matplotlib_practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b1fe037",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%matplotlin` not found.\n"
     ]
    }
   ],
   "source": [
    "%matplotlin inline\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae3ff6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Dependencies\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c7523b5",
   "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": 5,
   "id": "cab00113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1208272e0>]"
      ]
     },
     "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": [
    "#Create the plot\n",
    "plt.plot (x_axis, y_axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "72bc29ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1209a72b0>]"
      ]
     },
     "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": [
    "#Create the plot with ax.plt()\n",
    "fig, ax = plt.subplots ()\n",
    "ax.plot(x_axis, y_axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91c63f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x120acdca0>]"
      ]
     },
     "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2c11bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create labels for the x and y axes\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Fare $\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7905c23",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x120c3ceb0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAN1klEQVR4nO3cQYyc5X3H8e+vtpEbQgMymyj1GuFWJsQHqGBDUNW0pFGLTQ9WJA5AFFQUyUINUY6gSk0OXJpDpSgCYlnIQrnEhwYlTkWCKlUJlSgpawkMBoG2RoWpkVhMlEpELhj+PczQ3a7X7OuZd3fNPt+PtJLfeZ/d+e+j9devZ2cmVYUkaeP7nfUeQJK0Ngy+JDXC4EtSIwy+JDXC4EtSIwy+JDVixeAnOZTkjSTPn+N8knwvyVySY0mu639MSdKkulzhPwLs+ZDze4Fdo4/9wPcnH0uS1LcVg19VTwBvfciSfcAPaugp4NIkn+5rQElSPzb38DW2A68tOh6Mbnt96cIk+xn+L4CLL774+quvvrqHu5ekdhw9evTNqpoa53P7CH6WuW3Z92uoqoPAQYCZmZmanZ3t4e4lqR1J/nPcz+3jWToDYMei42ngZA9fV5LUoz6CfwS4c/RsnRuB31TVWQ/nSJLW14oP6ST5IXATcHmSAfBtYAtAVR0AHgNuAeaA3wJ3rdawkqTxrRj8qrp9hfMFfL23iSSpEe+++y6DwYDTp0+fdW7r1q1MT0+zZcuW3u6vj1/aSpLGMBgMuOSSS7jyyitJFp7/UlWcOnWKwWDAzp07e7s/31pBktbJ6dOn2bZt2/+LPUAStm3btuyV/yQMviSto6WxX+n2SRh8SWqEwZekRhh8SVpHwyc6dr99EgZfktbJ1q1bOXXq1Flx/+BZOlu3bu31/nxapiStk+npaQaDAfPz82ed++B5+H0y+JK0TrZs2dLr8+xX4kM6ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjegU/CR7kryUZC7Jfcuc/0SSnyZ5NsnxJHf1P6okaRIrBj/JJuBBYC+wG7g9ye4ly74OvFBV1wI3Af+Q5KKeZ5UkTaDLFf4NwFxVnaiqd4DDwL4lawq4JEmAjwNvAWd6nVSSNJEuwd8OvLboeDC6bbEHgM8CJ4HngG9W1ftLv1CS/Ulmk8zOz8+PObIkaRxdgp9lbqslxzcDzwC/D/wR8ECS3zvrk6oOVtVMVc1MTU2d56iSpEl0Cf4A2LHoeJrhlfxidwGP1tAc8ApwdT8jSpL60CX4TwO7kuwc/SL2NuDIkjWvAl8CSPIp4DPAiT4HlSRNZvNKC6rqTJJ7gMeBTcChqjqe5O7R+QPA/cAjSZ5j+BDQvVX15irOLUk6TysGH6CqHgMeW3LbgUV/Pgn8Zb+jSZL65CttJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGmHwJakRBl+SGtEp+En2JHkpyVyS+86x5qYkzyQ5nuSX/Y4pSZrU5pUWJNkEPAj8BTAAnk5ypKpeWLTmUuAhYE9VvZrkk6s0ryRpTF2u8G8A5qrqRFW9AxwG9i1ZcwfwaFW9ClBVb/Q7piRpUl2Cvx14bdHxYHTbYlcBlyX5RZKjSe5c7gsl2Z9kNsns/Pz8eBNLksbSJfhZ5rZacrwZuB74K+Bm4O+SXHXWJ1UdrKqZqpqZmpo672ElSeNb8TF8hlf0OxYdTwMnl1nzZlW9Dbyd5AngWuDlXqaUJE2syxX+08CuJDuTXATcBhxZsuYnwBeSbE7yMeDzwIv9jipJmsSKV/hVdSbJPcDjwCbgUFUdT3L36PyBqnoxyc+BY8D7wMNV9fxqDi5JOj+pWvpw/NqYmZmp2dnZdblvSfqoSnK0qmbG+VxfaStJjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjegU/CR7kryUZC7JfR+y7nNJ3ktya38jSpL6sGLwk2wCHgT2AruB25PsPse67wCP9z2kJGlyXa7wbwDmqupEVb0DHAb2LbPuG8CPgDd6nE+S1JMuwd8OvLboeDC67f8k2Q58GTjwYV8oyf4ks0lm5+fnz3dWSdIEugQ/y9xWS46/C9xbVe992BeqqoNVNVNVM1NTUx1HlCT1YXOHNQNgx6LjaeDkkjUzwOEkAJcDtyQ5U1U/7mNISdLkugT/aWBXkp3AfwG3AXcsXlBVOz/4c5JHgH8y9pJ0YVkx+FV1Jsk9DJ99swk4VFXHk9w9Ov+hj9tLki4MXa7wqarHgMeW3LZs6KvqrycfS5LUN19pK0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1AiDL0mNMPiS1IhOwU+yJ8lLSeaS3LfM+a8kOTb6eDLJtf2PKkmaxIrBT7IJeBDYC+wGbk+ye8myV4A/q6prgPuBg30PKkmaTJcr/BuAuao6UVXvAIeBfYsXVNWTVfXr0eFTwHS/Y0qSJtUl+NuB1xYdD0a3ncvXgJ8tdyLJ/iSzSWbn5+e7TylJmliX4GeZ22rZhckXGQb/3uXOV9XBqpqpqpmpqanuU0qSJra5w5oBsGPR8TRwcumiJNcADwN7q+pUP+NJkvrS5Qr/aWBXkp1JLgJuA44sXpDkCuBR4KtV9XL/Y0qSJrXiFX5VnUlyD/A4sAk4VFXHk9w9On8A+BawDXgoCcCZqppZvbElSecrVcs+HL/qZmZmanZ2dl3uW5I+qpIcHfeC2lfaSlIjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjOgU/yZ4kLyWZS3LfMueT5Huj88eSXNf/qJKkSawY/CSbgAeBvcBu4PYku5cs2wvsGn3sB77f85ySpAl1ucK/AZirqhNV9Q5wGNi3ZM0+4Ac19BRwaZJP9zyrJGkCmzus2Q68tuh4AHy+w5rtwOuLFyXZz/B/AAD/k+T585p247oceHO9h7hAuBcL3IsF7sWCz4z7iV2Cn2VuqzHWUFUHgYMASWaraqbD/W947sUC92KBe7HAvViQZHbcz+3ykM4A2LHoeBo4OcYaSdI66hL8p4FdSXYmuQi4DTiyZM0R4M7Rs3VuBH5TVa8v/UKSpPWz4kM6VXUmyT3A48Am4FBVHU9y9+j8AeAx4BZgDvgtcFeH+z449tQbj3uxwL1Y4F4scC8WjL0XqTrroXZJ0gbkK20lqREGX5IaserB920ZFnTYi6+M9uBYkieTXLsec66FlfZi0brPJXkvya1rOd9a6rIXSW5K8kyS40l+udYzrpUOf0c+keSnSZ4d7UWX3xd+5CQ5lOSNc71WaexuVtWqfTD8Je9/AH8AXAQ8C+xesuYW4GcMn8t/I/Cr1ZxpvT467sUfA5eN/ry35b1YtO5fGD4p4Nb1nnsdfy4uBV4Arhgdf3K9517Hvfhb4DujP08BbwEXrffsq7AXfwpcBzx/jvNjdXO1r/B9W4YFK+5FVT1ZVb8eHT7F8PUMG1GXnwuAbwA/At5Yy+HWWJe9uAN4tKpeBaiqjbofXfaigEuSBPg4w+CfWdsxV19VPcHwezuXsbq52sE/11sunO+ajeB8v8+vMfwXfCNacS+SbAe+DBxYw7nWQ5efi6uAy5L8IsnRJHeu2XRrq8tePAB8luELO58DvllV76/NeBeUsbrZ5a0VJtHb2zJsAJ2/zyRfZBj8P1nVidZPl734LnBvVb03vJjbsLrsxWbgeuBLwO8C/5bkqap6ebWHW2Nd9uJm4Bngz4E/BP45yb9W1X+v8mwXmrG6udrB920ZFnT6PpNcAzwM7K2qU2s021rrshczwOFR7C8Hbklypqp+vCYTrp2uf0ferKq3gbeTPAFcC2y04HfZi7uAv6/hA9lzSV4Brgb+fW1GvGCM1c3VfkjHt2VYsOJeJLkCeBT46ga8eltsxb2oqp1VdWVVXQn8I/A3GzD20O3vyE+ALyTZnORjDN+t9sU1nnMtdNmLVxn+T4ckn2L4zpEn1nTKC8NY3VzVK/xavbdl+MjpuBffArYBD42ubM/UBnyHwI570YQue1FVLyb5OXAMeB94uKo23FuLd/y5uB94JMlzDB/WuLeqNtzbJif5IXATcHmSAfBtYAtM1k3fWkGSGuErbSWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEf8LdSWpU0jmdX8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#add the legend\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ca4ff5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 45.0)"
      ]
     },
     "execution_count": 11,
     "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": [
    "#set the y limit between ) and 45\n",
    "plt.ylim (0,45)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "35c24ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PyBer Fare for Month')"
      ]
     },
