Overview

Dataset statistics

Number of variables15
Number of observations749
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory94.5 KiB
Average record size in memory129.2 B

Variable types

Categorical7
Numeric8

Dataset

Description전북특별자치도내 시군별 호소측정망 자료 (수심, 수온, 수소이온농도, 용존산소, 생화학적 산소요구량, 화학적 산소요구량, 부유물질 등)
Author전북특별자치도
URLhttps://www.data.go.kr/data/3038769/fileData.do

Alerts

측정소 명 is highly overall correlated with 시도 명 and 3 other fieldsHigh correlation
시군구 명 is highly overall correlated with 시도 명 and 3 other fieldsHigh correlation
읍면동 명 is highly overall correlated with 시도 명 and 3 other fieldsHigh correlation
동리 명 is highly overall correlated with 시도 명 and 3 other fieldsHigh correlation
시도 명 is highly overall correlated with 생화학적 산소요구량 and 5 other fieldsHigh correlation
수온 is highly overall correlated with 용존산소High correlation
용존산소 is highly overall correlated with 수온High correlation
생화학적 산소요구량 is highly overall correlated with 총질소 and 2 other fieldsHigh correlation
화학적 산소요구량 is highly overall correlated with 부유물질 and 2 other fieldsHigh correlation
부유물질 is highly overall correlated with 화학적 산소요구량 and 1 other fieldsHigh correlation
총질소 is highly overall correlated with 생화학적 산소요구량High correlation
총인 is highly overall correlated with 생화학적 산소요구량 and 2 other fieldsHigh correlation
시도 명 is highly imbalanced (65.2%)Imbalance
총인 has 24 (3.2%) zerosZeros

Reproduction

Analysis started2024-03-14 21:10:31.325608
Analysis finished2024-03-14 21:10:50.961016
Duration19.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2021
224 
2022
216 
2023
176 
2020
133 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2021 224
29.9%
2022 216
28.8%
2023 176
23.5%
2020 133
17.8%

Length

2024-03-15T06:10:51.166301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:10:51.411264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 224
29.9%
2022 216
28.8%
2023 176
23.5%
2020 133
17.8%


Categorical

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
03-01
73 
04-01
73 
05-01
73 
06-01
73 
07-01
73 
Other values (7)
384 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03-01
2nd row03-01
3rd row03-01
4th row03-01
5th row03-01

Common Values

ValueCountFrequency (%)
03-01 73
9.7%
04-01 73
9.7%
05-01 73
9.7%
06-01 73
9.7%
07-01 73
9.7%
08-01 73
9.7%
12-01 54
7.2%
02-01 54
7.2%
09-01 51
6.8%
10-01 51
6.8%
Other values (2) 101
13.5%

Length

2024-03-15T06:10:51.786781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03-01 73
9.7%
04-01 73
9.7%
05-01 73
9.7%
06-01 73
9.7%
07-01 73
9.7%
08-01 73
9.7%
12-01 54
7.2%
02-01 54
7.2%
09-01 51
6.8%
10-01 51
6.8%
Other values (2) 101
13.5%

시도 명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
전북특별자치도
670 
충청남도
 
67
금강
 
12

Length

Max length7
Median length7
Mean length6.6515354
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북특별자치도 670
89.5%
충청남도 67
 
8.9%
금강 12
 
1.6%

Length

2024-03-15T06:10:52.210229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:10:52.453149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북특별자치도 670
89.5%
충청남도 67
 
8.9%
금강 12
 
1.6%

시군구 명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
완주군
208 
진안군
168 
부안군
126 
임실군
84 
서천군
67 
Other values (3)
96 

Length

Max length5
Median length3
Mean length3.0320427
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
완주군 208
27.8%
진안군 168
22.4%
부안군 126
16.8%
임실군 84
11.2%
서천군 67
 
8.9%
정읍시 42
 
5.6%
장수군 42
 
5.6%
금강하구언 12
 
1.6%

Length

2024-03-15T06:10:52.670780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:10:52.917699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
완주군 208
27.8%
진안군 168
22.4%
부안군 126
16.8%
임실군 84
11.2%
서천군 67
 
