module1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lists, Tuples, Dictionaries, Sets, Graphing with matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list is collection of values with ONE name.  \n",
    "A list could also be imagined as an object with multiple \"slots\" for values.  \n",
    "The values in a list are INDEXED NUMERICALLY, starting at ZERO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 15, 8, 24, 5]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist = [10,15,8,24]\n",
    "mylist.append(5)\n",
    "mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 8, 10, 15, 24]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist.sort()\n",
    "mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 30, 8, 10, 15, 24]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist.insert(1,30)\n",
    "mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "5 30 8 10 15 24 "
     ]
    }
   ],
   "source": [
    "print(mylist[3])\n",
    "for n in mylist:\n",
    "    print(n, end = ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 30 8 10 15 24 "
     ]
    }
   ],
   "source": [
    "for n in range(len(mylist)):\n",
    "    print(mylist[n], end = ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 5\t1 30\t2 8\t3 10\t4 15\t5 24\t"
     ]
    }
   ],
   "source": [
    "for index,item in enumerate(mylist):   # enumerate function\n",
    "    print(index,item, end = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[11, 33, 55, 77, 99]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list comprehension examples\n",
    "\n",
    "squares = [x ** 2 for x in range(1,11)]\n",
    "print(squares)\n",
    "\n",
    "odd_11 = [a for a in range(1,101) if a % 11 == 0 and a % 2 == 1]\n",
    "odd_11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "# tuples are like lists but they are IMMUTABLE\n",
    "mytuple = (1, 3, 4, 2, 8)\n",
    "print(mytuple[2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dictionaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dictionary is a data collection in which each element has TWO PARTS: a KEY and a VALUE.  \n",
    "Individual values in a dictionary are accessed by their unique keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "President #42 : Bill Clinton\n",
      "President #43 : George W Bush\n",
      "President #44 : Barack Obama\n",
      "President #45 : Donald Trump\n"
     ]
    }
   ],
   "source": [
    "# dictionary of recent presidents\n",
