nb_intro.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Jupyter-Notebook---Quick-Introduction\" data-toc-modified-id=\"Jupyter-Notebook---Quick-Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Jupyter Notebook - Quick Introduction</a></span><ul class=\"toc-item\"><li><span><a href=\"#Python-code-cells\" data-toc-modified-id=\"Python-code-cells-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Python code cells</a></span></li><li><span><a href=\"#Notebook-managing\" data-toc-modified-id=\"Notebook-managing-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Notebook managing</a></span></li><li><span><a href=\"#Help-and-documentation\" data-toc-modified-id=\"Help-and-documentation-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Help and documentation</a></span><ul class=\"toc-item\"><li><span><a href=\"#Tab-completion-of-object-contents\" data-toc-modified-id=\"Tab-completion-of-object-contents-1.3.1\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>Tab-completion of object contents</a></span></li></ul></li><li><span><a href=\"#IPython-Magic-Commands\" data-toc-modified-id=\"IPython-Magic-Commands-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>IPython Magic Commands</a></span><ul class=\"toc-item\"><li><span><a href=\"#Running-External-Code:-%run\" data-toc-modified-id=\"Running-External-Code:-%run-1.4.1\"><span class=\"toc-item-num\">1.4.1&nbsp;&nbsp;</span>Running External Code: <code>%run</code></a></span></li><li><span><a href=\"#Timing-code-%timeit\" data-toc-modified-id=\"Timing-code-%timeit-1.4.2\"><span class=\"toc-item-num\">1.4.2&nbsp;&nbsp;</span>Timing code <code>%timeit</code></a></span></li><li><span><a href=\"#Shell-commands\" data-toc-modified-id=\"Shell-commands-1.4.3\"><span class=\"toc-item-num\">1.4.3&nbsp;&nbsp;</span>Shell commands</a></span></li></ul></li><li><span><a href=\"#Python-versions\" data-toc-modified-id=\"Python-versions-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Python versions</a></span><ul class=\"toc-item\"><li><span><a href=\"#Python3-vs-Python2\" data-toc-modified-id=\"Python3-vs-Python2-1.5.1\"><span class=\"toc-item-num\">1.5.1&nbsp;&nbsp;</span>Python3 vs Python2</a></span></li></ul></li></ul></li></ul></div>"
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    "# Jupyter Notebook - Quick Introduction \n",
    "\n",
    "A very attractive way to use python and work interactively is provided by **jupyter notebooks** \n",
    "- Start an interactive python environment in a browser window\n",
    "- Output and Graphs are shown in the notebook\n",
    "- you can add your own comments, descriptions, math formulas (Latex style) , ... \n",
    "\n",
    "Great way to develop and document an interactive analysis and to share with others:\n",
    "* Nice example provided by **Ligo gravitational wave analysis** (2017 Nobel prize):  \n",
    "  [Detailed notebook](https://losc.ligo.org/tutorials/) to present analysis in detail\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "** Starting:**  \n",
    "In shell-window:\n",
    "\n",
    "```jupyter notebook```\n",
    "\n",
    "Sometimes problems on cip nodes, you might need:\n",
    "\n",
    "```jupyter notebook --browser=firefox```\n",
    "\n",
    "More info at https://jupyter.readthedocs.io/en/latest/index.html"
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    "## Python code cells\n",
    "\n",
    "* you can enter  Python instructions (single line, many lines, ...) in *code cells*.\n",
    "* content of cells will be executed when you hit \n",
    "\n",
    "```<Shift><Enter>```"
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w5nw4i8WMUV5/C26MjuNWs2Nxr/f4ttEyObyx/QWudsk/JiFTLcMWwkTxEm+L+99fHG7A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\n9LVyEXoJ4WEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16d12b1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as pl\n",
    "\n",
    "t = np.arange(0.0, 1.0+0.01, 0.01)\n",
    "s = np.cos(2*2*np.pi*t)\n",
    "pl.plot(t, s)\n",
    "\n",
    "pl.xlabel('time (s)')\n",
    "pl.ylabel('voltage (mV)')\n",
    "pl.title('About as simple as it gets, folks')\n",
    "pl.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "7c2b3c9d-cf1b-4455-b2ec-d7b9934eaaae"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "** Code cells **\n",
