{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7 reasons why I love Vaex for data science"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T10:51:04.004565Z",
     "start_time": "2020-02-14T10:51:03.030891Z"
    }
   },
   "outputs": [],
   "source": [
    "import vaex\n",
    "\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "\n",
    "[Vaex](https://github.com/vaexio/vaex) is an open-source DataFrame library for Python with an API that closely resembles that of [Pandas](https://pandas.pydata.org/docs/index.html). I have been using Vaex for several years in both academic and industry environments, and it is my go-to library for several of the data science projects I am working on. In this article I would like to share some of my favourite Vaex features. Some may be obvious by now, but some may surprise you.\n",
    "\n",
    "The following code examples are run on a MacBook Pro (15\", 2018, 2.6GHz Intel Core i7, 32GB RAM)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Easy to work with very large datasets\n",
    "\n",
    "Nowadays, it becomes increasingly more common to encounter datasets that are larger than the available RAM on a typical laptop or a desktop workstation. [Vaex](https://github.com/vaexio/vaex) solves this problem rather elegantly by the use of memory mapping and lazy evaluations. As long as your data is stored in a memory mappable file format such as Apache Arrow or HDF5, Vaex will open it instantly, no matter how large it is, or how much RAM your machine has. In fact, the size of the files Vaex can read are only limited by the amount of free hard-disk space you have. If your data is not in a memory-mappable file format (e.g. CSV, JSON), you can easily convert it by using the rich Pandas I/O in combination with Vaex. [See this guide](https://docs.vaex.io/en/latest/faq.html#I-have-a-massive-CSV-file-which-I-can-not-fit-all-into-memory-at-one-time.-How-do-I-convert-it-to-HDF5?) on how to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T10:51:04.842737Z",
     "start_time": "2020-02-14T10:51:04.718847Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 jovan  staff   107G Jul  3  2019 ../vaex-taxi/data/yellow_taxi_2009_2015_f32.hdf5\r\n"
     ]
    }
   ],
   "source": [
    "# Check the file size on disk\n",
    "!ls -l -h ../vaex-taxi/data/yellow_taxi_2009_2015_f32.hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T10:51:05.825240Z",
     "start_time": "2020-02-14T10:51:05.765977Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                                        </th><th>vendor_id  </th><th>pickup_datetime              </th><th>dropoff_datetime             </th><th>passenger_count  </th><th>payment_type  </th><th>trip_distance     </th><th>pickup_longitude  </th><th>pickup_latitude   </th><th>rate_code  </th><th>store_and_fwd_flag  </th><th>dropoff_longitude  </th><th>dropoff_latitude  </th><th>fare_amount       </th><th>surcharge  </th><th>mta_tax  </th><th>tip_amount        </th><th>tolls_amount  </th><th>total_amount      </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i>            </td><td>VTS        </td><td>2009-01-04 02:52:00.000000000</td><td>2009-01-04 03:02:00.000000000</td><td>1                </td><td>CASH          </td><td>2.630000114440918 </td><td>-73.99195861816406</td><td>40.72156524658203 </td><td>nan        </td><td>nan                 </td><td>-73.99380493164062 </td><td>40.6959228515625  </td><td>8.899999618530273 </td><td>0.5        </td><td>nan      </td><td>0.0               </td><td>0.0           </td><td>9.399999618530273 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1</i>            </td><td>VTS        </td><td>2009-01-04 03:31:00.000000000</td><td>2009-01-04 03:38:00.000000000</td><td>3                </td><td>Credit        </td><td>4.550000190734863 </td><td>-73.98210144042969</td><td>40.736289978027344</td><td>nan        </td><td>nan                 </td><td>-73.95584869384766 </td><td>40.768028259277344</td><td>12.100000381469727</td><td>0.5        </td><td>nan      </td><td>2.0               </td><td>0.0           </td><td>14.600000381469727</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>2</i>            </td><td>VTS        </td><td>2009-01-03 15:43:00.000000000</td><td>2009-01-03 15:57:00.000000000</td><td>5                </td><td>Credit        </td><td>10.350000381469727</td><td>-74.0025863647461 </td><td>40.73974609375    </td><td>nan        </td><td>nan                 </td><td>-73.86997985839844 </td><td>40.770225524902344</td><td>23.700000762939453</td><td>0.0        </td><td>nan      </td><td>4.739999771118164 </td><td>0.0           </td><td>28.440000534057617</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>3</i>            </td><td>DDS        </td><td>2009-01-01 20:52:58.000000000</td><td>2009-01-01 21:14:00.000000000</td><td>1                </td><td>CREDIT        </td><td>5.0               </td><td>-73.9742660522461 </td><td>40.79095458984375 </td><td>nan        </td><td>nan                 </td><td>-73.9965591430664  </td><td>40.731849670410156</td><td>14.899999618530273</td><td>0.5        </td><td>nan      </td><td>3.049999952316284 </td><td>0.0           </td><td>18.450000762939453</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>4</i>            </td><td>DDS        </td><td>2009-01-24 16:18:23.000000000</td><td>2009-01-24 16:24:56.000000000</td><td>1                </td><td>CASH          </td><td>0.4000000059604645</td><td>-74.00157928466797</td><td>40.719383239746094</td><td>nan        </td><td>nan                 </td><td>-74.00837707519531 </td><td>40.7203483581543  </td><td>3.700000047683716 </td><td>0.0        </td><td>nan      </td><td>0.0               </td><td>0.0           </td><td>3.700000047683716 </td></tr>\n",
