matplotlib_practice_Chart Extras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5ef76c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Error Bars to a Line Chart\n",
    "# Import dependencies.\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics\n",
    "import numpy as np\n",
    "# plt.errorbar(x, y, yerr=<value>)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "325d599b",
   "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": 3,
   "id": "28bffdf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.208367917035753"
      ]
     },
     "execution_count": 3,
     "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": "code",
   "execution_count": 4,
   "id": "697a4653",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 4,
     "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": [
    "# line chart with error bars for the fare amounts\n",
    "plt.errorbar(x_axis, y_axis, yerr=stdev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f67b5ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 5,
     "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": [
    "# same chart but with caps on the error bars\n",
    "plt.errorbar(x_axis, y_axis, yerr=stdev, capsize=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56e78161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iQp410bFSvqY1cB96fWkJ+w41cH+Yt317Q0RIT4rltTvOYNq73/C7j9fz+bpd/PnKET6b1MJTT8/fQmZyLP82sndAy3U505i9fjflVXVkJPn2jk/lHV/frBdsWgP3kbrGZp5ZUMTY3O6c1s/72czDXVZKPC/d4uL/rjiFtdsrueCRhby2dFvA5vBcVXqAxVv2cftZ/YmN6vyQsd44PMGDzpMZdA9MHkTx9Is5I8fBGTkOiqdfTPH0i22ZvEFr4D7zxrIS9lbX8+TEkcEOJWSJCFNc2YzN7cEv3l3Dr95fy+drd/F/VwynVwcmePbGM/O3kBwXxbVnZPu1nPYM7ZVCXLR7goeLTskKePkqePxd4z9hAheROGAhEGut/44x5rci4gBmAU6gGLjaGNMlqxju2vcWRvd3cEZ//3dNs7s+aQn8/bYz+PvSEv742QbOf3ghv77kZK7K6+OXvtKby6v5Yv0ufjIhl8TYwNdZYqIiGNk3TdvBuyBfD89xJE+aUOqBicaYEcCpwAUiMhqYBswxxgwE5livu6S3CkrZXVnfZXuedISIcOPofnx+/zmc3CuZh95dw60v5bO7ss7nZc1YuIXYqAhuHuP0+bY95XKmsX5HJdX1TUGLQYWfEyZw41ZtvYy2fgxwGfCytfxl4N/8EWCoq29q5un5W3A50zhTa99ey+6ewBt3jOa3PziZJUX7mPy3Bby/ssxnbeM7D9by/srtTMnrS/fEWJ9ssyNcOQ5aDKzQiY6VD3l0EVNEIkVkFVAOzDbGLAUyjTE7AazHjGN8dqqIFIhIwZ49e3wUduh4Z3kZOw/Wcd+kgXqrdAdFRAi3jM3hH/efw8DMJB6YtZqpry5nT1V9p7