Übungsaufgaben
Übungen mit der Sakila Datenbank. Das Schema finden Sie hier:
Einfache Abfragen
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[00] - Zeigen Sie alle Spalten und alle Zeilen aus der Tabelle
customer.+-------------+----------+------------+-----------+-------------------------------------+------------+--------+---------------------+---------------------+ | customer_id | store_id | first_name | last_name | email | address_id | active | create_date | last_update | +-------------+----------+------------+-----------+-------------------------------------+------------+--------+---------------------+---------------------+ | 1 | 1 | MARY | SMITH | MARY.SMITH@sakilacustomer.org | 5 | 1 | 2006-02-14 22:04:36 | 2006-02-15 04:57:20 | | 2 | 1 | PATRICIA | JOHNSON | PATRICIA.JOHNSON@sakilacustomer.org | 6 | 1 | 2006-02-14 22:04:36 | 2006-02-15 04:57:20 | | ... | ... | ... | ... | ... | ... | ... | ... | ... | +-------------+----------+------------+-----------+-------------------------------------+------------+--------+---------------------+---------------------+ 599 rows in set (0.001 sec)
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[01] - Zeigen Sie nur die Spalten
first_name,last_nameundemailaus der Tabellecustomer.+------------+-----------+-------------------------------------+ | first_name | last_name | email | +------------+-----------+-------------------------------------+ | MARY | SMITH | MARY.SMITH@sakilacustomer.org | | PATRICIA | JOHNSON | PATRICIA.JOHNSON@sakilacustomer.org | | ... | ... | ... | +------------+-----------+-------------------------------------+ 599 rows in set (0.001 sec)
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[02] - Gleiche Abfrage wie zuvor, aber die Spalten
first_nameundlast_namesollen inVornameundNachnameumbenannt werden.spalten_name AS neuer_name
+----------+----------+-------------------------------------+ | Vorname | Nachname | email | +----------+----------+-------------------------------------+ | MARY | SMITH | MARY.SMITH@sakilacustomer.org | | PATRICIA | JOHNSON | PATRICIA.JOHNSON@sakilacustomer.org | | ... | ... | ... | +----------+----------+-------------------------------------+ 599 rows in set (0.001 sec)
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[03] - Gleiche Abfrage wie zuvor, aber es sollen nur die ersten 5 Kunden angezeigt werden.
Verwenden Sie LIMIT
+-----------+----------+-------------------------------------+ | Vorname | Nachname | email | +-----------+----------+-------------------------------------+ | MARY | SMITH | MARY.SMITH@sakilacustomer.org | | PATRICIA | JOHNSON | PATRICIA.JOHNSON@sakilacustomer.org | | LINDA | WILLIAMS | LINDA.WILLIAMS@sakilacustomer.org | | BARBARA | JONES | BARBARA.JONES@sakilacustomer.org | | ELIZABETH | BROWN | ELIZABETH.BROWN@sakilacustomer.org | +-----------+----------+-------------------------------------+ 5 rows in set (0.000 sec)
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[04] - Geben Sie die Vornamen (actor.first_name) aller Schauspieler aus. Sortieren Sie nach den Vornamen.
Verwenden Sie ORDER BY
ASC: Aufsteigend (Ascending)
DESC: Absteigend (Descending)+-------------+ | first_name | +-------------+ | ADAM | | ADAM | | AL | | ALAN | | ALBERT | | ALBERT | | ALEC | | ANGELA | | ANGELA | | ... | | WOODY | | WOODY | | ZERO | +-------------+ 200 rows in set (0.001 sec)
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[05] - Gleiche Abfrage wie zuvor, aber eliminieren Sie alle Duplikate.
