Overview

Dataset statistics

Number of variables13
Number of observations25
Missing cells59
Missing cells (%)18.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory113.3 B

Variable types

Text3
Numeric2
Unsupported1
Categorical7

Dataset

Description파일 다운로드
Author강남구
URLhttps://data.seoul.go.kr/dataList/OA-15010/S/1/datasetView.do

Alerts

관리기관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
부적합항목 is highly overall correlated with 수질검사결과구분High correlation
수질검사결과구분 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 수질검사결과구분 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 관리기관전화번호High correlation
수질검사일자 is highly overall correlated with 관리기관전화번호High correlation
관리기관전화번호 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
소재지도로명주소 has 22 (88.0%) missing valuesMissing
위도 has 6 (24.0%) missing valuesMissing
경도 has 6 (24.0%) missing valuesMissing
지정일자 has 25 (100.0%) missing valuesMissing
약수터명 has unique valuesUnique
지정일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 05:29:35.039117
Analysis finished2023-12-11 05:29:36.387690
Duration1.35 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

약수터명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-11T14:29:36.522051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.56
Min length2

Characters and Unicode

Total characters64
Distinct characters40
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

Unique25 ?
Unique (%)100.0%

Sample

1st row개암
2nd row천의
3rd row대룡
4th row대천
5th row구룡산
ValueCountFrequency (%)
개암 1
 
4.0%
대모천 1
 
4.0%
불국사 1
 
4.0%
용두천 1
 
4.0%
인수천 1
 
4.0%
임록천 1
 
4.0%
못골 1
 
4.0%
옥수천 1
 
4.0%
실로암 1
 
4.0%
성지 1
 
4.0%
Other values (15) 15
60.0%
2023-12-11T14:29:36.893696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 9
 
14.1%
룡 4
 
6.2%
대 3
 
4.7%
구 3
 
4.7%
암 3
 
4.7%
수 3
 
4.7%
옛 2
 
3.1%
봉 2
 
3.1%
지 2
 
3.1%
1 2
 
3.1%
Other values (30) 31
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60
93.8%
Decimal Number 4
 
6.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 9
 
15.0%
룡 4
 
6.7%
대 3
 
5.0%
구 3
 
5.0%
암 3
 
5.0%
수 3
 
5.0%
옛 2
 
3.3%
봉 2
 
3.3%
지 2
 
3.3%
성 1
 
1.7%
Other values (28) 28
46.7%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60
93.8%
Common 4
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 9
 
15.0%
룡 4
 
6.7%
대 3
 
5.0%
구 3
 
5.0%
암 3
 
5.0%
수 3
 
5.0%
옛 2
 
3.3%
봉 2
 
3.3%
지 2
 
3.3%
성 1
 
1.7%
Other values (28) 28
46.7%
Common
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60
93.8%
ASCII 4
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 9
 
15.0%
룡 4
 
6.7%
대 3
 
5.0%
구 3
 
5.0%
암 3
 
5.0%
수 3
 
5.0%
옛 2
 
3.3%
봉 2
 
3.3%
지 2
 
3.3%
성 1
 
1.7%
Other values (28) 28
46.7%
ASCII
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%

소재지도로명주소
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing22
Missing (%)88.0%
Memory size332.0 B
2023-12-11T14:29:37.095038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length26
Min length25

Characters and Unicode

Total characters78
Distinct characters33
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

Unique3 ?
Unique (%)100.0%

Sample

1st row서울특별시 강남구 개포로28길 47 (개포동)
2nd row서울특별시 강남구 학동로95길 19 (청담동)
3rd row서울특별시 강남구 광평로10길 30-71 (일원동)
ValueCountFrequency (%)
서울특별시 3
20.0%
강남구 3
20.0%
개포로28길 1
 
6.7%
47 1
 
6.7%
개포동 1
 
6.7%
학동로95길 1
 
6.7%
19 1
 
6.7%
청담동 1
 
6.7%
광평로10길 1
 
6.7%
30-71 1
 
6.7%
2023-12-11T14:29:37.401092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
15.4%
동 4
 
5.1%
서 3
 
3.8%
( 3
 
3.8%
길 3
 
3.8%
) 3
 
3.8%
로 3
 
3.8%
1 3
 
3.8%
구 3
 
3.8%
남 3
 
3.8%
Other values (23) 38
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45
57.7%
Decimal Number 14
 
