Overview

Dataset statistics

Number of variables15
Number of observations1376
Missing cells2987
Missing cells (%)14.5%
Duplicate rows3
Duplicate rows (%)0.2%
Total size in memory166.8 KiB
Average record size in memory124.1 B

Variable types

Text7
Numeric4
DateTime3
Categorical1

Dataset

Description(수집방법) 본 데이터는 물환경측정망 운영계획 및 조류경보제 운영매뉴얼, 환경정책기본법 제22조, 수질및수생태계보전에 관한 법률 제9조, 16조, 22조, 23조, 법 제 16조의 2(방사성물질 등의 유입 여부 조사) 및 시행규칙 제26조의 4에 따라 물환경정보시스템을 통해 자료를 수집함​(데이터 한계) 수질 데이터는 물환경측정망 운영계획, 조류경보제 운영매뉴얼 등을 기준으로 각 데이터에 대한 측정주기와 측정항목이 정해져 있음 「먹는물관리법」 등에 따라 지방자치단체에서 먹는 물 공동시설로 지정하여 관리하고 있는 약수터 정보
Author환경부 국립환경과학원
URLhttps://www.data.go.kr/data/15017324/standard.do

Alerts

Dataset has 3 (0.2%) duplicate rowsDuplicates
소재지도로명주소 has 1098 (79.8%) missing valuesMissing
위도 has 152 (11.0%) missing valuesMissing
경도 has 151 (11.0%) missing valuesMissing
지정일자 has 783 (56.9%) missing valuesMissing
부적합항목 has 793 (57.6%) missing valuesMissing
일평균이용인구수 has 25 (1.8%) zerosZeros

Reproduction

Analysis started2023-12-12 10:30:40.081611
Analysis finished2023-12-12 10:30:44.430316
Duration4.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

약수터명
Text

Distinct977
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2023-12-12T19:30:44.615252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length4.7979651
Min length2

Characters and Unicode

Total characters6602
Distinct characters374
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique866 ?
Unique (%)62.9%

Sample

1st row귀성약수터
2nd row감로천약수터
3rd row불로약수터
4th row은행약수터
5th row오봉산약수터
ValueCountFrequency (%)
약수터 39
 
2.6%
생골약수터 20
 
1.3%
88약수터 20
 
1.3%
창말약수터 20
 
1.3%
보은약수터 20
 
1.3%
시민약수터 20
 
1.3%
소요산일주문약수터 20
 
1.3%
소요산광장약수터 20
 
1.3%
무지개약수터 20
 
1.3%
보문사약수터 20
 
1.3%
Other values (987) 1291
85.5%
2023-12-12T19:30:45.073133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 816
 
12.4%
약 725
 
11.0%
터 710
 
10.8%
산 210
 
3.2%
145
 
2.2%
골 138
 
2.1%
사 123
 
1.9%
샘 115
 
1.7%
천 94
 
1.4%
봉 82
 
1.2%
Other values (364) 3444
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6240
94.5%
Space Separator 145
 
2.2%
Decimal Number 120
 
1.8%
Open Punctuation 45
 
0.7%
Close Punctuation 45
 
0.7%
Letter Number 3
 
< 0.1%
Other Punctuation 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 816
 
13.1%
약 725
 
11.6%
터 710
 
11.4%
산 210
 
3.4%
골 138
 
2.2%
사 123
 
2.0%
샘 115
 
1.8%
천 94
 
1.5%
봉 82
 
1.3%
동 70
 
1.1%
Other values (347) 3157
50.6%
Decimal Number
ValueCountFrequency (%)
8 41
34.2%
7 22
18.3%
2 22
18.3%
1 21
17.5%
3 5
 
4.2%
4 5
 
4.2%
5 2
 
1.7%
6 2
 
1.7%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
66.7%
Ⅱ 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
· 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
L 1
50.0%
Space Separator
ValueCountFrequency (%)
145
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6240
94.5%
Common 357
 
5.4%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 816
 
13.1%
약 725
 
11.6%
터 710
 
11.4%
산 210
 
3.4%
골 138
 
2.2%
사 123
 
2.0%
샘 115
 
1.8%
천 94
 
1.5%
봉 82
 
1.3%
동 70
 
1.1%
Other values (347) 3157
50.6%
Common
ValueCountFrequency (%)
145
40.6%
( 45
 
12.6%
) 45
 
12.6%
8 41
 
11.5%
7 22
 
6.2%
2 22
 
6.2%
1 21
 
5.9%
3 5
 
1.4%
4 5
 
1.4%
5 2
 
0.6%
Other values (3) 4
 
1.1%
Latin
ValueCountFrequency (%)
Ⅰ 2
40.0%
Ⅱ 1
20.0%
G 1
20.0%
L 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6240
94.5%
ASCII 358
 
