├── Analyzing Unicorn Companies
├── calculator.png
└── notebook.ipynb
├── Analyzing Motorcycle Part Sales
├── motorcycle.jpg
└── notebook.ipynb
├── When Was the Golden Age of Video Games
├── datasets
│ ├── game_sales_data.csv
│ ├── top_critic_scores.csv
│ ├── top_user_scores_more_than_four_games.csv
│ ├── top_critic_scores_more_than_four_games.csv
│ ├── createdb.sql
│ ├── game_reviews.csv
│ └── game_sales.csv
└── notebook.ipynb
├── Analyzing American Baby Name Trends
└── datasets
│ ├── createdb.sql
│ └── createdblocal.sql
├── Analyze International Debt Statistics
├── datasets
│ └── international_debt.sql
└── notebook.ipynb
├── Analyzing NYC Public School Test Result Scores
├── datasets
│ ├── createdb.sql
│ └── schools_modified.csv
└── notebook.ipynb
├── Optimizing Online Sports Retail Revenue
└── datasets
│ └── createdb.sql
└── README.md
/Analyzing Unicorn Companies/calculator.png:
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https://raw.githubusercontent.com/qanhnn12/DataCamp-SQL-projects/HEAD/Analyzing Unicorn Companies/calculator.png
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/Analyzing Motorcycle Part Sales/motorcycle.jpg:
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https://raw.githubusercontent.com/qanhnn12/DataCamp-SQL-projects/HEAD/Analyzing Motorcycle Part Sales/motorcycle.jpg
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/When Was the Golden Age of Video Games/datasets/game_sales_data.csv:
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https://raw.githubusercontent.com/qanhnn12/DataCamp-SQL-projects/HEAD/When Was the Golden Age of Video Games/datasets/game_sales_data.csv
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/When Was the Golden Age of Video Games/datasets/top_critic_scores.csv:
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1 | year,avg_critic_score
2 | 1990,9.80
3 | 1992,9.67
4 | 1998,9.32
5 | 2020,9.20
6 | 1993,9.10
7 | 1995,9.07
8 | 2004,9.03
9 | 1982,9.00
10 | 2002,8.99
11 | 1999,8.93
12 |
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/Analyzing American Baby Name Trends/datasets/createdb.sql:
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1 | DROP TABLE baby_names;
2 |
3 | CREATE TABLE baby_names (
4 | year INT,
5 | first_name VARCHAR(64),
6 | sex VARCHAR(64),
7 | num INT
8 | );
9 |
10 | \copy baby_names FROM 'usa_baby_names.csv' DELIMITER ',' CSV HEADER;
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/When Was the Golden Age of Video Games/datasets/top_user_scores_more_than_four_games.csv:
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1 | year,num_games,avg_user_score
2 | 1997,8,9.50
3 | 1998,10,9.40
4 | 2010,23,9.24
5 | 2009,20,9.18
6 | 2008,20,9.03
7 | 1996,5,9.00
8 | 2005,13,8.95
9 | 2006,16,8.95
10 | 2000,8,8.80
11 | 2002,9,8.80
12 |
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/Analyzing American Baby Name Trends/datasets/createdblocal.sql:
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1 | DROP TABLE baby_names;
2 |
3 | CREATE TABLE baby_names (
4 | year INT,
5 | first_name VARCHAR(64),
6 | sex VARCHAR(64),
7 | num INT
8 | );
9 |
10 | \copy baby_names FROM 'datasets/usa_baby_names.csv' DELIMITER ',' CSV HEADER;
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/When Was the Golden Age of Video Games/datasets/top_critic_scores_more_than_four_games.csv:
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1 | year,num_games,avg_critic_score
2 | 1998,10,9.32
3 | 2004,11,9.03
4 | 2002,9,8.99
5 | 1999,11,8.93
6 | 2001,13,8.82
7 | 2011,26,8.76
8 | 2016,13,8.67
9 | 2013,18,8.66
10 | 2008,20,8.63
11 | 2017,13,8.62
12 |
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/Analyze International Debt Statistics/datasets/international_debt.sql:
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1 | -- Table: international_debt
2 |
3 | CREATE TABLE international_debt
4 | (
5 | country_name character varying(50),
6 | country_code character varying(50),
7 | indicator_name text,
8 | indicator_code text,
9 | debt numeric
10 | );
11 |
12 | -- Copy over data from CSV
13 | \copy international_debt FROM 'international_debt.csv' DELIMITER ',' CSV HEADER;
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/Analyzing NYC Public School Test Result Scores/datasets/createdb.sql:
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1 | DROP TABLE schools;
2 |
3 | CREATE TABLE schools
4 | (
5 | school_name VARCHAR(100) PRIMARY KEY,
6 | borough VARCHAR(100),
7 | building_code VARCHAR(10),
8 | average_math INT,
9 | average_reading INT,
10 | average_writing INT,
11 | percent_tested FLOAT
12 | );
13 |
14 | \copy schools FROM 'schools_modified.csv' DELIMITER ',' CSV HEADER;
15 |
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/Optimizing Online Sports Retail Revenue/datasets/createdb.sql:
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1 | DROP TABLE info;
2 |
3 | CREATE TABLE info
4 | (
5 | product_name VARCHAR(100),
6 | product_id VARCHAR(11) PRIMARY KEY,
7 | description VARCHAR(700)
8 | );
9 |
10 | DROP TABLE finance;
11 |
12 | CREATE TABLE finance
13 | (
14 | product_id VARCHAR(11) PRIMARY KEY,
15 | listing_price FLOAT,
16 | sale_price FLOAT,
17 | discount FLOAT,
18 | revenue FLOAT
19 | );
20 |
21 | DROP TABLE reviews;
22 |
23 | CREATE TABLE reviews
24 | (
25 | product_id VARCHAR(11) PRIMARY KEY,
26 | rating FLOAT,
27 | reviews FLOAT
28 | );
29 |
30 | DROP TABLE traffic;
31 |
32 | CREATE TABLE traffic
33 | (
34 | product_id VARCHAR(11) PRIMARY KEY,
35 | last_visited TIMESTAMP
36 | );
37 |
38 | DROP TABLE brands;
39 |
40 | CREATE TABLE brands
41 | (
42 | product_id VARCHAR(11) PRIMARY KEY,
43 | brand VARCHAR(7)
44 | );
45 |
46 | \copy info FROM 'info_v2.csv' DELIMITER ',' CSV HEADER;
47 | \copy finance FROM 'finance.csv' DELIMITER ',' CSV HEADER;
48 | \copy reviews FROM 'reviews_v2.csv' DELIMITER ',' CSV HEADER;
49 | \copy traffic FROM 'traffic_v3.csv' DELIMITER ',' CSV HEADER;
50 | \copy brands FROM 'brands_v2.csv' DELIMITER ',' CSV HEADER;
51 |
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/When Was the Golden Age of Video Games/datasets/createdb.sql:
--------------------------------------------------------------------------------
1 | DROP TABLE game_sales;
2 |
3 | CREATE TABLE game_sales (
4 | game VARCHAR(100) PRIMARY KEY,
5 | platform VARCHAR(64),
6 | publisher VARCHAR(64),
7 | developer VARCHAR(64),
8 | games_sold NUMERIC(5, 2),
9 | year INT
10 | );
11 |
12 | DROP TABLE reviews;
13 |
14 | CREATE TABLE reviews (
15 | game VARCHAR(100) PRIMARY KEY,
16 | critic_score NUMERIC(4, 2),
17 | user_score NUMERIC(4, 2)
18 | );
19 |
20 | DROP TABLE top_critic_years;
21 |
22 | CREATE TABLE top_critic_years (
23 | year INT PRIMARY KEY,
24 | avg_critic_score NUMERIC(4, 2)
25 | );
26 |
27 | DROP TABLE top_critic_years_more_than_four_games;
28 |
29 | CREATE TABLE top_critic_years_more_than_four_games (
30 | year INT PRIMARY KEY,
31 | num_games INT,
32 | avg_critic_score NUMERIC(4, 2)
33 | );
34 |
35 | DROP TABLE top_user_years_more_than_four_games;
36 |
37 | CREATE TABLE top_user_years_more_than_four_games (
38 | year INT PRIMARY KEY,
39 | num_games INT,
40 | avg_user_score NUMERIC(4, 2)
41 | );
42 |
43 | \copy game_sales FROM 'game_sales.csv' DELIMITER ',' CSV HEADER;
44 | \copy reviews FROM 'game_reviews.csv' DELIMITER ',' CSV HEADER;
45 | \copy top_critic_years FROM 'top_critic_scores.csv' DELIMITER ',' CSV HEADER;
46 | \copy top_critic_years_more_than_four_games FROM 'top_critic_scores_more_than_four_games.csv' DELIMITER ',' CSV HEADER;
47 | \copy top_user_years_more_than_four_games FROM 'top_user_scores_more_than_four_games.csv' DELIMITER ',' CSV HEADER;
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/README.md:
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1 | # SQL projects on DataCamp
2 | ## 📊 Guided projects
3 |
4 | ### 1. Analyze International Debt Statistics
5 | * Aggregate data with ```SUM()```, ```MAX()```, ```MIN()```
6 | * Filter rows using ```WHERE```, ```GROUP BY```
7 | * Use subquery in ```WHERE```
8 |
9 | View my project [here](https://github.com/qanhnn12/DataCamp-SQL-projects/tree/main/Analyze%20International%20Debt%20Statistics)!
10 |
11 | ### 2. Analyzing NYC Public School Test Result Scores
12 | * Count unique values with ```COUNT(DISTINCT)```
13 | * Combine ```SUM()``` and ```COUNT()``` to calculate in each row
14 |
15 |
16 | View my project [here](https://github.com/qanhnn12/DataCamp-SQL-projects/tree/main/Analyzing%20NYC%20Public%20School%20Test%20Result%20Scores)!
17 |
18 | ### 3. When Was the Golden Age of Video Games?
19 | * Use ```HAVING``` to filter on results of ```GROUP BY```
20 | * ```JOIN``` and ```LEFT JOIN``` tables
21 |
22 | View my project [here](https://github.com/qanhnn12/DataCamp-SQL-projects/tree/main/When%20Was%20the%20Golden%20Age%20of%20Video%20Games)!
23 |
24 | ### 4. Analyzing American Baby Name Trends
25 | * Use```CASE``` to add a new column based on some conditions
26 | * Use window functions such as ```DENSE_RANK()```, ```SUM()```
27 | * Simplify the code with ```CTE```
28 |
29 | View my project [here](https://github.com/qanhnn12/DataCamp-SQL-projects/tree/main/Analyzing%20American%20Baby%20Name%20Trends)!
30 |
31 | ### 5. Optimizing Online Sports Retail Revenue
32 | * ```CAST``` numeric data
33 | * Calculate the correlation and median
34 | * Work with dates, times, and strings
35 |
36 | View my project [here](https://github.com/qanhnn12/DataCamp-SQL-projects/tree/main/Optimizing%20Online%20Sports%20Retail%20Revenue)!
37 |
38 | ## 📊 Unguided projects
39 | ### 1. Analyzing Unicorn Companies
40 |
41 | View my project [here](https://github.com/qanhnn12/DataCamp-SQL-projects/tree/main/Analyzing%20Unicorn%20Companies)!
42 |
43 | ### 2. Analyzing Motorcycle Part Sales
44 |
45 | View my project [here](https://github.com/qanhnn12/DataCamp-SQL-projects/tree/main/Analyzing%20Motorcycle%20Part%20Sales)!
46 |
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/When Was the Golden Age of Video Games/datasets/game_reviews.csv:
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1 | Name,Critic_Score,User_Score
2 | Wii Sports for Wii,7.7,8.0
3 | Super Mario Bros. for NES,10.0,8.2
4 | Counter-Strike: Global Offensive for PC,8.0,7.5
5 | Mario Kart Wii for Wii,8.2,9.1
6 | PLAYERUNKNOWN'S BATTLEGROUNDS for PC,8.6,4.7
7 | Minecraft for PC,10.0,7.8
8 | Wii Sports Resort for Wii,8.0,8.8
9 | Pokemon Red / Green / Blue Version for GB,9.4,8.8
10 | New Super Mario Bros. for DS,9.1,8.1
11 | New Super Mario Bros. Wii for Wii,8.6,9.2
12 | Tetris for GB,9.4,8.7
13 | Call of Duty: Modern Warfare for PS4,8.0,3.3
14 | Duck Hunt for NES,7.5,8.3
15 | Wii Play for Wii,5.9,6.5
16 | Mario Kart 8 Deluxe for NS,9.3,8.7
17 | Kinect Adventures! for X360,6.7,
18 | Nintendogs for DS,8.4,8.5
19 | Mario Kart DS for DS,9.1,9.4
20 | Pokemon Gold / Silver Version for GB,9.2,8.8
21 | Wii Fit for Wii,7.9,
22 | Wii Fit Plus for Wii,8.0,
23 | Super Mario World for SNES,8.5,9.3
24 | Grand Theft Auto V for PS3,9.4,8.3
25 | Grand Theft Auto V for PS4,9.7,8.4
26 | Brain Age: Train Your Brain in Minutes a Day for DS,8.1,
27 | Super Smash Bros. Ultimate for NS,9.4,9.7
28 | Mario Kart 7 for 3DS,8.2,8.3
29 | Pokemon Diamond / Pearl Version for DS,8.6,
30 | Super Mario Odyssey for NS,9.9,9.0
31 | The Legend of Zelda: Breath of the Wild for NS,9.9,10.0
32 | Pokemon Sword / Shield for NS,8.0,4.6
33 | Grand Theft Auto: San Andreas for PS2,9.5,9.1
34 | Super Mario Bros. 3 for NES,9.8,9.3
35 | Pokemon X/Y for 3DS,8.9,9.7
36 | Uncharted 4: A Thief's End for PS4,9.2,8.4
37 | Pokemon Ruby / Sapphire Version for GBA,8.8,8.6
38 | Pokemon Sun/Moon for 3DS,9.0,7.7
39 | Grand Theft Auto: Vice City for PS2,9.6,8.8
40 | Grand Theft Auto V for X360,10.0,
41 | Pokemon Black / White Version for DS,8.6,9.0
42 | Call of Duty: Black Ops 3 for PS4,8.1,4.9
43 | Counter-Strike: Source for PC,9.3,
44 | Sonic the Hedgehog for GEN,8.6,8.3
45 | Gran Turismo 3: A-Spec for PS2,9.3,8.9
46 | Brain Age 2: More Training in Minutes a Day for DS,8.0,
47 | Call of Duty: Modern Warfare 3 for X360,8.7,
48 | Call of Duty: Black Ops for X360,8.8,
49 | Pokemon Yellow: Special Pikachu Edition for GB,8.7,8.8
50 | Halo 3 for X360,9.6,9.5
51 | Pokemon Omega Ruby/Pokemon Alpha Sapphire for 3DS,8.3,7.4
52 | Terraria for PC,8.8,8.6
53 | Red Dead Redemption 2 for PS4,9.8,7.9
54 | Call of Duty: Black Ops II for X360,9.3,
55 | Call of Duty: Black Ops II for PS3,9.3,
56 | Call of Duty: Modern Warfare 2 for X360,9.5,9.0
57 | Animal Crossing: New Horizons for NS,9.0,5.4
58 | Call of Duty: WWII for PS4,8.0,4.3
59 | Call of Duty: Modern Warfare 3 for PS3,8.8,
60 | New Super Mario Bros. 2 for 3DS,7.5,7.3
61 | Super Smash Bros. Brawl for Wii,9.2,9.7
62 | Marvel's Spider-Man for PS4,9.1,8.7
63 | Grand Theft Auto III for PS2,9.5,
64 | Portal 2 for PC,9.7,9.0
65 | Minecraft for X360,8.5,
66 | Super Mario Galaxy for Wii,9.7,9.6
67 | Pokemon Heart Gold / Soul Silver Version for DS,8.6,
68 | Super Mario 3D Land for 3DS,8.9,8.4
69 | Call of Duty: Black Ops for PS3,8.7,
70 | Grand Theft Auto V for PC,9.6,7.7
71 | Animal Crossing: New Leaf for 3DS,8.6,8.8
72 | The Witcher 3: Wild Hunt for PC,9.3,9.4
73 | Pokemon FireRed / LeafGreen Version for GBA,8.3,
74 | World of Warcraft for PC,9.2,8.0
75 | Half-Life 2 for PC,9.7,
76 | Diablo III for PC,9.0,4.1
77 | "Pokemon: Let's Go, Pikachu/Eevee for NS",8.0,6.2
78 | Gran Turismo 5 for PS3,8.3,
79 | Super Mario 64 for N64,9.7,9.0
80 | FIFA 18 for PS4,8.3,3.4
81 | The Last of Us Remastered for PS4,9.5,9.1
82 | Gran Turismo 4 for PS2,8.7,
83 | Animal Crossing: Wild World for DS,8.5,
84 | Super Mario Land 2: 6 Golden Coins for GB,9.0,8.5
85 | Grand Theft Auto IV for X360,10.0,
86 | Super Mario 64 DS for DS,8.6,
87 | God of War (2018) for PS4,9.7,10.0
88 | StarCraft for PC,8.7,
89 | FIFA 17 for PS4,8.9,4.8
90 | Gran Turismo for PS,9.5,
