Created on Cognitive Class Labs

BarPlots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = {\"City\":[\"London\",\"Paris\",\"Rome\"],\n",
    "       \"Visits\":[20.42,17.95,9.7]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Visits')"
      ]
     },
     "execution_count": 8,
     "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": [
    "dataFrame = pd.DataFrame(data=data)\n",
    "dataFrame.plot(x=\"City\",y=\"Visits\",kind=\"bar\",rot=70)\n",
    "plt.title(\"Nuber of Tourists Visit in Year 2018\")\n",
    "plt.xlabel(\"City\")\n",
    "plt.ylabel(\"Visits\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "data = {\"Car_Price\":[24050,34850,38150],\n",
    "       \"Curb_Weight\":[3045,3572,3638]}\n",
    "index = [\"Variant1\",\"Variant2\",\"Variant3\"]\n",
    "dataFrame = pd.DataFrame(data=data,index = index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Price</th>\n",
       "      <th>Curb_Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Variant1</th>\n",
       "      <td>24050</td>\n",
       "      <td>3045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variant2</th>\n",
       "      <td>34850</td>\n",
       "      <td>3572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Variant3</th>\n",
       "      <td>38150</td>\n",
       "      <td>3638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Car_Price  Curb_Weight\n",
       "Variant1      24050         3045\n",
       "Variant2      34850         3572\n",
       "Variant3      38150         3638"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Car Price vs Car Weight comparision for sedans made by a Car Company')"
      ]
     },
     "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": [
    "dataFrame.plot(kind=\"bar\",rot=15)\n",
    "plt.title(\"Car Price vs Car Weight comparision for sedans made by a Car Company\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#stacked vertical graphs\n",
    "data = {\"Production\":[10000, 12000, 14000],\n",
    "\n",
    "        \"Sales\":[9000, 10500, 12000]\n",
    "\n",
    "        }\n",
    "\n",
    "index     = [\"2017\", \"2018\", \"2019\"];\n",
    "\n",
    " \n",
    "\n",
    "\n",
    "\n",
    "dataFrame = pd.DataFrame(data=data, index=index);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Production</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>12000</td>\n",
       "      <td>10500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>14000</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Production  Sales\n",
       "2017       10000   9000\n",
       "2018       12000  10500\n",
       "2019       14000  12000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "dataFrame.plot(kind=\"bar\",stacked=True,rot=15)\n",
    "plt.title(\"Annual Productions vs Annual Sales\")\n",
    "plt.show(block = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "growthData = {\"Countries\": [\"Country1\", \"Country2\", \"Country3\", \"Country4\", \"Country5\", \"Country6\", \"Country7\"],\n",
    "\n",
    "              \"Growth Rate\":[10.2, 7.5, 3.7, 2.1, 1.5, -1.7, -2.3]};\n",
    "\n",
    "dataFrame  = pd.DataFrame(data = growthData);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countries</th>\n",
       "      <th>Growth Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Country1</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Country2</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Country3</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Country4</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Country6</td>\n",
       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Country7</td>\n",
       "      <td>-2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Countries  Growth Rate\n",
       "0  Country1         10.2\n",
       "1  Country2          7.5\n",
       "2  Country3          3.7\n",
       "3  Country4          2.1\n",
       "4  Country5          1.5\n",
       "5  Country6         -1.7\n",
       "6  Country7         -2.3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Growth Rate of Various Countries')"
      ]
     },
     "execution_count": 28,
     "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": [
    "dataFrame.plot(x=\"Countries\",y=\"Growth Rate\",kind='barh')\n",
    "plt.title(\"Growth Rate of Various Countries\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "inflationAndGrowth  = {\"Growth rate\": [7, 1.6, 1.5, 6.2],\n",
    "\n",
    "                       \"Inflation rate\":[3.2, 3.4, 4.5, 2.7]};\n",
    "\n",
    "index               = [\"Country1\", \"Country2\", \"Country3\", \"Country4\"];\n",
    "\n",
    " \n",
    "\n",
    "# Python dictionary into a pandas DataFrame\n",
    "\n",
    "dataFrame       = pd.DataFrame(data = inflationAndGrowth);\n",
    "\n",
    "dataFrame.index = index;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Growth rate</th>\n",
       "      <th>Inflation rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Country1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country2</th>\n",
       "      <td>1.6</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country4</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Growth rate  Inflation rate\n",
       "Country1          7.0             3.2\n",
       "Country2          1.6             3.4\n",
       "Country3          1.5             4.5\n",
       "Country4          6.2             2.7"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Inflation and Growth Rate of Diffrent Countries')"
      ]
     },
     "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": [
    "dataFrame.plot(kind=\"barh\",stacked=False)\n",
    "plt.title(\"Inflation and Growth Rate of Diffrent Countries\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ata = {\"Appeared\":[50000, 49000, 55000],\n",
    "\n",
    "        \"Passed\":[4500, 5000, 4600]\n",
    "\n",
    "        }\n",
    "\n",
    "index     = [\"2017\", \"2018\", \"2019\"]\n",
    "\n",
    " \n",
    "\n",
    "# Python Dictionary loaded into a DataFrame\n",
    "\n",
    "dataFrame = pd.DataFrame(data=data, index=index)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}