SQL Query Primer

Query Clauses

SQL offers six clauses (Query Clauses) to query your data. All SQL queries will use at least two clauses, i.e., SELECT and FROM.

Table 1 Query Clauses

Clause Name	Purpose
SELECT	Determines which columns to include in the query’s result set
FROM	Identifies the tables from which to retrieve data and how the tables should be joined
WHERE	Filters out unwanted data
GROUP BY	Used to group rows together by common column values
HAVING	Filters out unwanted groups
ORDER BY	Sorts the rows of the final result set by one or more columns

Basic query

The most basic SQL query will have a SELECT and FROM clause. Select lets you choose the columns you want. In case you want all the columns, you can use *, which indicates to SQL you want all the columns. The FROM clause lets you specify the table you want to query. The following is the most basic SQL query: You can select all the columns by using * after the SELECT. Note all SQL queries are terminated by a semicolon (;). You can format your SQL with as many spaces and tabs as you like. To indicate to SQL that your query statement is complete, terminate it with a semicolon.

Query: Select all columns and all rows from the PatientCorePopulatedTable table.

SELECT
    *
FROM
    PatientCorePopulatedTable;

By default all the rows are selected since no filtering is applied.

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT
    *
FROM
    PatientCorePopulatedTable;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientID	PatientGender	PatientDateOfBirth	PatientRace	PatientMaritalStatus	PatientLanguage	PatientPopulationPercentageBelowPoverty
0	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	Male	1947-12-28 02:45:40.547	Unknown	Married	Icelandic	18.080000
1	64182B95-EB72-4E2B-BE77-8050B71498CE	Male	1952-01-18 19:51:12.917	African American	Separated	English	13.030000
2	DB22A4D9-7E4D-485C-916A-9CD1386507FB	Female	1970-07-25 13:04:20.717	Asian	Married	English	6.670000
3	6E70D84D-C75F-477C-BC37-9177C3698C66	Male	1979-01-04 05:45:29.580	White	Married	English	16.090000
4	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	Female	1921-04-11 11:39:49.197	White	Married	English	18.200000
5	7FD13988-E58A-4A5C-8680-89AC200950FA	Male	1965-07-12 15:41:20.523	White	Married	Spanish	12.410000
6	C60FE675-CA52-4C55-A233-F4B27E94987F	Male	1957-10-30 23:26:15.303	Asian	Married	Spanish	12.800000
7	B39DC5AC-E003-4E6A-91B6-FC07625A1285	Female	1935-11-03 21:07:09.040	White	Married	English	15.310000
8	FA157FA5-F488-4884-BF87-E144630D595C	Female	1932-11-01 06:19:56.577	White	Single	English	16.320000
9	B7E9FC4C-5182-4A34-954E-CEF5FC07E96D	Female	1985-12-11 02:48:16.907	Unknown	Single	English	11.430000
10	1A40AF35-C6D4-4D46-B475-A15D84E8A9D5	Male	1949-01-10 15:37:35.543	White	Married	English	11.250000
11	DA6CECFF-DE13-4C4C-919F-64E1A2B76C9D	Male	1924-06-27 19:37:58.823	Asian	Divorced	English	83.750000
12	0A9BA3E4-CF3C-49C4-9774-5EEA2EE7D123	Male	1980-04-04 07:59:15.820	White	Single	Spanish	14.280000
13	7C788499-7798-484B-A027-9FCDC4C0DADB	Male	1926-08-13 10:22:16.247	White	Married	English	11.890000
14	C54B5AAD-98E8-472D-BAA0-638D9F3BD024	Female	1972-11-13 22:50:43.600	African American	Single	Unknown	94.000000
15	6985D824-3269-4D12-A9DD-B932D640E26E	Female	1967-10-27 03:58:29.027	White	Married	English	12.010000
16	D8B53AA2-7953-4477-9EA4-68400EBAAC5C	Male	1951-06-11 20:11:10.003	African American	Unknown	English	16.890000
17	DB92CDC6-FA9B-4492-BC2C-0C588AD78956	Male	1977-06-30 01:46:05.467	Unknown	Single	Icelandic	1.700000
18	25B786AF-0F99-478C-9CFA-0EA607E45834	Male	1926-08-20 00:21:38.870	White	Married	English	93.600000
19	F00C64F8-2033-4640-80FE-F1F62CBE26A5	Female	1957-06-04 18:31:01.177	African American	Single	English	15.720000
20	B70E5A76-F2BC-41E4-B037-CD4D9ABA0967	Male	1962-12-15 01:59:01.737	White	Single	Icelandic	13.100000
21	F0B53A2C-98CA-415D-B928-E3FD0E52B22A	Male	1950-06-20 10:31:18.337	African American	Married	English	14.870000
22	1A8791E3-A61C-455A-8DEE-763EB90C9B2C	Male	1973-08-16 10:58:34.413	Asian	Single	English	13.970000
23	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	Female	1930-05-28 02:59:42.857	White	Married	Icelandic	19.220000
24	220C8D43-1322-4A9D-B890-D426942A3649	Male	1971-03-13 18:31:24.877	Asian	Married	English	16.710000
25	C242E3A4-E785-4DF1-A0E4-3B568DC88F2E	Male	1963-02-23 21:46:28.840	Asian	Single	Spanish	7.590000
26	0BC491C5-5A45-4067-BD11-A78BEA00D3BE	Female	1921-04-18 01:56:01.807	Unknown	Married	English	18.050000
27	79A7BA2A-D35A-4CB8-A835-6BAA13B0058C	Female	1952-05-03 02:30:21.563	White	Single	Spanish	16.240000
28	FFCDECD6-4048-4DCB-B910-1218160005B3	Male	1941-05-06 14:56:42.687	White	Single	English	14.490000
29	CD2ADB1B-97F7-4EF6-BC5C-3E0EC562A06F	Female	1963-04-16 08:54:08.647	White	Single	Icelandic	11.160000
30	A50BE9B4-8A0B-4169-B894-F7BD86D7D90B	Female	1987-04-18 16:31:16.333	Asian	Divorced	English	14.070000
31	69CC25ED-A54A-4BAF-97E3-774BB3C9DED1	Female	1974-10-13 14:51:28.997	Unknown	Married	English	17.720000
32	69B5D2A0-12FD-46EF-A5FF-B29C4BAFBE49	Male	1954-05-02 16:38:15.957	White	Married	English	18.650000
33	8D389A8C-A6D8-4447-9DDE-1A28AB4EC667	Female	1964-07-10 08:50:13.707	Asian	Married	English	4.340000
34	967987B9-FFEF-4776-85CF-AE05CA81F583	Male	1947-02-13 10:26:55.520	White	Widowed	English	88.660000
35	9BBF3A51-443D-438B-9289-B98B8E0577C0	Male	1944-11-25 06:12:56.860	White	Married	English	16.760000
36	6D8008ED-D623-4BE4-B93B-335F9797C170	Female	1978-03-14 13:41:28.337	White	Married	Icelandic	14.810000
37	B3892204-880B-40EF-B3BB-B824B50E99E5	Male	1978-08-06 08:38:27.330	White	Single	Spanish	16.860000
38	49DADA25-F2C2-42BB-8210-D78E6C7B0D48	Male	1962-04-08 10:18:26.263	African American	Married	English	19.500000
39	7548B6CF-79D9-461D-A0C5-20B861406FAC	Male	1956-11-13 14:08:00.253	White	Married	Icelandic	14.030000
40	B5D31F01-7273-4901-B56F-8139769A11EF	Female	1971-04-02 18:56:59.353	Asian	Unknown	Unknown	82.080000
41	80D356B4-F974-441F-A5F2-F95986D119A2	Female	1938-03-06 18:24:18.297	White	Single	English	18.880000
