Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells3
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory80.3 B

Variable types

Numeric7
DateTime2

Dataset

Description당뇨병 환자들이 시행한 혈액 검사 결과를 이용하여 공존질환과의 관련성을 평가할 수 있는 검사 데이터를 포함함. 검사 항목은 HbA1c, TC, TG, HDL, LDL로 신장병증, 망막병증, 심근경색, 백내장과 혈관성 질환의 평가가 가능함. - 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 - 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_coexlab-eunpyeong

Alerts

A1C_VAL is highly overall correlated with A1C_VAL_CHigh correlation
A1C_VAL_C is highly overall correlated with A1C_VALHigh correlation
TC_VAL is highly overall correlated with LDL_VALHigh correlation
LDL_VAL is highly overall correlated with TC_VALHigh correlation
TG_VAL has 3 (3.0%) missing valuesMissing
RID has unique valuesUnique

Reproduction

Analysis started2023-10-08 18:55:55.787139
Analysis finished2023-10-08 18:56:09.893502
Duration14.11 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
2023-10-09T03:56:10.093810image/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
2023-10-09T03:56:10.591115image/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%

A1C_DATE
Date

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-10-01 00:00:00
Maximum2020-01-07 00:00:00
2023-10-09T03:56:11.019495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:11.391575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

A1C_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.052
Minimum4.7
Maximum14.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:11.924996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.7
5-th percentile5.695
Q16.5
median7.2
Q39.3
95-th percentile12.805
Maximum14.7
Range10
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1686695
Coefficient of variation (CV)0.26933302
Kurtosis0.36793447
Mean8.052
Median Absolute Deviation (MAD)1.1
Skewness1.0440809
Sum805.2
Variance4.7031273
MonotonicityNot monotonic
2023-10-09T03:56:12.201477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 6
 
6.0%
6.5 6
 
6.0%
7.1 5
 
5.0%
8.0 5
 
5.0%
6.8 4
 
4.0%
10.0 3
 
3.0%
7.2 3
 
3.0%
8.3 3
 
3.0%
11.0 3
 
3.0%
5.8 3
 
3.0%
Other values (42) 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%
6.1 1
 
1.0%
6.2 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%
11.5 1
 
1.0%
11.1 1
 
1.0%
11.0 3
3.0%
10.9 1
 
1.0%

A1C_VAL_C
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.64
Minimum4
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:12.425076image/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 deviation2.2134575
Coefficient of variation (CV)0.28971956
Kurtosis0.1877693
Mean7.64
Median Absolute Deviation (MAD)1
Skewness0.94712288
Sum764
Variance4.8993939
MonotonicityNot monotonic
2023-10-09T03:56:12.642718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
6 29
29.0%
7 18
18.0%
8 15
15.0%
5 10
 
10.0%
10 8
 
8.0%
9 6
 
6.0%
11 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 18
18.0%
8 15
15.0%
9 6
 
6.0%
10 8
 
8.0%
11 5
 
5.0%
12 4
 
4.0%
13 3
 
3.0%
ValueCountFrequency (%)
14 1
 
1.0%
13 3
 
3.0%
12 4
 
4.0%
11 5
 
5.0%
10 8
 
8.0%
9 6
 
6.0%
8 15
15.0%
7 18
18.0%
6 29
29.0%
5 10
 
10.0%

TC/TG/HDL/LDL_DATE
Date

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-10-01 00:00:00
Maximum2020-01-13 00:00:00
2023-10-09T03:56:12.978140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:13.305195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TC_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.59
Minimum77
Maximum412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:13.578764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile102.9
Q1131.75
median164.5
Q3196.5
95-th percentile265.6
Maximum412
Range335
Interquartile range (IQR)64.75

Descriptive statistics

Standard deviation55.756704
Coefficient of variation (CV)0.32684626
Kurtosis2.9505278
Mean170.59
Median Absolute Deviation (MAD)34
Skewness1.2493937
Sum17059
Variance3108.81
MonotonicityNot monotonic
2023-10-09T03:56:14.232013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114 4
 
4.0%
154 3
 
3.0%
178 3
 
3.0%
166 3
 
3.0%
159 3
 
3.0%
115 2
 
2.0%
155 2
 
2.0%
209 2
 
2.0%
189 2
 
2.0%
174 2
 
2.0%
Other values (66) 74
74.0%
ValueCountFrequency (%)
77 1
1.0%
79 1
1.0%
84 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%
111 1
1.0%
ValueCountFrequency (%)
412 1
1.0%
329 1
1.0%
296 1
1.0%
291 1
1.0%
277 1
1.0%
265 1
1.0%
260 1
1.0%
255 1
1.0%
244 1
1.0%
243 1
1.0%

TG_VAL
Real number (ℝ)

MISSING 

Distinct78
Distinct (%)80.4%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean168.31959
Minimum49
Maximum839
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:14.497657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile58.6
Q182
median118
Q3179
95-th percentile619.4
Maximum839
Range790
Interquartile range (IQR)97

Descriptive statistics

Standard deviation158.04565
Coefficient of variation (CV)0.93896173
Kurtosis9.0201562
Mean168.31959
Median Absolute Deviation (MAD)42
Skewness2.9942776
Sum16327
Variance24978.428
MonotonicityNot monotonic
2023-10-09T03:56:14.806584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78 3
 
3.0%
95 2
 
2.0%
191 2
 
2.0%
104 2
 
2.0%
71 2
 
2.0%
82 2
 
2.0%
59 2
 
2.0%
75 2
 
2.0%
169 2
 
2.0%
136 2
 
2.0%
Other values (68) 76
76.0%
(Missing) 3
 
3.0%
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%
Mean43.27
Minimum9
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:15.113716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile26.95
Q135.75
median44
Q351
95-th percentile58.05
Maximum74
Range65
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation11.188789
Coefficient of variation (CV)0.25858074
Kurtosis0.51957714
Mean43.27
Median Absolute Deviation (MAD)8
Skewness-0.15377708
Sum4327
Variance125.18899
MonotonicityNot monotonic
2023-10-09T03:56:15.377378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
52 9
 