8.9%
정읍시 42
 
5.6%
장수군 42
 
5.6%
금강하구언 12
 
1.6%

읍면동 명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
동상면
126 
변산면
84 
안천면
84 
화산면
82 
화양면
47 
Other values (9)
326 

Length

Max length5
Median length3
Mean length3.0320427
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화산면
2nd row화산면
3rd row동상면
4th row동상면
5th row동상면

Common Values

ValueCountFrequency (%)
동상면 126
16.8%
변산면 84
11.2%
안천면 84
11.2%
화산면 82
10.9%
화양면 47
 
6.3%
상서면 42
 
5.6%
상전면 42
 
5.6%
정천면 42
 
5.6%
강진면 42
 
5.6%
운암면 42
 
5.6%
Other values (4) 116
15.5%

Length

2024-03-15T06:10:53.244100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동상면 126
16.8%
변산면 84
11.2%
안천면 84
11.2%
화산면 82
10.9%
화양면 47
 
6.3%
상서면 42
 
5.6%
상전면 42
 
5.6%
정천면 42
 
5.6%
강진면 42
 
5.6%
운암면 42
 
5.6%
Other values (4) 116
15.5%

동리 명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
대아리
126 
<NA>
117 
중계리
84 
운제리
82 
청림리
42 
Other values (10)
298 

Length

Max length4
Median length3
Mean length3.1562083
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row운제리
2nd row운제리
3rd row대아리
4th row대아리
5th row대아리

Common Values

ValueCountFrequency (%)
대아리 126
16.8%
<NA> 117
15.6%
중계리 84
11.2%
운제리 82
10.9%
청림리 42
 
5.6%
삼락리 42
 
5.6%
모정리 42
 
5.6%
용수리 42
 
5.6%
운정리 42
 
5.6%
죽림리 42
 
5.6%
Other values (5) 88
11.7%

Length

2024-03-15T06:10:53.678440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대아리 126
16.8%
na 117
15.6%
중계리 84
11.2%
운제리 82
10.9%
청림리 42
 
5.6%
삼락리 42
 
5.6%
모정리 42
 
5.6%
용수리 42
 
5.6%
운정리 42
 
5.6%
죽림리 42
 
5.6%
Other values (5) 88
11.7%

측정소 명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
대아지1
 
42
대아지3
 
42
부안댐1
 
42
부안댐2
 
42
부안댐3
 
42
Other values (21)
539 

Length

Max length10
Median length4
Mean length4.6275033
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경천지1
2nd row경천지2
3rd row대아지1
4th row대아지2
5th row대아지3

Common Values

ValueCountFrequency (%)
대아지1 42
 
5.6%
대아지3 42
 
5.6%
부안댐1 42
 
5.6%
부안댐2 42
 
5.6%
부안댐3 42
 
5.6%
용담댐4 42
 
5.6%
용담댐1 42
 
5.6%
용담댐2 42
 
5.6%
용담댐3 42
 
5.6%
동화호 42
 
5.6%
Other values (16) 329
43.9%

Length

2024-03-15T06:10:54.095815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대아지1 42
 
5.6%
부안댐1 42
 
5.6%
부안댐2 42
 
5.6%
부안댐3 42
 
5.6%
용담댐4 42
 
5.6%
용담댐1 42
 
5.6%
용담댐2 42
 
5.6%
용담댐3 42
 
5.6%
동화호 42
 
5.6%
대아지2 42
 
5.6%
Other values (16) 329
43.9%

수온
Real number (ℝ)

HIGH CORRELATION 

Distinct239
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.285848
Minimum2.2
Maximum30.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-15T06:10:54.488838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile4
Q18
median12.7
Q318.1
95-th percentile25.32
Maximum30.7
Range28.5
Interquartile range (IQR)10.1

Descriptive statistics

Standard deviation6.5356294
Coefficient of variation (CV)0.49192415
Kurtosis-0.76049466
Mean13.285848
Median Absolute Deviation (MAD)5
Skewness0.3676581
Sum9951.1
Variance42.714452
MonotonicityNot monotonic
2024-03-15T06:10:54.920168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.3 8
 