Verwenden Sie SELECT DISTINCT
+-------------+ | first_name | +-------------+ | ADAM | | AL | | ALAN | | ALBERT | | ALEC | | ANGELA | | ANGELINA | | ANNE | | AUDREY | | BELA | | BEN | | BETTE | | BOB | | ... | | WOODY | | ZERO | +-------------+ 128 rows in set (0.001 sec)
Abfragen mit Filter
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[06] - Finden Sie alle Filme (Tabelle
film) mit Länge (length) > 150 Minuten. Zeigen Sie nur die Spaltentitleundlength.+-------------------------+--------+ | Titel | Länge | +-------------------------+--------+ | AGENT TRUMAN | 169 | | ALLEY EVOLUTION | 180 | | ANALYZE HOOSIERS | 181 | | ANONYMOUS HUMAN | 179 | | ANTITRUST TOMATOES | 168 | | APOLLO TEEN | 153 | | ARTIST COLDBLOODED | 170 | | ATLANTIS CAUSE | 170 | | BABY HALL | 153 | | BADMAN DAWN | 162 | | BAKED CLEOPATRA | 182 | | BALLROOM MOCKINGBIRD | 173 | | BEAR GRACELAND | 160 | | BEAUTY GREASE | 175 | | BEETHOVEN EXORCIST | 151 | | BIRCH ANTITRUST | 162 | | BIRD INDEPENDENCE | 163 | | ... | ... | | YOUNG LANGUAGE | 183 | | YOUTH KICK | 179 | +-------------------------+--------+ 242 rows in set (0.000 sec)
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[07] - Finden Sie alle Filme, deren Mietdauer (rental_duration) genau 5 Tage beträgt. Zeigen Sie nur die Spalten
titleundrental_duration.+-------------------------+-----------+ | Filmtitel | Mietdauer | +-------------------------+-----------+ | AFFAIR PREJUDICE | 5 | | ALIEN CENTER | 5 | | ANTHEM LUKE | 5 | | ANTITRUST TOMATOES | 5 | | APACHE DIVINE | 5 | | APOLLO TEEN | 5 | | ARMAGEDDON LOST | 5 | | ARTIST COLDBLOODED | 5 | | ATTACKS HATE | 5 | | ATTRACTION NEWTON | 5 | | BABY HALL | 5 | | ... | ... | | YENTL IDAHO | 5 | | ZOOLANDER FICTION | 5 | +-------------------------+-----------+ 191 rows in set (0.001 sec)
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[08] - Finden Sie alle Filme mit Mietpreis (rental_rate) <= 2.99 und Mietdauer von genau 6 Tagen. Zeigen Sie nur die Spalten
title,rental_rateundrental_duration.+-------------------------+-----------+-----------+ | Filmtitel | Mietpreis | Mietdauer | +-------------------------+-----------+-----------+ | ACADEMY DINOSAUR | 0.99 | 6 | | AFRICAN EGG | 2.99 | 6 | | ALAMO VIDEOTAPE | 0.99 | 6 | | ALASKA PHANTOM | 0.99 | 6 | | ALICE FANTASIA | 0.99 | 6 | | ALLEY EVOLUTION | 2.99 | 6 | | ALTER VICTORY | 0.99 | 6 | | AMADEUS HOLY | 0.99 | 6 | | AMISTAD MIDSUMMER | 2.99 | 6 | | ANALYZE HOOSIERS | 2.99 | 6 | | ARABIA DOGMA | 0.99 | 6 | | ARK RIDGEMONT | 0.99 | 6 | | ATLANTIS CAUSE | 2.99 | 6 | | BADMAN DAWN | 2.99 | 6 | | ... | ... | ... | | YOUNG LANGUAGE | 0.99 | 6 | | ZHIVAGO CORE | 0.99 | 6 | +-------------------------+-----------+-----------+ 146 rows in set (0.001 sec)
Um nach Mustern in Spalten zu suchen, verwenden Sie LIKE
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[09] - Welche Schauspieler haben den Vornamen 'Tom'?
+----------+------------+-----------+ | actor_id | first_name | last_name | +----------+------------+-----------+ | 38 | TOM | MCKELLEN | | 42 | TOM | MIRANDA | +----------+------------+-----------+ 2 rows in set (0.000 sec)
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[10] - Welche Schauspieler haben den Nachnamen 'Johansson'?