5.4%
Number Forms 3
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 816
 
13.1%
약 725
 
11.6%
터 710
 
11.4%
산 210
 
3.4%
골 138
 
2.2%
사 123
 
2.0%
샘 115
 
1.8%
천 94
 
1.5%
봉 82
 
1.3%
동 70
 
1.1%
Other values (347) 3157
50.6%
ASCII
ValueCountFrequency (%)
145
40.5%
( 45
 
12.6%
) 45
 
12.6%
8 41
 
11.5%
7 22
 
6.1%
2 22
 
6.1%
1 21
 
5.9%
3 5
 
1.4%
4 5
 
1.4%
5 2
 
0.6%
Other values (4) 5
 
1.4%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
66.7%
Ⅱ 1
33.3%
None
ValueCountFrequency (%)
· 1
100.0%

소재지도로명주소
Text

MISSING 

Distinct215
Distinct (%)77.3%
Missing1098
Missing (%)79.8%
Memory size10.9 KiB
2023-12-12T19:30:45.425690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length29
Mean length21.007194
Min length9

Characters and Unicode

Total characters5840
Distinct characters237
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique183 ?
Unique (%)65.8%

Sample

1st row인천광역시남동구만의골로222
2nd row인천광역시 남동구 만월로 2-1
3rd row인천광역시 남동구 만월로 2-1
4th row전라남도 담양군 대전면 평장리 산 100-6
5th row전라남도 담양군 용면 쌍태리 산5
ValueCountFrequency (%)
전라남도 68
 
5.2%
충청남도 48
 
3.7%
경기도 43
 
3.3%
강원도 38
 
2.9%
해남군 35
 
2.7%
부산광역시 21
 
1.6%
충청북도 17
 
1.3%
송지면 14
 
1.1%
서구 12
 
0.9%
철원군 11
 
0.8%
Other values (571) 992
76.4%
2023-12-12T19:30:45.941819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1021
 
17.5%
1 250
 
4.3%
도 234
 
4.0%
남 182
 
3.1%
시 170
 
2.9%
로 137
 
2.3%
산 135
 
2.3%
길 130
 
2.2%
군 117
 
2.0%
3 116
 
2.0%
Other values (227) 3348
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3634
62.2%
Space Separator 1021
 
17.5%
Decimal Number 1013
 
17.3%
Dash Punctuation 111
 
1.9%
Close Punctuation 26
 
0.4%
Open Punctuation 26
 
0.4%
Other Punctuation 9
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 234
 
6.4%
남 182
 
5.0%
시 170
 
4.7%
로 137
 
3.8%
산 135
 
3.7%
길 130
 
3.6%
군 117
 
3.2%
면 101
 
2.8%
전 84
 
2.3%
구 83
 
2.3%
Other values (212) 2261
62.2%
Decimal Number
ValueCountFrequency (%)
1 250
24.7%
3 116
11.5%
2 105
10.4%
5 98
 
9.7%
7 80
 
7.9%
4 80
 
7.9%
0 76
 
7.5%
9 76
 
7.5%
6 73
 
7.2%
8 59
 
5.8%
Space Separator
ValueCountFrequency (%)
1021
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 111
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3634
62.2%
Common 2206
37.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 234
 
6.4%
남 182
 
5.0%
시 170
 
4.7%
로 137
 
3.8%
산 135
 
3.7%
길 130
 
3.6%
군 117
 
3.2%
면 101
 
2.8%
전 84
 
2.3%
구 83
 
2.3%
Other values (212) 2261
62.2%
Common
ValueCountFrequency (%)
1021
46.3%
1 250
 
11.3%
3 116
 
5.3%
- 111
 
5.0%
2 105
 
4.8%
5 98
 
4.4%
7 80
 
3.6%
4 80
 
3.6%
0 76
 
3.4%
9 76
 
3.4%
Other values (5) 193
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3634
62.2%
ASCII 2206
37.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1021
46.3%
1 250
 
11.3%
3 116
 
5.3%
- 111
 
5.0%
2 105
 
4.8%
5 98
 
4.4%
7 80
 
3.6%
4 80
 
3.6%
0 76
 
3.4%
9 76
 
3.4%
Other values (5) 193
 
8.7%
Hangul
ValueCountFrequency (%)
도 234
 
6.4%
남 182
 
5.0%
시 170
 
4.7%
로 137
 
3.8%
산 135
 
3.7%
길 130
 
3.6%
군 117
 
3.2%
면 101
 
2.8%
전 84
 
2.3%
구 83
 
2.3%
Other values (212) 2261
62.2%

소재지지번주소
Text

Distinct899
Distinct (%)65.8%
Missing10
Missing (%)0.7%
Memory size10.9 KiB
2023-12-12T19:30:46.365229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length19.066618
Min length12

Characters and Unicode

Total characters26045
Distinct characters268
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique768 ?
Unique (%)56.2%

