Overview

Dataset statistics

Number of variables13
Number of observations9930
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory110.0 B

Variable types

Numeric6
Text3
Categorical3
DateTime1

Dataset

Description한국농어촌공사가 관리하는 농업용수로 사용되는 저수지의 주소,담당관리기관,조사일자, 수질정보 등
Author한국농어촌공사
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20191014000000001273

Alerts

조사구분 has constant value ""Constant
시설구분 has constant value ""Constant
관리구분 has constant value ""Constant
TOC is highly overall correlated with T-P and 1 other fieldsHigh correlation
T-P is highly overall correlated with TOC and 1 other fieldsHigh correlation
SS is highly overall correlated with TOC and 1 other fieldsHigh correlation
T-P is highly skewed (γ1 = 40.40679774)Skewed

Reproduction

Analysis started2023-12-11 03:31:27.653311
Analysis finished2023-12-11 03:31:34.024394
Duration6.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

표준코드
Real number (ℝ)

Distinct2501
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.570514 × 109
Minimum2.6710101 × 109
Maximum4.97101 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.4 KiB
2023-12-11T12:31:34.104053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6710101 × 109
5-th percentile3.1710102 × 109
Q14.57201 × 109
median4.68301 × 109
Q34.7730106 × 109
95-th percentile4.8860101 × 109
Maximum4.97101 × 109
Range2.2999999 × 109
Interquartile range (IQR)2.0100054 × 108

Descriptive statistics

Standard deviation4.1491018 × 108
Coefficient of variation (CV)0.090779764
Kurtosis9.0641473
Mean4.570514 × 109
Median Absolute Deviation (MAD)1.0200012 × 108
Skewness-3.0498655
Sum4.5385204 × 1013
Variance1.7215046 × 1017
MonotonicityIncreasing
2023-12-11T12:31:34.265549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2671010085 4
 
< 0.1%
4713010444 4
 
< 0.1%
4713010406 4
 
< 0.1%
4713010425 4
 
< 0.1%
4713010428 4
 
< 0.1%
4713010429 4
 
< 0.1%
4713010432 4
 
< 0.1%
4713010442 4
 
< 0.1%
4713010443 4
 
< 0.1%
4713010445 4
 
< 0.1%
Other values (2491) 9890
99.6%
ValueCountFrequency (%)
2671010085 4
< 0.1%
2671010098 4
< 0.1%
2714010023 4
< 0.1%
2723010006 4
< 0.1%
2723010007 4
< 0.1%
2723010012 4
< 0.1%
2723010022 4
< 0.1%
2726010001 4
< 0.1%
2726010006 4
< 0.1%
2726010008 4
< 0.1%
ValueCountFrequency (%)
4971010003 4
< 0.1%
4971010002 4
< 0.1%
4971010001 4
< 0.1%
4889010340 4
< 0.1%
4889010339 4
< 0.1%
4889010338 4
< 0.1%
4889010337 4
< 0.1%
4889010335 4
< 0.1%
4889010334 4
< 0.1%
4889010333 4
< 0.1%

시설명
Text

Distinct1954
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
2023-12-11T12:31:34.653038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.2254783
Min length1

Characters and Unicode

Total characters22099
Distinct characters367
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

Unique7 ?
Unique (%)0.1%

Sample

1st row송정
2nd row송정
3rd row송정
4th row송정
5th row임기
ValueCountFrequency (%)
대곡 40
 
0.4%
연화 36
 
0.4%
학동 36
 
0.4%
방축 32
 
0.3%
신기 32
 
0.3%
백운 28
 
0.3%
신흥 27
 
0.3%
대천 24
 
0.2%
연동 24
 
0.2%
구룡 24
 
0.2%
Other values (1944) 9627
96.9%
2023-12-11T12:31:35.233120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
곡 848
 
3.8%
동 758
 
3.4%
산 708
 
3.2%
신 521
 
2.4%
대 428
 
1.9%
정 412
 
1.9%
지 386
 
1.7%
천 331
 
1.5%
용 313
 
1.4%
평 308
 
1.4%
Other values (357) 17086
77.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21183
95.9%
Decimal Number 638
 
