Overview

Dataset statistics

Number of variables12
Number of observations80
Missing cells67
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory105.7 B

Variable types

Numeric8
Categorical4

Dataset

Description한국서부발전 사업소별(태안,평택,군산,서인천) 발전소의 연간 발전용수 사용량 및 폐수발생량, 처리량, 재이용량 정보를 제공합니다. 제공데이터는 한국서부발전 발전용수 생산 및 사용량 정보입니다. 제공데이터는 년도,사업소,방지시설,용수사용량(톤),원단위1(kg_MWh),폐수발생량(톤),원단위2(kg_MWh),폐수처리량(톤),재이용량(톤),방류량(톤) 입니다.
URLhttps://www.data.go.kr/data/15044427/fileData.do

Alerts

단위1 has constant value ""Constant
단위2 has constant value ""Constant
용수사용량(톤) is highly overall correlated with 재이용량(톤)High correlation
폐수발생량(톤) is highly overall correlated with 폐수처리량(톤) and 2 other fieldsHigh correlation
원단위2 is highly overall correlated with 방지시설High correlation
폐수처리량(톤) is highly overall correlated with 폐수발생량(톤) and 2 other fieldsHigh correlation
재이용량(톤) is highly overall correlated with 용수사용량(톤) and 2 other fieldsHigh correlation
방류량(톤) is highly overall correlated with 폐수발생량(톤) and 1 other fieldsHigh correlation
사업소 is highly overall correlated with 방지시설High correlation
방지시설 is highly overall correlated with 원단위2 and 1 other fieldsHigh correlation
용수사용량(톤) has 10 (12.5%) missing valuesMissing
원단위1 has 11 (13.8%) missing valuesMissing
폐수발생량(톤) has 1 (1.2%) missing valuesMissing
원단위2 has 5 (6.2%) missing valuesMissing
폐수처리량(톤) has 1 (1.2%) missing valuesMissing
재이용량(톤) has 20 (25.0%) missing valuesMissing
방류량(톤) has 19 (23.8%) missing valuesMissing
용수사용량(톤) has 4 (5.0%) zerosZeros
원단위1 has 4 (5.0%) zerosZeros
재이용량(톤) has 4 (5.0%) zerosZeros
방류량(톤) has 4 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-12 03:39:28.883466
Analysis finished2023-12-12 03:39:37.151225
Duration8.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct6
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.45
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T12:39:37.209456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7276603
Coefficient of variation (CV)0.00085551031
Kurtosis-1.2889692
Mean2019.45
Median Absolute Deviation (MAD)1.5
Skewness0.041238905
Sum161556
Variance2.9848101
MonotonicityIncreasing
2023-12-12T12:39:37.366770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2022 13
16.2%
2021 13
16.2%
2020 13
16.2%
2019 13
16.2%
2018 14
17.5%
2017 14
17.5%

사업소
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
태안
52 
평택
16 
서인천
6 
군산
6 

Length

Max length3
Median length2
Mean length2.075
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
태안 52
65.0%
평택 16
 
20.0%
서인천 6
 
7.5%
군산 6
 
7.5%

Length

2023-12-12T12:39:37.566106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:39:37.737896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
태안 52
65.0%
평택 16
 
20.0%
서인천 6
 
7.5%
군산 6
 
7.5%

방지시설
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
일반폐수
12 
#5~8 탈황폐수
6 
#9,10 탈황폐수
6 
회처리방류수 #A
6 
회처리방류수 #B
6 
Other values (16)
44 

Length

Max length11
Median length10.5
Mean length8.025
Min length4

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row#1~6 일반폐수
2nd row#7,8 일반폐수
3rd row#9,10 일반폐수
4th rowIGCC 일반폐수
5th row#1~4 탈황폐수

