Overview

Dataset statistics

Number of variables27
Number of observations3818
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory876.3 KiB
Average record size in memory235.0 B

Variable types

Numeric15
Text4
Categorical7
DateTime1

Dataset

Description한국농어촌공사가 관리하는 농업용수로 사용되는 저수지의 주소, 담당관리기관, 조사일자, 수질정보 등
Author한국농어촌공사
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20191014000000001280

Alerts

조사구분 has constant value ""Constant
CN has constant value ""Constant
Cd has constant value ""Constant
Hg has constant value ""Constant
Cr6+ has constant value ""Constant
시설구분 is highly imbalanced (90.3%)Imbalance
Cu has 3560 (93.2%) zerosZeros
Pb has 3774 (98.8%) zerosZeros
As has 3789 (99.2%) zerosZeros

Reproduction

Analysis started2023-12-11 03:23:33.232538
Analysis finished2023-12-11 03:23:34.079749
Duration0.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

표준코드
Real number (ℝ)

Distinct950
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5149714 × 109
Minimum2.6710101 × 109
Maximum4.8890106 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:34.182204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6710101 × 109
5-th percentile4.15101 × 109
Q14.4135101 × 109
median4.6710102 × 109
Q34.7150101 × 109
95-th percentile4.8820101 × 109
Maximum4.8890106 × 109
Range2.2180005 × 109
Interquartile range (IQR)3.0149999 × 108

Descriptive statistics

Standard deviation3.8707837 × 108
Coefficient of variation (CV)0.085732185
Kurtosis9.6790803
Mean4.5149714 × 109
Median Absolute Deviation (MAD)1.5000016 × 108
Skewness-2.8933429
Sum1.7238161 × 1013
Variance1.4982966 × 1017
MonotonicityIncreasing
2023-12-11T12:23:34.367661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4375010009 8
 
0.2%
4418010052 8
 
0.2%
4784010100 8
 
0.2%
4728010036 8
 
0.2%
4885010137 8
 
0.2%
4687010176 4
 
0.1%
4687010167 4
 
0.1%
4687010165 4
 
0.1%
4687010154 4
 
0.1%
4687010026 4
 
0.1%
Other values (940) 3758
98.4%
ValueCountFrequency (%)
2671010056 4
0.1%
2671010067 4
0.1%
2671010097 4
0.1%
2714010005 4
0.1%
2723010024 4
0.1%
2771010011 4
0.1%
2771010062 4
0.1%
2771010077 4
0.1%
2771010098 4
0.1%
2871010001 4
0.1%
ValueCountFrequency (%)
4889010599 4
0.1%
4889010336 4
0.1%
4889010332 4
0.1%
4889010331 4
0.1%
4889010285 4
0.1%
4889010185 4
0.1%
4889010149 4
0.1%
4889010129 4
0.1%
4889010056 4
0.1%
4888010326 4
0.1%

시설명
Text

Distinct949
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
2023-12-11T12:23:34.673549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.9533787
Min length1

Characters and Unicode

Total characters11276
Distinct characters268
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

Unique0 ?
Unique (%)0.0%

Sample

1st row용천[기장]
2nd row용천[기장]
3rd row용천[기장]
4th row용천[기장]
5th row병산
ValueCountFrequency (%)
하동 12
 
0.3%
청천 8
 
0.2%
백곡 8
 
0.2%
성주 8
 
0.2%
경천[문경 8
 
0.2%
용암[영광 4
 
0.1%
대동 4
 
0.1%
금계[함평 4
 
0.1%
구산[함평 4
 
0.1%
길용 4
 
0.1%
Other values (939) 3754
98.3%
2023-12-11T12:23:35.237087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 811
 
7.2%
] 811
 
7.2%
산 400
 
3.5%
천 288
 
2.6%
성 260
 
2.3%
곡 228
 
2.0%
동 220
 
2.0%
대 176
 
1.6%
정 171
 
1.5%
고 160
 
1.4%
Other values (258) 7751
68.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9478
84.1%
Open Punctuation 867
 
7.7%
Close Punctuation 867
 
7.7%
Decimal Number 64
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 400
 
4.2%
천 288
 
3.0%
성 260
 
2.7%
곡 228
 
2.4%
동 220
 
2.3%
대 176
 
1.9%
정 171
 
1.8%
고 160
 
1.7%
주 143
 
1.5%
장 140
 
1.5%
Other values (252) 7292
76.9%
Open Punctuation
ValueCountFrequency (%)
[ 811
93.5%
( 56
 
6.5%
Close Punctuation
ValueCountFrequency (%)
] 811
93.5%
) 56
 
6.5%
Decimal Number
ValueCountFrequency (%)
2 40
62.5%
1 24
37.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9478
84.1%
Common 1798
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 400
 
4.2%
천 288
 
3.0%
성 260
 
2.7%
곡 228
 
2.4%
동 220
 
2.3%
대 176
 
1.9%
정 171
 
1.8%
고 160
 
1.7%
주 143
 
1.5%
장 140
 
1.5%
Other values (252) 7292
76.9%
Common
ValueCountFrequency (%)
[ 811
45.1%
] 811
45.1%
( 56
 
