level1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "url=\"https://drive.google.com/file/d/1dKZxapT3xLzOTOpy1LCyqctL8YEvzp4Y/view?usp=drive_link\"\n",
    "url=\"https://drive.google.com/uc?id=\"+ url.split(\"/\")[-2]\n",
    "df=pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 1: Top Cuisines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "determine the top three most common cuisine of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9551, 21)\n",
      "Index(['Restaurant ID', 'Restaurant Name', 'Country Code', 'City', 'Address',\n",
      "       'Locality', 'Locality Verbose', 'Longitude', 'Latitude', 'Cuisines',\n",
      "       'Average Cost for two', 'Currency', 'Has Table booking',\n",
      "       'Has Online delivery', 'Is delivering now', 'Switch to order menu',\n",
      "       'Price range', 'Aggregate rating', 'Rating color', 'Rating text',\n",
      "       'Votes'],\n",
      "      dtype='object')\n",
      "0             French, Japanese, Desserts\n",
      "1                               Japanese\n",
      "2       Seafood, Asian, Filipino, Indian\n",
      "3                        Japanese, Sushi\n",
      "4                       Japanese, Korean\n",
      "                      ...               \n",
      "9546                             Turkish\n",
      "9547     World Cuisine, Patisserie, Cafe\n",
      "9548              Italian, World Cuisine\n",
      "9549                     Restaurant Cafe\n",
      "9550                                Cafe\n",
      "Name: Cuisines, Length: 9551, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df.columns)\n",
    "print(df['Cuisines'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cuisines\n",
      "North Indian      3960\n",
      "Chinese           2735\n",
      "Fast Food         1986\n",
      "Mughlai            995\n",
      "Italian            764\n",
      "                  ... \n",
      "Fish and Chips       1\n",
      "Malwani              1\n",
      "Cuisine Varies       1\n",
      "Soul Food            1\n",
      "B�_rek               1\n",
      "Name: count, Length: 145, dtype: int64\n",
      "The Top 3 Cuisines are:  Cuisines\n",
      "North Indian    3960\n",
      "Chinese         2735\n",
      "Fast Food       1986\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "cuisine_count=df['Cuisines'].str.split(', ').explode('Cuisines').value_counts()\n",
    "print(cuisine_count)\n",
    "top_cuisine=cuisine_count.head(3)\n",
    "print(\"The Top 3 Cuisines are: \",top_cuisine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colours = ['blue', 'green', 'red']\n",
    "plt.bar(top_cuisine.index, top_cuisine.values, color=colours)\n",
    "plt.xlabel('Cuisine')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Top Three Cuisines')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the percentage of restaurants that serve each of the top cuisines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9551\n",
      "The Market share of Top 5 Cuisines are:  Cuisines\n",
      "North Indian    41.461627\n",
      "Chinese         28.635745\n",
      "Fast Food       20.793634\n",
      "Mughlai         10.417757\n",
      "Italian          7.999162\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "total_restaurant = len(df) \n",
    "print(total_restaurant)\n",
    "top_cuisine10=cuisine_count.head(5)\n",
    "percentages = (top_cuisine10 / total_restaurant) * 100\n",
