Overview

Dataset statistics

Number of variables12
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory106.7 B

Variable types

Numeric6
Categorical3
Text3

Dataset

Description인천광역시 미추홀구 비상급수시설 현황에 대한 데이터로 유형, 시설명, 관리자, 소재지,심도, 1일생산량,수질구분,위도,경도 등의 항목을 제공합니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15051598&srcSe=7661IVAWM27C61E190

Alerts

연번 is highly overall correlated with 유형 High correlation
설치연도 is highly overall correlated with 유형 High correlation
위도 is highly overall correlated with 관리자High correlation
경도 is highly overall correlated with 관리자High correlation
유형 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
관리자 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
수질구분 is highly imbalanced (50.9%)Imbalance
연번 has unique valuesUnique
시설명 has unique valuesUnique
도로명주소 has unique valuesUnique
지번주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-04-06 09:40:12.647393
Analysis finished2024-04-06 09:40:17.358672
Duration4.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T18:40:17.424269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q17.75
median14.5
Q321.25
95-th percentile26.65
Maximum28
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)0.56730863
Kurtosis-1.2
Mean14.5
Median Absolute Deviation (MAD)7
Skewness0
Sum406
Variance67.666667
MonotonicityStrictly increasing
2024-04-06T18:40:17.612733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
16 1
 
3.6%
28 1
 
3.6%
27 1
 
3.6%
26 1
 
3.6%
25 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
22 1
 
3.6%
21 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
4 1
3.6%
5 1
3.6%
6 1
3.6%
7 1
3.6%
8 1
3.6%
9 1
3.6%
10 1
3.6%
ValueCountFrequency (%)
28 1
3.6%
27 1
3.6%
26 1
3.6%
25 1
3.6%
24 1
3.6%
23 1
3.6%
22 1
3.6%
21 1
3.6%
20 1
3.6%
19 1
3.6%

유형
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size356.0 B
공공용시설
12 
지자체시설
10 
정부지원시설
6 

Length

Max length6
Median length5
Mean length5.2142857
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공용시설 12
42.9%
지자체시설 10
35.7%
정부지원시설 6
21.4%

Length

2024-04-06T18:40:17.827367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:40:18.000369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공용시설 12
42.9%
지자체시설 10
35.7%
정부지원시설 6
21.4%

시설명
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-04-06T18:40:18.217886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length5.8214286
Min length4

Characters and Unicode

Total characters163
Distinct characters96
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

Unique28 ?
Unique (%)100.0%

Sample

1st row미추홀구청
2nd row남부초등학교
3rd row로얄아파트
4th row주안남초등학교
5th row제물포여중
ValueCountFrequency (%)
미추홀구청 1
 
3.1%
남부초등학교 1
 
3.1%
관교탕 1
 
3.1%
미추홀구 1
 
3.1%
인천광역시 1
 
3.1%
삼원빌딩 1
 
3.1%
한일목욕탕 1
 
3.1%
세종빌딩 1
 
3.1%
에이스빌딩 1
 
3.1%
순일목욕탕 1
 
3.1%
Other values (22) 22
68.8%
2024-04-06T18:40:18.866651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 7
 
4.3%
교 6
 
3.7%
탕 5
 
3.1%
등 5
 
3.1%
원 5
 
3.1%
주 4
 
2.5%
초 4
 
2.5%
4
 
2.5%
미 3
 
1.8%
스 3
 
1.8%
Other values (86) 117
71.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 158
96.9%
Space Separator 4
 
2.5%
Decimal Number 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 7
 
4.4%
교 6
 
3.8%
탕 5
 
3.2%
등 5
 
3.2%
원 5
 
3.2%
주 4
 
2.5%
초 4
 
2.5%
미 3
 
1.9%
스 3
 
1.9%
목 3
 
1.9%
Other values (84) 113
71.5%
Space Separator
ValueCountFrequency (%)
4
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 158
96.9%
Common 5
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 7
 
