Overview

Dataset statistics

Number of variables15
Number of observations86
Missing cells107
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory129.5 B

Variable types

Categorical6
Text3
Numeric6

Alerts

기타수(개) is highly overall correlated with 위험표지판수(개) and 5 other fieldsHigh correlation
구조봉수(개) is highly overall correlated with 이동식거치대수(개) and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 이동식거치대수(개) and 2 other fieldsHigh correlation
읍면동명 is highly overall correlated with 위험표지판수(개) and 5 other fieldsHigh correlation
위험표지판수(개) is highly overall correlated with 읍면동명 and 1 other fieldsHigh correlation
인명구조함수(개) is highly overall correlated with 구명환수(개) and 1 other fieldsHigh correlation
이동식거치대수(개) is highly overall correlated with 시군명 and 3 other fieldsHigh correlation
구명조끼수(개) is highly overall correlated with 구명로프수(개) and 1 other fieldsHigh correlation
구명환수(개) is highly overall correlated with 인명구조함수(개) and 3 other fieldsHigh correlation
구명로프수(개) is highly overall correlated with 인명구조함수(개) and 3 other fieldsHigh correlation
구조봉수(개) is highly imbalanced (68.6%)Imbalance
기타수(개) is highly imbalanced (71.3%)Imbalance
위험표지판수(개) has 2 (2.3%) missing valuesMissing
인명구조함수(개) has 12 (14.0%) missing valuesMissing
이동식거치대수(개) has 39 (45.3%) missing valuesMissing
구명조끼수(개) has 8 (9.3%) missing valuesMissing
구명환수(개) has 7 (8.1%) missing valuesMissing
구명로프수(개) has 39 (45.3%) missing valuesMissing
위험표지판수(개) has 1 (1.2%) zerosZeros
인명구조함수(개) has 3 (3.5%) zerosZeros
이동식거치대수(개) has 15 (17.4%) zerosZeros
구명로프수(개) has 1 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-10 21:25:42.591730
Analysis finished2023-12-10 21:25:47.241596
Duration4.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
가평군
30 
양평군
21 
남양주시
14 
연천군
12 
포천시
9 

Length

Max length4
Median length3
Mean length3.1627907
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가평군 30
34.9%
양평군 21
24.4%
남양주시 14
16.3%
연천군 12
 
14.0%
포천시 9
 
10.5%

Length

2023-12-11T06:25:47.303070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:25:47.404781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가평군 30
34.9%
양평군 21
24.4%
남양주시 14
16.3%
연천군 12
 
14.0%
포천시 9
 
10.5%

물놀이장소명
Text

Distinct49
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-11T06:25:47.582516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length3
Mean length3.8604651
Min length2

Characters and Unicode

Total characters332
Distinct characters104
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)47.7%

Sample

1st row가평천
2nd row화악천
3rd row승안천
4th row경반천
5th row승안천
ValueCountFrequency (%)
조종천 11
 
12.0%
가평천 10
 
10.9%
영평천 6
 
6.5%
흑천 5
 
5.4%
벽계천 4
 
4.3%
한탄강 4
 
4.3%
승안천 3
 
3.3%
용문천 2
 
2.2%
마을 2
 
2.2%
물레울 1
 
1.1%
Other values (44) 44
47.8%
2023-12-11T06:25:47.922116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 50
 
15.1%
평 16
 
4.8%
조 11
 
3.3%
종 11
 
3.3%
가 10
 
3.0%
리 9
 
2.7%
강 7
 
2.1%
수 7
 
2.1%
지 7
 
2.1%
교 7
 
2.1%
Other values (94) 197
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 320
96.4%
Space Separator 6
 
1.8%
Decimal Number 4
 
1.2%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 50
 
15.6%
평 16
 
5.0%
조 11
 
3.4%
종 11
 
3.4%
가 10
 
3.1%
리 9
 
2.8%
강 7
 
2.2%
수 7
 
2.2%
지 7
 
2.2%
교 7
 
2.2%
Other values (87) 185
57.8%
Decimal Number
ValueCountFrequency (%)
5 1
25.0%
1 1
25.0%
6 1
25.0%
3 1
25.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 320
96.4%
Common 12
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 50
 
