categorical_variables

test.ipynb

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   "source": [
    "import pandas as pd"
   ]
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   "execution_count": 2,
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    {
     "data": {
      "text/plain": [
       "'0.15.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   "source": [
    "pd.__version__"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4 entries, 0 to 3\n",
      "Data columns (total 1 columns):\n",
      "A    4 non-null object\n",
      "dtypes: object(1)\n",
      "memory usage: 64.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({\"A\":[\"a\",\"b\",\"c\",\"a\"]})\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this my narrative, this was my results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A_a</th>\n",
       "      <th>A_b</th>\n",
       "      <th>A_c</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> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A_a  A_b  A_c\n",
       "0    1    0    0\n",
       "1    0    1    0\n",
       "2    0    0    1\n",
       "3    1    0    0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.get_dummies(df, 'A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>A_a</th>\n",
       "      <th>A_b</th>\n",
       "      <th>A_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> a</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> b</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> c</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> a</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   A  A_a  A_b  A_c\n",
       "0  a    1    0    0\n",
       "1  b    0    1    0\n",
       "2  c    0    0    1\n",
       "3  a    1    0    0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = pd.concat([df, pd.get_dummies(df, 'A')], axis=1)\n",
    "combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a list from a data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'b', 'c', 'a'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.A.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 'a']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both work in iterators, if your function requires that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "c\n",
      "a\n"
     ]
    }
   ],
   "source": [
    "for a in list(df.A):\n",
    "    print a"
   ]
  }
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