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YELP_DATASET_SQL_LOOKUP.txt
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YELP_DATASET_SQL_LOOKUP.txt
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Data Scientist Role Play: Profiling and Analyzing the Yelp Dataset Coursera Worksheet
This is a 2-part assignment. In the first part, you are asked a series of questions that will help you profile and understand the data just like a data scientist would. For this first part of the assignment, you will be assessed both on the correctness of your findings, as well as the code you used to arrive at your answer. You will be graded on how easy your code is to read, so remember to use proper formatting and comments where necessary.
In the second part of the assignment, you are asked to come up with your own inferences and analysis of the data for a particular research question you want to answer. You will be required to prepare the dataset for the analysis you choose to do. As with the first part, you will be graded, in part, on how easy your code is to read, so use proper formatting and comments to illustrate and communicate your intent as required.
For both parts of this assignment, use this "worksheet." It provides all the questions you are being asked, and your job will be to transfer your answers and SQL coding where indicated into this worksheet so that your peers can review your work. You should be able to use any Text Editor (Windows Notepad, Apple TextEdit, Notepad ++, Sublime Text, etc.) to copy and paste your answers. If you are going to use Word or some other page layout application, just be careful to make sure your answers and code are lined appropriately.
In this case, you may want to save as a PDF to ensure your formatting remains intact for you reviewer.
Part 1: Yelp Dataset Profiling and Understanding
1. Profile the data by finding the total number of records for each of the tables below:
SELECT COUNT(*) as total
FROM Attribute;
SELECT COUNT(*) as total
FROM Business;
SELECT COUNT(*) as total
FROM Category;
SELECT COUNT(*) as total
FROM Checkin;
SELECT COUNT(*) as total
FROM elite_years;
SELECT COUNT(*) as total
FROM friend;
SELECT COUNT(*) as total
FROM hours;
SELECT COUNT(*) as total
FROM photo;
SELECT COUNT(*) as total
FROM review;
SELECT COUNT(*) as total
FROM tip;
SELECT COUNT(*) as total
FROM user;
i. Attribute table = 10000
ii. Business table = 10000
iii. Category table = 10000
iv. Checkin table = 10000
v. elite_years table = 10000
vi. friend table = 10000
vii. hours table = 10000
viii. photo table = 10000
ix. review table = 10000
x. tip table = 10000
xi. user table = 10000
2. Find the total number of distinct records for each of the keys listed below:
SELECT COUNT(DISTINCT(key))
FROM Business;
SELECT COUNT(DISTINCT(key))
FROM Business;
SELECT COUNT(DISTINCT(key))
FROM Hours;
SELECT COUNT(DISTINCT(key))
FROM Category;
SELECT COUNT(DISTINCT(key))
FROM Review;
SELECT COUNT(DISTINCT(key))
FROM Checkin;
SELECT COUNT(DISTINCT(key))
FROM Photo;
SELECT COUNT(DISTINCT(key))
FROM Tip;
SELECT COUNT(DISTINCT(key))
FROM User;
SELECT COUNT(DISTINCT(key))
FROM Friend;
SELECT COUNT(DISTINCT(key))
FROM Elite_years;
