Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory683.6 KiB
Average record size in memory70.0 B

Variable types

DateTime1
Numeric6

Dataset

Description전주시 도시가스 사용량 정보입니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=8&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15093744

Alerts

비보정값(strNonCorrectValue) is highly overall correlated with 보정값(strCorrectValue)High correlation
보정값(strCorrectValue) is highly overall correlated with 비보정값(strNonCorrectValue)High correlation
온도(dmlTempeture) is highly overall correlated with 보정계수(dmlCorrectCoefficient)High correlation
압력(dmlPressure) is highly overall correlated with 보정계수(dmlCorrectCoefficient)High correlation
보정계수(dmlCorrectCoefficient) is highly overall correlated with 온도(dmlTempeture) and 1 other fieldsHigh correlation
비보정값(strNonCorrectValue) is highly skewed (γ1 = 66.26502655)Skewed
순간유량(strFlowRate) is highly skewed (γ1 = 99.22383878)Skewed
순간유량(strFlowRate) has 8537 (85.4%) zerosZeros

Reproduction

Analysis started2024-03-14 02:55:11.818281
Analysis finished2024-03-14 02:55:16.383463
Duration4.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자료수집일자(dtDataCollect)
Date

Distinct5958
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-01-01 00:00:00
Maximum2020-12-13 07:00:00
2024-03-14T11:55:16.439293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:16.542075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

비보정값(strNonCorrectValue)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3934
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56814.931
Minimum0
Maximum15617908
Zeros28
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:16.650840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1665
Q111381
median20516
Q380685
95-th percentile234436
Maximum15617908
Range15617908
Interquartile range (IQR)69304

Descriptive statistics

Standard deviation179381.19
Coefficient of variation (CV)3.1572896
Kurtosis5671.2397
Mean56814.931
Median Absolute Deviation (MAD)17336
Skewness66.265027
Sum5.6814931 × 108
Variance3.2177611 × 1010
MonotonicityNot monotonic
2024-03-14T11:55:16.776768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80685 614
 
6.1%
8995 552
 
5.5%
12000 285
 
2.9%
19842 206
 
2.1%
234436 201
 
2.0%
87377 147
 
1.5%
12993 144
 
1.4%
13457 126
 
1.3%
323133 109
 
1.1%
12984 98
 
1.0%
Other values (3924) 7518
75.2%
ValueCountFrequency (%)
0 28
0.3%
1 24
0.2%
5 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
18 1
 
< 0.1%
27 1
 
< 0.1%
ValueCountFrequency (%)
15617908 1
 
< 0.1%
1565108 14
 
0.1%
323133 109
1.1%
323083 29
 
0.3%
322921 10
 
0.1%
322759 1
 
< 0.1%
322585 3
 
< 0.1%
322527 1
 
< 0.1%
322335 8
 
0.1%
322275 3
 
< 0.1%

보정값(strCorrectValue)
Real number (ℝ)

HIGH CORRELATION 

Distinct3940
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57497.893
Minimum0
Maximum1994517
Zeros52
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:16.908796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1586.8
Q111183
median19629
Q380659
95-th percentile231207
Maximum1994517
Range1994517
Interquartile range (IQR)69476

Descriptive statistics

Standard deviation107992.51
Coefficient of variation (CV)1.8781995
Kurtosis155.24132
Mean57497.893
Median Absolute Deviation (MAD)16378
Skewness9.6967321
Sum5.7497893 × 108
Variance1.1662383 × 1010
MonotonicityNot monotonic
2024-03-14T11:55:17.026466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80659 614
 
6.1%
8739 552
 
5.5%
13841 286
 
2.9%
18306 205
 
2.1%
231207 201
 
2.0%
84318 147
 
1.5%
14934 144
 
1.4%
15401 121
 
1.2%
389734 109
 
1.1%
14923 98
 
1.0%
Other values (3930) 7523
75.2%
ValueCountFrequency (%)
0 52
0.5%
5 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
25 1
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
1994517 1
 
< 0.1%
1993717 14
 
0.1%
389734 109
1.1%
389674 29
 
0.3%
389479 10
 
0.1%
389285 1
 
< 0.1%
389076 3
 
< 0.1%
389005 1
 
< 0.1%
388772 8
 
0.1%
388700 3
 
< 0.1%

온도(dmlTempeture)
Real number (ℝ)

HIGH CORRELATION 

Distinct3277
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.399824
Minimum0.02
Maximum110.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:17.141126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile6.65
Q113.68
median18.23
Q323.61
95-th percentile43.76
Maximum110.99
Range110.97
Interquartile range (IQR)9.93

Descriptive statistics

Standard deviation16.967528
Coefficient of variation (CV)0.79288165
Kurtosis12.552865
Mean21.399824
Median Absolute Deviation (MAD)5.08
Skewness3.3711281
Sum213998.24
Variance287.897
MonotonicityNot monotonic
2024-03-14T11:55:17.255274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.55 16
 
