Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells46
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory165.0 B

Variable types

Numeric10
Text3
Categorical5

Dataset

Description수도법에 따라 2년마다 소규모수도시설에서 지하수를 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=3205
URLhttps://www.data.go.kr/data/15093996/fileData.do

Alerts

수은 has constant value ""Constant
연번 is highly overall correlated with 검사년도High correlation
검사년도 is highly overall correlated with 연번High correlation
다이아지논 is highly overall correlated with 파라티온High correlation
파라티온 is highly overall correlated with 다이아지논High correlation
수원 is highly imbalanced (93.4%)Imbalance
파라티온 is highly imbalanced (99.8%)Imbalance
페니트로티온 is highly imbalanced (99.9%)Imbalance
카드뮴 is highly skewed (γ1 = 69.84457045)Skewed
비소 is highly skewed (γ1 = 55.42790044)Skewed
시안 is highly skewed (γ1 = 47.09013109)Skewed
납 is highly skewed (γ1 = 42.32824821)Skewed
크롬 is highly skewed (γ1 = 42.97292943)Skewed
다이아지논 is highly skewed (γ1 = 54.58742005)Skewed
음이온 계면활성제 is highly skewed (γ1 = 49.95512198)Skewed
연번 has unique valuesUnique
카드뮴 has 9990 (99.9%) zerosZeros
비소 has 9007 (90.1%) zerosZeros
시안 has 9994 (99.9%) zerosZeros
납 has 9938 (99.4%) zerosZeros
크롬 has 9983 (99.8%) zerosZeros
다이아지논 has 9994 (99.9%) zerosZeros
음이온 계면활성제 has 9990 (99.9%) zerosZeros
불소 has 5797 (58.0%) zerosZeros

Reproduction

Analysis started2023-12-12 11:17:10.936754
Analysis finished2023-12-12 11:17:38.499989
Duration27.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16109.874
Minimum4
Maximum32316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:17:38.615299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile1679.95
Q18027.25
median16107.5
Q324155.25
95-th percentile30719.1
Maximum32316
Range32312
Interquartile range (IQR)16128

Descriptive statistics

Standard deviation9308.7915
Coefficient of variation (CV)0.57783142
Kurtosis-1.1960878
Mean16109.874
Median Absolute Deviation (MAD)8064
Skewness0.011337823
Sum1.6109874 × 108
Variance86653598
MonotonicityNot monotonic
2023-12-12T20:17:38.847648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16114 1
 
< 0.1%
3437 1
 
< 0.1%
10219 1
 
< 0.1%
20691 1
 
< 0.1%
7602 1
 
< 0.1%
27093 1
 
< 0.1%
23089 1
 
< 0.1%
6387 1
 
< 0.1%
8474 1
 
< 0.1%
19507 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
4 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
21 1
< 0.1%
34 1
< 0.1%
40 1
< 0.1%
46 1
< 0.1%
ValueCountFrequency (%)
32316 1
< 0.1%
32315 1
< 0.1%
32312 1
< 0.1%
32311 1
< 0.1%
32303 1
< 0.1%
32295 1
< 0.1%
32294 1
< 0.1%
32293 1
< 0.1%
32291 1
< 0.1%
32287 1
< 0.1%

검사년도
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.2872
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:17:39.033771image/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 deviation2.2604484
Coefficient of variation (CV)0.0011199835
Kurtosis-1.2528842
Mean2018.2872
Median Absolute Deviation (MAD)2
Skewness0.12572516
Sum20182872
Variance5.1096271
MonotonicityNot monotonic
2023-12-12T20:17:39.230884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2017 1592
15.9%
2021 1429
14.3%
2018 1362
13.6%
2016 1353
13.5%
2015 1325
13.2%
2020 1204
12.0%
2022 905
9.0%
2019 830
8.3%
ValueCountFrequency (%)
2015 1325
13.2%
2016 1353
13.5%
2017 1592
15.9%
2018 1362
13.6%
2019 830
8.3%
2020 1204
12.0%
2021 1429
14.3%
2022 905
9.0%
ValueCountFrequency (%)
2022 905
9.0%
2021 1429
14.3%
2020 1204
12.0%
2019 830
8.3%
2018 1362
13.6%
2017 1592
15.9%
2016 1353
13.5%
2015 1325
13.2%

지역
Text

Distinct129
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T20:17:39.684222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.7284
Min length5

Characters and Unicode

Total characters77284
Distinct characters109
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