1.1%
4.2 8
 
1.1%
6.8 8
 
1.1%
8.6 7
 
0.9%
8.7 7
 
0.9%
16.4 7
 
0.9%
3.8 7
 
0.9%
12.0 7
 
0.9%
13.6 7
 
0.9%
4.1 7
 
0.9%
Other values (229) 676
90.3%
ValueCountFrequency (%)
2.2 1
 
0.1%
2.4 1
 
0.1%
2.6 1
 
0.1%
2.9 1
 
0.1%
3.0 2
0.3%
3.1 3
0.4%
3.2 1
 
0.1%
3.3 1
 
0.1%
3.4 3
0.4%
3.5 1
 
0.1%
ValueCountFrequency (%)
30.7 1
0.1%
28.6 1
0.1%
28.5 2
0.3%
28.2 2
0.3%
27.9 1
0.1%
27.7 1
0.1%
27.6 1
0.1%
27.5 1
0.1%
27.4 1
0.1%
27.3 2
0.3%

수소이온농도
Real number (ℝ)

Distinct37
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5962617
Minimum5.9
Maximum9.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-15T06:10:55.324708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile6.6
Q17.2
median7.6
Q37.9
95-th percentile8.7
Maximum9.5
Range3.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.62107541
Coefficient of variation (CV)0.081760666
Kurtosis0.33031374
Mean7.5962617
Median Absolute Deviation (MAD)0.3
Skewness0.2359661
Sum5689.6
Variance0.38573467
MonotonicityNot monotonic
2024-03-15T06:10:55.794033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
7.7 63
 
8.4%
7.8 56
 
7.5%
7.3 56
 
7.5%
7.4 53
 
7.1%
7.5 52
 
6.9%
7.6 50
 
6.7%
7.9 48
 
6.4%
8.0 30
 
4.0%
7.0 26
 
3.5%
7.1 25
 
3.3%
Other values (27) 290
38.7%
ValueCountFrequency (%)
5.9 1
 
0.1%
6.0 1
 
0.1%
6.1 2
 
0.3%
6.2 5
 
0.7%
6.3 4
 
0.5%
6.4 8
 
1.1%
6.5 11
1.5%
6.6 19
2.5%
6.7 23
3.1%
6.8 16
2.1%
ValueCountFrequency (%)
9.5 3
 
0.4%
9.4 4
0.5%
9.3 2
 
0.3%
9.2 2
 
0.3%
9.1 5
0.7%
9.0 4
0.5%
8.9 6
0.8%
8.8 6
0.8%
8.7 7
0.9%
8.6 8
1.1%

용존산소
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5356475
Minimum3.6
Maximum18.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-15T06:10:56.207146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile6.64
Q18.2
median9.5
Q310.7
95-th percentile12.6
Maximum18.3
Range14.7
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.9532566
Coefficient of variation (CV)0.20483734
Kurtosis0.999826
Mean9.5356475
Median Absolute Deviation (MAD)1.3
Skewness0.45546006
Sum7142.2
Variance3.8152115
MonotonicityNot monotonic
2024-03-15T06:10:56.748787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.3 26
 
3.5%
8.2 21
 
2.8%
10.5 19
 
2.5%
10.4 18
 
2.4%
9.9 18
 
2.4%
8.5 18
 
2.4%
9.8 17
 
2.3%
9.6 17
 
2.3%
9.1 16
 
2.1%
8.6 16
 
2.1%
Other values (90) 563
75.2%
ValueCountFrequency (%)
3.6 1
 
0.1%
4.3 1
 
0.1%
4.7 1
 
0.1%
4.9 2
0.3%
5.0 2
0.3%
5.1 2
0.3%
5.3 1
 
0.1%
5.5 1
 
0.1%
5.6 2
0.3%
5.8 3
0.4%
ValueCountFrequency (%)
18.3 1
0.1%
16.4 2
0.3%
16.2 1
0.1%
16.1 2
0.3%
16.0 1
0.1%
15.8 1
0.1%
15.1 1
0.1%
14.9 1
0.1%
14.6 1
0.1%
14.5 2
0.3%