91 | The Witcher 3: Wild Hunt for PS4,9.2,9.2
92 | Call of Duty: Modern Warfare 2 for PS3,9.5,
93 | Grand Theft Auto IV for PS3,10.0,9.0
94 | Super Mario All-Stars for SNES,9.2,9.5
95 | Call of Duty: Ghosts for X360,6.9,
96 | Just Dance 3 for Wii,7.5,
97 | Call of Duty: Ghosts for PS3,7.5,
98 | Splatoon 2 for NS,8.2,8.5
99 | Super Mario Party for NS,7.3,7.5
100 | Horizon: Zero Dawn for PS4,9.1,8.0
101 | RollerCoaster Tycoon 3 for PC,8.3,
102 | Halo: Reach for X360,9.3,
103 | Portal for PC,9.0,
104 | Halo 4 for X360,9.8,
105 | Final Fantasy VII for PS,9.6,9.5
106 | Mario Kart 64 for N64,8.5,
107 | Super Smash Bros. for 3DS for 3DS,8.4,
108 | Call of Duty 4: Modern Warfare for X360,9.6,9.0
109 | Gran Turismo 2 for PS,9.2,
110 | Wii Party for Wii,7.0,
111 | Call of Duty: Black Ops IIII for PS4,8.3,4.0
112 | Mario Party DS for DS,7.1,
113 | Uncharted 3: Drake's Deception for PS3,9.3,6.3
114 | Donkey Kong Country for SNES,9.0,8.8
115 | Half-Life for PC,9.5,
116 | FIFA 19 for PS4,8.3,1.7
117 | Rust for PC,6.1,6.2
118 | The Elder Scrolls V: Skyrim for X360,9.3,
119 | Mario Party 8 for Wii,6.5,
120 | Pokemon: Ultra Sun and Ultra Moon for 3DS,8.1,
121 | Super Mario Kart for SNES,10.0,
122 | Grand Theft Auto V for XOne,9.0,9.0
123 | Final Fantasy X for PS2,9.0,10.0
124 | Final Fantasy VIII for PS,9.4,
125 | Pokemon Black 2 and White 2 for DS,8.0,8.3
126 | Call of Duty: Infinite Warfare for PS4,7.7,3.8
127 | Fallout 4 for PS4,8.6,6.6
128 | Mario Kart 8 for WiiU,8.8,8.9
129 | Tekken 3 for PS,9.6,
130 | FIFA 16 for PS4,8.5,4.4
131 | The Last of Us for PS3,9.5,
132 | GoldenEye 007 for N64,9.8,
133 | Star Wars Battlefront (2015) for PS4,7.1,
134 | Halo 2 for XB,9.6,
135 | NBA 2K20 for PS4,7.8,1.2
136 | The Sims 3 for PC,8.5,
137 | Grand Theft Auto: Liberty City Stories for PSP,8.8,
138 | Pac-Man for 2600,9.0,7.4
139 | Pokemon Platinum Version for DS,8.3,8.9
140 | The Legend of Zelda: Ocarina of Time for N64,9.9,10.0
141 | God of War III for PS3,9.2,9.4
142 | Crash Bandicoot 2: Cortex Strikes Back for PS,8.6,
143 | Call of Duty: Advanced Warfare for PS4,8.5,5.7
144 | Call of Duty: World at War for X360,8.5,7.8
145 | Super Mario Bros. 2 for NES,8.5,8.1
146 | Super Mario Galaxy 2 for Wii,9.7,9.8
147 | Super Smash Bros. Melee for GC,9.2,
148 | Battlefield 3 for X360,8.5,
149 | The Legend of Zelda: Twilight Princess for Wii,9.5,9.6
150 | Battlefield 1 for PS4,9.1,7.9
151 | Battlefield 3 for PS3,8.5,
152 | Need for Speed Underground for PS2,8.6,
153 | The Binding of Isaac for PC,8.3,8.4
154 | FIFA 20 for PS4,7.9,1.1
155 | Crash Bandicoot 3: Warped for PS,9.3,
156 | Pokemon Emerald Version for GBA,7.7,
157 | Guild Wars 2 for PC,9.0,8.1
158 | Need for Speed Underground 2 for PS2,8.3,
159 | Mario Kart: Double Dash!! for GC,8.5,
160 | Medal of Honor: Frontline for PS2,9.0,
161 | Crash Bandicoot for PS,7.8,
162 | Uncharted 2: Among Thieves for PS3,9.5,9.6
163 | Call of Duty 4: Modern Warfare for PS3,9.5,9.6
164 | FIFA Soccer 12 for PS3,9.2,
165 | FIFA 14 for PS3,9.0,
166 | New Super Mario Bros. U Deluxe for NS,8.0,6.6
167 | Fallout 4 for PC,9.0,5.5
168 | Tomodachi Life for 3DS,7.8,7.6
169 | Red Dead Redemption for PS3,9.5,
170 | Donkey Kong Country Returns for Wii,8.6,
171 | The Legend of Zelda for NES,8.4,8.8
172 | Assassin's Creed III for PS3,8.8,
173 | Guild Wars for PC,8.7,
174 | Red Dead Redemption for X360,9.5,10.0
175 | The Elder Scrolls V: Skyrim for PS3,9.0,
176 | Final Fantasy XII for PS2,9.4,9.5
177 | Pokemon Crystal Version for GBC,8.7,
178 | Halo 3: ODST for X360,8.7,
179 | Minecraft for PS4,9.4,
180 | Luigi's Mansion 3 for NS,8.6,8.5
181 | FIFA 15 for PS4,8.1,5.7
182 | Kingdom Hearts for PS2,8.5,
183 | Myst for PC,8.9,8.0
184 | Driver for PS,8.5,
185 | Call of Duty: WWII for XOne,8.0,4.0
186 | Luigi's Mansion: Dark Moon for 3DS,8.6,8.5
187 | Big Brain Academy for DS,7.7,
188 | Minecraft for PS3,9.5,
189 | Metal Gear Solid 2: Sons of Liberty for PS2,9.5,7.0
190 | The Legend of Zelda: Ocarina of Time 3D for 3DS,9.3,9.0
191 | Metal Gear Solid 4: Guns of the Patriots for PS3,9.3,9.8
192 | Metal Gear Solid for PS,9.3,
193 | Cities: Skylines for PC,8.5,8.9
194 | Euro Truck Simulator 2 for PC,8.5,8.7
195 | Mario Kart: Super Circuit for GBA,9.3,
196 | Super Mario Sunshine for GC,9.2,
197 | Super Mario 3D World for WiiU,9.5,8.9
198 | Age of Empires II: HD Edition for PC,6.8,7.9
199 | New Super Mario Bros. U for WiiU,8.4,8.0
200 | Link's Crossbow Training for Wii,6.9,
201 | Red Dead Redemption 2 for XOne,9.7,7.4
202 | Destiny for PS4,7.6,6.1
203 | Tekken 2 for PS,9.1,
204 | Uncharted: The Nathan Drake Collection for PS4,8.6,8.5
205 | Super Mario World: Super Mario Advance 2 for GBA,9.4,
206 | LEGO Star Wars: The Complete Saga for Wii,8.0,
207 | Cooking Mama for DS,6.6,
208 | Super Mario Advance for GBA,8.2,
209 | Assassin's Creed II for PS3,9.0,
210 | Assassin's Creed for X360,8.2,8.0
211 | Super Smash Bros. for N64,8.4,
212 | Batman: Arkham City for PS3,9.6,
213 | Forza Motorsport 3 for X360,9.2,9.8
214 | Dragon Quest IX: Sentinels of the Starry Skies for DS,8.6,
215 | Final Fantasy IX for PS,9.2,
216 | Final Fantasy X-2 for PS2,8.3,
217 | Sid Meier's Civilization VI for PC,8.8,7.0
218 | Tomb Raider (2013) for PC,8.8,
219 | Super Mario Maker 2 for NS,8.8,8.5
220 | Pokemon Stadium for N64,7.5,
221 | Super Mario Advance 4: Super Mario Bros. 3 for GBA,9.2,
222 | Call of Duty: World at War for PS3,8.4,
223 | Crash Bandicoot: The Wrath of Cortex for PS2,6.9,
224 | Super Smash Bros. for Wii U for WiiU,9.2,8.9
225 | Final Fantasy XIII for PS3,8.0,9.2
226 | Gran Turismo 5 Prologue for PS3,8.3,
227 | Pokemon Pinball for GBC,8.7,
228 | Assassin's Creed III for X360,8.5,
229 | Assassin's Creed II for X360,9.1,
230 | The Forest for PC,8.3,7.3
231 | Donkey Kong 64 for N64,9.3,
232 | Just Dance 2 for Wii,7.3,
233 | Tomb Raider II for PS,7.6,
234 | Madden NFL 2004 for PS2,9.5,
235 | Call of Duty: Advanced Warfare for XOne,8.3,5.5
236 | Fallout: New Vegas for PC,8.3,
237 | Kingdom Hearts II for PS2,8.3,
238 | Nintendo Land for WiiU,7.8,7.9
239 | Super Mario Land 3: Wario Land for GB,7.9,
240 | Donkey Kong Country 2: Diddy's Kong Quest for SNES,9.3,
241 | Medal of Honor: Rising Sun for PS2,5.9,
242 | Battlefield 1 for XOne,9.3,7.6
243 | Microsoft Flight Simulator for PC,7.0,
244 | Guitar Hero II for PS2,9.2,
245 | Resident Evil 5 for PS3,8.6,8.8
246 | Fable III for X360,7.8,
247 | Mario & Sonic at the Olympic Games for DS,7.0,
248 | Grand Theft Auto: Vice City Stories for PSP,8.4,
249 | FIFA Soccer 11 for PS3,8.7,
250 | Final Fantasy XV for PS4,8.1,9.5
251 | Super Mario Bros. Deluxe for GB,9.7,
252 | Fallout 4 for XOne,8.4,6.4
253 | Tony Hawk's Pro Skater for PS,9.6,
254 | Warzone 2100 for PS,7.5,
255 | Spyro the Dragon for PS,8.7,
256 | Gears of War for X360,9.4,9.4
257 | Gears of War 2 for X360,9.4,9.5
258 | Halo 5: Guardians for XOne,8.6,6.4
259 | Halo: Combat Evolved for XB,9.5,
260 | Guitar Hero III: Legends of Rock for PS2,8.2,
261 | Uncharted: Drake's Fortune for PS3,8.7,8.6
262 | Resident Evil 2 for PS,9.3,
263 | Fallout 3 for X360,9.0,8.6
264 | Splatoon for WiiU,8.4,8.7
265 | Madden NFL 06 for PS2,9.1,
266 | Stardew Valley for PC,9.2,8.7
267 | Dragon Quest VIII: Journey of the Cursed King for PS2,8.6,
268 | Pokemon Mystery Dungeon: Explorers of Time / Darkness for DS,6.4,
269 | Diddy Kong Racing for N64,8.3,
270 | Crash Bandicoot N. Sane Trilogy for PS4,8.0,8.5
271 | Assassin's Creed for PS3,8.2,8.5
272 | Call of Duty: Infinite Warfare for XOne,7.8,3.6
273 | Crash Team Racing for PS,9.2,
274 | LEGO Star Wars: The Complete Saga for DS,7.8,
275 | The Legend of Zelda: Phantom Hourglass for DS,9.1,
276 | Batman: Arkham City for X360,9.5,
277 | Driver 2 for PS,7.0,
278 | The Simpsons: Hit & Run for PS2,7.7,
279 | World of Warcraft: Cataclysm for PC,9.0,
280 | Tony Hawk's Pro Skater 2 for PS,9.3,
281 | Gran Turismo for PSP,7.7,9.5
282 | The Lord of the Rings: The Two Towers for PS2,8.3,
283 | Tomb Raider for PS,8.9,
284 | God of War for PS2,9.3,
285 | The Legend of Zelda: A Link to the Past for SNES,10.0,
286 | Left 4 Dead for PC,8.6,8.5
287 | Guitar Hero III: Legends of Rock for Wii,8.6,
288 | Forza Motorsport 4 for X360,9.1,
289 | Nintendogs + cats for 3DS,7.2,
290 | "Pokemon: Let's Go, Pikachu! for NS",8.3,6.1
291 | BioShock Infinite for PC,9.5,8.6
292 | Mario & Luigi: Bowser's Inside Story for DS,9.1,
293 | FIFA 15 for PS3,6.9,
294 | Overwatch for PS4,8.8,6.4
295 | Mario & Sonic at the Olympic Winter Games for Wii,6.8,
296 | Madden NFL 2005 for PS2,9.5,
297 | Guitar Hero III: Legends of Rock for X360,8.7,
298 | Dead Island for PC,7.1,6.9
299 | Warcraft III: Reign of Chaos for PC,9.3,
300 | StarCraft II: Wings of Liberty for PC,9.3,
301 | LittleBigPlanet for PS3,9.4,9.2
302 | ARK: Survival Evolved for PC,7.0,5.2
303 | Professor Layton and the Curious Village for DS,8.7,
304 | Madden NFL 07 for PS2,8.5,
305 | Spider-Man: The Movie for PS2,8.2,
306 | The Elder Scrolls IV: Oblivion for X360,9.2,
307 | The Legend of Zelda: The Wind Waker for GC,9.6,
308 | Tony Hawk's Pro Skater 3 for PS2,9.1,
309 | Winning Eleven: Pro Evolution Soccer 2007 for PS2,8.8,
310 | The Legend of Zelda: Link's Awakening for NS,8.7,8.4
311 | Michael Jackson: The Experience for Wii,5.6,
312 | Tom Clancy's The Division for PS4,8.0,7.0
313 | Need for Speed: Most Wanted for PS2,8.6,
314 | Resistance: Fall of Man for PS3,8.5,9.1
315 | Call of Duty: Advanced Warfare for X360,9.1,
316 | Animal Crossing: City Folk for Wii,7.1,8.8
317 | Watch Dogs for PS4,8.0,6.4
318 | Starbound for PC,8.1,7.1
319 | Asteroids for 2600,7.6,7.8
320 | Dragon Quest VII for PS,8.0,3.0
321 | Monster Hunter Generations for 3DS,8.8,8.0
322 | The Witcher 3: Wild Hunt for XOne,9.1,9.2
323 | Batman: Arkham Asylum for PS3,9.0,
324 | Call of Duty: Advanced Warfare for PS3,9.1,
325 | Namco Museum for GBA,8.5,
326 | God of War II for PS2,9.3,9.8
327 | Daxter for PSP,8.6,
328 | Super Paper Mario for Wii,8.5,
329 | Assassin's Creed: Revelations for PS3,8.8,
330 | Metal Gear Solid 3: Snake Eater for PS2,9.0,
331 | Assassin's Creed: Revelations for X360,7.9,
332 | FIFA Soccer 06 for PS2,8.4,
333 | Counter-Strike for PC,8.9,
334 | Monster Hunter 4 Ultimate for 3DS,8.8,8.6
335 | Mortal Kombat 11 for PS4,8.2,3.6
336 | Street Fighter IV for PS3,9.3,9.0
337 | FIFA Soccer 12 for X360,9.2,
338 | Call of Duty: Ghosts for PS4,7.5,3.8
339 | Teenage Mutant Ninja Turtles for NES,5.9,
340 | Just Cause 2 for PC,8.2,
341 | Excitebike for NES,8.4,
342 | Frogger for PS,2.0,
343 | Star Wars Battlefront (2015) for XOne,6.9,
344 | FIFA 14 for X360,9.1,
345 | Destiny 2 for PS4,8.5,4.9
346 | Madden NFL 2003 for PS2,9.4,
347 | Super Mario World 2: Yoshi's Island for SNES,9.0,
348 | Batman: Arkham Knight for PS4,8.1,7.8
349 | FIFA 07 Soccer for PS2,8.4,
350 | The Sims 4 for PC,7.0,4.0
351 | Street Fighter II Turbo for SNES,9.0,
352 | Fallout: New Vegas for X360,8.2,
353 | The Legend of Zelda: A Link Between Worlds for 3DS,9.3,9.0
354 | Far Cry 4 for PS4,8.4,7.7
355 | Carnival Games for Wii,4.2,
356 | World Soccer Winning Eleven 9 for PS2,8.9,
357 | Assassin's Creed Origins for PS4,8.1,7.2
358 | Forza Motorsport 2 for X360,9.1,
359 | Tekken Tag Tournament for PS2,8.7,
360 | Super Mario Maker for WiiU,8.8,8.7
361 | Monster Hunter: World for PS4,9.3,9.0
362 | Fallout 3 for PS3,8.8,
363 | The Last of Us: Part II for PS4,9.4,5.6
364 | Star Fox 64 for N64,9.0,
365 | World of Warcraft: Wrath of the Lich King for PC,9.3,9.3
366 | Just Dance for Wii,5.4,
367 | Diablo II for PC,8.8,
368 | EyeToy Play for PS2,7.5,
369 | The Elder Scrolls V: Skyrim for PC,9.2,8.2
370 | Namco Museum: 50th Anniversary for PS2,5.0,
371 | NBA 2K16 for PS4,8.7,6.7
372 | Left 4 Dead 2 for X360,9.0,
373 | Far Cry 5 for PS4,7.9,6.8
374 | Assassin's Creed IV: Black Flag for PS3,8.7,
375 | Battlefield 4 for PS4,8.3,7.0
376 | EA Sports Active for Wii,8.3,
377 | Half-Life 2: Episode One for PC,8.7,
378 | Dragon Quest III for NES,,8.0
379 | Tony Hawk's Underground for PS2,8.7,
380 | Just Dance 2014 for Wii,7.4,
381 | Professor Layton and the Diabolical Box for DS,8.5,
382 | Sports Champions for PS3,7.6,
383 | World Soccer Winning Eleven 8 International for PS2,9.3,
384 | Resident Evil VII: Biohazard for PS4,8.6,
385 | Monster Hunter Freedom Unite for PSP,7.7,9.7
386 | Madden NFL 20 for PS4,7.6,1.5
387 | Gran Turismo Sport for PS4,7.5,6.0
388 | LEGO Indiana Jones: The Original Adventures for X360,7.5,
389 | The Sims: Unleashed for PC,7.3,
390 | Ratchet & Clank: Size Matters for PSP,8.5,
391 | Harry Potter and the Sorcerer's Stone for PS,8.0,
392 | Pokemon Trading Card Game for GB,8.3,
393 | FIFA 17 for XOne,8.8,5.1
394 | Spyro: Year of the Dragon for PS,9.1,
395 | FIFA Soccer 2005 for PS2,8.4,
396 | Diablo III: Reaper of Souls for PC,8.7,6.6
397 | The Legend of Zelda: Skyward Sword for Wii,9.4,
398 | Tony Hawk's Pro Skater 4 for PS2,9.6,
399 | MySims for DS,6.7,
400 | Midnight Club 3: DUB Edition for PSP,7.2,
401 | Banjo-Kazooie for N64,9.3,9.4
402 |
--------------------------------------------------------------------------------
/Analyzing NYC Public School Test Result Scores/notebook.ipynb:
--------------------------------------------------------------------------------
1 | {"cells":[{"metadata":{"dc":{"key":"4"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 1. Inspecting the data\n![\"New](\"https://images.unsplash.com/photo-1550523049-a61d0deec4a4?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=774&q=80\")
\nPhoto by Jannis Lucas on Unsplash.\n
\nEvery year, American high school students take SATs, which are standardized tests intended to measure literacy, numeracy, and writing skills. There are three sections - reading, math, and writing, each with a maximum score of 800 points. These tests are extremely important for students and colleges, as they play a pivotal role in the admissions process.
