Practice-01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "a = 5\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world\n"
     ]
    }
   ],
   "source": [
    "print('Hello world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0.35182306901707566, 1: 0.6309973926255225, 2: 1.1428271143234112, 3: 0.36428624958318145, 4: 0.6178752970477182, 5: 0.5178803341050071, 6: -0.5220580896449305}\n"
     ]
    }
   ],
   "source": [
    "data ={i: np.random.randn() for i in range(7)}\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0.5371822192192613, 1: 1.1898753914223175, 2: 2.0507071201316625, 3: 0.6785151299660496, 4: 1.0890362844211425, 5: -0.10076011953169842, 6: 0.9218877470206427}\n"
     ]
    }
   ],
   "source": [
    "from numpy.random import randn\n",
    "data = {i : randn() for i in range(7)}\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/app\n"
     ]
    }
   ],
   "source": [
    "! pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/app'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        list\n",
       "\u001b[0;31mString form:\u001b[0m [1, 2, 3]\n",
       "\u001b[0;31mLength:\u001b[0m      3\n",
       "\u001b[0;31mDocstring:\u001b[0m  \n",
       "Built-in mutable sequence.\n",
       "\n",
       "If no argument is given, the constructor creates a new empty list.\n",
       "The argument must be an iterable if specified.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = [1,2,3]\n",
    "b?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def b():\n",
    "    \"\"\"this is a function\n",
    "    \"\"\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "this is a function\n",
       "    \n",
       "\u001b[0;31mFile:\u001b[0m      /app/<ipython-input-12-7c1477722918>\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function b in module __main__:\n",
      "\n",
      "b()\n",
      "    this is a function\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_numbers(a,b):\n",
    "    \"\"\"\n",
    "    Add two numbers together\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    \"\"\"\n",
    "    return a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0madd_numbers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Add two numbers together\n",
       "\n",
       "Returns\n",
       "-------\n",
       "\u001b[0;31mFile:\u001b[0m      /app/<ipython-input-15-30dbac16e37b>\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_numbers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0madd_numbers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mSource:\u001b[0m   \n",
       "\u001b[0;32mdef\u001b[0m \u001b[0madd_numbers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"\u001b[0m\n",
       "\u001b[0;34m    Add two numbers together\u001b[0m\n",
       "\u001b[0;34m    \u001b[0m\n",
       "\u001b[0;34m    Returns\u001b[0m\n",
       "\u001b[0;34m    -------\u001b[0m\n",
       "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m      /app/<ipython-input-15-30dbac16e37b>\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_numbers??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ad_a():\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ad_b():\n",
    "    return b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ad_a\n",
       "ad_b"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ad_*?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ad_a\n",
       "ad_b\n",
       "add_numbers"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ad*?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__loader__\n",
       "ad_a\n",
       "ad_b\n",
       "add_numbers"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "*ad*?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run ipython_script_test.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4666666666666666"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load ipython_script_test.py\n",
    "def f(x,y,z):\n",
    "    return(x+y)/z\n",
    "\n",
    "a=5\n",
    "b=6\n",
    "c=7.5\n",
    "\n",
    "result = f(a,b,c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'' was not found in history, as a file, url, nor in the user namespace.\n"
     ]
    }
   ],
   "source": [
    "%pastebin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.20840086, -0.58472911,  0.4812218 , ..., -0.84158768,\n",
       "         0.2435366 ,  0.52253601],\n",
       "       [-0.66801981, -1.14757088,  0.86080365, ...,  0.74177281,\n",
       "        -0.12947285,  0.76556144],\n",
       "       [ 0.61404372, -0.26746666, -0.06879127, ...,  1.66004918,\n",
       "        -1.83708943,  0.4661893 ],\n",
       "       ...,\n",
       "       [-0.4239755 , -0.29039151,  0.69373848, ..., -0.64808734,\n",
       "        -0.80887983,  0.47137375],\n",
       "       [-0.33241758,  1.38174709, -0.2058946 , ..., -1.55457333,\n",
       "         0.99453926, -0.38401958],\n",
       "       [ 1.01442802, -0.3014859 ,  1.71560202, ...,  0.00497564,\n",
       "        -0.013868  , -0.01714359]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.randn(100,100)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m            ndarray\n",
       "\u001b[0;31mString form:\u001b[0m    \n",
       "[[-0.20840086 -0.58472911  0.4812218  ... -0.84158768  0.2435366\n",
       "           0.52253601]\n",
       "           [-0.66801981 -1. <...> 26\n",
       "           -0.38401958]\n",
       "           [ 1.01442802 -0.3014859   1.71560202 ...  0.00497564 -0.013868\n",
       "           -0.01714359]]\n",
       "\u001b[0;31mLength:\u001b[0m          100\n",
       "\u001b[0;31mFile:\u001b[0m            /usr/local/lib/python3.8/site-packages/numpy/__init__.py\n",
       "\u001b[0;31mDocstring:\u001b[0m       <no docstring>\n",
