00-nhanes_data_basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started with Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"data/nhanes_2015_2016.csv\"\n",
    "da = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "       SEQN  ALQ101  ALQ110  ALQ130  SMQ020  RIAGENDR  RIDAGEYR  RIDRETH1  \\\n",
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     "execution_count": 4,
     "metadata": {},
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       "      <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</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>4</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SEQN  ALQ101  ALQ110  ALQ130  SMQ020  RIAGENDR  RIDAGEYR  RIDRETH1  \\\n",
       "0  83732     1.0     NaN     1.0       1         1        62         3   \n",
       "1  83733     1.0     NaN     6.0       1         1        53         3   \n",
       "2  83734     1.0     NaN     NaN       1         1        78         3   \n",
       "3  83735     2.0     1.0     1.0       2         2        56         3   \n",
       "4  83736     2.0     1.0     1.0       2         2        42         4   \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",
       "   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",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5735, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the contents of a data set\n",
    "\n",
    "Pandas has a number of basic ways to understand what is in a data set.  For example, above we used the '`shape`' method to determine the numbers of rows and columns in a data set.  The columns in a Pandas data frame have names, to see the names, use the '`columns`' method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da.columns     # .size "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These names correspond to variables in the NHANES study. Details of these are here: \n",
    "\n",
    "https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2015\n",
    "\n",
    "For convenience, direct links to some of the code books are included below:\n",
    "\n",
    "* [Demographics code book](https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/DEMO_I.htm)\n",
    "\n",
    "* [Body measures code book](https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/BMX_I.htm)\n",
    "\n",
    "* [Blood pressure code book](https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/BPX_I.htm)\n",
    "\n",
    "* [Alcohol questionaire code book](https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/ALQ_I.htm)\n",
    "\n",
    "* [Smoking questionaire code book](https://wwwn.cdc.gov/Nchs/Nhanes/2015-2016/SMQ_I.htm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  BMXWT is the weight in kg "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        94.8\n",
       "1        90.4\n",
       "2        83.4\n",
       "3       109.8\n",
       "4        55.2\n",
       "        ...  \n",
       "5730     59.1\n",
       "5731    112.1\n",
       "5732     71.7\n",
       "5733     78.2\n",
       "5734     58.3\n",
       "Name: BMXWT, Length: 5735, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da['BMXWT']    # also use with .head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare some data\n",
    "y = [6, 7, 2, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "plt.plot(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure \n",
    "plt.figure(figsize=(10, 5)) \n",
    "\n",
    "# add a line renderer with legend and line thickness\n",
    "plt.plot(y)\n",
    "\n",
    "# also need to make the figure readable -- very important from stats perspective!\n",
    "plt.title(\"First Example Plot\")\n",
    "plt.xlabel(\"x vals (s)\")\n",
    "plt.ylabel(\"y vals (m)\")\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5735"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da['BMXWT'].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting Weights \n",
    "# create a figure \n",
    "plt.figure(figsize=(20, 5)) \n",
    "\n",
    "# add a line renderer with legend and line thickness\n",
    "x =  range(da['BMXWT'].size)\n",
    "y =  da['BMXWT']\n",
    "\n",
    "plt.scatter(x, y, marker='x', s=0.5)\n",
    "\n",
    "# also need to make the figure readable -- very important from stats perspective!\n",
    "plt.title(\"Weights of Individuals\")\n",
    "plt.xlabel(\"ID of Individual\")\n",
    "plt.ylabel(\"Weight (kg)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "460887.5999999996"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.dropna()    # just remove the values with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.34267560889509"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y) / len(y)"
   ]
  },
  {
   "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",
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