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‎week_1/tutorial_week1.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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"__IMPORTANT INFO__\n",
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"<br>\n",
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"Note that you can find all the workshop materials under the following link, each session being marked as \"week_x\":\n",
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"<br>\n",
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"<br>\n",
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"&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[https://github.com/CodeHubOrg/python_workshops](https://github.com/CodeHubOrg/python_workshops)\n",
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"<br>\n",
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"<br>\n",
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"Download the data files by clicking on the 'Clone or download' green button, choose 'Download ZIP', then unzip from your downloads folder. We will update the material before each session."
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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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"# An Introduction to Python\n",
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"This workshop is divided into 5 parts:\n",
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"* An overview of the workshops\n",
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"* An introduction to how to install Python and a few Python tools on your machine using Anaconda\n",
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"* An short guide to using Jupyter notebooks\n",
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"* A short guide to using the PyCharm (for Anaconda) editor\n",
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"* A quick overview of the more important Python packages for data science projects"
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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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"# Part 1. Python Workshops"
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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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"| | | | |\n",
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"|:------|:-------|:--------------------------------------------|:----------------------------------------------------|\n",
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"| 1 |15.01.20|Setting up the Python environment |Overview of workshops |\n",
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"| | | |Python installation |\n",
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"| | | |Anaconda | \n",
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"| | | |Jupyter notebooks | \n",
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"| | | |PyCharm IDE for debugging | \n",
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"| | | |Jupyter notebooks |\n",
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"| | | |Packages and environments | "
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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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"| | | | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; |\n",
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"|:------|:-------|:--------------------------------------------|:----------------------------------------------------|\n",
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"| 2 |29.01.20|Data structures |Data types (string, integer, float) |\n",
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"| | | |Lists | \n",
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"| | | |Dictionaries |\n",
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"| | | |Arrays |"
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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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"| | | | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; |\n",
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"|:------|:-------|:--------------------------------------------|:----------------------------------------------------|\n",
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"| 3 |12.02.20|Programming fundamentals I |Conditionals | \n",
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"| | | |For/while loops | \n",
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"| | | |Try/except statements | "
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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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"| | | | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; |\n",
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"|:------|:-------|:--------------------------------------------|:----------------------------------------------------|\n",
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"| 4 |26.02.20|Programming fundamentals II |Functions | \n",
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"| | | |Debugging in PyCharm |"
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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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"| | | | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; |\n",
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"|:------|:-------|:--------------------------------------------|:----------------------------------------------------|\n",
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"| 5 |11.03.20|Working with data |Introduction to Pandas | \n",
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"| | | |Loading/saving/manipulating data with Pandas | "
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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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"| | | | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; |\n",
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"|:------|:-------|:--------------------------------------------|:----------------------------------------------------|\n",
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"| 6 |25.03.20|Vizualizations |Introduction to Matplotlib |"
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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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"| | | | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; |\n",
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"|:------|:-------|:--------------------------------------------|:----------------------------------------------------|\n",
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"| 7 |08.04.20|Testing |Test your Python code |"
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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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"# Part 2. Python/Python Tools Installation Guide \n",
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"We will be using Python 3.7 during this tutorial. To install Python, please use the instructions below.\n",
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"### Install Anaconda \n",
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"* go to the official Anaconda website **[https://www.anaconda.com/distribution/](https://www.anaconda.com/distribution/)** and download the installation corresponding to your machine, e.g, for MacOS:\n",
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"<img src=\"images/Screenshot1.png\" alt=\"Anaconda installation MacOS\" style=\"width: 600px;\"/>\n",
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"* Follow the installation instructions until the end. It might take a while if it's the first time you install it."