17.9%
Space Separator 12
 
15.4%
Open Punctuation 3
 
3.8%
Close Punctuation 3
 
3.8%
Dash Punctuation 1
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4
 
8.9%
서 3
 
6.7%
길 3
 
6.7%
로 3
 
6.7%
구 3
 
6.7%
남 3
 
6.7%
강 3
 
6.7%
시 3
 
6.7%
별 3
 
6.7%
특 3
 
6.7%
Other values (10) 14
31.1%
Decimal Number
ValueCountFrequency (%)
1 3
21.4%
0 2
14.3%
9 2
14.3%
7 2
14.3%
4 1
 
7.1%
8 1
 
7.1%
2 1
 
7.1%
5 1
 
7.1%
3 1
 
7.1%
Space Separator
ValueCountFrequency (%)
12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45
57.7%
Common 33
42.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4
 
8.9%
서 3
 
6.7%
길 3
 
6.7%
로 3
 
6.7%
구 3
 
6.7%
남 3
 
6.7%
강 3
 
6.7%
시 3
 
6.7%
별 3
 
6.7%
특 3
 
6.7%
Other values (10) 14
31.1%
Common
ValueCountFrequency (%)
12
36.4%
( 3
 
9.1%
) 3
 
9.1%
1 3
 
9.1%
0 2
 
6.1%
9 2
 
6.1%
7 2
 
6.1%
4 1
 
3.0%
8 1
 
3.0%
2 1
 
3.0%
Other values (3) 3
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45
57.7%
ASCII 33
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
36.4%
( 3
 
9.1%
) 3
 
9.1%
1 3
 
9.1%
0 2
 
6.1%
9 2
 
6.1%
7 2
 
6.1%
4 1
 
3.0%
8 1
 
3.0%
2 1
 
3.0%
Other values (3) 3
 
9.1%
Hangul
ValueCountFrequency (%)
동 4
 
8.9%
서 3
 
6.7%
길 3
 
6.7%
로 3
 
6.7%
구 3
 
6.7%
남 3
 
6.7%
강 3
 
6.7%
시 3
 
6.7%
별 3
 
6.7%
특 3
 
6.7%
Other values (10) 14
31.1%

소재지지번주소
Text

Distinct23
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-11T14:29:37.609993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.24
Min length17

Characters and Unicode

Total characters481
Distinct characters34
Distinct categories4 ?
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 (%)84.0%

Sample

1st row서울특별시 강남구 개포동 산53-20
2nd row서울특별시 강남구 개포동 산53-30
3rd row서울특별시 강남구 개포동 118-21
4th row서울특별시 강남구 개포동 산53-31
5th row서울특별시 강남구 개포동 1017-7
ValueCountFrequency (%)
서울특별시 25
25.5%
강남구 25
25.5%
개포동 10
 
10.2%
일원동 7
 
7.1%
산53-28 2
 
2.0%
산63-32 2
 
2.0%
자곡동 2
 
2.0%
개포당 1
 
1.0%
산63-51 1
 
1.0%
산53-32 1
 
1.0%
Other values (22) 22
22.4%
2023-12-11T14:29:37.959404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
73
 
15.2%
서 26
 
5.4%
특 25
 
5.2%
별 25
 
5.2%
시 25
 
5.2%
강 25
 
5.2%
남 25
 
5.2%
구 25
 
5.2%
울 25
 
5.2%
동 24
 
5.0%
Other values (24) 183
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 294
61.1%
Decimal Number 93
 
19.3%
Space Separator 73
 
15.2%
Dash Punctuation 21
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 26
8.8%
특 25
8.5%
별 25
8.5%
시 25
8.5%
강 25
8.5%
남 25
8.5%
구 25
8.5%
울 25
8.5%
동 24
8.2%
산 19
 
6.5%
Other values (12) 50
17.0%
Decimal Number
ValueCountFrequency (%)
3 23
24.7%
1 17
18.3%
2 14
15.1%
5 12
12.9%
4 7
 
7.5%
6 7
 
7.5%
0 4
 
4.3%
9 3
 
3.2%
8 3
 
3.2%
7 3
 
3.2%
Space Separator
ValueCountFrequency (%)
73
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 294
61.1%
Common 187
38.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 26
8.8%
특 25
8.5%
별 25
8.5%
시 25
8.5%
강 25
8.5%
남 25
8.5%
구 25
8.5%
울 25
8.5%
동 24
8.2%
산 19
 
6.5%
Other values (12) 50
17.0%
Common
ValueCountFrequency (%)
73
39.0%
3 23
 
12.3%
- 21
 
11.2%
1 17
 
9.1%
2 14
 
7.5%
5 12
 
6.4%
4 7
 
3.7%
6 7
 
3.7%
0 4
 
2.1%
9 3
 
1.6%
Other values (2) 6
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 294
61.1%
ASCII 187
38.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
73
39.0%
3 23
 
