variables and .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "url = \"https://github.com/raw/kshedden/statswpy-nhanes/master/merged/nhanes_2015_2016.csv\"\n",
    "da = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    2976\n",
       "1.0    2759\n",
       "Name: RIAGENDR, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender = da['RIAGENDR']\n",
    "gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a+O7IupKmvf7kwAeoqnuSvH4UDfXCM/1lLMllTP+9gl9dMwX2VtW+0XUlQZIbgbcw80nJk1V17ah6W+4MfUkj4UnJaBj6HUqyvf3dAkmd8eGsPmXUDUizaX89T2eIob+MJXlbkouTnH3S0NMjaUiaH09KziBDf5lK8lfA7cBfAg8nGfwD9H87mq6kefnlqBtYzrymv0wleQh4d1W9kGQ98A3gq1X190l+WFUXjLZDaWZJ/qeqfn/UfSxX3qe/fL2mql4AqKqnklwEfCPJm/G/zxqxJA/ONgSsHmYvvTH0l6+jSTZV1QGAdsb/p8Bu4A9H2pk0HeyXAs+dVA/wH8Nvpx+G/vJ1NXB8sFBVx4Grk3xpNC1Jv3IHcPaJk5JBSf596N10xGv6ktQR796RpI4Y+pLUEUNfkjpi6EtSR/4fdtMTlRR0ag8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender.value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": [
    "\n",
    "gender.value_counts().plot(kind='bar')\n",
    "plt.xticks([0, 1], ['Male', 'Female']) ## [0, 1] --> are indexies\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sizes of shirts \n",
    "sizes = np.array([22, 23,23, 29, 32, 39, 42, 42, 42, 42, 42, 42, 42, 42, 43, 44, 46, 51, 51, 55, 55, 57, 58, 78, 83, 97, 97])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.851851851851855"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sizes.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(42, 8),\n",
       " (23, 2),\n",
       " (51, 2),\n",
       " (55, 2),\n",
       " (97, 2),\n",
       " (22, 1),\n",
       " (29, 1),\n",
       " (32, 1),\n",
       " (39, 1),\n",
       " (43, 1),\n",
       " (44, 1),\n",
       " (46, 1),\n",
       " (57, 1),\n",
       " (58, 1),\n",
       " (78, 1),\n",
       " (83, 1)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter \n",
    "cnt = Counter()\n",
    "\n",
    "for size in sizes: \n",
    "    cnt[size] = cnt[size] + 1 \n",
    "    \n",
    "\n",
    "# gives us the common in the list or numpy array \n",
    "cnt.most_common() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from scipy import stats\n",
    "stats.mode(sizes) [0]   # index 0 has value, index 1 has count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender.mode()   # not too useful since we could have got that from the bar chart anyway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([2.]), count=array([2976]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mode(gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2., nan,  1.,  9.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da['HIQ210'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAADnCAYAAAAzUZtFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAW8klEQVR4nO3deZgcVb3G8e9vZjIJgbAFCZEAhRBAtqySkBiIoCIUi+hVFBAEBJFd4HJLARlBsFRAAdllvQKiIouWCohmYUlActlilM1ik01IhiVklu5z/6gaGSaT6e6Z7j5Vp3+f5+lnlu50vYG8Xfs5YoxBKeWmJtsBlFK1owVXymFacKUcpgVXymFacKUcpgVXymFacKUcpgVXymFacKUcpgVXymFacKUcpgVXymFacKUcpgVXymFacKUcpgVXymFacKUcpgVXymFacKUcpgVXymFacKUcpgVXymFacKUcpgVXymFacKUcpgVXymFacKUc1mI7gKo+L4iagLHAOGCj9NHz/brAcKC1n8cwYDmwtM9jWfr138CzwDPAC3Ho68R2GSc6+WC+eUG0KTAZmJg+tiUpc60/vFcATwOL08cTwENx6L9Y4+WqCmjBc8QLomZgOjA7/ToN+JDNTP14FpgLzAHmxqH/nN04jU0LnnFeEK0NfAbYM/062mqgyj1HUvY/A3fEob/MapoGowXPIC+INgf2ISn1x3HnWEkncCfwC+D2OPTftZzHeVrwjPCCaATwBeBwYJblOPWwHIhIyv77OPRXWM7jJC24ZV4