42	A0A976C8-9B30-4492-B8C4-5B25095B9192	Male	1931-05-26 14:54:15.847	Asian	Single	English	19.520000
43	016A590E-D093-4667-A5DA-D68EA6987D93	Male	1960-12-06 06:37:05.640	White	Unknown	English	15.020000
44	3B11D6B3-A36A-4B69-A437-C29BF425A941	Female	1954-05-18 10:19:19.110	Asian	Separated	English	10.800000
45	E483DE6E-D4E6-47FD-905B-22EE86EC7ACE	Male	1945-11-18 04:14:31.573	African American	Single	English	16.880000
46	B2EB15FA-5431-4804-9309-4215BDC778C0	Male	1951-04-13 20:14:02.953	White	Divorced	English	19.410000
47	53B9FFDD-F80B-43BE-93CF-C34A023EE7E9	Male	1921-08-22 19:17:09.227	White	Divorced	Icelandic	18.170000
48	C5D09468-574F-4802-B56F-DB38F4EB1687	Male	1939-07-07 19:39:49.753	African American	Married	Icelandic	10.870000
49	EA7C2F0F-DA1C-4CE8-9700-4BB1FC7AF3FB	Male	1969-01-05 00:15:09.627	Unknown	Married	English	3.300000
50	35FE7491-1A1D-48CB-810C-8DC2599AB3DD	Male	1969-11-02 06:34:34.527	White	Married	English	4.510000
51	868E700E-3C56-458F-A477-078D671DCB20	Female	1978-09-21 15:29:44.770	African American	Married	Icelandic	5.940000
52	56A35E74-90BE-44A0-B7BA-7743BB152133	Female	1968-11-09 15:29:19.557	White	Married	English	8.810000
53	CC12B481-B516-455B-884F-4CA900B29F2E	Female	1985-10-21 07:59:04.777	Unknown	Divorced	English	14.580000
54	714823AF-C52C-414C-B53B-C43EACD194C3	Male	1952-05-08 23:51:50.127	White	Married	English	13.230000
55	3231F930-2978-4F50-8234-755449851E7B	Male	1979-05-26 04:58:10.627	White	Single	English	18.360000
56	C2CCB1AB-6633-4CB3-B4E8-157E6FB02376	Female	1964-05-07 10:20:37.740	White	Single	Spanish	15.980000
57	1311FEE4-2FDC-46E4-83D3-1550A3E51D2C	Female	1988-03-28 03:09:22.807	White	Single	English	14.990000
58	21792512-2D40-4326-BEA2-A40127EB24FF	Male	1938-03-24 19:25:53.980	White	Single	Unknown	89.440000
59	EEAFC0B3-B835-4D99-AB33-2F9428E54E5F	Female	1961-01-08 15:19:15.490	Asian	Unknown	English	18.950000
60	2EE42DEF-37CA-4694-827E-FA4EAF882BFC	Male	1964-04-27 00:41:40.410	White	Married	English	18.910000
61	36775002-9EC3-4889-AD4F-80DC6855C8D8	Female	1963-07-16 22:16:48.477	Asian	Single	Spanish	15.560000
62	4C201C71-CCED-40D1-9642-F9C8C485B854	Male	1926-09-22 09:17:14.713	African American	Married	English	10.300000
63	2A8772FE-61DB-483E-B6BF-6C0A74BA9C2A	Female	1966-10-14 15:31:48.427	Asian	Single	English	11.150000
64	C65A4ADE-112E-49E4-B72A-0DED22C242ED	Male	1955-04-07 18:55:17.077	White	Married	English	19.660000
65	80AC01B2-BD55-4BE0-A59A-4024104CF4E9	Female	1965-05-14 15:25:09.740	African American	Married	English	19.740000
66	6A57AC0C-57F3-4C19-98A1-51135EFBC4FF	Female	1958-07-07 16:00:19.383	Unknown	Married	English	94.060000
67	7A025E77-7832-4F53-B9A7-09A3F98AC17E	Female	1951-07-12 08:23:45.520	Asian	Single	English	16.690000
68	DCE5AEB8-6DB9-4106-8AE4-02CCC5C23741	Female	1967-11-09 08:14:51.143	White	Single	Unknown	16.430000
69	886B5885-1EE2-49F3-98D5-A2F02EB8A9D4	Female	1970-04-27 09:15:11.080	Unknown	Married	English	10.350000
70	0E0EADE8-5592-4E0B-9F88-D7596E32EE08	Female	1942-07-13 21:15:37.517	White	Married	Spanish	18.800000
71	0681FA35-A794-4684-97BD-00B88370DB41	Female	1978-10-02 21:46:05.300	Asian	Unknown	Spanish	19.160000
72	2E26695A-EFB0-4C7F-9318-E3030B154E39	Female	1963-06-28 05:37:36.843	Asian	Married	English	14.200000
73	DDC0BC57-7A4E-4E02-9282-177750B74FBC	Male	1921-03-26 14:38:51.803	White	Single	English	18.410000
74	E250799D-F6DE-4914-ADB4-B08A6E5029B9	Female	1945-08-04 19:03:00.757	White	Single	Unknown	12.860000
75	9C75DF1F-9DA6-4C98-8F5B-E10BDC805ED0	Female	1952-07-19 13:35:56.833	White	Divorced	English	88.650000
76	A19A0B00-4C9A-4206-B1FE-17E6DA3CEB0B	Female	1967-07-01 01:44:24.907	Unknown	Married	English	17.840000
77	E5478913-6819-4977-BB11-4C8B61175B56	Female	1980-03-21 13:08:56.867	White	Single	English	12.920000
78	66154E24-D3EE-4311-89DB-6195278F9B3C	Female	1944-08-26 13:03:24.297	White	Divorced	English	19.490000
79	BC44CE19-9FC5-4AC9-A296-9EBC5E3D03AE	Female	1953-06-04 03:16:17.843	African American	Married	English	15.040000
80	CC9CDA72-B37A-4F8F-AFE4-B08F56A183BE	Female	1942-04-14 14:01:01.130	Asian	Married	Spanish	14.150000
81	EA9B67E2-15F0-450B-A8A6-14F6E4AE3D12	Female	1930-04-08 20:59:31.057	Asian	Separated	Icelandic	98.400000
82	3E462A8F-7B90-43A1-A8B6-AD82CB5002C9	Female	1986-12-20 16:41:34.793	White	Married	Spanish	18.330000
83	9E18822E-7D13-45C7-B50E-F95CFF92BC3E	Male	1954-10-16 06:45:56.257	Unknown	Single	English	15.240000
84	A7142B71-A144-4D56-BD14-3E966B01DB37	Male	1960-03-28 13:40:52.270	White	Divorced	Spanish	19.520000
85	FE0B9B59-1927-45B7-8556-E079DC1DE30A	Male	1955-08-23 01:01:22.260	White	Divorced	English	6.480000
86	98F593D2-8894-49BB-93B9-5A0E2CF85E2E	Female	1944-07-15 19:04:11.487	African American	Married	English	9.100000
87	1A220558-5996-43E1-AE5D-7B96180FED35	Male	1937-09-07 22:23:53.143	Asian	Married	English	15.110000
88	7A7332AD-88B1-4848-9356-E5260E477C59	Female	1944-12-01 06:30:01.543	Unknown	Married	English	19.460000
89	6D5DCAC1-17FE-4D7C-923B-806EFBA3E6DF	Male	1961-10-22 02:47:13.170	Asian	Married	English	13.900000
90	36E2F89E-777A-4D77-9D95-0D70A8AB416F	Male	1980-05-30 13:23:50.703	African American	Separated	English	19.360000
91	672D554B-D6D1-40B2-A6A4-21A4CB6B1AA6	Male	1983-06-17 04:46:13.753	African American	Single	Icelandic	11.040000
92	03A481F5-B32A-4A91-BD42-43EB78FEBA77	Female	1968-02-07 23:02:38.017	Asian	Single	Unknown	93.230000
93	2A5251B1-0945-47FA-A65C-7A6381562591	Female	1942-01-07 16:45:33.060	Unknown	Married	Spanish	18.050000
94	8AF47463-8534-4203-B210-C2290F6CE689	Female	1952-06-27 17:29:04.187	White	Divorced	English	11.880000
95	135C831F-7DA5-46C0-959C-EBCBD8810B43	Male	1971-05-13 04:40:05.623	White	Unknown	Spanish	12.380000
96	8856096E-E59C-4156-A767-C091AF799C80	Female	1988-11-25 02:59:36.373	White	Divorced	English	11.080000
97	6623F5D6-D581-4268-9F9B-21612FBBF7B5	Female	1943-02-17 15:36:13.787	Asian	Single	Spanish	14.490000
98	65A7FBE0-EA9F-49E9-9824-D8F3AD98DAC0	Female	1962-11-30 06:28:33.110	White	Separated	Spanish	17.980000
99	FB909FAE-72DD-4F6F-9828-D92183DF185F	Male	1940-07-15 12:18:41.080	White	Single	Spanish	14.900000

