matplotlib_practice_stats&chartextras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: We are importing a new dependency, **the statistics module**, which comes with the Anaconda installation. We’re going to use the statistics module so that we can add the y-error bars to line and bar charts. With the statistics module, we can calculate the standard deviation of the fare amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the x-axis to a list of strings for each month\n",
    "x_axis = [\"Jan\", \"Feb\", \"Mar\", \"April\", \"May\", \"June\", \"July\", \"Aug\", \"Sept\", \"Oct\", \"Nov\", \"Dec\"]\n",
    "\n",
    "# Set the y-axis to a list of floats as the total fare in US dollars accumulated for each month\n",
    "y_axis = [10.02, 23.24, 39.20, 35.42, 32.34, 27.04, 43.82, 10.56, 11.85, 27.90, 20.71, 20.09]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.208367917035753"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the standard deviation of the values in the y-axis.\n",
    "stdev = statistics.stdev(y_axis)\n",
    "stdev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add Error Bars to a Line Chart\n",
    "Adding error bars can show either a standard deviation, standard error, confidence intervals, or minimum or maximum values of a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 6,
     "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.errorbar(x_axis, y_axis, yerr=stdev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the error bars look more professional by adding a \"cap\" to the error bars that's equal to a scalar value. To do this, we use the capsize= parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 7,
     "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.errorbar(x_axis, y_axis, yerr=stdev, capsize=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add Error Bars and a Capsize using the Object-Oriented Interface Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.errorbar(x_axis, y_axis, yerr=stdev, capsize=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: The output is the same graph as using the MATLAB method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding Error Bars and a Capsize to a Bar Chart using the MATLAB Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 9,
