Overview

Dataset statistics

Number of variables15
Number of observations32
Missing cells38
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory136.1 B

Variable types

Numeric7
Categorical8

Dataset

Description한국서부발전 발전소 배출 수질 정보입니다. 제공데이터는 연도,사업소,방류량(톤),pH,COD기준(㎎/ℓ),COD평균(㎎/ℓ),SS기준(㎎/ℓ),SS평균(㎎/ℓ),광유 기준(㎎/ℓ),광유 평균(㎎/ℓ),T-N기준(㎎/ℓ),T-N평균(㎎/ℓ),T-P기준(㎎/ℓ),T-P평균(㎎/ℓ)입니다. - pH : 수소이온농도 - COD: 화학적산소요구량 - SS: 부유물질량 - T-N : 총질소 - T-P : 총인
URLhttps://www.data.go.kr/data/15083349/fileData.do

Alerts

단위 has constant value ""Constant
총질소기준(T_N) is highly overall correlated with 방류량(톤) and 9 other fieldsHigh correlation
부유물질량기준(SS) is highly overall correlated with 수소이온농도(pH) and 6 other fieldsHigh correlation
광유평균 is highly overall correlated with 연도 and 12 other fieldsHigh correlation
사업소 is highly overall correlated with 수소이온농도(pH) and 7 other fieldsHigh correlation
화학적산소요구량기준(COD) 또는 총유기탄소(TOC) is highly overall correlated with 수소이온농도(pH) and 7 other fieldsHigh correlation
총인기준(T_P) is highly overall correlated with 수소이온농도(pH) and 7 other fieldsHigh correlation
광유기준 is highly overall correlated with 방류량(톤) and 9 other fieldsHigh correlation
연도 is highly overall correlated with 광유평균High correlation
방류량(톤) is highly overall correlated with 총질소평균 and 3 other fieldsHigh correlation
수소이온농도(pH) is highly overall correlated with 총인평균 and 7 other fieldsHigh correlation
화학적산소요구량평균 또는 총유기탄소 is highly overall correlated with 화학적산소요구량기준(COD) 또는 총유기탄소(TOC) and 3 other fieldsHigh correlation
부유물질량평균 is highly overall correlated with 총질소평균 and 2 other fieldsHigh correlation
총질소평균 is highly overall correlated with 방류량(톤) and 7 other fieldsHigh correlation
총인평균 is highly overall correlated with 수소이온농도(pH) and 3 other fieldsHigh correlation
방류량(톤) has 3 (9.4%) missing valuesMissing
수소이온농도(pH) has 7 (21.9%) missing valuesMissing
화학적산소요구량평균 또는 총유기탄소 has 7 (21.9%) missing valuesMissing
부유물질량평균 has 7 (21.9%) missing valuesMissing
총질소평균 has 7 (21.9%) missing valuesMissing
총인평균 has 7 (21.9%) missing valuesMissing
수소이온농도(pH) has 1 (3.1%) zerosZeros
화학적산소요구량평균 또는 총유기탄소 has 1 (3.1%) zerosZeros
부유물질량평균 has 1 (3.1%) zerosZeros
총질소평균 has 1 (3.1%) zerosZeros
총인평균 has 2 (6.2%) zerosZeros

Reproduction

Analysis started2023-12-12 23:39:48.205591
Analysis finished2023-12-12 23:39:53.188492
Duration4.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:39:53.238137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016.75
median2018.5
Q32020.25
95-th percentile2022
Maximum2022
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.3279508
Coefficient of variation (CV)0.0011533073
Kurtosis-1.2420361
Mean2018.5
Median Absolute Deviation (MAD)2
Skewness0
Sum64592
Variance5.4193548
MonotonicityIncreasing
2023-12-13T08:39:53.337047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2015 4
12.5%
2016 4
12.5%
2017 4
12.5%
2018 4
12.5%
2019 4
12.5%
2020 4
12.5%
2021 4
12.5%
2022 4
12.5%
ValueCountFrequency (%)
2015 4
12.5%
2016 4
12.5%
2017 4
12.5%
2018 4
12.5%
2019 4
12.5%
2020 4
12.5%
2021 4
12.5%
2022 4
12.5%
ValueCountFrequency (%)
2022 4
12.5%
2021 4
12.5%
2020 4
12.5%
2019 4
12.5%
2018 4
12.5%
2017 4
12.5%
2016 4
12.5%
2015 4
12.5%