Limit rows

You can limit the number of rows to 10 by adding LIMIT 10 after the FROM clause.

Query: Select all columns and from the PatientCorePopulatedTable table and limit to 10 rows.

SELECT
    *
FROM
    PatientCorePopulatedTable
LIMIT 10;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT
    *
FROM
    PatientCorePopulatedTable
LIMIT 10;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientID	PatientGender	PatientDateOfBirth	PatientRace	PatientMaritalStatus	PatientLanguage	PatientPopulationPercentageBelowPoverty
0	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	Male	1947-12-28 02:45:40.547	Unknown	Married	Icelandic	18.080000
1	64182B95-EB72-4E2B-BE77-8050B71498CE	Male	1952-01-18 19:51:12.917	African American	Separated	English	13.030000
2	DB22A4D9-7E4D-485C-916A-9CD1386507FB	Female	1970-07-25 13:04:20.717	Asian	Married	English	6.670000
3	6E70D84D-C75F-477C-BC37-9177C3698C66	Male	1979-01-04 05:45:29.580	White	Married	English	16.090000
4	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	Female	1921-04-11 11:39:49.197	White	Married	English	18.200000
5	7FD13988-E58A-4A5C-8680-89AC200950FA	Male	1965-07-12 15:41:20.523	White	Married	Spanish	12.410000
6	C60FE675-CA52-4C55-A233-F4B27E94987F	Male	1957-10-30 23:26:15.303	Asian	Married	Spanish	12.800000
7	B39DC5AC-E003-4E6A-91B6-FC07625A1285	Female	1935-11-03 21:07:09.040	White	Married	English	15.310000
8	FA157FA5-F488-4884-BF87-E144630D595C	Female	1932-11-01 06:19:56.577	White	Single	English	16.320000
9	B7E9FC4C-5182-4A34-954E-CEF5FC07E96D	Female	1985-12-11 02:48:16.907	Unknown	Single	English	11.430000

Select some columns

You can select columns from a table by specifying them after the SELECT clause. Multiple columns are separated by a comma (,).

Query: Select the PatientID and PatientDateOfBirth columns and limit to 10 rows.

SELECT
    PatientID,
    PatientDateOfBirth
FROM
    PatientCorePopulatedTable
LIMIT 10;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT
    PatientID,
    PatientDateOfBirth
FROM
    PatientCorePopulatedTable
LIMIT 10;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientID	PatientDateOfBirth
0	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	1947-12-28 02:45:40.547
1	64182B95-EB72-4E2B-BE77-8050B71498CE	1952-01-18 19:51:12.917
2	DB22A4D9-7E4D-485C-916A-9CD1386507FB	1970-07-25 13:04:20.717
3	6E70D84D-C75F-477C-BC37-9177C3698C66	1979-01-04 05:45:29.580
4	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	1921-04-11 11:39:49.197
5	7FD13988-E58A-4A5C-8680-89AC200950FA	1965-07-12 15:41:20.523
6	C60FE675-CA52-4C55-A233-F4B27E94987F	1957-10-30 23:26:15.303
7	B39DC5AC-E003-4E6A-91B6-FC07625A1285	1935-11-03 21:07:09.040
8	FA157FA5-F488-4884-BF87-E144630D595C	1932-11-01 06:19:56.577
9	B7E9FC4C-5182-4A34-954E-CEF5FC07E96D	1985-12-11 02:48:16.907

Using column alias

Notice that all the column names are prefixed by Patient. You can give columns a different name, meaning an alias. There are two ways to do alias. You can put the alias name right after the actual column name, e.g., PatientID PTID or you can use the AS keyword to indicate explicitly that you are aliasing a column name, e.g., PatientID AS PTID. Note that if the alias has a space, then it should be in quotes.

Query: Select the PatientID and PatientDateOfBirth columns, but alias PatientID to PTID and PatientDateOfBirth to “Date of Birth” and limit to 10 rows.

