short_notes_exercises.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__abs__', '__add__', '__and__', '__bool__', '__ceil__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floor__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getnewargs__', '__gt__', '__hash__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__le__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', '__xor__', 'bit_length', 'conjugate', 'denominator', 'from_bytes', 'imag', 'numerator', 'real', 'to_bytes']\n",
      "\n",
      "bit length of 1: 1 bits\n",
      "      size of 1: 28 bytes\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "print(dir(1))\n",
    "\n",
    "print(\"\\nbit length of %d: %d bits\" % (1, (1).bit_length() ))\n",
    "print(\"      size of %d: %d bytes\" % (1, sys.getsizeof(1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abcabc abcabcabcabcabcabcabcabcabcabc 30\n",
      "\n",
      " bcabc abcabcab cbacbacba\n",
      "\n",
      " AbcAbcAbc\n",
      "\n",
      " a;b;c;d \n",
      " ['a', 'b', 'c', 'd']\n",
      "\n",
      " ['1', '2', '', '3', '', '', '4', '', '', '', '5'] \n",
      " ['1', '2', '3', '4', '5']\n",
      "0123456789\n",
      " \t\n",
      "\r",
      "\u000b",
      "\f",
      "\n",
      "' \\t\\n\\r\\x0b\\x0c'\n"
     ]
    }
   ],
   "source": [
    "s = 'abc'\n",
    "print(s+s, 10*s, len(10*s))\n",
    "\n",
    "print('\\n', (3*s)[1:6], (3*s)[:-1],  (3*s)[ : : -1])\n",
    "\n",
    "print('\\n', (3*s).replace('a', 'A'))\n",
    "\n",
    "s = ';'.join( ['a', 'b', 'c', 'd'] )\n",
    "print('\\n', s, '\\n', s.split(';'))\n",
    "\n",
    "s = '1 2  3   4    5' \n",
    "print('\\n', s.split(' '), '\\n', s.split())\n",
    "\n",
    "import string\n",
    "print(\"%s\\n%s\" % (string.digits, string.whitespace))\n",
    "\n",
    "#string.whitespace\n",
    "print(repr(string.whitespace))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variables and bindings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2] [1, 2]\n",
      "11 10\n",
      "10 11\n"
     ]
    }
   ],
   "source": [
    "a = [] \n",
    "b = a \n",
    "\n",
    "a.append(1) \n",
    "b.append(2)\n",
    "\n",
    "print(a, b)\n",
    "\n",
    "a = 10 \n",
    "b = a \n",
    "a += 1\n",
    "\n",
    "print(a, b)\n",
    "\n",
    "a, b = b, a\n",
    "print (a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id(a): 2975561921232\n",
      "id(b): 2975561921232\n"
     ]
    }
   ],
   "source": [
    "a = 'aaa'\n",
    "b = 'aaa'\n",
    "\n",
    "print (\"id(a): %d\\nid(b): %d\" % (id(a), id(b)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bindings and id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1740605136 1740605136 \n",
      "\n",
      "1740605168 1740605136 \n",
      "\n",
      "[] 2975560623624 \n",
      " [] 2975560623624 \n",
      "\n",
      "[1, 2] 2975560623624 \n",
      " [1, 2] 2975560623624 \n",
      "\n",
      "[1, 2]\n",
      "2975560623624\n"
     ]
    }
   ],
   "source": [
    "a = 10\n",
    "b = a \n",
    "print (id(a), id(b), '\\n')\n",
    "\n",
    "a += 1 \n",
    "print (id(a), id(b), '\\n')\n",
    "\n",
    "a = [] \n",
    "b = a \n",
    "print (a, id(a), '\\n', b, id(b), '\\n') \n",
    "\n",
    "a.append(1) \n",
    "b.append(2) \n",
    "print (a, id(a), '\\n', b, id(b), '\\n') \n",
    "\n",
    "del(a)\n",
    "\n",
    "#print(id(a))\n",
    "print(b)\n",
    "print(id(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Printing formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int: 3\n",
      "float: 3.141600\n",
      "string: abcdefgh\n",
      "\n",
      "Second argument:  11, first one:   47.42 \n",
      "Art:   453,  Price:    59.06 \n",
      "various precisions:    1.4 or  1.415\n"
     ]
    }
   ],
   "source": [
    "a, b, c = 3, 3.1416, 'abcdefgh'\n",
    "text = \"int: %d\\nfloat: %f\\nstring: %s\\n\" % (a, b, c)\n",
    "print (text)\n",
    "\n",
    "s1 = \"Second argument: {1:3d}, first one: {0:7.2f}\".format(47.42, 11) \n",
