Overview

Dataset statistics

Number of variables27
Number of observations3816
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory875.9 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=20191014000000001279

Alerts

조사구분 has constant value ""Constant
CN has constant value ""Constant
Cd has constant value ""Constant
Hg has constant value ""Constant
시설구분 is highly imbalanced (90.3%)Imbalance
Cr6+ is highly imbalanced (99.1%)Imbalance
Cu has 3591 (94.1%) zerosZeros
Pb has 3749 (98.2%) zerosZeros
As has 3779 (99.0%) zerosZeros

Reproduction

Analysis started2023-12-11 03:29:01.206753
Analysis finished2023-12-11 03:29:02.097220
Duration0.89 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%
Mean4.5149919 × 109
Minimum2.6710101 × 109
Maximum4.8890106 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:02.200745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6710101 × 109
5-th percentile4.15101 × 109
Q14.4130101 × 109
median4.6710102 × 109
Q34.7150101 × 109
95-th percentile4.8820101 × 109
Maximum4.8890106 × 109
Range2.2180005 × 109
Interquartile range (IQR)3.0199998 × 108

Descriptive statistics

Standard deviation3.8720096 × 108
Coefficient of variation (CV)0.085758949
Kurtosis9.6703776
Mean4.5149919 × 109
Median Absolute Deviation (MAD)1.5000016 × 108
Skewness-2.8921643
Sum1.7229209 × 1013
Variance1.4992458 × 1017
MonotonicityIncreasing
2023-12-11T12:29:02.385032image/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%
4885010137 8
 
0.2%
4728010036 8
 
0.2%
4687010181 4
 
0.1%
4687010176 4
 
0.1%
4687010167 4
 
0.1%
4687010165 4
 
0.1%
4687010097 4
 
0.1%
Other values (940) 3756
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 size29.9 KiB
2023-12-11T12:29:02.930042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.9536164
Min length1

Characters and Unicode

Total characters11271
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%
성주 8
 
0.2%
경천[문경 8
 
0.2%
청천 8
 
0.2%
백곡 8
 
0.2%
대동 4
 
0.1%
일로2 4
 
0.1%
월천 4
 
0.1%
백운[완도 4
 
0.1%
용천[기장 4
 
0.1%
Other values (939) 3752
98.3%
2023-12-11T12:29:03.631957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 811
 
7.2%
] 811
 
7.2%
산 400
 
3.5%
천 287
 
2.5%
성 260
 
2.3%
곡 228
 
2.0%
동 218
 
1.9%
대 176
 
1.6%
정 172
 
1.5%
고 159
 
1.4%
Other values (258) 7749
68.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9474
84.1%
Open Punctuation 867
 
7.7%
Close Punctuation 867
 
7.7%
Decimal Number 63
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 400
 
4.2%
천 287
 
3.0%
성 260
 
2.7%
곡 228
 
2.4%
동 218
 
2.3%
대 176
 
1.9%
정 172
 
1.8%
고 159
 
1.7%
주 144
 
1.5%
용 140
 
1.5%
Other values (252) 7290
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
63.5%
1 23
36.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9474
84.1%
Common 1797
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 400
 
4.2%
천 287
 
3.0%
성 260
 
2.7%
곡 228
 
2.4%
동 218
 
2.3%
대 176
 
1.9%
정 172
 
1.8%
고 159
 
1.7%
주 144
 
1.5%
용 140
 
1.5%
Other values (252) 7290
76.9%
Common
ValueCountFrequency (%)
[ 811
45.1%
] 811
45.1%
( 56
 
3.1%
) 56
 
3.1%
2 40
 
2.2%
1 23
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9474
84.1%
ASCII 1797
 
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 23
 
1.3%
Hangul
ValueCountFrequency (%)
산 400
 
4.2%
천 287
 
3.0%
성 260
 
2.7%
곡 228
 
2.4%
동 218
 
2.3%
대 176
 
1.9%
정 172
 
1.8%
고 159
 
1.7%
주 144
 
1.5%
용 140
 
1.5%
Other values (252) 7290
76.9%

지점코드
Text

Distinct955
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
2023-12-11T12:29:04.062831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters26712
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%
5302d70 4
 
0.1%
5302d45 4
 
0.1%
5006d10 4
 
0.1%
5006d20 4
 
0.1%
5005d30 4
 
0.1%
5005d20 4
 
0.1%
5005d10 4
 
0.1%
5302d30 4
 
0.1%
5302d40 4
 
0.1%
Other values (945) 3776
99.0%
2023-12-11T12:29:04.868932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7308
27.4%
1 4117
15.4%
D 3816
14.3%
2 3318
12.4%
3 2274
 
8.5%
5 2082
 
7.8%
4 1686
 
6.3%
6 687
 
2.6%
7 604
 
2.3%
8 432
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22896
85.7%
Uppercase Letter 3816
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7308
31.9%
1 4117
18.0%
2 3318
14.5%
3 2274
 
