Overview

Dataset statistics

Number of variables13
Number of observations70
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory107.8 B

Variable types

Numeric2
Categorical7
Text4

Dataset

Description광주광역시 관내 빗물 재이용 시설현황 을 붙임과 같이 공개하고자 합니다.빗물이용시설 설치대상은 수도법을 참고하시면 됩니다.
Author광주광역시
URLhttps://www.data.go.kr/data/3075557/fileData.do

Alerts

시도 has constant value ""Constant
연번 is highly overall correlated with 시군구High correlation
저류조용량(세제곱미터) is highly overall correlated with 건축물용도구분High correlation
시군구 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
건축물용도구분 is highly overall correlated with 저류조용량(세제곱미터)High correlation
법적시설여부 is highly overall correlated with 빗물활용용도High correlation
집수면 is highly overall correlated with 여과등처리시설여부High correlation
여과등처리시설여부 is highly overall correlated with 시군구 and 1 other fieldsHigh correlation
빗물활용용도 is highly overall correlated with 시군구 and 1 other fieldsHigh correlation
집수면 is highly imbalanced (89.2%)Imbalance
빗물활용용도 is highly imbalanced (52.6%)Imbalance
연번 has unique valuesUnique
시설명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 12:06:44.800820
Analysis finished2024-03-14 12:06:47.914595
Duration3.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct70
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.5
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.0 B
2024-03-14T21:06:48.045964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.45
Q118.25
median35.5
Q352.75
95-th percentile66.55
Maximum70
Range69
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation20.351085
Coefficient of variation (CV)0.57327
Kurtosis-1.2
Mean35.5
Median Absolute Deviation (MAD)17.5
Skewness0
Sum2485
Variance414.16667
MonotonicityStrictly increasing
2024-03-14T21:06:48.302830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
46 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
45 1
 
1.4%
54 1
 
1.4%
Other values (60) 60
85.7%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%
63 1
1.4%
62 1
1.4%
61 1
1.4%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size688.0 B
광주광역시
70 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광주광역시 70
100.0%

Length

2024-03-14T21:06:48.544114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:06:48.801009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주광역시 70
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size688.0 B
광산구
23 
북구
13 
서구
12 
남구
12 
동구
10 

Length

Max length3
Median length2
Mean length2.3285714
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광산구 23
32.9%
북구 13
18.6%
서구 12
17.1%
남구 12
17.1%
동구 10
14.3%

Length

2024-03-14T21:06:48.978431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:06:49.174207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광산구 23
32.9%
북구 13
18.6%
서구 12
17.1%
남구 12
17.1%
동구 10
14.3%

시설명
Text

UNIQUE 

Distinct70
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size688.0 B
2024-03-14T21:06:50.077852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length15
Mean length9.3857143
Min length4

Characters and Unicode

Total characters657
Distinct characters177
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)100.0%

Sample

1st row무등산아이파크
2nd row동구다목적체육관
3rd row동명동주민커뮤니티센터
4th row진아리채아파트 1BL
5th row진아리채아파트 2BL
ValueCountFrequency (%)
체육관 3
 
2.7%
광주관광공사 2
 
1.8%
진아리채아파트 2
 
1.8%
힐스테이트 2
 
1.8%
개방형 2
 
1.8%
모아엘가 2
 
1.8%
우산동 2
 
1.8%
성덕고등학교 1
 
0.9%
선운중학교 1
 
0.9%
선운초등학교 1
 
0.9%
Other values (92) 92
83.6%
2024-03-14T21:06:51.241024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
 
6.1%
주 21
 
3.2%
광 19
 
2.9%
아 18
 
2.7%
동 16
 
2.4%
산 15
 
2.3%
스 14
 
2.1%
리 14
 
2.1%
학 13
 
2.0%
관 13
 
2.0%
Other values (167) 474
72.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 589
89.6%
Space Separator 40
 
6.1%
Decimal Number 8
 
1.2%
Uppercase Letter 6
 
0.9%
Close Punctuation 4
 
0.6%
Open Punctuation 4
 
0.6%
Other Symbol 2
 
0.3%
Other Punctuation 2
 
0.3%
Lowercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 21
 
3.6%
광 19
 
3.2%
아 18
 
3.1%
동 16
 
2.7%
산 15
 
2.5%
스 14
 
2.4%
리 14
 
2.4%
학 13
 
2.2%
관 13
 
2.2%
교 12
 
2.0%
Other values (153) 434
73.7%
Uppercase Letter
ValueCountFrequency (%)
S 2
33.3%
L 2
33.3%
B 2
33.3%
Decimal Number
ValueCountFrequency (%)
2 5
62.5%
1 3
37.5%
Close Punctuation
ValueCountFrequency (%)
) 3
75.0%
] 1
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 3
75.0%
[ 1
 
