add lectures to the materials
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@@ -918,10 +918,10 @@ uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
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@@ -9,6 +9,10 @@ Extras:
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* in the `/exercises` folder for each book chapter you can find 10 additional
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exercises with solutions (they are meant for self study and are not discussed
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in the book)
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* in the `/lectures` folder for each book chapter you can find a Jupyter
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Notebook file with code from this chapter (note that the code is slightly
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adjusted in comparison to code contained in .jl files in the root folder
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to accomodate it for running in Jupyter Notebook).
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## Setting up your environment
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@@ -0,0 +1,74 @@
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# Julia for Data Analysis
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## Contents
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This folder contains Jupyter Notebooks with lecture templates to the book
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["Julia for Data Analysis"](https://www.manning.com/books/julia-for-data-analysis?utm_source=bkamins&utm_medium=affiliate&utm_campaign=book_kaminski2_julia_3_17_22)
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that is written by Bogumił Kamiński and is planned to be published in 2022
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by [Manning Publications Co.](https://www.manning.com/).
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The lecture tempates can be used as:
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* starting point for preparation of teaching materials by instructors giving
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couses about data analysis using the Julia language.
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* An alternative for the readers of the book to .jl files located in the root
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folder of this repository.
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The notebooks were prepared by [Bogumił Kamiński](https://github.com/bkamins)
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and [Daniel Kaszyński](https://www.linkedin.com/in/daniel-kaszy%C5%84ski-3a7807113/).
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The files containing lecture template have a naming convention
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`lectureDD.ipynb`, where `DD` is book chapter number for which the were
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prepared.
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The differences from the codes presented in the book (the .jl files
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in the root folder) are:
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* code layout was changed (so it is not identical to code presented in the book);
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* some codes adjusted or omitted in comparison to the book version, to make the
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presentation suitable for Jupyter Notebook format.
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## Running instructions
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The Jupyter Notebooks are stored in the *lectures* folder of the repository.
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Therefore they will automatically use the information from
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*Project.toml* and *Manifest.toml* files that are contained in the root
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folder of the repository to properly set up the working environment.
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However, before running Jupyter Notebooks you need to make sure that your
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environment is properly configured to run Julia in Jupyter.
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The notebooks were prepared under Julia 1.7, but they should work under newer
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Julia 1.x versions. In this case what you might need to do is change Jupyter
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Notebook kernel to the version of Julia that you have. In this case please
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make sure to run the following command in projects root folder before
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working with the notebooks:
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```
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julia --project -e "using Pkg; Pkg.instantiate()"
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```
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The simplest way to configure your environment is as follows:
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* Start Julia in a terminal using `julia` command in the folder containing the
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lecture materials.
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* press <kbd>]</kbd> to switch to package manager mode.
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* Make sure you are in a home project environment. Your prompt should look as
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`(@v1.8) pkg>`. If it is not then run the `activate` command. Then you will
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switch to the home project environment. The reason why we do it is to avoid
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adding IJulia.jl to the local project environment of the book (as it could
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update some of the packages used in the book).
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* Install the [IJulia.jl package](https://github.com/JuliaLang/IJulia.jl) by
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running command `add IJulia`.
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* Rress <kbd>Backspace</kbd> to leave package manager mode.
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* Run the command `using IJulia; notebook(dir=pwd())` that will start Jupyter
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in the current Julia directory. This directory should be the folder
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containing the lecture materials as we have started Julia in this folder. If
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you are not in this folder then instead of passing `pwd()` write a path to
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the folder where the lecture materials are located.