사업소
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
태안
8 
평택
8 
서인천
8 
군산
8 

Length

Max length3
Median length2
Mean length2.25
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row태안
2nd row평택
3rd row서인천
4th row군산
5th row태안

Common Values

ValueCountFrequency (%)
태안 8
25.0%
평택 8
25.0%
서인천 8
25.0%
군산 8
25.0%

Length

2023-12-13T08:39:53.438866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:39:53.523700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
태안 8
25.0%
평택 8
25.0%
서인천 8
25.0%
군산 8
25.0%

방류량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing3
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean1177698
Minimum39249
Maximum5754924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:39:53.615103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39249
5-th percentile52942.4
Q1117324
median382262
Q31704138
95-th percentile4484474.8
Maximum5754924
Range5715675
Interquartile range (IQR)1586814

Descriptive statistics

Standard deviation1686618.7
Coefficient of variation (CV)1.4321317
Kurtosis0.97869003
Mean1177698
Median Absolute Deviation (MAD)285986
Skewness1.503431
Sum34153243
Variance2.8446826 × 1012
MonotonicityNot monotonic
2023-12-13T08:39:53.716734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
252374 1
 
3.1%
96276 1
 
3.1%
527508 1
 
3.1%
3169648 1
 
3.1%
117324 1
 
3.1%
90399 1
 
3.1%
513578 1
 
3.1%
3962877 1
 
3.1%
85648 1
 
3.1%
451335 1
 
3.1%
Other values (19) 19
59.4%
(Missing) 3
 
9.4%
ValueCountFrequency (%)
39249 1
3.1%
51312 1
3.1%
55388 1
3.1%
58846 1
3.1%
85648 1
3.1%
90399 1
3.1%
96276 1
3.1%
117324 1
3.1%
131586 1
3.1%
141824 1
3.1%
ValueCountFrequency (%)
5754924 1
3.1%
4717812 1
3.1%
4134469 1
3.1%
3962877 1
3.1%
3310406 1
3.1%
3169648 1
3.1%
2430996 1
3.1%
1704138 1
3.1%
647792 1
3.1%
527508 1
3.1%

수소이온농도(pH)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)80.0%
Missing7
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean6.968
Minimum0
Maximum8.2
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:39:53.821515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.944
Q17.07
median7.15
Q37.41
95-th percentile7.532
Maximum8.2
Range8.2
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation1.4761239
Coefficient of variation (CV)0.21184326
Kurtosis23.192497
Mean6.968
Median Absolute Deviation (MAD)0.15
Skewness-4.7234967
Sum174.2
Variance2.1789417
MonotonicityNot monotonic
2023-12-13T08:39:53.908798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
7.37 2
 
6.2%
7.1 2
 
6.2%
7.0 2
 
6.2%
7.08 2
 
6.2%
7.41 2
 
6.2%
7.18 1
 
3.1%
7.04 1
 
3.1%
7.4 1
 
3.1%
7.42 1
 
3.1%
7.05 1
 
3.1%
Other values (10) 10
31.2%
(Missing) 7
21.9%
ValueCountFrequency (%)
0.0 1
3.1%
6.93 1
3.1%
7.0 2
6.2%
7.04 1
3.1%
7.05 1
3.1%
7.07 1
3.1%
7.08 2
6.2%
7.1 2
6.2%
7.12 1
3.1%
7.15 1
3.1%
ValueCountFrequency (%)
8.2 1
3.1%
7.54 1
3.1%
7.5 1
3.1%
7.49 1
3.1%
7.42 1
3.1%
7.41 2
6.2%
7.4 1
3.1%
7.37 2
6.2%
7.19 1
3.1%
7.18 1
3.1%

화학적산소요구량기준(COD) 또는 총유기탄소(TOC)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size388.0 B
40
17 
<NA>
7 
90
5 
22
2 
0
 
1

Length

Max length4
Median length2
Mean length2.40625
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row40
2nd row90
3rd row<NA>
4th row40
5th row40

Common Values

ValueCountFrequency (%)
40 17
53.1%
<NA> 7
21.9%
90 5
 
15.6%
22 2
 
6.2%
0 1
 
3.1%

Length

2023-12-13T08:39:54.003973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:39:54.105544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
40 17
53.1%
na 7
21.9%
90 5
 