15.6%
평 16
 
5.0%
조 11
 
3.4%
종 11
 
3.4%
가 10
 
3.1%
리 9
 
2.8%
강 7
 
2.2%
수 7
 
2.2%
지 7
 
2.2%
교 7
 
2.2%
Other values (87) 185
57.8%
Common
ValueCountFrequency (%)
6
50.0%
( 1
 
8.3%
) 1
 
8.3%
5 1
 
8.3%
1 1
 
8.3%
6 1
 
8.3%
3 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 320
96.4%
ASCII 12
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 50
 
15.6%
평 16
 
5.0%
조 11
 
3.4%
종 11
 
3.4%
가 10
 
3.1%
리 9
 
2.8%
강 7
 
2.2%
수 7
 
2.2%
지 7
 
2.2%
교 7
 
2.2%
Other values (87) 185
57.8%
ASCII
ValueCountFrequency (%)
6
50.0%
( 1
 
8.3%
) 1
 
8.3%
5 1
 
8.3%
1 1
 
8.3%
6 1
 
8.3%
3 1
 
8.3%

읍면동명
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size820.0 B
북면
11 
수동면
10 
단월면
7 
용문면
7 
가평읍
5 
Other values (23)
46 

Length

Max length7
Median length3
Mean length2.872093
Min length2

Unique

Unique12 ?
Unique (%)14.0%

Sample

1st row가평읍
2nd row북면
3rd row가평읍
4th row가평읍
5th row가평읍

Common Values

ValueCountFrequency (%)
북면 11
12.8%
수동면 10
 
11.6%
단월면 7
 
8.1%
용문면 7
 
8.1%
가평읍 5
 
5.8%
청평면 5
 
5.8%
상면 4
 
4.7%
전곡읍 4
 
4.7%
조종면 3
 
3.5%
진접읍 3
 
3.5%
Other values (18) 27
31.4%

Length

2023-12-11T06:25:48.035819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
북면 11
12.8%
수동면 10
 
11.6%
단월면 7
 
8.1%
용문면 7
 
8.1%
가평읍 5
 
5.8%
청평면 5
 
5.8%
상면 4
 
4.7%
전곡읍 4
 
4.7%
조종면 3
 
3.5%
진접읍 3
 
3.5%
Other values (18) 27
31.4%

도로명건물번호
Text

Distinct85
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-11T06:25:48.332700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length8.7906977
Min length5

Characters and Unicode

Total characters756
Distinct characters96
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)97.7%

Sample

1st row개곡리 836-3
2nd row화악리 1205
3rd row승안리 628
4th row경반리 516
5th row승안리 907
ValueCountFrequency (%)
수동면 10
 
5.8%
덕현리 4
 
2.3%
산 4
 
2.3%
도대리 4
 
2.3%
청평리 3
 
1.7%
제령리 3
 
1.7%
승안리 3
 
1.7%
송천리 3
 
1.7%
전곡리 2
 
1.2%
성동리 2
 
1.2%
Other values (129) 135
78.0%
2023-12-11T06:25:48.734798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87
 
11.5%
리 69
 
9.1%
1 67
 
8.9%
2 53
 
7.0%
- 50
 
6.6%
3 35
 
4.6%
6 32
 
4.2%
5 25
 
3.3%
7 23
 
3.0%
0 19
 
2.5%
Other values (86) 296
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 311
41.1%
Decimal Number 307
40.6%
Space Separator 87
 
11.5%
Dash Punctuation 50
 
6.6%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 69
22.2%
수 17
 
5.5%
동 16
 
5.1%
산 13
 
4.2%
길 13
 
4.2%
면 10
 
3.2%
대 7
 
2.3%
평 7
 
2.3%
도 7
 
2.3%
로 7
 
2.3%
Other values (73) 145
46.6%
Decimal Number
ValueCountFrequency (%)
1 67
21.8%
2 53
17.3%
3 35
11.4%
6 32
10.4%
5 25
 
8.1%
7 23
 
7.5%
0 19
 
6.2%
4 18
 
5.9%
8 18
 
5.9%
9 17
 
5.5%
Space Separator
ValueCountFrequency (%)
87
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 445
58.9%
Hangul 311
41.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 69
22.2%
수 17
 