       "<tr><td>...                                      </td><td>...        </td><td>...                          </td><td>...                          </td><td>...              </td><td>...           </td><td>...               </td><td>...               </td><td>...               </td><td>...        </td><td>...                 </td><td>...                </td><td>...               </td><td>...               </td><td>...        </td><td>...      </td><td>...               </td><td>...           </td><td>...               </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1,173,057,922</i></td><td>VTS        </td><td>2015-12-31 23:59:56.000000000</td><td>2016-01-01 00:08:18.000000000</td><td>5                </td><td>1             </td><td>1.2000000476837158</td><td>-73.99381256103516</td><td>40.72087097167969 </td><td>1.0        </td><td>0.0                 </td><td>-73.98621368408203 </td><td>40.722469329833984</td><td>7.5               </td><td>0.5        </td><td>0.5      </td><td>1.7599999904632568</td><td>0.0           </td><td>10.5600004196167  </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1,173,057,923</i></td><td>CMT        </td><td>2015-12-31 23:59:58.000000000</td><td>2016-01-01 00:05:19.000000000</td><td>2                </td><td>2             </td><td>2.0               </td><td>-73.96527099609375</td><td>40.76028060913086 </td><td>1.0        </td><td>0.0                 </td><td>-73.93951416015625 </td><td>40.75238800048828 </td><td>7.5               </td><td>0.5        </td><td>0.5      </td><td>0.0               </td><td>0.0           </td><td>8.800000190734863 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1,173,057,924</i></td><td>CMT        </td><td>2015-12-31 23:59:59.000000000</td><td>2016-01-01 00:12:55.000000000</td><td>2                </td><td>2             </td><td>3.799999952316284 </td><td>-73.98729705810547</td><td>40.739078521728516</td><td>1.0        </td><td>0.0                 </td><td>-73.9886703491211  </td><td>40.69329833984375 </td><td>13.5              </td><td>0.5        </td><td>0.5      </td><td>0.0               </td><td>0.0           </td><td>14.800000190734863</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1,173,057,925</i></td><td>VTS        </td><td>2015-12-31 23:59:59.000000000</td><td>2016-01-01 00:10:26.000000000</td><td>1                </td><td>2             </td><td>1.9600000381469727</td><td>-73.99755859375   </td><td>40.72569274902344 </td><td>1.0        </td><td>0.0                 </td><td>-74.01712036132812 </td><td>40.705322265625   </td><td>8.5               </td><td>0.5        </td><td>0.5      </td><td>0.0               </td><td>0.0           </td><td>9.800000190734863 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1,173,057,926</i></td><td>VTS        </td><td>2015-12-31 23:59:59.000000000</td><td>2016-01-01 00:21:30.000000000</td><td>1                </td><td>1             </td><td>1.059999942779541 </td><td>-73.9843978881836 </td><td>40.76725769042969 </td><td>1.0        </td><td>0.0                 </td><td>-73.99098205566406 </td><td>40.76057052612305 </td><td>13.5              </td><td>0.5        </td><td>0.5      </td><td>2.9600000381469727</td><td>0.0           </td><td>17.760000228881836</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "#              vendor_id    pickup_datetime                dropoff_datetime               passenger_count    payment_type    trip_distance       pickup_longitude    pickup_latitude     rate_code    store_and_fwd_flag    dropoff_longitude    dropoff_latitude    fare_amount         surcharge    mta_tax    tip_amount          tolls_amount    total_amount\n",
       "0              VTS          2009-01-04 02:52:00.000000000  2009-01-04 03:02:00.000000000  1                  CASH            2.630000114440918   -73.99195861816406  40.72156524658203   nan          nan                   -73.99380493164062   40.6959228515625    8.899999618530273   0.5          nan        0.0                 0.0             9.399999618530273\n",
       "1              VTS          2009-01-04 03:31:00.000000000  2009-01-04 03:38:00.000000000  3                  Credit          4.550000190734863   -73.98210144042969  40.736289978027344  nan          nan                   -73.95584869384766   40.768028259277344  12.100000381469727  0.5          nan        2.0                 0.0             14.600000381469727\n",
       "2              VTS          2009-01-03 15:43:00.000000000  2009-01-03 15:57:00.000000000  5                  Credit          10.350000381469727  -74.0025863647461   40.73974609375      nan          nan                   -73.86997985839844   40.770225524902344  23.700000762939453  0.0          nan        4.739999771118164   0.0             28.440000534057617\n",
