Overview

Dataset statistics

Number of variables10
Number of observations68
Missing cells4
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory87.9 B

Variable types

Numeric6
Categorical2
DateTime2

Dataset

Description2016년 5월 31일 기준 부평구 굴포천 모니터링결과 정보(2014년~2016년)를 제공합니다
Author인천광역시 부평구
URLhttps://www.data.go.kr/data/15051642/fileData.do

Alerts

COD is highly overall correlated with T-PHigh correlation
T-P is highly overall correlated with CODHigh correlation
채수장소 is highly overall correlated with 검사항목High correlation
검사항목 is highly overall correlated with 채수장소High correlation
검사항목 is highly imbalanced (53.6%)Imbalance
BOD has 1 (1.5%) missing valuesMissing
COD has 3 (4.4%) missing valuesMissing
일련번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:36:38.653595
Analysis finished2023-12-12 16:36:42.582539
Duration3.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.5
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T01:36:42.659796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.35
Q117.75
median34.5
Q351.25
95-th percentile64.65
Maximum68
Range67
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation19.77372
Coefficient of variation (CV)0.5731513
Kurtosis-1.2
Mean34.5
Median Absolute Deviation (MAD)17
Skewness0
Sum2346
Variance391
MonotonicityStrictly increasing
2023-12-13T01:36:42.812797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
45 1
 
1.5%
51 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
44 1
 
1.5%
36 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
68 1
1.5%
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%

채수장소
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size676.0 B
굴포1교
30 
삼산3교
30 
굴포3교
 
2
여울교 부근
 
1
삼산3교 부근
 
1
Other values (4)
4 

Length

Max length7
Median length4
Mean length4.0588235
Min length3

Unique

Unique6 ?
Unique (%)8.8%

Sample

1st row굴포1교
2nd row삼산3교
3rd row굴포1교
4th row삼산3교
5th row굴포1교

Common Values

ValueCountFrequency (%)
굴포1교 30
44.1%
삼산3교 30
44.1%
굴포3교 2
 
2.9%
여울교 부근 1
 
1.5%
삼산3교 부근 1
 
1.5%
천상교 1
 
1.5%
서부2교 1
 
1.5%
서부1교 1
 
1.5%
굴포2교 1
 
1.5%

Length

2023-12-13T01:36:42.992197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:36:43.167148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
삼산3교 31
44.3%
굴포1교 30
42.9%
굴포3교 2
 
2.9%
부근 2
 
2.9%
여울교 1
 
1.4%
천상교 1
 
1.4%
서부2교 1
 
1.4%
서부1교 1
 
1.4%
굴포2교 1
 
1.4%

의뢰일자
Date

Distinct34
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
Minimum2014-01-23 00:00:00
Maximum2016-05-12 00:00:00
2023-12-13T01:36:43.305230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:43.463467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)

검사항목
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size676.0 B
BOD, COD, SS, T-N, T-P
55 
BOD, COD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소
 
4
BOD, COD, TOC, SS, T-N, T-P
 
4
COD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소
 
3
BOD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소
 
2

Length

Max length58
Median length23
Mean length27.558824
Min length23

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOD, COD, SS, T-N, T-P
2nd rowBOD, COD, SS, T-N, T-P
3rd rowBOD, COD, SS, T-N, T-P
4th rowBOD, COD, SS, T-N, T-P
5th rowBOD, COD, SS, T-N, T-P

Common Values

ValueCountFrequency (%)
BOD, COD, SS, T-N, T-P 55
80.9%
BOD, COD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소 4
 
5.9%
BOD, COD, TOC, SS, T-N, T-P 4
 
5.9%
COD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소 3
 
4.4%
BOD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소 2
 
2.9%

Length

2023-12-13T01:36:43.636702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:36:43.761066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ss 68
16.9%
t-n 68
16.9%
cod 66
16.4%
bod 65
16.2%
t-p 59
14.7%
t-p,cu 9
 
2.2%
zn 9
 
2.2%
pb 9
 
2.2%
cd 9
 
2.2%
cr 9
 
2.2%
Other values (4) 31
7.7%

결과일자
Date

Distinct33
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
Minimum2014-02-07 00:00:00
Maximum2016-05-24 00:00:00
2023-12-13T01:36:43.912020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:44.055619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

BOD
Real number (ℝ)

