intital tutorial files

Python Tutorial - Advanced Topics.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python Tutorial Part 2 - Advanced Topics\n",
+    "\n",
+    "## Object Oriented Programming (OOP)\n",
+    "\n",
+    "OOP is a programming paradigm based on the concept of \"objects\", which can contain data, in the form of attributes and code, in the form of methods.\n",
+    "It's a different way of you thinking about your model/problem. Instead of thinking of a problem as a sequence of commands that need to be executed (procedural, **linear**), you identify single processes/actors and define their attributes and functionalities, so that they can interact with other actors (**nonlinear**).\n",
+    "\n",
+    "**Example Problem:\n",
+    "Create a plotting library**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Structering your code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Large Projects\n",
+    "Pycharm, Git, Debugging, Naming conventions, goot practice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

README.md

+
 # PythonTutorial
 
 The tutorial consists of multiple parts.
 - Python Basics
 - Python Advanced 
 - Pandas and Plotting
+
+## Requierements
+
+You must have python3 installed on your computer. Additionally we'll be using a few packages in the tutorial what we still need to install.
+
+### Installation Python
+***
+
+Go to the [official python website](https://www.python.org) click on downloads and select your operating system. 
+Then go ahead and download the latest stable release. Windows users most likely need the 64 bit version, alternatively you can check your system version in the system information.
+
+Run the downloaded file and make sure you select the add to Path option!
+
+In case you need further assistance please check out this [website](https://realpython.com/installing-python/#step-2-run-the-installer).
+
+**Check the Installation**
+
+After you finished installing python open a console and type `pip`. This commands output should look something like this:
+```bash
+Usage:   
+  pip <command> [options]
+
+Commands:
+  install                     Install packages.
+  download                    Download packages.
+  uninstall                   Uninstall packages.
+  freeze                      Output installed packages in requirements format.
+  list                        List installed packages.
+  show                        Show information about installed packages.
+  check                       Verify installed packages have compatible dependencies.
+  search                      Search PyPI for packages.
+  wheel                       Build wheels from your requirements.
+  hash                        Compute hashes of package archives.
+  completion                  A helper command used for command completion.
+  help                        Show help for commands.
+
+General Options:
+  -h, --help                  Show help.
+  --isolated                  Run pip in an isolated mode, ignoring environment variables and user configuration.
+  -v, --verbose               Give more output. Option is additive, and can be used up to 3 times.
+  -V, --version               Show version and exit.
+  -q, --quiet                 Give less output. Option is additive, and can be used up to 3 times (corresponding to WARNING, ERROR,
+                              and CRITICAL logging levels).
+  --log <path>                Path to a verbose appending log.
+  --proxy <proxy>             Specify a proxy in the form [user:passwd@]proxy.server:port.
+  --retries <retries>         Maximum number of retries each connection should attempt (default 5 times).
+  --timeout <sec>             Set the socket timeout (default 15 seconds).
+  --exists-action <action>    Default action when a path already exists: (s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.
+  --trusted-host <hostname>   Mark this host as trusted, even though it does not have valid or any HTTPS.
+  --cert <path>               Path to alternate CA bundle.
+  --client-cert <path>        Path to SSL client certificate, a single file containing the private key and the certificate in PEM
+                              format.
+  --cache-dir <dir>           Store the cache data in <dir>.
+  --no-cache-dir              Disable the cache.
+  --disable-pip-version-check
+                              Don't periodically check PyPI to determine whether a new version of pip is available for download.
+                              Implied with --no-index.
+```
+### Install Python Packages
+***
+
+We'll be using:
+- jupyter notebook -> lightweight IDE
+- pandas -> a library for working with data
+- numpy -> library for efficient matrix, array,... manipulation
+- matplotlib -> standard plotting library
+- plotly -> dynamic plots
+- cufflinks -> pandas adapter to plotly
+- seaborn -> fancy plots out of the box
+- sklearn -> machine learning library
+
+To install the packages open a console and run the following command.
+(Linux users should use pip3 instead of pip since they do have Python2 and Python3 installed by default.)
+```bash
+pip install pandas numpy matplotlib plotly cufflinks seaborn sklearn jupyter notebook
+```
+They're many dependencies involved, so the installation might take a minute.
+
+## Starting Jupyter Notebook
+
+I choose *jupyter notebook* (*JN*) as the appropriate IDE for this tutorial, as it is light weight, so it's easy to understand and does not need much resources. Additionally *JN* is great for plotting!
+
+One way to start *JN* is:
+-  to open a console and navigate to your working directory (using the cd command)
+- now you simply type jupyter notebook
+
+A browser tab will open and this is your IDE. If you don't know what to do from this point on forward check out this comprehensive [tutorial](https://realpython.com/jupyter-notebook-introduction/).
+
+### The rest we'll figure out together in the tutorial!
+

