diff --git a/Digital Clock/digitalclock.py b/Digital Clock/digitalclock.py
new file mode 100644
index 0000000000..a9132b7326
--- /dev/null
+++ b/Digital Clock/digitalclock.py	
@@ -0,0 +1,22 @@
+# Importing Modules
+from tkinter import *
+from time import strftime
+
+root = Tk()  # Creates tkinter window
+root.title("Digital Computer Clock")  # Adds title to tkinter window
+
+# Function used to display time on the label
+def time():
+    string = strftime("%H:%M:%S %p")
+    lbl.config(text = string)
+    lbl.after(1000, time)
+
+# Styling the label widget which displays the clock
+lbl = Label(root, font = ("arial", 160, "bold"),bg="black",fg="white")
+
+# Pack method in tkinter packs widgets into rows or columns. Positions label
+lbl.pack(anchor = "center",fill = "both",expand=1)
+
+time()  # Time function is called
+
+mainloop()   # Runs the application program
diff --git a/Hotel Recommendation System/Hotel.ipynb b/Hotel Recommendation System/Hotel.ipynb
new file mode 100644
index 0000000000..886115b98e
--- /dev/null
+++ b/Hotel Recommendation System/Hotel.ipynb	
@@ -0,0 +1,1520 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # linear algebra\n",
+    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from matplotlib import rcParams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>url</th>\n",
+       "      <th>address</th>\n",
+       "      <th>name</th>\n",
+       "      <th>online_order</th>\n",
+       "      <th>book_table</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>votes</th>\n",
+       "      <th>phone</th>\n",
+       "      <th>location</th>\n",
+       "      <th>rest_type</th>\n",
+       "      <th>dish_liked</th>\n",
+       "      <th>cuisines</th>\n",
+       "      <th>approx_cost(for two people)</th>\n",
+       "      <th>reviews_list</th>\n",
+       "      <th>menu_item</th>\n",
+       "      <th>listed_in(type)</th>\n",
+       "      <th>listed_in(city)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
+       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
+       "      <td>Jalsa</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>4.1/5</td>\n",
+       "      <td>775</td>\n",
+       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
+       "      <td>Banashankari</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
+       "      <td>North Indian, Mughlai, Chinese</td>\n",
+       "      <td>800</td>\n",
+       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
+       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
+       "      <td>Spice Elephant</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>4.1/5</td>\n",
+       "      <td>787</td>\n",
+       "      <td>080 41714161</td>\n",
+       "      <td>Banashankari</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
+       "      <td>Chinese, North Indian, Thai</td>\n",
+       "      <td>800</td>\n",
+       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
+       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
+       "      <td>San Churro Cafe</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3.8/5</td>\n",
+       "      <td>918</td>\n",
+       "      <td>+91 9663487993</td>\n",
+       "      <td>Banashankari</td>\n",
+       "      <td>Cafe, Casual Dining</td>\n",
+       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
+       "      <td>Cafe, Mexican, Italian</td>\n",
+       "      <td>800</td>\n",
+       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
+       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
+       "      <td>Addhuri Udupi Bhojana</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3.7/5</td>\n",
+       "      <td>88</td>\n",
+       "      <td>+91 9620009302</td>\n",
+       "      <td>Banashankari</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>Masala Dosa</td>\n",
+       "      <td>South Indian, North Indian</td>\n",
+       "      <td>300</td>\n",
+       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
+       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
+       "      <td>Grand Village</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3.8/5</td>\n",
+       "      <td>166</td>\n",
+       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
+       "      <td>Basavanagudi</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>Panipuri, Gol Gappe</td>\n",
+       "      <td>North Indian, Rajasthani</td>\n",
+       "      <td>600</td>\n",
+       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 url  \\\n",
+       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
+       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
+       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
+       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
+       "4  https://www.zomato.com/bangalore/grand-village...   \n",
+       "\n",
+       "                                             address                   name  \\\n",
+       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
+       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
+       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
+       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