       "\u001b[0;31mClass docstring:\u001b[0m\n",
       "ndarray(shape, dtype=float, buffer=None, offset=0,\n",
       "        strides=None, order=None)\n",
       "\n",
       "An array object represents a multidimensional, homogeneous array\n",
       "of fixed-size items.  An associated data-type object describes the\n",
       "format of each element in the array (its byte-order, how many bytes it\n",
       "occupies in memory, whether it is an integer, a floating point number,\n",
       "or something else, etc.)\n",
       "\n",
       "Arrays should be constructed using `array`, `zeros` or `empty` (refer\n",
       "to the See Also section below).  The parameters given here refer to\n",
       "a low-level method (`ndarray(...)`) for instantiating an array.\n",
       "\n",
       "For more information, refer to the `numpy` module and examine the\n",
       "methods and attributes of an array.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "(for the __new__ method; see Notes below)\n",
       "\n",
       "shape : tuple of ints\n",
       "    Shape of created array.\n",
       "dtype : data-type, optional\n",
       "    Any object that can be interpreted as a numpy data type.\n",
       "buffer : object exposing buffer interface, optional\n",
       "    Used to fill the array with data.\n",
       "offset : int, optional\n",
       "    Offset of array data in buffer.\n",
       "strides : tuple of ints, optional\n",
       "    Strides of data in memory.\n",
       "order : {'C', 'F'}, optional\n",
       "    Row-major (C-style) or column-major (Fortran-style) order.\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "T : ndarray\n",
       "    Transpose of the array.\n",
       "data : buffer\n",
       "    The array's elements, in memory.\n",
       "dtype : dtype object\n",
       "    Describes the format of the elements in the array.\n",
       "flags : dict\n",
       "    Dictionary containing information related to memory use, e.g.,\n",
       "    'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.\n",
       "flat : numpy.flatiter object\n",
       "    Flattened version of the array as an iterator.  The iterator\n",
       "    allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for\n",
       "    assignment examples; TODO).\n",
       "imag : ndarray\n",
       "    Imaginary part of the array.\n",
       "real : ndarray\n",
       "    Real part of the array.\n",
       "size : int\n",
       "    Number of elements in the array.\n",
       "itemsize : int\n",
       "    The memory use of each array element in bytes.\n",
       "nbytes : int\n",
       "    The total number of bytes required to store the array data,\n",
       "    i.e., ``itemsize * size``.\n",
       "ndim : int\n",
       "    The array's number of dimensions.\n",
       "shape : tuple of ints\n",
       "    Shape of the array.\n",
       "strides : tuple of ints\n",
       "    The step-size required to move from one element to the next in\n",
       "    memory. For example, a contiguous ``(3, 4)`` array of type\n",
       "    ``int16`` in C-order has strides ``(8, 2)``.  This implies that\n",
       "    to move from element to element in memory requires jumps of 2 bytes.\n",
       "    To move from row-to-row, one needs to jump 8 bytes at a time\n",
       "    (``2 * 4``).\n",
       "ctypes : ctypes object\n",
       "    Class containing properties of the array needed for interaction\n",
       "    with ctypes.\n",
       "base : ndarray\n",
       "    If the array is a view into another array, that array is its `base`\n",
       "    (unless that array is also a view).  The `base` array is where the\n",
       "    array data is actually stored.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "array : Construct an array.\n",
       "zeros : Create an array, each element of which is zero.\n",
       "empty : Create an array, but leave its allocated memory unchanged (i.e.,\n",
       "        it contains \"garbage\").\n",
       "dtype : Create a data-type.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "There are two modes of creating an array using ``__new__``:\n",
       "\n",
       "1. If `buffer` is None, then only `shape`, `dtype`, and `order`\n",
       "   are used.\n",
       "2. If `buffer` is an object exposing the buffer interface, then\n",
       "   all keywords are interpreted.\n",
       "\n",
       "No ``__init__`` method is needed because the array is fully initialized\n",
       "after the ``__new__`` method.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "These examples illustrate the low-level `ndarray` constructor.  Refer\n",
       "to the `See Also` section above for easier ways of constructing an\n",
       "ndarray.\n",
       "\n",
       "First mode, `buffer` is None:\n",
       "\n",
       ">>> np.ndarray(shape=(2,2), dtype=float, order='F')\n",
       "array([[0.0e+000, 0.0e+000], # random\n",
       "       [     nan, 2.5e-323]])\n",
       "\n",
       "Second mode:\n",
       "\n",
       ">>> np.ndarray((2,), buffer=np.array([1,2,3]),\n",
       "...            offset=np.int_().itemsize,\n",
       "...            dtype=int) # offset = 1*itemsize, i.e. skip first element\n",
       "array([2, 3])\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "327 µs ± 23.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.dot(a,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "::\n",
       "\n",
       "  %debug [--breakpoint FILE:LINE] [statement [statement ...]]\n",
       "\n",
       "Activate the interactive debugger.\n",
       "\n",
       "This magic command support two ways of activating debugger.\n",
       "One is to activate debugger before executing code.  This way, you\n",