    "* Output of commands is shown, both text and graphics\n",
    "* Cells are part of interactive python session\n",
    "  * all *variables, definitions, imports, ...,* which happen in a cell stay active and can be used in sub-sequent cells\n",
    "* usually cells are executed one after the other, starting from top\n",
    "  * just convention, not mandatory, **watch out!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "0d030db8-41ed-4ff9-98f5-fee845a06063"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "nbpresent": {
     "id": "de223c17-03f9-4e67-9213-d052886ab26a"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.    0.01  0.02  0.03  0.04  0.05  0.06  0.07  0.08  0.09]\n"
     ]
    }
   ],
   "source": [
    "print(t[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "be5afe67-aa35-4b2e-8105-749fc55e7a40"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Notebook managing\n",
    "notebooks\n",
    "* can be stored\n",
    "* re-used\n",
    "* shared with other\n",
    "* exported as html, slides, ...\n",
    "\n",
    "They can also be executed and stored on remote system and accessed via web-browser\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "cd42ba71-f1d0-4b23-babd-2a30f52ddd0a"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Help and documentation\n",
    "\n",
    "Jupyter/IPython uses the ``?`` character as a shorthand for accessing  documentation and other relevant information on any object, function or module:\n",
    "\n",
    "```ipython\n",
    "In [2]: len?\n",
    "Type:        builtin_function_or_method\n",
    "String form: <built-in function len>\n",
    "Namespace:   Python builtin\n",
    "Docstring:\n",
    "len(object) -> integer\n",
    "\n",
    "Return the number of items of a sequence or mapping.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d5280d21-27af-4e93-81b7-53328e78ddd2"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "This notation works for just about anything, including object methods:\n",
    "\n",
    "```ipython\n",
    "In [3]: L = [1, 2, 3]\n",
    "In [4]: L.insert?\n",
    "Type:        builtin_function_or_method\n",
    "String form: <built-in method insert of list object at 0x1024b8ea8>\n",
    "Docstring:   L.insert(index, object) -- insert object before index\n",
    "```\n",
    "\n",
    "or even objects themselves, with the documentation from their type:\n",
    "\n",
    "```ipython\n",
    "In [5]: L?\n",
    "Type:        list\n",
    "String form: [1, 2, 3]\n",
    "Length:      3\n",
    "Docstring:\n",
    "list() -> new empty list\n",
    "list(iterable) -> new list initialized from iterable's items\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ff8c88b6-5cd0-4901-9537-1af99700e813"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Tab-completion of object contents\n",
    "\n",
    "Every Python object has various attributes and methods associated with it. This can be accessed in Python with ``help``  or ``dir``.\n",
    "But much more convenient is the tab-completion interface.\n",
    "To see a list of all available attributes of an object, you can type the name of the object followed by a period (\"``.``\") character and the Tab key:\n",
    "\n",
    "```ipython\n",
    "In [10]: L.<TAB>\n",
    "L.append   L.copy     L.extend   L.insert   L.remove   L.sort     \n",
    "L.clear    L.count    L.index    L.pop      L.reverse\n",
    "```"
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    "## IPython Magic Commands\n",
    "\n",
    "IPython  *magic commands* are prefixed by the ``%`` character.\n",
    "\n",
    "We just discuss two quick examples, but will see more in later chapters.\n",
    "\n",
    "### Running External Code: ``%run``\n",
    "\n",
    "Rather than writing or loading code in a cell it can be convenient to just run code in an external python file. \n",
    "This can be done with the ``%run`` magic.\n",
    "\n",
    "\n",
    "```ipython\n",
    "In [6]: %run myscript.py\n",
    "```\n",
    "\n",
    "The output of the code will be shown and also *all declarations* (vars, functions, imports, ...)  are available, it is equivalent to load and execute the code in a cell.\n",
    "\n",
    "### Timing code ``%timeit``\n",
    "This will determine the execution time of the single-line Python statement that follows it.\n",
