1A.soft - Notions de SQL - correction¶

Links: notebook, html, PDF, python, slides, GitHub

Correction des exercices du premier notebooks relié au SQL.

from jyquickhelper import add_notebook_menu
add_notebook_menu()

Recupérer les données
Exercice 1
Exercice 2
Exercice 3 : plage horaires de cinq minutes où il n’y a aucun vélo disponible
Exercice 4 : distribution horaire par station et par tranche de 5 minutes
Zones de travail et zones de résidence
JOIN avec la table stations et les stations “travail”

%matplotlib inline

Recupérer les données ¶

import os
if not os.path.exists("td8_velib.db3"):
    from pyensae import download_data
    download_data("td8_velib.zip", website = 'xd')
    from pyensae.sql import import_flatfile_into_database
    dbf = "td8_velib.db3"
    import_flatfile_into_database(dbf, "td8_velib.txt")                   # 2 secondes
    import_flatfile_into_database(dbf, "stations.txt", table="stations")  # 2 minutes

%load_ext pyensae

%SQL_connect td8_velib.db3

<pyensae.sql.sql_interface_database.InterfaceSQLDatabase at 0x2867b9635f8>

Exercice 1 ¶

%%SQL
SELECT COUNT(*) FROM (
    SELECT DISTINCT last_update FROM td8_velib
) ;

	COUNT(*)
0	15294

%%SQL
SELECT MIN(last_update), MAX(last_update) FROM td8_velib ;

	MIN(last_update)	MAX(last_update)
0	2013-07-22 09:00:19	2013-09-13 11:25:19

Exercice 2 ¶

%%SQL
SELECT number, COUNT(*) AS nb
FROM td8_velib
WHERE available_bikes==0 AND last_update >= '2013-09-10 11:30:19'
GROUP BY number
ORDER BY nb DESC

	number	nb
0	7025	864
1	12124	864
2	15025	864
3	15108	864
4	16135	864
5	18023	864
6	18037	864
7	18048	864
8	18049	864
9	18102	864

Exercice 3 : plage horaires de cinq minutes où il n’y a aucun vélo disponible ¶

%%SQL
SELECT nb, COUNT(*) AS nb_station
FROM (
  -- requête de l'exercice précédent
  SELECT number, COUNT(*) AS nb
  FROM td8_velib
  WHERE available_bikes==0 AND last_update >= '2013-09-10 11:30:19'
  GROUP BY number
)
GROUP BY nb

	nb	nb_station
0	1	7
1	2	7
2	3	5
3	4	6
4	5	6
5	6	5
6	7	8
7	8	9
8	9	1
9	10	7

Exercice 4 : distribution horaire par station et par tranche de 5 minutes ¶

%%SQL
SELECT A.number, A.heure, A.minute, 1.0 * A.nb_velo / B.nb_velo_tot  AS distribution_temporelle
FROM (
  SELECT number, heure, minute, SUM(available_bikes) AS nb_velo
  FROM td8_velib
  WHERE last_update >= '2013-09-10 11:30:19'
  GROUP BY heure, minute, number
) AS A
JOIN (
  SELECT number, heure, minute, SUM(available_bikes) AS nb_velo_tot
  FROM td8_velib
  WHERE last_update >= '2013-09-10 11:30:19'
  GROUP BY number
) AS B
ON A.number == B.number
--WHERE A.number in (8001, 8003, 15024, 15031)  -- pour n'afficher que quelques stations
ORDER BY A.number, A.heure, A.minute

	number	heure	minute	distribution_temporelle
0	901	0	0	0.001104
1	901	0	5	0.001104
2	901	0	10	0.001104
3	901	0	15	0.001104
4	901	0	20	0.001104
5	901	0	25	0.000946
6	901	0	30	0.000946
7	901	0	35	0.000946
8	901	0	40	0.000946
9	901	0	45	0.000946

