Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.0 KiB
Average record size in memory133.3 B

Variable types

Numeric12
DateTime3

Dataset

Description당뇨병 환자들이 시행한 혈액 검사 결과 데이터. 검사 항목은 HbA1c, BUN, Creatinine, AST, ALT, MDRD-eGFR, Total Cholesterol, Triglyceride, HDL, LDL. - HbA1c(당화혈색소): 혈액 속 적혈구 내 혈색소에 포도당 일부가 결합한 상태. 일반 혈당 검사가 검사 시점 혈당만을 알 수 있는데 반해 당화혈색소를 통해 3개월 간의 평균 혈당을 알 수 있음 - 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 - Total Cholesterol(TC, 총콜레스테롤) : 혈액 내에 있는 모든 콜레스테롤을 뜻함 - Triglyceride(TG, 중성지방): 혈 중 트리글리세라이드의 양을 측정. 혈 중 트리글리세라이드가 증가하는 이유는 분명하지 않으나 심혈관 질환으로 진행될 위험의 증가와 관련이 있음 - HDL(High Density Lipoprotein) Cholesterol: 좋은 콜레스테롤이라고도 불리는 고밀도 지단백 콜레스테롤로 콜레스테롤을 흡수하여 간으로 다시 운반함. 높은 HDL cholesterol은 심장질환과 뇌졸중 위험을 낮출 수 있음 - LDL(Low Density Lipoprotein) Cholesterol: 나쁜 콜레스테롤이라고도 불리는 저밀도 지단백 콜레스테롤. 신체 콜레스테롤의 대부분을 차지하며 수치가 높으면 심장질환 및 뇌놀중 위험이 높아짐
Author가톨릭대학교 은평성모병원
URLhttp://cmcdata.net/data/dataset/diabetes_lab2-eunpyeong

Alerts

A1C_VAL is highly overall correlated with A1C_VAL_CHigh correlation
A1C_VAL_C is highly overall correlated with A1C_VALHigh correlation
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
TC_VAL is highly overall correlated with LDL_VALHigh correlation
LDL_VAL is highly overall correlated with TC_VALHigh correlation
RID has unique valuesUnique
MDRD_VAL has unique valuesUnique

Reproduction

Analysis started2023-10-08 18:57:01.497470
Analysis finished2023-10-08 18:57:36.986103
Duration35.49 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
2023-10-09T03:57:37.194145image/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
2023-10-09T03:57:37.550823image/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%
Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-10-01 00:00:00
Maximum2020-01-07 00:00:00
2023-10-09T03:57:37.880856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:38.349028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

A1C_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.158
Minimum4.7
Maximum14.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:38.816552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.7
5-th percentile5.695
Q16.5
median7.3
Q39.9
95-th percentile12.805
Maximum14.7
Range10
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation2.2409495
Coefficient of variation (CV)0.27469349
Kurtosis-0.058208298
Mean8.158
Median Absolute Deviation (MAD)1.1
Skewness0.9242083
Sum815.8
Variance5.0218545
MonotonicityNot monotonic
2023-10-09T03:57:39.168929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 6
 
6.0%
6.6 6
 
6.0%
7.1 5
 
5.0%
8.0 5
 
5.0%
11.0 4
 
4.0%
5.8 3
 
3.0%
8.3 3
 
3.0%
6.0 3
 
3.0%
6.8 3
 
3.0%
6.4 3
 
3.0%
Other values (41) 59
59.0%
ValueCountFrequency (%)
4.7 1
 
1.0%
5.4 1
 
1.0%
5.5 1
 
1.0%
5.6 2
2.0%
5.7 2
2.0%
5.8 3
3.0%
5.9 1
 
1.0%
6.0 3
3.0%
6.2 2
2.0%
6.3 3
3.0%
ValueCountFrequency (%)
14.7 1
1.0%
13.2 1
1.0%
13.1 2
2.0%
12.9 1
1.0%
12.8 1
1.0%
12.4 2
2.0%
11.9 1
1.0%
11.7 1
1.0%
11.5 1
1.0%
11.1 1
1.0%

