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
압력(dmlPressure) is highly overall correlated with 보정계수(dmlCorrectCoefficient)High correlation
보정계수(dmlCorrectCoefficient) is highly overall correlated with 압력(dmlPressure)High correlation
순간유량(strFlowRate) has 7868 (78.7%) zerosZeros

Reproduction

Analysis started2024-03-14 02:55:20.287600
Analysis finished2024-03-14 02:55:24.759333
Duration4.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자료수집일자(dtDataCollect)
Date

Distinct4435
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-01-01 00:00:00
Maximum2019-12-31 22:00:00
2024-03-14T11:55:24.815018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:24.920050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

비보정값(strNonCorrectValue)
Real number (ℝ)

HIGH CORRELATION 

Distinct4565
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72706.423
Minimum2236
Maximum309599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:25.031254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2236
5-th percentile4149.8
Q19887
median40148
Q379499
95-th percentile280172
Maximum309599
Range307363
Interquartile range (IQR)69612

Descriptive statistics

Standard deviation85491.349
Coefficient of variation (CV)1.1758431
Kurtosis1.0305937
Mean72706.423
Median Absolute Deviation (MAD)32161.5
Skewness1.4636253
Sum7.2706423 × 108
Variance7.3087707 × 109
MonotonicityNot monotonic
2024-03-14T11:55:25.153078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
217435 255
 
2.5%
4684 227
 
2.3%
78456 195
 
1.9%
301321 119
 
1.2%
301222 93
 
0.9%
63614 82
 
0.8%
9712 81
 
0.8%
63575 74
 
0.7%
63579 70
 
0.7%
63578 64
 
0.6%
Other values (4555) 8740
87.4%
ValueCountFrequency (%)
2236 9
0.1%
2253 1
 
< 0.1%
2344 1
 
< 0.1%
2413 8
0.1%
2458 1
 
< 0.1%
2500 1
 
< 0.1%
2565 2
 
< 0.1%
2616 1
 
< 0.1%
2651 4
< 0.1%
2853 1
 
< 0.1%
ValueCountFrequency (%)
309599 1
 
< 0.1%
309451 1
 
< 0.1%
309162 1
 
< 0.1%
309161 3
 
< 0.1%
309102 1
 
< 0.1%
309028 1
 
< 0.1%
308806 10
0.1%
308805 3
 
< 0.1%
308591 5
0.1%
308521 1
 
< 0.1%

보정값(strCorrectValue)
Real number (ℝ)

HIGH CORRELATION 

Distinct4556
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77019.335
Minimum2431
Maximum373323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:25.288849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2431
5-th percentile4281
Q19719.75
median39548.5
Q378709
95-th percentile338890
Maximum373323
Range370892
Interquartile range (IQR)68989.25

Descriptive statistics

Standard deviation98103.716
Coefficient of variation (CV)1.2737544
Kurtosis2.1611013
Mean77019.335
Median Absolute Deviation (MAD)31784.5
Skewness1.7468706
Sum7.7019335 × 108
Variance9.6243392 × 109
MonotonicityNot monotonic
2024-03-14T11:55:25.613314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214293 255
 
2.5%
4561 227
 
2.3%
78458 194
 
1.9%
363198 119
 
1.2%
363081 93
 
0.9%
61473 82
 
0.8%
9441 81
 
0.8%
61435 74
 
0.7%
61439 70
 
0.7%
61438 64
 
0.6%
Other values (4546) 8741
87.4%
ValueCountFrequency (%)
2431 9
0.1%
2450 1
 
< 0.1%
2548 1
 
< 0.1%
2622 8
0.1%
2672 1
 
< 0.1%
2718 1
 
< 0.1%
2789 2
 
< 0.1%
2845 1
 
< 0.1%
2883 4
< 0.1%
3104 1
 
< 0.1%
ValueCountFrequency (%)
373323 1
 
< 0.1%
373139 1
 
< 0.1%
372779 1
 
< 0.1%
372778 3
 
< 0.1%
372706 1
 
< 0.1%
372616 1
 
< 0.1%
372345 10
0.1%
372344 3
 
< 0.1%
372080 5
0.1%
371995 1
 
< 0.1%

온도(dmlTempeture)
Real number (ℝ)

Distinct3362
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.946262
Minimum0
Maximum108.43
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:25.720509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.87
Q110.87
median16.94
Q323.24
95-th percentile100.9305
Maximum108.43
Range108.43
Interquartile range (IQR)12.37

Descriptive statistics

Standard deviation22.987645
Coefficient of variation (CV)1.0474515
Kurtosis7.19018
Mean21.946262
Median Absolute Deviation (MAD)6.19
Skewness2.7965293
Sum219462.62
Variance528.4318
MonotonicityNot monotonic
2024-03-14T11:55:25.827587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.57 14
 