MISSING 

Distinct40
Distinct (%)59.7%
Missing1
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean3.1925373
Minimum0.3
Maximum9.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T01:36:44.196182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.79
Q11.95
median2.7
Q33.5
95-th percentile7.01
Maximum9.4
Range9.1
Interquartile range (IQR)1.55

Descriptive statistics

Standard deviation1.9322081
Coefficient of variation (CV)0.60522649
Kurtosis2.2459975
Mean3.1925373
Median Absolute Deviation (MAD)0.8
Skewness1.4423064
Sum213.9
Variance3.7334283
MonotonicityNot monotonic
2023-12-13T01:36:44.360194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3.4 5
 
7.4%
3.5 4
 
5.9%
1.9 3
 
4.4%
3.0 3
 
4.4%
2.7 3
 
4.4%
1.7 3
 
4.4%
1.8 3
 
4.4%
2.8 3
 
4.4%
2.0 3
 
4.4%
2.3 2
 
2.9%
Other values (30) 35
51.5%
ValueCountFrequency (%)
0.3 1
 
1.5%
0.5 1
 
1.5%
0.6 1
 
1.5%
0.7 1
 
1.5%
1.0 1
 
1.5%
1.2 1
 
1.5%
1.6 2
2.9%
1.7 3
4.4%
1.8 3
4.4%
1.9 3
4.4%
ValueCountFrequency (%)
9.4 1
1.5%
9.2 1
1.5%
8.5 1
1.5%
7.1 1
1.5%
6.8 1
1.5%
6.2 1
1.5%
5.9 1
1.5%
5.7 1
1.5%
5.5 1
1.5%
5.1 1
1.5%

COD
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)55.4%
Missing3
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean5.9569231
Minimum3.5
Maximum16.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T01:36:44.508043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile4.2
Q14.8
median5.7
Q36.7
95-th percentile7.6
Maximum16.8
Range13.3
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.8472927
Coefficient of variation (CV)0.31010854
Kurtosis18.149036
Mean5.9569231
Median Absolute Deviation (MAD)1
Skewness3.399315
Sum387.2
Variance3.4124904
MonotonicityNot monotonic
2023-12-13T01:36:44.665100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
4.6 6
 
8.8%
5.2 4
 
5.9%
7.2 4
 
5.9%
4.3 3
 
4.4%
6.0 3
 
4.4%
6.2 3
 
4.4%
5.7 3
 
4.4%
5.3 3
 
4.4%
5.4 2
 
2.9%
7.4 2
 
2.9%
Other values (26) 32
47.1%
(Missing) 3
 
4.4%
ValueCountFrequency (%)
3.5 1
 
1.5%
3.9 1
 
1.5%
4.1 1
 
1.5%
4.2 2
 
2.9%
4.3 3
4.4%
4.4 1
 
1.5%
4.6 6
8.8%
4.7 1
 
1.5%
4.8 1
 
1.5%
4.9 1
 
1.5%
ValueCountFrequency (%)
16.8 1
 
1.5%
10.4 1
 
1.5%
8.7 1
 
1.5%
7.6 2
2.9%
7.4 2
2.9%
7.2 4
5.9%
7.1 1
 
1.5%
7.0 1
 
1.5%
6.9 1
 
1.5%
6.8 2
2.9%

SS
Real number (ℝ)

Distinct50
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3970588
Minimum1.6
Maximum49.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T01:36:44.824656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile2.14
Q14.2
median6.3
Q39.7
95-th percentile21.285
Maximum49.1
Range47.5
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation7.4152922
Coefficient of variation (CV)0.88308208
Kurtosis13.060893
Mean8.3970588
Median Absolute Deviation (MAD)2.3
Skewness3.0292935
Sum571
Variance54.986558
MonotonicityNot monotonic
2023-12-13T01:36:44.967794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 4
 
5.9%
7.4 3
 
4.4%
4.0 3
 
4.4%
2.4 3
 
4.4%
3.2 2
 
2.9%
4.2 2
 
2.9%
4.6 2
 
2.9%
6.8 2
 
2.9%
1.6 2
 
2.9%
3.0 2
 
2.9%
Other values (40) 43
63.2%
ValueCountFrequency (%)
1.6 2
2.9%
1.8 1
 
1.5%
2.0 1
 
1.5%
2.4 3
4.4%
3.0 2
2.9%
3.2 2
2.9%
3.6 2
2.9%
4.0 3
4.4%
4.2 2
2.9%
4.4 4
5.9%
ValueCountFrequency (%)
49.1 1
1.5%
26.6 1
1.5%
23.1 1
1.5%
21.6 1
1.5%
20.7 1
1.5%
19.0 1
1.5%
15.9 1
1.5%
15.8 1
1.5%
15.6 1
1.5%
15.0 1
1.5%

