Filtering#

Logical Operators#

Operator	What it does?
`AND`	True if both conditions are true
`OR`	True if one of two conditions is true
`NOT`	Negate a specified condition
`IN`	Allows for multiple OR conditions
`NOT IN`	Negate multiple AND conditions
`EXISTS`	True if a record exists
`LIKE`	True if there is a string match using %

Relational Operators#

Assume a=1 and b=1

Relational Operators	What it does?
=	True if a has the same value as b
<>, !=	True if a does not have the same value as b
>	True if a is greater than b
<	True if a is less than b
>=	True if a is greater than or equal to b
<=	True if a is less than or equal to b

Conditional Evaluation#

SELECT *
FROM PatientCorePopulatedTable
WHERE PatientGender = 'Male' AND PatientDateOfBirth < '1950-01-01'
ORDER BY PatientDateOfBirth

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM PatientCorePopulatedTable
WHERE PatientGender = 'Male' AND PatientDateOfBirth < '1950-01-01'
ORDER BY PatientDateOfBirth
"""
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	DDC0BC57-7A4E-4E02-9282-177750B74FBC	Male	1921-03-26 14:38:51.803	White	Single	English	18.410000
1	53B9FFDD-F80B-43BE-93CF-C34A023EE7E9	Male	1921-08-22 19:17:09.227	White	Divorced	Icelandic	18.170000
2	DA6CECFF-DE13-4C4C-919F-64E1A2B76C9D	Male	1924-06-27 19:37:58.823	Asian	Divorced	English	83.750000
3	7C788499-7798-484B-A027-9FCDC4C0DADB	Male	1926-08-13 10:22:16.247	White	Married	English	11.890000
4	25B786AF-0F99-478C-9CFA-0EA607E45834	Male	1926-08-20 00:21:38.870	White	Married	English	93.600000
5	4C201C71-CCED-40D1-9642-F9C8C485B854	Male	1926-09-22 09:17:14.713	African American	Married	English	10.300000
6	A0A976C8-9B30-4492-B8C4-5B25095B9192	Male	1931-05-26 14:54:15.847	Asian	Single	English	19.520000
7	1A220558-5996-43E1-AE5D-7B96180FED35	Male	1937-09-07 22:23:53.143	Asian	Married	English	15.110000
8	21792512-2D40-4326-BEA2-A40127EB24FF	Male	1938-03-24 19:25:53.980	White	Single	Unknown	89.440000
9	C5D09468-574F-4802-B56F-DB38F4EB1687	Male	1939-07-07 19:39:49.753	African American	Married	Icelandic	10.870000
10	FB909FAE-72DD-4F6F-9828-D92183DF185F	Male	1940-07-15 12:18:41.080	White	Single	Spanish	14.900000
11	FFCDECD6-4048-4DCB-B910-1218160005B3	Male	1941-05-06 14:56:42.687	White	Single	English	14.490000
12	9BBF3A51-443D-438B-9289-B98B8E0577C0	Male	1944-11-25 06:12:56.860	White	Married	English	16.760000
13	E483DE6E-D4E6-47FD-905B-22EE86EC7ACE	Male	1945-11-18 04:14:31.573	African American	Single	English	16.880000
14	967987B9-FFEF-4776-85CF-AE05CA81F583	Male	1947-02-13 10:26:55.520	White	Widowed	English	88.660000
15	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	Male	1947-12-28 02:45:40.547	Unknown	Married	Icelandic	18.080000
16	1A40AF35-C6D4-4D46-B475-A15D84E8A9D5	Male	1949-01-10 15:37:35.543	White	Married	English	11.250000

Using Parenthesis#

SELECT *
FROM PatientCorePopulatedTable
WHERE (PatientGender = 'Male' OR PatientRace = 'White') AND PatientDateOfBirth < '1950-01-01'
ORDER BY PatientDateOfBirth

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM PatientCorePopulatedTable
WHERE (PatientGender = 'Male' OR PatientRace = 'White') AND PatientDateOfBirth < '1950-01-01'
ORDER BY PatientDateOfBirth
"""
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	DDC0BC57-7A4E-4E02-9282-177750B74FBC	Male	1921-03-26 14:38:51.803	White	Single	English	18.410000
1	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	Female	1921-04-11 11:39:49.197	White	Married	English	18.200000
2	53B9FFDD-F80B-43BE-93CF-C34A023EE7E9	Male	1921-08-22 19:17:09.227	White	Divorced	Icelandic	18.170000
3	DA6CECFF-DE13-4C4C-919F-64E1A2B76C9D	Male	1924-06-27 19:37:58.823	Asian	Divorced	English	83.750000
4	7C788499-7798-484B-A027-9FCDC4C0DADB	Male	1926-08-13 10:22:16.247	White	Married	English	11.890000
5	25B786AF-0F99-478C-9CFA-0EA607E45834	Male	1926-08-20 00:21:38.870	White	Married	English	93.600000
6	4C201C71-CCED-40D1-9642-F9C8C485B854	Male	1926-09-22 09:17:14.713	African American	Married	English	10.300000
7	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	Female	1930-05-28 02:59:42.857	White	Married	Icelandic	19.220000
8	A0A976C8-9B30-4492-B8C4-5B25095B9192	Male	1931-05-26 14:54:15.847	Asian	Single	English	19.520000
9	FA157FA5-F488-4884-BF87-E144630D595C	Female	1932-11-01 06:19:56.577	White	Single	English	16.320000
10	B39DC5AC-E003-4E6A-91B6-FC07625A1285	Female	1935-11-03 21:07:09.040	White	Married	English	15.310000
11	1A220558-5996-43E1-AE5D-7B96180FED35	Male	1937-09-07 22:23:53.143	Asian	Married	English	15.110000
12	80D356B4-F974-441F-A5F2-F95986D119A2	Female	1938-03-06 18:24:18.297	White	Single	English	18.880000
13	21792512-2D40-4326-BEA2-A40127EB24FF	Male	1938-03-24 19:25:53.980	White	Single	Unknown	89.440000
14	C5D09468-574F-4802-B56F-DB38F4EB1687	Male	1939-07-07 19:39:49.753	African American	Married	Icelandic	10.870000
15	FB909FAE-72DD-4F6F-9828-D92183DF185F	Male	1940-07-15 12:18:41.080	White	Single	Spanish	14.900000
16	FFCDECD6-4048-4DCB-B910-1218160005B3	Male	1941-05-06 14:56:42.687	White	Single	English	14.490000
17	0E0EADE8-5592-4E0B-9F88-D7596E32EE08	Female	1942-07-13 21:15:37.517	White	Married	Spanish	18.800000
18	66154E24-D3EE-4311-89DB-6195278F9B3C	Female	1944-08-26 13:03:24.297	White	Divorced	English	19.490000
19	9BBF3A51-443D-438B-9289-B98B8E0577C0	Male	1944-11-25 06:12:56.860	White	Married	English	16.760000
20	E250799D-F6DE-4914-ADB4-B08A6E5029B9	Female	1945-08-04 19:03:00.757	White	Single	Unknown	12.860000
21	E483DE6E-D4E6-47FD-905B-22EE86EC7ACE	Male	1945-11-18 04:14:31.573	African American	Single	English	16.880000
22	967987B9-FFEF-4776-85CF-AE05CA81F583	Male	1947-02-13 10:26:55.520	White	Widowed	English	88.660000
23	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	Male	1947-12-28 02:45:40.547	Unknown	Married	Icelandic	18.080000
24	1A40AF35-C6D4-4D46-B475-A15D84E8A9D5	Male	1949-01-10 15:37:35.543	White	Married	English	11.250000

Using the `NOT` Operator#

SELECT *
FROM PatientCorePopulatedTable
WHERE NOT (PatientGender = 'Male' OR PatientRace = 'White') AND PatientDateOfBirth < '1950-01-01'
ORDER BY PatientDateOfBirth

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM PatientCorePopulatedTable
WHERE NOT (PatientGender = 'Male' OR PatientRace = 'White') AND PatientDateOfBirth < '1950-01-01'
ORDER BY PatientDateOfBirth
"""
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	0BC491C5-5A45-4067-BD11-A78BEA00D3BE	Female	1921-04-18 01:56:01.807	Unknown	Married	English	18.050000
1	EA9B67E2-15F0-450B-A8A6-14F6E4AE3D12	Female	1930-04-08 20:59:31.057	Asian	Separated	Icelandic	98.400000
2	2A5251B1-0945-47FA-A65C-7A6381562591	Female	1942-01-07 16:45:33.060	Unknown	Married	Spanish	18.050000
3	CC9CDA72-B37A-4F8F-AFE4-B08F56A183BE	Female	1942-04-14 14:01:01.130	Asian	Married	Spanish	14.150000
4	6623F5D6-D581-4268-9F9B-21612FBBF7B5	Female	1943-02-17 15:36:13.787	Asian	Single	Spanish	14.490000
5	98F593D2-8894-49BB-93B9-5A0E2CF85E2E	Female	1944-07-15 19:04:11.487	African American	Married	English	9.100000
6	7A7332AD-88B1-4848-9356-E5260E477C59	Female	1944-12-01 06:30:01.543	Unknown	Married	English	19.460000

Inequality condition#

SELECT *
FROM PatientCorePopulatedTable
WHERE (PatientGender = 'Male' AND PatientRace <> 'White') AND PatientDateOfBirth < '1950-01-01'
ORDER BY PatientDateOfBirth

