Overview

Dataset statistics

Number of variables11
Number of observations256
Missing cells241
Missing cells (%)8.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.9 KiB
Average record size in memory95.5 B

Variable types

Numeric7
Categorical2
DateTime2

Dataset

Description인천광역시 부평구 굴포천 수질모니터링 분석자료 데이터입니다.(채수장소, 의뢰일자, 검사항목, 결과일자 등)ex) 1,굴포1교,2014-01-23,BOD, COD, SS, T-N, T-P,2014-02-07,1.8,4.3,6.1,2.59,0.019
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15051643&srcSe=7661IVAWM27C61E190

Alerts

일련번호 is highly overall correlated with 총 질소 and 1 other fieldsHigh correlation
생화학적 산소요구량 is highly overall correlated with 화학적 산소요구량High correlation
화학적 산소요구량 is highly overall correlated with 생화학적 산소요구량 and 1 other fieldsHigh correlation
총 유기탄소 is highly overall correlated with 총 질소High correlation
총 질소 is highly overall correlated with 일련번호 and 2 other fieldsHigh correlation
총 인 is highly overall correlated with 화학적 산소요구량 and 1 other fieldsHigh correlation
채수장소 is highly overall correlated with 검사항목High correlation
검사항목 is highly overall correlated with 일련번호 and 1 other fieldsHigh correlation
화학적 산소요구량 has 172 (67.2%) missing valuesMissing
총 유기탄소 has 68 (26.6%) missing valuesMissing
일련번호 has unique valuesUnique

Reproduction

Analysis started2024-01-28 11:14:44.417400
Analysis finished2024-01-28 11:14:48.603404
Duration4.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct256
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.5
Minimum1
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-28T20:14:48.664857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.75
Q164.75
median128.5
Q3192.25
95-th percentile243.25
Maximum256
Range255
Interquartile range (IQR)127.5

Descriptive statistics

Standard deviation74.045031
Coefficient of variation (CV)0.57622592
Kurtosis-1.2
Mean128.5
Median Absolute Deviation (MAD)64
Skewness0
Sum32896
Variance5482.6667
MonotonicityStrictly increasing
2024-01-28T20:14:48.769447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
130 1
 
0.4%
164 1
 
0.4%
165 1
 
0.4%
166 1
 
0.4%
167 1
 
0.4%
168 1
 
0.4%
169 1
 
0.4%
170 1
 
0.4%
171 1
 
0.4%
Other values (246) 246
96.1%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
256 1
0.4%
255 1
0.4%
254 1
0.4%
253 1
0.4%
252 1
0.4%
251 1
0.4%
250 1
0.4%
249 1
0.4%
248 1
0.4%
247 1
0.4%

채수장소
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
굴포1교
115 
굴포3교
72 
삼산3교
39 
서부1교
25 
여울교 부근
 
1
Other values (4)
 
4

Length

Max length7
Median length4
Mean length4.015625
Min length3

Unique

Unique5 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
굴포1교 115
44.9%
굴포3교 72
28.1%
삼산3교 39
 
15.2%
서부1교 25
 
9.8%
여울교 부근 1
 
0.4%
삼산3교 부근 1
 
0.4%
천상교 1
 
0.4%
서부2교 1
 
0.4%
굴포2교 1
 
0.4%

Length

2024-01-28T20:14:48.885185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:14:48.990075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
굴포1교 115
44.6%
굴포3교 72
27.9%
삼산3교 40
 
15.5%
서부1교 25
 
9.7%
부근 2
 
0.8%
여울교 1
 
0.4%
천상교 1
 
0.4%
서부2교 1
 
0.4%
굴포2교 1
 
0.4%

의뢰일자
Date

Distinct119
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2014-01-23 00:00:00
Maximum2023-04-19 00:00:00
2024-01-28T20:14:49.109417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:49.203543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

검사항목
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
BOD, SS, T-N, T-P, TOC
152 
BOD, COD, SS, T-N, T-P
55 
BOD, COD, TOC, SS, T-N, T-P
23 
BOD, TOC, SS, T-N, T-P
17 
BOD, COD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소
 
