api case study kernal project (part 2) question 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delhi-NCR=  7947\n",
      "Others=  705\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import json\n",
    "\n",
    "\n",
    "dataframe=pd.read_csv('datasets/zomato.csv', encoding='ISO-8859-1')\n",
    "df=dataframe.copy()\n",
    "#summarised all the other cities in Rest of India while those in New Delhi, Ghaziabad, Noida, Gurgaon, Faridabad to Delhi-NCR.\n",
    "lst=['New Delhi', 'Ghaziabad', 'Noida', 'Gurgaon', 'Faridabad']\n",
    "for i in lst:\n",
    "    df.loc[df['City']==i, 'City']='Delhi-NCR'\n",
    "#filtering and keeping all the indian cities in the data frame\n",
    "#filtered all the indian cities/states\n",
    "df=df.loc[df.Currency=='Indian Rupees(Rs.)']\n",
    "\n",
    "\n",
    "\n",
    "#now in the filtered dataframe i have copied all the elements of column 'City', in the filtered dataframe to a numpy array np_City\n",
    "cities=[]\n",
    "for i in df['City']:\n",
    "    cities.append(i)\n",
    "np_city=np.array(cities)\n",
    "#initialised two variables corresponding to the restaurants in delhi ncr and rest states\n",
    "count_delhi=0\n",
    "other=0\n",
    "for i in np_city:\n",
    "    if i=='Delhi-NCR':\n",
    "        count_delhi+=1\n",
    "    else:\n",
    "        other+=1\n",
    "#plotted a graph between number of restaurants present in delhi ncr and the rest of india.\n",
    "plt.bar(['Delhi-NCR', 'Other Cities'], [count_delhi, other], color='orange')\n",
    "plt.xlabel('states')\n",
    "plt.ylabel('number of restaurants')\n",
    "plt.show()\n",
    "print('Delhi-NCR= ', count_delhi)\n",
    "print('Others= ', other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BBQ', 'Cajun', 'German', 'Malwani'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaning all the null values and keeping all the non null values in the dataframe\n",
    "df=df.loc[df.Cuisines.notnull()]\n",
    "in_delhi=[]#i have created this list to append all the cuisines in delhi according to the dataset\n",
    "for i in df.loc[df['City']=='Delhi-NCR', 'Cuisines']:\n",
    "    for j in i.split(','):\n",
    "        in_delhi.append(j.strip())\n",
    "in_delhi=set(in_delhi)#creating a set will give me all the unique cusinies present in delhi\n",
    "#now i have created a list corresponding to the cuisines present in other states of india.\n",
    "other_states=[]#in this list i have appended all those cuisines which are present in rest parts of india\n",
    "for i in df.loc[df['City']!='Delhi-NCR', 'Cuisines']:\n",
    "    if i!=np.nan:\n",
    "        for j in i.split(','):\n",
    "            other_states.append(j.strip())\n",
    "other_states=set(other_states)#created a set to get all the unique cuisines in other parts of india\n",
    "not_in_delhi_ncr_acc_to_dataset=other_states-in_delhi #subtracting these will give me the cuisines which are present in all the other parts of india except new delhi.\n",
    "\n",
    "#cuisines, other_states, in_delhi\n",
    "not_in_delhi_ncr_acc_to_dataset\n",
    "#according to the dataset these cuisines are not present in delhi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "German\n",
      "BBQ\n"
     ]
    }
   ],
   "source": [
    "# api-key= 14fff80fb67efe097bc7033153debb2e\n",
    "#city id of Delhi-NCR is 1\n",
    "#now i will extract the cuisines present in delhi ncr according to the zomato api\n",
    "params={\"user-key\":\"14fff80fb67efe097bc7033153debb2e\", \"city_id\":1}\n",
    "headers={\"accept\":\"application/json\", \"user-key\":\"14fff80fb67efe097bc7033153debb2e\"}\n",
    "response=requests.get('https://developers.zomato.com/api/v2.1/cuisines', params=params, headers=headers)\n",
    "data=response.json()\n",
    "#\n",
