Delayed-Feather.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://dask.readthedocs.io/en/latest/_images/dask_horizontal.svg\"\n",
    "     align=\"right\"\n",
    "     width=\"40%\"\n",
    "     alt=\"Dask logo\">\n",
    "\n",
    "Custom Computations with `dask.delayed` and `for` loops\n",
    "===========\n",
    "\n",
    "*Because the real world is a messy place*\n",
    "\n",
    "This example uses [dask.delayed](http://dask.pydata.org/en/latest/delayed.html) to construct a parallel [dask.dataframe](http://dask.pydata.org/en/latest/dataframe.html) from a nested directory of data stored in a custom format, [feather](https://github.com/wesm/feather).  It is a good example of using dask.delayed to handle messy situations in the real world and then hand those situations off to dask.dataframe for clean processing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Hierarchically stored data in custom format\n",
    "\n",
    "*  Hierarchical storage: Custom directory structure with filenames encoding columns\n",
    "*  Feather: New Dataframe format that came out two weeks ago"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See contents of local directory\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-01-01  2016-01-09\t2016-01-17  2016-01-25\tdask.pdf\r\n",
      "2016-01-02  2016-01-10\t2016-01-18  2016-01-26\tImperative-Feather.ipynb\r\n",
      "2016-01-03  2016-01-11\t2016-01-19  2016-01-27\tmydask.png\r\n",
      "2016-01-04  2016-01-12\t2016-01-20  2016-01-28\twork.py\r\n",
      "2016-01-05  2016-01-13\t2016-01-21  2016-01-29\r\n",
      "2016-01-06  2016-01-14\t2016-01-22  2016-01-30\r\n",
      "2016-01-07  2016-01-15\t2016-01-23  2016-01-31\r\n",
      "2016-01-08  2016-01-16\t2016-01-24  create.py\r\n"
     ]
    }
   ],
   "source": [
    "!ls "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See contents of subdirectory, feather files named by symbol (like stocks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BV  EX\tHXMW  MNX  PL  SKHT  UU  XHV\r\n"
     ]
    }
   ],
   "source": [
    "!ls 2016-01-01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some symbol overlap in other directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BV  EX\tHXMW  MNX  PL  PWU  SKHT  UU  WNT  XHV\r\n"
     ]
    }
   ],
   "source": [
    "!ls 2016-01-02"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Each file is a feather encoded dataframe (with toy data inside)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.351246</td>\n",
       "      <td>-1.692870</td>\n",
       "      <td>-0.397125</td>\n",
       "      <td>0.384644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.423015</td>\n",
       "      <td>-0.745109</td>\n",
       "      <td>1.990852</td>\n",
       "      <td>-0.235062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.008925</td>\n",
       "      <td>0.196380</td>\n",
       "      <td>1.587710</td>\n",
       "      <td>-0.273950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.605530</td>\n",
       "      <td>0.538377</td>\n",
       "      <td>0.159150</td>\n",
       "      <td>0.224017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.647940</td>\n",
       "      <td>0.219201</td>\n",
       "      <td>0.939612</td>\n",
       "      <td>0.829770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.351246 -1.692870 -0.397125  0.384644\n",
       "1 -1.423015 -0.745109  1.990852 -0.235062\n",
       "2 -1.008925  0.196380  1.587710 -0.273950\n",
       "3  0.605530  0.538377  0.159150  0.224017\n",
       "4 -0.647940  0.219201  0.939612  0.829770"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import feather\n",
    "df = feather.read_dataframe('2016-01-01/BV')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We want to encode the filename and directory as column data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>date</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.351246</td>\n",
       "      <td>-1.692870</td>\n",
       "      <td>-0.397125</td>\n",
       "      <td>0.384644</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>BV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.423015</td>\n",
       "      <td>-0.745109</td>\n",
       "      <td>1.990852</td>\n",
       "      <td>-0.235062</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>BV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.008925</td>\n",
       "      <td>0.196380</td>\n",
       "      <td>1.587710</td>\n",
       "      <td>-0.273950</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>BV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.605530</td>\n",
       "      <td>0.538377</td>\n",
       "      <td>0.159150</td>\n",
       "      <td>0.224017</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>BV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.647940</td>\n",
       "      <td>0.219201</td>\n",
       "      <td>0.939612</td>\n",
       "      <td>0.829770</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>BV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D       date symbol\n",
       "0 -0.351246 -1.692870 -0.397125  0.384644 2016-01-01     BV\n",
       "1 -1.423015 -0.745109  1.990852 -0.235062 2016-01-01     BV\n",
       "2 -1.008925  0.196380  1.587710 -0.273950 2016-01-01     BV\n",
       "3  0.605530  0.538377  0.159150  0.224017 2016-01-01     BV\n",
       "4 -0.647940  0.219201  0.939612  0.829770 2016-01-01     BV"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df.assign(date=pd.Timestamp('2016-01-01'), symbol='BV').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### but we have lots of files, so we want to do it in parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[01;34m.\u001b[00m\n",