     "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.bar(x_axis, y_axis, yerr=stdev, capsize=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding Error Bars and a Capsize to a Bar Chart using the Object-Oriented Interface Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(x_axis, y_axis, yerr=stdev, capsize=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: The output looks like the same graph as using the MATLAB method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change the Major Ticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD4CAYAAAAKA1qZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAWM0lEQVR4nO3de7BlZXnn8e/Ppm1u0kbBFEFiawpkAKkGWxwiQxqSOGo0gGEGWp1IJEFNojGO5aAmjo6jYVTE64Qgg6hRvDtBR7kUl1LAAk9r0w2CINBGjSV2EVFEUZpn/tjrhO3h9OmzL2ft3azvp+rUXte9nvPW2fs577q8T6oKSVI3PWzSAUiSJsckIEkdZhKQpA4zCUhSh5kEJKnDdpp0APPZc889a9WqVZMOQ5J2KOvXr99SVXsNss9UJoFVq1YxMzMz6TAkaYeS5NuD7uPpIEnqMJOAJHWYSUCSOswkIEkdZhKQpA4zCUhSh5kEJKnDTAKS1GFT+bDYpu/dxarT/t+kw5gKm0//g0mHIOkhzJ6AJHWYSUCSOswkIEkdNnISSHL3OAKRJLXPnoAkddhYkkCS3ZNcmuRrSTYlObZZvirJjUnen+SGJBcn2WUcx5QkjW5cPYGfA8dX1WHA0cAZSdKs2w94X1UdBPwI+KP53iDJqUlmksxsveeuMYUlSVrIuJ4TCPCWJEcB9wP7AL/erLu9qjY00+uBVfO9QVWdDZwNsGLv/WpMcUmSFjCuJPB8YC/gyVX1yySbgZ2bdff2bbcV8HSQJE2JcZ0OWgnc0SSAo4HHjel9JUlLaKSeQJKd6P2n/xHgc0lmgA3ATWOITZK0xEY9HXQQcGtVbQGO2MY2B89OVNXbRzyeJGmMhj4dlOQlwPnA34wvHElSm1I1fTfirFmzpmZmZiYdhiTtUJKsr6o1g+zjE8OS1GEmAUnqMIvKqPMs3KMusycgSR1mEpCkDjMJSFKHDZUEkmxNsqHvZ9UC265N8vlhA5QkLZ1hLwz/rKpWjzUSSVLrxnY6KMmyJG9L8tUkG5O8uG/1Hkk+m+QbSc5K4mkoSZoCw/YEdkkyWyPg9qo6HjgFuKuqnpJkBXBVkoubbQ4HDgS+DVwIPBf4VP8bJjkVOBVg2R57DRmWJGkQ4zwd9HTgkCQnNPMr6VUV+wVwbVXdBpDkfOBI5iQBi8pIUvvG+bBYgJdV1UW/sjBZC8z9UvdLXpKmwDjPzV8EvDTJcoAk+yfZrVl3eJLHN9cCTgSuHONxJUlDGmdP4Bx69YO/1hSZ/yFwXLPuK8DpwJOALwGfHeNxJUlDGioJVNXu8yy7H3ht89PviuZHkjRlpnIAuSfts5IZB/WSpCXn/fqS1GEmAUnqMJOAJHXYVF4TsKiMpPlYAGj87AlIUoeZBCSpwwZOAkkqyYf75ndK8kNrBkjSjmeYnsBPgYOT7NLM/z7wvUHeIMlUXouQpK4Z9nTQF4HZKzTrgPNnVyQ5PMnVSb7evD6xWX5ykk8m+Rxw8YPfUpLUtmGTwMeAk5LsDBwCXNO37ibgqKo6FHg98Ja+dUcAL6yqY+a+YZJTk8wkmdl6z11DhiVJGsSwYwdtbOoKrwO+MGf1SuCDSfajN2T08r51l1TVndt4T+sJSFLLRrk76ALg7fSdCmq8Cbi8qg4GngPs3LfupyMcT5I0ZqNcoD2XXjnJTU3hmFkreeBC8ckjvL8kaYkN3ROoqu9W1bvmWfVW4O+SXAUsGzoySdKSG7gnsI1aAlfQ1Ayoqq8A+/et/ttm+XnAeYOHKElaKj4xLEkdNpUPbVlURpLaYU9AkjrMJCBJHTaVp4OsJyDtWBznf8dlT0CSOswkIEkdZhKQpA5bVBJIcnxTTOaAQQ+Q5JwkBzbTm5PsOeh7SJKWxmJ7AuuAK4GTBnnzJMuq6k+r6hsDRyZJWnLbTQJJdgeeBpxCkwSSrE3ypSSfTfKNJGcleViz7u4k/yPJNcARSa5IsmYpfwlJ0nAW0xM4Driwqm4G7kxyWLP8cOC/Ak8Cfgt4brN8N+D6qnpqVV252EAsKiNJ7VtMElhHr5IYzeu6ZvraqrqtqrbSqylwZLN8K/DpQQOpqrOrak1VrVm268pBd5ckDWHBh8WSPBo4hl5h+aI3NHTRqyY2t/rX7PzPm8QgSZpy2+sJnAB8qKoeV1Wrqmpf4HZ6//UfnuTxzbWAE+ldOJYk7UC2lwTWAZ+ds+zTwPOArwCnA9fTSwxzt5MkTbkFTwdV1dp5lr07yUbgVVV14jzrd58zv7ZvetWwgUqSxs8nhiWpw1I19/ru5K1Zs6ZmZmYmHYYk7VCSrK+qgZ7LsicgSR1mEpCkDrOojNRBFoHRLHsCktRhJgFJ6jCTgCR12EBJoCks8+G++Z2S/DDJ58cfmiRpqQ3aE/gpvcHkdmnmfx/43nhDkiS1ZZjTQV8EZm8tWEdvGGkAkhye5OokX29en9gs/3KS1X3bXZXkkFEClySNbpgk8DHgpCQ7A4cA1/Stuwk4qqoOBV4PvKVZfg5wMkCS/YEVVbWx/00tKiNJ7Rs4CTRf3qvo9QK+MGf1SuCTSa4HzgQOapZ/Enh2kuXAi4Dz5nlfi8pIUsuGvTvoAuDt9J0KarwJuLyqDgaeA+wMUFX3AJcAxwL/GfjokMeVJI3RsE8MnwvcVVWbkqztW76SBy4Unzxnn3OAzwFfrqo7hzyuJGmMhuoJVNV3q+pd86x6K/B3Sa6iV4qyf5/1wI+BDwxzTEnS+A3UE5hbMKZZdgVwRTP9FWD/vtV/OzuR5DfoJZ2Lh4hTkrQEWnliOMkf07uL6HVVdX8bx5QkbZ9FZSTpIcKiMpKkgZgEJKnDLCojtcAiLppW9gQkqcNMApLUYSYBSeqwRSeBJHcvZSCSpPbZE5CkDhu0vOTa/lKSSd6b5ORmenOSNyb5WpJNSQ5olu+W5NwkX22KzRw71t9AkjS0cfcEtlTVYcDfA69qlr0OuKyqngIcDbwtyW5zd7SojCS1b9xJ4DPN63p6hWcAng6clmQDvYHmdgZ+c+6OFpWRpPYN+rDYffxq4th5zvp7m9etfe8d4I+q6puDhydJWkqD9gS+DRyYZEWSlcDvLmKfi4CXJQlAkkMHPKYkaYksqieQZCfg3qr6TpJPABuBW4CvL2L3NwHvBDY2iWAz8OzhwpUkjdNiTwcdBNwKUFWvBl49d4OqWtU3PQOsbaZ/Brx4xDglSUtgu0kgyUuAlwOvWPpwep60z0pmHHBLkpbcdpNAVZ0FnNVCLJKklvnEsCR1mElAkjrMojKStAgP1cJA9gQkqcNMApLUYUMnge3VF0hyRZI1w76/JGnp2ROQpA4bKQksVF+gb9kpSc7sm/+zJO8Y5biSpPFooyfwMeAPkyxv5v8E+MDcjawnIEntW/IkUFU/BS4Dnt1UG1teVZvm2c56ApLUslGfE9hefYFZ5wCvBW5inl6AJGkyRk0C/1ZfgF4C+F3gyrkbVdU1SfYFDgMOGfGYkqQxGSoJDFlf4BPA6qr612GOKUkav2F7AoupL7B2zqIjgTPnbidJmpyBLww39QXOB/5mkds/MsnNwM+q6tJBjydJWjqpqknH8CBr1qypmZmZSYchSTuUJOuraqCRGnxiWJI6zCQgSR1mEpCkDrOozA7moVrYQtJk2BOQpA4zCUhShw2VBJIcn6SaAeEkSTuoYXsC6+iNEXTSGGORJLVsmCeGdweeBpxCkwQWKi6T5FlJbkpyZZJ3928nSZqsYXoCxwEXVtXNwJ1JDtvWhkl2Bv4BeGZVHQnstcC2FpWRpJYNkwTW0asWRvO6boFtDwBuq6rbm/nzt7WhRWUkqX0DPSeQ5NHAMcDBSQpYBhRwAfMXl8k4gpQkLY1BewInAB+qqsdV1aqq2heY/S//wCQrkqykV1wGepXEnpBkVTN/4qgBS5LGZ9AnhtcBp89Z9mngefSKxvxKcZmq+lmSPwcuTLIFuHa0cCVJ4zRQEpinUAxV9e6+2QcVlwEur6oDkgR4H+AY0ZI0Jdp4YvjPkmwAbgBW0rtbSJI0BSwqI0kPERaVkSQNxCQgSR1mPYGHIGsOSFosewKS1GEmAUnqMJOAJHXYopJAktcluSHJxiQbkjx10AM1w03/9uAhSpKWynYvDCc5Ang2cFhV3ZtkT+DhQxxrLXA3cPUQ+0qSlsBi7g7aG9hSVfcCVNUWgCRPBt4B7A5sAU6uqu8nuQLYABwO7AG8CLgDeAmwNckLgJdV1ZfH/LtIkga0mNNBFwP7Jrk5yf9O8jtJlgPvAU6oqicD5wJv7ttnt6r6beDPgXOrajNwFnBmVa2eLwFYVEaS2rfdnkBV3d381/8fgKOBjwP/EzgYuKQ3LhzLgO/37XZ+s++XkuyR5JGLOM7ZwNkAK/beb/rGspCkh6BFPSxWVVuBK4ArkmwC/gK4oaqO2NYu25mXJE2B7Z4OSvLEJPv1LVoN3Ajs1Vw0JsnyJAf1bXNis/xI4K6qugv4CfCIsUUuSRrZYnoCuwPvaU7p3Ad8CziV3qmbdzeVxHYC3klvuGiAf01yNQ9cGAb4HPCpJMfihWFJmgqLuSawHpjv/v4twFHb2O3TVfWaOe9zM3DIwBFKkpaMTwxLUoeNfRTR+UpQDupJ+6xkxpEwJWnJ2ROQpA4zCUhSh1lURpogCwBp0uwJSFKHmQQkqcNMApLUYSMlgSSPTfJPSW5JcmuSdyXZZq2BJK9Isusox5Qkjc/QSSC94UM/A/zfqtoP2J/eEBNvXmC3VwAmAUmaEqPcHXQM8POq+gD0RhpN8tfA7UneALwR+I/0RhB9PxDgN4DLk2ypqqNHilySNLJRksBBwPr+BVX14yT/DPwp8Hjg0Kq6L8mjqurOJK8Ejp6tTtYvyan0BqZj2R57jRCWJGmxRrkmEOavExB6A8udVVX3AVTVndt7s6o6u6rWVNWaZbuuHCEsSdJijZIEbgDW9C9IsgewL9tOEJKkKTJKErgU2DXJHwMkWQacAZxHry7xS5Ls1Kx7VLOPhWUkaYoMnQSqqoDjgf+U5BbgZuDnwGuBc4B/BjYmuQ54XrPb2cAXk1w+UtSSpLEYaeygqvoO8JxtrH5l89O//XuA94xyTEnS+EzlAHLWE5CkdjhshCR1mElAkjrMJCBJHTaV1wQsKqPFsiiLNBp7ApLUYSYBSeqwgZNAkkpyRt/8q5pRQyVJO5hhegL3As9Nsue4g5EktWuYJHAfveEf/nruiiSPS3Jpko3N628mWZlkc5KHNdvsmuQ7SZaPGLskaUTDXhN4H/D8JHPHfH4v8KGqOgT4CPDuqroLuA74nWab5wAXVdUv+3dMcmqSmSQzW++5a8iwJEmDGCoJVNWPgQ8BL5+z6gjgo830h4Ejm+mPAyc20yc183Pf03oCktSyUe4OeidwCrDbAtvM1hS4AHhmM6T0k4HLRjiuJGlMRhlK+k7gE/QSwayr6f2nD/B84Mpm27uBa4F3AZ+vqq3DHleSND6jPidwBtB/l9DLgT9JshH4L8Bf9a37OPAC5jkVJEmajIGHjaiq3fumfwDs2je/GThmG/t9il7ZSUnSlPCJYUnqsKkcQM6iMpLUDnsCktRhJgFJ6jCTgCR12FReE7CojLbFIjLSeNkTkKQOMwlIUocNfDooyVZgE7Cc3rDSHwTeWVX3jzk2SdISG+aawM+qajVAksfQGzV0JfDfxxmYJGnpjXQ6qKruAE4F/jI9y5K8LclXm8IyL57dNsmrk2xKcl2S00cNXJI0upHvDqqq25qqYY8BjgXuqqqnJFkBXJXkYuAA4DjgqVV1TzOk9K9Iciq9hMKyPfYaNSxJ0iKM6xbR2YHhng4ckuSEZn4lsB/we8AHquoe+LdhqH9FVZ1Nr2wlK/ber+aulySN38hJIMkTgK3AHfSSwcuq6qI52zyDBwrMSJKmxEjXBJLsBZwFvLeqCrgIeOlsEfkk+yfZDbgYeFGSXZvlDzodJElq3zA9gV2SbOCBW0Q/DLyjWXcOsAr4WpIAPwSOq6oLk6wGZpL8AvgC8NpRg5ckjWaYojLLFlh3P70v9wd9wVfV6YB3BUnSFPGJYUnqsKkcQM6iMpLUDnsCktRhJgFJ6jCTgCR1mElAkjrMJCBJHWYSkKQOMwlIUoeZBCSpw0wCktRh6Q3+OV2S/AT45qTjAPYEthgDMB1xTEMMMB1xTEMMMB1xTEMMMB1xPLGqHjHIDlM5bATwzapaM+kgksxMOo5piGFa4piGGKYljmmIYVrimIYYpiWOJDOD7uPpIEnqMJOAJHXYtCaBsycdQGMa4piGGGA64piGGGA64piGGGA64piGGGA64hg4hqm8MCxJase09gQkSS0wCUhSh01dEkjyjCTfTPKtJKdNKIbNSTYl2TDMLVcjHPfcJHckub5v2aOSXJLklub11yYQwxuSfK9pjw1JnrWUMTTH3DfJ5UluTHJDkr9qlrfWHgvE0Gp7JNk5ybVJrmvieGOz/PFJrmna4uNJHj6BGM5LcntfW6xeqhj6YlmW5OtJPt/Mt9YO24mj1baY73tqqM9HVU3ND7AMuBV4AvBw4DrgwAnEsRnYcwLHPQo4DLi+b9lbgdOa6dOA/zWBGN4AvKrlttgbOKyZfgRwM3Bgm+2xQAyttgcQYPdmejlwDfDvgU8AJzXLzwJeOoEYzgNOaPlv45XAR4HPN/OttcN24mi1Leb7nhrm8zFtPYHDgW9V1W1V9QvgY8CxE46pNVX1JeDOOYuPBT7YTH8QOG4CMbSuqr5fVV9rpn8C3AjsQ4vtsUAMraqeu5vZ5c1PAccAn2qWL3VbbCuGViV5LPAHwDnNfGixHbYVxxQZ+PMxbUlgH+A7ffPfZQIfOnp/3BcnWZ/k1Akcv9+vV9X3ofelBDxmQnH8ZZKNzemiJT0lNVeSVcCh9P77nEh7zIkBWm6P5tTDBuAO4BJ6PeYfVdV9zSZL/lmZG0NVzbbFm5u2ODPJiqWMAXgn8Grg/mb+0bTcDtuIY1abbTHf99TAn49pSwKZZ9kk7mF9WlUdBjwT+IskR00ghmny98BvAauB7wNntHXgJLsDnwZeUVU/buu424mh9faoqq1VtRp4LL0e87+bb7M2Y0hyMPAa4ADgKcCjgP+2VMdP8mzgjqpa3794vlCXKoYF4oAW26Ixlu+paUsC3wX27Zt/LPAvbQdRVf/SvN4BfJbeh25SfpBkb4Dm9Y62A6iqHzRfAPcD76el9kiynN6X70eq6jPN4lbbY74YJtUezbF/BFxB73z8I5PMjv/V2melL4ZnNKfMqqruBT7A0rbF04A/TLKZ3qniY+j9R952OzwojiT/2HJbbOt7auDPx7Qlga8C+zVX+x8OnARc0GYASXZL8ojZaeDpwPUL77WkLgBe2Ey/EPintgOY/aNqHE8L7dGc6/0/wI1V9Y6+Va21x7ZiaLs9kuyV5JHN9C7A79G7PnE5cEKz2VK3xXwx3NT3hRN655+XrC2q6jVV9diqWkXvu+Gyqno+LbbDAnG8oM22WOB7avDPR1tXsge44v0sendh3Aq8bgLHfwK9u5KuA25oMwbgfHqnF35Jr1d0Cr1znpcCtzSvj5pADB8GNgEbmz+yvVtoiyPpdes3Ahuan2e12R4LxNBqewCHAF9vjnc98Pq+v9VrgW8BnwRWTCCGy5q2uB74R5o7iFr4+1jLA3fltNYO24mjtbbY1vfUMJ8Ph42QpA6bttNBkqQWmQQkqcNMApLUYSYBSeowk4AkdZhJQJI6zCQgSR32/wHJriv4wU5kogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.barh(x_axis, y_axis)\n",
    "plt.xticks(np.arange(0, 51, step=5.0))\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: Compare this horizontal bar graph to the first one. The major ticks are spaced by 5 dollar increment, instead of 10 dollar increment, to better estimate the averages fares. To create the $5 increment for the x-ticks, we use the numpy module to create an array of numbers using the arange() function. We defined the lower and upper limit of the x-axis and the increment frequency with the code: np.arange(0, 51, step=5.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  5., 10., 15., 20., 25., 30., 35., 40., 45., 50.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.arange(0, 51, step=5.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 51)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the same graph using the Object-Oriented Interface Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.barh(x_axis, y_axis)\n",
    "ax.set_xticks(np.arange(0, 51, step=5.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can also use ax.xaxis.set_ticks(). If the y-axis had numerical values, you could change how often every major y-tick occurs by using the yticks() function for the MATLAB method and the set_yticks() function for the Object-Oriented Interface Method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add Minor Ticks\n",
    "To add minor tickets to the x-axis on horizontal bar chart, use the set_minor_locator function with the xaxis class in the object-oriented interface method. Inside the set_minor_locator functino, add the MultipleLocator method and pass how often you want the minor ticks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "\n",
    "# Increase the size of the plot figure\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.barh(x_axis, y_axis)\n",
    "ax.set_xticks(np.arange(0, 51, step=5.0))\n",
    "\n",
    "# Create minor ticks at an increment of 1\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have minor tickers every 1 dollar increment, and major ticks at every 5 dollar increment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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