5.5%
동 16
 
5.1%
산 13
 
4.2%
길 13
 
4.2%
면 10
 
3.2%
대 7
 
2.3%
평 7
 
2.3%
도 7
 
2.3%
로 7
 
2.3%
Other values (73) 145
46.6%
Common
ValueCountFrequency (%)
87
19.6%
1 67
15.1%
2 53
11.9%
- 50
11.2%
3 35
7.9%
6 32
 
7.2%
5 25
 
5.6%
7 23
 
5.2%
0 19
 
4.3%
4 18
 
4.0%
Other values (3) 36
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 445
58.9%
Hangul 311
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
87
19.6%
1 67
15.1%
2 53
11.9%
- 50
11.2%
3 35
7.9%
6 32
 
7.2%
5 25
 
5.6%
7 23
 
5.2%
0 19
 
4.3%
4 18
 
4.0%
Other values (3) 36
8.1%
Hangul
ValueCountFrequency (%)
리 69
22.2%
수 17
 
5.5%
동 16
 
5.1%
산 13
 
4.2%
길 13
 
4.2%
면 10
 
3.2%
대 7
 
2.3%
평 7
 
2.3%
도 7
 
2.3%
로 7
 
2.3%
Other values (73) 145
46.6%

세부지명
Text

Distinct85
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-11T06:25:48.962841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length5.9534884
Min length2

Characters and Unicode

Total characters512
Distinct characters163
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)97.7%

Sample

1st row까치유원지앞
2nd row넓은다락방
3rd row펜션마을 입구
4th row경반리 범소
5th row용추계곡유원지
ValueCountFrequency (%)
앞 14
 
12.0%
아래 5
 
4.3%
마을 2
 
1.7%
벽계천 2
 
1.7%
부안천 1
 
0.9%
준펜션 1
 
0.9%
소리산소금강야영장(산음천 1
 
0.9%
스마일펜션 1
 
0.9%
봉상마을 1
 
0.9%
물레울 1
 
0.9%
Other values (88) 88
75.2%
2023-12-11T06:25:49.294691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
 
6.1%
교 25
 
4.9%
지 22
 
4.3%
원 21
 
4.1%
앞 18
 
3.5%
유 17
 
3.3%
리 16
 
3.1%
수 11
 
2.1%
천 9
 
1.8%
계 9
 
1.8%
Other values (153) 333
65.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 464
90.6%
Space Separator 31
 
6.1%
Close Punctuation 5
 
1.0%
Open Punctuation 5
 
1.0%
Math Symbol 3
 
0.6%
Uppercase Letter 2
 
0.4%
Other Punctuation 1
 
0.2%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 25
 
5.4%
지 22
 
4.7%
원 21
 
4.5%
앞 18
 
3.9%
유 17
 
3.7%
리 16
 
3.4%
수 11
 
2.4%
천 9
 
1.9%
계 9
 
1.9%
마 8
 
1.7%
Other values (145) 308
66.4%
Uppercase Letter
ValueCountFrequency (%)
H 1
50.0%
J 1
50.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 464
90.6%
Common 46
 
9.0%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 25
 
5.4%
지 22
 
4.7%
원 21
 
4.5%
앞 18
 
3.9%
유 17
 
3.7%
리 16
 
3.4%
수 11
 
2.4%
천 9
 
1.9%
계 9
 
1.9%
마 8
 
1.7%
Other values (145) 308
66.4%
Common
ValueCountFrequency (%)
31
67.4%
) 5
 
10.9%
( 5
 
10.9%
~ 3
 
6.5%
, 1
 
2.2%
1 1
 
2.2%
Latin
ValueCountFrequency (%)
H 1
50.0%
J 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 464
90.6%
ASCII 48
 
9.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31
64.6%
) 5
 
10.4%
( 5
 
10.4%
~ 3
 
6.2%
, 1
 
2.1%
H 1
 
2.1%
1 1
 
2.1%
J 1
 
2.1%
Hangul
ValueCountFrequency (%)
교 25
 
5.4%
지 22
 
4.7%
원 21
 
4.5%
앞 18
 
3.9%
유 17
 
3.7%
리 16
 
3.4%
수 11
 
2.4%
천 9
 
1.9%
계 9
 
1.9%
마 8
 
1.7%
Other values (145) 308
66.4%

물놀이장소유형
Categorical

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size820.0 B
하천
70 
계곡
16 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row하천
2nd row계곡
3rd row계곡
4th row하천
5th row하천