Unique7 ?
Unique (%)0.1%

Sample

1st row인천광역시
2nd row전라남도 나주시
3rd row전라남도 강진군
4th row전라남도 보성군
5th row충청북도 음성군
ValueCountFrequency (%)
경상남도 2443
 
12.6%
경상북도 2427
 
12.5%
전라남도 1257
 
6.5%
충청북도 1094
 
5.6%
강원도 822
 
4.2%
충청남도 733
 
3.8%
합천군 383
 
2.0%
밀양시 351
 
1.8%
보성군 320
 
1.7%
전라북도 315
 
1.6%
Other values (126) 9240
47.7%
2023-12-12T20:17:40.426562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 9543
 
12.3%
9385
 
12.1%
군 5767
 
7.5%
경 5416
 
7.0%
상 5146
 
6.7%
남 4713
 
6.1%
시 4231
 
5.5%
북 3836
 
5.0%
청 2562
 
3.3%
충 1927
 
2.5%
Other values (99) 24758
32.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67899
87.9%
Space Separator 9385
 
12.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9543
14.1%
군 5767
 
8.5%
경 5416
 
8.0%
상 5146
 
7.6%
남 4713
 
6.9%
시 4231
 
6.2%
북 3836
 
5.6%
청 2562
 
3.8%
충 1927
 
2.8%
천 1824
 
2.7%
Other values (98) 22934
33.8%
Space Separator
ValueCountFrequency (%)
9385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67899
87.9%
Common 9385
 
12.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9543
14.1%
군 5767
 
8.5%
경 5416
 
8.0%
상 5146
 
7.6%
남 4713
 
6.9%
시 4231
 
6.2%
북 3836
 
5.6%
청 2562
 
3.8%
충 1927
 
2.8%
천 1824
 
2.7%
Other values (98) 22934
33.8%
Common
ValueCountFrequency (%)
9385
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67899
87.9%
ASCII 9385
 
12.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 9543
14.1%
군 5767
 
8.5%
경 5416
 
8.0%
상 5146
 
7.6%
남 4713
 
6.9%
시 4231
 
6.2%
북 3836
 
5.6%
청 2562
 
3.8%
충 1927
 
2.8%
천 1824
 
2.7%
Other values (98) 22934
33.8%
ASCII
ValueCountFrequency (%)
9385
100.0%

소규모수도시설명
Text

Distinct5891
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T20:17:40.911476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length2
Mean length2.922
Min length2

Characters and Unicode

Total characters29220
Distinct characters643
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

Unique3684 ?
Unique (%)36.8%

Sample

1st row금사
2nd row내동
3rd row백양
4th row봉산
5th row구례골
ValueCountFrequency (%)
화산 40
 
0.4%
신촌 39
 
0.4%
현산 38
 
0.4%
신기 37
 
0.4%
상촌 30
 
0.3%
계곡 30
 
0.3%
새터 27
 
0.3%
중촌 25
 
0.2%
본동 24
 
0.2%
새마을 23
 
0.2%
Other values (5859) 9950
97.0%
2023-12-12T20:17:41.849708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1008
 
3.4%
골 784
 
2.7%
리 675
 
2.3%
산 674
 
2.3%
) 540
 
1.8%
( 540
 
1.8%
곡 496
 
1.7%
촌 495
 
1.7%
마 494
 
1.7%
지 452
 
1.5%
Other values (633) 23062
78.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26790
91.7%
Decimal Number 844
 
2.9%
Close Punctuation 540
 
1.8%
Open Punctuation 540
 
1.8%
Space Separator 263
 
0.9%
Other Punctuation 171
 
0.6%
Uppercase Letter 63
 
0.2%
Dash Punctuation 4
 
< 0.1%
Lowercase Letter 4
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1008
 
3.8%
골 784
 
2.9%
리 675
 
2.5%
산 674
 
2.5%
곡 496
 
1.9%
촌 495
 
1.8%
마 494
 
1.8%
지 452
 
1.7%
상 436
 
1.6%
신 423
 
1.6%
Other values (601) 20853
77.8%
Decimal Number
ValueCountFrequency (%)
2 424
50.2%
1 272
32.2%
3 90
 
10.7%
4 29
 
3.4%
5 13
 
1.5%
6 5
 
0.6%
8 4
 
0.5%
7 4
 
0.5%
0 2
 
0.2%
9 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
C 35
55.6%
A 8
 
12.7%
T 7
 
11.1%
P 5
 
7.9%
K 2
 
3.2%
I 2
 
3.2%
G 1
 
1.6%
B 1
 
1.6%
S 1
 
1.6%
N 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
· 72
42.1%
, 68
39.8%
. 25
 