+----------+------------+-----------+ | actor_id | first_name | last_name | +----------+------------+-----------+ | 8 | MATTHEW | JOHANSSON | | 64 | RAY | JOHANSSON | | 146 | ALBERT | JOHANSSON | +----------+------------+-----------+ 3 rows in set (0.000 sec)
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[11] - Welche Schauspieler haben einen Nachnamen, der an der 2. Stelle ein 'i' hat?
+----------+------------+-------------+ | actor_id | first_name | last_name | +----------+------------+-------------+ | 6 | BETTE | NICHOLSON | | 23 | SANDRA | KILMER | | 42 | TOM | MIRANDA | | 45 | REESE | KILMER | | 54 | PENELOPE | PINKETT | | 55 | FAY | KILMER | | 68 | RIP | WINSLET | | 72 | SEAN | WILLIAMS | | 78 | GROUCHO | SINATRA | | 83 | BEN | WILLIS | | 84 | JAMES | PITT | | 96 | GENE | WILLIS | | 137 | MORGAN | WILLIAMS | | 144 | ANGELA | WITHERSPOON | | 147 | FAY | WINSLET | | 153 | MINNIE | KILMER | | 154 | MERYL | GIBSON | | 162 | OPRAH | KILMER | | 164 | HUMPHREY | WILLIS | | 168 | WILL | WILSON | | 172 | GROUCHO | WILLIAMS | | 180 | JEFF | SILVERSTONE | | 189 | CUBA | BIRCH | | 195 | JAYNE | SILVERSTONE | +----------+------------+-------------+ 24 rows in set (0.001 sec)
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[12] - Welche Schauspieler haben einen Vornamen, der mit einem 'A' anfängt und mit einem 'a' aufhört?
Bei der Suche nach Mustern in Strings ist Groß- und Kleinschreibung egal.
+----------+------------+-------------+ | actor_id | first_name | last_name | +----------+------------+-------------+ | 65 | ANGELA | HUDSON | | 76 | ANGELINA | ASTAIRE | | 144 | ANGELA | WITHERSPOON | +----------+------------+-------------+ 3 rows in set (0.000 sec)
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[13] - Welche Schauspieler haben einen Nachnamen, der genau 5 Zeichen lang ist?
+----------+-------------+-----------+ | actor_id | first_name | last_name | +----------+-------------+-----------+ | 3 | ED | CHASE | | 4 | JENNIFER | DAVIS | | 9 | JOE | SWANK | | 10 | CHRISTIAN | GABLE | | 12 | KARL | BERRY | | 20 | LUCILLE | TRACY | | 25 | KEVIN | BLOOM | | 29 | ALEC | WAYNE | | 39 | GOLDIE | BRODY | | 60 | HENRY | BERRY | | 66 | MARY | TANDY | | 71 | ADAM | GRANT | | 75 | BURT | POSEY | | 81 | SCARLETT | DAMON | | 82 | WOODY | JOLIE | | 88 | KENNETH | PESCI | | 89 | CHARLIZE | DENCH | | 91 | CHRISTOPHER | BERRY | | 97 | MEG | HAWKE | | 101 | SUSAN | DAVIS | | 103 | MATTHEW | LEIGH | | 105 | SIDNEY | CROWE | | 106 | GROUCHO | DUNST | | 108 | WARREN | NOLTE | | 110 | SUSAN | DAVIS | | 117 | RENEE | TRACY | | 118 | CUBA | ALLEN | | 122 | SALMA | NOLTE | | 123 | JULIANNE | DENCH | | 125 | ALBERT | NOLTE | | 126 | FRANCES | TOMEI | | 142 | JADA | RYDER | | 145 | KIM | ALLEN | | 150 | JAYNE | NOLTE | | 155 | IAN | TANDY | | 159 | LAURA | BRODY | | 176 | JON | CHASE | | 183 | RUSSELL | CLOSE | | 189 | CUBA | BIRCH | | 194 | MERYL | ALLEN | +----------+-------------+-----------+ 40 rows in set (0.000 sec)
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[14] - Finden Sie alle Schauspieler, deren Vorname genauso lang ist wie ihr Nachname.