Common Values

ValueCountFrequency (%)
하천 70
81.4%
계곡 16
 
18.6%

Length

2023-12-11T06:25:49.398435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:25:49.477677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하천 70
81.4%
계곡 16
 
18.6%

관리등급
Categorical

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size820.0 B
일반지역
66 
중점관리지역
20 

Length

Max length6
Median length4
Mean length4.4651163
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반지역
2nd row일반지역
3rd row중점관리지역
4th row일반지역
5th row일반지역

Common Values

ValueCountFrequency (%)
일반지역 66
76.7%
중점관리지역 20
 
23.3%

Length

2023-12-11T06:25:49.572365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:25:49.663121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반지역 66
76.7%
중점관리지역 20
 
23.3%

위험표지판수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)19.0%
Missing2
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean3.9047619
Minimum0
Maximum17
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T06:25:49.736517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q35
95-th percentile12.85
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7565013
Coefficient of variation (CV)0.96203083
Kurtosis2.8486158
Mean3.9047619
Median Absolute Deviation (MAD)2
Skewness1.81949
Sum328
Variance14.111302
MonotonicityNot monotonic
2023-12-11T06:25:49.842923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 24
27.9%
2 14
16.3%
3 14
16.3%
4 8
 
9.3%
5 7
 
8.1%
6 3
 
3.5%
12 2
 
2.3%
10 2
 
2.3%
15 2
 
2.3%
7 2
 
2.3%
Other values (6) 6
 
7.0%
(Missing) 2
 
2.3%
ValueCountFrequency (%)
0 1
 
1.2%
1 24
27.9%
2 14
16.3%
3 14
16.3%
4 8
 
9.3%
5 7
 
8.1%
6 3
 
3.5%
7 2
 
2.3%
8 1
 
1.2%
9 1
 
1.2%
ValueCountFrequency (%)
17 1
 
1.2%
15 2
2.3%
14 1
 
1.2%
13 1
 
1.2%
12 2
2.3%
10 2
2.3%
9 1
 
1.2%
8 1
 
1.2%
7 2
2.3%
6 3
3.5%

인명구조함수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)8.1%
Missing12
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean1.9054054
Minimum0
Maximum20
Zeros3
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T06:25:49.923946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.3476536
Coefficient of variation (CV)1.2321019
Kurtosis49.450369
Mean1.9054054
Median Absolute Deviation (MAD)0.5
Skewness6.4625688
Sum141
Variance5.5114772
MonotonicityNot monotonic
2023-12-11T06:25:50.010352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 37
43.0%
2 21
24.4%
4 6
 
7.0%
3 6
 
7.0%
0 3
 
3.5%
20 1
 
1.2%
(Missing) 12
 
14.0%
ValueCountFrequency (%)
0 3
 
3.5%
1 37
43.0%
2 21
24.4%
3 6
 
7.0%
4 6
 
7.0%
20 1
 
1.2%
ValueCountFrequency (%)
20 1
 
1.2%
4 6
 
7.0%
3 6
 
7.0%
2 21
24.4%
1 37
43.0%
0 3
 
3.5%

이동식거치대수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)12.8%
Missing39
Missing (%)45.3%
Infinite0
Infinite (%)0.0%
Mean1.1914894
Minimum0
Maximum8
Zeros15
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T06:25:50.092844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31.5
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.4983803
Coefficient of variation (CV)1.2575691
Kurtosis10.250948
Mean1.1914894
Median Absolute Deviation (MAD)1
Skewness2.816776
Sum56
Variance2.2451434
MonotonicityNot monotonic
2023-12-11T06:25:50.182993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 20
23.3%
0 15
 
