Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells87
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory78.3 B

Variable types

Numeric5
DateTime4

Dataset

Description고지혈증 환자들이 시행한 혈액 검사 중에 스타틴 약물의 효과를 평가할 수 있는 주요 검사 데이터를 포함하며 검체 채취 일자와 접수 일자를 이용하여 처방시점으로 부터의 기간을 계산한 시점 데이터를 생성함. 검사항목은 LDL Cholesterol, HDL Cholesterol, Total Cholesterol, Triglyceride 등 지질검사항목이 포함됨 - LDL(Low Density Lipoprotein) Cholesterol : 나쁜 콜레스테롤이라고도 불리는 저밀도 지단백 콜레스테롤. 신체 콜레스테롤의 대부분을 차지하며 수치가 높으면 심장질환 및 뇌놀중 위험이 높아짐 - 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 - Total Cholesterol(TC, 총콜레스테롤) : 혈액 내에 있는 모든 콜레스테롤을 뜻함 - Triglyceride(TG, 중성지방) : 대게 인체의 지방조직에 저장되며, 아주 일부만이 혈액 내에 존재합니다. ‘고중성지방혈증’ 또한 고지혈증의 하나이며, 피부와 내장, 혈관 등에 축적되어 비만과 각종 질환을 일으킴
Author가톨릭대학교 은평성모병원
URLhttp://cmcdata.net/data/dataset/main-effect-blood-test-data-dyslipidemia-eunpyeong

Alerts

TC_VAL is highly overall correlated with HDL_VAL and 1 other fieldsHigh correlation
HDL_VAL is highly overall correlated with TC_VALHigh correlation
LDL_VAL is highly overall correlated with TC_VALHigh correlation
TG_DCT has 4 (4.0%) missing valuesMissing
TG_VAL has 4 (4.0%) missing valuesMissing
HDL_DCT has 13 (13.0%) missing valuesMissing
HDL_VAL has 12 (12.0%) missing valuesMissing
LDL_DCT has 27 (27.0%) missing valuesMissing
LDL_VAL has 27 (27.0%) missing valuesMissing
RID has unique valuesUnique

Reproduction

Analysis started2023-10-08 18:56:44.559775
Analysis finished2023-10-08 18:56:52.689314
Duration8.13 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
2023-10-09T03:56:52.825235image/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
2023-10-09T03:56:53.152464image/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%

TC_DCT
Date

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2015-09-01 00:00:00
Maximum2015-10-08 00:00:00
2023-10-09T03:56:53.443770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:53.618104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

TC_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.25
Minimum64
Maximum257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:54.034913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile94.8
Q1117
median147.5
Q3181.5
95-th percentile242.25
Maximum257
Range193
Interquartile range (IQR)64.5

Descriptive statistics

Standard deviation44.502724
Coefficient of variation (CV)0.29423288
Kurtosis-0.36901554
Mean151.25
Median Absolute Deviation (MAD)31.5
Skewness0.52564741
Sum15125
Variance1980.4924
MonotonicityNot monotonic
2023-10-09T03:56:54.359303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165 4
 
4.0%
117 4
 
4.0%
156 3
 
3.0%
125 3
 
3.0%
104 2
 
2.0%
109 2
 
2.0%
105 2
 
2.0%
186 2
 
2.0%
124 2
 
2.0%
191 2
 
2.0%
Other values (62) 74
74.0%
ValueCountFrequency (%)
64 1
1.0%
78 1
1.0%
86 2
2.0%
91 1
1.0%
95 1
1.0%
97 1
1.0%
98 1
1.0%
99 1
1.0%
101 2
2.0%
102 1
1.0%
ValueCountFrequency (%)
257 1
1.0%
254 1
1.0%
252 1
1.0%
249 1
1.0%
247 1
1.0%
242 1
1.0%
228 1
1.0%
224 1
1.0%
222 1
1.0%
217 1
1.0%

TG_DCT
Date

MISSING 

Distinct23
Distinct (%)24.0%
Missing4
Missing (%)4.0%
Memory size932.0 B
Minimum2015-09-01 00:00:00
Maximum2015-10-08 00:00:00
2023-10-09T03:56:54.600561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:54.968906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

TG_VAL
Real number (ℝ)

MISSING 

Distinct78
Distinct (%)81.2%
Missing4
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean138.40625
Minimum22
Maximum758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:55.518356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile59.5
Q186
median113
Q3160
95-th percentile324.75
Maximum758
Range736
Interquartile range (IQR)74

