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airports.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Working with DataFrames.jl v0.22.1\n",
"\n",
"# Part 1\n",
"\n",
"## Bogumił Kamiński"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this part of the tutorial we will use the *airports2.csv* file that is available for download here: https://www.kaggle.com/flashgordon/usa-airport-dataset. Therefore before starting it please be sure to download it and and have it un-zipped in your working directory.\n",
"\n",
"Our objective is to show selected features of v0.22.1 release of the DataFrames.jl package.\n",
"\n",
"This tutorial is not meant to be a replacement of a documentation - it just shows some practical application examples. If you want to know all the details of the discussed functionalities please refer to docstrings of the functions we show."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As usual in Julia, for every project you should have *Project.toml* and *Manifest.toml* files that specify the dependencies. They are bundled with this file in a GitHub gist.\n",
"\n",
"If you do not have much experience with project dependencies and want to understand more about managing them I recommend you to read https://bkamins.github.io/julialang/2020/05/18/project-workflow.html.\n",
"\n",
"This tutorial was developed under Julia 1.5.3."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we start let us make sure that you have the right versions of packages installed.\n",
"\n",
"The output of the command below should be:\n",
"```\n",
" [6e4b80f9] BenchmarkTools v0.5.0\n",
" [336ed68f] CSV v0.8.0\n",
" [a93c6f00] DataFrames v0.22.1\n",
" [7073ff75] IJulia v1.23.0\n",
" [0f8b85d8] JSON3 v1.5.1\n",
" [b98c9c47] Pipe v1.3.0\n",
" [91a5bcdd] Plots v1.9.1\n",
" ```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32m\u001b[1mStatus\u001b[22m\u001b[39m `~/Downloads/c6b7b536aefda6c2be67f133972a62e8-10e8f32fdf4f6e15dea2d883ffd7311f11934e7e/Project.toml`\n",
" \u001b[90m [6e4b80f9] \u001b[39m\u001b[37mBenchmarkTools v0.5.0\u001b[39m\n",
" \u001b[90m [336ed68f] \u001b[39m\u001b[37mCSV v0.8.0\u001b[39m\n",
" \u001b[90m [a93c6f00] \u001b[39m\u001b[37mDataFrames v0.22.1\u001b[39m\n",
" \u001b[90m [7073ff75] \u001b[39m\u001b[37mIJulia v1.23.0\u001b[39m\n",
" \u001b[90m [0f8b85d8] \u001b[39m\u001b[37mJSON3 v1.5.1\u001b[39m\n",
" \u001b[90m [b98c9c47] \u001b[39m\u001b[37mPipe v1.3.0\u001b[39m\n",
" \u001b[90m [91a5bcdd] \u001b[39m\u001b[37mPlots v1.9.1\u001b[39m\n"
]
}
],
"source": [
"] status"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start with loading the required packages and reading in the CSV file to a `DataFrame`:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"using BenchmarkTools\n",
"using CSV\n",
"using DataFrames\n",
"using Dates\n",
"using Pipe\n",
"using Plots"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>Origin_airport</th><th>Destination_airport</th><th>Origin_city</th><th>Destination_city</th><th>Passengers</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th><th>Int64</th></tr></thead><tbody><p>3,606,803 rows × 15 columns (omitted printing of 10 columns)</p><tr><th>1</th><td>MHK</td><td>AMW</td><td>Manhattan, KS</td><td>Ames, IA</td><td>21</td></tr><tr><th>2</th><td>EUG</td><td>RDM</td><td>Eugene, OR</td><td>Bend, OR</td><td>41</td></tr><tr><th>3</th><td>EUG</td><td>RDM</td><td>Eugene, OR</td><td>Bend, OR</td><td>88</td></tr><tr><th>4</th><td>EUG</td><td>RDM</td><td>Eugene, OR</td><td>Bend, OR</td><td>11</td></tr><tr><th>5</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>0</td></tr><tr><th>6</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>11</td></tr><tr><th>7</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>2</td></tr><tr><th>8</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>7</td></tr><tr><th>9</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>7</td></tr><tr><th>10</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>8</td></tr><tr><th>11</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>453</td></tr><tr><th>12</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>784</td></tr><tr><th>13</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>749</td></tr><tr><th>14</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>11</td></tr><tr><th>15</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>349</td></tr><tr><th>16</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1376</td></tr><tr><th>17</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>444</td></tr><tr><th>18</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1949</td></tr><tr><th>19</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>381</td></tr><tr><th>20</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1559</td></tr><tr><th>21</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1852</td></tr><tr><th>22</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>483</td></tr><tr><th>23</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1965</td></tr><tr><th>24</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>494</td></tr><tr><th>25</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>459</td></tr><tr><th>26</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>553</td></tr><tr><th>27</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1887</td></tr><tr><th>28</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>773</td></tr><tr><th>29</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1800</td></tr><tr><th>30</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1173</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccccc}\n",
"\t& Origin\\_airport & Destination\\_airport & Origin\\_city & Destination\\_city & Passengers & \\\\\n",
"\t\\hline\n",
"\t& String & String & String & String & Int64 & \\\\\n",
"\t\\hline\n",
"\t1 & MHK & AMW & Manhattan, KS & Ames, IA & 21 & $\\dots$ \\\\\n",
"\t2 & EUG & RDM & Eugene, OR & Bend, OR & 41 & $\\dots$ \\\\\n",
"\t3 & EUG & RDM & Eugene, OR & Bend, OR & 88 & $\\dots$ \\\\\n",
"\t4 & EUG & RDM & Eugene, OR & Bend, OR & 11 & $\\dots$ \\\\\n",
"\t5 & MFR & RDM & Medford, OR & Bend, OR & 0 & $\\dots$ \\\\\n",
"\t6 & MFR & RDM & Medford, OR & Bend, OR & 11 & $\\dots$ \\\\\n",
"\t7 & MFR & RDM & Medford, OR & Bend, OR & 2 & $\\dots$ \\\\\n",
"\t8 & MFR & RDM & Medford, OR & Bend, OR & 7 & $\\dots$ \\\\\n",
"\t9 & MFR & RDM & Medford, OR & Bend, OR & 7 & $\\dots$ \\\\\n",
"\t10 & SEA & RDM & Seattle, WA & Bend, OR & 8 & $\\dots$ \\\\\n",
"\t11 & SEA & RDM & Seattle, WA & Bend, OR & 453 & $\\dots$ \\\\\n",
"\t12 & SEA & RDM & Seattle, WA & Bend, OR & 784 & $\\dots$ \\\\\n",
"\t13 & SEA & RDM & Seattle, WA & Bend, OR & 749 & $\\dots$ \\\\\n",
"\t14 & SEA & RDM & Seattle, WA & Bend, OR & 11 & $\\dots$ \\\\\n",
"\t15 & PDX & RDM & Portland, OR & Bend, OR & 349 & $\\dots$ \\\\\n",
"\t16 & PDX & RDM & Portland, OR & Bend, OR & 1376 & $\\dots$ \\\\\n",
"\t17 & PDX & RDM & Portland, OR & Bend, OR & 444 & $\\dots$ \\\\\n",
"\t18 & PDX & RDM & Portland, OR & Bend, OR & 1949 & $\\dots$ \\\\\n",
"\t19 & PDX & RDM & Portland, OR & Bend, OR & 381 & $\\dots$ \\\\\n",
"\t20 & PDX & RDM & Portland, OR & Bend, OR & 1559 & $\\dots$ \\\\\n",
"\t21 & PDX & RDM & Portland, OR & Bend, OR & 1852 & $\\dots$ \\\\\n",
"\t22 & PDX & RDM & Portland, OR & Bend, OR & 483 & $\\dots$ \\\\\n",
"\t23 & PDX & RDM & Portland, OR & Bend, OR & 1965 & $\\dots$ \\\\\n",
"\t24 & PDX & RDM & Portland, OR & Bend, OR & 494 & $\\dots$ \\\\\n",
"\t25 & PDX & RDM & Portland, OR & Bend, OR & 459 & $\\dots$ \\\\\n",
"\t26 & PDX & RDM & Portland, OR & Bend, OR & 553 & $\\dots$ \\\\\n",
"\t27 & PDX & RDM & Portland, OR & Bend, OR & 1887 & $\\dots$ \\\\\n",
"\t28 & PDX & RDM & Portland, OR & Bend, OR & 773 & $\\dots$ \\\\\n",
"\t29 & PDX & RDM & Portland, OR & Bend, OR & 1800 & $\\dots$ \\\\\n",
"\t30 & PDX & RDM & Portland, OR & Bend, OR & 1173 & $\\dots$ \\\\\n",
"\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m3606803×15 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m Origin_airport \u001b[0m\u001b[1m Destination_airport \u001b[0m\u001b[1m Origin_city \u001b[0m\u001b[1m Destinatio\u001b[0m ⋯\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m ⋯\n",
"─────────┼──────────────────────────────────────────────────────────────────────\n",
" 1 │ MHK AMW Manhattan, KS Ames, IA ⋯\n",
" 2 │ EUG RDM Eugene, OR Bend, OR\n",
" 3 │ EUG RDM Eugene, OR Bend, OR\n",
" 4 │ EUG RDM Eugene, OR Bend, OR\n",
" 5 │ MFR RDM Medford, OR Bend, OR ⋯\n",
" 6 │ MFR RDM Medford, OR Bend, OR\n",
" 7 │ MFR RDM Medford, OR Bend, OR\n",
" 8 │ MFR RDM Medford, OR Bend, OR\n",
" 9 │ MFR RDM Medford, OR Bend, OR ⋯\n",
" 10 │ SEA RDM Seattle, WA Bend, OR\n",
" 11 │ SEA RDM Seattle, WA Bend, OR\n",
" ⋮ │ ⋮ ⋮ ⋮ ⋱\n",
" 3606794 │ STL TBN St. Louis, MO Fort Leona\n",
" 3606795 │ STL TBN St. Louis, MO Fort Leona ⋯\n",
" 3606796 │ STL TBN St. Louis, MO Fort Leona\n",
" 3606797 │ STL TBN St. Louis, MO Fort Leona\n",
" 3606798 │ STL TBN St. Louis, MO Fort Leona\n",
" 3606799 │ STL TBN St. Louis, MO Fort Leona ⋯\n",
" 3606800 │ STL TBN St. Louis, MO Fort Leona\n",
" 3606801 │ STL TBN St. Louis, MO Fort Leona\n",
" 3606802 │ CGI TBN Cape Girardeau, MO Fort Leona\n",
" 3606803 │ FWA OH1 Fort Wayne, IN Washington ⋯\n",
"\u001b[31m 12 columns and 3606782 rows omitted\u001b[0m"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = CSV.File(\"Airports2.csv\") |> DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we move on let us change the display settings to allow for printing wider outupt and reduce the numbe of lines printed."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ENV[\"LINES\"] = 20 # lines are counted in number of rows printed"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1000"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ENV[\"COLUMNS\"] = 1000 # columns are counted in characters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After these changes Jupyter Notebook shoud show us all columns of the data frame we have just loaded. Let us check it."