T-N
Real number (ℝ)

Distinct67
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7068529
Minimum1.225
Maximum5.691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T01:36:45.105398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.225
5-th percentile1.4709
Q11.802
median2.5465
Q33.4675
95-th percentile4.9807
Maximum5.691
Range4.466
Interquartile range (IQR)1.6655

Descriptive statistics

Standard deviation1.1146382
Coefficient of variation (CV)0.41178381
Kurtosis0.080477986
Mean2.7068529
Median Absolute Deviation (MAD)0.772
Skewness0.90195818
Sum184.066
Variance1.2424184
MonotonicityNot monotonic
2023-12-13T01:36:45.258496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.811 2
 
2.9%
2.59 1
 
1.5%
1.805 1
 
1.5%
1.599 1
 
1.5%
1.586 1
 
1.5%
1.459 1
 
1.5%
1.493 1
 
1.5%
1.875 1
 
1.5%
1.757 1
 
1.5%
2.725 1
 
1.5%
Other values (57) 57
83.8%
ValueCountFrequency (%)
1.225 1
1.5%
1.402 1
1.5%
1.455 1
1.5%
1.459 1
1.5%
1.493 1
1.5%
1.512 1
1.5%
1.526 1
1.5%
1.543 1
1.5%
1.552 1
1.5%
1.565 1
1.5%
ValueCountFrequency (%)
5.691 1
1.5%
5.481 1
1.5%
5.253 1
1.5%
5.148 1
1.5%
4.67 1
1.5%
4.502 1
1.5%
4.409 1
1.5%
4.051 1
1.5%
4.046 1
1.5%
3.965 1
1.5%

T-P
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.083088235
Minimum0.019
Maximum0.468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T01:36:45.395162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.019
5-th percentile0.024
Q10.039
median0.0495
Q30.10175
95-th percentile0.2336
Maximum0.468
Range0.449
Interquartile range (IQR)0.06275

Descriptive statistics

Standard deviation0.078538086
Coefficient of variation (CV)0.94523714
Kurtosis8.9741218
Mean0.083088235
Median Absolute Deviation (MAD)0.021
Skewness2.6805106
Sum5.65
Variance0.0061682309
MonotonicityNot monotonic
2023-12-13T01:36:45.525459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.042 3
 
4.4%
0.046 3
 
4.4%
0.019 2
 
2.9%
0.043 2
 
2.9%
0.045 2
 
2.9%
0.024 2
 
2.9%
0.025 2
 
2.9%
0.035 2
 
2.9%
0.038 2
 
2.9%
0.041 2
 
2.9%
Other values (43) 46
67.6%
ValueCountFrequency (%)
0.019 2
2.9%
0.02 1
1.5%
0.024 2
2.9%
0.025 2
2.9%
0.026 1
1.5%
0.028 1
1.5%
0.029 1
1.5%
0.031 1
1.5%
0.032 1
1.5%
0.035 2
2.9%
ValueCountFrequency (%)
0.468 1
1.5%
0.313 1
1.5%
0.296 1
1.5%
0.242 1
1.5%
0.218 1
1.5%
0.181 1
1.5%
0.175 1
1.5%
0.162 1
1.5%
0.156 1
1.5%
0.148 1
1.5%