Zones de travail et zones de résidence ¶

%%SQL --df=df
SELECT number, SUM(distribution_temporelle) AS velo_jour
FROM (
  -- requête de l'exercice 4
    SELECT A.number, A.heure, A.minute, 1.0 * A.nb_velo / B.nb_velo_tot  AS distribution_temporelle
    FROM (
        SELECT number, heure, minute, SUM(available_bikes) AS nb_velo
        FROM td8_velib
        WHERE last_update >= '2013-09-10 11:30:19'
        GROUP BY heure, minute, number
    ) AS A
    JOIN (
        SELECT number, heure, minute, SUM(available_bikes) AS nb_velo_tot
        FROM td8_velib
        WHERE last_update >= '2013-09-10 11:30:19'
        GROUP BY number
    ) AS B
    ON A.number == B.number
)
WHERE heure >= 10 AND heure <= 16
GROUP BY number

	number	velo_jour
0	901	0.555188
1	903	0.473137
2	904	0.357640
3	905	0.323395
4	906	0.117566
5	908	0.501727
6	1001	0.486796
7	1002	0.355434
8	1003	0.364749
9	1004	0.440549

df = df.sort_values("velo_jour").reset_index()
df["index"] = range(0, df.shape[0])
df.head()

	index	number	velo_jour
0	0	18113	0.000000
1	1	35013	0.000000
2	2	10010	0.000000
3	3	20037	0.007382
4	4	20119	0.010623

df.plot(x="index", y="velo_jour")

<matplotlib.axes._subplots.AxesSubplot at 0x28602fd89b0>

../_images/td1a_sql_correction_19_1.png

JOIN avec la table stations et les stations “travail”¶

On trouve les arrondissements où les stations de vélib sont les plus remplies en journée au centre de Paris.

%%SQL
SELECT C.number, name, lat, lng, velo_jour FROM
(
  -- requête de la partie précédente
    SELECT number, SUM(distribution_temporelle) AS velo_jour
    FROM (
        -- requête de l'exercice 4
        SELECT A.number, A.heure, A.minute, 1.0 * A.nb_velo / B.nb_velo_tot  AS distribution_temporelle
        FROM (
            SELECT number, heure, minute, SUM(available_bikes) AS nb_velo
            FROM td8_velib
            WHERE last_update >= '2013-09-10 11:30:19'
            GROUP BY heure, minute, number
        ) AS A
        JOIN (
            SELECT number, heure, minute, SUM(available_bikes) AS nb_velo_tot
            FROM td8_velib
            WHERE last_update >= '2013-09-10 11:30:19'
            GROUP BY number
        ) AS B
        ON A.number == B.number
    )
    WHERE heure >= 10 AND heure <= 16
    GROUP BY number
) AS C
INNER JOIN stations
ON C.number == stations.number

	number	name	lat	lng	velo_jour
0	901	00901 - PORT SOLFÉRINO (STATION MOBILE)	48.861380	2.324420	0.555188
1	903	00903 - QUAI MAURIAC / PONT DE BERCY	48.837134	2.374341	0.473137
2	904	00904 - PLACE JOFFRE / ECOLE MILITAIRE	48.852136	2.301961	0.357640
3	905	00905 - CONCORDE/BERGES DE SEINE (STATION MOBILE)	48.863140	2.316690	0.323395
4	906	00906 - GARE DE L'EST	48.876420	2.358630	0.117566
5	908	00908 - PORT DU GROS CAILLOU (STATION MOBILE)	48.862880	2.306520	0.501727
6	1001	01001 - ILE DE LA CITE PONT NEUF	48.857092	2.341748	0.486796
7	1002	01002 - PLACE DU CHATELET	48.857940	2.347010	0.355434
8	1003	01003 - RIVOLI SAINT DENIS	48.859150	2.347620	0.364749
9	1004	01004 - MARGUERITE DE NAVARRE	48.859896	2.346757	0.440549