dataexp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration - My data exploration lab notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code Like a Pythonista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating over list/set/dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat\n",
      "monkey\n",
      "dog\n",
      "fish\n"
     ]
    }
   ],
   "source": [
    "# Iterating over set\n",
    "animals = {'cat', 'dog', 'fish', 'monkey'}\n",
    "for animal in animals:\n",
    "    print('{}'.format(animal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: cat\n",
      "1: monkey\n",
      "2: dog\n",
      "3: fish\n"
     ]
    }
   ],
   "source": [
    "# Iterating over set (and generating an index)\n",
    "animals = {'cat', 'dog', 'fish', 'monkey'}\n",
    "for idx, animal in enumerate(animals):\n",
    "    print('{}: {}'.format(idx,animal))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List/Set/Dict Comprehensions\n",
    "\n",
    "Python supports list comprehensions, that can be used to construct lists in a very natural way, similar to mathematic construction.\n",
    "\n",
    "\n",
    "[ output_expression() for(set of values to iterate) if(conditional filtering) ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n",
      "[0, 4, 16, 36, 64]\n",
      "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n",
      "{'a': 17, 'b': 34, 'z': 3}\n",
      "{'Alice', 'John', 'Bob'}\n",
      "[[1, 0, 0], [0, 1, 0], [0, 0, 1]]\n"
     ]
    }
   ],
   "source": [
    "#List comprehension\n",
    "list1 = [x**2 for x in range(10)]\n",
    "print(list1)\n",
    "\n",
    "list2 = [x for x in list1 if x % 2 == 0]\n",
    "print(list2)\n",
    "\n",
    "#Set comprehension\n",
    "nums = {x for x in range(10)}\n",
    "print(nums)  # Prints \"{0, 1, 2, 3, 4, 5}\"\n",
    "\n",
    "#Dict comprehension\n",
    "mcase = {'a':10, 'b': 34, 'A': 7, 'Z':3}\n",
    "mcase_frequency = { k.lower() : mcase.get(k.lower(), 0) + mcase.get(k.upper(), 0) for k in mcase.keys() }\n",
    "print(mcase_frequency)\n",
    "\n",
    "#Set comprehension from list\n",
    "names = [ 'Bob', 'JOHN', 'alice', 'bob', 'ALICE', 'J', 'Bob' ]\n",
    "names_set = { name[0].upper() + name[1:].lower() for name in names if len(name) > 1 }\n",
    "print(names_set)\n",
    "\n",
    "#Nested list comprehension\n",
    "matrix = [ [ 1 if item_idx == row_idx else 0 for item_idx in range(0, 3) ] for row_idx in range(0, 3) ]\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The power of comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 6, 10, 14, 18, 22, 26, 30, 34, 38]\n",
      "[2, 6, 10, 14, 18, 22, 26, 30, 34, 38]\n"
     ]
    }
   ],
   "source": [
    "numbers = range(20)\n",
    "\n",
    "numbers_doubled_odds = []\n",
    "for n in numbers:\n",
    "    if n%2 == 1:\n",
    "        numbers_doubled_odds.append(n*2)\n",
    "        \n",
    "print(numbers_doubled_odds)\n",
    "\n",
    "#vs\n",
    "\n",
    "numbers_doubled_odds = [n*2 for n in numbers if n%2==1]\n",
    "print(numbers_doubled_odds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]\n"
     ]
    }
   ],
   "source": [
    "# Calculating prime numbers\n",
    "noprimes = [j for i in range(2, 8) for j in range(i*2, 100, i)]\n",
    "primes = [x for x in range(2, 100) if x not in noprimes]\n",
    "print(primes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Numpy data structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data in arr1D:\n",
      " [ 1  2  3  4  5  6  7  8  9 10]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "array1D = np.array([1,2,3,4,5,6,7,8, 9, 10])\n",
    "\n",
    "#Standard print\n",
    "print('Data in arr1D:\\n', array1D)\n",
    "\n",
    "#The last line is evaluated\n",
    "array1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data in arr2D:\n",
      " [[1 2 3 4]\n",
      " [5 6 7 8]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array2D = np.array([[1,2,3,4],[5,6,7,8]])\n",
