Overview

Dataset statistics

Number of variables9
Number of observations140
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory78.9 B

Variable types

Categorical3
Numeric6

Dataset

Description경기도 안산시에서 운영하고 하는 하수처리시설에서 측정한 방류수질농도 현황입니다. 법적 방류수질농도 기준(해당 수치 이하) BOD(mg/L):10, COD(mg/L):40, SS(mg/L):10, T-N(mg/L):20, T-P(mg/L):2.0, 대장균(개/mL):3000
URLhttps://www.data.go.kr/data/15036748/fileData.do

Alerts

생화학적산소요구량(BOD(mg_L)) is highly overall correlated with 총유기탄소량(TOC(mg_L)) and 2 other fieldsHigh correlation
총유기탄소량(TOC(mg_L)) is highly overall correlated with 생화학적산소요구량(BOD(mg_L)) and 2 other fieldsHigh correlation
부유물질량(SS(mg_L)) is highly overall correlated with 생화학적산소요구량(BOD(mg_L)) and 4 other fieldsHigh correlation
총인(T-P(mg_L)) is highly overall correlated with 계열High correlation
대장균(개_mL) is highly overall correlated with 부유물질량(SS(mg_L)) and 1 other fieldsHigh correlation
처리장명 is highly overall correlated with 부유물질량(SS(mg_L)) and 1 other fieldsHigh correlation
계열 is highly overall correlated with 생화학적산소요구량(BOD(mg_L)) and 5 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 20:10:13.389240
Analysis finished2023-12-12 20:10:17.451581
Duration4.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처리장명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
공공하수1처리장
70 
공공하수2처리장
35 
대부하수처리장
35 

Length

Max length8
Median length8
Mean length7.75
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공공하수1처리장
2nd row공공하수1처리장
3rd row공공하수2처리장
4th row대부하수처리장
5th row공공하수1처리장

Common Values

ValueCountFrequency (%)
공공하수1처리장 70
50.0%
공공하수2처리장 35
25.0%
대부하수처리장 35
25.0%

Length

2023-12-13T05:10:17.511151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:10:17.603045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공하수1처리장 70
50.0%
공공하수2처리장 35
25.0%
대부하수처리장 35
25.0%

계열
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
생활계열
105 
공장계열
35 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활계열
2nd row공장계열
3rd row생활계열
4th row생활계열
5th row생활계열

Common Values

ValueCountFrequency (%)
생활계열 105
75.0%
공장계열 35
 
25.0%

Length

2023-12-13T05:10:17.708567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:10:17.816645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활계열 105
75.0%
공장계열 35
 
25.0%

생화학적산소요구량(BOD(mg_L))
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7264286
Minimum0.4
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:10:17.928498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.47
Q12.2
median2.5
Q33.225
95-th percentile4.4
Maximum5
Range4.6
Interquartile range (IQR)1.025

Descriptive statistics

Standard deviation0.94903288
Coefficient of variation (CV)0.34808646
Kurtosis0.05879047
Mean2.7264286
Median Absolute Deviation (MAD)0.4
Skewness0.41710103
Sum381.7
Variance0.90066341
MonotonicityNot monotonic
2023-12-13T05:10:18.069685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2.5 16
 
11.4%
2.6 15
 
10.7%
2.1 13
 
9.3%
2.4 12
 
8.6%
2.2 11
 
7.9%
2.3 10
 
7.1%
4.4 7
 
5.0%
2.0 5
 
3.6%
4.2 5
 
3.6%
4.3 4
 
2.9%
Other values (26) 42
30.0%
ValueCountFrequency (%)
0.4 1
 
0.7%
0.5 1
 
0.7%
0.6 1
 
0.7%
0.8 1
 
0.7%
0.9 3
2.1%
1.5 1
 
0.7%
1.6 1
 
0.7%
1.7 1
 
0.7%
1.9 3
2.1%
2.0 5
3.6%
ValueCountFrequency (%)
5.0 1
 
0.7%
4.8 1
 
0.7%
4.6 1
 
0.7%
4.5 3
2.1%
4.4 7
5.0%
4.3 4
2.9%
4.2 5
3.6%
4.1 1
 
0.7%
4.0 1
 
0.7%
3.9 2
 
1.4%

총유기탄소량(TOC(mg_L))
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9621429
Minimum2.6
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:10:18.223602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3
Q14.1
median5.3
Q37.5
95-th percentile11.105
Maximum14
Range11.4
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation2.5666502
Coefficient of variation (CV)0.43049123
Kurtosis0.34525148
Mean5.9621429
Median Absolute Deviation (MAD)1.5
Skewness1.015042
Sum834.7
Variance6.5876932
MonotonicityNot monotonic
2023-12-13T05:10:18.390477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3 6
 
