Overview

Dataset statistics

Number of variables11
Number of observations29
Missing cells65
Missing cells (%)20.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory101.6 B

Variable types

Text2
Numeric9

Dataset

Description교육부_학교건강검사 표본조사결과(건강조사) 결과 데이터 입니다. 해당 데이터는 영역,지표,학교급별(성별) 데이터 집계 결과를 제공합니다.
Author교육부
URLhttps://www.data.go.kr/data/15051012/fileData.do

Alerts

남자_초 is highly overall correlated with 여자_초 and 7 other fieldsHigh correlation
여자_초 is highly overall correlated with 남자_초 and 7 other fieldsHigh correlation
계_초 is highly overall correlated with 남자_초 and 7 other fieldsHigh correlation
남자_중 is highly overall correlated with 남자_초 and 7 other fieldsHigh correlation
여자_중 is highly overall correlated with 남자_초 and 7 other fieldsHigh correlation
계_중 is highly overall correlated with 남자_초 and 7 other fieldsHigh correlation
남자_고 is highly overall correlated with 남자_초 and 7 other fieldsHigh correlation
여자_고 is highly overall correlated with 남자_초 and 7 other fieldsHigh correlation
계_고 is highly overall correlated with 남자_초 and 7 other fieldsHigh correlation
영역 has 1 (3.4%) missing valuesMissing
지표 has 1 (3.4%) missing valuesMissing
남자_초 has 5 (17.2%) missing valuesMissing
여자_초 has 5 (17.2%) missing valuesMissing
계_초 has 5 (17.2%) missing valuesMissing
남자_중 has 8 (27.6%) missing valuesMissing
여자_중 has 8 (27.6%) missing valuesMissing
계_중 has 8 (27.6%) missing valuesMissing
남자_고 has 8 (27.6%) missing valuesMissing
여자_고 has 8 (27.6%) missing valuesMissing
계_고 has 8 (27.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 17:42:17.183276
Analysis finished2023-12-12 17:42:24.223061
Duration7.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영역
Text

MISSING 

Distinct14
Distinct (%)50.0%
Missing1
Missing (%)3.4%
Memory size364.0 B
2023-12-13T02:42:24.315216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.6785714
Min length2

Characters and Unicode

Total characters75
Distinct characters37
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)17.9%

Sample

1st row영양
2nd row섭취
3rd row섭취
4th row식습관
5th row식습관
ValueCountFrequency (%)
식습관 6
20.7%
안전 3
10.3%
섭취 2
 
6.9%
운동 2
 
6.9%
위생 2
 
6.9%
인터넷 2
 
6.9%
음주 2
 
6.9%
정서 2
 
6.9%
1학년만 2
 
6.9%
영양 1
 
3.4%
Other values (5) 5
17.2%
2023-12-13T02:42:24.615459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 6
 
8.0%
관 6
 
8.0%
습 6
 
8.0%
안 3
 
4.0%
전 3
 
4.0%
만 3
 
4.0%
음 2
 
2.7%
2
 
2.7%
년 2
 
2.7%
학 2
 
2.7%
Other values (27) 40
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
90.7%
Space Separator 2
 
2.7%
Decimal Number 2
 
2.7%
Uppercase Letter 2
 
2.7%
Other Punctuation 1
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 6
 
8.8%
관 6
 
8.8%
습 6
 
8.8%
안 3
 
4.4%
전 3
 
4.4%
만 3
 
4.4%
음 2
 
2.9%
년 2
 
2.9%
학 2
 
2.9%
서 2
 
2.9%
Other values (22) 33
48.5%
Uppercase Letter
ValueCountFrequency (%)
V 1
50.0%
T 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Decimal Number
ValueCountFrequency (%)
1 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
90.7%
Common 5
 
6.7%
Latin 2
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 6
 
8.8%
관 6
 
8.8%
습 6
 
8.8%
안 3
 
4.4%
전 3
 
4.4%
만 3
 
4.4%
음 2
 
2.9%
년 2
 
2.9%
학 2
 
2.9%
서 2
 
2.9%
Other values (22) 33
48.5%
Common
ValueCountFrequency (%)
2
40.0%
1 2
40.0%
, 1
20.0%
Latin
ValueCountFrequency (%)
V 1
50.0%
T 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
90.7%
ASCII 7
 
