Overview

Dataset statistics

Number of variables18
Number of observations3672
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory563.1 KiB
Average record size in memory157.0 B

Variable types

Categorical5
Numeric13

Dataset

Description한국인 성별, 연령별, 지역별, 고혈압약 복용자 포함, 복용자 제외 별 수축기/이완기 혈압 참조표준 확장불확도(조건: K =2, 신뢰수준 약 95 %) - 사용된 정보를 기초로 하여, 측정량에 대한 측정값의 분산 특성을 나타내는 음이 아닌 파라미터(측정결과의 불확도추정 및 표현을 위한 지침) - 측정의 한계에서 비롯된 측정값의 불확실한 정도 연령 : 20 ~ 75세 이상(20~24세, 25세~74세까지 2세 단위, 75세 이상 "참조표준" 이란 측정데이터 및 정보의 정확도와 신뢰도를 과학적으로 분석, 평가하여 공인된 것으로 국가사회의 모든 분야에서 널리 지속적으로 사용되거나 반복사용할 수 있도록 마련된 물리화학적 상수, 물성값, 과학기술적 통계 등을 말한다. (국가표준기본법 제3조6항) "한국인 건강지수 참조표준"이란 국가건강검진을 통해 측정, 수집한 데이터를 한국인의 특성에 맞게 지역별, 성별, 연령별로 나타낸 평균, 불확도, 분위수 < 이용시 준수사항 > - 한국인 건강지수 참조표준을 임의로 변경하거나 왜곡하지 않아야 합니다. - 데이터의 출처를 잘못 기재하지 않아야 합니다. - 관련 법률을 침해하거나 위반하지 않는 범위 내에서 활용해야 합니다. - 본 정보는 이용자에게 통보 없이 추가, 변경, 개선, 업데이트될 수 있습니다. - 국민건강보험공단은 본 정보에 포함된 오류, 누락 등 정보의 품질 또는 정보의 활용으로 인한 손해?손실에 대한 책임을 부담하지 않습니다. - 또한, 서비스 장애 등으로 발생한 활용자의 손해에 대한 책임을 지지 않습니다. - 국민건강보험공단은 본 정보를 매개로 제3자와 발생한 분쟁에 대하여 개입할 의무가 없음을 알려드립니다. - 본 자료의 이용에 대한 책임은 전적으로 이용자에게 있음을 알려드립니다.
Author국민건강보험공단
URLhttps://www.data.go.kr/data/15098383/fileData.do

Alerts

측정값평균(mmHg) is highly overall correlated with 1분위수(Percentile) and 10 other fieldsHigh correlation
확장불확도(mmHg) is highly overall correlated with 표준편차(mmHg)High correlation
표준편차(mmHg) is highly overall correlated with 확장불확도(mmHg)High correlation
1분위수(Percentile) is highly overall correlated with 측정값평균(mmHg) and 10 other fieldsHigh correlation
5분위수(Percentile) is highly overall correlated with 측정값평균(mmHg) and 10 other fieldsHigh correlation
10분위(Percentile) is highly overall correlated with 측정값평균(mmHg) and 10 other fieldsHigh correlation
25분위(Percentile) is highly overall correlated with 측정값평균(mmHg) and 10 other fieldsHigh correlation
50분위(Percentile) is highly overall correlated with 측정값평균(mmHg) and 10 other fieldsHigh correlation
75분위(Percentile) is highly overall correlated with 측정값평균(mmHg) and 10 other fieldsHigh correlation
90분위(Percentile) is highly overall correlated with 측정값평균(mmHg) and 10 other fieldsHigh correlation
95분위(Percentile) is highly overall correlated with 측정값평균(mmHg) and 9 other fieldsHigh correlation
99분위(Percentile) is highly overall correlated with 측정값평균(mmHg) and 9 other fieldsHigh correlation
참조표준명 is highly overall correlated with 측정값평균(mmHg) and 9 other fieldsHigh correlation
성별 is highly overall correlated with 측정값평균(mmHg) and 7 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 00:29:49.101676
Analysis finished2023-12-12 00:30:09.021187
Duration19.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

참조표준명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
수축기
1836 
이완기
1836 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수축기
2nd row수축기
3rd row수축기
4th row수축기
5th row수축기

Common Values

ValueCountFrequency (%)
수축기 1836
50.0%
이완기 1836
50.0%

Length

2023-12-12T09:30:09.298435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:09.372707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수축기 1836
50.0%
이완기 1836
50.0%

지역
Categorical

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
전국
 
216
서울
 
216
부산
 
216
대구
 
216
인천
 
216
Other values (12)
2592 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전국
2nd row전국
3rd row전국
4th row전국
5th row전국