3.1%
) 56
 
3.1%
2 40
 
2.2%
1 24
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9478
84.1%
ASCII 1798
 
15.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
[ 811
45.1%
] 811
45.1%
( 56
 
3.1%
) 56
 
3.1%
2 40
 
2.2%
1 24
 
1.3%
Hangul
ValueCountFrequency (%)
산 400
 
4.2%
천 288
 
3.0%
성 260
 
2.7%
곡 228
 
2.4%
동 220
 
2.3%
대 176
 
1.9%
정 171
 
1.8%
고 160
 
1.7%
주 143
 
1.5%
장 140
 
1.5%
Other values (252) 7292
76.9%

지점코드
Text

Distinct955
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
2023-12-11T12:23:35.704027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters26726
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2302D10
2nd row2302D10
3rd row2302D10
4th row2302D10
5th row2302D20
ValueCountFrequency (%)
2302d10 4
 
0.1%
5302d60 4
 
0.1%
2402d10 4
 
0.1%
5302d25 4
 
0.1%
5302d85 4
 
0.1%
5006d10 4
 
0.1%
5006d20 4
 
0.1%
5005d30 4
 
0.1%
5005d20 4
 
0.1%
5005d10 4
 
0.1%
Other values (945) 3778
99.0%
2023-12-11T12:23:36.308752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7313
27.4%
1 4116
15.4%
D 3818
14.3%
2 3319
12.4%
3 2280
 
8.5%
5 2083
 
7.8%
4 1687
 
6.3%
6 688
 
2.6%
7 604
 
2.3%
8 432
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22908
85.7%
Uppercase Letter 3818
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7313
31.9%
1 4116
18.0%
2 3319
14.5%
3 2280
 
10.0%
5 2083
 
9.1%
4 1687
 
7.4%
6 688
 
3.0%
7 604
 
2.6%
8 432
 
1.9%
9 386
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
D 3818
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22908
85.7%
Latin 3818
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7313
31.9%
1 4116
18.0%
2 3319
14.5%
3 2280
 
10.0%
5 2083
 
9.1%
4 1687
 
7.4%
6 688
 
3.0%
7 604
 
2.6%
8 432
 
1.9%
9 386
 
1.7%
Latin
ValueCountFrequency (%)
D 3818
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7313
27.4%
1 4116
15.4%
D 3818
14.3%
2 3319
12.4%
3 2280
 
8.5%
5 2083
 
7.8%
4 1687
 
6.3%
6 688
 
2.6%
7 604
 
2.3%
8 432
 
1.6%

조사구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
측정망
3818 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
측정망 3818
100.0%

Length

2023-12-11T12:23:36.546397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:23:36.680949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
측정망 3818
100.0%

주소
Text

Distinct930
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
2023-12-11T12:23:37.051123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length15.885018
Min length11

Characters and Unicode

Total characters60649
Distinct characters293
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

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 기장군 일광면 용천리
2nd row부산광역시 기장군 일광면 용천리
3rd row부산광역시 기장군 일광면 용천리
4th row부산광역시 기장군 일광면 용천리
5th row부산광역시 기장군 정관면 병산리
ValueCountFrequency (%)
전라남도 884
 
5.8%
경상북도 652
 
4.3%
경상남도 459
 
3.0%
충청남도 452
 
3.0%
전라북도 448
 
2.9%
충청북도 312
 
2.0%
강원도 240
 
1.6%
경기도 207
 
1.4%
고흥군 108
 
0.7%
강진군 96
 
0.6%
Other values (1549) 11414
74.7%
2023-12-11T12:23:37.666510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11454
18.9%
도 3962
 
6.5%
리 3681
 
6.1%
면 3187
 
5.3%
군 2440
 
4.0%
남 2251
 
3.7%
북 1628
 
2.7%
시 1622
 
2.7%
경 1474
 
2.4%
전 1468
 
2.4%
Other values (283) 27482
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49187
81.1%
Space Separator 11454
 
18.9%
Decimal Number 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 3962
 
8.1%
리 3681
 
7.5%
면 3187
 
6.5%
군 2440
 
5.0%
남 2251
 
4.6%
북 1628
 
3.3%
시 1622
 
3.3%
경 1474
 
3.0%
전 1468
 
3.0%
라 1360
 
2.8%
Other values (281) 26114
53.1%
Space Separator
ValueCountFrequency (%)
11454
100.0%
Decimal Number
ValueCountFrequency (%)
1 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49187
81.1%
Common 11462
 
18.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 3962
 
8.1%
리 3681
 
7.5%
면 3187
 
6.5%
군 2440
 
5.0%
남 2251
 
4.6%
북 1628
 
3.3%
시 1622
 
3.3%
경 1474
 
3.0%
전 1468
 
3.0%
라 1360
 
2.8%
Other values (281) 26114
53.1%
Common
ValueCountFrequency (%)
11454
99.9%
1 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49187
81.1%
ASCII 11462
 
