Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory77.3 B

Variable types

Numeric4
DateTime4
Text1

Dataset

Description알코올 사용 장애 환자들이 시행한 혈액 검사 중에 간기능의 효과를 평가할 수 있는 주요 검사 데이터를 포함하며 검체 채취 일자와 접수 일자를 이용하여 처방시점으로 부터의 기간을 계산한 시점 데이터를 생성함. 검사항목은AST(GOT), ALT(GPT), ALP, γ-GTP 등 간기능 개선 성과와 알코올 사용을 평가할 수 있는 주요 검사항목이 포함됨 - AST(Aspartate aminotransferase. GOT(Glutamic Oxalacetic Transaminase)), ALT(alanine aminotransferase, GPT(glutamic pyruvate transaminase)) : 간세포 손상을 반영하는 아미노전이효소(Aminotransferases)로 기본적인 간기능검사 항목임 - ALP(alkaline phosphatase, 알칼리인산분해효소) : 간세포 내 담관에 존재하는 효소로 즈로 담즙 배설 장애 시 빠르게 상승함 - γ-GTP(gamma(γ)-glutamyl transferase, GGT, 감마-글루타밀전이효소) : 간세포 내 담관에 존재하는 효소로 ALP와 함께 담즙 배설 장애를 판단하는데 사용되나, 간질환 없이도 알코올 중독자, 비만한 사람의 일부, 아세트아미노펜, 페니토인, 카르바마제핀 같은 약물의 과다복용 때도 상승할 수 있음
Author가톨릭대학교 은평성모병원
URLhttp://cmcdata.net/data/dataset/main-effect-blood-test-data-alcohol-use-disorder-eunpyeong

Alerts

AST_SRC is highly overall correlated with ALT_SRC and 1 other fieldsHigh correlation
ALT_SRC is highly overall correlated with AST_SRC High correlation
ALP_SRC is highly overall correlated with AST_SRC High correlation
RID has unique valuesUnique

Reproduction

Analysis started2023-10-08 18:55:59.154864
Analysis finished2023-10-08 18:56:04.627862
Duration5.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RID
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:04.993641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-10-09T03:56:05.299408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

AST_DCT
Date

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-09-03 00:00:00
Maximum2020-04-29 00:00:00
2023-10-09T03:56:05.607841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:06.089323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AST_SRC
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.48
Minimum11
Maximum767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:06.571853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile20.95
Q128
median43.5
Q3112.5
95-th percentile384.1
Maximum767
Range756
Interquartile range (IQR)84.5

Descriptive statistics

Standard deviation128.52532
Coefficient of variation (CV)1.3460967
Kurtosis10.665845
Mean95.48
Median Absolute Deviation (MAD)19
Skewness3.0903122
Sum9548
Variance16518.757
MonotonicityNot monotonic
2023-10-09T03:56:07.164672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 5
 
5.0%
28 4
 
4.0%
31 4
 
4.0%
32 4
 
4.0%
27 4
 
4.0%
143 2
 
2.0%
24 2
 
2.0%
23 2
 
2.0%
58 2
 
2.0%
47 2
 
2.0%
Other values (59) 69
69.0%
ValueCountFrequency (%)
11 1
 
1.0%
15 1
 
1.0%
18 1
 
1.0%
19 1
 
1.0%
20 1
 
1.0%
21 1
 
1.0%
22 1
 
1.0%
23 2
 
2.0%
24 2
 
2.0%
25 5
5.0%
ValueCountFrequency (%)
767 1
1.0%
601 1
1.0%
524 1
1.0%
504 1
1.0%
386 1
1.0%
384 1
1.0%
300 1
1.0%
294 1
1.0%
262 1
1.0%
210 1
1.0%

ALT_DCT
Date

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-09-03 00:00:00
Maximum2020-04-29 00:00:00
2023-10-09T03:56:07.951670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:08.626907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ALT_SRC
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.83
Minimum5
Maximum1480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:08.984345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.95
Q121
median33
Q356.25
95-th percentile143.5
Maximum1480
Range1475
Interquartile range (IQR)35.25

Descriptive statistics

Standard deviation150.65514
Coefficient of variation (CV)2.3238491
Kurtosis80.543838
Mean64.83
Median Absolute Deviation (MAD)18
Skewness8.5897645
Sum6483
Variance22696.971
MonotonicityNot monotonic
2023-10-09T03:56:09.217370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 6
 