Common Values

ValueCountFrequency (%)
전국 216
 
5.9%
서울 216
 
5.9%
부산 216
 
5.9%
대구 216
 
5.9%
인천 216
 
5.9%
광주 216
 
5.9%
대전 216
 
5.9%
울산 216
 
5.9%
경기 216
 
5.9%
강원 216
 
5.9%
Other values (7) 1512
41.2%

Length

2023-12-12T09:30:09.452446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전국 216
 
5.9%
강원 216
 
5.9%
경남 216
 
5.9%
경북 216
 
5.9%
전남 216
 
5.9%
전북 216
 
5.9%
충남 216
 
5.9%
충북 216
 
5.9%
경기 216
 
5.9%
서울 216
 
5.9%
Other values (7) 1512
41.2%

성별
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
남성
1836 
여성
1836 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남성
2nd row남성
3rd row남성
4th row남성
5th row남성

Common Values

ValueCountFrequency (%)
남성 1836
50.0%
여성 1836
50.0%

Length

2023-12-12T09:30:09.543001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:09.631960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남성 1836
50.0%
여성 1836
50.0%

나이(세)
Categorical

Distinct27
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
20~24세
 
136
25~26세
 
136
27~28세
 
136
29~30세
 
136
31~32세
 
136
Other values (22)
2992 

Length

Max length6
Median length6
Mean length5.962963
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20~24세
2nd row25~26세
3rd row27~28세
4th row29~30세
5th row31~32세

Common Values

ValueCountFrequency (%)
20~24세 136
 
3.7%
25~26세 136
 
3.7%
27~28세 136
 
3.7%
29~30세 136
 
3.7%
31~32세 136
 
3.7%
33~34세 136
 
3.7%
35~36세 136
 
3.7%
37~38세 136
 
3.7%
39~40세 136
 
3.7%
41~42세 136
 
3.7%
Other values (17) 2312
63.0%

Length

2023-12-12T09:30:09.718788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20~24세 136
 
3.7%
51~52세 136
 
3.7%
73~74세 136
 
3.7%
71~72세 136
 
3.7%
69~70세 136
 
3.7%
67~68세 136
 
3.7%
65~66세 136
 
3.7%
63~64세 136
 
3.7%
61~62세 136
 
3.7%
59~60세 136
 
3.7%
Other values (17) 2312
63.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
포함
1836 
제외
1836 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포함
2nd row포함
3rd row포함
4th row포함
5th row포함

Common Values

ValueCountFrequency (%)
포함 1836
50.0%
제외 1836
50.0%

Length

2023-12-12T09:30:09.816284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:30:09.906968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포함 1836
50.0%
제외 1836
50.0%

측정값평균(mmHg)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.248911
Minimum68
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:10.002291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile70
Q175
median96
Q3123
95-th percentile128
Maximum136
Range68
Interquartile range (IQR)48

Descriptive statistics

Standard deviation23.703152
Coefficient of variation (CV)0.24125613
Kurtosis-1.8651602
Mean98.248911
Median Absolute Deviation (MAD)23
Skewness0.055065433
Sum360770
Variance561.83942
MonotonicityNot monotonic
2023-12-12T09:30:10.108563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
76 234
 
6.4%
75 215
 
5.9%
74 213
 
5.8%
77 204
 
5.6%
78 197
 
5.4%
123 197
 
5.4%
124 169
 
4.6%
73 148
 
4.0%
122 145
 
3.9%
79 144
 
3.9%
Other values (32) 1806
49.2%
ValueCountFrequency (%)
68 16
 
0.4%
69 97
2.6%
70 106
2.9%
71 74
 
2.0%
72 96
2.6%
73 148
4.0%
74 213
5.8%
75 215
5.9%
76 234
6.4%
77 204
5.6%
ValueCountFrequency (%)
136 2
 
0.1%
135 2
 
0.1%
134 4
 
0.1%
133 9
 
0.2%
132 26
 
0.7%
131 27
 
0.7%
130 43
1.2%
129 43
1.2%
128 61
1.7%
127 98
2.7%

확장불확도(mmHg)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.787037
Minimum26
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:10.210031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile29
Q131
median33
Q334
95-th percentile37
Maximum41
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3936641
Coefficient of variation (CV)0.073006416
Kurtosis-0.12762482
Mean32.787037
Median Absolute Deviation (MAD)2
Skewness-0.02906397
Sum120394
Variance5.7296276
MonotonicityNot monotonic
2023-12-12T09:30:10.308503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
33 643
17.5%
32 500
13.6%
31 493
13.4%
34 490
13.3%
35 421
11.5%
30 374
10.2%
36 280
7.6%
29 143
 