9.9%
5 2082
 
9.1%
4 1686
 
7.4%
6 687
 
3.0%
7 604
 
2.6%
8 432
 
1.9%
9 388
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
D 3816
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22896
85.7%
Latin 3816
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7308
31.9%
1 4117
18.0%
2 3318
14.5%
3 2274
 
9.9%
5 2082
 
9.1%
4 1686
 
7.4%
6 687
 
3.0%
7 604
 
2.6%
8 432
 
1.9%
9 388
 
1.7%
Latin
ValueCountFrequency (%)
D 3816
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7308
27.4%
1 4117
15.4%
D 3816
14.3%
2 3318
12.4%
3 2274
 
8.5%
5 2082
 
7.8%
4 1686
 
6.3%
6 687
 
2.6%
7 604
 
2.3%
8 432
 
1.6%

조사구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
측정망
3816 

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 3816
100.0%

Length

2023-12-11T12:29:05.065338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:29:05.193001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
측정망 3816
100.0%

주소
Text

Distinct930
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
2023-12-11T12:29:05.541478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length15.88522
Min length11

Characters and Unicode

Total characters60618
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 883
 
5.8%
경상북도 652
 
4.3%
경상남도 460
 
3.0%
충청남도 452
 
3.0%
전라북도 446
 
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) 11408
74.7%
2023-12-11T12:29:06.108843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11448
18.9%
도 3960
 
6.5%
리 3680
 
6.1%
면 3185
 
5.3%
군 2438
 
4.0%
남 2251
 
3.7%
북 1626
 
2.7%
시 1622
 
2.7%
경 1475
 
2.4%
전 1465
 
2.4%
Other values (283) 27468
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49162
81.1%
Space Separator 11448
 
18.9%
Decimal Number 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 3960
 
8.1%
리 3680
 
7.5%
면 3185
 
6.5%
군 2438
 
5.0%
남 2251
 
4.6%
북 1626
 
3.3%
시 1622
 
3.3%
경 1475
 
3.0%
전 1465
 
3.0%
라 1357
 
2.8%
Other values (281) 26103
53.1%
Space Separator
ValueCountFrequency (%)
11448
100.0%
Decimal Number
ValueCountFrequency (%)
1 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49162
81.1%
Common 11456
 
18.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 3960
 
8.1%
리 3680
 
7.5%
면 3185
 
6.5%
군 2438
 
5.0%
남 2251
 
4.6%
북 1626
 
3.3%
시 1622
 
3.3%
경 1475
 
3.0%
전 1465
 
3.0%
라 1357
 
2.8%
Other values (281) 26103
53.1%
Common
ValueCountFrequency (%)
11448
99.9%
1 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49162
81.1%
ASCII 11456
 
18.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11448
99.9%
1 8
 
0.1%
Hangul
ValueCountFrequency (%)
도 3960
 
8.1%
리 3680
 
7.5%
면 3185
 
6.5%
군 2438
 
5.0%
남 2251
 
4.6%
북 1626
 
3.3%
시 1622
 
3.3%
경 1475
 
3.0%
전 1465
 
3.0%
라 1357
 
2.8%
Other values (281) 26103
53.1%

시설구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
저수지
3768 
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 3768
98.7%
담수호 48
 
1.3%

Length

2023-12-11T12:29:06.256379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:29:06.360065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
저수지 3768
98.7%
담수호 48
 
1.3%

관리구분
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
공사
3386 
시군
430 

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 3386
88.7%
시군 430
 
11.3%

Length

2023-12-11T12:29:06.517083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:29:06.630255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사 3386
88.7%
시군 430
 
11.3%

관리기관
Text

Distinct142
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
2023-12-11T12:29:06.950368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.8081761
Min length4

Characters and Unicode

Total characters18348
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%
경주지사 76
 
2.0%
장흥지사 76
 
2.0%
진주산청지사 76
 
2.0%
고흥지사 72
 
1.9%
무진장지사 68
 
1.8%
서산태안지사 64
 
1.7%
홍천춘천지사 64
 
1.7%
경산청도지사 64
 
1.7%
괴산증평지사 64
 
1.7%
Other values (132) 3100
81.2%
2023-12-11T12:29:07.500585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 3437
18.7%
지 3397
18.5%
주 675
 
3.7%
천 652
 
3.6%
청 651
 
3.5%
산 512
 
2.8%
성 451
 
2.5%
진 436
 
2.4%
군 364
 
2.0%
안 340
 
1.9%
Other values (92) 7433
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18348
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 3437
18.7%
지 3397
18.5%
주 675
 