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>Origin_airport</th><th>Destination_airport</th><th>Origin_city</th><th>Destination_city</th><th>Passengers</th><th>Seats</th><th>Flights</th><th>Distance</th><th>Fly_date</th><th>Origin_population</th><th>Destination_population</th><th>Org_airport_lat</th><th>Org_airport_long</th><th>Dest_airport_lat</th><th>Dest_airport_long</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Date</th><th>Int64</th><th>Int64</th><th>String</th><th>String</th><th>String</th><th>String</th></tr></thead><tbody><p>3,606,803 rows × 15 columns</p><tr><th>1</th><td>MHK</td><td>AMW</td><td>Manhattan, KS</td><td>Ames, IA</td><td>21</td><td>30</td><td>1</td><td>254</td><td>2008-10-01</td><td>122049</td><td>86219</td><td>39.140998840332</td><td>-96.6707992553711</td><td>NA</td><td>NA</td></tr><tr><th>2</th><td>EUG</td><td>RDM</td><td>Eugene, OR</td><td>Bend, OR</td><td>41</td><td>396</td><td>22</td><td>103</td><td>1990-11-01</td><td>284093</td><td>76034</td><td>44.1245994567871</td><td>-123.21199798584</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>3</th><td>EUG</td><td>RDM</td><td>Eugene, OR</td><td>Bend, OR</td><td>88</td><td>342</td><td>19</td><td>103</td><td>1990-12-01</td><td>284093</td><td>76034</td><td>44.1245994567871</td><td>-123.21199798584</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>4</th><td>EUG</td><td>RDM</td><td>Eugene, OR</td><td>Bend, OR</td><td>11</td><td>72</td><td>4</td><td>103</td><td>1990-10-01</td><td>284093</td><td>76034</td><td>44.1245994567871</td><td>-123.21199798584</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>5</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>0</td><td>18</td><td>1</td><td>156</td><td>1990-02-01</td><td>147300</td><td>76034</td><td>42.3741989135742</td><td>-122.873001098633</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>6</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>11</td><td>18</td><td>1</td><td>156</td><td>1990-03-01</td><td>147300</td><td>76034</td><td>42.3741989135742</td><td>-122.873001098633</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>7</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>2</td><td>72</td><td>4</td><td>156</td><td>1990-01-01</td><td>147300</td><td>76034</td><td>42.3741989135742</td><td>-122.873001098633</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>8</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>7</td><td>18</td><td>1</td><td>156</td><td>1990-09-01</td><td>147300</td><td>76034</td><td>42.3741989135742</td><td>-122.873001098633</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>9</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>7</td><td>36</td><td>2</td><td>156</td><td>1990-11-01</td><td>147300</td><td>76034</td><td>42.3741989135742</td><td>-122.873001098633</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>10</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>8</td><td>18</td><td>1</td><td>228</td><td>1990-02-01</td><td>5154164</td><td>76034</td><td>47.4490013122559</td><td>-122.30899810791</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>11</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>453</td><td>3128</td><td>23</td><td>228</td><td>1990-01-01</td><td>5154164</td><td>76034</td><td>47.4490013122559</td><td>-122.30899810791</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>12</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>784</td><td>2720</td><td>20</td><td>228</td><td>1990-02-01</td><td>5154164</td><td>76034</td><td>47.4490013122559</td><td>-122.30899810791</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>13</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>749</td><td>2992</td><td>22</td><td>228</td><td>1990-03-01</td><td>5154164</td><td>76034</td><td>47.4490013122559</td><td>-122.30899810791</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>14</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>11</td><td>18</td><td>1</td><td>228</td><td>1990-04-01</td><td>5154164</td><td>76034</td><td>47.4490013122559</td><td>-122.30899810791</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>15</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>349</td><td>851</td><td>23</td><td>116</td><td>1990-01-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>16</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1376</td><td>2898</td><td>161</td><td>116</td><td>1990-01-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>17</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>444</td><td>1110</td><td>30</td><td>116</td><td>1990-10-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>18</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1949</td><td>3261</td><td>187</td><td>116</td><td>1990-06-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>19</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>381</td><td>814</td><td>22</td><td>116</td><td>1990-02-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>20</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1559</td><td>2772</td><td>154</td><td>116</td><td>1990-02-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|ccccccccccccccc}\n",
"\t& Origin\\_airport & Destination\\_airport & Origin\\_city & Destination\\_city & Passengers & Seats & Flights & Distance & Fly\\_date & Origin\\_population & Destination\\_population & Org\\_airport\\_lat & Org\\_airport\\_long & Dest\\_airport\\_lat & Dest\\_airport\\_long\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String & Int64 & Int64 & Int64 & Int64 & Date & Int64 & Int64 & String & String & String & String\\\\\n",
"\t\\hline\n",
"\t1 & MHK & AMW & Manhattan, KS & Ames, IA & 21 & 30 & 1 & 254 & 2008-10-01 & 122049 & 86219 & 39.140998840332 & -96.6707992553711 & NA & NA \\\\\n",
"\t2 & EUG & RDM & Eugene, OR & Bend, OR & 41 & 396 & 22 & 103 & 1990-11-01 & 284093 & 76034 & 44.1245994567871 & -123.21199798584 & 44.2541008 & -121.1500015 \\\\\n",
"\t3 & EUG & RDM & Eugene, OR & Bend, OR & 88 & 342 & 19 & 103 & 1990-12-01 & 284093 & 76034 & 44.1245994567871 & -123.21199798584 & 44.2541008 & -121.1500015 \\\\\n",
"\t4 & EUG & RDM & Eugene, OR & Bend, OR & 11 & 72 & 4 & 103 & 1990-10-01 & 284093 & 76034 & 44.1245994567871 & -123.21199798584 & 44.2541008 & -121.1500015 \\\\\n",
"\t5 & MFR & RDM & Medford, OR & Bend, OR & 0 & 18 & 1 & 156 & 1990-02-01 & 147300 & 76034 & 42.3741989135742 & -122.873001098633 & 44.2541008 & -121.1500015 \\\\\n",
"\t6 & MFR & RDM & Medford, OR & Bend, OR & 11 & 18 & 1 & 156 & 1990-03-01 & 147300 & 76034 & 42.3741989135742 & -122.873001098633 & 44.2541008 & -121.1500015 \\\\\n",
"\t7 & MFR & RDM & Medford, OR & Bend, OR & 2 & 72 & 4 & 156 & 1990-01-01 & 147300 & 76034 & 42.3741989135742 & -122.873001098633 & 44.2541008 & -121.1500015 \\\\\n",
"\t8 & MFR & RDM & Medford, OR & Bend, OR & 7 & 18 & 1 & 156 & 1990-09-01 & 147300 & 76034 & 42.3741989135742 & -122.873001098633 & 44.2541008 & -121.1500015 \\\\\n",
"\t9 & MFR & RDM & Medford, OR & Bend, OR & 7 & 36 & 2 & 156 & 1990-11-01 & 147300 & 76034 & 42.3741989135742 & -122.873001098633 & 44.2541008 & -121.1500015 \\\\\n",
"\t10 & SEA & RDM & Seattle, WA & Bend, OR & 8 & 18 & 1 & 228 & 1990-02-01 & 5154164 & 76034 & 47.4490013122559 & -122.30899810791 & 44.2541008 & -121.1500015 \\\\\n",
"\t11 & SEA & RDM & Seattle, WA & Bend, OR & 453 & 3128 & 23 & 228 & 1990-01-01 & 5154164 & 76034 & 47.4490013122559 & -122.30899810791 & 44.2541008 & -121.1500015 \\\\\n",
"\t12 & SEA & RDM & Seattle, WA & Bend, OR & 784 & 2720 & 20 & 228 & 1990-02-01 & 5154164 & 76034 & 47.4490013122559 & -122.30899810791 & 44.2541008 & -121.1500015 \\\\\n",
"\t13 & SEA & RDM & Seattle, WA & Bend, OR & 749 & 2992 & 22 & 228 & 1990-03-01 & 5154164 & 76034 & 47.4490013122559 & -122.30899810791 & 44.2541008 & -121.1500015 \\\\\n",
"\t14 & SEA & RDM & Seattle, WA & Bend, OR & 11 & 18 & 1 & 228 & 1990-04-01 & 5154164 & 76034 & 47.4490013122559 & -122.30899810791 & 44.2541008 & -121.1500015 \\\\\n",
"\t15 & PDX & RDM & Portland, OR & Bend, OR & 349 & 851 & 23 & 116 & 1990-01-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 \\\\\n",
"\t16 & PDX & RDM & Portland, OR & Bend, OR & 1376 & 2898 & 161 & 116 & 1990-01-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 \\\\\n",
"\t17 & PDX & RDM & Portland, OR & Bend, OR & 444 & 1110 & 30 & 116 & 1990-10-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 \\\\\n",
"\t18 & PDX & RDM & Portland, OR & Bend, OR & 1949 & 3261 & 187 & 116 & 1990-06-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 \\\\\n",
"\t19 & PDX & RDM & Portland, OR & Bend, OR & 381 & 814 & 22 & 116 & 1990-02-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 \\\\\n",
"\t20 & PDX & RDM & Portland, OR & Bend, OR & 1559 & 2772 & 154 & 116 & 1990-02-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 \\\\\n",
"\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m3606803×15 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m Origin_airport \u001b[0m\u001b[1m Destination_airport \u001b[0m\u001b[1m Origin_city \u001b[0m\u001b[1m Destination_city \u001b[0m\u001b[1m Passengers \u001b[0m\u001b[1m Seats \u001b[0m\u001b[1m Flights \u001b[0m\u001b[1m Distance \u001b[0m\u001b[1m Fly_date \u001b[0m\u001b[1m Origin_population \u001b[0m\u001b[1m Destination_population \u001b[0m\u001b[1m Org_airport_lat \u001b[0m\u001b[1m Org_airport_long \u001b[0m\u001b[1m Dest_airport_lat \u001b[0m\u001b[1m Dest_airport_long \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Date \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\n",
"─────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
" 1 │ MHK AMW Manhattan, KS Ames, IA 21 30 1 254 2008-10-01 122049 86219 39.140998840332 -96.6707992553711 NA NA\n",
" 2 │ EUG RDM Eugene, OR Bend, OR 41 396 22 103 1990-11-01 284093 76034 44.1245994567871 -123.21199798584 44.2541008 -121.1500015\n",
" 3 │ EUG RDM Eugene, OR Bend, OR 88 342 19 103 1990-12-01 284093 76034 44.1245994567871 -123.21199798584 44.2541008 -121.1500015\n",
" 4 │ EUG RDM Eugene, OR Bend, OR 11 72 4 103 1990-10-01 284093 76034 44.1245994567871 -123.21199798584 44.2541008 -121.1500015\n",
" 5 │ MFR RDM Medford, OR Bend, OR 0 18 1 156 1990-02-01 147300 76034 42.3741989135742 -122.873001098633 44.2541008 -121.1500015\n",
" 6 │ MFR RDM Medford, OR Bend, OR 11 18 1 156 1990-03-01 147300 76034 42.3741989135742 -122.873001098633 44.2541008 -121.1500015\n",
" ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮\n",
" 3606799 │ STL TBN St. Louis, MO Fort Leonard Wood, MO 281 969 51 119 2009-02-01 2828990 46457 38.7486991882324 -90.370002746582 37.74160004 -92.14070129\n",
" 3606800 │ STL TBN St. Louis, MO Fort Leonard Wood, MO 245 1026 54 119 2009-11-01 2828990 46457 38.7486991882324 -90.370002746582 37.74160004 -92.14070129\n",
" 3606801 │ STL TBN St. Louis, MO Fort Leonard Wood, MO 363 1273 67 119 2009-08-01 2828990 46457 38.7486991882324 -90.370002746582 37.74160004 -92.14070129\n",
" 3606802 │ CGI TBN Cape Girardeau, MO Fort Leonard Wood, MO 2 19 1 146 2009-08-01 93712 46457 37.2252998352051 -89.57080078125 37.74160004 -92.14070129\n",
" 3606803 │ FWA OH1 Fort Wayne, IN Washington Court House, OH 0 0 1 135 2003-09-01 398574 28133 40.97850037 -85.19509888 NA NA\n",
"\u001b[31m 3606792 rows omitted\u001b[0m"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our data frame contains the following columns:\n",
"\n",
"1. `:Origin_airport`: Three letter airport code of the origin airport\n",
"2. `:Destination_airport`: Three letter airport code of the destination airport\n",
"3. `:Origin_city`: Origin city name\n",
"4. `:Destination_city`: Destination city name\n",
"5. `:Passengers`: Number of passengers transported from origin to destination\n",
"6. `:Seats`: Number of seats available on flights from origin to destination\n",
"7. `:Flights`: Number of flights between origin and destination (multiple records for one month, many with flights > 1)\n",
"8. `:Distance`: Distance (to nearest mile) flown between origin and destination\n",
"9. `:Fly_date`: The date (yyyymm) of flight\n",
"10. `:Origin_population`: Origin city's population as reported by US Census\n",
"11. `:Destination_population`: Destination city's population as reported by US Census\n",
"\n",
"Let us first investigate its metadata"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3606803, 15)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"size(df)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3606803"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nrow(df)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ncol(df)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15-element Array{String,1}:\n",
" \"Origin_airport\"\n",
" \"Destination_airport\"\n",
" \"Origin_city\"\n",
" \"Destination_city\"\n",
" \"Passengers\"\n",
" \"Seats\"\n",
" \"Flights\"\n",
" \"Distance\"\n",
" \"Fly_date\"\n",
" \"Origin_population\"\n",
" \"Destination_population\"\n",
" \"Org_airport_lat\"\n",
" \"Org_airport_long\"\n",
" \"Dest_airport_lat\"\n",
" \"Dest_airport_long\""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"names(df)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15-element Array{Symbol,1}:\n",
" :Origin_airport\n",
" :Destination_airport\n",
" :Origin_city\n",