6.0%
31 5
 
5.0%
17 4
 
4.0%
18 3
 
3.0%
15 3
 
3.0%
55 3
 
3.0%
30 3
 
3.0%
21 3
 
3.0%
33 3
 
3.0%
54 3
 
3.0%
Other values (51) 64
64.0%
ValueCountFrequency (%)
5 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
8 1
 
1.0%
10 1
 
1.0%
11 2
2.0%
12 2
2.0%
13 1
 
1.0%
14 1
 
1.0%
15 3
3.0%
ValueCountFrequency (%)
1480 1
1.0%
270 1
1.0%
252 1
1.0%
207 1
1.0%
153 1
1.0%
143 1
1.0%
135 1
1.0%
117 1
1.0%
116 2
2.0%
111 1
1.0%

ALP_DCT
Date

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-09-03 00:00:00
Maximum2020-04-29 00:00:00
2023-10-09T03:56:09.632215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:09.928927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ALP_SRC
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310.26
Minimum33
Maximum5304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:11.020905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile68.9
Q1117
median214
Q3290.5
95-th percentile746.75
Maximum5304
Range5271
Interquartile range (IQR)173.5

Descriptive statistics

Standard deviation549.93881
Coefficient of variation (CV)1.7725096
Kurtosis70.095623
Mean310.26
Median Absolute Deviation (MAD)95
Skewness7.8260164
Sum31026
Variance302432.7
MonotonicityNot monotonic
2023-10-09T03:56:11.431012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 2
 
2.0%
118 2
 
2.0%
67 2
 
2.0%
327 2
 
2.0%
94 2
 
2.0%
227 2
 
2.0%
71 2
 
2.0%
265 2
 
2.0%
125 1
 
1.0%
104 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
33 1
1.0%
42 1
1.0%
53 1
1.0%
67 2
2.0%
69 1
1.0%
71 2
2.0%
72 1
1.0%
73 2
2.0%
74 1
1.0%
76 1
1.0%
ValueCountFrequency (%)
5304 1
1.0%
1372 1
1.0%
989 1
1.0%
937 1
1.0%
780 1
1.0%
745 1
1.0%
727 1
1.0%
638 1
1.0%
628 1
1.0%
619 1
1.0%

GTP_DCT
Date

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-09-03 00:00:00
Maximum2020-04-29 00:00:00
2023-10-09T03:56:11.882816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:12.192119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

GTP_SRC
Text

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-10-09T03:56:12.698950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.76
Min length2

Characters and Unicode

Total characters276
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)85.0%

Sample

1st row54
2nd row1642
3rd row265
4th row24
5th row32
ValueCountFrequency (%)
89 3
 
3.0%
34 2
 
2.0%
70 2
 
2.0%
38 2
 
2.0%
26 2
 
2.0%
29 2
 
2.0%
189 2
 
2.0%
807 1
 
1.0%
278 1
 
1.0%
98 1
 
1.0%
Other values (83) 83
82.2%
2023-10-09T03:56:13.502701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 44
15.9%
2 39
14.1%
3 31
11.2%
7 30
10.9%
5 25
9.1%
6 24
8.7%
9 23
8.3%
8 20
7.2%
4 19
6.9%
0 18
6.5%
Other values (3) 3
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 273
98.9%
Other Letter 2
 
0.7%
Space Separator 1
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44
16.1%
2 39
14.3%
3 31
11.4%
7 30
11.0%
5 25
9.2%
6 24
8.8%
9 23
8.4%
8 20
7.3%
4 19
7.0%
0 18
6.6%
Other Letter
ValueCountFrequency (%)
이 1
50.0%
상 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274
99.3%
Hangul 2
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44
16.1%
2 39
14.2%
3 31
11.3%
7 30
10.9%
5 25
9.1%
6 24
8.8%
9 23
8.4%
8 20
7.3%
4 19
6.9%
0 18
6.6%
Hangul
ValueCountFrequency (%)
이 1
50.0%
상 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274
99.3%
Hangul 2
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44
16.1%
2 39
14.2%
3 31
11.3%
7 30
10.9%
5 25
9.1%
6 24
8.8%
9 23
8.4%
8 20
7.3%
4 19
6.9%
0 18
6.6%
Hangul
ValueCountFrequency (%)
이 1
50.0%
상 1
50.0%

