Overview

Dataset statistics

Number of variables15
Number of observations717
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory91.2 KiB
Average record size in memory130.2 B

Variable types

Categorical6
Numeric9

Dataset

Description전라북도내 시군별 호소측정망 자료 (수심, 수온, 수소이온농도, 용존산소, 생화학적 산소요구량, 화학적 산소요구량, 부유물질 등)
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=4&menuCd=DOM_000000103007001000&pListTypeStr=&pId=3038769

Alerts

읍면동 명 is highly overall correlated with 시도 명 and 3 other fieldsHigh correlation
측정소 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
시군구 명 is highly overall correlated with 시도 명 and 3 other fieldsHigh correlation
시도 명 is highly overall correlated with 생화학적 산소요구량 and 6 other fieldsHigh correlation
is highly overall correlated with 수온 and 1 other fieldsHigh correlation
수온 is highly overall correlated with and 1 other fieldsHigh correlation
용존산소 is highly overall correlated with and 1 other fieldsHigh 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 산소요구량 and 1 other fieldsHigh correlation
총인 is highly overall correlated with 생화학적 산소요구량 and 4 other fieldsHigh correlation
시도 명 is highly imbalanced (64.2%)Imbalance
총인 has 24 (3.3%) zerosZeros

Reproduction

Analysis started2024-03-14 00:41:23.013223
Analysis finished2024-03-14 00:41:31.043678
Duration8.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2021
224 
2022
216 
2023
144 
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
31.2%
2022 216
30.1%
2023 144
20.1%
2020 133
18.5%

Length

2024-03-14T09:41:31.091869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:41:31.186848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 224
31.2%
2022 216
30.1%
2023 144
20.1%
2020 133
18.5%


Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1492329
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-03-14T09:41:31.270053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1980773
Coefficient of variation (CV)0.52007744
Kurtosis-0.93061351
Mean6.1492329
Median Absolute Deviation (MAD)2
Skewness0.20116662
Sum4409
Variance10.227698
MonotonicityNot monotonic
2024-03-14T09:41:31.354007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 73
10.2%
4 73
10.2%
5 73
10.2%
6 73
10.2%
7 73
10.2%
8 73
10.2%
12 54
7.5%
2 54
7.5%
9 51
7.1%
1 50
7.0%
Other values (2) 70
9.8%
ValueCountFrequency (%)
1 50
7.0%
2 54
7.5%
3 73
10.2%
4 73
10.2%
5 73
10.2%
6 73
10.2%
7 73
10.2%
8 73
10.2%
9 51
7.1%
10 35
4.9%
ValueCountFrequency (%)
12 54
7.5%
11 35
4.9%
10 35
4.9%
9 51
7.1%
8 73
10.2%
7 73
10.2%
6 73
10.2%
5 73
10.2%
4 73
10.2%
3 73
10.2%

시도 명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
전라북도
638 
충청남도
67 
금강
 
12

Length

Max length4
Median length4
Mean length3.9665272
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 638
89.0%
충청남도 67
 
9.3%
금강 12
 
1.7%

Length

2024-03-14T09:41:31.479442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:41:31.573049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 638
89.0%
충청남도 67
 
9.3%
금강 12
 
1.7%

시군구 명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
완주군
198 
진안군
160 
부안군
120 
임실군
80 
서천군
67 
Other values (3)
92 

Length

Max length5
Median length3
Mean length3.0334728
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
완주군 198
27.6%
진안군 160
22.3%
부안군 120
16.7%
임실군 80
11.2%
서천군 67
 
9.3%
정읍시 40
 
5.6%
장수군 40
 
5.6%
금강하구언 12
 
1.7%

Length

2024-03-14T09:41:31.673847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:41:31.789535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
완주군 198
27.6%
진안군 160
22.3%
부안군 120
16.7%
임실군 80
11.2%
서천군 67
 
9.3%
정읍시 40
 
5.6%
장수군 40
 
5.6%
금강하구언 12
 
1.7%

읍면동 명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
동상면
120 
변산면
80 
안천면
80 
화산면
78 
화양면
47 
Other values (9)
312 