+       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
+       "\n",
+       "  online_order book_table   rate  votes                             phone  \\\n",
+       "0          Yes        Yes  4.1/5    775    080 42297555\\r\\n+91 9743772233   \n",
+       "1          Yes         No  4.1/5    787                      080 41714161   \n",
+       "2          Yes         No  3.8/5    918                    +91 9663487993   \n",
+       "3           No         No  3.7/5     88                    +91 9620009302   \n",
+       "4           No         No  3.8/5    166  +91 8026612447\\r\\n+91 9901210005   \n",
+       "\n",
+       "       location            rest_type  \\\n",
+       "0  Banashankari        Casual Dining   \n",
+       "1  Banashankari        Casual Dining   \n",
+       "2  Banashankari  Cafe, Casual Dining   \n",
+       "3  Banashankari          Quick Bites   \n",
+       "4  Basavanagudi        Casual Dining   \n",
+       "\n",
+       "                                          dish_liked  \\\n",
+       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
+       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
+       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
+       "3                                        Masala Dosa   \n",
+       "4                                Panipuri, Gol Gappe   \n",
+       "\n",
+       "                         cuisines approx_cost(for two people)  \\\n",
+       "0  North Indian, Mughlai, Chinese                         800   \n",
+       "1     Chinese, North Indian, Thai                         800   \n",
+       "2          Cafe, Mexican, Italian                         800   \n",
+       "3      South Indian, North Indian                         300   \n",
+       "4        North Indian, Rajasthani                         600   \n",
+       "\n",
+       "                                        reviews_list menu_item  \\\n",
+       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
+       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
+       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
+       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
+       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
+       "\n",
+       "  listed_in(type) listed_in(city)  \n",
+       "0          Buffet    Banashankari  \n",
+       "1          Buffet    Banashankari  \n",
+       "2          Buffet    Banashankari  \n",
+       "3          Buffet    Banashankari  \n",
+       "4          Buffet    Banashankari  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data=pd.read_csv('zomato.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percentage null or na values in df\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "url                             0.00\n",
+       "address                         0.00\n",
+       "name                            0.00\n",
+       "online_order                    0.00\n",
+       "book_table                      0.00\n",
+       "rate                           15.03\n",
+       "votes                           0.00\n",
+       "phone                           2.34\n",
+       "location                        0.04\n",
+       "rest_type                       0.44\n",
+       "dish_liked                     54.29\n",
+       "cuisines                        0.09\n",
+       "approx_cost(for two people)     0.67\n",
+       "reviews_list                    0.00\n",
+       "menu_item                       0.00\n",
+       "listed_in(type)                 0.00\n",
+       "listed_in(city)                 0.00\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"Percentage null or na values in df\")\n",
+    "((data.isnull() | data.isna()).sum() * 100 / data.index.size).round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.rate = data.rate.replace(\"NEW\", np.nan)\n",
+    "data.dropna(how ='any', inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>name</th>\n",
+       "      <th>online_order</th>\n",
+       "      <th>book_table</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>votes</th>\n",
+       "      <th>rest_type</th>\n",
+       "      <th>dish_liked</th>\n",
+       "      <th>cuisines</th>\n",
+       "      <th>average_cost</th>\n",
+       "      <th>reviews_list</th>\n",
+       "      <th>menu_item</th>\n",
+       "      <th>restaurant_type</th>\n",
+       "      <th>locality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Jalsa</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>4.1/5</td>\n",
+       "      <td>775</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
+       "      <td>North Indian, Mughlai, Chinese</td>\n",
+       "      <td>800</td>\n",
+       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Spice Elephant</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>4.1/5</td>\n",
+       "      <td>787</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
+       "      <td>Chinese, North Indian, Thai</td>\n",
+       "      <td>800</td>\n",
+       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>San Churro Cafe</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3.8/5</td>\n",
+       "      <td>918</td>\n",
+       "      <td>Cafe, Casual Dining</td>\n",