14.6%
/ 5
 
2.9%
& 1
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
n 2
50.0%
o 2
50.0%
Close Punctuation
ValueCountFrequency (%)
) 540
100.0%
Open Punctuation
ValueCountFrequency (%)
( 540
100.0%
Space Separator
ValueCountFrequency (%)
263
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26790
91.7%
Common 2363
 
8.1%
Latin 67
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1008
 
3.8%
골 784
 
2.9%
리 675
 
2.5%
산 674
 
2.5%
곡 496
 
1.9%
촌 495
 
1.8%
마 494
 
1.8%
지 452
 
1.7%
상 436
 
1.6%
신 423
 
1.6%
Other values (601) 20853
77.8%
Common
ValueCountFrequency (%)
) 540
22.9%
( 540
22.9%
2 424
17.9%
1 272
11.5%
263
11.1%
3 90
 
3.8%
· 72
 
3.0%
, 68
 
2.9%
4 29
 
1.2%
. 25
 
1.1%
Other values (10) 40
 
1.7%
Latin
ValueCountFrequency (%)
C 35
52.2%
A 8
 
11.9%
T 7
 
10.4%
P 5
 
7.5%
n 2
 
3.0%
K 2
 
3.0%
o 2
 
3.0%
I 2
 
3.0%
G 1
 
1.5%
B 1
 
1.5%
Other values (2) 2
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26790
91.7%
ASCII 2358
 
8.1%
None 72
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1008
 
3.8%
골 784
 
2.9%
리 675
 
2.5%
산 674
 
2.5%
곡 496
 
1.9%
촌 495
 
1.8%
마 494
 
1.8%
지 452
 
1.7%
상 436
 
1.6%
신 423
 
1.6%
Other values (601) 20853
77.8%
ASCII
ValueCountFrequency (%)
) 540
22.9%
( 540
22.9%
2 424
18.0%
1 272
11.5%
263
11.2%
3 90
 
3.8%
, 68
 
2.9%
C 35
 
1.5%
4 29
 
1.2%
. 25
 
1.1%
Other values (21) 72
 
3.1%
None
ValueCountFrequency (%)
· 72
100.0%

측정지점주소
Text

Distinct6219
Distinct (%)62.2%
Missing9
Missing (%)0.1%
Memory size156.2 KiB
2023-12-12T20:17:42.414943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length33
Mean length17.24392
Min length3

Characters and Unicode

Total characters172284
Distinct characters418
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3878 ?
Unique (%)38.8%

Sample

1st row인천광역시 강화군 양도면 조산리
2nd row전라남도 나주시 왕곡면 신포리 565-2
3rd row전라남도 강진군 병영면 삭양리
4th row전라남도 보성군 조성면 봉능리 봉산마을
5th row충청북도 음성군 음성읍 초천리 1
ValueCountFrequency (%)
경상남도 2438
 
5.7%
경상북도 2425
 
5.7%
전라남도 1250
 
2.9%
충청북도 1094
 
2.6%
강원도 821
 
1.9%
충청남도 733
 
1.7%
합천군 382
 
0.9%
밀양시 351
 
0.8%
보성군 320
 
0.7%
전라북도 314
 
0.7%
Other values (7395) 32547
76.3%
2023-12-12T20:17:43.281149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32748
19.0%
도 10088
 
5.9%
면 8826
 
5.1%
리 6885
 
4.0%
군 6259
 
3.6%
상 5911
 
3.4%
경 5480
 
3.2%
남 5410
 
3.1%
북 4531
 
2.6%
시 4424
 
2.6%
Other values (408) 81722
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 130059
75.5%
Space Separator 32748
 
19.0%
Decimal Number 8275
 
4.8%
Dash Punctuation 828
 
0.5%
Close Punctuation 158
 
0.1%
Open Punctuation 158
 
0.1%
Other Punctuation 57
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 10088
 
7.8%
면 8826
 
6.8%
리 6885
 
5.3%
군 6259
 
4.8%
상 5911
 
4.5%
경 5480
 
4.2%
남 5410
 
4.2%
북 4531
 
3.5%
시 4424
 
3.4%
산 3041
 
2.3%
Other values (390) 69204
53.2%
Decimal Number
ValueCountFrequency (%)
1 2259
27.3%
2 1975
23.9%
3 886
 