4.4%
교 6
 
3.8%
탕 5
 
3.2%
등 5
 
3.2%
원 5
 
3.2%
주 4
 
2.5%
초 4
 
2.5%
미 3
 
1.9%
스 3
 
1.9%
목 3
 
1.9%
Other values (84) 113
71.5%
Common
ValueCountFrequency (%)
4
80.0%
2 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 158
96.9%
ASCII 5
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 7
 
4.4%
교 6
 
3.8%
탕 5
 
3.2%
등 5
 
3.2%
원 5
 
3.2%
주 4
 
2.5%
초 4
 
2.5%
미 3
 
1.9%
스 3
 
1.9%
목 3
 
1.9%
Other values (84) 113
71.5%
ASCII
ValueCountFrequency (%)
4
80.0%
2 1
 
20.0%

관리자
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
인천광역시 미추홀구청
16 
김병수
 
1
서정렬
 
1
원미은
 
1
장병규
 
1
Other values (8)
8 

Length

Max length11
Median length11
Mean length7.5714286
Min length3

Unique

Unique12 ?
Unique (%)42.9%

Sample

1st row인천광역시 미추홀구청
2nd row인천광역시 미추홀구청
3rd row인천광역시 미추홀구청
4th row인천광역시 미추홀구청
5th row인천광역시 미추홀구청

Common Values

ValueCountFrequency (%)
인천광역시 미추홀구청 16
57.1%
김병수 1
 
3.6%
서정렬 1
 
3.6%
원미은 1
 
3.6%
장병규 1
 
3.6%
오성진 1
 
3.6%
김숙례 1
 
3.6%
김희태 1
 
3.6%
이종만 1
 
3.6%
오용진 1
 
3.6%
Other values (3) 3
 
10.7%

Length

2024-04-06T18:40:19.022628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천광역시 16
36.4%
미추홀구청 16
36.4%
김병수 1
 
2.3%
서정렬 1
 
2.3%
원미은 1
 
2.3%
장병규 1
 
2.3%
오성진 1
 
2.3%
김숙례 1
 
2.3%
김희태 1
 
2.3%
이종만 1
 
2.3%
Other values (4) 4
 
9.1%

도로명주소
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-04-06T18:40:19.256215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length26.071429
Min length22

Characters and Unicode

Total characters730
Distinct characters60
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

Unique28 ?
Unique (%)100.0%

Sample

1st row인천광역시 미추홀구 독정이로 95 (숭의동)
2nd row인천광역시 미추홀구 인주대로366번길 22 (주안동)
3rd row인천광역시 미추홀구 미추홀대로 589 (주안동)
4th row인천광역시 미추홀구 인주대로434번길 11 (주안동)
5th row인천광역시 미추홀구 인주대로470번길 20 (주안동)
ValueCountFrequency (%)
인천광역시 28
20.3%
미추홀구 28
20.3%
주안동 13
 
9.4%
용현동 3
 
2.2%
도화동 3
 
2.2%
경인로 3
 
2.2%
학익동 2
 
1.4%
독정이로 2
 
1.4%
인주대로 2
 
1.4%
숭의동 2
 
1.4%
Other values (48) 52
37.7%
2024-04-06T18:40:19.686936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
110
 
15.1%
인 38
 
5.2%
홀 31
 
4.2%
구 30
 
4.1%
미 29
 
4.0%
추 29
 
4.0%
( 28
 
3.8%
광 28
 
3.8%
역 28
 
3.8%
시 28
 
3.8%
Other values (50) 351
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 461
63.2%
Space Separator 110
 
15.1%
Decimal Number 100
 
13.7%
Open Punctuation 28
 
3.8%
Close Punctuation 28
 
3.8%
Dash Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 38
 
8.2%
홀 31
 
6.7%
구 30
 
6.5%
미 29
 
6.3%
추 29
 
6.3%
광 28
 
6.1%
역 28
 
6.1%
시 28
 
6.1%
천 28
 
6.1%
동 28
 
6.1%
Other values (36) 164
35.6%
Decimal Number
ValueCountFrequency (%)
4 19
19.0%
1 12
12.0%
2 12
12.0%
0 10
10.0%
5 9
9.0%
8 9
9.0%
3 9
9.0%
6 8
8.0%
9 8
8.0%
7 4
 