i. Business = id: 10000
ii. Hours = business_id: 1562
iii. Category = business_id: 2643
iv. Attribute = business_id: 1115
v. Review = id:10000, business_id: 8090, user_id: 9581
vi. Checkin = business_id: 493
vii. Photo = id: 10000, business_id: 6493
viii. Tip = user_id: 537, business_id: 3979
ix. User = id: 10000
x. Friend = user_id: 11
xi. Elite_years = user_id: 2780
3. Are there any columns with null values in the Users table? Indicate "yes," or "no."
SQL code used to arrive at answer:
SELECT COUNT(*)
FROM user
WHERE id IS NULL OR
name IS NULL OR
review_count IS NULL OR
yelping_since IS NULL OR
useful IS NULL OR
funny IS NULL OR
cool IS NULL OR
fans IS NULL OR
average_stars IS NULL OR
compliment_hot IS NULL OR
compliment_more IS NULL OR
compliment_profile IS NULL OR
compliment_cute IS NULL OR
compliment_list IS NULL OR
compliment_note IS NULL OR
compliment_plain IS NULL OR
compliment_cool IS NULL OR
compliment_funny IS NULL OR
compliment_writer IS NULL OR
compliment_photos IS NULL
Answer: "no"
4. Find the minimum, maximum, and average value for the following fields:
SELECT MIN (Stars), MAX (Stars), AVG(Stars)
FROM Review;
SELECT MIN (Stars), MAX (Stars), AVG(Stars)
FROM Business;
SELECT MIN (Likes), MAX (Likes), AVG(Likes)
FROM Tip;
SELECT MIN (Count), MAX (Count), AVG(Count)
FROM Checkin;
SELECT MIN (Review_count), MAX (Review_count), AVG(Review_count)
FROM User;
i. Table: Review, Column: Stars
min: 1 max: 5 avg: 3.7082
ii. Table: Business, Column: Stars
min: 1 max: 5 avg: 3.6549
iii. Table: Tip, Column: Likes
min: 0 max: 2 avg: 0.0144
iv. Table: Checkin, Column: Count
min: 1 max: 53 avg: 1.9414
v. Table: User, Column: Review_count
min: 0 max: 2000 avg: 24.2995
5. List the cities with the most reviews in descending order:
SQL code used to arrive at answer:
SELECT city,
SUM(review_count) AS reviews
FROM business
GROUP BY city
ORDER BY reviews DESC
Copy and Paste the Result Below:
+-----------------+---------+
| city | reviews |
+-----------------+---------+
| Las Vegas | 82854 |
| Phoenix | 34503 |
| Toronto | 24113 |
| Scottsdale | 20614 |
| Charlotte | 12523 |
| Henderson | 10871 |
| Tempe | 10504 |
| Pittsburgh | 9798 |
| Montréal | 9448 |
| Chandler | 8112 |
| Mesa | 6875 |
| Gilbert | 6380 |
| Cleveland | 5593 |
| Madison | 5265 |
| Glendale | 4406 |
| Mississauga | 3814 |
| Edinburgh | 2792 |
| Peoria | 2624 |
| North Las Vegas | 2438 |
| Markham | 2352 |
| Champaign | 2029 |
| Stuttgart | 1849 |
| Surprise | 1520 |
| Lakewood | 1465 |
| Goodyear | 1155 |
+-----------------+---------+
6. Find the distribution of star ratings to the business in the following cities:
i. Avon
SQL code used to arrive at answer:
SELECT stars,
SUM(review_count) AS count
FROM business
WHERE city == 'Avon'
GROUP BY stars
Copy and Paste the Resulting Table Below (2 columns - star rating and count):
+-------+-------+
| stars | count |
+-------+-------+
| 1.5 | 10 |
| 2.5 | 6 |
| 3.5 | 88 |
| 4.0 | 21 |
| 4.5 | 31 |
| 5.0 | 3 |
+-------+-------+
ii. Beachwood
SQL code used to arrive at answer:
SELECT stars,
SUM(review_count) AS count
FROM business
WHERE city == 'Beachwood'
GROUP BY stars
Copy and Paste the Resulting Table Below (2 columns - star rating and count):
+-------+-------+
| stars | count |
+-------+-------+
| 2.0 | 8 |
| 2.5 | 3 |
| 3.0 | 11 |
| 3.5 | 6 |
| 4.0 | 69 |
| 4.5 | 17 |
| 5.0 | 23 |
+-------+-------+
7. Find the top 3 users based on their total number of reviews:
SQL code used to arrive at answer:
SELECT id,
name,
review_count
FROM user
ORDER BY review_count DESC
LIMIT 3
Copy and Paste the Result Below:
+------------------------+--------+--------------+
| id | name | review_count |
+------------------------+--------+--------------+
| -G7Zkl1wIWBBmD0KRy_sCw | Gerald | 2000 |
| -3s52C4zL_DHRK0ULG6qtg | Sara | 1629 |
| -8lbUNlXVSoXqaRRiHiSNg | Yuri | 1339 |
+------------------------+--------+--------------+
8. Does posing more reviews correlate with more fans?
Please explain your findings and interpretation of the results:
SELECT id,
name,
review_count,
fans,
yelping_since
FROM user
ORDER BY fans DESC
+------------------------+-----------+--------------+------+---------------------+
| id | name | review_count | fans | yelping_since |
+------------------------+-----------+--------------+------+---------------------+
| -9I98YbNQnLdAmcYfb324Q | Amy | 609 | 503 | 2007-07-19 00:00:00 |
| -8EnCioUmDygAbsYZmTeRQ | Mimi | 968 | 497 | 2011-03-30 00:00:00 |
| --2vR0DIsmQ6WfcSzKWigw | Harald | 1153 | 311 | 2012-11-27 00:00:00 |
| -G7Zkl1wIWBBmD0KRy_sCw | Gerald | 2000 | 253 | 2012-12-16 00:00:00 |
| -0IiMAZI2SsQ7VmyzJjokQ | Christine | 930 | 173 | 2009-07-08 00:00:00 |
| -g3XIcCb2b-BD0QBCcq2Sw | Lisa | 813 | 159 | 2009-10-05 00:00:00 |
| -9bbDysuiWeo2VShFJJtcw | Cat | 377 | 133 | 2009-02-05 00:00:00 |
| -FZBTkAZEXoP7CYvRV2ZwQ | William | 1215 | 126 | 2015-02-19 00:00:00 |
| -9da1xk7zgnnfO1uTVYGkA | Fran | 862 | 124 | 2012-04-05 00:00:00 |
| -lh59ko3dxChBSZ9U7LfUw | Lissa | 834 | 120 | 2007-08-14 00:00:00 |
| -B-QEUESGWHPE_889WJaeg | Mark | 861 | 115 | 2009-05-31 00:00:00 |
| -DmqnhW4Omr3YhmnigaqHg | Tiffany | 408 | 111 | 2008-10-28 00:00:00 |
| -cv9PPT7IHux7XUc9dOpkg | bernice | 255 | 105 | 2007-08-29 00:00:00 |
| -DFCC64NXgqrxlO8aLU5rg | Roanna | 1039 | 104 | 2006-03-28 00:00:00 |
| -IgKkE8JvYNWeGu8ze4P8Q | Angela | 694 | 101 | 2010-10-01 00:00:00 |
| -K2Tcgh2EKX6e6HqqIrBIQ | .Hon | 1246 | 101 | 2006-07-19 00:00:00 |
| -4viTt9UC44lWCFJwleMNQ | Ben | 307 | 96 | 2007-03-10 00:00:00 |
| -3i9bhfvrM3F1wsC9XIB8g | Linda | 584 | 89 | 2005-08-07 00:00:00 |
| -kLVfaJytOJY2-QdQoCcNQ | Christina | 842 | 85 | 2012-10-08 00:00:00 |
| -ePh4Prox7ZXnEBNGKyUEA | Jessica | 220 | 84 | 2009-01-12 00:00:00 |
| -4BEUkLvHQntN6qPfKJP2w | Greg | 408 | 81 | 2008-02-16 00:00:00 |
| -C-l8EHSLXtZZVfUAUhsPA | Nieves | 178 | 80 | 2013-07-08 00:00:00 |
| -dw8f7FLaUmWR7bfJ_Yf0w | Sui | 754 | 78 | 2009-09-07 00:00:00 |
| -8lbUNlXVSoXqaRRiHiSNg | Yuri | 1339 | 76 | 2008-01-03 00:00:00 |
| -0zEEaDFIjABtPQni0XlHA | Nicole | 161 | 73 | 2009-04-30 00:00:00 |
+------------------------+-----------+--------------+------+---------------------+
Answer: Yes, but also the amount of time that they have been yelping. The longer they have been yelping and the more reviews they give has a higher fan count.
9. Are there more reviews with the word "love" or with the word "hate" in them?
SQL code used to arrive at answer:
SELECT COUNT(*)
FROM review
WHERE text LIKE "%love%"
= 1780
SELECT COUNT(*)
FROM review
WHERE text LIKE "%hate%"
= 232
Answer: love has 1780, while hate only has 232.
10. Find the top 10 users with the most fans:
SQL code used to arrive at answer:
SELECT id,
name,
fans
FROM user
ORDER BY fans DESC
LIMIT 10
Copy and Paste the Result Below:
+------------------------+-----------+------+
| id | name | fans |
+------------------------+-----------+------+
| -9I98YbNQnLdAmcYfb324Q | Amy | 503 |
| -8EnCioUmDygAbsYZmTeRQ | Mimi | 497 |
| --2vR0DIsmQ6WfcSzKWigw | Harald | 311 |
| -G7Zkl1wIWBBmD0KRy_sCw | Gerald | 253 |
| -0IiMAZI2SsQ7VmyzJjokQ | Christine | 173 |
| -g3XIcCb2b-BD0QBCcq2Sw | Lisa | 159 |
| -9bbDysuiWeo2VShFJJtcw | Cat | 133 |
| -FZBTkAZEXoP7CYvRV2ZwQ | William | 126 |
| -9da1xk7zgnnfO1uTVYGkA | Fran | 124 |
| -lh59ko3dxChBSZ9U7LfUw | Lissa | 120 |
+------------------------+-----------+------+
11. Is there a strong correlation between having a high number of fans and being listed
as "useful" or "funny?"
Yes, see interpretation.
