Overview

Dataset statistics

Number of variables16
Number of observations5897
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory800.6 KiB
Average record size in memory139.0 B

Variable types

Numeric4
Text3
Categorical9

Dataset

Description수도법에 따라 2년마다 소규모수도시설에서에서 표류수를 원수로 사용하는 원수 수질 검사 결과 데이터로, 수질검사결과, 수질검사기관 등을 포함 * 상세자료조회는 아래 URL을 참고 해주시기 바랍니다. https://www.waternow.go.kr/web/lawData7/?pMENUID=150&ATTR_1=3201
URLhttps://www.data.go.kr/data/15093997/fileData.do

Alerts

시안 has constant value ""Constant
수은 has constant value ""Constant
불소 has constant value ""Constant
유기인 has constant value ""Constant
연번 is highly overall correlated with 검사년도High correlation
검사년도 is highly overall correlated with 연번High correlation
납 is highly overall correlated with 크롬High correlation
크롬 is highly overall correlated with 납High correlation
수원 is highly imbalanced (65.3%)Imbalance
카드뮴 is highly imbalanced (99.6%)Imbalance
크롬 is highly imbalanced (99.8%)Imbalance
폴리크로리네이티드페닐 is highly imbalanced (99.4%)Imbalance
납 is highly skewed (γ1 = 33.50759482)Skewed
연번 has unique valuesUnique
비소 has 5731 (97.2%) zerosZeros
납 has 5869 (99.5%) zerosZeros

Reproduction

Analysis started2023-12-12 20:33:06.946370
Analysis finished2023-12-12 20:33:10.164061
Duration3.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5897
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2949
Minimum1
Maximum5897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.0 KiB
2023-12-13T05:33:10.234462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile295.8
Q11475
median2949
Q34423
95-th percentile5602.2
Maximum5897
Range5896
Interquartile range (IQR)2948

Descriptive statistics

Standard deviation1702.4616
Coefficient of variation (CV)0.57730132
Kurtosis-1.2
Mean2949
Median Absolute Deviation (MAD)1474
Skewness0
Sum17390253
Variance2898375.5
MonotonicityStrictly increasing
2023-12-13T05:33:10.380179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3929 1
 
< 0.1%
3938 1
 
< 0.1%
3937 1
 
< 0.1%
3936 1
 
< 0.1%
3935 1
 
< 0.1%
3934 1
 
< 0.1%
3933 1
 
< 0.1%
3932 1
 
< 0.1%
3931 1
 
< 0.1%
Other values (5887) 5887
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5897 1
< 0.1%
5896 1
< 0.1%
5895 1
< 0.1%
5894 1
< 0.1%
5893 1
< 0.1%
5892 1
< 0.1%
5891 1
< 0.1%
5890 1
< 0.1%
5889 1
< 0.1%
5888 1
< 0.1%

검사년도
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.2676
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.0 KiB
2023-12-13T05:33:10.496561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2018
Q32020
95-th percentile2022
Maximum2022
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2607251
Coefficient of variation (CV)0.0011201315
Kurtosis-1.3250452
Mean2018.2676
Median Absolute Deviation (MAD)2
Skewness0.072366218
Sum11901724
Variance5.1108781
MonotonicityIncreasing
2023-12-13T05:33:10.606477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2016 933
15.8%
2021 932
15.8%
2020 870
14.8%
2018 808
13.7%
2015 799
13.5%
2017 746
12.7%
2022 414
7.0%
2019 395
6.7%
ValueCountFrequency (%)
2015 799
13.5%
2016 933
15.8%
2017 746
12.7%
2018 808
13.7%
2019 395
6.7%
2020 870
14.8%
2021 932
15.8%
2022 414
7.0%
ValueCountFrequency (%)
2022 414
7.0%
2021 932
15.8%
2020 870
14.8%
2019 395
6.7%
2018 808
13.7%
2017 746
12.7%
2016 933
15.8%
2015 799
13.5%

지역
Text

Distinct101
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
2023-12-13T05:33:10.883271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.6257419
Min length5

Characters and Unicode

Total characters44969
Distinct characters92
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

Unique3 ?
Unique (%)0.1%

Sample

1st row강원도 고성군
2nd row강원도 고성군
3rd row강원도 고성군
4th row강원도 양구군
5th row강원도 양구군
ValueCountFrequency (%)
강원도 1471
 