9.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 6
 
8.8%
관 6
 
8.8%
습 6
 
8.8%
안 3
 
4.4%
전 3
 
4.4%
만 3
 
4.4%
음 2
 
2.9%
년 2
 
2.9%
학 2
 
2.9%
서 2
 
2.9%
Other values (22) 33
48.5%
ASCII
ValueCountFrequency (%)
2
28.6%
1 2
28.6%
V 1
14.3%
T 1
14.3%
, 1
14.3%

지표
Text

MISSING 

Distinct28
Distinct (%)100.0%
Missing1
Missing (%)3.4%
Memory size364.0 B
2023-12-13T02:42:24.861483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length12.321429
Min length4

Characters and Unicode

Total characters345
Distinct characters133
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row주1회 이상 라면 섭취율
2nd row주1회 이상 음료수 섭취율
3rd row주1회 이상 패스트푸드 섭취율
4th row육류 먹지 않는 비율
5th row우유 유제품 매일 섭취율
ValueCountFrequency (%)
이상 8
 
8.1%
비율 6
 
6.1%
섭취율 6
 
6.1%
하루 4
 
4.0%
주1회 3
 
3.0%
매일 3
 
3.0%
실천율 2
 
2.0%
가족내 2
 
2.0%
격렬한 2
 
2.0%
신체활동 2
 
2.0%
Other values (60) 61
61.6%
2023-12-13T02:42:25.256602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
 
20.6%
율 19
 
5.5%
이 15
 
4.3%
상 10
 
2.9%
주 7
 
2.0%
비 6
 
1.7%
섭 6
 
1.7%
취 6
 
1.7%
일 6
 
1.7%
시 5
 
1.4%
Other values (123) 194
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 262
75.9%
Space Separator 71
 
20.6%
Decimal Number 8
 
2.3%
Uppercase Letter 2
 
0.6%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
율 19
 
7.3%
이 15
 
5.7%
상 10
 
3.8%
주 7
 
2.7%
비 6
 
2.3%
섭 6
 
2.3%
취 6
 
2.3%
일 6
 
2.3%
시 5
 
1.9%
과 5
 
1.9%
Other values (113) 177
67.6%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
2 2
25.0%
3 1
 
12.5%
5 1
 
12.5%
6 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
V 1
50.0%
Space Separator
ValueCountFrequency (%)
71
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 262
75.9%
Common 81
 
23.5%
Latin 2
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
율 19
 
7.3%
이 15
 
5.7%
상 10
 
3.8%
주 7
 
2.7%
비 6
 
2.3%
섭 6
 
2.3%
취 6
 
2.3%
일 6
 
2.3%
시 5
 
1.9%
과 5
 
1.9%
Other values (113) 177
67.6%
Common
ValueCountFrequency (%)
71
87.7%
1 3
 
3.7%
2 2
 
2.5%
) 1
 
1.2%
( 1
 
1.2%
3 1
 
1.2%
5 1
 
1.2%
6 1
 
1.2%
Latin
ValueCountFrequency (%)
T 1
50.0%
V 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 262
75.9%
ASCII 83
 
24.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
85.5%
1 3
 
3.6%
2 2
 
2.4%
T 1
 
1.2%
V 1
 
1.2%
) 1
 
1.2%
( 1
 
1.2%
3 1
 
1.2%
5 1
 
1.2%
6 1
 
1.2%
Hangul
ValueCountFrequency (%)
율 19
 
7.3%
이 15
 
5.7%
상 10
 
3.8%
주 7
 
2.7%
비 6
 
2.3%
섭 6
 
2.3%
취 6
 
2.3%
일 6
 
2.3%
시 5
 
1.9%
과 5
 
1.9%
Other values (113) 177
67.6%

남자_초
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing5
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean37.305
Minimum0.43
Maximum92.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T02:42:25.705735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.43
5-th percentile1.831
Q18.465
median31.57
Q368.5775
95-th percentile89.002
Maximum92.35
Range91.92
Interquartile range (IQR)60.1125