3.7%
천 652
 
3.6%
청 651
 
3.5%
산 512
 
2.8%
성 451
 
2.5%
진 436
 
2.4%
군 364
 
2.0%
안 340
 
1.9%
Other values (92) 7433
40.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18348
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 3437
18.7%
지 3397
18.5%
주 675
 
3.7%
천 652
 
3.6%
청 651
 
3.5%
산 512
 
2.8%
성 451
 
2.5%
진 436
 
2.4%
군 364
 
2.0%
안 340
 
1.9%
Other values (92) 7433
40.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18348
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 3437
18.7%
지 3397
18.5%
주 675
 
3.7%
천 652
 
3.6%
청 651
 
3.5%
산 512
 
2.8%
성 451
 
2.5%
진 436
 
2.4%
군 364
 
2.0%
안 340
 
1.9%
Other values (92) 7433
40.5%

조사일자
Date

Distinct230
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
Minimum2017-02-13 00:00:00
Maximum2017-12-12 00:00:00
2023-12-11T12:29:07.688045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:29:07.855243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수온(℃)
Real number (ℝ)

Distinct296
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.747694
Minimum1.6
Maximum34.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:08.026977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile6.2
Q110.7
median15.25
Q320.6
95-th percentile26.9
Maximum34.1
Range32.5
Interquartile range (IQR)9.9

Descriptive statistics

Standard deviation6.4144762
Coefficient of variation (CV)0.40732797
Kurtosis-0.72215077
Mean15.747694
Median Absolute Deviation (MAD)4.85
Skewness0.24337743
Sum60093.2
Variance41.145505
MonotonicityNot monotonic
2023-12-11T12:29:08.219189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.6 34
 
0.9%
12.0 30
 
0.8%
18.1 29
 
0.8%
11.6 29
 
0.8%
13.2 27
 
0.7%
10.5 27
 
0.7%
11.1 26
 
0.7%
10.4 26
 
0.7%
11.5 26
 
0.7%
8.1 25
 
0.7%
Other values (286) 3537
92.7%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
2.0 1
 
< 0.1%
2.1 1
 
< 0.1%
2.2 1
 
< 0.1%
2.4 1
 
< 0.1%
2.6 3
0.1%
2.7 2
0.1%
3.0 1
 
< 0.1%
3.1 3
0.1%
3.3 2
0.1%
ValueCountFrequency (%)
34.1 1
< 0.1%
32.6 1
< 0.1%
32.2 1
< 0.1%
31.9 1
< 0.1%
31.8 1
< 0.1%
31.7 1
< 0.1%
31.6 1
< 0.1%
31.5 1
< 0.1%
31.4 1
< 0.1%
31.3 1
< 0.1%

pH
Real number (ℝ)

Distinct49
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5418763
Minimum5.7
Maximum10.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:08.425796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.7
5-th percentile6.6
Q17.1
median7.5
Q37.9
95-th percentile8.7
Maximum10.5
Range4.8
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.64316309
Coefficient of variation (CV)0.085278923
Kurtosis1.1224747
Mean7.5418763
Median Absolute Deviation (MAD)0.4
Skewness0.73858987
Sum28779.8
Variance0.41365876
MonotonicityNot monotonic
2023-12-11T12:29:08.628646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
7.4 268
 
7.0%
7.5 267
 
7.0%
7.1 262
 
6.9%
7.6 246
 
6.4%
7.2 245
 
6.4%
7.3 241
 
6.3%
7.7 219
 
5.7%
7.0 218
 
5.7%
7.9 205
 
5.4%
6.9 196
 
5.1%
Other values (39) 1449
38.0%
ValueCountFrequency (%)
5.7 2
 
0.1%
5.8 2
 
0.1%
5.9 1
 
< 0.1%
6.0 7
 
0.2%
6.1 9
 
0.2%
6.2 11
 
0.3%
6.3 19
 
0.5%
6.4 35
0.9%
6.5 57
1.5%
6.6 63
1.7%
ValueCountFrequency (%)
10.5 1
 
< 0.1%
10.4 1
 
< 0.1%
10.3 1
 
< 0.1%
10.2 1
 
< 0.1%
10.1 2
 
0.1%
10.0 4
0.1%
9.9 3
 
0.1%
9.8 6
0.2%
9.7 4
0.1%
9.6 9
0.2%

EC
Real number (ℝ)

Distinct583
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.53852
Minimum10
Maximum19050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:08.810505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile48
Q181
median121.5
Q3191
95-th percentile509.25
Maximum19050
Range19040
Interquartile range (IQR)110

Descriptive statistics

Standard deviation667.00468
Coefficient of variation (CV)2.9313923
Kurtosis323.67117
Mean227.53852
Median Absolute Deviation (MAD)50.5
Skewness15.232932
Sum868287
Variance444895.24
MonotonicityNot monotonic
2023-12-11T12:29:08.979713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108 33
 