    "\n",
    "print('Data in arr2D:\\n', array2D)\n",
    "\n",
    "array2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]\n",
      " [5 6 7 8]]\n",
      "[[2 3]\n",
      " [6 7]]\n"
     ]
    }
   ],
   "source": [
    "# Slicing works the same as in standard Python\n",
    "\n",
    "array2D = np.array([[1,2,3,4],[5,6,7,8]])\n",
    "print(array2D)\n",
    "\n",
    "mini_array2D = array2D[:2, 1:3]\n",
    "print(mini_array2D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.]]\n",
      "[[ 1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.]]\n",
      "[[ 0.8  0.8  0.8  0.8]\n",
      " [ 0.8  0.8  0.8  0.8]]\n",
      "[[ 0.32501545  0.64297193  0.04874627  0.06266212]\n",
      " [ 0.11432724  0.95436057  0.62908533  0.3934264 ]]\n",
      "<class 'numpy.ndarray'>\n",
      "(2, 4)\n"
     ]
    }
   ],
   "source": [
    "array2D = np.zeros((2,4))\n",
    "print(array2D)\n",
    "\n",
    "array2D = np.ones((2,4))\n",
    "print(array2D)\n",
    "\n",
    "array2D = np.full((2,4),0.8)\n",
    "print(array2D)\n",
    "\n",
    "array2D = np.random.random((2,4))\n",
    "print(array2D)\n",
    "\n",
    "print(type(array2D))\n",
    "print(array2D.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the shape of arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11] \n",
      "\n",
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]] \n",
      "\n",
      "[[ 0  1]\n",
      " [ 2  3]\n",
      " [ 4  5]\n",
      " [ 6  7]\n",
      " [ 8  9]\n",
      " [10 11]] \n",
      "\n",
      "[[[ 0  1  2]\n",
      "  [ 3  4  5]]\n",
      "\n",
      " [[ 6  7  8]\n",
      "  [ 9 10 11]]] \n",
      "\n",
      "[[[ 0  1]\n",
      "  [ 2  3]]\n",
      "\n",
      " [[ 4  5]\n",
      "  [ 6  7]]\n",
      "\n",
      " [[ 8  9]\n",
      "  [10 11]]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "array1D = np.arange(12)\n",
    "print(array1D,'\\n')\n",
    "\n",
    "array2D = array1D.reshape(2,6) \n",
    "print(array2D,'\\n')\n",
    "\n",
    "array2D = array1D.reshape(6,2) \n",
    "print(array2D,'\\n')\n",
    "\n",
    "array3D = array1D.reshape(2,2,3) \n",
    "print(array3D,'\\n')\n",
    "\n",
    "array3D = array1D.reshape(3,2,2) \n",
    "print(array3D,'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Numpy 2-dim array\n",
      "[[-1.91676919 -0.82903614  0.09831266 -0.26945091 -0.30614273]\n",
      " [-0.64949701  0.15896014  0.62038264 -0.50294463 -0.28051979]\n",
      " [-0.92855886 -0.7160973   1.77312128 -0.55484851  0.39640845]\n",
      " [ 0.53648903 -1.94113288  0.69364212  0.70478747  0.55072403]\n",
      " [-0.4904765   0.04574453  1.5269125  -1.16301585 -0.16958285]\n",
      " [-1.67551136 -1.19950876  1.48486584 -0.21099859  0.2339799 ]\n",
      " [-1.32631813 -0.71617488 -0.61762391 -1.06785925  1.5826729 ]\n",
      " [-0.08690198  0.53616966 -0.62694456  0.81235137 -1.43032088]\n",
      " [-0.41919501  0.25580246 -1.56213368  2.12270945 -1.49628911]\n",
      " [ 1.28077043 -1.0620417   0.57734305 -0.02029614  0.5593769 ]]\n",
      "\n",
      " Standard deviation of array\n",
      "0.978572096479\n"
     ]
    }
   ],
   "source": [
    "print('\\n Numpy 2-dim array')\n",
    "tab10n5 = np.random.randn(10,5)\n",
    "\n",
    "print(tab10n5)\n",
    "print('\\n Standard deviation of array')\n",
    "print(np.std(tab10n5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TODO Statistics functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacking together different arrays\n",
    "\n",
    "Take a quick look at [tutorial](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html)\n",
    "and fill the next cell\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TODO stacking arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qR9yBQTcd4dzc2ziCkItOaCqaZympEDIw8C98f+04LMfAd8D2dQHhbs7Pn1tB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\nChgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fdde4d438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0, 3*np.pi, 50)\n",