4
Other values (2)
 
5

Length

Max length58
Median length22
Mean length23.617188
Min length22

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, SS, T-N, T-P, TOC 152
59.4%
BOD, COD, SS, T-N, T-P 55
 
21.5%
BOD, COD, TOC, SS, T-N, T-P 23
 
9.0%
BOD, TOC, SS, T-N, T-P 17
 
6.6%
BOD, COD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소 4
 
1.6%
COD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소 3
 
1.2%
BOD, SS, T-N, T-P,Cu, Zn, Pb, Cd, Cr, Mn, Fe,암모니아성 질소 2
 
0.8%

Length

2024-01-28T20:14:49.304874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:14:49.387738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ss 256
18.8%
t-n 256
18.8%
bod 253
18.6%
t-p 247
18.1%
toc 192
14.1%
cod 85
 
6.2%
t-p,cu 9
 
0.7%
zn 9
 
0.7%
pb 9
 
0.7%
cd 9
 
0.7%
Other values (4) 36
 
2.6%

결과일자
Date

Distinct117
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2014-02-07 00:00:00
Maximum2023-05-03 00:00:00
2024-01-28T20:14:49.486317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:49.598676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

생화학적 산소요구량
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)25.1%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.7466667
Minimum0.3
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-28T20:14:49.704134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile1
Q11.5
median2.1
Q32.85
95-th percentile6.52
Maximum37
Range36.7
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation3.1108995
Coefficient of variation (CV)1.132609
Kurtosis66.086728
Mean2.7466667
Median Absolute Deviation (MAD)0.7
Skewness6.9882865
Sum700.4
Variance9.6776955
MonotonicityNot monotonic
2024-01-28T20:14:49.806642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 14
 
5.5%
2.2 13
 
5.1%
2.1 12
 
4.7%
2.4 12
 
4.7%
1.6 12
 
4.7%
1.7 11
 
4.3%
1.9 11
 
4.3%
1.4 11
 
4.3%
1.8 10
 
3.9%
2.0 10
 
3.9%
Other values (54) 139
54.3%
ValueCountFrequency (%)
0.3 2
 
0.8%
0.4 2
 
0.8%
0.5 1
 
0.4%
0.6 1
 
0.4%
0.7 1
 
0.4%
0.8 1
 
0.4%
0.9 3
 
1.2%
1.0 14
5.5%
1.1 9
3.5%
1.2 9
3.5%
ValueCountFrequency (%)
37.0 1
0.4%
23.9 1
0.4%
13.5 1
0.4%
10.6 1
0.4%
10.4 1
0.4%
9.4 1
0.4%
9.2 1
0.4%
8.5 1
0.4%
8.0 1
0.4%
7.1 2
0.8%

화학적 산소요구량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)52.4%
Missing172
Missing (%)67.2%
Infinite0
Infinite (%)0.0%
Mean7.275
Minimum3.5
Maximum67.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-28T20:14:49.914948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile4.1
Q14.775
median5.8
Q36.825
95-th percentile11.74
Maximum67.5
Range64
Interquartile range (IQR)2.05

Descriptive statistics

Standard deviation7.8838691
Coefficient of variation (CV)1.0836933
Kurtosis44.69867
Mean7.275
Median Absolute Deviation (MAD)1.05
Skewness6.3696239
Sum611.1
Variance62.155392
MonotonicityNot monotonic
2024-01-28T20:14:50.041174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4.6 6
 
2.3%
5.2 4
 
1.6%
7.2 4
 
1.6%
5.3 4
 
1.6%
6.0 3
 
1.2%
6.2 3
 
1.2%
5.7 3
 
1.2%
4.3 3
 
1.2%
6.4 2
 
0.8%
4.1 2
 
0.8%
Other values (34) 50
 
19.5%
(Missing) 172
67.2%
ValueCountFrequency (%)
3.5 1
 
0.4%
3.7 1
 
0.4%
3.9 2
 
0.8%
4.1 2
 
0.8%
4.2 2
 
0.8%
4.3 3
1.2%
4.4 2
 
0.8%
4.6 6
2.3%
4.7 2
 
0.8%
4.8 1
 
0.4%
ValueCountFrequency (%)
67.5 1
0.4%
39.6 1
0.4%
16.8 1
0.4%
14.5 1
0.4%
11.8 1
0.4%
11.4 1
0.4%
10.4 1
0.4%
9.1 1
0.4%
8.7 1
0.4%
7.6 2
0.8%