Length

Max length5
Median length3
Mean length3.0334728
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동상면 120
16.7%
변산면 80
11.2%
안천면 80
11.2%
화산면 78
10.9%
화양면 47
 
6.6%
상서면 40
 
5.6%
상전면 40
 
5.6%
정천면 40
 
5.6%
강진면 40
 
5.6%
운암면 40
 
5.6%
Other values (4) 112
15.6%

Length

2024-03-14T09:41:31.917705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동상면 120
16.7%
변산면 80
11.2%
안천면 80
11.2%
화산면 78
10.9%
화양면 47
 
6.6%
상서면 40
 
5.6%
상전면 40
 
5.6%
정천면 40
 
5.6%
강진면 40
 
5.6%
운암면 40
 
5.6%
Other values (4) 112
15.6%

동리 명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
대아리
120 
<NA>
113 
중계리
80 
운제리
78 
청림리
40 
Other values (10)
286 

Length

Max length4
Median length3
Mean length3.1576011
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대아리 120
16.7%
<NA> 113
15.8%
중계리 80
11.2%
운제리 78
10.9%
청림리 40
 
5.6%
삼락리 40
 
5.6%
모정리 40
 
5.6%
용수리 40
 
5.6%
운정리 40
 
5.6%
죽림리 40
 
5.6%
Other values (5) 86
12.0%

Length

2024-03-14T09:41:32.023243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대아리 120
16.7%
na 113
15.8%
중계리 80
11.2%
운제리 78
10.9%
청림리 40
 
5.6%
삼락리 40
 
5.6%
모정리 40
 
5.6%
용수리 40
 
5.6%
운정리 40
 
5.6%
죽림리 40
 
5.6%
Other values (5) 86
12.0%

측정소 명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
대아지1
 
40
대아지3
 
40
부안댐1
 
40
부안댐2
 
40
부안댐3
 
40
Other values (21)
517 

Length

Max length10
Median length4
Mean length4.6080893
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대아지1 40
 
5.6%
대아지3 40
 
5.6%
부안댐1 40
 
5.6%
부안댐2 40
 
5.6%
부안댐3 40
 
5.6%
용담댐4 40
 
5.6%
용담댐1 40
 
5.6%
용담댐2 40
 
5.6%
용담댐3 40
 
5.6%
동화호 40
 
5.6%
Other values (16) 317
44.2%

Length

2024-03-14T09:41:32.121292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대아지1 40
 
5.6%
부안댐1 40
 
5.6%
부안댐2 40
 
5.6%
부안댐3 40
 
5.6%
용담댐4 40
 
5.6%
용담댐1 40
 
5.6%
용담댐2 40
 
5.6%
용담댐3 40
 
5.6%
동화호 40
 
5.6%
대아지2 40
 
5.6%
Other values (16) 317
44.2%

수온
Real number (ℝ)

HIGH CORRELATION 

Distinct236
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.145188
Minimum2.2
Maximum30.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-03-14T09:41:32.219264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile4
Q17.8
median12.3
Q318
95-th percentile25.5
Maximum30.7
Range28.5
Interquartile range (IQR)10.2

Descriptive statistics

Standard deviation6.6182176
Coefficient of variation (CV)0.50347074
Kurtosis-0.75662999
Mean13.145188
Median Absolute Deviation (MAD)5.2
Skewness0.41873113
Sum9425.1
Variance43.800804
MonotonicityNot monotonic
2024-03-14T09:41:32.322603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.2 8
 
1.1%
8.3 8
 
1.1%
6.8 8
 
1.1%
8.6 7
 
1.0%
4.1 7
 
1.0%
5.0 7
 
1.0%
16.4 7
 
1.0%
12.0 7
 
1.0%
13.4 7
 
1.0%
13.6 7
 
1.0%
Other values (226) 644
89.8%
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)