+       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
+       "      <td>Cafe, Mexican, Italian</td>\n",
+       "      <td>800</td>\n",
+       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Addhuri Udupi Bhojana</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3.7/5</td>\n",
+       "      <td>88</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>Masala Dosa</td>\n",
+       "      <td>South Indian, North Indian</td>\n",
+       "      <td>300</td>\n",
+       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Grand Village</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3.8/5</td>\n",
+       "      <td>166</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>Panipuri, Gol Gappe</td>\n",
+       "      <td>North Indian, Rajasthani</td>\n",
+       "      <td>600</td>\n",
+       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    name online_order book_table   rate  votes  \\\n",
+       "0                  Jalsa          Yes        Yes  4.1/5    775   \n",
+       "1         Spice Elephant          Yes         No  4.1/5    787   \n",
+       "2        San Churro Cafe          Yes         No  3.8/5    918   \n",
+       "3  Addhuri Udupi Bhojana           No         No  3.7/5     88   \n",
+       "4          Grand Village           No         No  3.8/5    166   \n",
+       "\n",
+       "             rest_type                                         dish_liked  \\\n",
+       "0        Casual Dining  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
+       "1        Casual Dining  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
+       "2  Cafe, Casual Dining  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
+       "3          Quick Bites                                        Masala Dosa   \n",
+       "4        Casual Dining                                Panipuri, Gol Gappe   \n",
+       "\n",
+       "                         cuisines average_cost  \\\n",
+       "0  North Indian, Mughlai, Chinese          800   \n",
+       "1     Chinese, North Indian, Thai          800   \n",
+       "2          Cafe, Mexican, Italian          800   \n",
+       "3      South Indian, North Indian          300   \n",
+       "4        North Indian, Rajasthani          600   \n",
+       "\n",
+       "                                        reviews_list menu_item  \\\n",
+       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
+       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
+       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
+       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
+       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
+       "\n",
+       "  restaurant_type      locality  \n",
+       "0          Buffet  Banashankari  \n",
+       "1          Buffet  Banashankari  \n",
+       "2          Buffet  Banashankari  \n",
+       "3          Buffet  Banashankari  \n",
+       "4          Buffet  Banashankari  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "del data['url']\n",
+    "del data['address']\n",
+    "del data['phone']\n",
+    "del data['location']\n",
+    "data.rename(columns={'approx_cost(for two people)': 'average_cost', 'listed_in(city)': 'locality','listed_in(type)': 'restaurant_type'}, inplace=True)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>name</th>\n",
+       "      <th>online_order</th>\n",
+       "      <th>book_table</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>votes</th>\n",
+       "      <th>rest_type</th>\n",
+       "      <th>dish_liked</th>\n",
+       "      <th>cuisines</th>\n",
+       "      <th>average_cost</th>\n",
+       "      <th>reviews_list</th>\n",
+       "      <th>menu_item</th>\n",
+       "      <th>restaurant_type</th>\n",
+       "      <th>locality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Jalsa</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>775</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
+       "      <td>North Indian, Mughlai, Chinese</td>\n",
+       "      <td>800</td>\n",
+       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Spice Elephant</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>787</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
+       "      <td>Chinese, North Indian, Thai</td>\n",
+       "      <td>800</td>\n",
+       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>San Churro Cafe</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>918</td>\n",
+       "      <td>Cafe, Casual Dining</td>\n",
+       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
+       "      <td>Cafe, Mexican, Italian</td>\n",
+       "      <td>800</td>\n",
+       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Addhuri Udupi Bhojana</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>88</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>Masala Dosa</td>\n",
+       "      <td>South Indian, North Indian</td>\n",
+       "      <td>300</td>\n",
+       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Grand Village</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>166</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>Panipuri, Gol Gappe</td>\n",