10.7%
4 651
 
7.9%
5 526
 
6.4%
6 457
 
5.5%
8 414
 
5.0%
7 407
 
4.9%
0 357
 
4.3%
9 343
 
4.1%
Other Punctuation
ValueCountFrequency (%)
/ 27
47.4%
, 18
31.6%
. 12
21.1%
Space Separator
ValueCountFrequency (%)
32748
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 828
100.0%
Close Punctuation
ValueCountFrequency (%)
) 158
100.0%
Open Punctuation
ValueCountFrequency (%)
( 158
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 130047
75.5%
Common 42224
 
24.5%
Han 12
 
< 0.1%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 10088
 
7.8%
면 8826
 
6.8%
리 6885
 
5.3%
군 6259
 
4.8%
상 5911
 
4.5%
경 5480
 
4.2%
남 5410
 
4.2%
북 4531
 
3.5%
시 4424
 
3.4%
산 3041
 
2.3%
Other values (387) 69192
53.2%
Common
ValueCountFrequency (%)
32748
77.6%
1 2259
 
5.4%
2 1975
 
4.7%
3 886
 
2.1%
- 828
 
2.0%
4 651
 
1.5%
5 526
 
1.2%
6 457
 
1.1%
8 414
 
1.0%
7 407
 
1.0%
Other values (7) 1073
 
2.5%
Han
ValueCountFrequency (%)
岩 6
50.0%
岐 4
33.3%
基 2
 
16.7%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 130047
75.5%
ASCII 42225
 
24.5%
CJK 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32748
77.6%
1 2259
 
5.3%
2 1975
 
4.7%
3 886
 
2.1%
- 828
 
2.0%
4 651
 
1.5%
5 526
 
1.2%
6 457
 
1.1%
8 414
 
1.0%
7 407
 
1.0%
Other values (8) 1074
 
2.5%
Hangul
ValueCountFrequency (%)
도 10088
 
7.8%
면 8826
 
6.8%
리 6885
 
5.3%
군 6259
 
4.8%
상 5911
 
4.5%
경 5480
 
4.2%
남 5410
 
4.2%
북 4531
 
3.5%
시 4424
 
3.4%
산 3041
 
2.3%
Other values (387) 69192
53.2%
CJK
ValueCountFrequency (%)
岩 6
50.0%
岐 4
33.3%
基 2
 
16.7%

수도규모
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
소규모급수시설
6553 
마을상수도
3259 
전용상수도시설
 
188

Length

Max length7
Median length7
Mean length6.3482
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소규모급수시설 6553
65.5%
마을상수도 3259
32.6%
전용상수도시설 188
 
1.9%

Length

2023-12-12T20:17:43.539684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:17:43.765194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소규모급수시설 6553
65.5%
마을상수도 3259
32.6%
전용상수도시설 188
 
1.9%

수원
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
지하수
9788 
용천수
 
204
계곡수
 
6
기타
 
1
지표수
 
1

Length

Max length3
Median length3
Mean length2.9999
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
지하수 9788
97.9%
용천수 204
 
2.0%
계곡수 6
 
0.1%
기타 1
 
< 0.1%
지표수 1
 
< 0.1%

Length

2023-12-12T20:17:43.978187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:17:44.163065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하수 9788
97.9%
용천수 204
 
2.0%
계곡수 6
 
0.1%
기타 1
 
< 0.1%
지표수 1
 
< 0.1%

카드뮴
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.12 × 10-5
Minimum0
Maximum0.18
Zeros9990
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:17:44.347395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.18
Range0.18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0025562386
Coefficient of variation (CV)62.044627
Kurtosis4913.3135
Mean4.12 × 10-5
Median Absolute Deviation (MAD)0
Skewness69.84457
Sum0.412
Variance6.534356 × 10-6
MonotonicityNot monotonic
2023-12-12T20:17:44.553697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 9990
99.9%
0.002 4
 
< 0.1%
0.014 2
 
< 0.1%
0.18 2
 
< 0.1%
0.011 1
 
< 0.1%
0.005 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9990
99.9%
0.002 4
 
< 0.1%
0.005 1
 
< 0.1%
0.011 1
 
< 0.1%
0.014 2
 
< 0.1%
0.18 2
 
< 0.1%
ValueCountFrequency (%)
0.18 2
 
< 0.1%
0.014 2
 
< 0.1%
0.011 1
 
< 0.1%
0.005 1
 
< 0.1%
0.002 4
 
< 0.1%
0.0 9990
99.9%

비소
Real number (ℝ)

SKEWED  ZEROS 

Distinct72
Distinct (%)0.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.0015151515
Minimum0
Maximum1.27
Zeros9007
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:17:44.797368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.008
Maximum1.27
Range1.27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.016753951
Coefficient of variation (CV)11.057608
Kurtosis3741.8343
Mean0.0015151515
Median Absolute Deviation (MAD)0
Skewness55.4279
Sum15.15
Variance0.00028069488
MonotonicityNot monotonic
2023-12-12T20:17:45.102712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9007
90.1%
0.006 198
 