f9/JdbaTFw+9n+GTLWUyOz04iMEAq0GUX5kEcJ3BjTbIw5FegDnC4iwzwtwBgzwxiTZ4zJS09P72CYoamhqYWn5m1hVHYqZ+X2CHY4tpfToxtv3Xkmv7roJBZs2sN5Dy/g49U7Ory9/YcaeH1ZCZeO6BXwLotHSoyN4uSsZJZpAlc+5FU3QmPMAWA+cAGwW0SyAKzHcl8HF+reW1HG9gO1Wvv2ocgI4Y5z+vPZfWeT3b0b976xknteW8G+au9r468s2UZNQzN3+mnCBm+5nA5WlhygoalrTyKtfOeECVxE0kUk1XoeD5wLbAQ+Am6yVrsJ+NBPMYakxuYWnpy/mRF9Uhg3KLzOLEJBbkYi7951Jg9dMJjZ63dz3sML+XztTo8/X9PQxEuLtzJpSAZDeobGeOwuZxr1TS2s3XEw2KGoMOFJDTwLmCcia4B83G3gnwDTgckiUghMtl53GR+s3E5phda+/SkqMoIfj8/l43vPIis1jrv+voL73ljJ/kMNJ/zsrPxS9tc0cvd4/03Y4K0864ae/K3ajKJ844SdYo0xa4Cj7k4xxuwDJvkjqFDX1NzCk/M2M6x3MhOHtHvtVvnQ4J5JvP/jsTw1bwuPzy1kSdE+/nj5KZx7cma767cYw3MLi3A50w4nzVCQnhRLTo9u5Bfv585xwY5GhQO9lb4DPlq9g+J9Ndw3UWvfgRIdGcH95w7kw5+MpXu3GG5/pYCfv7Wag7WNR627r7qBHQfrQqr23crlTKNgW4VtZi8KFGNMyI0dbweawL3U3GJ4Yu5mTspKZvIxaoCeenj2JpzTPmXp1gqWbq3AOe1TnNM+5eHZm3wUbfgZ2iuFj35yFvdOzOWDVds5/+GFzP/2u+vnxhh2HqxjcGYSEwaH3tmRy+ngQE0jW/ZUn3jlLqR0fy0rSw5oN0sv6VgoXvpkzQ6K9h7i6U5MCNDKm3Gl1XdioiL4+XmDOfekTB58ezU3v5jPNa6+/OrikzhQ00htYzN3jx8QkmdHrQNbLSuuYGBmUpCjCQ27K+vYVVmHMXDD80t56vpRTBzSucpRV6E1cC+0tBgen7uZwZlJnZoQQPnGiL6pfHzvWdw1bgBvFZRywSNfUrq/ltioCC4ZHpqDRvXrnkB6UqxeyGzj2QVFGANDs5IZmJHEHa8s593lZcEOyxY0gXvhH2t3sbm8mnsn5RIRAtODKfd0btMuHMLbd40hNiqC2sZmeqbEERUZmoe2iOByppGvQ8sCsKeqnteWbqNHYgyJcVG8MXU0o/s7+Pnbq5n5ZVGwwwt5oXmUh6CWFsNjcwrJzUjkwmGhWbvryk7rl8Zn95/NoIxEMpOCN+aJJ1xOB9sP1LLjQG2wQwm6mV8W0djcQm9rOOHE2CheuNnFRaf05L8/3cAf/7EhYGPG25EmcA/9c/0uvt1dxb0Tc0Nicl51tLjoSNK6xYRk23dbre3gXX142YpDDbz69TYuHdGLuOjvJtmIjYrk8WtHccPobJ5dUMRD76yhqVnvXm2PJnAPGGN4dI57MtxLhvcKdjjK5k7KSiYxNqrLJ/DnFxVR29jMTyYePQVhZITwh8uGcf+kgby9vIy7/r6CusbmIEQZ2jSBe+BfG8rZsLOSeyZo7Vt1XmSEMKpfWpeeYu1ATQMvL97GRadkkZvRfm8cEeGByYP4r8uGMmfjbn70/LJ2+/13ZZrAT8Bd+95Ev+4JXHaq1r6Vb7j6pfHt7ioO1nTNhPTiV8VU1zdxbzu17yP96Ewnj10zkpWl+5ny7BLK/TDph11pAj+Bed+Ws3a7u/Ydqj0blP24chwYAwXbul4zSmVdIy98tZXzh2Z6PNDYD0b04oWbXZRU1HDFM4vZuveQn6O0B81Ix9Ha9t0nLZ7LR/YOdjgqjJzaN5XoSOmS3QlfWVxMVV0T9070bgrCswem88Ydo6mua+KqZxazdruO6qgJ/DgWFu5ldekB7pmQS7TWvpUPxUVHckrvlC53IbO6vomZi9zD/A7rneL150f0TeWdu8cQGxXJNTO+ZvGWvX6I0j40Kx2DMYZH/7WJ3qnxXDGqT7DDUWHI5XSwpuxAl+pd8eqSbRyoaeTeTkwAPiA9kXfvHkOv1DhufiHfq3Hiw40m8GOorGtiRckB7h4/gJgo/TUp33M5HTQ2G1aXHgh2KAFR09DEzC+LOGdQOqf2Te3UtnqmxPHWnWcyrHcyP35tBa8vLfFNkDajmakdxhi276+lZ3IcV+WFR+1bRz4MPXnONAAKushM9a8vLWHfoQbu86DniSdSE2J47fbRjBuUzn+8/w2Pzynscndt6miE7aiqa6KqvokHzx9MbFTkiT9gAzryYehJTYhhUGYiy7ZWcM+EYEfjX3WNzTy7sIgxA7r7dJKN+JhIZvwoj4feWcNfZ29i36EGfnPJyV1mrCJN4EcwxlB2oJboSGGKq2+ww1FhzuV08NGqHTS3mLC+SezNZSXsqarn8WuPmtyr06IjI/jrVSPo3i2GmYu2UnGogb9cNaJLNH2G/x566fO1u6iqa6JXavz3xmdQyh9cTgdV9U1s3FUZ7FD8pr6pmWcWFHG608Ho/t39UkZEhPCri09i2oVD+Gj1Dm57OZ9D9U1+KSuUaA28jdqGZv7wyXoSYiJDfkQ7FR5cOd9NdDy0l/fd6vzh4dmbeHRO4VHL7580sEPNcG8XlLGrso6/XDXCF+Edk4hw17gBOBJimPbeGq6buZQXb3bh6Bbj13KDSWvgbTw1fzM7Dtbh7J4Q8iPaqfDQOzWeXilx5IfQhcwHJg+iePrFnJHj4IwcB8XTL6Z4+sUdSt4NTS08PX8LI7NTGZvrn9r3ka529eWZG05jw85KrnxmMdvDeNheTeCW4r2HeHZBEZeP7E1SXHSww1FdiCvHQf7WirDsQfH+yjK2H6jlvkmBnQD8vKE9efXW09lTWc+VTy+mcHdVwMoOJE3glv/6ZD0xURH88sIhwQ5FdTEup4PyqnpKK8KrptjU3MKT87YwvE8K4welB7z8M/p3Z9adZ9LYbLjq2SWsKAmdsxxf0TZw4F/rdzN3Yzn/efFJZCTHBTsc1cW0neg4u3vCUe/7uk06UD5ctYOSihp+fUle0JokT+6VzHt3j+HGF5Zy/XNLeeqGUUwYnBGUWPyhy9fA6xqb+f0n68jNSOSmMc5gh6O6oIEZiaTER1NwjHFRfNkmHSjNLYYn523mpKxkzj0puAkzu3sC79w1hpwe3bjj5QI+WLk9qPH4UkjXwANR85ixsIjSilpev/0MHbDKh4787pzTPgVCv9YYDBERQl6/NJaF0cBWn6zZQdHeQzx9/aiQ6BCQnhTLm3eOZuorBfx01ir6ORLomWL/s+0TJnAR6Qu8AvQEWoAZxphHRcQBzAKcQDFwtTHGp41MrXcPTnl2CQCz7jzTl5untKKGJ+dt5uLhWYzJ7eHTbXd1euend1w5DuZsLGdvdT09Eu3dhbWlxfDE3M0Mykzk/KE9gx3OYclx0bx0y+nc/+ZKvli3mz3V9dz84jIcCTGkdYvBYf2kJbQ+jyYtIYbUhJiQvcnKkxp4E/BzY8wKEUkClovIbOBmYI4xZrqITAOmAb/wX6i+99+fridChF9ddFKwQ1FdnKt1XJTi/VwwLHSSXkd8vm4XheXVPHbtyJC7pT0uOpKnrj+Nc/40j+r6JvZVN1C4u5r9NQ3UNLQ/KqQIpMZHu5N8a7JPiMGR2Oa1lexb/wkkxkYF5MzjhAncGLMT2Gk9rxKRDUBv4DJgvLXay8B8bJTAF2zawxfrdvPQBYPplRof7HBUF3dK71RioyLIL66wdQJvaTE8NqeQ/unduPiUrGCH067ICKFPmvtvvu1ZfV1jM/trGthX3cD+mgYqDjWw/1ADFTWN7kfrp7SihtWlB9hf00Bjc/tdP6Mj5XBCT0uIobC8ml5+aLLxqg1cRJzASGApkGkld4wxO0Wk3SsVIjIVmAqQnZ3dqWB9pb6pmd9/tI6cHt247aycYIejFDFREZzaN/WYFzLt4l8bdrNxVxV/u3pEyDY7HEtcdCRZKfFkpXhWoTPGUF3fdDixu5O+lexrGqiodj/uP9RATUMTLX7o5+9xAheRROBd4KfGmEpPTw+MMTOAGQB5eXkhcafCC4uKKdp7iJducYXNaIPK/lxOB08v2MKh+ia6xYZ0/4J2GWN4fO5m+nVP4NIR4T8BuIiQFBdNUlw0/bp3O+66rdfxfM2jbhciEo07eb9mjHnPWrxbRLKs97OAcr9E6GM7D9by+NxCzjs5k/Fh1B9U2Z8rx0Fzi2GVTSd4mP/tHr7ZfpB7xusE4IFywt+yuKvazwMbjDF/a/PWR8BN1vObgA99H57v/c+nG2huMfz6kpODHYpS3zMqO5UIgWVb7deM4p4AvJDeqfFcPkonAA8UT/5NjgVuBCaKyCrr5yJgOjBZRAqBydbrkLZ4y14+WbOTu8cPoK/j6DvelAqmpLhoTspKtuVEx4s272VV6QF+PGGA3k8RQJ70QlkEHKvBe5Jvw/GfxuYWfvvhOvo64rlr3IBgh6NUu1xOB7PyS2lsbrFNIjTG3fMkKyWOK08LjykI7cIeR4gPvLy4mMLyan5zyVCdqEGFLJfTQW1jM+t22GeCh6+LKsgv3s9d4wZop4AA6xIJvLyyjkf+VciEwelBH5dBqeP57oYe+zSjPDankPSkWJ2CMAi6RAKf/o+NNDS18JsfDA2JcRmUOpaM5Dj6dU+wzYXM/OIKlhTt485z+uuZbRCEfQLPL67gvZXbueOcHHJ6HL+vplKhwOV0ULBtvy0meHhsTiE9EmO4/ox+wQ6lSwrrBN7U3MJvPlxHr5Q47pmQG+xwlPKIy5lGxaEGtuw5FOxQjmtlyX6+LNzL7Wf3Jz5Ga9/BENYJ/PVlJWzYWcl/XnIyCTH2u7NNdU2tEzyEenfCx+duJi0hmhtHa+07WMI2q+2rrucvX3zL2NzuXGjjwYFU15PToxs9EmPIL67g2tNDY/ygI63dfpC5G8t58LxBnbrtX8eN75ywTeB/+vxbahqa+f2leuFS2YuIkNfPEdI18MfmFJIcF8WPOjmLlY4b3zlh2YSyqvQAswpKufWsHHIzkoIdjlJec+U4KK2oZdfBumCHcpQNOyv55/rd3DI2h+S46GCH06WFXQ28ucXwmw/XkpEUy32TBgY7HBUg4XYq3tofPL+4gh+E2Mh+T8zdTGJsFLeO1aGYgy3sEvhbBaWsKTvIo9ecSqINh+RUHRNup+InZyWTEBNJQYgl8MLdVXy2dic/Hj+AlAStfQdbWDWhHKhp4E+fb+T0HEeXGI9Yha+oyAhGZaexrNin08x22hPzNhMfHcltZ/UPdiiKMEvgf/nnt1TWNemFSxUWXE4HG3dVUlnXGOxQACjaU83Hq3dw4+h+OLrFBDscRRgl8LXbD/La0hJuHN2Pk7KSgx2OUp3mcqZhDCzfFhq18CfnbSEmKoLbz9bad6gIiwTeYl247N4tJqzaQVXXNjI7jagIIT8ExkUp2VfDB6u2c93p/UhPig12OMoSFgn8vZXbWVFygF9cMISUeL2wosJDfEwkw3qnUBAC7eBPzd9MZIRw5zitfYcS2yfwyrpGpv9jAyOzU7lilA4mr8KLy5nGqrID1Dc1By2Gsv01vLuijGtcfclMjgtaHOpotk/gD8/exL5DDfzhsmFEROiFSxVeXE4HDU0tfFN2MGgxPLNgC4DOZBWCbJ3AN+6q5JUl27ju9GyG9U4JdjhK+VyeNbDVsiDdVt/Q1MJb+WVceVpfeqXGByUGdWy2TeDGGH774TqS4qJ48LzBwQ5HKb9wdIshNyMxaO3gOw7W0mIMPx6vte9QZNsE/tHqHSzdWsFD5w8hTfukqjDmcqZRUFwR8AkeGppaKK+q5/KRvenrSAho2coztkzg1fVN/O9nGzild4rOw6fCnsvpoLKuidrGwF7I3HmwDmPQyVBCmC0HC3l8TiG7K+t55obTiPThhctwGxBJhYfWCR6q6pr8OjFJTUMTW8oPUVheRWF5NeVVdXTvFoNTpyIMWbZL4JvLq3l+0VauzuvDyOw0n2473AZEUuGhT1o8PZPjqKprItMHNxlX1TWyubyawvJq9+Nud8Iu2197eJ2oCCE+OpI+aXrhMpTZKoEbY/jdR+tIiInkoQuGBDscpQJCRHDlOPh87U6v2sEP1jQerk0X7q6msLyKzeXV7GwzxnhMVAQD0hMZlZ3GlLy+DMxMJDcjkX7du3HDzKX+2B3lQ7ZK4J+v3cWizXv5/aVD6ZGot/OqrsPlTOPj1TtoaGo56r191fXuJF1ezWarNl1YXs2eqvrD68RHR5KbkciZ/buTm5nIwIwkBmYk0teR4NNmSBVYJ0zgIvICcAlQbowZZi1zALMAJ1AMXG2M8Ws/p+YWwx8+Wc+Qnklcf0ZozhOolL+0toPvrqrnpa+2fpewy6upONRweL3E2ChyMxIZPyidgVaizs1IpHdqvN7oFoY8qYG/BDwBvNJm2TRgjjFmuohMs17/wvfhfWfHwVp2HKzjkWtGEhVpy84zSnXYoMwkoiKEnQfr+N3H60mOi2JQZhLnD80k16pND8xMpGdynA6l3IWcMIEbYxaKiPOIxZcB463nLwPz8WMCr2tsZueBOi4f2ZvTcxz+KkapkBUZIQzpmURTi+GV204nPTFWE7XqcBt4pjFmJ4AxZqeIZPgwpqOU7a9FBH55oV64VF1XN2uKwIwkHVBKufn9IqaITAWmAmRnd6zt2tk9gfSkWDJ0JDSllDqsowl8t4hkWbXvLKD8WCsaY2YAMwDy8vI6dC9wVGQEKfHa7q2U6pxwu1mvown8I+AmYLr1+KHPIlJKKT8Jt5v1TlitFZE3gCXAYBEpE5HbcCfuySJSCEy2XiullAogT3qhXHuMtyb5OBallAor/m6ysdWdmEopZSf+brLRK4NKKWVTmsCVUsqmNIErpZRNaQJXSimb0gSulFI2pQlcKaVsShO4UkrZlCZwpZSyKU3gSillU5rAlVLKpjSBK6WUTWkCV0opm9IErpRSNqUJXCmlbEoTuFJK2ZQmcKWUsilN4EopZVOawJVSyqY0gSullE1pAldKKZvSBK6UUjalCVwppWxKE7hSStmUJnCllLIpTeBKKWVTmsCVUsqmojrzYRG5AHgUiARmGmOm+yQqpVTQPDx7E4/OKTz82jntUwDunzSQByYPClZYqh0dTuAiEgk8CUwGyoB8EfnIGLPeV8EppQLvgcmDNFHbRGeaUE4HNhtjiowxDcCbwGW+CUsppdSJdCaB9wZK27wus5Z9j4hMFZECESnYs2dPJ4pTSinVVmcSuLSzzBy1wJgZxpg8Y0xeenp6J4pTSinVVmcuYpYBfdu87gPs6Fw4Sqkj6UVFdSydSeD5wEARyQG2A9cA1/kkKqXUYXpRUR1LhxO4MaZJRH4CfIG7G+ELxph1PosMrXkopdTxiDFHNVv7TV5enikoKAhYeUopFQ5EZLkxJu/I5XonplJK2ZQmcKWUsilN4EopZVOawJVSyqY0gSullE1pAldKKZvSBK6UUjalCVwppWwqoDfyiMgeYFsHP94D2OvDcLpyeeG8b4EuL5z3LdDlhfO+dba8fsaYo0YDDGgC7wwRKWjvTiQtL7TLCvfywnnfAl1eOO+bv8rTJhSllLIpTeBKKWVTdkrgM7Q8W5YV7uWF874Furxw3je/lGebNnCllFLfZ6cauFJKqTY0gSullE2FXAIXkeoAldMsIqva/DiPs+58Eelw9x8RMSLyapvXUSKyR0Q+6eg2j1PW5VZ5Qzrw2ZkicrL1vFhEenjwmUDuW0CODW/L7ezxccS2Ovz9eVnOr0RknYissY7/MzqwjfEiMsbLz/QRkQ9FpFBEtojIoyISc5z1fyoiCR2IzYjIX9u8flBEfuftdrworzWfrBOR1SLyMxHxe34NuQQeQLXGmFPb/BT7saxDwDARibdeT8Y9j6jHRMTT6e+uBRbhnqPUm+1HGmNuN8as9+Zz+GDf1Pd06PvzhoicCVwCjDLGDAfOBUo7sKnxgMcJXEQEeA/4wBgzEBgEJAL/c5yP/RTwOoED9cAPPamE+EhrPhmK+2/gIuC3/i40JBO4iCSKyBwRWSEi34jIZdZyp4hsEJHnrP90/2yTOHxR7mkiskBElovIFyKS1ebtG0RksYisFZHTO7D5fwAXW8+vBd5oU+7p1rZXWo+DreU3i8jbIvIx8E8P4k8ExgK3YSUAq5a0UETeF5H1IvJMa81ARKpF5L9EZClwZidqkh3Zty9F5NQ2630lIsM92MfxbWv3IvKEiNxsPS8Wkd+3OW6GWMu7icgLIpJvxXGZtzt4vHLbLLtNRB5u8/oOEfmbF2Uc6/s71v5eJCIbRWSRiDzmxVlPFrDXGFMPYIzZa4zZcazj3zouHml7/Iv7jPUu4AGr5nm2B+VOBOqMMS9a5TYDDwC3Wt/RX6zvbY2I3Csi9wG9gHkiMs/DfWvVhLvXxwNHviEi/az8ssZ6zBaRFOv4af3bSBCRUhGJ9rJcjDHlwFTgJ+IWKSJ/to6/NSJyZ5tYHrL2ebWITPe2LIwxIfUDVOOebDnZet0D2AwI4MT9xZxqvfcWcEMHy2kGVlk/7wPRwGIg3Xp/Cu6JmgHmA89Zz88B1nZgn4YD7wBxVpnjgU+s95OBKOv5ucC71vObgTLA4WE5NwDPW88XA6OscuqA/rgnn54NXGmtY4Cr23x+PpBnPS8Gevhx324CHrGeDwIKPCzr8LatZU8AN7eJ+V7r+Y+Bmdbz/209ToBUYBPQzcvv73