One hot vector representation .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SEQN', 'ALQ101', 'ALQ110', 'ALQ130', 'SMQ020', 'RIAGENDR', 'RIDAGEYR',\n",
       "       'RIDRETH1', 'DMDCITZN', 'DMDEDUC2', 'DMDMARTL', 'DMDHHSIZ', 'INDFMPIR',\n",
       "       'BPXSY1', 'BPXDI1', 'BPXSY2', 'BPXDI2', 'BMXWT', 'BMXHT', 'BMXBMI',\n",
       "       'BMXLEG', 'BMXARML', 'BMXARMC', 'BMXWAIST', 'HIQ210'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.34267560889509"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da['BMXWT'].mean()         # we can get a mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.,  3.,  4.,  2., nan,  1.,  9.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da['DMDEDUC2'].unique()    # Categorical Ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1       1.0\n",
       "2       1.0\n",
       "3       2.0\n",
       "4       2.0\n",
       "       ... \n",
       "5730    2.0\n",
       "5731    1.0\n",
       "5732    2.0\n",
       "5733    1.0\n",
       "5734    2.0\n",
       "Name: RIAGENDR, Length: 5735, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "g = da['RIAGENDR']    # Categorical, Unordered \n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
       "1       1.0\n",
       "2       1.0\n",
       "3       2.0\n",
       "4       2.0\n",
       "       ... \n",
       "5730    2.0\n",
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       "5733    1.0\n",
       "5734    2.0\n",
       "Name: RIAGENDR, Length: 5735, dtype: float64"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot-vector representation \n",
    "#Categorical data are variables that contain label values rather than numeric values\n",
    "\n",
    "clr = ['blue' ,'balck','green','yellow','red', 'green']   # clr is basically a lists of dictionary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'categorical': clr}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "  categorical\n",
       "0        blue\n",
       "1       balck\n",
       "2       green\n",
       "3      yellow\n",
       "4         red\n",
       "5       green"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1     balck\n",
       "2     green\n",
       "3    yellow\n",
       "4       red\n",
       "5     green\n",
       "Name: categorical, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['categorical'] \n",
    "# 0 1 2 .. are the indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfdumies = pd.get_dummies(df['categorical']  , prefix ='category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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       "   category_balck  category_blue  category_green  category_red  \\\n",
       "0               0              1               0             0   \n",
       "1               1              0               0             0   \n",
       "2               0              0               1             0   \n",
       "3               0              0               0             0   \n",
       "4               0              0               0             1   \n",
       "5               0              0               1             0   \n",
       "\n",
       "   category_yellow  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                1  \n",
       "4                0  \n",
       "5                0  "
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     "execution_count": 35,
     "metadata": {},
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   "source": [
    "dfdumies\n"
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   "cell_type": "code",
   "execution_count": 36,
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       "array([[0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
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    "dfdumies.values "
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  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#how we can convert values from a categorical column in our dataset into their \n",
    "#numerical counterparts, via the one-hot vector encoding scheme.\n",
    "\n",
    "\n",
    "ids = [11, 22, 33, 44, 55, 66, 77]\n",
    "countries = ['Spain', 'France', 'Spain', 'Germany', 'France']\n",
    "\n",
    "df = pd.DataFrame(list(zip(ids, countries)),\n",
    "                  columns=['Ids', 'Countries'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Ids Countries\n",
       "0   11     Spain\n",
       "1   22    France\n",
       "2   33     Spain\n",
       "3   44   Germany\n",
       "4   55    France"
      ]
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     "metadata": {},
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    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.get_dummies(df.Countries,  prefix='Country')\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "      <th>Country_France</th>\n",
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      "text/plain": [
       "   Country_France  Country_Germany  Country_Spain\n",
       "0               0                0              1\n",
       "1               1                0              0\n",
       "2               0                0              1\n",
       "3               0                1              0\n",
       "4               1                0              0"
      ]
     },
     "execution_count": 47,
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   "source": [
    "y"
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   "cell_type": "code",
   "execution_count": 48,
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    "y.values"
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "B = ['bird','cat','dog', 'cat', 'bird', 'bird']\n",
    "# B is categorical unordered  variables representing numerical  using sacalar represntation which would intrinsic behaviour \n",
    "# variables make it unordered using --> One Hot Vector Encoding ...\n",
    "\n",
    "d = {'categorical': B}\n",
    "\n",
    "df = pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  categorical\n",
       "0        bird\n",
       "1         cat\n",
       "2         dog\n",
       "3         cat\n",
       "4        bird\n",
       "5        bird"
      ]
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     "execution_count": 50,
     "metadata": {},
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    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    bird\n",
       "1     cat\n",
       "2     dog\n",
       "3     cat\n",
       "4    bird\n",
       "5    bird\n",
       "Name: categorical, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df['categorical']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "dffrozenheart =pd.get_dummies(  df['categorical'], prefix= 'category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>category_bird</th>\n",
       "      <th>category_cat</th>\n",
       "      <th>category_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category_bird  category_cat  category_dog\n",
       "0              1             0             0\n",
       "1              0             1             0\n",
       "2              0             0             1\n",
       "3              0             1             0\n",
       "4              1             0             0\n",
       "5              1             0             0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dffrozenheart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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