Zainab Munir

ZOMATO COMPANY DATA ANALYSIS PROJECT : import import import import pandas as pd numpy as np matplotlib.pyplot as plt seaborn as sns # create the dataframe df= pd.read_csv("/content/Zomato data .csv") df.head() {"summary":"{\n \"name\": \"df\",\n \"rows\": 148,\n \"fields\": [\ n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 145,\n \"samples\": [\n \"The Biryani Cafe\",\n \"Melting Melodies\",\n \"Cuppa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"online_order\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"book_table\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"rate\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 20,\n \"samples\": [\n \"4.1/5\",\n \"2.6/5\"\n ],\ n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"votes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 653,\n \"min\": 0,\n \"max\": 4884,\n \"num_unique_values\": 90,\n \"samples\": [\n 244,\n 31\n ],\ n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"approx_cost(for two people)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 223,\n \"min\": 100,\n \"max\": 950,\n \"num_unique_values\": 18,\n \"samples\": [\n 800,\n 300\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"listed_in(type)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Cafes\",\n \"Dining\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n }\n ]\n}","type":"dataframe","variable_name":"df"} df {"summary":"{\n \"name\": \"df\",\n \"rows\": 148,\n \"fields\": [\ n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 145,\n \"samples\": [\n \"The Biryani Cafe\",\n \"Melting Melodies\",\n \"Cuppa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"online_order\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"book_table\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"rate\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 20,\n \"samples\": [\n \"4.1/5\",\n \"2.6/5\"\n ],\ n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"votes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 653,\n \"min\": 0,\n \"max\": 4884,\n \"num_unique_values\": 90,\n \"samples\": [\n 244,\n 31\n ],\ n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"approx_cost(for two people)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 223,\n \"min\": 100,\n \"max\": 950,\n \"num_unique_values\": 18,\n \"samples\": [\n 800,\n 300\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"listed_in(type)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Cafes\",\n \"Dining\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n }\n ]\n}","type":"dataframe","variable_name":"df"} DATA CLEANING : def HandleRate(value): value= str(value).split('/') value=value[0]; return float(value) df["rate"]= df["rate"].apply(HandleRate) df.head() {"summary":"{\n \"name\": \"df\",\n \"rows\": 148,\n \"fields\": [\ n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 145,\n \"samples\": [\n \"The Biryani Cafe\",\n \"Melting Melodies\",\n \"Cuppa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"online_order\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"book_table\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\":-,\n \"min\": 2.6,\n \"max\": 4.6,\n \"num_unique_values\": 19,\n \"samples\": [\n 4.1,\n 4.0\ n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"votes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 653,\n \"min\": 0,\n \"max\": 4884,\n \"num_unique_values\": 90,\n \"samples\": [\n 244,\n 31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"approx_cost(for two people)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 223,\n \"min\": 100,\n \"max\": 950,\n \"num_unique_values\": 18,\n \"samples\": [\n 800,\n 300\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"listed_in(type)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Cafes\",\n \"Dining\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n }\n ]\n}","type":"dataframe","variable_name":"df"} SUMMARY OF DATASET : df.info() RangeIndex: 148 entries, 0 to 147 Data columns (total 7 columns): # Column Non-Null Count --- ------------------0 name 148 non-null 1 online_order 148 non-null 2 book_table 148 non-null 3 rate 148 non-null Dtype ----object object object float64 4 votes 148 non-null 5 approx_cost(for two people) 148 non-null 6 listed_in(type) 148 non-null dtypes: float64(1), int64(2), object(4) memory usage: 8.2+ KB int64 int64 object TYPE OF RESTAURANT : df.head() {"summary":"{\n \"name\": \"df\",\n \"rows\": 148,\n \"fields\": [\ n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 145,\n \"samples\": [\n \"The Biryani Cafe\",\n \"Melting Melodies\",\n \"Cuppa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"online_order\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"book_table\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\":-,\n \"min\": 2.6,\n \"max\": 4.6,\n \"num_unique_values\": 19,\n \"samples\": [\n 4.1,\n 4.0\ n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"votes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 653,\n \"min\": 0,\n \"max\": 4884,\n \"num_unique_values\": 90,\n \"samples\": [\n 244,\n 31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"approx_cost(for two people)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 223,\n \"min\": 100,\n \"max\": 950,\n \"num_unique_values\": 18,\n \"samples\": [\n 800,\n 300\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"listed_in(type)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Cafes\",\n \"Dining\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n }\n ]\n}","type":"dataframe","variable_name":"df"} sns.countplot(x=df["listed_in(type)"]) plt.xlabel("Type Of Restaurant") Text(0.5, 0, 'Type Of Restaurant') # CONCLUSION: # The majority of the customers falls inte the Dining Category,Dining Restaurants are preferred by a larger number of individuals. VOTES RECEIVED FROM CUSTOMERS : df.head() {"summary":"{\n \"name\": \"df\",\n \"rows\": 148,\n \"fields\": [\ n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 145,\n \"samples\": [\n \"The Biryani Cafe\",\n \"Melting Melodies\",\n \"Cuppa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"online_order\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"book_table\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\":-,\n \"min\": 2.6,\n \"max\": 4.6,\n \"num_unique_values\": 19,\n \"samples\": [\n 4.1,\n 4.0\ n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"votes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 653,\n \"min\": 0,\n \"max\": 4884,\n \"num_unique_values\": 90,\n \"samples\": [\n 244,\n 31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"approx_cost(for two