jlzgfygG7AFiC6zXdwihflHOv7O6pc63ddCuRYy99ou94Jykm0vqdNwFPAODpw/AO/Ax70Yv/uAx5uZ/lK4H7g3TbHisOb4/AY31my9fkU4EHgd9Z7HwM3Wc9vxX1GAPAhMKHN/s/0prx2lu0HMnEn8/+0lsUCBUAOcKH1O09ou8/e/HR4Vno/E+B/ReQcoAXojfsXAbDVGLPKer4cd1LviFpjzKmHCxQZBgwDZosIuJPdzjbrvwFgjFkoIskikmqMOeBpYcaYNVat5VrgsyPeTgFeFpGBuJNq2//6s40xFR4Wcy3wiPX8Tev1p8AyY0wRgIi8AZyFO+E24/6j6ZQO7tvbwK9F5N9x/xG91Nk4LO9Zj8uBH1rPzwMuFZEHrddxQDawwUdlAmCMOSQic4FLRGQD7kT+jRebONb3154hQJExZqv1+g3cicKTOKtF5DTgbGACMAv4b7w8/j3bpe8R3MdAe8vPAZ4xxjRZ5Xh6zB+TMaZSRF7B/Y+jts1bZ/LdsfEq8Cfr+SzciXse7jOgpzoZgliP5wHDReRK63UKMBB3heZFY0yNFa/X+xyqCfx6IB04zRjTKCLFuP/owN221aoZ8FUTigDrjDFnHuP9Iw+8jnSg/wj4C+5aVfc2y/8AzDPGXG4lwvlt3jvkyYZFpDvuU9RhImJw/wEa3An1WLHXGfdprC94tW/GmBoRmQ1cBlyNuwbriSa+3/QXd8T7rcdHM98d3wJcYYz51sMyOlJuq5nAfwAbgRc93fhxvr+PjlGu0AnW9z4fmC8i3wD34P/jfx1wRdsFIpIM9AWKOrjNE3kEWMHxv4vWcj8C/igiDuA0YG5HCxWR/riPwXLc39W9xpgvjljnAjq5zyHZBo77P1S5lbwnAP0CUOa3QLq4L/AgItEiMrTN+1Os5WcBB40xBztQxgvAf7VTK0vhuwt/N3dguwBXAq8YY/oZY5zGmL7AVty17dNFJMdq35uC+yKZr3Vk32YCjwH5XtQ+tgEni0isiKQAkzz4zBfAvWJVLUVkpIdleV2uMWYp7oR0HW2uBXjgWN8fxyh3I9Bfvus9NcXTgkRksHVG1OpU3Gcj3h7/VUCSF/s4B0gQkR9Z24oE/or77OufwF1iXay3kigdKON7rOPqLdzXFVot5ruLxNdj/T0YY6qBZcCjuJujOlS5EZF04BngCeNuG/kCuLu1PV1EBolIN9z7fKtYvWza7LPHQiqBW19ePfAakCciBbh/wRv9XbYxpgH3H9H/ichq3G2Eba+w7xeRxbi/mNuO3oJHZZQZYx5t560/4f7P/xXumldHXIu7Lb+td3EnkiXAdGAt7qRw5Hqd1pF9M8YsByrxoKbaemwYY0px/0GuwX2crPQgvD/gbrpZIyJrrdce6WC5bwFfGWP2e1oOx//+jirXGFOLu53/cxFZBOwGPK1UJOJu1lovImuAk4Hf4P3x/zFwuacXMa1kdjlwlYgU4m6Dr8N9xjITKMH9Ha229hvcFyL/0YGLmG39Ffe1tFb3AbdY+34j7vb3VrNwX4uY5WUZ8dbvYR3wL9zJ+ffWezOB9cAK6/h7Fndb/+e4a/0FIrIKdzu9V0LqVnoRGYH7YklHenmodojIeNwXmi4JcihHEZFeuE/jhxhjWk6wblCOjY6UK+7eIA8bY+b4LzJ3rxWrPVuAJ4FCY8zDJ/pcB8qZj/sYKvD1tlXnhEwNXETuwn3K+Z/BjkX5n3UavRT4lQfJOyjHhrflikiqiGzCfYHcr8nbcodVc1uHu6nq2QCUqUJISNXAlVJKeS5kauBKKaW8owlcKaVsShO4UkrZlCZwpZSyKU3gSillU/8PU2DsvZ/M1XEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add error bars and a capsize using the object-oriented approach.\n",
    "fig, ax = plt.subplots()\n",
    "ax.errorbar(x_axis, y_axis, yerr=stdev, capsize=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8e7dae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 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": [