25.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
s 1
50.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 591
90.0%
Common 58
 
8.8%
Latin 8
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 21
 
3.6%
광 19
 
3.2%
아 18
 
3.0%
동 16
 
2.7%
산 15
 
2.5%
스 14
 
2.4%
리 14
 
2.4%
학 13
 
2.2%
관 13
 
2.2%
교 12
 
2.0%
Other values (154) 436
73.8%
Common
ValueCountFrequency (%)
40
69.0%
2 5
 
8.6%
1 3
 
5.2%
) 3
 
5.2%
( 3
 
5.2%
, 2
 
3.4%
] 1
 
1.7%
[ 1
 
1.7%
Latin
ValueCountFrequency (%)
S 2
25.0%
L 2
25.0%
B 2
25.0%
k 1
12.5%
s 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 589
89.6%
ASCII 66
 
10.0%
None 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
60.6%
2 5
 
7.6%
1 3
 
4.5%
) 3
 
4.5%
( 3
 
4.5%
S 2
 
3.0%
L 2
 
3.0%
B 2
 
3.0%
, 2
 
3.0%
] 1
 
1.5%
Other values (3) 3
 
4.5%
Hangul
ValueCountFrequency (%)
주 21
 
3.6%
광 19
 
3.2%
아 18
 
3.1%
동 16
 
2.7%
산 15
 
2.5%
스 14
 
2.4%
리 14
 
2.4%
학 13
 
2.2%
관 13
 
2.2%
교 12
 
2.0%
Other values (153) 434
73.7%
None
ValueCountFrequency (%)
㈜ 2
100.0%

건축물용도구분
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size688.0 B
공동주택
32 
공공업무시설
12 
교육시설
10 
체육관
 
3
학교
 
2
Other values (9)
11 

Length

Max length9
Median length4
Mean length4.4714286
Min length2

Unique

Unique7 ?
Unique (%)10.0%

Sample

1st row공동주택
2nd row운동시설
3rd row공공업무시설
4th row공동주택
5th row공동주택

Common Values

ValueCountFrequency (%)
공동주택 32
45.7%
공공업무시설 12
 
17.1%
교육시설 10
 
14.3%
체육관 3
 
4.3%
학교 2
 
2.9%
실내체육관 2
 
2.9%
체육시설 2
 
2.9%
운동시설 1
 
1.4%
운동시설(시) 1
 
1.4%
문화 및 집회시설 1
 
1.4%
Other values (4) 4
 
5.7%

Length

2024-03-14T21:06:51.674346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공동주택 32
43.2%
공공업무시설 12
 
16.2%
교육시설 10
 
13.5%
체육관 3
 
4.1%
학교 2
 
2.7%
실내체육관 2
 
2.7%
체육시설 2
 
2.7%
및 2
 
2.7%
교육 1
 
1.4%
수영장 1
 
1.4%
Other values (7) 7
 
9.5%

소재지도로명주소
Text

Distinct68
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size688.0 B
2024-03-14T21:06:52.883363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length25
Mean length20.6
Min length15

Characters and Unicode

Total characters1442
Distinct characters115
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)94.3%

Sample

1st row광주광역시 동구 남문로 753번길 20
2nd row광주광역시 동구 남문로 418-15
3rd row광주광역시 동구 동명로 20번길 43-5
4th row광주광역시 동구 육판서길 16
5th row광주광역시 동구 육판서길 17
ValueCountFrequency (%)
광주광역시 68
23.7%
광산구 23
 
8.0%
북구 13
 
4.5%
남구 12
 
4.2%
동구 9
 
3.1%
서구 5
 
1.7%
산정동 3
 
1.0%
우산동 3
 
1.0%
일원 3
 
1.0%
남문로 2
 
0.7%
Other values (137) 146
50.9%
2024-03-14T21:06:54.544427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
217
 
15.0%
광 164
 
11.4%
1 73
 
5.1%
주 72
 
5.0%
시 71
 
4.9%
역 70
 
4.9%
구 65
 
4.5%
동 62
 
4.3%
로 48
 
3.3%
산 36
 
2.5%
Other values (105) 564
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 863
59.8%
Decimal Number 281
 