+       "      <td>North Indian, Rajasthani</td>\n",
+       "      <td>600</td>\n",
+       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Buffet</td>\n",
+       "      <td>Banashankari</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    name online_order book_table  rate  votes  \\\n",
+       "0                  Jalsa          Yes        Yes   4.1    775   \n",
+       "1         Spice Elephant          Yes         No   4.1    787   \n",
+       "2        San Churro Cafe          Yes         No   3.8    918   \n",
+       "3  Addhuri Udupi Bhojana           No         No   3.7     88   \n",
+       "4          Grand Village           No         No   3.8    166   \n",
+       "\n",
+       "             rest_type                                         dish_liked  \\\n",
+       "0        Casual Dining  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
+       "1        Casual Dining  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
+       "2  Cafe, Casual Dining  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
+       "3          Quick Bites                                        Masala Dosa   \n",
+       "4        Casual Dining                                Panipuri, Gol Gappe   \n",
+       "\n",
+       "                         cuisines average_cost  \\\n",
+       "0  North Indian, Mughlai, Chinese          800   \n",
+       "1     Chinese, North Indian, Thai          800   \n",
+       "2          Cafe, Mexican, Italian          800   \n",
+       "3      South Indian, North Indian          300   \n",
+       "4        North Indian, Rajasthani          600   \n",
+       "\n",
+       "                                        reviews_list menu_item  \\\n",
+       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
+       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
+       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
+       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
+       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
+       "\n",
+       "  restaurant_type      locality  \n",
+       "0          Buffet  Banashankari  \n",
+       "1          Buffet  Banashankari  \n",
+       "2          Buffet  Banashankari  \n",
+       "3          Buffet  Banashankari  \n",
+       "4          Buffet  Banashankari  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = data\n",
+    "X.rate = X.rate.astype(str)\n",
+    "X.rate = X.rate.apply(lambda x: x.replace('/5',''))\n",
+    "X.rate = X.rate.apply(lambda x: float(x))\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'locality')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rcParams['figure.figsize'] = 15,7\n",
+    "g = sns.countplot(x=\"locality\",data=data, palette = \"Set1\")\n",
+    "g.set_xticklabels(g.get_xticklabels(), rotation=90, ha=\"right\")\n",
+    "g \n",
+    "plt.title('locality',size = 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'rest_type')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rcParams['figure.figsize'] = 15,7\n",
+    "g = sns.countplot(x=\"rest_type\",data=data, palette = \"Set1\")\n",
+    "g.set_xticklabels(g.get_xticklabels(), rotation=90, ha=\"right\")\n",
+    "g \n",
+    "plt.title('rest_type',size = 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams['figure.figsize'] = (3, 4)\n",
+    "plt.style.use('_classic_test')\n",
+    "\n",
+    "X['online_order'].value_counts().plot.bar(color = 'cyan')\n",
+    "plt.title('Online orders', fontsize = 20)\n",
+    "plt.ylabel('Number of orders', fontsize = 15)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams['figure.figsize'] = (7, 9)\n",
+    "plt.style.use('_classic_test')\n",
+    "\n",
+    "X['book_table'].value_counts().plot.bar(color = 'cyan')\n",
+    "plt.title('Table booking', fontsize = 20)\n",
+    "plt.ylabel('Number of bookings', fontsize = 15)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Shree\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5303: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self[name] = value\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Cost Distrubution for all restaurants')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1280x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X.head()\n",
+    "X.average_cost = X.average_cost.apply(lambda x: x.replace(',',''))\n",
+    "X.average_cost = X.average_cost.astype(int)\n",
+    "fig, ax = plt.subplots(figsize=[16,4])\n",
+    "sns.distplot(X['average_cost'],ax=ax)\n",
+    "ax.set_title('Cost Distrubution for all restaurants')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X= X.drop_duplicates(subset='name',keep='first')\n",
+    "# dups_name = X1.pivot_table(index=['name'],aggfunc='size')\n",
+    "newdf=X[['name','average_cost','locality','rest_type','cuisines']].groupby(['average_cost'], sort = True)\n",
+    "newdf=newdf.filter(lambda x: x.mean() <= 1500)\n",
+    "newdf=newdf.sort_values(by=['average_cost'])\n",
+    "\n",
+    "newdf_expensive=X[['name','average_cost','locality','rest_type','cuisines']].groupby(['average_cost'], sort = True)\n",
+    "newdf_expensive=newdf_expensive.filter(lambda x: x.mean() >= 3000)\n",
+    "newdf_expensive=newdf_expensive.sort_values(by=['average_cost'])\n",