Sample

1st row인천광역시 남동구 장수동 22-4
2nd row인천광역시 남동구 만수동 산 1-4
3rd row인천광역시 남동구 만수동 산 2-2
4th row인천광역시 남동구 만수동 산 2-2
5th row인천광역시 남동구 도림동 산 48-14
ValueCountFrequency (%)
경기도 560
 
8.9%
산 355
 
5.6%
동두천시 280
 
4.5%
서울특별시 155
 
2.5%
부산광역시 140
 
2.2%
70 100
 
1.6%
충청남도 100
 
1.6%
전라남도 99
 
1.6%
강원도 84
 
1.3%
생연동 80
 
1.3%
Other values (1717) 4338
69.0%
2023-12-12T19:30:46.949767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4935
 
18.9%
동 1420
 
5.5%
산 1321
 
5.1%
시 1147
 
4.4%
1 1088
 
4.2%
도 1021
 
3.9%
- 797
 
3.1%
경 646
 
2.5%
기 570
 
2.2%
천 553
 
2.1%
Other values (258) 12547
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16188
62.2%
Space Separator 4935
 
18.9%
Decimal Number 4090
 
15.7%
Dash Punctuation 797
 
3.1%
Close Punctuation 12
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Other Punctuation 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1420
 
8.8%
산 1321
 
8.2%
시 1147
 
7.1%
도 1021
 
6.3%
경 646
 
4.0%
기 570
 
3.5%
천 553
 
3.4%
구 529
 
3.3%
남 395
 
2.4%
리 388
 
2.4%
Other values (242) 8198
50.6%
Decimal Number
ValueCountFrequency (%)
1 1088
26.6%
3 465
11.4%
2 451
11.0%
7 405
 
9.9%
4 368
 
9.0%
5 337
 
8.2%
6 277
 
6.8%
0 272
 
6.7%
8 225
 
5.5%
9 202
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 10
90.9%
? 1
 
9.1%
Space Separator
ValueCountFrequency (%)
4935
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 797
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16188
62.2%
Common 9857
37.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1420
 
8.8%
산 1321
 
8.2%
시 1147
 
7.1%
도 1021
 
6.3%
경 646
 
4.0%
기 570
 
3.5%
천 553
 
3.4%
구 529
 
3.3%
남 395
 
2.4%
리 388
 
2.4%
Other values (242) 8198
50.6%
Common
ValueCountFrequency (%)
4935
50.1%
1 1088
 
11.0%
- 797
 
8.1%
3 465
 
4.7%
2 451
 
4.6%
7 405
 
4.1%
4 368
 
3.7%
5 337
 
3.4%
6 277
 
2.8%
0 272
 
2.8%
Other values (6) 462
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16188
62.2%
ASCII 9857
37.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4935
50.1%
1 1088
 
11.0%
- 797
 
8.1%
3 465
 
4.7%
2 451
 
4.6%
7 405
 
4.1%
4 368
 
3.7%
5 337
 
3.4%
6 277
 
2.8%
0 272
 
2.8%
Other values (6) 462
 
4.7%
Hangul
ValueCountFrequency (%)
동 1420
 
8.8%
산 1321
 
8.2%
시 1147
 
7.1%
도 1021
 
6.3%
경 646
 
4.0%
기 570
 
3.5%
천 553
 
3.4%
구 529
 
3.3%
남 395
 
2.4%
리 388
 
2.4%
Other values (242) 8198
50.6%

위도
Real number (ℝ)

MISSING 

Distinct808
Distinct (%)66.0%
Missing152
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean36.932828
Minimum33.333152
Maximum38.451226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T19:30:47.110642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.333152
5-th percentile34.907863
Q136.290788
median37.440372
Q337.899604
95-th percentile37.945348
Maximum38.451226
Range5.1180736
Interquartile range (IQR)1.6088165

Descriptive statistics

Standard deviation1.1159099
Coefficient of variation (CV)0.030214581
Kurtosis-0.56131216
Mean36.932828
Median Absolute Deviation (MAD)0.4800913
Skewness-0.90633032
Sum45205.782
Variance1.245255
MonotonicityNot monotonic
2023-12-12T19:30:47.293678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.899604 60
 
4.4%
37.9453481 40
 
2.9%
37.9221134301 40
 
2.9%
37.911061 20
 
1.5%
37.933087 20
 
1.5%
37.913589 20
 
1.5%
37.920463 20
 
1.5%
37.908502 20
 
1.5%
37.906331 20
 
1.5%
37.918715 20
 
1.5%
Other values (798) 944
68.6%
(Missing) 152
 
11.0%
ValueCountFrequency (%)
33.3331523531 1
 
0.1%
33.4377719736 1
 
0.1%
34.3172033 7
0.5%
34.34982599 7
0.5%
34.4668975 7
0.5%
34.4964073804 7
0.5%
34.5499439025 1
 
