Predicting Popularity on Spotify¶

This example uses the publicly-available Spotify from Kaggle to predict the popularity of Polish songs and artists on Spotify. We'll also use a model to group artists together based on how similar their songs are.

You can download the Jupyter notebook of this study here.

The "tracks" dataset (tracks.csv) have the following features:

id represents the Id of the track generated by Spotify

Numerical:

acousticness (range: [0,1])
danceability (range: [0,1])
energy, (range: [0,1])
duration_ms (range: [200000,300000])
instrumentalness (range: [0,1])
valence (range: [0,1])
popularity (range: [0,100])
tempo (range: [50,150])
liveness (range: [0,1])
loudness (range: [-60,0])
speechiness (range: [0,1])

Dummy:

mode (0 = Minor, 1 = Major)
explicit (0 = No explicit content and 1 = Explicit content)

Categorical:

key - keys on an octave encoded as integers in range [0,11] (C = 0, C# = 1, etc.)
timesignature - predicted time signature
artists - list of contributing artists
artists - list of IDs of contributing artists
release_date - date of release (yyyy-mm-dd)
name - track name

The "artists" dataset (artists.csv) has the following features:

id - ID of the artist
name - artist name
followers - how many followers the artist has
popularity - popularity of the artists based on their tracks
genres - list of genres covered by the artist's tracks

Import libraries¶

Start by importing VerticaPy and loading the SQL extension, which allows you to query the Vertica database with SQL.

In [1]:

import verticapy as vp
%load_ext verticapy.sql

This examlpe uses the following version of VerticaPy:

In [2]:

vp.__version__

Out[2]:

'0.9.0'

Connect to Vertica. This example uses an existing connection called "VerticaDSN." For details on how to create a connection, use see the connection tutorial.

In [3]:

vp.connect("VerticaDSN")

Create a new schema, "spotify."

In [4]:

vp.drop("spotify", method = "schema")
vp.create_schema("spotify")

Out[4]:

True

Data Loading¶

Load the datasets into the vDataFrame with read_csv() and then view them with display().

In [5]:

# load datasets as vDataFrame objects
artists = vp.read_csv("data/artists.csv", schema = "spotify", parse_nrows = 100)
tracks  = vp.read_csv("data/tracks.csv" , schema = "spotify", parse_nrows = 100)

The table "spotify"."artists" has been successfully created.
The table "spotify"."tracks" has been successfully created.

In [6]:

display(artists)
display(tracks)

