notebook1.ipynb

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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Modules loaded\n"
     ]
    }
   ],
   "source": [
    "#this first block loads several modules that we may or may not use\n",
    "#it serves as a check that you have them installed\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plot\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "print(\"Modules loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>filename</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>img_250.jpg</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hannover Medical School, Hannover, Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>img_251.jpg</td>\n",
       "      <td>M</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Qatif, Saudi Arabia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>img_252.jpg</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vivantes Klinikum Neukölln, Berlin, Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>img_253.jpg</td>\n",
       "      <td>M</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>img_254.jpg</td>\n",
       "      <td>M</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>img_339.jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>img_340.jpg</td>\n",
       "      <td>F</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>img_341.jpg</td>\n",
       "      <td>F</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Melbourne, Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>img_342.jpg</td>\n",
       "      <td>M</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Milan, Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>img_343.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taiwan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       filename gender   age                                     location\n",
       "0   img_250.jpg      M   NaN   Hannover Medical School, Hannover, Germany\n",
       "1   img_251.jpg      M  30.0                          Qatif, Saudi Arabia\n",
       "2   img_252.jpg      M   NaN  Vivantes Klinikum Neukölln, Berlin, Germany\n",
       "3   img_253.jpg      M  73.0                                          NaN\n",
       "4   img_254.jpg      M  51.0                                        Spain\n",
       "..          ...    ...   ...                                          ...\n",
       "89  img_339.jpg    NaN   NaN                                          NaN\n",
       "90  img_340.jpg      F  70.0                                        Spain\n",
       "91  img_341.jpg      F  45.0                         Melbourne, Australia\n",
       "92  img_342.jpg      M  55.0                                 Milan, Italy\n",
       "93  img_343.png    NaN   NaN                                       Taiwan\n",
       "\n",
       "[94 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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