생화학적 산소요구량
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4291055
Minimum0.1
Maximum5.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-15T06:10:57.202431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.8
median1.3
Q31.8
95-th percentile3.2
Maximum5.9
Range5.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89813049
Coefficient of variation (CV)0.6284564
Kurtosis4.021679
Mean1.4291055
Median Absolute Deviation (MAD)0.5
Skewness1.6489653
Sum1070.4
Variance0.80663837
MonotonicityNot monotonic
2024-03-15T06:10:57.694063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 52
 
6.9%
1.1 43
 
5.7%
1.0 43
 
5.7%
0.6 42
 
5.6%
1.5 39
 
5.2%
0.8 39
 
5.2%
1.4 38
 
5.1%
1.2 37
 
4.9%
1.7 35
 
4.7%
1.3 35
 
4.7%
Other values (42) 346
46.2%
ValueCountFrequency (%)
0.1 3
 
0.4%
0.2 20
 
2.7%
0.3 13
 
1.7%
0.4 21
2.8%
0.5 26
3.5%
0.6 42
5.6%
0.7 30
4.0%
0.8 39
5.2%
0.9 52
6.9%
1.0 43
5.7%
ValueCountFrequency (%)
5.9 1
0.1%
5.7 1
0.1%
5.6 1
0.1%
5.3 2
0.3%
4.9 1
0.1%
4.8 1
0.1%
4.7 2
0.3%
4.6 2
0.3%
4.4 2
0.3%
4.3 1
0.1%

화학적 산소요구량
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5481976
Minimum1
Maximum25.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-15T06:10:58.114072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.6
median3.1
Q33.7
95-th percentile7.36
Maximum25.6
Range24.6
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.7953557
Coefficient of variation (CV)0.50599091
Kurtosis34.930981
Mean3.5481976
Median Absolute Deviation (MAD)0.5
Skewness4.2131874
Sum2657.6
Variance3.2233022
MonotonicityNot monotonic
2024-03-15T06:10:58.547879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 47
 
6.3%
3.0 43
 
5.7%
3.6 42
 
5.6%
3.1 42
 
5.6%
2.6 38
 
5.1%
2.7 36
 
4.8%
2.4 31
 
4.1%
3.5 30
 
4.0%
2.9 30
 
4.0%
3.2 29
 
3.9%
Other values (72) 381
50.9%
ValueCountFrequency (%)
1.0 1
 
0.1%
1.1 1
 
0.1%
1.2 3
 
0.4%
1.3 4
0.5%
1.4 1
 
0.1%
1.5 1
 
0.1%
1.6 3
 
0.4%
1.7 1
 
0.1%
1.8 5
0.7%
1.9 8
1.1%
ValueCountFrequency (%)
25.6 1
0.1%
14.7 1
0.1%
13.6 1
0.1%
12.6 1
0.1%
10.1 1
0.1%
9.8 1
0.1%
9.7 1
0.1%
9.4 1
0.1%
9.0 2
0.3%
8.9 1
0.1%

부유물질
Real number (ℝ)

HIGH CORRELATION 

Distinct141
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6748999
Minimum0.1
Maximum166.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-15T06:10:58.964464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11
median1.8
Q32.8
95-th percentile18.56
Maximum166.4
Range166.3
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation12.058481
Coefficient of variation (CV)2.5794095
Kurtosis65.927184
Mean4.6748999
Median Absolute Deviation (MAD)0.8
Skewness7.0889409
Sum3501.5
Variance145.40696
MonotonicityNot monotonic
2024-03-15T06:10:59.403417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 42
 