    "print(\"The Market share of Top 5 Cuisines are: \",percentages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(top_cuisine10.index, percentages.values, color=colours)\n",
    "plt.xlabel('Cuisine Name')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Top 5 Cuisines %')\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 2:city Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify the city with the highest number of restaurants in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City with the highest number of restaurants is : New Delhi\n",
      "City\n",
      "New Delhi    5473\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "city_count =df['City'].value_counts()\n",
    "restaurants = city_count.head(1)\n",
    "print(\"City with the highest number of restaurants is :\", city_count.idxmax())\n",
    "print(restaurants)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the average rating for restaurants in each city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average Rating of each city  City\n",
      "Abu Dhabi          4.300000\n",
      "Agra               3.965000\n",
      "Ahmedabad          4.161905\n",
      "Albany             3.555000\n",
      "Allahabad          3.395000\n",
      "                     ...   \n",
      "Weirton            3.900000\n",
      "Wellington City    4.250000\n",
      "Winchester Bay     3.200000\n",
      "Yorkton            3.300000\n",
      "��stanbul          4.292857\n",
      "Name: Aggregate rating, Length: 141, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "rating_by_city= df.groupby('City')['Aggregate rating'].mean()\n",
    "print(\"The average Rating of each city \",rating_by_city)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine the city with the highest average rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The city with Highest Average Rating = Inner City , Rating = 4.9\n"
     ]
    }
   ],
   "source": [
    "top_rated_city=rating_by_city.idxmax()\n",
    "value=rating_by_city.max()\n",
    "print(\"The city with Highest Average Rating =\",top_rated_city, \",\",\"Rating =\", value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 3: Price range Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a histogram or bar chart to visualize the distribution of price ranges among the restaurants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column=\"Price range\")\n",
    "plt.title('Distribution Price Ranges')\n",
    "plt.xlabel('Price Range')\n",
    "plt.ylabel('Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the percentage of restaurants each price range category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The % of Resturent in each Price Range Category are : \n",
      "             count_x  count_y\n",
      "Price range                  \n",
      "1               4444    46.53\n",
      "2               3113    32.59\n",
      "3               1408    14.74\n",
      "4                586     6.14\n"
     ]
    }
   ],
   "source": [
    "price_range_count=df[\"Price range\"].value_counts()\n",
    "total_restaurant = len(df)\n",
    "percentage =round((price_range_count/total_restaurant)*100,2)\n",
    "percentage_m= pd.merge(price_range_count, percentage,on=\"Price range\")\n",
    "print(\"The % of Resturent in each Price Range Category are : \")\n",