Descriptive statistics

Standard deviation95.364959
Coefficient of variation (CV)0.68902205
Kurtosis18.621674
Mean138.40625
Median Absolute Deviation (MAD)34
Skewness3.5260075
Sum13287
Variance9094.4753
MonotonicityNot monotonic
2023-10-09T03:56:55.797220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106 4
 
4.0%
147 3
 
3.0%
86 2
 
2.0%
160 2
 
2.0%
99 2
 
2.0%
107 2
 
2.0%
116 2
 
2.0%
75 2
 
2.0%
113 2
 
2.0%
67 2
 
2.0%
Other values (68) 73
73.0%
(Missing) 4
 
4.0%
ValueCountFrequency (%)
22 1
1.0%
37 1
1.0%
44 1
1.0%
56 1
1.0%
58 1
1.0%
60 1
1.0%
61 1
1.0%
63 1
1.0%
65 1
1.0%
66 2
2.0%
ValueCountFrequency (%)
758 1
1.0%
378 1
1.0%
360 1
1.0%
352 1
1.0%
342 1
1.0%
319 1
1.0%
317 1
1.0%
245 1
1.0%
238 1
1.0%
214 1
1.0%

HDL_DCT
Date

MISSING 

Distinct22
Distinct (%)25.3%
Missing13
Missing (%)13.0%
Memory size932.0 B
Minimum2015-09-01 00:00:00
Maximum2015-10-08 00:00:00
2023-10-09T03:56:56.067993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:56.296891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

HDL_VAL
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)46.6%
Missing12
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean48.829545
Minimum5
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:56.534319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile31
Q140
median48
Q355.5
95-th percentile73.3
Maximum92
Range87
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation14.132559
Coefficient of variation (CV)0.28942639
Kurtosis1.1421612
Mean48.829545
Median Absolute Deviation (MAD)8
Skewness0.41900126
Sum4297
Variance199.72923
MonotonicityNot monotonic
2023-10-09T03:56:56.817652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
43 5
 
5.0%
51 5
 
5.0%
45 5
 
5.0%
48 5
 
5.0%
49 4
 
4.0%
31 4
 
4.0%
54 3
 
3.0%
35 3
 
3.0%
53 3
 
3.0%
41 3
 
3.0%
Other values (31) 48
48.0%
(Missing) 12
 
12.0%
ValueCountFrequency (%)
5 1
 
1.0%
28 1
 
1.0%
30 2
2.0%
31 4
4.0%
32 3
3.0%
34 1
 
1.0%
35 3
3.0%
36 3
3.0%
37 1
 
1.0%
38 1
 
1.0%
ValueCountFrequency (%)
92 1
1.0%
88 1
1.0%
76 1
1.0%
74 2
2.0%
72 1
1.0%
71 2
2.0%
70 1
1.0%
69 1
1.0%
68 1
1.0%
65 1
1.0%

LDL_DCT
Date

MISSING 

Distinct20
Distinct (%)27.4%
Missing27
Missing (%)27.0%
Memory size932.0 B
Minimum2015-09-01 00:00:00
Maximum2015-10-08 00:00:00
2023-10-09T03:56:57.090540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:57.371319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

LDL_VAL
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct55
Distinct (%)75.3%
Missing27
Missing (%)27.0%
Infinite0
Infinite (%)0.0%
Mean89.917808
Minimum30
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-10-09T03:56:57.729861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile49.6
Q162
median84
Q3111
95-th percentile155.6
Maximum182
Range152
Interquartile range (IQR)49

Descriptive statistics

Standard deviation34.433815
Coefficient of variation (CV)0.38294767
Kurtosis-0.13221148
Mean89.917808
Median Absolute Deviation (MAD)24
Skewness0.70450619
Sum6564
Variance1185.6876
MonotonicityNot monotonic
2023-10-09T03:56:58.507172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 4
 
4.0%
111 3
 
3.0%
63 3
 
3.0%
91 2
 
2.0%
82 2
 
2.0%
72 2
 
2.0%
56 2
 
2.0%
94 2
 
2.0%
112 2
 
2.0%
86 2
 
2.0%
Other values (45) 49
49.0%
(Missing) 27
27.0%
ValueCountFrequency (%)
30 1
1.0%
37 1
1.0%
43 1
1.0%
49 1
1.0%
50 1
1.0%
52 1
1.0%
53 1
1.0%
54 1
1.0%
56 2
2.0%
57 1
1.0%
ValueCountFrequency (%)
182 1
1.0%
173 1
1.0%
161 1
1.0%
158 1
1.0%
154 1
1.0%
150 1
1.0%
148 1
1.0%
138 1
1.0%
137 1
1.0%
133 1
1.0%