" :Destination_city\n",
" :Passengers\n",
" :Seats\n",
" :Flights\n",
" :Distance\n",
" :Fly_date\n",
" :Origin_population\n",
" :Destination_population\n",
" :Org_airport_lat\n",
" :Org_airport_long\n",
" :Dest_airport_lat\n",
" :Dest_airport_long"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"propertynames(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An important thing to note is that the `names` function returns a vector of `String`s and the `propertynames` function returns a vector of `Symbol`s.\n",
"\n",
"In DataFrames.jl both strings and `Symbol`s can be used for column indexing."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us now get some summary statistics of our data set using the `describe` function:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>variable</th><th>mean</th><th>min</th><th>median</th><th>max</th><th>nmissing</th><th>eltype</th></tr><tr><th></th><th>Symbol</th><th>Union…</th><th>Any</th><th>Union…</th><th>Any</th><th>Int64</th><th>DataType</th></tr></thead><tbody><p>15 rows × 7 columns</p><tr><th>1</th><td>Origin_airport</td><td></td><td>1B1</td><td></td><td>ZZV</td><td>0</td><td>String</td></tr><tr><th>2</th><td>Destination_airport</td><td></td><td>1B1</td><td></td><td>ZZV</td><td>0</td><td>String</td></tr><tr><th>3</th><td>Origin_city</td><td></td><td>Aberdeen, SD</td><td></td><td>Zanesville, OH</td><td>0</td><td>String</td></tr><tr><th>4</th><td>Destination_city</td><td></td><td>Aberdeen, SD</td><td></td><td>Zanesville, OH</td><td>0</td><td>String</td></tr><tr><th>5</th><td>Passengers</td><td>2688.91</td><td>0</td><td>1118.0</td><td>89597</td><td>0</td><td>Int64</td></tr><tr><th>6</th><td>Seats</td><td>4048.3</td><td>0</td><td>1998.0</td><td>147062</td><td>0</td><td>Int64</td></tr><tr><th>7</th><td>Flights</td><td>37.2289</td><td>0</td><td>25.0</td><td>1128</td><td>0</td><td>Int64</td></tr><tr><th>8</th><td>Distance</td><td>697.319</td><td>0</td><td>519.0</td><td>5095</td><td>0</td><td>Int64</td></tr><tr><th>9</th><td>Fly_date</td><td></td><td>1990-01-01</td><td></td><td>2009-12-01</td><td>0</td><td>Date</td></tr><tr><th>10</th><td>Origin_population</td><td>5.8715e6</td><td>13005</td><td>2.40019e6</td><td>38139592</td><td>0</td><td>Int64</td></tr><tr><th>11</th><td>Destination_population</td><td>5.89798e6</td><td>12887</td><td>2.40019e6</td><td>38139592</td><td>0</td><td>Int64</td></tr><tr><th>12</th><td>Org_airport_lat</td><td></td><td>19.721399307251</td><td></td><td>NA</td><td>0</td><td>String</td></tr><tr><th>13</th><td>Org_airport_long</td><td></td><td>-100.2860031</td><td></td><td>NA</td><td>0</td><td>String</td></tr><tr><th>14</th><td>Dest_airport_lat</td><td></td><td>19.721399307251</td><td></td><td>NA</td><td>0</td><td>String</td></tr><tr><th>15</th><td>Dest_airport_long</td><td></td><td>-100.2860031</td><td></td><td>NA</td><td>0</td><td>String</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|ccccccc}\n",
"\t& variable & mean & min & median & max & nmissing & eltype\\\\\n",
"\t\\hline\n",
"\t& Symbol & Union… & Any & Union… & Any & Int64 & DataType\\\\\n",
"\t\\hline\n",
"\t1 & Origin\\_airport & & 1B1 & & ZZV & 0 & String \\\\\n",
"\t2 & Destination\\_airport & & 1B1 & & ZZV & 0 & String \\\\\n",
"\t3 & Origin\\_city & & Aberdeen, SD & & Zanesville, OH & 0 & String \\\\\n",
"\t4 & Destination\\_city & & Aberdeen, SD & & Zanesville, OH & 0 & String \\\\\n",
"\t5 & Passengers & 2688.91 & 0 & 1118.0 & 89597 & 0 & Int64 \\\\\n",
"\t6 & Seats & 4048.3 & 0 & 1998.0 & 147062 & 0 & Int64 \\\\\n",
"\t7 & Flights & 37.2289 & 0 & 25.0 & 1128 & 0 & Int64 \\\\\n",
"\t8 & Distance & 697.319 & 0 & 519.0 & 5095 & 0 & Int64 \\\\\n",
"\t9 & Fly\\_date & & 1990-01-01 & & 2009-12-01 & 0 & Date \\\\\n",
"\t10 & Origin\\_population & 5.8715e6 & 13005 & 2.40019e6 & 38139592 & 0 & Int64 \\\\\n",
"\t11 & Destination\\_population & 5.89798e6 & 12887 & 2.40019e6 & 38139592 & 0 & Int64 \\\\\n",
"\t12 & Org\\_airport\\_lat & & 19.721399307251 & & NA & 0 & String \\\\\n",
"\t13 & Org\\_airport\\_long & & -100.2860031 & & NA & 0 & String \\\\\n",
"\t14 & Dest\\_airport\\_lat & & 19.721399307251 & & NA & 0 & String \\\\\n",
"\t15 & Dest\\_airport\\_long & & -100.2860031 & & NA & 0 & String \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m15×7 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m variable \u001b[0m\u001b[1m mean \u001b[0m\u001b[1m min \u001b[0m\u001b[1m median \u001b[0m\u001b[1m max \u001b[0m\u001b[1m nmissing \u001b[0m\u001b[1m eltype \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m Symbol \u001b[0m\u001b[90m Union… \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Union… \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m DataType \u001b[0m\n",
"─────┼───────────────────────────────────────────────────────────────────────────────────────────────────\n",
" 1 │ Origin_airport \u001b[90m \u001b[0m 1B1 \u001b[90m \u001b[0m ZZV 0 String\n",
" 2 │ Destination_airport \u001b[90m \u001b[0m 1B1 \u001b[90m \u001b[0m ZZV 0 String\n",
" 3 │ Origin_city \u001b[90m \u001b[0m Aberdeen, SD \u001b[90m \u001b[0m Zanesville, OH 0 String\n",
" 4 │ Destination_city \u001b[90m \u001b[0m Aberdeen, SD \u001b[90m \u001b[0m Zanesville, OH 0 String\n",
" 5 │ Passengers 2688.91 0 1118.0 89597 0 Int64\n",
" 6 │ Seats 4048.3 0 1998.0 147062 0 Int64\n",
" ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮\n",
" 11 │ Destination_population 5.89798e6 12887 2.40019e6 38139592 0 Int64\n",
" 12 │ Org_airport_lat \u001b[90m \u001b[0m 19.721399307251 \u001b[90m \u001b[0m NA 0 String\n",
" 13 │ Org_airport_long \u001b[90m \u001b[0m -100.2860031 \u001b[90m \u001b[0m NA 0 String\n",
" 14 │ Dest_airport_lat \u001b[90m \u001b[0m 19.721399307251 \u001b[90m \u001b[0m NA 0 String\n",
" 15 │ Dest_airport_long \u001b[90m \u001b[0m -100.2860031 \u001b[90m \u001b[0m NA 0 String\n",
"\u001b[31m 4 rows omitted\u001b[0m"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"describe(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In particular note that CSV.jl correctly idenfitied `:Fly_date` column as being a `Date` type."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we investigate the summary statistics we note that there are flights that potentially have `0` passengers, `0` seats, or `0` flights.\n",
"\n",
"In parctice if you get such data it is good to investigate it, as it shows some potential data quality issues.\n",
"\n",
"Let us investigate into it (with a first deep-dive into new features of DataFrames.jl 0.21 release)."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>Passengers</th><th>Seats</th><th>Flights</th><th>nrow</th></tr><tr><th></th><th>Bool</th><th>Bool</th><th>Bool</th><th>Int64</th></tr></thead><tbody><p>8 rows × 4 columns</p><tr><th>1</th><td>0</td><td>0</td><td>0</td><td>11239</td></tr><tr><th>2</th><td>0</td><td>0</td><td>1</td><td>322787</td></tr><tr><th>3</th><td>0</td><td>1</td><td>0</td><td>94</td></tr><tr><th>4</th><td>0</td><td>1</td><td>1</td><td>51772</td></tr><tr><th>5</th><td>1</td><td>0</td><td>0</td><td>7</td></tr><tr><th>6</th><td>1</td><td>0</td><td>1</td><td>3</td></tr><tr><th>7</th><td>1</td><td>1</td><td>0</td><td>56</td></tr><tr><th>8</th><td>1</td><td>1</td><td>1</td><td>3220845</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& Passengers & Seats & Flights & nrow\\\\\n",
"\t\\hline\n",
"\t& Bool & Bool & Bool & Int64\\\\\n",
"\t\\hline\n",
"\t1 & 0 & 0 & 0 & 11239 \\\\\n",
"\t2 & 0 & 0 & 1 & 322787 \\\\\n",
"\t3 & 0 & 1 & 0 & 94 \\\\\n",
"\t4 & 0 & 1 & 1 & 51772 \\\\\n",
"\t5 & 1 & 0 & 0 & 7 \\\\\n",
"\t6 & 1 & 0 & 1 & 3 \\\\\n",
"\t7 & 1 & 1 & 0 & 56 \\\\\n",
"\t8 & 1 & 1 & 1 & 3220845 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m8×4 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m Passengers \u001b[0m\u001b[1m Seats \u001b[0m\u001b[1m Flights \u001b[0m\u001b[1m nrow \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m Bool \u001b[0m\u001b[90m Bool \u001b[0m\u001b[90m Bool \u001b[0m\u001b[90m Int64 \u001b[0m\n",
"─────┼─────────────────────────────────────\n",
" 1 │ false false false 11239\n",
" 2 │ false false true 322787\n",
" 3 │ false true false 94\n",
" 4 │ false true true 51772\n",
" 5 │ true false false 7\n",
" 6 │ true false true 3\n",
" 7 │ true true false 56\n",
" 8 │ true true true 3220845"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@pipe df |>\n",
" select(_, :Passengers, :Seats, :Flights) |>\n",
" mapcols(x -> x .> 0, _) |>\n",
" groupby(_, :, sort=true) |>\n",
" combine(_, nrow)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before interpreting the results let us dissect the code:\n",
"1. `@pipe` is a macro from Pipe.jl package, that makes it easy to chain function calls and makes `_` a placeholder for a value returned by a function that was earlier in the chain (you can read about the details here: https://github.com/oxinabox/Pipe.jl)\n",
"2. `select` creates a new data frame that keeps only three columns that we are interested in\n",
"3. `mapcols` transforms each column of our data frame to have `Bool` element; it will have a value `true` if an entry is greater than `0` and `false` otherwise. In this way we are able to easily idenfity rows with `0` entries, as they are most interesting for us\n",
"4. `groupby` groups our data frame by all columns and tells DataFrames.jl that the gorups should be sorted\n",
"5. finally `combine` just counts the number of rows in each group"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now going to our results - some of entries can be explained, like `0` passenges, but some seats and some flights (I guess it means that just no one took some flight).\n",
"\n",
"However cases like some passengers, but `0` seats and `0` flights are probably a mistake in data (we have 7 rows that have this combination of values).\n",
"\n",
"Let us try to find these 7 rows in two ways:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>Origin_airport</th><th>Destination_airport</th><th>Origin_city</th><th>Destination_city</th><th>Passengers</th><th>Seats</th><th>Flights</th><th>Distance</th><th>Fly_date</th><th>Origin_population</th><th>Destination_population</th><th>Org_airport_lat</th><th>Org_airport_long</th><th>Dest_airport_lat</th><th>Dest_airport_long</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Date</th><th>Int64</th><th>Int64</th><th>String</th><th>String</th><th>String</th><th>String</th></tr></thead><tbody><p>7 rows × 15 columns</p><tr><th>1</th><td>ABR</td><td>PIR</td><td>Aberdeen, SD</td><td>Pierre, SD</td><td>11</td><td>0</td><td>0</td><td>117</td><td>1997-11-01</td><td>39903</td><td>18289</td><td>45.4491004943848</td><td>-98.4217987060547</td><td>44.38270187</td><td>-100.2860031</td></tr><tr><th>2</th><td>IAD</td><td>BKW</td><td>Washington, DC</td><td>Beckley, WV</td><td>2</td><td>0</td><td>0</td><td>215</td><td>2002-12-01</td><td>10029142</td><td>78851</td><td>38.94449997</td><td>-77.45580292</td><td>37.7873001099</td><td>-81.1241989136</td></tr><tr><th>3</th><td>IAD</td><td>BKW</td><td>Washington, DC</td><td>Beckley, WV</td><td>1</td><td>0</td><td>0</td><td>215</td><td>2003-02-01</td><td>10172752</td><td>78587</td><td>38.94449997</td><td>-77.45580292</td><td>37.7873001099</td><td>-81.1241989136</td></tr><tr><th>4</th><td>IAD</td><td>BKW</td><td>Washington, DC</td><td>Beckley, WV</td><td>2</td><td>0</td><td>0</td><td>215</td><td>2003-03-01</td><td>10172752</td><td>78587</td><td>38.94449997</td><td>-77.45580292</td><td>37.7873001099</td><td>-81.1241989136</td></tr><tr><th>5</th><td>BOS</td><td>LAS</td><td>Boston, MA</td><td>Las Vegas, NV</td><td>9</td><td>0</td><td>0</td><td>2381</td><td>1993-06-01</td><td>8260582</td><td>877917</td><td>42.36429977</td><td>-71.00520325</td><td>36.08010101</td><td>-115.1520004</td></tr><tr><th>6</th><td>BKW</td><td>IAD</td><td>Beckley, WV</td><td>Washington, DC</td><td>7</td><td>0</td><td>0</td><td>215</td><td>2002-12-01</td><td>78851</td><td>10029142</td><td>37.7873001099</td><td>-81.1241989136</td><td>38.94449997</td><td>-77.45580292</td></tr><tr><th>7</th><td>CMI</td><td>BMI</td><td>Champaign, IL</td><td>Bloomington, IL</td><td>6</td><td>0</td><td>0</td><td>46</td><td>1997-11-01</td><td>201107</td><td>141699</td><td>40.03919983</td><td>-88.27809906</td><td>40.47710037</td><td>-88.91590118</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|ccccccccccccccc}\n",