Descriptive statistics

Standard deviation32.385642
Coefficient of variation (CV)0.8681314
Kurtosis-1.353655
Mean37.305
Median Absolute Deviation (MAD)26.695
Skewness0.44599951
Sum895.32
Variance1048.8298
MonotonicityNot monotonic
2023-12-13T02:42:25.823877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
92.35 1
 
3.4%
9.72 1
 
3.4%
9.51 1
 
3.4%
5.33 1
 
3.4%
2.63 1
 
3.4%
14.88 1
 
3.4%
38.47 1
 
3.4%
24.89 1
 
3.4%
33.65 1
 
3.4%
55.36 1
 
3.4%
Other values (14) 14
48.3%
(Missing) 5
 
17.2%
ValueCountFrequency (%)
0.43 1
3.4%
1.69 1
3.4%
2.63 1
3.4%
2.9 1
3.4%
4.42 1
3.4%
5.33 1
3.4%
9.51 1
3.4%
9.72 1
3.4%
11.89 1
3.4%
14.88 1
3.4%
ValueCountFrequency (%)
92.35 1
3.4%
90.16 1
3.4%
82.44 1
3.4%
81.23 1
3.4%
79.25 1
3.4%
70.19 1
3.4%
68.04 1
3.4%
55.36 1
3.4%
51.0 1
3.4%
38.47 1
3.4%

여자_초
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing5
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean35.37875
Minimum0.48
Maximum94.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T02:42:25.956111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile1.5155
Q14.925
median32.41
Q362.64
95-th percentile89.836
Maximum94.4
Range93.92
Interquartile range (IQR)57.715

Descriptive statistics

Standard deviation31.901219
Coefficient of variation (CV)0.90170565
Kurtosis-1.1402318
Mean35.37875
Median Absolute Deviation (MAD)28.36
Skewness0.50568736
Sum849.09
Variance1017.6878
MonotonicityNot monotonic
2023-12-13T02:42:26.095891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
94.4 1
 
3.4%
4.93 1
 
3.4%
5.23 1
 
3.4%
1.4 1
 
3.4%
2.37 1
 
3.4%
16.18 1
 
3.4%
37.9 1
 
3.4%
19.65 1
 
3.4%
31.66 1
 
3.4%
61.63 1
 
3.4%
Other values (14) 14
48.3%
(Missing) 5
 
17.2%
ValueCountFrequency (%)
0.48 1
3.4%
1.4 1
3.4%
2.17 1
3.4%
2.37 1
3.4%
2.49 1
3.4%
4.91 1
3.4%
4.93 1
3.4%
5.23 1
3.4%
9.66 1
3.4%
16.18 1
3.4%
ValueCountFrequency (%)
94.4 1
3.4%
91.33 1
3.4%
81.37 1
3.4%
75.83 1
3.4%
73.36 1
3.4%
65.67 1
3.4%
61.63 1
3.4%
47.99 1
3.4%
45.25 1
3.4%
40.07 1
3.4%

계_초
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing5
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean36.36875
Minimum0.46
Maximum93.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T02:42:26.226191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.0155
Q16.705
median31.975
Q360.8
95-th percentile89.4085
Maximum93.34
Range92.88
Interquartile range (IQR)54.095

Descriptive statistics

Standard deviation32.04856
Coefficient of variation (CV)0.88121147
Kurtosis-1.2548871
Mean36.36875
Median Absolute Deviation (MAD)26.875
Skewness0.46825177
Sum872.85
Variance1027.1102
MonotonicityNot monotonic
2023-12-13T02:42:26.368282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
93.34 1
 