      "├── \u001b[01;34m2016-01-01\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-02\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-03\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-04\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-05\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── UU\n",
      "│   └── WNT\n",
      "├── \u001b[01;34m2016-01-06\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   └── UU\n",
      "├── \u001b[01;34m2016-01-07\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-08\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-09\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   └── WNT\n",
      "├── \u001b[01;34m2016-01-10\u001b[00m\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-11\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   └── WNT\n",
      "├── \u001b[01;34m2016-01-12\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── PWU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-13\u001b[00m\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── PL\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-14\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-15\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   └── WNT\n",
      "├── \u001b[01;34m2016-01-16\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-17\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-18\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-19\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   └── WNT\n",
      "├── \u001b[01;34m2016-01-20\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── MNX\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-21\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   └── UU\n",
      "├── \u001b[01;34m2016-01-22\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-23\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-24\u001b[00m\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PWU\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-25\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   └── PL\n",
      "├── \u001b[01;34m2016-01-26\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-27\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-28\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── PL\n",
      "│   ├── SKHT\n",
      "│   └── UU\n",
      "├── \u001b[01;34m2016-01-29\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── EX\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── SKHT\n",
      "│   └── UU\n",
      "├── \u001b[01;34m2016-01-30\u001b[00m\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── UU\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── \u001b[01;34m2016-01-31\u001b[00m\n",
      "│   ├── BV\n",
      "│   ├── HXMW\n",
      "│   ├── MNX\n",
      "│   ├── PL\n",
      "│   ├── PWU\n",
      "│   ├── SKHT\n",
      "│   ├── WNT\n",
      "│   └── XHV\n",
      "├── create.py\n",
      "├── dask.pdf\n",
      "├── Imperative-Feather.ipynb\n",
      "├── \u001b[01;35mmydask.png\u001b[00m\n",
      "└── work.py\n",
      "\n",
      "31 directories, 234 files\n"
     ]
    }
   ],
   "source": [
    "!tree ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### but no canned function exists for our use case :("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'module' object has no attribute 'read_nested_directory_of_feather_files'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-63ca6464b225>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataframe\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_nested_directory_of_feather_files\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'module' object has no attribute 'read_nested_directory_of_feather_files'"
     ]
    }
   ],
   "source": [
    "import dask.dataframe as dd\n",
    "\n",
    "dd.read_nested_directory_of_feather_files('.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fortunately we can use `dask.delayed` to create custom parallel computations\n",
    "\n",
    "The `dask.delayed` function turns a normal function call in normal code to instead produce a task in a task graph.  Can create strange computations very easily with generic Python code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from dask import delayed\n",
    "from glob import glob\n",
    "import os\n",
    "\n",
    "lazy_dataframes = []\n",
    "for directory in glob('2016-*'):\n",
    "    for symbol in os.listdir(directory):\n",
    "        filename = os.path.join(directory, symbol)\n",
    "        df = delayed(feather.read_dataframe)(filename)\n",
    "        df = delayed(pd.DataFrame.assign)(df, date=pd.Timestamp(directory),\n",
    "                                              symbol=symbol)\n",
    "        lazy_dataframes.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Value('assign-8ee86514-7230-4734-977d-7effa2499a0f')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lazy_dataframes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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CZKwFMhaAO5w6dapFixb9+vX78ssvja7FeIqixMTEbNiw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\n7FoCSXx8fI8ePXbu3BkVFWV2LQD8FxlrDBkLQA8y1hgyFoB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gsgNGisPKGxAMJUUVHh5ub24MGDuLg4AwMD6jn816tXr8TExHv37rm5ub16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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask import visualize\n",
    "visualize(*lazy_dataframes[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Can manually construct graph further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303.17973331250477"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sums = [df.A.sum() for df in lazy_dataframes]\n",
    "total = delayed(sum)(sums)\n",
    "total.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {

Unable to load the notebook

Unable to load the notebook

click the raw button, copy all content into a new file named xxx.ipynb(opend by a text editor), and launch jupyter notebook. done