15.6%
22 2
 
6.2%
0 1
 
3.1%

화학적산소요구량평균 또는 총유기탄소
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)96.0%
Missing7
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean4.71768
Minimum0
Maximum6.6
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:39:54.204832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8092
Q14
median5.112
Q36.049
95-th percentile6.5568
Maximum6.6
Range6.6
Interquartile range (IQR)2.049

Descriptive statistics

Standard deviation1.6753562
Coefficient of variation (CV)0.3551229
Kurtosis1.2921358
Mean4.71768
Median Absolute Deviation (MAD)1.112
Skewness-1.1747499
Sum117.942
Variance2.8068184
MonotonicityNot monotonic
2023-12-13T08:39:54.288487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4.0 2
 
6.2%
5.701 1
 
3.1%
5.55 1
 
3.1%
2.81 1
 
3.1%
2.23 1
 
3.1%
4.38 1
 
3.1%
3.98 1
 
3.1%
3.44 1
 
3.1%
0.0 1
 
3.1%
6.3 1
 
3.1%
Other values (14) 14
43.8%
(Missing) 7
21.9%
ValueCountFrequency (%)
0.0 1
3.1%
1.704 1
3.1%
2.23 1
3.1%
2.81 1
3.1%
3.44 1
3.1%
3.98 1
3.1%
4.0 2
6.2%
4.38 1
3.1%
4.44 1
3.1%
4.9 1
3.1%
ValueCountFrequency (%)
6.6 1
3.1%
6.575 1
3.1%
6.484 1
3.1%
6.423 1
3.1%
6.3 1
3.1%
6.284 1
3.1%
6.049 1
3.1%
5.701 1
3.1%
5.55 1
3.1%
5.4 1
3.1%

부유물질량기준(SS)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size388.0 B
30
15 
<NA>
7 
80
5 
10
4 
0
 
1

Length

Max length4
Median length2
Mean length2.40625
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row30
2nd row80
3rd row<NA>
4th row10
5th row30

Common Values

ValueCountFrequency (%)
30 15
46.9%
<NA> 7
21.9%
80 5
 
15.6%
10 4
 
12.5%
0 1
 
3.1%

Length

2023-12-13T08:39:54.403789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:39:54.512287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 15
46.9%
na 7
21.9%
80 5
 
15.6%
10 4
 
12.5%
0 1
 
3.1%

부유물질량평균
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)92.0%
Missing7
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean2.54244
Minimum0
Maximum19.1
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:39:54.597026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272
Q10.84
median1.47
Q32.29
95-th percentile6.6
Maximum19.1
Range19.1
Interquartile range (IQR)1.45

Descriptive statistics

Standard deviation3.8712214
Coefficient of variation (CV)1.5226402
Kurtosis14.675564
Mean2.54244
Median Absolute Deviation (MAD)0.801
Skewness3.5875495
Sum63.561
Variance14.986356
MonotonicityNot monotonic
2023-12-13T08:39:54.691606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1.0 2
 
6.2%
6.6 2
 
6.2%
0.669 1
 
3.1%
0.24 1
 
3.1%
0.51 1
 
3.1%
1.94 1
 
3.1%
2.29 1
 
3.1%
0.43 1
 
3.1%
0.0 1
 
3.1%
1.51 1
 
3.1%
Other values (13) 13
40.6%
(Missing) 7
21.9%
ValueCountFrequency (%)
0.0 1
3.1%
0.24 1
3.1%
0.4 1
3.1%
0.43 1
3.1%
0.51 1
3.1%
0.669 1
3.1%
0.84 1
3.1%
0.93 1
3.1%
0.953 1
3.1%
1.0 2
6.2%
ValueCountFrequency (%)
19.1 1
3.1%
6.6 2
6.2%
4.832 1
3.1%
2.921 1
3.1%
2.635 1
3.1%
2.29 1
3.1%
1.94 1
3.1%
1.9 1
3.1%
1.865 1
3.1%
1.7 1
3.1%

광유기준
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
5
14 
<NA>
10 
1
7 
0
 
1

Length

Max length4
Median length1
Mean length1.9375
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row1
2nd row5
3rd row<NA>
4th row5
5th row1

Common Values

ValueCountFrequency (%)
5 14
43.8%
<NA> 10
31.2%
1 7
21.9%
0 1
 
3.1%

Length

2023-12-13T08:39:54.798522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:39:54.877033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 14
43.8%
na 10
31.2%
1 7
21.9%
0 1
 
