Overview

Dataset statistics

Number of variables12
Number of observations27
Missing cells17
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory103.9 B

Variable types

Categorical4
Text3
DateTime1
Numeric3
Boolean1

Dataset

Description대구광역시 수성구_빗물이용시설현황_20200615
Author대구광역시 수성구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3075312&dataSetDetailId=30753121d3c4abea236d&provdMethod=FILE

Alerts

지 역(시·군·구) has constant value ""Constant
설치비(백만원) is highly overall correlated with 집수면적(㎥) and 3 other fieldsHigh correlation
집수면적(㎥) is highly overall correlated with 설치비(백만원) and 2 other fieldsHigh correlation
저류조 용량(㎥) is highly overall correlated with 설치비(백만원) and 3 other fieldsHigh correlation
여과 등 처리시설 유무 is highly overall correlated with 설치비(백만원) and 2 other fieldsHigh correlation
빗물활용 용도 is highly overall correlated with 여과 등 처리시설 유무High correlation
법적시설여부(대상/미대상) is highly overall correlated with 설치비(백만원) and 2 other fieldsHigh correlation
집수면 is highly imbalanced (77.1%)Imbalance
연간 사용량(㎥/년) has 17 (63.0%) missing valuesMissing
위치(주소) has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:51:47.971853
Analysis finished2023-12-10 17:51:51.443259
Duration3.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지 역(시·군·구)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
대구광역시 수성구
27 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 수성구
2nd row대구광역시 수성구
3rd row대구광역시 수성구
4th row대구광역시 수성구
5th row대구광역시 수성구

Common Values

ValueCountFrequency (%)
대구광역시 수성구 27
100.0%

Length

2023-12-11T02:51:51.595563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:51:51.818493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 27
50.0%
수성구 27
50.0%

시설명
Text

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T02:51:52.140709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length6.3703704
Min length2

Characters and Unicode

Total characters172
Distinct characters97
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)88.9%

Sample

1st row대구육상진흥센터
2nd row대구미술관
3rd row들안길초등학교
4th row노변중학교
5th row교보생명빌딩
ValueCountFrequency (%)
주택 3
 
10.3%
꿈나무어린이집 1
 
3.4%
아이숲유치원 1
 
3.4%
대구시장애인체육센터 1
 
3.4%
체육회관 1
 
3.4%
대구광역시 1
 
3.4%
범어센트럴푸르지오 1
 
3.4%
중동어린이집 1
 
3.4%
해맑은어린이집 1
 
3.4%
미소드림빌라 1
 
3.4%
Other values (17) 17
58.6%
2023-12-11T02:51:52.916007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 7
 
4.1%
원 6
 
3.5%
구 6
 
3.5%
어 6
 
3.5%
유 5
 
2.9%
치 5
 
2.9%
대 5
 
2.9%
센 4
 
2.3%
주 3
 
1.7%
라 3
 
1.7%
Other values (87) 122
70.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 169
98.3%
Space Separator 2
 
1.2%
Other Symbol 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 7
 
4.1%
원 6
 
3.6%
구 6
 
3.6%
어 6
 
3.6%
유 5
 
3.0%
치 5
 
3.0%
대 5
 
3.0%
센 4
 
2.4%
주 3
 
1.8%
라 3
 
1.8%
Other values (85) 119
70.4%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
98.8%
Common 2
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 7
 
4.1%
원 6
 
3.5%
구 6
 
3.5%
어 6
 
3.5%
유 5
 
2.9%
치 5
 
2.9%
대 5
 
2.9%
센 4
 
2.4%
주 3
 
1.8%
라 3
 
1.8%
Other values (86) 120
70.6%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 169
98.3%
ASCII 2
 
1.2%
None 1
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 7
 
4.1%
원 6
 
3.6%
구 6
 
3.6%
어 6
 
3.6%
유 5
 
3.0%
치 5
 
3.0%
대 5
 
3.0%
센 4
 
2.4%
주 3
 
1.8%
라 3
 
1.8%
Other values (85) 119
70.4%
ASCII
ValueCountFrequency (%)
2
100.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

위치(주소)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T02:51:54.239339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length27
Mean length23.407407
Min length16

Characters and Unicode

Total characters632
Distinct characters74
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

Unique27 ?
Unique (%)100.0%

Sample

1st row대구광역시 수성구 미술관로 88(삼덕동)
2nd row대구광역시 수성구 미술관로 40(삼덕동)
3rd row대구광역시 수성구 들안로 16길 58(두산동)
4th row대구광역시 수성구 시지로 66(노변동)
5th row대구광역시 수성구 달구벌대로 2330(수성동2가)
ValueCountFrequency (%)
대구광역시 28
22.4%
수성구 27
21.6%
달구벌대로 4
 
