VilniusPy (2017-10-18)

vilniuspy-2017-10-18.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dark Jupyter theme\n",
    "\n",
    "https://github.com/powerpak/jupyter-dark-theme\n",
    "\n",
    "\n",
    "```\n",
    "mkdir -p ~/.jupyter/custom\n",
    "wget https://github.com/powerpak/jupyter-dark-theme/raw/master/custom.css \\\n",
    "     -O ~/.jupyter/custom/custom.css\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter vim-mode\n",
    "\n",
    "https://github.com/lambdalisue/jupyter-vim-binding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter as a calculator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2 + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_**4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "math.log2(_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter for math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sympy\n",
    "sympy.init_printing(use_latex='mathjax')\n",
    "x = sympy.symbols('x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x**2 / 3 + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sympy.solve(_, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(sympy.latex(_5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\LaTeX$ works in markdown cells too: $\\frac{x^{2}}{3} + 1$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding things in Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "str.is*?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "str.isalnum?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute shell commands from Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!screenfetch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines = !ls -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "/print lines.n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines.s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines.grep('ipynb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines.grep('ipynb').fields(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines.grep('ipynb').fields(-1).p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for p in lines.grep('ipynb').fields(-1).p:\n",
    "    !echo $p.suffix {p.suffix.lstrip('.')}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for s, p in map(str.split, lines.grep('ipynb').fields(4, -1)):\n",
    "    du = !du -sh $p\n",
    "    !echo {du.fields(0).s} $s {du.fields(1).s}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!echo $USER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter variable storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run doctests from Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "\n",
    "def fib(n):\n",
    "    \"\"\"\n",
    "    >>> fib(10)\n",
    "    [0, 1, 2, 3, 5, 8, 13, 21, 34, 55]\n",
    "    \"\"\"\n",
    "    q = deque([0, 1], 2)\n",
    "    return [q.append(sum(q)) or q[0] for i in range(2, n + 2)]\n",
    "\n",
    "\n",
    "import doctest\n",
    "\n",
    "doctest.run_docstring_examples(fib, None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Profiling in Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%time fib(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%timeit fib(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%prun fib(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext line_profiler\n",
    "\n",
    "%lprun -f fib fib(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Requires `line_profiler` package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with databases from Jupyter\n",
    "\n",
    "Requires `ipython-sql` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%sql sqlite://\n",
    "    \n",
    "CREATE TABLE data (a, b);\n",
    "\n",
    "INSERT INTO data VALUES\n",
    "    ('a', 1),\n",
    "    ('b', 2),\n",
    "    ('c', 3),\n",
    "    ('d', 4),\n",
    "    ('e', 5),\n",
    "    ('f', 6);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sql select * from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = 3\n",
    "%sql select * from data where b = :v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = %sql select * from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.DataFrame().query('b > 3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Showing plots in Jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.style.use('seaborn-darkgrid')\n",
    "mpl.rc('figure', figsize=(12, 8))\n",
    "mpl.rc('font', size=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.pie();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive Jupyter\n",
    "\n",
    "Requires `ipywidgets` packages:\n",
    "\n",
    "    pip install ipywidgets\n",
    "    jupyter nbextension enable --py widgetsnbextension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interactive\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def f(m, b):\n",
    "    plt.figure(2)\n",
    "    x = np.linspace(-10, 10, num=100)\n",
    "    plt.axhline(0, color='gray')\n",
    "    plt.axvline(0, color='gray')\n",
    "    plt.plot(x, m * x + b, linewidth=3)\n",
    "    plt.ylim(-5, 5)\n",
    "    plt.show()\n",
    "\n",
    "interactive(f, m=(-2.0, 2.0), b=(-3, 3, 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embeding videos into Jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "mpl.rc('animation', html='html5')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "x = np.linspace(-10, 10, num=100)\n",
    "b = np.sin(np.linspace(-np.pi, np.pi, 200))\n",
    "ax.axhline(0, color='gray')\n",
    "ax.axvline(0, color='gray')\n",
    "ax.set_xlim(-5, 5)\n",
    "ax.set_ylim(-5, 5)\n",
    "line, = ax.plot([], [], lw=3)\n",
    "plt.close()\n",
    "\n",
    "def update(b):\n",
    "    line.set_data(x, b * x)\n",
    "    return line,\n",
    "\n",
    "animation.FuncAnimation(fig, update, frames=b, interval=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharing Jupyter notebooks\n",
    "\n",
    "https://gist.github.com/sirex/f8064d588fc518de2382f309390e1cf7\n",
    "\n",
    "http://nbviewer.jupyter.org/gist/sirex/f8064d588fc518de2382f309390e1cf7"
   ]
  }
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