x9w0hwP7xi/eM7o4py+OYFYxTb/Dvg6+eh9EL9j7d1FqR+CSP+ZFxPG/poQwb+3p7ck1TLKL0q+Hl78t59PfH18mDBpmM2iM6J1xQVwimcz4I9n0OvR8A/zDbnSHoITu2C9f+B2C5G9UZ3tnM+5B00Jvec2A7J78Dh1XD7DGjSrW7HykqBlS9C65HQ37ZdpHmFJSzZeZK7esYRGlC/d2pNwvz58vEBxDWqud+9vpzuDl270MQLV4pV1NOOj8FUDn2m2fY8o1+HZv1gyRNGF4+7KrsIP78OTZOMP1yjXzNq35QUwMwRsPZfYKqw7FiX+s0DGhvPJGzYbw6waMtxSstNPNg/4bqO0yw8AA8bjXRxqoTu5+dHXl6eSyRKrTV5eXn4+fk5OhRhK6YKY4Zhy2G2X1fSywfungv+jWDRvVDkpkNlt8yE8yfghpcvjxZqNQwe2wDtxxndTnNugrNHaz6O1rD8GaOi5Z2zITDCpmGXVZj4eONRBrWJoE10sE3PdT2cqsslLi6OzMxMcnNzHR2KRfz8/IiLc4tnwaIqh36E/ONw49/sc76gKJgwH2aPNopH3f+ldR/COlpxPqz9t1GmtsXgK7cFNIa75sCuT4xFJt4bYEzg8gkEdKWSCeav849D6hcw/E/QvL/NQ1+55xSnzhfz99s62fxc18OpErq3tzctWrRwdBhCGLbNMYbBtbPjbOGmPeDmt4xSAav+5PAJdla14W24eAZG/Lnq7UpB1wnmrqfHjHH6NelwMwz8jfXjrMKH64/QPDyAYe2i7HK++nKqhC6E0yg4Bfu+gf5PGd0h9tRtovGQNPld4yGpDWc82k1hrjFaKPFWaNK95raNmsODK6D00hBiZe6eueqzp49VKiXWJuX4ObYfO8efxyXarO/bWiShC1GVHfNAVzhuXcmRr0L2Hlj+LDRuBc36OCYOa1n7JpQXw/CXLGuvlDEBywnM2ZBBkK8XdyU5f/eqUz0UFcIpmEyw/SOjnze8lWNi8PSCuz6CkCYw7w44Vq8Cp87h7FHYMgu633vdKwLZW875Yr7adZI7e8YR7Of8zzMkoQtxtcM/GbMOez7o2DgCGhtdD0GRMO92OLrRsfHU1+p/gvKAIb93dCR1Nn/TMcpNmsnXOVTRXiShC3G1bXMgINwYRudooU3hwa+Nut7z7oCMdY6OqG5y0iFlobH+aqhrrfxVUl7B/E1HGdYuihYRgY4OxyKS0IWorDAH9n1tPIi8egFoRwmJNe7UQ+Ng3p3GrEpX8ePfjL7wQb91dCR1tmJXFqcLS697IpE9SUIXorKd842ZoT0edHQkVwqONpJ64xaw4B44+IOjI6rd8S2w9ytjpJCLrdCktebD9Rm0jgpiUBvbTlqyJknoQlxiMsG2j6D5QIuXmNtx7CxPLdzB0bwiGweH0Zc+eTmEt4aFE+HA97Y/Z31pDT/8FQIiLFt/1clsP3aW3Sfymdw/AWWHoZHWIgldiEsy1sDZI3V6GPr3FeksTznJ2LfW2mcx4MAII6lHtoNFE2H/t7Y/Z10U58PmGfBef8hYC0N+B77WLxNra7PXZxDs58UdPVyr318SuhCXbJtj1FLpcLNFzbdknGHr0bM8PrQVHZuE8ptPU3h20Q4KistsG2dAY5i8DKI7GnVf9n5t2/NZ4sR2WPok/Ks9fP2cUbLglv8ZVSpdzMGcAlbuOcWEXvEE+LjWVB3XilYIWynMhfSvoPc08Las4Np7qw/RONCHp4a3wcfLg3d+OshbPxxg27GzvDWhOz2aNbJdvP6NjCqF826HTx8w6qB0sPOonNIi2P05bJ0NWTvBO8CooJg0xShh4GL2nMhn+prDrNidha+XBw/0S3B0SHUmCV0IgJQFYCqDnpYtYpGedZ4f9+bw25Ft8fcxVhl6ekQbBrQO5+mFO7nr/Y38ZmRbHh3SynaLAvuHGQW85t1hFPO680NIvMU257pa3iH4cAwUZkNUIox9E7rcDX6h9jm/lWitWb0/lxlrDrPhUB5Bvl5MGZDAQwNa0KSWtUKdkSR0IbQ2HoY262f0TVvgg58PEejjec1dXM/mjfn6mUH88cvdvPHtPtYdOM3/3dONmFAblVn2C4X7vjAn9QeNUrLWWPe0JkWnYf6dUFFmjLxpPsAuNVWsqbTcxNKdJ5i59gj7sguICfHjD2PaM7FPM0JcYEZodSShC5GxDs4cMh7gWeD4mQss35XFlAEJVa5cE+rvzf8mdmdw20j+siyVm99ex8JH+tA6yka1SfxC4P4vjDHqn08BbYJOt9vmXGUXYeEEOH/SeDgb39s257GRnIJiFm0+zrzko+QUlNA+Jph/392VcV2a4OPl+o8Ua03oSql4YC4QA5iA6Vrrt65qo4C3gLHABeBBrfV264crhA1sm2Pc6SaOt6j5jLWH8VAwdWD1i14opbg7KZ5u8WFMmrGJCdM3sfCRPrZbHME3GO77HObfZSw4rU3Q+U7rnsNUAV88Aplb4e6PXCaZa63Zfuwcczdm8PXuLMoqNIPbRvLmXS0Y1CbCpYYl1saSO/Ry4Lda6+1KqWBgm1LqO611WqU2Y4A25o8+wHvmz0I4t/NZkL4Mej4E3rX3mZ4uLOGTLce5vXucRd0obaODWTStL5NmJDNhejILHulLuxgbJvV7P4cFdxuJV2vocpf1jr/qT5C+HEa9ZvEfP0cqLqtgWcpJ5m7MYM+J8wT7enFf3+Y80C/BZaby11Wt7zG01lmX7ra11gVAOnD14MzxwFxtSAbClFKxVo9WCGtb84aR+PpZNvnlow0ZlFaYmDbE8iXpWkcFsWhaX7w8FRNnJJN28nx9o62dbxDc+5nRr/3lNNj+MVSUX/9xk983FnPu86jTTxTSWvPW9wfo+48f+N3nuygtN/G3WzuR/OIIXr65o9smc6jjOHSlVALQHbi6lmdT4Hilf2dybdJHKTVNKbVVKbXVVZaZE27szBGjTG6PB6BRQq3NC0vK+WhDBqMSY2gVWbfJMi0jg1g0rR8+nh5MmplM6sn8egZtAZ9AmPQpJAyEZU/CP+Phw7Hw3cuwd4VRr6Yu0r+Clb83ipWNes3pH4C+u/oQ//f9fpKaN2LhI3359tnB3Ne3OYG+7v/I0OLvUCkVBCwGntVaX32LUdX/8DUrPWutpwPTAZKSkpx/JWjh3n5+HTy8YPDzFjVfuOkY54vLeXRo/Wqkt4gI5JNf9WXi9GQmzdjE/If70KmpjYb5+QQY3S97vzJqqmRuNlYMWv8fY3tYc4jrBU17QkwniOoIgeHXHuf4Flg81RhXfvsM8PC0TbxW8n1aNm+u2sf4bk34zz3d3Kp/3BIWJXSllDdGMp+vtf6iiiaZQHylf8cBJ68/PCFsJGevsSBxvyeMaoa1KCmvYOa6w/RvFU63+LB6n7Z5eCCf/KofE6YnM2lGMvMe7kOXuPofr0ZevsZEn053GP8uK4asFMg0J/ijG2DP55fbB8UYs0+jO0J0J2PR6sVTITgGJn5i/JFwYgeyC3j2k510ahLK63d0aXDJHCwb5aKAWUC61vrf1TRbBjyplFqE8TA0X2udZb0whbCyn/4O3oEw4NcWNV+y4wTZ50t4866u133q+MYBLJrWl4kzkrl35ibmTe1D1+v4I2Exbz9jKbvKy9kV5kB2aqWPPbDpfagoNbb7N4J7FxuFwaxIa01eUSkRQdYpUXzuQikPz92Kn7cn0x/oiZ+3c7+TsBVL7tAHAPcDu5VSO82vvQg0A9Bavw98jTFk8SDGsMWHrB6pENZycqcxsmXIC1V3M1ylwqT54OfDdGoawsDW1imlGt84wHynvpEpc7aw5IkBxDd2wB1wUJTx0WrY5dcqyoyZoDlpxoLOjVtY/bQz1x7h71+nM6pjNM+MaEtik5B6H6u8wsRTC3eQda6YhdP6EBvqejM8raXWhK61XkfVfeSV22jgCWsFJYRN/fg3486zn2U/sqtST3H4dBHvTOph1bfxTcP8+eih3tz27gamzNnC4sf7O8csRU9viGpvfNjAuQul/PfHA7SKDGTDoTy+TV3L6I4xPHNDGzrE1j2x/+Obvaw9cJr/d0cXejZ3rbrr1ub6U6OEqIujG+HgdzDgWYvqjmitef/nQySEBzC6U4zVw2kZGcR79/XgyOkinpi/nfIKk9XP4WzeXX2IwpJy3rm3B+teGM4zI9qw/uBpxry1lsfmbWPvKcuHdX6+LZNZ647wYP8E7u4VX/sObk4Sumg4tIYfX4WgaKOqogU2HTlDSmY+0wbbrshW/1YRvHZbZ9YeOM1fl6dhvOF1TyfOXWTOhgxu7x5H+5gQQv29+fXItqx7YThPD2/N2gOnGf2ftTw+fxtbM85QWFL9GPrtx87y4he76d8qnD/e1MGO34Xzcv+BmUJccuhHOLreqAxo4YiNuRszCAvw5nYbL3Rwd694Dp0u5IOfD9MyMpCHBli/39oZ/N93+wH4zY1tr3g9NMCb39zYjikDWzBz7RE+XH+Er3efAiA21I/WUUG0igyiVVQQrSODCAvw5tGPtxET6sc7k3rg7Sn3piAJXTQUl+7OQ5tBD8tK5GafL+bb1GymDmxhl1ETL4xqz5HcIl79Ko3m4QEMbx9t83Pa095T51m8PZOHB7agaTWlacMCfHhuVDseHtSC5MN5HMot4mBOIYdyC/ls63GKSit+aRvo48nHU/vQKNDHXt+C05OELhqGvV/ByR0w/l3wsiwBLNh0DJPW3NunmY2DM3h4KP4zoRt3f7CRpxbsYPHj/WkfU//RH87mjZX7CPL14vGhta/XGhbgw+hOV84P0Fpz6nwxB3MKOZxbRM/mjWxXF8dFyfsU4f5MFfDj3yG8DXS5x6JdyipMLNx8jCFtI2kebr/aHwE+Xsx8oBdBfl5MnbOVnIJiu53bljYdzuOHvTk8NrRVve+olVLEhvozqE0kk/sn2G6WrQuThC7c357FkJsOw14ET8velH6Xlk1OQQn3921u4+CuFRPqx6zJvThTVMq0udsoLquofScnprXmnyv3Eh3iy0P93fPZgLOQhC7cW94ho+xrTGdIvNXi3eZuzCCukT9D20XZLrYadGoaylsTupGSeY5JM5LJPu+6d+qr0rLZcewcv77h8nJ9wjYkoQv3lbvPWPfSVA63fQAelv24H8guIPnwGe7t09x264Fa4MaOMbwzqQd7TxUw7n/r2JJxxmGx1Fd5hYn/t3IvrSIDubNnnKPDcXuS0IV7yk41SsaCse5ldEeLd/04+Sg+nh7cneT4BDS2cyxfPj6AQB9PJk5PZu7GDJcap/75tkwO5Rbxu9Ht8ZKhhTYnV1i4n6wUmDMOPH3gwa/rNIW9sKScL7afYFyXWMKtVDjqerWLCWbpkwMZ0jaSPy9N5befpbhEv/rF0gr+7/v99GgWxo2J7jUE01lJQhfuJXMbfHSzscjDQysgovYhcpUt2XGCwpJy7utn/4ehNQn192bGA0k8e0Mbvth+gjve28DxMxccHVaNPtxwhOzzJfx+TIcGWcrWESShC/dxbBPMHQ9+YfDQ19DY8mXiwBiN8fHGo3RqGkJ3e5SzrSMPD8WzN7Rl1uQkjp25wC1vr2PtAeda+UtrzcGcQmavO8J7qw9xQ4coerdo2AWz7EkmFgnnV3AK0pYZCy2ExRur7fg3unIptIz1xor3wTEweTmE1n2q/paMs+zLLuD1Ozo79R3liA7RLHtyIL/6eCuTZ2/mzbu6cnsPx/X3518oY/2h06zZn8vaA6c5ce4iAG2ignhxrNRYsSdJ6ML5ffVr2Pf1la95B0JYM+MjJBZSPjGS/eTlRlKvh7kbMwjx8+KWrrat22INLSIC+fLxAUz7eCvPfZaCp4difDf7xa21Zta6I6zYnUXK8XOYNAT7eTGgVQRPDGvNoDYRjqnv3sBJQhfOLWOdkcyHvADtb4Jzx+DccfPnY5B/DI4lG6NYJi40Fmuoh5yCYlbuOcXk/gkuM1Y60NeYVfrgh5v5zacpeHl4cFOX2pfTs4Z/rdrP2z8dpEtcKE8Oa83gtpF0iw+TkSwO5n4J/dKQLid+yywsZDLBqpcgJA4G/hq8/SH2+peAq8onm49TbrJf3RZr8ffxZPaDvZg8ezPPLNqBl6diVEfr122vbF7yUd7+6SATe8fz2m3O3T3V0Ljfn9P1b8F/ukBF9XWUhYtI/cIoqDX8JSOZ20h5hYkFm48xqE0ELSODbHYeWwn09eLDh3rROS6UJxds54f0bJuda1XqKf68dA8j2kfx6vhOksydjHsldJMJtsw03oYf3+ToaMT1KCuG7/9qTNm3sKBWfX2fnkNWfrFD6rZYS7CfNx9N6U2H2BAem7ed1ftyrH6ObUfP8tTCHXSOC+N/k7pL94oTcq//kYy1kH/c+PrAKsfGIq7P5unGH+Yb/2bxlP36+jg5gyahfgxv75i6LdYS4ufNx1P60CY6iGkfb2PdgdNWO/ah3EIe/mgLsaF+zJ6cRICP+/XWuoNaf1OUUrOVUjlKqT3VbG+klPpSKbVLKbVZKdXJ+mFaKGUh+IZAfB9J6K7swhlY8ya0uRFaDrXpqVKOn2P9wTzu7dvcLe44QwO8mTe1Dy0jAnl47hY2HDx93aUCcgqKmTx7Mx5K8dGU3k4zg1Zcy5I/s3OAt4G51Wx/Ediptb5NKdUeeAcYYZ3w6qCkANKWQue7IKItrPqjMRoiTBaOdTlr3oDSAhj5ik1Po7Xmta/TCQ/0YXL/BJuey54aBfow/+E+TJyRzKSZm/Dx8iA80IfG5o/wQB8amT/HNQqgS1woLSICq+wPLywp56EPt5BXWMqiaX3tWhte1F2tCV1rvUYplVBDk0TgH+a2e5VSCUqpaK217Z7MVCVtGZRdgG6TwL+xkdAPrIJeU+0ahrhOZw7D5hnQ/X6Isu2klJ/25bDpyBleGd+RIF/36kIID/Jl0bR+fLE9k9yCEvKKSjlTVEpeUSkZeUWcKSy9Yjm3UH9vusaH0S0+jO7xYXSNDyPYz4vH5m1j76kCZj6QRFcnnD0rrmSNn+IU4HZgnVKqN9AciAOuSehKqWnANIBmzaw8PCxloTHVO76P8e9GCZLQXdH3fzWKag170aanqTBp/vnNXlpEBDKxt2sNVbRU40AfHh5UffmD4rIKMvKK2HnsHDuPGx9v/3gAk768/5miUl6/ozPDXPz5QkNhjYT+T+AtpdROYDewA6hyzKDWejowHSApKcl6NUDPHjUeiA576fL48zajYPtcKLto0yFvwoqOb4a0JTDk9/We7Wmpxdsy2Z9dyHv3NtwV4/28PWkfE0L7mBAmmP+oFZWUs/tEPjuPn2N3Zj4DWkdwTy/3/IPnjq47oWutzwMPASijE+6I