+    "# newdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name               3142\n",
+       "online_order          2\n",
+       "book_table            2\n",
+       "rate                 31\n",
+       "votes               965\n",
+       "rest_type            70\n",
+       "dish_liked         2903\n",
+       "cuisines           1475\n",
+       "average_cost         56\n",
+       "reviews_list       3107\n",
+       "menu_item           692\n",
+       "restaurant_type       7\n",
+       "locality             30\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "newdf_rate=X[['name','rate']].groupby(['rate'], sort = True)\n",
+    "newdf_rate=newdf_rate.filter(lambda x: x.mean() >= 4.5)\n",
+    "newdf_rate=newdf_rate.sort_values(by=['rate'])\n",
+    "X.rate.value_counts()\n",
+    "X.rate.unique()\n",
+    "X.nunique()\n",
+    "# newdf_rate\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cheap restaurants with low cost,high rating \n",
+      "\n"
+     ]
+    },
+    {
+     "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>name</th>\n",
+       "      <th>average_cost</th>\n",
+       "      <th>locality</th>\n",
+       "      <th>rest_type</th>\n",
+       "      <th>cuisines</th>\n",
+       "      <th>rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brahmin's Coffee Bar</td>\n",
+       "      <td>100</td>\n",
+       "      <td>Basavanagudi</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>South Indian</td>\n",
+       "      <td>4.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Taaza Thindi</td>\n",
+       "      <td>100</td>\n",
+       "      <td>Banashankari</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>South Indian</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CTR</td>\n",
+       "      <td>150</td>\n",
+       "      <td>Malleshwaram</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>South Indian</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Chikkanna Tiffin Room</td>\n",
+       "      <td>150</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>South Indian</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Veena Stores</td>\n",
+       "      <td>150</td>\n",
+       "      <td>Malleshwaram</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>South Indian</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>Opus Food Stories</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>Bellandur</td>\n",
+       "      <td>Bar, Casual Dining</td>\n",
+       "      <td>Goan, Asian, North Indian, European, Continental</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>Chianti</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>Italian</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>Sarjapur Social</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>Bellandur</td>\n",
+       "      <td>Bar, Casual Dining</td>\n",
+       "      <td>American, North Indian, Chinese, Finger Food</td>\n",
+       "      <td>4.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>Toast &amp; Tonic</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>European, Asian</td>\n",
+       "      <td>4.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>Communiti</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Microbrewery, Casual Dining</td>\n",
+       "      <td>Continental, BBQ, Salad</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>91 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     name  average_cost      locality  \\\n",
+       "0    Brahmin's Coffee Bar           100  Basavanagudi   \n",
+       "1            Taaza Thindi           100  Banashankari   \n",
+       "2                     CTR           150  Malleshwaram   \n",
+       "3   Chikkanna Tiffin Room           150  Brigade Road   \n",
+       "4            Veena Stores           150  Malleshwaram   \n",
+       "..                    ...           ...           ...   \n",
+       "86      Opus Food Stories          1500     Bellandur   \n",
+       "87                Chianti          1500  Brigade Road   \n",
+       "88        Sarjapur Social          1500     Bellandur   \n",
+       "89          Toast & Tonic          1500  Brigade Road   \n",
+       "90              Communiti          1500  Brigade Road   \n",
+       "\n",
+       "                      rest_type  \\\n",
+       "0                   Quick Bites   \n",
+       "1                   Quick Bites   \n",
+       "2                   Quick Bites   \n",
+       "3                   Quick Bites   \n",
+       "4                   Quick Bites   \n",
+       "..                          ...   \n",
+       "86           Bar, Casual Dining   \n",
+       "87                Casual Dining   \n",
+       "88           Bar, Casual Dining   \n",
+       "89                Casual Dining   \n",
+       "90  Microbrewery, Casual Dining   \n",
+       "\n",
+       "                                            cuisines  rate  \n",
+       "0                                       South Indian   4.8  \n",
+       "1                                       South Indian   4.7  \n",
+       "2                                       South Indian   4.7  \n",