4.0%
Space Separator
ValueCountFrequency (%)
110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 461
63.2%
Common 269
36.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 38
 
8.2%
홀 31
 
6.7%
구 30
 
6.5%
미 29
 
6.3%
추 29
 
6.3%
광 28
 
6.1%
역 28
 
6.1%
시 28
 
6.1%
천 28
 
6.1%
동 28
 
6.1%
Other values (36) 164
35.6%
Common
ValueCountFrequency (%)
110
40.9%
( 28
 
10.4%
) 28
 
10.4%
4 19
 
7.1%
1 12
 
4.5%
2 12
 
4.5%
0 10
 
3.7%
5 9
 
3.3%
8 9
 
3.3%
3 9
 
3.3%
Other values (4) 23
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 461
63.2%
ASCII 269
36.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110
40.9%
( 28
 
10.4%
) 28
 
10.4%
4 19
 
7.1%
1 12
 
4.5%
2 12
 
4.5%
0 10
 
3.7%
5 9
 
3.3%
8 9
 
3.3%
3 9
 
3.3%
Other values (4) 23
 
8.6%
Hangul
ValueCountFrequency (%)
인 38
 
8.2%
홀 31
 
6.7%
구 30
 
6.5%
미 29
 
6.3%
추 29
 
6.3%
광 28
 
6.1%
역 28
 
6.1%
시 28
 
6.1%
천 28
 
6.1%
동 28
 
6.1%
Other values (36) 164
35.6%

지번주소
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-04-06T18:40:19.952395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length21.107143
Min length19

Characters and Unicode

Total characters591
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
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 row인천광역시 미추홀구 숭의2동 131-1
2nd row인천광역시 미추홀구 주안7동 1444-2
3rd row인천광역시 미추홀구 주안7동 1458
4th row인천광역시 미추홀구 주안8동 1503
5th row인천광역시 미추홀구 주안8동 1529
ValueCountFrequency (%)
인천광역시 28
24.8%
미추홀구 28
24.8%
주안4동 5
 
4.4%
주안8동 4
 
3.5%
주안7동 3
 
2.7%
문학동 2
 
1.8%
도화1동 2
 
1.8%
학익2동 2
 
1.8%
490-1 1
 
0.9%
숭의동 1
 
0.9%
Other values (37) 37
32.7%
2024-04-06T18:40:20.341874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
14.4%
1 36
 
6.1%
인 28
 
4.7%
추 28
 
4.7%
동 28
 
4.7%
구 28
 
4.7%
천 28
 
4.7%
홀 28
 
4.7%
미 28
 
4.7%
시 28
 
4.7%
Other values (26) 246
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 336
56.9%
Decimal Number 146
24.7%
Space Separator 85
 
14.4%
Dash Punctuation 21
 
3.6%
Other Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 28
8.3%
추 28
8.3%
동 28
8.3%
구 28
8.3%
천 28
8.3%
홀 28
8.3%
미 28
8.3%
시 28
8.3%
역 28
8.3%
광 28
8.3%
Other values (13) 56
16.7%
Decimal Number
ValueCountFrequency (%)
1 36
24.7%
4 27
18.5%
3 17
11.6%
2 15
10.3%
5 14
 
9.6%
6 10
 
6.8%
8 9
 
6.2%
7 7
 
4.8%
9 6
 
4.1%
0 5
 
3.4%
Space Separator
ValueCountFrequency (%)
85
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 336
56.9%
Common 255
43.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 28
8.3%
추 28
8.3%
동 28
8.3%
구 28
8.3%
천 28
8.3%
홀 28
8.3%
미 28
8.3%
시 28
8.3%
역 28
8.3%
광 28
8.3%
Other values (13) 56
16.7%
Common
ValueCountFrequency (%)
85
33.3%
1 36
14.1%
4 27
 