Distinct36
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6181311
Minimum6
Maximum9.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-03-14T09:41:32.421216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.6
Q17.3
median7.6
Q38
95-th percentile8.7
Maximum9.5
Range3.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.61712559
Coefficient of variation (CV)0.081007478
Kurtosis0.26195066
Mean7.6181311
Median Absolute Deviation (MAD)0.3
Skewness0.2605402
Sum5462.2
Variance0.38084399
MonotonicityNot monotonic
2024-03-14T09:41:32.520434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
7.7 62
 
8.6%
7.8 55
 
7.7%
7.3 50
 
7.0%
7.6 49
 
6.8%
7.9 48
 
6.7%
7.5 48
 
6.7%
7.4 48
 
6.7%
8.0 30
 
4.2%
8.1 25
 
3.5%
7.0 25
 
3.5%
Other values (26) 277
38.6%
ValueCountFrequency (%)
6.0 1
 
0.1%
6.1 1
 
0.1%
6.2 2
 
0.3%
6.3 4
 
0.6%
6.4 8
 
1.1%
6.5 10
1.4%
6.6 19
2.6%
6.7 23
3.2%
6.8 15
2.1%
6.9 23
3.2%
ValueCountFrequency (%)
9.5 3
 
0.4%
9.4 4
0.6%
9.3 2
 
0.3%
9.2 2
 
0.3%
9.1 5
0.7%
9.0 4
0.6%
8.9 6
0.8%
8.8 6
0.8%
8.7 7
1.0%
8.6 8
1.1%

용존산소
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5857741
Minimum3.6
Maximum18.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-03-14T09:41:32.624374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile6.7
Q18.2
median9.6
Q310.8
95-th percentile12.6
Maximum18.3
Range14.7
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation1.9571723
Coefficient of variation (CV)0.20417468
Kurtosis0.95656105
Mean9.5857741
Median Absolute Deviation (MAD)1.3
Skewness0.45634664
Sum6873
Variance3.8305236
MonotonicityNot monotonic
2024-03-14T09:41:32.750271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.3 23
 
3.2%
10.5 19
 
2.6%
10.4 18
 
2.5%
8.2 18
 
2.5%
9.9 18
 
2.5%
9.6 17
 
2.4%
9.8 17
 
2.4%
10.0 16
 
2.2%
9.1 16
 
2.2%
11.2 16
 
2.2%
Other values (89) 539
75.2%
ValueCountFrequency (%)
3.6 1
 
0.1%
4.7 1
 
0.1%
4.9 1
 
0.1%
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%
5.9 1
 
0.1%
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 (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.432357
Minimum0.1
Maximum5.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-03-14T09:41:32.926758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.38
Q10.8
median1.3
Q31.8
95-th percentile3.3
Maximum5.9
Range5.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90674792
Coefficient of variation (CV)0.63304601
Kurtosis4.0169111
Mean1.432357
Median Absolute Deviation (MAD)0.5
Skewness1.6707957
Sum1027
Variance0.82219178
MonotonicityNot monotonic
2024-03-14T09:41:33.111071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 52
 
7.3%
1.1 43
 
6.0%
1.0 41
 
5.7%
0.6 40
 
5.6%
1.4 38
 
5.3%
1.2 37
 
5.2%
1.5 37
 
5.2%
0.8 37
 
5.2%
1.3 35
 
4.9%
1.7 33
 
4.6%
Other values (42) 324
45.2%
ValueCountFrequency (%)
0.1 3
 
0.4%
0.2 20
 
2.8%
0.3 13
 
1.8%
0.4 19
 
2.6%
0.5 22
3.1%
0.6 40
5.6%
0.7 28
3.9%
0.8 37
5.2%
0.9 52
7.3%
1.0 41
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 (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5504881
Minimum1
Maximum25.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-03-14T09:41:33.432070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.6
median3.1
Q33.7
95-th percentile7.44
Maximum25.6
Range24.6
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.824933
Coefficient of variation (CV)0.51399495
Kurtosis34.105575
Mean3.5504881
Median Absolute Deviation (MAD)0.5
Skewness4.1868004
Sum2545.7
Variance3.3303803
MonotonicityNot monotonic
2024-03-14T09:41:33.538148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 45
 
