{
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   "cell_type": "markdown",
   "id": "fa63f581-f411-4e3c-8a36-255c1c986389",
   "metadata": {},
   "source": [
    "# Querying databases using SQL\n",
    "\n",
    "The first step is to load and explore data. We use pandas for loading the data and all we do is store it in variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "91c81c85-dce7-4b20-9654-ad52d051743d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandasql import sqldf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd8dc6b-b640-4c2c-9f0d-82978a08db82",
   "metadata": {},
   "source": [
    "When working with SQL, common practice is to call tables (a.k.a. DataFrames) as singluar. This allows to read SQL code better. For example in a database containing experiments, we can later use `experiment.concentration` to describe the concentration of a drug that was used to expose cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a98379e-c60d-4ce2-81e7-2ad47c5225e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = pd.read_csv(\"../../data/experiments.csv\")\n",
    "measurement = pd.read_csv(\"../../data/measurements.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dfa47bc-7f85-49fb-8a62-57b6b65054ae",
   "metadata": {},
   "source": [
    "Next we combine these tables in a database. Therefore, we define a function that can query the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdec6f91-22c5-404f-a436-b23a29506e9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_database = lambda q: sqldf(q, globals())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe7d09c-fe64-4d17-a6f7-00b910980899",
   "metadata": {},
   "source": [
    "# Select From\n",
    "Next, we define a query in SQL. SQL has a quite nicely human readable format. Typically we start by defining what we want to read (`SELECT`) and from where (`FROM`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6fb1244e-70a6-4c6c-bb1f-8bfe7e550f2c",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "      <td>6</td>\n",
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       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>10</td>\n",
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       "      <td>54</td>\n",
       "    </tr>\n",
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       "      <th>98</th>\n",
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       "      <td>9</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  image_ID  time_after_exposure_in_s  number_of_cells\n",
       "0            0         1                         0               99\n",
       "1            1         1                         1               52\n",
       "2            2         1                         2               33\n",
       "3            3         1                         3               25\n",
       "4            4         1                         4               21\n",
       "..         ...       ...                       ...              ...\n",
       "95          95        10                         5               52\n",
       "96          96        10                         6               54\n",
       "97          97        10                         7               54\n",
       "98          98        10                         8               54\n",
       "99          99        10                         9               51\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT * \n",
    "FROM measurement\n",
    "\"\"\"\n",
    "\n",
    "query_database(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37e59421-2e1e-4ae8-9380-bbed2f0127d7",
   "metadata": {},
   "source": [
    "By the way, SQL is not case-sensitive per-se but you typically find SQL command words in capital letters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d7d7f3a-b3c4-403d-8de1-86d9f9ca9750",
   "metadata": {},
   "outputs": [
    {
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       "      <td>9</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  image_ID  time_after_exposure_in_s  number_of_cells\n",
       "0            0         1                         0               99\n",
       "1            1         1                         1               52\n",
       "2            2         1                         2               33\n",
       "3            3         1                         3               25\n",
       "4            4         1                         4               21\n",
       "..         ...       ...                       ...              ...\n",
       "95          95        10                         5               52\n",
       "96          96        10                         6               54\n",
       "97          97        10                         7               54\n",
       "98          98        10                         8               54\n",
       "99          99        10                         9               51\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "select * \n",
    "from measurement\n",
    "\"\"\"\n",
    "\n",
    "query_database(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "091108ac-f5fe-474d-b3b1-1a63492f3608",
   "metadata": {},
   "source": [
    "You can also select specific columns by name. Also here, SQL is not case-sensitive. The selected column `concentration` shows up as `Concentration` because that's its name in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c93f5a11-53e7-4318-987c-66cd39b25139",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Comment</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High dose</td>\n",
       "      <td>50</td>\n",
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       "      <th>1</th>\n",
       "      <td>Medium dose</td>\n",
       "      <td>20</td>\n",
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       "      <td>Control</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super high dose</td>\n",
       "      <td>1000</td>\n",
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      "text/plain": [
       "           Comment  Concentration\n",
       "0        High dose             50\n",
       "1      Medium dose             20\n",
       "2          Control              0\n",
       "3  Super high dose           1000"
      ]
     },
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   "source": [
    "query = \"\"\"\n",
    "select Comment, concentration\n",
    "from experiment\n",
    "\"\"\"\n",
    "\n",
    "query_database(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f18fe8a-1273-40d5-a68c-bd74c02d2eed",
   "metadata": {},
   "source": [
    "## Sorting tables\n",
    "\n",
    "This table can also be sorted using the `ORDER BY` statement, for example to see the highest number of cells. In this case we sort the column descending using the `DESC` statement. The ascenting order `ASC` would be default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9faf21e7-745d-49d7-bf9f-d11420cf5e81",
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    {
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       "      <td>7</td>\n",
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       "      <th>97</th>\n",
       "      <td>48</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
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       "      <th>99</th>\n",
       "      <td>49</td>\n",
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       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  image_ID  time_after_exposure_in_s  number_of_cells\n",
       "0           10         2                         0              201\n",
       "1           75         8                         5              161\n",
       "2           50         6                         0              158\n",
       "3           78         8                         8              158\n",
       "4           70         8                         0              157\n",
       "..         ...       ...                       ...              ...\n",
       "95          47         5                         7                8\n",
       "96          38         4                         8                7\n",
       "97          48         5                         8                7\n",
       "98          39         4                         9                6\n",
       "99          49         5                         9                6\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT * \n",
    "FROM measurement\n",
    "ORDER BY number_of_cells DESC\n",
    "\"\"\"\n",
    "\n",
    "query_database(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c7789a-de21-4b14-8126-b256e4f4a4df",
   "metadata": {},
   "source": [
    "## Specifying the number of row to query\n",
    "\n",
    "In case a table is very large, retrieving all rows may take a long time. To get insights into the data anyway, it may make sense to `LIMIT` the table by the top 10 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c586b76-44a2-4207-a987-b2b4cb0260e6",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>77</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>66</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  image_ID  time_after_exposure_in_s  number_of_cells\n",
       "0          10         2                         0              201\n",
       "1          75         8                         5              161\n",
       "2          50         6                         0              158\n",
       "3          78         8                         8              158\n",
       "4          70         8                         0              157\n",
       "5          77         8                         7              157\n",
       "6          76         8                         6              154\n",
       "7          60         7                         0              153\n",
       "8          64         7                         4              153\n",
       "9          66         7                         6              153"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT * \n",
    "FROM measurement\n",
    "ORDER BY number_of_cells DESC\n",
    "LIMIT 10;\n",
    "\"\"\"\n",
    "\n",
    "query_database(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e71d016-b617-47c2-997f-d8ad9ddeefde",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "Determine the longest `time_after_exposure_in_s` applied in any experient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9d6d5e3-b630-4a7b-9741-f9f9f289cb4f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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