still work in progress but getting there

Jupyter Notebooks.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %line_magic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%cell_magic (must be in the first line of the cell)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# list the help to magix commands\n",
+    "%magic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# list content of current folder\n",
+    "%ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%lsmagic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%file test_simple.py\n",
+    "# writing content of cell to file\n",
+    "\n",
+    "def test_one():\n",
+    "    assert 1 + 1 == 2\n",
+    "    \n",
+    "def test_two():\n",
+    "    \"\"\" this test will fail !\"\"\"\n",
+    "    a = [1, 2, 3]\n",
+    "    b = [3, 4]\n",
+    "    assert a + b == [1, 2, 3, 4]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get current working directory\n",
+    "%pwd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# return the history of a notebook\n",
+    "%hist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pip install in notebook\n",
+    "%pip install numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run a python script from inside a notebook\n",
+    "%run test_simple.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run shell commands and capture output!\n",
+    "%sx "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# time your code\n",
+    "%time some_list=[a+b**2 for a,b in enumerate(range(1000000))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# time your code - more suffisticated\n",
+    "%timeit some_list=[a+b**2 for a,b in enumerate(range(1000000))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a=\"22\"\n",
+    "b=33\n",
+    "\n",
+    "# list varaibles of certain type quickly\n",
+    "%who function str"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "There's a lot more o discover... convert cells to html, run javascript in cells, render as svg, ..."
+   ]
+  }
+ ],
+ "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Numpy.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Numpy\n",
+    "\n",
+    "For all of you that love matlab numpy will be very familiar. (https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html)\n",
+    "\n",
+    "Why use numpy? Because it's very fast! (https://stackoverflow.com/questions/7596612/benchmarking-python-vs-c-using-blas-and-numpy)\n",
+    "\n",
+    "**A more comprehensive tutorial:** (https://docs.scipy.org/doc/numpy/user/quickstart.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Arrays Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr = np.array([3,4,5,6])\n",
+    "rng = np.arange(100)\n",
+    "ones = np.ones(1000000)\n",
+    "zeros = np.zeros((100,100)) # 2d array shape passed as tuple\n",
+    "repeat = np.repeat(2,1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shapes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "repeat.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3d array\n",
+    "repeat.reshape((10,10,10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# flatten array to 1d\n",
+    "repeat.flatten()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing and Slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng[79]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng[3::5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng_2d = rng.reshape((10,10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all lines, starting with column 5\n",
+    "rng_2d[:,4:] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first 5 lines, all column\n",
+    "rng_2d[:-5,:] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng_2d[3:5,6:9]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boolean Indexing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2d boolean array\n",
+    "boolean_arr = rng_2d > 45\n",
+    "boolean_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# losing shape of course!\n",
+    "rng_2d[boolean_arr]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng_2d[rng_2d[:,0] > 40, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng = np.arange(10000000)\n",
+    "# symbols: & and, | or, ~ not\n",
+    "%timeit rng[(rng<90) & (rng>67)] # \n",
+    "%timeit rng[np.logical_and(rng<90,rng>67)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Performance Comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vector_len = 10000000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "square = []\n",
+    "for i in range(vector_len):\n",
+    "    square.append(i**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "square_np = np.square(np.arange(vector_len))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "square_even = []\n",
+    "for i in range(vector_len):\n",
+    "    i_2 = i**2\n",
+    "    if i_2 < (vector_len / 2) ** 2:\n",
+    "        square_even.append(i_2)\n",
+    "    else:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "square_even_np = np.square(np.arange(vector_len))\n",
+    "square_even_np = square_even_np[square_even_np < (vector_len / 2) ** 2]"
+   ]
+  },
+  {
+   "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

README.md

@@ -3,8 +3,8 @@
 
 The tutorial consists of multiple parts.
 - Python Basics
-- Python Advanced 
-- Pandas and Plotting
+- Python Pandas, Numpy, Plotting, Statsmodels 
+- Packaging
 
 ## Requierements
 
@@ -79,7 +79,7 @@ We'll be using:
 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
+pip install pandas numpy matplotlib cufflinks seaborn sklearn jupyter notebook
 ```
 They're many dependencies involved, so the installation might take a minute.
 

Tasks - Basic.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**1. Create a function that prints a list without commas or brackets.**\n",
+    "\n",
+    "Input: [1,2,3,4]  \n",
+    "Output: 1 2 3 4 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2. Create a function that negates all values of a boolean list.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3. Choose a name randomly. (Use the random modul)**  \n",
+    "Namen=[\"Karl\",\"Lisa\",\"Sven\",\"Birgit\",\"Igor\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**4. Create a prime number generator!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**5. Define your own sorting algorithm!**  \n",
+    "Input is a list!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**6. Define your own algorithm to get the max value of a list!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**7. Write a class called 'FileAnalyser'.**  \n",
+    "\n",
+    "When initiated it gets a filepath to a file you want to analyse.  \n",
+    "The class should have 2 attributes, 'path' and 'name'.  \n",
+    "Write at least 2 methods for that class, that returns you clever stats about the file like: \n",
+    "\n",
+    " - size (with os package)\n",
+    " - line number\n",
+    " - total words (by whitespace and newline separated string patterns)\n",
+    " - contains_numbers \n",
+    " - contains_uppercase\n",
+    " - contains_lowercase\n",
+    " - ...\n",
+    " \n",
+    "(This would be at least 6 methods now)"
+   ]
+  },
+  {
+   "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

data/data_file.txt

+line1
+line 2