ostwalprasad/Learn6Jan.ipynb

## Learn6Jan.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.random.random(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.arange(1,101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'count' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-f0ca0f9dd917>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'count' is not defined"
     ]
    }
   ],
   "source": [
    "count(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-0b8ac2916653>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'int' object is not callable"
     ]
    }
   ],
   "source": [
    "a.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xaa9b629240>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(b,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'random numbers')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.title(\"random numbers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'random numbers')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.title(\"random numbers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import interactive\n",
    "interactive(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'random numbers')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IMjrdbbsM6YwBywbm9wdum6xNkkXAPsDd0y1KkjR8XQL/GmB5koOT7AGcAKwb12Yd8Mb+9GuB/66qx13hS5Lmz5RDOv0x+VOBy4DdgY9X1cYkZwGjVbUO+Bjw6SRb6F3Zn9Dh2GtnUPdCY1/sYF/sYF/sYF/sMO2+iBfiktQGn7SVpEYY+JLUiFkPfF/LsEOHvnhHkk1Jrk/yzSQHzkedc2Gqvhho99oklWTBfiWvS18keV3/Z2Njks/NdY1zpcPvyAFJLk9yXf/35Lj5qHO2Jfl4kjsme1YpPR/q99P1SV7UacdVNWv/6N3k/QHwPGAP4HvAinFt3gKc358+Afj8bNY0X/869sXLgaf1p9/ccl/02+0NfBvYAIzMd93z+HOxHLgOeEZ//lnzXfc89sVa4M396RXATfNd9yz1xZ8ALwJumGT9ccCl9J6BOhK4ust+Z/sK39cy7DBlX1TV5VX1QH92A71nHhaiLj8XAGcD5wC/nMvi5liXvjgFWFNV9wBU1R1zXONc6dIXBTy9P70Pj38maEGoqm+z82eZjgc+VT0bgH2T7DfVfmc78Cd6LcPSydpU1SPA9tcyLDRd+mLQyfQ+wReiKfsiyeHAsqr6ylwWNg+6/FwcChya5DtJNiQ5Zs6qm1td+uK9wIlJxoD1wFvnprRdzhPNE6DbqxVmYmivZVgAOp9nkhOBEeBPZ7Wi+bPTvkiyG723rp40VwXNoy4/F4voDescRe+vviuTHFZVP53l2uZal75YBXyiqj6Y5A/pPf9zWFU9Ovvl7VKmlZuzfYXvaxl26NIXJHkF8B5gZVU9OEe1zbWp+mJv4DDgiiQ30RujXLdAb9x2/R35UlU9XFU/BDbT+wBYaLr0xcnAxQBVdRXwFHovVmtNpzwZb7YD39cy7DBlX/SHMT5KL+wX6jgtTNEXVXVvVS2uqoOq6iB69zNWVtW0Xxq1C+vyO/JFejf0SbKY3hDP1jmtcm506YtbgKMBkryQXuBvm9Mqdw3rgDf0v61zJHBvVd0+1UazOqRTs/daht84HfviXGAv4JL+fetbqmrlvBU9Szr2RRM69sVlwJ8l2QT8CnhnVd01f1XPjo59cRpwQZK30xvCOGkhXiAmuZDeEN7i/v2KM4EnAVTV+fTuXxwHbAEeAN7Uab8LsK8kSRPwSVtJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhrx/5Qd5gPOXlXaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"random numbers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.isinteractive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xaa9b81f860>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(b,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hi there')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(b,a)\n",
    "plt.title(\"Hi there\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Normal plotting')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(b,a)\n",
    "plt.title(\"Normal plotting\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(1950,2001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960,\n",
       "       1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n",
       "       1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n",
       "       1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n",
       "       1994, 1995, 1996, 1997, 1998, 1999, 2000])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd = np.random.randint(0, 10, size=(3, a.size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 8, 4, 0, 3, 1, 9, 1, 2, 6, 1, 7, 8, 2, 1, 2, 3, 1, 2, 7, 6, 6,\n",
       "        6, 7, 4, 4, 3, 5, 3, 1, 0, 7, 6, 6, 0, 1, 3, 0, 2, 7, 4, 8, 9, 8,\n",
       "        9, 5, 9, 0, 5, 5, 5],\n",
       "       [8, 8, 0, 2, 8, 3, 5, 3, 7, 9, 7, 5, 2, 8, 9, 7, 1, 6, 0, 5, 7, 7,\n",
       "        8, 8, 0, 0, 0, 4, 3, 3, 1, 5, 0, 9, 1, 4, 2, 3, 8, 4, 9, 6, 5, 6,\n",
       "        1, 8, 0, 8, 9, 5, 9],\n",
       "       [1, 2, 7, 3, 2, 0, 2, 5, 9, 9, 7, 7, 3, 6, 4, 5, 9, 2, 4, 0, 9, 7,\n",
       "        0, 1, 4, 6, 2, 3, 9, 1, 6, 7, 9, 4, 9, 8, 7, 7, 7, 0, 4, 2, 1, 5,\n",