SQL code used to arrive at answer:
SELECT name,
fans,
useful,
funny,
review_count,
yelping_since
FROM user
ORDER BY fans DESC
Copy and Paste the Result Below:
+-----------+------+--------+--------+--------------+---------------------+
| name | fans | useful | funny | review_count | yelping_since |
+-----------+------+--------+--------+--------------+---------------------+
| Amy | 503 | 3226 | 2554 | 609 | 2007-07-19 00:00:00 |
| Mimi | 497 | 257 | 138 | 968 | 2011-03-30 00:00:00 |
| Harald | 311 | 122921 | 122419 | 1153 | 2012-11-27 00:00:00 |
| Gerald | 253 | 17524 | 2324 | 2000 | 2012-12-16 00:00:00 |
| Christine | 173 | 4834 | 6646 | 930 | 2009-07-08 00:00:00 |
| Lisa | 159 | 48 | 13 | 813 | 2009-10-05 00:00:00 |
| Cat | 133 | 1062 | 672 | 377 | 2009-02-05 00:00:00 |
| William | 126 | 9363 | 9361 | 1215 | 2015-02-19 00:00:00 |
| Fran | 124 | 9851 | 7606 | 862 | 2012-04-05 00:00:00 |
| Lissa | 120 | 455 | 150 | 834 | 2007-08-14 00:00:00 |
| Mark | 115 | 4008 | 570 | 861 | 2009-05-31 00:00:00 |
| Tiffany | 111 | 1366 | 984 | 408 | 2008-10-28 00:00:00 |
| bernice | 105 | 120 | 112 | 255 | 2007-08-29 00:00:00 |
| Roanna | 104 | 2995 | 1188 | 1039 | 2006-03-28 00:00:00 |
| Angela | 101 | 158 | 164 | 694 | 2010-10-01 00:00:00 |
| .Hon | 101 | 7850 | 5851 | 1246 | 2006-07-19 00:00:00 |
| Ben | 96 | 1180 | 1155 | 307 | 2007-03-10 00:00:00 |
| Linda | 89 | 3177 | 2736 | 584 | 2005-08-07 00:00:00 |
| Christina | 85 | 158 | 34 | 842 | 2012-10-08 00:00:00 |
| Jessica | 84 | 2161 | 2091 | 220 | 2009-01-12 00:00:00 |
| Greg | 81 | 820 | 753 | 408 | 2008-02-16 00:00:00 |
| Nieves | 80 | 1091 | 774 | 178 | 2013-07-08 00:00:00 |
| Sui | 78 | 9 | 18 | 754 | 2009-09-07 00:00:00 |
| Yuri | 76 | 1166 | 220 | 1339 | 2008-01-03 00:00:00 |
| Nicole | 73 | 13 | 10 | 161 | 2009-04-30 00:00:00 |
+-----------+------+--------+--------+--------------+---------------------+
Please explain your findings and interpretation of the results:
From the result above, it was observed that Harald had outlying values for 'Useful' and 'Funny', when compared to the number of fans he has.
The other users seem to have a correlation with more `useful` and `funny` results in more fans, but also in conjunction with review_count and time they have been yelping.
Part 2: Inferences and Analysis
1. Pick one city and category of your choice and group the businesses in that city
or category by their overall star rating. Compare the businesses with 2-3 stars to
the businesses with 4-5 stars and answer the following questions. Include your code.
i. Do the two groups you chose to analyze have a different distribution of hours?
The 4-5 star group seems to have shorter hours then the 2-3 star group.
Please note the query returned only three businesses so not a great
sample size.
ii. Do the two groups you chose to analyze have a different number of reviews?
Yes and no, one of the 4-5 star group has a lot more reviews but then the other
4-5 star group has close to the same number of reviews as the 2-3 star group
iii. Are you able to infer anything from the location data provided between these two
groups? Explain.
No, every business is in a different zip-code.
SQL code used for analysis:
SELECT B.name,
B.review_count,
H.hours,
postal_code,
CASE
WHEN hours LIKE "%monday%" THEN 1
WHEN hours LIKE "%tuesday%" THEN 2
WHEN hours LIKE "%wednesday%" THEN 3
WHEN hours LIKE "%thursday%" THEN 4
WHEN hours LIKE "%friday%" THEN 5
WHEN hours LIKE "%saturday%" THEN 6
WHEN hours LIKE "%sunday%" THEN 7
END AS ord,
CASE
WHEN B.stars BETWEEN 2 AND 3 THEN '2-3 stars'
WHEN B.stars BETWEEN 4 AND 5 THEN '4-5 stars'
END AS star_rating
FROM business B INNER JOIN hours H
ON B.id = H.business_id
INNER JOIN category C
ON C.business_id = B.id
WHERE (B.city == 'Las Vegas'
AND
C.category LIKE 'shopping')
AND
(B.stars BETWEEN 2 AND 3
OR
B.stars BETWEEN 4 AND 5)
GROUP BY stars,ord
ORDER BY ord,star_rating ASC
2. Group business based on the ones that are open and the ones that are closed. What
differences can you find between the ones that are still open and the ones that are
closed? List at least two differences and the SQL code you used to arrive at your
answer.
i. Difference 1:
The businesses that are open tend to have more reviews than ones that
are closed on average.
Open: AVG(review_count) = 31.757
Closed: AVG(review_count0 = 23.198
ii. Difference 2:
The average star rating is higher for businesses that are open than
businesses that are closed.
