scipy_intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n",
      "(6L,)\n"
     ]
    }
   ],
   "source": [
    "# lists to numpy array is easy!!\n",
    "mylist = [1,2,3,4,5,6]\n",
    "myarray = numpy.array(mylist)\n",
    "\n",
    "print(myarray)\n",
    "print(myarray.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 4 5 6]\n",
      "[2 4 6 8]\n"
     ]
    }
   ],
   "source": [
    "#direct use of numpy in array for math!\n",
    "myarray1 = numpy.array([1,2,3,4])\n",
    "myarray2 = numpy.array([2,2,2,2])\n",
    "\n",
    "print(myarray1 + myarray2)\n",
    "print(myarray1 * myarray2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ht+9IkiZUCCIdUG4oyLy7yhk3oBv3PbaO5zbv8R1J0oAKQaSDysvOYsH08Yy4pAt/v2Qt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\nEEREJE6FICIiAPx/Aazi7zz6p1IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6fd7278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#testing it out for basic line plots\n",
    "\n",
    "myarray3 = numpy.array([4,3,-1])\n",
    "plt.plot(myarray3)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x732c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#testing it out for basic scattered plots!\n",
    "x = numpy.array([23,14,45, 48])\n",
    "y = numpy.array([32,10, 2, 47])\n",
    "plt.scatter(x,y)\n",
    "plt.xlabel(\"People\")\n",
    "plt.ylabel(\"Bruuuuuuuuuh\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "d    4\n",
      "dtype: int32\n"
     ]
    }
   ],
   "source": [
    "#testing with series.\n",
    "myarray4 = numpy.array([1,2,3,4])\n",
    "rownames = ['a', 'b', 'c', 'd']\n",
    "myseries = pandas.Series(myarray4, index = rownames)\n",
    "print(myseries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "#can access data in a series like a numpy array.\n",
    "print(myseries[0])\n",
    "print(myseries['a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   uno  dos  tres  cuatro\n",
      "a    1    2     3       4\n",
      "b    4    5     6       7\n"
     ]
    }
   ],
   "source": [
    "#testing out functionality of data frame\n",
    "myarray5 = numpy.array([[1,2,3,4], [4,5,6,7]])\n",
    "rownames = ['a', 'b']\n",
    "colnames = ['uno', 'dos', 'tres', 'cuatro']\n",
    "mydataframe = pandas.DataFrame(myarray5, index = rownames, columns = colnames)\n",
    "print(mydataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st way:\n",
      "one colomn: a    1\n",
      "b    4\n",
      "Name: uno, dtype: int32\n",
      "2nd way:\n",
      "one colomn: a    1\n",
      "b    4\n",
      "Name: uno, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "#data can be indexed using colomn names\n",
    "print(\"1st way:\")\n",
    "print(\"one colomn: %s\" ) % mydataframe['uno']\n",
    "\n",
    "print(\"2nd way:\")\n",
    "print(\"one colomn: %s\") % mydataframe.uno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
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}