	Abc id Varchar(44)	123 followers Numeric(7,2)	Abc Varchar(36)	Abc name Varchar(114)	123 popularity Int
1	0001K3ON4ACv7hte9Jlc6o	1.0		Chris	0
2	0001ZVMPt41Vwzt1zsmuzp	13667.0		Thyro & Yumi	39
3	0001cekkfdEBoMlwVQvpLg	80.0		Jordan Colle	0
4	0001wHqxbF2YYRQxGdbyER	6329.0		Motion Drive	19
5	0002XY9y3JhjzTZqNCqEcv	1828.0		Katie Price & Peter Andre	0
6	00045gNg7mLEf9UY9yhD0t	829.0		Kubus & BangBang	13
7	0004m70bDmySiyX0mbJMNS	0.0		Carla Sanders	0
8	0004mMWDlIBRhLMZP3p92i	4.0		Yung Danzy	0
9	00052qkj8osxzuMhyZA88Y	1.0		Stephen Olaussen	0
10	00062105bTKjdbdc1K1UpI	0.0		懾魂紫	0
11	0006sHOabJ20USMjG8cWtD	705.0		The Art Company	32
12	000778ICMgFxCapm7fVDAW	1.0		Polyphoniko Sygkrotima	0
13	0007DSGSwy4e3PdpExQESD	47.0		Detect Audio	0
14	0008EBLAehfRBSeStEUQmn	0.0		Hi Tide Wild	0
15	0008dSXw2QjJXEg3jNl6hG	0.0		prottaborton	0
16	00097QzAB3ui6aBPavao2O	0.0		Gastraws Gang	0
17	0009LMJQT62wdZVgnM4vBy	97.0		Bernd Mehler	34
18	0009v6e5hmLdAqSFTLcKry	1417.0		Komissar	0
19	000AK4sjc0Np5MbCQ6JC1U	11.0		Tony Camillo's Bazuka	0
20	000AreMafq1WYcwszMM61y	0.0		Da Brxkxnhxrt Boys	0
21	000BQxIqXh6LNfsVTt703l	0.0		CecilyCecily	0
22	000BblCiHJeKvtiq5aiHOs	1994.0		51 Koodia	34
23	000CkqJliuK4m3uNRl0jd2	0.0		Young Ranz	0
24	000Cz1Pts8aGpDLBvrc4wi	597.0		El Negrito	18
25	000DEqM4VjDJIUIFJ2LNk1	0.0		Han Young Chul	0
26	000DpPOWQC5JxdVapodOTM	0.0		Blu Bill$	0
27	000Dq0VqTZpxOP6jQMscVL	4923.0		Thug Brothers	14
28	000ESzt0wlQI41YbKYGIkf	192.0		Zach Russell	19
29	000EYNgg9zCfoWNtDBemdu	0.0		Coral Anointed vases	0
30	000Eamfs6aR2aDr5svq1XF	26.0		Scarlett Gonzalez	0
31	000FVR595IhUXTTTRwMgyQ	0.0		Mr. Todd	0
32	000G2jFbXDhyuRZGw3jX0l	0.0		Ci	0
33	000GXXsAUTgVJfJzOEMIQz	3.0		Seanine Seven	0
34	000Gam69yUQckclDmjLvlL	1331.0		Tony Victor	0
35	000IpCJToEX5T7oDZtXSeW	0.0		@Harlem Fuego	0
36	000JK7wzCV5X4mg3MbsuGA	17.0		Delmonaco	0
37	000JMMX1ZXl1WQIu9GGxCI	0.0		P.O.P	0
38	000JdbKW4RCTX2eV304DDR	5.0		Yuni Ayunda	0
39	000LGmf577qlcsgUZMWJLL	0.0		Mel Vine	0
40	000MS0paUmy5CYke9uzmAO	0.0		Zues Carter	0
41	000N5stG2Co67GJJeqBJpy	0.0		Hajoso	0
42	000NbZqISnuKaqmw33JxTV	1418.0		Carlos Nakai	9
43	000NkLnzi66UcbuTttHDqX	1.0		R.J. Paradee	0
44	000Nolc0A0ZuWkFzNYVYdU	16.0		Peter Clark	1
45	000OybtozoTUivAiSvHstz	3.0		Mohamad Sparow	0
46	000PIVxMsaaWl1FtigEyDo	0.0		Halo Kitty	0
47	000PWQyVzAHQICDwxkwEvy	0.0		TrulySincere	0
48	000QJOWUPdmFGneapRPzMx	144.0		The Red Carpet	0