0.9%
61 33
 
0.9%
82 33
 
0.9%
100 31
 
0.8%
72 31
 
0.8%
98 31
 
0.8%
96 31
 
0.8%
59 31
 
0.8%
89 31
 
0.8%
112 30
 
0.8%
Other values (573) 3501
91.7%
ValueCountFrequency (%)
10 1
< 0.1%
11 1
< 0.1%
24 2
0.1%
25 1
< 0.1%
26 2
0.1%
27 1
< 0.1%
28 2
0.1%
29 1
< 0.1%
30 2
0.1%
31 2
0.1%
ValueCountFrequency (%)
19050 1
< 0.1%
16388 1
< 0.1%
12694 1
< 0.1%
10284 1
< 0.1%
8104 1
< 0.1%
7230 1
< 0.1%
6448 1
< 0.1%
6343 1
< 0.1%
6301 1
< 0.1%
6018 1
< 0.1%

DO
Real number (ℝ)

Distinct168
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0090671
Minimum0
Maximum20
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:09.178159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.9
Q15.9
median8.2
Q310.2
95-th percentile12.7
Maximum20
Range20
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.0692277
Coefficient of variation (CV)0.38321913
Kurtosis-0.23185496
Mean8.0090671
Median Absolute Deviation (MAD)2.2
Skewness-0.15402725
Sum30562.6
Variance9.4201589
MonotonicityNot monotonic
2023-12-11T12:29:09.428115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 67
 
1.8%
8.1 65
 
1.7%
7.5 59
 
1.5%
9.4 58
 
1.5%
9.0 57
 
1.5%
9.1 54
 
1.4%
7.0 53
 
1.4%
10.6 52
 
1.4%
8.2 52
 
1.4%
6.9 51
 
1.3%
Other values (158) 3248
85.1%
ValueCountFrequency (%)
0.0 8
0.2%
0.1 10
0.3%
0.2 9
0.2%
0.3 5
0.1%
0.4 3
 
0.1%
0.5 7
0.2%
0.6 2
 
0.1%
0.7 3
 
0.1%
0.8 3
 
0.1%
0.9 5
0.1%
ValueCountFrequency (%)
20.0 1
 
< 0.1%
18.6 1
 
< 0.1%
17.2 1
 
< 0.1%
17.1 1
 
< 0.1%
16.7 1
 
< 0.1%
16.6 1
 
< 0.1%
16.3 1
 
< 0.1%
16.2 4
0.1%
16.1 2
0.1%
16.0 1
 
< 0.1%

COD
Real number (ℝ)

Distinct95
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3436583
Minimum0.8
Maximum35.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:09.589142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile2.6
Q14
median5.4
Q37.6
95-th percentile14
Maximum35.3
Range34.5
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation3.609059
Coefficient of variation (CV)0.56892393
Kurtosis7.0056057
Mean6.3436583
Median Absolute Deviation (MAD)1.6
Skewness2.1150959
Sum24207.4
Variance13.025307
MonotonicityNot monotonic
2023-12-11T12:29:09.744018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 151
 
4.0%
4.6 150
 
3.9%
4.4 145
 
3.8%
3.6 143
 
3.7%
3.8 141
 
3.7%
3.4 138
 
3.6%
4.8 130
 
3.4%
3.2 128
 
3.4%
4.2 127
 
3.3%
5.2 126
 
3.3%
Other values (85) 2437
63.9%
ValueCountFrequency (%)
0.8 1
 
< 0.1%
1.0 1
 
< 0.1%
1.2 2
 
0.1%
1.4 3
 
0.1%
1.6 7
 
0.2%
1.8 7
 
0.2%
2.0 18
 
0.5%
2.2 33
0.9%
2.4 65
1.7%
2.6 55
1.4%
ValueCountFrequency (%)
35.3 1
 
< 0.1%
33.7 1
 
< 0.1%
32.1 1
 
< 0.1%
31.3 2
0.1%
28.9 1
 
< 0.1%
25.7 1
 
< 0.1%
23.6 2
0.1%
23.2 4
0.1%
22.8 2
0.1%
22.3 1
 
< 0.1%

TOC
Real number (ℝ)

Distinct120
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4471436
Minimum0.5
Maximum17.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:09.918631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.3
Q12.1
median3
Q34.3
95-th percentile7.3
Maximum17.8
Range17.3
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.9339018
Coefficient of variation (CV)0.56101573
Kurtosis4.4844477
Mean3.4471436
Median Absolute Deviation (MAD)1.1
Skewness1.668263
Sum13154.3
Variance3.7399761
MonotonicityNot monotonic
2023-12-11T12:29:10.073659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 125
 