12.7%
경상북도 1308
 
11.3%
경상남도 1005
 
8.7%
전라북도 703
 
6.1%
전라남도 558
 
4.8%
충청북도 495
 
4.3%
평창군 283
 
2.4%
단양군 276
 
2.4%
영월군 276
 
2.4%
경주시 249
 
2.2%
Other values (98) 4952
42.8%
2023-12-13T05:33:11.369641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 5712
 
12.7%
5679
 
12.6%
군 4119
 
9.2%
경 2747
 
6.1%
북 2506
 
5.6%
상 2353
 
5.2%
남 1865
 
4.1%
시 1802
 
4.0%
원 1641
 
3.6%
강 1584
 
3.5%
Other values (82) 14961
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39290
87.4%
Space Separator 5679
 
12.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 5712
14.5%
군 4119
 
10.5%
경 2747
 
7.0%
북 2506
 
6.4%
상 2353
 
6.0%
남 1865
 
4.7%
시 1802
 
4.6%
원 1641
 
4.2%
강 1584
 
4.0%
전 1274
 
3.2%
Other values (81) 13687
34.8%
Space Separator
ValueCountFrequency (%)
5679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39290
87.4%
Common 5679
 
12.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 5712
14.5%
군 4119
 
10.5%
경 2747
 
7.0%
북 2506
 
6.4%
상 2353
 
6.0%
남 1865
 
4.7%
시 1802
 
4.6%
원 1641
 
4.2%
강 1584
 
4.0%
전 1274
 
3.2%
Other values (81) 13687
34.8%
Common
ValueCountFrequency (%)
5679
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39290
87.4%
ASCII 5679
 
12.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 5712
14.5%
군 4119
 
10.5%
경 2747
 
7.0%
북 2506
 
6.4%
상 2353
 
6.0%
남 1865
 
4.7%
시 1802
 
4.6%
원 1641
 
4.2%
강 1584
 
4.0%
전 1274
 
3.2%
Other values (81) 13687
34.8%
ASCII
ValueCountFrequency (%)
5679
100.0%

소규모수도시설명
Text

Distinct2345
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
2023-12-13T05:33:11.865332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length2
Mean length2.9720197
Min length1

Characters and Unicode

Total characters17526
Distinct characters501
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique806 ?
Unique (%)13.7%

Sample

1st row진부
2nd row진부
3rd row학야1리
4th row갯골터
5th row고대
ValueCountFrequency (%)
절골 39
 
0.6%
신기 30
 
0.5%
양지 20
 
0.3%
본마을 18
 
0.3%
주)소노호텔앤리조트,(주)대명티피앤이 14
 
0.2%
새마을 13
 
0.2%
장전 12
 
0.2%
본부락 12
 
0.2%
학동 12
 
0.2%
신촌 11
 
0.2%
Other values (2359) 5872
97.0%
2023-12-13T05:33:12.478158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 683
 
3.9%
골 643
 
3.7%
리 480
 
2.7%
) 384
 
2.2%
( 384
 
2.2%
마 315
 
1.8%
상 315
 
1.8%
대 265
 
1.5%
곡 237
 
1.4%
지 236
 
1.3%
Other values (491) 13584
77.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16053
91.6%
Decimal Number 471
 
2.7%
Close Punctuation 384
 
2.2%
Open Punctuation 384
 
2.2%
Space Separator 160
 
0.9%
Other Punctuation 44
 
0.3%
Uppercase Letter 20
 
0.1%
Dash Punctuation 4
 
< 0.1%
Math Symbol 4
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 683
 
4.3%
골 643
 
4.0%
리 480
 
3.0%
마 315
 
2.0%
상 315
 
2.0%
대 265
 
1.7%
곡 237
 
1.5%
지 236
 
1.5%
산 222
 
1.4%
원 212
 
1.3%
Other values (467) 12445
77.5%
Decimal Number
ValueCountFrequency (%)
2 172
36.5%
1 155
32.9%
3 82
17.4%
4 24
 
5.1%
5 19
 
4.0%
6 11
 
2.3%
9 5
 
1.1%
8 2
 
0.4%
0 1
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
O 5
25.0%
B 5
25.0%
C 3
15.0%
K 3
15.0%
S 3
15.0%
G 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 28
63.6%
· 15
34.1%
. 1
 
