Overview

Dataset statistics

Number of variables8
Number of observations27
Missing cells1
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory74.9 B

Variable types

Categorical2
Numeric6

Dataset

Description한국가스공사 생산기지(평택, 통영, 인천, 삼척, 제주)의 현황데이터로, 각 기지별 생산능력 및 설비용량 등의 자료를 포함하고 있습니다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15040982/fileData.do

Alerts

용량 is highly overall correlated with 단위High correlation
평택기지 is highly overall correlated with 인천기지 and 3 other fieldsHigh correlation
인천기지 is highly overall correlated with 평택기지 and 3 other fieldsHigh correlation
통영기지 is highly overall correlated with 평택기지 and 2 other fieldsHigh correlation
삼척기지 is highly overall correlated with 평택기지 and 2 other fieldsHigh correlation
제주기지 is highly overall correlated with 평택기지 and 1 other fieldsHigh correlation
구 분 is highly overall correlated with 단위High correlation
단위 is highly overall correlated with 용량 and 1 other fieldsHigh correlation
용량 has 1 (3.7%) missing valuesMissing
평택기지 has 12 (44.4%) zerosZeros
인천기지 has 13 (48.1%) zerosZeros
통영기지 has 16 (59.3%) zerosZeros
삼척기지 has 18 (66.7%) zerosZeros
제주기지 has 19 (70.4%) zerosZeros

Reproduction

Analysis started2023-12-23 07:06:22.185263
Analysis finished2023-12-23 07:06:50.605048
Duration28.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
재액화설비
7 
저장탱크
5 
기화설비(SCV)
3 
BOG압축기
3 
송출설비(저압펌프)
2 
Other values (5)
7 

Length

Max length11
Median length9
Mean length6.6666667
Min length3

Unique

Unique3 ?
Unique (%)11.1%

Sample

1st row하역설비
2nd row저장탱크
3rd row저장탱크
4th row저장탱크
5th row저장탱크

Common Values

ValueCountFrequency (%)
재액화설비 7
25.9%
저장탱크 5
18.5%
기화설비(SCV) 3
11.1%
BOG압축기 3
11.1%
송출설비(저압펌프) 2
 
7.4%
송출설비(고압펌프) 2
 
7.4%
기화설비(고압ORV) 2
 
7.4%
하역설비 1
 
3.7%
기화설비(저압ORV) 1
 
3.7%
AAV 1
 
3.7%

Length

2023-12-23T07:06:50.921781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:06:51.454442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재액화설비 7
25.9%
저장탱크 5
18.5%
기화설비(scv 3
11.1%
bog압축기 3
11.1%
송출설비(저압펌프 2
 
7.4%
송출설비(고압펌프 2
 
7.4%
기화설비(고압orv 2
 
7.4%
하역설비 1
 
3.7%
기화설비(저압orv 1
 
3.7%
aav 1
 
3.7%

용량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)92.3%
Missing1
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean30608.119
Minimum5
Maximum270000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-23T07:06:52.134172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile15.5
Q130.5
median100
Q39875
95-th percentile185000
Maximum270000
Range269995
Interquartile range (IQR)9844.5

Descriptive statistics

Standard deviation69541.106
Coefficient of variation (CV)2.2719823
Kurtosis5.9354107
Mean30608.119
Median Absolute Deviation (MAD)79.5
Skewness2.5367031
Sum795811.1
Variance4.8359655 × 109
MonotonicityNot monotonic
2023-12-23T07:06:52.884572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
60.0 2
 
7.4%
90.0 2
 
7.4%
120.0 1
 
3.7%
24000.0 1
 
3.7%
12000.0 1
 
3.7%
3500.0 1
 
3.7%
40.0 1
 
3.7%
30.0 1
 
3.7%
26.1 1
 
3.7%
21.0 1
 
3.7%
Other values (14) 14
51.9%
ValueCountFrequency (%)
5.0 1
3.7%
15.0 1
3.7%
17.0 1
3.7%
21.0 1
3.7%
25.0 1
3.7%
26.1 1
3.7%
30.0 1
3.7%
32.0 1
3.7%
40.0 1
3.7%
60.0 2
7.4%
ValueCountFrequency (%)
270000.0 1
3.7%
200000.0 1
3.7%
140000.0 1
3.7%
100000.0 1
3.7%
45000.0 1
3.7%
24000.0 1
3.7%
12000.0 1
3.7%
3500.0 1
3.7%
240.0 1
3.7%
180.0 1
3.7%