2.9%
Open Punctuation 131
 
0.6%
Close Punctuation 131
 
0.6%
Space Separator 12
 
0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
곡 848
 
4.0%
동 758
 
3.6%
산 708
 
3.3%
신 521
 
2.5%
대 428
 
2.0%
정 412
 
1.9%
지 386
 
1.8%
천 331
 
1.6%
용 313
 
1.5%
평 308
 
1.5%
Other values (347) 16170
76.3%
Decimal Number
ValueCountFrequency (%)
1 307
48.1%
2 275
43.1%
3 36
 
5.6%
4 12
 
1.9%
5 4
 
0.6%
6 4
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 131
100.0%
Close Punctuation
ValueCountFrequency (%)
) 131
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21183
95.9%
Common 916
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
곡 848
 
4.0%
동 758
 
3.6%
산 708
 
3.3%
신 521
 
2.5%
대 428
 
2.0%
정 412
 
1.9%
지 386
 
1.8%
천 331
 
1.6%
용 313
 
1.5%
평 308
 
1.5%
Other values (347) 16170
76.3%
Common
ValueCountFrequency (%)
1 307
33.5%
2 275
30.0%
( 131
14.3%
) 131
14.3%
3 36
 
3.9%
4 12
 
1.3%
12
 
1.3%
5 4
 
0.4%
- 4
 
0.4%
6 4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21183
95.9%
ASCII 916
 
4.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
곡 848
 
4.0%
동 758
 
3.6%
산 708
 
3.3%
신 521
 
2.5%
대 428
 
2.0%
정 412
 
1.9%
지 386
 
1.8%
천 331
 
1.6%
용 313
 
1.5%
평 308
 
1.5%
Other values (347) 16170
76.3%
ASCII
ValueCountFrequency (%)
1 307
33.5%
2 275
30.0%
( 131
14.3%
) 131
14.3%
3 36
 
3.9%
4 12
 
1.3%
12
 
1.3%
5 4
 
0.4%
- 4
 
0.4%
6 4
 
0.4%

조사구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
공사조사
9930 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공사조사 9930
100.0%

Length

2023-12-11T12:31:35.410938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:31:35.627133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사조사 9930
100.0%

주소
Text

Distinct1772
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
2023-12-11T12:31:35.997152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length15.851662
Min length11

Characters and Unicode

Total characters157407
Distinct characters307
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row부산광역시 기장군 철마면 송정리
2nd row부산광역시 기장군 철마면 송정리
3rd row부산광역시 기장군 철마면 송정리
4th row부산광역시 기장군 철마면 송정리
5th row부산광역시 기장군 철마면 임기리
ValueCountFrequency (%)
전라남도 3259
 
8.3%
경상북도 1906
 
4.8%
경상남도 1754
 
4.4%
전라북도 1249
 
3.2%
나주시 628
 
1.6%
영암군 516
 
1.3%
충청남도 474
 
1.2%
충청북도 437
 
1.1%
영천시 328
 
0.8%
울산광역시 287
 
0.7%
Other values (2162) 28588
72.5%
2023-12-11T12:31:36.535422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29566
18.8%
도 9958
 
6.3%
리 9372
 
6.0%
면 8011
 
5.1%
남 6860
 
4.4%
군 6377
 
4.1%
전 4893
 
3.1%
라 4551
 
2.9%
북 4304
 
2.7%
시 4280
 
2.7%
Other values (297) 69235
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127837
81.2%
Space Separator 29566
 
18.8%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9958
 
7.8%
리 9372
 
7.3%
면 8011
 
6.3%
남 6860
 
5.4%
군 6377
 
5.0%
전 4893
 
3.8%
라 4551
 
3.6%
북 4304
 
3.4%
시 4280
 
3.3%
경 4202
 
3.3%
Other values (295) 65029
50.9%
Space Separator
ValueCountFrequency (%)
29566
100.0%
Decimal Number
ValueCountFrequency (%)
1 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 127837
81.2%
Common 29570
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9958
 