\nAnalyzing the performance of schools is important for a variety of stakeholders, including policy and education professionals, researchers, government, and even parents considering which school their children should attend.
\nIn this notebook, we will take a look at data on SATs across public schools in New York City. Our database contains a single table:
\nschools
\n\n\n\n| column | \ntype | \ndescription | \n
\n\n\n\nschool_name | \nvarchar | \nName of school | \n
\n\nborough | \nvarchar | \nBorough that the school is located in | \n
\n\nbuilding_code | \nvarchar | \nCode for the building | \n
\n\naverage_math | \nint | \nAverage math score for SATs | \n
\n\naverage_reading | \nint | \nAverage reading score for SATs | \n
\n\naverage_writing | \nint | \nAverage writing score for SATs | \n
\n\npercent_tested | \nnumeric | \nPercentage of students completing SATs | \n
\n\n
\nLet's familiarize ourselves with the data by taking a looking at the first few schools!
"},{"metadata":{"dc":{"key":"4"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\npostgresql:///schools\n \n-- Select all columns from the database\n-- Display only the first ten rows\nSELECT *\nFROM schools\nLIMIT 10;","execution_count":2,"outputs":[{"output_type":"stream","text":"10 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":2,"data":{"text/plain":"[('New Explorations into Science, Technology and Math High School', 'Manhattan', 'M022', 657, 601, 601, None),\n ('Essex Street Academy', 'Manhattan', 'M445', 395, 411, 387, 78.9),\n ('Lower Manhattan Arts Academy', 'Manhattan', 'M445', 418, 428, 415, 65.1),\n ('High School for Dual Language and Asian Studies', 'Manhattan', 'M445', 613, 453, 463, 95.9),\n ('Henry Street School for International Studies', 'Manhattan', 'M056', 410, 406, 381, 59.7),\n ('Bard High School Early College', 'Manhattan', 'M097', 634, 641, 639, 70.8),\n ('Urban Assembly Academy of Government and Law', 'Manhattan', 'M445', 389, 395, 381, 80.8),\n ('Marta Valle High School', 'Manhattan', 'M025', 438, 413, 394, 35.6),\n ('University Neighborhood High School', 'Manhattan', 'M446', 437, 355, 352, 69.9),\n ('New Design High School', 'Manhattan', 'M445', 381, 396, 372, 73.7)]","text/html":"\n \n \n | school_name | \n borough | \n building_code | \n average_math | \n average_reading | \n average_writing | \n percent_tested | \n
\n \n \n \n | New Explorations into Science, Technology and Math High School | \n Manhattan | \n M022 | \n 657 | \n 601 | \n 601 | \n None | \n
\n \n | Essex Street Academy | \n Manhattan | \n M445 | \n 395 | \n 411 | \n 387 | \n 78.9 | \n
\n \n | Lower Manhattan Arts Academy | \n Manhattan | \n M445 | \n 418 | \n 428 | \n 415 | \n 65.1 | \n
\n \n | High School for Dual Language and Asian Studies | \n Manhattan | \n M445 | \n 613 | \n 453 | \n 463 | \n 95.9 | \n
\n \n | Henry Street School for International Studies | \n Manhattan | \n M056 | \n 410 | \n 406 | \n 381 | \n 59.7 | \n
\n \n | Bard High School Early College | \n Manhattan | \n M097 | \n 634 | \n 641 | \n 639 | \n 70.8 | \n
\n \n | Urban Assembly Academy of Government and Law | \n Manhattan | \n M445 | \n 389 | \n 395 | \n 381 | \n 80.8 | \n
\n \n | Marta Valle High School | \n Manhattan | \n M025 | \n 438 | \n 413 | \n 394 | \n 35.6 | \n
\n \n | University Neighborhood High School | \n Manhattan | \n M446 | \n 437 | \n 355 | \n 352 | \n 69.9 | \n
\n \n | New Design High School | \n Manhattan | \n M445 | \n 381 | \n 396 | \n 372 | \n 73.7 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"11"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 2. Finding missing values\nIt looks like the first school in our database had no data in the percent_tested column!
\nLet's identify how many schools have missing data for this column, indicating schools that did not report the percentage of students tested.
\nTo understand whether this missing data problem is widespread in New York, we will also calculate the total number of schools in the database.
"},{"metadata":{"dc":{"key":"11"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\n\n-- Count rows with percent_tested missing and total number of schools\nSELECT \n COUNT(school_name) - COUNT(percent_tested) AS num_tested_missing,\n COUNT(school_name) AS num_schools\nFROM schools;","execution_count":4,"outputs":[{"output_type":"stream","text":" * postgresql:///schools\n1 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"[(20, 375)]","text/html":"\n \n \n | num_tested_missing | \n num_schools | \n
\n \n \n \n | 20 | \n 375 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"18"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 3. Schools by building code\nThere are 20 schools with missing data for percent_tested, which only makes up 5% of all rows in the database.
\nNow let's turn our attention to how many schools there are. When we displayed the first ten rows of the database, several had the same value in the building_code column, suggesting there are multiple schools based in the same location. Let's find out how many unique school locations exist in our database.
"},{"metadata":{"dc":{"key":"18"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\n\n-- Count the number of unique building_code values\nSELECT COUNT(DISTINCT building_code) AS num_school_buildings\nFROM schools;","execution_count":6,"outputs":[{"output_type":"stream","text":" * postgresql:///schools\n1 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"[(233,)]","text/html":"\n \n \n | num_school_buildings | \n
\n \n \n \n | 233 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"25"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 4. Best schools for math\nOut of 375 schools, only 233 (62%) have a unique building_code!
\nNow let's start our analysis of school performance. As each school reports individually, we will treat them this way rather than grouping them by building_code.
\nFirst, let's find all schools with an average math score of at least 80% (out of 800).
"},{"metadata":{"dc":{"key":"25"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\n\n-- Select school and average_math\n-- Filter for average_math 640 or higher\n-- Display from largest to smallest average_math\n\nSELECT \n school_name,\n average_math\nFROM schools\nWHERE average_math >= 640\nORDER BY average_math DESC;","execution_count":8,"outputs":[{"output_type":"stream","text":" * postgresql:///schools\n10 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"[('Stuyvesant High School', 754),\n ('Bronx High School of Science', 714),\n ('Staten Island Technical High School', 711),\n ('Queens High School for the Sciences at York College', 701),\n ('High School for Mathematics, Science, and Engineering at City College', 683),\n ('Brooklyn Technical High School', 682),\n ('Townsend Harris High School', 680),\n ('High School of American Studies at Lehman College', 669),\n ('New Explorations into Science, Technology and Math High School', 657),\n ('Eleanor Roosevelt High School', 641)]","text/html":"\n \n \n | school_name | \n average_math | \n
\n \n \n \n | Stuyvesant High School | \n 754 | \n
\n \n | Bronx High School of Science | \n 714 | \n
\n \n | Staten Island Technical High School | \n 711 | \n
\n \n | Queens High School for the Sciences at York College | \n 701 | \n
\n \n | High School for Mathematics, Science, and Engineering at City College | \n 683 | \n
\n \n | Brooklyn Technical High School | \n 682 | \n
\n \n | Townsend Harris High School | \n 680 | \n
\n \n | High School of American Studies at Lehman College | \n 669 | \n
\n \n | New Explorations into Science, Technology and Math High School | \n 657 | \n
\n \n | Eleanor Roosevelt High School | \n 641 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"32"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 5. Lowest reading score\nWow, there are only ten public schools in New York City with an average math score of at least 640!
\nNow let's look at the other end of the spectrum and find the single lowest score for reading. We will only select the score, not the school, to avoid naming and shaming!
"},{"metadata":{"dc":{"key":"32"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\n\n-- Find lowest average_reading\n\nSELECT MIN(average_reading) AS lowest_reading\nFROM schools;","execution_count":10,"outputs":[{"output_type":"stream","text":" * postgresql:///schools\n1 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"[(302,)]","text/html":"\n \n \n | lowest_reading | \n
\n \n \n \n | 302 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"39"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 6. Best writing school\nThe lowest average score for reading across schools in New York City is less than 40% of the total available points!
\nNow let's find the school with the highest average writing score.
"},{"metadata":{"dc":{"key":"39"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\n\n-- Find the top score for average_writing\n-- Group the results by school\n-- Sort by max_writing in descending order\n-- Reduce output to one school\n\nSELECT \n school_name,\n MAX(average_writing) AS max_writing\nFROM schools\nGROUP BY school_name\nORDER BY max_writing DESC\nLIMIT 1;","execution_count":12,"outputs":[{"output_type":"stream","text":" * postgresql:///schools\n1 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"[('Stuyvesant High School', 693)]","text/html":"\n \n \n | school_name | \n max_writing | \n
\n \n \n \n | Stuyvesant High School | \n 693 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"46"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 7. Top 10 schools\nAn average writing score of 693 is pretty impressive!
\nThis top writing score was at the same school that got the top math score, Stuyvesant High School. Stuyvesant is widely known as a perennial top school in New York.
\nWhat other schools are also excellent across the board? Let's look at scores across reading, writing, and math to find out.
"},{"metadata":{"dc":{"key":"46"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\n\n-- Calculate average_sat\n-- Group by school_name\n-- Sort by average_sat in descending order\n-- Display the top ten results\n\nSELECT\n school_name,\n SUM(average_math) + SUM(average_reading) + SUM(average_writing) AS average_sat\nFROM schools\nGROUP BY school_name\nORDER BY average_sat DESC\nLIMIT 10;","execution_count":14,"outputs":[{"output_type":"stream","text":" * postgresql:///schools\n10 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"[('Stuyvesant High School', 2144),\n ('Staten Island Technical High School', 2041),\n ('Bronx High School of Science', 2041),\n ('High School of American Studies at Lehman College', 2013),\n ('Townsend Harris High School', 1981),\n ('Queens High School for the Sciences at York College', 1947),\n ('Bard High School Early College', 1914),\n ('Brooklyn Technical High School', 1896),\n ('Eleanor Roosevelt High School', 1889),\n ('High School for Mathematics, Science, and Engineering at City College', 1889)]","text/html":"\n \n \n | school_name | \n average_sat | \n
\n \n \n \n | Stuyvesant High School | \n 2144 | \n
\n \n | Staten Island Technical High School | \n 2041 | \n
\n \n | Bronx High School of Science | \n 2041 | \n
\n \n | High School of American Studies at Lehman College | \n 2013 | \n
\n \n | Townsend Harris High School | \n 1981 | \n
\n \n | Queens High School for the Sciences at York College | \n 1947 | \n
\n \n | Bard High School Early College | \n 1914 | \n
\n \n | Brooklyn Technical High School | \n 1896 | \n
\n \n | Eleanor Roosevelt High School | \n 1889 | \n
\n \n | High School for Mathematics, Science, and Engineering at City College | \n 1889 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"53"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 8. Ranking boroughs\nThere are four schools with average SAT scores of over 2000! Now let's analyze performance by New York City borough.
\nWe will build a query that calculates the number of schools and the average SAT score per borough!
"},{"metadata":{"dc":{"key":"53"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\n\n-- Select borough and a count of all schools, aliased as num_schools\n-- Calculate the sum of average_math, average_reading, and average_writing, divided by a count of all schools, aliased as average_borough_sat\n-- Organize results by borough\n-- Display by average_borough_sat in descending order\n\nSELECT \n borough,\n COUNT(school_name) AS num_schools,\n (SUM(average_math) + SUM(average_reading) + SUM(average_writing))/COUNT(school_name) AS average_borough_sat\nFROM schools\nGROUP BY borough\nORDER BY average_borough_sat DESC;","execution_count":16,"outputs":[{"output_type":"stream","text":" * postgresql:///schools\n5 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"[('Staten Island', 10, 1439),\n ('Queens', 69, 1345),\n ('Manhattan', 89, 1340),\n ('Brooklyn', 109, 1230),\n ('Bronx', 98, 1202)]","text/html":"\n \n \n | borough | \n num_schools | \n average_borough_sat | \n
\n \n \n \n | Staten Island | \n 10 | \n 1439 | \n
\n \n | Queens | \n 69 | \n 1345 | \n
\n \n | Manhattan | \n 89 | \n 1340 | \n
\n \n | Brooklyn | \n 109 | \n 1230 | \n
\n \n | Bronx | \n 98 | \n 1202 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"60"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 9. Brooklyn numbers\nIt appears that schools in Staten Island, on average, produce higher scores across all three categories. However, there are only 10 schools in Staten Island, compared to an average of 91 schools in the other four boroughs!
\nFor our final query of the database, let's focus on Brooklyn, which has 109 schools. We wish to find the top five schools for math performance.