18.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11454
99.9%
1 8
 
0.1%
Hangul
ValueCountFrequency (%)
도 3962
 
8.1%
리 3681
 
7.5%
면 3187
 
6.5%
군 2440
 
5.0%
남 2251
 
4.6%
북 1628
 
3.3%
시 1622
 
3.3%
경 1474
 
3.0%
전 1468
 
3.0%
라 1360
 
2.8%
Other values (281) 26114
53.1%

시설구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
저수지
3770 
담수호
 
48

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 3770
98.7%
담수호 48
 
1.3%

Length

2023-12-11T12:23:37.882364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:23:38.035287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
저수지 3770
98.7%
담수호 48
 
1.3%

관리구분
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
공사
3390 
시군
428 

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 3390
88.8%
시군 428
 
11.2%

Length

2023-12-11T12:23:38.185850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:23:38.636103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사 3390
88.8%
시군 428
 
11.2%

관리기관
Text

Distinct142
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
2023-12-11T12:23:38.971275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.8072289
Min length4

Characters and Unicode

Total characters18354
Distinct characters102
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

Unique0 ?
Unique (%)0.0%

Sample

1st row울산지사
2nd row울산지사
3rd row울산지사
4th row울산지사
5th row울산지사
ValueCountFrequency (%)
해남완도지사 92
 
2.4%
장흥지사 76
 
2.0%
경주지사 76
 
2.0%
진주산청지사 75
 
2.0%
고흥지사 72
 
1.9%
무진장지사 68
 
1.8%
경산청도지사 64
 
1.7%
홍천춘천지사 64
 
1.7%
고창지사 64
 
1.7%
포항울릉지사 64
 
1.7%
Other values (132) 3103
81.3%
2023-12-11T12:23:39.590141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 3438
18.7%
지 3398
18.5%
주 674
 
3.7%
천 652
 
3.6%
청 651
 
3.5%
산 511
 
2.8%
성 452
 
2.5%
진 435
 
2.4%
군 364
 
2.0%
안 340
 
1.9%
Other values (92) 7439
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18354
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 3438
18.7%
지 3398
18.5%
주 674
 
3.7%
천 652
 
3.6%
청 651
 
3.5%
산 511
 
2.8%
성 452
 
2.5%
진 435
 
2.4%
군 364
 
2.0%
안 340
 
1.9%
Other values (92) 7439
40.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18354
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 3438
18.7%
지 3398
18.5%
주 674
 
3.7%
천 652
 
3.6%
청 651
 
3.5%
산 511
 
2.8%
성 452
 
2.5%
진 435
 
2.4%
군 364
 
2.0%
안 340
 
1.9%
Other values (92) 7439
40.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18354
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 3438
18.7%
지 3398
18.5%
주 674
 
3.7%
천 652
 
3.6%
청 651
 
3.5%
산 511
 
2.8%
성 452
 
2.5%
진 435
 
2.4%
군 364
 
2.0%
안 340
 
1.9%
Other values (92) 7439
40.5%

조사일자
Date

Distinct216
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
Minimum2018-02-02 00:00:00
Maximum2018-12-17 00:00:00
2023-12-11T12:23:39.792017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:39.986967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수온(℃)
Real number (ℝ)

Distinct298
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.946674
Minimum2.5
Maximum34.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:40.161748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile6.6
Q110.3
median14.1
Q318.3
95-th percentile27.015
Maximum34.9
Range32.4
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.1406534
Coefficient of variation (CV)0.41083746
Kurtosis0.034046126
Mean14.946674
Median Absolute Deviation (MAD)4
Skewness0.68669558
Sum57066.4
Variance37.707625
MonotonicityNot monotonic
2023-12-11T12:23:40.358230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.4 40
 
1.0%
18.1 33
 
0.9%
15.0 33
 
0.9%
16.0 30
 
0.8%
12.6 30
 
0.8%
13.9 30
 
0.8%
12.8 30
 
0.8%
14.5 30
 
0.8%
10.3 29
 
0.8%
11.5 29
 
0.8%
Other values (288) 3504
91.8%
ValueCountFrequency (%)
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
3.3 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 2
0.1%
3.6 2
0.1%
3.7 1
 
< 0.1%
3.8 3
0.1%
3.9 3
0.1%
4.0 1
 
< 0.1%
ValueCountFrequency (%)
34.9 1
 
< 0.1%
33.8 2
0.1%
33.5 2
0.1%
32.9 1
 
< 0.1%
32.7 3
0.1%
32.6 1
 
< 0.1%
32.5 2
0.1%
32.4 2
0.1%
32.3 1
 
< 0.1%
32.1 1
 
< 0.1%

pH
Real number (ℝ)

Distinct53
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5199057
Minimum5.3
Maximum11.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:40.578183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.3
5-th percentile6.6
Q17.1
median7.5
Q37.9
95-th percentile8.615
Maximum11.3
Range6
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.67396806
Coefficient of variation (CV)0.089624536
Kurtosis1.6291549
Mean7.5199057
Median Absolute Deviation (MAD)0.4
Skewness0.66076689
Sum28711
Variance0.45423295
MonotonicityNot monotonic
2023-12-11T12:23:40.753685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5 268
 
