{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DataFrames in Julia\n", "\n", "Let's continue our exploration of Julia by looking at one of the most popular packages in the ecosystem, [**DataFrames**](https://dataframes.juliadata.org/stable/).\n", "\n", "DataFrames are a powerful and convenient way to work with *tabular data*. They are very popular in the R and Python ecosystems and Julia can speak DataFrames too." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DataFrames Package\n", "\n", "By now installing a new package in Julia should be something you know how to do, but for reference:\n", "\n", "```julia\n", " ➜ JuliaHEP-2023 git:(main) julia --project=.\n", " _\n", " _ _ _(_)_ | Documentation: https://docs.julialang.org\n", " (_) | (_) (_) |\n", " _ _ _| |_ __ _ | Type \"?\" for help, \"]?\" for Pkg help.\n", " | | | | | | |/ _` | |\n", " | | |_| | | | (_| | | Version 1.9.3 (2023-08-24)\n", " _/ |\\__'_|_|_|\\__'_| | Official https://julialang.org/ release\n", "|__/ |\n", "\n", "## At the julia> prompt type `]`\n", "\n", "(JuliaHEP-2023) pkg> add DataFrames\n", "...\n", "```\n", "\n", "Now we can see how to construct a simple data frame" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "using DataFrames" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating DataFrames" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "leptons (generic function with 1 method)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "function leptons()\n", " name = [\"electron\", \"muon\", \"tau\"]\n", " symbol = [\"e\", \"μ\", \"τ\"]\n", " mass = [0.5109989, 105.657, 1776.86]\n", " charge = -1.0\n", " DataFrame(; name, symbol, mass, charge)\n", "end" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
3×4 DataFrame
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1electrone0.510999-1.0
2muonμ105.657-1.0
3tauτ1776.86-1.0
" ], "text/latex": [ "\\begin{tabular}{r|cccc}\n", "\t& name & symbol & mass & charge\\\\\n", "\t\\hline\n", "\t& String & String & Float64 & Float64\\\\\n", "\t\\hline\n", "\t1 & electron & e & 0.510999 & -1.0 \\\\\n", "\t2 & muon & μ & 105.657 & -1.0 \\\\\n", "\t3 & tau & τ & 1776.86 & -1.0 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m3×4 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m name \u001b[0m\u001b[1m symbol \u001b[0m\u001b[1m mass \u001b[0m\u001b[1m charge \u001b[0m\n", " │\u001b[90m String \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", "─────┼────────────────────────────────────────\n", " 1 │ electron e 0.510999 -1.0\n", " 2 │ muon μ 105.657 -1.0\n", " 3 │ tau τ 1776.86 -1.0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_leptons = leptons()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the DataFrame is constructed with a bunch of vectors, of different types (but the same length!), and also using a scalar, when every row has the same value.\n", "\n", "There are different ways to construct DataFrames from dictionaries, named tuples, matrices and so on, as well as using alternative column names - read the docs for all the ways!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading Data from CSV\n", "\n", "Loading data from a CSV file is extremely common. You will need to use the `CSV` package." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "using CSV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just before we do that, as this table is pretty big, let's set some more appropriate display parameters for the tutorial" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "ENV[\"DATAFRAMES_ROWS\"] = 20;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we load the table.\n", "\n", "N.B. If you have this tutorial checked out from GitHub, or running in Binder, then the relative path below is correct, viz., in the `./assets` directory; you can also [download the data](julia-intro/docs/assets/atlas-higgs-challenge-2014-v2-reduced.csv) directly." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
50000×19 DataFrame
49980 rows omitted
RowEventIdPRI_tau_ptPRI_tau_etaPRI_tau_phiPRI_lep_ptPRI_lep_etaPRI_lep_phiPRI_metPRI_met_phiPRI_met_sumetPRI_jet_numPRI_jet_leading_ptPRI_jet_leading_etaPRI_jet_leading_phiPRI_jet_subleading_ptPRI_jet_subleading_etaPRI_jet_subleading_phiPRI_jet_all_ptLabel
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" ], "text/latex": [ "\\begin{tabular}{r|cccccccc}\n", "\t& EventId & PRI\\_tau\\_pt & PRI\\_tau\\_eta & PRI\\_tau\\_phi & PRI\\_lep\\_pt & PRI\\_lep\\_eta & PRI\\_lep\\_phi & \\\\\n", "\t\\hline\n", "\t& Int64 & Float64 & Float64 & Float64 & Float64 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & 100000 & 32.638 & 1.017 & 0.381 & 51.626 & 2.273 & -2.414 & $\\dots$ \\\\\n", "\t2 & 100001 & 42.014 & 2.039 & -3.011 & 36.918 & 0.501 & 0.103 & $\\dots$ \\\\\n", "\t3 & 100002 & 32.154 & -0.705 & -2.093 & 121.409 & -0.953 & 1.052 & $\\dots$ \\\\\n", "\t4 & 100003 & 22.647 & -1.655 & 0.01 & 53.321 & -0.522 & -3.1 & $\\dots$ \\\\\n", "\t5 & 100004 & 28.209 & -2.197 & -2.231 & 29.774 & 0.798 & 1.569 & $\\dots$ \\\\\n", "\t6 & 100005 & 53.651 & 0.371 & 1.329 & 31.565 & -0.884 & 1.857 & $\\dots$ \\\\\n", "\t7 & 100006 & 28.85 & 1.113 & 2.409 & 97.24 & 0.675 & -0.966 & $\\dots$ \\\\\n", "\t8 & 100007 & 78.8 & 0.654 & 1.547 & 28.74 & 0.506 & -1.347 & $\\dots$ \\\\\n", "\t9 & 100008 & 39.008 & 2.433 & -2.532 & 26.325 & 0.21 & 1.884 & $\\dots$ \\\\\n", "\t10 & 100009 & 54.646 & -1.533 & 0.416 & 32.742 & -0.317 & -0.636 & $\\dots$ \\\\\n", "\t11 & 100010 & 29.718 & -0.866 & 2.878 & 52.016 & 0.126 & -1.288 & $\\dots$ \\\\\n", "\t12 & 100011 & 35.976 & -0.669 & -0.342 & 38.188 & -0.165 & 2.502 & $\\dots$ \\\\\n", "\t13 & 100012 & 62.89 & -0.766 & -1.632 & 36.237 & 0.722 & -0.035 & $\\dots$ \\\\\n", "\t14 & 100013 & 25.47 & -0.654 & -2.99 & 33.179 & -1.665 & -0.354 & $\\dots$ \\\\\n", "\t15 & 100014 & 22.552 & 1.389 & 1.34 & 40.013 & 1.856 & 1.412 & $\\dots$ \\\\\n", "\t16 & 100015 & 30.606 & -1.107 & -1.903 & 39.043 & -1.944 & 1.191 & $\\dots$ \\\\\n", "\t17 & 100016 & 30.145 & 0.484 & -0.929 & 34.522 & -0.215 & 1.941 & $\\dots$ \\\\\n", "\t18 & 100017 & 30.739 & -0.635 & 2.603 & 48.764 & -0.343 & -0.862 & $\\dots$ \\\\\n", "\t19 & 100018 & 27.931 & 1.175 & 2.356 & 43.512 & 2.332 & 0.584 & $\\dots$ \\\\\n", "\t20 & 100019 & 31.046 & 1.38 & 0.451 & 27.165 & -1.486 & 0.724 & $\\dots$ \\\\\n", "\t21 & 100020 & 27.453 & 1.58 & 2.51 & 29.704 & 0.341 & 1.869 & $\\dots$ \\\\\n", "\t22 & 100021 & 37.06 & 1.537 & -2.616 & 27.773 & 1.161 & 1.335 & $\\dots$ \\\\\n", "\t23 & 100022 & 28.688 & 1.739 & -2.975 & 36.594 & 2.367 & -0.193 & $\\dots$ \\\\\n", "\t24 & 100023 & 98.565 & 0.19 & -1.506 & 64.285 & 1.405 & -0.952 & $\\dots$ \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m50000×19 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_tau_pt \u001b[0m\u001b[1m PRI_tau_eta \u001b[0m\u001b[1m PRI_tau_phi \u001b[0m\u001b[1m PRI_lep_pt \u001b[0m\u001b[1m PRI_lep_et\u001b[0m ⋯\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m ⋯\n", "───────┼────────────────────────────────────────────────────────────────────────\n", " 1 │ 100000 32.638 1.017 0.381 51.626 2.27 ⋯\n", " 2 │ 100001 42.014 2.039 -3.011 36.918 0.50\n", " 3 │ 100002 32.154 -0.705 -2.093 121.409 -0.95\n", " 4 │ 100003 22.647 -1.655 0.01 53.321 -0.52\n", " 5 │ 100004 28.209 -2.197 -2.231 29.774 0.79 ⋯\n", " 6 │ 100005 53.651 0.371 1.329 31.565 -0.88\n", " 7 │ 100006 28.85 1.113 2.409 97.24 0.67\n", " 8 │ 100007 78.8 0.654 1.547 28.74 0.50\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 49994 │ 149993 24.108 1.616 -2.424 72.108 1.40 ⋯\n", " 49995 │ 149994 26.691 -1.067 -2.872 45.83 -0.93\n", " 49996 │ 149995 73.174 0.819 1.32 30.581 0.95\n", " 49997 │ 149996 30.498 -0.34 1.375 68.136 0.86\n", " 49998 │ 149997 38.094 -0.936 -3.106 32.158 -0.94 ⋯\n", " 49999 │ 149998 29.225 0.746 0.521 50.01 2.07\n", " 50000 │ 149999 30.645 0.859 2.111 26.094 -0.48\n", "\u001b[36m 14 columns and 49985 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "higgs_ml = CSV.read(joinpath(\"assets\", \"atlas-higgs-challenge-2014-v2-reduced.csv\"), DataFrame)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second argument to `CSV.read` tells CSV to read the file into a DataFrame and is a nice illustration of how packages in Julia can remain independent, but still work together (DataFrames has really great [integration](https://dataframes.juliadata.org/stable/#DataFrames.jl-and-the-Julia-Data-Ecosystem) with the Julia ecosystem)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "N.B. this is a *reduced* version of the [Higgs ML](http://opendata.cern.ch/record/328) dataset, restricted to 50k events, where we have also stripped out columns of derived data and event weights. This is just to make this example more *visually* manageable in this notebook - everything which is done here works just fine on the full 818k events with all columns." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get the names of all the columns in a data frame, use `names()`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EventId\n", "PRI_tau_pt\n", "PRI_tau_eta\n", "PRI_tau_phi\n", "PRI_lep_pt\n", "PRI_lep_eta\n", "PRI_lep_phi\n", "PRI_met\n", "PRI_met_phi\n", "PRI_met_sumet\n", "PRI_jet_num\n", "PRI_jet_leading_pt\n", "PRI_jet_leading_eta\n", "PRI_jet_leading_phi\n", "PRI_jet_subleading_pt\n", "PRI_jet_subleading_eta\n", "PRI_jet_subleading_phi\n", "PRI_jet_all_pt\n", "Label\n" ] } ], "source": [ "println(join(names(higgs_ml), \"\\n\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also rather useful is the `describe` function:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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18PRI_jet_all_pt72.9546-0.040.44651417.330Float64
19Labelbs0String1
