Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 KiB
Average record size in memory73.3 B

Variable types

Numeric8

Dataset

Description고지혈증 환자들이 시행한 혈액 검사 중에 스타틴 약물의 부작요을 평가할 수 있는 검사 데이터를 포함함. 검체 채취 일장, 접수 일자를 이용하여 처방시점으로 부터의 기간을 계산한 시점 데이터를 생성함. 검사항목은 AST(GOT), ALT(GPT), ALP, γ-GTP, Creatinine, Glucose, CPK 등 고지혈증의 간독성과 신독성 등 다양한 부작용을 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 - Creatinine : 근육에서 크레틴(Creatine)으로부터 생성되며 신장 기능 이외의 영향이 적어 신기능을 평가하는데 유용함 - CPK(creatine phosphokinase, 크레아틴인산활성효소)
Author가톨릭대학교 은평성모병원
URLhttp://cmcdata.net/data/dataset/side-effect-blood-test-data-dyslipidemia-eunpyeong

Alerts

AST_VAL is highly overall correlated with ALT_VAL and 1 other fieldsHigh correlation
ALT_VAL is highly overall correlated with AST_VAL and 2 other fieldsHigh correlation
GTP_VAL is highly overall correlated with AST_VAL and 1 other fieldsHigh correlation
CPK_VAL is highly overall correlated with ALT_VALHigh correlation
일련번호 has unique valuesUnique

Reproduction

Analysis started2023-10-08 18:55:36.198890
Analysis finished2023-10-08 18:55:58.304172
Duration22.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
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
2023-10-09T03:55:58.518667image/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
2023-10-09T03:55:59.102953image/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_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.85
Minimum9
Maximum213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:00.047801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12
Q118
median23
Q337
95-th percentile70.1
Maximum213
Range204
Interquartile range (IQR)19

Descriptive statistics

Standard deviation27.149558
Coefficient of variation (CV)0.85241939
Kurtosis20.962556
Mean31.85
Median Absolute Deviation (MAD)7
Skewness3.8952817
Sum3185
Variance737.09848
MonotonicityNot monotonic
2023-10-09T03:56:00.638181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
18 9
 
9.0%
16 6
 
6.0%
15 5
 
5.0%
23 4
 
4.0%
26 4
 
4.0%
19 4
 
4.0%
22 4
 
4.0%
21 4
 
4.0%
17 4
 
4.0%
24 3
 
3.0%
Other values (35) 53
53.0%
ValueCountFrequency (%)
9 1
 
1.0%
10 2
 
2.0%
11 1
 
1.0%
12 2
 
2.0%
13 1
 
1.0%
14 2
 
2.0%
15 5
5.0%
16 6
6.0%
17 4
4.0%
18 9
9.0%
ValueCountFrequency (%)
213 1
1.0%
124 1
1.0%
115 1
1.0%
85 1
1.0%
72 1
1.0%
70 1
1.0%
69 1
1.0%
65 1
1.0%
60 2
2.0%
54 1
1.0%

ALT_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.62
Minimum5
Maximum142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:01.092229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q114
median21
Q341
95-th percentile73.35
Maximum142
Range137
Interquartile range (IQR)27

Descriptive statistics

Standard deviation25.168435
Coefficient of variation (CV)0.82196063
Kurtosis5.0189455
Mean30.62
Median Absolute Deviation (MAD)10
Skewness2.0066558
Sum3062
Variance633.4501
MonotonicityNot monotonic
2023-10-09T03:56:01.378524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
8 6
 
6.0%
16 6
 
6.0%
15 6
 
6.0%
18 5
 
5.0%
9 4
 
4.0%
13 4
 
4.0%
24 3
 
3.0%
14 3
 
3.0%
29 3
 
3.0%
19 3
 
3.0%
Other values (38) 57
57.0%
ValueCountFrequency (%)
5 1
 
1.0%
6 1
 
1.0%
8 6
6.0%
9 4
4.0%
10 3
3.0%
11 3
3.0%
12 1
 
1.0%
13 4
4.0%
14 3
3.0%
15 6
6.0%
ValueCountFrequency (%)
142 1
1.0%
127 1
1.0%
96 1
1.0%
94 1
1.0%
80 1
1.0%
73 1
1.0%
72 2
2.0%
69 1
1.0%
68 1
1.0%
64 1
1.0%

ALP_VAL
Real number (ℝ)

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.88
Minimum123
Maximum592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:01.634437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123
5-th percentile151.9
Q1210
median248.5
Q3321.5
95-th percentile443.3
Maximum592
Range469
Interquartile range (IQR)111.5

Descriptive statistics

Standard deviation94.109895
Coefficient of variation (CV)0.34614497
Kurtosis1.3036384
Mean271.88
Median Absolute Deviation (MAD)53.5
Skewness1.0852157
Sum27188
Variance8856.6723
MonotonicityNot monotonic
2023-10-09T03:56:01.946065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210 3
 