0.1%
34.6382181276 1
 
0.1%
34.642039651 1
 
0.1%
34.6543456953 1
 
0.1%
ValueCountFrequency (%)
38.451226 1
0.1%
38.4510706 1
0.1%
38.448909 1
0.1%
38.427703 1
0.1%
38.420843 1
0.1%
38.4018639 1
0.1%
38.394745 1
0.1%
38.377362 1
0.1%
38.372645 1
0.1%
38.334022 1
0.1%

경도
Real number (ℝ)

MISSING 

Distinct810
Distinct (%)66.1%
Missing151
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean127.44756
Minimum126.26242
Maximum130.90631
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T19:30:47.494254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.26242
5-th percentile126.64009
Q1126.97889
median127.07469
Q3127.69054
95-th percentile129.08033
Maximum130.90631
Range4.6438851
Interquartile range (IQR)0.7116497

Descriptive statistics

Standard deviation0.80953317
Coefficient of variation (CV)0.0063518925
Kurtosis0.34046586
Mean127.44756
Median Absolute Deviation (MAD)0.191934
Skewness1.2402117
Sum156123.25
Variance0.65534396
MonotonicityNot monotonic
2023-12-12T19:30:47.735215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.074691 60
 
4.4%
127.0864835 40
 
2.9%
127.0477996789 40
 
2.9%
127.068687 20
 
1.5%
127.064892 20
 
1.5%
127.043281 20
 
1.5%
127.072736 20
 
1.5%
127.041801 20
 
1.5%
127.034151 20
 
1.5%
127.043063 20
 
1.5%
Other values (800) 945
68.7%
(Missing) 151
 
11.0%
ValueCountFrequency (%)
126.2624234 1
 
0.1%
126.266907 1
 
0.1%
126.267089 1
 
0.1%
126.295742 1
 
0.1%
126.295773 1
 
0.1%
126.3070929 1
 
0.1%
126.3070929386 1
 
0.1%
126.3244165 7
0.5%
126.47264 1
 
0.1%
126.47381 1
 
0.1%
ValueCountFrequency (%)
130.9063084939 1
0.1%
130.9001521468 1
0.1%
129.418261 1
0.1%
129.399482 2
0.1%
129.389558 2
0.1%
129.380398 2
0.1%
129.379425 1
0.1%
129.347414 1
0.1%
129.338342 1
0.1%
129.338084 1
0.1%

지정일자
Date

MISSING 

Distinct232
Distinct (%)39.1%
Missing783
Missing (%)56.9%
Memory size10.9 KiB
Minimum1912-03-02 00:00:00
Maximum2019-02-27 00:00:00
2023-12-12T19:30:47.938607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:48.135106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

일평균이용인구수
Real number (ℝ)

ZEROS 

Distinct67
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.36555
Minimum0
Maximum1450
Zeros25
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T19:30:48.325694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q150
median100
Q3150
95-th percentile300
Maximum1450
Range1450
Interquartile range (IQR)100

Descriptive statistics

Standard deviation116.23133
Coefficient of variation (CV)0.94986968
Kurtosis21.807403
Mean122.36555
Median Absolute Deviation (MAD)50
Skewness3.3468386
Sum168375
Variance13509.722
MonotonicityNot monotonic
2023-12-12T19:30:48.476299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 206
15.0%
50 186
13.5%
150 120
 
8.7%
30 103
 
7.5%
60 92
 
6.7%
200 89
 
6.5%
80 66
 
4.8%
20 63
 
4.6%
180 52
 
3.8%
300 51
 
3.7%
Other values (57) 348
25.3%
ValueCountFrequency (%)
0 25
 
1.8%
1 1
 
0.1%
5 2
 
0.1%
6 1
 
0.1%
10 19
 
1.4%
15 7
 
0.5%
20 63
4.6%
25 1
 
0.1%
30 103
7.5%
31 1
 
0.1%
ValueCountFrequency (%)
1450 1
 
0.1%
1040 1
 
0.1%
1000 2
 
0.1%
700 2
 
0.1%
680 1
 
0.1%
650 1
 
0.1%
615 1
 
0.1%
550 3
 
0.2%
519 1
 
0.1%
500 15
1.1%

수질검사일자
Date

Distinct207
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
Minimum2015-09-14 00:00:00
Maximum2020-11-24 00:00:00
2023-12-12T19:30:48.670337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:48.823262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수질검사결과구분
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
적합
969 
부적합
407 

Length

Max length3
Median length2
Mean length2.2957849
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row적합
2nd row부적합
3rd row부적합
4th row적합
5th row적합

Common Values

ValueCountFrequency (%)
적합 969
70.4%
부적합 407
29.6%

Length

2023-12-12T19:30:48.972553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:30:49.429255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적합 969
70.4%
부적합 407
29.6%

부적합항목
Text

MISSING 

Distinct65
Distinct (%)11.1%
Missing793
Missing (%)57.6%
Memory size10.9 KiB
2023-12-12T19:30:49.606411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length28
Mean length5.9176672
Min length1

Characters and Unicode

Total characters3450
Distinct characters93
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)4.3%