QbQscARwIrGM5ji1vAdcAP4lDP7acxSlacAu8IBoG7A8cSXKwTCUKwG3AeXHoP2A5ixO04HWUFvurwLcBz2qY7FsAnA/8Jg79gu0weaUFrwMviFqAg4FTgU0tx8mbGDgTuC4O/aLlLLmjBa+htNgHAaehxR6qxcC349C/w3aQPNGC14gXRLsDFwDjbWdxzDzg+Dj0H7EdJA+04FXmBZFHUuy9LUdxWRG4Cjg1Dv3XbYfJMi14laSb4ycBZwCrWY7TKJYCx8ahf4PtIFmlBa8CL4gmAz8DJtnO0qBuAb6ha/OVacGHIL0t8zskB9GaLcdpdK8BR8ahf6vtIFmiBR8kL4g2AG4AdrGdRX3Az0k225fZDpIFWvBB8ILokyT/kMbYzqL69RKwXxz699kOYpsWvALp/dhtJFei6XBX2dYJHBWH/lW2g9ikBS9Tukl+E8lgCyo/LgJOjEO/23YQG7TgZfCCaCvgj8AmtrOoQbkH+GIc+m/aDlJvuplZghdE04F70XLn2a7Ag14QbWM7SL1pwQfgBdGeJJ/+eRsmSa1sM+ABL4h2sh2knrTgq+AF0WEk9yaPtBxFVc8o4A9eEDXMqU0teD+8IDqN5Mo0vXjFPSOB33lBtJvtIPWgBe/DC6I24CzbOVRNrQbc7gWRbztIrelR9F68IDoe+IntHKpuOkkuiLnNdpBa0YKnvCD6KnA1IJajqPrqJin5b2wHqQUtOOAF0b7Ar9B97kbVAXwqDv35toNUW8MX3AuiXUnG5x5uO4uyaikwMw79JbaDVFNDF9wLoqnAX4A1bGdRmfAcMC0O/VdtB6mWhi24F0RjgIeBDW1nUZmyAJgdh36H7SDV0JCnydLxyW9By61WNp3kGggnNGTBSQZFnGk7hMqsA70gOsl2iGpouE10L4gOIBmsQamBdAE7xqH/sO0gQ9FQBU9v+/wrsLrtLCoXngQmxaG/3HaQwWqYTfR0et5foeVW5duCnF/Z2DAFJxlqaVvbIVTuHO4F0edshxishthE94JoEvAg0GI7i8qlN4Ht49B/yXaQSjm/Bk9nHLkKLbcavHWB670gyt19Cs4XHDgZnXFEDd0uwIG2Q1TK6U10L4i2AB4FRtjOopzwMrBFHPrv2A5SLmfX4Onm1JVouVX1jAVOtx2iEs4WHDgAaKgB9lRdnJBuGeaCkwVPrzU/03YO5aRWcnRu3MmCA0cAm9oOoZy1ezqkduY5d5DNC6KRwDPABrazKKc9CWwdh37BdpCBuLgGPx4tt6q9LYAv2A5RilMF94JobeAU2zlUwwhsByjFqYKTlHtt2yFUw5iQ9bHVnSm4F0RrAEfbzqEazrdsBxiIMwUHDgLWtB1CNZyZWZ7Q0KWC69pb2ZLZtbgTp8m8IJpNMvyxUrZsF4f+E7ZD9FXzNbiIbCQifxGRv4nIYhE5vp/XiIhcKCJPi8hjIjK5wsXo2lvZdpjtAP2p+RpcRMYCY40xi0RkFMlY5J81xvyt12v2AI4F9gCmARcYY6aV8/5eEG0IxOj93squN4APx6HfaTtIbzVfgxtjXjbGLEq/fxtYwsrjke8DXG8SC4C10w+GcnwdLbeybzTJv+NMqetBNhHxSAZfWNjnqQ2BF3r9/CJlTEqQ3hL61SrFU2qoDrIdoK+6FVxE1iCZTeQEY8xbVXrb6cBGVXovpYZqNy+IRtsO0VtdCi4iPVMF3WCM6W8e5pf4YFHHpb8r5YtViKdUtQwjY9en1+MoupAMerjEGHP+Kl52B3BQejR9OtBujHl5oPdNN8//q7pplRqyL9sO0Fs91uAzga8Au4jII+ljDxE5UkSOTF/ze+BZ4GmSYZaOKuN9p5Cs6ZXKkhleEGXmisqaH302xtwLDDjcrEnO1VV6LnuvQYdSqnZagE8At9sOAvm+VFULrrLq07YD9Mhlwb0gGouOda6y61O2A/TIZcGBGbYDKDWA8V4QebZDQH4LvqPtAEqVkIm1uBZcqdrIxH547gruBVErySkypbIsEyuh3BWc5ODacNshlCphQy+I1rUdIo8Fz8Qno1Jl2N52gJIXuojIWsBneP/urpeAO40xy2qYayBacJUX2wNzbAYYcA0uIgcBi4DZwMj08Qng4fQ5G7a1tFylKrWd7QCl1uCnAlP6rq1FZB2Se7qvr1GugeicYyovrG+il9oHF6C/MZ2KlLi+vBa8INoAWK3ey1VqkLbxgsjqca5Sa/CzgUUichfvj7iyMclJ/LNqGWwVPmJhmUoN1uokW5zP2Aow4KeLMeY6YCowF+hIH3OAqcaYa2sdrh9acJU3H7a58JJH0Y0xS4Ff1CFLOXT/W+XN+jYXPuj9AxF5vJpByqRrcJU3Y2wufMA1uIh8blVPYWcO7k0sLFOpochuwYGbgRvo/0j6iOrHKSkzQ+EoVaZMF/wx4FxjzEpzLonIJ2sTaUCrW1imUkNhteCl9sFPAFY1hvm+1Y1SFi24ypvsFtwYM98Y8/wqnvtrbSINSAuu8madcl8oIseLyBPpJJ0n9PN8xZN0ljyKLiK7ichh6bRDvX9/aLnBq0gLrvKmrJGLRWRb4HBgB2ACsKeIbN7nZbsD49PHEcClpd631M0m55Bcj74dcI+IHNvr6WPKCV4tXhA1o/eBq/wpd2jyjwILjTHLjTHdJBeX9T2LVfEknaXW4HsBuxhjTiAZRWV3Eflx+ly9r0XXtbfKo+YyX/cEMEtERovISJKptPvOu1fxJJ2lPl1a0k8TjDHLRGQv4AoR+RXQWmbwahlW5+U1lFNabpz3teZo8wI0FUQKRTBFkUI3UiwIxQKYIlLsFikWoFgUMd2Q/iymKPT8nL4O042YgmAKiOkWKRYEupP3NV3JcxTS13Qnr6EApiBCd/JVen4uAOnrSH+W9L0pCFJAKArp74UiyffF5D2kKMkdUkWkKf1eCiBGRIqQPAQx0FQEMSBGaDIkzxtoSn5Ov09Wjk09rwOaTbLSazJJqXuefxv8kv/9jTFLROQHwF3Au8AjyV9haEoV/BkR2dkYMzcNUQAOE5HvAZ8f6sIr1FHn5TWUH3bvv9O84oTF1w8L11iNQnJBUc/VD/1dBaHKtbzcFxpjriKZx69n9/jFPi+peJLOUpvoXwAe7CfIadR/2t4VdV5ew1lQ3GabSR1XjH686M23ncUhneW+UETWT79uTLL/fWOfl1Q8Sack04INuNDVgAOArdNf/RX4tTGm7ODV4gVRN+Xv06ghOLD57gVntlyzZZOUf5pH9Wsxbe1ljUIkIvOB0UAXcKIx5p6eCTqNMZelM/X+lGQIteXAIaVOVw9YcBHZjuRTYx7wcPrrKSRl/xRwcro2rwsviJYBa9VreY1uLG+88tvhp/5rPXmr5PlWtUoP0da+g62Fl9oHvxA4whhzd+9fppepPgEsrlWwVWhHC143LzN6g6kdl445q+WauQc2/2m6iJ6mHITXbS681D742L7lBjDG/IlkM6Lel6suq/PyFCKndx+682c7z3xuhRn2lO00OfSazYWXKniTiKz0qS0iI4AuY0zZRwirZFmdl6dSj5rNt5jQceVGC4ofnWuMHlevQKYLfj1wi4j85z7s9JLVXwL/W8Ncq/IvC8tUqQ5aR3yp8/Sdj+s65uGCEav/cHMkuwU3xnwP+CMwX0T+LSJvkFxCd7cxxsagi9YGr1Pv+21xxtSPdVzS/KJZb6HtLDnwqs2Fl7zZxBjzU2PMxiTjoXnGmE2MMRfVPlq/nrW0XNXHm6w1+uMdF067uHvv+caUfzFHA7K6Bi81ZNOJ/fzuP98bY86vQaaB6Bo8Y37U/aVZtxdm/vM3rWe8t4as2Lr0n2g4Vv/NllqDjyrxqDddg2fQk2ajTSd2XDH+T4XJc42haDtPhqwA/mkzQMkr2bIknSViOXrbaGbt0rTo0SuGnb9eixQHvMupQTxKW/tEmwFKXcl24UB/2BhzXNUTleAF0RJgq3ovV5VvFO+239p6xuLNm/41w3YWy26irX1/mwFKbaI/3Ouxd5+fHx7gz9WSXmyRcW+z+lqf7Dx3xve7vnyfMbTbzmPR32wHGPAgWzp1EQAickLvny16iGQgCpVxlxf2mvmH4g4v3tF6Wry2vDvBdh4LltgOUMnMJlnZWb/PdgBVvufNmHGTOi7f7tbCzLnG0G07T509ZjuA1alNB2khVRjpQtWPoanpm11H7/ylztOe7DQtse08dfIKbe3WdydLDbr4toi8JSJvAdv3fN/z+zpl/IA49N8lA5+MqnILzdZbT+y44kOPFTdthAElMvF3LHWp6ihjzJrpo6XX96OMMTanEbrf4rLVECxnxOp7d54961tdhy0sGnnTdp4ammc7AORzEx204Ll3U2HXaTM6Lup6zaxl62xMrWnBh0AL7oBXWHfMDh2XTL6u+9NzjXFqUM2lgI3ptVeSqyvZevOC6EmSGR6UA7aXZ566ufVMVpMuF/6f3kFb+z62Q0B+1+AAt9oOoKrnMbPZ+AkdP9v4/sLWLgwoEdkO0CPPBb/FdgBVXZ0MG75/12k7H9t17KKCEav3UQ+BIRmodEAicrWIvCYiK03NnT5f8USD/clzwR/ig9O4KEf8rrjjlI91XNLyQjGXA0ospK39lTJedy3J8MerUvFEg/3JbcHj0DfoZrqz3mSt0bM6L5x2Ufdn5xvDu7bzVKCsLUtjzDxgoNOEFU802J/cFjylm+mOO6/7i7M+3fnD198xI6zfuFEGQzJeYTVUPNFgf/Je8HuxPCSOqr2nzDhvQseVW9xVmDIn4wNKLKSt/XnbIXrLdcHj0C8Cv7adQ9VegeaWI7pOmn1I1ylPdJumvpPyZcW1VXyviica7E+uC566wnYAVT9zihO3n9Rx+agnixtm7a7Ct4Ebqvh+FU802J/cXujSmxdEC4BptnOo+jq8Obr/2y03bCOSiemsLqGt/ehyXywiNwGzgfVIhlY+AxgGg59osN/lOFLwg6nu5pHKiY3l1Rdvbz39jXXkHdsDSmxHW3u/57RtcmETHeBmLE/ypux43owZN7njsu1uKcyaYwxdlmLcm8VygyMFj0N/BXCZ7RzKDkNT00ld35i9X+fpT3WaFhvDFA/qIpR6cKLgqUuATtshlD0Pmo9uPaHjyjH/V9ysnrdqPk+Gz+Q4U/A49F8Bfm47h7LrPYaP3LfzrJ1O6Tr8waKRN+qwyO/T1p7ZFYszBU+dCU7dV6wG6ZeFT+ywY8dFhVfN2hUfea7A88DVNXz/IXOq4HHoP4fui6vUq6y7/rSOi6dc073bPGNYUYNFnJPltTc4VvDU2SQXHSgFiHy3++Cd9u783gvvmdYnq/jGz5HxtTc4WPA49F8H6j3rqcq4x81Hxk/ouHKT+wrbVGtAiXNoa7d1Wq5szhU8dR56Xlz10cmw4Qd0nbrzMV3HLSoYKeee7VVZTA7W3uBowePQfxs4x3YOlU1RcfqUqR2Xtj5fXH/BIN/iaNraczFLi5MFT10KVHOfSzlkKWuuu1PnT6Zf0L1vpQNK3Ehb+9yaBasyJ65FXxUviGYBcwGxnUVl12by0nO3tX7nnVHy3jYlXvo2sCVt7RXf1WWLy2tw4tCfT3KFm1Kr9IzZcJOJHVdseWdh6hxjBpz3ri1P5QbHC54KSE5pKLVKBZpbvt514uyDu/5ncZdp7m9AiceBC+uda6icL3gc+u+QjEqpVEnzihO2n9Rx+Zr/KI7rPaBEF3BQXg6s9eZ8wQHi0L8LuM52DpUP7zByzd06fzjzzK6v3G8M7cB3aWt/xHauwWiIgqe+CeRq/0nZdXVh9xm7dJ47HwhtZxmshil4HPpLgf2A3G1mKWuW/dN8+Bja2gc68JZpDVNw+M9R9ZNt51C58bX0BqbcaqiCA8ShfwFwk+0cKvMujkM/9xNrNFzBU18jI/M3q0y6EzjBdohqcPpKtoF4QbQ5yQSGa1uOorJlMTAjDv23bAephkZdgxOH/tPAgZDpqXBUfb0G7OlKuaGBCw4Qh34EHGc7h8qEFcA+cejHtoNUU0MXHCAO/YtJxnJTjcsAB8ehP9jbRzOr4QsOEIf+GehNKY3sv+PQr9a0v5miBX/fMej0R43oxDj0z7MdolYa9ih6f7wgaiIZW/3LtrOoujguDv2LbIeoJV2D95LON34QeiGM6wxwtOvlBi34SuLQ7wYOQEdmdZUBjoxDvyGOuegm+gC8IDoROBcd8skVBeCIOPRzMSJqNWjBS/CCaD/geqDVdhY1JMuA/dKxARqGFrwMXhDNBm4D1rKbRA3SP4C949BvuFF2dR+8DHHozwFmATbmnlZDcycwvRHLDVrwssWh/zgwCcj9LYQN5MeAH4f+MttBbNFN9EHwgugokqPsw21nUf16DzgqDv1rbQexTQs+SF4QTQR+CYy3HEV90CLggDj0/247SBboJvogxaH/CDAZuNFyFJUoAj8g2d/Wcqd0DV4F6am0HwNjbWdpUP8ADolD/wHbQbJG1+BVEIf+zcBWwEUw4NQ3qroKwI+AiVru/ukavMq8IJoMXAZ8zHYWx90NnByH/mO2g2SZFrwG0rvSjiSZo1wvjqmuJSTF/r3tIHmgBa8hL4jWB04Fvo6eUhuq14EzgCvTG4JUGbTgdeAF0TjgdOAQYJjlOHnzLnAxcLZLgyHWixa8jrwg2gQ4BTgUGGE5Tta9QnLQ8rI49N+0HSavtOAWeEG0AclkiIcC61mOkzVPkFwleEMc+p22w+SdFtwiL4hagX1JZlrZlca979wA9wDnxqF/p+0wLtGCZ4QXRJuSrNEPATa0HKdeHiUZHusXeZ/kL6u04BnjBVEz8Bng88AewBi7iaruaZJS3xSH/hLbYVynBc8wL4gEmAL46WMq+duML5LcAHI3cGsc+g9ZztNQtOA54gXRGGB3kv31KcCWZO9y4yLJzK3zgbnAn/UouD1a8BzzgmgNYCJJ2XseW1G/0r8J/J3k6rK/kxT7AT1fnR1acMd4QTQS+AiwCeClX8cCG6SP9UgGkBzW69Hc522Wk5R3afq15/EG8CxpqePQf622fxs1VFpw1bOv31P27jj0OyxHUlWiBVfKYVk7QKOUqiItuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5TAtuFIO04Ir5bD/B9iGNCI3yTViAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "da['HIQ210'].value_counts().plot(kind='pie') ## highly not recommended \n",