0.1%
13.39 13
 
0.1%
14.22 12
 
0.1%
20.49 12
 
0.1%
17.41 11
 
0.1%
20.28 11
 
0.1%
17.77 11
 
0.1%
21.54 11
 
0.1%
12.55 11
 
0.1%
21.2 11
 
0.1%
Other values (3352) 9883
98.8%
ValueCountFrequency (%)
0.0 2
 
< 0.1%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.05 2
 
< 0.1%
0.07 1
 
< 0.1%
0.09 4
< 0.1%
0.1 1
 
< 0.1%
0.11 5
0.1%
0.13 2
 
< 0.1%
0.14 1
 
< 0.1%
ValueCountFrequency (%)
108.43 1
< 0.1%
107.96 1
< 0.1%
107.26 1
< 0.1%
107.08 1
< 0.1%
106.87 1
< 0.1%
106.75 1
< 0.1%
106.63 2
< 0.1%
106.61 1
< 0.1%
106.48 1
< 0.1%
106.3 1
< 0.1%

압력(dmlPressure)
Real number (ℝ)

HIGH CORRELATION 

Distinct7528
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.54534
Minimum99.6149
Maximum134.8828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:25.928754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99.6149
5-th percentile102.28239
Q1103.4693
median104.15965
Q3104.9152
95-th percentile131.38232
Maximum134.8828
Range35.2679
Interquartile range (IQR)1.4459

Descriptive statistics

Standard deviation9.0488307
Coefficient of variation (CV)0.084139683
Kurtosis2.4424173
Mean107.54534
Median Absolute Deviation (MAD)0.71265
Skewness2.0518909
Sum1075453.4
Variance81.881337
MonotonicityNot monotonic
2024-03-14T11:55:26.045230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104.4163 8
 
0.1%
103.9553 8
 
0.1%
103.6668 7
 
0.1%
104.1643 6
 
0.1%
132.7332 6
 
0.1%
104.6776 6
 
0.1%
103.6389 6
 
0.1%
104.6019 6
 
0.1%
104.0508 6
 
0.1%
103.7285 6
 
0.1%
Other values (7518) 9935
99.4%
ValueCountFrequency (%)
99.6149 2
< 0.1%
99.6229 1
< 0.1%
99.6249 1
< 0.1%
99.6329 1
< 0.1%
99.6389 1
< 0.1%
99.6449 1
< 0.1%
99.6489 1
< 0.1%
99.6609 1
< 0.1%
99.6629 1
< 0.1%
99.6668 1
< 0.1%
ValueCountFrequency (%)
134.8828 1
< 0.1%
134.6044 1
< 0.1%
134.5699 1
< 0.1%
134.5634 1
< 0.1%
134.5202 1
< 0.1%
134.5094 1
< 0.1%
134.4857 1
< 0.1%
134.4727 1
< 0.1%
134.3648 1
< 0.1%
134.3324 1
< 0.1%

보정계수(dmlCorrectCoefficient)
Real number (ℝ)

HIGH CORRELATION 

Distinct2626
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0070572
Minimum0.1
Maximum1.2829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:26.170968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.914795
Q10.950475
median0.9814
Q31.0242
95-th percentile1.2263
Maximum1.2829
Range1.1829
Interquartile range (IQR)0.073725

Descriptive statistics

Standard deviation0.089894688
Coefficient of variation (CV)0.089264727
Kurtosis2.2779458
Mean1.0070572
Median Absolute Deviation (MAD)0.0355
Skewness1.3491663
Sum10070.572
Variance0.0080810549
MonotonicityNot monotonic
2024-03-14T11:55:26.289562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9425 35
 
0.4%
0.9431 25
 
0.2%
0.9426 24
 
0.2%
0.9757 22
 
0.2%
0.9421 20
 
0.2%
0.9777 18
 
0.2%
0.9794 18
 
0.2%
0.943 18
 
0.2%
0.9785 18
 
0.2%
0.9432 16
 
0.2%
Other values (2616) 9786
97.9%
ValueCountFrequency (%)
0.1 1
< 0.1%
0.8532 1
< 0.1%
0.8555 1
< 0.1%
0.8587 1
< 0.1%
0.8606 1
< 0.1%
0.8608 1
< 0.1%
0.8627 1
< 0.1%
0.8631 1
< 0.1%
0.8634 1
< 0.1%
0.8636 1
< 0.1%
ValueCountFrequency (%)
1.2829 1
< 0.1%
1.2711 1
< 0.1%
1.2704 1
< 0.1%
1.2691 2
< 0.1%
1.2686 1
< 0.1%
1.2681 1
< 0.1%
1.2677 1
< 0.1%
1.2659 1
< 0.1%
1.2657 1
< 0.1%
1.2656 1
< 0.1%

