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{
 "cells": [
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   "cell_type": "markdown",
   "id": "500bd02c-eee8-45e6-a301-3482638767de",
   "metadata": {},
   "source": [
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    "# Data Engineering and Analytics\n",
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    "Master Software Engineering\n",
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    "\n",
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    "Eva Zangerle\n",
    "\n",
    "## General Notes\n",
    "* Code is partly taken from further sources, such as books.\n",
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    "* Sources are annotated (and acknowledged!) as follows:\n",
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    "    * (CleaningData): Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools; David Mertz; Packt Publishing, 2021; [Github repo](https://github.com/PacktPublishing/Cleaning-Data-for-Effective-Data-Science/)\n",
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    "* Unless marked otherwise, code was written by Eva Zangerle.\n",
    "\n",
    "\n",
    "\n",
    "## Virtual environments\n",
    "\n",
    "![xkcd python environment](https://imgs.xkcd.com/comics/python_environment.png)\n",
    "\n",
    "[Comic taken from XKCD Comics https://xkcd.com/1987/ (CC-BY)]\n",
    "\n",
    "\n",
    "\n",
    "Good tutorial on pipenv and jupyter(-lab): https://towardsdatascience.com/virtual-environments-for-data-science-running-python-and-jupyter-with-pipenv-c6cb6c44a405#\n",
    "\n",
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    "\n",
    "## Useful python stuff\n",
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    "* Startup files: https://ipython.readthedocs.io/en/stable/interactive/tutorial.html#startup-files\n",
    "* tqdm progress bars (also for Jupyter): https://github.com/tqdm/tqdm\n",
    "\n",
    "## Further tools\n",
    "* jq command linen json processor: https://stedolan.github.io/jq/\n"
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   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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   "display_name": "Python 3 (ipykernel)",
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   "language": "python",
   "name": "python3"
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    "version": 3
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   "name": "python",
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