" ], "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 & EventId & 1.25e5 & 100000 & 1.25e5 & 149999 & 0 & Int64 \\\\\n", "\t2 & PRI\\_tau\\_pt & 38.7584 & 20.0 & 31.848 & 396.875 & 0 & Float64 \\\\\n", "\t3 & PRI\\_tau\\_eta & -0.0141847 & -2.494 & -0.02 & 2.492 & 0 & Float64 \\\\\n", "\t4 & PRI\\_tau\\_phi & -0.00554088 & -3.142 & -0.024 & 3.141 & 0 & Float64 \\\\\n", "\t5 & PRI\\_lep\\_pt & 46.6231 & 26.0 & 40.501 & 423.438 & 0 & Float64 \\\\\n", "\t6 & PRI\\_lep\\_eta & -0.0234368 & -2.49 & -0.053 & 2.49 & 0 & Float64 \\\\\n", "\t7 & PRI\\_lep\\_phi & 0.0426212 & -3.142 & 0.084 & 3.142 & 0 & Float64 \\\\\n", "\t8 & PRI\\_met & 41.7448 & 0.2 & 34.9075 & 2842.62 & 0 & Float64 \\\\\n", "\t9 & PRI\\_met\\_phi & -0.00511272 & -3.142 & -0.036 & 3.142 & 0 & Float64 \\\\\n", "\t10 & PRI\\_met\\_sumet & 209.705 & 13.678 & 179.324 & 1512.24 & 0 & Float64 \\\\\n", "\t11 & PRI\\_jet\\_num & 0.97868 & 0 & 1.0 & 3 & 0 & Int64 \\\\\n", "\t12 & PRI\\_jet\\_leading\\_pt & -348.632 & -999.0 & 38.7805 & 755.235 & 0 & Float64 \\\\\n", "\t13 & PRI\\_jet\\_leading\\_eta & -399.447 & -999.0 & -1.8805 & 4.482 & 0 & Float64 \\\\\n", "\t14 & PRI\\_jet\\_leading\\_phi & -399.442 & -999.0 & -2.065 & 3.141 & 0 & Float64 \\\\\n", "\t15 & PRI\\_jet\\_subleading\\_pt & -692.799 & -999.0 & -999.0 & 557.18 & 0 & Float64 \\\\\n", "\t16 & PRI\\_jet\\_subleading\\_eta & -709.495 & -999.0 & -999.0 & 4.496 & 0 & Float64 \\\\\n", "\t17 & PRI\\_jet\\_subleading\\_phi & -709.492 & -999.0 & -999.0 & 3.141 & 0 & Float64 \\\\\n", "\t18 & PRI\\_jet\\_all\\_pt & 72.9546 & -0.0 & 40.4465 & 1417.33 & 0 & Float64 \\\\\n", "\t19 & Label & & b & & s & 0 & String1 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m19×7 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 ⋯\n", " │\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 ⋯\n", "─────┼──────────────────────────────────────────────────────────────────────────\n", " 1 │ EventId 1.25e5 100000 1.25e5 149999 0 ⋯\n", " 2 │ PRI_tau_pt 38.7584 20.0 31.848 396.875 0\n", " 3 │ PRI_tau_eta -0.0141847 -2.494 -0.02 2.492 0\n", " 4 │ PRI_tau_phi -0.00554088 -3.142 -0.024 3.141 0\n", " 5 │ PRI_lep_pt 46.6231 26.0 40.501 423.438 0 ⋯\n", " 6 │ PRI_lep_eta -0.0234368 -2.49 -0.053 2.49 0\n", " 7 │ PRI_lep_phi 0.0426212 -3.142 0.084 3.142 0\n", " 8 │ PRI_met 41.7448 0.2 34.9075 2842.62 0\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 13 │ PRI_jet_leading_eta -399.447 -999.0 -1.8805 4.482 0 ⋯\n", " 14 │ PRI_jet_leading_phi -399.442 -999.0 -2.065 3.141 0\n", " 15 │ PRI_jet_subleading_pt -692.799 -999.0 -999.0 557.18 0\n", " 16 │ PRI_jet_subleading_eta -709.495 -999.0 -999.0 4.496 0\n", " 17 │ PRI_jet_subleading_phi -709.492 -999.0 -999.0 3.141 0 ⋯\n", " 18 │ PRI_jet_all_pt 72.9546 -0.0 40.4465 1417.33 0\n", " 19 │ Label \u001b[90m \u001b[0m b \u001b[90m \u001b[0m s 0\n", "\u001b[36m 1 column and 4 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "describe(higgs_ml)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Operations\n", "\n", "### Accessing Data\n", "\n", "The template for accessing data from a DataFrame is:\n", "\n", "```julia\n", "my_data[selected_rows, selected_columns]\n", "```\n", "\n", "There are a few different patterns for this, but the template is always the same.\n", "\n", "Extracting data (without copying) works like this:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "50000-element Vector{Int64}:\n", " 2\n", " 1\n", " 1\n", " 0\n", " 0\n", " 3\n", " 2\n", " 1\n", " 0\n", " 1\n", " ⋮\n", " 1\n", " 0\n", " 1\n", " 0\n", " 3\n", " 2\n", " 2\n", " 0\n", " 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "higgs_ml[!, :PRI_jet_num]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the recommended way to do this, although `higgs_ml.PRI_jet_num` and `higgs_ml[!, \"PRI_jet_num\"]` will also work" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "higgs_ml[6, :PRI_jet_num]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you modify the data accessed this way, then you are modifying the primary DataFrame.\n", "\n", "This is why in the `[]` notation a `!` is used - *caveat emptor*! " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "666" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "higgs_ml[6, :PRI_jet_num] = 666 # Completely bonkers!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
DataFrameRow (1 columns)
RowPRI_jet_num
Int64
6666
" ], "text/latex": [ "\\begin{tabular}{r|c}\n", "\t& PRI\\_jet\\_num\\\\\n", "\t\\hline\n", "\t& Int64\\\\\n", "\t\\hline\n", "\t6 & 666 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1mDataFrameRow\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m PRI_jet_num \u001b[0m\n", " │\u001b[90m Int64 \u001b[0m\n", "─────┼─────────────\n", " 6 │ 666" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "higgs_ml[6, [:PRI_jet_num]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When the column specifier is a scalar, one retrieves the actual value. When it's a list of columns (even length 1) then a DataFrame object is returned." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "higgs_ml.PRI_jet_num[6] = 3 # Restore sanity!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Copying Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If a `:` notation is used for the row selection, then a copy of the data is made:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
5×2 DataFrame
RowEventIdPRI_tau_pt
Int64Float64
110000032.638
210000142.014
310000232.154
410000322.647
510000428.209
" ], "text/latex": [ "\\begin{tabular}{r|cc}\n", "\t& EventId & PRI\\_tau\\_pt\\\\\n", "\t\\hline\n", "\t& Int64 & Float64\\\\\n", "\t\\hline\n", "\t1 & 100000 & 32.638 \\\\\n", "\t2 & 100001 & 42.014 \\\\\n", "\t3 & 100002 & 32.154 \\\\\n", "\t4 & 100003 & 22.647 \\\\\n", "\t5 & 100004 & 28.209 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m5×2 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_tau_pt \u001b[0m\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", "─────┼─────────────────────\n", " 1 │ 100000 32.638\n", " 2 │ 100001 42.014\n", " 3 │ 100002 32.154\n", " 4 │ 100003 22.647\n", " 5 │ 100004 28.209" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mini_higgs = higgs_ml[1:5, [:EventId, :PRI_tau_pt]] # Select the given columns from rows 1 to 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To show this is a copy, let's reset the values of $\\tau_{p_T}$:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5-element Vector{Float64}:\n", " 1.2\n", " 2.3\n", " 3.4\n", " 4.5\n", " 5.6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mini_higgs[!, :PRI_tau_pt] = [1.2, 2.3, 3.4, 4.5, 5.6]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5-element Vector{Float64}:\n", " 32.638\n", " 42.014\n", " 32.154\n", " 22.647\n", " 28.209" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "higgs_ml[!, :PRI_tau_pt][1:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The primary data stayed unmodified." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can use an appropriate row vector to set any row in the data frame:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
5×2 DataFrame
RowEventIdPRI_tau_pt
Int64Float64
11000001.2
21000012.3
3666999.0
41000034.5
51000045.6
" ], "text/latex": [ "\\begin{tabular}{r|cc}\n", "\t& EventId & PRI\\_tau\\_pt\\\\\n", "\t\\hline\n", "\t& Int64 & Float64\\\\\n", "\t\\hline\n", "\t1 & 100000 & 1.2 \\\\\n", "\t2 & 100001 & 2.3 \\\\\n", "\t3 & 666 & 999.0 \\\\\n", "\t4 & 100003 & 4.5 \\\\\n", "\t5 & 100004 & 5.6 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m5×2 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_tau_pt \u001b[0m\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", "─────┼─────────────────────\n", " 1 │ 100000 1.2\n", " 2 │ 100001 2.3\n", " 3 │ 666 999.0\n", " 4 │ 100003 4.5\n", " 5 │ 100004 5.6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mini_higgs[3, 1:2] = [666, 999.0]\n", "mini_higgs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The selection of columns is very flexible:\n", "\n", "- `Not()` - exclude columns from the selection\n", "- `Cols()` - union of arguments in the selection\n", "- `regexp` - a regular expression match against column names\n", "- `N::Integer` - pick the Nth column (and `M:N` works as you would expect)\n", "- `:` - all columns *not yet selected*\n", "\n", "The selected output column ordering is respected, so allowing for easy reordering of columns. This is particularly useful when operating on a data frame with `select`, which we will meet below, and with the fact that `:` will not select columns already included." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RowEventIdPRI_jet_numPRI_jet_leading_ptPRI_jet_leading_etaPRI_jet_leading_phiPRI_jet_subleading_ptPRI_jet_subleading_etaPRI_jet_subleading_phiPRI_jet_all_pt
Int64Int64Float64Float64Float64Float64Float64Float64Float64
1100000267.4352.150.44446.0621.24-2.475113.497
2100001146.2260.7251.158-999.0-999.0-999.046.226
3100002144.2512.053-2.028-999.0-999.0-999.044.251
41000030-999.0-999.0-999.0-999.0-999.0-999.0-0.0
51000040-999.0-999.0-999.0-999.0-999.0-999.00.0
6100005390.547-2.412-0.65356.1650.2243.106193.66
71000062123.010.8641.4556.8670.131-2.767179.877
8100007130.638-0.715-1.724-999.0-999.0-999.030.638
91000080-999.0-999.0-999.0-999.0-999.0-999.00.0
101000091167.735-2.767-2.514-999.0-999.0-999.0167.735
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
499911499902166.372-2.844-0.14856.3-1.5342.692222.672
499921499911140.7460.5931.712-999.0-999.0-999.0140.746
499931499920-999.0-999.0-999.0-999.0-999.0-999.00.0
49994149993163.7052.8842.632-999.0-999.0-999.063.705
499951499940-999.0-999.0-999.0-999.0-999.0-999.00.0
499961499953193.2970.14-1.79892.691-0.5261.653398.099
499971499962149.11-0.234-2.23558.1840.5942.188207.294
499981499972122.3-1.4143.04368.4832.5180.046190.783
499991499980-999.0-999.0-999.0-999.0-999.0-999.00.0
50000149999149.353-2.641-2.038-999.0-999.0-999.049.353
