01-nyc-taxi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://dask.readthedocs.io/en/latest/_images/dask_horizontal.svg\"\n",
    "     align=\"right\"\n",
    "     width=\"30%\"\n",
    "     alt=\"Dask logo\">\n",
    "\n",
    "DataFrames on a Cluster\n",
    "=======================\n",
    "\n",
    "<img src=\"http://www.numfocus.org/uploads/6/0/6/9/60696727/6893890_orig.png\"\n",
    "     align=\"left\"\n",
    "     width=\"30%\"\n",
    "     alt=\"Pandas logo\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read single dataframe from S3 with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from s3fs import S3FileSystem\n",
    "\n",
    "s3 = S3FileSystem(anon=True)\n",
    "s3.ls('dask-data/nyc-taxi/2015/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "with s3.open('dask-data/nyc-taxi/2015/yellow_tripdata_2015-01.csv') as f:\n",
    "    df = pd.read_csv(f, nrows=5, parse_dates=['tpep_pickup_datetime', 'tpep_dropoff_datetime'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parallelize Pandas with Dask.dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from dask.distributed import Client, progress\n",
    "client = Client('127.0.0.1:8786')\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import dask.dataframe as dd\n",
    "\n",
    "df = dd.read_csv('s3://dask-data/nyc-taxi/2015/*.csv', \n",
    "                 parse_dates=['tpep_pickup_datetime', 'tpep_dropoff_datetime'],\n",
    "                 storage_options={'anon': True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = client.persist(df)\n",
    "progress(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Dask DataFrames\n",
    "---------------\n",
    "\n",
    "*  Coordinate many Pandas DataFrames across a cluster\n",
    "*  Faithfully implement a subset of the Pandas API\n",
    "*  Use Pandas under the hood (for speed and maturity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%time len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%time df.passenger_count.sum().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Compute average trip distance grouped by passenger count\n",
    "df.groupby(df.passenger_count).trip_distance.mean().compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tip Fraction, grouped by day-of-week and hour-of-day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = df[(df.tip_amount > 0) & (df.fare_amount > 0)]\n",
    "df2 = df2.assign(tip_fraction=df2.tip_amount / df2.fare_amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Group df.tpep_pickup_datetime by dayofweek and hour\n",
    "dayofweek = df2.groupby(df2.tpep_pickup_datetime.dt.dayofweek).tip_fraction.mean() \n",
    "hour = df2.groupby(df2.tpep_pickup_datetime.dt.hour).tip_fraction.mean()\n",
    "\n",
    "dayofweek, hour = client.persist([dayofweek, hour])\n",
    "progress(dayofweek, hour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, output_notebook, show\n",
    "output_notebook()\n",
    "\n",
    "fig = figure(title='Tip Fraction',\n",
    "             x_axis_label='Hour of day',\n",
    "             y_axis_label='Tip Fraction',\n",
    "             height=300)\n",
    "fig.line(x=hour.index.compute(), y=hour.compute(), line_width=3)\n",
    "fig.y_range.start = 0\n",
    "\n",
    "show(fig)"
   ]
  }
 ],
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02-custom-futures.ipynb

03-TimeSeries.ipynb

04-custom-etl.ipynb

05-image-manipulation.ipynb