17.4%
2 8
 
9.3%
3 2
 
2.3%
8 1
 
1.2%
6 1
 
1.2%
(Missing) 39
45.3%
ValueCountFrequency (%)
0 15
17.4%
1 20
23.3%
2 8
 
9.3%
3 2
 
2.3%
6 1
 
1.2%
8 1
 
1.2%
ValueCountFrequency (%)
8 1
 
1.2%
6 1
 
1.2%
3 2
 
2.3%
2 8
 
9.3%
1 20
23.3%
0 15
17.4%

구명조끼수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)20.5%
Missing8
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean10.089744
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T06:25:50.281721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6.5
Q315
95-th percentile22
Maximum53
Range52
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.4404296
Coefficient of variation (CV)0.93564614
Kurtosis5.7537151
Mean10.089744
Median Absolute Deviation (MAD)5.5
Skewness1.8365427
Sum787
Variance89.121712
MonotonicityNot monotonic
2023-12-11T06:25:50.370365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
15 22
25.6%
2 15
17.4%
1 9
10.5%
4 7
 
8.1%
20 5
 
5.8%
6 4
 
4.7%
12 3
 
3.5%
5 2
 
2.3%
22 2
 
2.3%
10 2
 
2.3%
Other values (6) 7
 
8.1%
(Missing) 8
 
9.3%
ValueCountFrequency (%)
1 9
10.5%
2 15
17.4%
3 2
 
2.3%
4 7
 
8.1%
5 2
 
2.3%
6 4
 
4.7%
7 1
 
1.2%
10 2
 
2.3%
12 3
 
3.5%
15 22
25.6%
ValueCountFrequency (%)
53 1
 
1.2%
44 1
 
1.2%
25 1
 
1.2%
22 2
 
2.3%
21 1
 
1.2%
20 5
 
5.8%
15 22
25.6%
12 3
 
3.5%
10 2
 
2.3%
7 1
 
1.2%

구명환수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)16.5%
Missing7
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean2.721519
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T06:25:50.454996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9.1
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.4527459
Coefficient of variation (CV)1.2686834
Kurtosis13.473878
Mean2.721519
Median Absolute Deviation (MAD)1
Skewness3.457144
Sum215
Variance11.921454
MonotonicityNot monotonic
2023-12-11T06:25:50.559804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 38
44.2%
2 20
23.3%
3 7
 
8.1%
4 5
 
5.8%
9 1
 
1.2%
21 1
 
1.2%
8 1
 
1.2%
13 1
 
1.2%
5 1
 
1.2%
6 1
 
1.2%
Other values (3) 3
 
3.5%
(Missing) 7
 
8.1%
ValueCountFrequency (%)
1 38
44.2%
2 20
23.3%
3 7
 
8.1%
4 5
 
5.8%
5 1
 
1.2%
6 1
 
1.2%
7 1
 
1.2%
8 1
 
1.2%
9 1
 
1.2%
10 1
 
1.2%
ValueCountFrequency (%)
21 1
 
1.2%
17 1
 
1.2%
13 1
 
1.2%
10 1
 
1.2%
9 1
 
1.2%
8 1
 
1.2%
7 1
 
1.2%
6 1
 
1.2%
5 1
 
1.2%
4 5
5.8%

구명로프수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)23.4%
Missing39
Missing (%)45.3%
Infinite0
Infinite (%)0.0%
Mean2.9574468
Minimum0
Maximum17
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-11T06:25:50.646768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile11.5
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.476364
Coefficient of variation (CV)1.1754612
Kurtosis7.0785032
Mean2.9574468
Median Absolute Deviation (MAD)1
Skewness2.6151997
Sum139
Variance12.085106
MonotonicityNot monotonic
2023-12-11T06:25:50.729632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 21
24.4%
2 9
 
10.5%
3 5
 
5.8%
4 4
 
4.7%
13 2
 
2.3%
8 1
 
1.2%
5 1
 
1.2%
0 1
 
1.2%
6 1
 
1.2%
17 1
 
1.2%
(Missing) 39
45.3%
ValueCountFrequency (%)
0 1
 
1.2%
1 21
24.4%
2 9
10.5%
3 5
 
5.8%
4 4
 
4.7%
5 1
 
1.2%
6 1
 
1.2%
7 1
 
1.2%
8 1
 
1.2%
13 2
 
2.3%
ValueCountFrequency (%)
17 1
 
1.2%
13 2
 
2.3%
8 1
 
1.2%
7 1
 
1.2%
6 1
 
1.2%
5 1
 
1.2%
4 4
 
4.7%
3 5
 
5.8%
2 9
10.5%
1 21
24.4%

구조봉수(개)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size820.0 B
<NA>
78 
0
 