"},{"metadata":{"dc":{"key":"60"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\n\n-- Select school and average_math\n-- Filter for schools in Brooklyn\n-- Aggregate on school_name\n-- Display results from highest average_math and restrict output to five rows\n\nSELECT \n school_name,\n average_math\nFROM schools\nWHERE borough = 'Brooklyn'\nORDER BY average_math DESC\nLIMIT 5;","execution_count":18,"outputs":[{"output_type":"stream","text":" * postgresql:///schools\n5 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"[('Brooklyn Technical High School', 682),\n ('Brooklyn Latin School', 625),\n ('Leon M. Goldstein High School for the Sciences', 563),\n ('Millennium Brooklyn High School', 553),\n ('Midwood High School', 550)]","text/html":"\n \n \n | school_name | \n average_math | \n
\n \n \n \n | Brooklyn Technical High School | \n 682 | \n
\n \n | Brooklyn Latin School | \n 625 | \n
\n \n | Leon M. Goldstein High School for the Sciences | \n 563 | \n
\n \n | Millennium Brooklyn High School | \n 553 | \n
\n \n | Midwood High School | \n 550 | \n
\n \n
"},"metadata":{}}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.6.7","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":2}
2 |
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/Analyzing Motorcycle Part Sales/notebook.ipynb:
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1 | {"cells":[{"source":"![\"Image](\"motorcycle.jpg\")
\n\nYou're working for a company that sells motorcycle parts, and they've asked with some help in analyzing their sales data!\n\nThey operate three warehouses in the area, selling both retail and wholesale. They offer a variety of parts and accept credit card, cash, and bank transfer as payment methods. However, each payment type incurs a different fee.\n\nThe board of directors want to gain a better understanding of wholesale revenue by product line, and how this varies month-to-month and across warehouses. You have been tasked with calculating net revenue for each product line, grouping results by month and warehouse. The results should be filtered so that only `\"Wholesale\"` orders are included.\n\nThey have provided you with access to their database, which contains the following table called `sales`:\n\n| Column | Data type | Description |\n|--------|-----------|-------------|\n| `order_number` | `VARCHAR` | Unique order number. |\n| `date` | `DATE` | Date of the order, from June to August 2021. |\n| `warehouse` | `VARCHAR` | The warehouse that the order was made from— `North`, `Central`, or `West`. |\n| `client_type` | `VARCHAR` | Whether the order was `Retail` or `Wholesale`. |\n| `product_line` | `VARCHAR` | Type of product ordered. |\n| `quantity` | `INT` | Number of products ordered. | \n| `unit_price` | `FLOAT` | Price per product (dollars). |\n| `total` | `FLOAT` | Total price of the order (dollars). |\n| `payment` | `VARCHAR` | Payment method—`Credit card`, `Transfer`, or `Cash`. |\n| `payment_fee` | `FLOAT` | Percentage of `total` charged as a result of the `payment` method. |\n\n\nYour query output should be presented in the following format:\n\n| `product_line` | `month` | `warehouse` |\t`net_revenue` |\n|----------------|-----------|----------------------------|--------------|\n| product_one | --- | --- | --- |\n| product_one | --- | --- | --- |\n| product_one | --- | --- | --- |\n| product_one | --- | --- | --- |\n| product_one | --- | --- | --- |\n| product_one | --- | --- | --- |\n| product_two | --- | --- | --- |\n| ... | ... | ... | ... |\n\n","metadata":{},"id":"13335817","cell_type":"markdown"},{"source":"**1. Explore the data**","metadata":{},"cell_type":"markdown","id":"f237c35c-4a96-46b5-b1bb-46c5ffe409d2"},{"source":"SELECT *\nFROM sales\nLIMIT 5","metadata":{"customType":"sql","dataFrameVariableName":"df","initial":false,"integrationId":"89e17161-a224-4a8a-846b-0adc0fe7a4b1","queuedAt":1667575034271,"executionStartedAt":1667575034533,"executionStoppedAt":1667575035860,"lastSuccessfullyExecutedCode":"SELECT *\nFROM sales\nLIMIT 5"},"cell_type":"code","id":"b95b9486-1ef7-44f4-9b45-7eac642bbf6a","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"application/com.datacamp.data-table.v1+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"order_number","type":"string"},{"name":"date","type":"datetime","tz":"UTC"},{"name":"warehouse","type":"string"},{"name":"client_type","type":"string"},{"name":"product_line","type":"string"},{"name":"quantity","type":"integer"},{"name":"unit_price","type":"number"},{"name":"total","type":"number"},{"name":"payment","type":"string"},{"name":"payment_fee","type":"number"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":0,"order_number":"N1","date":"2021-06-01T00:00:00.000Z","warehouse":"North","client_type":"Retail","product_line":"Breaking system","quantity":9,"unit_price":19.29,"total":173.61,"payment":"Cash","payment_fee":0},{"index":1,"order_number":"N2","date":"2021-06-01T00:00:00.000Z","warehouse":"North","client_type":"Retail","product_line":"Suspension & traction","quantity":8,"unit_price":32.93,"total":263.45,"payment":"Credit card","payment_fee":0.03},{"index":2,"order_number":"N3","date":"2021-06-01T00:00:00.000Z","warehouse":"North","client_type":"Wholesale","product_line":"Frame & body","quantity":16,"unit_price":37.84,"total":605.44,"payment":"Transfer","payment_fee":0.01},{"index":3,"order_number":"N4","date":"2021-06-01T00:00:00.000Z","warehouse":"North","client_type":"Wholesale","product_line":"Suspension & traction","quantity":40,"unit_price":37.37,"total":1494.8,"payment":"Transfer","payment_fee":0.01},{"index":4,"order_number":"N5","date":"2021-06-01T00:00:00.000Z","warehouse":"North","client_type":"Retail","product_line":"Frame & body","quantity":6,"unit_price":45.44,"total":272.61,"payment":"Credit card","payment_fee":0.03}]},"total_rows":5,"truncation_type":null},"text/plain":" order_number date warehouse client_type \\\n0 N1 2021-06-01 00:00:00+00:00 North Retail \n1 N2 2021-06-01 00:00:00+00:00 North Retail \n2 N3 2021-06-01 00:00:00+00:00 North Wholesale \n3 N4 2021-06-01 00:00:00+00:00 North Wholesale \n4 N5 2021-06-01 00:00:00+00:00 North Retail \n\n product_line quantity unit_price total payment \\\n0 Breaking system 9 19.29 173.61 Cash \n1 Suspension & traction 8 32.93 263.45 Credit card \n2 Frame & body 16 37.84 605.44 Transfer \n3 Suspension & traction 40 37.37 1494.80 Transfer \n4 Frame & body 6 45.44 272.61 Credit card \n\n payment_fee \n0 0.00 \n1 0.03 \n2 0.01 \n3 0.01 \n4 0.03 ","text/html":"\n\n
\n \n \n | \n order_number | \n date | \n warehouse | \n client_type | \n product_line | \n quantity | \n unit_price | \n total | \n payment | \n payment_fee | \n
\n \n \n \n | 0 | \n N1 | \n 2021-06-01 00:00:00+00:00 | \n North | \n Retail | \n Breaking system | \n 9 | \n 19.29 | \n 173.61 | \n Cash | \n 0.00 | \n
\n \n | 1 | \n N2 | \n 2021-06-01 00:00:00+00:00 | \n North | \n Retail | \n Suspension & traction | \n 8 | \n 32.93 | \n 263.45 | \n Credit card | \n 0.03 | \n
\n \n | 2 | \n N3 | \n 2021-06-01 00:00:00+00:00 | \n North | \n Wholesale | \n Frame & body | \n 16 | \n 37.84 | \n 605.44 | \n Transfer | \n 0.01 | \n
\n \n | 3 | \n N4 | \n 2021-06-01 00:00:00+00:00 | \n North | \n Wholesale | \n Suspension & traction | \n 40 | \n 37.37 | \n 1494.80 | \n Transfer | \n 0.01 | \n
\n \n | 4 | \n N5 | \n 2021-06-01 00:00:00+00:00 | \n North | \n Retail | \n Frame & body | \n 6 | \n 45.44 | \n 272.61 | \n Credit card | \n 0.03 | \n
\n \n
\n
\n\n
\n \n \n | \n product_line | \n month | \n warehouse | \n net_revenue | \n
\n \n \n \n | 0 | \n Breaking system | \n August | \n Central | \n 3009.10 | \n
\n \n | 1 | \n Breaking system | \n August | \n West | \n 2475.71 | \n
\n \n | 2 | \n Breaking system | \n August | \n North | \n 1753.19 | \n
\n \n | 3 | \n Breaking system | \n July | \n Central | \n 3740.94 | \n
\n \n | 4 | \n Breaking system | \n July | \n West | \n 3030.39 | \n
\n \n | 5 | \n Breaking system | \n July | \n North | \n 2568.55 | \n
\n \n | 6 | \n Breaking system | \n June | \n Central | \n 3648.14 | \n
\n \n | 7 | \n Breaking system | \n June | \n North | \n 1472.93 | \n
\n \n | 8 | \n Breaking system | \n June | \n West | \n 1200.64 | \n
\n \n | 9 | \n Electrical system | \n August | \n North | \n 4673.99 | \n
\n \n | 10 | \n Electrical system | \n August | \n Central | \n 3095.22 | \n
\n \n | 11 | \n Electrical system | \n August | \n West | \n 1229.45 | \n
\n \n | 12 | \n Electrical system | \n July | \n Central | \n 5521.94 | \n
\n \n | 13 | \n Electrical system | \n July | \n North | \n 1693.06 | \n
\n \n | 14 | \n Electrical system | \n July | \n West | \n 444.98 | \n
\n \n | 15 | \n Electrical system | \n June | \n Central | \n 2875.93 | \n
\n \n | 16 | \n Electrical system | \n June | \n North | \n 2002.30 | \n
\n \n | 17 | \n Engine | \n August | \n Central | \n 9433.48 | \n
\n \n | 18 | \n Engine | \n August | \n North | \n 2300.96 | \n
\n \n | 19 | \n Engine | \n July | \n Central | \n 1808.77 | \n
\n \n | 20 | \n Engine | \n July | \n North | \n 997.08 | \n
\n \n | 21 | \n Engine | \n June | \n Central | \n 6483.40 | \n
\n \n | 22 | \n Frame & body | \n August | \n Central | \n 8571.50 | \n
\n \n | 23 | \n Frame & body | \n August | \n North | \n 7819.95 | \n
\n \n | 24 | \n Frame & body | \n August | \n West | \n 821.40 | \n
\n \n | 25 | \n Frame & body | \n July | \n North | \n 6093.11 | \n
\n \n | 26 | \n Frame & body | \n July | \n Central | \n 3103.82 | \n
\n \n | 27 | \n Frame & body | \n June | \n Central | \n 5060.29 | \n
\n \n | 28 | \n Frame & body | \n June | \n North | \n 4861.08 | \n
\n \n | 29 | \n Frame & body | \n June | \n West | \n 2751.96 | \n
\n \n | 30 | \n Miscellaneous | \n August | \n North | \n 1823.03 | \n
\n \n | 31 | \n Miscellaneous | \n August | \n Central | \n 1722.40 | \n
\n \n | 32 | \n Miscellaneous | \n August | \n West | \n 805.31 | \n
\n \n | 33 | \n Miscellaneous | \n July | \n Central | \n 3087.31 | \n
\n \n | 34 | \n Miscellaneous | \n July | \n North | \n 2380.63 | \n
\n \n | 35 | \n Miscellaneous | \n July | \n West | \n 1145.26 | \n
\n \n | 36 | \n Miscellaneous | \n June | \n West | \n 2258.20 | \n
\n \n | 37 | \n Miscellaneous | \n June | \n Central | \n 1859.34 | \n
\n \n | 38 | \n Miscellaneous | \n June | \n North | \n 508.86 | \n
\n \n | 39 | \n Suspension & traction | \n August | \n Central | \n 5362.59 | \n
\n \n | 40 | \n Suspension & traction | \n August | \n North | \n 4874.51 | \n
\n \n | 41 | \n Suspension & traction | \n August | \n West | \n 1069.99 | \n
\n \n | 42 | \n Suspension & traction | \n July | \n Central | \n 6392.23 | \n
\n \n | 43 | \n Suspension & traction | \n July | \n North | \n 3677.21 | \n
\n \n | 44 | \n Suspension & traction | \n July | \n West | \n 2909.98 | \n
\n \n | 45 | \n Suspension & traction | \n June | \n North | \n 7985.17 | \n
\n \n | 46 | \n Suspension & traction | \n June | \n Central | \n 3291.80 | \n
\n \n | 47 | \n Suspension & traction | \n June | \n West | \n 2348.83 | \n
\n \n
\n
It's not that we humans only take debts to manage our necessities. A country may also take debt to manage its economy. For example, infrastructure spending is one costly ingredient required for a country's citizens to lead comfortable lives. The World Bank is the organization that provides debt to countries.
\nIn this notebook, we are going to analyze international debt data collected by The World Bank. The dataset contains information about the amount of debt (in USD) owed by developing countries across several categories. We are going to find the answers to questions like:
\n\n- What is the total amount of debt that is owed by the countries listed in the dataset?
\n- Which country owns the maximum amount of debt and what does that amount look like?
\n- What is the average amount of debt owed by countries across different debt indicators?
\n
\n![](\"https://storage.googleapis.com/deepnote-publishing-bucket/thumb/f2d65119-210b-41e6-a470-5dda818862ef-thumb.jpg\")
\nThe first line of code connects us to the international_debt database where the table international_debt is residing. Let's first SELECT all of the columns from the international_debt table. Also, we'll limit the output to the first ten rows to keep the output clean.
"},{"metadata":{"dc":{"key":"4"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\npostgresql:///international_debt\n \nSELECT *\nFROM international_debt\nLIMIT 10;","execution_count":50,"outputs":[{"output_type":"stream","text":"10 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":50,"data":{"text/plain":"[('Afghanistan', 'AFG', 'Disbursements on external debt, long-term (DIS, current US$)', 'DT.DIS.DLXF.CD', Decimal('72894453.700000003')),\n ('Afghanistan', 'AFG', 'Interest payments on external debt, long-term (INT, current US$)', 'DT.INT.DLXF.CD', Decimal('53239440.100000001')),\n ('Afghanistan', 'AFG', 'PPG, bilateral (AMT, current US$)', 'DT.AMT.BLAT.CD', Decimal('61739336.899999999')),\n ('Afghanistan', 'AFG', 'PPG, bilateral (DIS, current US$)', 'DT.DIS.BLAT.CD', Decimal('49114729.399999999')),\n ('Afghanistan', 'AFG', 'PPG, bilateral (INT, current US$)', 'DT.INT.BLAT.CD', Decimal('39903620.100000001')),\n ('Afghanistan', 'AFG', 'PPG, multilateral (AMT, current US$)', 'DT.AMT.MLAT.CD', Decimal('39107845')),\n ('Afghanistan', 'AFG', 'PPG, multilateral (DIS, current US$)', 'DT.DIS.MLAT.CD', Decimal('23779724.300000001')),\n ('Afghanistan', 'AFG', 'PPG, multilateral (INT, current US$)', 'DT.INT.MLAT.CD', Decimal('13335820')),\n ('Afghanistan', 'AFG', 'PPG, official creditors (AMT, current US$)', 'DT.AMT.OFFT.CD', Decimal('100847181.900000006')),\n ('Afghanistan', 'AFG', 'PPG, official creditors (DIS, current US$)', 'DT.DIS.OFFT.CD', Decimal('72894453.700000003'))]","text/html":"\n \n | country_name | \n country_code | \n indicator_name | \n indicator_code | \n debt | \n
\n \n | Afghanistan | \n AFG | \n Disbursements on external debt, long-term (DIS, current US$) | \n DT.DIS.DLXF.CD | \n 72894453.700000003 | \n
\n \n | Afghanistan | \n AFG | \n Interest payments on external debt, long-term (INT, current US$) | \n DT.INT.DLXF.CD | \n 53239440.100000001 | \n
\n \n | Afghanistan | \n AFG | \n PPG, bilateral (AMT, current US$) | \n DT.AMT.BLAT.CD | \n 61739336.899999999 | \n
\n \n | Afghanistan | \n AFG | \n PPG, bilateral (DIS, current US$) | \n DT.DIS.BLAT.CD | \n 49114729.399999999 | \n
\n \n | Afghanistan | \n AFG | \n PPG, bilateral (INT, current US$) | \n DT.INT.BLAT.CD | \n 39903620.100000001 | \n
\n \n | Afghanistan | \n AFG | \n PPG, multilateral (AMT, current US$) | \n DT.AMT.MLAT.CD | \n 39107845 | \n
\n \n | Afghanistan | \n AFG | \n PPG, multilateral (DIS, current US$) | \n DT.DIS.MLAT.CD | \n 23779724.300000001 | \n
\n \n | Afghanistan | \n AFG | \n PPG, multilateral (INT, current US$) | \n DT.INT.MLAT.CD | \n 13335820 | \n
\n \n | Afghanistan | \n AFG | \n PPG, official creditors (AMT, current US$) | \n DT.AMT.OFFT.CD | \n 100847181.900000006 | \n
\n \n | Afghanistan | \n AFG | \n PPG, official creditors (DIS, current US$) | \n DT.DIS.OFFT.CD | \n 72894453.700000003 | \n
\n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"12"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 2. Finding the number of distinct countries\nFrom the first ten rows, we can see the amount of debt owed by Afghanistan in the different debt indicators. But we do not know the number of different countries we have on the table. There are repetitions in the country names because a country is most likely to have debt in more than one debt indicator.
\nWithout a count of unique countries, we will not be able to perform our statistical analyses holistically. In this section, we are going to extract the number of unique countries present in the table.
"},{"metadata":{"dc":{"key":"12"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\nSELECT \n COUNT(DISTINCT(country_name)) AS total_distinct_countries\nFROM international_debt;","execution_count":52,"outputs":[{"output_type":"stream","text":" * postgresql:///international_debt\n1 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":52,"data":{"text/plain":"[(124,)]","text/html":"\n \n | total_distinct_countries | \n
\n \n | 124 | \n
\n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"20"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 3. Finding out the distinct debt indicators\nWe can see there are a total of 124 countries present on the table. As we saw in the first section, there is a column called indicator_name that briefly specifies the purpose of taking the debt. Just beside that column, there is another column called indicator_code which symbolizes the category of these debts. Knowing about these various debt indicators will help us to understand the areas in which a country can possibly be indebted to.
"},{"metadata":{"dc":{"key":"20"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\nSELECT DISTINCT indicator_code AS distinct_debt_indicators\nFROM international_debt\nORDER BY distinct_debt_indicators;","execution_count":54,"outputs":[{"output_type":"stream","text":" * postgresql:///international_debt\n25 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":54,"data":{"text/plain":"[('DT.AMT.BLAT.CD',),\n ('DT.AMT.DLXF.CD',),\n ('DT.AMT.DPNG.CD',),\n ('DT.AMT.MLAT.CD',),\n ('DT.AMT.OFFT.CD',),\n ('DT.AMT.PBND.CD',),\n ('DT.AMT.PCBK.CD',),\n ('DT.AMT.PROP.CD',),\n ('DT.AMT.PRVT.CD',),\n ('DT.DIS.BLAT.CD',),\n ('DT.DIS.DLXF.CD',),\n ('DT.DIS.MLAT.CD',),\n ('DT.DIS.OFFT.CD',),\n ('DT.DIS.PCBK.CD',),\n ('DT.DIS.PROP.CD',),\n ('DT.DIS.PRVT.CD',),\n ('DT.INT.BLAT.CD',),\n ('DT.INT.DLXF.CD',),\n ('DT.INT.DPNG.CD',),\n ('DT.INT.MLAT.CD',),\n ('DT.INT.OFFT.CD',),\n ('DT.INT.PBND.CD',),\n ('DT.INT.PCBK.CD',),\n ('DT.INT.PROP.CD',),\n ('DT.INT.PRVT.CD',)]","text/html":"\n \n | distinct_debt_indicators | \n
\n \n | DT.AMT.BLAT.CD | \n
\n \n | DT.AMT.DLXF.CD | \n
\n \n | DT.AMT.DPNG.CD | \n
\n \n | DT.AMT.MLAT.CD | \n
\n \n | DT.AMT.OFFT.CD | \n
\n \n | DT.AMT.PBND.CD | \n
\n \n | DT.AMT.PCBK.CD | \n
\n \n | DT.AMT.PROP.CD | \n
\n \n | DT.AMT.PRVT.CD | \n
\n \n | DT.DIS.BLAT.CD | \n
\n \n | DT.DIS.DLXF.CD | \n
\n \n | DT.DIS.MLAT.CD | \n
\n \n | DT.DIS.OFFT.CD | \n
\n \n | DT.DIS.PCBK.CD | \n
\n \n | DT.DIS.PROP.CD | \n
\n \n | DT.DIS.PRVT.CD | \n
\n \n | DT.INT.BLAT.CD | \n
\n \n | DT.INT.DLXF.CD | \n
\n \n | DT.INT.DPNG.CD | \n
\n \n | DT.INT.MLAT.CD | \n
\n \n | DT.INT.OFFT.CD | \n
\n \n | DT.INT.PBND.CD | \n
\n \n | DT.INT.PCBK.CD | \n
\n \n | DT.INT.PROP.CD | \n
\n \n | DT.INT.PRVT.CD | \n
\n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"28"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 4. Totaling the amount of debt owed by the countries\nAs mentioned earlier, the financial debt of a particular country represents its economic state. But if we were to project this on an overall global scale, how will we approach it?