2.0%
0.005 136
 
1.4%
0.008 126
 
1.3%
0.007 120
 
1.2%
0.009 80
 
0.8%
0.01 50
 
0.5%
0.012 28
 
0.3%
0.011 18
 
0.2%
0.016 17
 
0.2%
Other values (62) 219
 
2.2%
ValueCountFrequency (%)
0.0 9007
90.1%
0.003 1
 
< 0.1%
0.005 136
 
1.4%
0.006 198
 
2.0%
0.007 120
 
1.2%
0.008 126
 
1.3%
0.009 80
 
0.8%
0.01 50
 
0.5%
0.011 18
 
0.2%
0.012 28
 
0.3%
ValueCountFrequency (%)
1.27 1
< 0.1%
0.71 1
< 0.1%
0.57 1
< 0.1%
0.19 1
< 0.1%
0.131 1
< 0.1%
0.108 1
< 0.1%
0.103 1
< 0.1%
0.1 1
< 0.1%
0.095 2
< 0.1%
0.094 1
< 0.1%

시안
Real number (ℝ)

SKEWED  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9 × 10-6
Minimum0
Maximum0.013
Zeros9994
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:17:45.333461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.013
Range0.013
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00021042913
Coefficient of variation (CV)42.94472
Kurtosis2390.2502
Mean4.9 × 10-6
Median Absolute Deviation (MAD)0
Skewness47.090131
Sum0.049
Variance4.4280418 × 10-8
MonotonicityNot monotonic
2023-12-12T20:17:45.533012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 9994
99.9%
0.006 1
 
< 0.1%
0.007 1
 
< 0.1%
0.013 1
 
< 0.1%
0.01 1
 
< 0.1%
0.005 1
 
< 0.1%
0.008 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9994
99.9%
0.005 1
 
< 0.1%
0.006 1
 
< 0.1%
0.007 1
 
< 0.1%
0.008 1
 
< 0.1%
0.01 1
 
< 0.1%
0.013 1
 
< 0.1%
ValueCountFrequency (%)
0.013 1
 
< 0.1%
0.01 1
 
< 0.1%
0.008 1
 
< 0.1%
0.007 1
 
< 0.1%
0.006 1
 
< 0.1%
0.005 1
 
< 0.1%
0.0 9994
99.9%

수은
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

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 10000
100.0%

Length

2023-12-12T20:17:45.747483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:17:45.905393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

납
Real number (ℝ)

SKEWED  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.09 × 10-5
Minimum0
Maximum0.062
Zeros9938
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:17:46.029379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.062
Range0.062
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00085975759
Coefficient of variation (CV)16.891112
Kurtosis2751.0792
Mean5.09 × 10-5
Median Absolute Deviation (MAD)0
Skewness42.328248
Sum0.509
Variance7.3918311 × 10-7
MonotonicityNot monotonic
2023-12-12T20:17:46.219655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 9938
99.4%
0.006 18
 
0.2%
0.007 12
 
0.1%
0.008 9
 
0.1%
0.005 9
 
0.1%
0.009 8
 
0.1%
0.015 2
 
< 0.1%
0.011 2
 
< 0.1%
0.062 1
 
< 0.1%
0.014 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9938
99.4%
0.005 9
 
0.1%
0.006 18
 
0.2%
0.007 12
 
0.1%
0.008 9
 
0.1%
0.009 8
 
0.1%
0.011 2
 
< 0.1%
0.014 1
 
< 0.1%
0.015 2
 
< 0.1%
0.062 1
 
< 0.1%
ValueCountFrequency (%)
0.062 1
 
< 0.1%
0.015 2
 
< 0.1%
0.014 1
 
< 0.1%
0.011 2
 
< 0.1%
0.009 8
 
0.1%
0.008 9
 
0.1%
0.007 12
 
0.1%
0.006 18
 
0.2%
0.005 9
 
0.1%
0.0 9938
99.4%

크롬
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1 × 10-5
Minimum0
Maximum0.06
Zeros9983
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:17:46.426039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.06
Range0.06
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00094293579
Coefficient of variation (CV)30.417284
Kurtosis2215.4034
Mean3.1 × 10-5
Median Absolute Deviation (MAD)0
Skewness42.972929
Sum0.31
Variance8.8912791 × 10-7
MonotonicityNot monotonic
2023-12-12T20:17:46.580364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 9983
99.8%
0.01 11
 