3.9%
37 131
 
3.6%
27 58
 
1.6%
Other values (6) 139
 
3.8%
ValueCountFrequency (%)
26 14
 
0.4%
27 58
 
1.6%
28 51
 
1.4%
29 143
 
3.9%
30 374
10.2%
31 493
13.4%
32 500
13.6%
33 643
17.5%
34 490
13.3%
35 421
11.5%
ValueCountFrequency (%)
41 2
 
0.1%
40 5
 
0.1%
39 17
 
0.5%
38 50
 
1.4%
37 131
 
3.6%
36 280
7.6%
35 421
11.5%
34 490
13.3%
33 643
17.5%
32 500
13.6%

측정수(건)
Real number (ℝ)

Distinct1745
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21168.078
Minimum375
Maximum362523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:10.468426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum375
5-th percentile1537.15
Q14639.5
median7480
Q313614.5
95-th percentile107371.3
Maximum362523
Range362148
Interquartile range (IQR)8975

Descriptive statistics

Standard deviation45056.687
Coefficient of variation (CV)2.1285205
Kurtosis18.009537
Mean21168.078
Median Absolute Deviation (MAD)3569.5
Skewness4.1434533
Sum77729182
Variance2.030105 × 109
MonotonicityNot monotonic
2023-12-12T09:30:10.593801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4335 8
 
0.2%
7052 6
 
0.2%
5399 6
 
0.2%
4462 6
 
0.2%
7965 6
 
0.2%
3637 4
 
0.1%
6980 4
 
0.1%
5180 4
 
0.1%
5870 4
 
0.1%
9018 4
 
0.1%
Other values (1735) 3620
98.6%
ValueCountFrequency (%)
375 2
0.1%
425 2
0.1%
434 2
0.1%
446 2
0.1%
473 2
0.1%
511 2
0.1%
554 2
0.1%
575 2
0.1%
579 2
0.1%
592 2
0.1%
ValueCountFrequency (%)
362523 2
0.1%
305246 2
0.1%
294895 2
0.1%
292911 2
0.1%
289316 2
0.1%
288481 2
0.1%
283850 2
0.1%
283721 2
0.1%
282902 2
0.1%
281816 2
0.1%

표준편차(mmHg)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.963508
Minimum8
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:10.723864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11
Q112
median13
Q314
95-th percentile15
Maximum18
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5055613
Coefficient of variation (CV)0.11613842
Kurtosis-0.074559243
Mean12.963508
Median Absolute Deviation (MAD)1
Skewness-0.10996226
Sum47602
Variance2.2667148
MonotonicityNot monotonic
2023-12-12T09:30:10.838687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
13 961
26.2%
14 769
20.9%
12 760
20.7%
11 452
12.3%
15 441
12.0%
16 109
 
3.0%
10 96
 
2.6%
9 60
 
1.6%
17 20
 
0.5%
18 2
 
0.1%
ValueCountFrequency (%)
8 2
 
0.1%
9 60
 
1.6%
10 96
 
2.6%
11 452
12.3%
12 760
20.7%
13 961
26.2%
14 769
20.9%
15 441
12.0%
16 109
 
3.0%
17 20
 
0.5%
ValueCountFrequency (%)
18 2
 
0.1%
17 20
 
0.5%
16 109
 
3.0%
15 441
12.0%
14 769
20.9%
13 961
26.2%
12 760
20.7%
11 452
12.3%
10 96
 
2.6%
9 60
 
1.6%

1분위수(Percentile)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.033769
Minimum51
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:10.952586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile53
Q156
median74.5
Q394
95-th percentile99
Maximum102
Range51
Interquartile range (IQR)38

Descriptive statistics

Standard deviation19.292412
Coefficient of variation (CV)0.2571164
Kurtosis-1.9002355
Mean75.033769
Median Absolute Deviation (MAD)19.5
Skewness0.02304769
Sum275524
Variance372.19717
MonotonicityNot monotonic
2023-12-12T09:30:11.089162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
90 562
15.3%
55 256
 
7.0%
54 234
 
6.4%
60 220
 
6.0%
56 205
 
5.6%
58 201
 
5.5%
98 197
 
5.4%
53 195
 
5.3%
59 192
 
5.2%
96 178
 
4.8%
Other values (14) 1232
33.6%
ValueCountFrequency (%)
51 15
 
0.4%
52 162
4.4%
53 195
5.3%
54 234
6.4%
55 256
7.0%
56 205
5.6%
57 156
4.2%
58 201
5.5%
59 192
5.2%
60 220
6.0%
ValueCountFrequency (%)
102 2
 