3.3%
1.9 125
 
3.3%
2.0 119
 
3.1%
2.1 118
 
3.1%
2.2 118
 
3.1%
1.5 108
 
2.8%
2.7 108
 
2.8%
2.4 107
 
2.8%
2.6 104
 
2.7%
2.3 103
 
2.7%
Other values (110) 2681
70.3%
ValueCountFrequency (%)
0.5 1
 
< 0.1%
0.6 3
 
0.1%
0.7 6
 
0.2%
0.8 8
 
0.2%
0.9 21
 
0.6%
1.0 25
 
0.7%
1.1 38
1.0%
1.2 49
1.3%
1.3 59
1.5%
1.4 66
1.7%
ValueCountFrequency (%)
17.8 1
< 0.1%
17.6 1
< 0.1%
15.3 1
< 0.1%
14.9 1
< 0.1%
14.8 1
< 0.1%
13.3 1
< 0.1%
13.1 1
< 0.1%
12.9 1
< 0.1%
12.8 1
< 0.1%
12.7 1
< 0.1%

T-N
Real number (ℝ)

Distinct1680
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0813074
Minimum0.141
Maximum21.093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:10.241013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.141
5-th percentile0.35275
Q10.636
median0.9225
Q31.32525
95-th percentile2.28475
Maximum21.093
Range20.952
Interquartile range (IQR)0.68925

Descriptive statistics

Standard deviation0.76117706
Coefficient of variation (CV)0.70394142
Kurtosis134.70208
Mean1.0813074
Median Absolute Deviation (MAD)0.33
Skewness6.7996653
Sum4126.269
Variance0.57939052
MonotonicityNot monotonic
2023-12-11T12:29:10.424503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.819 12
 
0.3%
0.662 9
 
0.2%
0.796 9
 
0.2%
0.787 9
 
0.2%
0.393 9
 
0.2%
0.995 8
 
0.2%
0.652 8
 
0.2%
0.879 8
 
0.2%
0.848 8
 
0.2%
0.537 8
 
0.2%
Other values (1670) 3728
97.7%
ValueCountFrequency (%)
0.141 1
< 0.1%
0.176 2
0.1%
0.179 1
< 0.1%
0.181 1
< 0.1%
0.184 1
< 0.1%
0.185 1
< 0.1%
0.186 2
0.1%
0.193 1
< 0.1%
0.195 1
< 0.1%
0.199 1
< 0.1%
ValueCountFrequency (%)
21.093 1
< 0.1%
8.265 1
< 0.1%
8.023 1
< 0.1%
7.689 1
< 0.1%
6.565 1
< 0.1%
6.286 1
< 0.1%
6.12 1
< 0.1%
5.86 1
< 0.1%
5.611 1
< 0.1%
5.554 1
< 0.1%

T-P
Real number (ℝ)

Distinct211
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.031872904
Minimum0.001
Maximum0.795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:10.563170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.007
Q10.012
median0.019
Q30.033
95-th percentile0.095
Maximum0.795
Range0.794
Interquartile range (IQR)0.021

Descriptive statistics

Standard deviation0.047034198
Coefficient of variation (CV)1.4756798
Kurtosis70.388114
Mean0.031872904
Median Absolute Deviation (MAD)0.009
Skewness6.8093586
Sum121.627
Variance0.0022122158
MonotonicityNot monotonic
2023-12-11T12:29:10.703317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.012 170
 
4.5%
0.013 164
 
4.3%
0.01 162
 
4.2%
0.011 156
 
4.1%
0.009 153
 
4.0%
0.014 151
 
4.0%
0.016 148
 
3.9%
0.018 141
 
3.7%
0.015 124
 
3.2%
0.008 118
 
3.1%
Other values (201) 2329
61.0%
ValueCountFrequency (%)
0.001 3
 
0.1%
0.002 16
 
0.4%
0.003 16
 
0.4%
0.004 27
 
0.7%
0.005 53
 
1.4%
0.006 72
1.9%
0.007 111
2.9%
0.008 118
3.1%
0.009 153
4.0%
0.01 162
4.2%
ValueCountFrequency (%)
0.795 1
< 0.1%
0.702 1
< 0.1%
0.701 1
< 0.1%
0.569 1
< 0.1%
0.555 1
< 0.1%
0.538 1
< 0.1%
0.522 1
< 0.1%
0.505 1
< 0.1%
0.498 1
< 0.1%
0.436 1
< 0.1%

SS
Real number (ℝ)

Distinct310
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8694969
Minimum0.1
Maximum164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:10.844682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1
Q12.2
median3.9
Q37.7
95-th percentile21.4
Maximum164
Range163.9
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation9.8734816
Coefficient of variation (CV)1.4372933
Kurtosis61.640414
Mean6.8694969
Median Absolute Deviation (MAD)2.15
Skewness6.1090948
Sum26214
Variance97.485638
MonotonicityNot monotonic
2023-12-11T12:29:11.014064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 97
 