SELECT
    PatientID PTID, 
    PatientDateOfBirth AS "Date of Birth"
FROM
    PatientCorePopulatedTable
LIMIT 10;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT
    PatientID PTID, 
    PatientDateOfBirth AS "Date of Birth"
FROM
    PatientCorePopulatedTable
LIMIT 10;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PTID	Date of Birth
0	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	1947-12-28 02:45:40.547
1	64182B95-EB72-4E2B-BE77-8050B71498CE	1952-01-18 19:51:12.917
2	DB22A4D9-7E4D-485C-916A-9CD1386507FB	1970-07-25 13:04:20.717
3	6E70D84D-C75F-477C-BC37-9177C3698C66	1979-01-04 05:45:29.580
4	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	1921-04-11 11:39:49.197
5	7FD13988-E58A-4A5C-8680-89AC200950FA	1965-07-12 15:41:20.523
6	C60FE675-CA52-4C55-A233-F4B27E94987F	1957-10-30 23:26:15.303
7	B39DC5AC-E003-4E6A-91B6-FC07625A1285	1935-11-03 21:07:09.040
8	FA157FA5-F488-4884-BF87-E144630D595C	1932-11-01 06:19:56.577
9	B7E9FC4C-5182-4A34-954E-CEF5FC07E96D	1985-12-11 02:48:16.907

Adding columns not from the table

Besides selecting columns in the table, you can also add the following columns to your query:

1. literals such as numbers or strings

2. Math expressions such as PatientPopulationPercentageBelowPoverty + 1, or PatientPopulationPercentageBelowPoverty * 100

Query: Select the PatientID, Hospital, and PatientPopulationPercentageBelowPoverty columns, alias PatientID to PTID, make the Hospital column ‘Buffalo General’, multiply PatientPopulationPercentageBelowPoverty by 10 and alias it to Poverty Level, and limit to 10 rows. Note that if the alias name has a space, then the name needs to inside of quotes.

SELECT
    PatientID PTID, 
    'Buffalo General' Hospital,
    PatientPopulationPercentageBelowPoverty * 10 "Poverty Level"
FROM
    PatientCorePopulatedTable
LIMIT 10;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT
    PatientID PTID, 
    'Buffalo General' Hospital,
    PatientPopulationPercentageBelowPoverty * 10 "Poverty Level"
FROM
    PatientCorePopulatedTable
LIMIT 10;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PTID	Hospital	Poverty Level
0	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	Buffalo General	180.800000
1	64182B95-EB72-4E2B-BE77-8050B71498CE	Buffalo General	130.300000
2	DB22A4D9-7E4D-485C-916A-9CD1386507FB	Buffalo General	66.700000
3	6E70D84D-C75F-477C-BC37-9177C3698C66	Buffalo General	160.900000
4	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	Buffalo General	182.000000
5	7FD13988-E58A-4A5C-8680-89AC200950FA	Buffalo General	124.100000
6	C60FE675-CA52-4C55-A233-F4B27E94987F	Buffalo General	128.000000
7	B39DC5AC-E003-4E6A-91B6-FC07625A1285	Buffalo General	153.100000
8	FA157FA5-F488-4884-BF87-E144630D595C	Buffalo General	163.200000
9	B7E9FC4C-5182-4A34-954E-CEF5FC07E96D	Buffalo General	114.300000

Removing duplicates

In some cases you might get duplicate rows. You remove these duplicate rows by putting the DISTINCT keyword after the SELECT keyword. One use of this is to get distinct values of a given column.

Query: Select only the PatientMaritalStatus column from the PatientCorePopulatedTable table.

SELECT
    PatientMaritalStatus
FROM
    PatientCorePopulatedTable;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT
    PatientMaritalStatus
FROM
    PatientCorePopulatedTable;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientMaritalStatus
0	Married
1	Separated
2	Married
3	Married
4	Married
5	Married
6	Married
7	Married
8	Single
9	Single
10	Married
11	Divorced
12	Single
13	Married
14	Single
15	Married
16	Unknown
17	Single
18	Married
19	Single
20	Single
21	Married
22	Single
23	Married
24	Married
25	Single
26	Married
27	Single
28	Single
29	Single
30	Divorced
31	Married
32	Married
33	Married
34	Widowed
35	Married
36	Married
37	Single
38	Married
39	Married
40	Unknown
41	Single
42	Single
43	Unknown
44	Separated
45	Single
46	Divorced
47	Divorced
48	Married
49	Married
50	Married
51	Married
52	Married
53	Divorced
54	Married
55	Single
56	Single
57	Single
58	Single
59	Unknown
60	Married
61	Single
62	Married
63	Single
64	Married
65	Married
66	Married
67	Single
68	Single
69	Married
70	Married
71	Unknown
72	Married
73	Single
74	Single
75	Divorced
76	Married
77	Single
78	Divorced
79	Married
80	Married
81	Separated
82	Married
83	Single
84	Divorced
85	Divorced
86	Married
87	Married
88	Married
89	Married
90	Separated
91	Single
92	Single
93	Married
94	Divorced
95	Unknown
96	Divorced
97	Single
98	Separated
99	Single

Query: Select the PatientMaritalStatus column from the PatientCorePopulatedTable table but only select distinct values.

SELECT DISTINCT
    PatientMaritalStatus
FROM
    PatientCorePopulatedTable;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT DISTINCT
    PatientMaritalStatus
FROM
    PatientCorePopulatedTable;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientMaritalStatus
0	Married
1	Separated
2	Single
3	Divorced
4	Unknown
5	Widowed

Removing duplicates with multiple columns

The DISTINCT keyword can also be used to find distinct combination of columns. It is also used sometimes with joins to remove duplicate rows.

Query: Select PatientRace and PatientMaritalStatus columns from the PatientCorePopulatedTable table but only select distinct values. This query finds the distinct combinations of race and martial status.

SELECT DISTINCT
    PatientRace,
    PatientMaritalStatus
FROM
    PatientCorePopulatedTable
ORDER BY PatientRace, PatientMaritalStatus

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT DISTINCT
    PatientRace,
    PatientMaritalStatus
FROM
    PatientCorePopulatedTable
ORDER BY PatientRace, PatientMaritalStatus
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientRace	PatientMaritalStatus
0	African American	Married
1	African American	Separated
2	African American	Single
3	African American	Unknown
4	Asian	Divorced
5	Asian	Married
6	Asian	Separated
7	Asian	Single
8	Asian	Unknown
9	Unknown	Divorced
10	Unknown	Married
11	Unknown	Single
12	White	Divorced
13	White	Married
14	White	Separated
15	White	Single
16	White	Unknown
17	White	Widowed

FROM multiple tables

The power of SQL lies in the fact that you can combine tables together based on some shared column between tables. The FROM clause allows you to select from multiple tables.

You should now that there are four types of tables in SQL:

1. Permanent tables (i.e., created using the CREATE TABLE statement)

2. Derived tables (i.e., rows returned by a subquery and held in memory)

3. Temporary tables (i.e., volatile data held in memory)

4. Virtual tables (i.e, created using the CREATE VIEW statement)

We have been using permanent tables so far. Temporary and virtual tables will be covered later. The following is an example a derived table.