3.4%
7.39 1
 
3.4%
7.43 1
 
3.4%
3.42 1
 
3.4%
2.5 1
 
3.4%
15.51 1
 
3.4%
38.2 1
 
3.4%
22.34 1
 
3.4%
32.68 1
 
3.4%
58.4 1
 
3.4%
Other values (14) 14
48.3%
(Missing) 5
 
17.2%
ValueCountFrequency (%)
0.46 1
3.4%
1.93 1
3.4%
2.5 1
3.4%
2.7 1
3.4%
3.42 1
3.4%
4.65 1
3.4%
7.39 1
3.4%
7.43 1
3.4%
10.81 1
3.4%
15.51 1
3.4%
ValueCountFrequency (%)
93.34 1
3.4%
90.73 1
3.4%
81.92 1
3.4%
78.61 1
3.4%
76.39 1
3.4%
68.0 1
3.4%
58.4 1
3.4%
58.3 1
3.4%
48.2 1
3.4%
38.2 1
3.4%

남자_중
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing8
Missing (%)27.6%
Infinite0
Infinite (%)0.0%
Mean38.625238
Minimum0.64
Maximum90.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T02:42:26.526150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.64
5-th percentile0.79
Q112.17
median30.95
Q367.56
95-th percentile90.35
Maximum90.53
Range89.89
Interquartile range (IQR)55.39

Descriptive statistics

Standard deviation32.06232
Coefficient of variation (CV)0.8300873
Kurtosis-1.1520478
Mean38.625238
Median Absolute Deviation (MAD)21.93
Skewness0.5210507
Sum811.13
Variance1027.9923
MonotonicityNot monotonic
2023-12-13T02:42:26.664487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
90.12 1
 
3.4%
0.64 1
 
3.4%
5.56 1
 
3.4%
42.57 1
 
3.4%
41.79 1
 
3.4%
30.95 1
 
3.4%
67.56 1
 
3.4%
90.53 1
 
3.4%
78.65 1
 
3.4%
13.25 1
 
3.4%
Other values (11) 11
37.9%
(Missing) 8
27.6%
ValueCountFrequency (%)
0.64 1
3.4%
0.79 1
3.4%
1.64 1
3.4%
5.56 1
3.4%
9.02 1
3.4%
12.17 1
3.4%
13.25 1
3.4%
19.83 1
3.4%
26.33 1
3.4%
27.21 1
3.4%
ValueCountFrequency (%)
90.53 1
3.4%
90.35 1
3.4%
90.12 1
3.4%
80.64 1
3.4%
78.65 1
3.4%
67.56 1
3.4%
45.76 1
3.4%
42.57 1
3.4%
41.79 1
3.4%
35.77 1
3.4%

여자_중
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing8
Missing (%)27.6%
Infinite0
Infinite (%)0.0%
Mean35.832857
Minimum0.35
Maximum94.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T02:42:26.824145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile1.77
Q110.46
median26.05
Q365.51
95-th percentile85.52
Maximum94.97
Range94.62
Interquartile range (IQR)55.05

Descriptive statistics

Standard deviation31.679912
Coefficient of variation (CV)0.88410232
Kurtosis-0.96952841
Mean35.832857
Median Absolute Deviation (MAD)19.25
Skewness0.68438223
Sum752.49
Variance1003.6168
MonotonicityNot monotonic
2023-12-13T02:42:27.037129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
85.52 1
 
3.4%
0.35 1
 
3.4%
1.77 1
 
3.4%
35.99 1
 
3.4%
31.6 1
 
3.4%
36.67 1
 
3.4%
65.51 1
 
3.4%
94.97 1
 
3.4%
78.48 1
 
3.4%
10.46 1
 
3.4%
Other values (11) 11
37.9%
(Missing) 8
27.6%
ValueCountFrequency (%)
0.35 1
3.4%
1.77 1
3.4%
1.86 1
3.4%
2.19 1
3.4%
6.8 1
3.4%
10.46 1
3.4%
14.92 1
3.4%
16.13 1
3.4%
24.79 1
3.4%
25.25 1
3.4%
ValueCountFrequency (%)
94.97 1
3.4%
85.52 1
3.4%
84.82 1
3.4%
78.48 1
3.4%
76.18 1
3.4%
65.51 1
3.4%
36.67 1
3.4%
35.99 1
3.4%
32.18 1
3.4%
31.6 1
3.4%