" ], "text/latex": [ "\\begin{tabular}{r|cccccc}\n", "\t& EventId & PRI\\_jet\\_num & PRI\\_jet\\_leading\\_pt & PRI\\_jet\\_leading\\_eta & PRI\\_jet\\_leading\\_phi & \\\\\n", "\t\\hline\n", "\t& Int64 & Int64 & Float64 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & 100000 & 2 & 67.435 & 2.15 & 0.444 & $\\dots$ \\\\\n", "\t2 & 100001 & 1 & 46.226 & 0.725 & 1.158 & $\\dots$ \\\\\n", "\t3 & 100002 & 1 & 44.251 & 2.053 & -2.028 & $\\dots$ \\\\\n", "\t4 & 100003 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t5 & 100004 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t6 & 100005 & 3 & 90.547 & -2.412 & -0.653 & $\\dots$ \\\\\n", "\t7 & 100006 & 2 & 123.01 & 0.864 & 1.45 & $\\dots$ \\\\\n", "\t8 & 100007 & 1 & 30.638 & -0.715 & -1.724 & $\\dots$ \\\\\n", "\t9 & 100008 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t10 & 100009 & 1 & 167.735 & -2.767 & -2.514 & $\\dots$ \\\\\n", "\t11 & 100010 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t12 & 100011 & 3 & 76.773 & -0.79 & 0.303 & $\\dots$ \\\\\n", "\t13 & 100012 & 1 & 93.117 & -0.97 & 1.943 & $\\dots$ \\\\\n", "\t14 & 100013 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t15 & 100014 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t16 & 100015 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t17 & 100016 & 1 & 36.263 & -0.766 & -0.686 & $\\dots$ \\\\\n", "\t18 & 100017 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t19 & 100018 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t20 & 100019 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t21 & 100020 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t22 & 100021 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t23 & 100022 & 0 & -999.0 & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t24 & 100023 & 2 & 195.533 & 1.156 & 1.416 & $\\dots$ \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m50000×9 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_jet_num \u001b[0m\u001b[1m PRI_jet_leading_pt \u001b[0m\u001b[1m PRI_jet_leading_eta \u001b[0m\u001b[1m PRI_je\u001b[0m ⋯\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float6\u001b[0m ⋯\n", "───────┼────────────────────────────────────────────────────────────────────────\n", " 1 │ 100000 2 67.435 2.15 ⋯\n", " 2 │ 100001 1 46.226 0.725\n", " 3 │ 100002 1 44.251 2.053\n", " 4 │ 100003 0 -999.0 -999.0\n", " 5 │ 100004 0 -999.0 -999.0 ⋯\n", " 6 │ 100005 3 90.547 -2.412\n", " 7 │ 100006 2 123.01 0.864\n", " 8 │ 100007 1 30.638 -0.715\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 49994 │ 149993 1 63.705 2.884 ⋯\n", " 49995 │ 149994 0 -999.0 -999.0\n", " 49996 │ 149995 3 193.297 0.14\n", " 49997 │ 149996 2 149.11 -0.234\n", " 49998 │ 149997 2 122.3 -1.414 ⋯\n", " 49999 │ 149998 0 -999.0 -999.0\n", " 50000 │ 149999 1 49.353 -2.641\n", "\u001b[36m 5 columns and 49985 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "higgs_jets = higgs_ml[:, Cols(:EventId, r\"PRI_jet.*\")]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Selection from `bool`\n", "\n", "A powerful way to select data is to select rows on a boolean vector constructed from the data frame itself, e.g., to select all rows that are signal events do the following.\n", "\n", "(Below we explain why you need to use `.==` to broadcast the comparison.)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/latex": [ "\\begin{tabular}{r|cc}\n", "\t& EventId & Label\\\\\n", "\t\\hline\n", "\t& Int64 & String1\\\\\n", "\t\\hline\n", "\t1 & 100000 & s \\\\\n", "\t2 & 100006 & s \\\\\n", "\t3 & 100007 & s \\\\\n", "\t4 & 100009 & s \\\\\n", "\t5 & 100015 & s \\\\\n", "\t6 & 100017 & s \\\\\n", "\t7 & 100023 & s \\\\\n", "\t8 & 100026 & s \\\\\n", "\t9 & 100027 & s \\\\\n", "\t10 & 100028 & s \\\\\n", "\t11 & 100031 & s \\\\\n", "\t12 & 100032 & s \\\\\n", "\t13 & 100036 & s \\\\\n", "\t14 & 100037 & s \\\\\n", "\t15 & 100038 & s \\\\\n", "\t16 & 100039 & s \\\\\n", "\t17 & 100040 & s \\\\\n", "\t18 & 100042 & s \\\\\n", "\t19 & 100046 & s \\\\\n", "\t20 & 100047 & s \\\\\n", "\t21 & 100049 & s \\\\\n", "\t22 & 100051 & s \\\\\n", "\t23 & 100057 & s \\\\\n", "\t24 & 100058 & s \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m17065×2 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m Label \u001b[0m\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m String1 \u001b[0m\n", "───────┼──────────────────\n", " 1 │ 100000 s\n", " 2 │ 100006 s\n", " 3 │ 100007 s\n", " 4 │ 100009 s\n", " 5 │ 100015 s\n", " 6 │ 100017 s\n", " 7 │ 100023 s\n", " 8 │ 100026 s\n", " ⋮ │ ⋮ ⋮\n", " 17059 │ 149986 s\n", " 17060 │ 149990 s\n", " 17061 │ 149991 s\n", " 17062 │ 149993 s\n", " 17063 │ 149997 s\n", " 17064 │ 149998 s\n", " 17065 │ 149999 s\n", "\u001b[36m 17050 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "higgs_ml[higgs_ml.Label .== \"s\", [:EventId, :Label]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Views" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With `view()` or `@view` we create a view into a dataframe, which is fast and efficient" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RowPRI_jet_leading_ptPRI_jet_leading_etaPRI_jet_leading_phi
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167.4352.150.444
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344.2512.053-2.028
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7123.010.8641.45
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9-999.0-999.0-999.0
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49991166.372-2.844-0.148
49992140.7460.5931.712
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49998122.3-1.4143.043
49999-999.0-999.0-999.0
5000049.353-2.641-2.038
" ], "text/latex": [ "\\begin{tabular}{r|ccc}\n", "\t& PRI\\_jet\\_leading\\_pt & PRI\\_jet\\_leading\\_eta & PRI\\_jet\\_leading\\_phi\\\\\n", "\t\\hline\n", "\t& Float64 & Float64 & Float64\\\\\n", "\t\\hline\n", "\t1 & 67.435 & 2.15 & 0.444 \\\\\n", "\t2 & 46.226 & 0.725 & 1.158 \\\\\n", "\t3 & 44.251 & 2.053 & -2.028 \\\\\n", "\t4 & -999.0 & -999.0 & -999.0 \\\\\n", "\t5 & -999.0 & -999.0 & -999.0 \\\\\n", "\t6 & 90.547 & -2.412 & -0.653 \\\\\n", "\t7 & 123.01 & 0.864 & 1.45 \\\\\n", "\t8 & 30.638 & -0.715 & -1.724 \\\\\n", "\t9 & -999.0 & -999.0 & -999.0 \\\\\n", "\t10 & 167.735 & -2.767 & -2.514 \\\\\n", "\t11 & -999.0 & -999.0 & -999.0 \\\\\n", "\t12 & 76.773 & -0.79 & 0.303 \\\\\n", "\t13 & 93.117 & -0.97 & 1.943 \\\\\n", "\t14 & -999.0 & -999.0 & -999.0 \\\\\n", "\t15 & -999.0 & -999.0 & -999.0 \\\\\n", "\t16 & -999.0 & -999.0 & -999.0 \\\\\n", "\t17 & 36.263 & -0.766 & -0.686 \\\\\n", "\t18 & -999.0 & -999.0 & -999.0 \\\\\n", "\t19 & -999.0 & -999.0 & -999.0 \\\\\n", "\t20 & -999.0 & -999.0 & -999.0 \\\\\n", "\t21 & -999.0 & -999.0 & -999.0 \\\\\n", "\t22 & -999.0 & -999.0 & -999.0 \\\\\n", "\t23 & -999.0 & -999.0 & -999.0 \\\\\n", "\t24 & 195.533 & 1.156 & 1.416 \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m50000×3 SubDataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m PRI_jet_leading_pt \u001b[0m\u001b[1m PRI_jet_leading_eta \u001b[0m\u001b[1m PRI_jet_leading_phi \u001b[0m\n", " │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", "───────┼──────────────────────────────────────────────────────────────\n", " 1 │ 67.435 2.15 0.444\n", " 2 │ 46.226 0.725 1.158\n", " 3 │ 44.251 2.053 -2.028\n", " 4 │ -999.0 -999.0 -999.0\n", " 5 │ -999.0 -999.0 -999.0\n", " 6 │ 90.547 -2.412 -0.653\n", " 7 │ 123.01 0.864 1.45\n", " 8 │ 30.638 -0.715 -1.724\n", " ⋮ │ ⋮ ⋮ ⋮\n", " 49994 │ 63.705 2.884 2.632\n", " 49995 │ -999.0 -999.0 -999.0\n", " 49996 │ 193.297 0.14 -1.798\n", " 49997 │ 149.11 -0.234 -2.235\n", " 49998 │ 122.3 -1.414 3.043\n", " 49999 │ -999.0 -999.0 -999.0\n", " 50000 │ 49.353 -2.641 -2.038\n", "\u001b[36m 49985 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "leading_jets = @view higgs_ml[:, r\"PRI_jet_leading.*\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just to emphasise the point, *`higgs_jets` is an independent data frame, and `leading_jets` is a data frame view into the primary data*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Broadcast Assignment\n", "\n", "To broadcast operations across a data frame, we use Julia's `.=` operation" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
5×2 DataFrame
RowEventIdPRI_tau_pt
Int64Float64
1100000999.0
2100001999.0
3666999.0
4100003999.0
5100004999.0
" ], "text/latex": [ "\\begin{tabular}{r|cc}\n", "\t& EventId & PRI\\_tau\\_pt\\\\\n", "\t\\hline\n", "\t& Int64 & Float64\\\\\n", "\t\\hline\n", "\t1 & 100000 & 999.0 \\\\\n", "\t2 & 100001 & 999.0 \\\\\n", "\t3 & 666 & 999.0 \\\\\n", "\t4 & 100003 & 999.0 \\\\\n", "\t5 & 100004 & 999.0 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m5×2 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_tau_pt \u001b[0m\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", "─────┼─────────────────────\n", " 1 │ 100000 999.0\n", " 2 │ 100001 999.0\n", " 3 │ 666 999.0\n", " 4 │ 100003 999.0\n", " 5 │ 100004 999.0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mini_higgs[!, :PRI_tau_pt] .= 999.0\n", "mini_higgs" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
5×2 DataFrame
RowEventIdPRI_tau_pt
Int64Float64
1100010999.0
2100011999.0
3676999.0
4100013999.0
5100014999.0
" ], "text/latex": [ "\\begin{tabular}{r|cc}\n", "\t& EventId & PRI\\_tau\\_pt\\\\\n", "\t\\hline\n", "\t& Int64 & Float64\\\\\n", "\t\\hline\n", "\t1 & 100010 & 999.0 \\\\\n", "\t2 & 100011 & 999.0 \\\\\n", "\t3 & 676 & 999.0 \\\\\n", "\t4 & 100013 & 999.0 \\\\\n", "\t5 & 100014 & 999.0 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m5×2 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_tau_pt \u001b[0m\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\n", "─────┼─────────────────────\n", " 1 │ 100010 999.0\n", " 2 │ 100011 999.0\n", " 3 │ 676 999.0\n", " 4 │ 100013 999.0\n", " 5 │ 100014 999.0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mini_higgs[!, :EventId] .+= 10\n", "mini_higgs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding New Data\n", "\n", "Adding new data to a data frame is just a matter of assigning to a new column (using the Julia *symbol* for the name is useful)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
5×3 DataFrame
RowEventIdPRI_tau_ptname
Int64Float64String
1100010999.0alice
2100011999.0bob
3676999.0ciarn
4100013999.0dinah
5100014999.0elmer