10.6%
- 21
 
8.2%
3 17
 
6.7%
2 15
 
5.9%
5 14
 
5.5%
6 10
 
3.9%
8 9
 
3.5%
7 7
 
2.7%
Other values (3) 14
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 336
56.9%
ASCII 252
42.6%
None 3
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
33.7%
1 36
14.3%
4 27
 
10.7%
- 21
 
8.3%
3 17
 
6.7%
2 15
 
6.0%
5 14
 
5.6%
6 10
 
4.0%
8 9
 
3.6%
7 7
 
2.8%
Other values (2) 11
 
4.4%
Hangul
ValueCountFrequency (%)
인 28
8.3%
추 28
8.3%
동 28
8.3%
구 28
8.3%
천 28
8.3%
홀 28
8.3%
미 28
8.3%
시 28
8.3%
역 28
8.3%
광 28
8.3%
Other values (13) 56
16.7%
None
ValueCountFrequency (%)
· 3
100.0%

설치연도
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1994.8929
Minimum1978
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T18:40:20.474344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1978
5-th percentile1978
Q11988
median1995
Q31998.25
95-th percentile2012.65
Maximum2023
Range45
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation11.301718
Coefficient of variation (CV)0.005665326
Kurtosis0.19017985
Mean1994.8929
Median Absolute Deviation (MAD)4.5
Skewness0.40935955
Sum55857
Variance127.72884
MonotonicityNot monotonic
2024-04-06T18:40:20.587826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1995 5
17.9%
1997 3
10.7%
1978 3
10.7%
1982 3
10.7%
1996 2
 
7.1%
2009 1
 
3.6%
2001 1
 
3.6%
1999 1
 
3.6%
1998 1
 
3.6%
2023 1
 
3.6%
Other values (7) 7
25.0%
ValueCountFrequency (%)
1978 3
10.7%
1981 1
 
3.6%
1982 3
10.7%
1990 1
 
3.6%
1991 1
 
3.6%
1994 1
 
3.6%
1995 5
17.9%
1996 2
 
7.1%
1997 3
10.7%
1998 1
 
3.6%
ValueCountFrequency (%)
2023 1
 
3.6%
2013 1
 
3.6%
2012 1
 
3.6%
2009 1
 
3.6%
2008 1
 
3.6%
2001 1
 
3.6%
1999 1
 
3.6%
1998 1
 
3.6%
1997 3
10.7%
1996 2
7.1%

심도
Real number (ℝ)

Distinct12
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.285714
Minimum14
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T18:40:20.710308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile21.35
Q145.25
median85
Q3102.5
95-th percentile150
Maximum150
Range136
Interquartile range (IQR)57.25

Descriptive statistics

Standard deviation38.795625
Coefficient of variation (CV)0.46581368
Kurtosis-0.64591645
Mean83.285714
Median Absolute Deviation (MAD)30
Skewness-0.04807596
Sum2332
Variance1505.1005
MonotonicityNot monotonic
2024-04-06T18:40:20.828189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
100 5
17.9%
40 4
14.3%
150 3
10.7%
120 3
10.7%
80 3
10.7%
75 2
 
7.1%
14 2
 
7.1%
90 2
 
7.1%
110 1
 
3.6%
72 1
 
3.6%
Other values (2) 2
 
7.1%
ValueCountFrequency (%)
14 2
 
7.1%
35 1
 
3.6%
40 4
14.3%
47 1
 
3.6%
72 1
 
3.6%
75 2
 
7.1%
80 3
10.7%
90 2
 
7.1%
100 5
17.9%
110 1
 
3.6%
ValueCountFrequency (%)
150 3
10.7%
120 3
10.7%
110 1
 
3.6%
100 5
17.9%
90 2
 
7.1%
80 3
10.7%
75 2
 
7.1%
72 1
 
3.6%
47 1
 
3.6%
40 4
14.3%

1일생산량
Real number (ℝ)

