EncodingData.ipynb

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    "# In this exercise we'll load the titanic data (from Project 0)\n",
    "# And then perform one-hot encoding on the feature names\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
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     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>113803</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>373450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name     Sex  \\\n",
       "0                            Braund, Mr. Owen Harris    male   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   \n",
       "2                             Heikkinen, Miss. Laina  female   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   \n",
       "4                           Allen, Mr. William Henry    male   \n",
       "\n",
       "             Ticket Cabin Embarked  \n",
       "0         A/5 21171   NaN        S  \n",
       "1          PC 17599   C85        C  \n",
       "2  STON/O2. 3101282   NaN        S  \n",
       "3            113803  C123        S  \n",
       "4            373450   NaN        S  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset\n",
    "X = pd.read_csv('titanic_data.csv')\n",
    "# Limit to categorical data\n",
    "X = X.select_dtypes(include=[object])\n",
    "X.head() #X.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = list(X.columns.values)\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "for c in X.columns:\n",
    "    X[c] = le.fit_transform(X[c])\n",
    "    \n",
    "enc = OneHotEncoder()\n",
    "\n",
    "X = enc.fit_transform(X)\n",
    "\n",
    "onehotlabels = X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
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   "outputs": [],
   "source": [
    "# TODO: Create a LabelEncoder object, which will turn all labels present in\n",
    "#       in each feature to numbers. For example, the labels ['cat', 'dog', 'fish']\n",
    "#       might be transformed into [0, 1, 2]\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO: For each feature in X, apply the LabelEncoder's fit_transform\n",
    "#       function, which will first learn the labels for the feature (fit)\n",
    "#       and then change the labels to numbers (transform). \n",
    "\n",
    "le.fit(features);\n",
    "\n",
    "for feature in X:\n",
    "    X[feature] = le.fit_transform(X[feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO: Create a OneHotEncoder object, which will create a feature for each\n",
    "#       label present in the data. For example, for a feature 'animal' that had\n",
    "#       the labels ['cat','dog','fish'], the new features (instead of 'animal') \n",
    "#       could be ['animal_cat', 'animal_dog', 'animal_fish']\n",
    "\n",
    "ohe = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO: Apply the OneHotEncoder's fit_transform function to all of X, which will\n",
    "#       first learn of all the (now numerical) labels in the data (fit), and then\n",
    "#       change the data to one-hot encoded entries (transform).\n",
    "\n",
    "onehotlabels = ohe.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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