Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory63.3 B

Variable types

Numeric6
DateTime1

Dataset

Description당뇨병 환자들이 시행한 혈액 검사 중에 간, 신장 기능 평가할 수 있는 검사 데이터를 포함함. 검사항목은 Bun, Creatinine, AST(GOT), ALT(GPT), MDRD-eGFR - AST(Aspartate aminotransferase. GOT(Glutamic Oxalacetic Transaminase)), ALT(alanine aminotransferase, GPT(glutamic pyruvate transaminase)): 간세포 손상을 반영하는 아미노전이효소(Aminotransferases)로 기본적인 간기능검사 항목임 - BUN(Blood Urea Nitrogen): 간세포 손상이나 신장의 기능을 평가할 수 있는 항목 - Creatinine: 근육에서 크레틴(Creatine)으로부터 생성되며 신장 기능 이외의 영향이 적어 신기능을 평가하는데 유용함 - MDRD-eGFR(Modification of Diet in Renal Disease Study, MDRD-Estimated Glomerular Filtration Rate, eGFR): 혈액 내 크레아티닌 수치를 측정하고 그 결과를 MDRD공식을 사용하여 계산해 신장이 얼마나 잘 기능 하는지를 나태내는 수치
Author가톨릭대학교 은평성모병원
URLhttp://cmcdata.net/data/dataset/diabetes_lab-eunpyeong

Alerts

BUN_VAL is highly overall correlated with Cr_VAL and 1 other fieldsHigh correlation
Cr_VAL is highly overall correlated with BUN_VAL and 1 other fieldsHigh correlation
AST_VAL is highly overall correlated with ALT_VALHigh correlation
ALT_VAL is highly overall correlated with AST_VALHigh correlation
MDRD_VAL is highly overall correlated with BUN_VAL and 1 other fieldsHigh correlation
RID has unique valuesUnique
MDRD_VAL has unique valuesUnique

Reproduction

Analysis started2023-10-08 18:57:52.189115
Analysis finished2023-10-08 18:57:59.703802
Duration7.51 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
2023-10-09T03:57:59.864234image/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
2023-10-09T03:58:00.090636image/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%

BUN/Cr_DATE
Date

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-09-14 00:00:00
Maximum2020-01-30 00:00:00
2023-10-09T03:58:00.284728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:58:00.516802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BUN_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.778
Minimum8.5
Maximum121.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:58:00.861092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.5
5-th percentile9.9
Q112.475
median15.65
Q320.95
95-th percentile33.225
Maximum121.6
Range113.1
Interquartile range (IQR)8.475

Descriptive statistics

Standard deviation12.635636
Coefficient of variation (CV)0.67289575
Kurtosis44.395206
Mean18.778
Median Absolute Deviation (MAD)3.4
Skewness5.7269968
Sum1877.8
Variance159.65931
MonotonicityNot monotonic
2023-10-09T03:58:01.178581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.3 4
 
4.0%
14.4 3
 
3.0%
12.1 2
 
2.0%
15.8 2
 
2.0%
9.0 2
 
2.0%
11.4 2
 
2.0%
11.2 2
 
2.0%
16.9 2
 
2.0%
9.9 2
 
2.0%
18.4 2
 
2.0%
Other values (69) 77
77.0%
ValueCountFrequency (%)
8.5 1
1.0%
9.0 2
2.0%
9.1 1
1.0%
9.9 2
2.0%
10.2 1
1.0%
10.5 1
1.0%
10.7 1
1.0%
11.2 2
2.0%
11.3 1
1.0%
11.4 2
2.0%
ValueCountFrequency (%)
121.6 1
1.0%
44.4 1
1.0%
36.8 1
1.0%
36.7 1
1.0%
35.6 1
1.0%
33.1 1
1.0%
32.0 1
1.0%
30.6 1
1.0%
30.3 1
1.0%
30.1 1
1.0%

Cr_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1047
Minimum0.48
Maximum9.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:58:01.585980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.5295
Q10.66
median0.865
Q31.0225
95-th percentile2.519
Maximum9.07
Range8.59
Interquartile range (IQR)0.3625