Open: AVG(stars) = 3.679
Closed: AVG(stars) = 3.520
SQL code used for analysis:
SELECT COUNT(DISTINCT(id)),
AVG(review_count),
SUM(review_count),
AVG(stars),
is_open
FROM business
GROUP BY is_open
3. For this last part of your analysis, you are going to choose the type of analysis you
want to conduct on the Yelp dataset and are going to prepare the data for analysis.
Ideas for analysis include: Parsing out keywords and business attributes for sentiment
analysis, clustering businesses to find commonalities or anomalies between them,
predicting the overall star rating for a business, predicting the number of fans a
user will have, and so on. These are just a few examples to get you started, so feel
free to be creative and come up with your own problem you want to solve. Provide
answers, in-line, to all of the following:
i. Indicate the type of analysis you chose to do:
Predicting whether a business will stay open or close. We wish not to explicitly
examine the text of the reviews, but this would be an interesting analysis.
ii. Write 1-2 brief paragraphs on the type of data you will need for your analysis
and why you chose that data:
To better help businesses understand the importance of different factors which
will help their business stay open. Some data that may be important; number of
reviews, star rating of business, hours open, and of course location location
location. We will gather the latitude and longitude as well as city, state,
postal_code, and address to make processing easier later on. Categories and
attributes will be used to better distinguish between different types of
businesses. `is_open` will determine which business is open and which business
have closed (not hours) but permanently.
iii. Output of your finished dataset:
+------------------------+--------------------------------+-----------------------------+---------------+-------+-------------+----------+-----------+--------------+-------+--------------+---------------+-----------------+----------------+--------------+----------------+--------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+
| id | name | address | city | state | postal_code | latitude | longitude | review_count | stars | monday_hours | tuesday_hours | wednesday_hours | thursday_hours | friday_hours | saturday_hours | sunday_hours | categories | attributes | is_open |
+------------------------+--------------------------------+-----------------------------+---------------+-------+-------------+----------+-----------+--------------+-------+--------------+---------------+-----------------+----------------+--------------+----------------+--------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+
| -0DET7VdEQOJVJ_v6klEug | Flaming Kitchen | 3235 York Regional Road 7 | Markham | ON | L3R 3P9 | 43.8484 | -79.3487 | 25 | 3.0 | 12:00-23:00 | 12:00-23:00 | 12:00-23:00 | 12:00-23:00 | 12:00-23:00 | 12:00-23:00 | 12:00-23:00 | Asian Fusion,Restaurants | RestaurantsTableService,GoodForMeal,Alcohol,Caters,HasTV,RestaurantsGoodForGroups,NoiseLevel,WiFi,RestaurantsAttire,RestaurantsReservations,OutdoorSeating,RestaurantsPriceRange2,BikeParking,RestaurantsDelivery,Ambience,RestaurantsTakeOut,GoodForKids,BusinessParking | 1 |
| -2HjuT4yjLZ3b5f_abD87Q | Freeman's Car Stereo | 4821 South Blvd | Charlotte | NC | 28217 | 35.1727 | -80.8755 | 8 | 3.5 | 9:00-19:00 | 9:00-19:00 | 9:00-19:00 | 9:00-19:00 | 9:00-19:00 | 9:00-17:00 | None | Electronics,Shopping,Automotive,Car Stereo Installation | BusinessAcceptsCreditCards,RestaurantsPriceRange2,BusinessParking,WheelchairAccessible | 1 |