\nLet's switch gears from the debt indicators now and find out the total amount of debt (in USD) that is owed by the different countries. This will give us a sense of how the overall economy of the entire world is holding up.
"},{"metadata":{"dc":{"key":"28"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\nSELECT \n ROUND(SUM(debt)/1000000, 2) AS total_debt\nFROM international_debt; ","execution_count":56,"outputs":[{"output_type":"stream","text":" * postgresql:///international_debt\n1 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":56,"data":{"text/plain":"[(Decimal('3079734.49'),)]","text/html":"\n \n | total_debt | \n
\n \n | 3079734.49 | \n
\n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"36"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 5. Country with the highest debt\n\"Human beings cannot comprehend very large or very small numbers. It would be useful for us to acknowledge that fact.\" - Daniel Kahneman. That is more than 3 million million USD, an amount which is really hard for us to fathom.
\nNow that we have the exact total of the amounts of debt owed by several countries, let's now find out the country that owns the highest amount of debt along with the amount. Note that this debt is the sum of different debts owed by a country across several categories. This will help to understand more about the country in terms of its socio-economic scenarios. We can also find out the category in which the country owns its highest debt. But we will leave that for now.
"},{"metadata":{"dc":{"key":"36"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\nSELECT \n country_name, \n SUM(debt) AS total_debt\nFROM international_debt\nGROUP BY country_name\nORDER BY total_debt DESC \nLIMIT 1;","execution_count":58,"outputs":[{"output_type":"stream","text":" * postgresql:///international_debt\n1 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":58,"data":{"text/plain":"[('China', Decimal('285793494734.200001568'))]","text/html":"\n \n | country_name | \n total_debt | \n
\n \n | China | \n 285793494734.200001568 | \n
\n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"44"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 6. Average amount of debt across indicators\nSo, it was China. A more in-depth breakdown of China's debts can be found here.
\nWe now have a brief overview of the dataset and a few of its summary statistics. We already have an idea of the different debt indicators in which the countries owe their debts. We can dig even further to find out on an average how much debt a country owes? This will give us a better sense of the distribution of the amount of debt across different indicators.
"},{"metadata":{"dc":{"key":"44"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\nSELECT \n indicator_code AS debt_indicator,\n indicator_name,\n AVG(debt) AS average_debt\nFROM international_debt\nGROUP BY debt_indicator, indicator_name\nORDER BY average_debt DESC\nLIMIT 10;","execution_count":60,"outputs":[{"output_type":"stream","text":" * postgresql:///international_debt\n10 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":60,"data":{"text/plain":"[('DT.AMT.DLXF.CD', 'Principal repayments on external debt, long-term (AMT, current US$)', Decimal('5904868401.499193612')),\n ('DT.AMT.DPNG.CD', 'Principal repayments on external debt, private nonguaranteed (PNG) (AMT, current US$)', Decimal('5161194333.812658349')),\n ('DT.DIS.DLXF.CD', 'Disbursements on external debt, long-term (DIS, current US$)', Decimal('2152041216.890243888')),\n ('DT.DIS.OFFT.CD', 'PPG, official creditors (DIS, current US$)', Decimal('1958983452.859836046')),\n ('DT.AMT.PRVT.CD', 'PPG, private creditors (AMT, current US$)', Decimal('1803694101.963265321')),\n ('DT.INT.DLXF.CD', 'Interest payments on external debt, long-term (INT, current US$)', Decimal('1644024067.650806481')),\n ('DT.DIS.BLAT.CD', 'PPG, bilateral (DIS, current US$)', Decimal('1223139290.398230108')),\n ('DT.INT.DPNG.CD', 'Interest payments on external debt, private nonguaranteed (PNG) (INT, current US$)', Decimal('1220410844.421518983')),\n ('DT.AMT.OFFT.CD', 'PPG, official creditors (AMT, current US$)', Decimal('1191187963.083064523')),\n ('DT.AMT.PBND.CD', 'PPG, bonds (AMT, current US$)', Decimal('1082623947.653623188'))]","text/html":"\n \n | debt_indicator | \n indicator_name | \n average_debt | \n
\n \n | DT.AMT.DLXF.CD | \n Principal repayments on external debt, long-term (AMT, current US$) | \n 5904868401.499193612 | \n
\n \n | DT.AMT.DPNG.CD | \n Principal repayments on external debt, private nonguaranteed (PNG) (AMT, current US$) | \n 5161194333.812658349 | \n
\n \n | DT.DIS.DLXF.CD | \n Disbursements on external debt, long-term (DIS, current US$) | \n 2152041216.890243888 | \n
\n \n | DT.DIS.OFFT.CD | \n PPG, official creditors (DIS, current US$) | \n 1958983452.859836046 | \n
\n \n | DT.AMT.PRVT.CD | \n PPG, private creditors (AMT, current US$) | \n 1803694101.963265321 | \n
\n \n | DT.INT.DLXF.CD | \n Interest payments on external debt, long-term (INT, current US$) | \n 1644024067.650806481 | \n
\n \n | DT.DIS.BLAT.CD | \n PPG, bilateral (DIS, current US$) | \n 1223139290.398230108 | \n
\n \n | DT.INT.DPNG.CD | \n Interest payments on external debt, private nonguaranteed (PNG) (INT, current US$) | \n 1220410844.421518983 | \n
\n \n | DT.AMT.OFFT.CD | \n PPG, official creditors (AMT, current US$) | \n 1191187963.083064523 | \n
\n \n | DT.AMT.PBND.CD | \n PPG, bonds (AMT, current US$) | \n 1082623947.653623188 | \n
\n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"52"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 7. The highest amount of principal repayments\nWe can see that the indicator DT.AMT.DLXF.CD tops the chart of average debt. This category includes repayment of long term debts. Countries take on long-term debt to acquire immediate capital. More information about this category can be found here.
\nAn interesting observation in the above finding is that there is a huge difference in the amounts of the indicators after the second one. This indicates that the first two indicators might be the most severe categories in which the countries owe their debts.
\nWe can investigate this a bit more so as to find out which country owes the highest amount of debt in the category of long term debts (DT.AMT.DLXF.CD). Since not all the countries suffer from the same kind of economic disturbances, this finding will allow us to understand that particular country's economic condition a bit more specifically.
"},{"metadata":{"dc":{"key":"52"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\nSELECT \n country_name, \n indicator_name\nFROM international_debt\nWHERE debt = (SELECT \n MAX(debt)\n FROM international_debt\n WHERE indicator_code = 'DT.AMT.DLXF.CD');","execution_count":62,"outputs":[{"output_type":"stream","text":" * postgresql:///international_debt\n1 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":62,"data":{"text/plain":"[('China', 'Principal repayments on external debt, long-term (AMT, current US$)')]","text/html":"\n \n | country_name | \n indicator_name | \n
\n \n | China | \n Principal repayments on external debt, long-term (AMT, current US$) | \n
\n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"60"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 8. The most common debt indicator\nChina has the highest amount of debt in the long-term debt (DT.AMT.DLXF.CD) category. This is verified by The World Bank. It is often a good idea to verify our analyses like this since it validates that our investigations are correct.
\nWe saw that long-term debt is the topmost category when it comes to the average amount of debt. But is it the most common indicator in which the countries owe their debt? Let's find that out.
"},{"metadata":{"dc":{"key":"60"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\nSELECT \n indicator_code,\n COUNT(indicator_code) AS indicator_count\nFROM international_debt\nGROUP BY indicator_code\nORDER BY indicator_count DESC, indicator_code DESC\nLIMIT 20;","execution_count":64,"outputs":[{"output_type":"stream","text":" * postgresql:///international_debt\n20 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":64,"data":{"text/plain":"[('DT.INT.OFFT.CD', 124),\n ('DT.INT.MLAT.CD', 124),\n ('DT.INT.DLXF.CD', 124),\n ('DT.AMT.OFFT.CD', 124),\n ('DT.AMT.MLAT.CD', 124),\n ('DT.AMT.DLXF.CD', 124),\n ('DT.DIS.DLXF.CD', 123),\n ('DT.INT.BLAT.CD', 122),\n ('DT.DIS.OFFT.CD', 122),\n ('DT.AMT.BLAT.CD', 122),\n ('DT.DIS.MLAT.CD', 120),\n ('DT.DIS.BLAT.CD', 113),\n ('DT.INT.PRVT.CD', 98),\n ('DT.AMT.PRVT.CD', 98),\n ('DT.INT.PCBK.CD', 84),\n ('DT.AMT.PCBK.CD', 84),\n ('DT.INT.DPNG.CD', 79),\n ('DT.AMT.DPNG.CD', 79),\n ('DT.INT.PBND.CD', 69),\n ('DT.AMT.PBND.CD', 69)]","text/html":"\n \n | indicator_code | \n indicator_count | \n
\n \n | DT.INT.OFFT.CD | \n 124 | \n
\n \n | DT.INT.MLAT.CD | \n 124 | \n
\n \n | DT.INT.DLXF.CD | \n 124 | \n
\n \n | DT.AMT.OFFT.CD | \n 124 | \n
\n \n | DT.AMT.MLAT.CD | \n 124 | \n
\n \n | DT.AMT.DLXF.CD | \n 124 | \n
\n \n | DT.DIS.DLXF.CD | \n 123 | \n
\n \n | DT.INT.BLAT.CD | \n 122 | \n
\n \n | DT.DIS.OFFT.CD | \n 122 | \n
\n \n | DT.AMT.BLAT.CD | \n 122 | \n
\n \n | DT.DIS.MLAT.CD | \n 120 | \n
\n \n | DT.DIS.BLAT.CD | \n 113 | \n
\n \n | DT.INT.PRVT.CD | \n 98 | \n
\n \n | DT.AMT.PRVT.CD | \n 98 | \n
\n \n | DT.INT.PCBK.CD | \n 84 | \n
\n \n | DT.AMT.PCBK.CD | \n 84 | \n
\n \n | DT.INT.DPNG.CD | \n 79 | \n
\n \n | DT.AMT.DPNG.CD | \n 79 | \n
\n \n | DT.INT.PBND.CD | \n 69 | \n
\n \n | DT.AMT.PBND.CD | \n 69 | \n
\n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"68"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 9. Other viable debt issues and conclusion\nThere are a total of six debt indicators in which all the countries listed in our dataset have taken debt. The indicator DT.AMT.DLXF.CD is also there in the list. So, this gives us a clue that all these countries are suffering from a common economic issue. But that is not the end of the story, but just a part of the story.
\nLet's change tracks from debt_indicators now and focus on the amount of debt again. Let's find out the maximum amount of debt that each country has. With this, we will be in a position to identify the other plausible economic issues a country might be going through.
\nIn this notebook, we took a look at debt owed by countries across the globe. We extracted a few summary statistics from the data and unraveled some interesting facts and figures. We also validated our findings to make sure the investigations are correct.
"},{"metadata":{"dc":{"key":"68"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\nSELECT \n country_name,\n MAX(debt) AS maximum_debt\nFROM international_debt\nGROUP BY country_name\nORDER BY maximum_debt DESC\nLIMIT 10;","execution_count":66,"outputs":[{"output_type":"stream","text":" * postgresql:///international_debt\n10 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":66,"data":{"text/plain":"[('China', Decimal('96218620835.699996948')),\n ('Brazil', Decimal('90041840304.100006104')),\n ('Russian Federation', Decimal('66589761833.5')),\n ('Turkey', Decimal('51555031005.800003052')),\n ('South Asia', Decimal('48756295898.199996948')),\n ('Least developed countries: UN classification', Decimal('40160766261.599998474')),\n ('IDA only', Decimal('34531188113.199996948')),\n ('India', Decimal('31923507000.799999237')),\n ('Indonesia', Decimal('30916112653.799999237')),\n ('Kazakhstan', Decimal('27482093686.400001526'))]","text/html":"\n \n | country_name | \n maximum_debt | \n
\n \n | China | \n 96218620835.699996948 | \n
\n \n | Brazil | \n 90041840304.100006104 | \n
\n \n | Russian Federation | \n 66589761833.5 | \n
\n \n | Turkey | \n 51555031005.800003052 | \n
\n \n | South Asia | \n 48756295898.199996948 | \n
\n \n | Least developed countries: UN classification | \n 40160766261.599998474 | \n
\n \n | IDA only | \n 34531188113.199996948 | \n
\n \n | India | \n 31923507000.799999237 | \n
\n \n | Indonesia | \n 30916112653.799999237 | \n
\n \n | Kazakhstan | \n 27482093686.400001526 | \n
\n
"},"metadata":{}}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.6.7","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":2}
2 |
--------------------------------------------------------------------------------
/When Was the Golden Age of Video Games/notebook.ipynb:
--------------------------------------------------------------------------------
1 | {"cells":[{"metadata":{"dc":{"key":"7"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 1. The ten best-selling video games\n![\"A](\"https://images.unsplash.com/photo-1632256347173-298f7407d1df?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\")
\nPhoto by Dan Schleusser on Unsplash.
\nVideo games are big business: the global gaming market is projected to be worth more than $300 billion by 2027 according to Mordor Intelligence. With so much money at stake, the major game publishers are hugely incentivized to create the next big hit. But are games getting better, or has the golden age of video games already passed?
\nIn this project, we'll explore the top 400 best-selling video games created between 1977 and 2020. We'll compare a dataset on game sales with critic and user reviews to determine whether or not video games have improved as the gaming market has grown.
\nOur database contains two tables. We've limited each table to 400 rows for this project, but you can find the complete dataset with over 13,000 games on Kaggle.
\ngame_sales
\n\n\n\n| column | \ntype | \nmeaning | \n
\n\n\n\ngame | \nvarchar | \nName of the video game | \n
\n\nplatform | \nvarchar | \nGaming platform | \n
\n\npublisher | \nvarchar | \nGame publisher | \n
\n\ndeveloper | \nvarchar | \nGame developer | \n
\n\ngames_sold | \nfloat | \nNumber of copies sold (millions) | \n
\n\nyear | \nint | \nRelease year | \n
\n\n
\nreviews
\n\n\n\n| column | \ntype | \nmeaning | \n
\n\n\n\ngame | \nvarchar | \nName of the video game | \n
\n\ncritic_score | \nfloat | \nCritic score according to Metacritic | \n
\n\nuser_score | \nfloat | \nUser score according to Metacritic | \n
\n\n
\nLet's begin by looking at some of the top selling video games of all time!
"},{"metadata":{"dc":{"key":"7"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\npostgresql:///games\n\n-- Select all information for the top ten best-selling games\n-- Order the results from best-selling game down to tenth best-selling\nSELECT * \nFROM game_sales\nORDER BY games_sold DESC\nLIMIT 10;","execution_count":2,"outputs":[{"output_type":"stream","text":"10 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":2,"data":{"text/plain":"[('Wii Sports for Wii', 'Wii', 'Nintendo', 'Nintendo EAD', Decimal('82.90'), 2006),\n ('Super Mario Bros. for NES', 'NES', 'Nintendo', 'Nintendo EAD', Decimal('40.24'), 1985),\n ('Counter-Strike: Global Offensive for PC', 'PC', 'Valve', 'Valve Corporation', Decimal('40.00'), 2012),\n ('Mario Kart Wii for Wii', 'Wii', 'Nintendo', 'Nintendo EAD', Decimal('37.32'), 2008),\n (\"PLAYERUNKNOWN'S BATTLEGROUNDS for PC\", 'PC', 'PUBG Corporation', 'PUBG Corporation', Decimal('36.60'), 2017),\n ('Minecraft for PC', 'PC', 'Mojang', 'Mojang AB', Decimal('33.15'), 2010),\n ('Wii Sports Resort for Wii', 'Wii', 'Nintendo', 'Nintendo EAD', Decimal('33.13'), 2009),\n ('Pokemon Red / Green / Blue Version for GB', 'GB', 'Nintendo', 'Game Freak', Decimal('31.38'), 1998),\n ('New Super Mario Bros. for DS', 'DS', 'Nintendo', 'Nintendo EAD', Decimal('30.80'), 2006),\n ('New Super Mario Bros. Wii for Wii', 'Wii', 'Nintendo', 'Nintendo EAD', Decimal('30.30'), 2009)]","text/html":"\n \n \n | game | \n platform | \n publisher | \n developer | \n games_sold | \n year | \n
\n \n \n \n | Wii Sports for Wii | \n Wii | \n Nintendo | \n Nintendo EAD | \n 82.90 | \n 2006 | \n
\n \n | Super Mario Bros. for NES | \n NES | \n Nintendo | \n Nintendo EAD | \n 40.24 | \n 1985 | \n
\n \n | Counter-Strike: Global Offensive for PC | \n PC | \n Valve | \n Valve Corporation | \n 40.00 | \n 2012 | \n
\n \n | Mario Kart Wii for Wii | \n Wii | \n Nintendo | \n Nintendo EAD | \n 37.32 | \n 2008 | \n
\n \n | PLAYERUNKNOWN'S BATTLEGROUNDS for PC | \n PC | \n PUBG Corporation | \n PUBG Corporation | \n 36.60 | \n 2017 | \n
\n \n | Minecraft for PC | \n PC | \n Mojang | \n Mojang AB | \n 33.15 | \n 2010 | \n
\n \n | Wii Sports Resort for Wii | \n Wii | \n Nintendo | \n Nintendo EAD | \n 33.13 | \n 2009 | \n
\n \n | Pokemon Red / Green / Blue Version for GB | \n GB | \n Nintendo | \n Game Freak | \n 31.38 | \n 1998 | \n
\n \n | New Super Mario Bros. for DS | \n DS | \n Nintendo | \n Nintendo EAD | \n 30.80 | \n 2006 | \n
\n \n | New Super Mario Bros. Wii for Wii | \n Wii | \n Nintendo | \n Nintendo EAD | \n 30.30 | \n 2009 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"14"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 2. Missing review scores\nWow, the best-selling video games were released between 1985 to 2017! That's quite a range; we'll have to use data from the reviews table to gain more insight on the best years for video games.