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM PatientCorePopulatedTable
WHERE (PatientGender = 'Male' AND PatientRace <> 'White') AND PatientDateOfBirth < '1950-01-01'
ORDER BY PatientDateOfBirth
"""
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	DA6CECFF-DE13-4C4C-919F-64E1A2B76C9D	Male	1924-06-27 19:37:58.823	Asian	Divorced	English	83.750000
1	4C201C71-CCED-40D1-9642-F9C8C485B854	Male	1926-09-22 09:17:14.713	African American	Married	English	10.300000
2	A0A976C8-9B30-4492-B8C4-5B25095B9192	Male	1931-05-26 14:54:15.847	Asian	Single	English	19.520000
3	1A220558-5996-43E1-AE5D-7B96180FED35	Male	1937-09-07 22:23:53.143	Asian	Married	English	15.110000
4	C5D09468-574F-4802-B56F-DB38F4EB1687	Male	1939-07-07 19:39:49.753	African American	Married	Icelandic	10.870000
5	E483DE6E-D4E6-47FD-905B-22EE86EC7ACE	Male	1945-11-18 04:14:31.573	African American	Single	English	16.880000
6	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	Male	1947-12-28 02:45:40.547	Unknown	Married	Icelandic	18.080000

Range using `BETWEEN` condition#

SELECT *
FROM PatientCorePopulatedTable
WHERE PatientDateOfBirth BETWEEN '1920-01-01' AND '1965-01-01'
ORDER BY PatientDateOfBirth

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM PatientCorePopulatedTable
WHERE PatientDateOfBirth BETWEEN '1920-01-01' AND '1965-01-01'
ORDER BY PatientDateOfBirth
"""
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	DDC0BC57-7A4E-4E02-9282-177750B74FBC	Male	1921-03-26 14:38:51.803	White	Single	English	18.410000
1	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	Female	1921-04-11 11:39:49.197	White	Married	English	18.200000
2	0BC491C5-5A45-4067-BD11-A78BEA00D3BE	Female	1921-04-18 01:56:01.807	Unknown	Married	English	18.050000
3	53B9FFDD-F80B-43BE-93CF-C34A023EE7E9	Male	1921-08-22 19:17:09.227	White	Divorced	Icelandic	18.170000
4	DA6CECFF-DE13-4C4C-919F-64E1A2B76C9D	Male	1924-06-27 19:37:58.823	Asian	Divorced	English	83.750000
5	7C788499-7798-484B-A027-9FCDC4C0DADB	Male	1926-08-13 10:22:16.247	White	Married	English	11.890000
6	25B786AF-0F99-478C-9CFA-0EA607E45834	Male	1926-08-20 00:21:38.870	White	Married	English	93.600000
7	4C201C71-CCED-40D1-9642-F9C8C485B854	Male	1926-09-22 09:17:14.713	African American	Married	English	10.300000
8	EA9B67E2-15F0-450B-A8A6-14F6E4AE3D12	Female	1930-04-08 20:59:31.057	Asian	Separated	Icelandic	98.400000
9	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	Female	1930-05-28 02:59:42.857	White	Married	Icelandic	19.220000
10	A0A976C8-9B30-4492-B8C4-5B25095B9192	Male	1931-05-26 14:54:15.847	Asian	Single	English	19.520000
11	FA157FA5-F488-4884-BF87-E144630D595C	Female	1932-11-01 06:19:56.577	White	Single	English	16.320000
12	B39DC5AC-E003-4E6A-91B6-FC07625A1285	Female	1935-11-03 21:07:09.040	White	Married	English	15.310000
13	1A220558-5996-43E1-AE5D-7B96180FED35	Male	1937-09-07 22:23:53.143	Asian	Married	English	15.110000
14	80D356B4-F974-441F-A5F2-F95986D119A2	Female	1938-03-06 18:24:18.297	White	Single	English	18.880000
15	21792512-2D40-4326-BEA2-A40127EB24FF	Male	1938-03-24 19:25:53.980	White	Single	Unknown	89.440000
16	C5D09468-574F-4802-B56F-DB38F4EB1687	Male	1939-07-07 19:39:49.753	African American	Married	Icelandic	10.870000
17	FB909FAE-72DD-4F6F-9828-D92183DF185F	Male	1940-07-15 12:18:41.080	White	Single	Spanish	14.900000
18	FFCDECD6-4048-4DCB-B910-1218160005B3	Male	1941-05-06 14:56:42.687	White	Single	English	14.490000
19	2A5251B1-0945-47FA-A65C-7A6381562591	Female	1942-01-07 16:45:33.060	Unknown	Married	Spanish	18.050000
20	CC9CDA72-B37A-4F8F-AFE4-B08F56A183BE	Female	1942-04-14 14:01:01.130	Asian	Married	Spanish	14.150000
21	0E0EADE8-5592-4E0B-9F88-D7596E32EE08	Female	1942-07-13 21:15:37.517	White	Married	Spanish	18.800000
22	6623F5D6-D581-4268-9F9B-21612FBBF7B5	Female	1943-02-17 15:36:13.787	Asian	Single	Spanish	14.490000
23	98F593D2-8894-49BB-93B9-5A0E2CF85E2E	Female	1944-07-15 19:04:11.487	African American	Married	English	9.100000
24	66154E24-D3EE-4311-89DB-6195278F9B3C	Female	1944-08-26 13:03:24.297	White	Divorced	English	19.490000
25	9BBF3A51-443D-438B-9289-B98B8E0577C0	Male	1944-11-25 06:12:56.860	White	Married	English	16.760000
26	7A7332AD-88B1-4848-9356-E5260E477C59	Female	1944-12-01 06:30:01.543	Unknown	Married	English	19.460000
27	E250799D-F6DE-4914-ADB4-B08A6E5029B9	Female	1945-08-04 19:03:00.757	White	Single	Unknown	12.860000
28	E483DE6E-D4E6-47FD-905B-22EE86EC7ACE	Male	1945-11-18 04:14:31.573	African American	Single	English	16.880000
29	967987B9-FFEF-4776-85CF-AE05CA81F583	Male	1947-02-13 10:26:55.520	White	Widowed	English	88.660000
30	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	Male	1947-12-28 02:45:40.547	Unknown	Married	Icelandic	18.080000
31	1A40AF35-C6D4-4D46-B475-A15D84E8A9D5	Male	1949-01-10 15:37:35.543	White	Married	English	11.250000
32	F0B53A2C-98CA-415D-B928-E3FD0E52B22A	Male	1950-06-20 10:31:18.337	African American	Married	English	14.870000
33	B2EB15FA-5431-4804-9309-4215BDC778C0	Male	1951-04-13 20:14:02.953	White	Divorced	English	19.410000
34	D8B53AA2-7953-4477-9EA4-68400EBAAC5C	Male	1951-06-11 20:11:10.003	African American	Unknown	English	16.890000
35	7A025E77-7832-4F53-B9A7-09A3F98AC17E	Female	1951-07-12 08:23:45.520	Asian	Single	English	16.690000
36	64182B95-EB72-4E2B-BE77-8050B71498CE	Male	1952-01-18 19:51:12.917	African American	Separated	English	13.030000
37	79A7BA2A-D35A-4CB8-A835-6BAA13B0058C	Female	1952-05-03 02:30:21.563	White	Single	Spanish	16.240000
38	714823AF-C52C-414C-B53B-C43EACD194C3	Male	1952-05-08 23:51:50.127	White	Married	English	13.230000
39	8AF47463-8534-4203-B210-C2290F6CE689	Female	1952-06-27 17:29:04.187	White	Divorced	English	11.880000
40	9C75DF1F-9DA6-4C98-8F5B-E10BDC805ED0	Female	1952-07-19 13:35:56.833	White	Divorced	English	88.650000
41	BC44CE19-9FC5-4AC9-A296-9EBC5E3D03AE	Female	1953-06-04 03:16:17.843	African American	Married	English	15.040000
42	69B5D2A0-12FD-46EF-A5FF-B29C4BAFBE49	Male	1954-05-02 16:38:15.957	White	Married	English	18.650000
43	3B11D6B3-A36A-4B69-A437-C29BF425A941	Female	1954-05-18 10:19:19.110	Asian	Separated	English	10.800000
44	9E18822E-7D13-45C7-B50E-F95CFF92BC3E	Male	1954-10-16 06:45:56.257	Unknown	Single	English	15.240000
45	C65A4ADE-112E-49E4-B72A-0DED22C242ED	Male	1955-04-07 18:55:17.077	White	Married	English	19.660000
46	FE0B9B59-1927-45B7-8556-E079DC1DE30A	Male	1955-08-23 01:01:22.260	White	Divorced	English	6.480000
47	7548B6CF-79D9-461D-A0C5-20B861406FAC	Male	1956-11-13 14:08:00.253	White	Married	Icelandic	14.030000
48	F00C64F8-2033-4640-80FE-F1F62CBE26A5	Female	1957-06-04 18:31:01.177	African American	Single	English	15.720000
49	C60FE675-CA52-4C55-A233-F4B27E94987F	Male	1957-10-30 23:26:15.303	Asian	Married	Spanish	12.800000
50	6A57AC0C-57F3-4C19-98A1-51135EFBC4FF	Female	1958-07-07 16:00:19.383	Unknown	Married	English	94.060000
51	A7142B71-A144-4D56-BD14-3E966B01DB37	Male	1960-03-28 13:40:52.270	White	Divorced	Spanish	19.520000
52	016A590E-D093-4667-A5DA-D68EA6987D93	Male	1960-12-06 06:37:05.640	White	Unknown	English	15.020000
53	EEAFC0B3-B835-4D99-AB33-2F9428E54E5F	Female	1961-01-08 15:19:15.490	Asian	Unknown	English	18.950000
54	6D5DCAC1-17FE-4D7C-923B-806EFBA3E6DF	Male	1961-10-22 02:47:13.170	Asian	Married	English	13.900000
55	49DADA25-F2C2-42BB-8210-D78E6C7B0D48	Male	1962-04-08 10:18:26.263	African American	Married	English	19.500000
56	65A7FBE0-EA9F-49E9-9824-D8F3AD98DAC0	Female	1962-11-30 06:28:33.110	White	Separated	Spanish	17.980000
57	B70E5A76-F2BC-41E4-B037-CD4D9ABA0967	Male	1962-12-15 01:59:01.737	White	Single	Icelandic	13.100000
58	C242E3A4-E785-4DF1-A0E4-3B568DC88F2E	Male	1963-02-23 21:46:28.840	Asian	Single	Spanish	7.590000
59	CD2ADB1B-97F7-4EF6-BC5C-3E0EC562A06F	Female	1963-04-16 08:54:08.647	White	Single	Icelandic	11.160000
60	2E26695A-EFB0-4C7F-9318-E3030B154E39	Female	1963-06-28 05:37:36.843	Asian	Married	English	14.200000
61	36775002-9EC3-4889-AD4F-80DC6855C8D8	Female	1963-07-16 22:16:48.477	Asian	Single	Spanish	15.560000
62	2EE42DEF-37CA-4694-827E-FA4EAF882BFC	Male	1964-04-27 00:41:40.410	White	Married	English	18.910000
63	C2CCB1AB-6633-4CB3-B4E8-157E6FB02376	Female	1964-05-07 10:20:37.740	White	Single	Spanish	15.980000
64	8D389A8C-A6D8-4447-9DDE-1A28AB4EC667	Female	1964-07-10 08:50:13.707	Asian	Married	English	4.340000

