tutorials/BigQuery/SQL and Pandas DataFrames.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SQL and Pandas DataFrames\n",
    "\n",
    "Google Cloud Datalab allows you to use SQL to interact with Google BigQuery. However, SQL is just the starting point. Cloud Datalab enables you to do is combine the power of declarative SQL with imperative code (Python) to perform interesting data analysis, visualization, and transformation tasks.\n",
    "\n",
    "You can use a number of Python data analysis, data wrangling, and visualization libraries, such as `numpy`, `pandas`, `matplotlib`, and many others. Several of these libraries build on top of a `DataFrame` object.\n",
    "\n",
    "This notebook shows how to easily integrate these capabilites together in a single notebook. This functionality is provided by the BigQuery APIs, so the first step is, of course, to import the module along with pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datalab.bigquery as bq\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# From SQL to DataFrames\n",
    "\n",
    "This is straightforward. Define some SQL, execute it, and get a reference to the results in the form of a `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%sql --module requests\n",
    "SELECT timestamp, latency, endpoint\n",
    "FROM [cloud-datalab-samples:httplogs.logs_20140615]\n",
    "WHERE endpoint = 'Popular' OR endpoint = 'Recent'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    <div class=\"bqtv\" id=\"2_146281557057\"><table><tr><th>timestamp</th><th>latency</th><th>endpoint</th></tr><tr><td>2014-06-15 07:00:01.514197</td><td>22</td><td>Popular</td></tr><tr><td>2014-06-15 07:00:01.700211</td><td>21</td><td>Popular</td></tr><tr><td>2014-06-15 07:00:01.973441</td><td>21</td><td>Popular</td></tr><tr><td>2014-06-15 07:00:02.043231</td><td>21</td><td>Popular</td></tr><tr><td>2014-06-15 07:00:02.288373</td><td>21</td><td>Popular</td></tr></table></div>\n",
       "    <br />(rows: 5, time: 0.3s, cached, job: job_wdaC09ME9PFwl5cMDZctSHg5V90)<br />\n",
       "    <script>\n",
       "      require(['datalab/charting', 'datalab/element!2_146281557057', 'base/js/events',\n",
       "          'datalab/style!/nbextensions/gcpdatalab/charting.css'],\n",
       "        function(charts, dom, events) {\n",
       "          charts.render('gcharts', dom, events, 'table', [], {\"rows\": [{\"c\": [{\"v\": \"2014-06-15T07:00:01.514197\"}, {\"v\": 22}, {\"v\": \"Popular\"}]}, {\"c\": [{\"v\": \"2014-06-15T07:00:01.700211\"}, {\"v\": 21}, {\"v\": \"Popular\"}]}, {\"c\": [{\"v\": \"2014-06-15T07:00:01.973441\"}, {\"v\": 21}, {\"v\": \"Popular\"}]}, {\"c\": [{\"v\": \"2014-06-15T07:00:02.043231\"}, {\"v\": 21}, {\"v\": \"Popular\"}]}, {\"c\": [{\"v\": \"2014-06-15T07:00:02.288373\"}, {\"v\": 21}, {\"v\": \"Popular\"}]}], \"cols\": [{\"type\": \"datetime\", \"id\": \"timestamp\", \"label\": \"timestamp\"}, {\"type\": \"number\", \"id\": \"latency\", \"label\": \"latency\"}, {\"type\": \"string\", \"id\": \"endpoint\", \"label\": \"endpoint\"}]},\n",
       "            {\n",
       "              pageSize: 25,\n",
       "              cssClassNames:  {\n",
       "                tableRow: 'gchart-table-row',\n",
       "                headerRow: 'gchart-table-headerrow',\n",
       "                oddTableRow: 'gchart-table-oddrow',\n",
       "                selectedTableRow: 'gchart-table-selectedrow',\n",
       "                hoverTableRow: 'gchart-table-hoverrow',\n",
       "                tableCell: 'gchart-table-cell',\n",
       "                headerCell: 'gchart-table-headercell',\n",
       "                rowNumberCell: 'gchart-table-rownumcell'\n",
       "              }\n",
       "            },\n",
       "            {source_index: 0, fields: 'timestamp,latency,endpoint'},\n",
       "            0,\n",
       "            5);\n",
       "        }\n",
       "      );\n",
       "    </script>\n",
       "  "
      ],
      "text/plain": [
       "QueryResultsTable job_wdaC09ME9PFwl5cMDZctSHg5V90"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery sample --count 5 --query requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below constructs a BigQuery `Query` instance, executes the query, and converts the results into a `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8392"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = bq.Query(requests).to_dataframe()\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting `DataFrame` can be further transformed, sliced, projected, and inspected using the extensive set of APIs available on the class. Let's print out the first five rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>latency</th>\n",
       "      <th>endpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-06-15 07:00:01.514197</td>\n",
       "      <td>22</td>\n",