19.5%
Space Separator 217
 
15.0%
Open Punctuation 30
 
2.1%
Close Punctuation 30
 
2.1%
Dash Punctuation 21
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 164
19.0%
주 72
 
8.3%
시 71
 
8.2%
역 70
 
8.1%
구 65
 
7.5%
동 62
 
7.2%
로 48
 
5.6%
산 36
 
4.2%
남 16
 
1.9%
북 13
 
1.5%
Other values (91) 246
28.5%
Decimal Number
ValueCountFrequency (%)
1 73
26.0%
3 35
12.5%
2 31
11.0%
7 24
 
8.5%
0 24
 
8.5%
6 22
 
7.8%
4 21
 
7.5%
8 20
 
7.1%
5 17
 
6.0%
9 14
 
5.0%
Space Separator
ValueCountFrequency (%)
217
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 863
59.8%
Common 579
40.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 164
19.0%
주 72
 
8.3%
시 71
 
8.2%
역 70
 
8.1%
구 65
 
7.5%
동 62
 
7.2%
로 48
 
5.6%
산 36
 
4.2%
남 16
 
1.9%
북 13
 
1.5%
Other values (91) 246
28.5%
Common
ValueCountFrequency (%)
217
37.5%
1 73
 
12.6%
3 35
 
6.0%
2 31
 
5.4%
( 30
 
5.2%
) 30
 
5.2%
7 24
 
4.1%
0 24
 
4.1%
6 22
 
3.8%
- 21
 
3.6%
Other values (4) 72
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 863
59.8%
ASCII 579
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
217
37.5%
1 73
 
12.6%
3 35
 
6.0%
2 31
 
5.4%
( 30
 
5.2%
) 30
 
5.2%
7 24
 
4.1%
0 24
 
4.1%
6 22
 
3.8%
- 21
 
3.6%
Other values (4) 72
 
12.4%
Hangul
ValueCountFrequency (%)
광 164
19.0%
주 72
 
8.3%
시 71
 
8.2%
역 70
 
8.1%
구 65
 
7.5%
동 62
 
7.2%
로 48
 
5.6%
산 36
 
4.2%
남 16
 
1.9%
북 13
 
1.5%
Other values (91) 246
28.5%

법적시설여부
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size688.0 B
대상
33 
비대상
30 
미대상
7 

Length

Max length3
Median length3
Mean length2.5285714
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대상
2nd row대상
3rd row비대상
4th row비대상
5th row비대상

Common Values

ValueCountFrequency (%)
대상 33
47.1%
비대상 30
42.9%
미대상 7
 
10.0%

Length

2024-03-14T21:06:54.974221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:06:55.311141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대상 33
47.1%
비대상 30
42.9%
미대상 7
 
10.0%

설치완료일
Text

Distinct62
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Memory size688.0 B
2024-03-14T21:06:56.212977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.2571429
Min length6

Characters and Unicode

Total characters648
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)81.4%

Sample

1st row2017-01-04
2nd row2018-03-19
3rd row2013-11-01
4th row2019-05-09
5th row2020-03-01
ValueCountFrequency (%)
2015-03-01 4
 
5.7%
2016-12-01 3
 
4.3%
2013-03-01 2
 
2.9%
2012-03-01 2
 
2.9%
2003-12-02 2
 
2.9%
2017-01-04 1
 
1.4%
2011.2 1
 
1.4%
2013.3 1
 
1.4%
2016.7 1
 
1.4%
2021.12 1
 
1.4%
Other values (52) 52
74.3%
2024-03-14T21:06:57.625403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 174
26.9%
2 133
20.5%
- 112
17.3%
1 106
16.4%
3 33
 
5.1%
9 20
 
3.1%
. 16
 
2.5%
5 14
 
2.2%
6 13
 
2.0%
7 12
 
1.9%
Other values (2) 15
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520
80.2%
Dash Punctuation 112
 
17.3%
Other Punctuation 16
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 174
33.5%
2 133
25.6%
1 106
20.4%
3 33
 
6.3%
9 20
 
3.8%
5 14
 
2.7%
6 13
 
2.5%
7 12
 
2.3%
8 10
 
1.9%
4 5
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 648
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 174
26.9%
2 133
20.5%
- 112
17.3%
1 106
16.4%
3 33
 
5.1%
9 20
 
3.1%
. 16
 
2.5%
5 14
 
2.2%
6 13
 
2.0%
7 12
 
1.9%
Other values (2) 15
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 174
26.9%
2 133
20.5%
- 112
17.3%
1 106
16.4%
3 33
 