3.2%
대흥동 3
 
2.4%
유니버시아드로42길 3
 
2.4%
미술관로 2
 
1.6%
범어동 2
 
1.6%
21 2
 
1.6%
36길 1
 
0.8%
145 1
 
0.8%
Other values (52) 52
41.6%
2023-12-11T02:51:57.509378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
100
15.8%
구 62
 
9.8%
대 38
 
6.0%
시 33
 
5.2%
광 29
 
4.6%
역 29
 
4.6%
수 29
 
4.6%
성 29
 
4.6%
로 24
 
3.8%
1 24
 
3.8%
Other values (64) 235
37.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 400
63.3%
Decimal Number 104
 
16.5%
Space Separator 100
 
15.8%
Close Punctuation 11
 
1.7%
Open Punctuation 11
 
1.7%
Dash Punctuation 6
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 62
15.5%
대 38
 
9.5%
시 33
 
8.2%
광 29
 
7.2%
역 29
 
7.2%
수 29
 
7.2%
성 29
 
7.2%
로 24
 
6.0%
동 16
 
4.0%
길 14
 
3.5%
Other values (50) 97
24.2%
Decimal Number
ValueCountFrequency (%)
1 24
23.1%
2 18
17.3%
3 13
12.5%
5 11
10.6%
6 11
10.6%
4 8
 
7.7%
7 6
 
5.8%
0 5
 
4.8%
8 5
 
4.8%
9 3
 
2.9%
Space Separator
ValueCountFrequency (%)
100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 400
63.3%
Common 232
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 62
15.5%
대 38
 
9.5%
시 33
 
8.2%
광 29
 
7.2%
역 29
 
7.2%
수 29
 
7.2%
성 29
 
7.2%
로 24
 
6.0%
동 16
 
4.0%
길 14
 
3.5%
Other values (50) 97
24.2%
Common
ValueCountFrequency (%)
100
43.1%
1 24
 
10.3%
2 18
 
7.8%
3 13
 
5.6%
5 11
 
4.7%
) 11
 
4.7%
6 11
 
4.7%
( 11
 
4.7%
4 8
 
3.4%
7 6
 
2.6%
Other values (4) 19
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 400
63.3%
ASCII 232
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
100
43.1%
1 24
 
10.3%
2 18
 
7.8%
3 13
 
5.6%
5 11
 
4.7%
) 11
 
4.7%
6 11
 
4.7%
( 11
 
4.7%
4 8
 
3.4%
7 6
 
2.6%
Other values (4) 19
 
8.2%
Hangul
ValueCountFrequency (%)
구 62
15.5%
대 38
 
9.5%
시 33
 
8.2%
광 29
 
7.2%
역 29
 
7.2%
수 29
 
7.2%
성 29
 
7.2%
로 24
 
6.0%
동 16
 
4.0%
길 14
 
3.5%
Other values (50) 97
24.2%

설치년월
Date

Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum2010-03-19 00:00:00
Maximum2020-05-23 00:00:00
2023-12-11T02:51:59.096903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:51:59.623371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

설치비(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.511111
Minimum2.7
Maximum319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T02:51:59.974169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile2.8
Q13.3
median4
Q353.5
95-th percentile88.4
Maximum319
Range316.3
Interquartile range (IQR)50.2

Descriptive statistics

Standard deviation64.288983
Coefficient of variation (CV)1.8628488
Kurtosis15.304262
Mean34.511111
Median Absolute Deviation (MAD)1.2
Skewness3.5849836
Sum931.8
Variance4133.0733
MonotonicityNot monotonic
2023-12-11T02:52:00.350019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3.3 7
25.9%
4.0 3
11.1%
3.0 3
11.1%
2.8 2
 
7.4%
72.0 1
 
3.7%
66.0 1
 
3.7%
54.0 1
 
3.7%
20.0 1
 
3.7%
2.7 1
 
3.7%
319.0 1
 
3.7%
Other values (6) 6
22.2%
ValueCountFrequency (%)
2.7 1
 
3.7%
2.8 2
 
7.4%
3.0 3
11.1%
3.3 7
25.9%
4.0 3
11.1%
5.4 1
 
3.7%
20.0 1
 
3.7%
50.0 1
 
3.7%
53.0 1
 
3.7%
54.0 1
 
3.7%
ValueCountFrequency (%)
319.0 1
3.7%
92.0 1
3.7%
80.0 1
3.7%
72.0 1
3.7%
68.0 1
3.7%
66.0 1
3.7%
54.0 1
3.7%
53.0 1
3.7%
50.0 1
3.7%
20.0 1
3.7%