people)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 223,\n \"min\": 100,\n \"max\": 950,\n \"num_unique_values\": 18,\n \"samples\": [\n 800,\n 300\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"listed_in(type)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Cafes\",\n \"Dining\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n }\n ]\n}","type":"dataframe","variable_name":"df"} grouped_data= df.groupby("listed_in(type)")["votes"].sum() result= pd.DataFrame({'votes': grouped_data}) plt.plot(result, c='green', marker="o") plt.xlabel("Type of Restaurant",c="red", size=20) plt.ylabel("Votes",c="red",size=20) plt.show() # CONCLUSION : # Dining restaurants has received maximum Votes## MAJORITY RESTAURANTS RECEIVED RATINGS : df.head() {"summary":"{\n \"name\": \"df\",\n \"rows\": 148,\n \"fields\": [\ n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 145,\n \"samples\": [\n \"The Biryani Cafe\",\n \"Melting Melodies\",\n \"Cuppa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"online_order\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"book_table\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\":-,\n \"min\": 2.6,\n \"max\": 4.6,\n \"num_unique_values\": 19,\n \"samples\": [\n 4.1,\n 4.0\ n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"votes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 653,\n \"min\": 0,\n \"max\": 4884,\n \"num_unique_values\": 90,\n \"samples\": [\n 244,\n 31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"approx_cost(for two people)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 223,\n \"min\": 100,\n \"max\": 950,\n \"num_unique_values\": 18,\n \"samples\": [\n 800,\n 300\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"listed_in(type)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Cafes\",\n \"Dining\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n }\n ]\n}","type":"dataframe","variable_name":"df"} plt.hist(df["rate"],bins=5) plt.title("Rating Distribution") plt.show() # Conclusion: # The Majority Restaurants received Ratings from 3.5 to 4.00 AVERAGE SPENDING FROM COUPLES ON EACH ONLINE ORDER : df.head() {"summary":"{\n \"name\": \"df\",\n \"rows\": 148,\n \"fields\": [\ n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 145,\n \"samples\": [\n \"The Biryani Cafe\",\n \"Melting Melodies\",\n \"Cuppa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"online_order\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"book_table\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\":-,\n \"min\": 2.6,\n \"max\": 4.6,\n \"num_unique_values\": 19,\n \"samples\": [\n 4.1,\n 4.0\ n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"votes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 653,\n \"min\": 0,\n \"max\": 4884,\n \"num_unique_values\": 90,\n \"samples\": [\n 244,\n 31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"approx_cost(for two people)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 223,\n \"min\": 100,\n \"max\": 950,\n \"num_unique_values\": 18,\n \"samples\": [\n 800,\n 300\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"listed_in(type)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Cafes\",\n \"Dining\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n }\n ]\n}","type":"dataframe","variable_name":"df"} couple_data=df["approx_cost(for two people)"] sns.countplot(x=couple_data) # CONCLUSION: # The majority of Couples prefer Restaurants with approximate cost of 300 rupees WHICH MODE(ONLINE/OFFLINE) HAS RECEIVED THE MAXIMUM RATINGS : df.head() {"summary":"{\n \"name\": \"df\",\n \"rows\": 148,\n \"fields\": [\ n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 145,\n \"samples\": [\n \"The Biryani Cafe\",\n \"Melting Melodies\",\n \"Cuppa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"online_order\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"book_table\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\":-,\n \"min\": 2.6,\n \"max\": 4.6,\n \"num_unique_values\": 19,\n \"samples\": [\n 4.1,\n 4.0\ n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"votes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 653,\n \"min\": 0,\n \"max\": 4884,\n \"num_unique_values\": 90,\n \"samples\": [\n 244,\n 31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"approx_cost(for two people)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 223,\n \"min\": 100,\n \"max\": 950,\n \"num_unique_values\": 18,\n \"samples\": [\n 800,\n 300\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"listed_in(type)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Cafes\",\n \"Dining\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n }\n ]\n}","type":"dataframe","variable_name":"df"} plt.figure(figsize=(6,6)) sns.boxplot(x=df["online_order"],y=df["rate"]) # CONCLUSION : # ONLINE ORDERS RECEIVE HIGHER RATINGS THEN OFFLINE ORDERS WHICH TYPE OF RESTAURANTS RECEIVE MORE OFFLINE ORDERS ? : df.head() {"summary":"{\n \"name\": \"df\",\n \"rows\": 148,\n \"fields\": [\ n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 145,\n \"samples\": [\n \"The Biryani Cafe\",\n \"Melting Melodies\",\n \"Cuppa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"online_order\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"book_table\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No\",\n \"Yes\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\":-,\n \"min\": 2.6,\n \"max\": 4.6,\n \"num_unique_values\": 19,\n \"samples\": [\n 4.1,\n 4.0\ n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"votes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 653,\n \"min\": 0,\n \"max\": 4884,\n \"num_unique_values\": 90,\n \"samples\": [\n 244,\n 31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"approx_cost(for two people)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 223,\n \"min\": 100,\n \"max\": 950,\n \"num_unique_values\": 18,\n \"samples\": [\n 800,\n 300\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n },\n {\n \"column\": \"listed_in(type)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Cafes\",\n \"Dining\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\ n }\n ]\n}","type":"dataframe","variable_name":"df"} pivot_table= df.pivot_table(index="listed_in(type)", columns="online_order", aggfunc="size", fill_value=0) sns.heatmap(pivot_table, annot=True, cmap="YlGnBu", fmt='d') plt.title("HeatMap") plt.xlabel("Online Order") plt.ylabel("listed_in(type)") plt.show() # CONCLUSION: # DINING RESTAURANTS PRIMARILY RECEIVE OFFLINE ORDERS, WHEREAS CAFES PRIMARILY RECEIVE ONLINE ORDERS,THIS SUGGESTS THAT CLIENTS PREFER TO PLACE ORDERS IN PERSON AT RESTAURANTS,BUT PREFER ONLINE ORDERING AT CAFES