49	000R5IwxLTiwZ1O0lsEgMZ	2.0		Verónica Falco	0
50	000SLLJ1xQBlePMwMXtkK5	0.0		SlimStnr	0
51	000Saas96Bv97L2quWFvJl	4.0		3MYR	0
52	000SwGJYBhOXUnpXva6H3v	3.0		Lucrative Burrito	0
53	000SzAei1SOa0HERIjNNqb	0.0		Man Gogh	0
54	000TLrYTTOschyZq06KUyV	0.0		Sadia Naeem	0
55	000TPdyW9vCzS7vAboRLPP	0.0		Florence Mestais	0
56	000TcTgrYANFi4NWE4K0fi	0.0		B Greazy	0
57	000UmiuzgqlUjKHpvIWmAo	3834.0		Lisa Knowles-Smith	27
58	000UxvYLQuybj6iVRRCAw1	91.0		Primera Etica	1
59	000VNWB7V9Xt6HDlPnP1FM	1.0		JVC	0
60	000VtE5U1okZpIrAilYHpJ	0.0		Элиана Бублик	0
61	000WDFTEMnS2Ey1iyugp0x	3.0		Nyell Jeudy	0
62	000WMX8CCUlKyWxaOasSNZ	346.0		John Wang	9
63	000WyXOWRZlAYSCHOl6vbw	5.0		Naveen Hiremath	0
64	000XIsEmxd7gGlxWDFCD43	2.0		YkUno	0
65	000ZSUbEUZFtMiWHIYBv3W	0.0		Cathy Fishburn	0
66	000ZhE5mMcLVkkIMYScxGn	0.0		Torrober	0
67	000ZqKpgzccO8TcuxHK6VW	2.0		Alibastard	0
68	000dicAA3QWT6m118rFXLO	134.0		Wings of the ISANG	0
69	000e7vL2PldGExOPwRFmfF	0.0		Tea K	0
70	000eJgbruxYJJGYpzP33bd	0.0		Alonzo Garton	0
71	000fiSPJm8TYO2T7UPExMS	0.0		Bobbie Johnson	0
72	000h0et7HHi3pmuWZ80LNj	0.0		NT Money	0
73	000isQj1PXwsP5kJF0F1Fi	14.0		YoursTruly	1
74	000j0w6ospeDya5uUncVNQ	0.0		Pradeep Dutto	0
75	000j8IPyHeLwvo4ScFUJMn	0.0		El Bkno	0
76	000kGSv1ge0O6gJDYkOBLZ	11.0		Electra Vega	0
77	000lFylX9iGEN12AIz7qkF	0.0		Mr. Goodbarz	0
78	000lUERE6k2fnBFdDn58yb	6.0		Décio Pulini	0
79	000lc4PE8DuDC8O1n2jsao	0.0		Luh H	0
80	000nr5NQY4yigOdegZaKoV	7.0		Ghostfog	0
81	000ohFb1Uurub7P6UxvkJ3	0.0		Leek Sosa	0
82	000p4jMMhpEHq1h6PFCyO1	335.0		Anne Veski	24
83	000pQJoy8qFwpZwB5T8htu	2.0		RCHILL	0
84	000q9sWX0l76D7fijF5iOl	19.0		Room 404	0
85	000qAzhdvazIILeGHZhfHE	1.0		Deja Amari	0
86	000qxDglux9HycJpOEigNr	0.0		Em. Mâu Huỳnh Điệp	0
87	000rFz8r7IQznjS1qjIsyk	0.0		Soph vs. Hypa	0
88	000rLf9cJqQOstUbHCzl77	5.0		Raffaele Cardone	0
89	000rOFFfUyGGzKmB8XYIuu	132.0		Milton Cross	0
90	000rU47x3QblbKdSE2VRFm	2.0		Ron One	0
91	000s6SnJAGVnsoT33VKcSW	59.0		Poker Pets	8
92	000s7VhvjpDLaDf3L140Zc	0.0		古風な音楽を学ぶ	0
93	000sD0EGScyHwmiRNF0RRP	9.0		Doblado	0
94	000spuc3oKgwYmfg5IE26s	301.0		Parliament Syndicate	8
95	000tKel9gS8530Lh9kd8xb	2.0		Rupantar	1
96	000vMR3cHSTJ88w3PJWHJB	114.0		Jeff Kryka	20
97	000vYbdVeUF5Mx5RLgT2iR	0.0		608 A	0
98	000vgESUHZ8XyaYCCxapde	7.0		Maggie Lee	0
99	000vr8pSUPAUgtGPpJbGEH	0.0		Dro-Delecity	0
100	000vwlhkQBKs6RjdBkjltZ	41.0		Soul Creation feat. Kenny Bobien	0