    "# Add Error Bars to a Bar Chart\n",
    "plt.bar(x_axis, y_axis, yerr=stdev, capsize=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a6d9594",
   "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": [
    "# create the same graph using the object-oriented approach.\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(x_axis, y_axis, yerr=stdev, capsize=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "858c7803",
   "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": [
    "# Change the Major Ticks\n",
    "# a horizontal bar chart with the major ticks at every $5 increment\n",
    "plt.barh(x_axis, y_axis)\n",
    "plt.xticks(np.arange(0, 51, step=5.0))\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5c20ba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  5., 10., 15., 20., 25., 30., 35., 40., 45., 50.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a range between zero and 50 with an increment of 5\n",
    "np.arange(0, 51, step=5.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "096e77b4",
   "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": [
    "# same graph using the object-oriented approach with the following code\n",
    "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()\n",
    "# Alternatively, you can use ax.xaxis.set_ticks() instead of ax.set_xticks()."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22bddb73",
   "metadata": {},
   "source": [
    "If our y-axis had numerical values, we could change how often every major y-tick occurs by using the yticks() function for the MATLAB approach and the set_yticks() function for the object-oriented approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8302b074",
   "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": [
    "# 5.1.9 Add Minor Ticks\n",
    "# x.xaxis.set_minor_locator()\n",
    "\n",
    "from matplotlib.ticker import MultipleLocator\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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30c8b6a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5.1.10 Plot a Pandas DataFrame and Series\n"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}