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---
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*Preparation of these exercises have been supported by the Polish National Agency for Academic Exchange under the Strategic Partnerships programme, grant number BPI/PST/2021/1/00069/U/00001.*
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{
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"cells": [
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"cell_type": "markdown",
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"id": "7f8bec03",
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"metadata": {},
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"source": [
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"# Julia for Data Analysis\n",
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"\n",
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"## Bogumił Kamiński"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f190bea8",
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"metadata": {},
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"source": [
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"# Lecture 1. Introduction"
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]
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},
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{
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"cell_type": "markdown",
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"id": "90be7554",
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"metadata": {},
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"source": [
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"## What is Julia and why is it useful?\n",
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"\n",
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"### Comparison of execution speed of Julia, Java, Python, and C"
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]
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},
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"<path clip-path=\"url(#clip650)\" d=\"M2169.56 213.14 L2173.65 213.14 L2173.65 241.274 Q2173.65 246.742 2171.56 249.213 Q2169.49 251.684 2164.9 251.684 L2163.34 251.684 L2163.34 248.241 L2164.61 248.241 Q2167.33 248.241 2168.44 246.722 Q2169.56 245.203 2169.56 241.274 L2169.56 213.14 Z\" fill=\"#000000\" fill-rule=\"evenodd\" fill-opacity=\"1\" /><path clip-path=\"url(#clip650)\" d=\"M2181.24 234.428 L2181.24 220.695 L2184.97 220.695 L2184.97 234.286 Q2184.97 237.506 2186.22 239.127 Q2187.48 240.727 2189.99 240.727 Q2193.01 240.727 2194.75 238.803 Q2196.51 236.878 2196.51 233.557 L2196.51 220.695 L2200.24 220.695 L2200.24 243.38 L2196.51 243.38 L2196.51 239.896 Q2195.16 241.962 2193.35 242.975 Q2191.57 243.968 2189.2 243.968 Q2185.29 243.968 2183.27 241.537 Q2181.24 239.106 2181.24 234.428 M2190.62 220.148 L2190.62 220.148 Z\" fill=\"#000000\" fill-rule=\"evenodd\" fill-opacity=\"1\" /><path clip-path=\"url(#clip650)\" d=\"M2207.92 211.864 L2211.64 211.864 L2211.64 243.38 L2207.92 243.38 L2207.92 211.864 Z\" fill=\"#000000\" fill-rule=\"evenodd\" fill-opacity=\"1\" /><path clip-path=\"url(#clip650)\" d=\"M2219.44 220.695 L2223.17 220.695 L2223.17 243.38 L2219.44 243.38 L2219.44 220.695 M2219.44 211.864 L2223.17 211.864 L2223.17 216.583 L2219.44 216.583 L2219.44 211.864 Z\" fill=\"#000000\" fill-rule=\"evenodd\" fill-opacity=\"1\" /><path clip-path=\"url(#clip650)\" d=\"M2241.28 231.977 Q2236.76 231.977 2235.02 233.01 Q2233.28 234.043 2233.28 236.534 Q2233.28 238.519 2234.57 239.694 Q2235.89 240.848 