    "s2 = \"\\nArt: {a:5d},  Price: {p:8.2f}\".format(a=453, p=59.058)\n",
    "s3 = \"\\nvarious precisions: {0:6.1f} or {0:6.3f}\".format(1.4148) \n",
    "print(s1, s2, s3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4140625 1.99957275390625\n",
      "1.4142135381698608 1.9999999315429164\n"
     ]
    }
   ],
   "source": [
    "def sqroot(x, eps=1.e-3):\n",
    "    \"\"\"Computes the square root of x up to a precision eps by approximate binary search.\n",
    "    \n",
    "    :param x: number whose square root is to be computed\n",
    "    :param eps: precision between x and the square of the approximate root\n",
    "    :type x: float\n",
    "    :type eps: float\n",
    "    :return: approximate square root\n",
    "    :rtype: float\n",
    "    \"\"\"\n",
    "    \n",
    "    assert (x   >= 1 ), \"error: input %f < 1.\" % x\n",
    "    assert (eps >  0.), \"error: precision %f <= 0.\" % eps\n",
    "    \n",
    "    left = 1.\n",
    "    right = x\n",
    "    sqr = (left+right)/2 \n",
    "    \n",
    "    while abs (sqr**2 - x) > eps:\n",
    "        if sqr**2 < x:\n",
    "            left = sqr\n",
    "        else:\n",
    "            right = sqr\n",
    "        sqr = (left+right)/2\n",
    "    \n",
    "    return sqr\n",
    "\n",
    "s = sqroot(2, 1.e-3)\n",
    "print(s, s*s)\n",
    "\n",
    "s = sqroot(2, 1.e-7)\n",
    "print(s, s*s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## funcs as arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def square(n):\n",
    "    return n**2\n",
    "\n",
    "def list_func_values(n, f):\n",
    "    l_func_vals =[f(i) for i in range(n)] #list comprehension\n",
    "    return l_func_vals\n",
    "\n",
    "list_func_values(10, square)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# tuples, lists, dicts, sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "a = ('a', 1, 'b', 2) \n",
    "b = 'a', 1, 'b', 2\n",
    "print(a == b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "8\n",
      "27\n",
      "64\n",
      "125\n",
      "216\n",
      "343\n",
      "512\n",
      "729\n",
      "0\n",
      "1\n",
      "8\n",
      "27\n",
      "64\n",
      "125\n",
      "216\n",
      "343\n",
      "512\n",
      "729\n"
     ]
    }
   ],
   "source": [
    "l_1 = range(10) \n",
    "\n",
    "# list comprehension\n",
    "l_2 = [i*i for i in l_1]\n",
    "\n",
    "for l1, l2 in zip(l_1, l_2):\n",
    "    print (l1*l2)\n",
    "\n",
    "for l in zip(l_1, l_2):\n",
    "    print (l[0]*l[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## enumerate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "el cuadrado de  0 es    0\n",
      "el cuadrado de  1 es    1\n",
      "el cuadrado de  2 es    4\n",
      "el cuadrado de  3 es    9\n",
      "el cuadrado de  4 es   16\n",
      "el cuadrado de  5 es   25\n",
      "el cuadrado de  6 es   36\n",
      "el cuadrado de  7 es   49\n",
      "el cuadrado de  8 es   64\n",
      "el cuadrado de  9 es   81\n"
     ]
    }
   ],
   "source": [
    "l_2 = [i*i for i in l_1]\n",
    "\n",
    "for i, sq in enumerate(l_2):\n",
    "    print (\"el cuadrado de % 2d es % 4d\" % (i, sq))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keys dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
      "values dict_values([0, 1, 4, 9, 16, 25, 36, 49, 64, 81])\n",
      "items dict_items([(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25), (6, 36), (7, 49), (8, 64), (9, 81)])\n"
     ]
    }
   ],
   "source": [
    "d = {}\n",
    "\n",
    "for i, l in enumerate(l_2):\n",
    "    d[i] = l\n",
    "    \n",
    "print(\"keys\", d.keys())\n",
    "print(\"values\", d.values())\n",
    "print(\"items\", d.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l1: [1, 2, 3, 1, 2, 3]\n",
      "l2: [1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "l_1 = [1, 2, 3, 1, 2, 3]\n",
    "l_2 = list( set(l_1) ) \n",
    "\n",
    "print(\"l1:\", l_1)\n",