String Condition#

SELECT *
FROM PatientCorePopulatedTable
WHERE PatientID BETWEEN '2A' AND '53'
ORDER BY PatientID

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM PatientCorePopulatedTable
WHERE PatientID BETWEEN '2A' AND '53'
ORDER BY PatientID
"""
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	2A5251B1-0945-47FA-A65C-7A6381562591	Female	1942-01-07 16:45:33.060	Unknown	Married	Spanish	18.050000
1	2A8772FE-61DB-483E-B6BF-6C0A74BA9C2A	Female	1966-10-14 15:31:48.427	Asian	Single	English	11.150000
2	2E26695A-EFB0-4C7F-9318-E3030B154E39	Female	1963-06-28 05:37:36.843	Asian	Married	English	14.200000
3	2EE42DEF-37CA-4694-827E-FA4EAF882BFC	Male	1964-04-27 00:41:40.410	White	Married	English	18.910000
4	3231F930-2978-4F50-8234-755449851E7B	Male	1979-05-26 04:58:10.627	White	Single	English	18.360000
5	35FE7491-1A1D-48CB-810C-8DC2599AB3DD	Male	1969-11-02 06:34:34.527	White	Married	English	4.510000
6	36775002-9EC3-4889-AD4F-80DC6855C8D8	Female	1963-07-16 22:16:48.477	Asian	Single	Spanish	15.560000
7	36E2F89E-777A-4D77-9D95-0D70A8AB416F	Male	1980-05-30 13:23:50.703	African American	Separated	English	19.360000
8	3B11D6B3-A36A-4B69-A437-C29BF425A941	Female	1954-05-18 10:19:19.110	Asian	Separated	English	10.800000
9	3E462A8F-7B90-43A1-A8B6-AD82CB5002C9	Female	1986-12-20 16:41:34.793	White	Married	Spanish	18.330000
10	49DADA25-F2C2-42BB-8210-D78E6C7B0D48	Male	1962-04-08 10:18:26.263	African American	Married	English	19.500000
11	4C201C71-CCED-40D1-9642-F9C8C485B854	Male	1926-09-22 09:17:14.713	African American	Married	English	10.300000

Membership Condition#

SELECT *
FROM PatientCorePopulatedTable
WHERE PatientRace = 'White' OR PatientRace = 'Asian'
ORDER BY PatientDateOfBirth

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM PatientCorePopulatedTable
WHERE PatientRace = 'White' OR PatientRace = 'Asian'
ORDER BY PatientDateOfBirth
"""
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	DDC0BC57-7A4E-4E02-9282-177750B74FBC	Male	1921-03-26 14:38:51.803	White	Single	English	18.410000
1	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	Female	1921-04-11 11:39:49.197	White	Married	English	18.200000
2	53B9FFDD-F80B-43BE-93CF-C34A023EE7E9	Male	1921-08-22 19:17:09.227	White	Divorced	Icelandic	18.170000
3	DA6CECFF-DE13-4C4C-919F-64E1A2B76C9D	Male	1924-06-27 19:37:58.823	Asian	Divorced	English	83.750000
4	7C788499-7798-484B-A027-9FCDC4C0DADB	Male	1926-08-13 10:22:16.247	White	Married	English	11.890000
5	25B786AF-0F99-478C-9CFA-0EA607E45834	Male	1926-08-20 00:21:38.870	White	Married	English	93.600000
6	EA9B67E2-15F0-450B-A8A6-14F6E4AE3D12	Female	1930-04-08 20:59:31.057	Asian	Separated	Icelandic	98.400000
7	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	Female	1930-05-28 02:59:42.857	White	Married	Icelandic	19.220000
8	A0A976C8-9B30-4492-B8C4-5B25095B9192	Male	1931-05-26 14:54:15.847	Asian	Single	English	19.520000
9	FA157FA5-F488-4884-BF87-E144630D595C	Female	1932-11-01 06:19:56.577	White	Single	English	16.320000
10	B39DC5AC-E003-4E6A-91B6-FC07625A1285	Female	1935-11-03 21:07:09.040	White	Married	English	15.310000
11	1A220558-5996-43E1-AE5D-7B96180FED35	Male	1937-09-07 22:23:53.143	Asian	Married	English	15.110000
12	80D356B4-F974-441F-A5F2-F95986D119A2	Female	1938-03-06 18:24:18.297	White	Single	English	18.880000
13	21792512-2D40-4326-BEA2-A40127EB24FF	Male	1938-03-24 19:25:53.980	White	Single	Unknown	89.440000
14	FB909FAE-72DD-4F6F-9828-D92183DF185F	Male	1940-07-15 12:18:41.080	White	Single	Spanish	14.900000
15	FFCDECD6-4048-4DCB-B910-1218160005B3	Male	1941-05-06 14:56:42.687	White	Single	English	14.490000
16	CC9CDA72-B37A-4F8F-AFE4-B08F56A183BE	Female	1942-04-14 14:01:01.130	Asian	Married	Spanish	14.150000
17	0E0EADE8-5592-4E0B-9F88-D7596E32EE08	Female	1942-07-13 21:15:37.517	White	Married	Spanish	18.800000
18	6623F5D6-D581-4268-9F9B-21612FBBF7B5	Female	1943-02-17 15:36:13.787	Asian	Single	Spanish	14.490000
19	66154E24-D3EE-4311-89DB-6195278F9B3C	Female	1944-08-26 13:03:24.297	White	Divorced	English	19.490000
20	9BBF3A51-443D-438B-9289-B98B8E0577C0	Male	1944-11-25 06:12:56.860	White	Married	English	16.760000
21	E250799D-F6DE-4914-ADB4-B08A6E5029B9	Female	1945-08-04 19:03:00.757	White	Single	Unknown	12.860000
22	967987B9-FFEF-4776-85CF-AE05CA81F583	Male	1947-02-13 10:26:55.520	White	Widowed	English	88.660000
23	1A40AF35-C6D4-4D46-B475-A15D84E8A9D5	Male	1949-01-10 15:37:35.543	White	Married	English	11.250000
24	B2EB15FA-5431-4804-9309-4215BDC778C0	Male	1951-04-13 20:14:02.953	White	Divorced	English	19.410000
25	7A025E77-7832-4F53-B9A7-09A3F98AC17E	Female	1951-07-12 08:23:45.520	Asian	Single	English	16.690000
26	79A7BA2A-D35A-4CB8-A835-6BAA13B0058C	Female	1952-05-03 02:30:21.563	White	Single	Spanish	16.240000
27	714823AF-C52C-414C-B53B-C43EACD194C3	Male	1952-05-08 23:51:50.127	White	Married	English	13.230000
28	8AF47463-8534-4203-B210-C2290F6CE689	Female	1952-06-27 17:29:04.187	White	Divorced	English	11.880000
29	9C75DF1F-9DA6-4C98-8F5B-E10BDC805ED0	Female	1952-07-19 13:35:56.833	White	Divorced	English	88.650000
30	69B5D2A0-12FD-46EF-A5FF-B29C4BAFBE49	Male	1954-05-02 16:38:15.957	White	Married	English	18.650000
31	3B11D6B3-A36A-4B69-A437-C29BF425A941	Female	1954-05-18 10:19:19.110	Asian	Separated	English	10.800000
32	C65A4ADE-112E-49E4-B72A-0DED22C242ED	Male	1955-04-07 18:55:17.077	White	Married	English	19.660000
33	FE0B9B59-1927-45B7-8556-E079DC1DE30A	Male	1955-08-23 01:01:22.260	White	Divorced	English	6.480000
34	7548B6CF-79D9-461D-A0C5-20B861406FAC	Male	1956-11-13 14:08:00.253	White	Married	Icelandic	14.030000
35	C60FE675-CA52-4C55-A233-F4B27E94987F	Male	1957-10-30 23:26:15.303	Asian	Married	Spanish	12.800000
36	A7142B71-A144-4D56-BD14-3E966B01DB37	Male	1960-03-28 13:40:52.270	White	Divorced	Spanish	19.520000
37	016A590E-D093-4667-A5DA-D68EA6987D93	Male	1960-12-06 06:37:05.640	White	Unknown	English	15.020000
38	EEAFC0B3-B835-4D99-AB33-2F9428E54E5F	Female	1961-01-08 15:19:15.490	Asian	Unknown	English	18.950000
39	6D5DCAC1-17FE-4D7C-923B-806EFBA3E6DF	Male	1961-10-22 02:47:13.170	Asian	Married	English	13.900000
40	65A7FBE0-EA9F-49E9-9824-D8F3AD98DAC0	Female	1962-11-30 06:28:33.110	White	Separated	Spanish	17.980000
41	B70E5A76-F2BC-41E4-B037-CD4D9ABA0967	Male	1962-12-15 01:59:01.737	White	Single	Icelandic	13.100000
42	C242E3A4-E785-4DF1-A0E4-3B568DC88F2E	Male	1963-02-23 21:46:28.840	Asian	Single	Spanish	7.590000
43	CD2ADB1B-97F7-4EF6-BC5C-3E0EC562A06F	Female	1963-04-16 08:54:08.647	White	Single	Icelandic	11.160000
44	2E26695A-EFB0-4C7F-9318-E3030B154E39	Female	1963-06-28 05:37:36.843	Asian	Married	English	14.200000
45	36775002-9EC3-4889-AD4F-80DC6855C8D8	Female	1963-07-16 22:16:48.477	Asian	Single	Spanish	15.560000
46	2EE42DEF-37CA-4694-827E-FA4EAF882BFC	Male	1964-04-27 00:41:40.410	White	Married	English	18.910000
47	C2CCB1AB-6633-4CB3-B4E8-157E6FB02376	Female	1964-05-07 10:20:37.740	White	Single	Spanish	15.980000
48	8D389A8C-A6D8-4447-9DDE-1A28AB4EC667	Female	1964-07-10 08:50:13.707	Asian	Married	English	4.340000
49	7FD13988-E58A-4A5C-8680-89AC200950FA	Male	1965-07-12 15:41:20.523	White	Married	Spanish	12.410000
50	2A8772FE-61DB-483E-B6BF-6C0A74BA9C2A	Female	1966-10-14 15:31:48.427	Asian	Single	English	11.150000
51	6985D824-3269-4D12-A9DD-B932D640E26E	Female	1967-10-27 03:58:29.027	White	Married	English	12.010000
52	DCE5AEB8-6DB9-4106-8AE4-02CCC5C23741	Female	1967-11-09 08:14:51.143	White	Single	Unknown	16.430000
53	03A481F5-B32A-4A91-BD42-43EB78FEBA77	Female	1968-02-07 23:02:38.017	Asian	Single	Unknown	93.230000
54	56A35E74-90BE-44A0-B7BA-7743BB152133	Female	1968-11-09 15:29:19.557	White	Married	English	8.810000
55	35FE7491-1A1D-48CB-810C-8DC2599AB3DD	Male	1969-11-02 06:34:34.527	White	Married	English	4.510000
56	DB22A4D9-7E4D-485C-916A-9CD1386507FB	Female	1970-07-25 13:04:20.717	Asian	Married	English	6.670000
57	220C8D43-1322-4A9D-B890-D426942A3649	Male	1971-03-13 18:31:24.877	Asian	Married	English	16.710000
58	B5D31F01-7273-4901-B56F-8139769A11EF	Female	1971-04-02 18:56:59.353	Asian	Unknown	Unknown	82.080000
59	135C831F-7DA5-46C0-959C-EBCBD8810B43	Male	1971-05-13 04:40:05.623	White	Unknown	Spanish	12.380000
60	1A8791E3-A61C-455A-8DEE-763EB90C9B2C	Male	1973-08-16 10:58:34.413	Asian	Single	English	13.970000
61	6D8008ED-D623-4BE4-B93B-335F9797C170	Female	1978-03-14 13:41:28.337	White	Married	Icelandic	14.810000
62	B3892204-880B-40EF-B3BB-B824B50E99E5	Male	1978-08-06 08:38:27.330	White	Single	Spanish	16.860000
63	0681FA35-A794-4684-97BD-00B88370DB41	Female	1978-10-02 21:46:05.300	Asian	Unknown	Spanish	19.160000
64	6E70D84D-C75F-477C-BC37-9177C3698C66	Male	1979-01-04 05:45:29.580	White	Married	English	16.090000
65	3231F930-2978-4F50-8234-755449851E7B	Male	1979-05-26 04:58:10.627	White	Single	English	18.360000
66	E5478913-6819-4977-BB11-4C8B61175B56	Female	1980-03-21 13:08:56.867	White	Single	English	12.920000
67	0A9BA3E4-CF3C-49C4-9774-5EEA2EE7D123	Male	1980-04-04 07:59:15.820	White	Single	Spanish	14.280000
68	3E462A8F-7B90-43A1-A8B6-AD82CB5002C9	Female	1986-12-20 16:41:34.793	White	Married	Spanish	18.330000
69	A50BE9B4-8A0B-4169-B894-F7BD86D7D90B	Female	1987-04-18 16:31:16.333	Asian	Divorced	English	14.070000
70	1311FEE4-2FDC-46E4-83D3-1550A3E51D2C	Female	1988-03-28 03:09:22.807	White	Single	English	14.990000
71	8856096E-E59C-4156-A767-C091AF799C80	Female	1988-11-25 02:59:36.373	White	Divorced	English	11.080000