Interactions

2023-10-09T03:56:03.505272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.406415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.079694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.748425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.660733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.555150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.236955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.899528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.921726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.714477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.403800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.061055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:04.095692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.889922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.616236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.280488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:56:13.729982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDAST_DCTAST_SRCALT_DCTALT_SRCALP_DCTALP_SRCGTP_DCTGTP_SRC
RID1.0000.6490.2270.6490.2080.6490.3010.7260.694
AST_DCT0.6491.0000.9931.0001.0001.0001.0001.0000.994
AST_SRC0.2270.9931.0000.9930.9430.9970.7770.9990.999
ALT_DCT0.6491.0000.9931.0001.0001.0001.0001.0000.994
ALT_SRC0.2081.0000.9431.0001.0001.0000.2721.0001.000
ALP_DCT0.6491.0000.9971.0001.0001.0001.0001.0000.994
ALP_SRC0.3011.0000.7771.0000.2721.0001.0001.0000.000
GTP_DCT0.7261.0000.9991.0001.0001.0001.0001.0000.995
GTP_SRC0.6940.9940.9990.9941.0000.9940.0000.9951.000
2023-10-09T03:56:13.972796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDAST_SRCALT_SRCALP_SRC
RID1.000-0.314-0.187-0.185
AST_SRC-0.3141.0000.7500.501
ALT_SRC-0.1870.7501.0000.480
ALP_SRC-0.1850.5010.4801.000

Missing values

2023-10-09T03:56:04.301568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:56:04.535912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RIDAST_DCTAST_SRCALT_DCTALT_SRCALP_DCTALP_SRCGTP_DCTGTP_SRC
012020-02-18T00:00:00512020-02-18T00:00:00222020-02-18T00:00:00732020-02-18T00:00:0054
122019-04-24T00:00:005042019-04-24T00:00:002072019-04-24T00:00:002602019-04-24T00:00:001642
232016-01-26T00:00:001262016-01-26T00:00:00512016-01-26T00:00:002862016-01-26T00:00:00265
342019-07-01T00:00:00182019-07-01T00:00:00152019-07-01T00:00:00422019-07-01T00:00:0024
452017-06-27T00:00:006012017-06-27T00:00:001352017-06-27T00:00:002872017-06-27T00:00:0032
562018-04-24T00:00:003862018-04-24T00:00:001432018-04-24T00:00:006382018-04-24T00:00:001067
672018-04-09T00:00:001242018-04-09T00:00:00962018-04-09T00:00:003812018-04-09T00:00:00593
782018-01-06T00:00:001532018-01-06T00:00:00562018-01-06T00:00:001712018-01-06T00:00:00733
892017-04-05T00:00:001142017-04-05T00:00:001032017-04-05T00:00:004472017-04-05T00:00:00797
9102017-05-16T00:00:00712017-05-16T00:00:00832017-05-16T00:00:002622017-05-16T00:00:00614
RIDAST_DCTAST_SRCALT_DCTALT_SRCALP_DCTALP_SRCGTP_DCTGTP_SRC
90912020-01-08T00:00:00772020-01-08T00:00:00512020-01-08T00:00:001032020-01-08T00:00:00123
91922020-04-13T00:00:00262020-04-13T00:00:00272020-04-13T00:00:00742020-04-13T00:00:0034
92932017-09-11T00:00:00362017-09-11T00:00:00522017-09-11T00:00:002262017-09-11T00:00:00156
93942019-11-18T00:00:00282019-11-18T00:00:00552019-11-18T00:00:001252019-11-18T00:00:0038
94952019-04-16T00:00:00222019-04-16T00:00:00182019-04-16T00:00:00842019-04-16T00:00:0025
95962018-04-11T00:00:00582018-04-11T00:00:00542018-04-11T00:00:0053042018-04-11T00:00:0070
96972016-12-19T00:00:005242016-12-19T00:00:002702016-12-19T00:00:007802016-12-19T00:00:001906
97982015-11-20T00:00:00952015-11-20T00:00:00942015-11-20T00:00:002692015-11-20T00:00:00937
98992016-08-01T00:00:00312016-08-01T00:00:00312016-08-01T00:00:001882016-08-01T00:00:0069
991002020-01-28T00:00:00882020-01-28T00:00:00322020-01-28T00:00:002322020-01-28T00:00:00293