3.1%

광유평균
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
ND
22 
<NA>
10 

Length

Max length4
Median length2
Mean length2.625
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowND
2nd rowND
3rd row<NA>
4th rowND
5th rowND

Common Values

ValueCountFrequency (%)
ND 22
68.8%
<NA> 10
31.2%

Length

2023-12-13T08:39:54.966097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:39:55.049323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nd 22
68.8%
na 10
31.2%

총질소기준(T_N)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
60
19 
<NA>
7 
30
5 
0
 
1

Length

Max length4
Median length2
Mean length2.40625
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row30
2nd row60
3rd row<NA>
4th row60
5th row30

Common Values

ValueCountFrequency (%)
60 19
59.4%
<NA> 7
 
21.9%
30 5
 
15.6%
0 1
 
3.1%

Length

2023-12-13T08:39:55.133324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:39:55.216751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60 19
59.4%
na 7
 
21.9%
30 5
 
15.6%
0 1
 
3.1%

총질소평균
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)100.0%
Missing7
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean3.1918
Minimum0
Maximum7.286
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:39:55.294950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0122
Q11.854
median2.51
Q34.949
95-th percentile6.4648
Maximum7.286
Range7.286
Interquartile range (IQR)3.095

Descriptive statistics

Standard deviation1.9351136
Coefficient of variation (CV)0.60627658
Kurtosis-0.65004948
Mean3.1918
Median Absolute Deviation (MAD)0.955
Skewness0.61190448
Sum79.795
Variance3.7446646
MonotonicityNot monotonic
2023-12-13T08:39:55.385823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4.949 1
 
3.1%
0.88 1
 
3.1%
3.55 1
 
3.1%
2.51 1
 
3.1%
2.25 1
 
3.1%
2.0 1
 
3.1%
5.45 1
 
3.1%
2.8 1
 
3.1%
0.0 1
 
3.1%
1.7 1
 
3.1%
Other values (15) 15
46.9%
(Missing) 7
21.9%
ValueCountFrequency (%)
0.0 1
3.1%
0.88 1
3.1%
1.541 1
3.1%
1.555 1
3.1%
1.678 1
3.1%
1.7 1
3.1%
1.854 1
3.1%
1.895 1
3.1%
2.0 1
3.1%
2.096 1
3.1%
ValueCountFrequency (%)
7.286 1
3.1%
6.555 1
3.1%
6.104 1
3.1%
5.45 1
3.1%
5.2 1
3.1%
5.187 1
3.1%
4.949 1
3.1%
4.804 1
3.1%
3.55 1
3.1%
2.97 1
3.1%

총인기준(T_P)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size388.0 B
2
10 
8
9 
<NA>
7 
4
5 
0
 
1

Length

Max length4
Median length1
Mean length1.65625
Min length1

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row4
2nd row8
3rd row<NA>
4th row8
5th row4

Common Values

ValueCountFrequency (%)
2 10
31.2%
8 9
28.1%
<NA> 7
21.9%
4 5
15.6%
0 1
 
3.1%

Length

2023-12-13T08:39:55.484086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:39:55.578702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 10
31.2%
8 9
28.1%
na 7
21.9%
4 5
15.6%
0 1
 
3.1%

총인평균
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)92.0%
Missing7
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean0.08684
Minimum0
Maximum0.234
Zeros2
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:39:55.677165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0014
Q10.029
median0.08
Q30.134
95-th percentile0.1866
Maximum0.234
Range0.234
Interquartile range (IQR)0.105

Descriptive statistics

Standard deviation0.067629924
Coefficient of variation (CV)0.77878771
Kurtosis-0.77677836
Mean0.08684
Median Absolute Deviation (MAD)0.054
Skewness0.40216801
Sum2.171
Variance0.0045738067
MonotonicityNot monotonic
2023-12-13T08:39:56.085003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 2
 