0.2%
23.09 14
 
0.1%
18.99 14
 
0.1%
15.5 14
 
0.1%
23.2 13
 
0.1%
15.53 13
 
0.1%
23.34 13
 
0.1%
16.86 13
 
0.1%
17.42 13
 
0.1%
15.46 12
 
0.1%
Other values (3267) 9865
98.7%
ValueCountFrequency (%)
0.02 2
< 0.1%
0.05 1
< 0.1%
0.07 1
< 0.1%
0.11 2
< 0.1%
0.16 1
< 0.1%
0.17 2
< 0.1%
0.22 1
< 0.1%
0.25 1
< 0.1%
0.27 1
< 0.1%
0.29 1
< 0.1%
ValueCountFrequency (%)
110.99 1
< 0.1%
110.3 1
< 0.1%
109.99 1
< 0.1%
109.64 1
< 0.1%
109.3 1
< 0.1%
109.03 1
< 0.1%
108.71 1
< 0.1%
108.19 1
< 0.1%
107.87 1
< 0.1%
107.26 1
< 0.1%

압력(dmlPressure)
Real number (ℝ)

HIGH CORRELATION 

Distinct7599
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.37053
Minimum98.7028
Maximum134.3799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:17.387408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98.7028
5-th percentile101.5622
Q1102.64467
median103.58985
Q3104.61597
95-th percentile129.99022
Maximum134.3799
Range35.6771
Interquartile range (IQR)1.9713

Descriptive statistics

Standard deviation9.4129492
Coefficient of variation (CV)0.087667902
Kurtosis1.5242212
Mean107.37053
Median Absolute Deviation (MAD)0.9815
Skewness1.8222896
Sum1073705.3
Variance88.603612
MonotonicityNot monotonic
2024-03-14T11:55:17.535491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104.064 7
 
0.1%
104.4015 6
 
0.1%
102.8101 6
 
0.1%
104.3373 6
 
0.1%
104.3152 6
 
0.1%
103.976 6
 
0.1%
104.0238 6
 
0.1%
102.5316 5
 
0.1%
102.593 5
 
0.1%
103.8246 5
 
0.1%
Other values (7589) 9942
99.4%
ValueCountFrequency (%)
98.7028 1
< 0.1%
99.3615 1
< 0.1%
99.4054 1
< 0.1%
99.4852 1
< 0.1%
99.589 1
< 0.1%
99.6269 1
< 0.1%
99.6569 1
< 0.1%
99.6708 1
< 0.1%
99.7347 1
< 0.1%
99.7367 1
< 0.1%
ValueCountFrequency (%)
134.3799 1
< 0.1%
134.2375 1
< 0.1%
134.0325 1
< 0.1%
133.972 1
< 0.1%
133.8986 1
< 0.1%
133.8814 1
< 0.1%
133.8619 1
< 0.1%
133.8468 1
< 0.1%
133.7756 1
< 0.1%
133.7713 1
< 0.1%

보정계수(dmlCorrectCoefficient)
Real number (ℝ)

HIGH CORRELATION 

Distinct2516
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99472047
Minimum0.1
Maximum1.2727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:17.712457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.9111
Q10.9357
median0.9647
Q30.9982
95-th percentile1.2157
Maximum1.2727
Range1.1727
Interquartile range (IQR)0.0625

Descriptive statistics

Standard deviation0.09400113
Coefficient of variation (CV)0.094500046
Kurtosis1.7952525
Mean0.99472047
Median Absolute Deviation (MAD)0.0302
Skewness1.3892034
Sum9947.2047
Variance0.0088362125
MonotonicityNot monotonic
2024-03-14T11:55:17.855364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9608 20
 
0.2%
0.9763 20
 
0.2%
0.9644 19
 
0.2%
0.9642 18
 
0.2%
0.9686 18
 
0.2%
0.9721 18
 
0.2%
0.9641 17
 
0.2%
0.9692 17
 
0.2%
0.9674 17
 
0.2%
0.9331 17
 
0.2%
Other values (2506) 9819
98.2%
ValueCountFrequency (%)
0.1 1
< 0.1%
0.8464 1
< 0.1%
0.8495 1
< 0.1%
0.85 1
< 0.1%
0.8508 1
< 0.1%
0.8511 1
< 0.1%
0.8527 1
< 0.1%
0.853 1
< 0.1%
0.8531 2
< 0.1%
0.8532 1
< 0.1%
ValueCountFrequency (%)
1.2727 1
< 0.1%
1.2666 1
< 0.1%
1.2646 1
< 0.1%
1.262 1
< 0.1%
1.2619 1
< 0.1%
1.2613 1
< 0.1%
1.2612 1
< 0.1%
1.2601 1
< 0.1%
1.2593 1
< 0.1%
1.258 1
< 0.1%