5.1%
9 20
 
3.1%
. 16
 
2.5%
5 14
 
2.2%
6 13
 
2.0%
7 12
 
1.9%
Other values (2) 15
 
2.3%

설치비(백만원)
Text

Distinct51
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Memory size688.0 B
2024-03-14T21:06:58.435192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3571429
Min length1

Characters and Unicode

Total characters165
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)60.0%

Sample

1st row확인불가
2nd row120
3rd row49
4th row60
5th row46
ValueCountFrequency (%)
확인불가 5
 
7.1%
50 4
 
5.7%
60 4
 
5.7%
120 3
 
4.3%
80 3
 
4.3%
85 3
 
4.3%
49 2
 
2.9%
150 2
 
2.9%
55 2
 
2.9%
117 1
 
1.4%
Other values (41) 41
58.6%
2024-03-14T21:06:59.456387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 25
15.2%
0 24
14.5%
1 17
10.3%
8 16
9.7%
2 15
9.1%
4 12
7.3%
6 11
6.7%
9 9
 
5.5%
3 8
 
4.8%
7 6
 
3.6%
Other values (5) 22
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 143
86.7%
Other Letter 20
 
12.1%
Other Punctuation 2
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 25
17.5%
0 24
16.8%
1 17
11.9%
8 16
11.2%
2 15
10.5%
4 12
8.4%
6 11
7.7%
9 9
 
6.3%
3 8
 
5.6%
7 6
 
4.2%
Other Letter
ValueCountFrequency (%)
확 5
25.0%
인 5
25.0%
불 5
25.0%
가 5
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 145
87.9%
Hangul 20
 
12.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 25
17.2%
0 24
16.6%
1 17
11.7%
8 16
11.0%
2 15
10.3%
4 12
8.3%
6 11
7.6%
9 9
 
6.2%
3 8
 
5.5%
7 6
 
4.1%
Hangul
ValueCountFrequency (%)
확 5
25.0%
인 5
25.0%
불 5
25.0%
가 5
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145
87.9%
Hangul 20
 
12.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 25
17.2%
0 24
16.6%
1 17
11.7%
8 16
11.0%
2 15
10.3%
4 12
8.3%
6 11
7.6%
9 9
 
6.2%
3 8
 
5.5%
7 6
 
4.1%
Hangul
ValueCountFrequency (%)
확 5
25.0%
인 5
25.0%
불 5
25.0%
가 5
25.0%

집수면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size688.0 B
지붕면
69 
건물옥탑
 
1

Length

Max length4
Median length3
Mean length3.0142857
Min length3

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
지붕면 69
98.6%
건물옥탑 1
 
1.4%

Length

2024-03-14T21:06:59.693181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:06:59.872312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지붕면 69
98.6%
건물옥탑 1
 
1.4%

여과등처리시설여부
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size688.0 B
필터
40 
초기우수를 배제한 여과
5 
여과장치
5 
초기빗물배제방식
 
4
모래여과
 
3
Other values (10)
13 

Length

Max length18
Median length2
Mean length4.5714286
Min length2

Unique

Unique7 ?
Unique (%)10.0%

Sample

1st row필터
2nd row와류형 급속 중력침전여과
3rd row모래여과
4th row스크린여과, 염소소독
5th row스크린여과, 염소소독

Common Values

ValueCountFrequency (%)
필터 40
57.1%
초기우수를 배제한 여과 5
 
7.1%
여과장치 5
 
7.1%
초기빗물배제방식 4
 
5.7%
모래여과 3
 
4.3%
스크린여과, 염소소독 2
 
2.9%
침전형 2
 
2.9%
스크린 및 여과처리 2
 
2.9%
와류형 급속 중력침전여과 1
 
1.4%
침전형시설 1
 
1.4%
Other values (5) 5
 
7.1%

Length

2024-03-14T21:07:00.147640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
필터 40
44.4%
여과 6
 
6.7%
배제한 5
 
5.6%
여과장치 5
 
5.6%
초기우수를 5
 
5.6%
초기빗물배제방식 4
 
4.4%
모래여과 3
 
3.3%
스크린 2
 
2.2%
여과처리 2
 
2.2%
및 2
 
2.2%
Other values (13) 16
 
17.8%

저류조용량(세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.74586
Minimum3
Maximum2288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.0 B
2024-03-14T21:07:00.462853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile20.15
Q160.25
median141.62
Q3314.9925
95-th percentile1000.6
Maximum2288
Range2285
Interquartile range (IQR)254.7425