    "prez = {42:'Bill Clinton',43:'George W Bush',44:'Barack Obama',45:'Donald Trump'}\n",
    "prez[44]\n",
    "\n",
    "print(43 in prez)\n",
    "\n",
    "# for looping through dictionary\n",
    "for k in prez:\n",
    "    print('President #{} : {}'.format(k,prez[k]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "President #42 : Bill Clinton\n",
      "President #43 : George W Bush\n",
      "President #44 : Barack Obama\n",
      "President #45 : Donald Trump\n"
     ]
    }
   ],
   "source": [
    "# using items() function\n",
    "for n,p in prez.items():\n",
    "    print('President #{} : {}'.format(n,p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42 43 44 45 \n",
      "42 43 44 45 "
     ]
    }
   ],
   "source": [
    "# for looping through just the keys\n",
    "for k in prez:\n",
    "    print(k, end = ' ')\n",
    "\n",
    "print()    \n",
    "    \n",
    "for ky in prez.keys():\n",
    "    print(ky, end = ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bill Clinton George W Bush Barack Obama Donald Trump \n",
      "Bill Clinton George W Bush Barack Obama Donald Trump "
     ]
    }
   ],
   "source": [
    "# for looping through just the values\n",
    "for k in prez:\n",
    "    print(prez[k], end = ' ')\n",
    "\n",
    "print()    \n",
    "    \n",
    "for ky in prez.values():\n",
    "    print(ky, end = ' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A set is a collection of UNIQUE values.  \n",
    "There are NO DUPLICATES in a set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'c', 'b', 'a'}\n",
      "{'The Matrix', 'The Fifth Element', 'Alien'}\n",
      "c b a \n",
      "The Matrix The Fifth Element Alien "
     ]
    }
   ],
   "source": [
    "myset = set('abcbacb')\n",
    "print(myset)           ### no duplicates in a set\n",
    "\n",
    "movies = set(['Alien', 'The Matrix', 'The Fifth Element', 'Alien'])\n",
    "print(movies)\n",
    "\n",
    "for ch in myset:\n",
    "    print(ch, end = ' ')\n",
    "    \n",
    "print()\n",
    "    \n",
    "for film in movies:\n",
    "    print(film, end = ' ')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python's matplotlib module is used to plot charts and graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# matplotlib uses lists to specify points on a graph\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [-3,-2,-1,0,1,2,3,4]\n",
    "y = [-3,-2,-1,0,1,2,3,4]\n",