    "For example, we may want to check the performance of a list comprehension:\n",
    "\n",
    "```ipython\n",
    "In [8]: %timeit L = [n ** 2 for n in range(1000)]\n",
    "1000 loops, best of 3: 325 µs per loop\n",
    "```\n",
    "\n",
    "The benefit of ``%timeit`` is that for short commands it will automatically perform multiple runs in order to attain more robust results.\n",
    "For multi line statements, adding a **second** ``%`` sign will turn this into a cell magic that can handle multiple lines of input.\n",
    "For example, here's the equivalent construction with a ``for``-loop:\n",
    "\n",
    "```ipython\n",
    "In [9]: %%timeit\n",
    "   ...: L = []\n",
    "   ...: for n in range(1000):\n",
    "   ...:     L.append(n ** 2)\n",
    "   ...: \n",
    "1000 loops, best of 3: 373 µs per loop\n",
    "```\n"
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    "### Shell commands\n",
    "Simple executon of shell commands, just precede with **`!`**"
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     "text": [
      "/home/gduckeck/kurs_repo/ml\r\n"
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    "!pwd"
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      "['nb_intro.ipynb', 'NumpyIntro.ipynb', 'PythonDA_ML.ipynb', 'README.GD']\n"
     ]
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    "a=!ls  # ouput can be obtained as list\n",
    "print (a)"
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    "## Python versions\n",
    "We are using for the course the recent **Python 3.6** version together with corresponding Numpy, Scipy, Pandas, etc, packages.\n",
    "\n",
    "The installation is based on the **[Anaconda](https://www.anaconda.com/download)** and **[conda](https://conda.io/docs/user-guide/install/index.html)** tools, which provide scientific and machine learning python packages for many architectures (Linux, WIndows, Mac).\n",
    "\n",
    "Pandas provides a function to get detailed info on installed package versions:\n"
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      "\n",
      "INSTALLED VERSIONS\n",
      "------------------\n",
      "commit: None\n",
      "python: 3.5.2.final.0\n",
      "python-bits: 64\n",
      "OS: Linux\n",
      "OS-release: 4.4.0-91-generic\n",
      "machine: x86_64\n",
      "processor: x86_64\n",
      "byteorder: little\n",
      "LC_ALL: None\n",
      "LANG: en_US.UTF-8\n",
      "LOCALE: en_US.UTF-8\n",
      "\n",
      "pandas: 0.19.2\n",
      "nose: 1.3.7\n",
      "pip: 8.1.2\n",
      "setuptools: 23.0.0\n",
      "Cython: 0.24\n",
      "numpy: 1.11.3\n",
      "scipy: 0.18.1\n",
      "statsmodels: 0.6.1\n",
      "xarray: None\n",
      "IPython: 5.3.0\n",
      "sphinx: 1.4.1\n",
      "patsy: 0.4.1\n",
      "dateutil: 2.5.3\n",
      "pytz: 2016.4\n",
      "blosc: None\n",
      "bottleneck: 1.1.0\n",
      "tables: 3.2.2\n",
      "numexpr: 2.6.1\n",
      "matplotlib: 2.0.0\n",
      "openpyxl: 2.3.2\n",
      "xlrd: 1.0.0\n",
      "xlwt: 1.1.2\n",
      "xlsxwriter: 0.9.2\n",
      "lxml: 3.6.0\n",
      "bs4: 4.4.1\n",
      "html5lib: None\n",
      "httplib2: None\n",
      "apiclient: None\n",
      "sqlalchemy: 1.0.13\n",
      "pymysql: None\n",
      "psycopg2: None\n",
      "jinja2: 2.8\n",
      "boto: 2.40.0\n",
      "pandas_datareader: 0.2.1\n"
     ]
    }
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   "source": [
    "import pandas as pd\n",
    "pd.show_versions()"
   ]
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    "### Python3 vs Python2\n",
    "For most parts differences between Python3 and Python2 are small:\n",
    "* **most notable** : `print` is a function in Python3 and  requires `()`  *(was statement in Python2)*\n",
    "  * `print ('Hi there') # Python3`\n",
    "  * `print 'Hi there' # Python2`\n",
    "    * Python2 also accepts `print 'Hi there'`, but not v.v.\n",
    "* many detailed differences in Python modules\n",
    "  * existing Python2 code which relies on Python modules might require larger porting effort\n",
    "  more or less large re-writ \n",
    "\n",
    "More info e.g. in http://sebastianraschka.com/Articles/2014_python_2_3_key_diff.html"
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