4.3%
4.2 6
 
4.3%
4.7 5
 
3.6%
4.5 5
 
3.6%
3.6 4
 
2.9%
3.1 4
 
2.9%
4.1 4
 
2.9%
5.6 4
 
2.9%
4.6 4
 
2.9%
5.2 3
 
2.1%
Other values (61) 95
67.9%
ValueCountFrequency (%)
2.6 1
 
0.7%
2.7 2
1.4%
2.9 2
1.4%
3.0 3
2.1%
3.1 4
2.9%
3.2 3
2.1%
3.3 1
 
0.7%
3.4 3
2.1%
3.6 4
2.9%
3.7 3
2.1%
ValueCountFrequency (%)
14.0 1
0.7%
12.9 1
0.7%
12.7 1
0.7%
12.2 1
0.7%
12.1 1
0.7%
11.3 1
0.7%
11.2 1
0.7%
11.1 1
0.7%
11.0 1
0.7%
10.8 1
0.7%

부유물질량(SS(mg_L))
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.72
Minimum0.4
Maximum7.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:10:18.527831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.1
Q11.975
median2.5
Q33.4
95-th percentile5.705
Maximum7.1
Range6.7
Interquartile range (IQR)1.425

Descriptive statistics

Standard deviation1.3486577
Coefficient of variation (CV)0.49583005
Kurtosis1.1091804
Mean2.72
Median Absolute Deviation (MAD)0.8
Skewness1.0254251
Sum380.8
Variance1.8188777
MonotonicityNot monotonic
2023-12-13T05:10:19.048331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2.3 11
 
7.9%
2.2 11
 
7.9%
1.1 10
 
7.1%
1.4 7
 
5.0%
3.0 7
 
5.0%
2.6 6
 
4.3%
2.8 6
 
4.3%
3.4 6
 
4.3%
3.3 5
 
3.6%
2.1 5
 
3.6%
Other values (34) 66
47.1%
ValueCountFrequency (%)
0.4 1
 
0.7%
0.7 2
 
1.4%
1.0 3
 
2.1%
1.1 10
7.1%
1.2 4
 
2.9%
1.3 3
 
2.1%
1.4 7
5.0%
1.6 2
 
1.4%
1.7 2
 
1.4%
1.9 1
 
0.7%
ValueCountFrequency (%)
7.1 1
0.7%
7.0 1
0.7%
6.2 1
0.7%
6.0 2
1.4%
5.9 1
0.7%
5.8 1
0.7%
5.7 2
1.4%
5.4 2
1.4%
4.7 1
0.7%
4.6 1
0.7%

총질소(T-N(mg_L))
Real number (ℝ)

Distinct139
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5039143
Minimum2.455
Maximum12.389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:10:19.227635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.455
5-th percentile4.11685
Q16.06075
median7.665
Q39.03625
95-th percentile11.05845
Maximum12.389
Range9.934
Interquartile range (IQR)2.9755

Descriptive statistics

Standard deviation2.0244913
Coefficient of variation (CV)0.26979137
Kurtosis-0.42315005
Mean7.5039143
Median Absolute Deviation (MAD)1.386
Skewness-0.096039118
Sum1050.548
Variance4.0985651
MonotonicityNot monotonic
2023-12-13T05:10:19.391597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.847 2
 
1.4%
3.421 1
 
0.7%
6.627 1
 
0.7%
9.047 1
 
0.7%
5.715 1
 
0.7%
6.957 1
 
0.7%
5.511 1
 
0.7%
7.755 1
 
0.7%
3.683 1
 
0.7%
5.811 1
 
0.7%
Other values (129) 129
92.1%
ValueCountFrequency (%)
2.455 1
0.7%
3.019 1
0.7%
3.421 1
0.7%
3.572 1
0.7%
3.683 1
0.7%
3.819 1
0.7%
3.905 1
0.7%
4.128 1
0.7%
4.17 1
0.7%
4.273 1
0.7%
ValueCountFrequency (%)
12.389 1
0.7%
11.589 1
0.7%
11.377 1
0.7%
11.309 1
0.7%
11.297 1
0.7%
11.215 1
0.7%
11.105 1
0.7%
11.056 1
0.7%
10.51 1
0.7%
10.433 1
0.7%

총인(T-P(mg_L))
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71331429
Minimum0.204
Maximum1.597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:10:19.563380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.204
5-th percentile0.3698
Q10.56375
median0.7055
Q30.84725
95-th percentile1.0981
Maximum1.597
Range1.393
Interquartile range (IQR)0.2835