7.0%
7.4 249
 
6.5%
7.3 245
 
6.4%
7.1 241
 
6.3%
7.2 237
 
6.2%
7.8 210
 
5.5%
7.6 209
 
5.5%
7.0 206
 
5.4%
7.7 206
 
5.4%
7.9 167
 
4.4%
Other values (43) 1580
41.4%
ValueCountFrequency (%)
5.3 3
 
0.1%
5.4 2
 
0.1%
5.5 1
 
< 0.1%
5.7 1
 
< 0.1%
5.8 2
 
0.1%
5.9 9
 
0.2%
6.0 15
0.4%
6.1 18
0.5%
6.2 22
0.6%
6.3 25
0.7%
ValueCountFrequency (%)
11.3 1
 
< 0.1%
11.2 1
 
< 0.1%
10.6 1
 
< 0.1%
10.5 3
 
0.1%
10.4 2
 
0.1%
10.1 4
0.1%
10.0 1
 
< 0.1%
9.9 9
0.2%
9.8 7
0.2%
9.7 5
0.1%

EC
Real number (ℝ)

Distinct536
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.47695
Minimum6
Maximum12276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:40.900412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile44
Q173
median114
Q3186
95-th percentile442.15
Maximum12276
Range12270
Interquartile range (IQR)113

Descriptive statistics

Standard deviation396.03632
Coefficient of variation (CV)2.0901557
Kurtosis281.80936
Mean189.47695
Median Absolute Deviation (MAD)49
Skewness13.091617
Sum723423
Variance156844.77
MonotonicityNot monotonic
2023-12-11T12:23:41.057890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 40
 
1.0%
72 39
 
1.0%
65 36
 
0.9%
79 35
 
0.9%
70 35
 
0.9%
82 35
 
0.9%
77 34
 
0.9%
63 34
 
0.9%
86 31
 
0.8%
97 30
 
0.8%
Other values (526) 3469
90.9%
ValueCountFrequency (%)
6 1
 
< 0.1%
11 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
23 2
 
0.1%
25 1
 
< 0.1%
26 2
 
0.1%
27 7
0.2%
28 6
0.2%
29 9
0.2%
ValueCountFrequency (%)
12276 1
< 0.1%
6169 1
< 0.1%
5763 1
< 0.1%
4700 1
< 0.1%
4688 1
< 0.1%
4365 1
< 0.1%
4216 1
< 0.1%
4138 1
< 0.1%
3835 1
< 0.1%
3810 1
< 0.1%

DO
Real number (ℝ)

Distinct162
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6554217
Minimum0
Maximum18.9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:41.265511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q15.6
median7.8
Q39.8
95-th percentile11.9
Maximum18.9
Range18.9
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation2.8685421
Coefficient of variation (CV)0.37470726
Kurtosis-0.35388104
Mean7.6554217
Median Absolute Deviation (MAD)2.1
Skewness-0.11607567
Sum29228.4
Variance8.2285336
MonotonicityNot monotonic
2023-12-11T12:23:41.426044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 67
 
1.8%
7.4 64
 
1.7%
8.6 60
 
1.6%
9.5 59
 
1.5%
9.3 58
 
1.5%
8.7 56
 
1.5%
9.6 56
 
1.5%
8.3 55
 
1.4%
10.9 54
 
1.4%
10.5 53
 
1.4%
Other values (152) 3236
84.8%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.1 4
0.1%
0.2 3
0.1%
0.3 3
0.1%
0.4 4
0.1%
0.5 4
0.1%
0.6 2
 
0.1%
0.7 5
0.1%
0.8 6
0.2%
0.9 2
 
0.1%
ValueCountFrequency (%)
18.9 1
< 0.1%
17.3 1
< 0.1%
16.8 2
0.1%
16.7 1
< 0.1%
16.6 2
0.1%
16.3 1
< 0.1%
15.8 1
< 0.1%
15.6 2
0.1%
15.5 2
0.1%
15.4 1
< 0.1%

COD
Real number (ℝ)

Distinct87
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1756417
Minimum1.4
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:41.599746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2.8
Q14
median5.2
Q37.6
95-th percentile12.8
Maximum46
Range44.6
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation3.3969679
Coefficient of variation (CV)0.5500591
Kurtosis11.864321
Mean6.1756417
Median Absolute Deviation (MAD)1.6
Skewness2.4368021
Sum23578.6
Variance11.539391
MonotonicityNot monotonic
2023-12-11T12:23:41.788141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 159
 
