Revised with 1.6.1
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DataFrames/01__Environment_setup.ipynb
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DataFrames/01__Environment_setup.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Environment setup for data frames tutorial\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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"metadata": {},
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"source": [
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"Welcome to DataFrames.jl introduction!\n",
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"\n",
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"This set of Jupyter notebooks is intended to give you an overwiew of what functionality DataFrames.jl has based on practical examples.\n",
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"\n",
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"You can find reviews of functionality of DataFrames.jl (not as exercises as this tutorial but task-type oriented) in the following locations:\n",
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"* an official manual at https://juliadata.github.io/DataFrames.jl/stable/\n",
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"* a tutorial going through all functionalities of DataFrames.jl at https://github.com/bkamins/Julia-DataFrames-Tutorial\n",
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"\n",
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"We also assume that you have a basic knowledge of the Julia language and the Julia ecosystem. There are great tutorials on this topic in [JuliaAcademy](https://juliaacademy.com/), so I encourage you to check them out.\n",
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"\n",
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"As this is a hands-on tutorial you can expect that the examples will be implemented in a way as I would write them when doing actual project."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The notebooks were prepared under Julia 1.5.3 and tested under Julia 1.6.1. If you have a different version of Julia installed change the kernel in *Kernel/Change kernel* option in menu (assuming you are on a Julia 1.x all examples should work without a problem)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"v\"1.6.1\""
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"VERSION"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Jupyter Notebook automatically activates project environment if it is found in the working directory.\n",
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"\n",
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"So first let us check if we have Project.toml and Manifest.toml files present (they should be present if you cloned the repository of this tutorial)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2-element BitVector:\n",
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" 1\n",
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" 1"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"isfile.([\"Project.toml\", \"Manifest.toml\"])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You should get `1` printed (meaning `true`) in both entries of a vector.\n",
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"\n",
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"Now we are sure that you are going to use exactly the same versions of the packages that I use when running this tutorial.\n",
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"\n",
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"Let us check what packages (and in what versions) we will use."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"] status"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"These notebooks should work with DataFrames versions 0.22 and 1.1."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"if the command above gives a warning that some of the packages are not downloaded run the `instantiate` instruction from the following line"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"] instantiate"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"As you see we will use the following packages:\n",
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"\n",
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"Package | Description\n",
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":-|:-\n",
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"DataFrames.jl | a core package that is a subject of this tutorial; it is used for data manipulation; we use version 0.21.0 of this package\n",
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"CSV.jl | a package for reading/writing of CSV files\n",
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"FreqTables.jl | a very useful package for creating frequency tables\n",
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"GLM.jl | a package for fitting Generalized Linear Models (as no data science tutorial would be complete without building some predictive model)\n",
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"PyPlot.jl | a package for plotting; there are many options in the Julia ecosystem to choose from; in this tutorial we use PyPlot.jl as it is based on Matplotlib so if you have experience with the Python data science technology stack it should be familiar\n",
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"Pipe.jl | a package that makes chaining of operations super powerful (which is something you probably know from `%>%` in R)\n",
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"Arrow.jl | a package for working with data in Apache Arrow format\n",
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"Uniftul.jl | a package for working with physical units (like kg, cm, ...)"
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]
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}
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],
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"metadata": {
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"@webio": {
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"lastCommId": null,
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"lastKernelId": null
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},
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"kernelspec": {
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"display_name": "Julia 1.6.1",
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"language": "julia",
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"name": "julia-1.6"
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},
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"language_info": {
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "1.6.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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