    "print(\"l2:\", l_2)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " El volumen de la unidad D es Work\n",
      " El número de serie del volumen es: 4C39-D1E8\n",
      "\n",
      " Directorio de D:\\GoogleDriveBck\\Cursos\\Python\\master\\short_2018\n",
      "\n",
      "12/09/2018  14:55                81 squares.txt\n",
      "               1 archivos             81 bytes\n",
      "               0 dirs  16.836.710.400 bytes libres\n"
     ]
    }
   ],
   "source": [
    "l_s = []\n",
    "for i, sq in enumerate(l_2):\n",
    "    s = \"el cuadrado de % 2d es % 4d\\n\" % (i, sq)\n",
    "    l_s.append(s)\n",
    "\n",
    "f = open(\"squares.txt\", 'w')\n",
    "for s in l_s:\n",
    "    f.write(s)\n",
    "\n",
    "f.close()\n",
    "\n",
    "%ls squares.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'el cuadrado de  0 es    1\\n'\n",
      "['el', 'cuadrado', 'de', '0', 'es', '1']\n",
      "'el cuadrado de  1 es    2\\n'\n",
      "['el', 'cuadrado', 'de', '1', 'es', '2']\n",
      "'el cuadrado de  2 es    3\\n'\n",
      "['el', 'cuadrado', 'de', '2', 'es', '3']\n"
     ]
    }
   ],
   "source": [
    "f = open(\"squares.txt\", 'r')\n",
    "\n",
    "for l in f:\n",
    "    print(repr(l))\n",
    "    print(l.split())\n",
    "\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape: (10, 10)\n",
      "diagonal:\n",
      " [0.31869021 0.38733496 0.29467607 0.1512722  0.09155182 0.89824394\n",
      " 0.64725455 0.33110829 0.93384688 0.23502302]\n",
      "shape: (5, 20)\n",
      "\n",
      "mean -0.069 \tstd 0.997\n",
      "\n",
      "mean:  [-0.34391589 -0.76264527  0.19679118  0.82876777 -0.6744176  -0.27220697\n",
      " -1.06865091 -0.05658385 -0.12244107  0.04538311 -0.68390122  0.29999841\n",
      " -0.15019979 -0.11058104 -0.04770566 -0.14392788  0.99651717  0.35707418\n",
      "  0.45847035 -0.12251114] \n",
      "std:  [1.19751788 0.9807675  1.05206578 0.66472317 0.97485827]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.random.rand(10, 10)\n",
    "\n",
    "print(\"shape:\", x.shape)\n",
    "print(\"diagonal:\\n\", np.diag(x))\n",
    "\n",
    "x = np.random.randn(100).reshape(5, -1)\n",
    "\n",
    "print(\"shape:\", x.shape)\n",
    "print(\"\\nmean %.3f\" % x.mean(), \"\\tstd %.3f\" % x.std())\n",
    "print(\"\\nmean: \", x.mean(axis=0), \"\\nstd: \", x.std(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dilat: 5.000000 \tintercept: 9.806 \tcoef: 0.987\n"
     ]
    },
    {
     "data": {
      "image/png": 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8l0gwKEcvIVe5R1zrnPhD2+Hzf8KaqRSVuHir5GJeKB7MblJhTQEvrFlQ9jpv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\nIhLhFOhFRCLc/wPy/R9HrLQC/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b4cf518908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def fit_plot(x, y, func_2_fit):\n",
    "    xx = np.array([func_2_fit(z) for z in x])\n",
    "    xx = xx.reshape(-1, 1)\n",
    "    \n",
    "    lr_m = LinearRegression()\n",
    "    lr_m.fit(xx, y)\n",
    "    \n",
    "    y_pred = lr_m.predict(xx)\n",
    "    \n",
    "    return lr_m, y_pred\n",
    "    \n",
    "\n",
    "def n2(n):\n",
    "    return n**2. \n",
    "\n",
    "\n",
    "x = np.sort( 10. * np.random.rand(100) )\n",
    "x_2 = np.array([z**2. for z in x])\n",
    "\n",
    "dilat = 5.\n",
    "y_2 = 10. + x_2 + dilat * np.random.randn(100)\n",
    "\n",
    "lr_m, y_pred = fit_plot(x, y_2, n2)\n",
    "\n",
    "print(\"dilat: %3f\" % dilat, \"\\tintercept: %.3f\" % lr_m.intercept_, \"\\tcoef: %.3f\" % lr_m.coef_)\n",
    "_ = plt.plot(x, y_2, '*', x, y_pred, '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAFpCAYAAADtINuMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b4d1960e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "_ = plt.hist(np.random.rand(1000), bins=50 )\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "_ = plt.hist(np.random.randn(1000), bins=50 )"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}