       "        8, 9, 4, 1, 5, 0, 3]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (100,) and (51,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-4a661d500fd2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2811\u001b[0m     return gca().plot(\n\u001b[0;32m   2812\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[1;32m-> 2813\u001b[1;33m         is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2814\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2815\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1808\u001b[0m                         \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1810\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1811\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1609\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1610\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1611\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1612\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1613\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    391\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    392\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 393\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    395\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    368\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    369\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 370\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    371\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    372\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'plot'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[1;34m(self, x, y)\u001b[0m\n\u001b[0;32m    229\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    230\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[1;32m--> 231\u001b[1;33m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[0;32m    232\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (100,) and (51,)"
     ]
    },
    {
     "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": [
    "plt.plot(b,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "a= np.arange(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xaaa003deb8>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.random.random(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xaaa1052860>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(a,b,'g',label='line one',linewidth=0.5)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"Random Values\")\n",
    "plt.grid(True,color='K')\n",
    "plt.title(\"Random values in graph\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'geopandas'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-65-a90ec4546185>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# -> 2012/12/08/matplotlib-basemap-tutorial-plotting-global-earthquake-activity/\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m# -----------------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mgeopandas\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'geopandas'"
     ]
    }
   ],
   "source": [
    "# -----------------------------------------------------------------------------\n",
    "# Copyright (c) 2014, Nicolas P. Rougier. All Rights Reserved.\n",
    "# Distributed under the (new) BSD License.\n",
    "# -----------------------------------------------------------------------------\n",
    "# Based on : https://peak5390.wordpress.com\n",
    "# -> 2012/12/08/matplotlib-basemap-tutorial-plotting-global-earthquake-activity/\n",
    "# -----------------------------------------------------------------------------\n",
    "import geopandas\n",
    "import urllib\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "matplotlib.rcParams['toolbar'] = 'None'\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from  matplotlib.animation import FuncAnimation\n",
    "\n",
    "\n",
    "# Open the earthquake data\n",
    "# -------------------------\n",
    "# -> http://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php\n",
    "feed = \"http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/\"\n",
    "\n",
    "# Significant earthquakes in the past 30 days\n",
    "# url = urllib.urlopen(feed + \"significant_month.csv\")\n",
    "\n",
    "# Earthquakes of magnitude > 4.5 in the past 30 days\n",
    "url = urllib.request.urlopen(feed + \"4.5_month.csv\")\n",
    "\n",
    "# Earthquakes of magnitude > 2.5 in the past 30 days\n",
    "# url = urllib.urlopen(feed + \"2.5_month.csv\")\n",
    "\n",
    "# Earthquakes of magnitude > 1.0 in the past 30 days\n",
    "# url = urllib.urlopen(feed + \"1.0_month.csv\")\n",
    "\n",
    "# Set earthquake data\n",
    "data = url.read()\n",
    "data = data.split(b'\\n')[+1:-1]\n",
    "E = np.zeros(len(data), dtype=[('position',  float, 2),\n",
    "                               ('magnitude', float, 1)])\n",
    "for i in range(len(data)):\n",
    "    row = data[i].split(b',')\n",
    "    E['position'][i] = float(row[2]),float(row[1])\n",
    "    E['magnitude'][i] = float(row[4])\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(14,10))\n",
    "ax = plt.subplot(1,1,1)\n",
    "P = np.zeros(50, dtype=[('position', float, 2),\n",
    "                        ('size',     float, 1),\n",
    "                        ('growth',   float, 1),\n",