     "execution_count": 12,
     "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": [
    "#create a title\n",
    "plt.title(\"PyBer Fare for Month\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a1f97364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x120f01730>"
      ]
     },
     "execution_count": 13,
     "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": [
    "# Create the plot.\n",
    "plt.plot(x_axis, y_axis, marker=\"*\", color=\"blue\", linewidth=2, label='Boston')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Fare($)\")\n",
    "# Set the y limit between 0 and 45.\n",
    "plt.ylim(0, 45)\n",
    "# Create a title.\n",
    "plt.title(\"PyBer Fare by Month\")\n",
    "# Add a grid.\n",
    "plt.grid()\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e21ab39c",
   "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": 15,
   "id": "3b5be0c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 15,
     "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": [
    "#create the plot\n",
    "plt.bar(x_axis, y_axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8ca17be9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1211823d0>"
      ]
     },
     "execution_count": 16,
     "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": [
    "# Create the plot.\n",
    "plt.bar(x_axis, y_axis, color=\"green\", label='Boston')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Fare($)\")\n",
    "# Create a title.\n",
    "plt.title(\"PyBer Fare by Month\")\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "73085771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 17,
     "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": [
    "#create the plot in horizontal\n",
    "plt.barh(x_axis, y_axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1a5cfd55",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Rectangle' object has no property 'legend'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/r5/ztvxwh2507dbzw3svy6wgl400000gn/T/ipykernel_17181/2620302045.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#create the plot in opposite axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"magenta\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"boston\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Pyber fare by month\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mbarh\u001b[0;34m(y, width, height, left, align, **kwargs)\u001b[0m\n\u001b[1;32m   2664\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0m_copy_docstring_and_deprecators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2665\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mbarh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2666\u001b[0;31m     return gca().barh(\n\u001b[0m\u001b[1;32m   2667\u001b[0m         y, width, height=height, left=left, align=align, **kwargs)\n\u001b[1;32m   2668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbarh\u001b[0;34m(self, y, width, height, left, align, **kwargs)\u001b[0m\n\u001b[1;32m   2511\u001b[0m         \"\"\"\n\u001b[1;32m   