Verwenden Sie die eingebaute Funktion LENGTH
+----------+------------+-----------+--------+ | actor_id | first_name | last_name | Länge | +----------+------------+-----------+--------+ | 11 | ZERO | CAGE | 4 | | 14 | VIVIEN | BERGEN | 6 | | 21 | KIRSTEN | PALTROW | 7 | | 23 | SANDRA | KILMER | 6 | | 25 | KEVIN | BLOOM | 5 | | 35 | JUDY | DEAN | 4 | | 50 | NATALIE | HOPKINS | 7 | | 59 | DUSTIN | TAUTOU | 6 | | 60 | HENRY | BERRY | 5 | | 65 | ANGELA | HUDSON | 6 | | 69 | KENNETH | PALTROW | 7 | | 73 | GARY | PENN | 4 | | 78 | GROUCHO | SINATRA | 7 | | 82 | WOODY | JOLIE | 5 | | 101 | SUSAN | DAVIS | 5 | | 110 | SUSAN | DAVIS | 5 | | 117 | RENEE | TRACY | 5 | | 122 | SALMA | NOLTE | 5 | | 150 | JAYNE | NOLTE | 5 | | 153 | MINNIE | KILMER | 6 | | 159 | LAURA | BRODY | 5 | | 169 | KENNETH | HOFFMAN | 7 | | 175 | WILLIAM | HACKMAN | 7 | | 182 | DEBBIE | AKROYD | 6 | | 190 | AUDREY | BAILEY | 6 | | 191 | GREGORY | GOODING | 7 | | 194 | MERYL | ALLEN | 5 | +----------+------------+-----------+--------+ 27 rows in set (0.000 sec)
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[15] - Finden Sie alle Filme, deren Beschreibung (film.description) den Teilstring "database" enthält.
+---------+---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | film_id | title | description | +---------+---------------------+------------------------------------------------------------------------------------------------------------------------------------+ | 2 | ACE GOLDFINGER | A Astounding Epistle of a Database Administrator And a Explorer who must Find a Car in Ancient China | | 9 | ALABAMA DEVIL | A Thoughtful Panorama of a Database Administrator And a Mad Scientist who must Outgun a Mad Scientist in A Jet Boat | | 14 | ALICE FANTASIA | A Emotional Drama of a A Shark And a Database Administrator who must Vanquish a Pioneer in Soviet Georgia | | 21 | AMERICAN CIRCUS | A Insightful Drama of a Girl And a Astronaut who must Face a Database Administrator in A Shark Tank | | 27 | ANONYMOUS HUMAN | A Amazing Reflection of a Database Administrator And a Astronaut who must Outrace a Database Administrator in A Shark Tank | | 29 | ANTITRUST TOMATOES | A Fateful Yarn of a Womanizer And a Feminist who must Succumb a Database Administrator in Ancient India | | 40 | ARMY FLINTSTONES | A Boring Saga of a Database Administrator And a Womanizer who must Battle a Waitress in Nigeria | | 91 | BOUND CHEAPER | A Thrilling Panorama of a Database Administrator And a Astronaut who must Challenge a Lumberjack in A Baloon | | 108 | BUTCH PANTHER | A Lacklusture Yarn of a Feminist And a Database Administrator who must Face a Hunter in New Orleans | | 113 | CALIFORNIA BIRDS | A Thrilling Yarn of a Database Administrator And a Robot who must Battle a Database Administrator in Ancient India | | ... | ... | ... | | 969 | WEST LION | A Intrepid Drama of a Butler And a Lumberjack who must Challenge a Database Administrator in A Manhattan Penthouse | | 971 | WHALE BIKINI | A Intrepid Story of a Pastry Chef And a Database Administrator who must Kill a Feminist in A MySQL Convention | | 996 | YOUNG LANGUAGE | A Unbelieveable Yarn of a Boat And a Database Administrator who must Meet a Boy in The First Manned Space Station | +---------+---------------------+------------------------------------------------------------------------------------------------------------------------------------+ 76 rows in set (0.001 sec)
Abfragen mit Aggregationen
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[16] - Wie viele Filme sind in der Datenbank gespeichert?