\nFirst, it's important to explore the limitations of our database. One big shortcoming is that there is not any reviews data for some of the games on the game_sales table.
"},{"metadata":{"dc":{"key":"14"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql \n\n-- Join games_sales and reviews\n-- Select a count of the number of games where both critic_score and user_score are null\n\nSELECT COUNT(g.game)\nFROM game_sales g\nLEFT JOIN reviews r\nON g.game = r.game\nWHERE critic_score IS NULL\nAND user_score IS NULL;","execution_count":4,"outputs":[{"output_type":"stream","text":" * postgresql:///games\n1 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":4,"data":{"text/plain":"[(31,)]","text/html":"\n \n \n | count | \n
\n \n \n \n | 31 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"21"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 3. Years that video game critics loved\nIt looks like a little less than ten percent of the games on the game_sales table don't have any reviews data. That's a small enough percentage that we can continue our exploration, but the missing reviews data is a good thing to keep in mind as we move on to evaluating results from more sophisticated queries.
\nThere are lots of ways to measure the best years for video games! Let's start with what the critics think.
"},{"metadata":{"dc":{"key":"21"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql\n\n-- Select release year and average critic score for each year, rounded and aliased\n-- Join the game_sales and reviews tables\n-- Group by release year\n-- Order the data from highest to lowest avg_critic_score and limit to 10 results\n\nSELECT \n year, \n ROUND(AVG(critic_score), 2) AS avg_critic_score\nFROM game_sales g\nLEFT JOIN reviews r\nON g.game = r.game\nGROUP BY year\nORDER BY avg_critic_score DESC\nLIMIT 10;","execution_count":6,"outputs":[{"output_type":"stream","text":" * postgresql:///games\n10 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"[(1990, Decimal('9.80')),\n (1992, Decimal('9.67')),\n (1998, Decimal('9.32')),\n (2020, Decimal('9.20')),\n (1993, Decimal('9.10')),\n (1995, Decimal('9.07')),\n (2004, Decimal('9.03')),\n (1982, Decimal('9.00')),\n (2002, Decimal('8.99')),\n (1999, Decimal('8.93'))]","text/html":"\n \n \n | year | \n avg_critic_score | \n
\n \n \n \n | 1990 | \n 9.80 | \n
\n \n | 1992 | \n 9.67 | \n
\n \n | 1998 | \n 9.32 | \n
\n \n | 2020 | \n 9.20 | \n
\n \n | 1993 | \n 9.10 | \n
\n \n | 1995 | \n 9.07 | \n
\n \n | 2004 | \n 9.03 | \n
\n \n | 1982 | \n 9.00 | \n
\n \n | 2002 | \n 8.99 | \n
\n \n | 1999 | \n 8.93 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"28"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 4. Was 1982 really that great?\nThe range of great years according to critic reviews goes from 1982 until 2020: we are no closer to finding the golden age of video games!
\nHang on, though. Some of those avg_critic_score values look like suspiciously round numbers for averages. The value for 1982 looks especially fishy. Maybe there weren't a lot of video games in our dataset that were released in certain years.
\nLet's update our query and find out whether 1982 really was such a great year for video games.
"},{"metadata":{"dc":{"key":"28"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql \n\n-- Paste your query from the previous task; update it to add a count of games released in each year called num_games\n-- Update the query so that it only returns years that have more than four reviewed games\n\nSELECT \n year, \n COUNT(r.game) AS num_games,\n ROUND(AVG(critic_score), 2) AS avg_critic_score\nFROM game_sales g\nLEFT JOIN reviews r\nON g.game = r.game\nGROUP BY year\nHAVING COUNT(r.game) > 4\nORDER BY avg_critic_score DESC\nLIMIT 10;","execution_count":8,"outputs":[{"output_type":"stream","text":" * postgresql:///games\n10 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"[(1998, 10, Decimal('9.32')),\n (2004, 11, Decimal('9.03')),\n (2002, 9, Decimal('8.99')),\n (1999, 11, Decimal('8.93')),\n (2001, 13, Decimal('8.82')),\n (2011, 26, Decimal('8.76')),\n (2016, 13, Decimal('8.67')),\n (2013, 18, Decimal('8.66')),\n (2008, 20, Decimal('8.63')),\n (2017, 13, Decimal('8.62'))]","text/html":"\n \n \n | year | \n num_games | \n avg_critic_score | \n
\n \n \n \n | 1998 | \n 10 | \n 9.32 | \n
\n \n | 2004 | \n 11 | \n 9.03 | \n
\n \n | 2002 | \n 9 | \n 8.99 | \n
\n \n | 1999 | \n 11 | \n 8.93 | \n
\n \n | 2001 | \n 13 | \n 8.82 | \n
\n \n | 2011 | \n 26 | \n 8.76 | \n
\n \n | 2016 | \n 13 | \n 8.67 | \n
\n \n | 2013 | \n 18 | \n 8.66 | \n
\n \n | 2008 | \n 20 | \n 8.63 | \n
\n \n | 2017 | \n 13 | \n 8.62 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"35"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 5. Years that dropped off the critics' favorites list\nThat looks better! The num_games column convinces us that our new list of the critics' top games reflects years that had quite a few well-reviewed games rather than just one or two hits. But which years dropped off the list due to having four or fewer reviewed games? Let's identify them so that someday we can track down more game reviews for those years and determine whether they might rightfully be considered as excellent years for video game releases!
\nIt's time to brush off your set theory skills. To get started, we've created tables with the results of our previous two queries:
\ntop_critic_years
\n\n\n\n| column | \ntype | \nmeaning | \n
\n\n\n\nyear | \nint | \nYear of video game release | \n
\n\navg_critic_score | \nfloat | \nAverage of all critic scores for games released in that year | \n
\n\n
\ntop_critic_years_more_than_four_games
\n\n\n\n| column | \ntype | \nmeaning | \n
\n\n\n\nyear | \nint | \nYear of video game release | \n
\n\nnum_games | \nint | \nCount of the number of video games released in that year | \n
\n\navg_critic_score | \nfloat | \nAverage of all critic scores for games released in that year | \n
\n\n
"},{"metadata":{"dc":{"key":"35"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql \n\n-- Select the year and avg_critic_score for those years that dropped off the list of critic favorites \n-- Order the results from highest to lowest avg_critic_score\n\nSELECT \n t1.year,\n t1.avg_critic_score\nFROM top_critic_years t1\nWHERE t1.avg_critic_score NOT IN(\n SELECT t2.avg_critic_score\n FROM top_critic_years_more_than_four_games t2\n WHERE t1.year = t2.year)\nORDER BY t1.avg_critic_score DESC;","execution_count":10,"outputs":[{"output_type":"stream","text":" * postgresql:///games\n6 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"[(1990, Decimal('9.80')),\n (1992, Decimal('9.67')),\n (2020, Decimal('9.20')),\n (1993, Decimal('9.10')),\n (1995, Decimal('9.07')),\n (1982, Decimal('9.00'))]","text/html":"\n \n \n | year | \n avg_critic_score | \n
\n \n \n \n | 1990 | \n 9.80 | \n
\n \n | 1992 | \n 9.67 | \n
\n \n | 2020 | \n 9.20 | \n
\n \n | 1993 | \n 9.10 | \n
\n \n | 1995 | \n 9.07 | \n
\n \n | 1982 | \n 9.00 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"42"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 6. Years video game players loved\nBased on our work in the task above, it looks like the early 1990s might merit consideration as the golden age of video games based on critic_score alone, but we'd need to gather more games and reviews data to do further analysis.
\nLet's move on to looking at the opinions of another important group of people: players! To begin, let's create a query very similar to the one we used in Task Four, except this one will look at user_score averages by year rather than critic_score averages.
"},{"metadata":{"dc":{"key":"42"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql \n\n-- Select year, an average of user_score, and a count of games released in a given year, aliased and rounded\n-- Include only years with more than four reviewed games; group data by year\n-- Order data by avg_user_score, and limit to ten results\n\nSELECT \n year, \n ROUND(AVG(user_score), 2) AS avg_user_score,\n COUNT(r.game) AS num_games\nFROM game_sales g\nLEFT JOIN reviews r\nON g.game = r.game\nGROUP BY year\nHAVING COUNT(r.game) > 4\nORDER BY avg_user_score DESC\nLIMIT 10;","execution_count":12,"outputs":[{"output_type":"stream","text":" * postgresql:///games\n10 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"[(1997, Decimal('9.50'), 8),\n (1998, Decimal('9.40'), 10),\n (2010, Decimal('9.24'), 23),\n (2009, Decimal('9.18'), 20),\n (2008, Decimal('9.03'), 20),\n (1996, Decimal('9.00'), 5),\n (2006, Decimal('8.95'), 16),\n (2005, Decimal('8.95'), 13),\n (1999, Decimal('8.80'), 11),\n (2000, Decimal('8.80'), 8)]","text/html":"\n \n \n | year | \n avg_user_score | \n num_games | \n
\n \n \n \n | 1997 | \n 9.50 | \n 8 | \n
\n \n | 1998 | \n 9.40 | \n 10 | \n
\n \n | 2010 | \n 9.24 | \n 23 | \n
\n \n | 2009 | \n 9.18 | \n 20 | \n
\n \n | 2008 | \n 9.03 | \n 20 | \n
\n \n | 1996 | \n 9.00 | \n 5 | \n
\n \n | 2006 | \n 8.95 | \n 16 | \n
\n \n | 2005 | \n 8.95 | \n 13 | \n
\n \n | 1999 | \n 8.80 | \n 11 | \n
\n \n | 2000 | \n 8.80 | \n 8 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"49"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 7. Years that both players and critics loved\nAlright, we've got a list of the top ten years according to both critic reviews and user reviews. Are there any years that showed up on both tables? If so, those years would certainly be excellent ones!
\nRecall that we have access to the top_critic_years_more_than_four_games table, which stores the results of our top critic years query from Task 4:
\ntop_critic_years_more_than_four_games
\n\n\n\n| column | \ntype | \nmeaning | \n
\n\n\n\nyear | \nint | \nYear of video game release | \n
\n\nnum_games | \nint | \nCount of the number of video games released in that year | \n
\n\navg_critic_score | \nfloat | \nAverage of all critic scores for games released in that year | \n
\n\n
\nWe've also saved the results of our top user years query from the previous task into a table:
\ntop_user_years_more_than_four_games
\n\n\n\n| column | \ntype | \nmeaning | \n
\n\n\n\nyear | \nint | \nYear of video game release | \n
\n\nnum_games | \nint | \nCount of the number of video games released in that year | \n
\n\navg_user_score | \nfloat | \nAverage of all user scores for games released in that year | \n
\n\n
"},{"metadata":{"dc":{"key":"49"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql \n\n-- Select the year results that appear on both tables\nSELECT t1.year\nFROM top_critic_years_more_than_four_games t1\nJOIN top_user_years_more_than_four_games t2\nON t1.year = t2.year\n","execution_count":14,"outputs":[{"output_type":"stream","text":" * postgresql:///games\n3 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"[(1998,), (2002,), (2008,)]","text/html":"\n \n \n | year | \n
\n \n \n \n | 1998 | \n
\n \n | 2002 | \n
\n \n | 2008 | \n
\n \n
"},"metadata":{}}]},{"metadata":{"dc":{"key":"56"},"deletable":false,"editable":false,"run_control":{"frozen":true},"tags":["context"]},"cell_type":"markdown","source":"## 8. Sales in the best video game years\nLooks like we've got three years that both users and critics agreed were in the top ten! There are many other ways of measuring what the best years for video games are, but let's stick with these years for now. We know that critics and players liked these years, but what about video game makers? Were sales good? Let's find out.
\nThis time, we haven't saved the results from the previous task in a table for you. Instead, we'll use the query from the previous task as a subquery in this one! This is a great skill to have, as we don't always have write permissions on the database we are querying.