+       "3                                       South Indian   4.5  \n",
+       "4                                       South Indian   4.5  \n",
+       "..                                               ...   ...  \n",
+       "86  Goan, Asian, North Indian, European, Continental   4.7  \n",
+       "87                                           Italian   4.5  \n",
+       "88      American, North Indian, Chinese, Finger Food   4.6  \n",
+       "89                                   European, Asian   4.6  \n",
+       "90                           Continental, BBQ, Salad   4.7  \n",
+       "\n",
+       "[91 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s1 = pd.merge(newdf, newdf_rate, how='inner', on=['name'])\n",
+    "\n",
+    "s2= pd.merge(newdf_expensive, newdf_rate, how='inner', on=['name'])\n",
+    "\n",
+    "print(\"Cheap restaurants with low cost,high rating \\n\")\n",
+    "s1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expensive restaurants with high cost,high rating \n",
+      "\n"
+     ]
+    },
+    {
+     "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>name</th>\n",
+       "      <th>average_cost</th>\n",
+       "      <th>locality</th>\n",
+       "      <th>rest_type</th>\n",
+       "      <th>cuisines</th>\n",
+       "      <th>rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Rim Naam - The Oberoi</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Fine Dining</td>\n",
+       "      <td>Thai</td>\n",
+       "      <td>4.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Karavalli - The Gateway Hotel</td>\n",
+       "      <td>3500</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Fine Dining</td>\n",
+       "      <td>Mangalorean, Konkan, Seafood, Kerala</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alba - JW Marriott Bengaluru</td>\n",
+       "      <td>4000</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Fine Dining</td>\n",
+       "      <td>Italian</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            name  average_cost      locality    rest_type  \\\n",
+       "0          Rim Naam - The Oberoi          3000  Brigade Road  Fine Dining   \n",
+       "1  Karavalli - The Gateway Hotel          3500  Brigade Road  Fine Dining   \n",
+       "2   Alba - JW Marriott Bengaluru          4000  Brigade Road  Fine Dining   \n",
+       "\n",
+       "                               cuisines  rate  \n",
+       "0                                  Thai   4.6  \n",
+       "1  Mangalorean, Konkan, Seafood, Kerala   4.5  \n",
+       "2                               Italian   4.5  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"Expensive restaurants with high cost,high rating \\n\")\n",
+    "s2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "newdf_votes=X[['name','votes']].groupby(['votes'], sort = True)\n",
+    "newdf_votes=newdf_votes.filter(lambda x: x.mean() >= 175)\n",
+    "newdf_votes=newdf_votes.sort_values(by=['votes'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cheap restaurants,high rating,high votes\n"
+     ]
+    },
+    {
+     "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>name</th>\n",
+       "      <th>average_cost</th>\n",
+       "      <th>locality</th>\n",
+       "      <th>rest_type</th>\n",
+       "      <th>cuisines</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>votes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brahmin's Coffee Bar</td>\n",
+       "      <td>100</td>\n",
+       "      <td>Basavanagudi</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>South Indian</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>2679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Taaza Thindi</td>\n",
+       "      <td>100</td>\n",
+       "      <td>Banashankari</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>South Indian</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CTR</td>\n",
+       "      <td>150</td>\n",
+       "      <td>Malleshwaram</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>South Indian</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>4408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Chikkanna Tiffin Room</td>\n",
+       "      <td>150</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>South Indian</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Veena Stores</td>\n",
+       "      <td>150</td>\n",
+       "      <td>Malleshwaram</td>\n",
+       "      <td>Quick Bites</td>\n",
+       "      <td>South Indian</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>2407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>Toit</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>Indiranagar</td>\n",
+       "      <td>Microbrewery</td>\n",
+       "      <td>Italian, American, Pizza</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>14956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>Levitate Brewery and Kitchen</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>Jayanagar</td>\n",
+       "      <td>Microbrewery, Casual Dining</td>\n",
+       "      <td>Finger Food, North Indian, Continental</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>Toast &amp; Tonic</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Casual Dining</td>\n",