+-----------+ | num_films | +-----------+ | 1000 | +-----------+ 1 row in set (0,000 sec)
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[17] - Wie viele Schauspieler gibt es insgesamt?
+------------+ | num_actors | +------------+ | 200 | +------------+ 1 row in set (0,000 sec)
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[18] - Wie viele unterschiedliche Nachnamen (actor.last_name) gibt es in der actor Tabelle?
Verwenden Sie COUNT DISTINCT
+----------------------+ | distinct_actor_names | +----------------------+ | 121 | +----------------------+ 1 row in set (0.000 sec)
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[19] - Wie viele verschiedene Filmbewertungen (film.rating) gibt es?
+------------------------+ | disctinct_film_ratings | +------------------------+ | 5 | +------------------------+ 1 row in set (0,000 sec)
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[20] - Wie ist die durchschnittliche Laufzeit (film.length) aller Filme?
+-----------------+ | avg_film_length | +-----------------+ | 115.2720 | +-----------------+ 1 row in set (0.000 sec)
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[21] - Finden Sie den kleinsten Mietpreis (film.rental_rate) aller Filme.
+-----------------+ | min_rental_rate | +-----------------+ | 0.99 | +-----------------+ 1 row in set (0.000 sec)
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[22] - Finden Sie die maximale Mietdauer (film.rental_duration) aller Filme.
+-----------------+ | max_rental_days | +-----------------+ | 7 | +-----------------+ 1 row in set (0.001 sec)
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[23] - Finden Sie heraus, wieviele Zahlungen bisher gespeichert wurden.
Die Einträge zu Zahlungen sind in der Tabelle
paymentgespeichert.+--------------+ | num_payments | +--------------+ | 16044 | +--------------+ 1 row in set (0.003 sec)
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[24] - Summieren Sie die Beträge (payment.amount) aller Zahlungen.
+--------------+ | sum_payments | +--------------+ | 67406.56 | +--------------+ 1 row in set (0.004 sec)
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[25] - Finden Sie den kleinsten, größten und den durchschnittlichen Betrag (payment.amount) aller Zahlungen.
+------+-------+----------+ | min | max | avg | +------+-------+----------+ | 0.00 | 11.99 | 4.201356 | +------+-------+----------+ 1 row in set (0.004 sec)
Abfragen mit Aggregationen und Gruppierung
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[26] - Wie viele Filme gibt es pro Bewertung (film.rating)?
+--------+------------+ | rating | film_count | +--------+------------+ | G | 178 | | PG | 194 | | PG-13 | 223 | | R | 195 | | NC-17 | 210 | +--------+------------+ 5 rows in set (0,001 sec)
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[27] - Wie hoch ist die durchschnittliche Mietdauer (film.rental_duration) pro Bewertung (film.rating)?
+--------+---------------------+ | rating | avg_rental_duration | +--------+---------------------+ | G | 4.8371 | | PG | 5.0825 | | PG-13 | 5.0538 | | R | 4.7744 | | NC-17 | 5.1429 | +--------+---------------------+ 5 rows in set (0,001 sec)
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[28] - Wie ist die durchschnittliche Laufzeit (film.length) der Filme, gruppiert nach Bewertung (film.rating)?
+--------+------------+ | rating | avg_length | +--------+------------+ | G | 111.0506 | | PG | 112.0052 | | NC-17 | 113.2286 | | R | 118.6615 | | PG-13 | 120.4439 | +--------+------------+
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[29] - Finden Sie den kleinsten, größten und den durchschnittlichen Betrag (payment.amount) aller Zahlungen pro Kunde (payment.customer_id).