`IN` condition#

SELECT *
FROM PatientCorePopulatedTable
WHERE PatientRace IN ('White', 'Asian')
ORDER BY PatientDateOfBirth

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM PatientCorePopulatedTable
WHERE PatientRace IN ('White', 'Asian')
ORDER BY PatientDateOfBirth
"""
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	DDC0BC57-7A4E-4E02-9282-177750B74FBC	Male	1921-03-26 14:38:51.803	White	Single	English	18.410000
1	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	Female	1921-04-11 11:39:49.197	White	Married	English	18.200000
2	53B9FFDD-F80B-43BE-93CF-C34A023EE7E9	Male	1921-08-22 19:17:09.227	White	Divorced	Icelandic	18.170000
3	DA6CECFF-DE13-4C4C-919F-64E1A2B76C9D	Male	1924-06-27 19:37:58.823	Asian	Divorced	English	83.750000
4	7C788499-7798-484B-A027-9FCDC4C0DADB	Male	1926-08-13 10:22:16.247	White	Married	English	11.890000
5	25B786AF-0F99-478C-9CFA-0EA607E45834	Male	1926-08-20 00:21:38.870	White	Married	English	93.600000
6	EA9B67E2-15F0-450B-A8A6-14F6E4AE3D12	Female	1930-04-08 20:59:31.057	Asian	Separated	Icelandic	98.400000
7	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	Female	1930-05-28 02:59:42.857	White	Married	Icelandic	19.220000
8	A0A976C8-9B30-4492-B8C4-5B25095B9192	Male	1931-05-26 14:54:15.847	Asian	Single	English	19.520000
9	FA157FA5-F488-4884-BF87-E144630D595C	Female	1932-11-01 06:19:56.577	White	Single	English	16.320000
10	B39DC5AC-E003-4E6A-91B6-FC07625A1285	Female	1935-11-03 21:07:09.040	White	Married	English	15.310000
11	1A220558-5996-43E1-AE5D-7B96180FED35	Male	1937-09-07 22:23:53.143	Asian	Married	English	15.110000
12	80D356B4-F974-441F-A5F2-F95986D119A2	Female	1938-03-06 18:24:18.297	White	Single	English	18.880000
13	21792512-2D40-4326-BEA2-A40127EB24FF	Male	1938-03-24 19:25:53.980	White	Single	Unknown	89.440000
14	FB909FAE-72DD-4F6F-9828-D92183DF185F	Male	1940-07-15 12:18:41.080	White	Single	Spanish	14.900000
15	FFCDECD6-4048-4DCB-B910-1218160005B3	Male	1941-05-06 14:56:42.687	White	Single	English	14.490000
16	CC9CDA72-B37A-4F8F-AFE4-B08F56A183BE	Female	1942-04-14 14:01:01.130	Asian	Married	Spanish	14.150000
17	0E0EADE8-5592-4E0B-9F88-D7596E32EE08	Female	1942-07-13 21:15:37.517	White	Married	Spanish	18.800000
18	6623F5D6-D581-4268-9F9B-21612FBBF7B5	Female	1943-02-17 15:36:13.787	Asian	Single	Spanish	14.490000
19	66154E24-D3EE-4311-89DB-6195278F9B3C	Female	1944-08-26 13:03:24.297	White	Divorced	English	19.490000
20	9BBF3A51-443D-438B-9289-B98B8E0577C0	Male	1944-11-25 06:12:56.860	White	Married	English	16.760000
21	E250799D-F6DE-4914-ADB4-B08A6E5029B9	Female	1945-08-04 19:03:00.757	White	Single	Unknown	12.860000
22	967987B9-FFEF-4776-85CF-AE05CA81F583	Male	1947-02-13 10:26:55.520	White	Widowed	English	88.660000
23	1A40AF35-C6D4-4D46-B475-A15D84E8A9D5	Male	1949-01-10 15:37:35.543	White	Married	English	11.250000
24	B2EB15FA-5431-4804-9309-4215BDC778C0	Male	1951-04-13 20:14:02.953	White	Divorced	English	19.410000
25	7A025E77-7832-4F53-B9A7-09A3F98AC17E	Female	1951-07-12 08:23:45.520	Asian	Single	English	16.690000
26	79A7BA2A-D35A-4CB8-A835-6BAA13B0058C	Female	1952-05-03 02:30:21.563	White	Single	Spanish	16.240000
27	714823AF-C52C-414C-B53B-C43EACD194C3	Male	1952-05-08 23:51:50.127	White	Married	English	13.230000
28	8AF47463-8534-4203-B210-C2290F6CE689	Female	1952-06-27 17:29:04.187	White	Divorced	English	11.880000
29	9C75DF1F-9DA6-4C98-8F5B-E10BDC805ED0	Female	1952-07-19 13:35:56.833	White	Divorced	English	88.650000
30	69B5D2A0-12FD-46EF-A5FF-B29C4BAFBE49	Male	1954-05-02 16:38:15.957	White	Married	English	18.650000
31	3B11D6B3-A36A-4B69-A437-C29BF425A941	Female	1954-05-18 10:19:19.110	Asian	Separated	English	10.800000
32	C65A4ADE-112E-49E4-B72A-0DED22C242ED	Male	1955-04-07 18:55:17.077	White	Married	English	19.660000
33	FE0B9B59-1927-45B7-8556-E079DC1DE30A	Male	1955-08-23 01:01:22.260	White	Divorced	English	6.480000
34	7548B6CF-79D9-461D-A0C5-20B861406FAC	Male	1956-11-13 14:08:00.253	White	Married	Icelandic	14.030000
35	C60FE675-CA52-4C55-A233-F4B27E94987F	Male	1957-10-30 23:26:15.303	Asian	Married	Spanish	12.800000
36	A7142B71-A144-4D56-BD14-3E966B01DB37	Male	1960-03-28 13:40:52.270	White	Divorced	Spanish	19.520000
37	016A590E-D093-4667-A5DA-D68EA6987D93	Male	1960-12-06 06:37:05.640	White	Unknown	English	15.020000
38	EEAFC0B3-B835-4D99-AB33-2F9428E54E5F	Female	1961-01-08 15:19:15.490	Asian	Unknown	English	18.950000
39	6D5DCAC1-17FE-4D7C-923B-806EFBA3E6DF	Male	1961-10-22 02:47:13.170	Asian	Married	English	13.900000
40	65A7FBE0-EA9F-49E9-9824-D8F3AD98DAC0	Female	1962-11-30 06:28:33.110	White	Separated	Spanish	17.980000
41	B70E5A76-F2BC-41E4-B037-CD4D9ABA0967	Male	1962-12-15 01:59:01.737	White	Single	Icelandic	13.100000
42	C242E3A4-E785-4DF1-A0E4-3B568DC88F2E	Male	1963-02-23 21:46:28.840	Asian	Single	Spanish	7.590000
43	CD2ADB1B-97F7-4EF6-BC5C-3E0EC562A06F	Female	1963-04-16 08:54:08.647	White	Single	Icelandic	11.160000
44	2E26695A-EFB0-4C7F-9318-E3030B154E39	Female	1963-06-28 05:37:36.843	Asian	Married	English	14.200000
45	36775002-9EC3-4889-AD4F-80DC6855C8D8	Female	1963-07-16 22:16:48.477	Asian	Single	Spanish	15.560000
46	2EE42DEF-37CA-4694-827E-FA4EAF882BFC	Male	1964-04-27 00:41:40.410	White	Married	English	18.910000
47	C2CCB1AB-6633-4CB3-B4E8-157E6FB02376	Female	1964-05-07 10:20:37.740	White	Single	Spanish	15.980000
48	8D389A8C-A6D8-4447-9DDE-1A28AB4EC667	Female	1964-07-10 08:50:13.707	Asian	Married	English	4.340000
49	7FD13988-E58A-4A5C-8680-89AC200950FA	Male	1965-07-12 15:41:20.523	White	Married	Spanish	12.410000
50	2A8772FE-61DB-483E-B6BF-6C0A74BA9C2A	Female	1966-10-14 15:31:48.427	Asian	Single	English	11.150000