+       "      <td>European, Asian</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>1082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>Asia Kitchen By Mainland China</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>BTM</td>\n",
+       "      <td>Casual Dining, Bar</td>\n",
+       "      <td>Asian, Chinese, Thai, Momos</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>2178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>Communiti</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Microbrewery, Casual Dining</td>\n",
+       "      <td>Continental, BBQ, Salad</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3894</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>82 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              name  average_cost      locality  \\\n",
+       "0             Brahmin's Coffee Bar           100  Basavanagudi   \n",
+       "1                     Taaza Thindi           100  Banashankari   \n",
+       "2                              CTR           150  Malleshwaram   \n",
+       "3            Chikkanna Tiffin Room           150  Brigade Road   \n",
+       "4                     Veena Stores           150  Malleshwaram   \n",
+       "..                             ...           ...           ...   \n",
+       "68                            Toit          1500   Indiranagar   \n",
+       "67    Levitate Brewery and Kitchen          1500     Jayanagar   \n",
+       "80                   Toast & Tonic          1500  Brigade Road   \n",
+       "72  Asia Kitchen By Mainland China          1500           BTM   \n",
+       "81                       Communiti          1500  Brigade Road   \n",
+       "\n",
+       "                      rest_type                                cuisines  rate  \\\n",
+       "0                   Quick Bites                            South Indian   4.8   \n",
+       "1                   Quick Bites                            South Indian   4.7   \n",
+       "2                   Quick Bites                            South Indian   4.7   \n",
+       "3                   Quick Bites                            South Indian   4.5   \n",
+       "4                   Quick Bites                            South Indian   4.5   \n",
+       "..                          ...                                     ...   ...   \n",
+       "68                 Microbrewery                Italian, American, Pizza   4.7   \n",
+       "67  Microbrewery, Casual Dining  Finger Food, North Indian, Continental   4.5   \n",
+       "80                Casual Dining                         European, Asian   4.6   \n",
+       "72           Casual Dining, Bar             Asian, Chinese, Thai, Momos   4.9   \n",
+       "81  Microbrewery, Casual Dining                 Continental, BBQ, Salad   4.7   \n",
+       "\n",
+       "    votes  \n",
+       "0    2679  \n",
+       "1     651  \n",
+       "2    4408  \n",
+       "3     203  \n",
+       "4    2407  \n",
+       "..    ...  \n",
+       "68  14956  \n",
+       "67    456  \n",
+       "80   1082  \n",
+       "72   2178  \n",
+       "81   3894  \n",
+       "\n",
+       "[82 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = pd.merge(s1, newdf_votes, how='inner', on=['name'])\n",
+    "s=s.sort_values(by=['average_cost'])\n",
+    "print(\"Cheap restaurants,high rating,high votes\")\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>name</th>\n",
+       "      <th>average_cost</th>\n",
+       "      <th>locality</th>\n",
+       "      <th>rest_type</th>\n",
+       "      <th>cuisines</th>\n",
+       "      <th>rate</th>\n",
+       "      <th>votes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Rim Naam - The Oberoi</td>\n",
+       "      <td>3000</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Fine Dining</td>\n",
+       "      <td>Thai</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Karavalli - The Gateway Hotel</td>\n",
+       "      <td>3500</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Fine Dining</td>\n",
+       "      <td>Mangalorean, Konkan, Seafood, Kerala</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alba - JW Marriott Bengaluru</td>\n",
+       "      <td>4000</td>\n",
+       "      <td>Brigade Road</td>\n",
+       "      <td>Fine Dining</td>\n",
+       "      <td>Italian</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>583</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            name  average_cost      locality    rest_type  \\\n",
+       "0          Rim Naam - The Oberoi          3000  Brigade Road  Fine Dining   \n",
+       "1  Karavalli - The Gateway Hotel          3500  Brigade Road  Fine Dining   \n",
+       "2   Alba - JW Marriott Bengaluru          4000  Brigade Road  Fine Dining   \n",
+       "\n",
+       "                               cuisines  rate  votes  \n",
+       "0                                  Thai   4.6    979  \n",
+       "1  Mangalorean, Konkan, Seafood, Kerala   4.5    674  \n",
+       "2                               Italian   4.5    583  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = pd.merge(s2, newdf_votes, how='inner', on=['name'])\n",