Rows: 1-100 | Columns: 5

	Abc id Varchar(44)	Abc Varchar(96)	123 popularity Int	123 duration_ms Int	123 explicit Int	Abc Varchar(100)	Abc Varchar(156)	Abc release_date Varchar(20)	123 danceability Numeric(6,4)	123 energy Numeric(8,6)	123 key Int	123 loudness Numeric(8,4)	123 mode Int	123 speechiness Numeric(7,5)	123 acousticness Numeric(6,4)	123 instrumentalness Float	123 liveness Numeric(7,5)	123 valence Numeric(7,5)	123 tempo Numeric(9,4)	123 time_signature Int
1	0004Uy71ku11n3LMpuyf59		34	258907	0			2012-01-01	0.623	0.599	6	-9.255	1	0.0255	0.177	0.00148	0.0748	0.381	140.056	4
2	000CSYu4rvd8cQ7JilfxhZ		43	189107	0			2005-05-03	0.624	0.766	2	-7.86	1	0.0731	0.345	0.0	0.113	0.548	95.128	4
3	000DsoWJKHdaUmhgcnpr8j		16	234533	0			1977-07-11	0.572	0.608	7	-10.224	1	0.555	0.642	0.00022	0.344	0.559	106.453	4
4	000G1xMMuwxNHmwVsBdtj1		32	182347	0			1978-09-23	0.256	0.895	2	-4.86	1	0.0707	0.0131	0.000106	0.0821	0.555	191.307	4
5	000KblXP5csWFFFsD6smOy		35	240013	0			2006-07-08	0.619	0.518	4	-5.392	0	0.0534	0.805	0.0	0.102	0.314	143.757	3
6	000Npgk5e2SgwGaIsN3ztv		0	206972	0			1953-12-31	0.277	0.145	3	-19.898	1	0.0845	0.98	0.879	0.111	0.494	75.644	4
7	000P83HDtOHcNVFZy7Q2Yu		18	242493	0			1980	0.619	0.561	7	-8.377	0	0.0736	0.438	4.17e-05	0.126	0.696	88.94	4
8	000RDCYioLteXcutOjeweY		54	190203	0			2018-02-12	0.679	0.77	0	-3.537	1	0.19	0.0583	0.0	0.0825	0.839	161.721	4
9	000TXa2oEZLYfQGPCiv23U		6	243200	0			1982	0.61	0.91	6	-10.981	1	0.043	0.0606	0.0828	0.148	0.775	129.639	4
10	000TiSS4vK5su0MkoFyQbd		44	159250	0			2017-12-22	0.72	0.646	6	-7.691	0	0.106	0.237	0.0	0.127	0.0958	80.013	4
11	000d0lQMYaRR5ZXS9nTeiN		32	258533	1			1995	0.826	0.827	1	-8.393	1	0.205	0.0106	4.62e-05	0.17	0.49	92.137	4
12	000jBcNljWTnyjB4YO7ojf		0	179747	0			1954-01-01	0.788	0.808	5	-6.59	1	0.0395	0.656	0.0	0.154	0.969	113.046	4
13	000mGrJNc2GAgQdMESdgEc		0	498560	0			1951	0.0753	0.15	2	-16.705	0	0.0371	0.302	0.884	0.121	0.0351	76.558	3
14	000q9YBtesW8yPwlmus12C		3	178515	0			1975-01-01	0.715	0.254	5	-15.459	1	0.0343	0.878	0.0	0.0867	0.559	94.205	4
15	000u1dTg7y1XCDXi80hbBX		58	300600	0			1989-01-01	0.756	0.47	7	-12.615	1	0.0394	0.196	0.000487	0.126	0.43	120.484	4
16	000x2qE0ZI3hodeVrnJK8A		37	200627	0			1975	0.507	0.356	0	-14.24	1	0.0306	0.339	0.0	0.18	0.472	134.248	4
17	000xYdQfIZ4pDmBGzQalKU		53	187119	0			2016-11-04	0.509	0.803	0	-6.743	1	0.04	0.684	0.000539	0.463	0.651	166.018	4
18	000ydDsz4ijCNUsmoIeZcj		13	98667	0			1978-09-22	0.637	0.353	1	-13.978	0	0.039	0.233	8.44e-06	0.102	0.662	139.209	3
19	00105Q1NbnHkf8R5eXXeXm		44	234067	0			2011-08-23	0.777	0.832	6	-3.982	0	0.0403	0.334	1.08e-05	0.0765	0.72	94.964	4
20	00147h65HDYSncB3byziPP		12	241440	0			1961-01-01	0.238	0.253	10	-13.898	1	0.0315	0.877	0.0	0.0748	0.164	90.855	4
21	0018QzCxmMrpa0FubbNdak		31	147933	0			1993-01-01	0.804	0.796	9	-8.616	1	0.032	0.319	0.000127	0.0987	0.965	113.964	4
22	001AGGmHtn4FUzXgAUxz39		13	191504	0			1991-01-01	0.516	0.295	7	-15.558	0	0.0662	0.676	0.0	0.195	0.881	150.542	3