    "                        ('color',    float, 4)])\n",
    "\n",
    "# Basemap projection\n",
    "map = Basemap(projection='mill')\n",
    "map.drawcoastlines(color='0.50', linewidth=0.25)\n",
    "map.fillcontinents(color='0.95')\n",
    "scat = ax.scatter(P['position'][:,0], P['position'][:,1], P['size'], lw=0.5,\n",
    "                  edgecolors = P['color'], facecolors='None', zorder=10)\n",
    "\n",
    "\n",
    "def update(frame):\n",
    "    current = frame % len(E)\n",
    "    i = frame % len(P)\n",
    "\n",
    "    P['color'][:,3] = np.maximum(0, P['color'][:,3] - 1.0/len(P))\n",
    "    P['size'] += P['growth']\n",
    "\n",
    "    magnitude = E['magnitude'][current]\n",
    "    P['position'][i] = map(*E['position'][current])\n",
    "    P['size'][i] = 5\n",
    "    P['growth'][i]= np.exp(magnitude) * 0.1\n",
    "\n",
    "    if magnitude < 6:\n",
    "        P['color'][i]    = 0,0,1,1\n",
    "    else:\n",
    "        P['color'][i]    = 1,0,0,1\n",
    "    scat.set_edgecolors(P['color'])\n",
    "    scat.set_facecolors(P['color']*(1,1,1,0.25))\n",
    "    scat.set_sizes(P['size'])\n",
    "    scat.set_offsets(P['position'])\n",
    "\n",
    "plt.title(\"Earthquakes > 4.5 in the last 30 days\")\n",
    "animation = FuncAnimation(fig, update, interval=10)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import operator\n",
    "\n",
    "\n",
    "def start(url):\n",
    "    word_list = []\n",
    "    source_code = requests.get(url).text\n",
    "    soup = BeautifulSoup(source_code, \"html.parser\")\n",
    "    for post_text in soup.findAll('a', {'class': 'title'}):\n",
    "        content = post_text.string\n",
    "        words = content.lower().split()\n",
    "        for each_word in words:\n",
    "            word_list.append(each_word)\n",
    "    clean_up_list(word_list)\n",
    "\n",
    "\n",
    "def clean_up_list(word_list):\n",
    "    clean_word_list = []\n",
    "    for word in word_list:\n",
    "        symbols = \"!@#$%^&*()_+{}:\\\"<>?,./;'[]-='\"\n",
    "        for i in range(0, len(symbols)):\n",
    "            word = word.replace(symbols[i], \"\")\n",
    "        if len(word) > 0:\n",
    "            clean_word_list.append(word)\n",
    "    create_dictionary(clean_word_list)\n",
    "\n",
    "\n",
    "def create_dictionary(clean_word_list):\n",
    "    word_count = {}\n",
    "    for word in clean_word_list:\n",
    "        if word in word_count:\n",
    "            word_count[word] += 1\n",
    "        else:\n",
    "            word_count[word] = 1\n",
    "    for key, value in sorted(word_count.items(), key=operator.itemgetter(1)):\n",
    "        print(key, value)\n",
    "\n",
    "\n",
    "start('https://thenewboston.com/forum/')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ConnectionError",
     "evalue": "HTTPSConnectionPool(host='thenewboston.com', port=443): Max retries exceeded with url: /forum/ (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x00000004B89288D0>: Failed to establish a new connection: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond'))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    158\u001b[0m             conn = connection.create_connection(\n\u001b[1;32m--> 159\u001b[1;33m                 (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[0;32m    160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     79\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 80\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     69\u001b[0m                 \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m             \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTimeoutError\u001b[0m: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mNewConnectionError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    599\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 600\u001b[1;33m                                                   chunked=chunked)\n\u001b[0m\u001b[0;32m    601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    342\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 343\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    344\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m    838\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sock'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 839\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    840\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    300\u001b[0m         \u001b[1;31m# Add certificate verification\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 301\u001b[1;33m         \u001b[0mconn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    302\u001b[0m         \u001b[0mhostname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    167\u001b[0m             raise NewConnectionError(\n\u001b[1;32m--> 168\u001b[1;33m                 self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[0;32m    169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNewConnectionError\u001b[0m: <urllib3.connection.VerifiedHTTPSConnection object at 0x00000004B89288D0>: Failed to establish a new connection: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 449\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    450\u001b[0m                 )\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    637\u001b[0m             retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[1;32m--> 638\u001b[1;33m                                         _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[0;32m    639\u001b[0m             \u001b[0mretries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    397\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 398\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    399\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='thenewboston.com', port=443): Max retries exceeded with url: /forum/ (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x00000004B89288D0>: Failed to establish a new connection: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond'))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-5ed110a6bbdc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m \u001b[0mstart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'https://thenewboston.com/forum/'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-1-5ed110a6bbdc>\u001b[0m in \u001b[0;36mstart\u001b[1;34m(url)\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mword_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0msource_code\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0msoup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBeautifulSoup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"html.parser\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mpost_text\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msoup\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfindAll\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'title'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 75\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'get'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 60\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    531\u001b[0m         }\n\u001b[0;32m    532\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 533\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    534\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    535\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    514\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mClosedPoolError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionError\u001b[0m: HTTPSConnectionPool(host='thenewboston.com', port=443): Max retries exceeded with url: /forum/ (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x00000004B89288D0>: Failed to establish a new connection: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond'))"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import operator\n",
    "\n",
    "\n",
    "def start(url):\n",
    "    word_list = []\n",
    "    source_code = requests.get(url).text\n",
    "    soup = BeautifulSoup(source_code, \"html.parser\")\n",
    "    for post_text in soup.findAll('a', {'class': 'title'}):\n",
    "        content = post_text.string\n",
    "        words = content.lower().split()\n",
    "        for each_word in words:\n",
    "            word_list.append(each_word)\n",
    "    clean_up_list(word_list)\n",
    "\n",
    "\n",
    "def clean_up_list(word_list):\n",
    "    clean_word_list = []\n",
    "    for word in word_list:\n",
    "        symbols = \"!@#$%^&*()_+{}:\\\"<>?,./;'[]-='\"\n",
    "        for i in range(0, len(symbols)):\n",
    "            word = word.replace(symbols[i], \"\")\n",
    "        if len(word) > 0:\n",
    "            clean_word_list.append(word)\n",
    "    create_dictionary(clean_word_list)\n",
    "\n",
    "\n",
    "def create_dictionary(clean_word_list):\n",
    "    word_count = {}\n",
    "    for word in clean_word_list:\n",
    "        if word in word_count:\n",
    "            word_count[word] += 1\n",
    "        else:\n",
    "            word_count[word] = 1\n",
    "    for key, value in sorted(word_count.items(), key=operator.itemgetter(1)):\n",
    "        print(key, value)\n",
    "\n",
    "\n",
    "start('https://thenewboston.com/forum/')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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