A1C_VAL_C
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.73
Minimum4
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:39.482674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q16
median7
Q39
95-th percentile12
Maximum14
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2823654
Coefficient of variation (CV)0.29526073
Kurtosis-0.18890084
Mean7.73
Median Absolute Deviation (MAD)1
Skewness0.83978775
Sum773
Variance5.2091919
MonotonicityNot monotonic
2023-10-09T03:57:39.676906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
6 29
29.0%
7 17
17.0%
8 14
14.0%
5 10
 
10.0%
11 8
 
8.0%
10 8
 
8.0%
9 5
 
5.0%
12 4
 
4.0%
13 3
 
3.0%
4 1
 
1.0%
ValueCountFrequency (%)
4 1
 
1.0%
5 10
 
10.0%
6 29
29.0%
7 17
17.0%
8 14
14.0%
9 5
 
5.0%
10 8
 
8.0%
11 8
 
8.0%
12 4
 
4.0%
13 3
 
3.0%
ValueCountFrequency (%)
14 1
 
1.0%
13 3
 
3.0%
12 4
 
4.0%
11 8
 
8.0%
10 8
 
8.0%
9 5
 
5.0%
8 14
14.0%
7 17
17.0%
6 29
29.0%
5 10
 
10.0%
Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-10-01 00:00:00
Maximum2020-01-13 00:00:00
2023-10-09T03:57:39.919404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:40.272790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BUN_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.545
Minimum8.5
Maximum121.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:40.649395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.5
5-th percentile9.895
Q112.3
median15.35
Q321.425
95-th percentile33.225
Maximum121.6
Range113.1
Interquartile range (IQR)9.125

Descriptive statistics

Standard deviation12.687155
Coefficient of variation (CV)0.68412809
Kurtosis44.063619
Mean18.545
Median Absolute Deviation (MAD)3.65
Skewness5.69677
Sum1854.5
Variance160.96391
MonotonicityNot monotonic
2023-10-09T03:57:41.012897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.2 3
 
3.0%
15.3 2
 
2.0%
18.6 2
 
2.0%
15.8 2
 
2.0%
13.3 2
 
2.0%
9.0 2
 
2.0%
16.6 2
 
2.0%
11.4 2
 
2.0%
14.0 2
 
2.0%
11.2 2
 
2.0%
Other values (71) 79
79.0%
ValueCountFrequency (%)
8.5 1
1.0%
9.0 2
2.0%
9.1 1
1.0%
9.8 1
1.0%
9.9 2
2.0%
10.0 1
1.0%
10.2 1
1.0%
10.3 1
1.0%
10.4 1
1.0%
10.5 1
1.0%
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%
29.4 1
1.0%

Cr_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0899
Minimum0.41
Maximum9.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:41.271769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.5
Q10.6375
median0.86
Q31.015
95-th percentile2.519
Maximum9.07
Range8.66
Interquartile range (IQR)0.3775

Descriptive statistics

Standard deviation1.1997176
Coefficient of variation (CV)1.1007593
Kurtosis32.372028
Mean1.0899
Median Absolute Deviation (MAD)0.2
Skewness5.4222516
Sum108.99
Variance1.4393222
MonotonicityNot monotonic
2023-10-09T03:57:42.023762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.58 4
 
4.0%
0.96 4
 
4.0%
0.95 3
 
3.0%
0.97 3
 
3.0%
0.63 3
 
3.0%
1.08 3
 
3.0%
0.89 3
 
3.0%
0.91 3
 
3.0%
0.72 3
 
3.0%
0.61 3
 
3.0%
Other values (50) 68
68.0%
ValueCountFrequency (%)
0.41 1
1.0%
0.47 1
1.0%
0.48 1
1.0%
0.49 1
1.0%
0.5 2
2.0%
0.51 1
1.0%
0.52 1
1.0%
0.53 2
2.0%
0.54 1
1.0%
0.56 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%
1.4 1
1.0%

AST_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.23
Minimum12
Maximum564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:42.235604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile13.95
Q118
median23.5
Q332
95-th percentile71.2
Maximum564
Range552
Interquartile range (IQR)14

Descriptive statistics

Standard deviation55.854221
Coefficient of variation (CV)1.6808372
Kurtosis84.451955
Mean33.23
Median Absolute Deviation (MAD)6.5
Skewness8.8633626
Sum3323
Variance3119.694
MonotonicityNot monotonic
2023-10-09T03:57:42.664793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
20 8
 