       "3              DDS          2009-01-01 20:52:58.000000000  2009-01-01 21:14:00.000000000  1                  CREDIT          5.0                 -73.9742660522461   40.79095458984375   nan          nan                   -73.9965591430664    40.731849670410156  14.899999618530273  0.5          nan        3.049999952316284   0.0             18.450000762939453\n",
       "4              DDS          2009-01-24 16:18:23.000000000  2009-01-24 16:24:56.000000000  1                  CASH            0.4000000059604645  -74.00157928466797  40.719383239746094  nan          nan                   -74.00837707519531   40.7203483581543    3.700000047683716   0.0          nan        0.0                 0.0             3.700000047683716\n",
       "...            ...          ...                            ...                            ...                ...             ...                 ...                 ...                 ...          ...                   ...                  ...                 ...                 ...          ...        ...                 ...             ...\n",
       "1,173,057,922  VTS          2015-12-31 23:59:56.000000000  2016-01-01 00:08:18.000000000  5                  1               1.2000000476837158  -73.99381256103516  40.72087097167969   1.0          0.0                   -73.98621368408203   40.722469329833984  7.5                 0.5          0.5        1.7599999904632568  0.0             10.5600004196167\n",
       "1,173,057,923  CMT          2015-12-31 23:59:58.000000000  2016-01-01 00:05:19.000000000  2                  2               2.0                 -73.96527099609375  40.76028060913086   1.0          0.0                   -73.93951416015625   40.75238800048828   7.5                 0.5          0.5        0.0                 0.0             8.800000190734863\n",
       "1,173,057,924  CMT          2015-12-31 23:59:59.000000000  2016-01-01 00:12:55.000000000  2                  2               3.799999952316284   -73.98729705810547  40.739078521728516  1.0          0.0                   -73.9886703491211    40.69329833984375   13.5                0.5          0.5        0.0                 0.0             14.800000190734863\n",
       "1,173,057,925  VTS          2015-12-31 23:59:59.000000000  2016-01-01 00:10:26.000000000  1                  2               1.9600000381469727  -73.99755859375     40.72569274902344   1.0          0.0                   -74.01712036132812   40.705322265625     8.5                 0.5          0.5        0.0                 0.0             9.800000190734863\n",
       "1,173,057,926  VTS          2015-12-31 23:59:59.000000000  2016-01-01 00:21:30.000000000  1                  1               1.059999942779541   -73.9843978881836   40.76725769042969   1.0          0.0                   -73.99098205566406   40.76057052612305   13.5                0.5          0.5        2.9600000381469727  0.0             17.760000228881836"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Read data from S3\n",
    "# df = vaex.open('s3://vaex/taxi/yellow_taxi_2009_2015_f32.hdf5?anon=true')\n",
    "\n",
    "# Read data from local disk\n",
    "df = vaex.open('../vaex-taxi/data/yellow_taxi_2009_2015_f32.hdf5')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caption: Opening and previewing a 100GB file with Vaex is instant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. No memory copies\n",
    "\n",
    "Vaex has a zero memory copy policy. This means that filtering a DataFrames costs very little memory and does not copy the data. Consider the following example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T10:51:36.106131Z",
     "start_time": "2020-02-14T10:51:27.208332Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                                        </th><th>vendor_id  </th><th>pickup_datetime              </th><th>dropoff_datetime             </th><th>passenger_count  </th><th>payment_type  </th><th>trip_distance     </th><th>pickup_longitude  </th><th>pickup_latitude   </th><th>rate_code  </th><th>store_and_fwd_flag  </th><th>dropoff_longitude  </th><th>dropoff_latitude  </th><th>fare_amount       </th><th>surcharge  </th><th>mta_tax  </th><th>tip_amount        </th><th>tolls_amount  </th><th>total_amount      </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i>            </td><td>VTS        </td><td>2009-01-04 02:52:00.000000000</td><td>2009-01-04 03:02:00.000000000</td><td>1                </td><td>CASH          </td><td>2.630000114440918 </td><td>-73.99195861816406</td><td>40.72156524658203 </td><td>nan        </td><td>nan                 </td><td>-73.99380493164062 </td><td>40.6959228515625  </td><td>8.899999618530273 </td><td>0.5        </td><td>nan      </td><td>0.0               </td><td>0.0           </td><td>9.399999618530273 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1</i>            </td><td>VTS        </td><td>2009-01-04 03:31:00.000000000</td><td>2009-01-04 03:38:00.000000000</td><td>3                </td><td>Credit        </td><td>4.550000190734863 </td><td>-73.98210144042969</td><td>40.736289978027344</td><td>nan        </td><td>nan                 </td><td>-73.95584869384766 </td><td>40.768028259277344</td><td>12.100000381469727</td><td>0.5        </td><td>nan      </td><td>2.0               </td><td>0.0           </td><td>14.600000381469727</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>2</i>            </td><td>DDS        </td><td>2009-01-01 20:52:58.000000000</td><td>2009-01-01 21:14:00.000000000</td><td>1                </td><td>CREDIT        </td><td>5.0               </td><td>-73.9742660522461 </td><td>40.79095458984375 </td><td>nan        </td><td>nan                 </td><td>-73.9965591430664  </td><td>40.731849670410156</td><td>14.899999618530273</td><td>0.5        </td><td>nan      </td><td>3.049999952316284 </td><td>0.0           </td><td>18.450000762939453</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>3</i>            </td><td>DDS        </td><td>2009-01-24 16:18:23.000000000</td><td>2009-01-24 16:24:56.000000000</td><td>1                </td><td>CASH          </td><td>0.4000000059604645</td><td>-74.00157928466797</td><td>40.719383239746094</td><td>nan        </td><td>nan                 </td><td>-74.00837707519531 </td><td>40.7203483581543  </td><td>3.700000047683716 </td><td>0.0        </td><td>nan      </td><td>0.0               </td><td>0.0           </td><td>3.700000047683716 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>4</i>            </td><td>DDS        </td><td>2009-01-16 22:35:59.000000000</td><td>2009-01-16 22:43:35.000000000</td><td>2                </td><td>CASH          </td><td>1.2000000476837158</td><td>-73.98980712890625</td><td>40.73500442504883 </td><td>nan        </td><td>nan                 </td><td>-73.98502349853516 </td><td>40.72449493408203 </td><td>6.099999904632568 </td><td>0.5        </td><td>nan      </td><td>0.0               </td><td>0.0           </td><td>6.599999904632568 </td></tr>\n",
       "<tr><td>...                                      </td><td>...        </td><td>...                          </td><td>...                          </td><td>...              </td><td>...           </td><td>...               </td><td>...               </td><td>...               </td><td>...        </td><td>...                 </td><td>...                </td><td>...               </td><td>...               </td><td>...        </td><td>...      </td><td>...               </td><td>...           </td><td>...               </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1,061,605,165</i></td><td>CMT        </td><td>2015-12-31 23:59:56.000000000</td><td>2016-01-01 00:09:25.000000000</td><td>1                </td><td>1             </td><td>1.0               </td><td>-73.9738998413086 </td><td>40.74289321899414 </td><td>1.0        </td><td>0.0                 </td><td>-73.98957061767578 </td><td>40.75054931640625 </td><td>8.0               </td><td>0.5        </td><td>0.5      </td><td>1.850000023841858 </td><td>0.0           </td><td>11.149999618530273</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1,061,605,166</i></td><td>CMT        </td><td>2015-12-31 23:59:58.000000000</td><td>2016-01-01 00:05:19.000000000</td><td>2                </td><td>2             </td><td>2.0               </td><td>-73.96527099609375</td><td>40.76028060913086 </td><td>1.0        </td><td>0.0                 </td><td>-73.93951416015625 </td><td>40.75238800048828 </td><td>7.5               </td><td>0.5        </td><td>0.5      </td><td>0.0               </td><td>0.0           </td><td>8.800000190734863 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1,061,605,167</i></td><td>CMT        </td><td>2015-12-31 23:59:59.000000000</td><td>2016-01-01 00:12:55.000000000</td><td>2                </td><td>2             </td><td>3.799999952316284 </td><td>-73.98729705810547</td><td>40.739078521728516</td><td>1.0        </td><td>0.0                 </td><td>-73.9886703491211  </td><td>40.69329833984375 </td><td>13.5              </td><td>0.5        </td><td>0.5      </td><td>0.0               </td><td>0.0           </td><td>14.800000190734863</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1,061,605,168</i></td><td>VTS        </td><td>2015-12-31 23:59:59.000000000</td><td>2016-01-01 00:10:26.000000000</td><td>1                </td><td>2             </td><td>1.9600000381469727</td><td>-73.99755859375   </td><td>40.72569274902344 </td><td>1.0        </td><td>0.0                 </td><td>-74.01712036132812 </td><td>40.705322265625   </td><td>8.5               </td><td>0.5        </td><td>0.5      </td><td>0.0               </td><td>0.0           </td><td>9.800000190734863 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1,061,605,169</i></td><td>VTS        </td><td>2015-12-31 23:59:59.000000000</td><td>2016-01-01 00:21:30.000000000</td><td>1                </td><td>1             </td><td>1.059999942779541 </td><td>-73.9843978881836 </td><td>40.76725769042969 </td><td>1.0        </td><td>0.0                 </td><td>-73.99098205566406 </td><td>40.76057052612305 </td><td>13.5              </td><td>0.5        </td><td>0.5      </td><td>2.9600000381469727</td><td>0.0           </td><td>17.760000228881836</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "#              vendor_id    pickup_datetime                dropoff_datetime               passenger_count    payment_type    trip_distance       pickup_longitude    pickup_latitude     rate_code    store_and_fwd_flag    dropoff_longitude    dropoff_latitude    fare_amount         surcharge    mta_tax    tip_amount          tolls_amount    total_amount\n",
