ex12-Bonus from Interactive with Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ex12-Bonus: Interactive with Pandas\n",
    "\n",
    "In the previous notebooks, we praticed basic SQL syntax with sql% (SQL magic) just like using a command line tool. In fact, SQL magic has a nice integration with pandas library. Result from SQL query can be converted to regular pandas data frame via DataFrame call. After retrieving data into a Pandas DataFrame, we can do more Exploratory Data Analysis. However, that is another story beyond the scope of SQL itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load all needed libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Read the table of rch in the demo.db3\n",
    "\n",
    "#### 2.1 Connect database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'Connected: @data/demo.db3'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql sqlite:///data/demo.db3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Save query into a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///data/demo.db3\n",
      "Done.\n",
      "Returning data to local variable sqlres\n"
     ]
    }
   ],
   "source": [
    "%%sql sqlres << \n",
    "SELECT RCH, YR, MO, FLOW_OUTcms \n",
    "FROM rch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Retrieve dataframe from query results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>RCH</th>\n",
       "      <th>YR</th>\n",
       "      <th>MO</th>\n",
       "      <th>FLOW_OUTcms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1981</td>\n",
       "      <td>1</td>\n",
       "      <td>146.252487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1981</td>\n",
       "      <td>1</td>\n",
       "      <td>96.182854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1981</td>\n",
       "      <td>1</td>\n",
       "      <td>11.861368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1981</td>\n",
       "      <td>1</td>\n",
       "      <td>49.406513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1981</td>\n",
       "      <td>1</td>\n",
       "      <td>272.106018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RCH    YR  MO  FLOW_OUTcms\n",
       "0    1  1981   1   146.252487\n",
       "1    2  1981   1    96.182854\n",
       "2    3  1981   1    11.861368\n",
       "3    4  1981   1    49.406513\n",
       "4    5  1981   1   272.106018"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sqlres.DataFrame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Further process with Pandas\n",
    "\n",
    "#### 3.1 Custom quarters definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "quarters = {1: 'DJF', 2: 'DJF', 3: 'MAM', 4: 'MAM', 5: 'MAM', 6: 'JJA',\n",
    "            7: 'JJA', 8: 'JJA', 9: 'SON', 10: 'SON', 11: 'SON', 12: 'DJF'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 Carry out seasonal statistics for each reach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RCH     \n",
       "1    DJF      99.204991\n",
       "     JJA    1405.262298\n",
       "     MAM     559.746932\n",
       "     SON     454.737985\n",
       "2    DJF      56.328539\n",
       "Name: FLOW_OUTcms, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(['MO'])\n",
    "ssndf = df.groupby(['RCH',quarters])['FLOW_OUTcms'].mean()\n",
    "ssndf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reset index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RCH</th>\n",
       "      <th>level_1</th>\n",
       "      <th>FLOW_OUTcms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DJF</td>\n",
       "      <td>99.204991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>JJA</td>\n",
       "      <td>1405.262298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>MAM</td>\n",
       "      <td>559.746932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>SON</td>\n",
       "      <td>454.737985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>DJF</td>\n",
       "      <td>56.328539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RCH level_1  FLOW_OUTcms\n",
       "0    1     DJF    99.204991\n",
       "1    1     JJA  1405.262298\n",
       "2    1     MAM   559.746932\n",
       "3    1     SON   454.737985\n",
       "4    2     DJF    56.328539"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssndf = ssndf.reset_index()\n",
    "ssndf.set_index(['RCH'])\n",
    "ssndf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>RCH</th>\n",
       "      <th>SSN</th>\n",
       "      <th>FLOW_OUTcms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DJF</td>\n",
       "      <td>99.204991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>JJA</td>\n",
       "      <td>1405.262298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>MAM</td>\n",
       "      <td>559.746932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>SON</td>\n",
       "      <td>454.737985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>DJF</td>\n",
       "      <td>56.328539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RCH  SSN  FLOW_OUTcms\n",
       "0    1  DJF    99.204991\n",
       "1    1  JJA  1405.262298\n",
       "2    1  MAM   559.746932\n",
       "3    1  SON   454.737985\n",
       "4    2  DJF    56.328539"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssndf = ssndf.rename(index=str, columns={\"level_1\":\"SSN\"})\n",
    "ssndf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert data into pivoted table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>SSN</th>\n",
       "      <th>DJF</th>\n",
       "      <th>JJA</th>\n",
       "      <th>MAM</th>\n",
       "      <th>SON</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.204991</td>\n",
       "      <td>1405.262298</td>\n",
       "      <td>559.746932</td>\n",
       "      <td>454.737985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56.328539</td>\n",
       "      <td>773.664280</td>\n",
       "      <td>203.242828</td>\n",
       "      <td>297.934326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.838328</td>\n",
       "      <td>166.310382</td>\n",
       "      <td>379.771358</td>\n",
       "      <td>27.425488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.384519</td>\n",
       "      <td>619.184517</td>\n",
       "      <td>278.996801</td>\n",
       "      <td>155.766145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>336.824441</td>\n",
       "      <td>1635.518864</td>\n",
       "      <td>1215.423962</td>\n",
       "      <td>499.265579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "SSN         DJF          JJA          MAM         SON\n",
       "RCH                                                  \n",
       "1     99.204991  1405.262298   559.746932  454.737985\n",
       "2     56.328539   773.664280   203.242828  297.934326\n",
       "3     32.838328   166.310382   379.771358   27.425488\n",
       "4     28.384519   619.184517   278.996801  155.766145\n",
       "5    336.824441  1635.518864  1215.423962  499.265579"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivoted = ssndf.pivot(index='RCH', columns='SSN', values='FLOW_OUTcms')\n",
    "pivoted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Visualize\n",
    "Set some parameters to make figure pretty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc046b70>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot size to 15\" x 7\"\n",
    "matplotlib.rc('figure', figsize = (15, 7))\n",
    "# Font size to 14\n",
    "matplotlib.rc('font', size = 14)\n",
    "# Display top and right frame lines\n",
    "matplotlib.rc('axes.spines', top = True, right = True)\n",
    "# Remove grid lines\n",
    "matplotlib.rc('axes', grid = False)\n",
    "# Set backgound color to white\n",
    "matplotlib.rc('axes', facecolor = 'white')\n",
    "\n",
    "pivoted.plot(kind='bar', title='Seasonal Mean Discharge from 1981 to 2010 ($m^3/s$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "\n",
    "In this tutorial we have seen how easy it is to link a SQLite database operations to Pandas via sql%. Moreover, we use Python tools such as Pandas do more further analysis after retrieving data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}