Sample

1st row총대장균군,분원성대장균군
2nd row채수불가
3rd row없음
4th row없음
5th row없음
ValueCountFrequency (%)
총대장균군 153
22.3%
없음 105
15.3%
미검사(수원고갈 56
 
8.2%
49
 
7.2%
해당없음 34
 
5.0%
총대장균군(검출 32
 
4.7%
분원성대장균군 18
 
2.6%
시설폐쇄 18
 
2.6%
검출 17
 
2.5%
총대장균균 17
 
2.5%
Other values (60) 186
27.2%
2023-12-12T19:30:50.038426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
균 347
 
10.1%
장 307
 
8.9%
대 307
 
8.9%
군 272
 
7.9%
총 247
 
7.2%
없 139
 
4.0%
음 139
 
4.0%
검 134
 
3.9%
원 116
 
3.4%
) 112
 
3.2%
Other values (83) 1330
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2943
85.3%
Close Punctuation 112
 
3.2%
Open Punctuation 112
 
3.2%
Space Separator 102
 
3.0%
Other Punctuation 79
 
2.3%
Dash Punctuation 49
 
1.4%
Decimal Number 27
 
0.8%
Math Symbol 19
 
0.6%
Format 7
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
균 347
11.8%
장 307
 
10.4%
대 307
 
10.4%
군 272
 
9.2%
총 247
 
8.4%
없 139
 
4.7%
음 139
 
4.7%
검 134
 
4.6%
원 116
 
3.9%
수 84
 
2.9%
Other values (71) 851
28.9%
Decimal Number
ValueCountFrequency (%)
0 15
55.6%
1 7
25.9%
2 3
 
11.1%
9 2
 
7.4%
Other Punctuation
ValueCountFrequency (%)
, 78
98.7%
. 1
 
1.3%
Close Punctuation
ValueCountFrequency (%)
) 112
100.0%
Open Punctuation
ValueCountFrequency (%)
( 112
100.0%
Space Separator
ValueCountFrequency (%)
102
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Math Symbol
ValueCountFrequency (%)
+ 19
100.0%
Format
ValueCountFrequency (%)
­ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2943
85.3%
Common 507
 
14.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
균 347
11.8%
장 307
 
10.4%
대 307
 
10.4%
군 272
 
9.2%
총 247
 
8.4%
없 139
 
4.7%
음 139
 
4.7%
검 134
 
4.6%
원 116
 
3.9%
수 84
 
2.9%
Other values (71) 851
28.9%
Common
ValueCountFrequency (%)
) 112
22.1%
( 112
22.1%
102
20.1%
, 78
15.4%
- 49
9.7%
+ 19
 
3.7%
0 15
 
3.0%
1 7
 
1.4%
­ 7
 
1.4%
2 3
 
0.6%
Other values (2) 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2943
85.3%
ASCII 500
 
14.5%
None 7
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
균 347
11.8%
장 307
 
10.4%
대 307
 
10.4%
군 272
 
9.2%
총 247
 
8.4%
없 139
 
4.7%
음 139
 
4.7%
검 134
 
4.6%
원 116
 
3.9%
수 84
 
2.9%
Other values (71) 851
28.9%
ASCII
ValueCountFrequency (%)
) 112
22.4%
( 112
22.4%
102
20.4%
, 78
15.6%
- 49
9.8%
+ 19
 
3.8%
0 15
 
3.0%
1 7
 
1.4%
2 3
 
0.6%
9 2
 
0.4%
None
ValueCountFrequency (%)
­ 7
100.0%

관리기관전화번호
Text

Distinct191
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2023-12-12T19:30:50.396138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.009448
Min length11

Characters and Unicode

Total characters16525
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)2.8%

Sample

1st row032-453-2651
2nd row032-453-2651
3rd row032-453-2651
4th row032-453-2651
5th row032-453-2651
ValueCountFrequency (%)
031-860-2471 280
 
20.3%
061-531-3670 35
 
2.5%
02-2680-2339 30
 
2.2%
02-2116-3952 27
 
2.0%
051-605-4382 25
 
1.8%
02-3423-6284 25
 
1.8%
031-8082-6832 23
 
1.7%
031-828-4433 22
 
1.6%
031-729-4094 20
 
1.5%
051-310-5174 18
 
1.3%
Other values (181) 871
63.3%
2023-12-12T19:30:50.832347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2752
16.7%
0 2480
15.0%
3 2050
12.4%
1 1683
10.2%
2 1495
9.0%
4 1412
8.5%
6 1183
7.2%
5 1183
7.2%
8 911
 
5.5%
7 859
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13773
83.3%
Dash Punctuation 2752
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2480
18.0%
3 2050
14.9%
1 1683
12.2%
2 1495
10.9%
4 1412
10.3%
6 1183
8.6%
5 1183
8.6%
8 911
 