0.1%
0.02 2
 
< 0.1%
0.03 2
 
< 0.1%
0.06 1
 
< 0.1%
0.04 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9983
99.8%
0.01 11
 
0.1%
0.02 2
 
< 0.1%
0.03 2
 
< 0.1%
0.04 1
 
< 0.1%
0.06 1
 
< 0.1%
ValueCountFrequency (%)
0.06 1
 
< 0.1%
0.04 1
 
< 0.1%
0.03 2
 
< 0.1%
0.02 2
 
< 0.1%
0.01 11
 
0.1%
0.0 9983
99.8%

다이아지논
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6.4006401 × 10-7
Minimum0
Maximum0.0022
Zeros9994
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:17:46.761623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.0022
Range0.0022
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.062316 × 10-5
Coefficient of variation (CV)47.843902
Kurtosis3310.3554
Mean6.4006401 × 10-7
Median Absolute Deviation (MAD)0
Skewness54.58742
Sum0.0064
Variance9.3777791 × 10-10
MonotonicityNot monotonic
2023-12-12T20:17:46.940544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 9994
99.9%
0.0011 1
 
< 0.1%
0.0008 1
 
< 0.1%
0.0022 1
 
< 0.1%
0.0013 1
 
< 0.1%
0.001 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9994
99.9%
0.0008 1
 
< 0.1%
0.001 1
 
< 0.1%
0.0011 1
 
< 0.1%
0.0013 1
 
< 0.1%
0.0022 1
 
< 0.1%
ValueCountFrequency (%)
0.0022 1
 
< 0.1%
0.0013 1
 
< 0.1%
0.0011 1
 
< 0.1%
0.001 1
 
< 0.1%
0.0008 1
 
< 0.1%
0.0 9994
99.9%

파라티온
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0.0
9997 
0.0021
 
1
0.0019
 
1
0.003
 
1

Length

Max length6
Median length3
Mean length3.0008
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 9997
> 99.9%
0.0021 1
 
< 0.1%
0.0019 1
 
< 0.1%
0.003 1
 
< 0.1%

Length

2023-12-12T20:17:47.158731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:17:47.353765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9997
> 99.9%
0.0021 1
 
< 0.1%
0.0019 1
 
< 0.1%
0.003 1
 
< 0.1%

페니트로티온
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0.0
9999 
0.002
 
1

Length

Max length5
Median length3
Mean length3.0002
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 9999
> 99.9%
0.002 1
 
< 0.1%

Length

2023-12-12T20:17:47.572229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:17:47.754867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9999
> 99.9%
0.002 1
 
< 0.1%

음이온 계면활성제
Real number (ℝ)

SKEWED  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.000299
Minimum0
Maximum0.72
Zeros9990
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:17:47.897665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.72
Range0.72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.011786199
Coefficient of variation (CV)39.418726
Kurtosis2786.1461
Mean0.000299
Median Absolute Deviation (MAD)0
Skewness49.955122
Sum2.99
Variance0.00013891449
MonotonicityNot monotonic
2023-12-12T20:17:48.619220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 9990
99.9%
0.21 2
 
< 0.1%
0.72 1
 
< 0.1%
0.7 1
 
< 0.1%
0.1 1
 
< 0.1%
0.02 1
 
< 0.1%
0.22 1
 
< 0.1%
0.2 1
 
< 0.1%
0.37 1
 
< 0.1%
0.24 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9990
99.9%
0.02 1
 
< 0.1%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.21 2
 
< 0.1%
0.22 1
 
< 0.1%
0.24 1
 
< 0.1%
0.37 1
 
< 0.1%
0.7 1
 
< 0.1%
0.72 1
 
< 0.1%
ValueCountFrequency (%)
0.72 1
 
< 0.1%
0.7 1
 
< 0.1%
0.37 1
 
< 0.1%
0.24 1
 
< 0.1%
0.22 1
 
< 0.1%
0.21 2
 
< 0.1%
0.2 1
 
< 0.1%
0.1 1
 
< 0.1%
0.02 1
 
< 0.1%
0.0 9990
99.9%

불소
Real number (ℝ)

ZEROS 

Distinct233
Distinct (%)2.3%
Missing35
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.19086603
Minimum0
Maximum8.74
Zeros5797
Zeros (%)58.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:17:48.857291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.24
95-th percentile0.84
Maximum8.74
Range8.74
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.40205826
Coefficient of variation (CV)2.1064946
Kurtosis68.413156
Mean0.19086603
Median Absolute Deviation (MAD)0
Skewness6.1542376
Sum1901.98
Variance0.16165085
MonotonicityNot monotonic
2023-12-12T20:17:49.079731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5797
58.0%
0.16 236
 
