1 -  intro to Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hello IPython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello, Alex\n"
     ]
    }
   ],
   "source": [
    "print 'hello, Alex'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "x = 3\n",
    "print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 8,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "x += 5\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'adhjhkjh'"
      ]
     },
     "execution_count": 10,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "my_string = 'adhjhkjh'\n",
    "my_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 11,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "12 + 45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.0"
      ]
     },
     "execution_count": 12,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "24 * 3.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lists, Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1, 2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 3)"
      ]
     },
     "execution_count": 16,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "x[0], x[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 5, 10]"
      ]
     },
     "execution_count": 21,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "y = x[:2] + [5, 10]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 5]"
      ]
     },
     "execution_count": 24,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "y[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 5, 2, 1]"
      ]
     },
     "execution_count": 25,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "y[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'a': 1, 'b': 2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 1, 'b': 2, 'c': 3, 'casdsd': 34}"
      ]
     },
     "execution_count": 32,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "d['c'] = 3\n",
    "d['casdsd'] = 34\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a 1\n",
      "c 3\n",
      "b 2\n",
      "casdsd 34\n"
     ]
    }
   ],
   "source": [
    "for key, value in d.items():\n",
    "    print key, value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - numpy (numerics) + scipy (functions) \n",
    " - matplotlib - plotting\n",
    " - pandas - convenient operations on data for DS\n",
    " - scikit-learn - ML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hello, numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 43,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "x = numpy.arange(10)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [6, 7],\n",
       "       [8, 9]])"
      ]
     },
     "execution_count": 37,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "x.reshape(5, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 44,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "x[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n",
      "[3 4 5 6]\n",
      "[7 8 9]\n"
     ]
    }
   ],
   "source": [
    "print x[:3]\n",
    "print x[3:7]\n",
    "print x[7:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vector ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 6.45 ms per loop\n"
     ]
    }
   ],
   "source": [
    "x = numpy.arange(10 ** 6)\n",
    "%timeit 3 * x + 12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11],\n",
       "       [12, 13, 14]])"
      ]
     },
     "execution_count": 47,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "Z = numpy.arange(15).reshape(5, 3)\n",
    "Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   1.,   2.],\n",
       "       [  3.,   4.,   5.],\n",
       "       [  6.,   7.,   8.],\n",
       "       [  9.,  10.,  11.],\n",
       "       [ 12.,  13.,  14.]])"
      ]
     },
     "execution_count": 49,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "numpy.log(numpy.exp(Z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z += 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12],\n",
       "       [13, 14, 15],\n",
       "       [16, 17, 18]])"
      ]
     },
     "execution_count": 51,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6],\n",
       "       [10, 11, 12],\n",
       "       [16, 17, 18]])"
      ]
     },
     "execution_count": 55,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "Z[::2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 24, 33, 42, 51])"
      ]
     },
     "execution_count": 58,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "Z.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 55, 60])"
      ]
     },
     "execution_count": 59,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "Z.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  9, 12, 15, 18])"
      ]
     },
     "execution_count": 71,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "Z.max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -6,  -5,  -4],\n",
       "       [ -9,  -8,  -7],\n",
       "       [-12, -11, -10],\n",
       "       [-15, -14, -13],\n",
       "       [-18, -17, -16]])"
      ]
     },
     "execution_count": 74,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "Z2 = - Z\n",
    "Z2.sort(axis=1)\n",
    "Z2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  5,  6],\n",
       "       [10, 11, 12],\n",
       "       [16, 17, 18]])"
      ]
     },
     "execution_count": 61,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "Z[[0, 2, 4], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## indexing with boolean array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 63,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "x = numpy.arange(10)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True,  True,  True,  True,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 66,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "x > 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 69,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "x[x < 7.4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9] [0 1 2 3 4]\n",
      "[0 1 2 3 4 5 6 7 8 9] [ 0  1  2  3 10]\n"
     ]
    }
   ],
   "source": [
    "x = numpy.arange(10)\n",
    "y = x[:5].copy()\n",
    "print x, y\n",
    "y[4] = 10\n",
    "print x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 29.87106868,   1.48812383,   4.61406417, ...,  -7.62578366,\n",
       "        16.05328271,   3.29466941])"
      ]
     },
     "execution_count": 82,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "numpy.random.normal(loc=2, scale=12, size=10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "0. import numpy\n",
    "1. sample from normal distribution 1000 elements\n",
    "2. leave only positive numbers\n",
    "3. count number of left numbers, std, min, max, mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 85,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.arg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy.sin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.00044950888224008637,\n",
       " 3.5780563068443154,\n",
       " 0.61072462596428589,\n",
       " 0.7958258421168346,\n",
       " (4992,))"
      ]
     },
     "execution_count": 90,
     "output_type": "execute_result",
     "metadata": {}
    }
   ],
   "source": [
    "x = numpy.random.normal(size=10000)\n",
    "y = x[x > 0]\n",
    "y.min(), y.max(), y.std(), y.mean(), y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  }
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