6.2%
0.1 2
 
6.2%
0.171 1
 
3.1%
0.073 1
 
3.1%
0.01 1
 
3.1%
0.12 1
 
3.1%
0.181 1
 
3.1%
0.008 1
 
3.1%
0.188 1
 
3.1%
0.107 1
 
3.1%
Other values (13) 13
40.6%
(Missing) 7
21.9%
ValueCountFrequency (%)
0.0 2
6.2%
0.007 1
3.1%
0.008 1
3.1%
0.01 1
3.1%
0.014 1
3.1%
0.029 1
3.1%
0.031 1
3.1%
0.045 1
3.1%
0.049 1
3.1%
0.073 1
3.1%
ValueCountFrequency (%)
0.234 1
3.1%
0.188 1
3.1%
0.181 1
3.1%
0.171 1
3.1%
0.162 1
3.1%
0.14 1
3.1%
0.134 1
3.1%
0.12 1
3.1%
0.11 1
3.1%
0.107 1
3.1%

단위
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
(㎎/ℓ)
32 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(㎎/ℓ)
2nd row(㎎/ℓ)
3rd row(㎎/ℓ)
4th row(㎎/ℓ)
5th row(㎎/ℓ)

Common Values

ValueCountFrequency (%)
(㎎/ℓ) 32
100.0%

Length

2023-12-13T08:39:56.183568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:39:56.258268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
㎎/ℓ 32
100.0%

Interactions

2023-12-13T08:39:52.216021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:48.823601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.371128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.953273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.500165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.019094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.728370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:52.286524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:48.899502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.452734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.041169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.572813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.093072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.801040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:52.355577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:48.977236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.534612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.123363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.638769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.377776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.865788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:52.412925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.046294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.632133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.205670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.705452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.443689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.930079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:52.477887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.125813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.709475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.285868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.771515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.518661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.997612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:52.552092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.206681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.794240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.368239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.857016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.591190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:52.073309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:52.621107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.301741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:49.882487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.440732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:50.950029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:51.664907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:39:52.147594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:39:56.317786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도사업소방류량(톤)수소이온농도(pH)화학적산소요구량기준(COD) 또는 총유기탄소(TOC)화학적산소요구량평균 또는 총유기탄소부유물질량기준(SS)부유물질량평균광유기준총질소기준(T_N)총질소평균총인기준(T_P)총인평균
연도1.0000.0000.0000.0000.3810.5640.0000.0000.0000.0000.0000.4600.177
사업소0.0001.0000.4730.7310.9460.7210.9670.3611.0000.7940.7740.9460.709
방류량(톤)0.0000.4731.0000.2500.0000.7140.0000.9091.0000.8670.7250.8330.727
수소이온농도(pH)0.0000.7310.2501.0000.6810.8870.7740.0000.9280.9310.9530.6560.000
화학적산소요구량기준(COD) 또는 총유기탄소(TOC)0.3810.9460.0000.6811.0000.8840.9770.0000.9520.6730.6740.9370.000
화학적산소요구량평균 또는 총유기탄소0.5640.7210.7140.8870.8841.0000.6030.0000.7880.9240.7480.7300.000
부유물질량기준(SS)0.0000.9670.0000.7740.9770.6031.0000.0000.7490.6880.6440.9760.548
부유물질량평균0.0000.3610.9090.0000.0000.0000.0001.0000.4960.4190.7590.3630.993
광유기준0.0001.0001.0000.9280.9520.7880.7490.4961.0000.9920.8630.8370.219
총질소기준(T_N)0.0000.7940.8670.9310.6730.9240.6880.4190.9921.0000.9871.0000.288
총질소평균0.0000.7740.7250.9530.6740.7480.6440.7590.8630.9871.0000.8330.269
총인기준(T_P)0.4600.9460.8330.6560.9370.7300.9760.3630.8371.0000.8331.0000.318
총인평균0.1770.7090.7270.0000.0000.0000.5480.9930.2190.2880.2690.3181.000
2023-12-13T08:39:56.446436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총질소기준(T_N)부유물질량기준(SS)광유평균사업소화학적산소요구량기준(COD) 또는 총유기탄소(TOC)총인기준(T_P)광유기준
총질소기준(T_N)1.0000.7041.0000.8340.6840.9770.890
부유물질량기준(SS)0.7041.0001.0000.7540.7940.7870.776
광유평균1.0001.0001.0001.0001.0001.0001.000
사업소0.8340.7541.0001.0000.6860.6890.973
화학적산소요구량기준(COD) 또는 총유기탄소(TOC)0.6840.7941.0000.6861.0000.6640.731
총인기준(T_P)0.9770.7871.0000.6890.6641.0000.882
광유기준0.8900.7761.0000.9730.7310.8821.000
2023-12-13T08:39:56.564454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도방류량(톤)수소이온농도(pH)화학적산소요구량평균 또는 총유기탄소부유물질량평균총질소평균총인평균사업소화학적산소요구량기준(COD) 또는 총유기탄소(TOC)부유물질량기준(SS)광유기준광유평균총질소기준(T_N)총인기준(T_P)
연도1.0000.149-0.255-0.458-0.032-0.115-0.0720.0000.1910.0000.0001.0000.0000.273
방류량(톤)0.1491.000-0.186-0.0230.4830.5800.1680.2890.0000.0000.7951.0000.7410.448
수소이온농도(pH)-0.255-0.1861.0000.0900.3730.3110.5950.7560.6940.8090.6711.0000.6760.664
화학적산소요구량평균 또는 총유기탄소-0.458-0.0230.0901.0000.3290.2400.2650.4770.6890.3620.6471.0000.5730.487
부유물질량평균-0.0320.4830.3730.3291.0000.7120.7460.2830.0000.0000.3971.0000.3230.284
총질소평균-0.1150.5800.3110.2400.7121.0000.6950.5370.4280.3990.7021.0000.7320.614
총인평균-0.0720.1680.5950.2650.7460.6951.0000.4180.0000.2790.0001.0000.0790.103
사업소0.0000.2890.7560.4770.2830.5370.4181.0000.6860.7540.9731.0000.8340.689
화학적산소요구량기준(COD) 또는 총유기탄소(TOC)0.1910.0000.6940.6890.0000.4280.0000.6861.0000.7940.7311.0000.6840.664
부유물질량기준(SS)0.0000.0000.8090.3620.0000.3990.2790.7540.7941.0000.7761.0000.7040.787
광유기준0.0000.7950.6710.6470.3970.7020.0000.9730.7310.7761.0001.0000.8900.882
광유평균1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
총질소기준(T_N)0.0000.7410.6760.5730.3230.7320.0790.8340.6840.7040.8901.0001.0000.977
총인기준(T_P)0.2730.4480.6640.4870.2840.6140.1030.6890.6640.7870.8821.0000.9771.000