Distinct17
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.39286
Minimum50
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T18:40:21.025479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile50
Q1100
median160
Q3235
95-th percentile397.5
Maximum600
Range550
Interquartile range (IQR)135

Descriptive statistics

Standard deviation121.82299
Coefficient of variation (CV)0.66427334
Kurtosis4.3909779
Mean183.39286
Median Absolute Deviation (MAD)60
Skewness1.7991121
Sum5135
Variance14840.84
MonotonicityNot monotonic
2024-04-06T18:40:21.136702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100 4
14.3%
50 3
10.7%
130 3
10.7%
250 3
10.7%
200 2
 
7.1%
220 2
 
7.1%
80 1
 
3.6%
450 1
 
3.6%
150 1
 
3.6%
300 1
 
3.6%
Other values (7) 7
25.0%
ValueCountFrequency (%)
50 3
10.7%
70 1
 
3.6%
80 1
 
3.6%
100 4
14.3%
120 1
 
3.6%
130 3
10.7%
150 1
 
3.6%
170 1
 
3.6%
180 1
 
3.6%
200 2
7.1%
ValueCountFrequency (%)
600 1
 
3.6%
450 1
 
3.6%
300 1
 
3.6%
255 1
 
3.6%
250 3
10.7%
230 1
 
3.6%
220 2
7.1%
200 2
7.1%
180 1
 
3.6%
170 1
 
3.6%

수질구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
생활용수
25 
음용수
3 

Length

Max length4
Median length4
Mean length3.8928571
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활용수
2nd row음용수
3rd row생활용수
4th row생활용수
5th row음용수

Common Values

ValueCountFrequency (%)
생활용수 25
89.3%
음용수 3
 
10.7%

Length

2024-04-06T18:40:21.297486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:40:21.430572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활용수 25
89.3%
음용수 3
 
10.7%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.446399
Minimum37.274376
Maximum37.467965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T18:40:21.580447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.274376
5-th percentile37.438921
Q137.446368
median37.45135
Q337.457767
95-th percentile37.464925
Maximum37.467965
Range0.1935885
Interquartile range (IQR)0.011398868

Descriptive statistics

Standard deviation0.034580179
Coefficient of variation (CV)0.00092345806
Kurtosis24.992624
Mean37.446399
Median Absolute Deviation (MAD)0.00567828
Skewness-4.8719709
Sum1048.4992
Variance0.0011957888
MonotonicityNot monotonic
2024-04-06T18:40:21.758868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
37.46347788 1
 
3.6%
37.4437066 1
 
3.6%
37.43839941 1
 
3.6%
37.44571084 1
 
3.6%
37.4509393 1
 
3.6%
37.44572643 1
 
3.6%
37.4570674 1
 
3.6%
37.45752977 1
 
3.6%
37.4679645 1
 
3.6%
37.46479777 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
37.274376 1
3.6%
37.43839941 1
3.6%
37.4398904 1
3.6%
37.44306491 1
3.6%
37.4437066 1
3.6%
37.44571084 1
3.6%
37.44572643 1
3.6%
37.44658154 1
3.6%
37.44914564 1
3.6%
37.44955342 1
3.6%
ValueCountFrequency (%)
37.4679645 1
3.6%
37.46499344 1
3.6%
37.46479777 1
3.6%
37.46347788 1
3.6%
37.46136529 1
3.6%
37.46132153 1
3.6%
37.45847721 1
3.6%
37.45752977 1
3.6%
37.4570674 1
3.6%
37.45424402 1
3.6%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.66596
Minimum126.39281
Maximum126.69365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-04-06T18:40:21.895792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.39281
5-th percentile126.64512
Q1126.66674
median126.6777
Q3126.68754
95-th percentile126.69152
Maximum126.69365
Range0.300838
Interquartile range (IQR)0.020798475

Descriptive statistics

Standard deviation0.055177287
Coefficient of variation (CV)0.00043561259
Kurtosis24.440288
Mean126.66596
Median Absolute Deviation (MAD)0.0103567
Skewness-4.8091866
Sum3546.6469
Variance0.003044533
MonotonicityNot monotonic
2024-04-06T18:40:22.015411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
126.6500013 1
 