Descriptive statistics

Standard deviation1.1971502
Coefficient of variation (CV)1.0836881
Kurtosis32.395091
Mean1.1047
Median Absolute Deviation (MAD)0.195
Skewness5.4237198
Sum110.47
Variance1.4331686
MonotonicityNot monotonic
2023-10-09T03:58:01.954157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.95 5
 
5.0%
0.63 4
 
4.0%
0.58 3
 
3.0%
0.67 3
 
3.0%
0.89 3
 
3.0%
0.97 3
 
3.0%
0.91 3
 
3.0%
0.72 3
 
3.0%
0.96 2
 
2.0%
0.48 2
 
2.0%
Other values (52) 69
69.0%
ValueCountFrequency (%)
0.48 2
2.0%
0.49 1
 
1.0%
0.52 2
2.0%
0.53 2
2.0%
0.54 1
 
1.0%
0.56 2
2.0%
0.58 3
3.0%
0.59 2
2.0%
0.6 1
 
1.0%
0.61 2
2.0%
ValueCountFrequency (%)
9.07 1
1.0%
8.32 1
1.0%
3.55 1
1.0%
2.69 2
2.0%
2.51 1
1.0%
2.11 1
1.0%
1.73 1
1.0%
1.58 1
1.0%
1.56 1
1.0%
1.51 1
1.0%

AST_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.79
Minimum12
Maximum564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:58:02.116015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile13
Q118
median23
Q333.25
95-th percentile75
Maximum564
Range552
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation56.848069
Coefficient of variation (CV)1.6340347
Kurtosis77.615242
Mean34.79
Median Absolute Deviation (MAD)6
Skewness8.3782339
Sum3479
Variance3231.7029
MonotonicityNot monotonic
2023-10-09T03:58:02.253983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20 8
 
8.0%
16 7
 
7.0%
25 6
 
6.0%
19 6
 
6.0%
22 5
 
5.0%
15 5
 
5.0%
23 4
 
4.0%
12 4
 
4.0%
17 4
 
4.0%
27 4
 
4.0%
Other values (30) 47
47.0%
ValueCountFrequency (%)
12 4
4.0%
13 2
 
2.0%
15 5
5.0%
16 7
7.0%
17 4
4.0%
18 4
4.0%
19 6
6.0%
20 8
8.0%
21 3
 
3.0%
22 5
5.0%
ValueCountFrequency (%)
564 1
 
1.0%
127 1
 
1.0%
89 1
 
1.0%
86 1
 
1.0%
75 3
3.0%
71 2
2.0%
61 1
 
1.0%
55 1
 
1.0%
52 1
 
1.0%
48 1
 
1.0%

ALT_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.64
Minimum6
Maximum375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:58:02.413640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11.95
Q118
median24
Q334.5
95-th percentile69.05
Maximum375
Range369
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation45.840417
Coefficient of variation (CV)1.286207
Kurtosis36.852958
Mean35.64
Median Absolute Deviation (MAD)7
Skewness5.6436284
Sum3564
Variance2101.3438
MonotonicityNot monotonic
2023-10-09T03:58:02.636327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
21 9
 
9.0%
24 6
 
6.0%
19 6
 
6.0%
18 5
 
5.0%
31 4
 
4.0%
17 4
 
4.0%
15 4
 
4.0%
26 3
 
3.0%
29 3
 
3.0%
30 3
 
3.0%
Other values (34) 53
53.0%
ValueCountFrequency (%)
6 1
 
1.0%
10 2
 
2.0%
11 2
 
2.0%
12 2
 
2.0%
13 3
3.0%
14 3
3.0%
15 4
4.0%
16 1
 
1.0%
17 4
4.0%
18 5
5.0%
ValueCountFrequency (%)
375 1
 
1.0%
272 1
 
1.0%
99 1
 
1.0%
98 1
 
1.0%
89 1
 
1.0%
68 1
 
1.0%
63 2
2.0%
61 3
3.0%
59 1
 
1.0%
57 1
 
1.0%

MDRD_VAL
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.0442
Minimum4.71
Maximum176.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:58:02.823759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.71
5-th percentile23.069
Q161.83
median86.63
Q3103.4625
95-th percentile138.0005
Maximum176.02
Range171.31
Interquartile range (IQR)41.6325