" ], "text/latex": [ "\\begin{tabular}{r|ccc}\n", "\t& EventId & PRI\\_tau\\_pt & name\\\\\n", "\t\\hline\n", "\t& Int64 & Float64 & String\\\\\n", "\t\\hline\n", "\t1 & 100010 & 999.0 & alice \\\\\n", "\t2 & 100011 & 999.0 & bob \\\\\n", "\t3 & 676 & 999.0 & ciarn \\\\\n", "\t4 & 100013 & 999.0 & dinah \\\\\n", "\t5 & 100014 & 999.0 & elmer \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m5×3 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_tau_pt \u001b[0m\u001b[1m name \u001b[0m\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m String \u001b[0m\n", "─────┼─────────────────────────────\n", " 1 │ 100010 999.0 alice\n", " 2 │ 100011 999.0 bob\n", " 3 │ 676 999.0 ciarn\n", " 4 │ 100013 999.0 dinah\n", " 5 │ 100014 999.0 elmer" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mini_higgs[:, :name] = [\"alice\", \"bob\", \"ciarn\", \"dinah\", \"elmer\"]\n", "mini_higgs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Manipulation\n", "\n", "So much for selecting and replacing data - how do we do more interesting thing?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first thing we might want to do is ensure that we can select events that match some particular criteria - for that we can use the `filter` function, like this:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
4436×9 DataFrame
4416 rows omitted
RowEventIdPRI_jet_numPRI_jet_leading_ptPRI_jet_leading_etaPRI_jet_leading_phiPRI_jet_subleading_ptPRI_jet_subleading_etaPRI_jet_subleading_phiPRI_jet_all_pt
Int64Int64Float64Float64Float64Float64Float64Float64Float64
1100005390.547-2.412-0.65356.1650.2243.106193.66
2100011376.773-0.790.30356.8761.773-2.079165.64
31000313182.4491.3830.00138.006-1.257-0.609253.461
41000383114.6020.6190.16577.0532.433-2.637341.947
5100039388.399-2.168-1.42377.27-2.3851.876198.632
6100059380.042-0.8561.30452.5010.638-1.114182.413
7100060378.174-1.668-0.97858.097-0.989-1.727212.314
8100070359.4011.342-0.36953.711-2.5772.14162.577
9100077356.9510.749-0.29642.88-2.229-2.825140.06
10100082385.392-1.0620.16681.5590.513-2.255343.858
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
4427149845385.346-3.078-1.29266.050.022-2.06184.672
4428149855359.409-0.608-0.33937.6833.515-2.542164.274
4429149856361.1851.019-0.52758.4233.314-2.992155.665
44301498903273.5390.8072.5778.5410.353-0.696441.915
44311499003128.952-1.287-0.64337.8674.046-0.265199.454
44321499423118.8621.1740.39736.505-3.3571.89191.244
44331499753283.116-1.009-1.419124.487-1.4890.036489.347
4434149983364.0151.9381.02947.6731.8242.178148.696
44351499853320.4520.758-2.373143.8981.407-1.119505.049
44361499953193.2970.14-1.79892.691-0.5261.653398.099
" ], "text/latex": [ "\\begin{tabular}{r|cccccc}\n", "\t& EventId & PRI\\_jet\\_num & PRI\\_jet\\_leading\\_pt & PRI\\_jet\\_leading\\_eta & PRI\\_jet\\_leading\\_phi & \\\\\n", "\t\\hline\n", "\t& Int64 & Int64 & Float64 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & 100005 & 3 & 90.547 & -2.412 & -0.653 & $\\dots$ \\\\\n", "\t2 & 100011 & 3 & 76.773 & -0.79 & 0.303 & $\\dots$ \\\\\n", "\t3 & 100031 & 3 & 182.449 & 1.383 & 0.001 & $\\dots$ \\\\\n", "\t4 & 100038 & 3 & 114.602 & 0.619 & 0.165 & $\\dots$ \\\\\n", "\t5 & 100039 & 3 & 88.399 & -2.168 & -1.423 & $\\dots$ \\\\\n", "\t6 & 100059 & 3 & 80.042 & -0.856 & 1.304 & $\\dots$ \\\\\n", "\t7 & 100060 & 3 & 78.174 & -1.668 & -0.978 & $\\dots$ \\\\\n", "\t8 & 100070 & 3 & 59.401 & 1.342 & -0.369 & $\\dots$ \\\\\n", "\t9 & 100077 & 3 & 56.951 & 0.749 & -0.296 & $\\dots$ \\\\\n", "\t10 & 100082 & 3 & 85.392 & -1.062 & 0.166 & $\\dots$ \\\\\n", "\t11 & 100084 & 3 & 176.49 & -0.558 & 2.664 & $\\dots$ \\\\\n", "\t12 & 100090 & 3 & 86.379 & 1.365 & 0.155 & $\\dots$ \\\\\n", "\t13 & 100097 & 3 & 97.16 & -1.686 & 2.858 & $\\dots$ \\\\\n", "\t14 & 100102 & 3 & 90.445 & -1.63 & -1.166 & $\\dots$ \\\\\n", "\t15 & 100103 & 3 & 73.26 & -1.915 & -1.662 & $\\dots$ \\\\\n", "\t16 & 100118 & 3 & 148.174 & 1.109 & -1.21 & $\\dots$ \\\\\n", "\t17 & 100134 & 3 & 51.155 & 0.122 & -2.564 & $\\dots$ \\\\\n", "\t18 & 100144 & 3 & 52.541 & 0.194 & -0.861 & $\\dots$ \\\\\n", "\t19 & 100158 & 3 & 93.768 & 1.146 & 1.909 & $\\dots$ \\\\\n", "\t20 & 100192 & 3 & 138.456 & -1.117 & 1.43 & $\\dots$ \\\\\n", "\t21 & 100202 & 3 & 32.368 & 3.571 & -0.819 & $\\dots$ \\\\\n", "\t22 & 100205 & 3 & 95.508 & -2.44 & 2.287 & $\\dots$ \\\\\n", "\t23 & 100206 & 3 & 127.039 & -0.111 & 2.166 & $\\dots$ \\\\\n", "\t24 & 100211 & 3 & 71.071 & 0.469 & -0.191 & $\\dots$ \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m4436×9 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_jet_num \u001b[0m\u001b[1m PRI_jet_leading_pt \u001b[0m\u001b[1m PRI_jet_leading_eta \u001b[0m\u001b[1m PRI_jet\u001b[0m ⋯\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64\u001b[0m ⋯\n", "──────┼─────────────────────────────────────────────────────────────────────────\n", " 1 │ 100005 3 90.547 -2.412 ⋯\n", " 2 │ 100011 3 76.773 -0.79\n", " 3 │ 100031 3 182.449 1.383\n", " 4 │ 100038 3 114.602 0.619\n", " 5 │ 100039 3 88.399 -2.168 ⋯\n", " 6 │ 100059 3 80.042 -0.856\n", " 7 │ 100060 3 78.174 -1.668\n", " 8 │ 100070 3 59.401 1.342\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 4430 │ 149890 3 273.539 0.807 ⋯\n", " 4431 │ 149900 3 128.952 -1.287\n", " 4432 │ 149942 3 118.862 1.174\n", " 4433 │ 149975 3 283.116 -1.009\n", " 4434 │ 149983 3 64.015 1.938 ⋯\n", " 4435 │ 149985 3 320.452 0.758\n", " 4436 │ 149995 3 193.297 0.14\n", "\u001b[36m 5 columns and 4421 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lots_o_jets(n_jets) = n_jets >= 3\n", "filter(:PRI_jet_num => lots_o_jets, higgs_jets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Usually one would not want to bother with a named function for these kind of trivial selections - use an anonymous function:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
4436×9 DataFrame
4416 rows omitted
RowEventIdPRI_jet_numPRI_jet_leading_ptPRI_jet_leading_etaPRI_jet_leading_phiPRI_jet_subleading_ptPRI_jet_subleading_etaPRI_jet_subleading_phiPRI_jet_all_pt
Int64Int64Float64Float64Float64Float64Float64Float64Float64
1100005390.547-2.412-0.65356.1650.2243.106193.66
2100011376.773-0.790.30356.8761.773-2.079165.64
31000313182.4491.3830.00138.006-1.257-0.609253.461
41000383114.6020.6190.16577.0532.433-2.637341.947
5100039388.399-2.168-1.42377.27-2.3851.876198.632
6100059380.042-0.8561.30452.5010.638-1.114182.413
7100060378.174-1.668-0.97858.097-0.989-1.727212.314
8100070359.4011.342-0.36953.711-2.5772.14162.577
9100077356.9510.749-0.29642.88-2.229-2.825140.06
10100082385.392-1.0620.16681.5590.513-2.255343.858
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
4427149845385.346-3.078-1.29266.050.022-2.06184.672
4428149855359.409-0.608-0.33937.6833.515-2.542164.274
4429149856361.1851.019-0.52758.4233.314-2.992155.665
44301498903273.5390.8072.5778.5410.353-0.696441.915
44311499003128.952-1.287-0.64337.8674.046-0.265199.454
44321499423118.8621.1740.39736.505-3.3571.89191.244
44331499753283.116-1.009-1.419124.487-1.4890.036489.347
4434149983364.0151.9381.02947.6731.8242.178148.696
44351499853320.4520.758-2.373143.8981.407-1.119505.049
44361499953193.2970.14-1.79892.691-0.5261.653398.099
" ], "text/latex": [ "\\begin{tabular}{r|cccccc}\n", "\t& EventId & PRI\\_jet\\_num & PRI\\_jet\\_leading\\_pt & PRI\\_jet\\_leading\\_eta & PRI\\_jet\\_leading\\_phi & \\\\\n", "\t\\hline\n", "\t& Int64 & Int64 & Float64 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & 100005 & 3 & 90.547 & -2.412 & -0.653 & $\\dots$ \\\\\n", "\t2 & 100011 & 3 & 76.773 & -0.79 & 0.303 & $\\dots$ \\\\\n", "\t3 & 100031 & 3 & 182.449 & 1.383 & 0.001 & $\\dots$ \\\\\n", "\t4 & 100038 & 3 & 114.602 & 0.619 & 0.165 & $\\dots$ \\\\\n", "\t5 & 100039 & 3 & 88.399 & -2.168 & -1.423 & $\\dots$ \\\\\n", "\t6 & 100059 & 3 & 80.042 & -0.856 & 1.304 & $\\dots$ \\\\\n", "\t7 & 100060 & 3 & 78.174 & -1.668 & -0.978 & $\\dots$ \\\\\n", "\t8 & 100070 & 3 & 59.401 & 1.342 & -0.369 & $\\dots$ \\\\\n", "\t9 & 100077 & 3 & 56.951 & 0.749 & -0.296 & $\\dots$ \\\\\n", "\t10 & 100082 & 3 & 85.392 & -1.062 & 0.166 & $\\dots$ \\\\\n", "\t11 & 100084 & 3 & 176.49 & -0.558 & 2.664 & $\\dots$ \\\\\n", "\t12 & 100090 & 3 & 86.379 & 1.365 & 0.155 & $\\dots$ \\\\\n", "\t13 & 100097 & 3 & 97.16 & -1.686 & 2.858 & $\\dots$ \\\\\n", "\t14 & 100102 & 3 & 90.445 & -1.63 & -1.166 & $\\dots$ \\\\\n", "\t15 & 100103 & 3 & 73.26 & -1.915 & -1.662 & $\\dots$ \\\\\n", "\t16 & 100118 & 3 & 148.174 & 1.109 & -1.21 & $\\dots$ \\\\\n", "\t17 & 100134 & 3 & 51.155 & 0.122 & -2.564 & $\\dots$ \\\\\n", "\t18 & 100144 & 3 & 52.541 & 0.194 & -0.861 & $\\dots$ \\\\\n", "\t19 & 100158 & 3 & 93.768 & 1.146 & 1.909 & $\\dots$ \\\\\n", "\t20 & 100192 & 3 & 138.456 & -1.117 & 1.43 & $\\dots$ \\\\\n", "\t21 & 100202 & 3 & 32.368 & 3.571 & -0.819 & $\\dots$ \\\\\n", "\t22 & 100205 & 3 & 95.508 & -2.44 & 2.287 & $\\dots$ \\\\\n", "\t23 & 100206 & 3 & 127.039 & -0.111 & 2.166 & $\\dots$ \\\\\n", "\t24 & 100211 & 3 & 71.071 & 0.469 & -0.191 & $\\dots$ \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m4436×9 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_jet_num \u001b[0m\u001b[1m PRI_jet_leading_pt \u001b[0m\u001b[1m PRI_jet_leading_eta \u001b[0m\u001b[1m PRI_jet\u001b[0m ⋯\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64\u001b[0m ⋯\n", "──────┼─────────────────────────────────────────────────────────────────────────\n", " 1 │ 100005 3 90.547 -2.412 ⋯\n", " 2 │ 100011 3 76.773 -0.79\n", " 3 │ 100031 3 182.449 1.383\n", " 4 │ 100038 3 114.602 0.619\n", " 5 │ 100039 3 88.399 -2.168 ⋯\n", " 6 │ 100059 3 80.042 -0.856\n", " 7 │ 100060 3 78.174 -1.668\n", " 8 │ 100070 3 59.401 1.342\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 4430 │ 149890 3 273.539 0.807 ⋯\n", " 4431 │ 149900 3 128.952 -1.287\n", " 4432 │ 149942 3 118.862 1.174\n", " 4433 │ 149975 3 283.116 -1.009\n", " 4434 │ 149983 3 64.015 1.938 ⋯\n", " 4435 │ 149985 3 320.452 0.758\n", " 4436 │ 149995 3 193.297 0.14\n", "\u001b[36m 5 columns and 4421 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filter(:PRI_jet_num => nj -> nj >= 3, higgs_jets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Need to filter based on multiple columns? No problem:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
2302×9 DataFrame
2282 rows omitted
RowEventIdPRI_jet_numPRI_jet_leading_ptPRI_jet_leading_etaPRI_jet_leading_phiPRI_jet_subleading_ptPRI_jet_subleading_etaPRI_jet_subleading_phiPRI_jet_all_pt
Int64Int64Float64Float64Float64Float64Float64Float64Float64
11000313182.4491.3830.00138.006-1.257-0.609253.461
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91003113144.005-0.9252.87947.2040.41.11233.132
101003193406.435-0.3010.405132.52-1.7081.67640.728