Derived table

A derived query is a query held in memory. You surround it a pair of parenthesis and give it a name.

Query: Create a subquery called dx_codes which selects the PrimaryDiagnosisCode and PrimaryDiagnosisDescription columns from the AdmissionsDiagnosesCorePopulatedTable where the AdmissionID is equal to 1. Then use this derived query in another query that concatenates the diagnosis code and diagnosis description, e.g., “(M01.X) Direct infection of joint in infectious and parasitic diseases classified elsewhere”. Fields and string literals can be concatenated in SQLite using ||.

SELECT '(' || dx_codes.code || ') ' || dx_codes.description AS CodeWDescription
FROM
   (
     SELECT
       PrimaryDiagnosisCode code,
       PrimaryDiagnosisDescription description 
     FROM
       AdmissionsDiagnosesCorePopulatedTable
      WHERE AdmissionID = 1
   ) dx_codes

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT '(' || dx_codes.code || ') ' || dx_codes.description AS CodeWDescription
FROM
   (
     SELECT
       PrimaryDiagnosisCode code,
       PrimaryDiagnosisDescription description 
     FROM
       AdmissionsDiagnosesCorePopulatedTable
      WHERE AdmissionID = 1
   ) dx_codes
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	CodeWDescription
0	(C91.00) Acute lymphoblastic leukemia not having achieved remission
1	(M05.442) Rheumatoid myopathy with rheumatoid arthritis of left hand
2	(K91) Intraoperative and postprocedural complications and disorders of digestive system, not elsewhere classified
3	(M05.752) Rheumatoid arthritis with rheumatoid factor of left hip without organ or systems involvement
4	(C94.6) Myelodysplastic disease, not classified
5	(C92.51) Acute myelomonocytic leukemia, in remission
6	(D12.7) Benign neoplasm of rectosigmoid junction
7	(C79.72) Secondary malignant neoplasm of left adrenal gland
8	(M06.31) Rheumatoid nodule, shoulder
9	(Z13.810) Encounter for screening for upper gastrointestinal disorder
10	(Z13.83) Encounter for screening for respiratory disorder NEC
11	(D39.12) Neoplasm of uncertain behavior of left ovary
12	(O99.41) Diseases of the circulatory system complicating pregnancy
13	(O07.32) Renal failure following failed attempted termination of pregnancy
14	(E10.630) Type 1 diabetes mellitus with periodontal disease
15	(F11) Opioid related disorders
16	(D13.1) Benign neoplasm of stomach
17	(F31.4) Bipolar disorder, current episode depressed, severe, without psychotic features
18	(O10.113) Pre-existing hypertensive heart disease complicating pregnancy, third trimester
19	(N49.2) Inflammatory disorders of scrotum
20	(I25.82) Chronic total occlusion of coronary artery
21	(C51.2) Malignant neoplasm of clitoris
22	(H75.01) Mastoiditis in infectious and parasitic diseases classified elsewhere, right ear
23	(M01.X5) Direct infection of hip in infectious and parasitic diseases classified elsewhere
24	(E10.339) Type 1 diabetes mellitus with moderate nonproliferative diabetic retinopathy without macular edema
25	(C19) Malignant neoplasm of rectosigmoid junction
26	(I67.81) Acute cerebrovascular insufficiency
27	(C92.50) Acute myelomonocytic leukemia, not having achieved remission
28	(I25.84) Coronary atherosclerosis due to calcified coronary lesion
29	(M63) Disorders of muscle in diseases classified elsewhere
30	(O9A.1) Malignant neoplasm complicating pregnancy, childbirth and the puerperium
31	(C75.1) Malignant neoplasm of pituitary gland
32	(M11.071) Hydroxyapatite deposition disease, right ankle and foot
33	(F01.5) Vascular dementia
34	(G47) Sleep disorders
35	(G96.1) Disorders of meninges, not elsewhere classified
36	(C91.5) Adult T-cell lymphoma/leukemia (HTLV-1-associated)
37	(Z91.15) Patient's noncompliance with renal dialysis
38	(C92.12) Chronic myeloid leukemia, BCR/ABL-positive, in relapse
39	(M05.742) Rheumatoid arthritis with rheumatoid factor of left hand without organ or systems involvement
40	(J66.1) Flax-dressers' disease
41	(N18.1) Chronic kidney disease, stage 1
42	(C88.3) Immunoproliferative small intestinal disease
43	(Z12) Encounter for screening for malignant neoplasms
44	(M05.261) Rheumatoid vasculitis with rheumatoid arthritis of right knee
45	(B40.1) Chronic pulmonary blastomycosis
46	(H34) Retinal vascular occlusions
47	(M24.251) Disorder of ligament, right hip
48	(O99.712) Diseases of the skin and subcutaneous tissue complicating pregnancy, second trimester
49	(I79.0) Aneurysm of aorta in diseases classified elsewhere
50	(A98.3) Marburg virus disease
51	(M05.722) Rheumatoid arthritis with rheumatoid factor of left elbow without organ or systems involvement
52	(H16.433) Localized vascularization of cornea, bilateral
53	(N25.1) Nephrogenic diabetes insipidus
54	(M02.37) Reiter's disease, ankle and foot
55	(M10.31) Gout due to renal impairment, shoulder
56	(I25.110) Atherosclerotic heart disease of native coronary artery with unstable angina pectoris
57	(E72.4) Disorders of ornithine metabolism
58	(O24.111) Pre-existing diabetes mellitus, type 2, in pregnancy, first trimester
59	(Z13.0) Encounter for screening for diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism
60	(M24.22) Disorder of ligament, elbow
61	(C03.0) Malignant neoplasm of upper gum
62	(Z12.2) Encounter for screening for malignant neoplasm of respiratory organs
63	(C69.61) Malignant neoplasm of right orbit
64	(O10.213) Pre-existing hypertensive chronic kidney disease complicating pregnancy, third trimester
65	(M90.51) Osteonecrosis in diseases classified elsewhere, shoulder
66	(F63.81) Intermittent explosive disorder
67	(O10.13) Pre-existing hypertensive heart disease complicating the puerperium
68	(C94.22) Acute megakaryoblastic leukemia, in relapse
69	(C11) Malignant neoplasm of nasopharynx
70	(I43) Cardiomyopathy in diseases classified elsewhere
71	(E09.359) Drug or chemical induced diabetes mellitus with proliferative diabetic retinopathy without macular edema
72	(M90.6) Osteitis deformans in neoplastic diseases
73	(C72.1) Malignant neoplasm of cauda equina
74	(F12.180) Cannabis abuse with cannabis-induced anxiety disorder
75	(C18.3) Malignant neoplasm of hepatic flexure
76	(M05.272) Rheumatoid vasculitis with rheumatoid arthritis of left ankle and foot
77	(E10.32) Type 1 diabetes mellitus with mild nonproliferative diabetic retinopathy
78	(M06.361) Rheumatoid nodule, right knee
79	(H26.21) Cataract with neovascularization
80	(Z12.13) Encounter for screening for malignant neoplasm of small intestine
81	(D35.5) Benign neoplasm of carotid body
82	(D16.2) Benign neoplasm of long bones of lower limb
83	(M06.041) Rheumatoid arthritis without rheumatoid factor, right hand
84	(M05.732) Rheumatoid arthritis with rheumatoid factor of left wrist without organ or systems involvement
85	(M10.37) Gout due to renal impairment, ankle and foot
86	(C67.5) Malignant neoplasm of bladder neck
87	(M10.361) Gout due to renal impairment, right knee
88	(K08.121) Complete loss of teeth due to periodontal diseases, class I
89	(E30) Disorders of puberty, not elsewhere classified
90	(G71) Primary disorders of muscles
91	(C18.3) Malignant neoplasm of hepatic flexure
92	(O99.351) Diseases of the nervous system complicating pregnancy, first trimester
93	(M05.25) Rheumatoid vasculitis with rheumatoid arthritis of hip
94	(C57.21) Malignant neoplasm of right round ligament
95	(K00) Disorders of tooth development and eruption
96	(M63.832) Disorders of muscle in diseases classified elsewhere, left forearm
97	(C72.3) Malignant neoplasm of optic nerve
98	(M05.27) Rheumatoid vasculitis with rheumatoid arthritis of ankle and foot
99	(F06.1) Catatonic disorder due to known physiological condition