Descriptive statistics

Standard deviation0.22627042
Coefficient of variation (CV)0.31720999
Kurtosis0.93823489
Mean0.71331429
Median Absolute Deviation (MAD)0.142
Skewness0.53036543
Sum99.864
Variance0.051198303
MonotonicityNot monotonic
2023-12-13T05:10:19.745626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.879 2
 
1.4%
0.643 2
 
1.4%
0.712 2
 
1.4%
0.745 2
 
1.4%
0.645 2
 
1.4%
0.602 2
 
1.4%
0.593 2
 
1.4%
0.682 2
 
1.4%
0.678 1
 
0.7%
0.421 1
 
0.7%
Other values (122) 122
87.1%
ValueCountFrequency (%)
0.204 1
0.7%
0.254 1
0.7%
0.319 1
0.7%
0.343 1
0.7%
0.355 1
0.7%
0.356 1
0.7%
0.366 1
0.7%
0.37 1
0.7%
0.372 1
0.7%
0.388 1
0.7%
ValueCountFrequency (%)
1.597 1
0.7%
1.279 1
0.7%
1.211 1
0.7%
1.202 1
0.7%
1.172 1
0.7%
1.125 1
0.7%
1.1 1
0.7%
1.098 1
0.7%
1.092 1
0.7%
1.063 1
0.7%

대장균(개_mL)
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.957143
Minimum2
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:10:19.936458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.95
Q17
median10
Q315
95-th percentile34.2
Maximum92
Range90
Interquartile range (IQR)8

Descriptive statistics

Standard deviation15.627807
Coefficient of variation (CV)1.0448391
Kurtosis10.760832
Mean14.957143
Median Absolute Deviation (MAD)3
Skewness3.0342773
Sum2094
Variance244.22837
MonotonicityNot monotonic
2023-12-13T05:10:20.101746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
8 17
 
12.1%
10 12
 
8.6%
9 11
 
7.9%
7 10
 
7.1%
12 9
 
6.4%
11 8
 
5.7%
6 7
 
5.0%
2 7
 
5.0%
3 6
 
4.3%
13 6
 
4.3%
Other values (25) 47
33.6%
ValueCountFrequency (%)
2 7
5.0%
3 6
 
4.3%
4 3
 
2.1%
5 5
 
3.6%
6 7
5.0%
7 10
7.1%
8 17
12.1%
9 11
7.9%
10 12
8.6%
11 8
5.7%
ValueCountFrequency (%)
92 1
 
0.7%
85 2
1.4%
79 1
 
0.7%
67 1
 
0.7%
41 1
 
0.7%
38 1
 
0.7%
34 2
1.4%
33 1
 
0.7%
31 1
 
0.7%
30 3
2.1%

측정일(1주간)
Categorical

Distinct35
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-07-30
 
4
2022-10-31
 
4
2023-05-29
 
4
2023-04-30
 
4
2023-03-27
 
4
Other values (30)
120 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-30
2nd row2023-07-30
3rd row2023-07-30
4th row2023-07-30
5th row2023-06-26

Common Values

ValueCountFrequency (%)
2023-07-30 4
 
2.9%
2022-10-31 4
 
2.9%
2023-05-29 4
 
2.9%
2023-04-30 4
 
2.9%
2023-03-27 4
 
2.9%
2023-02-27 4
 
2.9%
2023-01-30 4
 
2.9%
2022-12-26 4
 
2.9%
2022-03-27 4
 
2.9%
2023-06-26 4
 
2.9%
Other values (25) 100
71.4%

Length

2023-12-13T05:10:20.254622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-07-30 4
 