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22931496943193.1320.8291.098111.8330.7412.557446.408
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22951497523140.332-1.1780.1352.2011.635-0.285242.703
22961497563114.993-2.137-1.43570.141.0061.465291.784
22971498903273.5390.8072.5778.5410.353-0.696441.915
22981499003128.952-1.287-0.64337.8674.046-0.265199.454
22991499423118.8621.1740.39736.505-3.3571.89191.244
23001499753283.116-1.009-1.419124.487-1.4890.036489.347
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" ], "text/latex": [ "\\begin{tabular}{r|cccccc}\n", "\t& EventId & PRI\\_jet\\_num & PRI\\_jet\\_leading\\_pt & PRI\\_jet\\_leading\\_eta & PRI\\_jet\\_leading\\_phi & \\\\\n", "\t\\hline\n", "\t& Int64 & Int64 & Float64 & Float64 & Float64 & \\\\\n", "\t\\hline\n", "\t1 & 100031 & 3 & 182.449 & 1.383 & 0.001 & $\\dots$ \\\\\n", "\t2 & 100038 & 3 & 114.602 & 0.619 & 0.165 & $\\dots$ \\\\\n", "\t3 & 100084 & 3 & 176.49 & -0.558 & 2.664 & $\\dots$ \\\\\n", "\t4 & 100118 & 3 & 148.174 & 1.109 & -1.21 & $\\dots$ \\\\\n", "\t5 & 100192 & 3 & 138.456 & -1.117 & 1.43 & $\\dots$ \\\\\n", "\t6 & 100206 & 3 & 127.039 & -0.111 & 2.166 & $\\dots$ \\\\\n", "\t7 & 100232 & 3 & 117.933 & -1.322 & 2.031 & $\\dots$ \\\\\n", "\t8 & 100243 & 3 & 230.617 & 1.242 & 1.609 & $\\dots$ \\\\\n", "\t9 & 100311 & 3 & 144.005 & -0.925 & 2.879 & $\\dots$ \\\\\n", "\t10 & 100319 & 3 & 406.435 & -0.301 & 0.405 & $\\dots$ \\\\\n", "\t11 & 100322 & 3 & 108.072 & 2.173 & 1.671 & $\\dots$ \\\\\n", "\t12 & 100327 & 3 & 155.923 & 0.986 & -0.658 & $\\dots$ \\\\\n", "\t13 & 100339 & 3 & 162.554 & 0.021 & 1.532 & $\\dots$ \\\\\n", "\t14 & 100368 & 3 & 217.017 & 1.167 & 1.736 & $\\dots$ \\\\\n", "\t15 & 100413 & 3 & 322.504 & 0.282 & 2.7 & $\\dots$ \\\\\n", "\t16 & 100423 & 3 & 229.22 & -2.108 & 0.119 & $\\dots$ \\\\\n", "\t17 & 100429 & 3 & 196.331 & 0.668 & -2.212 & $\\dots$ \\\\\n", "\t18 & 100437 & 3 & 112.822 & -3.219 & 0.041 & $\\dots$ \\\\\n", "\t19 & 100441 & 3 & 143.97 & 1.063 & 2.736 & $\\dots$ \\\\\n", "\t20 & 100462 & 3 & 444.036 & -0.195 & -0.203 & $\\dots$ \\\\\n", "\t21 & 100469 & 3 & 196.168 & -1.834 & 2.016 & $\\dots$ \\\\\n", "\t22 & 100475 & 3 & 100.439 & 2.551 & -0.949 & $\\dots$ \\\\\n", "\t23 & 100523 & 3 & 185.057 & 1.271 & 1.17 & $\\dots$ \\\\\n", "\t24 & 100531 & 3 & 101.411 & -2.449 & 1.507 & $\\dots$ \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m2302×9 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_jet_num \u001b[0m\u001b[1m PRI_jet_leading_pt \u001b[0m\u001b[1m PRI_jet_leading_eta \u001b[0m\u001b[1m PRI_jet\u001b[0m ⋯\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64\u001b[0m ⋯\n", "──────┼─────────────────────────────────────────────────────────────────────────\n", " 1 │ 100031 3 182.449 1.383 ⋯\n", " 2 │ 100038 3 114.602 0.619\n", " 3 │ 100084 3 176.49 -0.558\n", " 4 │ 100118 3 148.174 1.109\n", " 5 │ 100192 3 138.456 -1.117 ⋯\n", " 6 │ 100206 3 127.039 -0.111\n", " 7 │ 100232 3 117.933 -1.322\n", " 8 │ 100243 3 230.617 1.242\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 2296 │ 149756 3 114.993 -2.137 ⋯\n", " 2297 │ 149890 3 273.539 0.807\n", " 2298 │ 149900 3 128.952 -1.287\n", " 2299 │ 149942 3 118.862 1.174\n", " 2300 │ 149975 3 283.116 -1.009 ⋯\n", " 2301 │ 149985 3 320.452 0.758\n", " 2302 │ 149995 3 193.297 0.14\n", "\u001b[36m 5 columns and 2287 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filter([:PRI_jet_num, :PRI_jet_leading_pt] => (nj, ptj) -> (nj >= 3) && (ptj > 100), higgs_jets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the arguments to the `filter` method follow the Julia convention, with the filter parameters given first, followed by the data object.\n", "\n", "Of note is the selector pattern `columns => filter_funtion` - we shall see this repeated!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Derived Data" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "using Statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get summary data for a data frame, use the `combine()` function. There is a mini-language for applying functions to the data is the same as for `filter` (but note the arguments are reversed). The second `=>` determines the destination column (which will be created, if needed). If you do not give this then a plausible name will be generated for the output." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
1×1 DataFrame
Rowjet_pt_mean
Float64
172.9546
" ], "text/latex": [ "\\begin{tabular}{r|c}\n", "\t& jet\\_pt\\_mean\\\\\n", "\t\\hline\n", "\t& Float64\\\\\n", "\t\\hline\n", "\t1 & 72.9546 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m1×1 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m jet_pt_mean \u001b[0m\n", " │\u001b[90m Float64 \u001b[0m\n", "─────┼─────────────\n", " 1 │ 72.9546" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "combine(higgs_jets, :PRI_jet_all_pt => mean => :jet_pt_mean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The special property of `combine` is that it collapses the output down to unique values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scalar and vector outputs can also be combined:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
4×2 DataFrame
Rowjet_pt_meann_jets
Float64Int64
172.95462
272.95461
372.95460
472.95463
" ], "text/latex": [ "\\begin{tabular}{r|cc}\n", "\t& jet\\_pt\\_mean & n\\_jets\\\\\n", "\t\\hline\n", "\t& Float64 & Int64\\\\\n", "\t\\hline\n", "\t1 & 72.9546 & 2 \\\\\n", "\t2 & 72.9546 & 1 \\\\\n", "\t3 & 72.9546 & 0 \\\\\n", "\t4 & 72.9546 & 3 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m4×2 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m jet_pt_mean \u001b[0m\u001b[1m n_jets \u001b[0m\n", " │\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\n", "─────┼─────────────────────\n", " 1 │ 72.9546 2\n", " 2 │ 72.9546 1\n", " 3 │ 72.9546 0\n", " 4 │ 72.9546 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "combine(higgs_jets, :PRI_jet_all_pt => mean => :jet_pt_mean, :PRI_jet_num => unique => :n_jets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But this probably isn't quite what we wanted to do as the mean of $p_T$ is always calculated for all jets.\n", "\n", "To do this in a more useful way, we use the `groupby()` function to split the data frame up by a certain criterion:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
4×3 DataFrame
RowPRI_jet_numPRI_jet_all_pt_meannrow
Int64Float64Int64
100.019992
2164.652315518
32149.43710054
43257.4424436
" ], "text/latex": [ "\\begin{tabular}{r|ccc}\n", "\t& PRI\\_jet\\_num & PRI\\_jet\\_all\\_pt\\_mean & nrow\\\\\n", "\t\\hline\n", "\t& Int64 & Float64 & Int64\\\\\n", "\t\\hline\n", "\t1 & 0 & 0.0 & 19992 \\\\\n", "\t2 & 1 & 64.6523 & 15518 \\\\\n", "\t3 & 2 & 149.437 & 10054 \\\\\n", "\t4 & 3 & 257.442 & 4436 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m4×3 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m PRI_jet_num \u001b[0m\u001b[1m PRI_jet_all_pt_mean \u001b[0m\u001b[1m nrow \u001b[0m\n", " │\u001b[90m Int64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\n", "─────┼─────────────────────────────────────────\n", " 1 │ 0 0.0 19992\n", " 2 │ 1 64.6523 15518\n", " 3 │ 2 149.437 10054\n", " 4 │ 3 257.442 4436" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "combine(groupby(higgs_jets, :PRI_jet_num), :PRI_jet_all_pt => mean, nrow)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Derived Data and `missing` values\n", "\n", "For some analysis, it's pretty useful to add derived values, which we know how to do:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
50000×1 DataFrame
49980 rows omitted
Rowpt2
Float64
14547.48
22136.84
31958.15
4998001.0
5998001.0
68198.76
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9998001.0
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" ], "text/latex": [ "\\begin{tabular}{r|c}\n", "\t& pt2\\\\\n", "\t\\hline\n", "\t& Float64\\\\\n", "\t\\hline\n", "\t1 & 4547.48 \\\\\n", "\t2 & 2136.84 \\\\\n", "\t3 & 1958.15 \\\\\n", "\t4 & 998001.0 \\\\\n", "\t5 & 998001.0 \\\\\n", "\t6 & 8198.76 \\\\\n", "\t7 & 15131.5 \\\\\n", "\t8 & 938.687 \\\\\n", "\t9 & 998001.0 \\\\\n", "\t10 & 28135.0 \\\\\n", "\t11 & 998001.0 \\\\\n", "\t12 & 5894.09 \\\\\n", "\t13 & 8670.78 \\\\\n", "\t14 & 998001.0 \\\\\n", "\t15 & 998001.0 \\\\\n", "\t16 & 998001.0 \\\\\n", "\t17 & 1315.01 \\\\\n", "\t18 & 998001.0 \\\\\n", "\t19 & 998001.0 \\\\\n", "\t20 & 998001.0 \\\\\n", "\t21 & 998001.0 \\\\\n", "\t22 & 998001.0 \\\\\n", "\t23 & 998001.0 \\\\\n", "\t24 & 38233.2 \\\\\n", "\t$\\dots$ & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m50000×1 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m pt2 \u001b[0m\n", " │\u001b[90m Float64 \u001b[0m\n", "───────┼────────────\n", " 1 │ 4547.48\n", " 2 │ 2136.84\n", " 3 │ 1958.15\n", " 4 │ 998001.0\n", " 5 │ 998001.0\n", " 6 │ 8198.76\n", " 7 │ 15131.5\n", " 8 │ 938.687\n", " ⋮ │ ⋮\n", " 49994 │ 4058.33\n", " 49995 │ 998001.0\n", " 49996 │ 37363.7\n", " 49997 │ 22233.8\n", " 49998 │ 14957.3\n", " 49999 │ 998001.0\n", " 50000 │ 2435.72\n", "\u001b[36m 49985 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "select(higgs_ml, :PRI_jet_leading_pt => (x -> x.^2) => :pt2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far, so good, but notice that there are a lot of columns where `pt==-999.0`, which was the input dataset convention for a missing value, so this isn't quite what we wanted.\n", "\n", "We could filter out all the unphysical values, but with data frames there is an option to set such values to `missing`.\n", "\n", "First, for a data frame that does not yet have missing values, first we call the `allowmissing()` function - this changes our columns of type `T` into `Union{T, Missing}`. Then we convert all the negative values we find into `missing`." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
50000×9 DataFrame
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RowEventIdPRI_jet_numPRI_jet_leading_ptPRI_jet_leading_etaPRI_jet_leading_phiPRI_jet_subleading_ptPRI_jet_subleading_etaPRI_jet_subleading_phiPRI_jet_all_pt
Int64?Int64?Float64?Float64?Float64?Float64?Float64?Float64?Float64?
