Creating arrays in numpy

wk-03-01-creating arrays in numpy

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      "# Creating arrays \n",
      "# 1. Using the array module (not numpy)\n",
      "# 2. Using lists\n",
      "\n",
      "# Using the array module, not recommmended - but is is 'simple'\n",
      "import array as Array\n",
      "myonedim = Array.array('i', range(0,9))\n",
      "print myonedim[0]\n",
      "\n",
      "# What is the difference between python.array and numpy.array\n",
      "# Answer: See -- overflow.com/questions/111983/python-array-versus-numpy-array\n",
      "\n",
      "# From the slides\n",
      "# Make an array out of a nested sequence (can be nested tuples, nested lists, or combination of them)\n",
      "a = [[[1,2],[3,4]],[[5,6],[7,8]],[[9,10],[11,12]]]\n",
      "print a[0]\n",
      "print a[0][0]\n",
      "print \n",
      "In [ ]:\n",
      "\ufffc\n",
      "# Using numpy\n",
      "import numpy as np   # This is how numpy is 'typically' imported\n",
      " \n",
      "# Using 'arange'\n",
      "# This is the easiest way to create an array of sequences\n",
      "arr1 = np.arange(3)\n",
      "arr2 = np.arange(3.0)\n",
      "arr3 = np.arange(3,10) # start,stop\n",
      "arr4 = np.arange(3,10,2) # start,stop,step\n",
      "print arr1\n",
      "print arr2\n",
      "print arr3\n",
      "print arr4\n",
      "In [12]:\n",
      "\ufffc\n",
      "# Using numpy\n",
      "import numpy as np\n",
      " \n",
      "# Using 'linspace' and 'logspace'\n",
      "linarr1 = np.linspace(2.0, 3.0, num=5)\n",
      "linarr2 = np.linspace(2.0, 3.0, num=5, endpoint=False)\n",
      "linarr3 = np.linspace(2.0, 3.0, num=5, retstep=True)\n",
      " \n",
      "print linarr1\n",
      "print linarr2\n",
      "print linarr3\n",
      " \n",
      "logarr1 = np.logspace(0,4,5)\n",
      "print logarr1\n",
      " \n",
      "# if you want see this in non-scientific notation use 'set_printoptions'\n",
      "np.set_printoptions(suppress=True)\n",
      "print logarr1\n",
      " # Using numpy\n",
      "import numpy as np\n",
      "\n",
      "# Using 'linspace' and 'logspace'# Using numpy\n",
      "import numpy as np\n",
      "\n",
      "# Using 'linspace' and 'logspace'# Using numpy\n",
      "import numpy as np\n",
      "\n",
      "# Using 'linspace' and 'logspace'# Using numpy\n",
      "import numpy as np\n",
      "\n",
      "# Using 'linspace' and 'logspace'a[0][0][1]\n",
      "# Using numpy\n",
      "import numpy as np\n",
      "\n",
      "# Using 'linspace' and 'logspace'# Using numpy\n",
      "import numpy as np\n",
      "\n",
      "# Using 'linspace' and 'logspace'"
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     "input": [
      "# Using numpy\n",
      "import numpy as np   # This is how numpy is 'typically' imported\n",
      "\n",
      "# Using 'arange'\n",
      "# This is the easiest way to create an array of sequences\n",
      "arr1 = np.arange(3)\n",
      "arr2 = np.arange(3.0)\n",
      "arr3 = np.arange(3,10) # start,stop\n",
      "arr4 = np.arange(3,10,2) # start,stop,step\n",
      "print arr1\n",
      "print arr2\n",
      "print arr3\n",
      "print arr4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Using numpy\n",
      "import numpy as np\n",
      "\n",
      "# Using 'linspace' and 'logspace'\n",
      "linarr1 = np.linspace(2.0, 3.0, num=5)\n",
      "linarr2 = np.linspace(2.0, 3.0, num=5, endpoint=False)\n",
      "linarr3 = np.linspace(2.0, 3.0, num=5, retstep=True)\n",
      "\n",
      "print linarr1\n",
      "print linarr2\n",
      "print linarr3\n",
      "\n",
      "logarr1 = np.logspace(0,4,5)\n",
      "print logarr1\n",
      "\n",
      "# if you want see this in non-scientific notation use 'set_printoptions'\n",
      "np.set_printoptions(suppress=True)\n",
      "print logarr1\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
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     "input": [
      "# Using numpy\n",
      "import numpy as np\n",
      "\n",
      "# Using 'mgrid'\n",
      "mygrid = np.mgrid[0:2, 0:4]\n",
      "grid_x, grid_y = np.mgrid[0:2, 0:4]\n",
      "print 'mygrid', mygrid\n",
      "print 'grid_x', grid_x\n",
      "print 'grid_y', grid_y\n",
      "\n",