3.6%
126.6812456 1
 
3.6%
126.6814244 1
 
3.6%
126.6906483 1
 
3.6%
126.6824915 1
 
3.6%
126.6753003 1
 
3.6%
126.6916324 1
 
3.6%
126.6881872 1
 
3.6%
126.6718078 1
 
3.6%
126.6645412 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
126.392812 1
3.6%
126.6424979 1
3.6%
126.6500013 1
3.6%
126.6541043 1
3.6%
126.6600867 1
3.6%
126.6633622 1
3.6%
126.6645412 1
3.6%
126.6674738 1
3.6%
126.6707631 1
3.6%
126.6718078 1
3.6%
ValueCountFrequency (%)
126.69365 1
3.6%
126.6916324 1
3.6%
126.6913054 1
3.6%
126.6906483 1
3.6%
126.6899169 1
3.6%
126.6882045 1
3.6%
126.6881872 1
3.6%
126.6873231 1
3.6%
126.6850024 1
3.6%
126.6828332 1
3.6%

Interactions

2024-04-06T18:40:16.330393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:13.199190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:13.784119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:14.445809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:15.076680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:15.678653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:16.434631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:13.278781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:13.862653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:14.546441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:15.182520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:15.772190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:16.536743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:13.353570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:13.934739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:14.647328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:15.257426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:15.870385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:16.625646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:13.445252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:14.056473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:14.778547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:15.339096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:16.019938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:16.743594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:13.552338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:14.194009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:14.865381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:15.426330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:16.135483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:16.872150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:13.670931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:14.341130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:14.965324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:15.569123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:40:16.233630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T18:40:22.145202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번유형시설명관리자도로명주소지번주소설치연도심도1일생산량수질구분위도경도
연번1.0000.9561.0000.5891.0001.0000.7040.4120.3760.0000.0000.647
유형0.9561.0001.0000.5301.0001.0000.6830.3910.5320.1950.0000.000
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관리자0.5890.5301.0001.0001.0001.0000.3800.0000.7040.0000.8710.823
도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
설치연도0.7040.6831.0000.3801.0001.0001.0000.4790.0000.3750.7540.745
심도0.4120.3911.0000.0001.0001.0000.4791.0000.3060.0000.0000.000
1일생산량0.3760.5321.0000.7041.0001.0000.0000.3061.0000.0000.0000.000
수질구분0.0000.1951.0000.0001.0001.0000.3750.0000.0001.0000.0000.000
위도0.0000.0001.0000.8711.0001.0000.7540.0000.0000.0001.0000.944
경도0.6470.0001.0000.8231.0001.0000.7450.0000.0000.0000.9441.000
2024-04-06T18:40:22.327137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형관리자수질구분
유형1.0000.2450.309
관리자0.2451.0000.000
수질구분0.3090.0001.000
2024-04-06T18:40:22.482110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치연도심도1일생산량위도경도유형관리자수질구분
연번1.0000.3420.345-0.482-0.0780.1610.8090.2200.000
설치연도0.3421.0000.455-0.414-0.076-0.0690.5270.2770.222
심도0.3450.4551.000-0.2250.2240.0530.2220.0000.000
1일생산량-0.482-0.414-0.2251.0000.490-0.0280.3760.3340.000
위도-0.078-0.0760.2240.4901.000-0.1120.0000.5880.000
경도0.161-0.0690.053-0.028-0.1121.0000.0000.5260.000
유형0.8090.5270.2220.3760.0000.0001.0000.2450.309
관리자0.2200.2770.0000.3340.5880.5260.2451.0000.000
수질구분0.0000.2220.0000.0000.0000.0000.3090.0001.000

Missing values

2024-04-06T18:40:17.046409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T18:40:17.280119image/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