12.3%
- 21
 
11.2%
1 17
 
9.1%
2 14
 
7.5%
5 12
 
6.4%
4 7
 
3.7%
6 7
 
3.7%
0 4
 
2.1%
9 3
 
1.6%
Other values (2) 6
 
3.2%
Hangul
ValueCountFrequency (%)
서 26
8.8%
특 25
8.5%
별 25
8.5%
시 25
8.5%
강 25
8.5%
남 25
8.5%
구 25
8.5%
울 25
8.5%
동 24
8.2%
산 19
 
6.5%
Other values (12) 50
17.0%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)89.5%
Missing6
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean37.480087
Minimum37.472528
Maximum37.521317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-11T14:29:38.105959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.472528
5-th percentile37.472528
Q137.474299
median37.477934
Q337.479173
95-th percentile37.493202
Maximum37.521317
Range0.04878913
Interquartile range (IQR)0.0048733

Descriptive statistics

Standard deviation0.010901008
Coefficient of variation (CV)0.00029084798
Kurtosis12.44332
Mean37.480087
Median Absolute Deviation (MAD)0.00224207
Skewness3.32086
Sum712.12164
Variance0.00011883197
MonotonicityNot monotonic
2023-12-11T14:29:38.526339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
37.47322797 2
 
8.0%
37.4779343 2
 
8.0%
37.47890887 1
 
4.0%
37.47784281 1
 
4.0%
37.48562508 1
 
4.0%
37.47790641 1
 
4.0%
37.47943658 1
 
4.0%
37.4780218 1
 
4.0%
37.47252789 1
 
4.0%
37.47480977 1
 
4.0%
Other values (7) 7
28.0%
(Missing) 6
24.0%
ValueCountFrequency (%)
37.47252752 1
4.0%
37.47252789 1
4.0%
37.47322797 2
8.0%
37.47378908 1
4.0%
37.47480977 1
4.0%
37.47781101 1
4.0%
37.47784281 1
4.0%
37.47790641 1
4.0%
37.4779343 2
8.0%
37.4780218 1
4.0%
ValueCountFrequency (%)
37.52131665 1
4.0%
37.490078 1
4.0%
37.48562508 1
4.0%
37.48017637 1
4.0%
37.47943658 1
4.0%
37.47890887 1
4.0%
37.47854242 1
4.0%
37.4780218 1
4.0%
37.4779343 2
8.0%
37.47790641 1
4.0%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)89.5%
Missing6
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean127.07336
Minimum127.04618
Maximum127.10444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-11T14:29:38.637730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.04618
5-th percentile127.05163
Q1127.06935
median127.0713
Q3127.08167
95-th percentile127.09481
Maximum127.10444
Range0.0582544
Interquartile range (IQR)0.01232155

Descriptive statistics

Standard deviation0.013791992
Coefficient of variation (CV)0.00010853567
Kurtosis0.82107676
Mean127.07336
Median Absolute Deviation (MAD)0.004867
Skewness0.083980709
Sum2414.3939
Variance0.00019021904
MonotonicityNot monotonic
2023-12-11T14:29:38.738241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
127.0711679 2
 
8.0%
127.0821975 2
 
8.0%
127.0730502 1
 
4.0%
127.075498 1
 
4.0%
127.0758461 1
 
4.0%
127.0845147 1
 
4.0%
127.071297 1
 
4.0%
127.0811361 1
 
4.0%
127.06643 1
 
4.0%
127.0692829 1
 
4.0%
Other values (7) 7
28.0%
(Missing) 6
24.0%
ValueCountFrequency (%)
127.0461812 1
4.0%
127.0522313 1
4.0%
127.053228 1
4.0%
127.06643 1
4.0%
127.0692829 1
4.0%
127.0694076 1
4.0%
127.0708607 1
4.0%
127.0711679 2
8.0%
127.071297 1
4.0%
127.0730502 1
4.0%
ValueCountFrequency (%)
127.1044356 1
4.0%
127.0937442 1
4.0%
127.0845147 1
4.0%
127.0821975 2
8.0%
127.0811361 1
4.0%
127.0758461 1
4.0%
127.075498 1
4.0%
127.0730502 1
4.0%
127.071297 1
4.0%
127.0711679 2
8.0%

지정일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing25
Missing (%)100.0%
Memory size357.0 B