총 유기탄소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct64
Distinct (%)34.0%
Missing68
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean4.1248564
Minimum1.023
Maximum23.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-28T20:14:50.149593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.023
5-th percentile1.635
Q12.7
median3.9
Q34.3
95-th percentile8.4
Maximum23.6
Range22.577
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation2.7829807
Coefficient of variation (CV)0.67468548
Kurtosis19.594773
Mean4.1248564
Median Absolute Deviation (MAD)0.6
Skewness3.8501511
Sum775.473
Variance7.7449817
MonotonicityNot monotonic
2024-01-28T20:14:50.249873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 19
 
7.4%
3.9 16
 
6.2%
4.3 11
 
4.3%
3.8 9
 
3.5%
4.4 7
 
2.7%
4.2 7
 
2.7%
2.4 7
 
2.7%
4.1 6
 
2.3%
3.5 5
 
2.0%
2.6 5
 
2.0%
Other values (54) 96
37.5%
(Missing) 68
26.6%
ValueCountFrequency (%)
1.023 1
 
0.4%
1.075 1
 
0.4%
1.153 1
 
0.4%
1.372 1
 
0.4%
1.4 2
0.8%
1.5 1
 
0.4%
1.587 1
 
0.4%
1.6 2
0.8%
1.7 3
1.2%
1.768 1
 
0.4%
ValueCountFrequency (%)
23.6 1
0.4%
19.0 1
0.4%
16.7 1
0.4%
15.1 1
0.4%
13.2 1
0.4%
10.933 1
0.4%
9.4 1
0.4%
8.9 1
0.4%
8.6 1
0.4%
8.4 2
0.8%

부유물질
Real number (ℝ)

Distinct138
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.239609
Minimum1.5
Maximum392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-28T20:14:50.348917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.4
Q14.7
median8
Q313.825
95-th percentile39.375
Maximum392
Range390.5
Interquartile range (IQR)9.125

Descriptive statistics

Standard deviation33.75599
Coefficient of variation (CV)2.3705699
Kurtosis92.504697
Mean14.239609
Median Absolute Deviation (MAD)3.8
Skewness9.1513839
Sum3645.34
Variance1139.4668
MonotonicityNot monotonic
2024-01-28T20:14:50.459319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 6
 
2.3%
2.4 6
 
2.3%
4.4 5
 
2.0%
4.0 5
 
2.0%
9.2 5
 
2.0%
5.2 5
 
2.0%
6.8 4
 
1.6%
4.2 4
 
1.6%
8.0 4
 
1.6%
8.8 4
 
1.6%
Other values (128) 208
81.2%
ValueCountFrequency (%)
1.5 1
 
0.4%
1.6 2
 
0.8%
1.8 2
 
0.8%
2.0 3
1.2%
2.4 6
2.3%
2.6 1
 
0.4%
2.8 2
 
0.8%
3.0 4
1.6%
3.2 3
1.2%
3.4 1
 
0.4%
ValueCountFrequency (%)
392.0 1
0.4%
331.0 1
0.4%
160.0 1
0.4%
51.7 1
0.4%
51.2 1
0.4%
49.3 1
0.4%
49.1 1
0.4%
48.0 1
0.4%
46.0 1
0.4%
43.7 1
0.4%

총 질소
Real number (ℝ)

HIGH CORRELATION 

Distinct234
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4687734
Minimum1.18
Maximum14.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-28T20:14:50.569770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile1.57
Q12.4175
median5.347
Q310.905
95-th percentile12.5175
Maximum14.76
Range13.58
Interquartile range (IQR)8.4875

Descriptive statistics

Standard deviation4.2084797
Coefficient of variation (CV)0.65058387
Kurtosis-1.6410155
Mean6.4687734
Median Absolute Deviation (MAD)3.536
Skewness0.21093028
Sum1656.006
Variance17.711301
MonotonicityNot monotonic
2024-01-28T20:14:50.680247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.43 3
 