2.5%
2.4 79
 
2.1%
1.2 79
 
2.1%
2.2 74
 
1.9%
2.3 74
 
1.9%
1.9 71
 
1.9%
2.5 71
 
1.9%
2.7 69
 
1.8%
2.9 67
 
1.8%
1.4 66
 
1.7%
Other values (300) 3069
80.4%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
0.2 2
 
0.1%
0.3 2
 
0.1%
0.4 7
 
0.2%
0.5 7
 
0.2%
0.6 24
0.6%
0.7 32
0.8%
0.8 30
0.8%
0.9 49
1.3%
1.0 57
1.5%
ValueCountFrequency (%)
164.0 2
0.1%
132.0 1
< 0.1%
126.0 1
< 0.1%
96.5 1
< 0.1%
91.4 1
< 0.1%
89.4 1
< 0.1%
88.7 1
< 0.1%
87.5 1
< 0.1%
87.3 1
< 0.1%
85.4 1
< 0.1%

Cl-
Real number (ℝ)

Distinct643
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.833124
Minimum0.9
Maximum6071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:11.498386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile3.3
Q16.4
median9.9
Q316
95-th percentile85.225
Maximum6071
Range6070.1
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation204.28561
Coefficient of variation (CV)5.3996495
Kurtosis384.29189
Mean37.833124
Median Absolute Deviation (MAD)4.1
Skewness16.923875
Sum144371.2
Variance41732.61
MonotonicityNot monotonic
2023-12-11T12:29:11.707934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.4 41
 
1.1%
6.5 40
 
1.0%
6.3 37
 
1.0%
7.5 37
 
1.0%
6.1 36
 
0.9%
5.6 36
 
0.9%
8.3 34
 
0.9%
4.8 34
 
0.9%
5.8 34
 
0.9%
8.2 33
 
0.9%
Other values (633) 3454
90.5%
ValueCountFrequency (%)
0.9 1
 
< 0.1%
1.0 7
0.2%
1.1 6
0.2%
1.2 9
0.2%
1.3 4
0.1%
1.4 5
0.1%
1.5 6
0.2%
1.6 3
 
0.1%
1.7 3
 
0.1%
1.8 5
0.1%
ValueCountFrequency (%)
6071.0 1
< 0.1%
4806.0 1
< 0.1%
4783.0 1
< 0.1%
2985.0 1
< 0.1%
2451.8 1
< 0.1%
1926.0 1
< 0.1%
1893.2 1
< 0.1%
1881.0 1
< 0.1%
1857.0 1
< 0.1%
1789.6 1
< 0.1%

Chl-a
Real number (ℝ)

Distinct613
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.93032
Minimum0.2
Maximum295.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:11.895280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.8
Q14.3
median8
Q315.9
95-th percentile54.9
Maximum295.6
Range295.4
Interquartile range (IQR)11.6

Descriptive statistics

Standard deviation21.110473
Coefficient of variation (CV)1.4139331
Kurtosis28.557586
Mean14.93032
Median Absolute Deviation (MAD)4.6
Skewness4.2904523
Sum56974.1
Variance445.65206
MonotonicityNot monotonic
2023-12-11T12:29:12.079578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8 42
 
1.1%
3.0 41
 
1.1%
1.7 38
 
1.0%
3.1 37
 
1.0%
2.9 37
 
1.0%
3.3 37
 
1.0%
3.9 36
 
0.9%
2.2 36
 
0.9%
4.6 35
 
0.9%
5.7 35
 
0.9%
Other values (603) 3442
90.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.3 1
 
< 0.1%
0.5 3
 
0.1%
0.6 4
 
0.1%
0.7 9
0.2%
0.8 14
0.4%
0.9 13
0.3%
1.0 7
0.2%
1.1 9
0.2%
1.2 11
0.3%
ValueCountFrequency (%)
295.6 1
< 0.1%
244.4 1
< 0.1%
215.3 1
< 0.1%
213.0 1
< 0.1%
203.8 1
< 0.1%
186.5 1
< 0.1%
184.1 1
< 0.1%
181.4 1
< 0.1%
177.1 1
< 0.1%
168.6 1
< 0.1%

CN
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
3816 

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 3816
100.0%

Length

2023-12-11T12:29:12.223814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:29:12.350088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3816
100.0%

Cu
Real number (ℝ)

ZEROS 

Distinct154
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00018393082
Minimum0
Maximum0.01088
Zeros3591
Zeros (%)94.1%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:12.474794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.00216
Maximum0.01088
Range0.01088
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0007966683
Coefficient of variation (CV)4.3313476
Kurtosis32.727233
Mean0.00018393082
Median Absolute Deviation (MAD)0
Skewness5.1625266
Sum0.70188
Variance6.3468038 × 10-7
MonotonicityNot monotonic
2023-12-11T12:29:12.667337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3591
94.1%
0.00213 5
 