6.6%
7 859
 
6.2%
9 517
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 2752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16525
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2752
16.7%
0 2480
15.0%
3 2050
12.4%
1 1683
10.2%
2 1495
9.0%
4 1412
8.5%
6 1183
7.2%
5 1183
7.2%
8 911
 
5.5%
7 859
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2752
16.7%
0 2480
15.0%
3 2050
12.4%
1 1683
10.2%
2 1495
9.0%
4 1412
8.5%
6 1183
7.2%
5 1183
7.2%
8 911
 
5.5%
7 859
 
5.2%

관리기관명
Text

Distinct167
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2023-12-12T19:30:51.186670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length9.0763081
Min length3

Characters and Unicode

Total characters12489
Distinct characters140
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

Unique21 ?
Unique (%)1.5%

Sample

1st row인천광역시 남동구청
2nd row인천광역시 남동구청
3rd row인천광역시 남동구청
4th row인천광역시 남동구청
5th row인천광역시 남동구청
ValueCountFrequency (%)
경기도 412
 
15.5%
동두천시청 280
 
10.5%
부산광역시 114
 
4.3%
서울특별시 101
 
3.8%
공원녹지과 87
 
3.3%
상하수도사업소 62
 
2.3%
전라남도 51
 
1.9%
강원도 49
 
1.8%
해남군 35
 
1.3%
광명시청 30
 
1.1%
Other values (185) 1445
54.2%
2023-12-12T19:30:51.644172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1290
 
10.3%
청 1034
 
8.3%
시 1031
 
8.3%
도 836
 
6.7%
경 546
 
4.4%
천 475
 
3.8%
구 418
 
3.3%
기 416
 
3.3%
동 330
 
2.6%
두 280
 
2.2%
Other values (130) 5833
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11143
89.2%
Space Separator 1290
 
10.3%
Close Punctuation 24
 
0.2%
Open Punctuation 24
 
0.2%
Other Punctuation 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 1034
 
9.3%
시 1031
 
9.3%
도 836
 
7.5%
경 546
 
4.9%
천 475
 
4.3%
구 418
 
3.8%
기 416
 
3.7%
동 330
 
3.0%
두 280
 
2.5%
과 270
 
2.4%
Other values (126) 5507
49.4%
Space Separator
ValueCountFrequency (%)
1290
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11143
89.2%
Common 1346
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 1034
 
9.3%
시 1031
 
9.3%
도 836
 
7.5%
경 546
 
4.9%
천 475
 
4.3%
구 418
 
3.8%
기 416
 
3.7%
동 330
 
3.0%
두 280
 
2.5%
과 270
 
2.4%
Other values (126) 5507
49.4%
Common
ValueCountFrequency (%)
1290
95.8%
) 24
 
1.8%
( 24
 
1.8%
/ 8
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11143
89.2%
ASCII 1346
 
10.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1290
95.8%
) 24
 
1.8%
( 24
 
1.8%
/ 8
 
0.6%
Hangul
ValueCountFrequency (%)
청 1034
 
9.3%
시 1031
 
9.3%
도 836
 
7.5%
경 546
 
4.9%
천 475
 
4.3%
구 418
 
3.8%
기 416
 
3.7%
동 330
 
3.0%
두 280
 
2.5%
과 270
 
2.4%
Other values (126) 5507
49.4%

데이터기준일자
Date

Distinct111
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
Minimum2016-09-01 00:00:00
Maximum2021-01-27 00:00:00
2023-12-12T19:30:51.799509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:51.961424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제공기관코드
Real number (ℝ)

Distinct149
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4187326.7
Minimum3040000
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T19:30:52.112535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3040000
5-th percentile3120000
Q13660000
median3920000
Q34582500
95-th percentile6310000
Maximum6500000
Range3460000
Interquartile range (IQR)922500

Descriptive statistics

Standard deviation855885.26
Coefficient of variation (CV)0.20439897
Kurtosis0.45841882
Mean4187326.7
Median Absolute Deviation (MAD)530000
Skewness1.0075522
Sum5.7617615 × 109
Variance7.3253958 × 1011
MonotonicityNot monotonic
2023-12-12T19:30:52.257041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3920000 280
 
20.3%
6440000 60
 
4.4%
4930000 35
 
2.5%
5710000 30
 
2.2%
3900000 30
 
2.2%
3100000 27
 
2.0%
3290000 25
 
1.8%
3220000 25
 
1.8%
5590000 23
 
1.7%
4820000 22
 
1.6%
Other values (139) 819
59.5%
ValueCountFrequency (%)
3040000 1
 
0.1%
3060000 12
0.9%
3070000 7
 
0.5%
3080000 11
0.8%
3100000 27
2.0%
3110000 9
 
0.7%
3120000 15
1.1%
3140000 9
 
0.7%
3170000 13
0.9%
3190000 6
 
0.4%
ValueCountFrequency (%)
6500000 2
 
0.1%
6440000 60
4.4%
6310000 8
 
0.6%
5710000 30
2.2%
5700000 3
 
0.2%
5600000 8
 
0.6%
5590000 23
 
1.7%
5580000 3
 
0.2%
5570000 3
 
0.2%
5540000 7
 
0.5%

제공기관명
Text

Distinct149
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2023-12-12T19:30:52.599045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.8110465
Min length4