3.0%
299 2
 
2.0%
304 2
 
2.0%
181 2
 
2.0%
236 2
 
2.0%
258 2
 
2.0%
234 2
 
2.0%
267 1
 
1.0%
307 1
 
1.0%
222 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
123 1
1.0%
128 1
1.0%
132 1
1.0%
146 1
1.0%
150 1
1.0%
152 1
1.0%
155 1
1.0%
156 1
1.0%
162 1
1.0%
164 1
1.0%
ValueCountFrequency (%)
592 1
1.0%
572 1
1.0%
496 1
1.0%
492 1
1.0%
449 1
1.0%
443 1
1.0%
442 1
1.0%
425 1
1.0%
420 1
1.0%
407 1
1.0%

GTP_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.56
Minimum8
Maximum801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:02.220022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile13.9
Q122.75
median36
Q361.5
95-th percentile240.25
Maximum801
Range793
Interquartile range (IQR)38.75

Descriptive statistics

Standard deviation117.16735
Coefficient of variation (CV)1.6373302
Kurtosis22.962454
Mean71.56
Median Absolute Deviation (MAD)16.5
Skewness4.4788409
Sum7156
Variance13728.188
MonotonicityNot monotonic
2023-10-09T03:56:02.578045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 4
 
4.0%
35 4
 
4.0%
45 3
 
3.0%
15 3
 
3.0%
34 3
 
3.0%
33 3
 
3.0%
19 3
 
3.0%
44 2
 
2.0%
26 2
 
2.0%
22 2
 
2.0%
Other values (56) 71
71.0%
ValueCountFrequency (%)
8 1
 
1.0%
10 1
 
1.0%
11 2
2.0%
12 1
 
1.0%
14 2
2.0%
15 3
3.0%
16 1
 
1.0%
17 4
4.0%
18 1
 
1.0%
19 3
3.0%
ValueCountFrequency (%)
801 1
1.0%
700 1
1.0%
381 1
1.0%
346 1
1.0%
283 1
1.0%
238 1
1.0%
210 1
1.0%
164 1
1.0%
147 1
1.0%
136 2
2.0%

CR_VAL
Real number (ℝ)

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3335
Minimum0.52
Maximum6.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:02.825470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile0.6095
Q10.8075
median1.005
Q31.2925
95-th percentile3.8025
Maximum6.88
Range6.36
Interquartile range (IQR)0.485

Descriptive statistics

Standard deviation1.1141055
Coefficient of variation (CV)0.83547469
Kurtosis11.95857
Mean1.3335
Median Absolute Deviation (MAD)0.225
Skewness3.3046148
Sum133.35
Variance1.2412311
MonotonicityNot monotonic
2023-10-09T03:56:03.072086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.98 4
 
4.0%
0.78 3
 
3.0%
0.85 3
 
3.0%
0.64 2
 
2.0%
0.62 2
 
2.0%
0.86 2
 
2.0%
0.76 2
 
2.0%
1.3 2
 
2.0%
1.05 2
 
2.0%
0.88 2
 
2.0%
Other values (66) 76
76.0%
ValueCountFrequency (%)
0.52 1
1.0%
0.54 1
1.0%
0.56 1
1.0%
0.58 1
1.0%
0.6 1
1.0%
0.61 1
1.0%
0.62 2
2.0%
0.63 1
1.0%
0.64 2
2.0%
0.7 1
1.0%
ValueCountFrequency (%)
6.88 1
1.0%
6.72 1
1.0%
4.85 1
1.0%
4.28 1
1.0%
4.23 1
1.0%
3.78 1
1.0%
3.49 1
1.0%
2.87 1
1.0%
2.7 1
1.0%
2.39 1
1.0%

GLC_VAL
Real number (ℝ)

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.37
Minimum58
Maximum642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:03.430580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile88
Q1101.75
median117.5
Q3147.75
95-th percentile330.05
Maximum642
Range584
Interquartile range (IQR)46

Descriptive statistics

Standard deviation87.495299
Coefficient of variation (CV)0.59371174
Kurtosis12.190975
Mean147.37
Median Absolute Deviation (MAD)22.5
Skewness3.1192256
Sum14737
Variance7655.4274
MonotonicityNot monotonic
2023-10-09T03:56:03.683680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112 4
 
4.0%
95 3
 
3.0%
90 3
 
3.0%
110 3
 
3.0%
88 3
 
3.0%
97 3
 
3.0%
106 3
 
3.0%
105 2
 
2.0%
145 2
 
2.0%
111 2
 
2.0%
Other values (59) 72
72.0%
ValueCountFrequency (%)
58 1
 
1.0%
72 1
 
1.0%
84 1
 
1.0%
88 3
3.0%
90 3
3.0%
91 1
 
1.0%
92 2
2.0%
93 2
2.0%
95 3
3.0%
96 2
2.0%
ValueCountFrequency (%)
642 1
1.0%
495 1
1.0%
390 1
1.0%
337 1
1.0%
331 1
1.0%
330 1
1.0%
307 1
1.0%
287 1
1.0%
264 1
1.0%
261 1
1.0%