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]
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},
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"### Anaconda Tools\n",
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"* Launch the Anaconda app\n",
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"<br>\n",
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"<img src=\"images/Anaconda_navigator.png\" alt=\"Launch the Anaconda navigator\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"* Browse through the available tools\n",
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"<br>\n",
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"<img src=\"images/Anaconda_tools.png\" alt=\"Anaconda tools\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"* Open a Jupyter notebook\n",
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"<br>\n",
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"<img src=\"images/Jupyter_cmd.png\" alt=\"Automatic launch of the command line\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/Jupyter_web.png\" alt=\"Jupyter notebook interface\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/Jupyter_web_WTH.png\" alt=\"Jupyter notebook\" style=\"width: 600px;\"/>"
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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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"### Install PyCharm IDE\n",
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"* Install PyCharm IDE for Anaconda from **[https://www.jetbrains.com/pycharm/promo/anaconda/](https://www.jetbrains.com/pycharm/promo/anaconda/)** since we will be using this for part 4 of the tutorial. Make sure you install the free Community Edition version!\n",
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"<br>\n",
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"* IMPORTANT: Use the recommended installation defaults if it's the first time using PyCharm.\n",
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"<br>\n",
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"<img src=\"images/Screenshot6.png\" alt=\"PyCharm installation step 1\" style=\"width: 600px;\"/>"
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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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"# Part 3. Jupyter notebooks "
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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 modes"
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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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"### Command mode\n",
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"The cell is highlighted blue.\n",
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"<br>\n",
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"<img src=\"images/Jupyter_command_mode.png\" alt=\"Jupyter command mode\" style=\"width: 600px;\"/>\n",
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"### Edit mode\n",
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"The cell is highlighted green.\n",
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"<br>\n",
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"<img src=\"images/Jupyter_edit_mode.png\" alt=\"Jupyter edit mode\" style=\"width: 600px;\"/>"
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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 cell types"
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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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"### Code\n",
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"<br>\n",
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"<img src=\"images/Jupyter_code.png\" alt=\"Jupyter code\" style=\"width: 600px;\"/>"
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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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"### Markdown\n",
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"<br>\n",
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"<img src=\"images/Jupyter_markdown.png\" alt=\"Jupyter markdown\" style=\"width: 600px;\"/>"
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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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"### Raw NBConvert\n",
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"<br>\n",
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"<img src=\"images/Jupyter_raw_nbconvert.png\" alt=\"Jupyter raw NBConvert\" style=\"width: 600px;\"/>"
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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 keyboard shortcuts\n",
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"<br>\n",
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"<img src=\"images/Jupyter_keyboard_1.png\" alt=\"Jupyter keyboard 1\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/Jupyter_keyboard_2.png\" alt=\"Jupyter keyboard 2\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/Jupyter_keyboard_3.png\" alt=\"Jupyter keyboard 3\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/Jupyter_keyboard_4.png\" alt=\"Jupyter keyboard 4\" style=\"width: 600px;\"/>"
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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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"# Part 4. PyCharm for Anaconda\n",
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"### Setting up the Python environment\n",
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"* For your first Python project, use the Conda environment.\n",
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"<br>\n",
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"<img src=\"images/PyCharm_launch.png\" alt=\"PyCharm launch\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/PyCharm_create_project.png\" alt=\"PyCharm create project\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/Screenshot7.png\" alt=\"PyCharm set environment\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"* Congrats! You have created your first Python project in PyCharm! \n",
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"<br>\n",
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"* You can now try to run/debug a short example in PyCharm."
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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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"### Run a script in Pycharm\n",
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"* First, add a file to your project. The file should print a line of text of your choice to the screen.\n",
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"<br>\n",
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"<img src=\"images/PyCharm_add_python_file1.png\" alt=\"PyCharm add file\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/PyCharm_add_python_file2.png\" alt=\"PyCharm add file\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"* Run the script and check what happens.\n",
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"<br>\n",
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"<img src=\"images/PyCharm_run_python_file1.png\" alt=\"PyCharm run file 1\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/PyCharm_run_python_file2.png\" alt=\"PyCharm run file 2\" style=\"width: 600px;\"/>"
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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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"### Debug a script in Pycharm\n",
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"* Let's add a new print line and a breakpoint in front of this line. \n",
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"<br>\n",
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"<img src=\"images/PyCharm_debug_python_file1.png\" alt=\"PyCharm debug file 1\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"* Debug the file and check the variables.\n",
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"<br>\n",
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"<img src=\"images/PyCharm_debug_python_file2.png\" alt=\"PyCharm debug file 2\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/PyCharm_debug_python_file3.png\" alt=\"PyCharm debug file 3\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/PyCharm_debug_python_file4.png\" alt=\"PyCharm debug file 4\" style=\"width: 600px;\"/>"
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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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"# Part 5. Python Packages\n",
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"There are a few Python packages which are useful te get familiarized with if you plan to work on data science projects"
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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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"## Numpy\n",
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"The Numpy package is a powerful N-dimensional array object, whic allows you to do do fast operations with vectors and N-dimensional matrices. It is in particular useful for tasks which invlove linear algebra, Fourier transforms, and random number generation.\n",
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"<br>\n",
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"<img src=\"images/numpy_example.png\" alt=\"Numpy example\" style=\"width: 600px;\"/>"
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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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"## Scipy\n",
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"SciPy builds on Numpy and provides a large number of functions that operate on numpy arrays and are useful for scientific and engineering applications.\n",
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"<br>\n",
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"<img src=\"images/scipy_example_1.png\" alt=\"Scipy example 1\" style=\"width: 600px;\"/>\n",
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"<br>\n",
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"<img src=\"images/scipy_example_2.png\" alt=\"Scipy example 2\" style=\"width: 400px;\"/>\n",
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"<br>\n",
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"<img src=\"images/scipy_example_3.png\" alt=\"Scipy example 3\" style=\"width: 400px;\"/>"
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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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"## Matplotlib\n",
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"Matplotlib is a plotting package, which allows you to visualize your data.\n",
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"<br>\n",
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"<img src=\"images/matplotlib_example.png\" alt=\"Matplotlib example\" style=\"width: 600px;\"/>"
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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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"## Pandas\n",
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"The Pandas package offers data structures and operations for manipulating numerical tables and time series.\n",
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"<br>\n",
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"<img src=\"images/Pandas_example.png\" alt=\"Pandas example\" style=\"width: 600px;\"/>"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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