집수면
Categorical

IMBALANCE 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
지붕면
26 
기타
 
1

Length

Max length3
Median length3
Mean length2.962963
Min length2

Unique

Unique1 ?
Unique (%)3.7%

Sample

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

Common Values

ValueCountFrequency (%)
지붕면 26
96.3%
기타 1
 
3.7%

Length

2023-12-11T02:52:00.739520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:52:01.069648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지붕면 26
96.3%
기타 1
 
3.7%

집수면적(㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean852.16574
Minimum1.36
Maximum4627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T02:52:01.357549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile2.1705
Q16
median36.3
Q31402.465
95-th percentile3336.6
Maximum4627
Range4625.64
Interquartile range (IQR)1396.465

Descriptive statistics

Standard deviation1345.2382
Coefficient of variation (CV)1.578611
Kurtosis1.228826
Mean852.16574
Median Absolute Deviation (MAD)33.3
Skewness1.5103731
Sum23008.475
Variance1809665.9
MonotonicityNot monotonic
2023-12-11T02:52:01.672756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
6.0 3
 
11.1%
3504.0 1
 
3.7%
36.3 1
 
3.7%
1867.0 1
 
3.7%
1661.05 1
 
3.7%
1143.88 1
 
3.7%
644.62 1
 
3.7%
30.8 1
 
3.7%
20.0 1
 
3.7%
8.0 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
1.36 1
 
3.7%
1.815 1
 
3.7%
3.0 1
 
3.7%
3.9 1
 
3.7%
4.7 1
 
3.7%
6.0 3
11.1%
6.21 1
 
3.7%
8.0 1
 
3.7%
11.0 1
 
3.7%
20.0 1
 
3.7%
ValueCountFrequency (%)
4627.0 1
3.7%
3504.0 1
3.7%
2946.0 1
3.7%
2902.0 1
3.7%
2726.0 1
3.7%
1867.0 1
3.7%
1661.05 1
3.7%
1143.88 1
3.7%
676.8 1
3.7%
644.62 1
3.7%

여과 등 처리시설 유무
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Memory size348.0 B
여과시설
17 
스크린
4 
약품시설
2 
초기우수배재
 
1
자동여과
 
1
Other values (2)
2 

Length

Max length14
Median length4
Mean length4.4814815
Min length3

Unique

Unique4 ?
Unique (%)14.8%

Sample

1st row초기우수배재
2nd row자동여과
3rd row스크린
4th row스크린
5th row스크린

Common Values

ValueCountFrequency (%)
여과시설 17
63.0%
스크린 4
 
14.8%
약품시설 2
 
7.4%
초기우수배재 1
 
3.7%
자동여과 1
 
3.7%
초기우수배제, 여과, 살균 1
 
3.7%
스크린, 여과시설 1
 
3.7%

Length

2023-12-11T02:52:02.000014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:52:02.301713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여과시설 18
60.0%
스크린 5
 
16.7%
약품시설 2
 
6.7%
초기우수배재 1
 
3.3%
자동여과 1
 
3.3%
초기우수배제 1
 
3.3%
여과 1
 
3.3%
살균 1
 
3.3%

저류조 용량(㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.122222
Minimum1
Maximum677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T02:52:02.589053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q365
95-th percentile396.7
Maximum677
Range676
Interquartile range (IQR)64

Descriptive statistics

Standard deviation160.07266
Coefficient of variation (CV)2.1028375
Kurtosis7.8974621
Mean76.122222
Median Absolute Deviation (MAD)0.5
Skewness2.7755336
Sum2055.3
Variance25623.257
MonotonicityNot monotonic
2023-12-11T02:52:02.915013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1.0 12
44.4%
1.5 3
 
11.1%
677.0 1
 
3.7%
240.0 1
 
3.7%
30.0 1
 
3.7%
60.0 1
 
3.7%
40.0 1
 
3.7%
270.0 1
 
3.7%
2.0 1
 
3.7%
2.5 1
 
3.7%
Other values (4) 4
 
14.8%
ValueCountFrequency (%)
1.0 12
44.4%
1.5 3
 
11.1%
2.0 1
 
3.7%
2.5 1
 
3.7%
30.0 1
 
3.7%
40.0 1
 
3.7%
60.0 1
 
3.7%
70.0 1
 
3.7%
90.0 1
 
3.7%
106.3 1
 
3.7%
ValueCountFrequency (%)
677.0 1
3.7%
451.0 1
3.7%
270.0 1
3.7%
240.0 1
3.7%
106.3 1
3.7%
90.0 1
3.7%
70.0 1
3.7%
60.0 1
3.7%
40.0 1
3.7%
30.0 1
3.7%