8.0%
15 8
 
8.0%
25 7
 
7.0%
19 5
 
5.0%
16 4
 
4.0%
21 4
 
4.0%
28 4
 
4.0%
17 4
 
4.0%
24 4
 
4.0%
18 4
 
4.0%
Other values (29) 48
48.0%
ValueCountFrequency (%)
12 3
 
3.0%
13 2
 
2.0%
14 2
 
2.0%
15 8
8.0%
16 4
4.0%
17 4
4.0%
18 4
4.0%
19 5
5.0%
20 8
8.0%
21 4
4.0%
ValueCountFrequency (%)
564 1
 
1.0%
86 1
 
1.0%
75 3
3.0%
71 2
2.0%
61 1
 
1.0%
57 1
 
1.0%
55 1
 
1.0%
52 1
 
1.0%
47 1
 
1.0%
43 1
 
1.0%

ALT_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.05
Minimum6
Maximum375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:43.088956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q118
median24
Q333.25
95-th percentile62.05
Maximum375
Range369
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation38.244924
Coefficient of variation (CV)1.1932894
Kurtosis66.452631
Mean32.05
Median Absolute Deviation (MAD)7
Skewness7.4983024
Sum3205
Variance1462.6742
MonotonicityNot monotonic
2023-10-09T03:57:43.403909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
21 9
 
9.0%
24 7
 
7.0%
15 5
 
5.0%
18 5
 
5.0%
13 5
 
5.0%
19 4
 
4.0%
31 4
 
4.0%
26 4
 
4.0%
29 3
 
3.0%
53 3
 
3.0%
Other values (33) 51
51.0%
ValueCountFrequency (%)
6 1
 
1.0%
10 2
 
2.0%
11 1
 
1.0%
12 2
 
2.0%
13 5
5.0%
14 3
3.0%
15 5
5.0%
16 2
 
2.0%
17 2
 
2.0%
18 5
5.0%
ValueCountFrequency (%)
375 1
 
1.0%
98 1
 
1.0%
68 1
 
1.0%
63 2
2.0%
62 1
 
1.0%
61 2
2.0%
59 1
 
1.0%
57 1
 
1.0%
55 1
 
1.0%
53 3
3.0%

MDRD_VAL
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.3065
Minimum4.71
Maximum178.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:43.763022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.71
5-th percentile23.069
Q164.0475
median86.025
Q3107.4
95-th percentile138.0005
Maximum178.66
Range173.95
Interquartile range (IQR)43.3525

Descriptive statistics

Standard deviation34.029447
Coefficient of variation (CV)0.39890802
Kurtosis0.40231017
Mean85.3065
Median Absolute Deviation (MAD)22
Skewness0.034029896
Sum8530.65
Variance1158.0033
MonotonicityNot monotonic
2023-10-09T03:57:44.049490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.4 1
 
1.0%
80.06 1
 
1.0%
32.18 1
 
1.0%
64.07 1
 
1.0%
81.32 1
 
1.0%
104.01 1
 
1.0%
95.31 1
 
1.0%
60.27 1
 
1.0%
108.09 1
 
1.0%
96.82 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%
37.68 1
1.0%
ValueCountFrequency (%)
178.66 1
1.0%
176.02 1
1.0%
156.46 1
1.0%
148.95 1
1.0%
141.05 1
1.0%
137.84 1
1.0%
134.66 1
1.0%
132.17 1
1.0%
129.02 1
1.0%
127.63 1
1.0%
Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-10-01 00:00:00
Maximum2020-01-13 00:00:00
2023-10-09T03:57:44.376419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:44.665501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TC_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.59
Minimum79
Maximum329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:44.964438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile100.9
Q1133
median166
Q3201.25
95-th percentile274.15
Maximum329
Range250
Interquartile range (IQR)68.25

Descriptive statistics

Standard deviation52.636373
Coefficient of variation (CV)0.30675665
Kurtosis0.27251903
Mean171.59
Median Absolute Deviation (MAD)34
Skewness0.70108426
Sum17159
Variance2770.5878
MonotonicityNot monotonic
2023-10-09T03:57:45.297420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
159 3
 