일평균이용인구수
Categorical

Distinct5
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
150
7 
100
7 
200
6 
300
3 
250
2 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row150
2nd row200
3rd row150
4th row100
5th row150

Common Values

ValueCountFrequency (%)
150 7
28.0%
100 7
28.0%
200 6
24.0%
300 3
12.0%
250 2
 
8.0%

Length

2023-12-11T14:29:38.882441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:29:38.992759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
150 7
28.0%
100 7
28.0%
200 6
24.0%
300 3
12.0%
250 2
 
8.0%

수질검사일자
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2017-10-24
17 
2017-09-26
7 
2017-08-23
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row2017-10-24
2nd row2017-10-24
3rd row2017-10-24
4th row2017-10-24
5th row2017-10-24

Common Values

ValueCountFrequency (%)
2017-10-24 17
68.0%
2017-09-26 7
28.0%
2017-08-23 1
 
4.0%

Length

2023-12-11T14:29:39.101123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:29:39.188254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-10-24 17
68.0%
2017-09-26 7
28.0%
2017-08-23 1
 
4.0%

수질검사결과구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
부적합
19 
적합
6 

Length

Max length3
Median length3
Mean length2.76
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부적합 19
76.0%
적합 6
 
24.0%

Length

2023-12-11T14:29:39.295129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:29:39.404600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부적합 19
76.0%
적합 6
 
24.0%

부적합항목
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
총대장균군검출
14 
<NA>
6 
총대장균군, 대장균 검출
4 
일반세균기준초과, 총대장균군검출
 
1

Length

Max length17
Median length7
Mean length7.64
Min length4

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row총대장균군검출
2nd row총대장균군검출
3rd row일반세균기준초과, 총대장균군검출
4th row총대장균군검출
5th row<NA>

Common Values

ValueCountFrequency (%)
총대장균군검출 14
56.0%
<NA> 6
24.0%
총대장균군, 대장균 검출 4
 
16.0%
일반세균기준초과, 총대장균군검출 1
 
4.0%

Length

2023-12-11T14:29:39.526490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:29:39.655105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
총대장균군검출 15
44.1%
na 6
 
17.6%
총대장균군 4
 
11.8%
대장균 4
 
11.8%
검출 4
 
11.8%
일반세균기준초과 1
 
2.9%

관리기관전화번호
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
02-3423-6259
22 
02-3423-6245
3 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02-3423-6259
2nd row02-3423-6259
3rd row02-3423-6259
4th row02-3423-6259
5th row02-3423-6259

Common Values

ValueCountFrequency (%)
02-3423-6259 22
88.0%
02-3423-6245 3
 
12.0%

Length

2023-12-11T14:29:39.778983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:29:39.878016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
02-3423-6259 22
88.0%
02-3423-6245 3
 
12.0%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
서울특별시 강남구청
25 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 강남구청
2nd row서울특별시 강남구청
3rd row서울특별시 강남구청
4th row서울특별시 강남구청
5th row서울특별시 강남구청

Common Values

ValueCountFrequency (%)
서울특별시 강남구청 25
100.0%

Length

2023-12-11T14:29:39.991491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:29:40.084236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 25
50.0%
강남구청 25
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2018-04-26
25 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-04-26
2nd row2018-04-26
3rd row2018-04-26
4th row2018-04-26
5th row2018-04-26

Common Values

ValueCountFrequency (%)
2018-04-26 25
100.0%

Length

2023-12-11T14:29:40.175735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:29:40.259565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-04-26 25
100.0%

Interactions

2023-12-11T14:29:35.732975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:29:35.529149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:29:35.829235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:29:35.628056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:29:40.327303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
약수터명소재지도로명주소소재지지번주소위도경도일평균이용인구수수질검사일자수질검사결과구분부적합항목관리기관전화번호
약수터명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.000NaN1.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0001.0000.8020.0000.4330.4930.3630.615
경도1.0001.0001.0000.8021.0000.0000.7520.0000.0001.000
일평균이용인구수1.0001.0001.0000.0000.0001.0000.0000.0000.0920.090
수질검사일자1.0001.0001.0000.4330.7520.0001.0000.2540.4280.339
수질검사결과구분1.000NaN1.0000.4930.0000.0000.2541.000NaN0.000
부적합항목1.0001.0001.0000.3630.0000.0920.428NaN1.0000.000
관리기관전화번호1.0001.0001.0000.6151.0000.0900.3390.0000.0001.000
2023-12-11T14:29:40.498118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일평균이용인구수부적합항목관리기관전화번호수질검사결과구분수질검사일자
일평균이용인구수1.0000.0000.0610.0000.000
부적합항목0.0001.0000.0001.0000.140
관리기관전화번호0.0610.0001.0000.0000.528
수질검사결과구분0.0001.0000.0001.0000.401
수질검사일자0.0000.1400.5280.4011.000
2023-12-11T14:29:40.609607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도일평균이용인구수수질검사일자수질검사결과구분부적합항목관리기관전화번호
위도1.0000.2340.0000.4690.5360.0750.668
경도0.2341.0000.0000.4490.0000.0000.804
일평균이용인구수0.0000.0001.0000.0000.0000.0000.061
수질검사일자0.4690.4490.0001.0000.4010.1400.528
수질검사결과구분0.5360.0000.0000.4011.0001.0000.000
부적합항목0.0750.0000.0000.1401.0001.0000.000
관리기관전화번호0.6680.8040.0610.5280.0000.0001.000