       "0              VTS          2009-01-04 02:52:00.000000000  2009-01-04 03:02:00.000000000  1                  CASH            2.630000114440918   -73.99195861816406  40.72156524658203   nan          nan                   -73.99380493164062   40.6959228515625    8.899999618530273   0.5          nan        0.0                 0.0             9.399999618530273\n",
       "1              VTS          2009-01-04 03:31:00.000000000  2009-01-04 03:38:00.000000000  3                  Credit          4.550000190734863   -73.98210144042969  40.736289978027344  nan          nan                   -73.95584869384766   40.768028259277344  12.100000381469727  0.5          nan        2.0                 0.0             14.600000381469727\n",
       "2              DDS          2009-01-01 20:52:58.000000000  2009-01-01 21:14:00.000000000  1                  CREDIT          5.0                 -73.9742660522461   40.79095458984375   nan          nan                   -73.9965591430664    40.731849670410156  14.899999618530273  0.5          nan        3.049999952316284   0.0             18.450000762939453\n",
       "3              DDS          2009-01-24 16:18:23.000000000  2009-01-24 16:24:56.000000000  1                  CASH            0.4000000059604645  -74.00157928466797  40.719383239746094  nan          nan                   -74.00837707519531   40.7203483581543    3.700000047683716   0.0          nan        0.0                 0.0             3.700000047683716\n",
       "4              DDS          2009-01-16 22:35:59.000000000  2009-01-16 22:43:35.000000000  2                  CASH            1.2000000476837158  -73.98980712890625  40.73500442504883   nan          nan                   -73.98502349853516   40.72449493408203   6.099999904632568   0.5          nan        0.0                 0.0             6.599999904632568\n",
       "...            ...          ...                            ...                            ...                ...             ...                 ...                 ...                 ...          ...                   ...                  ...                 ...                 ...          ...        ...                 ...             ...\n",
       "1,061,605,165  CMT          2015-12-31 23:59:56.000000000  2016-01-01 00:09:25.000000000  1                  1               1.0                 -73.9738998413086   40.74289321899414   1.0          0.0                   -73.98957061767578   40.75054931640625   8.0                 0.5          0.5        1.850000023841858   0.0             11.149999618530273\n",
       "1,061,605,166  CMT          2015-12-31 23:59:58.000000000  2016-01-01 00:05:19.000000000  2                  2               2.0                 -73.96527099609375  40.76028060913086   1.0          0.0                   -73.93951416015625   40.75238800048828   7.5                 0.5          0.5        0.0                 0.0             8.800000190734863\n",
       "1,061,605,167  CMT          2015-12-31 23:59:59.000000000  2016-01-01 00:12:55.000000000  2                  2               3.799999952316284   -73.98729705810547  40.739078521728516  1.0          0.0                   -73.9886703491211    40.69329833984375   13.5                0.5          0.5        0.0                 0.0             14.800000190734863\n",
       "1,061,605,168  VTS          2015-12-31 23:59:59.000000000  2016-01-01 00:10:26.000000000  1                  2               1.9600000381469727  -73.99755859375     40.72569274902344   1.0          0.0                   -74.01712036132812   40.705322265625     8.5                 0.5          0.5        0.0                 0.0             9.800000190734863\n",
       "1,061,605,169  VTS          2015-12-31 23:59:59.000000000  2016-01-01 00:21:30.000000000  1                  1               1.059999942779541   -73.9843978881836   40.76725769042969   1.0          0.0                   -73.99098205566406   40.76057052612305   13.5                0.5          0.5        2.9600000381469727  0.0             17.760000228881836"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered = df[(df.passenger_count>0) & (df.passenger_count<5)]\n",
    "df_filtered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caption: Filtering a Vaex DataFrame does not copy the data and takes negligible amount of memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The creation of the `df_filtered` DataFrame takes no extra memory! This is because `df_filtered` is a _shallow_ copy of `df`. When creating filtered DataFrames, Vaex creates a binary mask which is then applied to the original data, without the need to make copies. The memory costs for these kind of filters are low: one needs ~1.2 GB of RAM to filter a 1 billion row DataFrame. This is negligible compared to other \"classical\" tools where one would need 100GB to simply read in the data, and another ~100GB for the filtered DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Virtual columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforming existing columns of a Vaex DataFrame into new ones results in the creation of _virtual columns_. Virtual columns behave just like normal ones, but they take up no memory what so ever. This is because Vaex only remembers the _expression_ the defines them, and does not calculate the values up front. These columns are lazily evaluated only when necessary, keeping memory usage low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T10:51:44.105206Z",