23	001GxQGaFwTjxM7tmKbMF3		30	214493	0			2004	0.547	0.731	11	-6.055	1	0.0737	0.206	0.0	0.0939	0.965	159.801	4
24	001I9iXPNwN0HlHfdQjDIX		21	267840	0			1988-01-01	0.453	0.688	5	-9.075	1	0.0556	0.0267	5.21e-06	0.293	0.42	120.953	4
25	001IcYypSE1ryXKY5KNIin		58	2704053	0			2016-10-08	0.111	0.376	10	-27.786	0	0.0863	0.566	0.716	0.31	1e-05	95.28	5
26	001LvKFwYbfKYPQF2Fiv77		34	190787	0			1954-01-01	0.555	0.533	3	-7.609	1	0.0372	0.802	0.211	0.27	0.882	82.68	4
27	001UI3J6PKAEnBgqrwGGQC		21	163840	0			1977	0.839	0.406	7	-17.732	1	0.1	0.0555	2.39e-05	0.107	0.559	139.367	4
28	001UkMQHw4zXfFNdKpwXAF		69	191053	0			2019-04-05	0.573	0.846	7	-4.866	1	0.0344	0.0037	7.87e-05	0.29	0.562	127.061	4
29	001YQlnDSduXd5LgBd66gT		36	177267	0			1984-02-06	0.554	0.921	2	-4.589	1	0.0758	0.0194	0.0881	0.329	0.7	183.571	1
30	001ZmOPuWEW5czwun7nkha		8	92867	0			1952-04-11	0.16	0.12	5	-15.244	1	0.0367	0.915	0.00024	0.28	0.162	169.722	1
31	001e2JrYMwnTeRnxf3sgIz		12	364280	0			1953-01-01	0.192	0.0763	3	-24.388	1	0.0425	0.951	0.284	0.0702	0.0554	91.097	3
32	001f6XLtM53gwKSauiUcKI		31	379987	0			1977-01-01	0.556	0.315	1	-12.923	0	0.228	0.151	0.0	0.102	0.364	129.998	4
33	001gx41rQo0bKh063TrC1I		22	382030	0			1984-01-01	0.503	0.233	10	-16.827	1	0.0675	0.813	0.0129	0.546	0.153	129.254	4
34	001hyVfKgE2R1a0TBXeLlV		52	219742	1			2019-05-20	0.666	0.554	11	-10.353	1	0.0325	0.67	0.00025	0.0631	0.414	130.052	4
35	001s02k3baVIxp6lIVRu6k		0	205473	0			1937-12-31	0.426	0.0884	3	-10.925	1	0.0639	0.988	0.00113	0.393	0.396	80.21	3
36	00218i6cYENiDszmBKsYYg		55	132657	1			2018-08-23	0.706	0.473	0	-8.085	0	0.0926	0.984	0.0242	0.106	0.59	87.412	4
37	0022sbR4gAWFhpMaYLbtnX		22	216561	0			2009-01-01	0.748	0.865	8	-5.663	0	0.0327	0.18	0.0	0.133	0.879	138.018	4
38	0024tEymsoc9FyKUauQngQ		41	546107	0			1977	0.305	0.234	1	-18.255	1	0.0259	0.752	0.929	0.132	0.078	82.453	3
39	0025JMWRhsWx0GXdlzhHMO		1	197293	0			2016-05-27	0.252	0.877	0	-7.876	0	0.0481	0.0001	0.0807	0.0685	0.332	103.784	4
40	0029TH4cSnQ12KKfHaq11C		38	198960	0			2000-01-01	0.616	0.819	5	-4.109	1	0.0303	0.0025	0.000114	0.215	0.962	126.816	4
41	002Ac7LJjVIcfFYMZ6Irue		3	177613	0			1969-08-23	0.4	0.391	6	-10.487	1	0.0276	0.485	1.18e-05	0.118	0.401	83.459	4
42	002CcxKpBE1tfKOy2CRaWr		0	194154	0			1955-12-31	0.505	0.419	3	-10.511	0	0.316	0.983	0.0512	0.148	0.654	134.658	3
43	002DkDzzQ7lrgaqWBF2o1M		37	205280	0			1985-01-01	0.509	0.715	7	-6.419	1	0.176	0.0251	0.0	0.419	0.833	178.144	4
44	002KIBXwb0pa66mPGuPKMr		33	294249	0			1990-03-01	0.641	0.854	9	-9.109	0	0.0291	0.0017	0.348	0.325	0.871	121.244	4
45	002TGKi4LBwxYodlfWoaN0		32	132093	0			2000-01-01	0.763	0.881	5	-7.409	1	0.0311	0.596	3.11e-05	0.148	0.987	143.164	4
46	002aR3zqP6SvscCnPT44on		0	726436	0			1955-01-01	0.203	0.107	4	-21.175	0	0.0399	0.925	0.832	0.112	0.0375	107.391	4
47	002ak2fuoNB5KW0plsN1jp		26	213533	0			1990-04-10	0.731	0.39	9	-12.023	0	0.0286	0.421	2.55e-06	0.0785	0.696	97.397	4
48	002c2TeuD0GPfL2ahcmWEF		0	178204	0			1945-12-31	0.676	0.245	6	-10.906	0	0.0945	0.995	0.571	0.134	0.748	75.444	4