Common Values

ValueCountFrequency (%)
일반폐수 12
15.0%
#5~8 탈황폐수 6
 
7.5%
#9,10 탈황폐수 6
 
7.5%
회처리방류수 #A 6
 
7.5%
회처리방류수 #B 6
 
7.5%
2복합 일반폐수 4
 
5.0%
IGCC 일반폐수 4
 
5.0%
#9,10 일반폐수 4
 
5.0%
#7,8 일반폐수 4
 
5.0%
탈황폐수 4
 
5.0%
Other values (11) 24
30.0%

Length

2023-12-12T12:39:38.304414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반폐수 36
25.7%
탈황폐수 20
14.3%
9,10 12
 
8.6%
회처리방류수 12
 
8.6%
종합폐수 8
 
5.7%
igcc 6
 
4.3%
1~6 6
 
4.3%
5~8 6
 
4.3%
7,8 6
 
4.3%
b 6
 
4.3%
Other values (8) 22
15.7%

용수사용량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct67
Distinct (%)95.7%
Missing10
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean733563.7
Minimum0
Maximum3763170
Zeros4
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T12:39:38.500991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7946.1
Q1227311.25
median533271
Q3946856
95-th percentile2213828.6
Maximum3763170
Range3763170
Interquartile range (IQR)719544.75

Descriptive statistics

Standard deviation725858.84
Coefficient of variation (CV)0.98949667
Kurtosis4.1474203
Mean733563.7
Median Absolute Deviation (MAD)342525
Skewness1.8558651
Sum51349459
Variance5.2687105 × 1011
MonotonicityNot monotonic
2023-12-12T12:39:38.721751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
5.0%
20990 1
 
1.2%
763677 1
 
1.2%
959527 1
 
1.2%
997576 1
 
1.2%
644275 1
 
1.2%
133416 1
 
1.2%
191136 1
 
1.2%
250383 1
 
1.2%
638398 1
 
1.2%
Other values (57) 57
71.2%
(Missing) 10
 
12.5%
ValueCountFrequency (%)
0 4
5.0%
17658 1
 
1.2%
20990 1
 
1.2%
105156 1
 
1.2%
133416 1
 
1.2%
142214 1
 
1.2%
164768 1
 
1.2%
166899 1
 
1.2%
170138 1
 
1.2%
176793 1
 
1.2%
ValueCountFrequency (%)
3763170 1
1.2%
2670474 1
1.2%
2572971 1
1.2%
2313505 1
1.2%
2092002 1
1.2%
2050036 1
1.2%
1552617 1
1.2%
1542559 1
1.2%
1522952 1
1.2%
1465699 1
1.2%

원단위1
Real number (ℝ)

MISSING  ZEROS 

Distinct66
Distinct (%)95.7%
Missing11
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean148.74032
Minimum0
Maximum884.91532
Zeros4
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T12:39:38.938222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.8490175
Q172.115233
median102.82714
Q3151.4
95-th percentile513.79842
Maximum884.91532
Range884.91532
Interquartile range (IQR)79.284767

Descriptive statistics

Standard deviation155.20732
Coefficient of variation (CV)1.0434784
Kurtosis8.1291455
Mean148.74032
Median Absolute Deviation (MAD)39.327138
Skewness2.6330798
Sum10263.082
Variance24089.311
MonotonicityNot monotonic
2023-12-12T12:39:39.146166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
5.0%
91.3 1
 
1.2%
83.06224288 1
 
1.2%
45.58080707 1
 
1.2%
22.97446406 1
 
1.2%
592.7246663 1
 
1.2%
82.94678523 1
 
1.2%
35.78085849 1
 
1.2%
69.12414836 1
 
1.2%
91.92290685 1
 
1.2%
Other values (56) 56
70.0%
(Missing) 11
 
13.8%
ValueCountFrequency (%)
0.0 4
5.0%
17.12254381 1
 
1.2%
22.97446406 1
 
1.2%
34.51356301 1
 
1.2%
35.78085849 1
 
1.2%
38.86828608 1
 
1.2%
43.0 1
 
1.2%
45.58080707 1
 
1.2%
63.5 1
 
1.2%
65.6 1
 
1.2%
ValueCountFrequency (%)
884.9153206 1
1.2%
592.7246663 1
1.2%
554.9 1
1.2%
554.1867816 1
1.2%
453.2158881 1
1.2%
418.0 1
1.2%
348.6 1
1.2%
306.0606218 1
1.2%
273.8 1
1.2%
216.9320024 1
1.2%