     "start_time": "2020-02-14T10:51:44.094212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                            </th><th style=\"text-align: right;\">  fare_amount</th><th style=\"text-align: right;\">  total_amount</th><th style=\"text-align: right;\">  tip_amount</th><th style=\"text-align: right;\">  tip_percentage</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i></td><td style=\"text-align: right;\">          8.9</td><td style=\"text-align: right;\">          9.4 </td><td style=\"text-align: right;\">        0   </td><td style=\"text-align: right;\">          0     </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1</i></td><td style=\"text-align: right;\">         12.1</td><td style=\"text-align: right;\">         14.6 </td><td style=\"text-align: right;\">        2   </td><td style=\"text-align: right;\">         13.6986</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>2</i></td><td style=\"text-align: right;\">         23.7</td><td style=\"text-align: right;\">         28.44</td><td style=\"text-align: right;\">        4.74</td><td style=\"text-align: right;\">         16.6667</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>3</i></td><td style=\"text-align: right;\">         14.9</td><td style=\"text-align: right;\">         18.45</td><td style=\"text-align: right;\">        3.05</td><td style=\"text-align: right;\">         16.5312</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>4</i></td><td style=\"text-align: right;\">          3.7</td><td style=\"text-align: right;\">          3.7 </td><td style=\"text-align: right;\">        0   </td><td style=\"text-align: right;\">          0     </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "  #    fare_amount    total_amount    tip_amount    tip_percentage\n",
       "  0            8.9            9.4           0               0\n",
       "  1           12.1           14.6           2              13.6986\n",
       "  2           23.7           28.44          4.74           16.6667\n",
       "  3           14.9           18.45          3.05           16.5312\n",
       "  4            3.7            3.7           0               0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tip_percentage'] = df.tip_amount / df.total_amount * 100\n",
    "df[['fare_amount', 'total_amount', 'tip_amount', 'tip_percentage']].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caption: The \"tip_percentage\" column is a virtual column: it take no extra memory and is lazily  evaluated on the fly when needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vaex is _fast_. I mean _seriously fast_. The evaluation of virtual columns is fully parallelized and done with one pass over the data. Column methods such as \"value_counts\", \"groupby\" , \"unique\" and the various string operations are  using fast and efficient algoithms, implemented in C++ under the hood. All of them work in an out-of-core fashion, meaning you can process much more data than you fit into RAM, and use all available cores of your processor. For example, doing a \"value_counts\" operation takes only a second for over 1 billion rows!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T10:51:52.994399Z",
     "start_time": "2020-02-14T10:51:52.273518Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "HBox(children=(FloatProgress(value=0.0, max=1.0), Label(value='In progress...')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1     812321234\n",
       "2     172864560\n",
       "5      81923912\n",
       "3      51435890\n",
       "6      25614703\n",
       "        ...    \n",
       "69            1\n",
       "66            1\n",
       "61            1\n",
       "53            1\n",
       "70            1\n",
       "Length: 62, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.passenger_count.value_counts(progress='widget')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caption: Using Vaex, the \"value_counts\" operation takes ~1s for over _1.1 billion rows!_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Just-In-Time Compilation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. No memory copies\n",
    "\n",
    "Vaex has a zero memory copy policy. This means that filtering DataFrame costs very little memory and does not copy the data. Consider the following example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As long as a virtual column is defined only using [Numpy](https://numpy.org/) or pure Python operations, Vaex can accelerate its evaluation by jitting, or Just-In-Time compilation via [Numba](http://numba.pydata.org/) or [Pythran](https://pythran.readthedocs.io/en/latest/). Vaex also supports acceleration via [CUDA](https://developer.nvidia.com/cuda-zone) if your machine has a CUDA enabled NVIDIA graphics card. This can be quite useful for speeding up the evaluation of rather computationally expensive virtual columns. \n",
    "\n",