7.8%
리 9372
 
7.3%
면 8011
 
6.3%
남 6860
 
5.4%
군 6377
 
5.0%
전 4893
 
3.8%
라 4551
 
3.6%
북 4304
 
3.4%
시 4280
 
3.3%
경 4202
 
3.3%
Other values (295) 65029
50.9%
Common
ValueCountFrequency (%)
29566
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 127837
81.2%
ASCII 29570
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29566
> 99.9%
1 4
 
< 0.1%
Hangul
ValueCountFrequency (%)
도 9958
 
7.8%
리 9372
 
7.3%
면 8011
 
6.3%
남 6860
 
5.4%
군 6377
 
5.0%
전 4893
 
3.8%
라 4551
 
3.6%
북 4304
 
3.4%
시 4280
 
3.3%
경 4202
 
3.3%
Other values (295) 65029
50.9%

시설구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
저수지
9930 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
저수지 9930
100.0%

Length

2023-12-11T12:31:36.683733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:31:36.796499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
저수지 9930
100.0%

관리구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
공사
9930 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공사 9930
100.0%

Length

2023-12-11T12:31:36.912032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:31:37.003401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사 9930
100.0%

관리기관
Text

Distinct93
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
2023-12-11T12:31:37.291052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.8127895
Min length4

Characters and Unicode

Total characters47791
Distinct characters99
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row울산지사
2nd row울산지사
3rd row울산지사
4th row울산지사
5th row울산지사
ValueCountFrequency (%)
나주지사 620
 
6.2%
영암지사 516
 
5.2%
영천지사 328
 
3.3%
울산지사 295
 
3.0%
무안신안지사 292
 
2.9%
전주완주임실지사 280
 
2.8%
해남완도지사 274
 
2.8%
남원지사 264
 
2.7%
합천지사 250
 
2.5%
사천지사 244
 
2.5%
Other values (83) 6567
66.1%
2023-12-11T12:31:37.991104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 10162
21.3%
지 9918
20.8%
주 2077
 
4.3%
천 1580
 
3.3%
영 1541
 
3.2%
산 1140
 
2.4%
양 1033
 
2.2%
성 919
 
1.9%
안 894
 
1.9%
남 766
 
1.6%
Other values (89) 17761
37.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47791
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 10162
21.3%
지 9918
20.8%
주 2077
 
4.3%
천 1580
 
3.3%
영 1541
 
3.2%
산 1140
 
2.4%
양 1033
 
2.2%
성 919
 
1.9%
안 894
 
1.9%
남 766
 
1.6%
Other values (89) 17761
37.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 47791
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 10162
21.3%
지 9918
20.8%
주 2077
 
4.3%
천 1580
 
3.3%
영 1541
 
3.2%
산 1140
 
2.4%
양 1033
 
2.2%
성 919
 
1.9%
안 894
 
1.9%
남 766
 
1.6%
Other values (89) 17761
37.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 47791
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 10162
21.3%
지 9918
20.8%
주 2077
 
4.3%
천 1580
 
3.3%
영 1541
 
3.2%
산 1140
 
2.4%
양 1033
 
2.2%
성 919
 
1.9%
안 894
 
1.9%
남 766
 
1.6%
Other values (89) 17761
37.2%

조사일자
Date

Distinct133
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
Minimum2015-01-14 00:00:00
Maximum2015-12-01 00:00:00
2023-12-11T12:31:38.174374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:38.315913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

pH
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7283585
Minimum3.7
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.4 KiB
2023-12-11T12:31:38.507468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile7
Q17.3
median7.6
Q38
95-th percentile9.1
Maximum11
Range7.3
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.6489278
Coefficient of variation (CV)0.083967093
Kurtosis2.6088805
Mean7.7283585
Median Absolute Deviation (MAD)0.3
Skewness1.0496644
Sum76742.6
Variance0.42110729
MonotonicityNot monotonic
2023-12-11T12:31:38.691479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5 952
 