    "##Difficult to understand in circular pie graph \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "wt = da['BMXWT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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": [
    "\n",
    "wt.value_counts().plot(kind='bar')    # this does not work at all \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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": [
    "wt.hist(bins=20) # no of bins increases easy to understand for specific point \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zJqiy+WgEcFU605QNgUsi4reSbgUul3Qs8DBwRB7/GtLpqPNIp6QeU2FsZmZWR2VJISIeBPaoU/4ocGCd8gCOqyoeMzPrna9oNjOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZoerbXJj1iW+xbdZarimYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKDSUFSbtXHYiZmbVeozWFH0q6RdInJG1TaURmZtYyDSWFiHgz8EFgDDBL0iWSDqo0MjMza7qGjylExFzgS8DngbcAZ0m6T9I/VxWcmZk1V6PHFP5J0hnAvcDbgEMjYpfcfUaF8ZmZWRM1WlP4PnAbsEdEHBcRtwFExCOk2kO3JA2RdLukX+X+HSTdLGmepMskbZzLN8n98/Lwsev8qczMbJ00+pCdQ4BnIuIFAEkbAJtGxNMRcVEv0x5PqmFsnftPA86IiEsl/Qg4Fjg7vz8eEa+WdGQe7/19+ziDS6MPnDEz6y+N1hT+AGxW6t88l/VI0mhSQjk394vU5HRlHmUacHjunpT7ycMPzOObmVmTNJoUNo2IVZ09uXvzBqb7HvA54MXc/3JgRUSszv0LgVG5exSwIM9/NfBEHr8LSVMkzZQ0c/ny5Q2Gb2ZmjWg0KTwlaXxnj6TXA8/0NIGkdwPLImLWS4hvLRExNSI6IqJj+PDh/TlrM7P1XqPHFE4ArpD0CCDglfTe3v9G4DBJE4FNSccUzgSGStow1wZGA4vy+ItI10EslLQhsA3waB8+i5mZvUSNXrx2K7Az8HHgY8AuvdUAIuLkiBgdEWOBI4E/RsQHgT8B78ujTQauzt3Tcz95+B8jIvrwWczM7CVqtKYAsDcwNk8zXhIRceE6LPPzwKWSvgHcDpyXy88DLpI0D3iMlEjMzKyJGkoKki4CdgJmAy/k4gAaSgoRcT1wfe5+ENinzjj/AP6lkfmZmVk1Gq0pdAC7ujnHzGxwa/Tsozmkg8tmZjaINVpTGAbcI+kW4NnOwog4rJKozMysJRpNCqdUGYSZmbWHhpJCRPxZ0vbAuIj4g6TNgSHVhmZmZs3W6K2zP0q6H9E5uWgU8IuKYjIzsxZp9EDzcaQrlFdC8cCdV1QVlJmZtUajSeHZiHiusyffhsKnp5qZDTKNJoU/S/oCsFl+NvMVwC+rC8vMzFqh0aRwErAcuAv4N+AaennimpmZDTyNnn30IvDj/DIzs0Gq0XsfPUSdYwgRsWO/R2RmZi3Tl3sfddqUdOO6bfs/HDMza6VGn6fwaOm1KCK+R3r2spmZDSKNNh+NL/VuQKo59OVZDGZmNgA0+sf+3VL3amA+cES/R2PWoLEn/bqh8eaf6gqtWV80evbRAVUHYmZmrddo89FnehoeEaf3TzhmZtZKfTn7aG9geu4/FLgFmFtFUGZm1hqNJoXRwPiIeBJA0inAryPiQ1UFZmZmzdfobS5GAM+V+p/LZWZmNog0WlO4ELhF0lW5/3BgWiURmZlZyzR68do3gWOAx/PrmIj4Vk/TSNpU0i2S7pB0t6Sv5fIdJN0saZ6kyyRtnMs3yf3z8vCxL+mTmZlZnzXafASwObAyIs4EFkraoZfxnwXeFhF7AHsC75I0ATgNOCMiXk1KMMfm8Y8FHs/lZ+TxzMysiRp9HOdXgc8DJ+eijYCf9DRNJKtK429Euqne20iP9oTUBHV47p7EmiapK4EDJamR+MzMrH80WlN4D3AY8BRARDwCbNXbRJKGSJoNLAOuBR4AVkTE6jzKQtLznsnvC/L8VwNPAC+vM88pkmZKmrl8+fIGwzczs0Y0mhSei4gg3z5b0haNTBQRL0TEnqRTWvcBdl6XIGvmOTUiOiKiY/jw4S91dmZmVtJoUrhc0jnAUEkfBf5AHx64ExErgD8B++Z5dJ71NBpYlLsXAWOgeAb0NsCjjS7DzMxeul6TQm7Xv4zUzv8z4LXAVyLi+71MN1zS0Ny9GXAQcC8pObwvjzYZuDp3T8/95OF/zLUTMzNrkl6vU4iIkHRNROxOOi7QqJHANElDSMnn8oj4laR7gEslfQO4HTgvj38ecJGkecBjwJF9+SBmZvbSNXrx2m2S9o6IWxudcUTcCexVp/xB0vGF2vJ/kJ7oZtZvfItts75pNCm8AfiQpPmkM5BEqkT8U1WBmZlZ8/WYFCRtFxH/A7yzSfGYmVkL9VZT+AXp7qgPS/pZRLy3CTGZmVmL9Hb2UfmK4h2rDMTMzFqvt6QQ3XSbmdkg1Fvz0R6SVpJqDJvlblhzoHnrSqMzM7Om6jEpRMSQZgViZmat1+gpqdaPGj133sys2fryPAUzMxvknBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCpUlBUljJP1J0j2S7pZ0fC7fVtK1kubm95flckk6S9I8SXdKGl9VbGZmVl+VNYXVwGcjYldgAnCcpF2Bk4DrImIccF3uBzgYGJdfU4CzK4zNzMzqqCwpRMTiiLgtdz8J3AuMAiYB0/Jo04DDc/ck4MJIbgKGShpZVXxmZra2pjx5TdJYYC/gZmBERCzOg5YAI3L3KGBBabKFuWxxqQxJU0g1Cbbbbrvqgrb1SqNPw5t/6iEVR2LWWpUfaJa0JfAz4ISIWFkeFhEBRF/mFxFTI6IjIjqGDx/ej5GamVmlSUHSRqSEcHFE/DwXL+1sFsrvy3L5ImBMafLRuczMzJqkyrOPBJwH3BsRp5cGTQcm5+7JwNWl8qPyWUgTgCdKzUxmZtYEVR5TeCPwYeAuSbNz2ReAU4HLJR0LPAwckYddA0wE5gFPA8dUGJuZmdVRWVKIiL8C6mbwgXXGD+C4quIxM7Pe+YpmMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZryjGazwcLPcrbBzjUFMzMruKbQjxrdizQza1euKZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRUqSwqSzpe0TNKcUtm2kq6VNDe/vyyXS9JZkuZJulPS+KriMjOz7lVZU7gAeFdN2UnAdRExDrgu9wMcDIzLrynA2RXGZWZm3agsKUTEDOCxmuJJwLTcPQ04vFR+YSQ3AUMljawqNjMzq6/ZxxRGRMTi3L0EGJG7RwELSuMtzGVrkTRF0kxJM5cvX15dpGZm66GWXdEcESEp1mG6qcBUgI6Ojj5Pb9YMfbm63fdJsnbS7JrC0s5mofy+LJcvAsaUxhudy8zMrImanRSmA5Nz92Tg6lL5UfkspAnAE6VmJjMza5LKmo8k/RR4KzBM0kLgq8CpwOWSjgUeBo7Io18DTATmAU8Dx1QVl5mZda+ypBARH+hm0IF1xg3guKpiMWtnfkaDtRNf0WxmZgUnBTMzKzgpmJlZwUnBzMwKfhyn2QDhA9LWDK4pmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFXz2kdkg47OU7KVwTcHMzApOCmZmVnBSMDOzgo8pmK2nfOzB6nFNwczMCq4pmFmPXKNYvzgp9KLRH4SZ2WDg5iMzMys4KZiZWcHNR2bWL3zsYXBwTcHMzAptVVOQ9C7gTGAIcG5EnNrikMysn/Xl5A3XKpqvbZKCpCHAD4CDgIXArZKmR8Q9rY3MzFql3c/+G4xJq22SArAPMC8iHgSQdCkwCagkKbT7xmZmg0cV/zdVJaR2SgqjgAWl/oXAG2pHkjQFmJJ7V0m6HxgG/L3yCPvXQIwZHHezDcS4B2LMsA5x67SKIunbstdlfW/f3YB2SgoNiYipwNRymaSZEdHRopDWyUCMGRx3sw3EuAdizOC4O7XT2UeLgDGl/tG5zMzMmqSdksKtwDhJO0jaGDgSmN7imMzM1itt03wUEaslfRL4HemU1PMj4u4GJ5/a+yhtZyDGDI672QZi3AMxZnDcACgi+nN+ZmY2gLVT85GZmbWYk4KZmRUGVFKQNEbSnyTdI+luScfn8lMkLZI0O78mtjrWWpLmS7orxzczl20r6VpJc/P7y1odZ5mk15bW6WxJKyWd0I7rW9L5kpZJmlMqq7t+lZwlaZ6kOyWNb6OYvyPpvhzXVZKG5vKxkp4prfMftSLmHuLudpuQdHJe1/dLemdrou427stKMc+XNDuXt8X67uE/r7ptOyIGzAsYCYzP3VsB/w3sCpwCnNjq+HqJfT4wrKbs28BJufsk4LRWx9lD/EOAJaSLXtpufQP7A+OBOb2tX2Ai8BtAwATg5jaK+R3Ahrn7tFLMY8vjteG6rrtN5N/nHcAmwA7AA8CQdom7Zvh3ga+00/ru4T+vsm17QNUUImJxRNyWu58E7iVdCT1QTQKm5e5pwOGtC6VXBwIPRMTDrQ6knoiYATxWU9zd+p0EXBjJTcBQSSObEmhJvZgj4vcRsTr33kS6XqetdLOuuzMJuDQino2Ih4B5pFvaNF1PcUsScATw06YG1Yse/vMq27YHVFIokzQW2Au4ORd9MleXzm+3ZpgsgN9LmpVv1QEwIiIW5+4lwIjWhNaQI+n6g2n39Q3dr996t1Rpx52L/0Pa6+u0g6TbJf1Z0ptbFVQP6m0TA2VdvxlYGhFzS2Vttb5r/vMq27YHZFKQtCXwM+CEiFgJnA3sBOwJLCZVA9vNmyJiPHAwcJyk/csDI9X92vL8YKWLCQ8DrshFA2F9d9HO67ceSV8EVgMX56LFwHYRsRfwGeASSVu3Kr46Btw2UeMDdN3paav1Xec/r9Df2/aASwqSNiKtnIsj4ucAEbE0Il6IiBeBH9Oi6mlPImJRfl8GXEWKcWln1S6/L2tdhD06GLgtIpbCwFjfWXfrt61vqSLpaODdwAfzD57c/PJo7p5Fapt/TcuCrNHDNtHW6xpA0obAPwOXdZa10/qu959Hhdv2gEoKud3vPODeiDi9VF5uM3sPMKd22laStIWkrTq7SQcT55Bu4zE5jzYZuLo1Efaqy15Uu6/vku7W73TgqHymxgTgiVJVvKWUHjT1OeCwiHi6VD5c6ZkjSNoRGAc82Joo19bDNjEdOFLSJpJ2IMV9S7Pj68XbgfsiYmFnQbus7+7+86hy22710fU+Hol/E6madCcwO78mAhcBd+Xy6cDIVsdaE/eOpDMw7gDuBr6Yy18OXAfMBf4AbNvqWOvEvgXwKLBNqazt1jcpaS0Gnie1ox7b3folnZnxA9Le311ARxvFPI/UJty5ff8oj/vevO3MBm4DDm2zdd3tNgF8Ma/r+4GD2ynuXH4B8LGacdtifffwn1fZtu3bXJiZWWFANR+ZmVm1nBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBLJN0hqQTSv2/k3Ruqf+7kj7TzbRfl/T2XuZ/iqQT65QPlfSJlxC6Wb9xUjBb42/AfgCSNgCGAbuVhu8H3FBvwoj4SkT8YR2XOxRwUrC24KRgtsYNwL65ezfSVblPSnqZpE2AXYDIN0iblWsSnbcauEDS+3L3RKVnIszK97b/VWkZu0q6XtKDkj6dy04Fdsr37f+OpJGSZuT+Oe1wMzZbf2zY6gDM2kVEPCJptaTtSLWCG0l3mNwXeIJ02+IzgEkRsVzS+4Fvku5mCoCkTYFzgP0j4iFJtbdi3hk4gHRv/PslnU26H/7rImLPPI/PAr+LiG/mWy1sXtmHNqvhpGDW1Q2khLAfcDopKexHSgqLSPetujbdkoYhpNsmlO0MPBjp2QGQbq0wpTT81xHxLPCspGXUv136rcD5+UZov4iI2f3wucwa4qRg1lXncYXdSc1HC4DPAiuB64FREbFvt1P37tlS9wvU+Q1GxIx8a/VDgAsknR4RF76EZZo1zMcUzLq6gXTb6sci3Qr6MdKB4H1Je/3DJe0L6ZbGknarmf5+YMf8QBSA9zewzCdJzUnk+W5PeuDLj4FzSY+QNGsK1xTMurqLdNbRJTVlW0bEsnww+SxJ25B+P98j3U0TgIh4Jp9e+ltJT5GagnoUEY9K+pvSA+V/Q6qh/F9JzwOrgKP656OZ9c53STXrZ5K2jIhV+V74PwDmRsQZrY7LrBFuPjLrfx+VNJtUg9iGdDaS2YDgmoKZmRVcUzAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMyv8L7m0VJvluTMvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(wt, bins=30) \n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Weights');\n",
    "plt.title(\"Histogram of Weights in NHANES Dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## latest lib work on matplotlib\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "sns.set_style(\"white\")     # See more styling options here: https://seaborn.pydata.org/tutorial/aesthetics.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/home/.local/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(wt, bins=20, kde=False )      # to get rid of the \"trend line\"\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Weights');\n",
    "plt.title(\"Histogram of Weights in NHANES Dataset\")\n",
    "sns.despine(offset=15, trim=True);  # move x axis and y axis away thats looks visually appealing\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[43.7, 52.5, 50.1]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics.quantiles(wt,n=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=wt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "198.9"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wt.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>SEQN</th>\n",
       "      <th>ALQ101</th>\n",
       "      <th>ALQ110</th>\n",
       "      <th>ALQ130</th>\n",
       "      <th>SMQ020</th>\n",
       "      <th>RIAGENDR</th>\n",
       "      <th>RIDAGEYR</th>\n",
       "      <th>RIDRETH1</th>\n",
       "      <th>DMDCITZN</th>\n",
       "      <th>DMDEDUC2</th>\n",
       "      <th>...</th>\n",
       "      <th>BPXSY2</th>\n",
       "      <th>BPXDI2</th>\n",
       "      <th>BMXWT</th>\n",
       "      <th>BMXHT</th>\n",
       "      <th>BMXBMI</th>\n",
       "      <th>BMXLEG</th>\n",
       "      <th>BMXARML</th>\n",
       "      <th>BMXARMC</th>\n",
       "      <th>BMXWAIST</th>\n",
       "      <th>HIQ210</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83732</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>124.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>94.8</td>\n",
       "      <td>184.5</td>\n",
       "      <td>27.8</td>\n",
       "      <td>43.3</td>\n",
       "      <td>43.6</td>\n",
       "      <td>35.9</td>\n",
       "      <td>101.1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83733</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>140.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>90.4</td>\n",
       "      <td>171.4</td>\n",
       "      <td>30.8</td>\n",
       "      <td>38.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>33.2</td>\n",
       "      <td>107.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83734</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>132.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>83.4</td>\n",
       "      <td>170.1</td>\n",
       "      <td>28.8</td>\n",