    "cuisines_from_api_in_delhi=[]\n",
    "for i in data['cuisines']:\n",
    "    cuisines_from_api_in_delhi.append(i['cuisine']['cuisine_name'])\n",
    "cuisines_from_api_in_delhi=set(cuisines_from_api_in_delhi)\n",
    "#i have created a set of all the uniques cuisines which i found from the zomato api.\n",
    "# now we have two things ---->   1. cuisines_from_api_in_delhi          2. not_in_delhi_ncr_acc_to_dataset\n",
    "#now i have printed all those cuisines which are actually in delhi ncr but not mentioned in the dataset.\n",
    "for i in cuisines_from_api_in_delhi:\n",
    "    if i in not_in_delhi_ncr_acc_to_dataset:\n",
    "        print(i)\n",
    "#following is the list of those cuisines which are not in delhi_ncr according to the dataset. but these are actually available\n",
    "#in delhi-ncr according to zomato api. it seems like it is just due to incomplete dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following data is corresponding to the restaurants in Delhi-NCR\n",
      "North Indian :  3597\n",
      "Chinese :  2448\n",
      "Fast Food :  1866\n",
      "Mughlai :  933\n",
      "Bakery :  697\n",
      "South Indian :  569\n",
      "Continental :  547\n",
      "Desserts :  542\n",
      "Street Food :  538\n",
      "Italian :  535\n",
      "\n",
      "The following data is corresponding to those restaurants which are not in Delhi-NCR\n",
      "North Indian :  349\n",
      "Chinese :  242\n",
      "Continental :  177\n",
      "Italian :  147\n",
      "Cafe :  136\n",
      "Fast Food :  97\n",
      "South Indian :  62\n",
      "Mughlai :  59\n",
      "Desserts :  55\n",
      "Mexican :  50\n"
     ]
    }
   ],
   "source": [
    "#i have divided this code into two parts\n",
    "#the first part i have derived the top 10 cuisines served by maximum number of restaurants in Delhi NCR\n",
    "#and in the second part i have derived the top 10 cuisines served by maximum number of restaurants in other indian states.\n",
    "#here we go... first of all i have created a new copy of the original dataframe so that there is no confusion around the previously used data.\n",
    "df=dataframe.copy()\n",
    "#now changing the data in accordance with the question\n",
    "lst=['New Delhi', 'Ghaziabad', 'Noida', 'Gurgaon', 'Faridabad', 'Delhi-NCR']\n",
    "for i in lst:\n",
    "    df.loc[df['City']==i, 'City']='Delhi-NCR'\n",
    "#taking indian cities only \n",
    "df=df.loc[df.Currency=='Indian Rupees(Rs.)']\n",
    "\n",
    "\n",
    "\n",
    "all_cuisines=[]#it indicates all cuisines in delhi ncr only\n",
    "df=df.loc[df.Cuisines.notnull()]#any cuisinie value should not be null, i have taken care of that\n",
    "df=df.loc[df['City']=='Delhi-NCR']#i have kept all the city values as Delhi-NCR\n",
    "for i in df.Cuisines:\n",
    "    for j in i.split(','):\n",
    "        all_cuisines.append(j.strip())#saves all the cuisinies in the variable all_cuisines\n",
    "dic_delhi=dict()# i have created this dictionary because here i will save the no. of restaurants serving a perticular cuisinie in delhi NCR\n",
    "for i in all_cuisines:\n",
    "    if i in dic_delhi.keys():\n",
    "        dic_delhi[i]+=1\n",
    "    else:\n",
    "        dic_delhi[i]=1#saved all the values in dictionary\n",
    "cus1=[]#from the dictionary i have saved the names and no. of restaurants in the variables times1(no. of times the perticular cuisinie occured in the restaurant in delhi ncr)\n",
    "times1=[]# and cus1 is having the names of all te cuisines. \n",
    "#i have created these tw variables because i am planning to create a numpy array of them and use the numpy functions for sort them.\n",
    "\n",
    "for i in dic_delhi.keys():\n",
    "    cus1.append(i)\n",
    "    times1.append(dic_delhi[i])\n",