1.2%
11.37 2
 
0.8%
2.18 2
 
0.8%
10.36 2
 
0.8%
10.59 2
 
0.8%
9.66 2
 
0.8%
2.38 2
 
0.8%
11.81 2
 
0.8%
2.1 2
 
0.8%
12.2 2
 
0.8%
Other values (224) 235
91.8%
ValueCountFrequency (%)
1.18 1
0.4%
1.225 1
0.4%
1.24 1
0.4%
1.402 1
0.4%
1.455 1
0.4%
1.459 1
0.4%
1.493 1
0.4%
1.512 1
0.4%
1.526 1
0.4%
1.543 1
0.4%
ValueCountFrequency (%)
14.76 1
0.4%
13.63 1
0.4%
13.44 1
0.4%
13.39 1
0.4%
13.36 1
0.4%
13.34 1
0.4%
13.09 1
0.4%
12.91 1
0.4%
12.85 1
0.4%
12.8 1
0.4%

총 인
Real number (ℝ)

HIGH CORRELATION 

Distinct161
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13462891
Minimum0.005
Maximum2.742
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-28T20:14:50.783956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.026
Q10.054
median0.103
Q30.15725
95-th percentile0.27675
Maximum2.742
Range2.737
Interquartile range (IQR)0.10325

Descriptive statistics

Standard deviation0.19369857
Coefficient of variation (CV)1.4387591
Kurtosis129.81009
Mean0.13462891
Median Absolute Deviation (MAD)0.053
Skewness10.014629
Sum34.465
Variance0.037519136
MonotonicityNot monotonic
2024-01-28T20:14:50.884646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.045 6
 
2.3%
0.042 4
 
1.6%
0.09 4
 
1.6%
0.148 4
 
1.6%
0.046 4
 
1.6%
0.019 3
 
1.2%
0.025 3
 
1.2%
0.026 3
 
1.2%
0.04 3
 
1.2%
0.049 3
 
1.2%
Other values (151) 219
85.5%
ValueCountFrequency (%)
0.005 1
 
0.4%
0.017 1
 
0.4%
0.019 3
1.2%
0.02 1
 
0.4%
0.022 1
 
0.4%
0.024 2
0.8%
0.025 3
1.2%
0.026 3
1.2%
0.028 1
 
0.4%
0.029 1
 
0.4%
ValueCountFrequency (%)
2.742 1
0.4%
0.683 1
0.4%
0.639 1
0.4%
0.628 1
0.4%
0.625 1
0.4%
0.553 1
0.4%
0.547 1
0.4%
0.468 1
0.4%
0.35 1
0.4%
0.324 1
0.4%

Interactions

2024-01-28T20:14:47.636156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:44.736702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.226939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.719519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.218801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.687825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.165720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.697139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:44.796186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.293699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.785642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.286382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.755198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.232943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.763290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:44.869956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.361946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.852268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.353644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.826735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.302158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.843318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:44.946910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.429278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.932417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.427334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.893957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.372565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.900819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.018029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.488498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.009070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.488487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.956025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.431929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.969114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.101060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.566851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.080550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.563717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.025717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.504216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:48.031334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.165424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:45.635469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.149318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:46.628029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.096347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:14:47.570342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T20:14:50.966875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호채수장소검사항목생화학적 산소요구량화학적 산소요구량총 유기탄소부유물질총 질소총 인
일련번호1.0000.5020.7960.1820.0000.5860.0910.7830.128
채수장소0.5021.0000.7900.2780.6240.3570.1520.4010.251
검사항목0.7960.7901.0000.2580.4790.7390.1460.5980.069
생화학적 산소요구량0.1820.2780.2581.0000.9930.7340.8100.4410.606
화학적 산소요구량0.0000.6240.4790.9931.0000.8681.0000.8560.780
총 유기탄소0.5860.3570.7390.7340.8681.0000.7510.5760.374
부유물질0.0910.1520.1460.8101.0000.7511.0000.4490.515
총 질소0.7830.4010.5980.4410.8560.5760.4491.0000.567
총 인0.1280.2510.0690.6060.7800.3740.5150.5671.000
2024-01-28T20:14:51.053710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
채수장소검사항목
채수장소1.0000.576
검사항목0.5761.000
2024-01-28T20:14:51.117925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호생화학적 산소요구량화학적 산소요구량총 유기탄소부유물질총 질소총 인채수장소검사항목
일련번호1.000-0.3820.2630.4730.1580.7370.3990.2550.576
생화학적 산소요구량-0.3821.0000.540-0.1130.116-0.4090.0170.1570.156
화학적 산소요구량0.2630.5401.0000.4330.4430.3900.6100.4580.405
총 유기탄소0.473-0.1130.4331.0000.1660.5590.4440.2320.436
부유물질0.1580.1160.4430.1661.0000.2300.2610.0870.092
총 질소0.737-0.4090.3900.5590.2301.0000.5340.1940.354
총 인0.3990.0170.6100.4440.2610.5341.0000.1610.046
채수장소0.2550.1570.4580.2320.0870.1940.1611.0000.576
검사항목0.5760.1560.4050.4360.0920.3540.0460.5761.000