51	6985D824-3269-4D12-A9DD-B932D640E26E	Female	1967-10-27 03:58:29.027	White	Married	English	12.010000
52	DCE5AEB8-6DB9-4106-8AE4-02CCC5C23741	Female	1967-11-09 08:14:51.143	White	Single	Unknown	16.430000
53	03A481F5-B32A-4A91-BD42-43EB78FEBA77	Female	1968-02-07 23:02:38.017	Asian	Single	Unknown	93.230000
54	56A35E74-90BE-44A0-B7BA-7743BB152133	Female	1968-11-09 15:29:19.557	White	Married	English	8.810000
55	35FE7491-1A1D-48CB-810C-8DC2599AB3DD	Male	1969-11-02 06:34:34.527	White	Married	English	4.510000
56	DB22A4D9-7E4D-485C-916A-9CD1386507FB	Female	1970-07-25 13:04:20.717	Asian	Married	English	6.670000
57	220C8D43-1322-4A9D-B890-D426942A3649	Male	1971-03-13 18:31:24.877	Asian	Married	English	16.710000
58	B5D31F01-7273-4901-B56F-8139769A11EF	Female	1971-04-02 18:56:59.353	Asian	Unknown	Unknown	82.080000
59	135C831F-7DA5-46C0-959C-EBCBD8810B43	Male	1971-05-13 04:40:05.623	White	Unknown	Spanish	12.380000
60	1A8791E3-A61C-455A-8DEE-763EB90C9B2C	Male	1973-08-16 10:58:34.413	Asian	Single	English	13.970000
61	6D8008ED-D623-4BE4-B93B-335F9797C170	Female	1978-03-14 13:41:28.337	White	Married	Icelandic	14.810000
62	B3892204-880B-40EF-B3BB-B824B50E99E5	Male	1978-08-06 08:38:27.330	White	Single	Spanish	16.860000
63	0681FA35-A794-4684-97BD-00B88370DB41	Female	1978-10-02 21:46:05.300	Asian	Unknown	Spanish	19.160000
64	6E70D84D-C75F-477C-BC37-9177C3698C66	Male	1979-01-04 05:45:29.580	White	Married	English	16.090000
65	3231F930-2978-4F50-8234-755449851E7B	Male	1979-05-26 04:58:10.627	White	Single	English	18.360000
66	E5478913-6819-4977-BB11-4C8B61175B56	Female	1980-03-21 13:08:56.867	White	Single	English	12.920000
67	0A9BA3E4-CF3C-49C4-9774-5EEA2EE7D123	Male	1980-04-04 07:59:15.820	White	Single	Spanish	14.280000
68	3E462A8F-7B90-43A1-A8B6-AD82CB5002C9	Female	1986-12-20 16:41:34.793	White	Married	Spanish	18.330000
69	A50BE9B4-8A0B-4169-B894-F7BD86D7D90B	Female	1987-04-18 16:31:16.333	Asian	Divorced	English	14.070000
70	1311FEE4-2FDC-46E4-83D3-1550A3E51D2C	Female	1988-03-28 03:09:22.807	White	Single	English	14.990000
71	8856096E-E59C-4156-A767-C091AF799C80	Female	1988-11-25 02:59:36.373	White	Divorced	English	11.080000

`NOT IN` condition#

SELECT *
FROM PatientCorePopulatedTable
WHERE PatientRace NOT IN ('White', 'Asian')
ORDER BY PatientDateOfBirth

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM PatientCorePopulatedTable
WHERE PatientRace NOT IN ('White', 'Asian')
ORDER BY PatientDateOfBirth
"""
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	0BC491C5-5A45-4067-BD11-A78BEA00D3BE	Female	1921-04-18 01:56:01.807	Unknown	Married	English	18.050000
1	4C201C71-CCED-40D1-9642-F9C8C485B854	Male	1926-09-22 09:17:14.713	African American	Married	English	10.300000
2	C5D09468-574F-4802-B56F-DB38F4EB1687	Male	1939-07-07 19:39:49.753	African American	Married	Icelandic	10.870000
3	2A5251B1-0945-47FA-A65C-7A6381562591	Female	1942-01-07 16:45:33.060	Unknown	Married	Spanish	18.050000
4	98F593D2-8894-49BB-93B9-5A0E2CF85E2E	Female	1944-07-15 19:04:11.487	African American	Married	English	9.100000
5	7A7332AD-88B1-4848-9356-E5260E477C59	Female	1944-12-01 06:30:01.543	Unknown	Married	English	19.460000
6	E483DE6E-D4E6-47FD-905B-22EE86EC7ACE	Male	1945-11-18 04:14:31.573	African American	Single	English	16.880000
7	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	Male	1947-12-28 02:45:40.547	Unknown	Married	Icelandic	18.080000
8	F0B53A2C-98CA-415D-B928-E3FD0E52B22A	Male	1950-06-20 10:31:18.337	African American	Married	English	14.870000
9	D8B53AA2-7953-4477-9EA4-68400EBAAC5C	Male	1951-06-11 20:11:10.003	African American	Unknown	English	16.890000
10	64182B95-EB72-4E2B-BE77-8050B71498CE	Male	1952-01-18 19:51:12.917	African American	Separated	English	13.030000
11	BC44CE19-9FC5-4AC9-A296-9EBC5E3D03AE	Female	1953-06-04 03:16:17.843	African American	Married	English	15.040000
12	9E18822E-7D13-45C7-B50E-F95CFF92BC3E	Male	1954-10-16 06:45:56.257	Unknown	Single	English	15.240000
13	F00C64F8-2033-4640-80FE-F1F62CBE26A5	Female	1957-06-04 18:31:01.177	African American	Single	English	15.720000
14	6A57AC0C-57F3-4C19-98A1-51135EFBC4FF	Female	1958-07-07 16:00:19.383	Unknown	Married	English	94.060000
15	49DADA25-F2C2-42BB-8210-D78E6C7B0D48	Male	1962-04-08 10:18:26.263	African American	Married	English	19.500000
16	80AC01B2-BD55-4BE0-A59A-4024104CF4E9	Female	1965-05-14 15:25:09.740	African American	Married	English	19.740000
17	A19A0B00-4C9A-4206-B1FE-17E6DA3CEB0B	Female	1967-07-01 01:44:24.907	Unknown	Married	English	17.840000
18	EA7C2F0F-DA1C-4CE8-9700-4BB1FC7AF3FB	Male	1969-01-05 00:15:09.627	Unknown	Married	English	3.300000
19	886B5885-1EE2-49F3-98D5-A2F02EB8A9D4	Female	1970-04-27 09:15:11.080	Unknown	Married	English	10.350000
20	C54B5AAD-98E8-472D-BAA0-638D9F3BD024	Female	1972-11-13 22:50:43.600	African American	Single	Unknown	94.000000
21	69CC25ED-A54A-4BAF-97E3-774BB3C9DED1	Female	1974-10-13 14:51:28.997	Unknown	Married	English	17.720000
22	DB92CDC6-FA9B-4492-BC2C-0C588AD78956	Male	1977-06-30 01:46:05.467	Unknown	Single	Icelandic	1.700000
23	868E700E-3C56-458F-A477-078D671DCB20	Female	1978-09-21 15:29:44.770	African American	Married	Icelandic	5.940000
24	36E2F89E-777A-4D77-9D95-0D70A8AB416F	Male	1980-05-30 13:23:50.703	African American	Separated	English	19.360000
25	672D554B-D6D1-40B2-A6A4-21A4CB6B1AA6	Male	1983-06-17 04:46:13.753	African American	Single	Icelandic	11.040000
26	CC12B481-B516-455B-884F-4CA900B29F2E	Female	1985-10-21 07:59:04.777	Unknown	Divorced	English	14.580000
27	B7E9FC4C-5182-4A34-954E-CEF5FC07E96D	Female	1985-12-11 02:48:16.907	Unknown	Single	English	11.430000