       "      <td>35.6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>116.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83735</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>134.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>109.8</td>\n",
       "      <td>160.9</td>\n",
       "      <td>42.4</td>\n",
       "      <td>38.5</td>\n",
       "      <td>37.7</td>\n",
       "      <td>38.3</td>\n",
       "      <td>110.1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83736</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>55.2</td>\n",
       "      <td>164.9</td>\n",
       "      <td>20.3</td>\n",
       "      <td>37.4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>27.2</td>\n",
       "      <td>80.4</td>\n",
       "      <td>2.0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5730</th>\n",
       "      <td>93695</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>112.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>59.1</td>\n",
       "      <td>165.8</td>\n",
       "      <td>21.5</td>\n",
       "      <td>38.2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5731</th>\n",
       "      <td>93696</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>116.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>112.1</td>\n",
       "      <td>182.2</td>\n",
       "      <td>33.8</td>\n",
       "      <td>43.4</td>\n",
       "      <td>41.8</td>\n",
       "      <td>42.3</td>\n",
       "      <td>110.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5732</th>\n",
       "      <td>93697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>146.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>71.7</td>\n",
       "      <td>152.2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31.3</td>\n",
       "      <td>37.5</td>\n",
       "      <td>28.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5733</th>\n",
       "      <td>93700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>106.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>173.3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>40.3</td>\n",
       "      <td>37.5</td>\n",
       "      <td>30.6</td>\n",
       "      <td>98.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>93702</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>58.3</td>\n",
       "      <td>165.0</td>\n",
       "      <td>21.4</td>\n",
       "      <td>38.2</td>\n",
       "      <td>33.5</td>\n",
       "      <td>26.2</td>\n",
       "      <td>72.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5735 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SEQN  ALQ101  ALQ110  ALQ130  SMQ020  RIAGENDR  RIDAGEYR  RIDRETH1  \\\n",
       "0     83732     1.0     NaN     1.0     1.0       1.0      62.0       3.0   \n",
       "1     83733     1.0     NaN     6.0     1.0       1.0      53.0       3.0   \n",
       "2     83734     1.0     NaN     NaN     1.0       1.0      78.0       3.0   \n",
       "3     83735     2.0     1.0     1.0     2.0       2.0      56.0       3.0   \n",
       "4     83736     2.0     1.0     1.0     2.0       2.0      42.0       4.0   \n",
       "...     ...     ...     ...     ...     ...       ...       ...       ...   \n",
       "5730  93695     2.0     2.0     NaN     1.0       2.0      76.0       3.0   \n",
       "5731  93696     2.0     2.0     NaN     2.0       1.0      26.0       3.0   \n",
       "5732  93697     1.0     NaN     1.0     1.0       2.0      80.0       3.0   \n",