The WHERE Clause

The WHERE clause allows you to filter out unwanted rows. For string fields, you can use the equality operator (=) or the LIKE operator. For numerical and date fields, you can use all the usually operators such as greater than, less than, etc. WHERE clauses can be combined using AND and OR. Parenthesis can be used to clarify grouping of the clauses. The WHERE clauses are put after the FROM clause.

Query: Select all patients from PatientCorePopulatedTable table that are either married and african american or married and white and the PatientPopulationPercentageBelowPoverty is above 15. Select the following columns: PatientID, PatientRace, PatientMaritalStatus and PatientPopulationPercentageBelowPoverty.

SELECT 
  PatientID, 
  PatientRace, 
  PatientMaritalStatus,
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
WHERE ((PatientRace = 'White' AND PatientMaritalStatus	= 'Married') OR (PatientRace = 'African American' AND PatientMaritalStatus = 'Married'))
  AND PatientPopulationPercentageBelowPoverty > 15

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT 
  PatientID, 
  PatientRace, 
  PatientMaritalStatus,
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
WHERE (
	(
		PatientRace = 'White' AND PatientMaritalStatus	= 'Married') 
		OR (PatientRace = 'African American' AND PatientMaritalStatus	= 'Married')
	)
  AND PatientPopulationPercentageBelowPoverty > 15
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientID	PatientRace	PatientMaritalStatus	PatientPopulationPercentageBelowPoverty
0	6E70D84D-C75F-477C-BC37-9177C3698C66	White	Married	16.090000
1	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	White	Married	18.200000
2	B39DC5AC-E003-4E6A-91B6-FC07625A1285	White	Married	15.310000
3	25B786AF-0F99-478C-9CFA-0EA607E45834	White	Married	93.600000
4	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	White	Married	19.220000
5	69B5D2A0-12FD-46EF-A5FF-B29C4BAFBE49	White	Married	18.650000
6	9BBF3A51-443D-438B-9289-B98B8E0577C0	White	Married	16.760000
7	49DADA25-F2C2-42BB-8210-D78E6C7B0D48	African American	Married	19.500000
8	2EE42DEF-37CA-4694-827E-FA4EAF882BFC	White	Married	18.910000
9	C65A4ADE-112E-49E4-B72A-0DED22C242ED	White	Married	19.660000
10	80AC01B2-BD55-4BE0-A59A-4024104CF4E9	African American	Married	19.740000
11	0E0EADE8-5592-4E0B-9F88-D7596E32EE08	White	Married	18.800000
12	BC44CE19-9FC5-4AC9-A296-9EBC5E3D03AE	African American	Married	15.040000
13	3E462A8F-7B90-43A1-A8B6-AD82CB5002C9	White	Married	18.330000

The ORDER BY Clauses

You can order the the rows by column(s) using the ORDER BY clause. This clause is put after the WHERE clause. You can specify multiple columns separated by comma. You can also specify ascending order using the ASC keyword after the column name and descending order by using the DESC keyword. The default sorting order is ascending. A shortcut for descending is putting - before the column name. Finally you can sort the columns by its numerical position.

Sort by columns

Query: Select the PatientMaritalStatus and PatientPopulationPercentageBelowPoverty columns from the PatientCorePopulatedTable table and sort by PatientPopulationPercentageBelowPoverty.

SELECT 
  PatientMaritalStatus, 
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
ORDER BY PatientPopulationPercentageBelowPoverty;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT 
  PatientMaritalStatus, 
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
ORDER BY PatientPopulationPercentageBelowPoverty;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientMaritalStatus	PatientPopulationPercentageBelowPoverty
0	Single	1.700000
1	Married	3.300000
2	Married	4.340000
3	Married	4.510000
4	Married	5.940000
5	Divorced	6.480000
6	Married	6.670000
7	Single	7.590000
8	Married	8.810000
9	Married	9.100000
10	Married	10.300000
11	Married	10.350000
12	Separated	10.800000
13	Married	10.870000
14	Single	11.040000
15	Divorced	11.080000
16	Single	11.150000
17	Single	11.160000
18	Married	11.250000
19	Single	11.430000
20	Divorced	11.880000
21	Married	11.890000
22	Married	12.010000
23	Unknown	12.380000
24	Married	12.410000
25	Married	12.800000
26	Single	12.860000
27	Single	12.920000
28	Separated	13.030000
29	Single	13.100000
30	Married	13.230000
31	Married	13.900000
32	Single	13.970000
33	Married	14.030000
34	Divorced	14.070000
35	Married	14.150000
36	Married	14.200000
37	Single	14.280000
38	Single	14.490000
39	Single	14.490000
40	Divorced	14.580000
41	Married	14.810000
42	Married	14.870000
43	Single	14.900000
44	Single	14.990000
45	Unknown	15.020000
46	Married	15.040000
47	Married	15.110000
48	Single	15.240000
49	Married	15.310000
50	Single	15.560000
51	Single	15.720000
52	Single	15.980000
53	Married	16.090000
54	Single	16.240000
55	Single	16.320000
56	Single	16.430000
57	Single	16.690000
58	Married	16.710000
59	Married	16.760000
60	Single	16.860000
61	Single	16.880000
62	Unknown	16.890000
63	Married	17.720000
64	Married	17.840000
65	Separated	17.980000
66	Married	18.050000
67	Married	18.050000
68	Married	18.080000
69	Divorced	18.170000
70	Married	18.200000
71	Married	18.330000
72	Single	18.360000
73	Single	18.410000
74	Married	18.650000
75	Married	18.800000
76	Single	18.880000
77	Married	18.910000
78	Unknown	18.950000
79	Unknown	19.160000
80	Married	19.220000
81	Separated	19.360000
82	Divorced	19.410000
83	Married	19.460000
84	Divorced	19.490000
85	Married	19.500000
86	Single	19.520000
87	Divorced	19.520000
88	Married	19.660000
89	Married	19.740000
90	Unknown	82.080000
91	Divorced	83.750000
92	Divorced	88.650000
93	Widowed	88.660000
94	Single	89.440000
95	Single	93.230000
96	Married	93.600000
97	Single	94.000000
98	Married	94.060000
99	Separated	98.400000

Query: Select the PatientMaritalStatus and PatientPopulationPercentageBelowPoverty columns from the PatientCorePopulatedTable table and sort by PatientMaritalStatus and PatientPopulationPercentageBelowPoverty.

