<jupyter_start><jupyter_text>Restaurant Data AnalysisThis is an Indian restaurant data set founded in 2010. This aggregator provides information, menus and user-reviews of restaurants as well as food delivery options from partner restaurants in select cities. Problem StatementToday, we are going to analyze the restaurant business data. We want to study the food habbits of users, their preferances, ratings etc.  Data description:Every Restaurant contains the following variables:• **Restaurant Id**: Unique id of every restaurant • **Restaurant Name**: Restaurant Name • **Country Code**: Countery of a restaurant • **City**: City of a  restaurant • **Address**: Address • **Locality**: City Location • **Locality Verbose**: Locality description• **Longitude**: Longitude coordinates • **Latitude**: Latitude coordinates • **Cuisines**: Cuisines by restaurant• **Average Cost for two**: Cost for two people  • **Currency**: Country Currency • **Has Table booking**: y/n• **Has Online delivery**: y/ n• **Is delivering**: y/n• **Switch to order menu**: y/n• **Price range**: Food price range • **Aggregate Rating**: out of 5 rating• **Rating color**: rating color based on rating• **Rating text**: text on the basis of rating• **Votes**: Number of ratings casted by customers **There is another dataset which has the country codes in it**  Import Libraries<jupyter_code>import os
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline<jupyter_output><empty_output><jupyter_text>Read the data and merge the two different datasets<jupyter_code>df = pd.read_csv('../data/FoodAgg.csv',encoding="ISO-8859-1")
df.head(10)

#Typical FIle Handling Sequence 



# country = pd.read_excel('../data/Country-Code.xlsx')

# data = pd.merge(df, country, on='Country Code')<jupyter_output><empty_output><jupyter_text>Basic Data Study<jupyter_code>#info

#describe about data

#Find Null Values Count<jupyter_output><empty_output><jupyter_text>Number of Restaurants by Country on FoodAggregator - Count<jupyter_code># No of restaurant that are on foodagrregator in each country<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10682768230376219, 'fraction_numerical': 0.00557361820715281, 'mean_word_length': 6.062295081967213, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '609290', 'license': 'MIT', 'n_tokens_mistral': 616, 'n_tokens_neox': 594, 'n_words': 256, 'path': 'Day-4/notebook/Restaurant_data_analysis-solution.ipynb', 'repo_name': 'BahugunaP/SKillpy-PythonForDataScience-BeginnerLevel', 'size': '2153'}