0.1%
101 2
 
0.1%
100 117
3.2%
99 87
2.4%
98 197
5.4%
97 162
4.4%
96 178
4.8%
95 146
4.0%
94 132
3.6%
93 56
 
1.5%

5분위수(Percentile)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.735022
Minimum56
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:11.244870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile58
Q160
median78.5
Q3101
95-th percentile106
Maximum110
Range54
Interquartile range (IQR)41

Descriptive statistics

Standard deviation20.487661
Coefficient of variation (CV)0.25376424
Kurtosis-1.9080787
Mean80.735022
Median Absolute Deviation (MAD)18.5
Skewness0.051144031
Sum296459
Variance419.74426
MonotonicityNot monotonic
2023-12-12T09:30:11.395197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
60 828
22.5%
100 307
 
8.4%
62 220
 
6.0%
104 206
 
5.6%
61 170
 
4.6%
102 165
 
4.5%
59 161
 
4.4%
63 154
 
4.2%
103 136
 
3.7%
105 126
 
3.4%
Other values (21) 1199
32.7%
ValueCountFrequency (%)
56 16
 
0.4%
57 59
 
1.6%
58 113
 
3.1%
59 161
 
4.4%
60 828
22.5%
61 170
 
4.6%
62 220
 
6.0%
63 154
 
4.2%
64 98
 
2.7%
65 13
 
0.4%
ValueCountFrequency (%)
110 12
 
0.3%
109 17
 
0.5%
108 58
 
1.6%
107 60
 
1.6%
106 118
3.2%
105 126
3.4%
104 206
5.6%
103 136
3.7%
102 165
4.5%
101 79
 
2.2%

10분위(Percentile)
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.566721
Minimum59
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:11.509895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile60
Q163
median83
Q3108
95-th percentile110
Maximum115
Range56
Interquartile range (IQR)45

Descriptive statistics

Standard deviation21.569079
Coefficient of variation (CV)0.25505399
Kurtosis-1.8903751
Mean84.566721
Median Absolute Deviation (MAD)21
Skewness0.041651879
Sum310529
Variance465.22518
MonotonicityNot monotonic
2023-12-12T09:30:11.657190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
60 456
 
12.4%
110 449
 
12.2%
100 261
 
7.1%
108 218
 
5.9%
65 206
 
5.6%
62 202
 
5.5%
64 196
 
5.3%
109 179
 
4.9%
66 165
 
4.5%
63 165
 
4.5%
Other values (22) 1175
32.0%
ValueCountFrequency (%)
59 10
 
0.3%
60 456
12.4%
61 131
 
3.6%
62 202
5.5%
63 165
 
4.5%
64 196
5.3%
65 206
5.6%
66 165
 
4.5%
67 157
 
4.3%
68 132
 
3.6%
ValueCountFrequency (%)
115 2
 
0.1%
114 10
 
0.3%
113 15
 
0.4%
112 50
 
1.4%
111 36
 
1.0%
110 449
12.2%
109 179
 
4.9%
108 218
5.9%
107 84
 
2.3%
106 90
 
2.5%

25분위(Percentile)
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.6378
Minimum61
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:11.795782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile64
Q170
median88.5
Q3114
95-th percentile119
Maximum125
Range64
Interquartile range (IQR)44

Descriptive statistics

Standard deviation22.474843
Coefficient of variation (CV)0.24796325
Kurtosis-1.868655
Mean90.6378
Median Absolute Deviation (MAD)21.5
Skewness0.050661658
Sum332822
Variance505.11857
MonotonicityNot monotonic
2023-12-12T09:30:11.944977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
70 629
 
17.1%
116 183
 
5.0%
118 182
 
5.0%
68 179
 
4.9%
115 170
 
4.6%
114 165
 
4.5%
110 158
 
4.3%
72 147
 
4.0%
69 130
 
3.5%
71 121
 
3.3%
Other values (28) 1608
43.8%
ValueCountFrequency (%)
61 5
 
0.1%
62 51
 
1.4%
63 115
 
3.1%
64 103
 
2.8%
65 77
 
2.1%
66 87
 
2.4%
67 92
 
2.5%
68 179
 
4.9%
69 130
 
3.5%
70 629
17.1%
ValueCountFrequency (%)
125 1
 
< 0.1%
124 4
 
0.1%
122 20
 
0.5%
121 16
 
0.4%
120 89
2.4%
119 62
 
1.7%
118 182
5.0%
117 100
2.7%
116 183
5.0%
115 170
4.6%

50분위(Percentile)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.911492
Minimum68
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:12.088219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile70
Q175
median96
Q3121
95-th percentile129
Maximum135
Range67
Interquartile range (IQR)46