2.3%
Close Punctuation
ValueCountFrequency (%)
) 384
100.0%
Open Punctuation
ValueCountFrequency (%)
( 384
100.0%
Space Separator
ValueCountFrequency (%)
160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16053
91.6%
Common 1451
 
8.3%
Latin 22
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 683
 
4.3%
골 643
 
4.0%
리 480
 
3.0%
마 315
 
2.0%
상 315
 
2.0%
대 265
 
1.7%
곡 237
 
1.5%
지 236
 
1.5%
산 222
 
1.4%
원 212
 
1.3%
Other values (467) 12445
77.5%
Common
ValueCountFrequency (%)
) 384
26.5%
( 384
26.5%
2 172
11.9%
160
11.0%
1 155
10.7%
3 82
 
5.7%
, 28
 
1.9%
4 24
 
1.7%
5 19
 
1.3%
· 15
 
1.0%
Other values (7) 28
 
1.9%
Latin
ValueCountFrequency (%)
O 5
22.7%
B 5
22.7%
C 3
13.6%
K 3
13.6%
S 3
13.6%
c 2
 
9.1%
G 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16053
91.6%
ASCII 1458
 
8.3%
None 15
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 683
 
4.3%
골 643
 
4.0%
리 480
 
3.0%
마 315
 
2.0%
상 315
 
2.0%
대 265
 
1.7%
곡 237
 
1.5%
지 236
 
1.5%
산 222
 
1.4%
원 212
 
1.3%
Other values (467) 12445
77.5%
ASCII
ValueCountFrequency (%)
) 384
26.3%
( 384
26.3%
2 172
11.8%
160
11.0%
1 155
10.6%
3 82
 
5.6%
, 28
 
1.9%
4 24
 
1.6%
5 19
 
1.3%
6 11
 
0.8%
Other values (13) 39
 
2.7%
None
ValueCountFrequency (%)
· 15
100.0%

측정지점주소
Text

Distinct2240
Distinct (%)38.0%
Missing1
Missing (%)< 0.1%
Memory size46.2 KiB
2023-12-13T05:33:12.857946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length17.229986
Min length3

Characters and Unicode

Total characters101588
Distinct characters343
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

Unique807 ?
Unique (%)13.7%

Sample

1st row강원도 고성군 간성읍 진부리
2nd row강원도 고성군 간성읍 진부리
3rd row강원도 고성군 토성면 학야1리 학야1리
4th row강원도 양구군 남면 대월리
5th row강원도 양구군 양구읍 고대리2반
ValueCountFrequency (%)
강원도 1471
 
5.8%
경상북도 1308
 
5.1%
경상남도 1005
 
3.9%
전라북도 703
 
2.8%
전라남도 552
 
2.2%
충청북도 495
 
1.9%
산 344
 
1.3%
평창군 283
 
1.1%
영월군 276
 
1.1%
단양군 276
 
1.1%
Other values (3210) 18822
73.7%
2023-12-13T05:33:13.463558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19704
 
19.4%
도 5899
 
5.8%
면 5002
 
4.9%
군 4209
 
4.1%
리 4021
 
4.0%
북 3079
 
3.0%
상 2779
 
2.7%
경 2777
 
2.7%
남 2256
 
2.2%
원 1840
 
1.8%
Other values (333) 50022
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74777
73.6%
Space Separator 19704
 
19.4%
Decimal Number 6335
 
6.2%
Dash Punctuation 621
 
0.6%
Other Punctuation 79
 
0.1%
Close Punctuation 36
 
< 0.1%
Open Punctuation 36
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 5899
 
7.9%
면 5002
 
6.7%
군 4209
 
5.6%
리 4021
 
5.4%
북 3079
 
4.1%
상 2779
 
3.7%
경 2777
 
3.7%
남 2256
 
3.0%
원 1840
 
2.5%
시 1838
 
2.5%
Other values (316) 41077
54.9%
Decimal Number
ValueCountFrequency (%)
1 1700
26.8%
2 1369
21.6%
3 650
 
10.3%
4 483
 
7.6%
5 458
 
7.2%
6 405
 
6.4%
9 327
 
5.2%
8 324
 
5.1%
7 319
 
5.0%
0 300
 
4.7%
Other Punctuation
ValueCountFrequency (%)
/ 55
69.6%
, 22
 
27.8%
. 2
 
2.5%
Space Separator
ValueCountFrequency (%)
19704
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 621
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74777
73.6%
Common 26811
 