"\t& Origin\\_airport & Destination\\_airport & Origin\\_city & Destination\\_city & Passengers & Seats & Flights & Distance & Fly\\_date & Origin\\_population & Destination\\_population & Org\\_airport\\_lat & Org\\_airport\\_long & Dest\\_airport\\_lat & Dest\\_airport\\_long\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String & Int64 & Int64 & Int64 & Int64 & Date & Int64 & Int64 & String & String & String & String\\\\\n",
"\t\\hline\n",
"\t1 & ABR & PIR & Aberdeen, SD & Pierre, SD & 11 & 0 & 0 & 117 & 1997-11-01 & 39903 & 18289 & 45.4491004943848 & -98.4217987060547 & 44.38270187 & -100.2860031 \\\\\n",
"\t2 & IAD & BKW & Washington, DC & Beckley, WV & 2 & 0 & 0 & 215 & 2002-12-01 & 10029142 & 78851 & 38.94449997 & -77.45580292 & 37.7873001099 & -81.1241989136 \\\\\n",
"\t3 & IAD & BKW & Washington, DC & Beckley, WV & 1 & 0 & 0 & 215 & 2003-02-01 & 10172752 & 78587 & 38.94449997 & -77.45580292 & 37.7873001099 & -81.1241989136 \\\\\n",
"\t4 & IAD & BKW & Washington, DC & Beckley, WV & 2 & 0 & 0 & 215 & 2003-03-01 & 10172752 & 78587 & 38.94449997 & -77.45580292 & 37.7873001099 & -81.1241989136 \\\\\n",
"\t5 & BOS & LAS & Boston, MA & Las Vegas, NV & 9 & 0 & 0 & 2381 & 1993-06-01 & 8260582 & 877917 & 42.36429977 & -71.00520325 & 36.08010101 & -115.1520004 \\\\\n",
"\t6 & BKW & IAD & Beckley, WV & Washington, DC & 7 & 0 & 0 & 215 & 2002-12-01 & 78851 & 10029142 & 37.7873001099 & -81.1241989136 & 38.94449997 & -77.45580292 \\\\\n",
"\t7 & CMI & BMI & Champaign, IL & Bloomington, IL & 6 & 0 & 0 & 46 & 1997-11-01 & 201107 & 141699 & 40.03919983 & -88.27809906 & 40.47710037 & -88.91590118 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m7×15 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m Origin_airport \u001b[0m\u001b[1m Destination_airport \u001b[0m\u001b[1m Origin_city \u001b[0m\u001b[1m Destination_city \u001b[0m\u001b[1m Passengers \u001b[0m\u001b[1m Seats \u001b[0m\u001b[1m Flights \u001b[0m\u001b[1m Distance \u001b[0m\u001b[1m Fly_date \u001b[0m\u001b[1m Origin_population \u001b[0m\u001b[1m Destination_population \u001b[0m\u001b[1m Org_airport_lat \u001b[0m\u001b[1m Org_airport_long \u001b[0m\u001b[1m Dest_airport_lat \u001b[0m\u001b[1m Dest_airport_long \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Date \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\n",
"─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
" 1 │ ABR PIR Aberdeen, SD Pierre, SD 11 0 0 117 1997-11-01 39903 18289 45.4491004943848 -98.4217987060547 44.38270187 -100.2860031\n",
" 2 │ IAD BKW Washington, DC Beckley, WV 2 0 0 215 2002-12-01 10029142 78851 38.94449997 -77.45580292 37.7873001099 -81.1241989136\n",
" 3 │ IAD BKW Washington, DC Beckley, WV 1 0 0 215 2003-02-01 10172752 78587 38.94449997 -77.45580292 37.7873001099 -81.1241989136\n",
" 4 │ IAD BKW Washington, DC Beckley, WV 2 0 0 215 2003-03-01 10172752 78587 38.94449997 -77.45580292 37.7873001099 -81.1241989136\n",
" 5 │ BOS LAS Boston, MA Las Vegas, NV 9 0 0 2381 1993-06-01 8260582 877917 42.36429977 -71.00520325 36.08010101 -115.1520004\n",
" 6 │ BKW IAD Beckley, WV Washington, DC 7 0 0 215 2002-12-01 78851 10029142 37.7873001099 -81.1241989136 38.94449997 -77.45580292\n",
" 7 │ CMI BMI Champaign, IL Bloomington, IL 6 0 0 46 1997-11-01 201107 141699 40.03919983 -88.27809906 40.47710037 -88.91590118"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filter(row -> row.Passengers > 0 && row.Seats == 0 && row.Flights == 0, df)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>Origin_airport</th><th>Destination_airport</th><th>Origin_city</th><th>Destination_city</th><th>Passengers</th><th>Seats</th><th>Flights</th><th>Distance</th><th>Fly_date</th><th>Origin_population</th><th>Destination_population</th><th>Org_airport_lat</th><th>Org_airport_long</th><th>Dest_airport_lat</th><th>Dest_airport_long</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Date</th><th>Int64</th><th>Int64</th><th>String</th><th>String</th><th>String</th><th>String</th></tr></thead><tbody><p>7 rows × 15 columns</p><tr><th>1</th><td>ABR</td><td>PIR</td><td>Aberdeen, SD</td><td>Pierre, SD</td><td>11</td><td>0</td><td>0</td><td>117</td><td>1997-11-01</td><td>39903</td><td>18289</td><td>45.4491004943848</td><td>-98.4217987060547</td><td>44.38270187</td><td>-100.2860031</td></tr><tr><th>2</th><td>IAD</td><td>BKW</td><td>Washington, DC</td><td>Beckley, WV</td><td>2</td><td>0</td><td>0</td><td>215</td><td>2002-12-01</td><td>10029142</td><td>78851</td><td>38.94449997</td><td>-77.45580292</td><td>37.7873001099</td><td>-81.1241989136</td></tr><tr><th>3</th><td>IAD</td><td>BKW</td><td>Washington, DC</td><td>Beckley, WV</td><td>1</td><td>0</td><td>0</td><td>215</td><td>2003-02-01</td><td>10172752</td><td>78587</td><td>38.94449997</td><td>-77.45580292</td><td>37.7873001099</td><td>-81.1241989136</td></tr><tr><th>4</th><td>IAD</td><td>BKW</td><td>Washington, DC</td><td>Beckley, WV</td><td>2</td><td>0</td><td>0</td><td>215</td><td>2003-03-01</td><td>10172752</td><td>78587</td><td>38.94449997</td><td>-77.45580292</td><td>37.7873001099</td><td>-81.1241989136</td></tr><tr><th>5</th><td>BOS</td><td>LAS</td><td>Boston, MA</td><td>Las Vegas, NV</td><td>9</td><td>0</td><td>0</td><td>2381</td><td>1993-06-01</td><td>8260582</td><td>877917</td><td>42.36429977</td><td>-71.00520325</td><td>36.08010101</td><td>-115.1520004</td></tr><tr><th>6</th><td>BKW</td><td>IAD</td><td>Beckley, WV</td><td>Washington, DC</td><td>7</td><td>0</td><td>0</td><td>215</td><td>2002-12-01</td><td>78851</td><td>10029142</td><td>37.7873001099</td><td>-81.1241989136</td><td>38.94449997</td><td>-77.45580292</td></tr><tr><th>7</th><td>CMI</td><td>BMI</td><td>Champaign, IL</td><td>Bloomington, IL</td><td>6</td><td>0</td><td>0</td><td>46</td><td>1997-11-01</td><td>201107</td><td>141699</td><td>40.03919983</td><td>-88.27809906</td><td>40.47710037</td><td>-88.91590118</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|ccccccccccccccc}\n",
"\t& Origin\\_airport & Destination\\_airport & Origin\\_city & Destination\\_city & Passengers & Seats & Flights & Distance & Fly\\_date & Origin\\_population & Destination\\_population & Org\\_airport\\_lat & Org\\_airport\\_long & Dest\\_airport\\_lat & Dest\\_airport\\_long\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String & Int64 & Int64 & Int64 & Int64 & Date & Int64 & Int64 & String & String & String & String\\\\\n",
"\t\\hline\n",
"\t1 & ABR & PIR & Aberdeen, SD & Pierre, SD & 11 & 0 & 0 & 117 & 1997-11-01 & 39903 & 18289 & 45.4491004943848 & -98.4217987060547 & 44.38270187 & -100.2860031 \\\\\n",
"\t2 & IAD & BKW & Washington, DC & Beckley, WV & 2 & 0 & 0 & 215 & 2002-12-01 & 10029142 & 78851 & 38.94449997 & -77.45580292 & 37.7873001099 & -81.1241989136 \\\\\n",
"\t3 & IAD & BKW & Washington, DC & Beckley, WV & 1 & 0 & 0 & 215 & 2003-02-01 & 10172752 & 78587 & 38.94449997 & -77.45580292 & 37.7873001099 & -81.1241989136 \\\\\n",
"\t4 & IAD & BKW & Washington, DC & Beckley, WV & 2 & 0 & 0 & 215 & 2003-03-01 & 10172752 & 78587 & 38.94449997 & -77.45580292 & 37.7873001099 & -81.1241989136 \\\\\n",
"\t5 & BOS & LAS & Boston, MA & Las Vegas, NV & 9 & 0 & 0 & 2381 & 1993-06-01 & 8260582 & 877917 & 42.36429977 & -71.00520325 & 36.08010101 & -115.1520004 \\\\\n",
"\t6 & BKW & IAD & Beckley, WV & Washington, DC & 7 & 0 & 0 & 215 & 2002-12-01 & 78851 & 10029142 & 37.7873001099 & -81.1241989136 & 38.94449997 & -77.45580292 \\\\\n",
"\t7 & CMI & BMI & Champaign, IL & Bloomington, IL & 6 & 0 & 0 & 46 & 1997-11-01 & 201107 & 141699 & 40.03919983 & -88.27809906 & 40.47710037 & -88.91590118 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m7×15 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m Origin_airport \u001b[0m\u001b[1m Destination_airport \u001b[0m\u001b[1m Origin_city \u001b[0m\u001b[1m Destination_city \u001b[0m\u001b[1m Passengers \u001b[0m\u001b[1m Seats \u001b[0m\u001b[1m Flights \u001b[0m\u001b[1m Distance \u001b[0m\u001b[1m Fly_date \u001b[0m\u001b[1m Origin_population \u001b[0m\u001b[1m Destination_population \u001b[0m\u001b[1m Org_airport_lat \u001b[0m\u001b[1m Org_airport_long \u001b[0m\u001b[1m Dest_airport_lat \u001b[0m\u001b[1m Dest_airport_long \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Date \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\n",
"─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
" 1 │ ABR PIR Aberdeen, SD Pierre, SD 11 0 0 117 1997-11-01 39903 18289 45.4491004943848 -98.4217987060547 44.38270187 -100.2860031\n",
" 2 │ IAD BKW Washington, DC Beckley, WV 2 0 0 215 2002-12-01 10029142 78851 38.94449997 -77.45580292 37.7873001099 -81.1241989136\n",
" 3 │ IAD BKW Washington, DC Beckley, WV 1 0 0 215 2003-02-01 10172752 78587 38.94449997 -77.45580292 37.7873001099 -81.1241989136\n",
" 4 │ IAD BKW Washington, DC Beckley, WV 2 0 0 215 2003-03-01 10172752 78587 38.94449997 -77.45580292 37.7873001099 -81.1241989136\n",
" 5 │ BOS LAS Boston, MA Las Vegas, NV 9 0 0 2381 1993-06-01 8260582 877917 42.36429977 -71.00520325 36.08010101 -115.1520004\n",
" 6 │ BKW IAD Beckley, WV Washington, DC 7 0 0 215 2002-12-01 78851 10029142 37.7873001099 -81.1241989136 38.94449997 -77.45580292\n",
" 7 │ CMI BMI Champaign, IL Bloomington, IL 6 0 0 46 1997-11-01 201107 141699 40.03919983 -88.27809906 40.47710037 -88.91590118"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filter([:Passengers, :Seats, :Flights] => (x...) -> x[1] > 0 && x[2] == 0 && x[3] == 0, df)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>Origin_airport</th><th>Destination_airport</th><th>Origin_city</th><th>Destination_city</th><th>Passengers</th><th>Seats</th><th>Flights</th><th>Distance</th><th>Fly_date</th><th>Origin_population</th><th>Destination_population</th><th>Org_airport_lat</th><th>Org_airport_long</th><th>Dest_airport_lat</th><th>Dest_airport_long</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Date</th><th>Int64</th><th>Int64</th><th>String</th><th>String</th><th>String</th><th>String</th></tr></thead><tbody><p>7 rows × 15 columns</p><tr><th>1</th><td>ABR</td><td>PIR</td><td>Aberdeen, SD</td><td>Pierre, SD</td><td>11</td><td>0</td><td>0</td><td>117</td><td>1997-11-01</td><td>39903</td><td>18289</td><td>45.4491004943848</td><td>-98.4217987060547</td><td>44.38270187</td><td>-100.2860031</td></tr><tr><th>2</th><td>IAD</td><td>BKW</td><td>Washington, DC</td><td>Beckley, WV</td><td>2</td><td>0</td><td>0</td><td>215</td><td>2002-12-01</td><td>10029142</td><td>78851</td><td>38.94449997</td><td>-77.45580292</td><td>37.7873001099</td><td>-81.1241989136</td></tr><tr><th>3</th><td>IAD</td><td>BKW</td><td>Washington, DC</td><td>Beckley, WV</td><td>1</td><td>0</td><td>0</td><td>215</td><td>2003-02-01</td><td>10172752</td><td>78587</td><td>38.94449997</td><td>-77.45580292</td><td>37.7873001099</td><td>-81.1241989136</td></tr><tr><th>4</th><td>IAD</td><td>BKW</td><td>Washington, DC</td><td>Beckley, WV</td><td>2</td><td>0</td><td>0</td><td>215</td><td>2003-03-01</td><td>10172752</td><td>78587</td><td>38.94449997</td><td>-77.45580292</td><td>37.7873001099</td><td>-81.1241989136</td></tr><tr><th>5</th><td>BOS</td><td>LAS</td><td>Boston, MA</td><td>Las Vegas, NV</td><td>9</td><td>0</td><td>0</td><td>2381</td><td>1993-06-01</td><td>8260582</td><td>877917</td><td>42.36429977</td><td>-71.00520325</td><td>36.08010101</td><td>-115.1520004</td></tr><tr><th>6</th><td>BKW</td><td>IAD</td><td>Beckley, WV</td><td>Washington, DC</td><td>7</td><td>0</td><td>0</td><td>215</td><td>2002-12-01</td><td>78851</td><td>10029142</td><td>37.7873001099</td><td>-81.1241989136</td><td>38.94449997</td><td>-77.45580292</td></tr><tr><th>7</th><td>CMI</td><td>BMI</td><td>Champaign, IL</td><td>Bloomington, IL</td><td>6</td><td>0</td><td>0</td><td>46</td><td>1997-11-01</td><td>201107</td><td>141699</td><td>40.03919983</td><td>-88.27809906</td><td>40.47710037</td><td>-88.91590118</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|ccccccccccccccc}\n",