+-------------+------+-------+----------+ | customer_id | min | max | avg | +-------------+------+-------+----------+ | 1 | 0.99 | 9.99 | 3.708750 | | 2 | 0.99 | 10.99 | 4.767778 | | 3 | 0.99 | 10.99 | 5.220769 | | 4 | 0.99 | 8.99 | 3.717273 | | 5 | 0.99 | 9.99 | 3.805789 | | 6 | 0.99 | 7.99 | 3.347143 | | 7 | 0.99 | 8.99 | 4.596061 | | 8 | 0.99 | 9.99 | 3.865000 | | 9 | 0.99 | 7.99 | 3.903043 | | 10 | 0.99 | 8.99 | 3.990000 | | 11 | 0.99 | 9.99 | 4.448333 | | 12 | 0.99 | 10.99 | 3.704286 | | ... | ... | ... | ... | | 597 | 0.99 | 8.99 | 3.990000 | | 598 | 0.99 | 7.99 | 3.808182 | | 599 | 0.99 | 9.99 | 4.411053 | +-------------+------+-------+----------+ 599 rows in set (0.017 sec)
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[30] - Welche Bewertung (film.rating) hat die meisten Filme?
Den 1. Teil dieser Aufgabe haben Sie schon in Aufgabe [26] erledigt.
Sortieren Sie nach der aggregierten Spalte und limitieren Sie das Ergebnis auf eine Zeile.+--------+------------+ | rating | film_count | +--------+------------+ | PG-13 | 223 | +--------+------------+ 1 row in set (0,001 sec)
Abfragen mit Joins
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[31] - Wie viele Filme hat jeder Schauspieler gedreht?
Tabellen: actor, film_actor
Gemeinsame Spalten: actor_id+----------+-------------+--------------+------------+ | actor_id | first_name | last_name | film_count | +----------+-------------+--------------+------------+ | 1 | PENELOPE | GUINESS | 19 | | 2 | NICK | WAHLBERG | 25 | | 3 | ED | CHASE | 22 | | 4 | JENNIFER | DAVIS | 22 | | 5 | JOHNNY | LOLLOBRIGIDA | 29 | | 6 | BETTE | NICHOLSON | 20 | | 7 | GRACE | MOSTEL | 30 | | 8 | MATTHEW | JOHANSSON | 20 | | 9 | JOE | SWANK | 25 | | 10 | CHRISTIAN | GABLE | 22 | | 11 | ZERO | CAGE | 25 | | 12 | KARL | BERRY | 31 | | 13 | UMA | WOOD | 35 | | ... | ... | ... | ... | | 194 | MERYL | ALLEN | 22 | | 195 | JAYNE | SILVERSTONE | 27 | | 196 | BELA | WALKEN | 30 | | 197 | REESE | WEST | 33 | | 198 | MARY | KEITEL | 40 | | 199 | JULIA | FAWCETT | 15 | | 200 | THORA | TEMPLE | 20 | +----------+-------------+--------------+------------+ 200 rows in set (0,005 sec)
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[32] - Welche Schauspieler spielen in dem Film „ACADEMY DINOSAUR“ mit?