3.0%
178 3
 
3.0%
166 3
 
3.0%
154 3
 
3.0%
189 2
 
2.0%
195 2
 
2.0%
124 2
 
2.0%
114 2
 
2.0%
115 2
 
2.0%
230 2
 
2.0%
Other values (67) 76
76.0%
ValueCountFrequency (%)
79 1
1.0%
84 1
1.0%
93 1
1.0%
95 1
1.0%
99 1
1.0%
101 1
1.0%
103 1
1.0%
104 1
1.0%
108 2
2.0%
110 1
1.0%
ValueCountFrequency (%)
329 1
1.0%
304 1
1.0%
296 1
1.0%
291 1
1.0%
277 1
1.0%
274 1
1.0%
265 1
1.0%
260 1
1.0%
255 1
1.0%
244 1
1.0%

TG_VAL
Real number (ℝ)

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.75
Minimum49
Maximum839
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:45.589132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile58.9
Q187.25
median117
Q3178.25
95-th percentile611.6
Maximum839
Range790
Interquartile range (IQR)91

Descriptive statistics

Standard deviation155.77868
Coefficient of variation (CV)0.92863595
Kurtosis9.3780936
Mean167.75
Median Absolute Deviation (MAD)41.5
Skewness3.0405561
Sum16775
Variance24266.997
MonotonicityNot monotonic
2023-10-09T03:57:46.017417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78 3
 
3.0%
76 3
 
3.0%
95 2
 
2.0%
55 2
 
2.0%
74 2
 
2.0%
100 2
 
2.0%
81 2
 
2.0%
104 2
 
2.0%
136 2
 
2.0%
191 2
 
2.0%
Other values (71) 78
78.0%
ValueCountFrequency (%)
49 1
1.0%
50 1
1.0%
55 2
2.0%
57 1
1.0%
59 2
2.0%
62 1
1.0%
68 1
1.0%
71 2
2.0%
72 1
1.0%
74 2
2.0%
ValueCountFrequency (%)
839 1
1.0%
829 1
1.0%
752 1
1.0%
686 1
1.0%
661 1
1.0%
609 1
1.0%
328 1
1.0%
295 1
1.0%
287 1
1.0%
265 1
1.0%

HDL_VAL
Real number (ℝ)

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.3
Minimum9
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:46.273237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile26.95
Q136
median45.5
Q352
95-th percentile59.1
Maximum87
Range78
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.960795
Coefficient of variation (CV)0.26999536
Kurtosis1.4807985
Mean44.3
Median Absolute Deviation (MAD)6.5
Skewness0.1818692
Sum4430
Variance143.06061
MonotonicityNot monotonic
2023-10-09T03:57:46.581240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
52 9
 
9.0%
47 7
 
7.0%
50 6
 
6.0%
36 6
 
6.0%
51 5
 
5.0%
44 4
 
4.0%
39 4
 
4.0%
49 4
 
4.0%
43 4
 
4.0%
57 3
 
3.0%
Other values (30) 48
48.0%
ValueCountFrequency (%)
9 1
 
1.0%
15 1
 
1.0%
25 2
2.0%
26 1
 
1.0%
27 2
2.0%
28 3
3.0%
29 2
2.0%
31 2
2.0%
32 2
2.0%
33 1
 
1.0%
ValueCountFrequency (%)
87 1
 
1.0%
74 1
 
1.0%
71 1
 
1.0%
68 1
 
1.0%
61 1
 
1.0%
59 1
 
1.0%
58 3
3.0%
57 3
3.0%
56 1
 
1.0%
54 2
2.0%

LDL_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.14
Minimum34
Maximum214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:57:46.853612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile41.95
Q169
median88
Q3117.25
95-th percentile166.4
Maximum214
Range180
Interquartile range (IQR)48.25

Descriptive statistics

Standard deviation39.180241
Coefficient of variation (CV)0.40753319
Kurtosis0.21793721
Mean96.14
Median Absolute Deviation (MAD)23.5
Skewness0.72141491
Sum9614
Variance1535.0913
MonotonicityNot monotonic
2023-10-09T03:57:47.189973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 4
 
4.0%
102 3
 
3.0%
107 3
 
3.0%
81 3
 
3.0%
69 3
 
3.0%
104 3
 
3.0%
163 2
 
2.0%
79 2
 
2.0%
95 2
 
2.0%
86 2
 
2.0%
Other values (63) 73
73.0%
ValueCountFrequency (%)
34 2
2.0%
37 1
1.0%
38 1
1.0%
41 1
1.0%
42 1
1.0%
44 2
2.0%
45 1
1.0%
47 1
1.0%
48 2
2.0%
51 1
1.0%
ValueCountFrequency (%)
214 1
1.0%
194 1
1.0%
193 1
1.0%
180 1
1.0%
174 1
1.0%
166 1
1.0%
163 2
2.0%
154 1
1.0%
153 1
1.0%
152 1
1.0%