| -CdstAUdEvci8GeJG8owpQ | Motors & More | 2315 Highland Dr | Las Vegas | NV | 89102 | 36.1465 | -115.167 | 7 | 5.0 | 7:00-17:00 | 7:00-17:00 | 7:00-17:00 | 7:00-17:00 | 7:00-17:00 | 8:00-12:00 | None | Home Services,Solar Installation,Heating & Air Conditioning/HVAC | BusinessAcceptsCreditCards,BusinessAcceptsBitcoin,ByAppointmentOnly | 1 |
| -K4gAv8_vjx8-2BxkVeRkA | Baby Cakes | 4145 Erie St | Willoughby | OH | 44094 | 41.6399 | -81.4064 | 5 | 3.5 | None | 11:00-17:00 | 11:00-17:00 | 11:00-20:00 | 11:00-17:00 | 10:00-17:00 | None | Bakeries,Food | BusinessAcceptsCreditCards,RestaurantsTakeOut,WheelchairAccessible,RestaurantsDelivery | 1 |
| -PtTGvWsckUL8tTutHr6Ew | Snip-its Rocky River | 21609 Center Ridge Rd | Rocky River | OH | 44116 | 41.4595 | -81.8587 | 18 | 2.5 | 10:00-19:00 | 10:00-19:00 | 10:00-19:00 | 10:00-19:00 | 10:00-19:00 | 9:00-17:30 | 10:00-16:00 | Beauty & Spas,Hair Salons | BusinessAcceptsCreditCards,RestaurantsPriceRange2,GoodForKids,BusinessParking,ByAppointmentOnly | 1 |
| -ayZoW_iNDsunYXX_0x1YQ | Standard Restaurant Supply | 2922 E McDowell Rd | Phoenix | AZ | 85008 | 33.4664 | -112.018 | 15 | 3.5 | 8:00-18:00 | 8:00-18:00 | 8:00-18:00 | 8:00-18:00 | 8:00-18:00 | 9:00-17:00 | None | Shopping,Wholesalers,Restaurant Supplies,Professional Services,Wholesale Stores | BusinessAcceptsCreditCards,RestaurantsPriceRange2,BusinessParking,BikeParking,WheelchairAccessible | 1 |
| -d9qyfNhLMQwVVg_raBKeg | What A Bagel | 973 Eglinton Avenue W | York | ON | M6C 2C4 | 43.6999 | -79.4295 | 8 | 3.0 | 6:00-15:30 | 6:00-15:30 | 6:00-15:30 | 6:00-15:30 | 6:00-15:30 | 6:00-15:30 | None | Restaurants,Bagels,Breakfast & Brunch,Food | NoiseLevel,RestaurantsAttire,RestaurantsTableService,OutdoorSeating | 1 |
| -hjbcaxaU9yYXY2iI-49sw | Pinnacle Fencing Solutions | | Phoenix | AZ | 85060 | 33.4805 | -111.997 | 13 | 4.0 | 8:00-16:00 | 8:00-16:00 | 8:00-16:00 | 8:00-16:00 | 8:00-16:00 | None | None | Home Services,Contractors,Fences & Gates | BusinessAcceptsCreditCards,ByAppointmentOnly | 1 |
| -iu4FxdfxN4rU4Fu9BjiFw | Alterations Express | 17240 Royalton Rd | Strongsville | OH | 44136 | 41.3141 | -81.8207 | 3 | 4.0 | 8:00-19:00 | 8:00-19:00 | 8:00-19:00 | 8:00-19:00 | 8:00-19:00 | 8:00-18:00 | None | Shopping,Bridal,Dry Cleaning & Laundry,Local Services,Sewing & Alterations | BusinessParking,BusinessAcceptsCreditCards,RestaurantsPriceRange2,BusinessAcceptsBitcoin,BikeParking,ByAppointmentOnly,WheelchairAccessible | 1 |
| -j4NsiRzSMrMk2N_bGH_SA | Extra Space Storage | 2880 W Elliot Rd | Chandler | AZ | 85224 | 33.3496 | -111.892 | 5 | 4.0 | 8:00-17:30 | 8:00-17:30 | 8:00-17:30 | 8:00-17:30 | 8:00-17:30 | 8:00-17:30 | 10:00-14:00 | Home Services,Self Storage,Movers,Shopping,Local Services,Home Decor,Home & Garden | BusinessAcceptsCreditCards | 1 |
| -uiBBVWI6tMDm2JFbZFrOw | Gussied Up | 1090 Bathurst St | Toronto | ON | M5R 1W5 | 43.6727 | -79.4142 | 6 | 4.5 | None | 11:00-19:00 | 11:00-19:00 | 11:00-19:00 | 11:00-19:00 | 11:00-17:00 | 12:00-16:00 | Women's Clothing,Shopping,Fashion | BusinessAcceptsCreditCards,RestaurantsPriceRange2,BusinessParking,BikeParking | 1 |
| 0-aPEeNc2zVb5Gp-i7Ckqg | Buddy's Muffler & Exhaust | 1509 Hickory Grove Rd | Gastonia | NC | 28056 | 35.2772 | -81.06 | 4 | 5.0 | 8:30-17:00 | 8:30-17:00 | 8:30-17:00 | 8:30-17:00 | 8:30-17:00 | 9:00-15:00 | None | Automotive,Auto Repair | BusinessAcceptsCreditCards | 1 |