9.6%
7.6 945
 
9.5%
7.4 873
 
8.8%
7.3 794
 
8.0%
7.7 777
 
7.8%
7.8 667
 
6.7%
7.2 624
 
6.3%
7.9 510
 
5.1%
7.1 443
 
4.5%
8.0 384
 
3.9%
Other values (47) 2961
29.8%
ValueCountFrequency (%)
3.7 1
< 0.1%
3.8 2
< 0.1%
3.9 1
< 0.1%
4.0 1
< 0.1%
4.3 1
< 0.1%
4.4 1
< 0.1%
5.3 1
< 0.1%
5.4 1
< 0.1%
5.6 1
< 0.1%
5.8 1
< 0.1%
ValueCountFrequency (%)
11.0 1
 
< 0.1%
10.6 1
 
< 0.1%
10.4 4
 
< 0.1%
10.3 10
 
0.1%
10.2 10
 
0.1%
10.1 18
0.2%
10.0 21
0.2%
9.9 25
0.3%
9.8 29
0.3%
9.7 35
0.4%

TOC
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7886203
Minimum0.1
Maximum56.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.4 KiB
2023-12-11T12:31:38.878674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.1
Q12.2
median3.4
Q35
95-th percentile7.7
Maximum56.9
Range56.8
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.2250511
Coefficient of variation (CV)0.58729852
Kurtosis39.172974
Mean3.7886203
Median Absolute Deviation (MAD)1.3
Skewness2.7849018
Sum37621
Variance4.9508525
MonotonicityNot monotonic
2023-12-11T12:31:39.038793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 255
 
2.6%
2.7 241
 
2.4%
2.4 231
 
2.3%
2.3 225
 
2.3%
3.0 224
 
2.3%
1.7 223
 
2.2%
3.3 213
 
2.1%
2.2 210
 
2.1%
3.2 210
 
2.1%
1.6 207
 
2.1%
Other values (136) 7691
77.5%
ValueCountFrequency (%)
0.1 2
 
< 0.1%
0.2 12
 
0.1%
0.3 18
 
0.2%
0.4 36
 
0.4%
0.5 31
 
0.3%
0.6 54
0.5%
0.7 65
0.7%
0.8 67
0.7%
0.9 96
1.0%
1.0 92
0.9%
ValueCountFrequency (%)
56.9 1
< 0.1%
33.4 1
< 0.1%
25.1 1
< 0.1%
22.7 1
< 0.1%
21.1 1
< 0.1%
19.7 1
< 0.1%
18.8 1
< 0.1%
18.3 1
< 0.1%
18.2 1
< 0.1%
17.7 1
< 0.1%

T-N
Real number (ℝ)

Distinct2548
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0959802
Minimum0.007
Maximum28.866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.4 KiB
2023-12-11T12:31:39.198213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.29745
Q10.539
median0.8235
Q31.35675
95-th percentile2.65255
Maximum28.866
Range28.859
Interquartile range (IQR)0.81775

Descriptive statistics

Standard deviation1.0502849
Coefficient of variation (CV)0.95830653
Kurtosis121.41182
Mean1.0959802
Median Absolute Deviation (MAD)0.357
Skewness7.2590635
Sum10883.083
Variance1.1030985
MonotonicityNot monotonic
2023-12-11T12:31:39.361835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.546 21
 
0.2%
0.449 17
 
0.2%
0.447 16
 
0.2%
0.363 16
 
0.2%
0.58 16
 
0.2%
0.418 16
 
0.2%
0.365 15
 
0.2%
0.735 15
 
0.2%
0.756 15
 
0.2%
0.695 15
 
0.2%
Other values (2538) 9768
98.4%
ValueCountFrequency (%)
0.007 1
< 0.1%
0.014 1
< 0.1%
0.022 1
< 0.1%
0.031 1
< 0.1%
0.043 1
< 0.1%
0.047 2
< 0.1%
0.06 1
< 0.1%
0.08 1
< 0.1%
0.082 1
< 0.1%
0.088 1
< 0.1%
ValueCountFrequency (%)
28.866 1
< 0.1%
28.15 1
< 0.1%
14.929 1
< 0.1%
14.811 1
< 0.1%
14.51 1
< 0.1%
14.384 1
< 0.1%
12.63 1
< 0.1%
12.242 1
< 0.1%
11.929 1
< 0.1%
11.771 1
< 0.1%