49	002dEfJAJnpoDU5cUhMmZJ		0	158145	0			1940	0.354	0.353	9	-12.506	1	0.068	0.985	0.278	0.0957	0.649	152.424	4
50	002dh6a4LfxfGGnhPZY4fG		0	309347	0			1951	0.389	0.312	7	-9.839	1	0.04	0.919	4.08e-06	0.425	0.289	119.479	4
51	002jsFzKzBLqDS71IZpnd6		5	211293	0			1971	0.691	0.4	4	-8.828	1	0.0367	0.755	0.000125	0.11	0.716	121.355	4
52	002lZtb0gYsfOJKqIouc0j		14	53600	0			1972-09-11	0.606	0.954	3	-5.939	0	0.934	0.959	0.0	0.915	0.288	76.856	3
53	002sGwDZYna3CKXbYIilHz		0	166949	0			2009-12-16	0.525	0.659	9	-14.273	0	0.0314	0.218	0.865	0.119	0.739	128.002	4
54	002x5YvGv3c0OW9HOoa4lG		10	173035	0			1977-01-01	0.745	0.586	10	-6.647	1	0.0627	0.629	0.000382	0.34	0.95	90.111	4
55	002zOHMdBKYgNGtmmHSE2D		5	250893	0			1959-02-02	0.39	0.699	7	-9.87	1	0.807	0.797	0.0	0.7	0.791	164.58	4
56	00309sp8DDeN07RtAyBKIX		34	251646	1			1992-02-24	0.57	0.881	7	-12.808	1	0.0483	0.0007	0.19	0.334	0.623	128.91	4
57	0033rRpGIViA0RngXc0jtL		0	245957	1			2012-11-01	0.175	0.0682	5	-16.955	1	0.0419	0.937	0.888	0.226	0.0354	118.248	4
58	0034Fs63aj5J6FTxe5U2Ru		11	777667	0			1983-06-10	0.466	0.419	0	-10.342	1	0.0371	0.698	6.09e-05	0.209	0.277	122.637	4
59	0036VchQkckr6Xxz7WYEh0		31	232587	0			2010-03-08	0.707	0.266	1	-12.338	1	0.0307	0.766	3.72e-05	0.105	0.473	92.008	4
60	0038DNjmwf6yFivn7PLwqP		21	336653	0			1983	0.898	0.739	9	-7.487	0	0.0605	0.225	0.000128	0.285	0.883	113.884	4
61	003FTlCpBTM4eSqYSWPv4H		69	233267	0			2002-10-15	0.553	0.717	7	-5.857	1	0.0318	0.0001	0.128	0.0385	0.318	127.947	4
62	003IDC4FUadw6ocSKqWHtq		22	193760	0			1988-01-01	0.574	0.59	7	-7.879	0	0.0241	0.0844	0.0	0.0676	0.346	102.683	4
63	003IdD0Ir5LSZHlrPpLZlm		27	120907	0			1975	0.293	0.186	7	-27.67	1	0.0419	0.989	0.848	0.109	0.796	152.045	4
64	003IvyvD1M7gdSDK3z5Kfd		36	143200	0			1996	0.716	0.33	5	-11.764	0	0.0413	0.57	1.79e-05	0.138	0.554	117.835	4
65	003JzPprzThp8SHUctgXnn		10	182413	0			1952	0.461	0.454	7	-8.499	1	0.0419	0.797	0.00612	0.107	0.298	142.197	4
66	003KPastz3K8HNyHb8dixS		26	303120	0			2000-04-09	0.65	0.83	0	-5.758	0	0.0281	0.106	0.0	0.318	0.632	132.353	4
67	003PmYxl19KBg6m4uxClbR		22	217267	0			1990-01-01	0.47	0.712	5	-5.462	1	0.0317	0.164	0.0	0.225	0.28	146.181	4
68	003ShLCyC9ACAsdUWOcnk0		19	228573	0			1993-03-12	0.773	0.587	7	-12.196	1	0.039	0.829	3.94e-05	0.0719	0.86	130.138	4
69	003WuNd8vTwCW4JyhFQMYT		0	42267	0			1954	0.656	0.518	4	-18.673	1	0.101	0.987	0.132	0.762	0.932	142.216	3
70	003YOvQRIurKekUuUMDtkZ		44	293616	0			1999-03-17	0.801	0.555	6	-7.871	1	0.0321	0.461	0.0	0.236	0.806	142.343	4
71	003Ymyk0DvgqFmqEmA5Vzc		24	249160	0			1978	0.693	0.57	0	-9.949	0	0.0309	0.38	0.0	0.28	0.855	123.193	4
72	003d3VbyJTZiiOYT2W7fnQ		41	290933	0			1973-01-01	0.461	0.32	0	-16.337	1	0.0327	0.266	3.5e-06	0.211	0.487	134.881	3
73	003fzMu2jBvZqXNPMYNYox		32	213107	0			2009-09-29	0.567	0.603	7	-8.223	1	0.044	0.317	3.7e-05	0.13	0.681	159.934	4
74	003gesN9g85fPpsH1Gx5OO		47	196040	0			2001	0.792	0.952	2	-6.749	1	0.055	0.134	0.000263	0.254	0.968	106.009	4