SELECT 
  PatientMaritalStatus, 
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
ORDER BY PatientMaritalStatus, PatientPopulationPercentageBelowPoverty;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT 
  PatientMaritalStatus, 
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
ORDER BY PatientMaritalStatus, PatientPopulationPercentageBelowPoverty;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientMaritalStatus	PatientPopulationPercentageBelowPoverty
0	Divorced	6.480000
1	Divorced	11.080000
2	Divorced	11.880000
3	Divorced	14.070000
4	Divorced	14.580000
5	Divorced	18.170000
6	Divorced	19.410000
7	Divorced	19.490000
8	Divorced	19.520000
9	Divorced	83.750000
10	Divorced	88.650000
11	Married	3.300000
12	Married	4.340000
13	Married	4.510000
14	Married	5.940000
15	Married	6.670000
16	Married	8.810000
17	Married	9.100000
18	Married	10.300000
19	Married	10.350000
20	Married	10.870000
21	Married	11.250000
22	Married	11.890000
23	Married	12.010000
24	Married	12.410000
25	Married	12.800000
26	Married	13.230000
27	Married	13.900000
28	Married	14.030000
29	Married	14.150000
30	Married	14.200000
31	Married	14.810000
32	Married	14.870000
33	Married	15.040000
34	Married	15.110000
35	Married	15.310000
36	Married	16.090000
37	Married	16.710000
38	Married	16.760000
39	Married	17.720000
40	Married	17.840000
41	Married	18.050000
42	Married	18.050000
43	Married	18.080000
44	Married	18.200000
45	Married	18.330000
46	Married	18.650000
47	Married	18.800000
48	Married	18.910000
49	Married	19.220000
50	Married	19.460000
51	Married	19.500000
52	Married	19.660000
53	Married	19.740000
54	Married	93.600000
55	Married	94.060000
56	Separated	10.800000
57	Separated	13.030000
58	Separated	17.980000
59	Separated	19.360000
60	Separated	98.400000
61	Single	1.700000
62	Single	7.590000
63	Single	11.040000
64	Single	11.150000
65	Single	11.160000
66	Single	11.430000
67	Single	12.860000
68	Single	12.920000
69	Single	13.100000
70	Single	13.970000
71	Single	14.280000
72	Single	14.490000
73	Single	14.490000
74	Single	14.900000
75	Single	14.990000
76	Single	15.240000
77	Single	15.560000
78	Single	15.720000
79	Single	15.980000
80	Single	16.240000
81	Single	16.320000
82	Single	16.430000
83	Single	16.690000
84	Single	16.860000
85	Single	16.880000
86	Single	18.360000
87	Single	18.410000
88	Single	18.880000
89	Single	19.520000
90	Single	89.440000
91	Single	93.230000
92	Single	94.000000
93	Unknown	12.380000
94	Unknown	15.020000
95	Unknown	16.890000
96	Unknown	18.950000
97	Unknown	19.160000
98	Unknown	82.080000
99	Widowed	88.660000

Ascending versus Descending Sort Order

Query: Select the PatientMaritalStatus and PatientPopulationPercentageBelowPoverty columns from the PatientCorePopulatedTable table and sort by PatientMaritalStatus and PatientPopulationPercentageBelowPoverty by descending order using the keyword DESC.

SELECT 
  PatientMaritalStatus, 
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
ORDER BY PatientMaritalStatus, PatientPopulationPercentageBelowPoverty DESC;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT 
  PatientMaritalStatus, 
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
ORDER BY PatientMaritalStatus, PatientPopulationPercentageBelowPoverty DESC;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientMaritalStatus	PatientPopulationPercentageBelowPoverty
0	Divorced	88.650000
1	Divorced	83.750000
2	Divorced	19.520000
3	Divorced	19.490000
4	Divorced	19.410000
5	Divorced	18.170000
6	Divorced	14.580000
7	Divorced	14.070000
8	Divorced	11.880000
9	Divorced	11.080000
10	Divorced	6.480000
11	Married	94.060000
12	Married	93.600000
13	Married	19.740000
14	Married	19.660000
15	Married	19.500000
16	Married	19.460000
17	Married	19.220000
18	Married	18.910000
19	Married	18.800000
20	Married	18.650000
21	Married	18.330000
22	Married	18.200000
23	Married	18.080000
24	Married	18.050000
25	Married	18.050000
26	Married	17.840000
27	Married	17.720000
28	Married	16.760000
29	Married	16.710000
30	Married	16.090000
31	Married	15.310000
32	Married	15.110000
33	Married	15.040000
34	Married	14.870000
35	Married	14.810000
36	Married	14.200000
37	Married	14.150000
38	Married	14.030000
39	Married	13.900000
40	Married	13.230000
41	Married	12.800000
42	Married	12.410000
43	Married	12.010000
44	Married	11.890000
45	Married	11.250000
46	Married	10.870000
47	Married	10.350000
48	Married	10.300000
49	Married	9.100000
50	Married	8.810000
51	Married	6.670000
52	Married	5.940000
53	Married	4.510000
54	Married	4.340000
55	Married	3.300000
56	Separated	98.400000
57	Separated	19.360000
58	Separated	17.980000
59	Separated	13.030000
60	Separated	10.800000
61	Single	94.000000
62	Single	93.230000
63	Single	89.440000
64	Single	19.520000
65	Single	18.880000
66	Single	18.410000
67	Single	18.360000
68	Single	16.880000
69	Single	16.860000
70	Single	16.690000
71	Single	16.430000
72	Single	16.320000
73	Single	16.240000
74	Single	15.980000
75	Single	15.720000
76	Single	15.560000
77	Single	15.240000
78	Single	14.990000
79	Single	14.900000
80	Single	14.490000
81	Single	14.490000
82	Single	14.280000
83	Single	13.970000
84	Single	13.100000
85	Single	12.920000
86	Single	12.860000
87	Single	11.430000
88	Single	11.160000
89	Single	11.150000
90	Single	11.040000
91	Single	7.590000
92	Single	1.700000
93	Unknown	82.080000
94	Unknown	19.160000
95	Unknown	18.950000
96	Unknown	16.890000
97	Unknown	15.020000
98	Unknown	12.380000
99	Widowed	88.660000

Query: Select the PatientMaritalStatus and PatientPopulationPercentageBelowPoverty columns from the PatientCorePopulatedTable table and sort by PatientMaritalStatus and PatientPopulationPercentageBelowPoverty by descending order using the -.

SELECT 
  PatientMaritalStatus, 
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
ORDER BY PatientMaritalStatus, -PatientPopulationPercentageBelowPoverty;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT 
  PatientMaritalStatus, 
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
ORDER BY PatientMaritalStatus, -PatientPopulationPercentageBelowPoverty;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientMaritalStatus	PatientPopulationPercentageBelowPoverty
0	Divorced	88.650000
1	Divorced	83.750000
2	Divorced	19.520000
3	Divorced	19.490000
4	Divorced	19.410000
5	Divorced	18.170000
6	Divorced	14.580000
7	Divorced	14.070000
8	Divorced	11.880000
9	Divorced	11.080000
10	Divorced	6.480000
11	Married	94.060000
12	Married	93.600000
13	Married	19.740000
14	Married	19.660000
15	Married	19.500000
16	Married	19.460000
17	Married	19.220000
18	Married	18.910000
19	Married	18.800000
20	Married	18.650000
21	Married	18.330000
22	Married	18.200000
23	Married	18.080000
24	Married	18.050000
25	Married	18.050000
26	Married	17.840000
27	Married	17.720000
28	Married	16.760000
29	Married	16.710000
30	Married	16.090000
31	Married	15.310000
32	Married	15.110000
33	Married	15.040000
34	Married	14.870000
35	Married	14.810000
36	Married	14.200000
37	Married	14.150000
38	Married	14.030000
39	Married	13.900000
40	Married	13.230000
41	Married	12.800000
42	Married	12.410000
43	Married	12.010000
44	Married	11.890000
45	Married	11.250000
46	Married	10.870000
47	Married	10.350000
48	Married	10.300000
49	Married	9.100000
50	Married	8.810000
51	Married	6.670000
52	Married	5.940000
53	Married	4.510000
54	Married	4.340000
55	Married	3.300000
56	Separated	98.400000
57	Separated	19.360000
58	Separated	17.980000
59	Separated	13.030000
60	Separated	10.800000
61	Single	94.000000
62	Single	93.230000
63	Single	89.440000
64	Single	19.520000
65	Single	18.880000
66	Single	18.410000
67	Single	18.360000
68	Single	16.880000
69	Single	16.860000
70	Single	16.690000
71	Single	16.430000
72	Single	16.320000
73	Single	16.240000
74	Single	15.980000
75	Single	15.720000
76	Single	15.560000
77	Single	15.240000
78	Single	14.990000
79	Single	14.900000
80	Single	14.490000
81	Single	14.490000
82	Single	14.280000
83	Single	13.970000
84	Single	13.100000
85	Single	12.920000
86	Single	12.860000
87	Single	11.430000
88	Single	11.160000
89	Single	11.150000
90	Single	11.040000
91	Single	7.590000
92	Single	1.700000
93	Unknown	82.080000
94	Unknown	19.160000
95	Unknown	18.950000
96	Unknown	16.890000
97	Unknown	15.020000
98	Unknown	12.380000
99	Widowed	88.660000

Sorting using numerical position

When sorting by numerical position, you cannot use -. You must use the keyword DESC. Column names make your code more explicit, which you should prefer.

Query: Select the PatientMaritalStatus and PatientPopulationPercentageBelowPoverty columns from the PatientCorePopulatedTable table and sort by PatientMaritalStatus and PatientPopulationPercentageBelowPoverty by descending order using the keyword DESC, but using the numerical position of PatientPopulationPercentageBelowPoverty.

SELECT 
  PatientMaritalStatus, 
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
ORDER BY PatientMaritalStatus, 2 DESC;

import sqlite3
import pandas as pd

conn = sqlite3.connect('100_patients.db')

sql_statement = """
SELECT 
  PatientMaritalStatus, 
  PatientPopulationPercentageBelowPoverty
FROM
  PatientCorePopulatedTable
ORDER BY PatientMaritalStatus, 2 DESC;
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientMaritalStatus	PatientPopulationPercentageBelowPoverty
0	Divorced	88.650000
1	Divorced	83.750000
2	Divorced	19.520000
3	Divorced	19.490000
4	Divorced	19.410000
5	Divorced	18.170000
6	Divorced	14.580000
7	Divorced	14.070000
8	Divorced	11.880000
9	Divorced	11.080000
10	Divorced	6.480000
11	Married	94.060000
12	Married	93.600000
13	Married	19.740000
14	Married	19.660000
15	Married	19.500000
16	Married	19.460000
17	Married	19.220000
18	Married	18.910000
19	Married	18.800000
20	Married	18.650000
21	Married	18.330000
22	Married	18.200000
23	Married	18.080000
24	Married	18.050000
25	Married	18.050000
26	Married	17.840000
27	Married	17.720000
28	Married	16.760000
29	Married	16.710000
30	Married	16.090000
31	Married	15.310000
32	Married	15.110000
33	Married	15.040000
34	Married	14.870000
35	Married	14.810000
36	Married	14.200000
37	Married	14.150000
38	Married	14.030000
39	Married	13.900000
40	Married	13.230000
41	Married	12.800000
42	Married	12.410000
43	Married	12.010000
44	Married	11.890000
45	Married	11.250000
46	Married	10.870000
47	Married	10.350000
48	Married	10.300000
49	Married	9.100000
50	Married	8.810000
51	Married	6.670000
52	Married	5.940000
53	Married	4.510000
54	Married	4.340000
55	Married	3.300000
56	Separated	98.400000
57	Separated	19.360000
58	Separated	17.980000
59	Separated	13.030000
60	Separated	10.800000
61	Single	94.000000
62	Single	93.230000
63	Single	89.440000
64	Single	19.520000
65	Single	18.880000
66	Single	18.410000
67	Single	18.360000
68	Single	16.880000
69	Single	16.860000
70	Single	16.690000
71	Single	16.430000
72	Single	16.320000
73	Single	16.240000
74	Single	15.980000
75	Single	15.720000
76	Single	15.560000
77	Single	15.240000
78	Single	14.990000
79	Single	14.900000
80	Single	14.490000
81	Single	14.490000
82	Single	14.280000
83	Single	13.970000
84	Single	13.100000
85	Single	12.920000
86	Single	12.860000
87	Single	11.430000
88	Single	11.160000
89	Single	11.150000
90	Single	11.040000
91	Single	7.590000
92	Single	1.700000
93	Unknown	82.080000
94	Unknown	19.160000
95	Unknown	18.950000
96	Unknown	16.890000
97	Unknown	15.020000
98	Unknown	12.380000
99	Widowed	88.660000