1100000267.4352.150.44446.0621.24-2.475113.497
2100001146.2260.7251.158-999.0-999.0-999.046.226
3100002144.2512.053-2.028-999.0-999.0-999.044.251
41000030missing-999.0-999.0-999.0-999.0-999.0-0.0
51000040missing-999.0-999.0-999.0-999.0-999.00.0
6100005390.547-2.412-0.65356.1650.2243.106193.66
71000062123.010.8641.4556.8670.131-2.767179.877
8100007130.638-0.715-1.724-999.0-999.0-999.030.638
91000080missing-999.0-999.0-999.0-999.0-999.00.0
101000091167.735-2.767-2.514-999.0-999.0-999.0167.735
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499911499902166.372-2.844-0.14856.3-1.5342.692222.672
499921499911140.7460.5931.712-999.0-999.0-999.0140.746
499931499920missing-999.0-999.0-999.0-999.0-999.00.0
49994149993163.7052.8842.632-999.0-999.0-999.063.705
499951499940missing-999.0-999.0-999.0-999.0-999.00.0
499961499953193.2970.14-1.79892.691-0.5261.653398.099
499971499962149.11-0.234-2.23558.1840.5942.188207.294
499981499972122.3-1.4143.04368.4832.5180.046190.783
499991499980missing-999.0-999.0-999.0-999.0-999.00.0
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" ], "text/latex": [ "\\begin{tabular}{r|cccccc}\n", "\t& EventId & PRI\\_jet\\_num & PRI\\_jet\\_leading\\_pt & PRI\\_jet\\_leading\\_eta & PRI\\_jet\\_leading\\_phi & \\\\\n", "\t\\hline\n", "\t& Int64? & Int64? & Float64? & Float64? & Float64? & \\\\\n", "\t\\hline\n", "\t1 & 100000 & 2 & 67.435 & 2.15 & 0.444 & $\\dots$ \\\\\n", "\t2 & 100001 & 1 & 46.226 & 0.725 & 1.158 & $\\dots$ \\\\\n", "\t3 & 100002 & 1 & 44.251 & 2.053 & -2.028 & $\\dots$ \\\\\n", "\t4 & 100003 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t5 & 100004 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t6 & 100005 & 3 & 90.547 & -2.412 & -0.653 & $\\dots$ \\\\\n", "\t7 & 100006 & 2 & 123.01 & 0.864 & 1.45 & $\\dots$ \\\\\n", "\t8 & 100007 & 1 & 30.638 & -0.715 & -1.724 & $\\dots$ \\\\\n", "\t9 & 100008 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t10 & 100009 & 1 & 167.735 & -2.767 & -2.514 & $\\dots$ \\\\\n", "\t11 & 100010 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t12 & 100011 & 3 & 76.773 & -0.79 & 0.303 & $\\dots$ \\\\\n", "\t13 & 100012 & 1 & 93.117 & -0.97 & 1.943 & $\\dots$ \\\\\n", "\t14 & 100013 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t15 & 100014 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t16 & 100015 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t17 & 100016 & 1 & 36.263 & -0.766 & -0.686 & $\\dots$ \\\\\n", "\t18 & 100017 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t19 & 100018 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t20 & 100019 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t21 & 100020 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t22 & 100021 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t23 & 100022 & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t24 & 100023 & 2 & 195.533 & 1.156 & 1.416 & $\\dots$ \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m50000×9 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_jet_num \u001b[0m\u001b[1m PRI_jet_leading_pt \u001b[0m\u001b[1m PRI_jet_leading_eta \u001b[0m\u001b[1m PRI_je\u001b[0m ⋯\n", " │\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m Float6\u001b[0m ⋯\n", "───────┼────────────────────────────────────────────────────────────────────────\n", " 1 │ 100000 2 67.435 2.15 ⋯\n", " 2 │ 100001 1 46.226 0.725\n", " 3 │ 100002 1 44.251 2.053\n", " 4 │ 100003 0 \u001b[90m missing \u001b[0m -999.0\n", " 5 │ 100004 0 \u001b[90m missing \u001b[0m -999.0 ⋯\n", " 6 │ 100005 3 90.547 -2.412\n", " 7 │ 100006 2 123.01 0.864\n", " 8 │ 100007 1 30.638 -0.715\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 49994 │ 149993 1 63.705 2.884 ⋯\n", " 49995 │ 149994 0 \u001b[90m missing \u001b[0m -999.0\n", " 49996 │ 149995 3 193.297 0.14\n", " 49997 │ 149996 2 149.11 -0.234\n", " 49998 │ 149997 2 122.3 -1.414 ⋯\n", " 49999 │ 149998 0 \u001b[90m missing \u001b[0m -999.0\n", " 50000 │ 149999 1 49.353 -2.641\n", "\u001b[36m 5 columns and 49985 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "higgs_set_missing_jets = allowmissing(higgs_ml)[:, Cols(:EventId, r\"PRI_jet.*\")] # Just work with jets for now\n", "missing_value(v) = if (v===missing || v<0) missing else v end\n", "transform!(higgs_set_missing_jets, :PRI_jet_leading_pt => ByRow(missing_value) => :PRI_jet_leading_pt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of note is the convenience function `ByRow()` that takes care of broadcasting the function to each row in the column(s).\n", "\n", "Also we used here the `transform` function - this is very like `filter`, but more specialised for DataFrames (the argument order is different, with the data frame coming first). In fact we used `transform!`, so we modified directly the `higgs_set_missing_jets` data frame." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
50000×10 DataFrame
49980 rows omitted
RowEventIdpt2PRI_jet_numPRI_jet_leading_ptPRI_jet_leading_etaPRI_jet_leading_phiPRI_jet_subleading_ptPRI_jet_subleading_etaPRI_jet_subleading_phiPRI_jet_all_pt
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11000004547.48267.4352.150.44446.0621.24-2.475113.497
21000012136.84146.2260.7251.158-999.0-999.0-999.046.226
31000021958.15144.2512.053-2.028-999.0-999.0-999.044.251
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710000615131.52123.010.8641.4556.8670.131-2.767179.877
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4999114999027679.62166.372-2.844-0.14856.3-1.5342.692222.672
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49999149998missing0missing-999.0-999.0-999.0-999.0-999.00.0
500001499992435.72149.353-2.641-2.038-999.0-999.0-999.049.353
" ], "text/latex": [ "\\begin{tabular}{r|ccccccc}\n", "\t& EventId & pt2 & PRI\\_jet\\_num & PRI\\_jet\\_leading\\_pt & PRI\\_jet\\_leading\\_eta & PRI\\_jet\\_leading\\_phi & \\\\\n", "\t\\hline\n", "\t& Int64? & Float64? & Int64? & Float64? & Float64? & Float64? & \\\\\n", "\t\\hline\n", "\t1 & 100000 & 4547.48 & 2 & 67.435 & 2.15 & 0.444 & $\\dots$ \\\\\n", "\t2 & 100001 & 2136.84 & 1 & 46.226 & 0.725 & 1.158 & $\\dots$ \\\\\n", "\t3 & 100002 & 1958.15 & 1 & 44.251 & 2.053 & -2.028 & $\\dots$ \\\\\n", "\t4 & 100003 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t5 & 100004 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t6 & 100005 & 8198.76 & 3 & 90.547 & -2.412 & -0.653 & $\\dots$ \\\\\n", "\t7 & 100006 & 15131.5 & 2 & 123.01 & 0.864 & 1.45 & $\\dots$ \\\\\n", "\t8 & 100007 & 938.687 & 1 & 30.638 & -0.715 & -1.724 & $\\dots$ \\\\\n", "\t9 & 100008 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t10 & 100009 & 28135.0 & 1 & 167.735 & -2.767 & -2.514 & $\\dots$ \\\\\n", "\t11 & 100010 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t12 & 100011 & 5894.09 & 3 & 76.773 & -0.79 & 0.303 & $\\dots$ \\\\\n", "\t13 & 100012 & 8670.78 & 1 & 93.117 & -0.97 & 1.943 & $\\dots$ \\\\\n", "\t14 & 100013 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t15 & 100014 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t16 & 100015 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t17 & 100016 & 1315.01 & 1 & 36.263 & -0.766 & -0.686 & $\\dots$ \\\\\n", "\t18 & 100017 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t19 & 100018 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t20 & 100019 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t21 & 100020 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t22 & 100021 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t23 & 100022 & \\emph{missing} & 0 & \\emph{missing} & -999.0 & -999.0 & $\\dots$ \\\\\n", "\t24 & 100023 & 38233.2 & 2 & 195.533 & 1.156 & 1.416 & $\\dots$ \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m50000×10 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m pt2 \u001b[0m\u001b[1m PRI_jet_num \u001b[0m\u001b[1m PRI_jet_leading_pt \u001b[0m\u001b[1m PRI_jet_leadin\u001b[0m ⋯\n", " │\u001b[90m Int64? \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m Float64? \u001b[0m ⋯\n", "───────┼────────────────────────────────────────────────────────────────────────\n", " 1 │ 100000 4547.48 2 67.435 ⋯\n", " 2 │ 100001 2136.84 1 46.226\n", " 3 │ 100002 1958.15 1 44.251\n", " 4 │ 100003 \u001b[90m missing \u001b[0m 0 \u001b[90m missing \u001b[0m -99\n", " 5 │ 100004 \u001b[90m missing \u001b[0m 0 \u001b[90m missing \u001b[0m -99 ⋯\n", " 6 │ 100005 8198.76 3 90.547 -\n", " 7 │ 100006 15131.5 2 123.01\n", " 8 │ 100007 938.687 1 30.638 -\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 49994 │ 149993 4058.33 1 63.705 ⋯\n", " 49995 │ 149994 \u001b[90m missing \u001b[0m 0 \u001b[90m missing \u001b[0m -99\n", " 49996 │ 149995 37363.7 3 193.297\n", " 49997 │ 149996 22233.8 2 149.11 -\n", " 49998 │ 149997 14957.3 2 122.3 - ⋯\n", " 49999 │ 149998 \u001b[90m missing \u001b[0m 0 \u001b[90m missing \u001b[0m -99\n", " 50000 │ 149999 2435.72 1 49.353 -\n", "\u001b[36m 6 columns and 49985 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "select!(higgs_set_missing_jets, :EventId, :PRI_jet_leading_pt => (x -> x.^2) => :pt2, :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that `missing` values were handled nicely!\n", "\n", "We also used the `select`function here. This is very similar to `transform`, just that only the columns which we ask for are included in the result, rather than all columns. As `select` returns the columns in the order we ask for it was simple to add the new `pt2` column where we wanted it to be." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It should be noted that `missing` values are *not some special magical implementation*. They are a well defined data type in Julia (the type is `Missing`), for which common arithmetic operations are well defined:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "missing * missing = missing\n", "1.0 + missing = missing\n", "missing * 3 = missing\n" ] } ], "source": [ "@show missing * missing\n", "@show 1.0 + missing\n", "@show missing * 3;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a great example of how Julia's type system works so powerfully with multiple dispatch!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transform, Select, Combine, GroupBy, Filter\n", "\n", "Just as a short summary of the data frame manipulation functions we met:\n", "\n", "| **Function** | **Description** |\n", "|---|---|\n", "| `transform` | Apply a transformation operation to one or more columns, return all columns plus any new ones |\n", "| `select` | Apply a transformation operation to one or more columns, only return columns that are selected, in the order requested |\n", "| `combine` | Apply a transformation operation, then collapse the result for identical output rows |\n", "| `groupby` | Split a data frame into pieces according to a certain criterion |\n", "| `filter` | Apply a selection operation to a data frame - argument order follows the method convention |\n", "\n", "The use of `groupby` and `combine` allows us to powerfully manipulate data in Julia using the well known [*Split, Combine, Apply* strategy](http://www.jstatsoft.org/v40/i01), originally introduced for S." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualising\n", "\n", "There is extremely good integration between the Julia plotting ecosystem and data frames. Here we give a quick tour of some of the plots that we can easily make with this data:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "using Plots\n", "using StatsPlots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For convenience, we'll create a subset of our data, selecting higher $p_T$ jets. We also want to benefit from `missing` columns." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1980 rows omitted
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" ], "text/latex": [ "\\begin{tabular}{r|cccccc}\n", "\t& EventId & PRI\\_jet\\_subleading\\_pt & PRI\\_jet\\_num & PRI\\_jet\\_leading\\_pt & PRI\\_jet\\_leading\\_eta & \\\\\n", "\t\\hline\n", "\t& Int64? & Float64? & Int64? & Float64? & Float64? & \\\\\n", "\t\\hline\n", "\t1 & 100006 & 56.867 & 2 & 123.01 & 0.864 & $\\dots$ \\\\\n", "\t2 & 100009 & \\emph{missing} & 1 & 167.735 & -2.767 & $\\dots$ \\\\\n", "\t3 & 100023 & 82.477 & 2 & 195.533 & 1.156 & $\\dots$ \\\\\n", "\t4 & 100027 & 43.458 & 2 & 170.712 & -1.961 & $\\dots$ \\\\\n", "\t5 & 100031 & 38.006 & 3 & 182.449 & 1.383 & $\\dots$ \\\\\n", "\t6 & 100038 & 77.053 & 3 & 114.602 & 0.619 & $\\dots$ \\\\\n", "\t7 & 100057 & 56.31 & 2 & 214.449 & -0.058 & $\\dots$ \\\\\n", "\t8 & 100078 & 70.786 & 2 & 101.934 & 3.139 & $\\dots$ \\\\\n", "\t9 & 100079 & \\emph{missing} & 1 & 116.316 & -1.171 & $\\dots$ \\\\\n", "\t10 & 100084 & 73.566 & 3 & 176.49 & -0.558 & $\\dots$ \\\\\n", "\t11 & 100098 & 92.256 & 2 & 111.656 & -0.987 & $\\dots$ \\\\\n", "\t12 & 100101 & 39.356 & 2 & 135.815 & -2.087 & $\\dots$ \\\\\n", "\t13 & 100110 & \\emph{missing} & 1 & 127.907 & 1.837 & $\\dots$ \\\\\n", "\t14 & 100118 & 140.818 & 3 & 148.174 & 1.109 & $\\dots$ \\\\\n", "\t15 & 100125 & 55.71 & 2 & 111.193 & 0.243 & $\\dots$ \\\\\n", "\t16 & 100127 & \\emph{missing} & 1 & 132.014 & 1.418 & $\\dots$ \\\\\n", "\t17 & 100135 & 81.532 & 2 & 113.256 & 2.447 & $\\dots$ \\\\\n", "\t18 & 100147 & 67.594 & 2 & 115.476 & -0.454 & $\\dots$ \\\\\n", "\t19 & 100154 & 43.856 & 2 & 146.463 & -2.567 & $\\dots$ \\\\\n", "\t20 & 100182 & \\emph{missing} & 1 & 121.069 & 1.697 & $\\dots$ \\\\\n", "\t21 & 100184 & \\emph{missing} & 1 & 103.885 & -2.069 & $\\dots$ \\\\\n", "\t22 & 100192 & 54.651 & 3 & 138.456 & -1.117 & $\\dots$ \\\\\n", "\t23 & 100206 & 115.897 & 3 & 127.039 & -0.111 & $\\dots$ \\\\\n", "\t24 & 100208 & \\emph{missing} & 1 & 107.118 & -2.261 & $\\dots$ \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m2000×9 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m PRI_jet_subleading_pt \u001b[0m\u001b[1m PRI_jet_num \u001b[0m\u001b[1m PRI_jet_leading_pt \u001b[0m\u001b[1m PRI_j\u001b[0m ⋯\n", " │\u001b[90m Int64? \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m Float\u001b[0m ⋯\n", "──────┼─────────────────────────────────────────────────────────────────────────\n", " 1 │ 100006 56.867 2 123.01 ⋯\n", " 2 │ 100009 \u001b[90m missing \u001b[0m 1 167.735\n", " 3 │ 100023 82.477 2 195.533\n", " 4 │ 100027 43.458 2 170.712\n", " 5 │ 100031 38.006 3 182.449 ⋯\n", " 6 │ 100038 77.053 3 114.602\n", " 7 │ 100057 56.31 2 214.449\n", " 8 │ 100078 70.786 2 101.934\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 1994 │ 112550 \u001b[90m missing \u001b[0m 1 112.601 ⋯\n", " 1995 │ 112553 60.941 2 199.28\n", " 1996 │ 112554 123.696 3 154.292\n", " 1997 │ 112556 71.009 3 106.372\n", " 1998 │ 112568 199.574 3 208.952 ⋯\n", " 1999 │ 112581 70.183 2 117.877\n", " 2000 │ 112582 48.515 3 106.257\n", "\u001b[36m 5 columns and 1985 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "interesting_jets = allowmissing(filter([:PRI_jet_num, :PRI_jet_leading_pt] => (nj, ptj) -> (nj >= 1) && (ptj > 100), higgs_jets)[1:2000, :])\n", "select!(interesting_jets, :EventId, :PRI_jet_subleading_pt => ByRow(missing_value) => :PRI_jet_subleading_pt, :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first example is a simple scatter plot of the $(\\eta, \\phi)$ coordinates of the leading jet:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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"markdown", "metadata": {}, "source": [ "For this data the marginal histogram is always an interesting way to look at the data distribution" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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"execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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jYmJGjRrl7+9fw10DAFBDqrRFuGfPnl69eu3atau4uHjy5Mnjxo1TxysqKrp27frTTz+Vlpbefvvt//73v9XxL7/88u677zabzUuXLu3du7fD4ait9gEAqJ4qbREmJSWdOnXK19dXCJGenp6QkPDqq682bNjwyy+/DAoKWr58uU6na9iw4SuvvNKnTx9FUWbOnDlv3rw//OEPNpstOTl5zZo1AwcOrOVfBACA61GlLcKAgAA1BYUQ6gOTySSEWLdu3cCBA3U6nRBi0KBBmzdvtlgsp0+fPnTokJp8JpOpX79+P/30U221DwBA9VR1H6HLX/7ylzFjxkRGRgohcnJybr/9dnU8NjZWUZScnJyCgoLg4ODAwEDX+P79+91Nraio6O23316+fLnryY899tg1/xJaFEWpkeloslgstTdxD7NYLAaDQf00gytQ/+gGg8Hbjdzo1HeU0+n0diM3OqvVWqurqXrDbrfbbDa9/joP7TQajVf9t722IHzhhRcyMzM3bNig/qjT6Vx/SPWBwWDQ6/UX/3WdTucVmjAYDCEhIeHh4eqPYWFh1/3belKdaLKK9Hq9Xq8nCK9K/z/ebuRGx4KqInVVyYK6qmq+o6qycruGIJw1a9a33367YcOGsLAwdSQuLi43N1d9nJOTo9PpYmJi9Hp9WVlZaWlpcHCwECIvLy82NtbdNMPDw8eNG9e5c+eqt1FFtbpmV78Zrh9MJpPJZCIIr0pdUGwRXpXD4TAYDPXpf6SWKIqiKAoL6qrUtVOtLqiqZuybb765cOHCn376KSoqyjXYv3//7777Tv0OZNWqVX369PH19W3UqNHNN9+8atUqIYTFYlmzZs2AAQNqo3UAAKqvSluEO3bsePLJJ3v06PHEE0+oIzNnzmzduvWIESPefPPNu+66q3Xr1gsXLly5cqWr+uCDD+7atWvHjh3Nmzfv27dvbbUPAED1VCkIExISLjnyU/2208/Pb+vWratWrTp79uyOHTtatmypVocMGZKYmLh+/fpu3boNHjyYL8EBADesKgVheHi4u626gICAkSNHXj7epk2bNm3aVKs1AABqH9tqAACpEYQAAKkRhAAAqRGEAACpEYQAAKkRhAAAqRGEAACpEYQAAKkRhAAAqRGEAACpEYQAAKkRhAAAqRGEAACpEYQAAKkRhAAAqRGEAACpEYQAAKkRhAAAqRGEAACpEYQAAKkRhAAAqRGEAACpEYQAAKkRhAAAqRGEAACpGb3dQN3jdDjWrVvnrtq4ceNWrVp5sh8AQHUQhNfIUmapLL/vqVc1iw5zRZjjQtbhAx5uCgBw3QjCa+R0CJPvuUd+0K7mHQn4dJhnGwIAVAv7CAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFLjots1SlGs5sor3KQpISGhWbNmHmwIAHAVBGGNOv/7uYJcdzdpspeebxUbuv3n9R5uCgBwBQRhjXI6RXC025s0Hdls3fKKZxsCAFwF+wgBAFIjCAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFIjCAEAUiMIAQBSIwgBAFIzersBuVgt5hMnTrirxsXF+fn5ebIfAABB6EE5hzL37rqlx52aRVtl2aAB/b9avMDDTQGA5AhCD3LY9I1vLnlum3Z1x7KyolWebQgAwD5CAIDcCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1GogCAsLC48cOeJ0Oi8edDgcmZmZ586dq/70AQCoPVUNwqlTpzZr1kyn073zzjuuQUVRHn/88TZt2gwZMiQlJeX06dPq+KFDh5KSkkaMGJGYmDhz5sya7xoAgBpirOLz+vTpM378+ClTplw8uHHjxm+++ebgwYORkZEPPfTQjBkz5s+fL4SYNm3aqFGj/vrXv2ZnZ6ekpAwfPvymm26q+d7rmbKzhw/se+aZZzSLOp3unnvu6dKli4ebAoB6r6pBePfddwshTCbTxYNffvnl8OHDIyMjhRAPP/xw9+7dP/744+Li4jVr1nzwwQdCiCZNmtx1111Llix55ZVXarrzeif/aFap8tqxEO3qsV/OlVYQhABQ46oahJqysrIGDRqkPk5ISKisrCwoKMjPzzeZTI0aNVLHW7ZsmZ2d7W4KVqs1MzPTaPxvG6GhoQkJCdVpqU7TxyQ4+/9Fu/bveUJkebQbAJBDtYKwvLzcz89Pfezv7y+EKC0tLSsrUx+rAgICSkpK3E0hNzd39uzZgYGB6o9NmzZdtGhRdVpyUf7/g3fqAZvNVlZWVuOTraiocDqdOp2uxqdcz1RUVNhsNoPB4O1GbnRms9lgMFzy7REuZ7VaFUWx2WzebuRGZ7fbbTabw+G4vpf7+fm5trXcqVYQxsTEnD9/Xn2sHiAaExOj0+lKSkocDoe6yjh37lxsbKy7KTRt2vSf//xn586dq9OGJp2+vp0ZYjKZgoKCanyyOp0uICCAILwqvV7v6+tLEF6V0WgkCKtCDUJfX19vN3KjU4Pw4u2rGlettGjfvv22bdvUx9u2bUtISAgJCWnSpEl4ePj27dvV8V9++eWWW26pbpsAANSOqm4Rrl+//tixY2fOnNm6davJZLrzzjubN2+enp7epk2b999/Pzk5+ZlnnlGPKfXx8Xn44YenTp365ptvbtmy5ciRI6NGjarNXwEAgOtX1SA8efJkRkZGz549hRAZGRmdOnVq3rx5fHz82rVr586du3z58kceeeSxxx5Tn/zSSy8FBgbOmDEjJiZm48aNwcHBtdU+AADVU9UgnDhx4sSJEy8f79y587Jlyy6dqNE4ffr06dOnV7c7AABqWX07ogQAgGtCEAIApEYQAgCkRhACAKRGEAIApEYQAgCkRhACAKRGEAIApEYQAgCkVq27T8BzFMVisbju9XG50NBQfb274QYAeABBWEec3L5w1zdLl36lWbRbKue+/uoTTzzh4aYAoB4gCOsIu1V/+0TzqLc0i4YVM8rLyz3cEQDUD3yZBgCQGkEIAJAaQQgAkBpBCACQGkEIAJAaQQgAkBpBCACQGucR1gfK6f1z3tz51gefaFYNBsOij9/v3bu3h7sCgDqBIKwPlIrisk5jynpO0qwGrHouKyvLsx0BQJ1BENYXAWEiqrlmRe8X5OFeAKAOYR8hAEBqBCEAQGoEIQBAagQhAEBqBCEAQGoEIQBAagQhAEBqBCEAQGoEIQBAagQhAEBqBCEAQGoEIQBAagQhAEBqBCEAQGoEIQBAagQhAEBqBCEAQGoEIQBAagQhAEBqRm83gBta/8H3/rxpo7uqn5/v8cOHwsLCPNgRANQwgrD+s+UefXbmllf/+a5m1WjQf7Vwftu2bTWrx7OyKx76SjRO0Z70zPbl5eUEIYA6jSCs/xzlF/JvGZnfbpBmNejrJ7OystwFoRBC+AWLgHDNik7PV+sA6jyCUA4RTUTTDpoVQ0CIh3sBgBsKn+gBAFJji1B2isOWl5d34sQJzarNbvNwPwDgYQSh7CpOH3l02rOmgGDt6oVzHu4HADyMIJSeTm/940fWlLs0i/ppjRQP9wMAnsU+QgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNQIQgCA1AhCAIDUCEIAgNSM3m4AdZjDal68eHFYWJhmNSQkZOTIkR5uCQCuFUGI62cxW55fsVvvF6RZtW6aP3z4cKOR9xiAGxorKVSDTtjufVWExWkW9Zs/82w3AHA92EcIAJAaQQgAkBpBCACQGkEIAJAaQQgAkBpBCACQGkEIAJAaQQgAkBpBCACQGkEIAJAal1hDbVEMPrf16S90Os3qTUmJn374rodbAoDLEYSoLYrNvL3Dn4TeoFGrLDn45aPugrC0tHTmrDk2h9PdlG/t0H7MKO5rAaBmEISoTcm9hV7rPVZWdIUXnTp1at6H8829p2iXC453/PVjghBATSEIcSPyCY009/+Ldu3QerHjb55tB0B9RhDCOxSn88SJE5ql06dPe7gZADIjCOEN5tLykuJbetypWbRVlluM/h7uCIC0CEJ4g8MmfANKXs7Uru5eqf9qmmcbAiAvziMEAEiNLULUNaVFx48eGXjvH9zVR4+4l2NKAVQdQYi65vzpEsX3++h7tavHthpXfHejBeHy5cuPHz/ururj4zNlyhSDQeuESwC1jyBE3aMLihCdhmnXHFZR8qNn27m6v8z4a1ZEexEcrVk1bXx39OjRMTExHu4KgIogRP3idBSfP5eRkaFZ1Ov1iYmJQUFBmtWSkpKjR4+6m7DD4UhJSbn+7bY+j4vGKZoV4/aF1zlNADWBIET9krVzyy9b7hz9kGbRfL5g8rjRb/ztNc3qrDmvvv3xQr8I7S2zyrysj97559ixY2usVQA3BoIQ9YvTqW/T9/ykpdrVH/9ucxS4e6nN7qjs+Whlf+0zN4IWpdvt9hrpEcANhSCETE7v+3jdT0uWf6tZLC8tEX3+7OGOAHgdQQiZVBRbbupfOfA5zaL+gxvrWFMAnkEQQi66gDAR1Vy7ZvK9wgvt5858/NmijVt+0az6+pheeuG5hg0bVr9DAB5GEAJVYi/O3xp661Zre81qwI9vDxsymCAE6iKCEKiyVj1FV+2jRo0Zi3fv3q3Xa1+z0Gw212ZbAKqFIAR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"cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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"\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "@df interesting_jets marginalhist(:PRI_jet_leading_pt, :PRI_jet_subleading_pt, label=\"Jet Location\", bins=20, xlims=(0, 550), ylims=(0,550))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's look at the distance in $(\\eta, \\phi)$ space between the leading and subleading jet." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# Clean up the \"missing data\" columns for the subleading jet\n", "select!(interesting_jets, :EventId, \n", " :PRI_jet_subleading_eta => ByRow(missing_value) => :PRI_jet_subleading_eta, \n", " :PRI_jet_subleading_phi => ByRow(missing_value) => :PRI_jet_subleading_phi, :);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define a function `dist` for the cartesian distance:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dist (generic function with 2 methods)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "δϕ(ϕ1, ϕ2) = begin\n", " δ = ϕ1 - ϕ2\n", " while δ > pi\n", " δ -= 2π\n", " end\n", " while δ < -pi\n", " δ += 2π\n", " end\n", " δ\n", "end\n", "dist(η1::Number, ϕ1::Number, η2::Number, ϕ2::Number) = sqrt((η1-η2)^2 + δϕ(ϕ1, ϕ2)^2)\n", "dist(η1, ϕ1, η2, ϕ2) = missing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a little bit tricksy - we need to ensure that\n", "- when the ϕ separation is calculated we normalise it to [-π, π]\n", "- if any of the values are `missing` then the distance is `missing`\n", " - we use Julia's multiple dispatch to achieve that by defining two implementations for the `dist` method" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now run this function to calculate the angle between the two jets:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
2000×10 DataFrame
1980 rows omitted
RowEventIdJet_distancePRI_jet_subleading_etaPRI_jet_subleading_phiPRI_jet_subleading_ptPRI_jet_numPRI_jet_leading_ptPRI_jet_leading_etaPRI_jet_leading_phiPRI_jet_all_pt
Int64?Float64?Float64?Float64?Float64?Int64?Float64?Float64?Float64?Float64?
1100006missing0.131missing56.8672123.010.8641.45179.877
2100009missingmissingmissingmissing1167.735-2.767-2.514167.735
3100023missingmissingmissing82.4772195.5331.1561.416278.009
4100027missing2.974missing43.4582170.712-1.9612.22214.17
5100031missingmissingmissing38.0063182.4491.3830.001253.461
6100038missing2.433missing77.0533114.6020.6190.165341.947
7100057missing1.151missing56.312214.449-0.0581.525270.759
8100078missingmissingmissing70.7862101.9343.1390.444172.721
9100079missingmissingmissingmissing1116.316-1.1710.641116.316
10100084missing0.49missing73.5663176.49-0.5582.664333.586
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
1991112539missingmissingmissingmissing1104.845-1.334-2.465104.845
1992112545missing2.135missing62.0193164.8750.848-1.408274.187
1993112547missing1.034missing49.6743114.0130.941-2.16235.454
1994112550missingmissingmissingmissing1112.6010.1241.47112.601
1995112553missing1.675missing60.9412199.28-0.427-1.397260.221
1996112554missingmissing2.389123.6963154.292-1.860.095376.563
1997112556missingmissing3.11871.0093106.3720.209-1.612223.655
1998112568missingmissing1.357199.5743208.952-1.6861.2440.235
1999112581missingmissingmissing70.1832117.8770.31-2.573188.06
2000112582missingmissingmissing48.5153106.2572.108-1.887202.71
" ], "text/latex": [ "\\begin{tabular}{r|cccccc}\n", "\t& EventId & Jet\\_distance & PRI\\_jet\\_subleading\\_eta & PRI\\_jet\\_subleading\\_phi & PRI\\_jet\\_subleading\\_pt & \\\\\n", "\t\\hline\n", "\t& Int64? & Float64? & Float64? & Float64? & Float64? & \\\\\n", "\t\\hline\n", "\t1 & 100006 & \\emph{missing} & 0.131 & \\emph{missing} & 56.867 & $\\dots$ \\\\\n", "\t2 & 100009 & \\emph{missing} & \\emph{missing} & \\emph{missing} & \\emph{missing} & $\\dots$ \\\\\n", "\t3 & 100023 & \\emph{missing} & \\emph{missing} & \\emph{missing} & 82.477 & $\\dots$ \\\\\n", "\t4 & 100027 & \\emph{missing} & 2.974 & \\emph{missing} & 43.458 & $\\dots$ \\\\\n", "\t5 & 100031 & \\emph{missing} & \\emph{missing} & \\emph{missing} & 38.006 & $\\dots$ \\\\\n", "\t6 & 100038 & \\emph{missing} & 2.433 & \\emph{missing} & 77.053 & $\\dots$ \\\\\n", "\t7 & 100057 & \\emph{missing} & 1.151 & \\emph{missing} & 56.31 & $\\dots$ \\\\\n", "\t8 & 100078 & \\emph{missing} & \\emph{missing} & \\emph{missing} & 70.786 & $\\dots$ \\\\\n", "\t9 & 100079 & \\emph{missing} & \\emph{missing} & \\emph{missing} & \\emph{missing} & $\\dots$ \\\\\n", "\t10 & 100084 & \\emph{missing} & 0.49 & \\emph{missing} & 73.566 & $\\dots$ \\\\\n", "\t11 & 100098 & \\emph{missing} & 1.617 & \\emph{missing} & 92.256 & $\\dots$ \\\\\n", "\t12 & 100101 & \\emph{missing} & \\emph{missing} & 0.49 & 39.356 & $\\dots$ \\\\\n", "\t13 & 100110 & \\emph{missing} & \\emph{missing} & \\emph{missing} & \\emph{missing} & $\\dots$ \\\\\n", "\t14 & 100118 & 2.57311 & 0.796 & 1.344 & 140.818 & $\\dots$ \\\\\n", "\t15 & 100125 & \\emph{missing} & \\emph{missing} & 0.213 & 55.71 & $\\dots$ \\\\\n", "\t16 & 100127 & \\emph{missing} & \\emph{missing} & \\emph{missing} & \\emph{missing} & $\\dots$ \\\\\n", "\t17 & 100135 & \\emph{missing} & \\emph{missing} & 1.503 & 81.532 & $\\dots$ \\\\\n", "\t18 & 100147 & \\emph{missing} & 2.14 & \\emph{missing} & 67.594 & $\\dots$ \\\\\n", "\t19 & 100154 & \\emph{missing} & 3.195 & \\emph{missing} & 43.856 & $\\dots$ \\\\\n", "\t20 & 100182 & \\emph{missing} & \\emph{missing} & \\emph{missing} & \\emph{missing} & $\\dots$ \\\\\n", "\t21 & 100184 & \\emph{missing} & \\emph{missing} & \\emph{missing} & \\emph{missing} & $\\dots$ \\\\\n", "\t22 & 100192 & 4.32512 & 3.144 & 0.688 & 54.651 & $\\dots$ \\\\\n", "\t23 & 100206 & \\emph{missing} & 0.352 & \\emph{missing} & 115.897 & $\\dots$ \\\\\n", "\t24 & 100208 & \\emph{missing} & \\emph{missing} & \\emph{missing} & \\emph{missing} & $\\dots$ \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m2000×10 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m EventId \u001b[0m\u001b[1m Jet_distance \u001b[0m\u001b[1m PRI_jet_subleading_eta \u001b[0m\u001b[1m PRI_jet_subleading_ph\u001b[0m ⋯\n", " │\u001b[90m Int64? \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m Float64? \u001b[0m ⋯\n", "──────┼─────────────────────────────────────────────────────────────────────────\n", " 1 │ 100006 \u001b[90m missing \u001b[0m 0.131 \u001b[90m missing \u001b[0m ⋯\n", " 2 │ 100009 \u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\n", " 3 │ 100023 \u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\n", " 4 │ 100027 \u001b[90m missing \u001b[0m 2.974 \u001b[90m missing \u001b[0m\n", " 5 │ 100031 \u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m ⋯\n", " 6 │ 100038 \u001b[90m missing \u001b[0m 2.433 \u001b[90m missing \u001b[0m\n", " 7 │ 100057 \u001b[90m missing \u001b[0m 1.151 \u001b[90m missing \u001b[0m\n", " 8 │ 100078 \u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\n", " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋱\n", " 1994 │ 112550 \u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m ⋯\n", " 1995 │ 112553 \u001b[90m missing \u001b[0m 1.675 \u001b[90m missing \u001b[0m\n", " 1996 │ 112554 \u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m 2.38\n", " 1997 │ 112556 \u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m 3.11\n", " 1998 │ 112568 \u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m 1.35 ⋯\n", " 1999 │ 112581 \u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\n", " 2000 │ 112582 \u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m\n", "\u001b[36m 7 columns and 1985 rows omitted\u001b[0m" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "select!(interesting_jets, :EventId,\n", " [:PRI_jet_leading_eta, :PRI_jet_leading_phi, :PRI_jet_subleading_eta, :PRI_jet_subleading_phi] => ByRow(dist) => :Jet_distance, :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can look at the distribution of the separation:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { 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