"\t& Origin\\_airport & Destination\\_airport & Origin\\_city & Destination\\_city & Passengers & Seats & Flights & Distance & Fly\\_date & Origin\\_population & Destination\\_population & Org\\_airport\\_lat & Org\\_airport\\_long & Dest\\_airport\\_lat & Dest\\_airport\\_long\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String & Int64 & Int64 & Int64 & Int64 & Date & Int64 & Int64 & String & String & String & String\\\\\n",
"\t\\hline\n",
"\t1 & ABR & PIR & Aberdeen, SD & Pierre, SD & 11 & 0 & 0 & 117 & 1997-11-01 & 39903 & 18289 & 45.4491004943848 & -98.4217987060547 & 44.38270187 & -100.2860031 \\\\\n",
"\t2 & IAD & BKW & Washington, DC & Beckley, WV & 2 & 0 & 0 & 215 & 2002-12-01 & 10029142 & 78851 & 38.94449997 & -77.45580292 & 37.7873001099 & -81.1241989136 \\\\\n",
"\t3 & IAD & BKW & Washington, DC & Beckley, WV & 1 & 0 & 0 & 215 & 2003-02-01 & 10172752 & 78587 & 38.94449997 & -77.45580292 & 37.7873001099 & -81.1241989136 \\\\\n",
"\t4 & IAD & BKW & Washington, DC & Beckley, WV & 2 & 0 & 0 & 215 & 2003-03-01 & 10172752 & 78587 & 38.94449997 & -77.45580292 & 37.7873001099 & -81.1241989136 \\\\\n",
"\t5 & BOS & LAS & Boston, MA & Las Vegas, NV & 9 & 0 & 0 & 2381 & 1993-06-01 & 8260582 & 877917 & 42.36429977 & -71.00520325 & 36.08010101 & -115.1520004 \\\\\n",
"\t6 & BKW & IAD & Beckley, WV & Washington, DC & 7 & 0 & 0 & 215 & 2002-12-01 & 78851 & 10029142 & 37.7873001099 & -81.1241989136 & 38.94449997 & -77.45580292 \\\\\n",
"\t7 & CMI & BMI & Champaign, IL & Bloomington, IL & 6 & 0 & 0 & 46 & 1997-11-01 & 201107 & 141699 & 40.03919983 & -88.27809906 & 40.47710037 & -88.91590118 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m7×15 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m Origin_airport \u001b[0m\u001b[1m Destination_airport \u001b[0m\u001b[1m Origin_city \u001b[0m\u001b[1m Destination_city \u001b[0m\u001b[1m Passengers \u001b[0m\u001b[1m Seats \u001b[0m\u001b[1m Flights \u001b[0m\u001b[1m Distance \u001b[0m\u001b[1m Fly_date \u001b[0m\u001b[1m Origin_population \u001b[0m\u001b[1m Destination_population \u001b[0m\u001b[1m Org_airport_lat \u001b[0m\u001b[1m Org_airport_long \u001b[0m\u001b[1m Dest_airport_lat \u001b[0m\u001b[1m Dest_airport_long \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Date \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\n",
"─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
" 1 │ ABR PIR Aberdeen, SD Pierre, SD 11 0 0 117 1997-11-01 39903 18289 45.4491004943848 -98.4217987060547 44.38270187 -100.2860031\n",
" 2 │ IAD BKW Washington, DC Beckley, WV 2 0 0 215 2002-12-01 10029142 78851 38.94449997 -77.45580292 37.7873001099 -81.1241989136\n",
" 3 │ IAD BKW Washington, DC Beckley, WV 1 0 0 215 2003-02-01 10172752 78587 38.94449997 -77.45580292 37.7873001099 -81.1241989136\n",
" 4 │ IAD BKW Washington, DC Beckley, WV 2 0 0 215 2003-03-01 10172752 78587 38.94449997 -77.45580292 37.7873001099 -81.1241989136\n",
" 5 │ BOS LAS Boston, MA Las Vegas, NV 9 0 0 2381 1993-06-01 8260582 877917 42.36429977 -71.00520325 36.08010101 -115.1520004\n",
" 6 │ BKW IAD Beckley, WV Washington, DC 7 0 0 215 2002-12-01 78851 10029142 37.7873001099 -81.1241989136 38.94449997 -77.45580292\n",
" 7 │ CMI BMI Champaign, IL Bloomington, IL 6 0 0 46 1997-11-01 201107 141699 40.03919983 -88.27809906 40.47710037 -88.91590118"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filter(AsTable(:) => row -> row.Passengers > 0 && row.Seats == 0 && row.Flights == 0, df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second form is a new feature of DataFrames.jl 0.21 release and it allows passing only selected columns to a predicate function. The benefit is that this operation is type stable.\n",
"\n",
"Let us compare the timing of both options:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.765999 seconds (18.73 M allocations: 307.128 MiB)\n"
]
}
],
"source": [
"@time filter(row -> row.Passengers > 0 && row.Seats == 0 && row.Flights == 0, df);"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.168150 seconds (425.74 k allocations: 20.100 MiB)\n"
]
}
],
"source": [
"@time filter([:Passengers, :Seats, :Flights] => (x...) -> x[1] > 0 && x[2] == 0 && x[3] == 0, df);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let us move to another analysis.\n",
"\n",
"We want to get an information about the occupancy of each flight. It can be calculated as ratio of the number of passengers and number of seats time number of flights. The problem is that if there are `0` seats or flights we would be dividing by `0` and get a `NaN` result. We prefer to get a `missing` value in this case. Therefore first define a helper function:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"get_occupied (generic function with 1 method)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function get_occupied(passengers, seats, flights)\n",
" if seats == 0 || flights == 0\n",
" return missing\n",
" else\n",
" return passengers / (seats * flights)\n",
" end\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and now we use it to add a new column to our data frame. Additionally we create three new columns:\n",
"1. `:ost`: state of the origin city\n",
"2. `:dst`: state of the destination city\n",
"3. `:year`: year of flight"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>Origin_airport</th><th>Destination_airport</th><th>Origin_city</th><th>Destination_city</th><th>Passengers</th><th>Seats</th><th>Flights</th><th>Distance</th><th>Fly_date</th><th>Origin_population</th><th>Destination_population</th><th>Org_airport_lat</th><th>Org_airport_long</th><th>Dest_airport_lat</th><th>Dest_airport_long</th><th>occupied</th><th>ost</th><th>dst</th><th>year</th></tr><tr><th></th><th>String</th><th>String</th><th>String</th><th>String</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Date</th><th>Int64</th><th>Int64</th><th>String</th><th>String</th><th>String</th><th>String</th><th>Float64?</th><th>String</th><th>String</th><th>Int64</th></tr></thead><tbody><p>3,606,803 rows × 19 columns</p><tr><th>1</th><td>MHK</td><td>AMW</td><td>Manhattan, KS</td><td>Ames, IA</td><td>21</td><td>30</td><td>1</td><td>254</td><td>2008-10-01</td><td>122049</td><td>86219</td><td>39.140998840332</td><td>-96.6707992553711</td><td>NA</td><td>NA</td><td>0.7</td><td>KS</td><td>IA</td><td>2008</td></tr><tr><th>2</th><td>EUG</td><td>RDM</td><td>Eugene, OR</td><td>Bend, OR</td><td>41</td><td>396</td><td>22</td><td>103</td><td>1990-11-01</td><td>284093</td><td>76034</td><td>44.1245994567871</td><td>-123.21199798584</td><td>44.2541008</td><td>-121.1500015</td><td>0.00470615</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>3</th><td>EUG</td><td>RDM</td><td>Eugene, OR</td><td>Bend, OR</td><td>88</td><td>342</td><td>19</td><td>103</td><td>1990-12-01</td><td>284093</td><td>76034</td><td>44.1245994567871</td><td>-123.21199798584</td><td>44.2541008</td><td>-121.1500015</td><td>0.0135426</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>4</th><td>EUG</td><td>RDM</td><td>Eugene, OR</td><td>Bend, OR</td><td>11</td><td>72</td><td>4</td><td>103</td><td>1990-10-01</td><td>284093</td><td>76034</td><td>44.1245994567871</td><td>-123.21199798584</td><td>44.2541008</td><td>-121.1500015</td><td>0.0381944</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>5</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>0</td><td>18</td><td>1</td><td>156</td><td>1990-02-01</td><td>147300</td><td>76034</td><td>42.3741989135742</td><td>-122.873001098633</td><td>44.2541008</td><td>-121.1500015</td><td>0.0</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>6</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>11</td><td>18</td><td>1</td><td>156</td><td>1990-03-01</td><td>147300</td><td>76034</td><td>42.3741989135742</td><td>-122.873001098633</td><td>44.2541008</td><td>-121.1500015</td><td>0.611111</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>7</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>2</td><td>72</td><td>4</td><td>156</td><td>1990-01-01</td><td>147300</td><td>76034</td><td>42.3741989135742</td><td>-122.873001098633</td><td>44.2541008</td><td>-121.1500015</td><td>0.00694444</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>8</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>7</td><td>18</td><td>1</td><td>156</td><td>1990-09-01</td><td>147300</td><td>76034</td><td>42.3741989135742</td><td>-122.873001098633</td><td>44.2541008</td><td>-121.1500015</td><td>0.388889</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>9</th><td>MFR</td><td>RDM</td><td>Medford, OR</td><td>Bend, OR</td><td>7</td><td>36</td><td>2</td><td>156</td><td>1990-11-01</td><td>147300</td><td>76034</td><td>42.3741989135742</td><td>-122.873001098633</td><td>44.2541008</td><td>-121.1500015</td><td>0.0972222</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>10</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>8</td><td>18</td><td>1</td><td>228</td><td>1990-02-01</td><td>5154164</td><td>76034</td><td>47.4490013122559</td><td>-122.30899810791</td><td>44.2541008</td><td>-121.1500015</td><td>0.444444</td><td>WA</td><td>OR</td><td>1990</td></tr><tr><th>11</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>453</td><td>3128</td><td>23</td><td>228</td><td>1990-01-01</td><td>5154164</td><td>76034</td><td>47.4490013122559</td><td>-122.30899810791</td><td>44.2541008</td><td>-121.1500015</td><td>0.00629656</td><td>WA</td><td>OR</td><td>1990</td></tr><tr><th>12</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>784</td><td>2720</td><td>20</td><td>228</td><td>1990-02-01</td><td>5154164</td><td>76034</td><td>47.4490013122559</td><td>-122.30899810791</td><td>44.2541008</td><td>-121.1500015</td><td>0.0144118</td><td>WA</td><td>OR</td><td>1990</td></tr><tr><th>13</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>749</td><td>2992</td><td>22</td><td>228</td><td>1990-03-01</td><td>5154164</td><td>76034</td><td>47.4490013122559</td><td>-122.30899810791</td><td>44.2541008</td><td>-121.1500015</td><td>0.0113788</td><td>WA</td><td>OR</td><td>1990</td></tr><tr><th>14</th><td>SEA</td><td>RDM</td><td>Seattle, WA</td><td>Bend, OR</td><td>11</td><td>18</td><td>1</td><td>228</td><td>1990-04-01</td><td>5154164</td><td>76034</td><td>47.4490013122559</td><td>-122.30899810791</td><td>44.2541008</td><td>-121.1500015</td><td>0.611111</td><td>WA</td><td>OR</td><td>1990</td></tr><tr><th>15</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>349</td><td>851</td><td>23</td><td>116</td><td>1990-01-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td><td>0.0178307</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>16</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1376</td><td>2898</td><td>161</td><td>116</td><td>1990-01-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td><td>0.00294913</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>17</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>444</td><td>1110</td><td>30</td><td>116</td><td>1990-10-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td><td>0.0133333</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>18</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1949</td><td>3261</td><td>187</td><td>116</td><td>1990-06-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td><td>0.00319609</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>19</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>381</td><td>814</td><td>22</td><td>116</td><td>1990-02-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td><td>0.0212754</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>20</th><td>PDX</td><td>RDM</td><td>Portland, OR</td><td>Bend, OR</td><td>1559</td><td>2772</td><td>154</td><td>116</td><td>1990-02-01</td><td>1534762</td><td>76034</td><td>45.58869934</td><td>-122.5979996</td><td>44.2541008</td><td>-121.1500015</td><td>0.00365201</td><td>OR</td><td>OR</td><td>1990</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|ccccccccccccccccccc}\n",