0.1%
0.00222 5
 
0.1%
0.00244 5
 
0.1%
0.00208 4
 
0.1%
0.00206 4
 
0.1%
0.0029 4
 
0.1%
0.00245 4
 
0.1%
0.00242 4
 
0.1%
0.00313 3
 
0.1%
Other values (144) 187
 
4.9%
ValueCountFrequency (%)
0.0 3591
94.1%
0.00153 1
 
< 0.1%
0.00169 2
 
0.1%
0.0019 1
 
< 0.1%
0.00198 1
 
< 0.1%
0.002 1
 
< 0.1%
0.00201 1
 
< 0.1%
0.00202 2
 
0.1%
0.00203 1
 
< 0.1%
0.00204 1
 
< 0.1%
ValueCountFrequency (%)
0.01088 1
< 0.1%
0.00836 1
< 0.1%
0.00821 1
< 0.1%
0.00727 1
< 0.1%
0.00689 1
< 0.1%
0.0066 1
< 0.1%
0.00651 1
< 0.1%
0.00607 1
< 0.1%
0.00606 1
< 0.1%
0.00597 1
< 0.1%

Pb
Real number (ℝ)

ZEROS 

Distinct62
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4868973 × 10-5
Minimum0
Maximum0.02367
Zeros3749
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:12.888978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.02367
Range0.02367
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00061725345
Coefficient of variation (CV)9.515388
Kurtosis604.41216
Mean6.4868973 × 10-5
Median Absolute Deviation (MAD)0
Skewness19.486818
Sum0.24754
Variance3.8100182 × 10-7
MonotonicityNot monotonic
2023-12-11T12:29:13.074660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3749
98.2%
0.0023 2
 
0.1%
0.00261 2
 
0.1%
0.00202 2
 
0.1%
0.00223 2
 
0.1%
0.0022 2
 
0.1%
0.00305 2
 
0.1%
0.00792 1
 
< 0.1%
0.00233 1
 
< 0.1%
0.02367 1
 
< 0.1%
Other values (52) 52
 
1.4%
ValueCountFrequency (%)
0.0 3749
98.2%
0.00156 1
 
< 0.1%
0.00184 1
 
< 0.1%
0.00201 1
 
< 0.1%
0.00202 2
 
0.1%
0.00203 1
 
< 0.1%
0.00205 1
 
< 0.1%
0.00207 1
 
< 0.1%
0.00211 1
 
< 0.1%
0.0022 2
 
0.1%
ValueCountFrequency (%)
0.02367 1
< 0.1%
0.00891 1
< 0.1%
0.00797 1
< 0.1%
0.00792 1
< 0.1%
0.00662 1
< 0.1%
0.00634 1
< 0.1%
0.00542 1
< 0.1%
0.00516 1
< 0.1%
0.00513 1
< 0.1%
0.00452 1
< 0.1%

Cd
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
3816 

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 3816
100.0%

Length

2023-12-11T12:29:13.239302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:29:13.347071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3816
100.0%

As
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00015182914
Minimum0
Maximum0.04504
Zeros3779
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-11T12:29:13.472689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.04504
Range0.04504
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0017858386
Coefficient of variation (CV)11.76216
Kurtosis268.83609
Mean0.00015182914
Median Absolute Deviation (MAD)0
Skewness15.083206
Sum0.57938
Variance3.1892196 × 10-6
MonotonicityNot monotonic
2023-12-11T12:29:13.636111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 3779
99.0%
0.00722 2
 
0.1%
0.0148 1
 
< 0.1%
0.01459 1
 
< 0.1%
0.0109 1
 
< 0.1%
0.01656 1
 
< 0.1%
0.00838 1
 
< 0.1%
0.0083 1
 
< 0.1%
0.00854 1
 
< 0.1%
0.00689 1
 
< 0.1%
Other values (27) 27
 
0.7%
ValueCountFrequency (%)
0.0 3779
99.0%
0.00624 1
 
< 0.1%
0.00637 1
 
< 0.1%
0.0064 1
 
< 0.1%
0.00642 1
 
< 0.1%
0.00689 1
 
< 0.1%
0.00722 2
 
0.1%
0.00824 1
 
< 0.1%
0.0083 1
 
< 0.1%
0.00838 1
 
< 0.1%
ValueCountFrequency (%)
0.04504 1
< 0.1%
0.03638 1
< 0.1%
0.03442 1
< 0.1%
0.03192 1
< 0.1%
0.02879 1
< 0.1%
0.02442 1
< 0.1%
0.02262 1
< 0.1%
0.0217 1
< 0.1%
0.02044 1
< 0.1%
0.01697 1
< 0.1%

Hg
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0
3816 

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 3816
100.0%

Length

2023-12-11T12:29:13.795189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:29:13.909452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3816
100.0%

Cr6+
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
0.0
3810 
0.001
 
4
0.002
 
1
0.003
 
1

Length

Max length5
Median length3
Mean length3.0031447
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 3810
99.8%
0.001 4
 