Descriptive statistics

Standard deviation400.82803
Coefficient of variation (CV)1.3152862
Kurtosis8.0252126
Mean304.74586
Median Absolute Deviation (MAD)96.62
Skewness2.4638208
Sum21332.21
Variance160663.11
MonotonicityNot monotonic
2024-03-14T21:07:00.712452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 3
 
4.3%
90.0 3
 
4.3%
300.0 3
 
4.3%
30.0 3
 
4.3%
70.0 2
 
2.9%
100.0 2
 
2.9%
50.0 2
 
2.9%
98.0 2
 
2.9%
3.0 2
 
2.9%
745.0 1
 
1.4%
Other values (47) 47
67.1%
ValueCountFrequency (%)
3.0 2
2.9%
13.0 1
 
1.4%
17.0 1
 
1.4%
24.0 1
 
1.4%
27.0 1
 
1.4%
30.0 3
4.3%
40.0 3
4.3%
50.0 2
2.9%
51.0 1
 
1.4%
51.44 1
 
1.4%
ValueCountFrequency (%)
2288.0 1
1.4%
1353.0 1
1.4%
1100.0 1
1.4%
1042.0 1
1.4%
950.0 1
1.4%
933.4 1
1.4%
877.0 1
1.4%
806.0 1
1.4%
790.0 1
1.4%
780.0 1
1.4%

빗물활용용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size688.0 B
조경
53 
조경용수
7 
청소화장실용수
 
4
기타
 
3
청소화장실
 
1
Other values (2)
 
2

Length

Max length7
Median length2
Mean length2.6571429
Min length2

Unique

Unique3 ?
Unique (%)4.3%

Sample

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

Common Values

ValueCountFrequency (%)
조경 53
75.7%
조경용수 7
 
10.0%
청소화장실용수 4
 
5.7%
기타 3
 
4.3%
청소화장실 1
 
1.4%
세척살수용수 1
 
1.4%
조경, 화장실 1
 
1.4%

Length

2024-03-14T21:07:01.156355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:07:01.386512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조경 54
76.1%
조경용수 7
 
9.9%
청소화장실용수 4
 
5.6%
기타 3
 
4.2%
청소화장실 1
 
1.4%
세척살수용수 1
 
1.4%
화장실 1
 
1.4%

Interactions

2024-03-14T21:06:46.688129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:06:46.189114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:06:46.937369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:06:46.448440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:07:01.613274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구시설명건축물용도구분소재지도로명주소법적시설여부설치완료일설치비(백만원)집수면여과등처리시설여부저류조용량(세제곱미터)빗물활용용도
연번1.0000.9931.0000.6710.9720.5920.9790.8050.0600.8210.3380.624
시군구0.9931.0001.0000.6561.0000.5600.9630.6100.0670.9270.2180.670
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
건축물용도구분0.6710.6561.0001.0001.0000.5400.0000.6330.0000.5890.8980.804
소재지도로명주소0.9721.0001.0001.0001.0000.9400.9970.8601.0000.0000.8870.000
법적시설여부0.5920.5601.0000.5400.9401.0001.0000.0000.0000.6360.4110.632
설치완료일0.9790.9631.0000.0000.9971.0001.0000.8731.0000.9750.0000.000
설치비(백만원)0.8050.6101.0000.6330.8600.0000.8731.0001.0000.8050.0000.913
집수면0.0600.0671.0000.0001.0000.0001.0001.0001.0000.6540.2070.000
여과등처리시설여부0.8210.9271.0000.5890.0000.6360.9750.8050.6541.0000.0000.576
저류조용량(세제곱미터)0.3380.2181.0000.8980.8870.4110.0000.0000.2070.0001.0000.724
빗물활용용도0.6240.6701.0000.8040.0000.6320.0000.9130.0000.5760.7241.000
2024-03-14T21:07:01.851227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축물용도구분시군구빗물활용용도여과등처리시설여부집수면법적시설여부
건축물용도구분1.0000.3830.4070.2390.0000.323
시군구0.3831.0000.5050.6060.0750.497
빗물활용용도0.4070.5051.0000.2800.0000.514
여과등처리시설여부0.2390.6060.2801.0000.5420.331
집수면0.0000.0750.0000.5421.0000.000
법적시설여부0.3230.4970.5140.3310.0001.000
2024-03-14T21:07:02.036199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번저류조용량(세제곱미터)시군구건축물용도구분법적시설여부집수면여과등처리시설여부빗물활용용도
연번1.000-0.0320.8480.3320.4110.0000.4550.367
저류조용량(세제곱미터)-0.0321.0000.1340.5320.2920.2100.0000.326
시군구0.8480.1341.0000.3830.4970.0750.6060.505
건축물용도구분0.3320.5320.3831.0000.3230.0000.2390.407
법적시설여부0.4110.2920.4970.3231.0000.0000.3310.514
집수면0.0000.2100.0750.0000.0001.0000.5420.000
여과등처리시설여부0.4550.0000.6060.2390.3310.5421.0000.280
빗물활용용도0.3670.3260.5050.4070.5140.0000.2801.000