Descriptive statistics

Standard deviation23.474905
Coefficient of variation (CV)0.23975638
Kurtosis-1.8394773
Mean97.911492
Median Absolute Deviation (MAD)23
Skewness0.061370625
Sum359531
Variance551.07116
MonotonicityNot monotonic
2023-12-12T09:30:12.229005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
78 304
 
8.3%
120 254
 
6.9%
70 227
 
6.2%
76 186
 
5.1%
74 186
 
5.1%
80 148
 
4.0%
75 147
 
4.0%
122 141
 
3.8%
110 134
 
3.6%
77 133
 
3.6%
Other values (31) 1812
49.3%
ValueCountFrequency (%)
68 23
 
0.6%
69 70
 
1.9%
70 227
6.2%
71 67
 
1.8%
72 106
2.9%
73 115
3.1%
74 186
5.1%
75 147
4.0%
76 186
5.1%
77 133
3.6%
ValueCountFrequency (%)
135 3
 
0.1%
134 1
 
< 0.1%
133 8
 
0.2%
132 24
 
0.7%
131 16
 
0.4%
130 95
2.6%
129 38
 
1.0%
128 88
2.4%
127 62
1.7%
126 102
2.8%

75분위(Percentile)
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.32516
Minimum73
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:12.370661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile76
Q180
median102.5
Q3130
95-th percentile138
Maximum148
Range75
Interquartile range (IQR)50

Descriptive statistics

Standard deviation25.026413
Coefficient of variation (CV)0.23761096
Kurtosis-1.8544468
Mean105.32516
Median Absolute Deviation (MAD)23.5
Skewness0.067132556
Sum386754
Variance626.32137
MonotonicityNot monotonic
2023-12-12T09:30:12.528711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
80 522
 
14.2%
130 333
 
9.1%
84 193
 
5.3%
82 182
 
5.0%
132 145
 
3.9%
81 137
 
3.7%
83 123
 
3.3%
85 121
 
3.3%
135 111
 
3.0%
134 110
 
3.0%
Other values (37) 1695
46.2%
ValueCountFrequency (%)
73 13
 
0.4%
74 53
 
1.4%
75 81
 
2.2%
76 86
 
2.3%
77 35
 
1.0%
78 98
 
2.7%
79 71
 
1.9%
80 522
14.2%
81 137
 
3.7%
82 182
 
5.0%
ValueCountFrequency (%)
148 1
 
< 0.1%
146 1
 
< 0.1%
145 3
 
0.1%
144 1
 
< 0.1%
143 5
 
0.1%
142 11
 
0.3%
141 16
 
0.4%
140 50
1.4%
139 39
1.1%
138 67
1.8%

90분위(Percentile)
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.13943
Minimum79
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:12.691928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile81
Q188
median109
Q3138
95-th percentile147
Maximum160
Range81
Interquartile range (IQR)50

Descriptive statistics

Standard deviation26.188661
Coefficient of variation (CV)0.23353659
Kurtosis-1.8376927
Mean112.13943
Median Absolute Deviation (MAD)25
Skewness0.072309813
Sum411776
Variance685.84598
MonotonicityNot monotonic
2023-12-12T09:30:12.849585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 319
 
8.7%
138 295
 
8.0%
89 292
 
8.0%
90 228
 
6.2%
139 205
 
5.6%
85 177
 
4.8%
86 174
 
4.7%
140 171
 
4.7%
80 142
 
3.9%
84 108
 
2.9%
Other values (46) 1561
42.5%
ValueCountFrequency (%)
79 18
 
0.5%
80 142
3.9%
81 58
 
1.6%
82 81
 
2.2%
83 52
 
1.4%
84 108
 
2.9%
85 177
4.8%
86 174
4.7%
87 100
 
2.7%
88 319
8.7%
ValueCountFrequency (%)
160 1
 
< 0.1%
158 1
 
< 0.1%
157 1
 
< 0.1%
156 2
 
0.1%
155 5
 
0.1%
154 3
 
0.1%
153 8
 
0.2%
152 12
 
0.3%
151 16
 
0.4%
150 64
1.7%

95분위(Percentile)
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.71678
Minimum80
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:13.022988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile85
Q190
median114.5
Q3143
95-th percentile153
Maximum168
Range88
Interquartile range (IQR)53

Descriptive statistics

Standard deviation27.253691
Coefficient of variation (CV)0.23350278
Kurtosis-1.8088494
Mean116.71678
Median Absolute Deviation (MAD)25.5
Skewness0.085575356
Sum428584
Variance742.76367
MonotonicityNot monotonic
2023-12-12T09:30:13.181001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 363
 