       "5733  93700     NaN     NaN     NaN     1.0       1.0      35.0       3.0   \n",
       "5734  93702     1.0     NaN     2.0     2.0       2.0      24.0       3.0   \n",
       "\n",
       "      DMDCITZN  DMDEDUC2  ...  BPXSY2  BPXDI2  BMXWT  BMXHT  BMXBMI  BMXLEG  \\\n",
       "0          1.0       5.0  ...   124.0    64.0   94.8  184.5    27.8    43.3   \n",
       "1          2.0       3.0  ...   140.0    88.0   90.4  171.4    30.8    38.0   \n",
       "2          1.0       3.0  ...   132.0    44.0   83.4  170.1    28.8    35.6   \n",
       "3          1.0       5.0  ...   134.0    68.0  109.8  160.9    42.4    38.5   \n",
       "4          1.0       4.0  ...   114.0    54.0   55.2  164.9    20.3    37.4   \n",
       "...        ...       ...  ...     ...     ...    ...    ...     ...     ...   \n",
       "5730       1.0       3.0  ...   112.0    46.0   59.1  165.8    21.5    38.2   \n",
       "5731       1.0       5.0  ...   116.0    76.0  112.1  182.2    33.8    43.4   \n",
       "5732       1.0       4.0  ...   146.0    58.0   71.7  152.2    31.0    31.3   \n",
       "5733       2.0       1.0  ...   106.0    66.0   78.2  173.3    26.0    40.3   \n",
       "5734       1.0       5.0  ...   114.0    68.0   58.3  165.0    21.4    38.2   \n",
       "\n",
       "      BMXARML  BMXARMC  BMXWAIST  HIQ210  \n",
       "0        43.6     35.9     101.1     2.0  \n",
       "1        40.0     33.2     107.9     NaN  \n",
       "2        37.0     31.0     116.5     2.0  \n",
       "3        37.7     38.3     110.1     2.0  \n",
       "4        36.0     27.2      80.4     2.0  \n",
       "...       ...      ...       ...     ...  \n",
       "5730     37.0     29.5      95.0     2.0  \n",
       "5731     41.8     42.3     110.2     2.0  \n",
       "5732     37.5     28.8       NaN     2.0  \n",
       "5733     37.5     30.6      98.9     2.0  \n",
       "5734     33.5     26.2      72.5     2.0  \n",
       "\n",
       "[5735 rows x 25 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wt.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_wt = da.loc[da['RIAGENDR']==2 , 'BMXWT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.51676280963693"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_wt.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.57326222876058"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_wt = da.loc[da['RIAGENDR']==1, 'BMXWT'] ## take as a SQL query\n",
    "female_wt.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,8))\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.xticks([0, 1], ['Male', 'Female'])\n",
    "plt.title(\"Weight plotting Example  \")\n",
    "\n",
    "ax = sns.boxplot( x = 'RIAGENDR' ,  y ='BMXWT' ,  data=da,    palette='Set3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,8))\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.xticks([0, 1], ['Male', 'Female'])\n",
    "plt.title(\"Height plotting Example \")\n",
    "\n",
    "ax = sns.boxplot( x = 'RIAGENDR' ,  y ='BMXHT' ,  data=da,    palette='Set3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,10))\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "plt.title(\"Body Mass Index  plotting Example \")\n",
    "\n",
    "ax = sns.boxplot( x = 'RIAGENDR' ,  y ='BMXBMI' ,  data=da,    palette='Set3')"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}