Overview

Dataset statistics

Number of variables9
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory83.3 B

Variable types

DateTime1
Categorical2
Numeric6

Dataset

Description한국서부발전 환경감시시스템 발전소 주변 수질정보 입니다.제공항목은 처리일,사업소,호기,수소 이온 농도 지수(PH),화학적산소요구량(COD),부유물질량(SS),총질소(TN),총인(TP),유량 입니다.
Author한국서부발전(주)
URLhttps://www.data.go.kr/data/15123109/fileData.do

Alerts

사업소 has constant value ""Constant
호기 has constant value ""Constant
수소 이온 농도 지수(PH) is highly overall correlated with 총질소(TN)High correlation
총질소(TN) is highly overall correlated with 수소 이온 농도 지수(PH)High correlation
처리일 has unique valuesUnique
유량 has unique valuesUnique
화학적산소요구량(COD) has 5 (16.1%) zerosZeros

Reproduction

Analysis started2023-12-12 01:42:43.229017
Analysis finished2023-12-12 01:42:46.977570
Duration3.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처리일
Date

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2022-12-01 00:00:00
Maximum2022-12-31 00:00:00
2023-12-12T10:42:47.039986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:47.172788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

사업소
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
군산
31 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군산 31
100.0%

Length

2023-12-12T10:42:47.640223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:42:47.768634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군산 31
100.0%

호기
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
1
31 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 31
100.0%

Length

2023-12-12T10:42:47.902504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:42:48.013405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 31
100.0%

수소 이온 농도 지수(PH)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3912903
Minimum5.39
Maximum7.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T10:42:48.123251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.39
5-th percentile5.595
Q17.58
median7.64
Q37.69
95-th percentile7.81
Maximum7.82
Range2.43
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.68172693
Coefficient of variation (CV)0.092233819
Kurtosis4.0363257
Mean7.3912903
Median Absolute Deviation (MAD)0.06
Skewness-2.2801839
Sum229.13
Variance0.46475161
MonotonicityNot monotonic
2023-12-12T10:42:48.353128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
7.65 3
 
9.7%
7.6 3
 
9.7%
7.68 2
 
6.5%
7.61 2
 
6.5%
7.66 2
 
6.5%
7.58 2
 
6.5%
7.81 2
 
6.5%
7.8 2
 
6.5%
7.76 1
 
3.2%
5.75 1
 
3.2%
Other values (11) 11
35.5%
ValueCountFrequency (%)
5.39 1
 
3.2%
5.44 1
 
3.2%
5.75 1
 
3.2%
6.42 1
 
3.2%
6.81 1
 
3.2%
7.51 1
 
3.2%
7.56 1
 
3.2%
7.58 2
6.5%
7.59 1
 
3.2%
7.6 3
9.7%
ValueCountFrequency (%)
7.82 1
 
3.2%
7.81 2
6.5%
7.8 2
6.5%
7.76 1
 
3.2%
7.71 1
 
3.2%
7.7 1
 
3.2%
7.68 2
6.5%
7.66 2
6.5%
7.65 3
9.7%
7.64 1
 
3.2%

화학적산소요구량(COD)
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7229032
Minimum0
Maximum9.18
Zeros5
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T10:42:48.523206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.005
median4.81
Q36.44
95-th percentile8.295
Maximum9.18
Range9.18
Interquartile range (IQR)2.435

Descriptive statistics

Standard deviation2.6577386
Coefficient of variation (CV)0.56273409
Kurtosis-0.40806518
Mean4.7229032
Median Absolute Deviation (MAD)1.51
Skewness-0.50502032
Sum146.41
Variance7.0635746
MonotonicityNot monotonic
2023-12-12T10:42:48.675489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 5
 
16.1%
4.25 2
 
6.5%
6.44 2
 
6.5%
3.77 1
 
3.2%
7.51 1
 
3.2%
1.86 1
 
3.2%
4.99 1
 
3.2%
4.6 1
 
3.2%
7.19 1
 
3.2%
9.18 1
 
3.2%
Other values (15) 15
48.4%
ValueCountFrequency (%)
0.0 5
16.1%
1.86 1
 
3.2%
3.3 1
 
3.2%
3.77 1
 
3.2%
4.24 1
 
3.2%
4.25 2
 
6.5%
4.28 1
 
3.2%
4.58 1
 
3.2%
4.6 1
 
3.2%
4.68 1
 
3.2%
ValueCountFrequency (%)
9.18 1
3.2%
8.39 1
3.2%
8.2 1
3.2%
7.88 1
3.2%
7.63 1
3.2%
7.51 1
3.2%
7.19 1
3.2%
6.44 2
6.5%
6.06 1
3.2%
5.93 1
3.2%

