Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells146
Missing cells (%)0.2%
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) has 154 (1.5%) zerosZeros
순간유량(strFlowRate) has 7808 (78.1%) zerosZeros

Reproduction

Analysis started2024-03-14 02:55:29.046727
Analysis finished2024-03-14 02:55:33.715159
Duration4.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자료수집일자(dtDataCollect)
Date

Distinct5851
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-01-03 14:50:00
Maximum2018-08-15 03:00:00
2024-03-14T11:55:33.773317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:33.884667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

비보정값(strNonCorrectValue)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4107
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155090.85
Minimum0
Maximum705288
Zeros154
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:34.015689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1389
Q14053.75
median36012.5
Q3157678
95-th percentile705288
Maximum705288
Range705288
Interquartile range (IQR)153624.25

Descriptive statistics

Standard deviation238491.27
Coefficient of variation (CV)1.537752
Kurtosis1.1310105
Mean155090.85
Median Absolute Deviation (MAD)34149.5
Skewness1.6501759
Sum1.5509085 × 109
Variance5.6878085 × 1010
MonotonicityNot monotonic
2024-03-14T11:55:34.160655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
705288 1314
 
13.1%
76890 311
 
3.1%
157678 211
 
2.1%
1863 205
 
2.1%
0 154
 
1.5%
124814 126
 
1.3%
234673 83
 
0.8%
127003 64
 
0.6%
32282 62
 
0.6%
234796 51
 
0.5%
Other values (4097) 7419
74.2%
ValueCountFrequency (%)
0 154
1.5%
1 6
 
0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
705288 1314
13.1%
704673 1
 
< 0.1%
704371 1
 
< 0.1%
704196 1
 
< 0.1%
703144 1
 
< 0.1%
703053 1
 
< 0.1%
702993 1
 
< 0.1%
702962 1
 
< 0.1%
702605 1
 
< 0.1%
702575 1
 
< 0.1%

보정값(strCorrectValue)
Real number (ℝ)

HIGH CORRELATION 

Distinct4220
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150405.43
Minimum0
Maximum644486
Zeros23
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:34.306858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1711.95
Q14316
median36319.5
Q3157802
95-th percentile644486
Maximum644486
Range644486
Interquartile range (IQR)153486

Descriptive statistics

Standard deviation218773.92
Coefficient of variation (CV)1.4545613
Kurtosis0.82231379
Mean150405.43
Median Absolute Deviation (MAD)34493.5
Skewness1.531381
Sum1.5040543 × 109
Variance4.786203 × 1010
MonotonicityNot monotonic
2024-03-14T11:55:34.425515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
644486 1313
 
13.1%
76899 311
 
3.1%
157802 211
 
2.1%
1826 205
 
2.1%
124485 126
 
1.3%
284808 83
 
0.8%
151507 64
 
0.6%
31720 62
 
0.6%
2029 52
 
0.5%
284953 51
 
0.5%
Other values (4210) 7522
75.2%
ValueCountFrequency (%)
0 23
0.2%
1 6
 
0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
644486 1313
13.1%
643913 1
 
< 0.1%
643632 1
 
< 0.1%
643469 1
 
< 0.1%
642487 1
 
< 0.1%
642402 1
 
< 0.1%
642345 1
 
< 0.1%
642316 1
 
< 0.1%
641982 1
 
< 0.1%
641954 1
 
< 0.1%

온도(dmlTempeture)
Real number (ℝ)

HIGH CORRELATION 

Distinct3378
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.134984
Minimum0
Maximum113.26
Zeros74
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:34.554196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.7295
Q114.49
median20.38
Q324.95
95-th percentile36.0805
Maximum113.26
Range113.26
Interquartile range (IQR)10.46

Descriptive statistics

Standard deviation17.256886
Coefficient of variation (CV)0.77962044
Kurtosis14.515499
Mean22.134984
Median Absolute Deviation (MAD)5.315
Skewness3.5170851
Sum221349.84
Variance297.80012
MonotonicityNot monotonic
2024-03-14T11:55:34.660307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 74
 