4.2%
4.2 157
 
4.1%
4.0 155
 
4.1%
4.6 153
 
4.0%
3.8 152
 
4.0%
3.2 142
 
3.7%
5.0 141
 
3.7%
3.4 131
 
3.4%
4.4 128
 
3.4%
5.6 126
 
3.3%
Other values (77) 2374
62.2%
ValueCountFrequency (%)
1.4 1
 
< 0.1%
1.6 7
 
0.2%
1.8 10
 
0.3%
2.0 16
 
0.4%
2.2 18
 
0.5%
2.4 43
 
1.1%
2.6 80
2.1%
2.8 86
2.3%
3.0 101
2.6%
3.2 142
3.7%
ValueCountFrequency (%)
46.0 1
< 0.1%
32.0 1
< 0.1%
31.2 2
0.1%
29.6 2
0.1%
28.8 1
< 0.1%
28.0 1
< 0.1%
27.2 2
0.1%
23.6 1
< 0.1%
22.4 1
< 0.1%
22.0 1
< 0.1%

TOC
Real number (ℝ)

Distinct119
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6756941
Minimum0.7
Maximum19.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:41.996657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.6
Q12.3
median3.2
Q34.6
95-th percentile7.2
Maximum19.9
Range19.2
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.8999417
Coefficient of variation (CV)0.51689332
Kurtosis5.9358023
Mean3.6756941
Median Absolute Deviation (MAD)1
Skewness1.7473166
Sum14033.8
Variance3.6097785
MonotonicityNot monotonic
2023-12-11T12:23:42.211481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4 129
 
3.4%
2.8 117
 
3.1%
2.9 114
 
3.0%
1.8 114
 
3.0%
2.0 114
 
3.0%
2.5 111
 
2.9%
2.2 111
 
2.9%
2.7 110
 
2.9%
2.6 108
 
2.8%
2.3 104
 
2.7%
Other values (109) 2686
70.4%
ValueCountFrequency (%)
0.7 1
 
< 0.1%
0.8 1
 
< 0.1%
0.9 5
 
0.1%
1.0 5
 
0.1%
1.1 9
 
0.2%
1.2 31
0.8%
1.3 37
1.0%
1.4 30
0.8%
1.5 56
1.5%
1.6 69
1.8%
ValueCountFrequency (%)
19.9 1
< 0.1%
19.1 1
< 0.1%
17.9 1
< 0.1%
16.6 1
< 0.1%
15.6 1
< 0.1%
13.8 1
< 0.1%
13.6 1
< 0.1%
12.6 1
< 0.1%
12.2 1
< 0.1%
12.0 2
0.1%

T-N
Real number (ℝ)

Distinct1895
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3260498
Minimum0.094
Maximum12.472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:42.432162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.094
5-th percentile0.42985
Q10.767
median1.137
Q31.62275
95-th percentile2.81845
Maximum12.472
Range12.378
Interquartile range (IQR)0.85575

Descriptive statistics

Standard deviation0.91012626
Coefficient of variation (CV)0.68634397
Kurtosis22.955173
Mean1.3260498
Median Absolute Deviation (MAD)0.412
Skewness3.4350343
Sum5062.858
Variance0.8283298
MonotonicityNot monotonic
2023-12-11T12:23:42.665821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.972 9
 
0.2%
1.308 8
 
0.2%
0.773 8
 
0.2%
0.872 8
 
0.2%
0.807 8
 
0.2%
0.926 7
 
0.2%
0.801 7
 
0.2%
0.81 7
 
0.2%
1.022 7
 
0.2%
1.382 7
 
0.2%
Other values (1885) 3742
98.0%
ValueCountFrequency (%)
0.094 1
< 0.1%
0.098 1
< 0.1%
0.113 1
< 0.1%
0.128 1
< 0.1%
0.157 1
< 0.1%
0.173 1
< 0.1%
0.188 1
< 0.1%
0.205 1
< 0.1%
0.208 1
< 0.1%
0.213 1
< 0.1%
ValueCountFrequency (%)
12.472 1
< 0.1%
11.349 1
< 0.1%
10.212 2
0.1%
9.038 1
< 0.1%
7.997 1
< 0.1%
7.878 1
< 0.1%
7.731 1
< 0.1%
7.62 1
< 0.1%
7.415 1
< 0.1%
7.0 1
< 0.1%

T-P
Real number (ℝ)

Distinct210
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.032869041
Minimum0.001
Maximum0.921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:42.917709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.008
Q10.013
median0.0195
Q30.033
95-th percentile0.09215
Maximum0.921
Range0.92
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.052835801
Coefficient of variation (CV)1.607464
Kurtosis93.277124
Mean0.032869041
Median Absolute Deviation (MAD)0.0085
Skewness8.0858867
Sum125.494
Variance0.0027916218
MonotonicityNot monotonic
2023-12-11T12:23:43.087081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.014 195
 
5.1%
0.011 184
 
4.8%
0.012 173
 
4.5%
0.015 173
 
4.5%
0.016 153
 
4.0%
0.013 145
 
3.8%
0.01 141
 
3.7%
0.018 132
 
3.5%
0.017 130
 
3.4%
0.009 128
 
3.4%
Other values (200) 2264
59.3%
ValueCountFrequency (%)
0.001 1
 
< 0.1%
0.002 3
 
0.1%
0.003 6
 
0.2%
0.004 12
 
0.3%
0.005 27
 
0.7%
0.006 48
 
1.3%
0.007 56
 
1.5%
0.008 97
2.5%
0.009 128
3.4%
0.01 141
3.7%
ValueCountFrequency (%)
0.921 2
0.1%
0.802 1
< 0.1%
0.707 1
< 0.1%
0.653 1
< 0.1%
0.648 1
< 0.1%
0.638 1
< 0.1%
0.597 1
< 0.1%
0.551 1
< 0.1%
0.49 1
< 0.1%
0.486 1
< 0.1%