"},{"metadata":{"dc":{"key":"56"},"tags":["sample_code"],"trusted":true},"cell_type":"code","source":"%%sql \n\n-- Select year and sum of games_sold, aliased as total_games_sold; order results by total_games_sold descending\n-- Filter game_sales based on whether each year is in the list returned in the previous task\n\nSELECT \n g.year,\n SUM(games_sold) AS total_games_sold\nFROM game_sales g\nWHERE g.year IN (\n SELECT t1.year\n FROM top_critic_years_more_than_four_games t1\n JOIN top_user_years_more_than_four_games t2\n ON t1.year = t2.year\n )\nGROUP BY g.year\nORDER BY total_games_sold DESC\n","execution_count":16,"outputs":[{"output_type":"stream","text":" * postgresql:///games\n3 rows affected.\n","name":"stdout"},{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"[(2008, Decimal('175.07')),\n (1998, Decimal('101.52')),\n (2002, Decimal('58.67'))]","text/html":"\n \n \n | year | \n total_games_sold | \n
\n \n \n \n | 2008 | \n 175.07 | \n
\n \n | 1998 | \n 101.52 | \n
\n \n | 2002 | \n 58.67 | \n
\n \n
"},"metadata":{}}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.6.7","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":2}
2 |
--------------------------------------------------------------------------------
/Analyzing NYC Public School Test Result Scores/datasets/schools_modified.csv:
--------------------------------------------------------------------------------
1 | school_name,borough,building_code,average_math,average_reading,average_writing,percent_tested
2 | "New Explorations into Science, Technology and Math High School",Manhattan,M022,657,601,601,
3 | Essex Street Academy,Manhattan,M445,395,411,387,78.9
4 | Lower Manhattan Arts Academy,Manhattan,M445,418,428,415,65.1
5 | High School for Dual Language and Asian Studies,Manhattan,M445,613,453,463,95.9
6 | Henry Street School for International Studies,Manhattan,M056,410,406,381,59.7
7 | Bard High School Early College,Manhattan,M097,634,641,639,70.8
8 | Urban Assembly Academy of Government and Law,Manhattan,M445,389,395,381,80.8
9 | Marta Valle High School,Manhattan,M025,438,413,394,35.6
10 | University Neighborhood High School,Manhattan,M446,437,355,352,69.9
11 | New Design High School,Manhattan,M445,381,396,372,73.7
12 | Pace High School,Manhattan,M131,430,435,427,87.8
13 | High School for Health Professions and Human Services,Manhattan,M475,452,445,430,86.9
14 | High School for Language and Diplomacy,Manhattan,M460,446,433,411,70.2
15 | International High School at Union Square,Manhattan,M460,403,330,316,53.2
16 | Institute for Collaborative Education,Manhattan,M475,501,550,541,78.6
17 | Gramercy Arts High School,Manhattan,M460,446,459,455,79.6
18 | Urban Assembly New York Harbor School,Manhattan,M877,446,453,428,78.8
19 | Richard R. Green High School of Teaching,Manhattan,M282,411,415,409,60.0
20 | Millennium High School,Manhattan,M824,577,560,567,94.0
21 | Urban Assembly School of Business for Young Women,Manhattan,M282,418,420,417,
22 | High School of Economics and Finance,Manhattan,M833,469,442,447,66.8
23 | Leadership and Public Service High School,Manhattan,M894,390,396,392,54.9
24 | East Side Community School,Manhattan,M060,454,428,445,85.2
25 | Baruch College Campus High School,Manhattan,M874,592,526,531,94.3
26 | School of the Future High School,Manhattan,M660,534,533,522,89.9
27 | Manhattan Village Academy,Manhattan,M873,522,479,510,98.8
28 | NYC Lab School for Collaborative Studies,Manhattan,M070,595,550,555,79.8
29 | Hudson High School of Learning Technologies,Manhattan,M440,378,407,388,71.8
30 | Manhattan Business Academy,Manhattan,M440,410,407,399,69.7
31 | Landmark High School,Manhattan,M440,386,386,386,30.6
32 | High School of Fashion Industries,Manhattan,M600,433,442,427,80.6
33 | NYC Museum School,Manhattan,M070,560,530,522,90.0
34 | Chelsea Career and Technical Education High School,Manhattan,M615,439,418,400,80.4
35 | NYC iSchool,Manhattan,M615,518,515,503,96.0
36 | Manhattan Academy for Arts and Language,Manhattan,M620,350,334,321,46.1
37 | Unity Center for Urban Technologies,Manhattan,M620,375,385,387,62.8
38 | Business of Sports School,Manhattan,M625,395,386,371,67.5
39 | Facing History School,Manhattan,M535,366,356,371,57.3
40 | High School of Hospitality Management,Manhattan,M535,451,433,424,
41 | Urban Assembly School of Design and Construction,Manhattan,M535,445,417,403,60.6
42 | High School for Environmental Studies,Manhattan,M834,469,454,444,74.0
43 | Art and Design High School,Manhattan,M488,455,475,458,70.1
44 | Manhattan Bridges High School,Manhattan,M535,409,360,347,84.4
45 | Urban Assembly Gateway School for Technology,Manhattan,M625,445,432,415,78.4
46 | Food and Finance High School,Manhattan,M535,428,435,421,52.0
47 | Eleanor Roosevelt High School,Manhattan,M855,641,617,631,86.0
48 | "High School for Arts, Imagination, and Inquiry",Manhattan,M490,375,389,380,58.8
49 | Urban Assembly School for Media Studies,Manhattan,M490,392,419,396,49.5
50 | Beacon High School,Manhattan,M837,583,586,595,82.8
51 | Fiorello H. LaGuardia High School of Music and Art and Performing Arts,Manhattan,M485,592,592,597,88.5
52 | High School of Arts and Technology,Manhattan,M490,421,428,406,44.4
53 | Manhattan / Hunter Science High School,Manhattan,M490,581,531,535,97.2
54 | "High School for Law, Advocacy, and Community Justice",Manhattan,M490,415,417,402,60.5
55 | Urban Assembly School for Green Careers,Manhattan,M470,393,371,378,60.0
56 | Frank McCourt High School,Manhattan,M470,489,491,500,89.1
57 | Global Learning Collaborative,Manhattan,M470,406,407,413,59.4
58 | Wadleigh Secondary School for the Performing and Visual Arts,Manhattan,M088,381,401,392,52.6
59 | Frederick Douglass Academy II Secondary School,Manhattan,M088,390,384,373,
60 | Columbia Secondary School,Manhattan,M125,583,561,546,88.2
61 | Academy for Social Action (College Board),Manhattan,M043,357,349,365,54.0
62 | Urban Assembly School for the Performing Arts,Manhattan,M043,379,399,388,76.7
63 | Heritage School,Manhattan,M107,385,380,370,56.8
64 | Central Park East High School,Manhattan,M013,483,468,439,97.9
65 | Park East High School,Manhattan,M495,429,433,435,89.6
66 | Young Women's Leadership School,Manhattan,M895,478,465,472,100.0
67 | Manhattan Center for Science and Mathematics,Manhattan,M435,556,505,495,87.6
68 | Mott Hall High School,Manhattan,M136,416,445,440,36.9
69 | Thurgood Marshall Academy for Learning and Social Change,Manhattan,M970,402,394,400,84.7
70 | "High School for Mathematics, Science, and Engineering at City College",Manhattan,M812,683,610,596,92.6
71 | A. Philip Randolph Campus High School,Manhattan,M540,459,453,447,74.0
72 | Gregorio Luperon High School for Science and Mathematics,Manhattan,M876,383,355,352,71.0
73 | Community Health Academy of the Heights,Manhattan,M814,387,389,385,55.3
74 | Washington Heights Expeditionary Learning School,Manhattan,M143,443,423,434,87.0
75 | Coalition School for Social Change,Manhattan,M045,344,368,367,40.5
76 | Repertory Company High School for Theatre Arts,Manhattan,M896,425,451,458,89.5
77 | Professional Performing Arts High School,Manhattan,M017,496,520,516,72.2
78 | Jacqueline Kennedy Onassis High School,Manhattan,M486,418,422,415,
79 | Murry Bergtraum High School for Business Careers,Manhattan,M520,418,415,398,44.3
80 | Frederick Douglass Academy,Manhattan,M010,463,452,450,65.1
81 | High School for Media and Communications,Manhattan,M465,378,381,383,33.0
82 | College Academy,Manhattan,M465,367,377,363,41.9
83 | High School for Law and Public Service,Manhattan,M465,401,394,381,44.1
84 | City College Academy of the Arts,Manhattan,M218,495,445,450,95.3
85 | High School for Health Careers and Sciences,Manhattan,M465,374,385,389,56.2
86 | Manhattan International High School,Manhattan,M480,438,358,382,58.6
87 | Talent Unlimited High School,Manhattan,M480,485,498,496,89.3
88 | Vanguard High School,Manhattan,M480,431,409,396,63.8
89 | Life Sciences Secondary School,Manhattan,M645,435,440,425,63.0
90 | Stuyvesant High School,Manhattan,M477,754,697,693,97.4
91 | Ralph R. McKee Career and Technical Education High School,Staten Island,R600,420,429,409,38.0
92 | Michael J. Petrides School,Staten Island,R880,483,473,470,79.2
93 | Curtis High School,Staten Island,R450,453,458,444,64.2
94 | Port Richmond High School,Staten Island,R445,432,427,425,44.6
95 | Staten Island Technical High School,Staten Island,R440,711,660,670,99.7
96 | New Dorp High School,Staten Island,R435,454,446,444,63.7
97 | Tottenville High School,Staten Island,R455,494,476,476,
98 | Susan E. Wagner High School,Staten Island,R460,496,490,487,66.3
99 | Gaynor McCown Expeditionary Learning School,Staten Island,R043,442,458,454,55.8
100 | CSI High School for International Studies,Staten Island,R043,477,468,464,83.5
101 | New Explorers High School,Bronx,X790,390,398,376,30.4
102 | "Bronx School for Law, Government, and Justice",Bronx,X460,403,409,415,63.8
103 | Urban Assembly School for Careers in Sports,Bronx,X790,402,405,395,55.5
104 | Bronx Leadership Academy II High School,Bronx,X790,384,355,361,61.3
105 | Urban Assembly Bronx Academy of Letters,Bronx,X183,398,400,405,57.8
106 | Health Opportunities High School,Bronx,X884,374,386,382,57.9
107 | Community School for Social Justice,Bronx,X884,380,382,362,38.4
108 | Alfred E. Smith Career and Technical Education High School,Bronx,X600,390,373,371,29.8
109 | Academy for Language and Technology,Bronx,X082,371,334,348,78.9
110 | South Bronx Preparatory (College Board),Bronx,X149,419,414,394,76.9
111 | International Community High School,Bronx,X139,345,338,312,58.2
112 | Hostos-Lincoln Academy of Science,Bronx,X162,463,451,435,80.0
113 | University Heights Secondary School,Bronx,X470,420,446,424,87.8
114 | Mott Haven Village Preparatory High School,Bronx,X470,377,373,369,44.9
115 | High School for Violin and Dance,Bronx,X400,363,401,396,31.3
116 | Bronx International High School,Bronx,X400,355,330,320,
117 | Bronx Career and College Preparatory High School,Bronx,X158,370,379,381,40.7
118 | Bronx Center for Science and Mathematics,Bronx,X002,513,468,485,73.9
119 | Bronx Early College Academy for Teaching and Learning,Bronx,X166,424,413,409,93.3
120 | Eximius College Preparatory Academy (College Board),Bronx,X002,400,391,397,78.2
121 | Bronx Latin,Bronx,X158,394,381,363,81.0
122 | School for Excellence,Bronx,X400,377,385,383,38.4
123 | Morris Academy for Collaborative Studies,Bronx,X400,407,389,386,44.8
124 | Frederick Douglass Academy III Secondary School,Bronx,X148,387,386,386,50.5
125 | Urban Assembly School for Applied Math and Science,Bronx,X970,445,418,422,87.8
126 | Bronx Collegiate Academy,Bronx,X410,395,401,382,45.5
127 | Mott Hall Bronx High School,Bronx,X970,417,399,398,46.2
128 | Bronx Leadership Academy High School,Bronx,X876,375,372,374,49.1
129 | Validus Preparatory Academy: An Expeditionary Learning School,Bronx,X970,382,375,384,59.2
130 | Bronx High School for Medical Science,Bronx,X410,431,421,426,83.9
131 | Eagle Academy for Young Men,Bronx,X465,398,410,397,65.7
132 | Bronx High School of Business,Bronx,X410,365,360,346,33.3
133 | DreamYard Preparatory School,Bronx,X410,385,385,388,51.1
134 | Theatre Arts Production Company School,Bronx,X137,402,421,410,77.7
135 | West Bronx Academy for the Future,Bronx,X435,404,383,378,
136 | Bronx High School for Law and Community Service,Bronx,X435,401,402,386,64.8
137 | Belmont Preparatory High School,Bronx,X435,413,408,402,55.3
138 | Fordham High School for the Arts,Bronx,X435,398,404,412,56.2
139 | Knowledge and Power Preparatory Academy International High School (KAPPA),Bronx,X435,408,417,404,66.7
140 | Fordham Leadership Academy for Business and Technology,Bronx,X435,355,373,368,44.3
141 | Metropolitan High School,Bronx,X099,387,388,386,65.9
142 | Holcombe L. Rucker School of Community Research,Bronx,X039,376,372,369,59.0
143 | Banana Kelly High School,Bronx,X039,378,391,378,25.7
144 | Bronx Studio School for Writers and Artists,Bronx,X392,417,412,403,71.6
145 | Peace and Diversity Academy,Bronx,X099,366,362,365,27.1
146 | East Bronx Academy for the Future,Bronx,X973,418,406,408,41.1
147 | Bronx Envision Academy,Bronx,X098,364,385,366,76.5
148 | Fannie Lou Hamer Freedom High School,Bronx,X878,345,347,339,75.0
149 | Explorations Academy,Bronx,X098,377,372,365,43.0
150 | Urban Assembly School for Wildlife Conservation,Bronx,X067,393,397,379,71.1
151 | Wings Academy,Bronx,X879,379,372,373,49.2
152 | Renaissance High School for Musical Theater and Technology,Bronx,X405,390,411,393,52.3
153 | Herbert H. Lehman High School,Bronx,X405,426,419,407,28.9
154 | Bronx High School for the Visual Arts,Bronx,X839,408,428,404,
155 | In-Tech Academy,Bronx,X368,426,419,404,85.9
156 | Bronx School of Law and Finance,Bronx,X475,384,394,366,48.3
157 | Bronx Engineering and Technology Academy,Bronx,X475,394,406,391,59.6
158 | Bronx Theatre High School,Bronx,X475,382,384,390,66.7
159 | Marble Hill High School for International Studies,Bronx,X475,451,417,423,92.9
160 | "Marie Curie School for Medicine, Nursing, and Health Professions",Bronx,X143,423,420,425,68.6
161 | Riverdale/Kingsbridge Academy,Bronx,X141,496,485,476,69.4
162 | New World High School,Bronx,X362,407,363,358,93.0
163 | Bronxwood Preparatory Academy,Bronx,X362,392,408,392,43.3
164 | Academy for Scholarship and Entrepreneurship (College Board),Bronx,X362,392,408,400,63.4
165 | High School for Contemporary Arts,Bronx,X425,367,381,361,41.0
166 | Bronx Aerospace High School,Bronx,X425,403,410,393,50.5
167 | Bronx Academy of Health Careers,Bronx,X425,386,380,391,59.4
168 | High School of Computers and Technology,Bronx,X425,396,399,377,65.6
169 | Bronx Lab School,Bronx,X425,407,416,401,77.4
170 | Bronx High School for Writing and Communication Arts,Bronx,X425,380,437,425,38.1
171 | High School for Teaching and the Professions,Bronx,X430,372,361,369,46.5
172 | Bronx High School of Science,Bronx,X445,714,660,667,97.0
173 | Kingsbridge International High School,Bronx,X430,366,311,310,
174 | Discovery High School,Bronx,X430,432,396,395,39.9
175 | Celia Cruz Bronx High School of Music,Bronx,X430,417,434,425,75.6
176 | High School of American Studies at Lehman College,Bronx,X905,669,672,672,91.8
177 | DeWitt Clinton High School,Bronx,X440,445,436,433,31.8
178 | International School for Liberal Arts,Bronx,X430,390,387,379,19.2
179 | Astor Collegiate Academy,Bronx,X415,422,417,409,57.9
180 | Pelham Preparatory Academy,Bronx,X415,420,433,425,77.0
181 | High School of Language and Innovation,Bronx,X415,356,340,320,72.7
182 | Collegiate Institute for Math and Science,Bronx,X415,488,461,458,79.8
183 | Bronxdale High School,Bronx,X415,418,432,436,72.5
184 | Pan American International High School at Monroe,Bronx,X420,317,315,292,65.6
185 | Cinema School,Bronx,X423,430,449,448,88.3
186 | Monroe Academy for Visual Arts and Design,Bronx,X420,361,354,351,46.8
187 | Mott Hall V,Bronx,X423,430,422,414,77.5
188 | Metropolitan Soundview High School,Bronx,X420,444,428,422,52.0
189 | High School of World Cultures,Bronx,X420,356,359,347,38.2
190 | Millennium Art Academy,Bronx,X450,396,413,395,39.2
191 | Bronx Guild,Bronx,X450,365,393,357,41.3
192 | "Archimedes Academy for Math, Science, and Technology Applications",Bronx,X174,418,430,403,
193 | Pablo Neruda Academy,Bronx,X450,394,384,383,44.2
194 | Women's Academy of Excellence,Bronx,X174,386,390,389,45.2
195 | Felisa Rincon de Gautier Institute for Law and Public Policy,Bronx,X972,389,408,413,31.3
196 | Antonia Pantoja Preparatory Academy (College Board),Bronx,X450,435,415,423,44.9
197 | Bronx Health Sciences High School,Bronx,X455,407,421,427,87.1
198 | Harry S. Truman High School,Bronx,X455,400,401,391,41.5
199 | Queens Vocational and Technical High School,Queens,Q600,467,436,432,53.4
200 | High School of Applied Communication,Queens,Q735,435,437,441,70.9
201 | Middle College High School at LaGuardia Community College,Queens,Q520,377,389,377,48.7
202 | Academy for Careers in Television and Film,Queens,Q404,444,458,444,95.0
203 | Aviation Career and Technical Education High School,Queens,Q610,511,464,456,78.6
204 | Academy of American Studies,Queens,Q451,495,482,479,84.8
205 | Newcomers High School,Queens,Q450,490,374,381,47.1
206 | Bard High School Early College Queens,Queens,Q735,631,598,610,94.1
207 | International High School at LaGuardia Community College,Queens,Q520,425,367,365,71.7
208 | Information Technology High School,Queens,Q725,443,420,411,58.8
209 | Robert F. Wagner Jr. Secondary School for Arts and Technology,Queens,Q891,478,445,445,71.1
210 | Academy of Finance and Enterprise,Queens,Q735,489,456,459,92.7
211 | Young Women's Leadership School in Astoria,Queens,Q739,483,464,477,
212 | William Cullen Bryant High School,Queens,Q445,466,424,426,56.9
213 | Long Island City High School,Queens,Q452,430,423,412,51.6
214 | Frank Sinatra School of the Arts High School,Queens,Q570,536,543,543,89.5
215 | Baccalaureate School for Global Education,Queens,Q798,633,620,628,98.5
216 | "Science Skills Center High School for Science, Technology, and the Creative Arts",Brooklyn,K805,399,397,386,65.9
217 | Brooklyn International High School,Brooklyn,K805,432,334,333,92.7
218 | School for International Studies,Brooklyn,K293,417,406,394,65.1
219 | Brooklyn School for Global Studies,Brooklyn,K293,393,381,402,61.8
220 | "City Polytechnic High School of Engineering, Architecture, and Technology",Brooklyn,K580,503,477,427,18.5