Using subqueries#

SELECT *
FROM PatientCorePopulatedTable
WHERE PatientID IN (SELECT PatientID FROM PatientCorePopulatedTable WHERE PatientDateOfBirth > '1980-01-01')
ORDER BY PatientDateOfBirth

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM PatientCorePopulatedTable
WHERE PatientID IN (SELECT PatientID FROM PatientCorePopulatedTable WHERE PatientDateOfBirth > '1980-01-01')
ORDER BY PatientDateOfBirth
"""
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	E5478913-6819-4977-BB11-4C8B61175B56	Female	1980-03-21 13:08:56.867	White	Single	English	12.920000
1	0A9BA3E4-CF3C-49C4-9774-5EEA2EE7D123	Male	1980-04-04 07:59:15.820	White	Single	Spanish	14.280000
2	36E2F89E-777A-4D77-9D95-0D70A8AB416F	Male	1980-05-30 13:23:50.703	African American	Separated	English	19.360000
3	672D554B-D6D1-40B2-A6A4-21A4CB6B1AA6	Male	1983-06-17 04:46:13.753	African American	Single	Icelandic	11.040000
4	CC12B481-B516-455B-884F-4CA900B29F2E	Female	1985-10-21 07:59:04.777	Unknown	Divorced	English	14.580000
5	B7E9FC4C-5182-4A34-954E-CEF5FC07E96D	Female	1985-12-11 02:48:16.907	Unknown	Single	English	11.430000
6	3E462A8F-7B90-43A1-A8B6-AD82CB5002C9	Female	1986-12-20 16:41:34.793	White	Married	Spanish	18.330000
7	A50BE9B4-8A0B-4169-B894-F7BD86D7D90B	Female	1987-04-18 16:31:16.333	Asian	Divorced	English	14.070000
8	1311FEE4-2FDC-46E4-83D3-1550A3E51D2C	Female	1988-03-28 03:09:22.807	White	Single	English	14.990000
9	8856096E-E59C-4156-A767-C091AF799C80	Female	1988-11-25 02:59:36.373	White	Divorced	English	11.080000

Using Wildcards#

SELECT *
FROM AdmissionsDiagnosesCorePopulatedTable
WHERE PrimaryDiagnosisCode LIKE 'M%'
ORDER BY PrimaryDiagnosisCode

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM AdmissionsDiagnosesCorePopulatedTable
WHERE PrimaryDiagnosisCode LIKE 'M%'
ORDER BY PrimaryDiagnosisCode
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientID	AdmissionID	PrimaryDiagnosisCode	PrimaryDiagnosisDescription
0	80AC01B2-BD55-4BE0-A59A-4024104CF4E9	2	M01.X	Direct infection of joint in infectious and parasitic diseases classified elsewhere
1	4C201C71-CCED-40D1-9642-F9C8C485B854	5	M01.X1	Direct infection of shoulder joint in infectious and parasitic diseases classified elsewhere
2	25B786AF-0F99-478C-9CFA-0EA607E45834	1	M01.X5	Direct infection of hip in infectious and parasitic diseases classified elsewhere
3	21792512-2D40-4326-BEA2-A40127EB24FF	4	M01.X72	Direct infection of left ankle and foot in infectious and parasitic diseases classified elsewhere
4	DA6CECFF-DE13-4C4C-919F-64E1A2B76C9D	2	M02.35	Reiter's disease, hip
5	25B786AF-0F99-478C-9CFA-0EA607E45834	7	M02.352	Reiter's disease, left hip
6	6623F5D6-D581-4268-9F9B-21612FBBF7B5	4	M02.352	Reiter's disease, left hip
7	EA7C2F0F-DA1C-4CE8-9700-4BB1FC7AF3FB	1	M02.37	Reiter's disease, ankle and foot
8	0681FA35-A794-4684-97BD-00B88370DB41	2	M02.38	Reiter's disease, vertebrae
9	2A8772FE-61DB-483E-B6BF-6C0A74BA9C2A	2	M05.13	Rheumatoid lung disease with rheumatoid arthritis of wrist
10	A7142B71-A144-4D56-BD14-3E966B01DB37	3	M05.132	Rheumatoid lung disease with rheumatoid arthritis of left wrist
11	135C831F-7DA5-46C0-959C-EBCBD8810B43	5	M05.17	Rheumatoid lung disease with rheumatoid arthritis of ankle and foot
12	2A5251B1-0945-47FA-A65C-7A6381562591	3	M05.23	Rheumatoid vasculitis with rheumatoid arthritis of wrist
13	EEAFC0B3-B835-4D99-AB33-2F9428E54E5F	1	M05.25	Rheumatoid vasculitis with rheumatoid arthritis of hip
14	E483DE6E-D4E6-47FD-905B-22EE86EC7ACE	3	M05.252	Rheumatoid vasculitis with rheumatoid arthritis of left hip
15	7548B6CF-79D9-461D-A0C5-20B861406FAC	1	M05.261	Rheumatoid vasculitis with rheumatoid arthritis of right knee
16	C65A4ADE-112E-49E4-B72A-0DED22C242ED	1	M05.27	Rheumatoid vasculitis with rheumatoid arthritis of ankle and foot
17	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	1	M05.272	Rheumatoid vasculitis with rheumatoid arthritis of left ankle and foot
18	B39DC5AC-E003-4E6A-91B6-FC07625A1285	5	M05.441	Rheumatoid myopathy with rheumatoid arthritis of right hand
19	7A025E77-7832-4F53-B9A7-09A3F98AC17E	1	M05.442	Rheumatoid myopathy with rheumatoid arthritis of left hand
20	0E0EADE8-5592-4E0B-9F88-D7596E32EE08	2	M05.49	Rheumatoid myopathy with rheumatoid arthritis of multiple sites
21	69B5D2A0-12FD-46EF-A5FF-B29C4BAFBE49	2	M05.5	Rheumatoid polyneuropathy with rheumatoid arthritis
22	80AC01B2-BD55-4BE0-A59A-4024104CF4E9	5	M05.51	Rheumatoid polyneuropathy with rheumatoid arthritis of shoulder
23	7548B6CF-79D9-461D-A0C5-20B861406FAC	3	M05.511	Rheumatoid polyneuropathy with rheumatoid arthritis of right shoulder
24	80D356B4-F974-441F-A5F2-F95986D119A2	6	M05.561	Rheumatoid polyneuropathy with rheumatoid arthritis of right knee
25	EEAFC0B3-B835-4D99-AB33-2F9428E54E5F	2	M05.59	Rheumatoid polyneuropathy with rheumatoid arthritis of multiple sites
26	B2EB15FA-5431-4804-9309-4215BDC778C0	1	M05.722	Rheumatoid arthritis with rheumatoid factor of left elbow without organ or systems involvement
27	FA157FA5-F488-4884-BF87-E144630D595C	1	M05.732	Rheumatoid arthritis with rheumatoid factor of left wrist without organ or systems involvement
28	35FE7491-1A1D-48CB-810C-8DC2599AB3DD	3	M05.741	Rheumatoid arthritis with rheumatoid factor of right hand without organ or systems involvement
29	967987B9-FFEF-4776-85CF-AE05CA81F583	1	M05.742	Rheumatoid arthritis with rheumatoid factor of left hand without organ or systems involvement
30	25B786AF-0F99-478C-9CFA-0EA607E45834	4	M05.75	Rheumatoid arthritis with rheumatoid factor of hip without organ or systems involvement
31	886B5885-1EE2-49F3-98D5-A2F02EB8A9D4	1	M05.752	Rheumatoid arthritis with rheumatoid factor of left hip without organ or systems involvement
32	B39DC5AC-E003-4E6A-91B6-FC07625A1285	1	M06.041	Rheumatoid arthritis without rheumatoid factor, right hand
33	E483DE6E-D4E6-47FD-905B-22EE86EC7ACE	4	M06.051	Rheumatoid arthritis without rheumatoid factor, right hip
34	98F593D2-8894-49BB-93B9-5A0E2CF85E2E	5	M06.2	Rheumatoid bursitis
35	A19A0B00-4C9A-4206-B1FE-17E6DA3CEB0B	3	M06.222	Rheumatoid bursitis, left elbow
36	7FD13988-E58A-4A5C-8680-89AC200950FA	3	M06.25	Rheumatoid bursitis, hip
37	E250799D-F6DE-4914-ADB4-B08A6E5029B9	1	M06.31	Rheumatoid nodule, shoulder
38	FA157FA5-F488-4884-BF87-E144630D595C	2	M06.34	Rheumatoid nodule, hand
39	DB22A4D9-7E4D-485C-916A-9CD1386507FB	1	M06.361	Rheumatoid nodule, right knee
40	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	3	M06.37	Rheumatoid nodule, ankle and foot
41	35FE7491-1A1D-48CB-810C-8DC2599AB3DD	1	M10.31	Gout due to renal impairment, shoulder
42	21792512-2D40-4326-BEA2-A40127EB24FF	6	M10.322	Gout due to renal impairment, left elbow
43	CC12B481-B516-455B-884F-4CA900B29F2E	1	M10.361	Gout due to renal impairment, right knee
44	868E700E-3C56-458F-A477-078D671DCB20	1	M10.37	Gout due to renal impairment, ankle and foot
45	CD2ADB1B-97F7-4EF6-BC5C-3E0EC562A06F	2	M10.38	Gout due to renal impairment, vertebrae
46	BC44CE19-9FC5-4AC9-A296-9EBC5E3D03AE	3	M11.05	Hydroxyapatite deposition disease, hip
47	D8B53AA2-7953-4477-9EA4-68400EBAAC5C	5	M11.052	Hydroxyapatite deposition disease, left hip
48	79A7BA2A-D35A-4CB8-A835-6BAA13B0058C	1	M11.071	Hydroxyapatite deposition disease, right ankle and foot
49	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	2	M11.072	Hydroxyapatite deposition disease, left ankle and foot
50	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	5	M12.111	Kaschin-Beck disease, right shoulder
51	220C8D43-1322-4A9D-B890-D426942A3649	4	M12.161	Kaschin-Beck disease, right knee
52	7A025E77-7832-4F53-B9A7-09A3F98AC17E	4	M12.162	Kaschin-Beck disease, left knee
53	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	4	M12.162	Kaschin-Beck disease, left knee
54	B39DC5AC-E003-4E6A-91B6-FC07625A1285	3	M1A.352	Chronic gout due to renal impairment, left hip
55	A7142B71-A144-4D56-BD14-3E966B01DB37	1	M24.22	Disorder of ligament, elbow
56	36775002-9EC3-4889-AD4F-80DC6855C8D8	2	M24.242	Disorder of ligament, left hand
57	A0A976C8-9B30-4492-B8C4-5B25095B9192	1	M24.251	Disorder of ligament, right hip
58	36775002-9EC3-4889-AD4F-80DC6855C8D8	5	M24.272	Disorder of ligament, left ankle
59	6985D824-3269-4D12-A9DD-B932D640E26E	2	M26.6	Temporomandibular joint disorders
60	6D5DCAC1-17FE-4D7C-923B-806EFBA3E6DF	3	M49.84	Spondylopathy in diseases classified elsewhere, thoracic region
61	135C831F-7DA5-46C0-959C-EBCBD8810B43	2	M49.87	Spondylopathy in diseases classified elsewhere, lumbosacral region
62	6A57AC0C-57F3-4C19-98A1-51135EFBC4FF	3	M51	Thoracic, thoracolumbar, and lumbosacral intervertebral disc disorders
63	220C8D43-1322-4A9D-B890-D426942A3649	1	M63	Disorders of muscle in diseases classified elsewhere
64	25B786AF-0F99-478C-9CFA-0EA607E45834	2	M63.831	Disorders of muscle in diseases classified elsewhere, right forearm
65	4C201C71-CCED-40D1-9642-F9C8C485B854	1	M63.832	Disorders of muscle in diseases classified elsewhere, left forearm
66	CC9CDA72-B37A-4F8F-AFE4-B08F56A183BE	2	M63.851	Disorders of muscle in diseases classified elsewhere, right thigh
67	220C8D43-1322-4A9D-B890-D426942A3649	2	M72	Fibroblastic disorders
68	2A5251B1-0945-47FA-A65C-7A6381562591	4	M84	Disorder of continuity of bone
69	0E0EADE8-5592-4E0B-9F88-D7596E32EE08	3	M84.51	Pathological fracture in neoplastic disease, shoulder
70	E5478913-6819-4977-BB11-4C8B61175B56	2	M84.552	Pathological fracture in neoplastic disease, left femur
71	A0A976C8-9B30-4492-B8C4-5B25095B9192	4	M90	Osteopathies in diseases classified elsewhere
72	6D5DCAC1-17FE-4D7C-923B-806EFBA3E6DF	1	M90.51	Osteonecrosis in diseases classified elsewhere, shoulder
73	967987B9-FFEF-4776-85CF-AE05CA81F583	4	M90.511	Osteonecrosis in diseases classified elsewhere, right shoulder
74	8AF47463-8534-4203-B210-C2290F6CE689	2	M90.522	Osteonecrosis in diseases classified elsewhere, left upper arm
75	1A40AF35-C6D4-4D46-B475-A15D84E8A9D5	5	M90.53	Osteonecrosis in diseases classified elsewhere, forearm
76	9E18822E-7D13-45C7-B50E-F95CFF92BC3E	2	M90.6	Osteitis deformans in neoplastic diseases
77	8856096E-E59C-4156-A767-C091AF799C80	1	M90.6	Osteitis deformans in neoplastic diseases
78	135C831F-7DA5-46C0-959C-EBCBD8810B43	4	M90.632	Osteitis deformans in neoplastic diseases, left forearm
79	21792512-2D40-4326-BEA2-A40127EB24FF	3	M90.642	Osteitis deformans in neoplastic diseases, left hand
80	C60FE675-CA52-4C55-A233-F4B27E94987F	3	M90.86	Osteopathy in diseases classified elsewhere, lower leg

SELECT *
FROM AdmissionsDiagnosesCorePopulatedTable
WHERE PrimaryDiagnosisCode LIKE '%4'
ORDER BY PrimaryDiagnosisCode

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM AdmissionsDiagnosesCorePopulatedTable
WHERE PrimaryDiagnosisCode LIKE '%4'
ORDER BY PrimaryDiagnosisCode
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientID	AdmissionID	PrimaryDiagnosisCode	PrimaryDiagnosisDescription
0	868E700E-3C56-458F-A477-078D671DCB20	2	B33.4	Hantavirus (cardio)-pulmonary syndrome [HPS] [HCPS]
1	66154E24-D3EE-4311-89DB-6195278F9B3C	4	B97.34	Human T-cell lymphotrophic virus, type II [HTLV-II] as the cause of diseases classified elsewhere
2	DA6CECFF-DE13-4C4C-919F-64E1A2B76C9D	3	C71.4	Malignant neoplasm of occipital lobe
3	6E70D84D-C75F-477C-BC37-9177C3698C66	2	D13.4	Benign neoplasm of liver
4	79A7BA2A-D35A-4CB8-A835-6BAA13B0058C	3	D14	Benign neoplasm of middle ear and respiratory system
5	6D8008ED-D623-4BE4-B93B-335F9797C170	3	D41.4	Neoplasm of uncertain behavior of bladder
6	35FE7491-1A1D-48CB-810C-8DC2599AB3DD	2	D48.4	Neoplasm of uncertain behavior of peritoneum
7	25B786AF-0F99-478C-9CFA-0EA607E45834	5	E08.64	Diabetes mellitus due to underlying condition with hypoglycemia
8	016A590E-D093-4667-A5DA-D68EA6987D93	2	E11.64	Type 2 diabetes mellitus with hypoglycemia
9	EA9B67E2-15F0-450B-A8A6-14F6E4AE3D12	1	E72.4	Disorders of ornithine metabolism
10	CC12B481-B516-455B-884F-4CA900B29F2E	2	E87.4	Mixed disorder of acid-base balance
11	0A9BA3E4-CF3C-49C4-9774-5EEA2EE7D123	1	F31.4	Bipolar disorder, current episode depressed, severe, without psychotic features
12	64182B95-EB72-4E2B-BE77-8050B71498CE	2	F45.4	Pain disorders related to psychological factors
13	80D356B4-F974-441F-A5F2-F95986D119A2	1	H34	Retinal vascular occlusions
14	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	1	I25.84	Coronary atherosclerosis due to calcified coronary lesion
15	220C8D43-1322-4A9D-B890-D426942A3649	3	J14	Pneumonia due to Hemophilus influenzae
16	80D356B4-F974-441F-A5F2-F95986D119A2	2	K50.014	Crohn's disease of small intestine with abscess
17	FA157FA5-F488-4884-BF87-E144630D595C	2	M06.34	Rheumatoid nodule, hand
18	6D5DCAC1-17FE-4D7C-923B-806EFBA3E6DF	3	M49.84	Spondylopathy in diseases classified elsewhere, thoracic region
19	2A5251B1-0945-47FA-A65C-7A6381562591	4	M84	Disorder of continuity of bone
20	F0B53A2C-98CA-415D-B928-E3FD0E52B22A	3	N18.4	Chronic kidney disease, stage 4 (severe)
21	53B9FFDD-F80B-43BE-93CF-C34A023EE7E9	2	Q60.4	Renal hypoplasia, bilateral
22	3B11D6B3-A36A-4B69-A437-C29BF425A941	2	T82.4	Mechanical complication of vascular dialysis catheter