Interactions

2023-10-09T03:57:32.691038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:03.886145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.903919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:10.054353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:12.302339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.063233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:17.987605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.022867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.229593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:25.452702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:27.482095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:29.632003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:32.864359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:04.094684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.046813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:10.388498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:12.454330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.297053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.127210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.207504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.357695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:25.599330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:27.723513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:29.891463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:32.998850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:04.598336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.166527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:10.591612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:12.704024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.428028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.318842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.339972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.510058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:25.754002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:27.872067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:30.038166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:33.179292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:05.137908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.351631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:10.746945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:12.967329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.552351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.528319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.493650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.706085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:25.907553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:28.034432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:30.722520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:33.339198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:06.061514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.530280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:10.900842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:13.232607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.740241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:18.664294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.628747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.857852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:26.041325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:28.291538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:30.897259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:33.564554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:06.688392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.670267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.052061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:13.540080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:16.875809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:19.393285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.779325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:24.091478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:26.171814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:28.425789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:31.129840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:33.739274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:06.899595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.803165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.191749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:13.920893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:17.041599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:19.807562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:21.928861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:24.256787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:26.307352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:28.589590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:31.286674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:34.068324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.058819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:08.956352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.332648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:14.159742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:17.191154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:20.066777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:22.225082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:24.446100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:26.467998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:28.781292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:31.531704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:34.617724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.218962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:09.129646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.506242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:14.330681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:17.342730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:20.219281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:22.469509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:24.742873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:26.669997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:28.928733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:31.817114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:34.941105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.346631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:09.294055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.732357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:14.729561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:17.484514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:20.361575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:22.647111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:24.909522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:26.822252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:29.072723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:32.013363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:35.189749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.570359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:09.525994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:11.923751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:15.150907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:17.673314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:20.518504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:22.809100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:25.087595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:27.057614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:29.231106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:32.210849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:35.480964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:07.745227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:09.684083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:12.140045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:15.704923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:17.804273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:20.817446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:23.065182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:25.282621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:27.224031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:29.426177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:57:32.427301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:57:47.615667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDA1C_DATEA1C_VALA1C_VAL_CBUN/Cr_DATEBUN_VALCr_VALAST_VALALT_VALMDRD_VALTC/TG/HDL/LDL_DATETC_VALTG_VALHDL_VALLDL_VAL
RID1.0000.8930.2350.3460.8340.3780.1510.0000.1790.0000.9220.0000.2030.0000.000
A1C_DATE0.8931.0000.9540.9500.9990.7300.9300.8860.9720.9260.9970.8620.9750.0000.778
A1C_VAL0.2350.9541.0000.9760.9580.2440.3020.0000.0000.3110.8960.2620.0000.0990.421
A1C_VAL_C0.3460.9500.9761.0000.9650.2710.5610.0000.2990.1990.9090.0000.0000.0000.326
BUN/Cr_DATE0.8340.9990.9580.9651.0000.9410.9870.9440.9670.9110.9980.8780.8390.3690.861
BUN_VAL0.3780.7300.2440.2710.9411.0000.9450.0000.0000.7740.9050.2040.0000.4990.000
Cr_VAL0.1510.9300.3020.5610.9870.9451.0000.0000.0000.8930.9440.6040.0000.4760.000
AST_VAL0.0000.8860.0000.0000.9440.0000.0001.0000.7430.1430.9020.0000.1550.0000.000
ALT_VAL0.1790.9720.0000.2990.9670.0000.0000.7431.0000.0750.9240.0000.0000.0000.000
MDRD_VAL0.0000.9260.3110.1990.9110.7740.8930.1430.0751.0000.8800.3710.0000.5450.000
TC/TG/HDL/LDL_DATE0.9220.9970.8960.9090.9980.9050.9440.9020.9240.8801.0000.8260.7200.9290.835
TC_VAL0.0000.8620.2620.0000.8780.2040.6040.0000.0000.3710.8261.0000.5940.3960.901
TG_VAL0.2030.9750.0000.0000.8390.0000.0000.1550.0000.0000.7200.5941.0000.0000.000
HDL_VAL0.0000.0000.0990.0000.3690.4990.4760.0000.0000.5450.9290.3960.0001.0000.000
LDL_VAL0.0000.7780.4210.3260.8610.0000.0000.0000.0000.0000.8350.9010.0000.0001.000
2023-10-09T03:57:48.004211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDA1C_VALA1C_VAL_CBUN_VALCr_VALAST_VALALT_VALMDRD_VALTC_VALTG_VALHDL_VALLDL_VAL
RID1.000-0.076-0.0570.010-0.144-0.023-0.0550.033-0.060-0.0690.056-0.018
A1C_VAL-0.0761.0000.983-0.0190.091-0.2260.1530.0880.0460.119-0.2290.083
A1C_VAL_C-0.0570.9831.0000.0160.074-0.2440.1470.0890.0540.111-0.2160.092
BUN_VAL0.010-0.0190.0161.0000.674-0.017-0.129-0.689-0.214-0.069-0.221-0.119
Cr_VAL-0.1440.0910.0740.6741.000-0.025-0.049-0.903-0.1550.209-0.443-0.045
AST_VAL-0.023-0.226-0.244-0.017-0.0251.0000.645-0.024-0.0750.0150.094-0.076
ALT_VAL-0.0550.1530.147-0.129-0.0490.6451.0000.153-0.0270.138-0.032-0.080
MDRD_VAL0.0330.0880.089-0.689-0.903-0.0240.1531.0000.244-0.1380.3780.151
TC_VAL-0.0600.0460.054-0.214-0.155-0.075-0.0270.2441.0000.3980.3600.875
TG_VAL-0.0690.1190.111-0.0690.2090.0150.138-0.1380.3981.000-0.1890.212
HDL_VAL0.056-0.229-0.216-0.221-0.4430.094-0.0320.3780.360-0.1891.0000.228
LDL_VAL-0.0180.0830.092-0.119-0.045-0.076-0.0800.1510.8750.2120.2281.000