+    "s=s.sort_values(by=['average_cost'])\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# No surprises there!!\n",
+    "\n",
+    "The Oberoi Hotel, Karavalli and JW Marriott make this high profile list\n",
+    "\n",
+    "Interestingly, all these restaurants have the same location- Brigade Road and same restaurant type- Fine dining"
+   ]
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/IP/ip.py b/IP/ip.py
new file mode 100644
index 0000000000..e04c69f71f
--- /dev/null
+++ b/IP/ip.py
@@ -0,0 +1,6 @@
+# Python Program to Get IP Address 
+import socket 
+hostname = socket.gethostname() 
+IPAddr = socket.gethostbyname(hostname) 
+print("Your Computer Name is:" + hostname) 
+print("Your Computer IP Address is:" + IPAddr) 
diff --git a/SmileEmoji/Emoji.ipynb b/SmileEmoji/Emoji.ipynb
new file mode 100644
index 0000000000..1e9afd253b
--- /dev/null
+++ b/SmileEmoji/Emoji.ipynb
@@ -0,0 +1,87 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import turtle\n",
+    "\n",
+    "\n",
+    "pen = turtle.Turtle()\n",
+    "\n",
+    "\n",
+    "def eye(col, rad):\n",
+    "\tpen.down()\n",
+    "\tpen.fillcolor(col)\n",
+    "\tpen.begin_fill()\n",
+    "\tpen.circle(rad)\n",
+    "\tpen.end_fill()\n",
+    "\tpen.up()\n",
+    "\n",
+    "pen.fillcolor('yellow')\n",
+    "pen.begin_fill()\n",
+    "pen.circle(100)\n",
+    "pen.end_fill()\n",
+    "pen.up()\n",
+    "\n",
+    "pen.goto(-40, 120)\n",
+    "eye('white', 15)\n",
+    "pen.goto(-37, 125)\n",
+    "eye('black', 5)\n",
+    "pen.goto(40, 120)\n",
+    "eye('white', 15)\n",
+    "pen.goto(40, 125)\n",
+    "eye('black', 5)\n",
+    "\n",
+    "pen.goto(0, 75)\n",
+    "eye('black', 8)\n",
+    "\n",
+    "pen.goto(-40, 85)\n",
+    "pen.down()\n",
+    "pen.right(90)\n",
+    "pen.circle(40, 180)\n",
+    "pen.up()\n",
+    "\n",
+    "pen.goto(-10, 45)\n",
+    "pen.down()\n",
+    "pen.right(180)\n",
+    "pen.fillcolor('red')\n",
+    "pen.begin_fill()\n",
+    "pen.circle(10, 180)\n",
+    "pen.end_fill()\n",
+    "pen.hideturtle()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/TypingSpeed/TypingSpeedTest.ipynb b/TypingSpeed/TypingSpeedTest.ipynb
new file mode 100644
index 0000000000..777cafe923
--- /dev/null
+++ b/TypingSpeed/TypingSpeedTest.ipynb
@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8.1866481\n",
+      "Wrong Input\n",
+      "11.4879198\n"
+     ]
+    }
+   ],
+   "source": [
+    "# importing all libraries \n",
+    "from tkinter import *\n",
+    "from timeit import default_timer as timer \n",
+    "import random \n",
+    "\n",
+    "# creating window using gui \n",
+    "window = Tk() \n",
+    "\n",
+    "# the size of the window is defined \n",
+    "window.geometry(\"450x200\") \n",
+    "\n",
+    "x = 0\n",
+    "\n",
+    "# defining the function for the test \n",
+    "def game(): \n",
+    "\tglobal x \n",
+    "\n",
+    "\t# loop for destroying the window \n",
+    "\t# after on test \n",
+    "\tif x == 0: \n",
+    "\t\twindow.destroy() \n",
+    "\t\tx = x+1\n",
+    "\n",
+    "\t# defining function for results of test \n",
+    "\tdef check_result(): \n",
+    "\t\tif entry.get() == words[word]: \n",
+    "\n",
+    "\t\t\t# here start time is when the window \n",
+    "\t\t\t# is opened and end time is when \n",
+    "\t\t\t# window is destroyed \n",
+    "\t\t\tend = timer() \n",
+    "\n",
+    "\t\t\t# we deduct the start time from end \n",
+    "\t\t\t# time and calculate results using \n",
+    "\t\t\t# timeit function \n",
+    "\t\t\tprint(end-start) \n",
+    "\t\telse: \n",
+    "\t\t\tprint(\"Wrong Input\") \n",
+    "\n",
+    "\twords = ['programming', 'coding', 'algorithm', \n",
+    "\t\t\t'systems', 'python', 'software'] \n",
+    "\n",
+    "\t# Give random words for testing the speed of user \n",
+    "\tword = random.randint(0, (len(words)-1)) \n",
+    "\n",
+    "\t# start timer using timeit function \n",
+    "\tstart = timer() \n",
+    "\twindows = Tk() \n",
+    "\twindows.geometry(\"450x200\") \n",
+    "\n",
+    "\t# use lable method of tkinter for labling in window \n",
+    "\tx2 = Label(windows, text=words[word], font=\"times 20\") \n",
+    "\n",
+    "\t# place of labling in window \n",
+    "\tx2.place(x=150, y=10) \n",
+    "\tx3 = Label(windows, text=\"Start Typing\", font=\"times 20\") \n",
+    "\tx3.place(x=10, y=50) \n",
+    "\n",
+    "\tentry = Entry(windows) \n",
+    "\tentry.place(x=280, y=55) \n",
+    "\n",
+    "\t# buttons to submit output and check results \n",
+    "\tb2 = Button(windows, text=\"Done\", \n",
+    "\t\t\t\tcommand=check_result, width=12, bg='grey') \n",
+    "\tb2.place(x=150, y=100) \n",
+    "\n",
+    "\tb3 = Button(windows, text=\"Try Again\", \n",
+    "\t\t\t\tcommand=game, width=12, bg='grey') \n",
+    "\tb3.place(x=250, y=100) \n",
+    "\twindows.mainloop() \n",
+    "\n",
+    "\n",
+    "x1 = Label(window, text=\"Lets start playing..\", font=\"times 20\") \n",
+    "x1.place(x=10, y=50) \n",
+    "\n",
+    "b1 = Button(window, text=\"Go\", command=game, width=12, bg='grey') \n",
+    "b1.place(x=150, y=100) \n",
+    "\n",
+    "# calling window \n",
+    "window.mainloop() \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}