+cN+UhYZn7tWGt7W5kbY/IEx07DNSLuG0yAV5oJPgFHlsD60NiYRBUVD/6esG9tVLpZW8K/v9tG9WRijO9n2D4erCfT1om/LcPq2DHd0KKIervvWRCkVppS6tOrrw8Aac5K3D62N7paEQUZdj0sSBoJ3gIx2sYcja+C/3eDdfkYt8vpIX27MHRj2IvjadnLP7PVGWdc/SFlX4WYsGba4ENgItFNKZSqlpiqlHlVKPWpu0gFIVUrtBcYAz9gu3CocS4azR4yHoZV5+0GLIbD/28vlAIT1pX8F8+6AkKZQUQYzRxpdXXVRdhG+fxkiO0C3+2wTp1leYQnvrT7EyMRoKesq3I4lo1wm1rJ9I9DGahHVVcoCo/Jeh1uu3db2Rtj/DZzeD5Ht7B+bu9sxD5Y9BU17wqRPQZtg8VTjteObYOybNT+/qCg3/v9W/xPOn4B7F4OnbUeb/O/Hg1wsq+CF0ZavYiSEq3Dtp0GlFyB1CSSOr/ptemtz37l0u1jf+v/C0ieMiT8PLIWAxhAYAfd9AYOfN5L9rJHGMMSraW3MGXi3r5H8g2ONsrdtbrBpyBmni5iXfJR7esXTOsr1arYIURvXTuh7V0DJeehWzZuIsHiI6mh0uwjr0Bq+exm++xN0vB0mfnLlg1APT6OY1qRPjYldHwyFvZVqmR/+GWYMh08fAOUB98yHh7+HFoNtHvobq/bh7enBsyMc94ZSCFty7dkUKQsgtBk0H1h9m7Y3wob/QXE++IXaLzZ3ZKqAr541+siTphhdKh7V1A5vOwp+9bORuBdNhD6PQu4+OPyTUQ53/LvQdUL1+1vZzuPnWLEri6dHtCEqxM8u5xTC3lz3Dv38STi82pwUavg22twIpnI49JPdQnNL5SXw2WQjmQ9+Hm76d+3JuFECTFkFPSbDpvfh1C4Y9Q94aht0v9duyVxrzT++TiciyIdpg+u2zqgQrsR179B3fWI8hOs6oeZ2cb2NRYMPfAcdb7VHZO6jrNiYsLX/W9j3DZzPNBJyv8ctP4a3H9zyX+jxgFFjxy/EdvFW48e9xhT/V2/t5HZT/IWozDV/urWGnQsgvi+Et6q5racXtB5hPBg1mWxeitXlnc+CA98aSfzwaqM+jncAtBwGN/0L2o2u33HjkqwapqVO5Rfzl+WptIwIZEIvKdQm3JtrJvQT242hiDe/ZVn7NqNgz2LI2glNe9g0NJd1cgcsf+byxKDQeOh2L7QdbZ6k5Xr9zifPXWTijGTOFpUxd2rvBjvFXzQcrpnQUxaAlx90vM2y9q1HAMrodpGEXrVvX4L8EzDiZSOJR3Vw6XVZM89eYOKMZM4VlfHx1N50b9bI0SEJYXOud8tSXgK7P4f24ywftRIYYbzlPyDDF6uUtQuOroMBz8Cg30B0oksn8+NnLnDPB8nkXyhj3sN9JJmLBsP1Evq+b6D4XPVjz6vTZpTRVVOYa5OwXNqm943Ztj3ud3Qk1+1oXhH3fLCRwpJyFjzSV2p4iwbF9RJ6XC+44a/GQ7q6aHsjoOHgdzYJy2UV5sLuz4w/kP6ufSd75HQR93yQzMWyChY80odOTWXegWhYXC+hhzaFgc/WfQxzTBcIipEyAFfbOhsqSo2JPy7sUG4h93ywkbIKEwun9aVjE0nmouFxzYei9aGUURc9bZlRFdDT29EROV55CWydZdS8iXDe6fD7swuYvuYwnkrh7+OJn7cn/t6eBPh44ufjiZeH4l+r9gOahdP60jY62NEhC+EQDSehgzEdfcfHRiXAhBrKBTQUqV9CYTb0fczRkVSrtNzE4/O3c+LsRUL8vbhYWkFxmYnSCtMV7SKDfVn4SF9aR0kyFw1Xw0roLYeCh7fR7dLQE7rWkPwuRLSDVsMdHU21Zqw9zMGcQmZNTmJEh+hfXi+vMHGxrIKLZRUUl5qICPYhwKdh/TgLcbWG9RvgGwzN+hoV/xq6Y8nGJKJx/+e0QxQzThfx3x8OMLZzzBXJHMDL04NgTw+C/aTrTIhLXO+haC1MJk1JeUX1DWK7Qu5eo3JgQ7bpPaPGTZdaauE4iNaaPy3dg7enBy/f3NHR4QjhEtwuof/3xwMMev2n6pN6dEcoL6564YWG4twxYw3Png8aCzs7oWUpJ1l74DTPj2pHtJS7FcIibpXQS8ormLvxKDkFJaw/eLrqRlGJxufsVPsF5mw2zwAU9H7E0ZFUKf9CGa9+lUbXuFDu69vc0eEI4TLcKqGv3HOKM0WleCj4ZvepqhtFtjNWyslJs29wzqK0CLZ/BIm3QGico6Op0j9X7uXshTJeu70znh7O2b8vhDOqNaErpWYrpXKUUnuq2R6qlFqulEpRSqUqpR6yfpiWmZd8lITwAG7u2oTv0rMpu2poG2AsWty4ZcO9Q09ZaKze1Mc5hypuzTjDws3HmDIgQSYHCVFHltyhzwFqKoL9BJCmte4KDAX+pZTyuf7Q6mbvqfNsyTjLpD7NGNMplnMXykg+nFd146jEhnmHbjJB8vvQpAfE93Z0NNcoqzDxxy/30CTUj2dvaOvocIRwObUmdK31GuBMTU2AYKWUAoLMbcutE57l5icfw8fLg7t6xjO0XSQBPp58s6eabpfojnDmCJResG+QjnboR8g7YEwkcsKhijPWHmZfdgGvjO9EoKwsJESdWaMP/W2gA3AS2A08o7Wuoq8DlFLTlFJblVJbc3OtV/WwqKScL3ecYFznWBoF+uDn7cmwdlGsSj1FhUlfu0NUIqCN4YsNSfK7Rj2bxFutetiyChOvfZ3OvOSjaF3F9bbAsbwLvPX9AUZ3jOGGxOjadxBCXMMaCX0UsBNoAnQD3lZKVblwpNZ6utY6SWudFBkZaYVTG5buPElhSTn3VhoRMbpTDKcLS9mSUcWbi2jzuOaG1O1y+gAc+gF6PQxe1usRKy6r4Fcfb2P6msO8tGQPv/t8V83zAKqgteYl85jzv9wiY86FqC9rJPSHgC+04SBwBGhvheNaRGvNvOSjtI8JpkezsF9eH9Y+Cl8vD1ZW1e3SKAG8/CG7ASX03Z8Zo3t6PGC1QxYUlzF59mZ+2pfDq7d24ukRbfhsWyYTpyeTU1Bs0TGy8i/y2LztrNmfy29vbEtMqIw5F6K+rJHQjwEjAJRS0UA7wG6zdnYeP0da1nnu69scValfOMjXi8FtI1m55xSmq7tdPDwhqj3kNKCRLmlLofkACLZOd8bZolLum7mJrUfP8p97unF/3+b8ZmRb3pnUg/SsAsa/vZ7dmfnV7l9eYWLm2sPc8K+fWb0/h9+NbsfkfglWiU2IhsqSYYsLgY1AO6VUplJqqlLqUaXUpQLarwL9lVK7gR+AF7TW1czqsb55yccI9PHk1u5Nr9k2plMMp84XszPz3LU7RiU2nDv03H3G84IOt1jlcNnni7n7g42knyrgg/t6Mr7b5Wt/U5dYPn+sHx5Kcef7G1i688Q1+287epab317P31ak07tFY7779RAeH9oaDxlzLsR1qXUogda6xrXetNYngRutFlEdnLtQyle7TnJnzziCqhgVMaJDNN6eim92Z9Hj6nUloxJh53woOm2sOerO0pYZnzvcfN2HOn7mAvfO3EReYQlzHupF/1bXXruOTUJZ+uQAHp+3nWcW7WTvqQKeu7EdBcVlvL5yHws3HyM21I/37+vBqI4xV7yzEkLUn0uPDft8WyYl5aZqp4eH+nszoHUE3+w5xYtjO1yZOKIrlQBoOcQO0TpQ2lKI7wshsdd1mAPZBdw3axPFZaZaF1+OCPJl3sN9eHlZKu+tPsT2o2c5mFPIuYtlPDywBc+ObFvlH2EhRP257NR/rTULNh2jZ/NGdIitclANYHS7ZJ69SOrJ81duiGogI13yDkH2bmOqfz2ZTJo1+3O5+4ONmDR88qu+NSbzS3y8PHjttk68Or4j246epVl4AMufHMhL4xIlmQthAy77W7XhUB6HTxfx7+Gta2w3MjGGF7/cwzd7sq5cNDgoCgLC3b8EQPql7pa6J/T92QUs2XGCpTtPcuLcReIa+TNvah8SIgItPoZSivv7JTCuSxNC/b2ln1wIG3LZhD4v+SiNArwZ27nmboTGgT70bdmYb3af4rkb213udlHKXAIg3Q7ROlDaUmjaE8LiLWp+Kr+YZSknWLLjJGlZ5/FQMKhNJM+NasuojjH1XhWoUaDdq0EI0eC4ZELPPl/MqrRspg5sgZ+3Z63tR3eK5U9L9rA/u5B2MZXWnIzuCNs/NmqceLhs71P1zh2Dkztg5Cu1Nj2YU8DLy1LZcCgPraFrXCgv35zIuC5NiAz2tUOwQojr5ZIJ/ZMtx6kwaSb1bmZR+1Edo/nzUqPb5YqEHpUIZUVw7ig0bmGjaB0ozfLulj8vTWXPiXyeHt6G8d2a0DIyyMbBCSGszeVuS8srTCzcfIxBbSIs7suNCvajV/PG184adfcSAGlLIaZLrX+sdh4/x4ZDeTw5vDW/HtlWkrkQLsrlEvpP+3LJyi/m3j51W8lmdKcY9p4q4HBu4eUXI9sZn91xgtH5k5C5GRLH19r03Z8OEurvzaQ6XlMhhHNxuYTeLjqYJ4a14oYOUXXab3SnGIArS+r6BkNYc/csAZC+3PhcS0I/kF3AqrRsJvdrLkMJhXBxLpfQm4UH8Pyo9nh51i30JmH+dIsPq7rbxR3v0NOWGs8IItrU2Oy91Yfw9/bkwQFu+AxBiAbG5RL69RjTKYbdJ/I5fqbSwhZRiZB3EMpLHBeYtRXmwNENtd6dHz9zgaUpJ5nYuxmNZVihEC6vgSV0Y8z6FXfp0YmgK4wCVu4ifTmgax3dMmPtYTwUPDJY7s6FcAcNKqE3Cw8gMTaE79KzL7/4SwkAN5pglLYUwttAVIdqm+QWlPDJluPc1r0psaH+dgxOCGErDSqhAyQlNCL1RP7lGunhrcDTx30ejBblQcY6o7ulhiqGH64/QmmFiUeHtLJjcEIIW2pwCT0xNoSi0gqOnzX3o3t6Q0Q793kwum+F0YVUQzGu88VlfLzxKGM7xcqYcyHcSINL6JcqM6ZVrr4Yneg+k4vSlhpL7MV0qbbJxxuPUlBSzmND5e5cCHfS4BJ6u5hgPBSkZ1VK6FEd4PwJuHjWcYFZw8WzcPjnGrtbissq+HD9EQa3jbyy+qQQwuU1uITu5+1Jy8gg0q5I6G7yYHTfSjCV1Thc8dOtxzldWMrjcncuhNtpcAkdjH709KyCyy9UXr3IlaUthdB4aNKjys1lFSY++PkwPZs3ok+LxnYOTghhaw1yrneH2BCWpZzk3IVSwgJ8IKQp+IY6fz+61lB8DvIz4dxx43N+pc8ntkOfR6vtbllmXqjilfEdZR1PIdxQrQldKTUbGAfkaK07VbH9eeDeSsfrAERqrc9YM1BrSmxiPBhNzyqgX6twIwFGJzr3SBetYfYoOL7pytc9fSA0zrgz7zYJ+j5a5e4mk+a9nw/RPiaY4e3rVgdHCOEaLLlDnwO8DcytaqPW+g3gDQCl1M3Ar505mQN0iDVqoqdlnTcSOhglAHZ/biROZ7x7PbXbSObd74fWNxgrEIXGQ0CERYtzrDmQy8GcQt6a0E3uzoVwU7UmdK31GqVUgoXHmwgsvK6I7CAq2I+IIN8rR7pEJ8LWfGO0S2ic44KrTuqXoDzhhr9CYHidd1+ekkWIn9cv5Q+EEO7Hag9FlVIBwGhgcQ1tpimltiqltubm5lrr1PXSITb4yrHol0a6OGO3i9aQtgRaDqlXMi8pr2BV2ilGdYzBx6tBPgcXokGw5m/3zcD6mrpbtNbTtdZJWuukyMhIK5667hKbhHAwp5DScpPxQlR747MzlgA4tQvOHIaOt9Vr97X7T1NQXM5NXeTuXAh3Zs2EPgEX6G65JDE2hNIKE4curWDk38gY7eKMd+iXulvaj6vX7it2ZxHq782A1hFWDkwI4UysktCVUqHAEGCpNY5nD4mxl0a6VO52ccISAFobCb3lUAio+9jx4rIKvkvLZnTHGLzruCiIEMK11PobrpRaCGwE2imlMpVSU5VSjyqlKo+Puw1YpbUuslWg1tYiIhBfL49ra7rk7oOKMscFdrWsnXA2o97dLT/vz6WwpJxxXaW7RQh3Z8kol4kWtJmDMbzRZXh5etAuJpj0U1c9GDWVGSsY1VBL3K5SvwQPL2h/U712X7Eri0YB3vRrWfeHqUII19Kg34MnxoaQdvI8WptroztbCYBfuluG1au75WJpBd+nZzO6U2yd12AVQrieBv1b3iE2hLMXysg+b15PNKKt8fDx1G7HBnbJye1w7li9u1tW78vhQmkFN8voFiEahAad0C+VAEjLyjde8PKFhAGw+zPn6EdPXQIe3tB+bL12/2p3FhFBPvSWQlxCNAgNOqG3jzFKAFxRebHv48Zs0TQHD9jR2kjorYYZQyrr6EJpOT+m5zC6U4x0twjRQDTo3/RgP2+aNQ64cqRLm1HQuCUkv+e4wMConJhf/+6WH/fmcLGsgnFdmlg5MCGEs2rQCR2MEgBXjEX38IA+j8GJrXB8s+MCS/3C6G5pV7/ulhW7sogM9qVXgnS3CNFQNPiEnhgbypG8Ii6Ull9+sdsk8AuFje84JqhL3S2tR4B/WJ13Lywp58e9OYztFIOnh1RWFKKhaPAJvUNsMFrD3lOV+tF9g6DHZEhfZowysbfMrXA+s97dLT+kZ1NSbuIm6W4RokFp8An9l5EulfvRAXpPAxRsnm7/oFK/NBauaDemXruv2JVFdIgvSc3r/jBVCOG6GnxCbxrmT4if15X96GAsIJF4C2ybCyWF9gvIZDJK5bYaYXT71FFBcRmr9+cytnMsHtLdIkSD0uATulKKDrEhpF2d0AH6PgEl+bBzvv0COrHVGDZZz+6W79OzKS03MU4mEwnR4DT4hA7GjNF9pwqoMOkrN8T3grhexhBGk8k+waR+CZ6+19Xd0iTUj+7x0t0iREMjCR2jH/1CaQVH86ooFtn3cTh7BPavtH0gJpN5dMsN4BdS593zL5axZv9p6W4RooGShE7l2ugF127scAuExEHyu7YPJHMzFJysd3fLd2nZlFaYZGUiIRooSehA66ggvDzU5ZoulXl6QZ9pkLEWsnbVfKDSIjj4AxRXcZzanD4Aa94wd7eMrvv+wIpdJ2ka5k+3+LB67S+EcG211kNvCPy8PWkdFVT1HToYY9JXv270pd9WRUmAinLYMRdW/xMKs8HLz+gD73KPMVrFy6fq45aXwt7lsPVD4w+GhxcMfh58g6tsfqG0nCOni8g6V0zW+WJO5V8kK7+YU/nFZOUXk5FXxLRBLVFKuluEaIgkoZt1iA1h46G8qjf6h0H3e2HbHLjhLxAcbbyuNexdAT/8FU7vh/i+MOZ1OLoB9iw2HnD6N4KOtxvJPb43KAVnjsD2j2DHPCjKhbBmMOLP0O2+y8e+ismkGfPWWo7mXfjlNU8PRUyIHzGhfiQ2CeHGxGimDmxh1esihHAdktDNEmND+HLHCc4UldI4sIo76j6PwuYZsGUmDP8jHNsE3/0ZjicbddQnLDDqrihl9IGPeg0O/Qi7PoGdC2DrLGiUYCTvI2tAeUDbMZA0BVoNN2rI1GDTkTMczbvA0yPaMLx9FE1C/QgP8pWp/UKIX0hCN+tQadHoAa0jrm0Q3grajjYSc04a7P0KgqJh3H+g+/1GX3tlnt7QdpTxUVIA6V/BrkVw7jgM/YOxT2hTi+NbuvMEgT6ePDakFf4+ntfxnQoh3JUkdLMOsZdqo1eT0AH6PQ4ffQOHV8OwP0K/J8AnsPaD+wZDt4nGRz2UlFfw9e4sRnWMkWQuhKhWrQldKTUbGAfkaK07VdNmKPAfwBs4rbUeYr0Q7SM8yJfoEN9ra7pU1mIw3PcFxHSBoEi7xbZ6Xy7ni8u5pZsU2xJCVM+SYYtzgGrH0SmlwoB3gVu01h2Bu6wSmQMkVlcCoLLWI+yazMHobgkP9GFgde8chBACCxK61noNcKaGJpOAL7TWx8ztc6wUm911iA3hYE4hJeUVjg7lFwXFZXyfnsO4LrGylJwQokbWyBBtgUZKqdVKqW1KqQeqa6iUmqaU2qqU2pqbm2uFU1tXYpMQyk2aA9l2rK5Yi5V7TlFabmJ8d8sfoAohGiZrJHQvoCdwEzAK+JNSqm1VDbXW07XWSVrrpMhI+3ZbWKLySBdnsSzlJM0aB9BdZn8KIWphjYSeCazUWhdprU8Da4CuVjiu3SWEBxLg48m2o2cdHQoAOQXFrD94mvHdmsjsTyFErayR0JcCg5RSXkqpAKAPkG6F49qdp4fips6xLN15krNFpY4Oh69SsjBpGN9NuluEELWrNaErpRYCG4F2SqlMpdRUpdSjSqlHAbTW6cBKYBewGZiptd5jy6Bt6ZHBLblYVsH8TUcdHQpLd56gU9MQWkcFOToUIYQLqHUcuta61tkwWus3gDesEpGDtY0OZmi7SOZsOMrDg1ri5+2YiTxHTheRkpnPH8d2cMj5hRCuR8bBVWHaoJacLixh6c4TDoth6c4TKAU3d5XJREIIy0hCr0K/VuEkxoYwY+0RTFcvS2cHWmuW7jxJ3xbhxIT62f38QgjXJAm9Ckoppg1uycGcQn7eb//x8rtP5HPkdBG3dpe7cyGE5SShV+OmLrHEhvoxfc1hu597yY6T+Hh6MLqTLCUnhLCcJPRqeHt6MGVACzYezmN3Zj2WlKunCpNm+a6TDGsfSai/t93OK4RwfZLQazChdzzBvl7MWGu/u/SNh/LILSjhVhl7LoSoI0noNQj282ZC73hW7M4i8+yF2newgiU7TxDs68Ww9lF2OZ8Qwn1IQq/FQwNaoIAP12fY/FzFZRWs3HOK0Z1iHDb+XQjhuiSh16JJmD/jusSyaPMx8i+W2fRcP+7NobCknFulsqIQoh4koVvg4UEtKSqtYNHmYzY5fmFJObPWHeGV5WlEBfvSt2W4Tc4jhHBvsqaoBTo1DWVA63A+XJ/BQwNa4ON17d/BM0WlLE85yeaMM3SNC2VI2yjaRgfVWCUxK/8iczZksGDTMQqKy+md0JgXxrTD00MqKwoh6k4SuoUeGdSSBz/cwle7TnJ7jzgASstN/LQvh8XbMvlpXw5lFZrIYF9W7Mrita/3EhPix6A2EQxuG8nA1hE0CvQBIPVkPjPXHmF5yklMWjOmcyyPDGpJN6l5LoS4DpLQLTSkbSRto4OYvuYwraOCWLwtk2UpJzl7oYyIIF8e7J/A7T3i6BAbQlb+RdbuP83P+3NZlZbNZ9syUQq6xIXh5+XBpiNnCPDx5P5+zZkyoAXxjQMc/e0JIdyA0tr+tUoAkpKS9NatWx1y7vr6dOtxfvf5LgB8vDy4MTGaO3rGMah1RLXrfVaYNCmZ51izP5c1+3M5U1TKhN7NmNi7mUwcEkLUmVJqm9Y6qaptcodeB+O7NWHfqQJaRQZxU5dYixKyp4eiR7NG9GjWiGdvqHJlPiGEsApJ6HXg6+XJn8YlOjoMIYSokgxbFEIINyEJXQgh3IQkdCGEcBOS0IUQwk3UmtCVUrOVUjlKqT3VbB+qlMpXSu00f/zZ+mEKIYSojSWjXOYAbwNza2izVms9zioRCSGEqJda79C11muAM3aIRQghxHWwVh96P6VUilLqG6VURysdUwghRB1YY2LRdqC51rpQKTUWWAK0qaqhUmoaMM38z0Kl1D4rnL8qEcBpGx37ekls9SOx1Y/EVj/OHFvz6jZYVMtFKZUAfKW17mRB2wwgSWvtsIuhlNpaXa0DR5PY6kdiqx+JrX6cObaaXHeXi1IqRpmLfiulepuPmXe9xxVCCFE3tXa5KKUWAkOBCKVUJvAy4A2gtX4fuBN4TClVDlwEJmhHlXAUQogGrNaErrWeWMv2tzGGNTqT6Y4OoAYSW/1IbPUjsdWPM8dWLYfVQxdCCGFdMvVfCCHchCR0IYRwEy6T0KuqKaOU6qqU2qiU2q2UWq6UCjG/fm+l2jI7lVImpVQ387ae5vYHlVL/vTRCx0liW62U2ldpW5SdY/NWSn1kfj1dKfWHSvs4+rrVFJujr5uPUupD8+spSqmhlfZx9HWrKTarXjelVLxS6ifz/0+qUuoZ8+uNlVLfKaUOmD83qrTPH8zXZp9SalSl16163awcm9V/3qxGa+0SH8BgoAewp9JrW4Ah5q+nAK9WsV9n4HClf28G+gEK+AYY40SxrcYYw++Q6wZMAhaZvw4AMoAEZ7hutcTm6Ov2BPCh+esoYBvg4STXrabYrHrdgFigh/nrYGA/kAj8P+D35td/D7xu/joRSAF8gRbAIcDTFtfNyrFZ/efNWh8uc4euq64p0w5YY/76O+COKnadCCwEUErFAiFa643a+J+ZC9zqDLHZSh1j00CgUsoL8AdKgfNOct2qjO16Y7BSbInAD+b9coBzQJKTXLcqY7veGKqJK0trvd38dQGQDjQFxgMfmZt9xOVrMB7jj3SJ1voIcBDobYvrZq3YricGe3CZhF6NPcAt5q/vAuKraHMPl5NmUyCz0rZM82vOENslH5rfxv3JGm/P6xjb50ARkAUcA97UWp/BOa5bdbFd4sjrlgKMV0p5KaVaAD3N25zhulUX2yU2uW7KmF3eHdgERGuts8BIrBjvFMC4Fscr7Xbp+tj0ul1nbJfY4+etzlw9oU8BnlBKbcN4G1VaeaNSqg9wQWt9qa+xqgtvq3GbdY0N4F6tdWdgkPnjfjvH1huoAJpgvM38rVKqJc5x3aqLDRx/3WZj/MJvBf4DbADKcY7rVl1sYKPrppQKAhYDz2qta3oXVd31sdl1s0JsYL+ftzqzRnEuh9Fa7wVuBFBKtQVuuqrJBK68A84E4ir9Ow446SSxobU+Yf5coJRagJHEaqpDb+3YJgErtdZlQI5Saj3G2/O1OP66VRfbYUdfN611OfDrS+2UUhuAA8BZHHzdaojNJj9vSilvjIQ5X2v9hfnlbKVUrNY6y9ydkmN+PZMr3y1cuj42+T21Umx2+z2tD5e+Q7/0dFkp5QG8BLxfaZsHxlvPRZdeM7+lKlBK9TW/TXoAWOoMsZnfEkeYv/YGxmG8jbZnbMeA4coQCPQF9jrJdasyNme4bkqpAHNMKKVGAuVa6zRnuG7VxWaL62b+HmcB6Vrrf1fatAyYbP56MpevwTJgglLK19wd1AbYbIvrZq3Y7PnzVi+Ofipr6QfG3WwWUIbx13Mq8AzG0+r9wD8xz3w1tx8KJFdxnCSM/4BDGCULlDPEBgRijEDYBaQCb2F+qm6v2IAg4DPz+dOA553lulUXm5NctwRgH8aDtu8xykk7y3WrMjZbXDdgIEa3xC5gp/ljLBCO8WD2gPlz40r7/NF8bfZRaSSLta+btWKz1c+btT5k6r8QQrgJl+5yEUIIcZkkdCGEcBOS0IUQwk1IQhdCCDchCV0IIdyEJHQhhHATktCFuA5KKU9HxyDEJZLQRYOhlHpVmetgm//9d6XU00qp55VSW5RSu5RSf620fYlSapsy6mdPq/R6oVLqFaXUJowSr0I4BUnooiGZhXmat3mK/AQgG2Nad2+gG9BTKTXY3H6K1ronxqzFp5VS4ebXAzFqkffRWq+zY/xC1Mili3MJURda6wylVJ5SqjsQDewAemEUtdphbhaEkeDXYCTx28yvx5tfz8Oo+rjYnrELYQlJ6KKhmQk8CMRglJYdAfxDa/1B5UbKWKrtBqCf1vqCUmo14GfeXKy1rrBTvEJYTLpcREPzJTAa4878W/PHFHOdbJRSTc2VC0OBs+Zk3h6juqMQTk3u0EWDorUuVUr9BJwz32WvUkp1ADaaF54pBO4DVgKPKqV2YVTbS3ZUzEJYSqotigbF/DB0O3CX1vqAo+MRwpqky0U0GEqpRIzFfn+QZC7ckdyhCyGEm5A7dCGEcBOS0IUQwk1IQhdCCDchCV0IIdyEJHQhhHAT/x+zssMv78ccWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.pivot_table(columns= 'gender', index= 'year', values= 'births', aggfunc= 'sum').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, getting mistyped data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day gender  births\n",
       "0  1969      1  1.0      F    4046\n",
       "1  1969      1  1.0      M    4440\n",
       "2  1969      1  2.0      F    4454\n",
       "3  1969      1  2.0      M    4548\n",
       "4  1969      1  3.0      F    4548"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "quartiles = np.percentile(data['births'], [25, 50, 75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4358. , 4814. , 5289.5])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quartiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4814.0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = quartiles[1]\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "689.31"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = 0.74 * (quartiles[2] - quartiles[0])\n",
    "std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "newData = data[ (data['births'] > median - 5 * std) & (data['births'] < median + 5 * std) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1367.4500000000003"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median - 5 * std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8260.55"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median + 5 * std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>15063</th>\n",
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       "      <td>F</td>\n",
       "      <td>5742</td>\n",
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       "    <tr>\n",
       "      <th>15064</th>\n",
       "      <td>1988</td>\n",
       "      <td>12</td>\n",
       "      <td>30.0</td>\n",
       "      <td>M</td>\n",
       "      <td>6095</td>\n",
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       "    <tr>\n",
       "      <th>15065</th>\n",
       "      <td>1988</td>\n",
       "      <td>12</td>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4435</td>\n",
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       "    <tr>\n",
       "      <th>15066</th>\n",
       "      <td>1988</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14610 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month   day gender  births\n",
       "0      1969      1   1.0      F    4046\n",
       "1      1969      1   1.0      M    4440\n",
       "2      1969      1   2.0      F    4454\n",
       "3      1969      1   2.0      M    4548\n",
       "4      1969      1   3.0      F    4548\n",
       "...     ...    ...   ...    ...     ...\n",
       "15062  1988     12  29.0      M    5944\n",