9.9%
89 238
 
6.5%
88 216
 
5.9%
150 164
 
4.5%
139 156
 
4.2%
140 139
 
3.8%
92 125
 
3.4%
138 118
 
3.2%
94 111
 
3.0%
93 104
 
2.8%
Other values (50) 1938
52.8%
ValueCountFrequency (%)
80 14
 
0.4%
81 7
 
0.2%
82 27
 
0.7%
83 33
 
0.9%
84 62
 
1.7%
85 64
 
1.7%
86 84
 
2.3%
87 43
 
1.2%
88 216
5.9%
89 238
6.5%
ValueCountFrequency (%)
168 1
 
< 0.1%
166 1
 
< 0.1%
163 2
 
0.1%
162 3
 
0.1%
161 2
 
0.1%
160 23
0.6%
159 8
 
0.2%
158 27
0.7%
157 16
0.4%
156 25
0.7%

99분위(Percentile)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.89542
Minimum87
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2023-12-12T09:30:13.341652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum87
5-th percentile92
Q1100
median125
Q3158
95-th percentile168
Maximum186
Range99
Interquartile range (IQR)58

Descriptive statistics

Standard deviation29.706518
Coefficient of variation (CV)0.23227194
Kurtosis-1.7831645
Mean127.89542
Median Absolute Deviation (MAD)28
Skewness0.090789458
Sum469632
Variance882.47721
MonotonicityNot monotonic
2023-12-12T09:30:13.480095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 535
 