2512\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'orientation'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'horizontal'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2513\u001b[0;31m         patches = self.bar(x=left, height=height, width=width, bottom=y,\n\u001b[0m\u001b[1;32m   2514\u001b[0m                            align=align, **kwargs)\n\u001b[1;32m   2515\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mpatches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1359\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1360\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1361\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1363\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2363\u001b[0m                 \u001b[0mhatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhtch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2364\u001b[0m                 )\n\u001b[0;32m-> 2365\u001b[0;31m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2366\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2367\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0morientation\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'vertical'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, props)\u001b[0m\n\u001b[1;32m   1060\u001b[0m                     \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"set_{k}\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1061\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1062\u001b[0;31m                         raise AttributeError(f\"{type(self).__name__!r} object \"\n\u001b[0m\u001b[1;32m   1063\u001b[0m                                              f\"has no property {k!r}\")\n\u001b[1;32m   1064\u001b[0m                     \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Rectangle' object has no property 'legend'"
     ]
    },
    {
     "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 plot in opposite axes\n",
    "plt.barh(x_axis, y_axis, color=\"magenta\", legend=\"boston\")\n",
    "plt.title(\"Pyber fare by month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "388b05c8",
   "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 plot.\n",
    "\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fb8982d6",
   "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]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a3de3a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 29,
     "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": [
    "#create the plot\n",
    "plt.barh(y_axis, x_axis)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2468a96b",
   "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 plot.\n",
    "plt.barh(x_axis, y_axis, color= \"magenta\", label='Boston')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.ylabel(\"Date\")\n",
    "plt.xlabel(\"fare$\")\n",
    "plt.title (\"PyBer Fare by month\")\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1ff7fe34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 53,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "# Set the x-axis to a list of strings for each month.\n",