26.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 5899
 
7.9%
면 5002
 
6.7%
군 4209
 
5.6%
리 4021
 
5.4%
북 3079
 
4.1%
상 2779
 
3.7%
경 2777
 
3.7%
남 2256
 
3.0%
원 1840
 
2.5%
시 1838
 
2.5%
Other values (316) 41077
54.9%
Common
ValueCountFrequency (%)
19704
73.5%
1 1700
 
6.3%
2 1369
 
5.1%
3 650
 
2.4%
- 621
 
2.3%
4 483
 
1.8%
5 458
 
1.7%
6 405
 
1.5%
9 327
 
1.2%
8 324
 
1.2%
Other values (7) 770
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74777
73.6%
ASCII 26811
 
26.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19704
73.5%
1 1700
 
6.3%
2 1369
 
5.1%
3 650
 
2.4%
- 621
 
2.3%
4 483
 
1.8%
5 458
 
1.7%
6 405
 
1.5%
9 327
 
1.2%
8 324
 
1.2%
Other values (7) 770
 
2.9%
Hangul
ValueCountFrequency (%)
도 5899
 
7.9%
면 5002
 
6.7%
군 4209
 
5.6%
리 4021
 
5.4%
북 3079
 
4.1%
상 2779
 
3.7%
경 2777
 
3.7%
남 2256
 
3.0%
원 1840
 
2.5%
시 1838
 
2.5%
Other values (316) 41077
54.9%

수도규모
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
소규모급수시설
4403 
마을상수도
1421 
전용상수도시설
 
73

Length

Max length7
Median length7
Mean length6.51806
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소규모급수시설
2nd row소규모급수시설
3rd row마을상수도
4th row소규모급수시설
5th row소규모급수시설

Common Values

ValueCountFrequency (%)
소규모급수시설 4403
74.7%
마을상수도 1421
 
24.1%
전용상수도시설 73
 
1.2%

Length

2023-12-13T05:33:13.613223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:33:13.736732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소규모급수시설 4403
74.7%
마을상수도 1421
 
24.1%
전용상수도시설 73
 
1.2%

수원
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
계곡수
4853 
지표수
616 
복류수
 
376
하천수
 
35
용천수
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
계곡수 4853
82.3%
지표수 616
 
10.4%
복류수 376
 
6.4%
하천수 35
 
0.6%
용천수 9
 
0.2%
지하수 8
 
0.1%

Length

2023-12-13T05:33:13.869319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:33:13.976771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계곡수 4853
82.3%
지표수 616
 
10.4%
복류수 376
 
6.4%
하천수 35
 
0.6%
용천수 9
 
0.2%
지하수 8
 
0.1%

카드뮴
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
0.0
5894 
0.004
 
2
0.0049
 
1

Length

Max length6
Median length3
Mean length3.001187
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5894
99.9%
0.004 2
 
< 0.1%
0.0049 1
 
< 0.1%

Length

2023-12-13T05:33:14.164017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:33:14.298928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5894
99.9%
0.004 2
 
< 0.1%
0.0049 1
 
< 0.1%

비소
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00032491097
Minimum0
Maximum0.057
Zeros5731
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size52.0 KiB
2023-12-13T05:33:14.434995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.057
Range0.057
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0024649328
Coefficient of variation (CV)7.5864869
Kurtosis186.99492
Mean0.00032491097
Median Absolute Deviation (MAD)0
Skewness11.942625
Sum1.916
Variance6.0758939 × 10-6
MonotonicityNot monotonic
2023-12-13T05:33:14.573891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 5731
97.2%
0.005 25
 
0.4%
0.006 22
 
0.4%
0.009 20
 
0.3%
0.007 20
 
0.3%
0.008 19
 
0.3%
0.011 10
 
0.2%
0.012 9
 
0.2%
0.01 5
 
0.1%
0.017 4
 
0.1%
Other values (21) 32
 
0.5%
ValueCountFrequency (%)
0.0 5731
97.2%
0.002 1
 
< 0.1%
0.005 25
 
0.4%
0.006 22
 
0.4%
0.007 20
 
0.3%
0.008 19
 
0.3%
0.009 20
 
0.3%
0.01 5
 
0.1%
0.011 10
 
0.2%
0.012 9
 
0.2%
ValueCountFrequency (%)
0.057 1
< 0.1%
0.056 1
< 0.1%
0.05 1
< 0.1%
0.037 1
< 0.1%
0.036 1
< 0.1%
0.033 1
< 0.1%
0.032 2
< 0.1%
0.031 2
< 0.1%
0.03 1
< 0.1%
0.029 2
< 0.1%