"\t& Origin\\_airport & Destination\\_airport & Origin\\_city & Destination\\_city & Passengers & Seats & Flights & Distance & Fly\\_date & Origin\\_population & Destination\\_population & Org\\_airport\\_lat & Org\\_airport\\_long & Dest\\_airport\\_lat & Dest\\_airport\\_long & occupied & ost & dst & year\\\\\n",
"\t\\hline\n",
"\t& String & String & String & String & Int64 & Int64 & Int64 & Int64 & Date & Int64 & Int64 & String & String & String & String & Float64? & String & String & Int64\\\\\n",
"\t\\hline\n",
"\t1 & MHK & AMW & Manhattan, KS & Ames, IA & 21 & 30 & 1 & 254 & 2008-10-01 & 122049 & 86219 & 39.140998840332 & -96.6707992553711 & NA & NA & 0.7 & KS & IA & 2008 \\\\\n",
"\t2 & EUG & RDM & Eugene, OR & Bend, OR & 41 & 396 & 22 & 103 & 1990-11-01 & 284093 & 76034 & 44.1245994567871 & -123.21199798584 & 44.2541008 & -121.1500015 & 0.00470615 & OR & OR & 1990 \\\\\n",
"\t3 & EUG & RDM & Eugene, OR & Bend, OR & 88 & 342 & 19 & 103 & 1990-12-01 & 284093 & 76034 & 44.1245994567871 & -123.21199798584 & 44.2541008 & -121.1500015 & 0.0135426 & OR & OR & 1990 \\\\\n",
"\t4 & EUG & RDM & Eugene, OR & Bend, OR & 11 & 72 & 4 & 103 & 1990-10-01 & 284093 & 76034 & 44.1245994567871 & -123.21199798584 & 44.2541008 & -121.1500015 & 0.0381944 & OR & OR & 1990 \\\\\n",
"\t5 & MFR & RDM & Medford, OR & Bend, OR & 0 & 18 & 1 & 156 & 1990-02-01 & 147300 & 76034 & 42.3741989135742 & -122.873001098633 & 44.2541008 & -121.1500015 & 0.0 & OR & OR & 1990 \\\\\n",
"\t6 & MFR & RDM & Medford, OR & Bend, OR & 11 & 18 & 1 & 156 & 1990-03-01 & 147300 & 76034 & 42.3741989135742 & -122.873001098633 & 44.2541008 & -121.1500015 & 0.611111 & OR & OR & 1990 \\\\\n",
"\t7 & MFR & RDM & Medford, OR & Bend, OR & 2 & 72 & 4 & 156 & 1990-01-01 & 147300 & 76034 & 42.3741989135742 & -122.873001098633 & 44.2541008 & -121.1500015 & 0.00694444 & OR & OR & 1990 \\\\\n",
"\t8 & MFR & RDM & Medford, OR & Bend, OR & 7 & 18 & 1 & 156 & 1990-09-01 & 147300 & 76034 & 42.3741989135742 & -122.873001098633 & 44.2541008 & -121.1500015 & 0.388889 & OR & OR & 1990 \\\\\n",
"\t9 & MFR & RDM & Medford, OR & Bend, OR & 7 & 36 & 2 & 156 & 1990-11-01 & 147300 & 76034 & 42.3741989135742 & -122.873001098633 & 44.2541008 & -121.1500015 & 0.0972222 & OR & OR & 1990 \\\\\n",
"\t10 & SEA & RDM & Seattle, WA & Bend, OR & 8 & 18 & 1 & 228 & 1990-02-01 & 5154164 & 76034 & 47.4490013122559 & -122.30899810791 & 44.2541008 & -121.1500015 & 0.444444 & WA & OR & 1990 \\\\\n",
"\t11 & SEA & RDM & Seattle, WA & Bend, OR & 453 & 3128 & 23 & 228 & 1990-01-01 & 5154164 & 76034 & 47.4490013122559 & -122.30899810791 & 44.2541008 & -121.1500015 & 0.00629656 & WA & OR & 1990 \\\\\n",
"\t12 & SEA & RDM & Seattle, WA & Bend, OR & 784 & 2720 & 20 & 228 & 1990-02-01 & 5154164 & 76034 & 47.4490013122559 & -122.30899810791 & 44.2541008 & -121.1500015 & 0.0144118 & WA & OR & 1990 \\\\\n",
"\t13 & SEA & RDM & Seattle, WA & Bend, OR & 749 & 2992 & 22 & 228 & 1990-03-01 & 5154164 & 76034 & 47.4490013122559 & -122.30899810791 & 44.2541008 & -121.1500015 & 0.0113788 & WA & OR & 1990 \\\\\n",
"\t14 & SEA & RDM & Seattle, WA & Bend, OR & 11 & 18 & 1 & 228 & 1990-04-01 & 5154164 & 76034 & 47.4490013122559 & -122.30899810791 & 44.2541008 & -121.1500015 & 0.611111 & WA & OR & 1990 \\\\\n",
"\t15 & PDX & RDM & Portland, OR & Bend, OR & 349 & 851 & 23 & 116 & 1990-01-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 & 0.0178307 & OR & OR & 1990 \\\\\n",
"\t16 & PDX & RDM & Portland, OR & Bend, OR & 1376 & 2898 & 161 & 116 & 1990-01-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 & 0.00294913 & OR & OR & 1990 \\\\\n",
"\t17 & PDX & RDM & Portland, OR & Bend, OR & 444 & 1110 & 30 & 116 & 1990-10-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 & 0.0133333 & OR & OR & 1990 \\\\\n",
"\t18 & PDX & RDM & Portland, OR & Bend, OR & 1949 & 3261 & 187 & 116 & 1990-06-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 & 0.00319609 & OR & OR & 1990 \\\\\n",
"\t19 & PDX & RDM & Portland, OR & Bend, OR & 381 & 814 & 22 & 116 & 1990-02-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 & 0.0212754 & OR & OR & 1990 \\\\\n",
"\t20 & PDX & RDM & Portland, OR & Bend, OR & 1559 & 2772 & 154 & 116 & 1990-02-01 & 1534762 & 76034 & 45.58869934 & -122.5979996 & 44.2541008 & -121.1500015 & 0.00365201 & OR & OR & 1990 \\\\\n",
"\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m3606803×19 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m Origin_airport \u001b[0m\u001b[1m Destination_airport \u001b[0m\u001b[1m Origin_city \u001b[0m\u001b[1m Destination_city \u001b[0m\u001b[1m Passengers \u001b[0m\u001b[1m Seats \u001b[0m\u001b[1m Flights \u001b[0m\u001b[1m Distance \u001b[0m\u001b[1m Fly_date \u001b[0m\u001b[1m Origin_population \u001b[0m\u001b[1m Destination_population \u001b[0m\u001b[1m Org_airport_lat \u001b[0m\u001b[1m Org_airport_long \u001b[0m\u001b[1m Dest_airport_lat \u001b[0m\u001b[1m Dest_airport_long \u001b[0m\u001b[1m occupied \u001b[0m\u001b[1m ost \u001b[0m\u001b[1m dst \u001b[0m\u001b[1m year \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Date \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\n",
"─────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
" 1 │ MHK AMW Manhattan, KS Ames, IA 21 30 1 254 2008-10-01 122049 86219 39.140998840332 -96.6707992553711 NA NA 0.7 KS IA 2008\n",
" 2 │ EUG RDM Eugene, OR Bend, OR 41 396 22 103 1990-11-01 284093 76034 44.1245994567871 -123.21199798584 44.2541008 -121.1500015 0.00470615 OR OR 1990\n",
" 3 │ EUG RDM Eugene, OR Bend, OR 88 342 19 103 1990-12-01 284093 76034 44.1245994567871 -123.21199798584 44.2541008 -121.1500015 0.0135426 OR OR 1990\n",
" 4 │ EUG RDM Eugene, OR Bend, OR 11 72 4 103 1990-10-01 284093 76034 44.1245994567871 -123.21199798584 44.2541008 -121.1500015 0.0381944 OR OR 1990\n",
" 5 │ MFR RDM Medford, OR Bend, OR 0 18 1 156 1990-02-01 147300 76034 42.3741989135742 -122.873001098633 44.2541008 -121.1500015 0.0 OR OR 1990\n",
" 6 │ MFR RDM Medford, OR Bend, OR 11 18 1 156 1990-03-01 147300 76034 42.3741989135742 -122.873001098633 44.2541008 -121.1500015 0.611111 OR OR 1990\n",
" ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮\n",
" 3606799 │ STL TBN St. Louis, MO Fort Leonard Wood, MO 281 969 51 119 2009-02-01 2828990 46457 38.7486991882324 -90.370002746582 37.74160004 -92.14070129 0.00568607 MO MO 2009\n",
" 3606800 │ STL TBN St. Louis, MO Fort Leonard Wood, MO 245 1026 54 119 2009-11-01 2828990 46457 38.7486991882324 -90.370002746582 37.74160004 -92.14070129 0.00442206 MO MO 2009\n",
" 3606801 │ STL TBN St. Louis, MO Fort Leonard Wood, MO 363 1273 67 119 2009-08-01 2828990 46457 38.7486991882324 -90.370002746582 37.74160004 -92.14070129 0.00425602 MO MO 2009\n",
" 3606802 │ CGI TBN Cape Girardeau, MO Fort Leonard Wood, MO 2 19 1 146 2009-08-01 93712 46457 37.2252998352051 -89.57080078125 37.74160004 -92.14070129 0.105263 MO MO 2009\n",
" 3606803 │ FWA OH1 Fort Wayne, IN Washington Court House, OH 0 0 1 135 2003-09-01 398574 28133 40.97850037 -85.19509888 NA NA \u001b[90m missing \u001b[0m IN OH 2003\n",
"\u001b[31m 3606792 rows omitted\u001b[0m"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = transform(df,\n",
" [:Passengers, :Seats, :Flights] => ByRow(get_occupied) => :occupied,\n",
" [:Origin_city, :Destination_city] .=> ByRow(x -> last(x, 2)) .=> [:ost, :dst],\n",
" :Fly_date => ByRow(year)=> :year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that in the computation we used `ByRow` wrapper that instructs DataFrames.jl to apply the function for each row of the passed data.\n",
"\n",
"Here is an alternative way to compute `:occupied` without using `ByRow`, but instead using standard broadcasting:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"get_occupied2 (generic function with 1 method)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_occupied2(passengers, seats, flights) =\n",
" @. ifelse((seats == 0) | (flights == 0), missing, passengers / (seats * flights))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>occupied</th></tr><tr><th></th><th>Float64?</th></tr></thead><tbody><p>3,606,803 rows × 1 columns</p><tr><th>1</th><td>0.7</td></tr><tr><th>2</th><td>0.00470615</td></tr><tr><th>3</th><td>0.0135426</td></tr><tr><th>4</th><td>0.0381944</td></tr><tr><th>5</th><td>0.0</td></tr><tr><th>6</th><td>0.611111</td></tr><tr><th>7</th><td>0.00694444</td></tr><tr><th>8</th><td>0.388889</td></tr><tr><th>9</th><td>0.0972222</td></tr><tr><th>10</th><td>0.444444</td></tr><tr><th>11</th><td>0.00629656</td></tr><tr><th>12</th><td>0.0144118</td></tr><tr><th>13</th><td>0.0113788</td></tr><tr><th>14</th><td>0.611111</td></tr><tr><th>15</th><td>0.0178307</td></tr><tr><th>16</th><td>0.00294913</td></tr><tr><th>17</th><td>0.0133333</td></tr><tr><th>18</th><td>0.00319609</td></tr><tr><th>19</th><td>0.0212754</td></tr><tr><th>20</th><td>0.00365201</td></tr><tr><th>&vellip;</th><td>&vellip;</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|c}\n",
"\t& occupied\\\\\n",
"\t\\hline\n",
"\t& Float64?\\\\\n",
"\t\\hline\n",
"\t1 & 0.7 \\\\\n",
"\t2 & 0.00470615 \\\\\n",
"\t3 & 0.0135426 \\\\\n",
"\t4 & 0.0381944 \\\\\n",
"\t5 & 0.0 \\\\\n",
"\t6 & 0.611111 \\\\\n",
"\t7 & 0.00694444 \\\\\n",
"\t8 & 0.388889 \\\\\n",
"\t9 & 0.0972222 \\\\\n",
"\t10 & 0.444444 \\\\\n",
"\t11 & 0.00629656 \\\\\n",
"\t12 & 0.0144118 \\\\\n",
"\t13 & 0.0113788 \\\\\n",
"\t14 & 0.611111 \\\\\n",
"\t15 & 0.0178307 \\\\\n",
"\t16 & 0.00294913 \\\\\n",
"\t17 & 0.0133333 \\\\\n",
"\t18 & 0.00319609 \\\\\n",
"\t19 & 0.0212754 \\\\\n",
"\t20 & 0.00365201 \\\\\n",
"\t$\\dots$ & $\\dots$ \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m3606803×1 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m occupied \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m Float64? \u001b[0m\n",
"─────────┼──────────────────\n",
" 1 │ 0.7\n",
" 2 │ 0.00470615\n",
" 3 │ 0.0135426\n",
" 4 │ 0.0381944\n",
" 5 │ 0.0\n",
" 6 │ 0.611111\n",
" ⋮ │ ⋮\n",
" 3606799 │ 0.00568607\n",
" 3606800 │ 0.00442206\n",
" 3606801 │ 0.00425602\n",
" 3606802 │ 0.105263\n",
" 3606803 │\u001b[90m missing \u001b[0m\n",