       "      <td>Popular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-06-15 07:00:01.700211</td>\n",
       "      <td>21</td>\n",
       "      <td>Popular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-06-15 07:00:01.973441</td>\n",
       "      <td>21</td>\n",
       "      <td>Popular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-06-15 07:00:02.043231</td>\n",
       "      <td>21</td>\n",
       "      <td>Popular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-06-15 07:00:02.288373</td>\n",
       "      <td>21</td>\n",
       "      <td>Popular</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   timestamp  latency endpoint\n",
       "0 2014-06-15 07:00:01.514197       22  Popular\n",
       "1 2014-06-15 07:00:01.700211       21  Popular\n",
       "2 2014-06-15 07:00:01.973441       21  Popular\n",
       "3 2014-06-15 07:00:02.043231       21  Popular\n",
       "4 2014-06-15 07:00:02.288373       21  Popular"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or it can be inspected for schema,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp    datetime64[ns]\n",
       "latency               int64\n",
       "endpoint             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or further transformed locally, for example to perform grouping,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latency</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>endpoint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Popular</th>\n",
       "      <td>int64</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recent</th>\n",
       "      <td>int64</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         latency timestamp\n",
       "endpoint                  \n",
       "Popular    int64       NaT\n",
       "Recent     int64       NaT"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = df.groupby('endpoint')\n",
    "groups.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Popular - 7658 items\n",
      "                   timestamp  latency endpoint\n",
      "0 2014-06-15 07:00:01.514197       22  Popular\n",
      "1 2014-06-15 07:00:01.700211       21  Popular\n",
      "2 2014-06-15 07:00:01.973441       21  Popular\n",
      "\n",
      "Recent - 734 items\n",
      "                    timestamp  latency endpoint\n",
      "15 2014-06-15 07:02:21.453438      427   Recent\n",
      "21 2014-06-15 07:03:48.191783      331   Recent\n",
      "25 2014-06-15 07:04:57.352634      317   Recent\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for name, df_group in groups:\n",
    "  print '%s - %d items' % (name, len(df_group))\n",
    "  print df_group.head(3)\n",
    "  print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then analyze a dimension per group,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "endpoint       \n",
       "Popular   count     7658.000000\n",
       "          mean       274.439018\n",
       "          std        187.050788\n",
       "          min          2.000000\n",
       "          25%        132.000000\n",
       "          50%        313.000000\n",
       "          75%        392.000000\n",
       "          max       6443.000000\n",
       "Recent    count      734.000000\n",
       "          mean       246.268392\n",
       "          std        690.888990\n",
       "          min          2.000000\n",
       "          25%        184.250000\n",
       "          50%        229.000000\n",
       "          75%        288.000000\n",
       "          max      18715.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups['latency'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or even run a set of custom aggregation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "      <th>list</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>endpoint</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Popular</th>\n",
       "      <td>[2, 3, 4, 5, 6, 7, 8, 9, 11, 15, 19, 20, 21, 2...</td>\n",
       "      <td>[22, 21, 21, 21, 21, 316, 402, 405, 126, 351, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recent</th>\n",
       "      <td>[2, 3, 4, 5, 6, 10, 21, 22, 23, 24, 29, 31, 35...</td>\n",
       "      <td>[427, 331, 317, 329, 75, 231, 197, 293, 187, 2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     unique  \\\n",
       "endpoint                                                      \n",
       "Popular   [2, 3, 4, 5, 6, 7, 8, 9, 11, 15, 19, 20, 21, 2...   \n",
       "Recent    [2, 3, 4, 5, 6, 10, 21, 22, 23, 24, 29, 31, 35...   \n",
       "\n",
       "                                                       list  \n",
       "endpoint                                                     \n",
       "Popular   [22, 21, 21, 21, 21, 316, 402, 405, 126, 351, ...  \n",
       "Recent    [427, 331, 317, 329, 75, 231, 197, 293, 187, 2...  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def unique(x):\n",