6.3%
3.6 41
 
5.7%
3.0 40
 
5.6%
3.1 39
 
5.4%
2.6 38
 
5.3%
2.7 35
 
4.9%
2.4 31
 
4.3%
3.5 30
 
4.2%
3.2 28
 
3.9%
3.4 28
 
3.9%
Other values (72) 362
50.5%
ValueCountFrequency (%)
1.0 1
 
0.1%
1.1 1
 
0.1%
1.2 3
 
0.4%
1.3 4
0.6%
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 

Distinct137
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7690377
Minimum0.1
Maximum166.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-03-14T09:41:33.640932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11
median1.8
Q32.7
95-th percentile19.26
Maximum166.4
Range166.3
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation12.308251
Coefficient of variation (CV)2.5808667
Kurtosis63.146931
Mean4.7690377
Median Absolute Deviation (MAD)0.8
Skewness6.9423546
Sum3419.4
Variance151.49303
MonotonicityNot monotonic
2024-03-14T09:41:33.744764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 39
 
5.4%
0.9 38
 
5.3%
1.8 38
 
5.3%
1.1 30
 
4.2%
1.3 30
 
4.2%
1.2 30
 
4.2%
0.7 27
 
3.8%
1.5 24
 
3.3%
0.5 23
 
3.2%
2.0 23
 
3.2%
Other values (127) 415
57.9%
ValueCountFrequency (%)
0.1 2
 
0.3%
0.2 4
 
0.6%
0.3 8
 
1.1%
0.4 15
 
2.1%
0.5 23
3.2%
0.6 19
2.6%
0.7 27
3.8%
0.8 18
2.5%
0.9 38
5.3%
1.0 39
5.4%
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 

Distinct594
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9592357
Minimum0.769
Maximum5.056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-03-14T09:41:33.878956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.769
5-th percentile1.1902
Q11.559
median1.869
Q32.173
95-th percentile3.168
Maximum5.056
Range4.287
Interquartile range (IQR)0.614

Descriptive statistics

Standard deviation0.65158703
Coefficient of variation (CV)0.33257205
Kurtosis5.2556832
Mean1.9592357
Median Absolute Deviation (MAD)0.309
Skewness1.8639904
Sum1404.772
Variance0.42456566
MonotonicityNot monotonic
2024-03-14T09:41:34.018809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.773 3
 
0.4%
1.632 3
 
0.4%
1.745 3
 
0.4%
1.945 3
 
0.4%
1.786 3
 
0.4%
1.449 3
 
0.4%
1.891 3
 
0.4%
2.181 3
 
0.4%
1.804 3
 
0.4%
1.822 3
 
0.4%
Other values (584) 687
95.8%
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%
0.949 1
0.1%
0.969 1
0.1%
0.972 1
0.1%
0.974 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 

Distinct84
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016573222
Minimum0
Maximum0.174
Zeros24
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-03-14T09:41:34.129286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q10.006
median0.009
Q30.014
95-th percentile0.0634
Maximum0.174
Range0.174
Interquartile range (IQR)0.008

Descriptive statistics

Standard deviation0.024378
Coefficient of variation (CV)1.470927
Kurtosis12.903698
Mean0.016573222
Median Absolute Deviation (MAD)0.004
Skewness3.3946895
Sum11.883
Variance0.00059428688
MonotonicityNot monotonic
2024-03-14T09:41:34.242182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 84
 
11.7%
0.009 66
 
9.2%
0.008 57
 
7.9%
0.004 44
 
6.1%
0.007 42
 
5.9%
0.006 36
 
5.0%
0.005 35
 
4.9%
0.002 30
 
4.2%
0.012 27
 
3.8%
0.011 25
 
3.5%
Other values (74) 271
37.8%
ValueCountFrequency (%)
0.0 24
 
3.3%
0.001 17
 
2.4%
0.002 30
4.2%
0.003 22
 
3.1%
0.004 44
6.1%
0.005 35
4.9%
0.006 36
5.0%
0.007 42
5.9%
0.008 57
7.9%
0.009 66
9.2%
ValueCountFrequency (%)
0.174 1
0.1%
0.16 1
0.1%
0.155 1
0.1%
0.152 1
0.1%
0.15 1
0.1%
0.142 1
0.1%
0.135 1
0.1%
0.125 1
0.1%
0.123 2
0.3%
0.122 1
0.1%