75	003hDp0MCmLiYKYRfzbhMo		18	229227	0			1996	0.621	0.817	4	-3.34	1	0.0526	0.509	0.0	0.156	0.976	155.129	4
76	003noSDLb7rvHSJ2FPYmDX		38	193867	0			1995-01-01	0.566	0.95	2	-9.146	0	0.0376	0.0035	0.00493	0.227	0.759	148.029	4
77	003oRl3zfRZvhttUxM9Gr5		12	238707	0			1986-11-12	0.76	0.746	2	-5.179	1	0.271	0.322	2.04e-06	0.0457	0.892	137.078	4
78	003vvx7Niy0yvhvHt4a68B		82	222973	0			2004	0.352	0.911	1	-5.23	1	0.0747	0.0012	0.0	0.0995	0.236	148.033	4
79	0043vCcTBYYsmUPzCNd1JQ		11	224627	0			1989-01-01	0.749	0.756	9	-6.095	1	0.0374	0.0061	0.0	0.0332	0.861	122.414	4
80	00469l4g2MMTCWwPudV9Qq		1	255547	0			1960-01-01	0.682	0.262	5	-14.67	0	0.0495	0.765	0.0	0.228	0.598	123.795	4
81	0046quUYhSAFccrKIC3Iht		43	284973	0			1985-06-01	0.48	0.435	1	-9.959	1	0.0305	0.207	5.83e-06	0.196	0.328	144.435	4
82	0047CfWoMPpXkZlizPQAb2		27	236213	0			2004-11-23	0.856	0.754	7	-2.728	0	0.0416	0.0294	1.49e-05	0.206	0.722	98.018	4
83	004ADkC8JLeDkT5HGsPDBm		4	239867	0			2005-11-29	0.559	0.713	8	-4.029	0	0.0318	0.123	0.0	0.1	0.734	164.048	4
84	004Me6nm9MknKeGKLnWx79		4	223240	0			1979-04-30	0.705	0.584	9	-9.997	1	0.0296	0.158	5.39e-06	0.234	0.831	98.844	4
85	004NJ3y5ED5zhn5adktc8g		0	184812	0			1926	0.378	0.598	3	-6.381	1	0.0434	0.996	0.607	0.43	0.649	198.492	4
86	004Oj2hkXAaenToKOLbYla		0	107760	0			1956	0.764	0.302	2	-14.698	1	0.037	0.911	0.000446	0.261	0.829	117.191	3
87	004TG0nRHejwSKisvwTcAB		31	204853	0			1963	0.22	0.0447	4	-25.514	1	0.0384	0.995	0.941	0.101	0.197	68.349	4
88	004TP6xsBixiK3zqOfPBEx		2	121060	0			1958	0.708	0.178	10	-17.491	1	0.958	0.608	0.0	0.234	0.561	100.121	3
89	004UHJTfLZH02Xds3ETd7e		0	254640	0			1953-01-01	0.414	0.425	2	-9.299	1	0.0378	0.891	0.0164	0.37	0.533	98.013	4
90	004VesjMxIn4AzXd8c52n1		5	392507	0			1990-01-01	0.568	0.62	11	-11.878	0	0.0274	0.58	0.00539	0.0846	0.418	100.897	3
91	004XgNPueIQSJrK25rzP3D		23	243533	0			2012	0.536	0.506	0	-9.836	1	0.0277	0.0009	0.0	0.0964	0.306	83.462	4
92	004a0SY8lPBwVnLCtO9aOO		36	72223	0			2016-12-25	0.0	0.00125	1	-27.592	1	0.0	0.908	1.0	0.111	0.0	0.0	0
93	004cCP7Csq7U0m67DDzEFs		0	72147	0			1946	0.419	0.0874	3	-27.612	0	0.0419	0.995	0.922	0.12	0.507	64.008	5
94	004dqsaJ3B8BBpqpN0F4YT		31	186333	0			1989	0.665	0.5	7	-9.579	0	0.0345	0.0862	2.33e-06	0.104	0.95	119.969	4
95	004ltC6j7SM8J06Egoa4wN		19	251960	0			1999	0.762	0.824	7	-7.571	1	0.0723	0.155	9.56e-05	0.097	0.756	98.072	4
96	004q4eDxR33ci4f8m4flwl		43	171627	0			2016-08-26	0.838	0.703	1	-6.195	0	0.05	0.768	1.56e-06	0.0861	0.658	104.982	4
97	004sbHg1qMzdrDXdxJpkj1		5	262840	0			2021-04-02	0.568	0.517	6	-7.316	0	0.0894	0.146	0.0	0.343	0.578	104.441	4
98	004tIr2Xy3FJVesPxRBLqm		2	226853	0			2020-09-25	0.683	0.572	7	-7.086	1	0.0447	0.11	1.54e-05	0.0895	0.583	100.035	4
99	004tQ7CGOm6ZKbmhdTgKsM		17	140120	0			1995-06-20	0.683	0.479	1	-11.574	1	0.942	0.367	0.0	0.337	0.707	76.782	1
100	004uWjBm1cOgxJAwQb6X8P		39	260467	0			1993-11-01	0.774	0.634	0	-11.401	0	0.0487	0.2	0.00976	0.22	0.933	119.953	4