"\u001b[31m 3606792 rows omitted\u001b[0m"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2′ = select(df, [:Passengers, :Seats, :Flights] => get_occupied2 => :occupied)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we check if both methods produced the same result:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"true"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"isequal(df2.occupied, df2′.occupied)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second thing we can do is compare the performance of both options:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: \n",
" memory estimate: 58.48 MiB\n",
" allocs estimate: 97\n",
" --------------\n",
" minimum time: 9.587 ms (0.00% GC)\n",
" median time: 10.809 ms (0.00% GC)\n",
" mean time: 11.749 ms (1.08% GC)\n",
" maximum time: 24.712 ms (0.00% GC)\n",
" --------------\n",
" samples: 426\n",
" evals/sample: 1"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@benchmark select($df, [:Passengers, :Seats, :Flights] => ByRow(get_occupied) => :occupied)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BenchmarkTools.Trial: \n",
" memory estimate: 58.48 MiB\n",
" allocs estimate: 94\n",
" --------------\n",
" minimum time: 11.247 ms (0.00% GC)\n",
" median time: 13.610 ms (0.00% GC)\n",
" mean time: 15.025 ms (1.57% GC)\n",
" maximum time: 34.100 ms (7.50% GC)\n",
" --------------\n",
" samples: 334\n",
" evals/sample: 1"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@benchmark select($df, [:Passengers, :Seats, :Flights] => get_occupied2 => :occupied)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see DataFrames.jl does a decent job in this case - the option with `ByRow` is a tad faster than the broadcasting approach."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A crucial value of piping with `@pipe` is that it composes also with functions outside of DataFrames.jl package.\n",
"\n",
"Here is a simple example in which we will plot the total number of passengers that flew by year:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": false
},
"outputs": [
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" \n",
" \"/>\n",
"</svg>\n"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@pipe df2 |>\n",
" groupby(_, :year, sort=true) |>\n",
" combine(_, :Passengers => sum) |>\n",
" plot(_.year, _.Passengers_sum ./ 10^6, legend=nothing,\n",
" xlab=\"year\", ylab=\"passengers (in millions)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I particular note that `_` notation from Pipe.jl package allows you to write any valid code you could use on the passed value. In our case `plot` is passed a `DataFrame`, so we e.g. write `_.year` to get a `:year` column from it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following example we will want to find pairs of origin-destination states that have the highest occupancy:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>ost</th><th>dst</th><th>occupied</th><th>Passengers_sum</th></tr><tr><th></th><th>String</th><th>String</th><th>Float64</th><th>Int64</th></tr></thead><tbody><p>2,369 rows × 4 columns</p><tr><th>1</th><td>NE</td><td>AK</td><td>1.0</td><td>180</td></tr><tr><th>2</th><td>MD</td><td>ID</td><td>1.0</td><td>150</td></tr><tr><th>3</th><td>ID</td><td>AR</td><td>1.0</td><td>124</td></tr><tr><th>4</th><td>ID</td><td>MS</td><td>1.0</td><td>181</td></tr><tr><th>5</th><td>ND</td><td>RI</td><td>1.0</td><td>50</td></tr><tr><th>6</th><td>WY</td><td>NY</td><td>0.991935</td><td>123</td></tr><tr><th>7</th><td>OR</td><td>RI</td><td>0.991935</td><td>123</td></tr><tr><th>8</th><td>WV</td><td>NE</td><td>0.984615</td><td>176</td></tr><tr><th>9</th><td>KS</td><td>ID</td><td>0.983871</td><td>122</td></tr><tr><th>10</th><td>AK</td><td>TN</td><td>0.981723</td><td>376</td></tr><tr><th>11</th><td>NM</td><td>KY</td><td>0.979204</td><td>285</td></tr><tr><th>12</th><td>HI</td><td>NE</td><td>0.977472</td><td>1327</td></tr><tr><th>13</th><td>PA</td><td>HI</td><td>0.974296</td><td>699</td></tr><tr><th>14</th><td>SD</td><td>MS</td><td>0.971098</td><td>168</td></tr><tr><th>15</th><td>WV</td><td>NV</td><td>0.970803</td><td>133</td></tr><tr><th>16</th><td>MI</td><td>ID</td><td>0.966667</td><td>145</td></tr><tr><th>17</th><td>MS</td><td>SD</td><td>0.965318</td><td>167</td></tr><tr><th>18</th><td>MD</td><td>HI</td><td>0.963899</td><td>267</td></tr><tr><th>19</th><td>MT</td><td>OK</td><td>0.959677</td><td>164</td></tr><tr><th>20</th><td>OR</td><td>VA</td><td>0.955169</td><td>653</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& ost & dst & occupied & Passengers\\_sum\\\\\n",
"\t\\hline\n",
"\t& String & String & Float64 & Int64\\\\\n",
"\t\\hline\n",
"\t1 & NE & AK & 1.0 & 180 \\\\\n",
"\t2 & MD & ID & 1.0 & 150 \\\\\n",
"\t3 & ID & AR & 1.0 & 124 \\\\\n",
"\t4 & ID & MS & 1.0 & 181 \\\\\n",
"\t5 & ND & RI & 1.0 & 50 \\\\\n",
"\t6 & WY & NY & 0.991935 & 123 \\\\\n",
"\t7 & OR & RI & 0.991935 & 123 \\\\\n",
"\t8 & WV & NE & 0.984615 & 176 \\\\\n",
"\t9 & KS & ID & 0.983871 & 122 \\\\\n",
"\t10 & AK & TN & 0.981723 & 376 \\\\\n",
"\t11 & NM & KY & 0.979204 & 285 \\\\\n",
"\t12 & HI & NE & 0.977472 & 1327 \\\\\n",
"\t13 & PA & HI & 0.974296 & 699 \\\\\n",
"\t14 & SD & MS & 0.971098 & 168 \\\\\n",
"\t15 & WV & NV & 0.970803 & 133 \\\\\n",
"\t16 & MI & ID & 0.966667 & 145 \\\\\n",
"\t17 & MS & SD & 0.965318 & 167 \\\\\n",
"\t18 & MD & HI & 0.963899 & 267 \\\\\n",
"\t19 & MT & OK & 0.959677 & 164 \\\\\n",
"\t20 & OR & VA & 0.955169 & 653 \\\\\n",
"\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m2369×4 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m ost \u001b[0m\u001b[1m dst \u001b[0m\u001b[1m occupied \u001b[0m\u001b[1m Passengers_sum \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
"──────┼──────────────────────────────────────────\n",
" 1 │ NE AK 1.0 180\n",
" 2 │ MD ID 1.0 150\n",
" 3 │ ID AR 1.0 124\n",
" 4 │ ID MS 1.0 181\n",
" 5 │ ND RI 1.0 50\n",
" 6 │ WY NY 0.991935 123\n",
" ⋮ │ ⋮ ⋮ ⋮ ⋮\n",
" 2365 │ AK AR 0.0 0\n",
" 2366 │ DE MN 0.0 0\n",
" 2367 │ AL SD 0.0 0\n",
" 2368 │ SC SD 0.0 0\n",
" 2369 │ AK VA 0.0 0\n",
"\u001b[31m 2358 rows omitted\u001b[0m"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3 = @pipe df2 |>\n",
" groupby(_, [:ost, :dst]) |>\n",
" combine(_, :occupied => mean∘skipmissing => :occupied, :Passengers => sum) |>\n",
" filter!(:occupied => isfinite, _) |>\n",
" sort!(_, :occupied, rev=true)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above code note two things: we use `filter!` and `sort!` functions to process the data frame returned by `combine` in place (which reduces the number of allocations we make).\n",
"\n",
"Additionally note that when we aggregate `:occupied` column in `combine` using `mean∘skipmissing` then for some origin-destination state combinations we get zero observations, which produce `NaN`, so we filter them out using `isfinite` predicate."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just to make sure what happens when we apply `mean∘skipmissing` to a vector containing only missing values consider the following simple example:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>g</th><th>v_mean_skipmissing</th></tr><tr><th></th><th>Int64</th><th>Float64</th></tr></thead><tbody><p>2 rows × 2 columns</p><tr><th>1</th><td>1</td><td>1.5</td></tr><tr><th>2</th><td>2</td><td>NaN</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cc}\n",
"\t& g & v\\_mean\\_skipmissing\\\\\n",
"\t\\hline\n",
"\t& Int64 & Float64\\\\\n",
"\t\\hline\n",
"\t1 & 1 & 1.5 \\\\\n",
"\t2 & 2 & NaN \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m2×2 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m g \u001b[0m\u001b[1m v_mean_skipmissing \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\n",
"─────┼───────────────────────────\n",
" 1 │ 1 1.5\n",
" 2 │ 2 NaN"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@pipe DataFrame(g=[1,1,2,2], v=[1,2,missing, missing]) |>\n",
" groupby(_, :g) |>\n",
" combine(_, :v => mean∘skipmissing)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a final example let us consider the following case. Assume that you want to query `df3` data frame by `:ost` and `:dst` columns very often.\n",
"\n",
"The simplest way to do it is to use `filter` as we have already discussed:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>ost</th><th>dst</th><th>occupied</th><th>Passengers_sum</th></tr><tr><th></th><th>String</th><th>String</th><th>Float64</th><th>Int64</th></tr></thead><tbody><p>1 rows × 4 columns</p><tr><th>1</th><td>NE</td><td>AK</td><td>1.0</td><td>180</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& ost & dst & occupied & Passengers\\_sum\\\\\n",
"\t\\hline\n",
"\t& String & String & Float64 & Int64\\\\\n",
"\t\\hline\n",
"\t1 & NE & AK & 1.0 & 180 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m1×4 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m ost \u001b[0m\u001b[1m dst \u001b[0m\u001b[1m occupied \u001b[0m\u001b[1m Passengers_sum \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
"─────┼──────────────────────────────────────────\n",
" 1 │ NE AK 1.0 180"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filter([:ost, :dst] => (ost, dst) -> ost == \"NE\" && dst == \"AK\", df3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However this method is expensive. Consider e.g. the timing of this operation done for all combinations of `:ost` and `:dst`. We just want to calculate the total number of passengers in a fancy way:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.306238 seconds (472.01 k allocations: 34.803 MiB)\n"
]
}
],
"source": [
"total1 = 0\n",
"@time for o in unique(df3.ost), d in unique(df3.dst)\n",
" res = filter([:ost, :dst] => (ost, dst) -> ost == o && dst == d, df3)\n",
" global total1 += sum(res.Passengers_sum)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.361516 seconds (471.98 k allocations: 34.803 MiB)\n"
]
}
],
"source": [
"total1 = 0\n",
"@time for o in unique(df3.ost), d in unique(df3.dst)\n",
" res = filter([:ost, :dst] => (ost, dst) -> ost == o && dst == d, df3)\n",
" global total1 += sum(res.Passengers_sum)\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that we have run the operation twice to make sure we correctly handle compilation overhead."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let us use a bit different technique. We `groupby` the `df3` data frame first:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<p><b>GroupedDataFrame with 2369 groups based on keys: ost, dst</b></p><p><i>First Group (1 row): ost = \"NE\", dst = \"AK\"</i></p><table class=\"data-frame\"><thead><tr><th></th><th>ost</th><th>dst</th><th>occupied</th><th>Passengers_sum</th></tr><tr><th></th><th>String</th><th>String</th><th>Float64</th><th>Int64</th></tr></thead><tbody><tr><th>1</th><td>NE</td><td>AK</td><td>1.0</td><td>180</td></tr></tbody></table><p>&vellip;</p><p><i>Last Group (1 row): ost = \"AK\", dst = \"VA\"</i></p><table class=\"data-frame\"><thead><tr><th></th><th>ost</th><th>dst</th><th>occupied</th><th>Passengers_sum</th></tr><tr><th></th><th>String</th><th>String</th><th>Float64</th><th>Int64</th></tr></thead><tbody><tr><th>1</th><td>AK</td><td>VA</td><td>0.0</td><td>0</td></tr></tbody></table>"
],
"text/latex": [
"GroupedDataFrame with 2369 groups based on keys: ost, dst\n",
"\n",
"First Group (1 row): ost = \"NE\", dst = \"AK\"\n",
"\n",
"\\begin{tabular}{r|cccc}\n",
"\t& ost & dst & occupied & Passengers\\_sum\\\\\n",
"\t\\hline\n",
"\t& String & String & Float64 & Int64\\\\\n",
"\t\\hline\n",
"\t1 & NE & AK & 1.0 & 180 \\\\\n",
"\\end{tabular}\n",
"\n",
"$\\dots$\n",
"\n",
"Last Group (1 row): ost = \"AK\", dst = \"VA\"\n",
"\n",
"\\begin{tabular}{r|cccc}\n",
"\t& ost & dst & occupied & Passengers\\_sum\\\\\n",
"\t\\hline\n",
"\t& String & String & Float64 & Int64\\\\\n",