부유물질량(SS)
Real number (ℝ)

Distinct7
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26483871
Minimum0.19
Maximum0.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T10:42:48.809754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.2
Q10.21
median0.3
Q30.3
95-th percentile0.3
Maximum0.3
Range0.11
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.045743558
Coefficient of variation (CV)0.17272233
Kurtosis-1.6016889
Mean0.26483871
Median Absolute Deviation (MAD)0
Skewness-0.6275464
Sum8.21
Variance0.0020924731
MonotonicityNot monotonic
2023-12-12T10:42:48.938989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.3 18
58.1%
0.2 5
 
16.1%
0.21 4
 
12.9%
0.26 1
 
3.2%
0.29 1
 
3.2%
0.19 1
 
3.2%
0.23 1
 
3.2%
ValueCountFrequency (%)
0.19 1
 
3.2%
0.2 5
 
16.1%
0.21 4
 
12.9%
0.23 1
 
3.2%
0.26 1
 
3.2%
0.29 1
 
3.2%
0.3 18
58.1%
ValueCountFrequency (%)
0.3 18
58.1%
0.29 1
 
3.2%
0.26 1
 
3.2%
0.23 1
 
3.2%
0.21 4
 
12.9%
0.2 5
 
16.1%
0.19 1
 
3.2%

총질소(TN)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3596774
Minimum1.51
Maximum2.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T10:42:49.075215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.51
5-th percentile1.73
Q12.03
median2.47
Q32.695
95-th percentile2.915
Maximum2.95
Range1.44
Interquartile range (IQR)0.665

Descriptive statistics

Standard deviation0.40530222
Coefficient of variation (CV)0.17176171
Kurtosis-0.95170279
Mean2.3596774
Median Absolute Deviation (MAD)0.38
Skewness-0.19595006
Sum73.15
Variance0.16426989
MonotonicityNot monotonic
2023-12-12T10:42:49.223234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2.51 2
 
6.5%
2.1 2
 
6.5%
2.0 2
 
6.5%
1.8 1
 
3.2%
2.44 1
 
3.2%
2.95 1
 
3.2%
2.91 1
 
3.2%
2.85 1
 
3.2%
2.81 1
 
3.2%
2.5 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
1.51 1
3.2%
1.66 1
3.2%
1.8 1
3.2%
1.95 1
3.2%
1.96 1
3.2%
2.0 2
6.5%
2.02 1
3.2%
2.04 1
3.2%
2.05 1
3.2%
2.1 2
6.5%
ValueCountFrequency (%)
2.95 1
3.2%
2.92 1
3.2%
2.91 1
3.2%
2.88 1
3.2%
2.87 1
3.2%
2.85 1
3.2%
2.81 1
3.2%
2.74 1
3.2%
2.65 1
3.2%
2.56 1
3.2%

총인(TP)
Real number (ℝ)

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11870968
Minimum0.09
Maximum0.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T10:42:49.358674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.09
Q10.115
median0.12
Q30.13
95-th percentile0.13
Maximum0.14
Range0.05
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.013100111
Coefficient of variation (CV)0.11035419
Kurtosis0.36461529
Mean0.11870968
Median Absolute Deviation (MAD)0.01
Skewness-0.98273942
Sum3.68
Variance0.0001716129
MonotonicityNot monotonic
2023-12-12T10:42:49.514594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.12 12
38.7%
0.13 10
32.3%
0.09 3
 
9.7%
0.11 3
 
9.7%
0.1 2
 
6.5%
0.14 1
 
3.2%
ValueCountFrequency (%)
0.09 3
 
9.7%
0.1 2
 
6.5%
0.11 3
 
9.7%
0.12 12
38.7%
0.13 10
32.3%
0.14 1
 
3.2%
ValueCountFrequency (%)
0.14 1
 
3.2%
0.13 10
32.3%
0.12 12
38.7%
0.11 3
 
9.7%
0.1 2
 
6.5%
0.09 3
 
9.7%

유량
Real number (ℝ)

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.48387
Minimum83.5
Maximum442.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T10:42:49.655930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83.5
5-th percentile86.75
Q1128.85
median186.2
Q3249.4
95-th percentile356.35
Maximum442.6
Range359.1
Interquartile range (IQR)120.55