7
1
 
1

Length

Max length4
Median length4
Mean length3.7209302
Min length1

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 78
90.7%
0 7
 
8.1%
1 1
 
1.2%

Length

2023-12-11T06:25:50.823610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:25:50.905762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 78
90.7%
0 7
 
8.1%
1 1
 
1.2%

기타수(개)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size820.0 B
<NA>
79 
0
 
6
5
 
1

Length

Max length4
Median length4
Mean length3.755814
Min length1

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 79
91.9%
0 6
 
7.0%
5 1
 
1.2%

Length

2023-12-11T06:25:50.991627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:25:51.075439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 79
91.9%
0 6
 
7.0%
5 1
 
1.2%

Interactions

2023-12-11T06:25:46.038350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:43.585395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.032687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.519109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.030554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.509820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:46.112456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:43.653970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.109584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.598290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.109317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.618903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:46.398476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:43.730219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.191221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.683511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.190058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.704841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:46.490811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:43.811820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.272871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.769823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.268853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.791751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:46.567582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:43.885375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.345555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.862166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.333581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.875684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:46.643579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:43.961814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.424668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:44.954566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.406546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:25:45.958793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:25:51.146424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명물놀이장소명읍면동명도로명건물번호세부지명물놀이장소유형관리등급위험표지판수(개)인명구조함수(개)이동식거치대수(개)구명조끼수(개)구명환수(개)구명로프수(개)구조봉수(개)기타수(개)
시군명1.0000.9881.0001.0001.0000.1060.0000.4360.2350.8080.6000.1920.0190.396NaN
물놀이장소명0.9881.0000.9671.0001.0000.5240.0000.7850.6780.5810.8580.5830.0001.0001.000
읍면동명1.0000.9671.0001.0001.0000.6230.0390.8910.3350.9510.8860.9240.7591.0000.293
도로명건물번호1.0001.0001.0001.0000.9991.0001.0000.9830.0001.0000.0000.9801.0001.0001.000
세부지명1.0001.0001.0000.9991.0000.0001.0001.0000.0000.9731.0000.0000.9201.0001.000
물놀이장소유형0.1060.5240.6231.0000.0001.0000.0000.1310.0000.0810.0000.4000.3440.0000.293
관리등급0.0000.0000.0391.0001.0000.0001.0000.0210.3270.0000.2740.0000.1100.0000.000
위험표지판수(개)0.4360.7850.8910.9831.0000.1310.0211.0000.6730.6020.4500.4780.3620.0001.000
인명구조함수(개)0.2350.6780.3350.0000.0000.0000.3270.6731.0000.6710.8150.9320.7800.0000.000
이동식거치대수(개)0.8080.5810.9511.0000.9730.0810.0000.6020.6711.0000.6960.7470.719NaNNaN
구명조끼수(개)0.6000.8580.8860.0001.0000.0000.2740.4500.8150.6961.0000.8220.9500.0000.293
구명환수(개)0.1920.5830.9240.9800.0000.4000.0000.4780.9320.7470.8221.0000.8910.3021.000
구명로프수(개)0.0190.0000.7591.0000.9200.3440.1100.3620.7800.7190.9500.8911.0000.0001.000
구조봉수(개)0.3961.0001.0001.0001.0000.0000.0000.0000.000NaN0.0000.3020.0001.000NaN
기타수(개)NaN1.0000.2931.0001.0000.2930.0001.0000.000NaN0.2931.0001.000NaN1.000
2023-12-11T06:25:51.279705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기타수(개)관리등급구조봉수(개)시군명물놀이장소유형읍면동명
기타수(개)1.0000.0001.0001.0000.0910.091
관리등급0.0001.0000.0000.0000.0000.000
구조봉수(개)1.0000.0001.0000.2180.0000.913
시군명1.0000.0000.2181.0000.1250.846
물놀이장소유형0.0910.0000.0000.1251.0000.413
읍면동명0.0910.0000.9130.8460.4131.000
2023-12-11T06:25:51.381536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위험표지판수(개)인명구조함수(개)이동식거치대수(개)구명조끼수(개)구명환수(개)구명로프수(개)시군명읍면동명물놀이장소유형관리등급구조봉수(개)기타수(개)
위험표지판수(개)1.0000.3920.171-0.0980.4590.2900.2610.5160.1210.0000.0000.632
인명구조함수(개)0.3921.0000.1140.1380.8590.6220.0000.2370.0000.1120.0000.000
이동식거치대수(개)0.1710.1141.0000.3390.3330.4230.6420.6360.0270.0001.0001.000
구명조끼수(개)-0.0980.1380.3391.0000.1860.6950.4340.5300.0000.2810.0000.091
구명환수(개)0.4590.8590.3330.1861.0000.8370.1520.5960.1300.0000.0000.894
구명로프수(개)0.2900.6220.4230.6950.8371.0000.0000.3050.3440.0980.0000.775
시군명0.2610.0000.6420.4340.1520.0001.0000.8460.1250.0000.2181.000
읍면동명0.5160.2370.6360.5300.5960.3050.8461.0000.4130.0000.9130.091
물놀이장소유형0.1210.0000.0270.0000.1300.3440.1250.4131.0000.0000.0000.091
관리등급0.0000.1120.0000.2810.0000.0980.0000.0000.0001.0000.0000.000
구조봉수(개)0.0000.0001.0000.0000.0000.0000.2180.9130.0000.0001.0001.000
기타수(개)0.6320.0001.0000.0910.8940.7751.0000.0910.0910.0001.0001.000