"\t\\hline\n",
"\t1 & AK & VA & 0.0 & 0 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"GroupedDataFrame with 2369 groups based on keys: ost, dst\n",
"First Group (1 row): ost = \"NE\", dst = \"AK\"\n",
"\u001b[1m Row \u001b[0m│\u001b[1m ost \u001b[0m\u001b[1m dst \u001b[0m\u001b[1m occupied \u001b[0m\u001b[1m Passengers_sum \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
"─────┼──────────────────────────────────────────\n",
" 1 │ NE AK 1.0 180\n",
"⋮\n",
"Last Group (1 row): ost = \"AK\", dst = \"VA\"\n",
"\u001b[1m Row \u001b[0m│\u001b[1m ost \u001b[0m\u001b[1m dst \u001b[0m\u001b[1m occupied \u001b[0m\u001b[1m Passengers_sum \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
"─────┼──────────────────────────────────────────\n",
" 1 │ AK VA 0.0 0"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf3 = groupby(df3, [:ost, :dst])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can easiliy query it by group values (here we show such indexing by `Tuple` or `NamedTuple`):"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>ost</th><th>dst</th><th>occupied</th><th>Passengers_sum</th></tr><tr><th></th><th>String</th><th>String</th><th>Float64</th><th>Int64</th></tr></thead><tbody><p>1 rows × 4 columns</p><tr><th>1</th><td>NE</td><td>AK</td><td>1.0</td><td>180</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& ost & dst & occupied & Passengers\\_sum\\\\\n",
"\t\\hline\n",
"\t& String & String & Float64 & Int64\\\\\n",
"\t\\hline\n",
"\t1 & NE & AK & 1.0 & 180 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m1×4 SubDataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m ost \u001b[0m\u001b[1m dst \u001b[0m\u001b[1m occupied \u001b[0m\u001b[1m Passengers_sum \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
"─────┼──────────────────────────────────────────\n",
" 1 │ NE AK 1.0 180"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf3[(\"NE\", \"AK\")]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"data-frame\"><thead><tr><th></th><th>ost</th><th>dst</th><th>occupied</th><th>Passengers_sum</th></tr><tr><th></th><th>String</th><th>String</th><th>Float64</th><th>Int64</th></tr></thead><tbody><p>1 rows × 4 columns</p><tr><th>1</th><td>NE</td><td>AK</td><td>1.0</td><td>180</td></tr></tbody></table>"
],
"text/latex": [
"\\begin{tabular}{r|cccc}\n",
"\t& ost & dst & occupied & Passengers\\_sum\\\\\n",
"\t\\hline\n",
"\t& String & String & Float64 & Int64\\\\\n",
"\t\\hline\n",
"\t1 & NE & AK & 1.0 & 180 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m1×4 SubDataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m ost \u001b[0m\u001b[1m dst \u001b[0m\u001b[1m occupied \u001b[0m\u001b[1m Passengers_sum \u001b[0m\n",
"\u001b[1m \u001b[0m│\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
"─────┼──────────────────────────────────────────\n",
" 1 │ NE AK 1.0 180"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf3[(ost=\"NE\", dst=\"AK\")]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the code below we will actually use a third way of querying the `GroupedDataFrame`:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.117862 seconds (230.52 k allocations: 11.196 MiB)\n"
]
}
],
"source": [
"total2 = 0\n",
"@time for k in keys(gdf3)\n",
" res = gdf3[k]\n",
" global total2 += sum(res.Passengers_sum)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0.001509 seconds (20.89 k allocations: 844.609 KiB)\n"
]
}
],
"source": [
"total2 = 0\n",
"@time for k in keys(gdf3)\n",
" res = gdf3[k]\n",
" global total2 += sum(res.Passengers_sum)\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the speed is of two orders of magnitude better now."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that what we addidionally did is we used `keys` on `GroupedDataFrame`:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2369-element DataFrames.GroupKeys{GroupedDataFrame{DataFrame}}:\n",
" GroupKey: (ost = \"NE\", dst = \"AK\")\n",
" GroupKey: (ost = \"MD\", dst = \"ID\")\n",
" GroupKey: (ost = \"ID\", dst = \"AR\")\n",
" GroupKey: (ost = \"ID\", dst = \"MS\")\n",
" GroupKey: (ost = \"ND\", dst = \"RI\")\n",
" GroupKey: (ost = \"WY\", dst = \"NY\")\n",
" GroupKey: (ost = \"OR\", dst = \"RI\")\n",
" GroupKey: (ost = \"WV\", dst = \"NE\")\n",
" ⋮\n",
" GroupKey: (ost = \"RI\", dst = \"RI\")\n",
" GroupKey: (ost = \"MN\", dst = \"DE\")\n",
" GroupKey: (ost = \"AK\", dst = \"AR\")\n",
" GroupKey: (ost = \"DE\", dst = \"MN\")\n",
" GroupKey: (ost = \"AL\", dst = \"SD\")\n",
" GroupKey: (ost = \"SC\", dst = \"SD\")\n",
" GroupKey: (ost = \"AK\", dst = \"VA\")"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"keys(gdf3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this way we got only the origin-destination state combinations that are actually present in our `GroupedDataFrame`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ability to query a `GroupedDataFrame` by grouping columns fast makes it a very useful way to add an index to a data frame."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally let us check that the results in both cases are correct."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(9698370217, 9698370217, 9698370217)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total1, total2, sum(df3.Passengers_sum)"
]
}
],
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"display_name": "Julia 1.5.3",
"language": "julia",
"name": "julia-1.5"
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"file_extension": ".jl",
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"name": "julia",
"version": "1.5.3"
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},
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"nbformat_minor": 4
}
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bison.json
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[[StatsBase]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
git-tree-sha1 = "7bab7d4eb46b225b35179632852b595a3162cb61"
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
version = "0.33.2"
[[StructArrays]]
deps = ["Adapt", "DataAPI", "Tables"]
git-tree-sha1 = "8099ed9fb90b6e754d6ba8c6ed8670f010eadca0"
uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
version = "0.4.4"
[[StructTypes]]
deps = ["Dates", "UUIDs"]
git-tree-sha1 = "1ed04f622a39d2e5a6747c3a70be040c00333933"
uuid = "856f2bd8-1eba-4b0a-8007-ebc267875bd4"
version = "1.1.0"
[[TableTraits]]
deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "b1ad568ba658d8cbb3b892ed5380a6f3e781a81e"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
version = "1.0.0"
[[Tables]]
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"]
git-tree-sha1 = "5131a624173d532299d1c7eb05341c18112b21b8"
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.2.1"
[[Test]]
deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[[UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[[Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
[[VersionParsing]]
git-tree-sha1 = "80229be1f670524750d905f8fc8148e5a8c4537f"
uuid = "81def892-9a0e-5fdd-b105-ffc91e053289"
version = "1.2.0"
[[Wayland_jll]]
deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "dc643a9b774da1c2781413fd7b6dcd2c56bb8056"
uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89"
version = "1.17.0+4"
[[Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll"]
git-tree-sha1 = "2839f1c1296940218e35df0bbb220f2a79686670"
uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
version = "1.18.0+4"
[[XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "be0db24f70aae7e2b89f2f3092e93b8606d659a6"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.10+3"
[[XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "2b3eac39df218762d2d005702d601cd44c997497"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.33+4"
[[Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"
[[Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"
[[Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"
[[Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"
[[Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"
[[Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"
[[Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"
[[Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"
[[Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"
[[Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"
[[Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"
[[Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"
[[Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.0+4"
[[Xorg_xcb_util_image_jll]]
deps = ["Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "16eb9a5aa027fb877207bf9915686366c2d5c064"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+0"
[[Xorg_xcb_util_jll]]
deps = ["Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "6b47a94261a67078fe3d3922363bd9fd83b6eb1d"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+0"
[[Xorg_xcb_util_keysyms_jll]]
deps = ["Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "40771f688e17baa121136b649e631e1868a6678e"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+0"
[[Xorg_xcb_util_renderutil_jll]]
deps = ["Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "72c9b59211a97f763a9ca82351d37ebc04a6858a"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+0"
[[Xorg_xcb_util_wm_jll]]
deps = ["Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "6ed52e9bfb2421f01ee62e1a5a30eba5f3f29c74"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+0"
[[Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"
[[Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"
[[Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"
[[ZMQ]]
deps = ["FileWatching", "Sockets", "ZeroMQ_jll"]
git-tree-sha1 = "fc68e8a3719166950a0f3e390a14c7302c48f8de"
uuid = "c2297ded-f4af-51ae-bb23-16f91089e4e1"
version = "1.2.1"
[[ZeroMQ_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "bba617292e040408cb72baa03c20f43583bf239f"
uuid = "8f1865be-045e-5c20-9c9f-bfbfb0764568"
version = "4.3.2+5"
[[Zlib_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "320228915c8debb12cb434c59057290f0834dbf6"
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.11+18"
[[Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6f1abcb0c44f184690912aa4b0ba861dd64f11b9"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.4.5+2"
[[libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "acc685bcf777b2202a904cdcb49ad34c2fa1880c"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.14.0+4"
[[libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "7a5780a0d9c6864184b3a2eeeb833a0c871f00ab"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "0.1.6+4"
[[libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "6abbc424248097d69c0c87ba50fcb0753f93e0ee"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.37+6"
[[libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "fa14ac25af7a4b8a7f61b287a124df7aab601bcd"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.6+6"
[[x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "d713c1ce4deac133e3334ee12f4adff07f81778f"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2020.7.14+2"
[[x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "487da2f8f2f0c8ee0e83f39d13037d6bbf0a45ab"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.0.0+3"
[[xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
Raw
Project.toml
[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a"
JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
Pipe = "b98c9c47-44ae-5843-9183-064241ee97a0"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
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