5.6%
1.8 41
 
5.5%
0.9 39
 
5.2%
1.2 32
 
4.3%
1.1 31
 
4.1%
1.3 30
 
4.0%
0.7 28
 
3.7%
1.5 26
 
3.5%
2.0 24
 
3.2%
0.5 24
 
3.2%
Other values (131) 432
57.7%
ValueCountFrequency (%)
0.1 2
 
0.3%
0.2 4
 
0.5%
0.3 8
 
1.1%
0.4 15
 
2.0%
0.5 24
3.2%
0.6 20
2.7%
0.7 28
3.7%
0.8 18
2.4%
0.9 39
5.2%
1.0 42
5.6%
ValueCountFrequency (%)
166.4 1
0.1%
108.6 1
0.1%
94.4 1
0.1%
90.5 1
0.1%
87.9 1
0.1%
84.5 1
0.1%
64.9 1
0.1%
63.9 1
0.1%
63.1 1
0.1%
61.5 1
0.1%

총질소
Real number (ℝ)

HIGH CORRELATION 

Distinct613
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9533071
Minimum0.769
Maximum5.056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-15T06:10:59.785028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.769
5-th percentile1.1874
Q11.551
median1.866
Q32.173
95-th percentile3.1286
Maximum5.056
Range4.287
Interquartile range (IQR)0.622

Descriptive statistics

Standard deviation0.64441759
Coefficient of variation (CV)0.32991105
Kurtosis5.3498665
Mean1.9533071
Median Absolute Deviation (MAD)0.313
Skewness1.8575253
Sum1463.027
Variance0.41527403
MonotonicityNot monotonic
2024-03-15T06:11:00.146035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.822 3
 
0.4%
1.773 3
 
0.4%
1.891 3
 
0.4%
1.945 3
 
0.4%
2.181 3
 
0.4%
1.749 3
 
0.4%
1.978 3
 
0.4%
1.449 3
 
0.4%
2.39 3
 
0.4%
2.043 3
 
0.4%
Other values (603) 719
96.0%
ValueCountFrequency (%)
0.769 1
0.1%
0.815 1
0.1%
0.82 1
0.1%
0.873 1
0.1%
0.934 1
0.1%
0.945 1
0.1%
0.948 1
0.1%
0.949 1
0.1%
0.969 1
0.1%
0.972 1
0.1%
ValueCountFrequency (%)
5.056 1
0.1%
4.944 1
0.1%
4.864 1
0.1%
4.822 1
0.1%
4.668 1
0.1%
4.659 1
0.1%
4.62 1
0.1%
4.605 1
0.1%
4.59 1
0.1%
4.579 1
0.1%

총인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050184246
Minimum0
Maximum2.127
Zeros24
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-15T06:11:00.536757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q10.006
median0.009
Q30.014
95-th percentile0.091
Maximum2.127
Range2.127
Interquartile range (IQR)0.008

Descriptive statistics

Standard deviation0.23906187
Coefficient of variation (CV)4.7636837
Kurtosis53.15199
Mean0.050184246
Median Absolute Deviation (MAD)0.004
Skewness7.2214077
Sum37.588
Variance0.057150578
MonotonicityNot monotonic
2024-03-15T06:11:00.843798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 88
 
11.7%
0.009 67
 
8.9%
0.008 58
 
7.7%
0.004 44
 
5.9%
0.007 43
 
5.7%
0.005 36
 
4.8%
0.006 36
 
4.8%
0.002 31
 
4.1%
0.012 28
 
3.7%
0.011 26
 
3.5%
Other values (90) 292
39.0%
ValueCountFrequency (%)
0.0 24
 
3.2%
0.001 17
 
2.3%
0.002 31
4.1%
0.003 25
 
3.3%
0.004 44
5.9%
0.005 36
4.8%
0.006 36
4.8%
0.007 43
5.7%
0.008 58
7.7%
0.009 67
8.9%
ValueCountFrequency (%)
2.127 1
0.1%
2.121 1
0.1%
2.11 1
0.1%
2.08 1
0.1%
2.013 1
0.1%
1.989 1
0.1%
1.786 1
0.1%
1.737 1
0.1%
1.687 1
0.1%
1.337 1
0.1%

