Filtering

13.3. Filtering#

13.3.1. 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 %

13.3.2. 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

13.3.3. Conditional Evaluation#

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

13.3.4. Using Parenthesis#

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

13.3.5. Using the NOT Operator#

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

13.3.6. Inequality condition#

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

13.3.7. Range using BETWEEN condition#

SELECT *
FROM PatientCorePopulatedTable
WHERE PatientDateOfBirth BETWEEN '1920-01-01' AND '1965-01-01'
ORDER BY PatientDateOfBirth
Hide code cell source 
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

13.3.8. String Condition#

SELECT *
FROM PatientCorePopulatedTable
WHERE PatientID BETWEEN '2A' AND '53'
ORDER BY PatientID
Hide code cell source 
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

13.3.9. Membership Condition#

SELECT *
FROM PatientCorePopulatedTable
WHERE PatientRace = 'White' OR PatientRace = 'Asian'
ORDER BY PatientDateOfBirth
Hide code cell source 
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

13.3.9.1. IN condition#

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

13.3.9.2. NOT IN condition#

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

13.3.9.3. Using subqueries#

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

13.3.10. Using Wildcards#

SELECT *
FROM AdmissionsDiagnosesCorePopulatedTable
WHERE PrimaryDiagnosisCode LIKE 'M%'
ORDER BY PrimaryDiagnosisCode
Hide code cell source 
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
Hide code cell source 
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
Hide code cell source 
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

13.3.11. Checking for NULL#

SELECT *
FROM Cars
WHERE color_id IS NULL
Hide code cell source 
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
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