    "  return sorted(set(list(x)))\n",
    "\n",
    "groups['latency'].agg({ 'list': lambda x: list(x), 'unique': lambda x: unique(x) })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization\n",
    "\n",
    "DataFrame provides built-in visualization capabilities using `matplotlib`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conversion to TimeSeries Data**\n",
    "\n",
    "Our DataFrame instance contains timestamps, latencies, and endpoints. Let's reshape this DataFrame so that each endpoint is a column, that is, an independent series. The timestamp, itself, will be the index row. For timestamps for which one of the endpoints has no data point, we'll use the previous value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>endpoint</th>\n",
       "      <th>Popular</th>\n",
       "      <th>Recent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-06-15 07:00:20.649279</th>\n",
       "      <td>177.0</td>\n",
       "      <td>427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-15 07:00:54.666471</th>\n",
       "      <td>352.0</td>\n",
       "      <td>427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-15 07:01:28.139716</th>\n",
       "      <td>471.0</td>\n",
       "      <td>427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-15 07:01:33.672656</th>\n",
       "      <td>394.0</td>\n",
       "      <td>427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-15 07:01:37.050927</th>\n",
       "      <td>321.0</td>\n",
       "      <td>427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-15 07:02:21.453438</th>\n",
       "      <td>390.0</td>\n",
       "      <td>427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-15 07:02:31.533299</th>\n",
       "      <td>390.0</td>\n",
       "      <td>331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-15 07:02:51.365249</th>\n",
       "      <td>130.0</td>\n",
       "      <td>331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-15 07:02:57.402798</th>\n",
       "      <td>121.0</td>\n",
       "      <td>331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-15 07:02:57.703736</th>\n",
       "      <td>139.0</td>\n",
       "      <td>331.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "endpoint                    Popular  Recent\n",
       "timestamp                                  \n",
       "2014-06-15 07:00:20.649279    177.0   427.0\n",
       "2014-06-15 07:00:54.666471    352.0   427.0\n",
       "2014-06-15 07:01:28.139716    471.0   427.0\n",
       "2014-06-15 07:01:33.672656    394.0   427.0\n",
       "2014-06-15 07:01:37.050927    321.0   427.0\n",
       "2014-06-15 07:02:21.453438    390.0   427.0\n",
       "2014-06-15 07:02:31.533299    390.0   331.0\n",
       "2014-06-15 07:02:51.365249    130.0   331.0\n",
       "2014-06-15 07:02:57.402798    121.0   331.0\n",
       "2014-06-15 07:02:57.703736    139.0   331.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_series = df.pivot(index='timestamp', columns='endpoint', values='latency').fillna(method = 'backfill')\n",
    "df_series[10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8392"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe79b8a7d50>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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fVfKXN0uDfwdm3QHKqtqCa7GxogUCHKmUlw1e2XQSC2YU4L3P1BOUirpOXY/T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\n0P5sTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe79b7378d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_series.plot(logy = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Resampling**\n",
    "\n",
    "The plot above is not meaningful. Our time-series data is taken arbitrary timestamps, whenever a request was processed. It is irregularly spaced, and there are a large number of data points. DataFrames provide the ability to resample a time-series into more meaningful time windows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe79b599e90>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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63e8OZJnJYQBEhwcQHxlIQWUrDqeL4mozBw5qWYTN0EJSTDDtVrunNO327vpi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe79b6d6dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_series.resample(rule='10min').mean().plot(logy = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking Ahead\n",
    "\n",
    "Python pandas provide an extensive toolbox of capabilities for working with data. Cloud Datalab combines the power of BigQuery and DataFrames.\n",
    "\n",
    "Subsequent notebooks cover additional SQL capabilities that you to use the full power of BigQuery, allowing you to run queries close to your entire dataset before bringing a useful subset of data into the notebook."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}