    "Consider the example below. I have defined the arc distance between two geographical locations, a calculation that involves quite some algebra and trigonometry. Calculating the mean value will force the execution of this rather computationally expensive virtual column. When the execution is done purely with Numpy, it takes only 30 seconds, which I find impressive given that it is done for over **1.1 billion rows**. Now, when we do the same with the numba pre-compiled expression, we get ~2.5 times faster execution time, at least on my laptop. Unfortunately, I do not have an NVIDIA graphics card so I can not do the same using CUDA at this time. If you do, I'll be very happy if you could try this out and share the results.\n",
    "\n",
    "A small but important bonus: Notice how you do not need to call `.compute` or any such method - Vaex automatically knows when to be lazy and when execute a computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T10:52:44.836057Z",
     "start_time": "2020-02-14T10:52:02.107778Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2c9bc4c513c4b42a76a5dd90bd4df87",
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       "HBox(children=(FloatProgress(value=0.0, max=1.0), Label(value='In progress...')))"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean arc distance comuted with numpy: 12.70720\n",
      "Mean arc distance comuted with numba: 12.70720\n"
     ]
    }
   ],
   "source": [
    "def arc_distance(theta_1, phi_1, theta_2, phi_2):\n",
    "    temp = (np.sin((theta_2-theta_1)/2*np.pi/180)**2\n",
    "           + np.cos(theta_1*np.pi/180)*np.cos(theta_2*np.pi/180) * np.sin((phi_2-phi_1)/2*np.pi/180)**2)\n",
    "    distance = 2 * np.arctan2(np.sqrt(temp), np.sqrt(1-temp))\n",
    "    return distance * 3958.8\n",
    "\n",
    "# Expression to be evaluated with numpy as usual\n",
    "df['arc_distance_numpy'] = arc_distance(df.pickup_longitude, \n",
    "                                        df.pickup_latitude, \n",
    "                                        df.dropoff_longitude, \n",
    "                                        df.dropoff_latitude)\n",
    "\n",
    "# Expression to be pre-compiled with numba, and then executed\n",
    "df['arc_distance_numba'] = arc_distance(df.pickup_longitude, \n",
    "                                        df.pickup_latitude, \n",
    "                                        df.dropoff_longitude, \n",
    "                                        df.dropoff_latitude).jit_numba()\n",
    "\n",
    "# Expression to be pre-compiled with CUDA, and then executed on you GPU \n",
    "# provided you have a CUDA compatible NVIDIA GPU.\n",
    "# df['arc_distance_cuda'] = arc_distance(df.pickup_longitude, \n",
    "#                                        df.pickup_latitude, \n",
    "#                                        df.dropoff_longitude, \n",
    "#                                        df.dropoff_latitude).jit_cuda()\n",
    "\n",
    "# Calculate the mean \n",
    "mean_numpy = df.arc_distance_numpy.mean(progress='widget')\n",
    "mean_numba = df.arc_distance_numba.mean(progress='widget')\n",
    "print(f'Mean arc distance comuted with numpy: {mean_numpy:.5f}')\n",
    "print(f'Mean arc distance comuted with numba: {mean_numba:.5f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caption: Jitting can lead to ~2.5 time faster execution times for a computationally expensive virtual column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Selections\n",
    "\n",
    "Vaex implements a concept called _selections_ which is used to, ah, select the data. This is useful when you want to explore the data by, for example, calculating statistics on different portions of it without making a new reference DataFrame each time. The true power of using selections is that we can calculate a statistic for multiple selections with just one pass over the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T10:53:35.752782Z",
     "start_time": "2020-02-14T10:53:28.969348Z"
    }
   },
   "outputs": [
    {
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    },
    {
     "data": {
      "text/plain": [
       "array([11.21730816, 11.2078196 , 11.26832503])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_n_passengers_lt3 = df.passenger_count < 3\n",
    "select_n_passengers_ge3 = df.passenger_count >= 3\n",
    "\n",
    "df.fare_amount.mean(selection=[None, select_n_passengers_lt3, select_n_passengers_ge3], progress='widget')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caption: You can calculate statistics for multiple selections with one pass over the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be be also useful for making various visualisations. For example, we can use the `.count` method to create a couple of histograms on different selections with just one pass over the data. Quite efficient!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T10:53:46.734642Z",
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "binned_dist_npass_lt3, binned_dist_npass_ge3 = df.count(binby=['total_amount'],\n",
    "                                                        limits=[5, 50],\n",
    "                                                        shape=64,\n",
    "                                                        selection=[select_n_passengers_lt3, \n",
    "                                                                   select_n_passengers_ge3], \n",