    "print(percentage_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l5XK56NevH2+++aZXthdKhg0bxiOPPMLmzZsLHUotwUNjChIUjhw5UmjZsGHDcLvdtG3b1kCi0Hb27yM3N5cRI0aQmZmpghDkNKYgQeHll1/mp59+okOHDoSHhzNx4kQmTpzIfffdR3p6uul4IeeGG26gcuXKNG7cmH379vHxxx+zYsUKxowZYzqalJCKggSFVq1aMWXKFJ599lkOHjxI5cqVGTJkiGf+HvGvzp07M2rUKMaMGUNeXh5169Zl3Lhx3HzzzaajSQlpTEFERDw0piAiIh4qCiIi4qGiICIiHioKIiLioaIgIiIeKgoiIuKhoiAiIh4qCiIi4qGiICIiHioKIiLioaIgIiIeKgoiIuKhoiAiIh4qCiIi4qGiICIiHioKIiLioaIgIiIeKgoiIuKhoiAiIh4qCiIi4qGiIOIDM2fOpFu3blSqVAmXy8WECRNMRxIpEhUFER84dOgQjRo14q233jIdReSShJsOIOJEXbp0oUuXLqZjiFwytRRERMRDRUFERDxUFERExENFQUREPFQURETEQ0cfifjAwYMHWbNmjed+dnY2ixcvJjExkcqVKxtMJnJhLsuyLNMhRJxm+vTpdOjQodDyXr16MXr0aP8HEikiFQUREfHQmIKIiHioKIiIiIeKgoiIeKgoiIiIh4qCiIh4qCiIiIiHioKIiHioKIiIiIemuRCHOgrsBnad/Hr2bQ/gwv4XiDj5tSjfxwPlgQonb+VObkfEGVQUJAgdAVYAy07e1lP4zf+Qn7KEAylARSANqHrWrRpQ2k9ZREpO01xIAMsFfsV+41/O6SKQDeQbzHWpygKNgGYnb82B6kYTiZyPioIEiBxgOrCA00VgHcH15n8pEoGm2AXiVLFIN5pIBFQUxJjDwGzgW2AqsBjnFoCiKk/B1sQVQILRRBJ6VBTET04AP2IXgKnAHOCY0USBLwJoC3Q7eVOXk/ieioL4iAUs5XQRmAEcMJoo+NXldIHIQkeUiy+oKIiXrQA+AsZiHxUkvlEOuBa7QHTGPlRWpORUFMQLdgD/wi4GPxnOEooigXbA9cDNQLLZOBLUVBSkmI4AE7ALwRTsMQMxLxK4AbgfaG82igQlFQW5BPnANOxC8DkaIwh0tYH7gF7Yh8CKXJyKghTBBuBtYAywxXAWuXTRwE3YrYfWhrNIoFNRkAtYCrwEjEPdQ05RH7s43Imm35BzUVGQc/gBGAr8F/vQUnGeGOAW4CGgsdkoElBUFOQM/wVexD7TWEKDC+gOPIPdipBQp6IQ8vKwu4deBn4xnEXMcWMfzjoEqGk2ihilohCyjgDvA69izzoqAhAG3AEMxp72W0KNikLIyQOGA89in3Qmci4RQB/gKezrREioUFEIKdOAh4ElpoNI0IjCPtfhz9hXmhOnU1EICRuBQcB400EkaJUC+gGPA0mGs4gvqSg4Wi72eQYvYY8hiJRUOexxqLtMBxEfUVFwrM+Ax9BMpeIbnYB3gAzTQcTLNCG74yzD/of9PSoI4jvfAg2wW6E6291J1FJwjL3YhxG+jf5Jxb8aAe9iX0JUgp2KgiN8iX2EiA4xFVPc2FNmPAfEGc4iJaGiENQOYB9i+oHpICInVcY+D+Za00GkmFQUgtYs7HnydTayBKKewOtAedNB5BJpoDnoHAP+hH1VLRUECVSfAvWASaaDyCVSSyGorMaetGyR6SAiReTGnirjr+gzaHBQUQgaY4EH0CUwJTh1xr5yn86GDnQq3QHvMHA3cDsqCBK8JgGXAfNMB5GLUFEIaMuwj/1+33QQES/YBLQF3jIdRC5A3UcBawJ26+Cw4RwivnAr9glvsaaDyFnUUghI/wBuRAVBnOtfQAtghekgchYVhYCSDwwAHj35vYiTLcfuHv3EdBA5g4pCwDgM9ADeMB1ExI8OArdgX6dBAoHGFALCNqAbsMB0EBGD7gZGYF8nWkxRUTBuOfY8MRtMBxEJADdin5MTaTpIyFL3kVFTgVaoIIic8hnQFbtbSUxQUTBmNNAF2Gc4h0ig+Rb7QlE5poOEJBUFI/4K9AGOmw4iEqDmYZ/o9pvpICFHYwp+9wgwzHQIkSBRFZgC1DCcI3SopeBXT6KCIHIp1gNXAL8YzhE6VBT85nngBdMhRILQNqAd8IPpICFB3Ud+8Q/ss5RFpPhisK9H3sl0EEdTUfC5d4AHTYcQcYhY7EO5LzcdxLFUFHzqn0BvQD9iEe9JxL5GeV3TQRxJRcFnPgVuA/JMBxFxoFTge6CK6SCOo6LgE18Bv0fnIYj4UiYwG0gxHcRRdPSR100GeqKCIOJrq4Fr0GVqvUtFwatmA92Bo4ZziISKRdgfwk6YDuIYKgpesxG4AThiOohIiPkG+IPpEI6houAVudgXyNlpOohIiHoXGGo6hCOoKHjFfcBC0yFEQtyT2Nd+lpLQ0Ucl9how0HQIEQEgCvvkttamgwQttRRKZDrwmOkQUkQvvgguFwwcWHD5nDlw5ZUQGwsJCdC2LRy5wNDQkCH2ds681a5dcJ1HH4XEREhPhzFjCj72739Dt27eeEVS2FHgZmCX6SBBK9x0gOC1ER31EDzmz4cRI6Bhw4LL58yBa66BJ56AN96A8HD4+WdwX+TjUr168O23p++Hn/Gf9PXXMHYsTJ4Mq1dD377QuTOUKwf79sGTTxZ8rnjbFuAu4L+Ay3CW4KOWQrHkYh9ppIHlYHDwINx+O7z7LpQtW/CxRx6BAQPg8cftN/pataBnT4iKuvA2w8OhQoXTt3LlTj/266/Qvj00awa33mq3PrKz7cf+7//gwQehcmWvvkQpZCLwiukQQUlFoVjuA34yHUKKqF8/6NoVOp01ueaOHTBvHqSkQKtWUL48tGsHs2dffJurV0OlSlC9ul1wNm48/VijRrBgAezZAz/9ZHdF1ahhb3fhQrsIiT88CcwxHSLoqChcsteAj0yHkCIaN85+Ix56jqMV162zvw4ZAvfeC998A02aQMeO9pv++Vx+OYweba8/fLjdCrjiCjhw8sTazp3hjjugeXPo3Rs+/NAer3jwQXjnHfs5tWpB69awbJmXX7Cc4QRwK7DHdJDgYsklmGZZVrhlWegWBLeNG7FSUrB+/vn0snbtsB5+2P7++++xAOuJJwo+r0EDrMcfL/p+9uzBSkjAGjXq/OsMGYI1cKCdpXx5rB07sN5/H6tJE/M/J+ffultSdGopFNlu4BY0sBw8fvrJ7iJq0sQeAwgPhxkz4PXX7e/Ll7fXq3vWDMx16hTsDrqYMmWgZk1Ys+bcj69YAR9/DM8+C9On20c3JSfbYxcLF55uYYivTABeNx0iaKgoFNlDwHbTIeQSdOwIS5bA4sWnb82a2WMAixfb4wGVKsHKlQWft2oVVLmEGZkPHoS1a6FixcKPWRbcfz/8/e8QFwd5eXD85FyJp77maXZ1P/gjGgcsGh2SWiRfojMlg098PNSvX3BZbCwkJZ1e/sc/wuDB9uBw48Z2//+KFTB+/OnndOwIPXpA//72/cces88zqFIFfvvNfn5YmH2k0dlGjbJbBafOS2jd2h7DmDsXJk60Wyllynj5hcs5HMM+f2EhkGA4S2BTUbioPcADpkOIjwwcCLm59qGpOTl2cZgyBTIyTq+zdi3sOuNcqM2b7QKwe7f9ht+mjf0mn5xccNvbt8Pzz8MPZ1xvvkULGDTIPhoqJcUuQuIva7GPHBxnOkhA0zQXF9UL+7KaIuIMo4C7TYcIWCoKF/Q/oKvpECLiVWWBlUDyxVYMSRpoPq992E1NEXGWPdgDz3IuKgrnNQh7DhURcZ5/ArNMhwhI6j46p8lAZ9MhRMSn6mNfzlPH25xJLYVCDgD3mg4hIj63FBhmOkTAUUuhkAeAEaZDiIhfxAG/AmmmgwQMtRQKmA2MNB1CRPzmILpyYkFqKRSQBcw1HUJE/G4icI3pEAFBLQWPL1BBEAlV/bEvniUqCgDkAX82HUJEjFkLvGg6REBQUQBgNLDCdAgRMeol4Dzzn4cQjSlwBKgJbDYdRESM+z3wb9MhjFJLgTdQQRAR22fActMhjArxlsIeIANdw1VETrsVGGs6hDEh3lJ4ERUEESnoE+xZVENTCBeFLdhdRyIiZ8oHnjcdwpgQLgqDsQeZRUTONhb7MNXQE6JF4Vfsw1BFRM4lD3jBdAgjQrQoPIv9SxcROZ+PgPWmQ/hdCBaFrcB40yFEJOAdJxTPcg7BovAO9i9bRORiPiDUzmMKsaJwDF0rQUSK7hih1loIsaLwb2C76RAiElTew+52Dg0hVhTeNB1ARIJOLvCW6RB+E0LTXCwAmpsOISJBKRXYSCh8jnb+K/TQ2csiUlxbgEmmQ/hFiBSFndjzmYiIFNf7pgP4RYgUhXeBo6ZDiEhQ+wrYbTqEz4VAUTgBDDcdQkSC3jFgjOkQPhcCRWECoXbyiYj4ivO7kEKgKITOoWQi4ms/AwtNh/AphxeFrcAM0yFExFGc3VpweFH4AgiR0zBExE/G4uQDVxxeFD43HUBEHGcP9gdOZ3JwUchBXUci4hvO7UJycFH4CvtwVBERb5sKbDIdwiccXBSc27wTEdPygf+YDuETDi0Kh4DJpkOIiKN9YzqATzi0KPwPe7pbERFf+Q4nXsXRoUVBRx2JiK8dBGabDuF1DiwKR4H/mg4hIiHBeV1IDiwK3wIHTIcQkZCgohAE1HUkIv7yC/Cb6RBe5bCiYGGfnyAi4i/Oai04rCgsBXaZDiEiIUVFIYDNMh1ARELOt0Ce6RBeo6IgIlIie4B5pkN4jYqCiEiJOacLyUFFIRvYYjqEiIQk50yr46CioFaCiJiyGKfMyhxuOoC3vPrDcrYebEdW2lGy0rOpFL/ddCQRCRlHgRVAfdNBSswxReHjJZNYvG2x5356QiWy0qvQMjWCrPTdNKm4isgw501eJSKBYjFOKAouy7KC/iLGR44fIeHFBE7kn7/5FhUWRZOKmbRMSyQr7RhZ6etJS9jmx5Qi4myPAa+YDlFijigK32/8njYftLnk56XGVyArvSpZaVFkpdmtiajwYz5IKCLO1wmYYjpEiTmi+2j+b/OL9bwtB7Yxfvk2xi+370eGRXJZhXpkpSXRMu0YWekbqVzaWfOaiIiv/Gw6gFc4oij8sv0Xr2znWN4x5m1ZxrwteM5FqRRfgay0qrRMiyQrbQ9NK60iOvyoV/YnIk6yE3tyvEqmg5SII7qPWr3Xijmb5/hlXxHuCBpXyCQrrRxZ6cfJSttIlTI6P0JEwL7qYxfTIUrEEUUh6eUkco7kGNt/xbjytEyrSlZaNC3TcmhWaTWlInQ5UJHQ8wLwhOkQJRL0RWHnoZ2k/C3FdIwCItwRNKpQg6y0ZFqmHScrbRPVym42HUtEfK4n8InpECUS9EVh1oZZtB3d1nSMiyofm0zLtGpkpZUiK30PzSqtJibiiOlYIuJVtbBPYgteQT/QvHL3StMRimT7oZ18uXInX56MG+4Op2H52mSlpZCVdoKs9E1UL7vJbEgRKaHVwGEgxnSQYgv6orBiV3BW5RP5J1i4dQULt67grZNH1KbElqNlWnWy0krRMm0vzSutJjbysNmgInIJ8oElwOWmgxRb0BeFYGkpFMWOQ7v4auUuvjr5ksJcYTQsX5uWaclkpeWTlb6JGokbzYYUkYvIJpiLQtCPKWS+kcmanDWmY/hNuZikk62JGLLS9tEidZVaEyIB5R/AQNMhii2oi0K+lU/Uc1EXnPPI6cJcYdRPySArrTxZ6fm0TNtMzaQNpmOJhLDHgaGmQxRbUBeFQDwcNRAklUqkZVoGLdNiyErbT4vU1cRHHTQdSyRE9AY+MB2i2IJ6TGH7IV0z4Vx2H8nhv6tz+O9q+77b5aZ+Sk1aptqtiay036iZlI3LZTaniDMF9/tScBeFg8H9w/eXfCufX7av4pftqxi50F6WWKosl6dmkJUWS1b6flqkriEh6oDZoCKOENxT8gd3UVBLodhyjuxh4poFTDw5Ru92uambXIOstIq0TMsnK20rtcutU2tC5JKpKBijloL35Fv5LN2xhqU71vDuydZE2egytEjNICstjqz0A1yeuobS0fvNBhUJeDsBCwjOT1TBXRTUUvCpPbl7mbT2Jyatte+7cFEnOYOstIpkpUFW+lbqlFur1oRIASeAXUCy6SDFoqIgRWZhsXznWpbvXMt7i+xlpaMSuDwtk6y0OFqmHaBl2hrKqDUhIW8bKgoGqPvIvH1H9zN57U9MPqM1UbtcdbLSKtEyDbLSt1E3eS1uV9Ae+SxSDNuBBqZDFEtQF4Udh3aYjiBnsbD4ddc6ft21jvcX28sSohJOjk0kkJV2iJZpqylbap/RnCK+FbyDzUFdFA4dP2Q6ghTB/qP7+XbdIr5dZ9934aJmUjWy0lPJSnPRMm0r9VPUmhAnCd5ejKAuCsfyjpmOIMVgYbFydzYrd2czerG9LD4ynhapNchKS6Bl2iFapq0lKWaP0ZwixbfLdIBiC+qicPTEUdMRxEsOHDvA1OxFTM0+vaxmUlVapqWSleYmK2079VPWEObONxdSpMiC9wNrcBeFPBUFJ1u1ez2rdq/nnz/b9+Mi42heqQZZaaXJSj9My7S1lIsxd21ukfML3kk6g7soqKUQUg4eO8i09YuZtv70shqJVchKS6NlmpustB00LL9arQkJACoKRmhMQdbkbGBNzgY++sW+HxsRS/PUTFqmliYr/QhZaWtJjt1tNqSEIBUFI1QU5GyHjh9i+vrFTF9/ellG2cq0TEsjKy2crHS7NRHuzjOWUUKBioLfHcs7hoUOYZSLW7tnI2v3bGTMEvt+TEQMzSplkpVWhqy0I2SlryMlNniPFpFApKLgd2olSHEdPn6YmRt+ZuYZF6hrX6YJ0/KvhbqzID94jzGXAJFaBhqaDlE8QVsURLxp+t6FHPu6H5GbU+DmndDmezgxHSwNWksxlM0ynaDY3KYDFFep8FKmI4jDrK53BPJcMDYF/tADhr8CB/pCZAXT0STYuIL383bQJg9zhxEZFqluJPGaiRWXUo96pxf8Ggl/uQwiG8Pt26DFTDj2PWgsSy7GHbRvrcHbUgB7wFDEW4aX+hrLfY6LQxxzwQcV4cGb4YOX4cidEFHO/wEleLgiTCcotuAtZ9hFYW/uXtMxxCHWsYXDtWOIXX6BiRYXR8PiFhDTDHr9Bo2mwdEf/RdSgoNaCmZoXEG87ZdaRZyE77AbhqfBA3fCuJfg+C0QXtq34SR4aEzBDHUfibd9UX4BWbS6tCfNiYE5raF0FvTeCHW/hdyffRNQgkNY8H5gDeqWgoqCeNvI8K+xosOK9+R9bnitKtx/D3z5AuTfCGGxXs0nQaJU8B6xppaCyBn2cZA99aNIXHC4ZBv6Lh6+aw/lroA+2VBjCuQu90pGCQKlKppOUGxBXRTiIuNMRxAH+jFjK9cs8NL4wK4weKUGUAOu3QvX/AiuKZCf653tS2CKDt6WQlB3H5WPLW86gjjQv5Jm+2bD/ysDA66GF4fCjn4QneGb/Yh5QdxSCOqiUCm+kukI4kBj3FPILx3pux1sCYfna8MDD8PMpyHsmoA/rn1LDtzxNiTdD6V6Q4M/wYJ1519/6x647U2oOQjcd8DAjwqvM2WJ/XjC3XDn23DsjDnk9h22H9uw0+svxT/UUjBDRUF8IY88tjb0w7+G5YLPEqF/V/jHS5BzH0RV9v1+L9GeQ9D6aYgIg4n/B8tfhldvh7IXGEM/egKSE+Av3aHROV5Sfj7c9hY80BHmPA0LsmHkd6cff3yc/ViVZK+/HN+LTIQwH36o8LGgHlNQURBfmVU1m1tm+bELIDsCnm4AYfUDbkK+l76G9CT44P7Ty6qlXPg5VZPhtbvs79+fUfjxXQfs2x86QXQkXN8Eft1iP/bDKpi/Dt7s7ZX4/hfEXUegloLIOb2fMMXMjgtNyHc3RJp9k/nqJ2hWDW56DVIehMv+DO9+d/HnXUhyAlQsA5OXwOGjMGslNKwMx0/Agx/AiLshLFjfnVQUzElNSDUdQRxqims+JyoaPgHp10j4S2N4+AlY/GeIvAI4x9xMPrZuJwyfCpkVYNKf4MFOMOCf8OHM4m/T5YJPB8CzE6Den+CyKtC3Hbz4NXSoC9ER0HoI1HoM3pzsrVfiJ9HBXRSCuvsoJTaFcHc4J/KD9ypHEriyGxwnc6vpFJyekO+DntDoerh1CST8D47752px+fnQrDq8cLN9/7KqsHQTvDMVerUt/nbb1IL5z56+v2or/HMWLHoB2j4LD3eGLo2g/uPQtrbdkggKQXziGgR5S8HtcuuwVPGZyWm/mo5Q2M/R8HhzeOwp+PVPEHW5z3dZsQzUPatRXicVNu727n7uf88ewM7Ph0Xr4abLIaU0tKsNMwLwV3Fe6j4yS11I4ivDY/5jOsL5HXbDO2nwwB3wr5fg2K0+m5CvdU1YeVaLadVWqOLF2cPfmw6JcXB9U8g7ebmK43mnv+aZH28vuiDvPgr6opBRVicAiW8sYx25mUEwd9HcGPhjK/jzM7B2EEQ39urmH+kCc9fAC1/Cmm0w9nsYOQ36XXV6nSfGwV3DCz5v8Xr7djAXdu63v1++ufD2d+yD5ybAG73s+2VjoU4lGDYR5qyGqcvswhQ0grylENRjCgD1kutdfCWRYlpe5wBNVgfJZ6d9bni9KnA3dDgI1y+AiG8g7wLXhyiC5hnwxUB44hN45guolgzD7oDbW59eZ+vewt1Jlz15+vufsmHsD3brYv1rBdd7+CMYdC1UKnt62egHoNc78Ppk+GNXO0PQiAnu3guXZVlBfW3BCSsm0OOTHqZjiEMNOdqbwe82NR2j+MrlQe9syNSEfH4RVgpuOgDuYs60GwCC5CPQ+amlIL40POorrPAg/jfZFQZ/qwH3PwjfPAeu68EdbTqVc5WuG9QFARxQFKqXrU50uP7IxTe2k8P+esF7wZQCJpaGAVfB0KGwvR9E1zCdyHnKNDSdoMSCviiEucOolVTLdAxxsEU1g3VWtvP4LRxeqA33PwzTn4awLgE/IV/QUFEIDPVS1IUkvvNpuTmmI/jOF4nQ/1r4+0uQc39ATsgXVMo08NqmXnzxRVwuFwMHDvTaNovCGUVB4wriQ6PdE7Fig/5AvQtbHwFP14d+j8Hcv0JER3AFd9+4EWUaeWUz8+fPZ8SIETRs6P+WhyOKQt3kuqYjiIMdcR1lZ8MQ6V7Jc8G/kuEP3eHtl+BAX+MT8gWNUhUhuuRn9B08eJDbb7+dd999l7Jly178CV7miKLQuEJj0xHE4X6otsl0BP9bEQV/ucz4hHxBI7G5VzbTr18/unbtSqdOnbyyvUvliKJQtUxVUuOD+4QRCWwflZ1mOoI5pybke7AnvP8yHLkLIrw4x4VTJLUo8SbGjRvHwoULGTp0qBcCFY8jigJA68qtL76SSDF9wSzyykWZjmHeqQn5BvlvQr6gUcKisGnTJh5++GHGjBlDdLS5w+yD/ozmU96Y9wYDvhlgOoY4WPbyEVT97pjpGIGn5WG4cTHETIQTe02nMcQFv98DkcWflHDChAn06NGDsLDTA/x5eXm4XC7cbjdHjx4t8JivOOaQijaV25iOIA73XeXV9KWK6RiBZ24MzG0F8S2h90ao/x3kLjKdyr8SapaoIAB07NiRJUuWFFjWp08fateuzZ/+9Ce/FARwUFFoWL4hCVEJ7D+633QUcaiRsf+jLw+ajhG4DrjhjapAX2h/EH7308kJ+Q6aTuZ7iSUfT4iPj6d+/foFlsXGxpKUlFRouS85ZkwhzB1Gy7SWpmOIg81zLedYlRjTMYLD9Dh4pB0MeQ42PQzRDj+XKDnLdAKvcUxLAaBNehsmrw22C7pKMFld7wj1NphOEUROTchHDeiyD66ZD+5JkJ9rOpl3Vezik81Onz7dJ9u9EGcVBY0riI/9r9IS6uG/pryjTCwNEztBpfbQay1UnQS5q02nKrnS9SGuqukUXuOY7iOAy9MuJypMhw2K77wd/RWWWydwlchv4TC0Ftw/AKY5YEK+1OtMJ/AqRxWFmIgY2lZpazqGONh6tnK4tsYVvGbCGRPy7Q7SCflUFALbtZnXmo4gDvdL7T2mIzjP+gh45uSEfHP+ChGdgmNCvqgkKOecQWZwYFHomtnVdARxuC+S55uO4Fx5LhiXDH/4Hbz1Muy/O7An5KvYBVzOeht1zBnNZ8p8I5M1OWtMxxCHKk0ce0a9jCs3z3SU0BBpwW3b4fKZcGw2EEBvWa0/gSo9TafwKmeVuJOur3m96QjiYPs4SE4DHdDgN8dcMLrC6Qn5Dt8FEcmmU4E7Aip2Np3C6xxZFLrX7m46gjjc/IytpiOEpp+j4YnmMOgvsNzwhHzJbUo8tUUgcmRRaF25NckxAfBJQhzrX4mzTEcIbUfcMCINHrgDxr4Ex26F8DL+zVDJWUcdneLIouB2uelWs5vpGOJgY9zfkl860nQMAZgXA39sBY8/DWsGQfRl/tmvww5FPcWRRQHgxro3mo4gDpZHHlsbOfbfJzidmpDv/r7wxVDI+z2ExflmX/E17ZlRHcixf9VXZ1xNSmyK6RjiYDOrrDMdQc5nehw82g7++hxsGuj9Cfkc2koABxeFcHc4tze43XQMcbD3E6aYjiAXkxMGf8uA+x+Aic8B3SGsVMm3m/77km8jQDnyPIVTlmxfQsN3GpqOIQ52/PNRhP92xHQMuRQVTkDvtVCtmBPyJdSB65Z7P1eAcGxLAaBB+QZcVsFPg04SktY10OU5g862cHixBBPyZdztu2wBwNFFAaB3496mI4iDTU791XQEKYlTE/K9empCvotcbtUdAdXu8k82QxzdfQSw+/BuKv29Esfy9IlOvK8uVVn61iBcjv4vCiFhFty0G674HvKmgXXWVCbpN8IV481k8xPHtxSSYpI0SZ74zHLWk1sj1nQM8ZY8F4wrB/3OmJAvqtLpxx3edQQhUBRAXUjiW7/W2W86gvjCykh4qjH0fxwWPgmxPRw519HZQqIoXJt5LRXjAnj6XQlqX1VYZDqC+NIJF3xYAVY/7rhpss/F+a8Q+5yF/i36m44hDvVO5NdYESHxrxS6Itxwp5dPgAtQIfOX/ECzB4iJ0GUUxfu2k8P+el44IUoCV9cMKB8aY0fhpgP4S2KpRPo07sNb898yHcW75p+87T15PwVoB2QCh4HpwFpgHxAD1AauBKIvst2dwBRgA5APJAM9gTInH/8GWAxEAp2AM88RXAb8DNxWvJcUjBbW3EGHxaHxphGS+jYwncBvQqalAPBIy0dwO61PMAH7Tfl+4D6gGvAvYAdw4OTtauAPQHdgDfDlRbaZA7wPlAN6Aw8CbTn9EWIlsAS4E7gK+Ao4dPKxXGAqEGKXyv534hzTEcRXmpWHrEoXX88hHPYOeWEZiRn8rtbvTMfwrlpATSAJ+028I/an981AeeDmk+skAtVPPr4KuNCVJKditzSuBiqefG5t4NSEkzuBqkAq0ACI4nRLZQrQnNMtihDxfvhErLhLOCtWgsfDzUwn8KuQKgoAg7IGmY7gO/nYn+CPA2nnWScX+0087ALbWI1dZD4CXgbeBc48cbcC8Btw5OTX49iFYwOwFTB4MSxTjnKMHQ190xs78/gauu0bQaWcJ3HteogJR38+77oPHByHa9dDDDsy7YLbrJozGNeuhwrd+h381LPOowc/J3H3n0jPeYoxufMLPP/fRxfRbd+Ikr2wYFAnETpXNZ3Cr0JmTOGU1pVbc3nq5czbMs90FO/ZDowCTmC3Em7GHls42yFgJtD0Ats6BBwDZmOPPXTC7nL6BLsrqSpQA3sMYSQQAfQ4+fW/2F1U84Efsccwup0niwP9UG0TPX4o5/XtHrKO0ig8lb7RLbnhwKjzrvfF0Z+Ze3w9ldwXv0Tk/DKPkcfp07CXnviNq/a/xU2R9lxhXx9dwtijC5ic0I/V+Tvoe2AsnSPrUM4dx778Izx56Gu+LR0CR/QNaAoul+kUfhVyLQVwYGshCXgAuBe762YC9pjCmXKBsdgDxu0vsK1T7xO1gCzs7qMrsLuoFpyxXgfgYeyxijrYRaQ69l/UTKAv0AT4olivKCh9VObCn86Lq0tkPZ6LvY4eUY3Ou86WvL08dGg8Y+J7EXHeZuBpye54KrgTPLf/HFtGhrsc7SJqAPBr3nbaR2TSLKIyt0Y1I8EVTXbebgD+7/CXPFiqDZXDEr3zAgNV1QTokWk6hd+FZFG4se6N1ClXx3QM7wnHLgyVsD/ZlwfObAgdBT7mdCviQu8ZMdh/FWdf4joZ+wimc9kJ/IJdKNYDVYBYoB52d9LRIr+SoPaFayZ55aL8vt98K587D/6TP5bqSL3wSz9J85h1go+PzqdvdEtcJz8VNwpPZcGJjezJP8xPJzZyhOPUCEtm9vG1LDyxiQHR7b38KgJQ/yYQFnpvkaH3irGv4fzclc+ZjuE7FnZXEtgthI+wC8Gt2N08FxKOXVx2n7V8N3CuXgkL+A/QGXuswsIel4DTg9n553ieQ21s5P+Z8V468i3hhDEgul2xnj/h2C/stY7QO6qlZ1nnyDrcEdWc5ntfofeBj/kw7g5iXZE8ePAT3om7meG5s6i151la7/07y05s9dZLCRzlY+AWB31wvAQhWRQAbqhzA80rNTcdo+S+xf50vgd7bOHU/YacLgjHgN9hf2I/dZjqmW/Ub1BwILk1sBT4CbsYzMM+DPVcP66F2K2LWifvpwPZwCZgLnYLI4TO6/oufZVf9/fTiY28dmQ6o+Pu8HzKv1Tv5c6hS0RdKoUVrPpDYq9lTeJglpT9Mz2iGjH0yGQ6RdQigjCeOzyJ2aUHck90K+468JE3Xkpg6XcZRF28G86JQrYoAAztONR0hJI7hN1v/ybwIbAF+/yBDOyumy3Y4wuvA6+ecTuzK2g3dgE5pQ5wHfA9MBz7jf9m7G6hMx3EHj/ocsayNOyxiLHYJ7E57AjgixkZN9Gv+5t1fC07rINU3vNXwnc9TPiuh9mQn8OgQ19QNWfwRZ+/IS+Hb4+v5J7orAuut+LENj7OXcCzsdcx/fhq2kbUINkdT8+oy1iYt4kD+bkXfH5QSY+HvqF7xcaQO/roTB2rd6RjtY5MzZ5qOkrxXehNtxowpAjbONc6TU7eLiQOeOQcy9tz4cFsB/uR5RyrEkPkhsN+2d+dUS3oFFGrwLLO+9/mzqjm9DmjO+h8PsidS4ornq6R55/Xx7Is7j/4CX+P7UGcK4o88jl+sm/w+MnrDeQ5qY/wzy1DtpUAId5SAHih4wumI4jDrKrv3YJw0DrK4hObWXxiMwDZ+btZfGIzG/NySHLHUj+8UoFbBGFUcCdQK7y8Zxsd973Bm0dmFNhuvpXPB0fn0iu6BeGu878Jjjr6A8nuOLpF2VM9tA6vznfHVzH3eDb/ODKNumEVKON2yLxiDZPhxpqmUxgV0i0FgBapLehRuwdfrAihYyfFp/5bcQn18d5cOQuOb6TD/tc99x89ZP+t9opqwej4O4u0jbV5u9iVf6jAsm+Pr2Rj/h76XqDraHv+fp4/PJkfSj/qWdYioiqDSl1J1/3vkOKO58O4Oy7l5QS2Ia1D7ryEszn+cpxFsXznchoOb0je2ZfeEymGylRg/dt/wZUf8v9awaVjFRjXzXQK40K++wigbnJd+jTuYzqGOMRGtnGojkO6U0JFmAsGtzKdIiCoKJz0YqcXSSqVZDqGOMQvtXJMR5BLcXNtqKP/f1BR8EiKSeKlTi+ZjiEO8Xny/IuvJIGhdBQ8eeFDckOJisIZ+l7Wl1bpakJKyY0M/xorOnQPawwqT2VBirr7TlFROIPL5eKdru8Q7g75g7KkhA64DpPTwP/zIMklal4B7gqNay8XlYrCWRqUb8CAFgNMxxAH+LGGA+cEcpIIN7zaIeQPQT2bisI5PN3hadISzneVGpGiGVtmpukIciEPNtbg8jmoKJxDXGQcwzoPMx1Dgty/wqaSXybSdAw5l6oJ8FgL0ykCkorCedxY