14.6%
160 278
 
7.6%
150 128
 
3.5%
102 125
 
3.4%
98 123
 
3.3%
96 109
 
3.0%
156 99
 
2.7%
158 96
 
2.6%
97 87
 
2.4%
101 80
 
2.2%
Other values (65) 2012
54.8%
ValueCountFrequency (%)
87 1
 
< 0.1%
88 21
 
0.6%
89 66
1.8%
90 73
2.0%
91 16
 
0.4%
92 57
1.6%
93 26
 
0.7%
94 46
1.3%
95 47
1.3%
96 109
3.0%
ValueCountFrequency (%)
186 1
 
< 0.1%
184 1
 
< 0.1%
181 1
 
< 0.1%
179 3
 
0.1%
178 5
0.1%
177 5
0.1%
176 8
0.2%
175 6
0.2%
174 9
0.2%
173 8
0.2%

Interactions

2023-12-12T09:30:07.673166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:51.176895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:52.546759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:54.098444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:55.471996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:56.784591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:58.206573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:59.502259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:00.970569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:02.511301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:04.042649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.636740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.688796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.747020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:51.279112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:52.654599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:54.212813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:55.583355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:56.885483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:58.300908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:59.594547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:01.096774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:02.649251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:04.419605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.720288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.761833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.822743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:51.386049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:52.798411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:54.320193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:55.695671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:56.977441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:58.398854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:59.731667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:01.246819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:02.769361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:04.549359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.805548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.838944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.908086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:51.507672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:52.921782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:54.447432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:55.805078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:57.063436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:58.506796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:59.857183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:01.378206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:02.898769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:04.693791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.888604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.917242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.994502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:51.620012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:53.041902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:54.539262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:55.893638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:57.145027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:58.605832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:59.977140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:01.468094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:03.025850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:04.838262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.963928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.993074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:08.086697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:51.717811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:53.145369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:54.627628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:55.993551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:57.471372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:58.764361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:00.092068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:01.576899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:03.146410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:04.948563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.039951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.069028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:08.160521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:51.811281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:53.247670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:54.730175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:56.087858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:57.567542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:58.874716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:00.184467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:01.678469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:03.258234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.045826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.123801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.140039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:08.227968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:51.908766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:53.357302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:54.847318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:56.180485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:57.664089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:58.958566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:00.290526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:01.778996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:03.354417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.137717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.200333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.224724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:08.301889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:52.054491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:53.464089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:54.967834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:56.298955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:57.748123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:59.046922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:00.390534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:01.890865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:03.493957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.230564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.282199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.305609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:08.376061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:52.150358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:53.585326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:55.071508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:56.417816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:57.840113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:59.142901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:00.483737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:02.027606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:03.614805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.319349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.360533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.380418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:08.454767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:52.247967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:53.699951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:55.186174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:56.524314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:57.933068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:59.238235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:00.589380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:02.170003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:03.723745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.407895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.442948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.460658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:08.535342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:52.356995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:53.860697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:55.286825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:56.628572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:58.049257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:59.333906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:00.711260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:02.302279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:03.843812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.489611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.526079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.539721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:08.608772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:52.457178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:53.982826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:55.377385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:56.706217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:58.127604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:59.413482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:00.850277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:02.413111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:03.940811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:05.562670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:06.610155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:30:07.607591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:30:13.580949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
참조표준명지역성별나이(세)고혈압약복용자포함여부측정값평균(mmHg)확장불확도(mmHg)측정수(건)표준편차(mmHg)1분위수(Percentile)5분위수(Percentile)10분위(Percentile)25분위(Percentile)50분위(Percentile)75분위(Percentile)90분위(Percentile)95분위(Percentile)99분위(Percentile)
참조표준명1.0000.0000.0000.0000.0001.0000.2920.0000.1921.0001.0001.0001.0001.0001.0001.0001.0001.000
지역0.0001.0000.0000.0000.0000.2460.5570.7210.5340.2430.3720.2810.2780.2530.3400.2430.2900.272
성별0.0000.0001.0000.0000.0000.5530.2570.0240.2850.5960.8130.8740.5900.5450.6940.6790.5670.429
나이(세)0.0000.0000.0001.0000.0000.5400.6860.2340.6990.3780.4310.4780.5410.5460.5420.5590.6010.621
고혈압약복용자포함여부0.0000.0000.0000.0001.0000.2140.5090.1590.4940.0930.2190.2480.1760.2000.2890.3720.3370.293
측정값평균(mmHg)1.0000.2460.5530.5400.2141.0000.5280.0630.4750.8170.8240.8400.9780.9940.9040.8840.8710.859
확장불확도(mmHg)0.2920.5570.2570.6860.5090.5281.0000.2030.9760.4180.3740.4010.4850.5220.5450.6130.6610.659
측정수(건)0.0000.7210.0240.2340.1590.0630.2031.0000.2040.0400.0640.0810.0680.0810.0550.1120.1500.127
표준편차(mmHg)0.1920.5340.2850.6990.4940.4750.9760.2041.0000.3940.3330.3760.4600.4800.5170.5770.6290.627
1분위수(Percentile)1.0000.2430.5960.3780.0930.8170.4180.0400.3941.0000.8660.8800.8250.8150.8200.8120.7860.806
5분위수(Percentile)1.0000.3720.8130.4310.2190.8240.3740.0640.3330.8661.0000.9830.8330.8310.8620.8480.8240.857
10분위(Percentile)1.0000.2810.8740.4780.2480.8400.4010.0810.3760.8800.9831.0000.8530.8480.8910.8720.8410.868
25분위(Percentile)1.0000.2780.5900.5410.1760.9780.4850.0680.4600.8250.8330.8531.0000.9780.8680.8720.8810.853
50분위(Percentile)1.0000.2530.5450.5460.2000.9940.5220.0810.4800.8150.8310.8480.9781.0000.8880.9130.8750.863
75분위(Percentile)1.0000.3400.6940.5420.2890.9040.5450.0550.5170.8200.8620.8910.8680.8881.0000.9820.9650.850
90분위(Percentile)1.0000.2430.6790.5590.3720.8840.6130.1120.5770.8120.8480.8720.8720.9130.9821.0000.9840.876
95분위(Percentile)1.0000.2900.5670.6010.3370.8710.6610.1500.6290.7860.8240.8410.8810.8750.9650.9841.0000.928
99분위(Percentile)1.0000.2720.4290.6210.2930.8590.6590.1270.6270.8060.8570.8680.8530.8630.8500.8760.9281.000
2023-12-12T09:30:14.093607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나이(세)성별고혈압약복용자포함여부참조표준명지역
나이(세)1.0000.0000.0000.0000.000
성별0.0001.0000.0000.0000.000
고혈압약복용자포함여부0.0000.0001.0000.0000.000
참조표준명0.0000.0000.0001.0000.000
지역0.0000.0000.0000.0001.000
2023-12-12T09:30:14.202139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정값평균(mmHg)확장불확도(mmHg)측정수(건)표준편차(mmHg)1분위수(Percentile)5분위수(Percentile)10분위(Percentile)25분위(Percentile)50분위(Percentile)75분위(Percentile)90분위(Percentile)95분위(Percentile)99분위(Percentile)참조표준명지역성별나이(세)고혈압약복용자포함여부
측정값평균(mmHg)1.0000.3890.0040.2960.9470.9760.9850.9920.9960.9930.9880.9830.9710.9990.1130.5940.2580.229
확장불확도(mmHg)0.3891.0000.0430.9650.2050.2870.2930.3470.3780.4030.4470.4600.4340.2270.2350.2230.3250.383
측정수(건)0.0040.0431.0000.0370.0010.003-0.002-0.012-0.006-0.0020.0130.0250.0390.0000.3810.0180.0860.122
표준편차(mmHg)0.2960.9650.0371.0000.1130.1930.2010.2550.2860.3100.3540.3660.3390.1470.2420.2190.3430.381
1분위수(Percentile)0.9470.2050.0010.1131.0000.9690.9710.9570.9440.9310.9160.9070.9021.0000.1270.7190.1920.114
5분위수(Percentile)0.9760.2870.0030.1930.9691.0000.9820.9790.9740.9680.9540.9490.9400.9990.1850.6170.2040.157
10분위(Percentile)0.9850.293-0.0020.2010.9710.9821.0000.9870.9830.9750.9630.9560.9460.9990.1360.6850.2320.179
25분위(Percentile)0.9920.347-0.0120.2550.9570.9790.9871.0000.9910.9850.9760.9690.9570.9990.1290.6340.2590.188
50분위(Percentile)0.9960.378-0.0060.2860.9440.9740.9830.9911.0000.9920.9870.9800.9670.9990.1170.5850.2620.214
75분위(Percentile)0.9930.403-0.0020.3100.9310.9680.9750.9850.9921.0000.9910.9870.9730.9990.1450.5290.2700.214
90분위(Percentile)0.9880.4470.0130.3540.9160.9540.9630.9760.9870.9911.0000.9930.9810.9990.1040.5160.2610.279
95분위(Percentile)0.9830.4600.0250.3660.9070.9490.9560.9690.9800.9870.9931.0000.9870.9990.1260.4280.2880.253
99분위(Percentile)0.9710.4340.0390.3390.9020.9400.9460.9570.9670.9730.9810.9871.0000.9990.1120.4290.2560.292
참조표준명0.9990.2270.0000.1471.0000.9990.9990.9990.9990.9990.9990.9990.9991.0000.0000.0000.0000.000
지역0.1130.2350.3810.2420.1270.1850.1360.1290.1170.1450.1040.1260.1120.0001.0000.0000.0000.000
성별0.5940.2230.0180.2190.7190.6170.6850.6340.5850.5290.5160.4280.4290.0000.0001.0000.0000.000
나이(세)0.2580.3250.0860.3430.1920.2040.2320.2590.2620.2700.2610.2880.2560.0000.0000.0001.0000.000
고혈압약복용자포함여부0.2290.3830.1220.3810.1140.1570.1790.1880.2140.2140.2790.2530.2920.0000.0000.0000.0001.000