2238.14 240.848 Q2241.24 240.848 2243.1 238.661 Q2244.98 236.453 2244.98 232.807 L2244.98 231.977 L2241.28 231.977 M2248.71 230.438 L2248.71 243.38 L2244.98 243.38 L2244.98 239.937 Q2243.71 242.003 2241.8 242.995 Q2239.9 243.968 2237.14 243.968 Q2233.66 243.968 2231.59 242.023 Q2229.55 240.058 2229.55 236.777 Q2229.55 232.949 2232.1 231.005 Q2234.67 229.06 2239.76 229.06 L2244.98 229.06 L2244.98 228.696 Q2244.98 226.123 2243.28 224.726 Q2241.6 223.308 2238.54 223.308 Q2236.6 223.308 2234.75 223.774 Q2232.91 224.24 2231.21 225.171 L2231.21 221.728 Q2233.26 220.938 2235.18 220.553 Q2237.1 220.148 2238.93 220.148 Q2243.85 220.148 2246.28 222.7 Q2248.71 225.252 2248.71 230.438 Z\" fill=\"#000000\" fill-rule=\"evenodd\" fill-opacity=\"1\" /><polyline clip-path=\"url(#clip650)\" style=\"stroke:#ff0000; stroke-linecap:butt; stroke-linejoin:round; stroke-width:3.5; stroke-opacity:1; fill:none\" points=\"\n",
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" 2088.82,273.62 2152.69,273.62 \n",
|
||||
" \"/>\n",
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||||
"<path clip-path=\"url(#clip650)\" d=\"M2187.72 260.829 L2187.72 265.144 Q2185.66 263.219 2183.31 262.267 Q2180.98 261.316 2178.35 261.316 Q2173.16 261.316 2170.41 264.495 Q2167.65 267.655 2167.65 273.651 Q2167.65 279.626 2170.41 282.806 Q2173.16 285.965 2178.35 285.965 Q2180.98 285.965 2183.31 285.013 Q2185.66 284.061 2187.72 282.137 L2187.72 286.411 Q2185.58 287.869 2183.17 288.598 Q2180.78 289.328 2178.1 289.328 Q2171.24 289.328 2167.29 285.135 Q2163.34 280.922 2163.34 273.651 Q2163.34 266.359 2167.29 262.166 Q2171.24 257.953 2178.1 257.953 Q2180.82 257.953 2183.21 258.682 Q2185.62 259.391 2187.72 260.829 Z\" fill=\"#000000\" fill-rule=\"evenodd\" fill-opacity=\"1\" /></svg>\n"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data = \"\"\"\n",
|
||||
"problem,language,time,size\n",
|
||||
"n-body,C,2.13,1633\n",
|
||||
"mandelbrot,C,1.3,1135\n",
|
||||
"spectral norm,C,0.41,1197\n",
|
||||
"fannkuch-redux,C,7.58,910\n",
|
||||
"fasta,C,0.78,1463\n",
|
||||
"k-nucleotide,C,3.96,1506\n",
|
||||
"binary-trees,C,1.58,809\n",
|
||||
"reverse-complement,C,0.41,1965\n",
|
||||
"pidigits,C,0.56,1090\n",
|
||||
"regex-redux,C,0.8,1397\n",
|
||||
"n-body,Java,6.77,1489\n",
|
||||
"mandelbrot,Java,4.1,796\n",
|
||||
"spectral norm,Java,1.55,756\n",
|
||||
"fannkuch-redux,Java,10.48,1282\n",
|
||||
"fasta,Java,1.2,2543\n",
|
||||
"k-nucleotide,Java,4.83,1812\n",
|
||||
"binary-trees,Java,2.51,835\n",
|
||||
"reverse-complement,Java,1.57,2183\n",
|
||||
"pidigits,Java,0.79,764\n",
|
||||
"regex-redux,Java,5.34,929\n",
|
||||
"n-body,Python,541.34,1196\n",
|
||||
"mandelbrot,Python,177.35,688\n",
|
||||
"spectral norm,Python,112.97,407\n",
|
||||
"fannkuch-redux,Python,341.45,950\n",
|
||||
"fasta,Python,36.9,1947\n",
|
||||
"k-nucleotide,Python,46.31,1967\n",
|
||||
"binary-trees,Python,44.7,660\n",
|
||||
"reverse-complement,Python,6.62,814\n",
|
||||
"pidigits,Python,1.16,567\n",
|
||||
"regex-redux,Python,1.34,1403\n",
|
||||
"n-body,Julia,4.21,1111\n",
|
||||
"mandelbrot,Julia,1.42,619\n",
|
||||
"spectral norm,Julia,1.11,429\n",
|
||||
"fannkuch-redux,Julia,7.83,1067\n",
|
||||
"fasta,Julia,1.13,1082\n",
|
||||
"k-nucleotide,Julia,4.94,951\n",
|
||||
"binary-trees,Julia,7.28,634\n",
|
||||
"reverse-complement,Julia,1.44,522\n",
|
||||
"pidigits,Julia,0.97,506\n",
|
||||
"regex-redux,Julia,1.74,759\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"using CSV\n",
|
||||
"using DataFrames\n",
|
||||
"using Plots\n",
|
||||
"\n",
|
||||
"df = CSV.read(IOBuffer(data), DataFrame)\n",
|
||||