       "can set a break point, to step through the code from the point.\n",
       "You can use this mode by giving statements to execute and optionally\n",
       "a breakpoint.\n",
       "\n",
       "The other one is to activate debugger in post-mortem mode.  You can\n",
       "activate this mode simply running %debug without any argument.\n",
       "If an exception has just occurred, this lets you inspect its stack\n",
       "frames interactively.  Note that this will always work only on the last\n",
       "traceback that occurred, so you must call this quickly after an\n",
       "exception that you wish to inspect has fired, because if another one\n",
       "occurs, it clobbers the previous one.\n",
       "\n",
       "If you want IPython to automatically do this on every exception, see\n",
       "the %pdb magic for more details.\n",
       "\n",
       ".. versionchanged:: 7.3\n",
       "    When running code, user variables are no longer expanded,\n",
       "    the magic line is always left unmodified.\n",
       "\n",
       "positional arguments:\n",
       "  statement             Code to run in debugger. You can omit this in cell\n",
       "                        magic mode.\n",
       "\n",
       "optional arguments:\n",
       "  --breakpoint <FILE:LINE>, -b <FILE:LINE>\n",
       "                        Set break point at LINE in FILE.\n",
       "\u001b[0;31mFile:\u001b[0m      /usr/local/lib/python3.8/site-packages/IPython/core/magics/execution.py\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%debug?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo = %pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/app'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: agg\n"
     ]
    }
   ],
   "source": [
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4f02c5dcd0>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(np.random.randn(50).cumsum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1,2,3]\n",
    "b = a\n",
    "a.append(4)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def append_element(some_list,element):\n",
    "    some_list.append(element)\n",
    "data = [1,2,3]\n",
    "append_element(data, 4)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 5\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 'foo'\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"int\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-e84694abbbbf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m'5'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
     ]
    }
   ],
   "source": [
    "'5'+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.25"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 4.5\n",
    "b = 2\n",
    "a / b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 5\n",
    "isinstance(a, int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance (a, (int, float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = 4.5\n",
    "isinstance( b, (int,float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = \"foo\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function str.split(sep=None, maxsplit=-1)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getattr(a,'split')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isiterable(obj):\n",
    "    try:\n",
    "        iter(obj)\n",
    "        return True\n",
    "    except TypeError: # not iterable\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isiterable('a string')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isiterable([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isiterable(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isiterable('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isiterable([5])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "if not isinstance(x,list) and isiterable(x):\n",
    "    x = list(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import some_module\n",
    "result = some_module.f(5)\n",
    "pi = some_module.PI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.14159"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from some_module import PI, f, g\n",
    "result = g(5,PI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.14159"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import some_module as sm\n",
    "from some_module import PI as pi, g as gf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "r1 = sm.f(pi)\n",
    "r2 = gf(6,pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.14159"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.14159"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 -7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "12+21.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5<=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1,2,3]\n",
    "b = a\n",
    "c = list(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a is b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b is a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a is not c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a == c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = None\n",
    "a is None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 2, [3, 4]]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_list = ['foo',2,[4,5]]\n",
    "a_list[2] = [3,4]\n",
    "a_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-1990528cab52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma_tuple\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma_tuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'four'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "a_tuple = (3,5,(4,5))\n",
    "a_tuple[1] = 'four'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('foo', 2, [3, 4])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_tuple = tuple(a_list)\n",