    "                                                        progress='widget')\n",
    "\n",
    "xvalues = np.linspace(5, 50, 64)\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.plot(xvalues, binned_dist_npass_lt3, drawstyle=\"steps-pre\", label='num passengers < 3', lw=3)\n",
    "plt.plot(xvalues, binned_dist_npass_ge3, drawstyle=\"steps-pre\", label='num passengers >=3', lw=3)\n",
    "plt.legend(fontsize=14)\n",
    "plt.xlabel('Total amount [$]', fontsize=14)\n",
    "plt.ylabel('Number of trips', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caption: One can create multiple histograms on different selections with just one pass over the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Groupby aggregations with selections\n",
    "\n",
    "One of my favourite features of [Vaex](https://github.com/vaexio/vaex) is the ability to use selections inside aggregation functions. I often find myself wanting to do a group-by operation, in which the aggregations follow some additional rule or filter. The SQL-esque way of doing this would be to run several separate queries in which one would first filter the data and then do the group-by aggregation, and later join the outputs of those aggregations into one table. With Vaex, one can do this with a single operation, and with just one pass over the data! The following group-by example, ran on over 1.1 billion rows takes only 30 seconds to execute on my laptop!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-14T10:55:26.617858Z",
     "start_time": "2020-02-14T10:54:56.586569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                            </th><th>vendor_id  </th><th style=\"text-align: right;\">      count</th><th style=\"text-align: right;\">  count_fare_n_pass_lt3</th><th style=\"text-align: right;\">  count_fare_n_pass_ge3</th><th style=\"text-align: right;\">  mean_fare_n_pass_lt3</th><th style=\"text-align: right;\">  mean_fare_n_pass_ge3</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i></td><td>CMT        </td><td style=\"text-align: right;\">5.71882e+08</td><td style=\"text-align: right;\">            5.37345e+08</td><td style=\"text-align: right;\">            3.4537e+07 </td><td style=\"text-align: right;\">               11.0698</td><td style=\"text-align: right;\">               11.917 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1</i></td><td>DDS        </td><td style=\"text-align: right;\">1.41691e+07</td><td style=\"text-align: right;\">            1.30247e+07</td><td style=\"text-align: right;\">            1.14446e+06</td><td style=\"text-align: right;\">               10.3088</td><td style=\"text-align: right;\">               10.8373</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>2</i></td><td>VTS        </td><td style=\"text-align: right;\">5.87007e+08</td><td style=\"text-align: right;\">            4.38728e+08</td><td style=\"text-align: right;\">            1.48279e+08</td><td style=\"text-align: right;\">               11.4036</td><td style=\"text-align: right;\">               11.1206</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "  #  vendor_id          count    count_fare_n_pass_lt3    count_fare_n_pass_ge3    mean_fare_n_pass_lt3    mean_fare_n_pass_ge3\n",
       "  0  CMT          5.71882e+08              5.37345e+08              3.4537e+07                  11.0698                 11.917\n",
       "  1  DDS          1.41691e+07              1.30247e+07              1.14446e+06                 10.3088                 10.8373\n",
       "  2  VTS          5.87007e+08              4.38728e+08              1.48279e+08                 11.4036                 11.1206"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='vendor_id', agg={'count': vaex.agg.count(),\n",
    "                                'count_fare_n_pass_lt3': vaex.agg.count(selection='passenger_count<3'),\n",
    "                                'count_fare_n_pass_ge3': vaex.agg.count(selection='passenger_count>=3'),\n",
    "                                'mean_fare_n_pass_lt3': vaex.agg.mean('fare_amount', selection='passenger_count<3'),\n",
    "                                'mean_fare_n_pass_ge3': vaex.agg.mean('fare_amount', selection='passenger_count>=3'),\n",
    "                               })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caption: Selections can be used inside groupby aggregation functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus: Progress bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the examples above, you may have noticed many of the methods implemented in Vaex are accompanied by a progress bar. I absolutely love this feature! Since I often use Vaex with larger datasets, knowing how long an operation is likely to take is very handy. Besides, it looks pretty and it makes me feel good as a user :)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "Vaex is a rather powerful DataFrame library, especially when it comes to larger datasets. It has a variety of useful methods for data manipulation and visualization. These are just my top 7 reasons why I like working with this library. What are yours?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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