Characters and Unicode

Total characters10748
Distinct characters116
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)1.3%

Sample

1st row인천광역시 남동구
2nd row인천광역시 남동구
3rd row인천광역시 남동구
4th row인천광역시 남동구
5th row인천광역시 남동구
ValueCountFrequency (%)
경기도 560
20.9%
동두천시 280
 
10.4%
서울특별시 158
 
5.9%
부산광역시 149
 
5.6%
충청남도 112
 
4.2%
전라남도 99
 
3.7%
강원도 84
 
3.1%
충청북도 69
 
2.6%
경상남도 42
 
1.6%
해남군 35
 
1.3%
Other values (142) 1094
40.8%
2023-12-12T19:30:53.055558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1306
 
12.2%
시 1110
 
10.3%
도 1013
 
9.4%
경 639
 
5.9%
기 562
 
5.2%
천 454
 
4.2%
구 377
 
3.5%
남 360
 
3.3%
동 328
 
3.1%
두 280
 
2.6%
Other values (106) 4319
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9442
87.8%
Space Separator 1306
 
12.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1110
 
11.8%
도 1013
 
10.7%
경 639
 
6.8%
기 562
 
6.0%
천 454
 
4.8%
구 377
 
4.0%
남 360
 
3.8%
동 328
 
3.5%
두 280
 
3.0%
광 262
 
2.8%
Other values (105) 4057
43.0%
Space Separator
ValueCountFrequency (%)
1306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9442
87.8%
Common 1306
 
12.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1110
 
11.8%
도 1013
 
10.7%
경 639
 
6.8%
기 562
 
6.0%
천 454
 
4.8%
구 377
 
4.0%
남 360
 
3.8%
동 328
 
3.5%
두 280
 
3.0%
광 262
 
2.8%
Other values (105) 4057
43.0%
Common
ValueCountFrequency (%)
1306
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9442
87.8%
ASCII 1306
 
12.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1306
100.0%
Hangul
ValueCountFrequency (%)
시 1110
 
11.8%
도 1013
 
10.7%
경 639
 
6.8%
기 562
 
6.0%
천 454
 
4.8%
구 377
 
4.0%
남 360
 
3.8%
동 328
 
3.5%
두 280
 
3.0%
광 262
 
2.8%
Other values (105) 4057
43.0%

Interactions

2023-12-12T19:30:43.221590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:41.508459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:42.178019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:42.679832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:43.345477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:41.764447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:42.293200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:42.803986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:43.470745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:41.909964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:42.416342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:42.920181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:43.613961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:42.040064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:42.546546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:30:43.084171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:30:53.151528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도일평균이용인구수수질검사결과구분부적합항목제공기관코드
위도1.0000.6770.2540.4060.7990.763
경도0.6771.0000.0000.3000.8920.672
일평균이용인구수0.2540.0001.0000.1910.0000.180
수질검사결과구분0.4060.3000.1911.0000.9960.210
부적합항목0.7990.8920.0000.9961.0000.868
제공기관코드0.7630.6720.1800.2100.8681.000
2023-12-12T19:30:53.276582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도일평균이용인구수제공기관코드수질검사결과구분
위도1.000-0.233-0.239-0.1840.311
경도-0.2331.0000.007-0.0460.224
일평균이용인구수-0.2390.0071.000-0.1370.143
제공기관코드-0.184-0.046-0.1371.0000.208
수질검사결과구분0.3110.2240.1430.2081.000

Missing values

2023-12-12T19:30:43.835398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:30:44.132650image/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.
2023-12-12T19:30:44.331524image/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

약수터명소재지도로명주소소재지지번주소위도경도지정일자일평균이용인구수수질검사일자수질검사결과구분부적합항목관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명
0귀성약수터인천광역시남동구만의골로222인천광역시 남동구 장수동 22-437.462277126.7729282008-10-013002019-10-01적합<NA>032-453-2651인천광역시 남동구청2019-12-243530000인천광역시 남동구
1감로천약수터<NA>인천광역시 남동구 만수동 산 1-437.468825126.7325452003-01-011002019-10-01부적합총대장균군,분원성대장균군032-453-2651인천광역시 남동구청2019-12-243530000인천광역시 남동구
2불로약수터인천광역시 남동구 만월로 2-1인천광역시 남동구 만수동 산 2-237.46883126.7242532003-01-011002019-10-01부적합채수불가032-453-2651인천광역시 남동구청2019-12-243530000인천광역시 남동구
3은행약수터인천광역시 남동구 만월로 2-1인천광역시 남동구 만수동 산 2-237.46883126.7242532003-01-011002019-10-01적합<NA>032-453-2651인천광역시 남동구청2019-12-243530000인천광역시 남동구
4오봉산약수터<NA>인천광역시 남동구 도림동 산 48-1437.415001126.7336742003-01-012502019-10-01적합<NA>032-453-2651인천광역시 남동구청2019-12-243530000인천광역시 남동구
5담양군 한재골약수터전라남도 담양군 대전면 평장리 산 100-6전라남도 담양군 대전면 평장리 산100-635.309127126.8745912008-03-011002019-02-18적합없음061-380-3327담양군 물순환사업소2019-09-174850000전라남도 담양군
6담양군 물통공약수터전라남도 담양군 용면 쌍태리 산5전라남도 담양군 용면 쌍태리 산535.395011126.9764292008-03-01502019-02-18적합없음061-380-3327담양군 물순환사업소2019-09-174850000전라남도 담양군
7담양군 옥천골약수터전라남도 담양군 대덕면 문학리 산66전라남도 담양군 대덕면 문학리 산6635.242159127.0723742008-03-012502019-02-18적합없음061-380-3327담양군 물순환사업소2019-09-174850000전라남도 담양군
8담양군 죽림약수터전라남도 담양군 가사문학면 인암리 산95-3전라남도 담양군 가사문학면 인암리 산95-335.137645127.0694882008-03-01502019-02-18적합없음061-380-3327담양군 물순환사업소2019-09-174850000전라남도 담양군
9보문사약수터<NA>경기도 동두천시 생연동 산 15-537.911061127.068687<NA>802019-05-22적합<NA>031-860-2471경기도 동두천시청2019-08-313920000경기도 동두천시
약수터명소재지도로명주소소재지지번주소위도경도지정일자일평균이용인구수수질검사일자수질검사결과구분부적합항목관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명
1366휴양림약수<NA>천안시 동남구 풍세면 삼태리 406-3<NA><NA><NA>1502019-09-18부적합<NA>041-521-3152충청남도 천안시청2019-10-014490000충청남도 천안시
1367매봉약수<NA>천안시 동남구 병천면 탑원리 127-4<NA><NA><NA>5502019-09-18부적합<NA>041-521-3152충청남도 천안시청2019-10-014490000충청남도 천안시
1368발산약수<NA>천안시 동남구 수신면 발산리 산48<NA><NA><NA>1502019-09-18적합<NA>041-521-3153충청남도 천안시청2019-10-014490000충청남도 천안시
1369신대약수<NA>천안시 서북구 신당동 304-1<NA><NA><NA>2002019-09-18부적합<NA>041-521-3154충청남도 천안시청2019-10-014490000충청남도 천안시
1370냉천골약수터강원도 양구군 양구읍 관공서로 38번길 33-8강원도 양구군 양구읍 하리 15<NA><NA><NA>1002017-03-23부적합총대장규균033-480-2444양구군청2019-10-104320000강원도 양구군
1371한계골약수터강원도 양구군 남면 구암리길 103-12강원도 양구군 남면 구암리 974-2<NA><NA><NA>1202017-03-23적합<NA>033-480-2444양구군청2019-10-104320000강원도 양구군
1372대암약수터<NA>강원도 양구군 해안면 오유리 산1<NA><NA><NA>502017-03-23부적합총대장규균033-480-2444양구군청2019-10-104320000강원도 양구군
1373용화약수터<NA>경기도 안산시 상록구 수암동 산5-137.364565126.8897881980-02-092402019-06-11적합<NA>031-481-3685경기도 안산시 상하수도사업소2019-07-053930000경기도 안산시
1374수암약수터<NA>경기도 안산시 상록구 수암동 산1-137.372041126.8977311980-05-083002019-06-11적합<NA>031-481-3685경기도 안산시 상하수도사업소2019-07-053930000경기도 안산시
1375성덕사<NA>서울특별시 중랑구 중화동37.604114127.085838<NA>3002019-03-28적합<NA>02-2094-2367중랑구청 공원녹지과2019-09-183060000서울특별시 중랑구

Duplicate rows

Most frequently occurring

약수터명소재지도로명주소소재지지번주소위도경도지정일자일평균이용인구수수질검사일자수질검사결과구분부적합항목관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명# duplicates
0남산약수터<NA>경상남도 고성군 고성읍 동외리 369-234.970648128.3262741996-01-291002016-12-12적합<NA>055-670-4414경상남도 고성군청2019-09-065420000경상남도 고성군2
1연화약수터<NA>경상남도 고성군 영현면 연화리 산2435.074266128.2501071998-09-25502016-12-12적합<NA>055-670-4414경상남도 고성군청2019-09-065420000경상남도 고성군2
2용건<NA>서울특별시 중랑구 면목동37.580833127.098052<NA>1502019-03-28적합<NA>02-2094-2367중랑구청 공원녹지과2019-09-183060000서울특별시 중랑구2