연번유형시설명관리자도로명주소지번주소설치연도심도1일생산량수질구분위도경도
01정부지원시설미추홀구청인천광역시 미추홀구청인천광역시 미추홀구 독정이로 95 (숭의동)인천광역시 미추홀구 숭의2동 131-11996110200생활용수37.463478126.650001
12정부지원시설남부초등학교인천광역시 미추홀구청인천광역시 미추홀구 인주대로366번길 22 (주안동)인천광역시 미추홀구 주안7동 1444-2198240250음용수37.45033126.675873
23정부지원시설로얄아파트인천광역시 미추홀구청인천광역시 미추홀구 미추홀대로 589 (주안동)인천광역시 미추홀구 주안7동 1458198240250생활용수37.449941126.679524
34정부지원시설주안남초등학교인천광역시 미추홀구청인천광역시 미추홀구 인주대로434번길 11 (주안동)인천광역시 미추홀구 주안8동 1503199072220생활용수37.449553126.685002
45정부지원시설제물포여중인천광역시 미추홀구청인천광역시 미추홀구 인주대로470번길 20 (주안동)인천광역시 미추홀구 주안8동 15292013150130음용수37.449146126.688204
56정부지원시설문학초등학교인천광역시 미추홀구청인천광역시 미추홀구 매소홀로 553 (문학동)인천광역시 미추홀구 문학동 343-2199175130생활용수37.43989126.682833
67지자체시설숭의운동장인천광역시 미추홀구청인천광역시 미추홀구 석정로49번길 30 (숭의동)인천광역시 미추홀구 숭의1동 424-34201240255생활용수37.464993126.642498
78지자체시설용일초등학교인천광역시 미추홀구청인천광역시 미추홀구 인주대로 244 (용현동)인천광역시 미추홀구 용현1·4동 74-1197814170생활용수37.45245126.663362
89지자체시설학익2동 주민센터인천광역시 미추홀구청인천광역시 미추홀구 한나루로 444 (학익동)인천광역시 미추홀구 학익2동 4-16198140250생활용수37.446582126.667474
910지자체시설종합건설본부인천광역시 미추홀구청인천광역시 미추홀구 경인로 280 (도화동)인천광역시 미추홀구 도화1동 421-21978100180생활용수37.461365126.672179
연번유형시설명관리자도로명주소지번주소설치연도심도1일생산량수질구분위도경도
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1920공공용시설콩나물 제조업장병규인천광역시 미추홀구 학익소로 49-12 (학익동)인천광역시 미추홀구 학익2동 35-1519997570생활용수37.443065126.670763
2021공공용시설도화충전소오성진인천광역시 미추홀구 경인로 200 (도화동)인천광역시 미추홀구 도화1동 624-61997100300생활용수37.464798126.664541
2122공공용시설순일목욕탕김숙례인천광역시 미추홀구 석정로 327 (도화동)인천광역시 미추홀구 도화2·3동 119-441995120150생활용수37.467965126.671808
2223공공용시설에이스빌딩김희태인천광역시 미추홀구 경인로 434 (주안동)인천광역시 미추홀구 주안4동 300-31997100450생활용수37.45753126.688187
2324공공용시설세종빌딩이종만인천광역시 미추홀구 구월로8번길 16 (주안동)인천광역시 미추홀구 주안4동 1543-111997120100생활용수37.457067126.691632
2425공공용시설한일목욕탕오용진인천광역시 미추홀구 신기길58번길 26 (주안동)인천광역시 미추홀구 주안7동 1335-6199510080생활용수37.445726126.6753
2526공공용시설삼원빌딩박용정인천광역시 미추홀구 인주대로 416 (주안동)인천광역시 미추홀구 주안8동 1485-5199580200생활용수37.450939126.682491
2627공공용시설인천광역시 미추홀구 관교탕윤순종인천광역시 미추홀구 경원대로712번길 6-15 (관교동)인천광역시 미추홀구 관교동 472-919953550생활용수37.445711126.690648
2728공공용시설큰나무주유소조용구인천광역시 미추홀구 매소홀로 536 (문학동)인천광역시 미추홀구 문학동 366-1200115050생활용수37.438399126.681424