    "a_tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        tuple\n",
       "\u001b[0;31mString form:\u001b[0m ('foo', 2, [3, 4])\n",
       "\u001b[0;31mLength:\u001b[0m      3\n",
       "\u001b[0;31mDocstring:\u001b[0m  \n",
       "Built-in immutable sequence.\n",
       "\n",
       "If no argument is given, the constructor returns an empty tuple.\n",
       "If iterable is specified the tuple is initialized from iterable's items.\n",
       "\n",
       "If the argument is a tuple, the return value is the same object.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_tuple?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26254519291092456596965462913230729701102721"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ival = 17239871\n",
    "ival ** 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "fval = 7.243\n",
    "fval2 = 7.78e-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.78e-05"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fval2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3//2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 'one way of writing a string'\n",
    "b = \"another way\"\n",
    "c = \"\"\"\n",
    " This is a longer string that\n",
    " spans multiple lines\n",
    " \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.count('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = \"\"\"\n",
    " This is a longer string that spans multiple lines\n",
    " \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.count('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-58-cfa170a67205>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'this is a string'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'f'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "a = 'this is a string'\n",
    "a[10] = 'f'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'this is a long string'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a.replace('string','long string')\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'this is a string'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 5.6\n",
    "s = str(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5.6'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.6\n"
     ]
    }
   ],
   "source": [
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['p', 'y', 't', 'h', 'o', 'n']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 'python'\n",
    "list(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pyt'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\\34\n"
     ]
    }
   ],
   "source": [
    "s = '12\\\\34'  #the back slash character \\ is an escape character, meaning that it is used to specify special character like newlinw \\n or Unicode characters. need escape them.\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "34\n"
     ]
    }
   ],
   "source": [
    "s = '12\\n34'  #the back slash character \\ is an escape character, meaning that it is used to specify special character like newlinw \\n or Unicode characters. need escape them.\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12\\\\34'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = r'12\\34' # the 'r' stand for raw.\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'this\\\\has\\\\no\\\\special\\\\characters'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = r'this\\has\\no\\special\\characters'\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12\\\\n34'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = r'12\\n34'\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'this is the first half and this is the second half'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 'this is the first half '\n",
    "b = 'and this is the second half'\n",
    "a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = '{0:.2f}{1:s} are worth US${2:d}' \n",
    "#-{0:.2f} means to format the first argument as a floating-point number with two decimal places.\n",
    "#- {1:s} means to format the second argument as a string.\n",
    "#- {2:d} means to format the third argument as an exact interger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.56Argentine Pesos are worth US$1'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template.format(4.5560,'Argentine Pesos', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bytes and Unicode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'espanol'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = 'espanol'\n",
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'espanol'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_utf8 = val.encode('utf-8')\n",
    "val_utf8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bytes"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(val_utf8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'espanol'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_utf8.decode('utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'espanol'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.encode('latin1')  # this is different with the textbook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\xff\\xfee\\x00s\\x00p\\x00a\\x00n\\x00o\\x00l\\x00'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.encode('utf-16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'e\\x00s\\x00p\\x00a\\x00n\\x00o\\x00l\\x00'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.encode('utf-16le')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'this is bytes'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bytes_val = b'this is bytes'\n",
    "bytes_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'this is bytes'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoded = bytes_val.decode('utf8')\n",