T-P
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct315
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.040214804
Minimum0.001
Maximum6.656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.4 KiB
2023-12-11T12:31:39.512263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.007
Q10.014
median0.024
Q30.044
95-th percentile0.111
Maximum6.656
Range6.655
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.096915744
Coefficient of variation (CV)2.4099519
Kurtosis2449.0413
Mean0.040214804
Median Absolute Deviation (MAD)0.012
Skewness40.406798
Sum399.333
Variance0.0093926614
MonotonicityNot monotonic
2023-12-11T12:31:39.664291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.012 319
 
3.2%
0.013 315
 
3.2%
0.015 307
 
3.1%
0.014 298
 
3.0%
0.011 281
 
2.8%
0.016 267
 
2.7%
0.009 261
 
2.6%
0.02 261
 
2.6%
0.01 259
 
2.6%
0.017 256
 
2.6%
Other values (305) 7106
71.6%
ValueCountFrequency (%)
0.001 17
 
0.2%
0.002 47
 
0.5%
0.003 39
 
0.4%
0.004 45
 
0.5%
0.005 103
 
1.0%
0.006 158
1.6%
0.007 185
1.9%
0.008 228
2.3%
0.009 261
2.6%
0.01 259
2.6%
ValueCountFrequency (%)
6.656 1
< 0.1%
3.851 1
< 0.1%
1.458 1
< 0.1%
1.428 1
< 0.1%
1.277 1
< 0.1%
1.214 1
< 0.1%
1.099 1
< 0.1%
1.002 1
< 0.1%
1.0 1
< 0.1%
0.992 1
< 0.1%

SS
Real number (ℝ)

HIGH CORRELATION 

Distinct411
Distinct (%)4.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean8.7032732
Minimum0
Maximum504
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size87.4 KiB
2023-12-11T12:31:39.846512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.7
median5
Q39.7
95-th percentile25.7
Maximum504
Range504
Interquartile range (IQR)7

Descriptive statistics

Standard deviation16.147714
Coefficient of variation (CV)1.8553611
Kurtosis293.12395
Mean8.7032732
Median Absolute Deviation (MAD)2.8
Skewness13.433846
Sum86414.8
Variance260.74867
MonotonicityNot monotonic
2023-12-11T12:31:40.000797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 265
 
2.7%
4.0 258
 
2.6%
3.0 255
 
2.6%
3.3 244
 
2.5%
2.0 243
 
2.4%
4.3 231
 
2.3%
2.3 213
 
2.1%
3.7 210
 
2.1%
5.0 207
 
2.1%
6.0 201
 
2.0%
Other values (401) 7602
76.6%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.1 3
 
< 0.1%
0.2 39
 
0.4%
0.3 45
 
0.5%
0.4 52
0.5%
0.5 30
 
0.3%
0.6 41
 
0.4%
0.7 89
0.9%
0.8 128
1.3%
0.9 40
 
0.4%
ValueCountFrequency (%)
504.0 1
< 0.1%
467.3 1
< 0.1%
395.3 1
< 0.1%
392.0 1
< 0.1%
356.0 1
< 0.1%
348.0 1
< 0.1%
254.0 1
< 0.1%
246.7 1
< 0.1%
227.6 1
< 0.1%
224.0 1
< 0.1%