단위1
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
kg/MWh
80 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/MWh
2nd rowkg/MWh
3rd rowkg/MWh
4th rowkg/MWh
5th rowkg/MWh

Common Values

ValueCountFrequency (%)
kg/MWh 80
100.0%

Length

2023-12-12T12:39:39.318086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:39:39.438603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/mwh 80
100.0%

폐수발생량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct79
Distinct (%)100.0%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean435002.42
Minimum9680
Maximum2367597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T12:39:39.597898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9680
5-th percentile40458.7
Q1137737.5
median210259
Q3474658.5
95-th percentile1739591.8
Maximum2367597
Range2357917
Interquartile range (IQR)336921

Descriptive statistics

Standard deviation533393.34
Coefficient of variation (CV)1.2261848
Kurtosis3.9043486
Mean435002.42
Median Absolute Deviation (MAD)110017
Skewness2.1248253
Sum34365191
Variance2.8450846 × 1011
MonotonicityNot monotonic
2023-12-12T12:39:39.828521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
304702.0 1
 
1.2%
215859.0 1
 
1.2%
104698.0 1
 
1.2%
358448.0 1
 
1.2%
857273.0 1
 
1.2%
273839.0 1
 
1.2%
147794.0 1
 
1.2%
103512.0 1
 
1.2%
84098.0 1
 
1.2%
9680.0 1
 
1.2%
Other values (69) 69
86.2%
ValueCountFrequency (%)
9680.0 1
1.2%
32576.0 1
1.2%
33899.0 1
1.2%
37135.0 1
1.2%
40828.0 1
1.2%
43800.0 1
1.2%
54293.0 1
1.2%
75356.0 1
1.2%
75790.0 1
1.2%
81123.0 1
1.2%
ValueCountFrequency (%)
2367597.0 1
1.2%
2137938.0 1
1.2%
1995446.0 1
1.2%
1934575.0 1
1.2%
1717927.0 1
1.2%
1710054.0 1
1.2%
1419680.0 1
1.2%
1277357.0 1
1.2%
1116380.0 1
1.2%
1000748.0 1
1.2%

원단위2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct74
Distinct (%)98.7%
Missing5
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean63.73115
Minimum2.9646698
Maximum293.70874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T12:39:40.059955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.9646698
5-th percentile8.6328448
Q118.796058
median47.612266
Q383.411473
95-th percentile178.54323
Maximum293.70874
Range290.74407
Interquartile range (IQR)64.615415

Descriptive statistics

Standard deviation59.065039
Coefficient of variation (CV)0.92678444
Kurtosis3.1394718
Mean63.73115
Median Absolute Deviation (MAD)29.876722
Skewness1.6716153
Sum4779.8363
Variance3488.6788
MonotonicityNot monotonic
2023-12-12T12:39:40.268166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.7 2
 
2.5%
62.3537372 1
 
1.2%
9.9 1
 
1.2%
71.31921159 1
 
1.2%
30.41381866 1
 
1.2%
16.50027727 1
 
1.2%
103.1800193 1
 
1.2%
254.749655 1
 
1.2%
60.45162958 1
 
1.2%
75.3 1
 
1.2%
Other values (64) 64
80.0%
(Missing) 5
 
6.2%
ValueCountFrequency (%)
2.964669756 1
1.2%
4.475181268 1
1.2%
7.371707164 1
1.2%
7.590780539 1
1.2%
9.079443767 1
1.2%
9.9 1
1.2%
10.0 1
1.2%
11.44 1
1.2%
14.0960118 1
1.2%
14.7 2
2.5%
ValueCountFrequency (%)
293.7087378 1
1.2%
254.749655 1
1.2%
182.2661723 1
1.2%
180.0907598 1
1.2%
177.88 1
1.2%
163.6 1
1.2%
163.5323848 1
1.2%
153.7564337 1
1.2%
137.0618766 1
1.2%
134.2902739 1
1.2%