    "\n",
    "plt.plot(x, y, 'o')\n",
    "plt.grid(b=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1HaA7V0r8HHC27RBhtqKpeWSpS9xdZ6XUPDZXFj02N0JF1nQsfsasv3BNrmXaa8yMGHd2yeijQZc4P8KuBs7A/wftU8aYJ0VkKHA3/q1i6oGhvT2XKyV26l+2MDq1tbUWY9oRsf6mU7ZCqv46Sxb+dVaESM5kFzxnNi9fndsToAUaDQV6nkpjzCIRWQZ8FX8g+wzQYoyZKyJzgV7XiLtSYt04vchqDEMOz+U276momGc7S3e5iFSsmyHzuhZoHN9ovBWrzFuzXzLHVLq7QONvfXzcwd5w6vr6A/k/18M7166fCvwHgDFmi4hs6e1FXCnxBvwfTK8GKqIlrW17Hhnu8M63IuJNleO9qf6n7yzQeMFMqcoy1W64d+yubajv603xdgOjDvjaaKDrPlzt+T+zvLeL/Xq32Y0rblKZDLDDdoywW9HUPNp2hv5omCS13/hIxemX/O/KqcnLKp7960x5rL3S+qmX19cHGmOagFdF5CwAERkNnA88dYj/7Angkvzj5wBze3sdV0Zi8A8pelxtowpjSWvbTIxpRsTh4bhnz4+X6d967wKNFxc9Y8YNLf0Cjaf7+fhLge+KSNcOAl8zxuwQOehB553A3fnD6E3Amt5ewI15YoBU9DrgG7ZjhN0Zkyasf7OyYqHtHIUy7i3z8vI1uR0nbzNjhrUzuwQLNM6vbaj/fZFfo19cGonX2g5QDpa2tjb9ZsRw2zEK5vXRMvGu8ysm3nV+foHG2tz2U7aa6IhWjpfCb2aXBf5a4OccNJdG4iHAW/RhXkwN3NohNdsuHz/OmUsGi6VICzTW1zbUF+0a9IFyp8QAqegjwHm2Y4RZFrLzY5OaEInazlIqw1vNnnM3mK1nb8pVj9k7qAUat9c21H+hoOEKwKXDaYBH0RIXVQVUHJXNPvNaZeUi21lKpWmoHP7AybL0gZMjDG03+87aZNafuyEXGdf/BRpO3r3TxRKrIju9pbXt5yPDshahf1prZMRDi+X9Dy2OUN1hWk7zzKoL1uWyE/wFGoe6L1gWeLxUOfvDrcNpgFT0FeBo2zHCzKuu3v7xCUfNsJ3DJfkFGpuXrc3tzy/QOPAijcdqG+rPsBKuF66NxACPAJfbDhFmc/bvny7G7DYiY2xncUX3BRqRnOlc/IxZf+HqXMsx7y7Q+I3tjAfjYol/gZa4qARkYmfnszurqrTEPchFpHJlrSxcWRtBjMkteM5sXLE699+ubvvpxmWX7/Un4HXbIcLurObWDtsZgsCIRNZPj7Rd9If6l2xnORj3SpzKZIH7bccIu+VNza6uEHLRfbYDHIp7JfbdaztA2M3o6JgaMeY12zkCwPlBxc0SpzKr8LfsUUU0taND/x/37s9ewnP69M7NEvvusR0g7M5pbnXmpmAO+5ntAL1xucTfB/bbDhFmFzY1u7LQ3lV/x/HzYXC5xKnM6/jTTapIpnR2Tqww5mXbORx2u5fwnB9I3C2x7w7bAcJuxv6ORtsZHPU28J+2Q/SF2yVOZdbj4PrNMDm/uVn3NevZd72E58w9iA/F7RL7dDQuomVNLcfYzuCgFgL0exeEEv+awu3zqw5wVDZ7VJUxL/T+yLLyQy/hOXPr0t64X2L/Cq4bbccIs1nt+3fazuCQDqBP90lyhfsl9v0Kf+c/VQQXNDdX287gkPu8hOfsddI9CUaJUxkD3GA7Rlhd0NQyHecWllthgH+3HaK/glFigFTmIWCV7RhhNDqXGzPEGNubsrvg117C22Y7RH8Fp8S+L9oOEFZz2/f39dYkYbUPuMZ2iIEIVolTmSeAn9iOEUbxpuZy3yr4ei/hvWI7xEAEq8S+a/GvplEFdHZLy3EYU64LIlYB37MdYqCCV+JU5g3gy7ZjhM3InIkeZswztnNY0AF82kt4OdtBBip4JfZ9H73tS8EtaGt/w3YGC271Et5W2yEGI5glTmVywJVAp+0oYXJhU3O5bUb9LHCT7RCDFcwSA6QyG4GU7RhhckZLay3GlNMGeld4Ca/NdojBCm6Jfbfg35RZFcBQY4aNzOXqbecokR95Ce8x2yEKIdgl9g+rPwHssR0lLBa1tZfDO/+v4c9yhEKwSwyQyuzEPz9WBbCiqflw2xmKrB24yEt4ofnHKvglBkhl7gd+ZDtGGJzc0joTY1pt5yiiK72Et9J2iEIKR4l9/wuddhq0aqgZHd7z4tu8hBe6XVTDU+JUpg24CL0FzKCd1Nq2z3aGIvgdIb32PjwlBkhlXgY+BAR+2sCmD+xrDtuN1rYBFwf5qqxDKVqJRSQrIptEZKuI/FZESvOGSSqzEr2r4qCc2NY2E2PCMhrvBlZ4CW+v7SDFUsyRuNUYM98YMwd4C/+ctTRSmfuA60