{
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"
    }
   },
   "cell_type": "markdown",
   "id": "e03eca50",
   "metadata": {},
   "source": [
    "# Using pymssql to attach to MS Sql Server\n",
    "\n",
    "You may have a set or tables (or better yet a view of cleaned data in MS SQL Server) and want to bring it in as a DataFrame. This simple .ipynb should be enough to get your started using the [pymssql library](https://pymssql.readthedocs.io/en/stable/index.html).\n",
    "\n",
    "## Prior to editing and running this notebook, ensure the following:\n",
    "\n",
    "1. You have a SQL account to connect to the database (i.e. you can't just use a Windows account as you can in SSMS). The account used in the example code below is `db_reader` and the password is `passme123`. Create an account such as this to access the desired database on your server. \n",
    "2. You have enabled TCP/IP in SQL Server Configuration Manager, if it is not enabled, your connection will fail. See image below:  \n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "3. Test the output at each stage, when looping through each row in the cursor each row will be returned as a dictionary. This may not be the most efficient way to load the data, but gives you control over the data on the import and uses concepts you are comfortable with:  \n",
    "* dictionaries (each returned row - test with print() if you like)\n",
    "* lists (used to store each row in a dictionary format) \n",
    "* DataFrames (used to convert the list of dictionaries into a usable df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7ddbd18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment and run this if not loaded...\n",
    "# !pip install pymssql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "735d6752",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbc311f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymssql\n",
    "conn = pymssql.connect('localhost', 'db_reader', 'passme123', 'Titanic')  \n",
    "cursor = conn.cursor(as_dict=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e4a2df46",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = []\n",
    "cursor.execute('SELECT p.* FROM dev.passengers p;')\n",
    "for row in cursor:\n",
    "    lst.append(row)\n",
    "    \n",
    "df = pd.DataFrame(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5cbbb8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# close your cursor, followed by your connection\n",
    "cursor.close()\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0017f695",
   "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>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Boarded</th>\n",
       "      <th>Class</th>\n",
       "      <th>Yrs</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>1313</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>1314</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>1315</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>1316</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>1317</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1317 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Gender  Boarded  Class   Yrs  Survived\n",
       "0        1       0        2      3  26.0         1\n",
       "1        2       0        1      3  42.0         0\n",
       "2        3       1        1      3  39.0         1\n",
       "3        4       0        1      3  16.0         0\n",
       "4        5       0        1      3  14.0         0\n",
       "...    ...     ...      ...    ...   ...       ...\n",
       "1312  1313       1        1      2  24.0         0\n",
       "1313  1314       0        2      3  27.0         0\n",
       "1314  1315       0        2      3  22.0         0\n",
       "1315  1316       0        2      3  22.0         1\n",
       "1316  1317       0        1      3  29.0         0\n",
       "\n",
       "[1317 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access your df (sql results like any other df)\n",
    "df"
   ]
  }
 ],
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