       "15063  1988     12  30.0      F    5742\n",
       "15064  1988     12  30.0      M    6095\n",
       "15065  1988     12  31.0      F    4435\n",
       "15066  1988     12  31.0      M    4698\n",
       "\n",
       "[14610 rows x 5 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here the unnecessary data has been clipped!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "newData = data[ (data['births'] > median - 5 * std) & (data['births'] < median + 5 * std) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   year  month  day gender  births\n",
       "0  1969      1  1.0      F    4046\n",
       "1  1969      1  1.0      M    4440\n",
       "2  1969      1  2.0      F    4454\n",
       "3  1969      1  2.0      M    4548\n",
       "4  1969      1  3.0      F    4548"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIGABYTE\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1745: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(ilocs[0], value)\n"
     ]
    }
   ],
   "source": [
    "newData.iloc[:,2] = newData['day'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>14610 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month  day gender  births\n",
       "0      1969      1    1      F    4046\n",
       "1      1969      1    1      M    4440\n",
       "2      1969      1    2      F    4454\n",
       "3      1969      1    2      M    4548\n",
       "4      1969      1    3      F    4548\n",
       "...     ...    ...  ...    ...     ...\n",
       "15062  1988     12   29      M    5944\n",
       "15063  1988     12   30      F    5742\n",
       "15064  1988     12   30      M    6095\n",
       "15065  1988     12   31      F    4435\n",
       "15066  1988     12   31      M    4698\n",
       "\n",
       "[14610 rows x 5 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "newData.index = pd.to_datetime(10000 * newData.year + 100 * newData.month + newData.day, format= '%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 2, 3, 3, 4, 4, 5, 5, 6, 6,\n",
       "            ...\n",
       "            1, 1, 2, 2, 3, 3, 4, 4, 5, 5],\n",
       "           dtype='int64', length=14610)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "newData.index.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-169-c56e9e74dedc>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  newData['weekday'] = newData.index.dayofweek\n"
     ]
    }
   ],
   "source": [
    "newData['weekday'] = newData.index.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
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       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-29</th>\n",
       "      <td>1988</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>M</td>\n",
       "      <td>5944</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-30</th>\n",
       "      <td>1988</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>F</td>\n",
       "      <td>5742</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-30</th>\n",
       "      <td>1988</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>M</td>\n",
       "      <td>6095</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-31</th>\n",
       "      <td>1988</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>4435</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-12-31</th>\n",
       "      <td>1988</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>4698</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14610 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            year  month  day gender  births  weekday\n",
       "1969-01-01  1969      1    1      F    4046        2\n",