계_중
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing8
Missing (%)27.6%
Infinite0
Infinite (%)0.0%
Mean37.285238
Minimum0.5
Maximum92.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T02:42:27.204080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.3
Q112.43
median30.73
Q366.58
95-th percentile87.91
Maximum92.66
Range92.16
Interquartile range (IQR)54.15

Descriptive statistics

Standard deviation31.69492
Coefficient of variation (CV)0.85006619
Kurtosis-1.0492782
Mean37.285238
Median Absolute Deviation (MAD)18.82
Skewness0.61457278
Sum782.99
Variance1004.568
MonotonicityNot monotonic
2023-12-13T02:42:27.357825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
87.91 1
 
3.4%
0.5 1
 
3.4%
3.74 1
 
3.4%
39.41 1
 
3.4%
36.91 1
 
3.4%
33.7 1
 
3.4%
66.58 1
 
3.4%
92.66 1
 
3.4%
78.57 1
 
3.4%
11.91 1
 
3.4%
Other values (11) 11
37.9%
(Missing) 8
27.6%
ValueCountFrequency (%)
0.5 1
3.4%
1.3 1
3.4%
1.91 1
3.4%
3.74 1
3.4%
11.91 1
3.4%
12.43 1
3.4%
13.49 1
3.4%
13.57 1
3.4%
26.2 1
3.4%
29.59 1
3.4%
ValueCountFrequency (%)
92.66 1
3.4%
87.91 1
3.4%
87.69 1
3.4%
78.57 1
3.4%
78.5 1
3.4%
66.58 1
3.4%
39.41 1
3.4%
36.91 1
3.4%
35.69 1
3.4%
33.7 1
3.4%

남자_고
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing8
Missing (%)27.6%
Infinite0
Infinite (%)0.0%
Mean37.198571
Minimum0.83
Maximum92.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T02:42:27.481998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.83
5-th percentile1.22
Q115.28
median24.96
Q365.24
95-th percentile91.94
Maximum92.38
Range91.55
Interquartile range (IQR)49.96

Descriptive statistics

Standard deviation31.418841
Coefficient of variation (CV)0.84462494
Kurtosis-0.93119652
Mean37.198571
Median Absolute Deviation (MAD)14.01
Skewness0.72616028
Sum781.17
Variance987.14357
MonotonicityNot monotonic
2023-12-13T02:42:27.637295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
92.38 1
 
3.4%
1.63 1
 
3.4%
8.45 1
 
3.4%
28.75 1
 
3.4%
38.97 1
 
3.4%
24.96 1
 
3.4%
65.24 1
 
3.4%
91.94 1
 
3.4%
75.79 1
 
3.4%
15.28 1
 
3.4%
Other values (11) 11
37.9%
(Missing) 8
27.6%
ValueCountFrequency (%)
0.83 1
3.4%
1.22 1
3.4%
1.63 1
3.4%
8.45 1
3.4%
14.63 1
3.4%
15.28 1
3.4%
17.72 1
3.4%
18.46 1
3.4%
22.24 1
3.4%
23.64 1
3.4%
ValueCountFrequency (%)
92.38 1
3.4%
91.94 1
3.4%
84.97 1
3.4%
82.77 1
3.4%
75.79 1
3.4%
65.24 1
3.4%
38.97 1
3.4%
36.37 1
3.4%
34.93 1
3.4%
28.75 1
3.4%

여자_고
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing8
Missing (%)27.6%
Infinite0
Infinite (%)0.0%
Mean35.08619
Minimum0.34
Maximum96.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T02:42:27.773921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.78
Q112.22
median22.54
Q366.27
95-th percentile86.57
Maximum96.92
Range96.58
Interquartile range (IQR)54.05

Descriptive statistics

Standard deviation32.304777
Coefficient of variation (CV)0.92072627
Kurtosis-1.0520813
Mean35.08619
Median Absolute Deviation (MAD)19.57
Skewness0.69059174
Sum736.81
Variance1043.5986
MonotonicityNot monotonic
2023-12-13T02:42:27.890372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
86.57 1
 
3.4%
0.34 1
 
3.4%
0.78 1
 
3.4%
24.59 1
 
3.4%
28.84 1
 
3.4%
32.65 1
 
3.4%
66.27 1
 
3.4%
96.92 1
 
3.4%
78.11 1
 
3.4%
12.22 1
 
3.4%
Other values (11) 11
37.9%
(Missing) 8
27.6%
ValueCountFrequency (%)
0.34 1
3.4%
0.78 1
3.4%
1.24 1
3.4%
2.97 1
3.4%
4.44 1
3.4%
12.22 1
3.4%
12.99 1
3.4%
16.48 1
3.4%
18.52 1
3.4%
21.93 1
3.4%
ValueCountFrequency (%)
96.92 1
3.4%
86.57 1
3.4%
78.11 1
3.4%
77.99 1
3.4%
77.48 1
3.4%
66.27 1
3.4%
52.94 1
3.4%
32.65 1
3.4%
28.84 1
3.4%
24.59 1
3.4%

계_고
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing8
Missing (%)27.6%
Infinite0
Infinite (%)0.0%
Mean36.183333
Minimum1.01
Maximum94.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T02:42:28.023579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile1.23
Q113.81
median24.38
Q365.73
95-th percentile89.59
Maximum94.33
Range93.32
Interquartile range (IQR)51.92

Descriptive statistics

Standard deviation31.608305
Coefficient of variation (CV)0.87355979
Kurtosis-0.96882075
Mean36.183333
Median Absolute Deviation (MAD)19.62
Skewness0.72580324
Sum759.85
Variance999.08495
MonotonicityNot monotonic
2023-12-13T02:42:28.140305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
89.59 1
 
3.4%
1.01 1
 
3.4%
4.76 1
 
3.4%
26.75 1
 
3.4%
34.1 1
 
3.4%
28.66 1
 
3.4%
65.73 1
 
3.4%
94.33 1
 
3.4%
76.91 1
 
3.4%
13.81 1
 
3.4%
Other values (11) 11
37.9%
(Missing) 8
27.6%
ValueCountFrequency (%)
1.01 1
3.4%
1.23 1
3.4%
1.86 1
3.4%
4.76 1
3.4%
9.73 1
3.4%
13.81 1
3.4%
18.11 1
3.4%
19.47 1
3.4%
20.42 1
3.4%
22.82 1
3.4%
ValueCountFrequency (%)
94.33 1
3.4%
89.59 1
3.4%
81.37 1
3.4%
80.47 1
3.4%
76.91 1
3.4%
65.73 1
3.4%
44.34 1
3.4%
34.1 1
3.4%
28.66 1
3.4%
26.75 1
3.4%