SS
Real number (ℝ)

Distinct265
Distinct (%)6.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.4225046
Minimum0.1
Maximum146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:43.255771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.8
Q11.8
median3.2
Q36.2
95-th percentile17.02
Maximum146
Range145.9
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation7.2481118
Coefficient of variation (CV)1.3366723
Kurtosis63.077438
Mean5.4225046
Median Absolute Deviation (MAD)1.8
Skewness5.8061413
Sum20697.7
Variance52.535125
MonotonicityNot monotonic
2023-12-11T12:23:43.437641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3 100
 
2.6%
2.0 95
 
2.5%
1.4 90
 
2.4%
2.7 89
 
2.3%
1.9 88
 
2.3%
1.5 87
 
2.3%
1.8 86
 
2.3%
1.7 84
 
2.2%
1.6 83
 
2.2%
1.2 80
 
2.1%
Other values (255) 2935
76.9%
ValueCountFrequency (%)
0.1 3
 
0.1%
0.2 7
 
0.2%
0.3 7
 
0.2%
0.4 18
 
0.5%
0.5 15
 
0.4%
0.6 37
1.0%
0.7 58
1.5%
0.8 48
1.3%
0.9 65
1.7%
1.0 68
1.8%
ValueCountFrequency (%)
146.0 1
< 0.1%
97.3 1
< 0.1%
77.0 1
< 0.1%
76.5 1
< 0.1%
75.3 1
< 0.1%
66.7 1
< 0.1%
62.5 1
< 0.1%
62.0 1
< 0.1%
61.3 1
< 0.1%
61.0 1
< 0.1%

Cl-
Real number (ℝ)

Distinct607
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.564405
Minimum0.9
Maximum3922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:43.616543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile4.1
Q16.2
median9
Q315.4
95-th percentile68.615
Maximum3922
Range3921.1
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation111.13884
Coefficient of variation (CV)4.1837502
Kurtosis463.44756
Mean26.564405
Median Absolute Deviation (MAD)3.5
Skewness17.335576
Sum101422.9
Variance12351.841
MonotonicityNot monotonic
2023-12-11T12:23:43.834082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.5 51
 
1.3%
5.2 47
 
1.2%
5.1 45
 
1.2%
5.9 45
 
1.2%
5.0 44
 
1.2%
6.6 43
 
1.1%
7.3 43
 
1.1%
6.0 43
 
1.1%
6.4 42
 
1.1%
8.4 41
 
1.1%
Other values (597) 3374
88.4%
ValueCountFrequency (%)
0.9 1
 
< 0.1%
1.2 2
 
0.1%
1.5 2
 
0.1%
1.6 2
 
0.1%
1.7 1
 
< 0.1%
1.8 1
 
< 0.1%
1.9 2
 
0.1%
2.0 1
 
< 0.1%
2.1 5
0.1%
2.2 2
 
0.1%
ValueCountFrequency (%)
3922.0 1
< 0.1%
1839.0 1
< 0.1%
1694.1 1
< 0.1%
1668.4 1
< 0.1%
1240.2 1
< 0.1%
1238.7 1
< 0.1%
1214.7 1
< 0.1%
1209.5 1
< 0.1%
1015.3 1
< 0.1%
1000.8 1
< 0.1%

Chl-a
Real number (ℝ)

Distinct570
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.214694
Minimum0.2
Maximum654.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:44.005662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2.3
Q14.8
median8
Q315.6
95-th percentile46.315
Maximum654.5
Range654.3
Interquartile range (IQR)10.8

Descriptive statistics

Standard deviation21.435706
Coefficient of variation (CV)1.5079964
Kurtosis240.10184
Mean14.214694
Median Absolute Deviation (MAD)4.15
Skewness10.667035
Sum54271.7
Variance459.48951
MonotonicityNot monotonic
2023-12-11T12:23:44.190852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8 43
 
1.1%
4.2 40
 
1.0%
7.1 40
 
1.0%
6.5 40
 
1.0%
3.0 39
 
1.0%
4.9 38
 
1.0%
6.7 37
 
1.0%
5.6 37
 
1.0%
3.6 36
 
0.9%
4.0 36
 
0.9%
Other values (560) 3432
89.9%
ValueCountFrequency (%)
0.2 3
0.1%
0.3 2
 
0.1%
0.5 2
 
0.1%
0.6 4
0.1%
0.7 3
0.1%
0.8 3
0.1%
0.9 6
0.2%
1.0 7
0.2%
1.1 5
0.1%
1.2 4
0.1%
ValueCountFrequency (%)
654.5 1
< 0.1%
340.3 1
< 0.1%
287.7 1
< 0.1%
239.7 1
< 0.1%
207.2 1
< 0.1%
195.7 1
< 0.1%
165.9 1
< 0.1%
158.8 1
< 0.1%
145.3 1
< 0.1%
142.7 1
< 0.1%