Missing values

2024-01-28T20:14:48.132077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T20:14:48.474912image/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.
2024-01-28T20:14:48.557735image/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

일련번호채수장소의뢰일자검사항목결과일자생화학적 산소요구량화학적 산소요구량총 유기탄소부유물질총 질소총 인
01굴포1교2014-01-23BOD, COD, SS, T-N, T-P2014-02-071.84.3<NA>6.12.590.019
12삼산3교2014-01-23BOD, COD, SS, T-N, T-P2014-02-071.84.4<NA>4.42.7250.029
23굴포1교2014-02-01BOD, COD, SS, T-N, T-P2014-02-260.35.0<NA>9.02.7780.039
34삼산3교2014-02-01BOD, COD, SS, T-N, T-P2014-02-262.45.5<NA>10.02.640.061
45굴포1교2014-03-27BOD, COD, SS, T-N, T-P2014-04-042.04.3<NA>7.21.8970.039
56삼산3교2014-03-27BOD, COD, SS, T-N, T-P2014-04-042.84.8<NA>7.92.1450.06
67굴포1교2014-04-25BOD, COD, SS, T-N, T-P2014-05-072.24.2<NA>8.21.7930.047
78삼산3교2014-04-25BOD, COD, SS, T-N, T-P2014-05-074.45.3<NA>15.02.8970.104
89삼산3교2014-05-29BOD, COD, SS, T-N, T-P2014-06-136.26.9<NA>23.11.5520.101
910굴포1교2014-05-29BOD, COD, SS, T-N, T-P2014-06-133.45.2<NA>6.01.5650.074
일련번호채수장소의뢰일자검사항목결과일자생화학적 산소요구량화학적 산소요구량총 유기탄소부유물질총 질소총 인
246247서부1교2023-01-27BOD, SS, T-N, T-P, TOC2023-02-031.7<NA>4.017.811.810.149
247248굴포1교2023-02-16BOD, SS, T-N, T-P, TOC2023-03-031.6<NA>8.69.211.20.212
248249굴포3교2023-02-16BOD, SS, T-N, T-P, TOC2023-03-031.4<NA>8.24.610.730.164
249250서부1교2023-02-16BOD, SS, T-N, T-P, TOC2023-03-031.4<NA>7.23.810.930.203
250251굴포1교2023-03-23BOD, SS, T-N, T-P, TOC2023-04-042.4<NA>7.210.410.930.156
251252굴포3교2023-03-23BOD, SS, T-N, T-P, TOC2023-04-042.6<NA>8.48.411.10.146
252253서부1교2023-03-23BOD, SS, T-N, T-P, TOC2023-04-042.8<NA>8.216.510.750.148
253254굴포1교2023-04-19BOD, SS, T-N, T-P, TOC2023-05-031.2<NA>3.96.511.390.09
254255굴포3교2023-04-19BOD, SS, T-N, T-P, TOC2023-05-031.5<NA>4.49.211.50.071
255256서부1교2023-04-19BOD, SS, T-N, T-P, TOC2023-05-032.2<NA>4.027.211.370.076