연간 사용량(㎥/년)
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing17
Missing (%)63.0%
Memory size348.0 B
2023-12-11T02:52:03.302494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1
Min length1

Characters and Unicode

Total characters21
Distinct characters8
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

Unique10 ?
Unique (%)100.0%

Sample

1st row210
2nd row42
3rd row30
4th row100
5th row35
ValueCountFrequency (%)
210 1
11.1%
42 1
11.1%
30 1
11.1%
100 1
11.1%
35 1
11.1%
115 1
11.1%
3 1
11.1%
1 1
11.1%
1.2 1
11.1%
2023-12-11T02:52:03.976445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6
28.6%
0 4
19.0%
2 3
14.3%
3 3
14.3%
5 2
 
9.5%
4 1
 
4.8%
. 1
 
4.8%
1
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19
90.5%
Other Punctuation 1
 
4.8%
Space Separator 1
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6
31.6%
0 4
21.1%
2 3
15.8%
3 3
15.8%
5 2
 
10.5%
4 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6
28.6%
0 4
19.0%
2 3
14.3%
3 3
14.3%
5 2
 
9.5%
4 1
 
4.8%
. 1
 
4.8%
1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6
28.6%
0 4
19.0%
2 3
14.3%
3 3
14.3%
5 2
 
9.5%
4 1
 
4.8%
. 1
 
4.8%
1
 
4.8%

빗물활용 용도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size348.0 B
조경용수
14 
조경용
9 
농업용수
3 
조경 및 청소
 
1

Length

Max length7
Median length4
Mean length3.7777778
Min length3

Unique

Unique1 ?
Unique (%)3.7%

Sample

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

Common Values

ValueCountFrequency (%)
조경용수 14
51.9%
조경용 9
33.3%
농업용수 3
 
11.1%
조경 및 청소 1
 
3.7%

Length

2023-12-11T02:52:04.352093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:52:04.621896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조경용수 14
48.3%
조경용 9
31.0%
농업용수 3
 
10.3%
조경 1
 
3.4%
및 1
 
3.4%
청소 1
 
3.4%

법적시설여부(대상/미대상)
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size159.0 B
False
23 
True
4 
ValueCountFrequency (%)
False 23
85.2%
True 4
 
14.8%
2023-12-11T02:52:04.847964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-11T02:51:50.217663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:51:48.942602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:51:49.606698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:51:50.429173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:51:49.145400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:51:49.830535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:51:50.628580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:51:49.357471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:51:50.003347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:52:05.005687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명위치(주소)설치년월설치비(백만원)집수면집수면적(㎥)여과 등 처리시설 유무저류조 용량(㎥)연간 사용량(㎥/년)빗물활용 용도법적시설여부(대상/미대상)
시설명1.0001.0000.8831.0001.0001.0001.0001.0001.0001.0001.000
위치(주소)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
설치년월0.8831.0001.0000.9640.0000.7300.9521.0001.0000.8801.000
설치비(백만원)1.0001.0000.9641.0000.0000.9400.7780.7211.0000.0000.825
집수면1.0001.0000.0000.0001.0000.0000.0000.000NaN0.0000.000
집수면적(㎥)1.0001.0000.7300.9400.0001.0000.7560.9261.0000.0001.000
여과 등 처리시설 유무1.0001.0000.9520.7780.0000.7561.0000.7561.0000.7190.243
저류조 용량(㎥)1.0001.0001.0000.7210.0000.9260.7561.0001.0000.0001.000
연간 사용량(㎥/년)1.0001.0001.0001.000NaN1.0001.0001.0001.0001.0001.000
빗물활용 용도1.0001.0000.8800.0000.0000.0000.7190.0001.0001.0000.000
법적시설여부(대상/미대상)1.0001.0001.0000.8250.0001.0000.2431.0001.0000.0001.000
2023-12-11T02:52:05.362921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법적시설여부(대상/미대상)빗물활용 용도여과 등 처리시설 유무집수면
법적시설여부(대상/미대상)1.0000.0000.2140.000
빗물활용 용도0.0001.0000.5450.000
여과 등 처리시설 유무0.2140.5451.0000.000
집수면0.0000.0000.0001.000
2023-12-11T02:52:05.623156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치비(백만원)집수면적(㎥)저류조 용량(㎥)집수면여과 등 처리시설 유무빗물활용 용도법적시설여부(대상/미대상)
설치비(백만원)1.0000.7440.8730.0000.6180.0000.592
집수면적(㎥)0.7441.0000.7950.0000.4960.0000.849
저류조 용량(㎥)0.8730.7951.0000.0000.5850.0000.938
집수면0.0000.0000.0001.0000.0000.0000.000
여과 등 처리시설 유무0.6180.4960.5850.0001.0000.5450.214
빗물활용 용도0.0000.0000.0000.0000.5451.0000.000
법적시설여부(대상/미대상)0.5920.8490.9380.0000.2140.0001.000