2.4%
0.18 212
 
2.1%
0.17 187
 
1.9%
0.2 178
 
1.8%
0.19 157
 
1.6%
0.21 126
 
1.3%
0.22 125
 
1.2%
0.15 124
 
1.2%
0.24 111
 
1.1%
Other values (223) 2712
27.1%
ValueCountFrequency (%)
0.0 5797
58.0%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 8
 
0.1%
0.05 4
 
< 0.1%
0.06 6
 
0.1%
0.07 11
 
0.1%
0.08 18
 
0.2%
0.09 11
 
0.1%
0.1 16
 
0.2%
ValueCountFrequency (%)
8.74 1
< 0.1%
6.61 1
< 0.1%
6.24 1
< 0.1%
5.97 1
< 0.1%
5.58 1
< 0.1%
5.52 1
< 0.1%
5.2 1
< 0.1%
5.0 1
< 0.1%
4.99 1
< 0.1%
4.9 1
< 0.1%

Interactions

2023-12-12T20:17:35.816340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:18.770514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:20.568344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:22.190907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:24.373665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:26.226328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:28.162358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:30.009911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:31.701792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:33.269989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:36.000243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:18.953830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:20.732190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:22.349362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:24.512807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:26.410794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:28.349589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:30.174654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:31.865162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:33.463032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:36.176373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:19.138604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:20.916429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:22.530535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:24.690564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:26.601086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:28.558596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:30.351105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:32.018833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:33.667064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:36.386847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:19.333781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:21.091883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:22.722584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:24.948917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:26.827506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:28.731514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:30.533288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:32.170778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:33.870426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:36.554743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:19.499764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:21.246311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:22.956316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:25.210129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:27.010744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:28.898332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:30.702220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:32.328218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:34.053045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:36.728458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:19.684556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:21.398080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:23.136752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:25.375704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:27.190766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:29.096604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:30.852772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:32.465898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:34.249129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:36.914200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:19.869819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:21.554101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:23.300018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:25.578513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:27.386267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:29.279509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:30.999653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:32.610784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:34.449905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:37.100293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:20.074673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:21.735138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:23.908499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:25.762399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:27.586800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:29.474301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:31.171260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:32.777743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:35.200988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:37.260261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:20.231021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:21.880040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:24.055751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:25.918391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:27.778575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:29.657159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:31.348709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:32.922925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:35.411869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:37.445171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:20.426347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:22.040208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:24.230990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:26.082563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:27.985561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:29.858782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:31.568090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:33.085890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:17:35.622931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:17:49.218573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번검사년도수도규모수원카드뮴비소시안납크롬다이아지논파라티온페니트로티온음이온 계면활성제불소
연번1.0000.9200.1390.0540.0390.0000.0000.0290.0630.0390.0100.0040.0390.043
검사년도0.9201.0000.0820.0400.0220.0000.0000.0170.0520.0220.0070.0000.0280.051
수도규모0.1390.0821.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.094
수원0.0540.0400.0351.0000.0000.0000.0000.0000.0000.0760.0430.0000.0000.000
카드뮴0.0390.0220.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
비소0.0000.0000.0000.0000.0001.0000.5030.0000.0000.0000.0000.0000.0000.000
시안0.0000.0000.0000.0000.0000.5031.0000.0000.0000.0000.0000.0000.0000.000
납0.0290.0170.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
크롬0.0630.0520.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.055
다이아지논0.0390.0220.0000.0760.0000.0000.0000.0000.0001.0000.6470.0000.0000.000
파라티온0.0100.0070.0000.0430.0000.0000.0000.0000.0000.6471.0000.0000.0000.000
페니트로티온0.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
음이온 계면활성제0.0390.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
불소0.0430.0510.0940.0000.0000.0000.0000.0000.0550.0000.0000.0000.0001.000
2023-12-12T20:17:49.419104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
페니트로티온수도규모파라티온수원
페니트로티온1.0000.0000.0000.000
수도규모0.0001.0000.0000.026
파라티온0.0000.0001.0000.035
수원0.0000.0260.0351.000
2023-12-12T20:17:49.563031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번검사년도카드뮴비소시안납크롬다이아지논음이온 계면활성제불소수도규모수원파라티온페니트로티온
연번1.0000.991-0.0070.011-0.012-0.0100.0060.020-0.0280.0160.0830.0230.0060.003
검사년도0.9911.000-0.0060.011-0.017-0.0130.0060.016-0.0240.0170.0530.0210.0060.000
카드뮴-0.007-0.0061.0000.030-0.0010.038-0.001-0.001-0.001-0.0050.0000.0000.0000.000
비소0.0110.0110.0301.0000.007-0.0090.026-0.0070.0090.2060.0000.0000.0000.000
시안-0.012-0.017-0.0010.0071.000-0.002-0.001-0.001-0.001-0.0200.0000.0000.0000.000
납-0.010-0.0130.038-0.009-0.0021.000-0.003-0.002-0.0020.0120.0000.0000.0000.000
크롬0.0060.006-0.0010.026-0.001-0.0031.000-0.001-0.0010.0170.0000.0000.0000.000
다이아지논0.0200.016-0.001-0.007-0.001-0.002-0.0011.000-0.0010.0010.0000.0280.5770.000
음이온 계면활성제-0.028-0.024-0.0010.009-0.001-0.002-0.001-0.0011.0000.0070.0000.0000.0000.000
불소0.0160.017-0.0050.206-0.0200.0120.0170.0010.0071.0000.0410.0000.0000.000
수도규모0.0830.0530.0000.0000.0000.0000.0000.0000.0000.0411.0000.0260.0000.000
수원0.0230.0210.0000.0000.0000.0000.0000.0280.0000.0000.0261.0000.0350.000
파라티온0.0060.0060.0000.0000.0000.0000.0000.5770.0000.0000.0000.0351.0000.000
페니트로티온0.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T20:17:37.687409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:17:38.119366image/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.
2023-12-12T20:17:38.382435image/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