v2eiFTCZVjs9kw7GvWCXzES3g7bAcpplIdTq8EJnR9IiLXichaEdkiIl/Lfy0mIg0ick/+678SkWH579WJyLb81/u2/1EqcwtQV4SfpSyc0tLWYjtDAXzOS3h/sR2i2IpeYhGpAM4CHsx/fi4wHVgEzAcWisip+YcfB/yXMWYusBf4rIiMxj/PnZ3/+s19fvFU5kvAtwv0o5SVDzQ1jbOdYZBu9hJeIDZ/H6xilnioiGzCPycZDfwh//Vz8x8bgQ3ATPxSA+w0xjyd//tPgaX4ZW4D7hKRi/D3BO6Pq9E55H6b175/hhgT1CvhvuolvLLZk63o58TAFKCad8+JBbglf7483xhzrDHmh/nvHbhZmzHGdOKP2r8GPgg80q8U/iZ7nyYAN8ZySQQi4zuzQVxffL2X8P7VdohSKvrhtDEmA/wLcK2IVAG/By4XkeEAIjJBRMbmHz5ZRE7K//1i4Kn846LGmN/h74E0v98h/GusPwn8YFA/TJk5s6Wl3XaGfrrOS3i32A5RaiV5Y8sYsxHYDHzMGPMocC+wUkQ8/D2lu9ax1gMJEdmCfwh+Z/57D+W/9jjw+QGFSGWypDJX0J9z6jK3vKl5Qu+PckIWf1lhoDZ9LxRxZbthEYkBD+WnpIorFf1n/HvthOtilyKYG5u0y4gcaTvHIbQCH/MS3oO2g9hSnr/Eqcx38A/Xnb/3rG1TOjqfs53hEN4Czu5LgUWkX3c4FJHTReShAScrIWdKbIxpLMko3MVf+XQ6/t3g1UGc3dLi6hZILwBLvYRX9re+dabEVviXaC4EnrQdxVXLm5on287Qg58D872E1+/VVvkR9rH8xUQNIvIzEZH8987Pf+0p/MU0gVDeJQZIZV4DzgT+w3YUF03r6JxSYYwrRyvNwOVewrt4kNdCvw9/pmMWMA04WUSG4M9eLAdOAY4abNhS0RIDpDKdpDJX42/102w7jmumdXS4cP/ijcACL+HdXYDnWmOMedkYk8O/22YM/6KjF4wxzxr/3d6fFuB1SkJL3F0q8zNgHnp4/R7nNVm9jNoAtwNLvIS3vUDP2X3+OwtUdnutwNESHyiV2YH/hte16LpkAC5sbp5q6aXfAOJewvuCl/CKPZPQAEwVkWPyn19c5NcrGC1xT1KZHKnMN4EFwDrbcWyb0Jk9utKYUt94+w/APC/hPVyKFzPGtAFXAOn8G1svluJ1C8GZiz2clYpW4l82eiMQtZzGmouPHvfk1pqaU0rwUjuBrwA/8RKe/nL2gZa4r1LRscDXgX+iDI9g/u/IEStvHTPqpN4fOWAZ/JsB3BGGuzKUkpa4v1LRhfjTUe+3HaWUdlVEdp05eWIxLr/swL+t6E1ewttdhOcPPS3xQKWi/4h/iD3bdpRSWThl0o79kXfe+CmEXwJfCvv2OcVWdoeFBeNftnk88GH8ucbQm7O//ZUCPdVT+FNG/6gFHjwdiQslFV0O3ACcaDtKsdw/Yviqm44YvWSA/3kOfy3597yEF4iFBUGhJS60VHQp/r2hPgLUWE5TUHsikbdPmTwhikh/juBext8e6Ydewiv1NFVZ0BIXSyo6BrgMv9DTD/3g4Fg0ZWJDayQys5eHZfHvuPBfwMNewssWP1n50hIXWyoq+FeAfRx/Zcxoq3kG6YpxRz6+ctjQ0w7y7ReBu4C7vYRXqPNn1QstcSmlolX42/d+CFhBgFbKdHlw+LC1Xz7yiO7n/TvwNy98EPhjWG+V4jItsS3+CH0icAb+SH0y7+415qwmkb+fNGXiOkQeBR7Rd5ft0xK7IhWtwN+g4DT89azzANsL8rPAdmALsAZ/o8JNpDJ6jusQLbHLUtGRwBz8+eg5+BeWTAKOBoYV8JXexn8X+WXeLe1m4G/5W8Yqh2mJgyoVjeKX+WhgPDAcGNLDRwf+XTO6Pprzf+6iq7ipjG6EEGBaYqUCTi+7VCrgtMRKBZyWWKmA0xIrFXBaYqUCTkusVMBpiZUKOC2xUgGnJVYq4LTESgWcllipgNMSKxVwWmKlAk5LrFTAaYmVCjgtsVIBpyVWKuC0xEoFnJZYqYDTEisVcFpipQJOS6xUwGmJlQo4LbFSAaclVirgtMRKBZyWWKmA0xIrFXBaYqUCTkusVMBpiZUKuP8Pw92y1IYj7nEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "voters = [43,36,16,5]\n",
    "labs = ['Dems','Reps','Ind','Und']\n",
    "plt.pie(voters,labels = labs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pylab module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Charts and graphs can also be created with the pylab module.\n",