Descriptive statistics

Standard deviation32.983801
Coefficient of variation (CV)0.39245779
Kurtosis0.41744229
Mean84.0442
Median Absolute Deviation (MAD)21
Skewness-0.0075265577
Sum8404.42
Variance1087.9311
MonotonicityNot monotonic
2023-10-09T03:58:03.425678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.4 1
 
1.0%
103.28 1
 
1.0%
90.45 1
 
1.0%
148.95 1
 
1.0%
63.98 1
 
1.0%
16.88 1
 
1.0%
107.17 1
 
1.0%
79.18 1
 
1.0%
75.71 1
 
1.0%
110.8 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
4.71 1
1.0%
5.11 1
1.0%
16.88 1
1.0%
19.55 1
1.0%
22.67 1
1.0%
23.09 1
1.0%
24.5 1
1.0%
32.18 1
1.0%
37.55 1
1.0%
42.74 1
1.0%
ValueCountFrequency (%)
176.02 1
1.0%
164.88 1
1.0%
156.46 1
1.0%
148.95 1
1.0%
141.05 1
1.0%
137.84 1
1.0%
129.02 1
1.0%
126.95 1
1.0%
123.49 1
1.0%
121.41 1
1.0%

Interactions

2023-10-09T03:57:58.488452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:52.640744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:53.626907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:55.193238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:56.563838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:57.533440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:58.656505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:52.793413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:54.134557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:55.932250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:56.744889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:57.674822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:58.803530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:52.940257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:54.413618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:56.044721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:56.915092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:57.889963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:58.948022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:53.081050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:54.574769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:56.137442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:57.052015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:58.050918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:59.121106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:53.217209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:54.782035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:56.258918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:57.209953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:58.190234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:59.273331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:53.393929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:55.014382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:56.414963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:57.366930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:58.319687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:58:03.560083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDBUN/Cr_DATEBUN_VALCr_VALAST_VALALT_VALMDRD_VAL
RID1.0000.6990.3030.0000.0690.0000.000
BUN/Cr_DATE0.6991.0000.9520.9570.8990.0000.968
BUN_VAL0.3030.9521.0000.9430.0000.0000.798
Cr_VAL0.0000.9570.9431.0000.0000.0000.903
AST_VAL0.0690.8990.0000.0001.0000.8750.487
ALT_VAL0.0000.0000.0000.0000.8751.0000.627
MDRD_VAL0.0000.9680.7980.9030.4870.6271.000
2023-10-09T03:58:03.740565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDBUN_VALCr_VALAST_VALALT_VALMDRD_VAL
RID1.0000.039-0.1560.0070.0510.074
BUN_VAL0.0391.0000.682-0.062-0.160-0.690
Cr_VAL-0.1560.6821.000-0.050-0.043-0.895
AST_VAL0.007-0.062-0.0501.0000.6400.022
ALT_VAL0.051-0.160-0.0430.6401.0000.151
MDRD_VAL0.074-0.690-0.8950.0220.1511.000

Missing values

2023-10-09T03:57:59.480018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:57:59.639659image/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

RIDBUN/Cr_DATEBUN_VALCr_VALAST_VALALT_VALMDRD_VAL
012019-08-1929.41.4231150.4
122016-12-2715.30.95899976.67
232019-07-2312.00.562413107.02
342017-10-3112.70.87212789.51
452017-04-149.10.75714477.8
562019-04-1630.32.51251819.55
672019-06-2022.91.03322274.17
782019-05-2911.60.491721137.84
892018-10-0210.20.722529108.24
9102019-10-1821.60.95222477.71
RIDBUN/Cr_DATEBUN_VALCr_VALAST_VALALT_VALMDRD_VAL
90912018-07-2616.11.11253064.07
91922019-05-0832.01.56191732.18
92932019-11-2612.50.481619164.88
93942016-07-2919.41.35401454.67
94952019-10-2411.30.67171392.07
95962018-02-2027.60.97202055.98
96972019-04-2515.80.615555100.74
97982017-03-1317.21.08431451.93
98992019-06-0714.00.532018117.28
991002019-08-0114.90.632024123.49