Missing values

2024-03-14T21:06:47.286641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:06:47.792916image/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

연번시도시군구시설명건축물용도구분소재지도로명주소법적시설여부설치완료일설치비(백만원)집수면여과등처리시설여부저류조용량(세제곱미터)빗물활용용도
01광주광역시동구무등산아이파크공동주택광주광역시 동구 남문로 753번길 20대상2017-01-04확인불가지붕면필터527.0조경
12광주광역시동구동구다목적체육관운동시설광주광역시 동구 남문로 418-15대상2018-03-19120지붕면와류형 급속 중력침전여과225.0조경
23광주광역시동구동명동주민커뮤니티센터공공업무시설광주광역시 동구 동명로 20번길 43-5비대상2013-11-0149지붕면모래여과17.0조경
34광주광역시동구진아리채아파트 1BL공동주택광주광역시 동구 육판서길 16비대상2019-05-0960지붕면스크린여과, 염소소독61.0조경
45광주광역시동구진아리채아파트 2BL공동주택광주광역시 동구 육판서길 17비대상2020-03-0146지붕면스크린여과, 염소소독70.0조경
56광주광역시동구그랜드센트럴공동주택광주광역시 동구 경양로 234대상2020-08-30136지붕면초기우수를 배제한 여과790.0조경
67광주광역시동구무등산골드클래스공동주택광주광역시동구 학소로 109비대상2022-01-28150지붕면초기우수를 배제한 여과160.0조경
78광주광역시동구계림아이파크sk뷰공동주택광주광역시 동구 계림동 1340대상2022-06-07150지붕면초기우수를 배제한 여과643.0조경
89광주광역시동구광주고등법원공공업무시설광주광역시 동구 준법로 7-12대상2023-10-1915.4지붕면초기우수를 배제한 여과90.0조경
910광주광역시동구선교2차 우방아이유쉘 리포레공동주택광주광역시 동구 선교로 38-33비대상2023-10-3042지붕면초기우수를 배제한 여과142.0조경
연번시도시군구시설명건축물용도구분소재지도로명주소법적시설여부설치완료일설치비(백만원)집수면여과등처리시설여부저류조용량(세제곱미터)빗물활용용도
6061광주광역시광산구운남진아리채리버힐즈공동주택광주광역시 광산구 운남동 524-15비대상2019-11-2064지붕면필터51.44조경
6162광주광역시광산구신창 유탑유블레스리버뷰공동주택광주광역시 광산구 장신로 306-37비대상2020-01-03120지붕면필터145.0조경
6263광주광역시광산구산정동 어등산한양수자인테라스공동주택광주광역시 광산구 산정동 831-1대상2020-10-3049지붕면필터1100.0조경
6364광주광역시광산구우산동 쌍용더플래티넘공동주택광주광역시 광산구 우산동 1625대상2020-11-30280지붕면필터644.0조경
6465광주광역시광산구수완종합체육관체육관광주광역시 광산구 수완동 918대상2021-08-0578지붕면필터147.0조경
6566광주광역시광산구우산동 진아리채공동주택광주광역시 광산구 우산동 666 외비대상2021-12-0148지붕면필터70.0조경
6667광주광역시광산구모아엘가 더 수완공동주택광주광역시 광산구 목련로381번길 27비대상2022-02-1880지붕면필터141.24조경
6768광주광역시광산구예미지파크힐스오피스텔업무시설광주광역시 광산구 하남산단1번로 61비대상2022-08-0875지붕면필터94.14조경
6869광주광역시광산구빛그린산단 개방형 체육관체육관광주광역시 광산구 빛응유로 33(삼거동 476)대상2023-06-205.1지붕면필터61.25조경
6970광주광역시광산구평동산단 개방형 체육관체육관광주광역시 광산구 평동로 821(옥동 1241-1)대상2023-06-265지붕면필터58.75조경