      "# Change the numbers and see what happens"
     ],
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     "input": [
      "# Using numpy\n",
      "import numpy as np\n",
      "\n",
      "# Using 'random' to generate an array of random numbers\n",
      "# Check this location out -- http://docs.scipy.org/doc/numpy/reference/routines.random.html\n",
      "\n",
      "from numpy import random as nprandom\n",
      "\n",
      "# uniform random numbers in [0,1]\n",
      "a = nprandom.rand(5,5)\n",
      "print a\n",
      "\n",
      "# standard normal distributed random numbers\n",
      "b = nprandom.randn(5,5)\n",
      "print b\n",
      "\n",
      "# Draw samples from a binomial distribution\n",
      "# p(success) = 0.156\n",
      "# N = number of trials = 20\n",
      "c = nprandom.binomial(20, 0.156, [2,3])\n",
      "\n",
      "print c\n",
      "In [ ]:\n",
      "\ufffc\n",
      "# NPY format\n",
      "from numpy import random as nrand\n",
      "import os\n",
      " \n",
      "a = nrand.randn(2,3)\n",
      " \n",
      "# Get working directory\n",
      "wd = os.getcwd()\n",
      "print \"Working directory is\", wd\n",
      " \n",
      "\n",
      "In [ ]:\n",
      "\ufffc\n",
      "# NPY format\n",
      "from numpy import random as nrand\n",
      "import os\n",
      " \n",
      "a = nrand.randn(2,3)\n",
      " \n",
      "# Get working directory\n",
      "wd = os.getcwd()\n",
      "print \"Working directory is\", wd\n",
      " \n"
     ],
     "language": "python",
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     "input": [
      "# Using numpy\n",
      "import numpy as np\n",
      "\n",
      "# a diagonal matrix\n",
      "a = np.diag([1,2,3])\n",
      "print a\n",
      "\n",
      "# diagonal with offset from main diagonal\n",
      "b = np.diag([1,2,3], k=2)\n",
      "\n",
      "# matrix with all zeros\n",
      "c = np.zeros((3,3))\n",
      "print c\n",
      "\n",
      "# matrix with all ones\n",
      "d = np.ones((3,3))\n",
      "print d\n",
      "In [ ]:\n",
      "\ufffc\n",
      "# NPY format\n",
      "from numpy import random as nrand\n",
      "import os\n",
      " \n",
      "a = nrand.randn(2,3)\n",
      " \n",
      "# Get working directory\n",
      "wd = os.getcwd()\n",
      "print \"Working directory is\", wd\n",
      " \n"
     ],
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     "input": [
      "# Creating a numpy matrix from a csv file\n",
      "\n",
      "# First create teh csv file. You can do it using a spreadsheet\n",
      "# Here I will create one using Python code   \n",
      "import csv\n",
      "mylist = [['Name', 'Age', 'Height'],\n",
      "          ['Tom', 23, 167],\n",
      "          ['Dick', 28, 172],\n",
      "          ['Mary', 27, 168]]\n",
      "\n",
      "with open(\"mycsv.csv\", \"wb\") as f:\n",
      "    mywriter = csv.writer(f)\n",
      "    mywriter.writerows(mylist)\n",
      "\n",
      "# Now read the file that you created into a numpy array\n",
      "# Using numpy\n",
      "import numpy as np    \n",
      "mydata = np.loadtxt('mycsv.csv', delimiter=',', dtype=str)\n",
      "print mydata\n",
      "print 'dtype is ', mydata.dtype\n",
      "\n",
      "# Note what happens when you do not force the data type to str\n",
      "# You should obtain an array of tuples \n",
      "mydata = np.genfromtxt('mycsv.csv', dtype=None, delimiter=',', names=True) \n",
      "print mydata\n",
      "print \"Dick's height is\", mydata[1][2], \"centimeters\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
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     "input": [
      "# NPY format\n",
      "from numpy import random as nrand\n",
      "import os\n",
      "\n",
      "a = nrand.randn(2,3)\n",
      "\n",
      "# Get working directory\n",
      "wd = os.getcwd()\n",
      "print \"Working directory is\", wd\n",
      "\n",
      "fullfilepath = wd + '/' + 'myarray'\n",
      "np.save('fullfilepath', a)\n",
      "\n",
      "retrievepath = wd + '/' + 'myarray' + '.npy'\n",
      "\n",
      "b = np.load(retrievepath)\n",
      "print b"
     ],
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     "outputs": []
    }
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