"\n",
|
||||
"plot(map([:time, :size],\n",
|
||||
" [\"execution time (relative to C)\",\n",
|
||||
" \"code size (relative to C)\"]) do col, title\n",
|
||||
" df_plot = unstack(df, :problem, :language, col)\n",
|
||||
" df_plot[!, Not(:problem)] ./= df_plot.C\n",
|
||||
" select!(df_plot, Not(:C))\n",
|
||||
" scatter(df_plot.problem, Matrix(select(df_plot, Not(:problem)));\n",
|
||||
" labels=permutedims(names(df_plot, Not(:problem))),\n",
|
||||
" ylabel=title,\n",
|
||||
" yaxis = col == :time ? :log : :none,\n",
|
||||
" xrotation=20,\n",
|
||||
" markershape=[:rect :diamond :circle],\n",
|
||||
" markersize=[4 5 5],\n",
|
||||
" markercolor=[:lightgray :lightblue :gold],\n",
|
||||
" xtickfontsize=7, ytickfontsize=7,\n",
|
||||
" legendfontsize=7, ylabelfontsize=7)\n",
|
||||
" hline!([1.0]; color=:red, labels=\"C\")\n",
|
||||
"end...)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "fddc5ffc",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Key features of Julia from data scientist’s perspective"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "979e1ae9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Julia is fast because it is a compiled language"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "eb06fc1d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 0.000004 seconds\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"500000000500000000"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"function sum_n(n)\n",
|
||||
" s = 0\n",
|
||||
" for i in 1:n\n",
|
||||
" s += i\n",
|
||||
" end\n",
|
||||
" return s\n",
|
||||
"end\n",
|
||||
"@time sum_n(1_000_000_000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e804cf4a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Julia provides a full support for interactive workflows"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "eb17d0d3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Julia programs are highly reusable and easy to compose together"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f870f256",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Julia has an in-built package manager"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1dca4066",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### It is easy to integrate existing code with Julia"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b25b96cd",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Tabular data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "7f0fe481",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div class=\"data-frame\"><p>3 rows × 5 columns</p><table class=\"data-frame\"><thead><tr><th></th><th>id</th><th>name</th><th>age</th><th>friends</th><th>location</th></tr><tr><th></th><th title=\"Int64\">Int64</th><th title=\"String\">String</th><th title=\"Int64\">Int64</th><th title=\"Vector{Int64}\">Array…</th><th title=\"NamedTuple{(:city, :state), Tuple{String, String}}\">NamedTup…</th></tr></thead><tbody><tr><th>1</th><td>1</td><td>Alice</td><td>19</td><td>[2]</td><td>(city = "Atlanta", state = "GA")</td></tr><tr><th>2</th><td>2</td><td>Bob</td><td>24</td><td>[1, 3]</td><td>(city = "Boston", state = "MA")</td></tr><tr><th>3</th><td>3</td><td>Clyde</td><td>21</td><td>[2]</td><td>(city = "Austin", state = "TX")</td></tr></tbody></table></div>"
|
||||
],
|
||||
"text/latex": [
|
||||
"\\begin{tabular}{r|ccccc}\n",
|
||||
"\t& id & name & age & friends & location\\\\\n",
|
||||
"\t\\hline\n",
|
||||
"\t& Int64 & String & Int64 & Array… & NamedTup…\\\\\n",
|
||||
"\t\\hline\n",
|
||||
"\t1 & 1 & Alice & 19 & [2] & (city = \"Atlanta\", state = \"GA\") \\\\\n",