Interactions

2024-03-15T06:10:48.054163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:33.169035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:35.470120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:38.072402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:39.678718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:41.643345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:43.715748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:45.662180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:48.315476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:33.433341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:35.787741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:38.239986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:39.843306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:41.897035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:43.965569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:45.941587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:48.650938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:33.703953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:36.095264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:38.431176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:40.087103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:42.172968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:44.152124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:46.228152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:48.866202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:33.978523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:36.401879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:38.626914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:40.372639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:42.506913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:44.342207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:46.511626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:49.134506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:34.330513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:36.691946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:38.805429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:40.636512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:42.784630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:44.518733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:46.780381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:49.416213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:34.609904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:37.187966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:39.029670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:40.899240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:43.028571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:44.757963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:47.189446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:49.674583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:34.859378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:37.504122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:39.341523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:41.170788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:43.414501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:45.026168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:47.471154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:49.941398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:35.102683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:37.783442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:39.503561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:41.394159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:43.562999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:45.239827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:10:47.761375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T06:11:01.024932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도 명시군구 명읍면동 명동리 명측정소 명수온수소이온농도용존산소생화학적 산소요구량화학적 산소요구량부유물질총질소총인
연도1.0000.3640.2430.3670.2580.2060.5030.2260.2460.2230.2470.0970.0890.3060.182
0.3641.0000.0740.0000.0000.0000.0000.7280.2770.6360.1150.1530.1680.2850.368
시도 명0.2430.0741.0001.0001.0001.0001.0000.4150.5410.4720.7230.7130.5970.5960.000
시군구 명0.3670.0001.0001.0001.0001.0000.9950.3340.6310.3820.6990.6220.4650.6240.000
읍면동 명0.2580.0001.0001.0001.0001.0000.9960.4030.6400.3880.7150.8170.6440.6790.000
동리 명0.2060.0001.0001.0001.0001.0000.9930.3530.6490.4250.7260.8180.7860.6800.000
측정소 명0.5030.0001.0000.9950.9960.9931.0000.3930.6650.4620.7630.7610.5590.7060.026
수온0.2260.7280.4150.3340.4030.3530.3931.0000.3980.7210.4620.4800.3470.3110.076
수소이온농도0.2460.2770.5410.6310.6400.6490.6650.3981.0000.6490.6460.4320.2620.6120.240
용존산소0.2230.6360.4720.3820.3880.4250.4620.7210.6491.0000.6000.4260.1880.7240.000
생화학적 산소요구량0.2470.1150.7230.6990.7150.7260.7630.4620.6460.6001.0000.7030.6310.7310.000
화학적 산소요구량0.0970.1530.7130.6220.8170.8180.7610.4800.4320.4260.7031.0000.7390.4870.000
부유물질0.0890.1680.5970.4650.6440.7860.5590.3470.2620.1880.6310.7391.0000.3830.000
총질소0.3060.2850.5960.6240.6790.6800.7060.3110.6120.7240.7310.4870.3831.0000.000
총인0.1820.3680.0000.0000.0000.0000.0260.0760.2400.0000.0000.0000.0000.0001.000
2024-03-15T06:11:01.387289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소 명시군구 명읍면동 명동리 명시도 명연도
측정소 명1.0000.9560.9550.9390.9840.2820.000
시군구 명0.9561.0000.9960.9940.9970.1700.000
읍면동 명0.9550.9961.0000.9980.9930.1470.000
동리 명0.9390.9940.9981.0000.9900.1170.000
시도 명0.9840.9970.9930.9901.0000.2310.033
연도0.2820.1700.1470.1170.2311.0000.174
0.0000.0000.0000.0000.0330.1741.000
2024-03-15T06:11:01.594180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수온수소이온농도용존산소생화학적 산소요구량화학적 산소요구량부유물질총질소총인연도시도 명시군구 명읍면동 명동리 명측정소 명
수온1.0000.028-0.7620.1940.4110.4090.1420.3260.1380.4130.2830.1720.1770.1530.154
수소이온농도0.0281.0000.1370.2680.0900.1840.1560.3250.1610.1170.3870.3640.3220.3290.308
용존산소-0.7620.1371.000-0.059-0.295-0.201-0.011-0.1670.1340.3290.3200.1930.1670.1850.184
생화학적 산소요구량0.1940.268-0.0591.0000.4600.4200.5070.6530.1510.0510.5820.4280.3870.3950.393
화학적 산소요구량0.4110.090-0.2950.4601.0000.5860.3340.5230.0670.0740.6300.3950.4480.4480.445
부유물질0.4090.184-0.2010.4200.5861.0000.2640.5720.0610.0820.4880.2710.2930.4120.273
총질소0.1420.156-0.0110.5070.3340.2641.0000.4920.1870.1230.4370.3590.3530.3540.340
총인0.3260.325-0.1670.6530.5230.5720.4921.0000.1250.1880.0000.0000.0000.0000.009
연도0.1380.1610.1340.1510.0670.0610.1870.1251.0000.1740.2310.1700.1470.1170.282
0.4130.1170.3290.0510.0740.0820.1230.1880.1741.0000.0330.0000.0000.0000.000
시도 명0.2830.3870.3200.5820.6300.4880.4370.0000.2310.0331.0000.9970.9930.9900.984
시군구 명0.1720.3640.1930.4280.3950.2710.3590.0000.1700.0000.9971.0000.9960.9940.956
읍면동 명0.1770.3220.1670.3870.4480.2930.3530.0000.1470.0000.9930.9961.0000.9980.955
동리 명0.1530.3290.1850.3950.4480.4120.3540.0000.1170.0000.9900.9940.9981.0000.939
측정소 명0.1540.3080.1840.3930.4450.2730.3400.0090.2820.0000.9840.9560.9550.9391.000