Interactions

2023-10-09T03:56:50.038531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:45.278798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:46.532872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.603193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.589812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.253819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:45.448156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:46.702078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.778381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.902570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.569979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:45.629855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:46.999202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.988352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.145651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:50.917864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:45.802674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.265918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.209433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.395627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:51.290341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:45.958332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:47.392167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:48.354064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T03:56:49.620116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-09T03:56:58.684345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDTC_DCTTC_VALTG_DCTTG_VALHDL_DCTHDL_VALLDL_DCTLDL_VAL
RID1.0000.5740.3520.8200.0000.5830.2960.5360.500
TC_DCT0.5741.0000.0001.0000.0001.0000.0001.0000.263
TC_VAL0.3520.0001.0000.0950.4360.0000.5220.0000.931
TG_DCT0.8201.0000.0951.0000.0001.0000.0001.0000.000
TG_VAL0.0000.0000.4360.0001.0000.0000.0480.3670.577
HDL_DCT0.5831.0000.0001.0000.0001.0000.0001.0000.000
HDL_VAL0.2960.0000.5220.0000.0480.0001.0000.2380.583
LDL_DCT0.5361.0000.0001.0000.3671.0000.2381.0000.263
LDL_VAL0.5000.2630.9310.0000.5770.0000.5830.2631.000
2023-10-09T03:56:58.888739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RIDTC_VALTG_VALHDL_VALLDL_VAL
RID1.000-0.0270.097-0.1100.068
TC_VAL-0.0271.0000.4430.6130.882
TG_VAL0.0970.4431.000-0.0150.322
HDL_VAL-0.1100.613-0.0151.0000.324
LDL_VAL0.0680.8820.3220.3241.000

Missing values

2023-10-09T03:56:51.614211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-09T03:56:52.115829image/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.
2023-10-09T03:56:52.524675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

RIDTC_DCTTC_VALTG_DCTTG_VALHDL_DCTHDL_VALLDL_DCTLDL_VAL
012015-09-11T00:00:001652015-09-11T00:00:00372015-09-11T00:00:0088<NA><NA>
122015-09-02T00:00:001472015-09-02T00:00:001932015-09-02T00:00:0050<NA><NA>
232015-09-04T00:00:001352015-09-04T00:00:002072015-09-04T00:00:00482015-09-04T00:00:0062
342015-09-08T00:00:00642015-09-08T00:00:001092015-09-08T00:00:00342015-09-08T00:00:0030
452015-09-11T00:00:001042015-09-11T00:00:00832015-09-11T00:00:00492015-09-11T00:00:0049
562015-10-05T00:00:00782015-10-05T00:00:00852015-10-05T00:00:00362015-10-05T00:00:0043
672015-09-02T00:00:002172015-09-02T00:00:001202015-09-02T00:00:0054<NA><NA>
782015-09-07T00:00:001612015-09-07T00:00:001492015-09-07T00:00:00432015-09-07T00:00:00110
892015-10-06T00:00:001222015-10-06T00:00:00662015-10-06T00:00:0035<NA><NA>
9102015-09-04T00:00:001902015-09-04T00:00:001392015-09-04T00:00:0051<NA><NA>
RIDTC_DCTTC_VALTG_DCTTG_VALHDL_DCTHDL_VALLDL_DCTLDL_VAL
90912015-09-10T00:00:001142015-09-10672015-09-10T00:00:00462015-09-10T00:00:0063
91922015-10-06T00:00:00912015-10-061392015-10-06T00:00:00282015-10-06T00:00:0063
92932015-10-06T00:00:00972015-10-061322015-10-06T00:00:00312015-10-06T00:00:0056
93942015-10-08T00:00:001942015-10-081652015-10-08T00:00:0061<NA><NA>
94952015-09-09T00:00:002522015-09-091622015-09-09T00:00:00572015-09-09T00:00:00173
95962015-09-07T00:00:00992015-09-07982015-09-07T00:00:00482015-09-07T00:00:0050
96972015-09-11T00:00:001012015-09-11582015-09-11T00:00:00452015-09-11T00:00:0060
97982015-10-06T00:00:001372015-10-061912015-10-06T00:00:00412015-10-06T00:00:0084
98992015-10-05T00:00:00982015-10-05602015-10-05T00:00:00412015-10-05T00:00:0060
991002015-09-08T00:00:001562015-09-081082015-09-08T00:00:00452015-09-08T00:00:00120