90a61dSJLFJ8eeTxWyN1TQSkl9tDKY0dnouKwgWMun4UKbEhci1J8YmZVbJNR5Cz3VwbOlQ2nSJgqShcQEpsCu9d/57pGBLE3o+fYjqCnKlqArzY1nSKgKaicBHX1byO+5vebzqGBKmprgWcSA2hqwwFsjAXvH0VxGmc50JUFIrg753/Ts2k0J5OV4pvbf1jpiMIwKPNoPmlX8M61KgoFEFMRAxjbhhDhPtiFzgWKWxy2nLTEaR5BRjkgMvv+oGKQhE1q9SMwe0ufnlDkbO9U+o/WDoIyZy4CBh+NYTp7a4o9FO6BE9c8QRtKrcxHUOCzHLWk1sj1nSM0PViO6iSYDpF0FBRuARul5sxN4whOSbZdBQJMsvr7jcdITT1rGUfgipFpqJwiSqXrsynN32qSfPkknxZfqHpCKGnUbI9t5FcEhWFYmhftT2vXv2q6RgSRN6J/AorQv9uflOuFHx4LUTrw9ul0l9pMQ24fAC9G/c2HUOCxE72sr++zlfwi3A3jLoGUuNNJwlKKgol8E7Xd2heSYe5SdEsrLHDdITQ8HRraJ1qOkXQUlEogajwKD6/+XPKx5Y3HUWCwCflfjAdwflurg33NTKdIqipKJRQWkIa43uO14ltclGjw77BitPfic80ToG/tTedIuipKHhBm8pteOvat0zHkAB3lGNsb6iBT5+oHA8fd9XAsheoKHjJvU3vZUi7IaZjSID7ofpG0xGcJykaPr0eyusEQW9QUfCiwe0H0795f9MxJID9M+E70xGcJSYCxnaDjLKmkziGioKXvd7ldW6pf4vpGBKgvnTPJi852nQMZwh3w3vXQBMd6OFNKgpe5nK5+Gf3f3J1xtWmo0iA2tgwz3QEZ/hHB+hUxXQKx1FR8IGIsAg+7/k5LVJ1YXApbGrl1aYjBL8nW8ItdUyncCQVBR+JjYzlf7f9jzrl9IcrBb0bO9F0hODW/zIY2Mx0CsdSUfChpJgkJt0xiaplqpqOIgHkR5ZzrGqM6RjB6aEmMLi16RSOpqLgY+ml05nRewYZZTNMR5EAsrLeYdMRgs/ApvDXVqZTOJ6Kgh9ULl2ZGb1nkJmYaTqKBIj/VvrFdITgMqg5PJllOkVIUFHwk9SEVGb0nkHtcrrgh8DwqK+xwnSNziL5Y3N4/HLTKUKGioIfVYyvyMzeM7mswmWmo4hhG9nGodoaV7ioxy+H/1NB8CcVBT9Ljk1mWq9ptE7XYFmo+7lWjukIgcsFDG5ldxuJX6koGFA6ujST75xM54zOpqOIQZ8n/2g6QmCKcMNbV0H/JqaThCQVBUNiImL46tavdPW2EPZu+H+wosNMxwgssREw5jq4qZbpJCFLRcGgyLBIPvjdBwztOBQXGnQMNQdch8lpGGU6RuAoHwNf9oAOlU0nCWkqCkU0dOhQmjdvTnx8PCkpKXTv3p2VK1d6ZduPt3mc8T3HExOhgcdQM6/6b6YjBIY6iTDpJmiUYjpJyFNRKKIZM2bQr18/5s6dy5QpUzh+/DhXX301hw4d8sr2b6hzAzN7z6RSfCWvbE+Cw5jEmaYjmNc+Hf77e0iNN51EAJdlWZbpEMFo586dpKSkMGPGDNq2beu17W7Zv4Xrx13Pwq0LvbZNCVxhhHFszNu49xwzHcWMfpfBU1kQps+ngUK/iWLat28fAImJiV7dbmpCKjN7z6R77e5e3a4Epjzy2NIwBMeTYiPsayEMaa2CEGD02yiG/Px8Bg4cSOvWralfv77Xtx8bGcvnPT/n6fZPE+bS0SlON7PKOtMR/CuzLEy+Ca6vYTqJnIOKQjH069ePpUuXMm7cOJ/tw+Vy8dd2f2XqXVM1zuBw78dPNh3Bf7pl2AWhZslb2MOHD6dhw4YkJCSQkJBAVlYWEydqWvKS0pjCJerfvz9ffvklM2fOpFq1an7Z567Du+g1oRf/W/0/v+xP/O/YhFFEbD5iOobvhLngL1lePSHt66+/JiwsjMzMTCzL4sMPP+SVV15h0aJF1KtXz2v7CTUqCkVkWRYPPfQQX3zxBdOnTycz078znlqWxatzXuXPU//M8fzjft23+N6KNW9T6xuHXqYzPR7e7AStUn2+q8TERF555RXuvvtun+/LqdR9VET9+vXj448/ZuzYscTHx7Nt2za2bdvGkSP++XTncrl4rNVjzO47m2pl/NNCEf+ZlLrcdATfuLk2zLjV5wUhLy+PcePGcejQIbKyNMV2SailUEQu17mPEPnggw/o3bu3X7Psy93HPV/fw/jl4/26X/GdulRl6VuDcDnlvzEpGv7WAa7z7cWllixZQlZWFrm5ucTFxTF27FiuvfZan+7T6VQUgtinyz7loYkPsePQDtNRxAsOT36fUqu8czKkUVdVgWEdIcX3Z+gfO3aMjRs3sm/fPsaPH8+oUaOYMWMGdevW9fm+nUpFIcjlHMnh0UmP8uHPH5qOIiU0f9M/aPZluOkYxRcTAc+2hru8f5h2UXXq1ImMjAxGjBhhLEOw05hCkEsslcjo7qOZdMckqpapajqOlMCX5YP4LParqsDMW40WBLDPITp69KjRDMFORcEhrs64mqUPLmXg5QNxu/RrDUYjIr/Gigiy311qHIzuAmO7QZUEv+76iSeeYObMmaxfv54lS5bwxBNPMH36dG6//Xa/5nCaIPsLlAuJjYzlH9f8gx/6/kD9FLOf2OTS7WQv++uXMh2jaCLc8FAT+P526OrbweTz2bFjB3fddRe1atWiY8eOzJ8/n0mTJnHVVVcZyeMUGlNwqBP5JxixYARDZgxh1+FdpuNIEU3dMZQrP40zHePCsirBy+2gdpLpJOIDaik4VLg7nH4t+rHmoTX8X6v/IypMF3MJBuOSfjAd4fwqxtonoX11gwqCg6mlECLW713PE1OfYNxS383XJCUXRSRHPnwd14EAOmu9TBQMaAr3NIRSQXx0lBSJikKImbd5HoMmD+L7Td+bjiLnsXXRKCp8HwDzIJUKh3sb2gWhtFqaoULdRyHm8rTLmd13NuNvGk+9ZE0aFoh+qLrRbIBwN/SqBz/eAU+1UkEIMWophDDLspiwYgJDZw9l/m/zTceRk7pZrfnqrVv8v+MwF/yuBvxfC8go6//9S0BQURAApqydwguzX2D6+ummowhw4tN3CduR65+dxYTDLXXgD5f5/VwDCTwqClLAnE1zeGH2C/xn1X9MRwlpa1e8Q/VvfTzYnBgNdzeAuxtCUpCcHyE+p6Ig5/Tztp955YdXGL98PEfzNG2Av4089Cj3fuCjKdIrx8MDjeH2uvZ8RSJnUFGQC9p1eBcfLv6QkQtHsmr3KtNxQkYz6jD/zT94b4NuF7RPhzvrwTXV7MFkkXNQUZAim5Y9jZELR/L5r59zLO+Y6TiOl/vf94jKPlyyjaTGwa114LY6kK7xArk4FQW5ZLsO72L04tGM/Gkkq3NWm47jWD+vf4OGxRnaCXfD1VXhjrrQsYrdShApIhUFKTbLsvh+0/