순간유량(strFlowRate)
Real number (ℝ)

ZEROS 

Distinct79
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3128
Minimum0
Maximum171
Zeros7868
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T11:55:26.419576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum171
Range171
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.5093217
Coefficient of variation (CV)4.1116057
Kurtosis105.94721
Mean2.3128
Median Absolute Deviation (MAD)0
Skewness8.5594995
Sum23128
Variance90.427199
MonotonicityNot monotonic
2024-03-14T11:55:26.574177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7868
78.7%
1 462
 
4.6%
2 251
 
2.5%
3 246
 
2.5%
4 147
 
1.5%
7 138
 
1.4%
6 118
 
1.2%
8 100
 
1.0%
5 84
 
0.8%
11 48
 
0.5%
Other values (69) 538
 
5.4%
ValueCountFrequency (%)
0 7868
78.7%
1 462
 
4.6%
2 251
 
2.5%
3 246
 
2.5%
4 147
 
1.5%
5 84
 
0.8%
6 118
 
1.2%
7 138
 
1.4%
8 100
 
1.0%
9 46
 
0.5%
ValueCountFrequency (%)
171 2
< 0.1%
168 4
< 0.1%
164 1
 
< 0.1%
147 1
 
< 0.1%
137 2
< 0.1%
135 1
 
< 0.1%
133 1
 
< 0.1%
129 1
 
< 0.1%
125 1
 
< 0.1%
104 2
< 0.1%

Interactions

2024-03-14T11:55:23.923531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.121328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.640094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:22.309239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:22.835706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:23.375570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:24.008003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.201307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.721433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:22.389715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:22.925705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:23.461735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:24.091553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.301354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.804210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:22.469758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:23.034995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:23.546817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:24.165797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.377706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.881092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:22.545701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:23.123060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:23.639360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:24.321895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.465590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.989342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:22.627582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:23.210877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:23.743373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:24.515214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:21.553932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:22.184764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:22.735611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:23.289057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:55:23.836572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:55:26.710862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
비보정값(strNonCorrectValue)1.0000.9550.4240.6530.6280.285
보정값(strCorrectValue)0.9551.0000.2940.7880.5840.286
온도(dmlTempeture)0.4240.2941.0000.4610.5310.120
압력(dmlPressure)0.6530.7880.4611.0000.7970.278
보정계수(dmlCorrectCoefficient)0.6280.5840.5310.7971.0000.241
순간유량(strFlowRate)0.2850.2860.1200.2780.2411.000
2024-03-14T11:55:26.819678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
비보정값(strNonCorrectValue)1.0000.999-0.0370.0560.0370.036
보정값(strCorrectValue)0.9991.000-0.0420.0670.0500.032
온도(dmlTempeture)-0.037-0.0421.000-0.289-0.445-0.114
압력(dmlPressure)0.0560.067-0.2891.0000.859-0.020
보정계수(dmlCorrectCoefficient)0.0370.050-0.4450.8591.0000.020
순간유량(strFlowRate)0.0360.032-0.114-0.0200.0201.000

Missing values

2024-03-14T11:55:24.618987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:55:24.713727image/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)
317892019-08-23 5:009272903024.26102.85730.93160
287662019-03-12 19:004248414015.19103.12720.96370
41922019-01-11 6:002682473241668.22129.77421.243947
422922019-09-25 21:00449984445922.95103.41010.94080
172742019-02-12 8:001296312699104.05106.16751.06390
695572019-12-28 6:002221322189497.72105.01231.00770
89702019-01-22 23:00694066001.0104.12381.02380
81362019-01-20 22:0068636524101.08104.70851.03760
342212019-08-31 0:00783687718619.81102.95530.94670
96802019-01-24 17:0077514775258.48104.15790.99670
자료수집일자(dtDataCollect)비보정값(strNonCorrectValue)보정값(strCorrectValue)온도(dmlTempeture)압력(dmlPressure)보정계수(dmlCorrectCoefficient)순간유량(strFlowRate)
213232019-02-22 6:003077129865100.59104.09971.02970
361732019-09-06 6:00635756143524.37102.71480.930
380402019-09-12 6:00784567845821.6100.15590.91530
393942019-09-16 14:00784567845821.36100.11990.91570
576952019-11-17 2:0030183236381417.88131.84611.22140
453932019-10-05 19:00375113465323.27102.95460.93560
460872019-10-08 1:004684456123.28103.72080.94260
139962019-02-04 7:0015078014891916.62104.27990.969633
620172019-12-02 2:00460984550715.2103.35210.96571
684742019-12-24 12:0030832337175416.62132.94251.2370