CPK_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.02
Minimum19
Maximum1529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:03.942868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile33.85
Q159
median87
Q3135
95-th percentile275.05
Maximum1529
Range1510
Interquartile range (IQR)76

Descriptive statistics

Standard deviation195.98634
Coefficient of variation (CV)1.4845201
Kurtosis33.245952
Mean132.02
Median Absolute Deviation (MAD)34
Skewness5.4439889
Sum13202
Variance38410.646
MonotonicityNot monotonic
2023-10-09T03:56:04.187407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 4
 
4.0%
78 3
 
3.0%
39 3
 
3.0%
121 3
 
3.0%
181 2
 
2.0%
56 2
 
2.0%
72 2
 
2.0%
94 2
 
2.0%
51 2
 
2.0%
43 2
 
2.0%
Other values (72) 75
75.0%
ValueCountFrequency (%)
19 1
 
1.0%
24 1
 
1.0%
26 1
 
1.0%
30 1
 
1.0%
31 1
 
1.0%
34 1
 
1.0%
37 1
 
1.0%
39 3
3.0%
40 1
 
1.0%
43 2
2.0%
ValueCountFrequency (%)
1529 1
1.0%
1146 1
1.0%
725 1
1.0%
336 1
1.0%
333 1
1.0%
272 1
1.0%
250 1
1.0%
229 1
1.0%
226 1
1.0%
208 1
1.0%

Interactions

2023-10-09T03:55:56.051966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:38.094548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:44.346464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:46.917020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:48.474209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:50.878430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:52.982481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:54.744671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:56.305424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:38.874616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:45.152675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:47.150560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:48.736543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:51.152299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:53.120963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:54.876598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:56.468548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:39.186527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:45.774472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:47.306816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:49.063103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:51.394554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:53.279856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:55.032137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:56.614828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:39.820316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:45.982960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:47.452987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:49.323466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:51.655425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:53.453069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:55.183012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:56.724503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:40.364841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:46.261193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:47.663325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:49.599066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:52.007470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:53.627943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:55.330708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:56.889859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:41.396347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:46.462849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:47.871237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:49.871098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:52.182855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:53.891926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:55.494665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:57.100080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:42.340299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:46.609714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:48.046721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:50.312656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:52.341531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:54.044291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:55.660541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:57.349355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:43.351928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:46.756044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:48.215960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:50.631099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:52.803410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:54.324472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:55.843977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:56:04.384797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호AST_VALALT_VALALP_VALGTP_VALCR_VALGLC_VALCPK_VAL
일련번호1.0000.2380.1100.0000.0000.0000.2450.163
AST_VAL0.2381.0000.8920.6250.8030.4880.0000.000
ALT_VAL0.1100.8921.0000.6710.7040.5320.0000.000
ALP_VAL0.0000.6250.6711.0000.6320.1050.1030.000
GTP_VAL0.0000.8030.7040.6321.0000.2780.0000.000
CR_VAL0.0000.4880.5320.1050.2781.0000.5260.000
GLC_VAL0.2450.0000.0000.1030.0000.5261.0000.376
CPK_VAL0.1630.0000.0000.0000.0000.0000.3761.000
2023-10-09T03:56:04.590026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호AST_VALALT_VALALP_VALGTP_VALCR_VALGLC_VALCPK_VAL
일련번호1.0000.0290.0310.018-0.031-0.1240.0950.202
AST_VAL0.0291.0000.8050.1510.5420.040-0.1380.438
ALT_VAL0.0310.8051.0000.0810.5310.083-0.1550.508
ALP_VAL0.0180.1510.0811.0000.3270.0530.190-0.142
GTP_VAL-0.0310.5420.5310.3271.0000.080-0.0910.189
CR_VAL-0.1240.0400.0830.0530.0801.0000.064-0.149
GLC_VAL0.095-0.138-0.1550.190-0.0910.0641.000-0.159
CPK_VAL0.2020.4380.508-0.1420.189-0.149-0.1591.000

Missing values

2023-10-09T03:55:57.828613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:55:58.218055image/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

일련번호AST_VALALT_VALALP_VALGTP_VALCR_VALGLC_VALCPK_VAL
0140392991361.0815050
121310235240.7211178
232918251151.3614726
3495123120.912839
452033443611.2512330
561614200510.958859
671818291351.23112121
781820206331.2811293
89168404190.8912558
9102929182450.8114095
일련번호AST_VALALT_VALALP_VALGTP_VALCR_VALGLC_VALCPK_VAL
909150521901111.3101151
91922621276400.58146250
92932234215530.61226725
93942630155200.5211594
94952110233240.63264105
95961913234270.7626161
96971812369150.779260
9798151632681.414524
98991816232460.7590121
99100148367560.8615443