2.9%
2022-01-30 4
 
2.9%
2021-11-28 4
 
2.9%
2021-10-31 4
 
2.9%
2021-09-26 4
 
2.9%
2021-08-29 4
 
2.9%
2021-07-25 4
 
2.9%
2021-06-27 4
 
2.9%
2021-12-26 4
 
2.9%
2021-05-30 4
 
2.9%
Other values (25) 100
71.4%

Interactions

2023-12-13T05:10:16.734726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:13.798724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:14.438470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:15.051261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:15.686819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.187398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.818319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:13.902049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:14.558004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:15.149071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:15.779695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.281792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.902420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:14.011386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:14.651496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:15.266655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:15.870121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.377079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.980949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:14.135316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:14.755007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:15.368844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:15.962417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.473531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:17.057186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:14.231816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:14.851312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:15.466860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.039517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.556838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:17.145098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:14.340476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:14.960243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:15.564610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.119875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:10:16.650128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:10:20.377102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리장명계열생화학적산소요구량(BOD(mg_L))총유기탄소량(TOC(mg_L))부유물질량(SS(mg_L))총질소(T-N(mg_L))총인(T-P(mg_L))대장균(개_mL)측정일(1주간)
처리장명1.0000.3570.5390.6460.8240.5940.5580.5030.000
계열0.3571.0000.9900.9390.9240.4620.6250.9620.000
생화학적산소요구량(BOD(mg_L))0.5390.9901.0000.6680.8040.7090.3820.7670.237
총유기탄소량(TOC(mg_L))0.6460.9390.6681.0000.7830.4770.4600.5060.000
부유물질량(SS(mg_L))0.8240.9240.8040.7831.0000.4490.5650.7090.000
총질소(T-N(mg_L))0.5940.4620.7090.4770.4491.0000.2570.5980.272
총인(T-P(mg_L))0.5580.6250.3820.4600.5650.2571.0000.2710.000
대장균(개_mL)0.5030.9620.7670.5060.7090.5980.2711.0000.000
측정일(1주간)0.0000.0000.2370.0000.0000.2720.0000.0001.000
2023-12-13T05:10:20.547665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일(1주간)계열처리장명
측정일(1주간)1.0000.0000.000
계열0.0001.0000.567
처리장명0.0000.5671.000
2023-12-13T05:10:20.647968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생화학적산소요구량(BOD(mg_L))총유기탄소량(TOC(mg_L))부유물질량(SS(mg_L))총질소(T-N(mg_L))총인(T-P(mg_L))대장균(개_mL)처리장명계열측정일(1주간)
생화학적산소요구량(BOD(mg_L))1.0000.6430.6310.3700.2990.4810.3720.8840.061
총유기탄소량(TOC(mg_L))0.6431.0000.7040.4290.3090.4590.4800.7660.000
부유물질량(SS(mg_L))0.6310.7041.0000.3690.2110.5570.7080.7460.000
총질소(T-N(mg_L))0.3700.4290.3691.0000.2200.2440.4260.3450.075
총인(T-P(mg_L))0.2990.3090.2110.2201.0000.3470.2870.6140.000
대장균(개_mL)0.4810.4590.5570.2440.3471.0000.3670.8130.000
처리장명0.3720.4800.7080.4260.2870.3671.0000.5670.000
계열0.8840.7660.7460.3450.6140.8130.5671.0000.000
측정일(1주간)0.0610.0000.0000.0750.0000.0000.0000.0001.000

Missing values

2023-12-13T05:10:17.248814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:10:17.399296image/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

처리장명계열생화학적산소요구량(BOD(mg_L))총유기탄소량(TOC(mg_L))부유물질량(SS(mg_L))총질소(T-N(mg_L))총인(T-P(mg_L))대장균(개_mL)측정일(1주간)
0공공하수1처리장생활계열0.84.32.33.4210.879922023-07-30
1공공하수1처리장공장계열3.99.75.76.7970.984792023-07-30
2공공하수2처리장생활계열1.53.72.35.0390.34372023-07-30
3대부하수처리장생활계열0.42.90.44.8510.593852023-07-30
4공공하수1처리장생활계열0.55.11.74.5780.74622023-06-26
5공공하수1처리장공장계열0.910.24.58.2691.152023-06-26
6공공하수2처리장생활계열2.04.64.36.5350.643112023-06-26
7대부하수처리장생활계열0.63.60.72.4550.676672023-06-26
8공공하수1처리장생활계열5.05.24.34.5740.413152023-05-29
9공공하수1처리장공장계열4.211.36.29.0341.597212023-05-29
처리장명계열생화학적산소요구량(BOD(mg_L))총유기탄소량(TOC(mg_L))부유물질량(SS(mg_L))총질소(T-N(mg_L))총인(T-P(mg_L))대장균(개_mL)측정일(1주간)
130공공하수2처리장생활계열3.56.92.18.3260.556112020-11-29
131대부하수처리장생활계열2.66.81.16.4040.7322020-11-29
132공공하수1처리장생활계열2.510.72.811.3090.533102020-10-25
133공공하수1처리장공장계열4.311.25.89.8661.172292020-10-25
134공공하수2처리장생활계열3.27.32.58.9250.8112020-10-25
135대부하수처리장생활계열2.56.01.05.8470.92442020-10-25
136공공하수1처리장생활계열2.67.82.86.9590.564102020-09-27
137공공하수1처리장공장계열4.214.06.08.351.019342020-09-27
138공공하수2처리장생활계열3.27.02.66.9320.534122020-09-27
139대부하수처리장생활계열2.66.91.17.1840.91132020-09-27