연번검사년도지역소규모수도시설명측정지점주소수도규모수원카드뮴비소시안수은납크롬다이아지논파라티온페니트로티온음이온 계면활성제불소
16113161142018인천광역시금사인천광역시 강화군 양도면 조산리마을상수도지하수0.00.00.000.00.00.00.00.00.00.0
31022310232022전라남도 나주시내동전라남도 나주시 왕곡면 신포리 565-2소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.17
20167201682019전라남도 강진군백양전라남도 강진군 병영면 삭양리마을상수도지하수0.00.00.000.00.00.00.00.00.00.0
16536165372018전라남도 보성군봉산전라남도 보성군 조성면 봉능리 봉산마을소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.0
29300293012021충청북도 음성군구례골충청북도 음성군 음성읍 초천리 1소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.43
949394942017경상남도 거창군원기경상남도 거창군 고제면 봉계소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.0
22350223512020경상북도 영주시일우실경상북도 영주시 이산면 지동2리마을상수도지하수0.00.00.000.00.00.00.00.00.00.28
19843198442019경상북도 고령군오리동경상북도 고령군 덕곡면 용흥소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.0
26487264882021경상남도 함양군평촌경상남도 함양군 백전면 들말1길 27소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.0
564056412016경상북도 상주시신곡1경상북도 상주시 공성면 신곡1리소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.0
연번검사년도지역소규모수도시설명측정지점주소수도규모수원카드뮴비소시안수은납크롬다이아지논파라티온페니트로티온음이온 계면활성제불소
27281272822021경상북도 예천군허리골경상북도 예천군 용문면 원류리소규모급수시설지하수0.00.00.000.00.00.00.00.00.01.29
18948189492019경상남도 산청군평지땀경상남도 산청군 차황면 철수리소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.0
369336942015전라북도 진안군금당전라북도 진안군 상전면 구룡리소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.2
640364042016경상북도 청송군구례경상북도 청송군 파천면 신기1소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.0
349834992015전라북도 군산시광야전라북도 군산시 옥구읍 어은리 광야1소규모급수시설지하수0.00.0060.000.00.00.00.00.00.00.0
644164422016경상북도 청송군새터경상북도 청송군 파천면 신기1마을상수도지하수0.00.00.000.00.00.00.00.00.00.0
18269182702019강원도 춘천시안비강원도 춘천시 서면 안보1/3소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.0
10290102912017경상남도 창원시미천경상남도 창원시 마산합포구 진전면 미천1길 98소규모급수시설지하수0.00.00.000.00.00.00.00.00.00.0
15854158552018경상북도 의성군송산경상북도 의성군 봉양면 삼산3마을상수도지하수0.00.00.000.00.010.00.00.00.00.15
24075240762020충청남도 천안시신덕2리(산양)충청남도 천안시 성남면 신덕2리 산양마을상수도지하수0.00.00.000.00.00.00.00.00.00.0