순간유량(strFlowRate)
Real number (ℝ)

SKEWED  ZEROS 

Distinct69
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3536
Minimum0
Maximum10002
Zeros8537
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:17.961585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum10002
Range10002
Interquartile range (IQR)0

Descriptive statistics

Standard deviation100.26793
Coefficient of variation (CV)42.601941
Kurtosis9896.0755
Mean2.3536
Median Absolute Deviation (MAD)0
Skewness99.223839
Sum23536
Variance10053.657
MonotonicityNot monotonic
2024-03-14T11:55:18.091346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8537
85.4%
1 422
 
4.2%
3 199
 
2.0%
2 172
 
1.7%
4 102
 
1.0%
5 73
 
0.7%
6 66
 
0.7%
8 46
 
0.5%
11 36
 
0.4%
7 27
 
0.3%
Other values (59) 320
 
3.2%
ValueCountFrequency (%)
0 8537
85.4%
1 422
 
4.2%
2 172
 
1.7%
3 199
 
2.0%
4 102
 
1.0%
5 73
 
0.7%
6 66
 
0.7%
7 27
 
0.3%
8 46
 
0.5%
9 16
 
0.2%
ValueCountFrequency (%)
10002 1
< 0.1%
222 1
< 0.1%
167 1
< 0.1%
138 1
< 0.1%
131 1
< 0.1%
123 1
< 0.1%
103 1
< 0.1%
100 1
< 0.1%
96 1
< 0.1%
92 1
< 0.1%

Interactions

2024-03-14T11:55:15.684791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:12.620114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:13.173806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:13.800854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:14.558791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:15.068777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:15.781704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:12.707636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:13.328554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:13.892968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:14.651576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:15.160670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:15.865762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:12.795346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:13.431121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:13.976058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:14.739107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:15.245902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:15.952355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:12.874748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:13.529712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:14.066603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:14.816344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:15.348377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:16.041527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:12.960818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:13.630095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:14.402142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:14.898394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:15.493155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:16.116436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:13.051658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:13.716207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:14.479094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:14.984668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:15.582927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:55:18.170398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
비보정값(strNonCorrectValue)1.0000.9430.0600.0870.0001.000
보정값(strCorrectValue)0.9431.0000.2270.5110.2640.156
온도(dmlTempeture)0.0600.2271.0000.3250.6840.000
압력(dmlPressure)0.0870.5110.3251.0000.7710.000
보정계수(dmlCorrectCoefficient)0.0000.2640.6840.7711.0000.000
순간유량(strFlowRate)1.0000.1560.0000.0000.0001.000
2024-03-14T11:55:18.553410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
비보정값(strNonCorrectValue)1.0000.995-0.165-0.0720.0180.054
보정값(strCorrectValue)0.9951.000-0.174-0.0460.0500.043
온도(dmlTempeture)-0.165-0.1741.000-0.421-0.6430.045
압력(dmlPressure)-0.072-0.046-0.4211.0000.882-0.033
보정계수(dmlCorrectCoefficient)0.0180.050-0.6430.8821.000-0.071
순간유량(strFlowRate)0.0540.0430.045-0.033-0.0711.000

Missing values

2024-03-14T11:55:16.212402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:55:16.328188image/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

자료수집일자(dtDataCollect)비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
933442020-11-20 04:00:00235692243988.87103.36010.96650
47842020-01-17 14:00:0010584121499.37127.7591.21950
452872020-06-06 17:00:00107501048225.46102.55890.92510
189642020-03-07 05:00:007464726815.65103.63990.96690
572242020-07-18 04:00:00123211419522.04123.0861.12380
893482020-11-06 18:00:00170201617015.49103.69920.9680
297622020-04-13 17:00:00106631039816.33103.19870.96050
23072020-01-09 00:00:00472624664611.82103.62140.97990
7282020-01-03 12:00:009610109907.96128.01111.22810
807912020-10-08 01:00:00873778431821.77104.22140.9520
자료수집일자(dtDataCollect)비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
202822020-03-11 19:00:0010516102526.47103.94591.00190
695902020-08-30 03:00:0079172927.42102.35150.91720
103042020-02-05 18:00:0011360130728.45129.10531.23640
292452020-04-11 22:00:0010655103918.66103.65180.99120
177592020-03-03 00:00:00192791848818.81104.24230.9620
521072020-06-30 09:00:0020619023.95100.23630.90881
56362020-01-20 13:00:0010584121498.34127.34951.22010
452982020-06-06 18:00:00819947933620.85102.54240.93960
734682020-09-12 14:00:00947109302124.93102.91440.930
400602020-05-19 13:00:00806858065915.299.40540.92880