Missing values

2023-12-11T14:29:35.972780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:29:36.181431image/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.
2023-12-11T14:29:36.326422image/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

약수터명소재지도로명주소소재지지번주소위도경도지정일자일평균이용인구수수질검사일자수질검사결과구분부적합항목관리기관전화번호관리기관명데이터기준일자
0개암<NA>서울특별시 강남구 개포동 산53-20<NA><NA><NA>1502017-10-24부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
1천의<NA>서울특별시 강남구 개포동 산53-3037.472528127.06643<NA>2002017-10-24부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
2대룡<NA>서울특별시 강남구 개포동 118-2137.47481127.069283<NA>1502017-10-24부적합일반세균기준초과, 총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
3대천<NA>서울특별시 강남구 개포동 산53-3137.472528127.069408<NA>1002017-10-24부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
4구룡산<NA>서울특별시 강남구 개포동 1017-7<NA><NA><NA>1502017-10-24적합<NA>02-3423-6259서울특별시 강남구청2018-04-26
5습지원<NA>서울특별시 강남구 개포동 산53-4237.473789127.070861<NA>1002017-10-24부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
6율암<NA>서울특별시 강남구 세곡동 산52-44<NA><NA><NA>1002017-09-26적합<NA>02-3423-6259서울특별시 강남구청2018-04-26
7만수정<NA>서울특별시 강남구 자곡동 산1537.480176127.104436<NA>2002017-10-24부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
8구룡천2<NA>서울특별시 강남구 개포동 산53-2837.473228127.071168<NA>3002017-10-24부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
9매봉<NA>서울특별시 강남구 도곡동 산31-337.490078127.046181<NA>2002017-09-26적합<NA>02-3423-6245서울특별시 강남구청2018-04-26
약수터명소재지도로명주소소재지지번주소위도경도지정일자일평균이용인구수수질검사일자수질검사결과구분부적합항목관리기관전화번호관리기관명데이터기준일자
15옛2<NA>서울특별시 강남구 일원동 산63-3237.477934127.082198<NA>2502017-10-24부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
16성지<NA>서울특별시 강남구 일원동 산63-3237.477934127.082198<NA>2502017-10-24부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
17실로암<NA>서울특별시 강남구 일원동 산63-5137.478022127.081136<NA>1002017-10-24부적합총대장균군, 대장균 검출02-3423-6259서울특별시 강남구청2018-04-26
18옥수천<NA>서울특별시 강남구 개포동 산53-3237.479437127.071297<NA>1002017-09-26적합<NA>02-3423-6259서울특별시 강남구청2018-04-26
19못골<NA>서울특별시 강남구 자곡동 산39-10<NA><NA><NA>1502017-09-26부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
20임록천<NA>서울특별시 강남구 개포당 산53-41<NA><NA><NA>1002017-08-23부적합총대장균군, 대장균 검출02-3423-6259서울특별시 강남구청2018-04-26
21인수천<NA>서울특별시 강남구 일원동 산63-137.477906127.084515<NA>3002017-09-26적합<NA>02-3423-6259서울특별시 강남구청2018-04-26
22용두천<NA>서울특별시 강남구 개포동 19237.485625127.075846<NA>1502017-10-24적합<NA>02-3423-6259서울특별시 강남구청2018-04-26
23불국사<NA>서울특별시 강남구 일원동 44137.477843127.075498<NA>2002017-10-24부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26
24구룡천1<NA>서울특별시 강남구 개포동 산53-2837.473228127.071168<NA>3002017-10-24부적합총대장균군검출02-3423-6259서울특별시 강남구청2018-04-26