0.7%
24.26 14
 
0.1%
20.53 14
 
0.1%
23.59 14
 
0.1%
23.57 13
 
0.1%
23.02 13
 
0.1%
21.06 13
 
0.1%
24.21 12
 
0.1%
24.09 12
 
0.1%
22.48 12
 
0.1%
Other values (3368) 9809
98.1%
ValueCountFrequency (%)
0.0 74
0.7%
0.02 2
 
< 0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 2
 
< 0.1%
0.07 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 2
 
< 0.1%
0.11 1
 
< 0.1%
ValueCountFrequency (%)
113.26 1
< 0.1%
112.82 1
< 0.1%
112.6 1
< 0.1%
112.49 1
< 0.1%
111.8 1
< 0.1%
111.69 1
< 0.1%
111.27 1
< 0.1%
111.16 1
< 0.1%
111.06 1
< 0.1%
110.61 1
< 0.1%

압력(dmlPressure)
Real number (ℝ)

HIGH CORRELATION 

Distinct7308
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.476
Minimum0
Maximum134.6432
Zeros73
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:34.765407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100.9186
Q1102.14238
median103.0061
Q3104.1061
95-th percentile127.83071
Maximum134.6432
Range134.6432
Interquartile range (IQR)1.963725

Descriptive statistics

Standard deviation11.701189
Coefficient of variation (CV)0.11199882
Kurtosis45.783567
Mean104.476
Median Absolute Deviation (MAD)0.97465
Skewness-4.4615725
Sum1044760
Variance136.91782
MonotonicityNot monotonic
2024-03-14T11:55:34.892828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 73
 
0.7%
101.881 21
 
0.2%
101.8448 14
 
0.1%
101.6075 12
 
0.1%
101.8679 12
 
0.1%
102.2238 12
 
0.1%
101.8745 11
 
0.1%
101.8712 11
 
0.1%
102.2271 11
 
0.1%
101.8876 10
 
0.1%
Other values (7298) 9813
98.1%
ValueCountFrequency (%)
0.0 73
0.7%
0.9087 1
 
< 0.1%
99.4633 1
 
< 0.1%
99.4712 1
 
< 0.1%
99.4872 1
 
< 0.1%
99.5032 1
 
< 0.1%
99.5072 1
 
< 0.1%
99.5191 3
 
< 0.1%
99.5231 1
 
< 0.1%
99.5311 1
 
< 0.1%
ValueCountFrequency (%)
134.6432 1
< 0.1%
134.3583 1
< 0.1%
134.3066 1
< 0.1%
134.2979 1
< 0.1%
134.1943 1
< 0.1%
134.1598 1
< 0.1%
134.1253 1
< 0.1%
134.0411 1
< 0.1%
133.9785 1
< 0.1%
133.9763 1
< 0.1%

보정계수(dmlCorrectCoefficient)
Real number (ℝ)

HIGH CORRELATION 

Distinct2419
Distinct (%)24.4%
Missing73
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.97312653
Minimum0.1
Maximum1.3094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:35.012586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.9039
Q10.9245
median0.9473
Q30.98715
95-th percentile1.18737
Maximum1.3094
Range1.2094
Interquartile range (IQR)0.06265

Descriptive statistics

Standard deviation0.079932528
Coefficient of variation (CV)0.082139912
Kurtosis5.2296491
Mean0.97312653
Median Absolute Deviation (MAD)0.0275
Skewness1.8909752
Sum9660.2271
Variance0.0063892091
MonotonicityNot monotonic
2024-03-14T11:55:35.159430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9253 25
 
0.2%
0.9265 24
 
0.2%
0.9271 24
 
0.2%
0.9188 24
 
0.2%
0.9249 24
 
0.2%
0.9267 24
 
0.2%
0.9166 23
 
0.2%
0.9243 23
 
0.2%
0.9443 22
 
0.2%
0.9165 22
 
0.2%
Other values (2409) 9692
96.9%
(Missing) 73
 
0.7%
ValueCountFrequency (%)
0.1 1
< 0.1%
0.8621 1
< 0.1%
0.8639 2
< 0.1%
0.8645 1
< 0.1%
0.865 1
< 0.1%
0.8653 1
< 0.1%
0.8658 1
< 0.1%
0.8664 2
< 0.1%
0.8665 1
< 0.1%
0.8666 1
< 0.1%
ValueCountFrequency (%)
1.3094 1
< 0.1%
1.3029 1
< 0.1%
1.3002 1
< 0.1%
1.297 1
< 0.1%
1.2964 1
< 0.1%
1.2931 1
< 0.1%
1.2921 1
< 0.1%
1.2918 1
< 0.1%
1.2915 2
< 0.1%
1.2913 1
< 0.1%

순간유량(strFlowRate)
Real number (ℝ)

ZEROS 

Distinct119
Distinct (%)1.2%
Missing73
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean43.84386
Minimum0
Maximum2223
Zeros7808
Zeros (%)78.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:35.269299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20
Maximum2223
Range2223
Interquartile range (IQR)0