Missing values

2023-12-13T08:39:52.726348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:39:52.913889image/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.
2023-12-13T08:39:53.065746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연도사업소방류량(톤)수소이온농도(pH)화학적산소요구량기준(COD) 또는 총유기탄소(TOC)화학적산소요구량평균 또는 총유기탄소부유물질량기준(SS)부유물질량평균광유기준광유평균총질소기준(T_N)총질소평균총인기준(T_P)총인평균단위
02015태안17041387.08401.704300.41ND301.67840.007(㎎/ℓ)
12015평택2523747.12906.423800.6695ND604.94980.171(㎎/ℓ)
22015서인천39249<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>(㎎/ℓ)
32015군산1606437.54404.44101.475ND602.88180.08(㎎/ℓ)
42016태안33104067.49405.298304.8321ND304.80440.234(㎎/ℓ)
52016평택6477927.08906.484801.8655ND605.18780.045(㎎/ℓ)
62016서인천55388<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>(㎎/ℓ)
72016군산2114377.5405.4100.935ND601.89580.049(㎎/ℓ)
82017태안24309967.18406.5753019.11ND306.10440.162(㎎/ℓ)
92017평택3949557.04905.282800.845ND602.09680.031(㎎/ℓ)
연도사업소방류량(톤)수소이온농도(pH)화학적산소요구량기준(COD) 또는 총유기탄소(TOC)화학적산소요구량평균 또는 총유기탄소부유물질량기준(SS)부유물질량평균광유기준광유평균총질소기준(T_N)총질소평균총인기준(T_P)총인평균단위
222020서인천<NA>0.000.000.00ND00.000.0(㎎/ℓ)
232020군산856488.2404.0301.05ND602.820.1(㎎/ℓ)
242021태안39628777.37403.44306.61ND605.4520.188(㎎/ℓ)
252021평택5135787.1403.98300.435ND602.020.008(㎎/ℓ)
262021서인천90399<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>(㎎/ℓ)
272021군산1173247.1404.38302.295ND602.2520.181(㎎/ℓ)
282022태안31696487.41222.23301.94<NA><NA>602.5120.12(㎎/ℓ)
292022평택5275086.93222.81300.51<NA><NA>603.5520.01(㎎/ℓ)
302022서인천<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>(㎎/ℓ)
312022군산962767.15405.55300.24<NA><NA>600.8820.073(㎎/ℓ)