    "plt.plot(x, np.sin(x**2))\n",
    "plt.title('A simple chirp');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[      nan  7363000.  7736000.  7864000.  7984000.  8121000.  8233000.\n",
      "   8330000.  8441000.]\n",
      " [      nan  5187000.  5171000.  5199000.  5243000.  5176000.  5270000.\n",
      "   5337000.  5359000.]\n",
      " [      nan   191000.   209000.   229000.   272000.   277000.   285000.\n",
      "    279000.   290000.]\n",
      " [      nan    47000.   113000.   111000.   127000.   151000.   145000.\n",
      "    143000.   178000.]\n",
      " [      nan    56000.    97000.    85000.    91000.   104000.   123000.\n",
      "    135000.   134000.]]\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "data = np.genfromtxt('country-of-birth-london-min.csv', skip_header=1, delimiter=';')\n",
    "print(data[:5,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Country']\n",
      " ['Total']\n",
      " ['United Kingdom']\n",
      " ['India']\n",
      " ['Poland']]\n"
     ]
    }
   ],
   "source": [
    "raw_data = np.genfromtxt('country-of-birth-london-min.csv', delimiter=';', dtype=None)\n",
    "names = raw_data[:5,:1].astype(str)\n",
    "print(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TODO - Calculate summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TODO - Visualize summary using matplotlib charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Miniproject - data exploration\n",
    "\n",
    "[Global Terrorism Database](https://www.kaggle.com/START-UMD/gtd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Instructions:\n",
    "\n",
    "1. Download data set, Global Terrorism Database, from https://www.kaggle.com/START-UMD/gtd\n",
    "2. Take a quick look at the data set. Check what's inside, how the data is structured, and where the data is corrupted (missing values, bad structure, etc).\n",
    "3. Think and create 5 questions to the data. Try to ask yourself what's really interesting in the data set. What's not so obvious. E.g. some trends, patterns, correlations.\n",
    "4. Create a jupyter notebook and use python, numpy, pandas, matplotlib (at least) to provide all the answers to your questions.\n",
    "5. Create a new github repository, and put your jupyter notebook there.\n",
    "6. Create readme.md file as well in your github root directory with all necessary instructions (what is in the repo, what libs are necessary to run the code, where to find data set and where to save it  - this is necessary because the dataset is too big for github repo).\n",
    "7. Provide the necessary documentation and introduction in your notebook using markdown language, at least: data source description, data structure, importing process, data processing process.\n",
    "8. Put some data visualization in your notebook. Sometimes it's much easier to present the answer using a chart rather than numbers\n",
    "9. Check if your notebook run smoothly - use 'Reset & Run All' command from the menu. Save it.\n",
    "10. Export the notebook as HTML as well, and save the file in the repo.\n",
    "11. Do not forget to commit/push all the changes to your repo on hithub.\n",
    "12. Smile :) You did a good job!\n",
    "\n",
    "FAQ:\n",
    "1. Can I take a look at different solution provided at kaggle?  Yes, you can. But check more than one solution. Try to understand what the authors are trying to solve, and how could it be used in your project. Try to find really good examples - easy to understand and not so complicated. Remember - you create the notebook as an instruction to someone else! Try to not complicate the process.\n",
    "2. Jupyter notebook provide R kernel, so can I use R instead? No. Even if you love R, try to solve the project using Python.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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