Interactions

2023-12-11T12:31:33.036572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:29.632630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:30.263853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:31.034563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:31.687842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:32.369188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:33.125142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:29.729084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:30.400168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:31.127492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:31.781544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:32.505163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:33.255222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:29.828637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:30.558204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:31.246795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:31.889522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:32.625715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:33.359668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:29.922794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:30.681651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:31.348252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:32.012309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:32.730240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:33.470825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:30.018160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:30.816901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:31.467073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:32.146867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:32.833119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:33.592462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:30.134783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:30.935338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:31.589104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:32.264140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:31:32.941487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:31:40.109647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준코드관리기관pHTOCT-NT-PSS
표준코드1.0000.9940.1260.1120.1120.0000.076
관리기관0.9941.0000.5510.4630.2430.0000.169
pH0.1260.5511.0000.1500.0550.0000.045
TOC0.1120.4630.1501.0000.1330.2250.335
T-N0.1120.2430.0550.1331.0000.6710.199
T-P0.0000.0000.0000.2250.6711.0000.055
SS0.0760.1690.0450.3350.1990.0551.000
2023-12-11T12:31:40.237022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준코드pHTOCT-NT-PSS
표준코드1.0000.093-0.067-0.170-0.141-0.107
pH0.0931.0000.1520.0390.0470.076
TOC-0.0670.1521.000-0.0560.6340.555
T-N-0.1700.039-0.0561.0000.2160.138
T-P-0.1410.0470.6340.2161.0000.707
SS-0.1070.0760.5550.1380.7071.000

Missing values

2023-12-11T12:31:33.740053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:31:33.930829image/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

표준코드시설명조사구분주소시설구분관리구분관리기관조사일자pHTOCT-NT-PSS
02671010085송정공사조사부산광역시 기장군 철마면 송정리저수지공사울산지사2015-03-107.41.90.560.0144.7
12671010085송정공사조사부산광역시 기장군 철마면 송정리저수지공사울산지사2015-06-097.83.30.5920.0143.0
22671010085송정공사조사부산광역시 기장군 철마면 송정리저수지공사울산지사2015-09-087.52.10.5180.0112.0
32671010085송정공사조사부산광역시 기장군 철마면 송정리저수지공사울산지사2015-11-177.11.81.7940.1663.2
42671010098임기공사조사부산광역시 기장군 철마면 임기리저수지공사울산지사2015-03-107.41.30.3630.012.0
52671010098임기공사조사부산광역시 기장군 철마면 임기리저수지공사울산지사2015-06-097.62.60.4980.013.3
62671010098임기공사조사부산광역시 기장군 철마면 임기리저수지공사울산지사2015-09-087.52.70.5290.0174.7
72671010098임기공사조사부산광역시 기장군 철마면 임기리저수지공사울산지사2015-11-177.11.20.4480.0120.9
82714010023신공사조사대구광역시 동구 각산동저수지공사경산청도지사2015-03-178.27.00.6030.0216.8
92714010023신공사조사대구광역시 동구 각산동저수지공사경산청도지사2015-05-208.06.20.6220.0196.0
표준코드시설명조사구분주소시설구분관리구분관리기관조사일자pHTOCT-NT-PSS
99204971010001광령공사조사제주특별자치도 제주시 애월읍 광령리저수지공사제주본부2015-09-306.97.40.8360.06120.5
99214971010001광령공사조사제주특별자치도 제주시 애월읍 광령리저수지공사제주본부2015-11-277.07.71.2250.08521.7
99224971010002귀엄공사조사제주특별자치도 제주시 애월읍 수산리저수지공사제주본부2015-03-257.44.02.2260.07917.0
99234971010002귀엄공사조사제주특별자치도 제주시 애월읍 수산리저수지공사제주본부2015-06-197.33.31.8160.02214.7
99244971010002귀엄공사조사제주특별자치도 제주시 애월읍 수산리저수지공사제주본부2015-09-307.24.61.4950.04424.5
99254971010002귀엄공사조사제주특별자치도 제주시 애월읍 수산리저수지공사제주본부2015-11-277.14.41.4990.0398.7
99264971010003용수공사조사제주특별자치도 제주시 한경면 용수리저수지공사제주본부2015-03-258.55.52.2410.07521.3
99274971010003용수공사조사제주특별자치도 제주시 한경면 용수리저수지공사제주본부2015-06-197.55.71.4730.0357.7
99284971010003용수공사조사제주특별자치도 제주시 한경면 용수리저수지공사제주본부2015-09-307.25.81.1190.0517.5
99294971010003용수공사조사제주특별자치도 제주시 한경면 용수리저수지공사제주본부2015-11-277.74.02.350.03711.3