eMXz6ez379jM37N5uO5CgvHL2HJ95tVLSVXUCzCnB9DeiRCeVjfZpNnEtFQbzCsizmbp7rKRAb9m0wHSnoVaYC64f/BVfeef5F3S64vCJ0y7BvFQJ8Ij0JCioK4hPzt8z3FIi1e9aajhO0Dkx/n7ilZ1yiM8wFrVPtax93zfDLJS8ltKgoiM8t3bGUKWunMGXdFGZsmMHh4yUcPA0hs397hdbzk6FDZfvoobbpmnZCfEpFQfzqWN4xftj0A9OypzF9w3TmbZ6n8yDOUj62PB2qdaBD1Q50Tr+KKik+Ol9B5BxUFMSo3BO5zNs8j1kbZ7Fw60IWbVvE+r3rTcfym1LhpWhUoRFNKjShaaWmZKVlUSe5julYEsJUFCTg7Dmyh8XbFrN422IWbVvEom2L+HXnr+RZeaajlUhcZByNKzT2FIAmFZtQp1wdwtxhpqOJeKgoSFDIPZHL0h1LWb17Nev3rrdv+9aTvSebjfs2BkwXVJnoMlQvW53qZatTrUw1z/enbm6XziSWwKaiIEHPsiy2HtzqKRa/HfiNfbn72Ju7l71H93q+33f05LLcvRw4egAL+0/fhQuXy4UL+ySvU9+7XC5KhZcisVQiZUuVpWx02dNfz/i+XEw5qpW1C0CZ6DIGfxIiJaeiICIiHmrLioiIh4qCiIh4qCiIiIiHioKIiHioKIiIiIeKgoiIeKgoiIiIh4qCiIh4qCiIiIiHioKIiHioKIiIiIeKgoiIeKgoiIiIh4qCiIh4qCiIiIiHioKIiHioKIiIiIeKgoiIeKgoiIiIh4qCiIh4qCiIiIiHioKIiHioKIiIiIeKgoiIeKgoiIiIh4qCiIh4qCiIiIiHioKIiHioKIiIiIeKgoiIeKgoiIiIh4qCiIh4qCiIiIiHioKIiHioKIiIiIeKgoiIeKgoiIiIh4qCiIh4qCiIiIiHioKIiHioKIiIiIeKgoiIeKgoiIiIh4qCiIh4qCiIiIiHioKIiHj8P3y+1KkVtdopAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(percentage, labels=percentage.index, autopct='%1.1f%%',colors=[\"yellow\", \"green\",\"deeppink\", \"orange\"])\n",
    "plt.title(\"Price range percentage \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 4: online Delivery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine the percentage of restaurants at offer online delivery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Resturent with Online Delivery:  2451\n",
      "Resturent with NO Online Delivery :  7100\n"
     ]
    }
   ],
   "source": [
    "online_delivery=df['Has Online delivery'].value_counts().get('Yes', 0)\n",
    "print(\"Total Resturent with Online Delivery: \",online_delivery)\n",
    "\n",
    "no=df['Has Online delivery'].value_counts().get('No', 0)\n",
    "print(\"Resturent with NO Online Delivery : \",no)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total % of Online Delivery  25.66\n",
      "Total % where Online Delivery is not present :  74.34\n"
     ]
    }
   ],
   "source": [
    "Row_count= len(df)\n",
    "percentage_of_online_delivery= round((online_delivery/Row_count)*100,2)\n",
    "print(\"Total % of Online Delivery \", percentage_of_online_delivery)\n",
    "\n",
    "percentage_of_NO_online_delivery =round((no/Row_count) *100,2)\n",
    "print(\"Total % where Online Delivery is not present : \",percentage_of_NO_online_delivery)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the average ratings of restaurants the and without online delivery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Rating with online Delivery :  3.25\n",
      "Average Rating without online Delivery:  2.47\n"
     ]
    }
   ],
   "source": [
    "Avg_rating_yes= round(df [df['Has Online delivery'] == 'Yes']['Aggregate rating'].mean(),2)\n",
    "print(\"Average Rating with online Delivery : \",Avg_rating_yes)\n",
    "\n",
    "Avg_rating_No= round(df[df['Has Online delivery'] == 'No']['Aggregate rating'].mean(),2)\n",
    "print(\"Average Rating without online Delivery: \",Avg_rating_No)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels =['YES', 'NO']\n",
    "average_rating =[Avg_rating_yes, Avg_rating_No]\n",
    "plt.barh(labels, average_rating, color=('green', 'orange'))\n",
    "plt.ylabel('Online Delivery')\n",
    "plt.xlabel('Average Rating')\n",
    "plt.title('Comparison of Average Ratings of Online delivery')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}