Solutions.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read in some of your data with pandas, plot it and get the main statistics on the data.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "%matplotlib inline\n",
+    "import cufflinks as cf\n",
+    "cf.go_offline()\n",
+    "\n",
+    "fp=r\"M:\\Projekte\\2013_UWRM\\3_messen und daten\\Messstellen\\Pegel Kreischa\\Q_Kreischa.txt\"\n",
+    "my_df=pd.read_csv(fp,parse_dates=True,index_col=0,sep=\",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_df.iplot(secondary_y=\"q\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(my_df.describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a class, that takes a filepath as an argument when it is initialized. Create a method, that takes a string as an argument and returns the number of occurances of that string in that file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "\n",
+    "class StrCount():\n",
+    "    def __init__(self,filepath):\n",
+    "        self.filepath=filepath\n",
+    "        return\n",
+    "    \n",
+    "    def count(self,string):\n",
+    "        count=0\n",
+    "        with open(self.filepath) as file:\n",
+    "            for line in file:\n",
+    "                count+=len(line.split(string))-1\n",
+    "        return count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig,ax=plt.subplots(2,2)\n",
+    "fig.text(0,0.5,r\"\\sum$\",rotation=90, verticalalignment='center')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filepath=r\"C:\\Users\\Gabi\\Documents\\Python\\NN\\Inputdata\\PCM_MEZ_good_shape.txt\"\n",
+    "count=StrCount(filepath).count(\";\")\n",
+    "print(count)\n",
+    "\n",
+    "# if this takes too long it might be the server connection... (depending on the file size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a function that takes an arbitrary number of inputs and prints them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#this function only takes list like entries\n",
+    "def arbitrary_list(*arguments):\n",
+    "    print(arguments)\n",
+    "    \n",
+    "arbitrary_list(2,3,4,5,4,3,2,4,[432],[{2:3}])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#this function only takes list like entries\n",
+    "def arbitrary_dict(**dictarguments):\n",
+    "    print(dictarguments)\n",
+    "    \n",
+    "arbitrary_dict(a=3,b=4,wer=88)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#this function can do all list arguments must come before dict arguments\n",
+    "def arbitrary(*list_args,**dict_args):\n",
+    "    print(list_args)\n",
+    "    print(dict_args)\n",
+    "    \n",
+    "arbitrary(2,3,4,5,6,a=3,b=33,d=\"dd\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a function, that creates the fibonacci sequence up to the n'th term."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fib(n):\n",
+    "    fibo=[1,1]\n",
+    "    if n==1:\n",
+    "        return fibo[0]\n",
+    "    elif n==2:\n",
+    "        return fibo\n",
+    "    else:\n",
+    "        for run in range(n-2):\n",
+    "            fibo.append(fibo[-1]+fibo[-2])\n",
+    "        return fibo\n",
+    "\n",
+    "fib(16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make python list of all files in the current directory and sort them by their file type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# list all contest\n",
+    "foldercontent=os.listdir()\n",
+    "\n",
+    "#filter files\n",
+    "files=[file for file in foldercontent if os.path.isfile(file)]\n",
+    "\n",
+    "#sort key makes sure i sort by the file ending\n",
+    "def sortkey(x):\n",
+    "    return x.split(\".\")[1]\n",
+    "# sorting\n",
+    "files.sort(key=sortkey)\n",
+    "\n",
+    "print(files)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Tasks.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tasks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read in some of your data with pandas, plot it and get the main statistics on the data.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a class, that takes a filepath as an argument when it is initialized. Create a method, that takes a string as an argument and returns the number of occurances of that string in that file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a function that takes an arbitrary number of inputs and prints them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a function, that creates the fibonacci sequence up to the n'th term."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make python list all files in the current directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}