Missing values

2024-03-15T06:10:50.221200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T06:10:50.718620image/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

연도시도 명시군구 명읍면동 명동리 명측정소 명수온수소이온농도용존산소생화학적 산소요구량화학적 산소요구량부유물질총질소총인
0202003-01전북특별자치도완주군화산면운제리경천지17.77.911.70.72.12.13.1660.011
1202003-01전북특별자치도완주군화산면운제리경천지28.37.711.60.81.91.93.1760.011
2202003-01전북특별자치도완주군동상면대아리대아지16.17.712.60.21.20.71.6020.006
3202003-01전북특별자치도완주군동상면대아리대아지26.57.612.50.61.81.51.6610.004
4202003-01전북특별자치도완주군동상면대아리대아지36.77.511.80.31.72.21.6590.001
5202003-01전북특별자치도부안군변산면중계리부안댐17.56.710.90.23.62.31.1910.0
6202003-01전북특별자치도부안군변산면중계리부안댐27.96.710.90.92.71.31.5540.006
7202003-01전북특별자치도부안군상서면청림리부안댐38.36.711.00.82.31.51.9860.008
8202003-01전북특별자치도진안군상전면<NA>용담댐46.17.612.11.73.40.51.8860.009
9202003-01전북특별자치도진안군안천면삼락리용담댐16.47.610.51.03.10.51.7910.007
연도시도 명시군구 명읍면동 명동리 명측정소 명수온수소이온농도용존산소생화학적 산소요구량화학적 산소요구량부유물질총질소총인
739202311-01전북특별자치도부안군변산면중계리부안댐216.96.18.21.84.51.81.8061.786
740202311-01전북특별자치도부안군상서면청림리부안댐315.46.29.21.94.91.81.9632.013
741202311-01전북특별자치도진안군상전면<NA>용담댐414.47.411.02.23.11.42.3112.11
742202311-01전북특별자치도진안군안천면삼락리용담댐112.97.311.31.52.31.02.342.127
743202311-01전북특별자치도진안군안천면<NA>용담댐212.97.211.01.72.21.02.3912.121
744202311-01전북특별자치도진안군정천면모정리용담댐312.97.411.02.22.01.22.3312.08
745202311-01전북특별자치도임실군강진면용수리섬진강댐1(옥정호)17.57.79.71.82.32.31.3541.244
746202311-01전북특별자치도임실군운암면운정리섬진강댐3(옥정호)18.27.68.32.43.03.81.4450.934
747202311-01전북특별자치도정읍시산내면장금리섬진강댐2(옥정호)17.47.58.22.22.72.81.2691.105
748202311-01전북특별자치도장수군번암면죽림리동화호15.57.49.40.42.90.60.9480.92