|
||||
"\t2 & 2 & Bob & 24 & [1, 3] & (city = \"Boston\", state = \"MA\") \\\\\n",
|
||||
"\t3 & 3 & Clyde & 21 & [2] & (city = \"Austin\", state = \"TX\") \\\\\n",
|
||||
"\\end{tabular}\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"\u001b[1m3×5 DataFrame\u001b[0m\n",
|
||||
"\u001b[1m Row \u001b[0m│\u001b[1m id \u001b[0m\u001b[1m name \u001b[0m\u001b[1m age \u001b[0m\u001b[1m friends \u001b[0m\u001b[1m location \u001b[0m\n",
|
||||
"\u001b[1m \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Array… \u001b[0m\u001b[90m NamedTup… \u001b[0m\n",
|
||||
"─────┼─────────────────────────────────────────────────────────────────\n",
|
||||
" 1 │ 1 Alice 19 [2] (city = \"Atlanta\", state = \"GA\")\n",
|
||||
" 2 │ 2 Bob 24 [1, 3] (city = \"Boston\", state = \"MA\")\n",
|
||||
" 3 │ 3 Clyde 21 [2] (city = \"Austin\", state = \"TX\")"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"using DataFrames\n",
|
||||
"\n",
|
||||
"DataFrame(id=1:3,\n",
|
||||
" name=[\"Alice\", \"Bob\", \"Clyde\"],\n",
|
||||
" age=[19, 24, 21], \n",
|
||||
" friends=[[2], [1, 3], [2]],\n",
|
||||
" location=[(city=\"Atlanta\", state=\"GA\"),\n",
|
||||
" (city=\"Boston\", state=\"MA\"),\n",
|
||||
" (city=\"Austin\", state=\"TX\")])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3b9d5fed",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Julia’s drawbacks\n",
|
||||
"\n",
|
||||
"### Compilation takes time which is noticable in short sessions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5158177c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# First steps with Julia"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "64319fda",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Getting help in and on Julia using `?`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "fcc88738",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"search: \u001b[0m\u001b[1m&\u001b[22m\u001b[0m\u001b[1m&\u001b[22m\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/latex": [
|
||||
"\\begin{verbatim}\n",
|
||||
"x && y\n",
|
||||
"\\end{verbatim}\n",
|
||||
"Short-circuiting boolean AND.\n",
|
||||
"\n",
|
||||
"See also \\href{@ref}{\\texttt{\\&}}, the ternary operator \\texttt{? :}, and the manual section on \\href{@ref man-conditional-evaluation}{control flow}.\n",
|
||||
"\n",
|
||||
"\\section{Examples}\n",
|
||||
"\\begin{verbatim}\n",
|
||||
"julia> x = 3;\n",
|
||||
"\n",
|
||||
"julia> x > 1 && x < 10 && x isa Int\n",
|
||||
"true\n",
|
||||
"\n",
|
||||
"julia> x < 0 && error(\"expected positive x\")\n",
|
||||
"false\n",
|
||||
"\\end{verbatim}\n"
|
||||
],
|
||||
"text/markdown": [
|
||||
"```\n",
|
||||
"x && y\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Short-circuiting boolean AND.\n",
|
||||
"\n",
|
||||
"See also [`&`](@ref), the ternary operator `? :`, and the manual section on [control flow](@ref man-conditional-evaluation).\n",
|
||||
"\n",
|
||||
"# Examples\n",
|
||||
"\n",
|
||||
"```jldoctest\n",
|
||||
"julia> x = 3;\n",
|
||||
"\n",
|
||||
"julia> x > 1 && x < 10 && x isa Int\n",
|
||||
"true\n",
|
||||
"\n",
|
||||
"julia> x < 0 && error(\"expected positive x\")\n",
|
||||
"false\n",
|
||||
"```\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"\u001b[36m x && y\u001b[39m\n",
|
||||
"\n",
|
||||
" Short-circuiting boolean AND.\n",
|
||||
"\n",
|
||||
" See also \u001b[36m&\u001b[39m, the ternary operator \u001b[36m? :\u001b[39m, and the manual section on control\n",
|
||||
" flow.\n",
|
||||
"\n",
|
||||
"\u001b[1m Examples\u001b[22m\n",
|
||||
"\u001b[1m ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n",