시안
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
0
5897 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5897
100.0%

Length

2023-12-13T05:33:14.730957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:33:14.844096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5897
100.0%

수은
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
0
5897 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5897
100.0%

Length

2023-12-13T05:33:14.941505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:33:15.049694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5897
100.0%

납
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7985416 × 10-5
Minimum0
Maximum0.037
Zeros5869
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size52.0 KiB
2023-12-13T05:33:15.174759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.037
Range0.037
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00074238459
Coefficient of variation (CV)19.543937
Kurtosis1422.0119
Mean3.7985416 × 10-5
Median Absolute Deviation (MAD)0
Skewness33.507595
Sum0.224
Variance5.5113488 × 10-7
MonotonicityNot monotonic
2023-12-13T05:33:15.319119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 5869
99.5%
0.005 9
 
0.2%
0.006 4
 
0.1%
0.007 3
 
0.1%
0.009 3
 
0.1%
0.003 2
 
< 0.1%
0.004 1
 
< 0.1%
0.037 1
 
< 0.1%
0.008 1
 
< 0.1%
0.012 1
 
< 0.1%
Other values (3) 3
 
0.1%
ValueCountFrequency (%)
0.0 5869
99.5%
0.002 1
 
< 0.1%
0.003 2
 
< 0.1%
0.004 1
 
< 0.1%
0.005 9
 
0.2%
0.006 4
 
0.1%
0.007 3
 
0.1%
0.008 1
 
< 0.1%
0.009 3
 
0.1%
0.01 1
 
< 0.1%
ValueCountFrequency (%)
0.037 1
 
< 0.1%
0.028 1
 
< 0.1%
0.012 1
 
< 0.1%
0.01 1
 
< 0.1%
0.009 3
 
0.1%
0.008 1
 
< 0.1%
0.007 3
 
0.1%
0.006 4
0.1%
0.005 9
0.2%
0.004 1
 
< 0.1%

크롬
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
0.0
5896 
0.01
 
1

Length

Max length4
Median length3
Mean length3.0001696
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5896
> 99.9%
0.01 1
 
< 0.1%

Length

2023-12-13T05:33:15.502214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:33:15.628034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5896
> 99.9%
0.01 1
 
< 0.1%

불소
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
0
5897 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5897
100.0%

Length

2023-12-13T05:33:15.777553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:33:15.904667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5897
100.0%

유기인
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
0
5897 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5897
100.0%

Length

2023-12-13T05:33:16.043286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:33:16.501277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5897
100.0%

폴리크로리네이티드페닐
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
0.0
5891 
0.1
 
3
0.07
 
1
0.04
 
1
0.09
 
1

Length

Max length4
Median length3
Mean length3.0005087
Min length3

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5891
99.9%
0.1 3
 
0.1%
0.07 1
 
< 0.1%
0.04 1
 
< 0.1%
0.09 1
 
< 0.1%

Length

2023-12-13T05:33:16.627181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:33:16.765259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5891
99.9%
0.1 3
 