CN
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3818 

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 3818
100.0%

Length

2023-12-11T12:23:44.672461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:23:44.779357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3818
100.0%

Cu
Real number (ℝ)

ZEROS 

Distinct198
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00026694605
Minimum0
Maximum0.0195
Zeros3560
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:44.897518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0024945
Maximum0.0195
Range0.0195
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0011814553
Coefficient of variation (CV)4.4258206
Kurtosis62.566105
Mean0.00026694605
Median Absolute Deviation (MAD)0
Skewness6.6496382
Sum1.0192
Variance1.3958366 × 10-6
MonotonicityNot monotonic
2023-12-11T12:23:45.111215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3560
93.2%
0.00212 3
 
0.1%
0.00375 3
 
0.1%
0.00304 3
 
0.1%
0.00299 3
 
0.1%
0.00206 3
 
0.1%
0.00214 3
 
0.1%
0.00273 3
 
0.1%
0.00224 3
 
0.1%
0.00221 3
 
0.1%
Other values (188) 231
 
6.1%
ValueCountFrequency (%)
0.0 3560
93.2%
0.002 2
 
0.1%
0.00201 1
 
< 0.1%
0.00202 1
 
< 0.1%
0.00203 1
 
< 0.1%
0.00204 2
 
0.1%
0.00206 3
 
0.1%
0.00208 1
 
< 0.1%
0.00209 2
 
0.1%
0.0021 2
 
0.1%
ValueCountFrequency (%)
0.0195 1
< 0.1%
0.01717 1
< 0.1%
0.01544 1
< 0.1%
0.01373 1
< 0.1%
0.01345 1
< 0.1%
0.01162 1
< 0.1%
0.01142 1
< 0.1%
0.01118 1
< 0.1%
0.00961 1
< 0.1%
0.00959 1
< 0.1%

Pb
Real number (ℝ)

ZEROS 

Distinct41
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4643793 × 10-5
Minimum0
Maximum0.01267
Zeros3774
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:45.328105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.01267
Range0.01267
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00047313911
Coefficient of variation (CV)10.598094
Kurtosis251.83892
Mean4.4643793 × 10-5
Median Absolute Deviation (MAD)0
Skewness14.120073
Sum0.17045
Variance2.2386061 × 10-7
MonotonicityNot monotonic
2023-12-11T12:23:45.502696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 3774
98.8%
0.00352 2
 
0.1%
0.00516 2
 
0.1%
0.00265 2
 
0.1%
0.0021 2
 
0.1%
0.00215 1
 
< 0.1%
0.00791 1
 
< 0.1%
0.00329 1
 
< 0.1%
0.00309 1
 
< 0.1%
0.00358 1
 
< 0.1%
Other values (31) 31
 
0.8%
ValueCountFrequency (%)
0.0 3774
98.8%
0.00205 1
 
< 0.1%
0.0021 2
 
0.1%
0.00215 1
 
< 0.1%
0.00221 1
 
< 0.1%
0.00223 1
 
< 0.1%
0.00224 1
 
< 0.1%
0.00227 1
 
< 0.1%
0.00234 1
 
< 0.1%
0.00243 1
 
< 0.1%
ValueCountFrequency (%)
0.01267 1
< 0.1%
0.00912 1
< 0.1%
0.00791 1
< 0.1%
0.00761 1
< 0.1%
0.00675 1
< 0.1%
0.0058 1
< 0.1%
0.00555 1
< 0.1%
0.00516 2
0.1%
0.00513 1
< 0.1%
0.00458 1
< 0.1%

Cd
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3818 

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 3818
100.0%

Length

2023-12-11T12:23:45.642686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:23:45.764101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3818
100.0%

As
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00011039026
Minimum0
Maximum0.04353
Zeros3789
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:23:45.895579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.04353
Range0.04353
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0015366209
Coefficient of variation (CV)13.919896
Kurtosis401.75438
Mean0.00011039026
Median Absolute Deviation (MAD)0
Skewness18.500794
Sum0.42147
Variance2.3612039 × 10-6
MonotonicityNot monotonic
2023-12-11T12:23:46.095116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 3789
99.2%
0.00657 2
 
0.1%
0.0077 2
 
0.1%
0.04353 1
 
< 0.1%
0.00695 1
 
< 0.1%
0.01216 1
 
< 0.1%
0.02642 1
 
< 0.1%
0.02063 1
 
< 0.1%
0.00654 1
 
< 0.1%
0.00662 1
 
< 0.1%
Other values (18) 18
 
0.5%
ValueCountFrequency (%)
0.0 3789
99.2%
0.00619 1
 
< 0.1%
0.00654 1
 
< 0.1%
0.00657 2
 
0.1%
0.00661 1
 
< 0.1%
0.00662 1
 
< 0.1%
0.00687 1
 
< 0.1%
0.00695 1
 
< 0.1%
0.00702 1
 
< 0.1%
0.0075 1
 
< 0.1%
ValueCountFrequency (%)
0.04353 1
< 0.1%
0.0399 1
< 0.1%
0.02896 1
< 0.1%
0.02642 1
< 0.1%
0.02474 1
< 0.1%
0.02393 1
< 0.1%
0.0224 1
< 0.1%
0.02063 1
< 0.1%
0.01708 1
< 0.1%
0.01324 1
< 0.1%