221 | Urban Assembly Institute of Math and Science for Young Women,Brooklyn,K313,397,415,407,82.4
222 | Urban Assembly School for Law and Justice,Brooklyn,K313,446,443,430,87.6
223 | Cobble Hill School of American Studies,Brooklyn,K804,416,420,402,55.3
224 | George Westinghouse Career and Technical Education High School,Brooklyn,K580,394,422,400,45.3
225 | Urban Assembly High School of Music and Art,Brooklyn,K805,372,372,361,55.1
226 | Cultural Academy for the Arts and Sciences,Brooklyn,K415,382,395,384,57.3
227 | Kurt Hahn Expeditionary Learning School,Brooklyn,K415,378,394,388,72.6
228 | School for Human Rights,Brooklyn,K470,398,411,400,60.8
229 | It Takes a Village Academy,Brooklyn,K415,364,374,361,62.2
230 | High School for Public Service: Heroes of Tomorrow,Brooklyn,K470,468,454,464,
231 | Arts and Media Preparatory Academy,Brooklyn,K232,370,383,374,64.2
232 | School for Democracy and Leadership,Brooklyn,K470,386,385,390,53.5
233 | Urban Assembly School for Criminal Justice,Brooklyn,K223,428,413,417,83.6
234 | Franklin Delano Roosevelt High School,Brooklyn,K505,504,411,407,54.0
235 | "Brooklyn Community High School of Communication, Arts, and Media",Brooklyn,K117,358,386,380,51.7
236 | Dr. Susan S. McKinney Secondary School of the Arts,Brooklyn,K265,332,346,350,55.1
237 | Benjamin Banneker Academy,Brooklyn,K914,479,484,472,85.5
238 | Juan Morel Campos Secondary School,Brooklyn,K071,366,356,353,56.3
239 | Brooklyn Latin School,Brooklyn,K049,625,588,591,97.5
240 | Green School: An Academy for Environmental Careers,Brooklyn,K049,384,398,399,41.1
241 | Lyons Community School,Brooklyn,K049,374,376,357,56.3
242 | World Academy for Total Community Health High School,Brooklyn,K435,380,389,384,40.9
243 | FDNY High School for Fire and Life Safety,Brooklyn,K435,365,369,357,47.7
244 | W. H. Maxwell Career and Technical Education High School,Brooklyn,K660,326,333,350,38.5
245 | High School for Civil Rights,Brooklyn,K435,391,373,376,26.4
246 | Performing Arts and Technology High School,Brooklyn,K435,375,393,394,39.8
247 | East New York Family Academy,Brooklyn,K819,464,451,421,66.7
248 | Brooklyn Lab School,Brooklyn,K420,383,376,370,46.8
249 | Multicultural High School,Brooklyn,K420,319,323,284,
250 | "School for Classics: An Academy of Thinkers, Writers, and Performers",Brooklyn,K218,387,391,383,52.8
251 | Transit Tech Career and Technical Education High School,Brooklyn,K615,387,387,384,37.2
252 | Cypress Hills Collegiate Preparatory School,Brooklyn,K420,365,370,362,53.8
253 | Academy for Young Writers,Brooklyn,K422,382,413,391,81.2
254 | Academy of Innovative Technology,Brooklyn,K420,395,376,359,57.4
255 | Fort Hamilton High School,Brooklyn,K490,513,456,451,68.7
256 | Midwood High School,Brooklyn,K405,550,514,516,85.7
257 | PROGRESS High School for Professional Careers,Brooklyn,K450,380,377,384,44.9
258 | "High School for Enterprise, Business, and Technology",Brooklyn,K450,463,446,425,54.5
259 | Brooklyn Preparatory High School,Brooklyn,K650,391,406,391,74.5
260 | El Puente Academy for Peace and Justice,Brooklyn,K778,344,380,379,62.5
261 | School for Legal Studies,Brooklyn,K450,393,390,394,34.8
262 | Williamsburg Preparatory School,Brooklyn,K650,443,440,430,87.8
263 | Williamsburg High School for Architecture and Design,Brooklyn,K650,415,424,407,60.1
264 | Frederick Douglass Academy VII High School,Brooklyn,K175,400,407,394,65.4
265 | Teachers Preparatory High School,Brooklyn,K175,394,399,412,56.6
266 | Pathways in Technology Early College High School,Brooklyn,K625,446,442,410,69.4
267 | Academy for Health Careers,Brooklyn,K625,406,405,396,48.1
268 | Boys and Girls High School,Brooklyn,K455,399,392,394,
269 | High School of Sports Management,Brooklyn,K400,367,374,361,56.2
270 | Brooklyn Studio Secondary School,Brooklyn,K721,458,434,434,67.8
271 | International High School at Lafayette,Brooklyn,K400,424,343,337,84.3
272 | Expeditionary Learning School for Community Leaders,Brooklyn,K400,420,396,396,75.0
273 | New Utrecht High School,Brooklyn,K445,488,422,417,63.2
274 | Life Academy High School for Film and Music,Brooklyn,K400,369,379,378,70.2
275 | Kingsborough Early College School,Brooklyn,K400,474,462,449,65.8
276 | Secondary School for Journalism,Brooklyn,K460,408,435,415,58.8
277 | Millennium Brooklyn High School,Brooklyn,K460,553,551,539,79.6
278 | Park Slope Collegiate,Brooklyn,K460,405,377,395,59.6
279 | Secondary School for Law,Brooklyn,K460,420,424,414,74.7
280 | Bedford Academy High School,Brooklyn,K994,505,464,456,96.2
281 | Brooklyn High School of the Arts,Brooklyn,K655,439,441,436,80.6
282 | Brooklyn Technical High School,Brooklyn,K430,682,608,606,95.5
283 | Brooklyn College Academy,Brooklyn,K917,502,495,493,98.1
284 | High School of Telecommunication Arts and Technology,Brooklyn,K485,475,440,445,81.6
285 | EBC High School for Public Service in Bushwick,Brooklyn,K913,389,374,378,50.0
286 | Brooklyn High School for Law and Technology,Brooklyn,K987,395,401,383,62.7
287 | Brooklyn Academy of Global Finance,Brooklyn,K057,395,382,391,
288 | Bushwick Leaders High School for Academic Excellence,Brooklyn,K865,393,368,382,46.8
289 | Automotive High School,Brooklyn,K610,367,381,328,31.6
290 | Frances Perkins Academy,Brooklyn,K610,399,413,400,46.3
291 | John Dewey High School,Brooklyn,K540,512,418,396,62.7
292 | Rachel Carson High School for Coastal Studies,Brooklyn,K303,436,430,421,68.4
293 | Brooklyn School for Music and Theatre,Brooklyn,K440,382,393,377,72.2
294 | Brooklyn Academy of Science and the Environment,Brooklyn,K440,419,411,416,58.5
295 | Medgar Evers College Preparatory School,Brooklyn,K590,525,500,481,89.0
296 | Clara Barton High School,Brooklyn,K600,408,424,407,55.0
297 | International High School at Prospect Heights,Brooklyn,K440,344,302,300,81.7
298 | High School for Global Citizenship,Brooklyn,K440,377,386,375,61.9
299 | "Science, Technology, and Research Early College High School at Erasmus",Brooklyn,K465,496,491,484,91.5
300 | Academy for College Preparation and Career Exploration (College Board),Brooklyn,K465,386,397,393,57.3
301 | High School for Youth and Community Development at Erasmus,Brooklyn,K465,360,382,359,63.2
302 | High School for Service and Learning at Erasmus,Brooklyn,K465,377,382,356,45.5
303 | Academy of Hospitality and Tourism,Brooklyn,K465,374,385,375,62.0
304 | James Madison High School,Brooklyn,K425,492,450,444,69.0
305 | Edward R. Murrow High School,Brooklyn,K525,500,479,472,75.2
306 | Brooklyn Secondary School for Collaborative Studies,Brooklyn,K142,401,411,404,
307 | Sunset Park High School,Brooklyn,K564,398,380,381,61.6
308 | Brooklyn Collegiate (College Board),Brooklyn,K055,394,395,399,64.4
309 | Gotham Professional Arts Academy,Brooklyn,K040,377,396,386,50.7
310 | Leon M. Goldstein High School for the Sciences,Brooklyn,K535,563,534,543,94.0
311 | Abraham Lincoln High School,Brooklyn,K410,441,422,422,54.9
312 | William E. Grady Career and Technical Education High School,Brooklyn,K620,416,423,387,31.4
313 | Urban Action Academy,Brooklyn,K500,362,396,393,44.9
314 | Brooklyn Generation School,Brooklyn,K515,392,406,403,50.0
315 | High School for Medical Professions,Brooklyn,K500,404,427,424,60.2
316 | High School for Innovation in Advertising and Media,Brooklyn,K500,390,410,397,59.1
317 | Victory Collegiate High School,Brooklyn,K515,386,408,402,72.7
318 | Brooklyn Theatre Arts High School,Brooklyn,K515,379,393,373,69.1
319 | Academy for Conservation and the Environment,Brooklyn,K515,381,397,390,50.8
320 | All City Leadership Secondary School,Brooklyn,K554,467,446,448,85.0
321 | Bushwick School for Social Justice,Brooklyn,K480,365,357,357,50.0
322 | Academy for Environmental Leadership,Brooklyn,K480,365,366,348,71.3
323 | Academy of Urban Planning,Brooklyn,K480,392,374,379,41.9
324 | ACORN Community High School,Brooklyn,K909,379,395,385,61.3
325 | Flushing High School,Queens,Q460,444,407,405,
326 | East-West School of International Studies,Queens,Q237,521,457,451,89.4
327 | Flushing International High School,Queens,Q189,481,323,323,67.7
328 | World Journalism Preparatory (College Board),Queens,Q025,484,491,487,89.2
329 | Bayside High School,Queens,Q405,523,479,485,76.7
330 | Benjamin N. Cardozo High School,Queens,Q415,563,505,510,75.6
331 | Francis Lewis High School,Queens,Q430,562,483,485,80.7
332 | Queens School of Inquiry,Queens,Q168,516,493,486,88.2
333 | Robert F. Kennedy Community High School,Queens,Q707,460,426,423,67.3
334 | John Bowne High School,Queens,Q425,467,422,425,62.1
335 | Townsend Harris High School,Queens,Q515,680,640,661,97.1
336 | High School for Arts and Business,Queens,Q456,413,405,393,76.7
337 | Civic Leadership Academy,Queens,Q744,435,424,418,68.9
338 | Maspeth High School,Queens,Q585,476,471,480,90.0
339 | Pan American International High School,Queens,Q744,340,320,318,31.9
340 | Newtown High School,Queens,Q455,434,401,389,39.1
341 | Forest Hills High School,Queens,Q440,517,485,483,72.8
342 | Queens Metropolitan High School,Queens,Q686,460,448,449,76.4
343 | Grover Cleveland High School,Queens,Q485,452,422,416,38.9
344 | Humanities and Arts Magnet High School,Queens,Q490,384,409,401,
345 | "Mathematics, Science Research, and Technology Magnet High School",Queens,Q490,380,418,388,54.9
346 | Pathways College Preparatory School (College Board),Queens,Q192,405,427,409,72.4
347 | Excelsior Preparatory High School,Queens,Q420,384,407,400,70.1
348 | Preparatory Academy for Writers (College Board),Queens,Q420,398,410,393,69.5
349 | Queens Preparatory Academy,Queens,Q420,423,422,403,83.7
350 | George Washington Carver High School for the Sciences,Queens,Q420,439,428,419,63.5
351 | "High School for Construction Trades, Engineering, and Architecture",Queens,Q650,489,457,451,78.9
352 | John Adams High School,Queens,Q480,418,401,395,38.4
353 | Robert H. Goddard High School of Communication Arts and Technology,Queens,Q202,426,435,424,85.1
354 | Richmond Hill High School,Queens,Q475,413,406,399,37.9
355 | Cambria Heights Academy,Queens,Q799,418,424,411,58.5
356 | "Queens High School of Teaching, Liberal Arts, and the Sciences",Queens,Q566,453,434,439,72.4
357 | Martin Van Buren High School,Queens,Q435,397,396,391,40.0
358 | Queens Gateway to Health Sciences Secondary School,Queens,Q695,524,511,514,92.5
359 | Jamaica Gateway to the Sciences,Queens,Q470,487,460,463,78.6
360 | High School for Community Leadership,Queens,Q470,415,392,386,72.3
361 | Young Women's Leadership School in Queens,Queens,Q680,415,420,433,90.4
362 | Queens Collegiate (College Board),Queens,Q470,455,439,441,74.8
363 | Hillcrest High School,Queens,Q505,448,432,426,
364 | Thomas A. Edison Career and Technical Education High School,Queens,Q620,514,473,470,80.0
365 | Hillside Arts and Letters Academy,Queens,Q470,409,399,404,62.6
366 | York Early College Academy,Queens,Q008,496,481,473,92.9
367 | Queens High School for the Sciences at York College,Queens,Q774,701,621,625,97.9
368 | High School for Law Enforcement and Public Safety,Queens,Q690,410,431,409,56.6
369 | August Martin High School,Queens,Q400,366,372,364,26.3
370 | Frederick Douglass Academy VI High School,Queens,Q465,410,418,407,30.1
371 | Academy of Medical Technology (College Board),Queens,Q465,422,424,415,67.6
372 | "Queens High School for Information, Research, and Technology",Queens,Q465,372,362,352,44.6
373 | Rockaway Park High School for Environmental Sustainability,Queens,Q410,357,381,376,38.5
374 | Channel View School for Research,Queens,Q410,427,430,423,76.6
375 | Rockaway Collegiate High School,Queens,Q410,399,403,405,46.5
376 | Scholars' Academy,Queens,Q180,588,560,568,99.2
377 |
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/Analyzing Unicorn Companies/notebook.ipynb:
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1 | {"cells":[{"source":"![Hand with calculator](calculator.png \"Calculator\")\n\nDid you know that the average return from investing in stocks is 10% per year! But who wants to be average?! \n\nYou have been asked to support an investment firm by analyzing trends in high-growth companies. They are interested in understanding which industries are producing the highest valuations and the rate at which new high-value companies are emerging. Providing them with this information gives them a competitive insight as to industry trends and how they should structure their portfolio looking forward.\n\nYou have been given access to their `unicorns` database, which contains the following tables:\n\n`dates`\n\n| Column | Description |\n|------------- |--------------------------------------------- |\n| company_id | A unique ID for the company. |\n| date_joined | The date that the company became a unicorn. |\n| year_founded | The year that the company was founded. |\n\n`funding`\n\n| Column | Description |\n|----------------- |--------------------------------------------- |\n| company_id | A unique ID for the company. |\n| valuation | Company value in US dollars. |\n| funding | The amount of funding raised in US dollars. |\n| select_investors | A list of key investors in the company. |\n\n`industries`\n\n| Column | Description |\n|------------- |--------------------------------------------- |\n| company_id | A unique ID for the company. |\n| industry | The industry that the company operates in. |\n\n`companies`\n\n| Column | Description |\n|------------- |-------------------------------------------------- |\n| company_id | A unique ID for the company. |\n| company | The name of the company. |\n| city | The city where the company is headquartered. |\n| country | The country where the company is headquartered. |\n| continent | The continent where the company is headquartered. |\n","metadata":{},"id":"1fe66cd5-4e11-4046-9e68-016430b84ecd","cell_type":"markdown"},{"source":"**1. Find the 3 best-performing industries based on the number of new unicorns created over three years (2019, 2020, and 2021) combined.**","metadata":{},"id":"cff298d0-66c0-4c8b-8016-becbd3852b86","cell_type":"markdown"},{"source":"SELECT \n i.industry,\n COUNT(i.*) AS count_new_unicorns\nFROM industries i\nJOIN dates d ON i.company_id = d.company_id\nWHERE DATE_PART('year', d.date_joined) IN ('2019', '2020', '2021')\nGROUP BY i.industry\nORDER BY count_new_unicorns DESC\nLIMIT 3","metadata":{"customType":"sql","dataFrameVariableName":"df","initial":false,"integrationId":"89e17161-a224-4a8a-846b-0adc0fe7a4b1","queuedAt":1667572241569,"executionStartedAt":1667572241845,"executionStoppedAt":1667572243030,"lastSuccessfullyExecutedCode":"SELECT \n i.industry,\n COUNT(i.*) AS count_new_unicorns\nFROM industries i\nJOIN dates d ON i.company_id = d.company_id\nWHERE DATE_PART('year', d.date_joined) IN ('2019', '2020', '2021')\nGROUP BY i.industry\nORDER BY count_new_unicorns DESC\nLIMIT 3"},"id":"a534c175-bbb3-4584-9ee3-ed488900dfbe","cell_type":"code","execution_count":84,"outputs":[{"output_type":"execute_result","execution_count":84,"data":{"application/com.datacamp.data-table.v1+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"industry","type":"string"},{"name":"count_new_unicorns","type":"integer"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":0,"industry":"Fintech","count_new_unicorns":173},{"index":1,"industry":"Internet software & services","count_new_unicorns":152},{"index":2,"industry":"E-commerce & direct-to-consumer","count_new_unicorns":75}]},"total_rows":3,"truncation_type":null},"text/plain":" industry count_new_unicorns\n0 Fintech 173\n1 Internet software & services 152\n2 E-commerce & direct-to-consumer 75","text/html":"\n\n
\n \n \n | \n industry | \n count_new_unicorns | \n
\n \n \n \n | 0 | \n Fintech | \n 173 | \n
\n \n | 1 | \n Internet software & services | \n 152 | \n
\n \n | 2 | \n E-commerce & direct-to-consumer | \n 75 | \n
\n \n
\n
\n\n
\n \n \n | \n industry | \n year | \n num_unicorns | \n average_valuation | \n
\n \n \n \n | 0 | \n Mobile & telecommunications | \n 2017 | \n 5 | \n 2.800000e+09 | \n
\n \n | 1 | \n Internet software & services | \n 2015 | \n 4 | \n 1.250000e+09 | \n
\n \n | 2 | \n Fintech | \n 2018 | \n 10 | \n 8.600000e+09 | \n
\n \n | 3 | \n Mobile & telecommunications | \n 2019 | \n 4 | \n 2.000000e+09 | \n
\n \n | 4 | \n Artificial intelligence | \n 2012 | \n 1 | \n 2.000000e+09 | \n
\n \n | ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n | 119 | \n E-commerce & direct-to-consumer | \n 2022 | \n 7 | \n 1.571429e+09 | \n
\n \n | 120 | \n Other | \n 2015 | \n 2 | \n 1.000000e+09 | \n
\n \n | 121 | \n Consumer & retail | \n 2017 | \n 4 | \n 1.050000e+10 | \n
\n \n | 122 | \n Fintech | \n 2019 | \n 20 | \n 6.800000e+09 | \n
\n \n | 123 | \n Travel | \n 2022 | \n 1 | \n 1.000000e+09 | \n
\n \n
\n
124 rows × 4 columns
\n
\n\n
\n \n \n | \n industry | \n year | \n num_unicorns | \n average_valuation_billions | \n
\n \n \n \n | 0 | \n E-commerce & direct-to-consumer | \n 2021 | \n 47 | \n 2.47 | \n
\n \n | 1 | \n E-commerce & direct-to-consumer | \n 2020 | \n 16 | \n 4.00 | \n
\n \n | 2 | \n E-commerce & direct-to-consumer | \n 2019 | \n 12 | \n 2.58 | \n
\n \n | 3 | \n Fintech | \n 2021 | \n 138 | \n 2.75 | \n
\n \n | 4 | \n Fintech | \n 2020 | \n 15 | \n 4.33 | \n
\n \n | 5 | \n Fintech | \n 2019 | \n 20 | \n 6.80 | \n
\n \n | 6 | \n Internet software & services | \n 2021 | \n 119 | \n 2.15 | \n
\n \n | 7 | \n Internet software & services | \n 2020 | \n 20 | \n 4.35 | \n
\n \n | 8 | \n Internet software & services | \n 2019 | \n 13 | \n 4.23 | \n
\n \n
\n