    "#i have apended all the dictionary values to the two lists.\n",
    "np_cus1=np.array(cus1)#created a numpy array out of those two lists\n",
    "np_times1=np.array(times1)\n",
    "np_cus1=np_cus1[np.argsort(np_times1)][::-1]#sorted\n",
    "np_times1=np.sort(np_times1)[::-1]#sorted\n",
    "print(\"The following data is corresponding to the restaurants in Delhi-NCR\")\n",
    "for i in range(10):#printed\n",
    "    print(np_cus1[i], \": \", np_times1[i])\n",
    "\n",
    "#=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n",
    "print()\n",
    "#this is the second part of the code. i just copied it from above and made some changes accordingly.\n",
    "#created a new copy of the dataframe because i dont want to mess with is previous changed dataset.\n",
    "df=dataframe.copy()\n",
    "\n",
    "lst=['New Delhi', 'Ghaziabad', 'Noida', 'Gurgaon', 'Faridabad', 'Delhi-NCR']\n",
    "for i in lst:\n",
    "    df.loc[df['City']==i, 'City']='Delhi-NCR'\n",
    "df=df.loc[df.Currency=='Indian Rupees(Rs.)']\n",
    "#did the similar thing that i did in the first part.\n",
    "all_cuisines=[]\n",
    "df=df.loc[df['City']!='Delhi-NCR']\n",
    "for i in df.Cuisines:\n",
    "    for j in i.split(','):\n",
    "        all_cuisines.append(j.strip())\n",
    "dic_other=dict()\n",
    "for i in all_cuisines:\n",
    "    if i in dic_other.keys():\n",
    "        dic_other[i]+=1\n",
    "    else:\n",
    "        dic_other[i]=1\n",
    "cus2=[]\n",
    "times2=[]\n",
    "for i in dic_other.keys():\n",
    "    cus2.append(i)\n",
    "    times2.append(dic_other[i])\n",
    "np_cus2=np.array(cus2)\n",
    "np_times2=np.array(times2)\n",
    "np_cus2=np_cus2[np.argsort(np_times2)][::-1]\n",
    "np_times2=np.sort(np_times2)[::-1]\n",
    "print(\"The following data is corresponding to those restaurants which are not in Delhi-NCR\")\n",
    "for i in range(10):\n",
    "\n",
    "    print(np_cus2[i], \": \", np_times2[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cusi=[]\n",
    "inside_delhi=[]#list containing number of restaurants with a perticular cuisine in delhi\n",
    "inside_others=[]#list containing number of restaurants with a perticular cuisinie in rest of the indian states\n",
    "for i in dic_delhi.keys():\n",
    "    if i in dic_other:\n",
    "        cusi.append(i)\n",
    "        inside_delhi.append(dic_delhi[i])\n",
    "        inside_others.append(dic_other[i])\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.scatter(inside_delhi[0:30], cusi[0:30], c='blue', s=50)\n",
    "plt.scatter(inside_others[0:30], cusi[0:30], c='green', s=50)\n",
    "plt.yticks(size=12)\n",
    "plt.grid()\n",
    "plt.xlabel('Number of restaurants--->')\n",
    "plt.ylabel('Cuisines--->')\n",
    "plt.title('Cuisines Versus number of restaurants associated')\n",
    "plt.legend(['Cusines in Delhi-NCR', 'Cusines in Rest of India'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Detailed Analysis</h1><br>\n",
    "From the above graph we can infer that \"North Indian\" cusinie is the most popular among restaurants of Delhi_NCR as well as other states. Here I have plotted only those 30 important cusinies which are common in both Delhi-NCR and other states. There are some cusinies present in other states and are not present in any restaurant of Delhi-NCR. After North Indian cusinie we have chinese cusinie with 2448 restaurants in Delhi-NCR and 242 in other states of india. After these two we have fast food with 1866 restaurants in Delhi_NCR and 97 in other states(a huge difference). After this we have Mughlai cusinie in Delhi-NCR with 933 restaurants and 59 in the states other than Delhi-NCR. and there are many more cusinies as show in the graph."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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