Interactions

2024-03-14T09:41:29.968230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:23.783890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.617914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.300640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.037450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.902974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.586925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:28.310107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.303751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:30.039313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:23.856579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.705250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.374009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.128070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.977027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.662634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:28.400928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.375557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:30.110177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:23.943368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.782308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.458186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.278394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.054307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.742517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:28.511729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.442015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:30.190127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.072368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.861010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.542639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.394482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.129049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.818455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:28.610878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.513139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:30.273040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.184756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.936543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.643286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.477416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.212284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.893222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:28.924107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.587359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:30.348149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.258672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.011080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.725631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.556514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.284385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.981315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.002133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.667495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:30.420925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.331610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.078248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.804781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.649548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.354031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:28.057456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.076301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.739992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:30.525615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.406069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.157782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.881503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.743189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.423685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:28.149874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.145771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.822113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:30.628353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:24.484513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.223819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:25.950101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:26.819200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:27.496097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:28.224629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.223857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:41:29.897229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:41:34.327646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도 명시군구 명읍면동 명동리 명측정소 명수온수소이온농도용존산소생화학적 산소요구량화학적 산소요구량부유물질총질소총인
연도1.0000.3140.2370.3550.2430.1890.4960.2170.2260.2200.2570.0880.0730.3160.153
0.3141.0000.0680.0000.0000.0000.0000.8280.3480.7400.1670.1520.1800.3340.347
시도 명0.2370.0681.0001.0001.0001.0001.0000.4130.5450.4700.7230.7130.5960.5960.785
시군구 명0.3550.0001.0001.0001.0001.0000.9940.3310.6470.3710.7030.6230.4630.6190.619
읍면동 명0.2430.0001.0001.0001.0001.0000.9960.4010.6570.3800.7180.8170.6420.6730.645
동리 명0.1890.0001.0001.0001.0001.0000.9930.3500.6670.4160.7300.8180.7850.6730.691
측정소 명0.4960.0001.0000.9940.9960.9931.0000.3870.6810.4280.7660.7600.5550.6990.712
수온0.2170.8280.4130.3310.4010.3500.3871.0000.4210.7270.4600.4770.3460.3090.564
수소이온농도0.2260.3480.5450.6470.6570.6670.6810.4211.0000.6530.6540.4160.2640.6190.531
용존산소0.2200.7400.4700.3710.3800.4160.4280.7270.6531.0000.6040.4210.1870.7240.488
생화학적 산소요구량0.2570.1670.7230.7030.7180.7300.7660.4600.6540.6041.0000.7040.6300.7280.795
화학적 산소요구량0.0880.1520.7130.6230.8170.8180.7600.4770.4160.4210.7041.0000.7380.4820.706
부유물질0.0730.1800.5960.4630.6420.7850.5550.3460.2640.1870.6300.7381.0000.3820.808
총질소0.3160.3340.5960.6190.6730.6730.6990.3090.6190.7240.7280.4820.3821.0000.596
총인0.1530.3470.7850.6190.6450.6910.7120.5640.5310.4880.7950.7060.8080.5961.000
2024-03-14T09:41:34.453196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동 명측정소 명동리 명시군구 명시도 명연도
읍면동 명1.0000.9530.9980.9960.9920.139
측정소 명0.9531.0000.9370.9540.9840.277
동리 명0.9980.9371.0000.9940.9900.107
시군구 명0.9960.9540.9941.0000.9960.164
시도 명0.9920.9840.9900.9961.0000.225
연도0.1390.2770.1070.1640.2251.000
2024-03-14T09:41:34.557360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수온수소이온농도용존산소생화학적 산소요구량화학적 산소요구량부유물질총질소총인연도시도 명시군구 명읍면동 명동리 명측정소 명
1.0000.5980.031-0.6180.0480.1940.1420.0020.1290.1910.0400.0000.0000.0000.000
수온0.5981.0000.056-0.7670.1950.4070.4040.1470.3290.1310.2820.1700.1760.1510.151
수소이온농도0.0310.0561.0000.1140.2850.1130.1960.1630.3660.1190.4070.3860.3410.3520.330
용존산소-0.618-0.7670.1141.000-0.074-0.283-0.196-0.028-0.1760.1330.3190.1860.1630.1810.167
생화학적 산소요구량0.0480.1950.285-0.0741.0000.4850.4350.5050.6790.1580.5820.4320.3900.3990.395
화학적 산소요구량0.1940.4070.113-0.2830.4851.0000.5830.3490.5610.0600.6300.3960.4470.4480.444
부유물질0.1420.4040.196-0.1960.4350.5831.0000.2800.6120.0500.4870.2700.2910.4110.270
총질소0.0020.1470.163-0.0280.5050.3490.2801.0000.5200.1930.4380.3540.3480.3480.335
총인0.1290.3290.366-0.1760.6790.5610.6120.5201.0000.0920.6660.3550.3250.3640.346
연도0.1910.1310.1190.1330.1580.0600.0500.1930.0921.0000.2250.1640.1390.1070.277
시도 명0.0400.2820.4070.3190.5820.6300.4870.4380.6660.2251.0000.9960.9920.9900.984
시군구 명0.0000.1700.3860.1860.4320.3960.2700.3540.3550.1640.9961.0000.9960.9940.954
읍면동 명0.0000.1760.3410.1630.3900.4470.2910.3480.3250.1390.9920.9961.0000.9980.953
동리 명0.0000.1510.3520.1810.3990.4480.4110.3480.3640.1070.9900.9940.9981.0000.937
측정소 명0.0000.1510.3300.1670.3950.4440.2700.3350.3460.2770.9840.9540.9530.9371.000