       "1969-01-01  1969      1    1      M    4440        2\n",
       "1969-01-02  1969      1    2      F    4454        3\n",
       "1969-01-02  1969      1    2      M    4548        3\n",
       "1969-01-03  1969      1    3      F    4548        4\n",
       "...          ...    ...  ...    ...     ...      ...\n",
       "1988-12-29  1988     12   29      M    5944        3\n",
       "1988-12-30  1988     12   30      F    5742        4\n",
       "1988-12-30  1988     12   30      M    6095        4\n",
       "1988-12-31  1988     12   31      F    4435        5\n",
       "1988-12-31  1988     12   31      M    4698        5\n",
       "\n",
       "[14610 rows x 6 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1969-01-01    19690101\n",
       "1969-01-01    19690101\n",
       "1969-01-02    19690102\n",
       "1969-01-02    19690102\n",
       "1969-01-03    19690103\n",
       "                ...   \n",
       "1988-12-29    19881229\n",
       "1988-12-30    19881230\n",
       "1988-12-30    19881230\n",
       "1988-12-31    19881231\n",
       "1988-12-31    19881231\n",
       "Length: 14610, dtype: int64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10000 * newData.year + 100 * newData.month + newData.day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on this... we can make the index of dates. And get the weekday out of it by `.dayofweek`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will look a new way to create a pivot table - without `Col` parameter!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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></th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>4009.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4247.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4500.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4571.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4603.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">12</th>\n",
       "      <th>27</th>\n",
       "      <td>4850.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5044.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5120.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5172.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4859.200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         births\n",
       "1  1   4009.225\n",
       "   2   4247.400\n",
       "   3   4500.900\n",
       "   4   4571.350\n",
       "   5   4603.625\n",
       "...         ...\n",
       "12 27  4850.150\n",
       "   28  5044.200\n",
       "   29  5120.150\n",
       "   30  5172.350\n",
       "   31  4859.200\n",
       "\n",
       "[366 rows x 1 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newData.pivot_table(index= [newData.index.month, newData.index.day], values= 'births')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yeah!, who says, you need row and col to build a Pivot table? \n",
    "(Well it is more like just a group by!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   1     4009.225\n",
       "    2     4247.400\n",
       "    3     4500.900\n",
       "    4     4571.350\n",
       "    5     4603.625\n",
       "            ...   \n",
       "12  27    4850.150\n",
       "    28    5044.200\n",
       "    29    5120.150\n",
       "    30    5172.350\n",
       "    31    4859.200\n",
       "Name: births, Length: 366, dtype: float64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newData.groupby([newData.index.month, newData.index.day])['births'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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