| 01xXe2m_z048W5gcBFpoJA | Five Guys | 2641 N 44th St, Ste 100 | Phoenix | AZ | 85008 | 33.478 | -111.986 | 63 | 3.5 | 10:00-22:00 | 10:00-22:00 | 10:00-22:00 | 10:00-22:00 | 10:00-22:00 | 10:00-22:00 | 10:00-22:00 | American (New),Burgers,Fast Food,Restaurants | RestaurantsTableService,GoodForMeal,Alcohol,Caters,HasTV,RestaurantsGoodForGroups,NoiseLevel,WiFi,RestaurantsAttire,RestaurantsReservations,OutdoorSeating,BusinessAcceptsCreditCards,RestaurantsPriceRange2,BikeParking,RestaurantsDelivery,Ambience,RestaurantsTakeOut,GoodForKids,DriveThru,BusinessParking | 1 |
| 06I2r8S3tHP_LwGnnkk6Uw | All Storage - Anthem | 2620 W Horizon Ridge Pkwy | Henderson | NV | 89052 | 36.0021 | -115.102 | 3 | 3.5 | 9:00-16:30 | 9:00-16:30 | 9:00-16:30 | 9:00-16:30 | 9:00-16:30 | 9:00-16:30 | None | Truck Rental,Local Services,Self Storage,Parking,Automotive | BusinessAcceptsCreditCards,BusinessAcceptsBitcoin | 1 |
| 07h3mGtTovPJE660nX6E-A | Mood | 1 Greenside Place | Edinburgh | EDH | EH1 3AA | 55.957 | -3.18502 | 11 | 2.0 | None | None | None | 22:30-3:00 | 22:00-3:00 | 22:00-3:00 | 22:30-3:00 | Dance Clubs,Nightlife | Alcohol,OutdoorSeating,BusinessAcceptsCreditCards,RestaurantsPriceRange2,AgesAllowed,Music,Smoking,RestaurantsGoodForGroups,WheelchairAccessible | 0 |
| 0AJF-USLN6K5T4caooDdjw | Starbucks | 4605 E Chandler Blvd, Ste A | Phoenix | AZ | 85048 | 33.3044 | -111.984 | 52 | 3.0 | 5:00-20:00 | 5:00-20:00 | 5:00-20:00 | 5:00-20:30 | 5:00-20:00 | 5:00-20:00 | 5:00-20:00 | Coffee & Tea,Food | BusinessParking,Caters,WiFi,OutdoorSeating,BusinessAcceptsCreditCards,RestaurantsPriceRange2,BikeParking,RestaurantsTakeOut | 1 |
| 0B3W6KxkD3o4W4l6cq735w | Big Smoke Burger | 260 Yonge Street | Toronto | ON | M4B 2L9 | 43.6546 | -79.3805 | 47 | 3.0 | 10:30-21:00 | 10:30-21:00 | 10:30-21:00 | 10:30-21:00 | 10:30-21:00 | 10:30-21:00 | 11:00-19:00 | Poutineries,Burgers,Restaurants | RestaurantsTableService,GoodForMeal,Alcohol,Caters,HasTV,RestaurantsGoodForGroups,NoiseLevel,WiFi,RestaurantsAttire,RestaurantsReservations,OutdoorSeating,BusinessAcceptsCreditCards,RestaurantsPriceRange2,WheelchairAccessible,BikeParking,RestaurantsDelivery,Ambience,RestaurantsTakeOut,GoodForKids,DriveThru,BusinessParking | 1 |
| 0IySwcfqwJjpHPsYwjpAkg | Subway | 2904 Yorkmont Rd | Charlotte | NC | 28208 | 35.1903 | -80.9288 | 7 | 3.5 | 6:00-22:00 | 6:00-22:00 | 6:00-22:00 | 6:00-22:00 | 6:00-22:00 | 10:00-21:00 | None | Fast Food,Restaurants,Sandwiches | Ambience,RestaurantsPriceRange2,GoodForKids | 1 |
| 0K2rKvqdBmiOAUTebcUohQ | Red Rock Canyon Visitor Center | 1000 Scenic Loop Dr | Las Vegas | NV | 89161 | 36.1357 | -115.428 | 32 | 4.5 | 8:00-16:30 | 8:00-16:30 | 8:00-16:30 | 8:00-16:30 | 8:00-16:30 | 8:00-16:30 | 8:00-16:30 | Education,Visitor Centers,Professional Services,Special Education,Local Services,Community Service/Non-Profit,Hotels & Travel,Travel Services,Gift Shops,Shopping,Parks,Hiking,Flowers & Gifts,Active Life | BusinessAcceptsCreditCards,GoodForKids | 1 |
| 0Ni7Stqt4RFWDGjOYRi2Bw | Scent From Above Company | 2501 W Behrend Dr, Ste 67 | Scottsdale | AZ | 85027 | 33.6656 | -112.111 | 14 | 4.5 | 6:00-16:00 | 6:00-16:00 | 6:00-16:00 | 6:00-16:00 | 6:00-16:00 | None | None | Home Cleaning,Local Services,Professional Services,Carpet Cleaning,Home Services,Office Cleaning,Window Washing | BusinessAcceptsCreditCards,ByAppointmentOnly | 1 |