단위2
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
kg/MWh
80 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkg/MWh
2nd rowkg/MWh
3rd rowkg/MWh
4th rowkg/MWh
5th rowkg/MWh

Common Values

ValueCountFrequency (%)
kg/MWh 80
100.0%

Length

2023-12-12T12:39:40.443094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:39:40.567828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kg/mwh 80
100.0%

폐수처리량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct79
Distinct (%)100.0%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean434100.43
Minimum9680
Maximum2367597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T12:39:40.708981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9680
5-th percentile43502.8
Q1140851.5
median216432
Q3512356
95-th percentile1739591.8
Maximum2367597
Range2357917
Interquartile range (IQR)371504.5

Descriptive statistics

Standard deviation528704.9
Coefficient of variation (CV)1.2179322
Kurtosis4.1559431
Mean434100.43
Median Absolute Deviation (MAD)126033
Skewness2.1748366
Sum34293934
Variance2.7952887 × 1011
MonotonicityNot monotonic
2023-12-12T12:39:40.862031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
349655 1
 
1.2%
317009 1
 
1.2%
111671 1
 
1.2%
333780 1
 
1.2%
634767 1
 
1.2%
273839 1
 
1.2%
147794 1
 
1.2%
103512 1
 
1.2%
63598 1
 
1.2%
9680 1
 
1.2%
Other values (69) 69
86.2%
ValueCountFrequency (%)
9680 1
1.2%
32576 1
1.2%
33899 1
1.2%
40828 1
1.2%
43800 1
1.2%
54293 1
1.2%
57812 1
1.2%
63598 1
1.2%
75790 1
1.2%
81123 1
1.2%
ValueCountFrequency (%)
2367597 1
1.2%
2137938 1
1.2%
1995446 1
1.2%
1934575 1
1.2%
1717927 1
1.2%
1710054 1
1.2%
1419680 1
1.2%
1277357 1
1.2%
1116380 1
1.2%
1001574 1
1.2%

재이용량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct57
Distinct (%)95.0%
Missing20
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean161315.28
Minimum0
Maximum740186
Zeros4
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T12:39:41.007828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123023.25
median129496.5
Q3238049.75
95-th percentile487632
Maximum740186
Range740186
Interquartile range (IQR)215026.5

Descriptive statistics

Standard deviation168467.77
Coefficient of variation (CV)1.0443386
Kurtosis1.7103532
Mean161315.28
Median Absolute Deviation (MAD)107454
Skewness1.3645553
Sum9678917
Variance2.838139 × 1010
MonotonicityNot monotonic
2023-12-12T12:39:41.163027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
5.0%
32576 1
 
1.2%
237185 1
 
1.2%
70478 1
 
1.2%
142885 1
 
1.2%
190371 1
 
1.2%
67409 1
 
1.2%
20500 1
 
1.2%
17864 1
 
1.2%
147794 1
 
1.2%
Other values (47) 47
58.8%
(Missing) 20
25.0%
ValueCountFrequency (%)
0 4
5.0%
6887 1
 
1.2%
7382 1
 
1.2%
11996 1
 
1.2%
13242 1
 
1.2%
16826 1
 
1.2%
16880 1
 
1.2%
17544 1
 
1.2%
17864 1
 
1.2%
19333 1
 
1.2%
ValueCountFrequency (%)
740186 1
1.2%
608518 1
1.2%
511230 1
1.2%
486390 1
1.2%
457805 1
1.2%
427473 1
1.2%
384229 1
1.2%
371865 1
1.2%
336200 1
1.2%
326296 1
1.2%

방류량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct58
Distinct (%)95.1%
Missing19
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean410002.91
Minimum0
Maximum2367597
Zeros4
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T12:39:41.316276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q157812
median153637
Q3319563
95-th percentile1934575
Maximum2367597
Range2367597
Interquartile range (IQR)261751