0.1%
0.002 1
 
< 0.1%
0.003 1
 
< 0.1%

Length

2023-12-11T12:29:14.079165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:29:14.247168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3810
99.8%
0.001 4
 
0.1%
0.002 1
 
< 0.1%
0.003 1
 
< 0.1%

Sample

표준코드시설명지점코드조사구분주소시설구분관리구분관리기관조사일자수온(℃)pHECDOCODTOCT-NT-PSSCl-Chl-aCNCuPbCdAsHgCr6+
02671010056용천[기장]2302D10측정망부산광역시 기장군 일광면 용천리저수지공사울산지사2017-02-276.67.78513.03.21.20.6010.0211.87.410.000.00.000.000.0
12671010056용천[기장]2302D10측정망부산광역시 기장군 일광면 용천리저수지공사울산지사2017-05-1920.17.6769.53.42.00.5540.0175.27.62.900.00.000.000.0
22671010056용천[기장]2302D10측정망부산광역시 기장군 일광면 용천리저수지공사울산지사2017-08-0327.47.71027.76.03.70.3310.0183.47.93.900.00.000.000.0
32671010056용천[기장]2302D10측정망부산광역시 기장군 일광면 용천리저수지공사울산지사2017-10-2615.27.16910.75.02.80.6590.00922.78.13.100.00.000.000.0
42671010067병산2302D20측정망부산광역시 기장군 정관면 병산리저수지공사울산지사2017-04-1012.97.115111.03.41.51.4540.0224.210.04.800.00.000.000.0
52671010067병산2302D20측정망부산광역시 기장군 정관면 병산리저수지공사울산지사2017-05-2921.27.31948.15.22.80.690.0396.910.314.600.002190.000.000.0
62671010067병산2302D20측정망부산광역시 기장군 정관면 병산리저수지공사울산지사2017-08-0327.37.12405.27.84.00.4150.0278.412.720.100.00.000.000.0
72671010067병산2302D20측정망부산광역시 기장군 정관면 병산리저수지공사울산지사2017-10-1319.06.72072.25.63.01.5120.0244.215.516.700.00.000.000.0
82671010097안평2302D30측정망부산광역시 기장군 철마면 안평리저수지공사울산지사2017-02-287.17.815512.92.41.21.8460.0164.310.74.700.00.000.000.0
92671010097안평2302D30측정망부산광역시 기장군 철마면 안평리저수지공사울산지사2017-05-2622.77.71449.83.21.51.5160.0281.510.73.000.00.000.000.0
표준코드시설명지점코드조사구분주소시설구분관리구분관리기관조사일자수온(℃)pHECDOCODTOCT-NT-PSSCl-Chl-aCNCuPbCdAsHgCr6+
38064889010332장계[합천]2016D10측정망경상남도 합천군 합천읍 장계리저수지공사합천지사2017-09-0624.07.51225.36.23.90.8450.0187.58.231.400.00.000.000.0
38074889010332장계[합천]2016D10측정망경상남도 합천군 합천읍 장계리저수지공사합천지사2017-11-1512.47.2969.54.82.31.1980.0094.29.48.300.00.000.000.0
38084889010336중촌2018D85측정망경상남도 합천군 쌍백면 평지리저수지공사합천지사2017-04-2413.07.49611.15.62.70.5480.013.03.42.200.00.000.000.0
38094889010336중촌2018D85측정망경상남도 합천군 쌍백면 평지리저수지공사합천지사2017-06-1417.28.09810.25.22.90.2990.0111.71.68.300.00.000.000.0
38104889010336중촌2018D85측정망경상남도 합천군 쌍백면 평지리저수지공사합천지사2017-08-1622.07.31053.34.42.60.2660.0163.42.95.400.00.000.000.0
38114889010336중촌2018D85측정망경상남도 합천군 쌍백면 평지리저수지공사합천지사2017-10-1719.77.11025.84.62.90.2970.0154.92.37.200.00.000.000.0
38124889010599노곡2015D70측정망경상남도 합천군 봉산면 노곡리저수지시군합천군청2017-04-2511.27.212610.94.01.50.6040.0184.35.99.500.00.000.000.0
38134889010599노곡2015D70측정망경상남도 합천군 봉산면 노곡리저수지시군합천군청2017-06-1314.87.31109.55.62.90.2180.0274.95.912.400.003470.000.000.0
38144889010599노곡2015D70측정망경상남도 합천군 봉산면 노곡리저수지시군합천군청2017-08-1722.57.01383.26.23.00.9120.05713.64.523.400.00.000.000.0
38154889010599노곡2015D70측정망경상남도 합천군 봉산면 노곡리저수지시군합천군청2017-10-1816.37.11163.95.62.30.7510.05212.46.024.500.00.000.000.0