Hg
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3818 

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 3818
100.0%

Length

2023-12-11T12:23:46.282257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:23:46.386750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3818
100.0%

Cr6+
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3818 

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 3818
100.0%

Length

2023-12-11T12:23:46.542420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:23:46.669604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3818
100.0%

Sample

표준코드시설명지점코드조사구분주소시설구분관리구분관리기관조사일자수온(℃)pHECDOCODTOCT-NT-PSSCl-Chl-aCNCuPbCdAsHgCr6+
02671010056용천[기장]2302D10측정망부산광역시 기장군 일광면 용천리저수지공사울산지사2018-04-109.57.46711.34.62.90.8420.0151.78.43.600.00.000.000
12671010056용천[기장]2302D10측정망부산광역시 기장군 일광면 용천리저수지공사울산지사2018-06-0114.27.17410.24.63.00.5040.022.27.94.900.00.000.000
22671010056용천[기장]2302D10측정망부산광역시 기장군 일광면 용천리저수지공사울산지사2018-08-2125.37.3894.54.63.00.2930.011.07.64.700.00.000.000
32671010056용천[기장]2302D10측정망부산광역시 기장군 일광면 용천리저수지공사울산지사2018-10-1814.57.0607.14.62.30.8470.0133.68.43.600.00.000.000
42671010067병산2302D20측정망부산광역시 기장군 정관면 병산리저수지공사울산지사2018-03-278.36.913810.74.62.71.6510.0261.711.17.800.00.000.000
52671010067병산2302D20측정망부산광역시 기장군 정관면 병산리저수지공사울산지사2018-05-2816.56.81703.74.22.31.1690.0263.010.210.500.00.000.000
62671010067병산2302D20측정망부산광역시 기장군 정관면 병산리저수지공사울산지사2018-08-0730.97.21957.05.23.20.7130.0276.310.429.400.00.000.000
72671010067병산2302D20측정망부산광역시 기장군 정관면 병산리저수지공사울산지사2018-10-2614.77.418610.33.41.81.1490.0113.610.412.100.00.000.000
82671010097안평2302D30측정망부산광역시 기장군 철마면 안평리저수지공사울산지사2018-04-109.37.412210.64.02.41.8470.0163.111.22.400.00.000.000
92671010097안평2302D30측정망부산광역시 기장군 철마면 안평리저수지공사울산지사2018-06-0114.37.51327.04.42.61.5180.0222.510.611.200.00.000.000
표준코드시설명지점코드조사구분주소시설구분관리구분관리기관조사일자수온(℃)pHECDOCODTOCT-NT-PSSCl-Chl-aCNCuPbCdAsHgCr6+
38084889010332장계[합천]2016D10측정망경상남도 합천군 합천읍 장계리저수지공사합천지사2018-08-3119.57.3762.98.64.81.7580.04213.86.024.500.00.000.000
38094889010332장계[합천]2016D10측정망경상남도 합천군 합천읍 장계리저수지공사합천지사2018-11-1512.66.3682.85.03.01.9950.0163.234.614.100.00.000.000
38104889010336중촌2018D85측정망경상남도 합천군 쌍백면 평지리저수지공사합천지사2018-04-0210.37.51008.64.42.40.6280.0162.24.15.000.00.000.000
38114889010336중촌2018D85측정망경상남도 합천군 쌍백면 평지리저수지공사합천지사2018-05-3011.97.31007.25.23.50.5240.0122.54.19.300.01950.000.000
38124889010336중촌2018D85측정망경상남도 합천군 쌍백면 평지리저수지공사합천지사2018-08-1016.88.01065.85.63.80.5430.0122.22.77.700.00.000.000
38134889010336중촌2018D85측정망경상남도 합천군 쌍백면 평지리저수지공사합천지사2018-10-1714.97.6915.65.63.10.6540.01813.53.35.700.00.000.000
38144889010599노곡2015D70측정망경상남도 합천군 봉산면 노곡리저수지시군합천군청2018-04-028.27.71118.34.42.80.8390.0191.96.612.900.00.000.000
38154889010599노곡2015D70측정망경상남도 합천군 봉산면 노곡리저수지시군합천군청2018-06-0610.57.11202.95.62.90.6120.0294.05.330.200.00.000.000
38164889010599노곡2015D70측정망경상남도 합천군 봉산면 노곡리저수지시군합천군청2018-08-1726.78.21069.95.83.70.580.0319.05.224.600.00.000.000
38174889010599노곡2015D70측정망경상남도 합천군 봉산면 노곡리저수지시군합천군청2018-10-1515.17.3836.75.63.01.0850.0296.15.310.500.00.000.000