Tabellen: film, actor, film_actor
+----------+------------+-----------+ | actor_id | first_name | last_name | +----------+------------+-----------+ | 1 | PENELOPE | GUINESS | | 10 | CHRISTIAN | GABLE | | 20 | LUCILLE | TRACY | | 30 | SANDRA | PECK | | 40 | JOHNNY | CAGE | | 53 | MENA | TEMPLE | | 108 | WARREN | NOLTE | | 162 | OPRAH | KILMER | | 188 | ROCK | DUKAKIS | | 198 | MARY | KEITEL | +----------+------------+-----------+ 10 rows in set (0,001 sec)
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[33] - Verknüpfen Sie die beiden Tabellen
cityundcountryüber das gemeinsame Attributcountry_idund finden Sie heraus, wie viele Cities es pro Country gibt.+------------+---------------------------------------+------------+ | country_id | country | num_cities | +------------+---------------------------------------+------------+ | 1 | Afghanistan | 1 | | 2 | Algeria | 3 | | 3 | American Samoa | 1 | | 4 | Angola | 2 | | 5 | Anguilla | 1 | | 6 | Argentina | 13 | | 7 | Armenia | 1 | | 8 | Australia | 1 | | 9 | Austria | 3 | | 10 | Azerbaijan | 2 | | 11 | Bahrain | 1 | | 12 | Bangladesh | 3 | | 13 | Belarus | 2 | | 14 | Bolivia | 2 | | 15 | Brazil | 28 | | 16 | Brunei | 1 | | 17 | Bulgaria | 2 | | 18 | Cambodia | 2 | | 19 | Cameroon | 2 | | 20 | Canada | 7 | | 21 | Chad | 1 | | 22 | Chile | 3 | | 23 | China | 53 | | 24 | Colombia | 6 | | 25 | Congo, The Democratic Republic of the | 2 | | 26 | Czech Republic | 1 | | 27 | Dominican Republic | 3 | | 28 | Ecuador | 3 | | 29 | Egypt | 6 | | 30 | Estonia | 1 | | 31 | Ethiopia | 1 | | 32 | Faroe Islands | 1 | | 33 | Finland | 1 | | 34 | France | 4 | | 35 | French Guiana | 1 | | 36 | French Polynesia | 2 | | 37 | Gambia | 1 | | 38 | Germany | 7 | | 39 | Greece | 2 | | 40 | Greenland | 1 | | 41 | Holy See (Vatican City State) | 1 | | 42 | Hong Kong | 1 | | 43 | Hungary | 1 | | 44 | India | 60 | | 45 | Indonesia | 14 | | 46 | Iran | 8 | | 47 | Iraq | 1 | | 48 | Israel | 4 | | 49 | Italy | 7 | | 50 | Japan | 31 | | 51 | Kazakstan | 2 | | 52 | Kenya | 2 | | 53 | Kuwait | 1 | | 54 | Latvia | 2 | | 55 | Liechtenstein | 1 | | 56 | Lithuania | 1 | | 57 | Madagascar | 1 | | 58 | Malawi | 1 | | 59 | Malaysia | 3 | | 60 | Mexico | 30 | | 61 | Moldova | 1 | | 62 | Morocco | 3 | | 63 | Mozambique | 3 | | 64 | Myanmar | 2 | | 65 | Nauru | 1 | | 66 | Nepal | 1 | | 67 | Netherlands | 5 | | 68 | New Zealand | 1 | | 69 | Nigeria | 13 | | 70 | North Korea | 1 | | 71 | Oman | 2 | | 72 | Pakistan | 5 | | 73 | Paraguay | 3 | | 74 | Peru | 4 | | 75 | Philippines | 20 | | 76 | Poland | 8 | | 77 | Puerto Rico | 2 | | 79 | Réunion | 1 | | 78 | Romania | 2 | | 80 | Russian Federation | 28 | | 81 | Saint Vincent and the Grenadines | 1 | | 82 | Saudi Arabia | 5 | | 83 | Senegal | 1 | | 84 | Slovakia | 1 | | 85 | South Africa | 11 | | 86 | South Korea | 5 | | 87 | Spain | 5 | | 88 | Sri Lanka | 1 | | 89 | Sudan | 2 | | 90 | Sweden | 1 | | 91 | Switzerland | 3 | | 92 | Taiwan | 10 | | 93 | Tanzania | 3 | | 94 | Thailand | 3 | | 95 | Tonga | 1 | | 96 | Tunisia | 1 | | 97 | Turkey | 15 | | 98 | Turkmenistan | 1 | | 99 | Tuvalu | 1 | | 100 | Ukraine | 6 | | 101 | United Arab Emirates | 3 | | 102 | United Kingdom | 8 | | 103 | United States | 35 | | 104 | Venezuela | 7 | | 105 | Vietnam | 6 | | 106 | Virgin Islands, U.S. | 1 | | 107 | Yemen | 4 | | 108 | Yugoslavia | 2 | | 109 | Zambia | 1 | +------------+---------------------------------------+------------+ 109 rows in set (0.001 sec)