| 0WBMEfqXQnEOAIkV-uCW6w | The Charlotte Room | 19 Charlotte Street | Toronto | ON | M5V 2H5 | 43.6466 | -79.3938 | 10 | 3.5 | 15:00-1:00 | 15:00-1:00 | 15:00-1:00 | 15:00-1:00 | 15:00-2:00 | 18:00-2:00 | None | Event Planning & Services,Bars,Nightlife,Lounges,Pool Halls,Venues & Event Spaces | BusinessParking,HasTV,CoatCheck,NoiseLevel,OutdoorSeating,BusinessAcceptsCreditCards,RestaurantsPriceRange2,Music,WheelchairAccessible,Smoking,Ambience,BestNights,RestaurantsGoodForGroups,HappyHour,GoodForDancing,Alcohol | 0 |
| 0Y3lHyqRHfWOBuQlS1bM0g | PC Savants | 11966 W Candelaria Ct | Sun City | AZ | 85373 | 33.6901 | -112.319 | 11 | 5.0 | 10:00-19:00 | 10:00-19:00 | 10:00-19:00 | 10:00-19:00 | 10:00-19:00 | 11:00-18:00 | 11:00-18:00 | IT Services & Computer Repair,Electronics Repair,Local Services,Mobile Phone Repair | BusinessAcceptsCreditCards,BusinessAcceptsBitcoin | 1 |
| 0aKsGxx7XP2TMs_fn_9xVw | Sweet Ruby Jane Confections | 8975 S Eastern Ave, Ste 3-B | Las Vegas | NV | 89123 | 36.015 | -115.118 | 30 | 4.0 | 10:00-19:00 | 10:00-19:00 | 10:00-19:00 | 10:00-19:00 | 10:00-19:00 | 10:00-19:00 | None | Food,Chocolatiers & Shops,Bakeries,Specialty Food,Desserts | BusinessAcceptsCreditCards,RestaurantsPriceRange2,BusinessParking,WheelchairAccessible | 0 |
| 0cxO1Lx2Pi7u6ftWX3Wksg | Oinky's Pork Chop Heaven | 22483 Emery Rd | North Randall | OH | 44128 | 41.4352 | -81.5214 | 3 | 3.0 | 6:00-23:00 | 6:00-23:00 | 6:00-23:00 | 6:00-23:00 | 6:00-23:00 | 6:00-23:00 | 6:00-23:00 | Soul Food,Restaurants | RestaurantsAttire,RestaurantsGoodForGroups,GoodForKids,RestaurantsReservations,RestaurantsTakeOut | 1 |
| 0e-j5VcEn54EZT-FKCUZdw | Sushi Osaka | 5084 Dundas Street W | Toronto | ON | M9A 1C2 | 43.6452 | -79.5324 | 8 | 4.5 | 11:00-23:00 | 11:00-23:00 | 11:00-23:00 | 11:00-23:00 | 11:00-23:00 | 11:00-23:00 | 14:00-23:00 | Sushi Bars,Restaurants,Japanese,Korean | RestaurantsTakeOut,WiFi,RestaurantsGoodForGroups,RestaurantsReservations | 1 |
+------------------------+--------------------------------+-----------------------------+---------------+-------+-------------+----------+-----------+--------------+-------+--------------+---------------+-----------------+----------------+--------------+----------------+--------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+
iv. Provide the SQL code you used to create your final dataset:
SELECT B.id,
B.name,
B.address,
B.city,
B.state,
B.postal_code,
B.latitude,
B.longitude,
B.review_count,
B.stars,
MAX(CASE
WHEN H.hours LIKE "%monday%" THEN TRIM(H.hours,'%MondayTuesWednesThursFriSatSun|%')
END) AS monday_hours,
MAX(CASE
WHEN H.hours LIKE "%tuesday%" THEN TRIM(H.hours,'%MondayTuesWednesThursFriSatSun|%')
END) AS tuesday_hours,
MAX(CASE
WHEN H.hours LIKE "%wednesday%" THEN TRIM(H.hours,'%MondayTuesWednesThursFriSatSun|%')
END) AS wednesday_hours,
MAX(CASE
WHEN H.hours LIKE "%thursday%" THEN TRIM(H.hours,'%MondayTuesWednesThursFriSatSun|%')
END) AS thursday_hours,
MAX(CASE
WHEN H.hours LIKE "%friday%" THEN TRIM(H.hours,'%MondayTuesWednesThursFriSatSun|%')
END) AS friday_hours,
MAX(CASE
WHEN H.hours LIKE "%saturday%" THEN TRIM(H.hours,'%MondayTuesWednesThursFriSatSun|%')
END) AS saturday_hours,
MAX(CASE
WHEN H.hours LIKE "%sunday%" THEN TRIM(H.hours,'%MondayTuesWednesThursFriSatSun|%')
END) AS sunday_hours,
GROUP_CONCAT(DISTINCT(C.category)) AS categories,
GROUP_CONCAT(DISTINCT(A.name)) AS attributes,
B.is_open
FROM business B
INNER JOIN hours H
ON B.id = H.business_id
INNER JOIN category C
ON B.id = C.business_id
INNER JOIN attribute A
ON B.id = A.business_id
GROUP BY B.id