Missing values

2023-10-09T03:57:36.056280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:57:36.690434image/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

RIDA1C_DATEA1C_VALA1C_VAL_CBUN/Cr_DATEBUN_VALCr_VALAST_VALALT_VALMDRD_VALTC/TG/HDL/LDL_DATETC_VALTG_VALHDL_VALLDL_VAL
012019-08-195.752019-08-1929.41.4231150.42019-08-191032342834
122019-07-236.562019-07-2312.00.562413107.022019-07-23143595073
232017-10-116.762017-10-3112.70.87212789.512017-10-131111275334
342017-04-176.562017-04-149.10.75714477.82017-04-171621034797
452019-04-164.742019-04-1630.32.51251819.552019-04-161751255187
562019-06-209.992019-06-2022.91.03322274.172019-06-201331093481
672019-05-296.462019-05-2911.60.491721137.842019-05-291949671100
782018-10-029.292018-10-0210.20.722529108.242018-10-0217811649116
892019-10-188.082019-10-1821.60.95222477.712019-10-18141915272
9102018-11-086.362018-11-0836.72.69171022.672018-11-081669827124
RIDA1C_DATEA1C_VALA1C_VAL_CBUN/Cr_DATEBUN_VALCr_VALAST_VALALT_VALMDRD_VALTC/TG/HDL/LDL_DATETC_VALTG_VALHDL_VALLDL_VAL
90912019-06-215.852019-06-2124.51.08191572.672019-06-2120220147126
91922019-06-136.762019-06-1310.00.55753127.632019-06-131591045279
92932016-06-2710.0102016-06-2720.10.96151377.442016-06-2723617552152
93942019-05-228.882019-05-2212.60.412421178.662019-05-221551914882
94952018-07-1811.7112018-07-189.80.85233697.922018-07-1819517847117
95962019-10-2811.0112019-10-2813.20.582126114.012019-10-2830416758214
96972019-07-266.362019-06-2713.80.81272368.562019-07-26951143245
97982019-10-166.362019-10-1614.80.73291679.522019-10-162747687149
98992018-06-0111.9112018-06-0110.40.92141359.842018-06-0293763637
991002019-07-297.572019-07-2913.80.512424120.652019-07-291561155744