Missing values

2023-12-11T06:25:46.778096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:25:46.994179image/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.
2023-12-11T06:25:47.136155image/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

시군명물놀이장소명읍면동명도로명건물번호세부지명물놀이장소유형관리등급위험표지판수(개)인명구조함수(개)이동식거치대수(개)구명조끼수(개)구명환수(개)구명로프수(개)구조봉수(개)기타수(개)
0가평군가평천가평읍개곡리 836-3까치유원지앞하천일반지역211151<NA><NA><NA>
1가평군화악천북면화악리 1205넓은다락방계곡일반지역11<NA>151<NA><NA><NA>
2가평군승안천가평읍승안리 628펜션마을 입구계곡중점관리지역11<NA>151<NA><NA><NA>
3가평군경반천가평읍경반리 516경반리 범소하천일반지역311151<NA><NA><NA>
4가평군승안천가평읍승안리 907용추계곡유원지하천일반지역11<NA>151<NA><NA><NA>
5가평군벽계천설악면가일리 350어비계곡(밤벌유원지)하천일반지역111151<NA><NA><NA>
6가평군벽계천설악면방일리 산106용소유원지하천중점관리지역111151<NA><NA><NA>
7가평군조종천청평면하천리 612산장유원지하천일반지역311151<NA><NA><NA>
8가평군조종천청평면청평리 738안전유원지하천일반지역44<NA>224<NA><NA><NA>
9가평군조종천청평면청평리 642-7청평대교 아래하천일반지역21<NA>151<NA><NA><NA>
시군명물놀이장소명읍면동명도로명건물번호세부지명물놀이장소유형관리등급위험표지판수(개)인명구조함수(개)이동식거치대수(개)구명조끼수(개)구명환수(개)구명로프수(개)구조봉수(개)기타수(개)
76연천군동막리유원지연천읍동막리 181-2동막리자라바위계곡일반지역171<NA>633<NA><NA>
77포천시한탄강관인면사정리 26-1화적연하천중점관리지역1422231<NA><NA>
78포천시한탄강창수면운산리 96-1영로교하천중점관리지역9<NA>2222<NA><NA>
79포천시영평천영중면성동리 640-2백교~성동보하천일반지역5<NA><NA>22<NA><NA><NA>
80포천시영평천영중면영송리 121-1사은교하천일반지역3<NA>211<NA><NA><NA>
81포천시영평천이동면도평리 105-3HJ글램핑장 앞하천일반지역2<NA>2221<NA><NA>
82포천시영평천이동면도평리 36 일대백운계곡계곡일반지역828777<NA><NA>
83포천시수동천신북면덕둔리 산220-5열두개울하천일반지역023333<NA><NA>
84포천시영평천이동면장암리 382-10장암1교~파인트리캠핑장 앞하천일반지역3<NA>224<NA><NA><NA>
85포천시영평천영중면~이동면성동리 281-1~노곡리 1242-8삼산대교~삼팔교하천일반지역124610102<NA><NA>