|
||||
"\n",
|
||||
"\u001b[36m julia> x = 3;\u001b[39m\n",
|
||||
"\u001b[36m \u001b[39m\n",
|
||||
"\u001b[36m julia> x > 1 && x < 10 && x isa Int\u001b[39m\n",
|
||||
"\u001b[36m true\u001b[39m\n",
|
||||
"\u001b[36m \u001b[39m\n",
|
||||
"\u001b[36m julia> x < 0 && error(\"expected positive x\")\u001b[39m\n",
|
||||
"\u001b[36m false\u001b[39m"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"? &&"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9e3ba862",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Julia documentation: https://docs.julialang.org/en/v1/\n",
|
||||
"### Julia Slack: https://julialang.org/slack/\n",
|
||||
"### Julia Discourse: https://discourse.julialang.org/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "99f11d44",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Managing packages in Julia"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1ea7572a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Project.toml and Manifest.toml define project environment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "8519aeca",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[deps]\n",
|
||||
"Arrow = \"69666777-d1a9-59fb-9406-91d4454c9d45\"\n",
|
||||
"BenchmarkTools = \"6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf\"\n",
|
||||
"CSV = \"336ed68f-0bac-5ca0-87d4-7b16caf5d00b\"\n",
|
||||
"CategoricalArrays = \"324d7699-5711-5eae-9e2f-1d82baa6b597\"\n",
|
||||
"CodecBzip2 = \"523fee87-0ab8-5b00-afb7-3ecf72e48cfd\"\n",
|
||||
"Conda = \"8f4d0f93-b110-5947-807f-2305c1781a2d\"\n",
|
||||
"DataFrames = \"a93c6f00-e57d-5684-b7b6-d8193f3e46c0\"\n",
|
||||
"DataFramesMeta = \"1313f7d8-7da2-5740-9ea0-a2ca25f37964\"\n",
|
||||
"Distributions = \"31c24e10-a181-5473-b8eb-7969acd0382f\"\n",
|
||||
"FreqTables = \"da1fdf0e-e0ff-5433-a45f-9bb5ff651cb1\"\n",
|
||||
"GLM = \"38e38edf-8417-5370-95a0-9cbb8c7f171a\"\n",
|
||||
"Genie = \"c43c736e-a2d1-11e8-161f-af95117fbd1e\"\n",
|
||||
"Graphs = \"86223c79-3864-5bf0-83f7-82e725a168b6\"\n",
|
||||
"HTTP = \"cd3eb016-35fb-5094-929b-558a96fad6f3\"\n",
|
||||
"Impute = \"f7bf1975-0170-51b9-8c5f-a992d46b9575\"\n",
|
||||
"InlineStrings = \"842dd82b-1e85-43dc-bf29-5d0ee9dffc48\"\n",
|
||||
"JSON3 = \"0f8b85d8-7281-11e9-16c2-39a750bddbf1\"\n",
|
||||
"Loess = \"4345ca2d-374a-55d4-8d30-97f9976e7612\"\n",
|
||||
"Missings = \"e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28\"\n",
|
||||
"Plots = \"91a5bcdd-55d7-5caf-9e0b-520d859cae80\"\n",
|
||||
"PooledArrays = \"2dfb63ee-cc39-5dd5-95bd-886bf059d720\"\n",
|
||||
"PyCall = \"438e738f-606a-5dbb-bf0a-cddfbfd45ab0\"\n",
|
||||
"RCall = \"6f49c342-dc21-5d91-9882-a32aef131414\"\n",
|
||||
"ROCAnalysis = \"f535d66d-59bb-5153-8d2b-ef0a426c6aff\"\n",
|
||||
"SQLite = \"0aa819cd-b072-5ff4-a722-6bc24af294d9\"\n",
|
||||
"StatsBase = \"2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91\"\n",
|
||||
"ThreadsX = \"ac1d9e8a-700a-412c-b207-f0111f4b6c0d\"\n",
|
||||
"ZipFile = \"a5390f91-8eb1-5f08-bee0-b1d1ffed6cea\"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"foreach(println, readlines(\"../Project.toml\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "48b4da05",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"# This file is machine-generated - editing it directly is not advised\n",
|
||||
"\n",
|
||||
"julia_version = \"1.7.3\"\n",
|
||||
"manifest_format = \"2.0\"\n",
|
||||
"\n",
|
||||
"[[deps.AbstractFFTs]]\n",
|
||||
"deps = [\"ChainRulesCore\", \"LinearAlgebra\"]\n",
|
||||
"git-tree-sha1 = \"69f7020bd72f069c219b5e8c236c1fa90d2cb409\"\n",
|
||||
"uuid = \"621f4979-c628-5d54-868e-fcf4e3e8185c\"\n",