9.0%
50 6
 
6.0%
47 6
 
6.0%
36 5
 
5.0%
51 5
 
5.0%
49 5
 
5.0%
43 4
 
4.0%
44 4
 
4.0%
39 4
 
4.0%
40 4
 
4.0%
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 3
3.0%
30 1
 
1.0%
31 2
2.0%
32 2
2.0%
ValueCountFrequency (%)
74 1
1.0%
71 1
1.0%
68 1
1.0%
61 1
1.0%
59 1
1.0%
58 2
2.0%
57 2
2.0%
56 1
1.0%
54 2
2.0%
53 2
2.0%

LDL_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.46
Minimum28
Maximum231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:15.613259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile41.95
Q168.75
median87
Q3115.25
95-th percentile166.4
Maximum231
Range203
Interquartile range (IQR)46.5

Descriptive statistics

Standard deviation39.268673
Coefficient of variation (CV)0.41136259
Kurtosis0.83643485
Mean95.46
Median Absolute Deviation (MAD)22.5
Skewness0.88642099
Sum9546
Variance1542.0287
MonotonicityNot monotonic
2023-10-09T03:56:15.872431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 4
 
4.0%
81 3
 
3.0%
62 3
 
3.0%
104 3
 
3.0%
102 3
 
3.0%
107 3
 
3.0%
69 3
 
3.0%
34 2
 
2.0%
86 2
 
2.0%
79 2
 
2.0%
Other values (63) 72
72.0%
ValueCountFrequency (%)
28 1
1.0%
34 2
2.0%
38 1
1.0%
41 1
1.0%
42 1
1.0%
44 1
1.0%
47 1
1.0%
48 2
2.0%
51 1
1.0%
54 2
2.0%
ValueCountFrequency (%)
231 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

2023-10-09T03:56:06.815215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:56.772741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:58.572435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:00.017382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.519669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.846805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:04.170813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:07.292487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:56.915970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:58.841653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:00.195920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.676343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.002280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:04.306912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:07.753287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:57.046110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:59.115885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:00.444484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.833059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.205656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:04.436837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:08.350890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:57.222361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:59.343509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:00.716832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.034251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.456558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:05.211323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:08.716794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:57.511182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:59.513068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:00.992200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.195976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.596005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:05.759622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:08.950327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:57.838207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:59.670944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.203072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.360949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.798218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:06.093421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:09.099637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:58.163689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:55:59.829622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:01.343342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:02.636555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:03.991273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:06.498493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:56:16.089162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDA1C_DATEA1C_VALA1C_VAL_CTC/TG/HDL/LDL_DATETC_VALTG_VALHDL_VALLDL_VAL
RID1.0000.9410.3020.3150.9530.0000.2420.0000.000
A1C_DATE0.9411.0000.9430.9390.9990.9440.9720.9600.889
A1C_VAL0.3020.9431.0000.9790.9020.0000.0000.0830.000
A1C_VAL_C0.3150.9390.9791.0000.8980.0000.0000.0000.113
TC/TG/HDL/LDL_DATE0.9530.9990.9020.8981.0000.9380.7350.7980.863
TC_VAL0.0000.9440.0000.0000.9381.0000.6290.4440.856
TG_VAL0.2420.9720.0000.0000.7350.6291.0000.0000.000
HDL_VAL0.0000.9600.0830.0000.7980.4440.0001.0000.000
LDL_VAL0.0000.8890.0000.1130.8630.8560.0000.0001.000
2023-10-09T03:56:16.320396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDA1C_VALA1C_VAL_CTC_VALTG_VALHDL_VALLDL_VAL
RID1.000-0.113-0.121-0.092-0.070-0.004-0.031
A1C_VAL-0.1131.0000.9780.0790.101-0.2260.120
A1C_VAL_C-0.1210.9781.0000.0810.080-0.2100.123
TC_VAL-0.0920.0790.0811.0000.4120.3320.881
TG_VAL-0.0700.1010.0800.4121.000-0.1720.207
HDL_VAL-0.004-0.226-0.2100.332-0.1721.0000.210
LDL_VAL-0.0310.1200.1230.8810.2070.2101.000

Missing values

2023-10-09T03:56:09.416201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:56:09.785400image/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_CTC/TG/HDL/LDL_DATETC_VALTG_VALHDL_VALLDL_VAL
012019-08-195.752019-08-191032342834
122019-07-236.572019-07-23143595073
232017-10-116.762017-10-131111275334
342017-04-176.562017-04-171621034797
452019-04-164.742019-04-161751255187
562019-06-209.992019-06-201331093481
672019-05-296.462019-05-291949671100
782018-10-029.292018-10-0217811649116
892019-10-188.082019-10-18141915272
9102018-11-086.362018-11-081669827124
RIDA1C_DATEA1C_VALA1C_VAL_CTC/TG/HDL/LDL_DATETC_VALTG_VALHDL_VALLDL_VAL
90912019-11-236.462019-11-23110494748
91922019-02-2510.0102019-02-251197839148
92932019-08-276.262019-08-271771915289
93942019-12-067.672019-12-06142<NA>4962
94952020-01-036.462020-01-031781695886
95962016-01-136.562016-01-13991063642
96972019-06-215.852019-06-2120220147126
97982019-06-136.762019-06-131591045279
98992016-06-2710.0102016-06-2723617552152
991002019-05-228.882019-05-221551914882