Missing values

2024-03-14T09:41:30.811752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:41:30.988353image/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
020203전라북도완주군화산면운제리경천지17.77.911.70.72.12.13.1660.011
120203전라북도완주군화산면운제리경천지28.37.711.60.81.91.93.1760.011
220203전라북도완주군동상면대아리대아지16.17.712.60.21.20.71.6020.006
320203전라북도완주군동상면대아리대아지26.57.612.50.61.81.51.6610.004
420203전라북도완주군동상면대아리대아지36.77.511.80.31.72.21.6590.001
520203전라북도부안군변산면중계리부안댐17.56.710.90.23.62.31.1910.0
620203전라북도부안군변산면중계리부안댐27.96.710.90.92.71.31.5540.006
720203전라북도부안군상서면청림리부안댐38.36.711.00.82.31.51.9860.008
820203전라북도진안군상전면<NA>용담댐46.17.612.11.73.40.51.8860.009
920203전라북도진안군안천면삼락리용담댐16.47.610.51.03.10.51.7910.007
연도시도 명시군구 명읍면동 명동리 명측정소 명수온수소이온농도용존산소생화학적 산소요구량화학적 산소요구량부유물질총질소총인
70720239전라북도부안군변산면중계리부안댐226.37.26.21.84.21.71.7840.006
70820239전라북도부안군상서면청림리부안댐327.47.47.42.25.05.41.9450.017
70920239전라북도진안군상전면<NA>용담댐419.58.49.62.42.71.42.620.02
71020239전라북도진안군안천면삼락리용담댐118.68.59.32.13.41.22.3510.013
71120239전라북도진안군안천면<NA>용담댐218.18.48.82.03.01.12.5270.012
71220239전라북도진안군정천면모정리용담댐318.38.29.32.13.01.52.5430.014
71320239전라북도임실군강진면용수리섬진강댐1(옥정호)16.77.45.01.03.43.21.7490.008
71420239전라북도임실군운암면운정리섬진강댐3(옥정호)22.97.25.61.73.55.22.0810.011
71520239전라북도정읍시산내면장금리섬진강댐2(옥정호)23.87.35.11.13.04.41.8910.01
71620239전라북도장수군번암면죽림리동화호21.37.77.31.43.11.11.1660.002