Interactions

2023-12-13T02:42:22.891304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:17.549602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.215165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.820091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.672998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.284975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.938846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.541494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.180079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.955588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:17.655649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.275929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.141205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.734942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.353709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.010422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.612991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.244831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:23.019875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:17.730311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.342987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.204433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.801326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.432858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.078023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.681136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.312856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:23.112149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:17.805593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.409195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.274616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.862823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.501396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.145424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.749336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.389198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:23.209359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:17.873266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.474665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.343158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.931845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.577165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.212251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.829681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.458031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:23.309318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:17.938021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.542443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.405996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.995463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.652740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.278163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.907914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.534349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:23.399940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.004543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.617875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.469870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.060772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.726064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.346805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.979205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.659988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:23.479954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.068459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.689629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.534801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.134862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.794924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.410701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.048988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.738666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:23.563093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.143677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:18.754843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:19.600825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.210384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:20.870640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:21.479280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.115157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:42:22.822970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:42:28.237125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영역지표남자_초여자_초계_초남자_중여자_중계_중남자_고여자_고계_고
영역1.0001.0000.8250.8760.7930.0000.2810.0000.0000.7560.526
지표1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
남자_초0.8251.0001.0000.9000.9500.9050.2340.8790.5890.3630.000
여자_초0.8761.0000.9001.0000.9740.9050.5480.8150.2870.6180.000
계_초0.7931.0000.9500.9741.0000.8060.1570.8280.4630.0000.000
남자_중0.0001.0000.9050.9050.8061.0000.8740.9050.9340.9200.843
여자_중0.2811.0000.2340.5480.1570.8741.0000.9030.8480.8870.939
계_중0.0001.0000.8790.8150.8280.9050.9031.0000.9740.7810.926
남자_고0.0001.0000.5890.2870.4630.9340.8480.9741.0000.7740.966
여자_고0.7561.0000.3630.6180.0000.9200.8870.7810.7741.0000.913
계_고0.5261.0000.0000.0000.0000.8430.9390.9260.9660.9131.000
2023-12-13T02:42:28.368699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남자_초여자_초계_초남자_중여자_중계_중남자_고여자_고계_고
남자_초1.0000.9880.9970.9340.9040.9290.8330.7970.831
여자_초0.9881.0000.9930.9260.9170.9260.8280.8110.836
계_초0.9970.9931.0000.9260.9170.9260.8280.8110.836
남자_중0.9340.9260.9261.0000.9350.9880.9160.8470.897
여자_중0.9040.9170.9170.9351.0000.9580.9170.9480.938
계_중0.9290.9260.9260.9880.9581.0000.9320.8900.922
남자_고0.8330.8280.8280.9160.9170.9321.0000.9400.986
여자_고0.7970.8110.8110.8470.9480.8900.9401.0000.974
계_고0.8310.8360.8360.8970.9380.9220.9860.9741.000

Missing values

2023-12-13T02:42:23.715172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:42:23.917226image/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.
2023-12-13T02:42:24.096026image/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

영역지표남자_초여자_초계_초남자_중여자_중계_중남자_고여자_고계_고
0영양주1회 이상 라면 섭취율79.2573.3676.3990.3584.8287.6984.9777.4881.37
1섭취주1회 이상 음료수 섭취율81.2375.8378.6190.1285.5287.9192.3886.5789.59
2섭취주1회 이상 패스트푸드 섭취율70.1965.6768.080.6476.1878.582.7777.9980.47
3식습관육류 먹지 않는 비율1.692.171.931.642.191.911.221.241.23
4식습관우유 유제품 매일 섭취율51.045.2548.235.7725.2530.7322.2416.4819.47
5식습관과일 매일 섭취율35.440.0737.6727.2132.1829.5918.4622.5420.42
6식습관채소 매일 섭취율29.4933.1631.2726.3326.0526.223.6421.9322.82
7식습관아침식사 결식률4.424.914.6512.1714.9213.4917.7218.5218.11
8식습관다이어트 약물 경험률0.430.480.460.791.861.30.832.971.86
9운동주3일 이상 격렬한 신체활동 실천율68.0447.9958.345.7624.7935.6934.9312.9924.38
영역지표남자_초여자_초계_초남자_중여자_중계_중남자_고여자_고계_고
19인터넷하루 2시간 이상 인터넷이나 게임24.8919.6522.3442.5735.9939.4128.7524.5926.75
20인터넷음란물이나 성인사이트에서 채팅<NA><NA><NA>5.561.773.748.450.784.76
21음주가족내 흡연자 비율38.4737.938.2<NA><NA><NA><NA><NA><NA>
22음주가족내 과음자 비율14.8816.1815.51<NA><NA><NA><NA><NA><NA>
23흡연음주 흡연 전문가와 상담 요청율<NA><NA><NA>0.640.350.51.630.341.01
24정서무기력과 우울 비율2.632.372.5<NA><NA><NA><NA><NA><NA>
25정서수업 적응 어려움(수업시간 혼남)5.331.43.42<NA><NA><NA><NA><NA><NA>
261학년만과잉행동9.515.237.43<NA><NA><NA><NA><NA><NA>
271학년만주의력 부족 및 산만9.724.937.39<NA><NA><NA><NA><NA><NA>
28<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>