Interactions

2023-12-13T01:36:41.672544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:38.985262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.454169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.926430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:40.630003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:41.086023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:41.776812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.064744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.540193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:40.001254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:40.710782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:41.167507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:41.854793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.146814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.608323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:40.066723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:40.777480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:41.265524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:41.957652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.227621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.685121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:40.425007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:40.858485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:41.412015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:42.038773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.303229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.762874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:40.500267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:40.935140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:41.504424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:42.112538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.380072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:39.838721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:40.564907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:41.004891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:36:41.584911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:36:45.881004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호채수장소의뢰일자검사항목결과일자BODCODSST-NT-P
일련번호1.0000.1790.9890.8510.9890.3920.4590.3420.6660.405
채수장소0.1791.0000.6840.8720.0000.4610.6030.5060.7040.633
의뢰일자0.9890.6841.0001.0000.9980.7790.8270.8880.9170.757
검사항목0.8510.8721.0001.0000.9530.5710.5510.3940.8120.480
결과일자0.9890.0000.9980.9531.0000.7860.6320.8160.7600.162
BOD0.3920.4610.7790.5710.7861.0000.6520.4370.6450.789
COD0.4590.6030.8270.5510.6320.6521.0000.6870.6500.785
SS0.3420.5060.8880.3940.8160.4370.6871.0000.4730.817
T-N0.6660.7040.9170.8120.7600.6450.6500.4731.0000.604
T-P0.4050.6330.7570.4800.1620.7890.7850.8170.6041.000
2023-12-13T01:36:45.990067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검사항목채수장소
검사항목1.0000.716
채수장소0.7161.000
2023-12-13T01:36:46.072966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호BODCODSST-NT-P채수장소검사항목
일련번호1.0000.0280.385-0.3750.2200.0640.0660.495
BOD0.0281.0000.4330.3060.3010.4860.2410.361
COD0.3850.4331.0000.3120.4780.5440.4100.380
SS-0.3750.3060.3121.0000.1250.2320.2890.259
T-N0.2200.3010.4780.1251.0000.3910.4110.449
T-P0.0640.4860.5440.2320.3911.0000.3920.326
채수장소0.0660.2410.4100.2890.4110.3921.0000.716
검사항목0.4950.3610.3800.2590.4490.3260.7161.000

Missing values

2023-12-13T01:36:42.252008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:36:42.404582image/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.
2023-12-13T01:36:42.534222image/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

일련번호채수장소의뢰일자검사항목결과일자BODCODSST-NT-P
01굴포1교2014-01-23BOD, COD, SS, T-N, T-P2014-02-071.84.36.12.590.019
12삼산3교2014-01-23BOD, COD, SS, T-N, T-P2014-02-071.84.44.42.7250.029
23굴포1교2014-02-01BOD, COD, SS, T-N, T-P2014-02-260.35.09.02.7780.039
34삼산3교2014-02-01BOD, COD, SS, T-N, T-P2014-02-262.45.510.02.640.061
45굴포1교2014-03-27BOD, COD, SS, T-N, T-P2014-04-042.04.37.21.8970.039
56삼산3교2014-03-27BOD, COD, SS, T-N, T-P2014-04-042.84.87.92.1450.06
67굴포1교2014-04-25BOD, COD, SS, T-N, T-P2014-05-072.24.28.21.7930.047
78삼산3교2014-04-25BOD, COD, SS, T-N, T-P2014-05-074.45.315.02.8970.104
89삼산3교2014-05-29BOD, COD, SS, T-N, T-P2014-06-136.26.923.11.5520.101
910굴포1교2014-05-29BOD, COD, SS, T-N, T-P2014-06-133.45.26.01.5650.074
일련번호채수장소의뢰일자검사항목결과일자BODCODSST-NT-P
5859굴포3교2016-02-18BOD, COD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소2016-02-250.56.23.63.9650.162
5960굴포3교2016-02-19BOD, COD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소2016-02-251.96.13.63.8280.106
6061굴포1교2016-02-25BOD, COD, SS, T-N, T-P2016-03-092.86.815.63.4410.046
6162삼산3교2016-02-25BOD, COD, SS, T-N, T-P2016-03-093.57.113.63.5990.063
6263굴포1교2016-03-23BOD, COD, SS, T-N, T-P2016-04-112.66.510.62.5240.032
6364삼산3교2016-03-23BOD, COD, SS, T-N, T-P2016-04-112.35.87.42.5450.031
6465굴포1교2016-04-22BOD, COD, TOC, SS, T-N, T-P2016-05-102.14.71.62.1580.046
6566삼산3교2016-04-22BOD, COD, TOC, SS, T-N, T-P2016-05-104.77.22.42.5830.09
6667굴포1교2016-05-12BOD, COD, TOC, SS, T-N, T-P2016-05-241.75.24.41.8110.045
6768삼산3교2016-05-12BOD, COD, TOC, SS, T-N, T-P2016-05-248.58.79.64.0460.218