SELECT *
FROM AdmissionsDiagnosesCorePopulatedTable
WHERE PrimaryDiagnosisCode LIKE '%5.%'
ORDER BY PrimaryDiagnosisCode

import sqlite3
import pandas as pd

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

sql_statement = """
SELECT *
FROM AdmissionsDiagnosesCorePopulatedTable
WHERE PrimaryDiagnosisCode LIKE '%5.%'
ORDER BY PrimaryDiagnosisCode
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	PatientID	AdmissionID	PrimaryDiagnosisCode	PrimaryDiagnosisDescription
0	967987B9-FFEF-4776-85CF-AE05CA81F583	2	B95.1	Streptococcus, group B, as the cause of diseases classified elsewhere
1	DDC0BC57-7A4E-4E02-9282-177750B74FBC	2	B95.62	Methicillin resistant Staphylococcus aureus infection as the cause of diseases classified elsewhere
2	DDC0BC57-7A4E-4E02-9282-177750B74FBC	3	C05.1	Malignant neoplasm of soft palate
3	1A8791E3-A61C-455A-8DEE-763EB90C9B2C	3	C15.8	Malignant neoplasm of overlapping sites of esophagus
4	0BC491C5-5A45-4067-BD11-A78BEA00D3BE	1	C75.1	Malignant neoplasm of pituitary gland
5	FE0B9B59-1927-45B7-8556-E079DC1DE30A	3	C75.3	Malignant neoplasm of pineal gland
6	69B5D2A0-12FD-46EF-A5FF-B29C4BAFBE49	3	D15.0	Benign neoplasm of thymus
7	C65A4ADE-112E-49E4-B72A-0DED22C242ED	3	D35.2	Benign neoplasm of pituitary gland
8	7FD13988-E58A-4A5C-8680-89AC200950FA	1	D35.5	Benign neoplasm of carotid body
9	8AF47463-8534-4203-B210-C2290F6CE689	5	D55.2	Anemia due to disorders of glycolytic enzymes
10	7548B6CF-79D9-461D-A0C5-20B861406FAC	2	E75.01	Sandhoff disease
11	0BC491C5-5A45-4067-BD11-A78BEA00D3BE	2	F25.0	Schizoaffective disorder, bipolar type
12	64182B95-EB72-4E2B-BE77-8050B71498CE	2	F45.4	Pain disorders related to psychological factors
13	A19A0B00-4C9A-4206-B1FE-17E6DA3CEB0B	2	F45.41	Pain disorder exclusively related to psychological factors
14	2A5251B1-0945-47FA-A65C-7A6381562591	6	F95.1	Chronic motor or vocal tic disorder
15	DB92CDC6-FA9B-4492-BC2C-0C588AD78956	1	H75.01	Mastoiditis in infectious and parasitic diseases classified elsewhere, right ear
16	B2EB15FA-5431-4804-9309-4215BDC778C0	2	H75.03	Mastoiditis in infectious and parasitic diseases classified elsewhere, bilateral
17	C54B5AAD-98E8-472D-BAA0-638D9F3BD024	2	I15.2	Hypertension secondary to endocrine disorders
18	135C831F-7DA5-46C0-959C-EBCBD8810B43	3	I25.10	Atherosclerotic heart disease of native coronary artery without angina pectoris
19	CC9CDA72-B37A-4F8F-AFE4-B08F56A183BE	1	I25.110	Atherosclerotic heart disease of native coronary artery with unstable angina pectoris
20	53B9FFDD-F80B-43BE-93CF-C34A023EE7E9	4	I25.711	Atherosclerosis of autologous vein coronary artery bypass graft(s) with angina pectoris with documented spasm
21	69B5D2A0-12FD-46EF-A5FF-B29C4BAFBE49	5	I25.75	Atherosclerosis of native coronary artery of transplanted heart with angina pectoris
22	016A590E-D093-4667-A5DA-D68EA6987D93	3	I25.812	Atherosclerosis of bypass graft of coronary artery of transplanted heart without angina pectoris
23	6985D824-3269-4D12-A9DD-B932D640E26E	1	I25.82	Chronic total occlusion of coronary artery
24	81C5B13B-F6B2-4E57-9593-6E7E4C13B2CE	1	I25.84	Coronary atherosclerosis due to calcified coronary lesion
25	2A8772FE-61DB-483E-B6BF-6C0A74BA9C2A	2	M05.13	Rheumatoid lung disease with rheumatoid arthritis of wrist
26	A7142B71-A144-4D56-BD14-3E966B01DB37	3	M05.132	Rheumatoid lung disease with rheumatoid arthritis of left wrist
27	135C831F-7DA5-46C0-959C-EBCBD8810B43	5	M05.17	Rheumatoid lung disease with rheumatoid arthritis of ankle and foot
28	2A5251B1-0945-47FA-A65C-7A6381562591	3	M05.23	Rheumatoid vasculitis with rheumatoid arthritis of wrist
29	EEAFC0B3-B835-4D99-AB33-2F9428E54E5F	1	M05.25	Rheumatoid vasculitis with rheumatoid arthritis of hip
30	E483DE6E-D4E6-47FD-905B-22EE86EC7ACE	3	M05.252	Rheumatoid vasculitis with rheumatoid arthritis of left hip
31	7548B6CF-79D9-461D-A0C5-20B861406FAC	1	M05.261	Rheumatoid vasculitis with rheumatoid arthritis of right knee
32	C65A4ADE-112E-49E4-B72A-0DED22C242ED	1	M05.27	Rheumatoid vasculitis with rheumatoid arthritis of ankle and foot
33	FB2ABB23-C9D0-4D09-8464-49BF0B982F0F	1	M05.272	Rheumatoid vasculitis with rheumatoid arthritis of left ankle and foot
34	B39DC5AC-E003-4E6A-91B6-FC07625A1285	5	M05.441	Rheumatoid myopathy with rheumatoid arthritis of right hand
35	7A025E77-7832-4F53-B9A7-09A3F98AC17E	1	M05.442	Rheumatoid myopathy with rheumatoid arthritis of left hand
36	0E0EADE8-5592-4E0B-9F88-D7596E32EE08	2	M05.49	Rheumatoid myopathy with rheumatoid arthritis of multiple sites
37	69B5D2A0-12FD-46EF-A5FF-B29C4BAFBE49	2	M05.5	Rheumatoid polyneuropathy with rheumatoid arthritis
38	80AC01B2-BD55-4BE0-A59A-4024104CF4E9	5	M05.51	Rheumatoid polyneuropathy with rheumatoid arthritis of shoulder
39	7548B6CF-79D9-461D-A0C5-20B861406FAC	3	M05.511	Rheumatoid polyneuropathy with rheumatoid arthritis of right shoulder
40	80D356B4-F974-441F-A5F2-F95986D119A2	6	M05.561	Rheumatoid polyneuropathy with rheumatoid arthritis of right knee
41	EEAFC0B3-B835-4D99-AB33-2F9428E54E5F	2	M05.59	Rheumatoid polyneuropathy with rheumatoid arthritis of multiple sites
42	B2EB15FA-5431-4804-9309-4215BDC778C0	1	M05.722	Rheumatoid arthritis with rheumatoid factor of left elbow without organ or systems involvement
43	FA157FA5-F488-4884-BF87-E144630D595C	1	M05.732	Rheumatoid arthritis with rheumatoid factor of left wrist without organ or systems involvement
44	35FE7491-1A1D-48CB-810C-8DC2599AB3DD	3	M05.741	Rheumatoid arthritis with rheumatoid factor of right hand without organ or systems involvement
45	967987B9-FFEF-4776-85CF-AE05CA81F583	1	M05.742	Rheumatoid arthritis with rheumatoid factor of left hand without organ or systems involvement
46	25B786AF-0F99-478C-9CFA-0EA607E45834	4	M05.75	Rheumatoid arthritis with rheumatoid factor of hip without organ or systems involvement
47	886B5885-1EE2-49F3-98D5-A2F02EB8A9D4	1	M05.752	Rheumatoid arthritis with rheumatoid factor of left hip without organ or systems involvement
48	C5D09468-574F-4802-B56F-DB38F4EB1687	1	N25.1	Nephrogenic diabetes insipidus
49	65A7FBE0-EA9F-49E9-9824-D8F3AD98DAC0	2	S35.415	Laceration of left renal vein
50	C8556CC0-32FC-4CA5-A8CD-9CCF38816167	2	Z95.810	Presence of automatic (implantable) cardiac defibrillator

Checking for NULL#

SELECT *
FROM Cars
WHERE color_id IS NULL

import sqlite3
import pandas as pd

conn = sqlite3.connect('cars.db')

sql_statement = """
SELECT *
FROM Cars
WHERE color_id IS NULL
"""
df = pd.read_sql_query(sql_statement, conn)
df.style.set_table_attributes('style="font-size: 12px"')

	car_id	make_model_id	color_id	available

EAS503

Filtering

Contents

Filtering#

Logical Operators#

Relational Operators#

Conditional Evaluation#

Using Parenthesis#

Using the `NOT` Operator#

Inequality condition#

Range using `BETWEEN` condition#

String Condition#

Membership Condition#

`IN` condition#

`NOT IN` condition#

Using subqueries#

Using Wildcards#

Checking for NULL#

EAS503

Filtering

Contents

Filtering#

Logical Operators#

Relational Operators#

Conditional Evaluation#

Using Parenthesis#

Using the NOT Operator#

Inequality condition#

Range using BETWEEN condition#

String Condition#

Membership Condition#

IN condition#

NOT IN condition#

Using subqueries#

Using Wildcards#

Checking for NULL#

Using the `NOT` Operator#

Range using `BETWEEN` condition#

`IN` condition#

`NOT IN` condition#