Descriptive statistics

Standard deviation603702.74
Coefficient of variation (CV)1.4724353
Kurtosis2.8782619
Mean410002.91
Median Absolute Deviation (MAD)105974
Skewness1.9917846
Sum25010177
Variance3.64457 × 1011
MonotonicityNot monotonic
2023-12-12T12:39:41.460562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
5.0%
96276.0 1
 
1.2%
1717927.0 1
 
1.2%
507752.0 1
 
1.2%
232369.0 1
 
1.2%
1934575.0 1
 
1.2%
1995446.0 1
 
1.2%
149443.0 1
 
1.2%
244053.0 1
 
1.2%
9680.0 1
 
1.2%
Other values (48) 48
60.0%
(Missing) 19
 
23.8%
ValueCountFrequency (%)
0.0 4
5.0%
3341.0 1
 
1.2%
5122.0 1
 
1.2%
9680.0 1
 
1.2%
33630.0 1
 
1.2%
33899.0 1
 
1.2%
40828.0 1
 
1.2%
43800.0 1
 
1.2%
47663.0 1
 
1.2%
51312.0 1
 
1.2%
ValueCountFrequency (%)
2367597.0 1
1.2%
2137938.0 1
1.2%
1995446.0 1
1.2%
1934575.0 1
1.2%
1717927.0 1
1.2%
1710054.0 1
1.2%
1419680.0 1
1.2%
1277357.0 1
1.2%
1116380.0 1
1.2%
886036.0 1
1.2%

Interactions

2023-12-12T12:39:35.808306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:29.314605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:30.255442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:31.583086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:32.493582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:33.347604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:34.169275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:35.024922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:35.918105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:29.408441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:30.378885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:31.684067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:32.580215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:33.447928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:34.299181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:35.121071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:36.025472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:29.588243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:30.521289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:31.836674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:32.681053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:33.564610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:34.434744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:35.216620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:36.128904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:29.692854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:30.631764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:31.919782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:32.777843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:33.673548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:34.532184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:35.303307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:36.211841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:29.799009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:30.767575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:32.019900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:32.865765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:33.770915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:34.619975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:35.404555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:36.297750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:29.902697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:30.867746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:32.134263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:32.991418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:33.864183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:34.716679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:35.490693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:36.386592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:30.004440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:31.009260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:32.271479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:33.100698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:33.959774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:34.826495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:35.590025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:36.498963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:30.128605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:31.121876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:32.387758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:33.242216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:34.069754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:34.943396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:39:35.687577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:39:41.574396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도사업소방지시설용수사용량(톤)원단위1폐수발생량(톤)원단위2폐수처리량(톤)재이용량(톤)방류량(톤)
연도1.0000.0000.0000.2760.0000.0000.0000.0000.0000.000
사업소0.0001.0000.9050.3620.4820.0000.5030.0000.1890.000
방지시설0.0000.9051.0000.7420.8000.5990.8660.5590.8180.000
용수사용량(톤)0.2760.3620.7421.0000.5140.4380.0000.3170.8410.542
원단위10.0000.4820.8000.5141.0000.0000.7840.0000.0000.000
폐수발생량(톤)0.0000.0000.5990.4380.0001.0000.0000.9980.8450.986
원단위20.0000.5030.8660.0000.7840.0001.0000.0000.2700.000
폐수처리량(톤)0.0000.0000.5590.3170.0000.9980.0001.0000.7750.992
재이용량(톤)0.0000.1890.8180.8410.0000.8450.2700.7751.0000.309
방류량(톤)0.0000.0000.0000.5420.0000.9860.0000.9920.3091.000
2023-12-12T12:39:41.705454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방지시설사업소
방지시설1.0000.656
사업소0.6561.000
2023-12-12T12:39:41.805000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도용수사용량(톤)원단위1폐수발생량(톤)원단위2폐수처리량(톤)재이용량(톤)방류량(톤)사업소방지시설
연도1.000-0.009-0.0340.0630.0810.019-0.1080.0160.0000.000
용수사용량(톤)-0.0091.0000.3780.192-0.3890.2370.679-0.0320.2260.352
원단위1-0.0340.3781.000-0.0890.399-0.0840.181-0.0180.2210.431
폐수발생량(톤)0.0630.192-0.0891.0000.3160.9880.5300.7330.0000.254
원단위20.081-0.3890.3990.3161.0000.267-0.1120.2970.3310.510
폐수처리량(톤)0.0190.237-0.0840.9880.2671.0000.5580.7430.0000.230
재이용량(톤)-0.1080.6790.1810.530-0.1120.5581.000-0.1520.1050.440
방류량(톤)0.016-0.032-0.0180.7330.2970.743-0.1521.0000.0000.000
사업소0.0000.2260.2210.0000.3310.0000.1050.0001.0000.656
방지시설0.0000.3520.4310.2540.5100.2300.4400.0000.6561.000