0.1%
0.07 1
 
< 0.1%
0.04 1
 
< 0.1%
0.09 1
 
< 0.1%

Interactions

2023-12-13T05:33:09.393098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:08.174978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:08.609612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:09.001835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:09.494816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:08.269187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:08.707430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:09.093933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:09.600995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:08.392903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:08.800453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:09.199283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:09.720599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:08.500220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:08.895824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:33:09.286107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:33:16.848220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번검사년도수도규모수원카드뮴비소납크롬폴리크로리네이티드페닐
연번1.0000.9220.1830.2680.0000.0310.0590.0020.000
검사년도0.9221.0000.0630.1870.0000.0290.0460.0320.000
수도규모0.1830.0631.0000.6950.0000.0000.0000.0000.000
수원0.2680.1870.6951.0000.0610.0430.0000.0340.000
카드뮴0.0000.0000.0000.0611.0000.0000.0000.0000.000
비소0.0310.0290.0000.0430.0001.0000.0000.0000.061
납0.0590.0460.0000.0000.0000.0001.0001.0000.000
크롬0.0020.0320.0000.0340.0000.0001.0001.0000.000
폴리크로리네이티드페닐0.0000.0000.0000.0000.0000.0610.0000.0001.000
2023-12-13T05:33:16.976431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수원크롬폴리크로리네이티드페닐카드뮴수도규모
수원1.0000.0250.0000.0250.378
크롬0.0251.0000.0000.0000.000
폴리크로리네이티드페닐0.0000.0001.0000.0000.000
카드뮴0.0250.0000.0001.0000.000
수도규모0.3780.0000.0000.0001.000
2023-12-13T05:33:17.121015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번검사년도비소납수도규모수원카드뮴크롬폴리크로리네이티드페닐
연번1.0000.991-0.022-0.0320.1100.1440.0000.0010.000
검사년도0.9911.000-0.030-0.0300.0590.1080.0000.0340.000
비소-0.022-0.0301.000-0.0120.0000.0210.0000.0000.035
납-0.032-0.030-0.0121.0000.0000.0000.0001.0000.000
수도규모0.1100.0590.0000.0001.0000.3780.0000.0000.000
수원0.1440.1080.0210.0000.3781.0000.0250.0250.000
카드뮴0.0000.0000.0000.0000.0000.0251.0000.0000.000
크롬0.0010.0340.0001.0000.0000.0250.0001.0000.000
폴리크로리네이티드페닐0.0000.0000.0350.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T05:33:09.872051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:33:10.082150image/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

연번검사년도지역소규모수도시설명측정지점주소수도규모수원카드뮴비소시안수은납크롬불소유기인폴리크로리네이티드페닐
012015강원도 고성군진부강원도 고성군 간성읍 진부리소규모급수시설계곡수0.00.0000.00.0000.0
122015강원도 고성군진부강원도 고성군 간성읍 진부리소규모급수시설계곡수0.00.0000.00.0000.0
232015강원도 고성군학야1리강원도 고성군 토성면 학야1리 학야1리마을상수도계곡수0.00.0000.00.0000.0
342015강원도 양구군갯골터강원도 양구군 남면 대월리소규모급수시설계곡수0.00.0000.00.0000.0
452015강원도 양구군고대강원도 양구군 양구읍 고대리2반소규모급수시설계곡수0.00.0000.00.0000.0
562015강원도 양구군도일강원도 양구군 양구읍 상무룡2리1반소규모급수시설계곡수0.00.0000.00.0000.0
672015강원도 양구군동수강원도 양구군 양구읍 동수리2반소규모급수시설계곡수0.00.0000.00.0000.0
782015강원도 양구군두무1강원도 양구군 남면 두무리 (초입)소규모급수시설계곡수0.00.0000.00.0000.0
892015강원도 양구군만대리강원도 양구군 해안면 만대리마을상수도계곡수0.00.0000.00.0000.0
9102015강원도 양구군뱅이골강원도 양구군 방산면 송현리소규모급수시설계곡수0.00.0000.00.0000.0
연번검사년도지역소규모수도시설명측정지점주소수도규모수원카드뮴비소시안수은납크롬불소유기인폴리크로리네이티드페닐
588758882022충청북도 옥천군솔미기충청북도 옥천군 안내면 용촌소규모급수시설계곡수0.00.0000.00.0000.0
588858892022충청북도 옥천군신대충청북도 옥천군 안내면 용촌리소규모급수시설계곡수0.00.0000.00.0000.0
588958902022충청북도 옥천군탄부충청북도 옥천군 청성면 구음1소규모급수시설계곡수0.00.0000.00.0000.0
589058912022충청북도 옥천군학골충청북도 옥천군 안내면 답양리소규모급수시설계곡수0.00.0000.00.0000.0
589158922022충청북도 청주시공군제3629부대충청북도 청주시 내수읍 원통리 308-14전용상수도시설하천수0.00.0000.00.0000.0
589258932022충청북도 청주시떼제베컨트리클럽충청북도 청주시 옥산면 환희 산102전용상수도시설복류수0.00.0000.00.0000.0
589358942022충청북도 청주시산정말(낭성)충청북도 청주시 상당구 낭성면 추정리 산 87소규모급수시설계곡수0.00.0000.00.0000.0
589458952022충청북도 청주시작은고디미충청북도 청주시 상당구 낭성면 귀래리 17소규모급수시설계곡수0.00.0000.00.0000.0
589558962022충청북도 청주시현도지방공단충청북도 청주시 현도면 노산 700전용상수도시설복류수0.00.0000.00.0000.0
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