    "\n",
    "from pylab import plot, show\n",
    "x = [-2,-1,0,1,2,3]\n",
    "y = [1,3,5,7,9,11]\n",
    "plot(x, y, marker = 'o')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average temp in LA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rmhOn22zH8Uta4Mrv1TS0sDK/lOuuSuL2yam24ygfynY6aO1w83Khjly7EC1w5fceW7+bDrebxxdM1PUxQszYIf2YPjyRNVvKcbl1qaXzaYErv5a/p4b8PUd4+KujGTYwznYcZcFSZwYVx5v5qFSHfp1PC1z5rcaWdh57Yw9jh/Tl27OH246jLLnp6sEM7hfNKl0f5S9ogSu/9fQ7+zjS2MJTd04iMlyfqqEqMjyMe6c7+HhfHQePnrYdx6/oT4XySzsr6llVUEZ2loMp6Qm24yjL7pmeTkSYsFqPws+hBa78TrvLzaOvfcrgvjH8SEdrKWBQvxhumZjCK9sraGrrsB3Hb2iBK7/z/MaD7K1p5Ofzr6ZvTKTtOMpPZDsdNLZ0kFdUbTuK39ACV36l4ngT//HePm4aP5i5Vw+xHUf5kUzHAMal9COnoAyd3thJC1z5DWMMP87bTURYGD+ff7XtOMrPiAhLnQ721jSyreyE7Th+QQtc+Y31u6r5eF8dy+aOIaV/rO04yg/Nn5JGv5gIXR+lixa48gv1TW3885ufMSU9gcVZDttxlJ+KjQpnUWY6b++uofZki+041mmBK7/w5Ia91De38+TCiYTriDR1CYuzHHS4DWu36sg1LXBl3eYDx1hXWMF3rhvOuJR+tuMoP5eR1IcbxiSzdssh2l1u23Gs0gJXVrV2uFjxejHpibE8PGe07TgqQGQ7HdQ2tpK/J7RHrmmBK6v+68MvOFB3mscXTCQ2SkekqZ65fvQghiXGkbMptN+ZqQWurNlfe4rffPQF86ek8pXRybbjqAASHiYszhrG1rLjlBw+aTuONVrgygq327Ait5jYqHB+Om+87TgqAC3KTCc6IoycEF4fRQtcWfFyYQVby47z41vHkRQfbTuOCkAJcVHMn5JKXlEVDc3ttuNYoQWufK6usZUnNpQwY3gi38gcajuOCmDZzgya2128ur3SdhQrtMCVz/3LW5/R0u7miYU6Ik1dmQlp/blmWAJrNpfjDsGRa1rgyqc+Kq1l/a5qvvdXIxmZHG87jgoCS2dmcPDoaf68/6jtKD6nBa58pqmtg5/k7WZkch8euGGk7TgqSNw8YQhJ8VGsLiizHcXntMCVz/z6vc+pPNHMkwsnER2h13wrz4iOCOee6cN4f28tFcebbMfxKS1w5RN7qhv43caD3D0tnenDE23HUUHm3hnDCBNhzZbQuqRQC1x5navrmu8BcZEsv2Wc7TgqCKX0j+Wm8YNZt62ClnaX7Tg+022Bi0i6iHwoIiUiskdEHjrv8z8SESMiSd6LqQJRXlEVs576gJErNrCrsoFbJqbQP05HpCnvWOJ0UN/Uzpu7QmfkWk+OwDuAR4wx44As4PsiMh46yx34a0DXdVTnyCuqYnluMVX1zV9ue7WwgryiKoupVDBzjhjIVYPiySkoD5mRa90WuDHmsDFmR9fHjUAJkNb16X8H/gEIjb8t1WMr80tpPu9X2eZ2NyvzSy0lUsFORMh2OiiuamBnRb3tOD7Rq3PgIpIBTAW2iMjtQJUxZlc3t7lfRApFpLCuru6yg6rAUn3WkXdPtivlCXdcM5T46IiQWR+lxwUuIvHAa8DDdJ5W+THws+5uZ4x5zhiTaYzJTE7WFedCwab9R7nYGyxTE3TWpfKe+OgI7rwmjT9+epijp1ptx/G6HhW4iETSWd4vGGNygZHAcGCXiJQBQ4EdIjLEW0GV/zPG8N8fH2Dx81tIjo8mOuLcp1dsZDjL5o6xlE6FiiXODNpcbtZtq7Adxet6chWKAM8DJcaYXwEYY4qNMYOMMRnGmAygErjGGBPa4zFCWFNbB3//0k4e31DC3KuH8P6PbuBf75xEWkIsAqQlxPLkwoksmJrW7fdS6kqMGhTPrFEDeWFzOR1BPnItogdfMwtYAhSLyM6ubSuMMRu8F0sFkvJjp/m71dvZd6SRf7h5DA9cPxIRYcHUNC1sZUW2M4O/W72d90pquXlC8J4Y6LbAjTEbgUsuGdd1FK5C0Eeltfz9i0WICH/45nSdrKP8wpyxg0jtH8PqzWVBXeD6Tkx1Wdxuw39+8Dnf/MM20gbE8daDs7W8ld+ICA/jviwHn+w/xv7aRttxvEYLXPVaY0s7312znX97Zx/zJ6eS+8BM0hPjbMdS6hx3T0snKjyM1UF8SaEWuOqV/bWnWPDsJ7y/t5afzhvPv981RafJK780MD6aeZNSeG1HFadaO2zH8QotcNVj7+ypYcGzn1Df1M6ab8/g27OH60Qd5deWOB2cau3g9R3BOXJNC1x1y+U2PP1OKfev3s7I5D68+eBsnCMH2o6lVLempCcwaWh/VgXp+iha4OqSGpra+faqbTzzwX4WZQ5l3d859d2UKmCICEuyHOyvPUXBgWO243icFri6qL01J7n92Y18sv8ov1gwgX+9cxIxkXq+WwWW2yanMiAukpxNwfdipha4uqA3d1Vzx7ObaG5z8dL9ThZnOfR8twpIMZHhLJqWzrslR4JuMTUtcHWODpebJzaU8OCLRVyd2o+3HpzNtY4BtmMpdUUWz3DgNoa1W4JrdIEWuPrS8dNtLP2frTz38QGynQ7W/m0Wg/rF2I6l1BVLT4xjzthBvLTtEK0dwTNyTQtcAbC7qoHbntnItrITrPz6JP55/gSiIvTpoYJHtjODo6fa+FNx8Ky5pz+hite2V3LnbzZhjOHV7zr5Rma67UhKedzsUUkMT+rDqoIy21E8Rgs8hLW73Dz2xm4eeWUXU4cl8OaDs5k0NMF2LKW8Iiys85LCokP1FFc22I7jET1ZTlYFibyiKlbml1Jd38zgfjHERoVx8GgT35k9nEdvGUtEuP57roLbndcO5ckNJdz1XAHNbS5SE2JZNndMwC57rAUeIs5MiT8zaLjmZAsAS7KG8ZN5421GU8pnPtxbixtoauv8Oaiqb2Z5bjFAQJa4HnKFiAtNiQf4YK8OmlahY2V+KS73uW+pb253sTK/1FKiK6MFHiJ0SrxSwfdzoAUeIi62fomua6JCSbD9HGiBh4jbJqf8xTadEq9CzbK5Y4g9bz2fQP450BcxQ0Bzm4s/Fh8mOT6KyPAwDje0BPyr70pdjjPP95X5pVR1nTb5f389OmB/DrTAQ8Cv3/+ciuPNvHR/FlkjdB1vFdoWTE1jwdQ0ak+2MPOpD768IisQ6SmUIFdy+CT//ecDLMocquWt1FkG9Yvh5glDeLmwgqa2wBy5pgUexFxuw/LcYhJiI1lx6zjbcZTyO0tnZtDY0sEbO6ttR7ksWuBBbM3mcnZW1PPTeeNJiIuyHUcpv5PpGMDYIX1ZtaksIEeuaYEHqZqGFlbml3LdVUnMn5JqO45SfklEWDozg701jRSWn7Adp9e0wIPUY+t30+F28/iCiTpJR6lLmD8llb4xEazaVGY7Sq9pgQeh/D015O85wkNzRjNsYJztOEr5tbioCBZlpvP27hpqA+yKFC3wINPY0s5jb+xh7JC+fOe64bbjKBUQFmc56HAb1m4NrJFrWuBB5ul39nGksYUnF04kUpeHVapHhif14frRyazdcoh2l9t2nB7Tn/AgsrOinlUFZWRnOZg6TAcRK9Ub2U4HtY2t5O8JnJFrWuBBot3lZnluMYP7xvCjAF3XQSmbbhgziPTEWHIKym1H6TEt8CDx/MaDlBw+yT/dfjV9YyJtx1Eq4ISHCYtnONh68Dh7a07ajtMjWuBBoOJ4E//x3j5uGj+YmycMsR1HqYC1KDOd6IiwgDkK1wIPcMYYfpy3m4iwMH4+/2rbcZQKaAP6RHH75FRe31FFQ3O77Tjd0gIPcOt3VfPxvjp+dNNoUvoH5qL0SvmTpTMzaG538dr2SttRutVtgYtIuoh8KCIlIrJHRB7q2r5SRPaKyKci8rqIJHg/rjpbfVMb//zmZ0xOT2CJM8N2HKWCwoS0/kwdlsDqzeW43f69PkpPjsA7gEeMMeOALOD7IjIeeBeYYIyZBOwDlnsvprqQJzfspb65nSfvmEh4mL5dXilPWerM4ODR02zcf9R2lEvqtsCNMYeNMTu6Pm4ESoA0Y8w7xpgzi+huBoZ6L6Y635YDx1hXWMF3rhvO+NR+tuMoFVRumTiEgX2iyCkosx3lknp1DlxEMoCpwJbzPvUt4E8Xuc39IlIoIoV1dXWXk1Gdp7XDxfLXi0lPjOXhOaNtx1Eq6ERHhHPP9GG8v7eWiuNNtuNcVI8LXETigdeAh40xJ8/a/mM6T7O8cKHbGWOeM8ZkGmMyk5OTrzSvAv7rwy84UHeaXyyYSGxUePc3UEr12r0zhiHAmi3+e0lhjwpcRCLpLO8XjDG5Z21fCswD7jOBuBp6ANpfe4rffPQF86ekcv1o/QdRKW9JTYjlpvFDeHlbBS3tLttxLqgnV6EI8DxQYoz51Vnbbwb+EbjdGOO/v2MEEbfbsOL1YmKjwvnpvPG24ygV9LKdDk40tfPmLv8cudaTI/BZwBLgRhHZ2fXfrcB/An2Bd7u2/dabQRW8sr2CrQePs+LWsSTFR9uOo1TQc44cyKhB8eQUlPvlyLWI7r7AGLMRuNA1ahs8H0ddTF1jK4//sYQZwxNZlJluO45SIUFEyHY6+Nkbe9hZUe93q3zqOzEDxL+89Rkt7W6eWKgj0pTypYXXDCU+OoLVfrg+ihZ4APiotJb1u6r53l+NZGRyvO04SoWU+OgIFl6TxlufHuboqVbbcc6hBe7nmto6+EnebkYm9+GBG0bajqNUSMp2OmhzuVm3rcJ2lHNogfu5X7/3OZUnmnnijolER+g130rZMGpQX2aOHMgLm8vp8KORa1rgfmxPdQO/23iQu6elM2PEQNtxlApp2c4MqhtaeH9vre0oX9IC91Mut2FFbjED4iJZfss423GUCnlfHTeI1P4xfrU+iha4n1pdUMauygZ+Om88/eN0RJpStkWEh3FfloNP9h9jf22j7TiAFrhfqq5vZmV+KdePTub2yam24yilutw1LZ2o8DC/uaRQC9wPPbZ+Dy5j+MWCCXrNt1J+JCk+mq9NSuG1HVWcau3o/gZepgXuZ97eXcO7nx3hh18dTXpinO04SqnzLHE6ONXawes77I9c0wL3I40t7Ty2fjfjUvrxrdnDbcdRSl3A1PQEJqb194v1UbTA/cjK/FJqG1t5auFEIsP1oVHKH4kIS5wOPq89RcGBY1azaEv4iR2HTrB6czlLnRlMTtf50Er5s9snp5IQF2n9xcxuVyMMNXlFVazML6W6vpnUhFiWzR3DgqlpXt3fL/P3Ul3fQpjA2CF9vbYvpZRnxESGc1dmOr/beJDDDc2k9I+1kkOPwM+SV1TF8txiquqbMUBVfTPLc4vJK6ry6v6q61sAcBv4+ZufeW1/SinPWZzlwG0Ma7ccspZBj8DPsjK/lObzRic1t7tY9uount940OP721tzknbXuS+CNLe7WJlf6tWjfqXUlUtPjOPGMYN4ceshfnDjKCtrFWmBn6W6vvmC29tdhuS+np+AU1x14VewL5ZDKeVfsmdmsPT3W3l7dw3zp/j+oEsLvMvrRZVc7IKgtIRYfv830zy+z1lPfUDVBco6NcHO+TSlVO9cNyqJjIFxrNpUZqXAQ/4ceLvLzT+t38MP1+1iZFIfYiLP/SuJjQxn2dwxXtn3srljiI0899cub+5PKeVZYWHCEmcGOw7Vs7uqwff79/ke/UhdYyv3/W4Lf9hUxrdmDeftH36FpxZOIi0hFqHzyPvJhRO9dj56wdQ0nlw40Wf7U0p53tevHUpsZLiVVQrFl+8kyszMNIWFhT7b36XsrKjnu6u3U9/cxr/eOcnKrz9KqeCwPLeY3B2VbFkxh4S4KI9/fxHZbozJPH97SB6Bv7T1EIt+W0BkhPDaAzO1vJVSVyTb6aC1w83Lhb4duRZSBd7a4WJ5bjGP5hYzY0Qib/5gNlen9rcdSykV4Mal9GN6RiJrNh/C5fbdWY2QKfCahhbufm4zL249xAM3jOQP35zulV91lFKhaYnTwaHjTfzvPt+NXAuJywi3lR3ngTU7aGrr4Df3XcMtE1NsR1JKBZm5Vw9hUN9ocgrKuXHsYJ/sM6iPwI0xrNpUxj3PbaZvTARvfH+WlrdSyiuiIsK4Z/owPiqto2rJCdkAAAZ+SURBVOzoaZ/sM2gLvKXdxSOv7OKx9Xu4YUwyb/xgFlcN1oWilFLec++MYUSECWs2+2aVwqAs8MoTTXz9t5vI3VHFw1+9iueWZNIvRgcDK6W8a3C/GOZOGMLLhRU0t7m6v8EVCroC37T/KLc9s5Hyo008vzSTh786mrAwnSuplPKNpc4MTrZ08MZO768qGjQFbozhuY+/YPHzW0iKj2b9g7OZM843LyQopdQZ0zIGMHZIX1b5YORaUBR4U1sHD75YxBMb9nLzhCHkfX8Ww5P62I6llApBZ0aulRw+yfbyE17dV8AXePmx0yz8r01sKD7MP948lmfvvYY+0SFxdaRSyk8tmJJG35gIVnl55FpAF/iHpbXc9sxGak62sOpb03nghpGI6PlupZRdfaIj+Pq1Q/lT8WFqT7Z4bT8BWeBut+GZ9z/nW3/YxtABcbz5g9lcd1Wy7VhKKfWlJVkOOtyGF7d6b32UgCvwxpZ2vrtmO0+/u48FU9J47YGZpCfG2Y6llFLnGJEcz3VXJbF2azntLrdX9tHtyWIRSQdygCGAG3jOGPNrEUkE1gEZQBmwyBjj8TP2Z0+J7xxrZjh2up3HbhvP38zM0FMmSim/tdSZwXdyCpn++HvUN7WTmhDLsrljPLbmf0+OwDuAR4wx44As4PsiMh54FHjfGHMV8H7Xnz3q/CnxtY2t1Da28cD1I/jmrOFa3kopv3ayuR0BTjS1Y4Cq+maW5xaTV+SZa8S7LXBjzGFjzI6ujxuBEiANmA+s6vqyVcACjyQ6y4WmxAO8XlTt6V0ppZTHPf3uvr+Ytdvc7mJlfqlHvn+vzoGLSAYwFdgCDDbGHIbOkgcGXeQ294tIoYgU1tXV9Srcxaaz69R2pVQg8HaH9bjARSQeeA142Bhzsqe3M8Y8Z4zJNMZkJif37kqRi01n16ntSqlA4O0O61GBi0gkneX9gjEmt2vzERFJ6fp8CuDxVcx1artSKpB5u8O6LXDpfKXweaDEGPOrsz61Hlja9fFS4A2PJDqLTm1XSgUyb3dYt1PpRWQ28GegmM7LCAFW0Hke/GVgGHAI+IYx5vilvpc/TaVXSqlAcbGp9N1eB26M2Qhc7Hq9OVcaTCml1OUJuHdiKqWU6qQFrpRSAUoLXCmlApQWuFJKBahur0Lx6M5E6oDLXeE8CTjqwTj+Jpjvn963wBXM9y+Q7pvDGPMX74T0aYFfCREpvNBlNMEimO+f3rfAFcz3Lxjum55CUUqpAKUFrpRSASqQCvw52wG8LJjvn963wBXM9y/g71vAnANXSil1rkA6AldKKXUWLXCllApQAVHgInKziJSKyH4R8fjsTVtEJF1EPhSREhHZIyIP2c7kaSISLiJFIvKW7SyeJiIJIvKqiOztegydtjN5ioj8sOs5uVtEXhSRGNuZroSI/F5EakVk91nbEkXkXRH5vOv/A2xmvBx+X+AiEg48C9wCjAfu6RqqHAwuNjA6mDxE5xzVYPRr4G1jzFhgMkFyP0UkDfh7INMYMwEIB+62m+qK/QG4+bxtXh/M7m1+X+DAdGC/MeaAMaYNeInOgcoB7xIDo4OCiAwFvgb8znYWTxORfsBX6Bx2gjGmzRhTbzeVR0UAsSISAcQBAT1J3BjzMXD+vAKvD2b3tkAo8DSg4qw/VxJEJXfGeQOjg8V/AP/A/w0CCSYjgDrgf7pOEf1ORPrYDuUJxpgq4N/oHNRyGGgwxrxjN5VX9Ggwuz8LhAK/0DCJoLr28XIHRvszEZkH1BpjttvO4iURwDXAb4wxU4HTBOCv4BfSdS54PjAcSAX6iMhiu6nUhQRCgVcC6Wf9eSgB/uvc2S4yMDoYzAJuF5EyOk973Sgia+xG8qhKoNIYc+Y3plfpLPRg8FXgoDGmzhjTDuQCMy1n8gavD2b3tkAo8G3AVSIyXESi6HwxZb3lTB5xiYHRAc8Ys9wYM9QYk0HnY/aBMSZojuKMMTVAhYicGS8+B/jMYiRPOgRkiUhc13N0DkHyAu15vD6Y3du6nYlpmzGmQ0R+AOTT+Wr4740xeyzH8pRZwBKgWER2dm1bYYzZYDGT6rkHgRe6DiwOAN+0nMcjjDFbRORVYAedV0oVEeBvOxeRF4EbgCQRqQQeA54CXhaRb9M1mN1ewsujb6VXSqkAFQinUJRSSl2AFrhSSgUoLXCllApQWuBKKRWgtMCVUipAaYErpVSA0gJXSqkA9f8BfdQIZdKZoaEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot annual temperature in LA\n",
    "\n",
    "la_temp = [20,21,21,23,24,26,29,29,29,26,23,20]\n",
    "plot(la_temp, marker = 'o')\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dice Rolling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7, 166573)\n",
      "(6, 138821)\n",
      "(8, 138585)\n",
      "(9, 111216)\n",
      "(5, 110782)\n",
      "(10, 84088)\n",
      "(4, 83073)\n",
      "(3, 55668)\n",
      "(11, 55583)\n",
      "(12, 27852)\n",
      "(2, 27759)\n"
     ]
    },
    {
     "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": [
    "# simulate 1 million dice rolls\n",
    "\n",
    "from collections import Counter\n",
    "import random as r\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "num = 1000000\n",
    "rolls = [(r.randint(1,6) + r.randint(1,6)) for n in range(num)]\n",
    "cnt = Counter(rolls)\n",
    "freq = cnt.most_common()\n",
    "for f in freq:\n",
    "    print(f)\n",
    "\n",
    "plt.bar(*zip(*freq))\n",
    "plt.title(format(num,',d') + ' Dice Rolls')\n",
    "plt.xlabel('Roll Values')\n",
    "plt.ylabel('Number of Rolls')\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}