|
||||
"version = \"1.2.1\"\n",
|
||||
"\n",
|
||||
"[[deps.Adapt]]\n",
|
||||
"deps = [\"LinearAlgebra\"]\n",
|
||||
"git-tree-sha1 = \"195c5505521008abea5aee4f96930717958eac6f\"\n",
|
||||
"uuid = \"79e6a3ab-5dfb-504d-930d-738a2a938a0e\"\n",
|
||||
"version = \"3.4.0\"\n",
|
||||
"\n",
|
||||
"[[deps.ArgCheck]]\n",
|
||||
"git-tree-sha1 = \"a3a402a35a2f7e0b87828ccabbd5ebfbebe356b4\"\n",
|
||||
"uuid = \"dce04be8-c92d-5529-be00-80e4d2c0e197\"\n",
|
||||
"version = \"2.3.0\"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"foreach(println, readlines(\"../Manifest.toml\")[1:21])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "93628a3b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### In Jupyter Notebook project environment is automatically activated"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "db6cd87f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Managing packages"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "29a58ed5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Please refer to the \"Julia for Data Analysis\" book, Appendix A, for details how to manage packages."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "fbd15b5e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[32m\u001b[1m Status\u001b[22m\u001b[39m `C:\\Users\\bogum\\Desktop\\Manning\\book_v2\\codes\\JuliaForDataAnalysis\\Project.toml`\n",
|
||||
" \u001b[90m [69666777] \u001b[39mArrow v2.3.0\n",
|
||||
" \u001b[90m [6e4b80f9] \u001b[39mBenchmarkTools v1.3.1\n",
|
||||
" \u001b[90m [336ed68f] \u001b[39mCSV v0.10.4\n",
|
||||
" \u001b[90m [324d7699] \u001b[39mCategoricalArrays v0.10.6\n",
|
||||
" \u001b[90m [523fee87] \u001b[39mCodecBzip2 v0.7.2\n",
|
||||
" \u001b[90m [8f4d0f93] \u001b[39mConda v1.7.0\n",
|
||||
" \u001b[90m [a93c6f00] \u001b[39mDataFrames v1.3.4\n",
|
||||
" \u001b[90m [1313f7d8] \u001b[39mDataFramesMeta v0.12.0\n",
|
||||
" \u001b[90m [31c24e10] \u001b[39mDistributions v0.25.70\n",
|
||||
" \u001b[90m [da1fdf0e] \u001b[39mFreqTables v0.4.5\n",
|
||||
" \u001b[90m [38e38edf] \u001b[39mGLM v1.8.0\n",
|
||||
" \u001b[90m [c43c736e] \u001b[39mGenie v5.3.1\n",
|
||||
" \u001b[90m [86223c79] \u001b[39mGraphs v1.7.2\n",
|
||||
" \u001b[90m [cd3eb016] \u001b[39mHTTP v1.3.3\n",
|
||||
" \u001b[90m [f7bf1975] \u001b[39mImpute v0.6.8\n",
|
||||
" \u001b[90m [842dd82b] \u001b[39mInlineStrings v1.1.4\n",
|
||||
" \u001b[90m [0f8b85d8] \u001b[39mJSON3 v1.9.5\n",
|
||||
" \u001b[90m [4345ca2d] \u001b[39mLoess v0.5.4\n",
|
||||
" \u001b[90m [e1d29d7a] \u001b[39mMissings v1.0.2\n",
|
||||
" \u001b[90m [91a5bcdd] \u001b[39mPlots v1.31.7\n",
|
||||
" \u001b[90m [2dfb63ee] \u001b[39mPooledArrays v1.4.2\n",
|
||||
" \u001b[90m [438e738f] \u001b[39mPyCall v1.94.1\n",
|
||||
" \u001b[90m [6f49c342] \u001b[39mRCall v0.13.13\n",
|
||||
" \u001b[90m [f535d66d] \u001b[39mROCAnalysis v0.3.5\n",
|
||||
" \u001b[90m [0aa819cd] \u001b[39mSQLite v1.4.2\n",
|
||||
" \u001b[90m [2913bbd2] \u001b[39mStatsBase v0.33.21\n",
|
||||
" \u001b[90m [ac1d9e8a] \u001b[39mThreadsX v0.1.10\n",
|
||||
" \u001b[90m [a5390f91] \u001b[39mZipFile v0.10.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"] status"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Julia 1.7.3",
|
||||
"language": "julia",
|
||||
"name": "julia-1.7"
|
||||
},
|
||||
"language_info": {
|
||||
"file_extension": ".jl",
|
||||
"mimetype": "application/julia",
|
||||
"name": "julia",
|
||||
"version": "1.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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|
After Width: | Height: | Size: 12 KiB |
Reference in new issue
Block a user