Descriptive statistics

Standard deviation93.612232
Coefficient of variation (CV)0.46461403
Kurtosis0.022543259
Mean201.48387
Median Absolute Deviation (MAD)64
Skewness0.69389458
Sum6246
Variance8763.2501
MonotonicityNot monotonic
2023-12-12T10:42:49.814975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
91.6 1
 
3.2%
206.3 1
 
3.2%
88.7 1
 
3.2%
213.9 1
 
3.2%
169.8 1
 
3.2%
184.7 1
 
3.2%
268.3 1
 
3.2%
325.3 1
 
3.2%
254.3 1
 
3.2%
290.0 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
83.5 1
3.2%
84.8 1
3.2%
88.7 1
3.2%
89.1 1
3.2%
91.6 1
3.2%
93.6 1
3.2%
101.7 1
3.2%
122.2 1
3.2%
135.5 1
3.2%
144.3 1
3.2%
ValueCountFrequency (%)
442.6 1
3.2%
360.2 1
3.2%
352.5 1
3.2%
331.8 1
3.2%
325.3 1
3.2%
290.0 1
3.2%
268.3 1
3.2%
254.3 1
3.2%
244.5 1
3.2%
235.5 1
3.2%

Interactions

2023-12-12T10:42:46.148520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:43.437007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:43.949114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.440688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.960991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:45.561163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:46.248659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:43.518191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.020144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.527134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:45.045905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:45.652205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:46.330840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:43.593844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.097019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.609144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:45.133529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:45.752131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:46.420919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:43.672431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.182945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.690872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:45.231228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:45.843480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:46.533586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:43.766272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.275336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.776717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:45.337318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:45.938308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:46.619898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:43.852010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.356736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:44.862257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:45.454933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:42:46.041641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:42:49.949821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리일수소 이온 농도 지수(PH)화학적산소요구량(COD)부유물질량(SS)총질소(TN)총인(TP)유량
처리일1.0001.0001.0001.0001.0001.0001.000
수소 이온 농도 지수(PH)1.0001.0000.8030.6380.8100.9050.599
화학적산소요구량(COD)1.0000.8031.0000.8160.9260.5300.723
부유물질량(SS)1.0000.6380.8161.0000.7950.3400.510
총질소(TN)1.0000.8100.9260.7951.0000.4620.778
총인(TP)1.0000.9050.5300.3400.4621.0000.000
유량1.0000.5990.7230.5100.7780.0001.000
2023-12-12T10:42:50.074448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수소 이온 농도 지수(PH)화학적산소요구량(COD)부유물질량(SS)총질소(TN)총인(TP)유량
수소 이온 농도 지수(PH)1.0000.4790.0100.5040.4400.045
화학적산소요구량(COD)0.4791.000-0.472-0.1670.2370.031
부유물질량(SS)0.010-0.4721.0000.3760.369-0.276
총질소(TN)0.504-0.1670.3761.0000.392-0.163
총인(TP)0.4400.2370.3690.3921.000-0.153
유량0.0450.031-0.276-0.163-0.1531.000

Missing values

2023-12-12T10:42:46.777413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:42:46.922160image/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

처리일사업소호기수소 이온 농도 지수(PH)화학적산소요구량(COD)부유물질량(SS)총질소(TN)총인(TP)유량
02022-12-01군산15.443.770.211.80.0991.6
12022-12-02군산17.564.680.32.880.13206.3
22022-12-03군산17.74.280.32.920.13101.7
32022-12-04군산17.714.250.32.870.1484.8
42022-12-05군산17.684.580.32.740.13214.0
52022-12-06군산17.614.810.32.510.13179.8
62022-12-07군산17.685.080.32.470.13215.5
72022-12-08군산15.393.30.21.510.09442.6
82022-12-09군산17.584.240.262.020.13244.5
92022-12-10군산17.515.050.32.10.1393.6
처리일사업소호기수소 이온 농도 지수(PH)화학적산소요구량(COD)부유물질량(SS)총질소(TN)총인(TP)유량
212022-12-22군산16.817.190.22.10.189.1
222022-12-23군산17.664.60.32.220.11122.2
232022-12-24군산17.654.250.32.050.12290.0
242022-12-25군산17.644.990.32.20.12254.3
252022-12-26군산15.751.860.231.660.09325.3
262022-12-27군산17.660.00.32.50.12268.3
272022-12-28군산17.580.00.32.810.12184.7
282022-12-29군산17.60.00.32.850.11169.8
292022-12-30군산17.60.00.32.910.12213.9
302022-12-31군산17.650.00.32.950.1188.7