Missing values

2023-12-12T09:30:08.738004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:30:08.933777image/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

참조표준명지역성별나이(세)고혈압약복용자포함여부측정값평균(mmHg)확장불확도(mmHg)측정수(건)표준편차(mmHg)1분위수(Percentile)5분위수(Percentile)10분위(Percentile)25분위(Percentile)50분위(Percentile)75분위(Percentile)90분위(Percentile)95분위(Percentile)99분위(Percentile)
0수축기전국남성20~24세포함120311403801295100106111119128135139150
1수축기전국남성25~26세포함121311390401296102108113120130136139152
2수축기전국남성27~28세포함122311827581297103108114120130137139153
3수축기전국남성29~30세포함122321940111298103109114120130138140155
4수축기전국남성31~32세포함123322002751298104109115121130138142158
5수축기전국남성33~34세포함123322132391298104110115122130138143159
6수축기전국남성35~36세포함124332392091398105110116122131138144160
7수축기전국남성37~38세포함124332601451399105110116123132139145160
8수축기전국남성39~40세포함124332929111398104110116123132139146160
9수축기전국남성41~42세포함124332647331398104110116123132139146160
참조표준명지역성별나이(세)고혈압약복용자포함여부측정값평균(mmHg)확장불확도(mmHg)측정수(건)표준편차(mmHg)1분위수(Percentile)5분위수(Percentile)10분위(Percentile)25분위(Percentile)50분위(Percentile)75분위(Percentile)90분위(Percentile)95분위(Percentile)99분위(Percentile)
3662이완기제주여성57~58세제외7434212114546062687481879099
3663이완기제주여성59~60세제외7534190614556063687582879098
3664이완기제주여성61~62세제외7536160815556062687481879098
3665이완기제주여성63~64세제외7534140414556062687581878998
3666이완기제주여성65~66세제외753494614566062687582879097
3667이완기제주여성67~68세제외753482514566163697580869098
3668이완기제주여성69~70세제외753467714576063697581879099
3669이완기제주여성71~72세제외753659215546062687481879098
3670이완기제주여성73~74세제외753342514576064707480858896
3671이완기제주여성75세이상제외7435126115526060697480869095