    "decoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Booleans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True and True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False or True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Type casting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s ='3.14159'\n",
    "fval = float(s)\n",
    "type(fval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(fval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(fval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(0) ## 0  default means False, but i is default mean True. any non 0 value is true. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# None  #None is a python null value type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = None\n",
    "a is None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = 5\n",
    "b is not None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_and_multiply(a,b,c=None):\n",
    "    result = a + b\n",
    "    if c is not None:\n",
    "        result = result * c\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 3\n",
    "b = 2\n",
    "c = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'result' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-40-0ac921c19f1a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'result' is not defined"
     ]
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dates and times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, date, time\n",
    "dt = datetime(2011,10,29,20,30,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 10, 29, 20, 30, 21)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2011, 10, 29)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.time(20, 30, 21)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10/29/2011 20:30'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.strftime('%m/%d/%Y %H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2009, 10, 31, 0, 0)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.strptime('20091031','%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 10, 29, 20, 0)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.replace(minute=0,second=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt2 = datetime(2011, 11, 15, 22, 30)\n",
    "delta = dt2 - dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=17, seconds=7179)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(delta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 10, 29, 20, 30, 21)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 11, 15, 22, 30)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt + delta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Control flow\n",
    "## if, elif and else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 5; b = 7\n",
    "c = 8; d = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Made it\n"
     ]
    }
   ],
   "source": [
    "if a<b or c>d:\n",
    "    print('Made it')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4>3>2>1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "sequence = [1,2,None,4,None,5]\n",
    "total = 0\n",
    "for value in sequence:\n",
    "    if value is None:\n",
    "        continue\n",
    "    total += value\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    }
   ],
   "source": [
    "sequence =[1,2,0,4,6,5,2,1]\n",
    "total_until_5 = 0\n",
    "for value in sequence:\n",
    "    if value == 5:\n",
    "        break\n",
    "    total_until_5 += value\n",
    "print(total_until_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0)\n",
      "(1, 0)\n",
      "(1, 1)\n",
      "(2, 0)\n",
      "(2, 1)\n",
      "(2, 2)\n",
      "(3, 0)\n",
      "(3, 1)\n",
      "(3, 2)\n",
      "(3, 3)\n"
     ]
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        if j > i:\n",
    "            break\n",
    "        print((i,j))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "for a,b,c in iterator:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## while loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "504\n"
     ]
    }
   ],
   "source": [
    "x = 256\n",
    "total = 0\n",
    "while x>0:\n",
    "    if total >500:\n",
    "        break\n",
    "    total +=x\n",
    "    x = x//2\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive!\n"
     ]
    }
   ],
   "source": [
    "if x<0:\n",
    "    print('negative!')\n",
    "elif x==0:\n",
    "    pass\n",
    "else:\n",
    "    print('positive!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 10)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(0,20,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 4, 3, 2, 1]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(5,0,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1\n",
      "1 2\n",
      "2 3\n",
      "3 4\n"
     ]
    }
   ],
   "source": [
    "seq =[1,2,3,4]\n",
    "for i in range(len(seq)):\n",
    "    val = seq[i]\n",
    "    print(i,val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2333316668\n"
     ]
    }
   ],
   "source": [
    "sum = 0\n",
    "for i in range(100000):\n",
    "    if i%3 ==0 or i%5==0:\n",
    "        sum+=i\n",
    "print(sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ternary expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Negative'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = -5\n",
    "'Non-negative' if x>=0 else 'Negative'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=2\n",
    "b=1\n",
    "x=a if b > 1 else a-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Program to demonstrate conditional operator \n",
    "a, b = 10, 20\n",
    "  \n",
    "# Copy value of a in min if a < b else copy b \n",
    "min = a if a < b else b "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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