Rows: 1-100 | Columns: 20

Data Exploration¶

Our "artists" dataset is too broad for us to use right now; we're only concerned with Polish artists, so let's extract and save them to our Vertica database.

In [7]:

# filter polish artists out of the 'artists' dataset using information in 'genres' column
polish_artists = artists.search("genres ilike '%disco polo%' or genres ilike '%polish%'") 

# save it to the database
polish_artists.to_db('"spotify"."polish_artists"', relation_type = "table")

Out[7]:

	Abc id Varchar(44)	123 followers Numeric(7,2)	Abc Varchar(36)	Abc Varchar(114)	123 popularity Integer
1	004reCzVFOidvBuYrYia9Y	12726.0			27
2	00drc18J6PkIXn24widBC5	3.0			0
3	00ekfPE5ZS3NwF8H8o8GBk	17574.0			47
4	0138Hlfmap3fEvKTqmZhE9	1289.0			5
5	018gIUaP08hROTOiVdiEQ3	584.0			8
6	01BlTZ696EkKe6xr56Gu6G	362.0			3
7	01TgMAgIALWvVXlKjUwpfn	1063.0			9
8	0244q9rIqAIzBFXKNNRN6O	27393.0			40
9	02Cq85QmaYHDi4dW7AxTRZ	749.0			9
10	02ESuuto8Jwyo4PeiJ1Xim	149.0			1
11	02JmHOSFJi2bLjGnO274di	8817.0			31
12	02LrsTMdnHVvKmXxN0epQF	753.0			22
13	02eZEXslMzAjHDkygNJHSX	8010.0			26
14	02keDoJak6YO12KBJFMFNm	1873.0			15
15	02tQ309SzZZ0bYs2yyO60G	11734.0			30
16	0338weYyACbkc5ERuLnFTa	270.0			17
17	033WIygOyXwUjc1vfCGxJ2	126.0			0
18	03Dy3XKBUsC3vJLCuF0T7I	152.0			1
19	03KLzHVK6la8dVop1iVI5x	63817.0			50
20	03ZzgzybQr8UyvWCMSCvRy	1363.0			14
21	03jLJnyfZXs1ssrIALfGRm	2633.0			22
22	03ohDYwWFrXfgp0VEtSTiF	706.0			9
23	03qKjVTzyKc3SyTjHaOpFc	2376.0			22
24	03rREATXGWcD2CfG3OXDZY	10155.0			44
25	03xKZpOUZOQjf7g5WBN4ee	3676.0			30
26	03yP3BHBnpGyvddEoIGnsx	2089.0			16
27	04Lio76CKJCMPbK5hV6J4w	1876.0			5
28	04Loj16dRX1yZodeEQlCOv	308.0			1
29	04WxKoI0kS5JclvQ8rn8qp	13.0			0
30	04bDWf1u7HxKdskC3N2nIk	27127.0			37
31	05AVHcWP9DF6y6LEU845uz	1545.0			15
32	05Fgqq7GfWeNol1TR5H3og	15868.0			35
33	05UsyksBcAUVdfyREMxbDm	295.0			11
34	063D0MKbIbbBjKgtYRGBga	7458.0			36
35	0690wuO0NVERuqxuoi2mTF	319.0			4
36	06O52v4thQuBoLC6jWatGW	21.0			0
37	06UcKJxYJXthEwn0c8XOCt	11024.0			20
38	06wBGqhkbyUAtVNMbbcK1x	607.0			8
39	070tdNOiP3pIsGlqNfVkG3	86130.0			51
40	072HrG3T5BaaBj4YhKIkxv	1166.0			8
41	07ILo13zpakvXxTL3VtqwS	540.0			10
42	098RsUTij7grC7evZUhWwA	720.0			31
43	09MjLGtslj39ILxA1MqUny	556.0			7
44	09ScR35g0VzipHacuPtXZd	440.0			7
45	09Z3SI4GkhYjpCB6884vC8	10395.0			25
46	09j4UTVH7vk7fVfVB71roU	348.0			1
47	09u3N07jllcbrnkvjxNBSl	1117.0			15
48	0AYJ3eg4zKi9ilGrhVaINs	2186.0			18
49	0AZgkXW6n0zfyOhVAnIopA	1109.0			33
50	0At3wjxYzZL9WwqbFR0JL8	24.0			0
51	0BBB9DjvskQV0oReJMxTP1	30889.0			45
52	0BQIhJ61mCyaOrVrMJ7e8k	5.0			0
53	0CEw36eWG0dYKCXOX8eUoO	77804.0			47
54	0CI4rQj50Dcr30HpiD2LF6	165.0			8
55	0CgCy79P84g1meaXcwwFqZ	80.0			0
56	0CsrftI3Zs3nvfSW6MRglc	50.0			0
57	0D5kXlS7UOApMpTyuSrFAW	40370.0			39
58	0D9mwbJP5sUH7XYXg4F7u9	580.0			4
59	0E6TslMisIITlZ1QjjPXeo	110.0			0
60	0EDBV0NVPOftbsEM0fg7WZ	2004.0			18
61	0EMDndPZcpfg9Qqgos0S7G	73.0			0
62	0EPzUAW8kwuPedmmVP6n9S	99964.0			53
63	0EQaqT3oKtxAGR0Y5c1Jme	3572.0			11
64	0EYfWGAHPugeWUKKvoMU79	336.0			4
65	0EalHNt7jDL6Tc7XSunjXu	21.0