Missing values

2023-12-11T02:51:50.933883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:51:51.294166image/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

지 역(시·군·구)시설명위치(주소)설치년월설치비(백만원)집수면집수면적(㎥)여과 등 처리시설 유무저류조 용량(㎥)연간 사용량(㎥/년)빗물활용 용도법적시설여부(대상/미대상)
0대구광역시 수성구대구육상진흥센터대구광역시 수성구 미술관로 88(삼덕동)2013-05-23319.0지붕면3504.0초기우수배재677.0210조경용Y
1대구광역시 수성구대구미술관대구광역시 수성구 미술관로 40(삼덕동)2010-03-1953.0지붕면2902.0자동여과240.042조경용N
2대구광역시 수성구들안길초등학교대구광역시 수성구 들안로 16길 58(두산동)2010-08-0850.0지붕면2726.0스크린30.030조경용N
3대구광역시 수성구노변중학교대구광역시 수성구 시지로 66(노변동)2012-02-0180.0지붕면2946.0스크린60.0100조경용N
4대구광역시 수성구교보생명빌딩대구광역시 수성구 달구벌대로 2330(수성동2가)2015-09-1192.0지붕면676.8스크린40.035조경용N
5대구광역시 수성구삼성라이온즈파크대구광역시 수성구 야구전설로 1 (연호동)2016-01-2568.0지붕면4627.0초기우수배제, 여과, 살균270.0115조경용N
6대구광역시 수성구비컨영어마을학원대구광역시 수성구 범어로 133 (범어동)2016-07-294.0지붕면6.21스크린, 여과시설2.0<NA>조경용N
7대구광역시 수성구단비유치원대구광역시 수성구 신매로 512016-08-183.0기타1.815여과시설1.0<NA>조경용N
8대구광역시 수성구아너스유치원대구광역시 수성구 천을로 197 (매호동)2016-08-183.0지붕면1.36여과시설1.0<NA>조경용N
9대구광역시 수성구이남희헤어대구광역시 수성구 동대구로8길 212016-07-292.8지붕면73.91스크린1.0<NA>조경 및 청소N
지 역(시·군·구)시설명위치(주소)설치년월설치비(백만원)집수면집수면적(㎥)여과 등 처리시설 유무저류조 용량(㎥)연간 사용량(㎥/년)빗물활용 용도법적시설여부(대상/미대상)
17대구광역시 수성구안나수이빌라대구광역시 수성구 희망로 36길 712018-06-153.3지붕면53.0약품시설1.0<NA>농업용수N
18대구광역시 수성구달구벌식품공장 영농기술㈜대구광역시 수성구 지범로 1452018-05-284.0지붕면6.0약품시설1.5<NA>농업용수N
19대구광역시 수성구미소드림빌라대구광역시 수성구 달구벌대로 661길 112018-05-203.3지붕면3.9여과시설1.0<NA>농업용수N
20대구광역시 수성구해맑은어린이집대구광역시 수성구 들안로32길 172019-07-023.3지붕면8.0여과시설1.0<NA>조경용수N
21대구광역시 수성구주택대구광역시 수성구 범안로3안길 33-172019-06-242.7지붕면20.0여과시설1.0<NA>조경용수N
22대구광역시 수성구중동어린이집대구광역시 수성구 중동 316번지2019-06-243.0지붕면30.8여과시설1.5<NA>조경용수N
23대구광역시 수성구범어센트럴푸르지오대구광역시 수성구 범어동 556-12번지 일대2019-05-0120.0지붕면644.62여과시설451.0<NA>조경용수N
24대구광역시 수성구대구광역시 체육회관대구광역시 수성구 유니버시아드로42길 127 대구광역시 체육회관 (대흥동)2020-05-2354.0지붕면1143.88여과시설70.0<NA>조경용수Y
25대구광역시 수성구대구시장애인체육센터대구광역시 수성구 유니버시아드로42길 125 대구광역시장애인국민체육센터 (대흥동)2020-05-2366.0지붕면1661.05여과시설90.0<NA>조경용수Y
26대구광역시 수성구대구스포츠단훈련센터대구광역시 수성구 유니버시아드로42길 139 (대흥동)2020-05-2372.0지붕면1867.0여과시설106.3<NA>조경용수Y