Missing values

2023-12-12T12:39:36.641300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:39:36.864026image/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.
2023-12-12T12:39:37.042549image/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

연도사업소방지시설용수사용량(톤)원단위1단위1폐수발생량(톤)원단위2단위2폐수처리량(톤)재이용량(톤)방류량(톤)
02017태안#1~6 일반폐수146569975.876605kg/MWh304702.015.773875kg/MWh34965529710752548.0
12017태안#7,8 일반폐수63839892.3556kg/MWh215859.031.227835kg/MWh317009317009<NA>
22017태안#9,10 일반폐수1542559111.685917kg/MWh1000748.079.894637kg/MWh1001574740186261388.0
32017태안IGCC 일반폐수<NA><NA>kg/MWh210259.0163.532385kg/MWh24055813242227316.0
42017태안#1~4 탈황폐수2313505183.160002kg/MWh114683.09.079444kg/MWh136810136810<NA>
52017태안#5~8 탈황폐수52853938.868286kg/MWh100242.07.371707kg/MWh175501175501<NA>
62017태안#9,10 탈황폐수1287997102.827138kg/MWh37135.02.96467kg/MWh8588285882<NA>
72017태안회처리방류수 #A<NA><NA>kg/MWh1116380.027.881026kg/MWh1116380<NA>1116380.0
82017태안회처리방류수 #B<NA><NA>kg/MWh773364.019.314381kg/MWh773364<NA>773364.0
92017평택기력,1복합 일반폐수608532554.186782kg/MWh197751.0180.09076kg/MWh197751<NA>197751.0
연도사업소방지시설용수사용량(톤)원단위1단위1폐수발생량(톤)원단위2단위2폐수처리량(톤)재이용량(톤)방류량(톤)
702022태안IGCC 종합폐수538003273.8kg/MWh349501.0177.88kg/MWh419349336200135761.9
712022태안#1~8 탈황폐수2050036216.4kg/MWh139488.014.7kg/MWh16888016880<NA>
722022태안#5~8 탈황폐수<NA><NA>kg/MWh<NA><NA>kg/MWh<NA><NA><NA>
732022태안#9,10 탈황폐수71630765.6kg/MWh196124.9818.0kg/MWh182462182462<NA>
742022태안회처리방류수 #A<NA><NA>kg/MWh2137938.0<NA>kg/MWh2137938<NA>2137938.0
752022태안회처리방류수 #B<NA><NA>kg/MWh552048.0<NA>kg/MWh552048<NA>552048.0
762022평택기력발전폐수764230554.9kg/MWh153637.0111.6kg/MWh153637<NA>153637.0
772022평택2복합발전폐수560401117.8kg/MWh296226.062.3kg/MWh3112096887256319.0
782022서인천일반폐수28657963.5kg/MWh75356.016.7kg/MWh578121754457812.0
792022군산일반폐수142214151.4kg/MWh108272.0115.24kg/MWh1082721199696276.0