Overview

Dataset statistics

Number of variables4
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory36.1 B

Variable types

DateTime1
Numeric3

Dataset

Description한국가스공사에서 공급하는 월별 용도별 천연가스 공급량에 대한 데이터입니다. 단위는 천톤 기준이며 문의사항은 기재되어있는 담당자 번호로 연락 부탁드립니다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15112209/fileData.do

Alerts

민수용 공급량 is highly overall correlated with 산업용 공급량 and 1 other fieldsHigh correlation
산업용 공급량 is highly overall correlated with 민수용 공급량 and 1 other fieldsHigh correlation
발전용 공급량 is highly overall correlated with 민수용 공급량 and 1 other fieldsHigh correlation
연월 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:04:33.002750
Analysis finished2023-12-12 05:04:34.512038
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2013-01-01 00:00:00
Maximum2022-12-01 00:00:00
2023-12-12T14:04:34.612782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:04:34.817454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

민수용 공급량
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean779.00833
Minimum217
Maximum1920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T14:04:35.056733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum217
5-th percentile239
Q1297.5
median591.5
Q31227.5
95-th percentile1650.15
Maximum1920
Range1703
Interquartile range (IQR)930

Descriptive statistics

Standard deviation516.32104
Coefficient of variation (CV)0.66279271
Kurtosis-1.0309332
Mean779.00833
Median Absolute Deviation (MAD)331
Skewness0.60572102
Sum93481
Variance266587.42
MonotonicityNot monotonic
2023-12-12T14:04:35.265208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239 2
 
1.7%
273 2
 
1.7%
422 2
 
1.7%
251 2
 
1.7%
1090 2
 
1.7%
1579 1
 
0.8%
274 1
 
0.8%
448 1
 
0.8%
910 1
 
0.8%
1483 1
 
0.8%
Other values (105) 105
87.5%
ValueCountFrequency (%)
217 1
0.8%
219 1
0.8%
220 1
0.8%
227 1
0.8%
233 1
0.8%
239 2
1.7%
242 1
0.8%
247 1
0.8%
250 1
0.8%
251 2
1.7%
ValueCountFrequency (%)
1920 1
0.8%
1901 1
0.8%
1836 1
0.8%
1784 1
0.8%
1717 1
0.8%
1672 1
0.8%
1649 1
0.8%
1647 1
0.8%
1628 1
0.8%
1617 1
0.8%

산업용 공급량
Real number (ℝ)

HIGH CORRELATION 

Distinct104
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean775.43333
Minimum492
Maximum1275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T14:04:35.478113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum492
5-th percentile572.75
Q1638.75
median713.5
Q3868
95-th percentile1157.1
Maximum1275
Range783
Interquartile range (IQR)229.25

Descriptive statistics

Standard deviation181.56309
Coefficient of variation (CV)0.23414403
Kurtosis-0.080378952
Mean775.43333
Median Absolute Deviation (MAD)106
Skewness0.89856659
Sum93052
Variance32965.155
MonotonicityNot monotonic
2023-12-12T14:04:35.701984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
656 3
 
2.5%
631 2
 
1.7%
1106 2
 
1.7%
607 2
 
1.7%
658 2
 
1.7%
555 2
 
1.7%
586 2
 
1.7%
625 2
 
1.7%
619 2
 
1.7%
977 2
 
1.7%
Other values (94) 99
82.5%
ValueCountFrequency (%)
492 1
0.8%
523 1
0.8%
555 2
1.7%
563 1
0.8%
568 1
0.8%
573 1
0.8%
575 1
0.8%
577 1
0.8%
578 1
0.8%
579 1
0.8%
ValueCountFrequency (%)
1275 1
0.8%
1193 1
0.8%
1191 1
0.8%
1185 1
0.8%
1179 1
0.8%
1159 1
0.8%
1157 1
0.8%
1153 1
0.8%
1113 1
0.8%
1106 2
1.7%

발전용 공급량
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1343.375
Minimum652
Maximum2036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T14:04:35.936632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum652
5-th percentile872.4
Q11163.25
median1341.5
Q31541.25
95-th percentile1813
Maximum2036
Range1384
Interquartile range (IQR)378

Descriptive statistics

Standard deviation279.01502
Coefficient of variation (CV)0.20769704
Kurtosis-0.15668701
Mean1343.375
Median Absolute Deviation (MAD)194.5
Skewness-0.019596989
Sum161205
Variance77849.379
MonotonicityNot monotonic
2023-12-12T14:04:36.193913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1536 2
 
1.7%
1387 2
 
1.7%
1271 2
 
1.7%
1563 2
 
1.7%
1313 2
 
1.7%
1315 1
 
0.8%
1379 1
 
0.8%
1613 1
 
0.8%
1724 1
 
0.8%
1298 1
 
0.8%
Other values (105) 105
87.5%
ValueCountFrequency (%)
652 1
0.8%
741 1
0.8%
790 1
0.8%
804 1
0.8%
810 1
0.8%
823 1
0.8%
875 1
0.8%
893 1
0.8%
911 1
0.8%
916 1
0.8%
ValueCountFrequency (%)
2036 1
0.8%
1962 1
0.8%
1877 1
0.8%
1874 1
0.8%
1859 1
0.8%
1851 1
0.8%
1811 1
0.8%
1802 1
0.8%
1789 1
0.8%
1778 1
0.8%

Interactions

2023-12-12T14:04:33.929349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:04:33.123228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:04:33.494427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:04:34.049559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:04:33.251831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:04:33.631872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:04:34.175478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:04:33.388594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:04:33.801579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:04:36.363918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
민수용 공급량산업용 공급량발전용 공급량
민수용 공급량1.0000.8290.542
산업용 공급량0.8291.0000.756
발전용 공급량0.5420.7561.000
2023-12-12T14:04:36.504451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
민수용 공급량산업용 공급량발전용 공급량
민수용 공급량1.0000.7930.529
산업용 공급량0.7931.0000.765
발전용 공급량0.5290.7651.000

Missing values

2023-12-12T14:04:34.338340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:04:34.462635image/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

연월민수용 공급량산업용 공급량발전용 공급량
02013-01164912751859
12013-02132410481536
22013-0310689451537
32013-048108361564
42013-054786861493
52013-062986821501
62013-072517491573
72013-082177131577
82013-092396781161
92013-104108211554
연월민수용 공급량산업용 공급량발전용 공급량
1102022-0312298261811
1112022-047266881303
1122022-054726541399
1132022-063276881471
1142022-072847291581
1152022-082606641372
1162022-092966071340
1172022-105466801375
1182022-118727241612
1192022-12178411912036