Descriptive statistics

Standard deviation234.36366
Coefficient of variation (CV)5.3454157
Kurtosis26.940861
Mean43.84386
Median Absolute Deviation (MAD)0
Skewness5.3646248
Sum435238
Variance54926.324
MonotonicityNot monotonic
2024-03-14T11:55:35.409045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7808
78.1%
1 405
 
4.0%
2 259
 
2.6%
3 213
 
2.1%
4 169
 
1.7%
7 124
 
1.2%
5 88
 
0.9%
6 86
 
0.9%
8 61
 
0.6%
9 50
 
0.5%
Other values (109) 664
 
6.6%
(Missing) 73
 
0.7%
ValueCountFrequency (%)
0 7808
78.1%
1 405
 
4.0%
2 259
 
2.6%
3 213
 
2.1%
4 169
 
1.7%
5 88
 
0.9%
6 86
 
0.9%
7 124
 
1.2%
8 61
 
0.6%
9 50
 
0.5%
ValueCountFrequency (%)
2223 1
 
< 0.1%
1364 7
0.1%
1363 3
< 0.1%
1362 3
< 0.1%
1361 1
 
< 0.1%
1360 4
< 0.1%
1359 4
< 0.1%
1358 4
< 0.1%
1356 4
< 0.1%
1355 1
 
< 0.1%

Interactions

2024-03-14T11:55:32.595332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:29.877365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.389948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.933143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:31.561974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:32.098953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:32.677233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:29.959973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.472859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:31.083547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:31.648407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:32.179777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:32.769960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.038421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.551865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:31.224819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:31.728899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:32.262717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:33.077242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.118302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.640040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:31.314490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:31.818195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:32.339647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:33.162052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.219457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.728481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:31.405067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:31.916591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:32.425663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:33.292531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.313970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:30.832709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:31.485187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:32.015604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:32.519195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:55:35.565138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
비보정값(strNonCorrectValue)1.0000.9920.2680.5880.7060.542
보정값(strCorrectValue)0.9921.0000.2890.6500.7520.493
온도(dmlTempeture)0.2680.2891.0000.1860.5160.129
압력(dmlPressure)0.5880.6500.1861.0000.6350.072
보정계수(dmlCorrectCoefficient)0.7060.7520.5160.6351.0000.123
순간유량(strFlowRate)0.5420.4930.1290.0720.1231.000
2024-03-14T11:55:35.755178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
비보정값(strNonCorrectValue)1.0000.9760.056-0.177-0.066-0.035
보정값(strCorrectValue)0.9761.0000.027-0.206-0.060-0.046
온도(dmlTempeture)0.0560.0271.000-0.391-0.6620.149
압력(dmlPressure)-0.177-0.206-0.3911.0000.850-0.102
보정계수(dmlCorrectCoefficient)-0.066-0.060-0.6620.8501.000-0.145
순간유량(strFlowRate)-0.035-0.0460.149-0.102-0.1451.000

Missing values

2024-03-14T11:55:33.473131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:55:33.573578image/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.
2024-03-14T11:55:33.669263image/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

자료수집일자(dtDataCollect)비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
683992018-05-29 16:00115711162528.08102.60650.91744
941622018-07-31 19:003476331636.61101.65550.88370
429662018-03-28 7:0021323021272817.57102.96890.954329
145612018-01-27 15:0091990310.22104.65470.99530
281312018-02-19 22:00401540943.43104.60351.01940
553522018-04-27 16:00768907689913.92103.23570.9690
619802018-05-13 22:00359653583519.06102.39290.9440
59792018-01-14 6:5570528864448618.96102.57650.9460
427712018-03-27 20:005109520716.52103.28340.96070
375442018-03-15 0:0012421612390617.82103.74340.96060
자료수집일자(dtDataCollect)비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
963882018-08-06 6:00367183570435.84102.47120.8930
777022018-06-21 11:0012676312629923.85102.41610.928931
627922018-05-15 22:003334341625.24103.43630.93370
821852018-07-02 10:006342635024.18102.21270.9267
925972018-07-27 23:003362321730.29102.47950.90960
46862018-01-12 20:1470528864448624.37102.41170.92720
294762018-02-23 5:0021124521082416.53103.54110.9630
785442018-06-23 12:005943595127.88103.97560.93030
327582018-03-03 6:005062650835101.04103.3011.02351
325572018-03-02 18:00294829787.7103.40050.99220