    "x = [\"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 = [10.02, 23.24, 39.20, 35.42, 32.34, 27.04, 43.82, 10.56, 11.85, 27.90, 20.71, 20.09]\n",
    "plt.scatter(x,y, c=\"green\", marker=\"*\",s=100 )\n",
    "plt.xlabel('Total number of riders per city')\n",
    "plt.ylabel ('Average fare ($)')\n",
    "plt.title ('Average fare vs total rides')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a1d97dcf",
   "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": [
    "plt.scatter(x,y)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f3702595",
   "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": 57,
   "id": "04697b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4160c578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.208367917035753"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stdev =statistics.stdev(y_axis)\n",
    "stdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "301d7a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "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.errorbar(x_axis, y_axis, yerr=stdev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "adcc729b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 60,
     "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": "code",
   "execution_count": 61,
   "id": "c681583c",
   "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": "code",
   "execution_count": 62,
   "id": "6f3fa75b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 62,
     "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": "code",
   "execution_count": 63,
   "id": "71ef47a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUkUlEQVR4nO3df7RlZX3f8fenQKOCgISBTqLj6Fqoy1DFcBephThjEBdVVtDEH6VooZJMbatGW5uQH62HpE0hiRGzTJaZEM2YEgKJWhAbkU68WMClziAiCJEEGaESZrSAYpQU/PaPvQePM/fHOffec+c+975fa5119q+zn2ffve/nPvvZZ++bqkKS1J5/cKArIElaGANckhplgEtSowxwSWqUAS5JjTp4OQs7+uija+PGjctZpCQ1b+fOnV+rqnX7Tl/WAN+4cSM7duxYziIlqXlJds003S4USWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcGkMg8GAJPu9BoPBga6a1qAs5z90mJqaKu/E1GqwefNmAKanpw9oPbQ2JNlZVVP7TrcFLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1KiDR1koyd3AN4HHgEerairJUcDlwEbgbuA1VfXAZKopSdrXOC3wF1fVCUOPNDwf2F5VxwHb+3EtAZ85LWkUi+lCORPY1g9vA16x6NoI6AK8qti0aRObNm2iqqgqA1zS9xk1wAv4eJKdSbb0046tqvsA+vdjZvpgki1JdiTZsWfPnsXXWJIEjNgHDpxcVV9NcgxwbZI7Ri2gqrYCW6H7jzwLqKMkaQYjtcCr6qv9+27gw8BJwP1J1gP077snVUlJ0v7mDfAkhyZ58t5h4KXArcBVwDn9YucAV06qkpKk/Y3ShXIs8OEke5f/k6r6WJLPAlckOQ/4CvDqyVVTkrSveQO8qu4Cnj/D9K8Dp06iUpKk+XknpiQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAb6G+dRDqW2jPgtFq9BgMGAwGLB582YApqenD2h9JI3HFrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUd7II+1j4/kfnXeZv73r6yMvC3D3hS9fVJ2kmdgC18R5y740GbbANXHesi9Nhi1wSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaNXKAJzkoyeeSXN2PH5Xk2iR39u9PmVw1JUn7GqcF/nPA7UPj5wPbq+o4YHs/LklaJiMFeJKnAi8HLhmafCawrR/eBrxiSWsmSZrTqC3wi4GfB747NO3YqroPoH8/ZqYPJtmSZEeSHXv27FlMXSVJQ+YN8CRnALuraudCCqiqrVU1VVVT69atW8gqJEkzGOVhVicDP5nkZcATgMOT/Hfg/iTrq+q+JOuB3ZOsqCTp+80b4FX1i8AvAiTZDLy9ql6X5DeBc4AL+/crJ1fN1Wepnznt86a1kg0GAy644IL9pr/jHe/wscKLsJjvgV8InJbkTuC0flyS9jMYDKgqNm3axKZNm6gqqsrwXqSxngdeVdPAdD/8deDUpa+SJGkU3ompVcH/+qO1yP/Io1XB//qjtcgWuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalR3sizyvnQLGn1sgUuSY0ywCWpUQa4JDXKAB+DT7yTtJJ4EXMMPvFO0kpiC1ySGmWAS1KjDHBJapQBLkmN8iKmNIYHr7+Uh2647PHxXRedAcARJ5/FkaecfaCqpTXKANeijXILPqyOW/aPPOVsg1orhl0oktQoA1zSqrAWb7SzC0XSqrAWb7SzBS5JY1hJLf15AzzJE5J8Jsnnk9yW5IJ++lFJrk1yZ//+lMlXV5IOrMFgQFWxadMmNm3aRFVRVSszwIFHgJ+oqucDJwCnJ/knwPnA9qo6Dtjej0uSlsm8AV6dh/vRQ/pXAWcC2/rp24BXTKKCkqSZjdQHnuSgJDcDu4Frq+rTwLFVdR9A/37MLJ/dkmRHkh179uxZompLkkYK8Kp6rKpOAJ4KnJTk+FELqKqtVTVVVVPr1q1bYDUlSfsa61soVfUgMA2cDtyfZD1A/757qSsnSZrdKN9CWZfkyH74icBLgDuAq4Bz+sXOAa6cUB0lSTMY5Uae9cC2JAfRBf4VVXV1kk8BVyQ5D/gK8OoJ1lOStI95A7yqbgFeMMP0rwOnTqJSkqT5eSemJDXKZ6GsQD5zWtIoDPAVyGdOqzVr6ZnwK4ldKJLUKANckhplF4qaMcpp9zin6OBputpmC1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhrlnZhrmE89lNpmgK9hPvVQaptdKJLUKFvg+/CBSZJaYQtcWsMGgwFJ9nsNBoMDXTWNwBa4tIYNBgMGgwGbN28GYHp6+oDWR+OxBS5JjTLAJalRBri0Atk3rVHYBy6tQPZNaxSrogVua2Vle/D6S9l10Rk8cs+tPHLPrey66Ax2XXQGD15/6YGumtS0eVvgSZ4GfAD4R8B3ga1V9e4kRwGXAxuBu4HXVNUDk6vq7GytrGze8SlNxihdKI8C/6GqbkryZGBnkmuBc4HtVXVhkvOB84FfmFxVJa113mj3/ebtQqmq+6rqpn74m8DtwA8DZwLb+sW2Aa+YUB0lSTMY6yJmko3AC4BPA8dW1X3QhXySY2b5zBZgC8CGDRsWVVlJWg6ttPRHvoiZ5DDgg8Bbq+obo36uqrZW1VRVTa1bt24hdZQkzWCkAE9yCF14X1pVH+on359kfT9/PbB7MlWUJM1k3gBPEuAPgdur6reHZl0FnNMPnwNcufTVkyTNZpQ+8JOB1wNfSHJzP+2XgAuBK5KcB3wFePVEaihJmtG8AV5V1wOZZfapS1sdSdKoVsWdmJK0FhngktQoA1ySGmWAS1KjDHBJapQBLkmN8h86SJq4B6+/lIduuOzx8V0XnQHAESef5aOGF8EAlzRxPhN+MuxCkaRG2QLXquAputYiA1yrgqfoWovsQpGkRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhrls1DG4AOTJK0kBvgYfGCSWrTx/I/Ou8zf3vX1kZe9+8KXL7pOWhp2oUhSowxwSWrUvAGe5H1Jdie5dWjaUUmuTXJn//6UyVZTkrSvUVrgfwScvs+084HtVXUcsL0flyQto3kvYlbVJ5Ns3GfymcDmfngbMA38wlJWTFrtvLioxVpoH/ixVXUfQP9+zNJVSZI0iol/jTDJFmALwIYNGxa8nqVurYAtFkltW2iA359kfVXdl2Q9sHu2BatqK7AVYGpqqhZYniTNaS3eaLfQAL8KOAe4sH+/cslqJEkLsBZvtBvla4SXAZ8Cnp3k3iTn0QX3aUnuBE7rxyVJy2iUb6GcNcusU5e4LpK04q2krhqfhSJJY1hJXTXeSi9JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBy/mw0lOB94NHARcUlUXLkmtJC2LB6+/lIduuOzx8V0XnQHAESefxZGnnH2gqqURLTjAkxwE/C5wGnAv8NkkV1XVF5eqcpIm68hTzjaoG7aYLpSTgL+uqruq6u+BPwXOXJpqSZLmk6pa2AeTVwGnV9XP9OOvB36sqt60z3JbgC0AGzZsOHHXrl2Lq/EcNm/eDMD09PTEypCWk8e0AJLsrKqpfacvpgWeGabt99egqrZW1VRVTa1bt24RxUmShi0mwO8FnjY0/lTgq4urjiSAwWBAEq677jquu+46kpCEwWBwoKumFWQxAf5Z4Lgkz0jyD4F/Dly1NNWS1rbBYEBV7fcywDVswQFeVY8CbwKuAW4Hrqiq25aqYuOwtSJpLVrwRcyFmJqaqh07dixbeZK0GkziIqYk6QAywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJatSy3siTZA8wuccRdo4GvjbhMixnZZezmrbFclZuGctZztOrar+nAS5rgC+HJDtmumPJctZOOatpWyxn5ZaxnOXMxi4USWqUAS5JjVqNAb7VctZ8OatpWyxn5ZaxnOXMaNX1gUvSWrEaW+CStCYY4JLUqGYDPMnDE17/Y0luHnptnGPZ6SQL+ipRkkryx0PjByfZk+TqhaxvhvW/si/jOQv47CVJntsP353k6DmWneh2DK13ovt93PIWs++H1rHgfTTi+n85yW1JbumP5R9bwDo2J/mnYyz/1CRXJrkzyd8keXf/rxdnW/6tSZ40xvoryTuHxt+eZDDq58coZ28O3Jbk80n+fZIVk5srpiIr0Ler6oSh190TKudbwPFJntiPnwb8n3FWkOTgOWafBVxP9z9Lx1nnQVX1M1X1xRE/sujtWMMWtI9GkeSFwBnAj1bV84CXAPcsYFWbgZECPEmADwH/o6qOA54FHAb81zk+9lZg5AAHHgF+aq5GxRLZmwM/QndMvwx4x4TLHFnTAZ7ksCTbk9yU5AtJzuynb0xye5I/6P9yfnwoWBZT3olJrkuyM8k1SdYPzX5dkhuT3JrkpDFX/RfAy/vhs4DLhso8qV/v5/r3Z/fTz03yZ0k+Anx8lvoeBpwMnEcfDn1L6pNJPpzki0neu7dFkeThJL+a5NPACxfQulzIdvzvJCcMLXdDkufNVUi/DVcPjb8nybn98N1JLhg6Jp7TTz80yfuSfLavw5mjbtRc5Q1NOy/Ju4bGfzbJb4+w7tn20Wzb97IkdyS5PsnvjHCGsx74WlU9AlBVX6uqr852LPf7/OLhYznd2ecbgbf1rdEfn6fMnwC+U1Xv78t8DHgb8IZ+P/xWv29uSfLmJG8Bfgj4RJJPzPcz6z1K9w2Qt+07I8nT+1y4pX/fkOSI/tjYe6w/Kck9SQ4ZsTyqajewBXhTOgcl+c3+mLolyb8eqsPP99v4+SQXjlrG2Gb6z9ctvICHgYOBw/vxo4G/BgJspNvBJ/TzrgBeN+b6HwNu7l8fBg4BbgTW9fNfC7yvH54G/qAffhFw65jb8Tzgz4En9OVtBq7u5x8OHNwPvwT4YD98LnAvcNQc634d8If98I3Aj/br/g7wTOAg4FrgVf0yBbxm6PPTwFQ/fDdw9AS24xzg4n74WcCOEX5ej6+3n/Ye4Nyher65H/63wCX98K/vPQaAI4EvAYeOuH/mKm8amAIOBf4GOGTo5/2PR1j/bPtov/L6n+s9wDP66ZcNLzfL+g/r98WXgN8DNrGAYxkYAG8f8Zh+C/CuGaZ/Dvg54INDx8JRoxxfs+yXw/vPHQG8HRj08z4CnNMPv4HuTADgSuDFQ9t8ySjlzDDtAeBYujD/lX7aDwA7gGcA/6z/+T5peBsn8Zrr1LsFAX49yYuA7wI/TPeDBfhyVd3cD++kC/VxfLuqTni8oOR44Hjg2iTQhd99Q8tfBlBVn0xyeJIjq+rBUQqqqlv6Vs5ZwP/cZ/YRwLYkx9EF7HCL4dqq+r9zrPos4OJ++E/78Y8Cn6mqu/rtugw4hS54H6P75VqQBW7HnwH/Kcl/pPtl+6OFlj/kQ/37TuCn+uGXAj+Z5O39+BOADcDtS1AeVfWtJH8JnJHkdrog/8IIH51tH83kOcBdVfXlfvwyuhCZq14PJzkR+HHgxcDlwH9hzGN5hO0YFrp9PNP0FwHvrapH+zLmOn7nVFXfSPIBuj8Y3x6a9UK+t9//GPiNfvhyuuD+BN3Zzu8tsOj07y8FnpfkVf34EcBxdA2U91fV3/X1XPA2zqf1AD8bWAecWFX/L8nddL+Y0PWR7fUYsNgulAC3VdULZ5m/7wE77hfsrwJ+i6719YND038N+ERVvbIPx+mhed+atbLJD9Kdyh6fpOh+SYsuWGer63eqO91djLG2o6r+Lsm1wJnAa+has/N5lO/v/nvCPvP37vvH+N4xHuCnq+qvRt2QMcrb6xLgl4A7gPfPt9I59tFVs5QXFqDfp9PAdJIvAP+OyR7LtwE/PTwhyeHA04C7FrC+uVwM3MTcP++95V0F/LckRwEnAn85bmFJnkl3XO2m2x9vrqpr9lnmdJZ2G2fVdB843V+83X14vxh4+gTL+itgXbqLQiQ5JMmPDM1/bT/9FOChqnpozPW/D/jVGVptR/C9i4HnjrG+VwEfqKqnV9XGqnoa8GW61vZJSZ7R9we+lu4C2lJZyHZcAvwO8NkRWyu7gOcm+YEkRwCnjvCZa4A3p29yJnnBCJ8Zq7yq+jRdSP0Lhvr/5zDbPmKW8u4AnpnvfSPqtfMVkOTZ/VnPXifQnXWMeyx/E3jyCNsEsB14UpJ/2a/nIOCddGdXHwfemP7Cex+mjLn+x/XHyxV01xD2upHvXRA+m/74rqqHgc8A76brehqrsZJkHfBe4D3V9Y1cA/ybvf3oSZ6V5NB+G9+Q/ls1Q9u45JoM8H7nPwJcCkwl2UG3o+6YVJlV9fd0v3AXJfk8Xb/i8FX5B5LcSLeDz9t/DfOu/96qevcMs36DrtVwA10LbVRn0fXdD/sgXbh8CrgQuJUuMPZdbsEWsh1VtRP4BvO0Wvfu96q6h+6X9ha6Y+BzI1Tt1+i6bW5Jcms/PqcFlncFcENVPTBCnebaR/uVV1XfpuvX/1iS64H7gfkaCofRdV19McktwHOB/8z4x/JHgFeOchGzD7dXAq9Ocidd//t36M5OLgG+QrcfPt9vK3QXJP9ijIuYw95Jdw1sr7cA/6rf3tfT9bvvdTnddYfLR1z3E/ttvg34X3ThfEE/7xLgi8BN/TH1+3R9+x+ja+3vSHIzXf/8RDR5K32S59NdaBn32x5rXpLNdBejzjjAVXlckh+iO8V/TlV9d47llnW/L6S8dN8KeVdVbZ9QnQ7r+7UD/C5wZ1W9a77PjbH+abrjY8dSrVOT01wLPMkb6U5Pf+VA10WL159mfxr45XnCe1n3+7jlJTkyyZfoLn5PJLx7P9u36m6j65b6/QmWpRWuyRa4JKnBFrgkqWOAS1KjDHBJapQBLkmNMsAlqVH/H2RRAlYWXvGeAAAAAElFTkSuQmCC\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": "code",
   "execution_count": 64,
   "id": "aa3113dc",
   "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": [
    "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": "code",
   "execution_count": 65,
   "id": "c2854c1f",
   "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",
    "# 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": "d7f6ab3a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}