단위
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size348.0 B
T/H
18 
㎘
5 
Nm3/H
3 
선좌
 
1

Length

Max length5
Median length3
Mean length2.8148148
Min length1

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row선좌
2nd row㎘
3rd row㎘
4th row㎘
5th row㎘

Common Values

ValueCountFrequency (%)
T/H 18
66.7%
㎘ 5
 
18.5%
Nm3/H 3
 
11.1%
선좌 1
 
3.7%

Length

2023-12-23T07:06:53.674160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:06:54.126157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
t/h 18
66.7%
㎘ 5
 
18.5%
nm3/h 3
 
11.1%
선좌 1
 
3.7%

평택기지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7407407
Minimum0
Maximum59
Zeros12
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-23T07:06:54.700314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39.5
95-th percentile34
Maximum59
Range59
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation13.668022
Coefficient of variation (CV)2.0276735
Kurtosis9.4209362
Mean6.7407407
Median Absolute Deviation (MAD)1
Skewness3.0339002
Sum182
Variance186.81481
MonotonicityNot monotonic
2023-12-23T07:06:55.184767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 12
44.4%
2 3
 
11.1%
10 2
 
7.4%
1 2
 
7.4%
4 1
 
3.7%
9 1
 
3.7%
59 1
 
3.7%
43 1
 
3.7%
3 1
 
3.7%
13 1
 
3.7%
Other values (2) 2
 
7.4%
ValueCountFrequency (%)
0 12
44.4%
1 2
 
7.4%
2 3
 
11.1%
3 1
 
3.7%
4 1
 
3.7%
9 1
 
3.7%
10 2
 
7.4%
11 1
 
3.7%
12 1
 
3.7%
13 1
 
3.7%
ValueCountFrequency (%)
59 1
 
3.7%
43 1
 
3.7%
13 1
 
3.7%
12 1
 
3.7%
11 1
 
3.7%
10 2
7.4%
9 1
 
3.7%
4 1
 
3.7%
3 1
 
3.7%
2 3
11.1%

인천기지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5925926
Minimum0
Maximum58
Zeros13
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-23T07:06:55.703194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38.5
95-th percentile43
Maximum58
Range58
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation15.039075
Coefficient of variation (CV)1.9807563
Kurtosis6.5398355
Mean7.5925926
Median Absolute Deviation (MAD)1
Skewness2.6177426
Sum205
Variance226.17379
MonotonicityNot monotonic
2023-12-23T07:06:56.334971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 13
48.1%
2 3
 
11.1%
1 2
 
7.4%
10 1
 
3.7%
11 1
 
3.7%
52 1
 
3.7%
58 1
 
3.7%
7 1
 
3.7%
21 1
 
3.7%
22 1
 
3.7%
Other values (2) 2
 
7.4%
ValueCountFrequency (%)
0 13
48.1%
1 2
 
7.4%
2 3
 
11.1%
4 1
 
3.7%
7 1
 
3.7%
10 1
 
3.7%
11 1
 
3.7%
12 1
 
3.7%
21 1
 
3.7%
22 1
 
3.7%
ValueCountFrequency (%)
58 1
 
3.7%
52 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
12 1
 
3.7%
11 1
 
3.7%
10 1
 
3.7%
7 1
 
3.7%
4 1
 
3.7%
2 3
11.1%

통영기지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5555556
Minimum0
Maximum41
Zeros16
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-23T07:06:57.020448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.5
95-th percentile24.2
Maximum41
Range41
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation9.6808031
Coefficient of variation (CV)2.1250543
Kurtosis8.4123184
Mean4.5555556
Median Absolute Deviation (MAD)0
Skewness2.8670683
Sum123
Variance93.717949
MonotonicityNot monotonic
2023-12-23T07:06:57.658540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16
59.3%
2 3
 
11.1%
13 1
 
3.7%
4 1
 
3.7%
41 1
 
3.7%
29 1
 
3.7%
12 1
 
3.7%
3 1
 
3.7%
5 1
 
3.7%
10 1
 
3.7%
ValueCountFrequency (%)
0 16
59.3%
2 3
 
11.1%
3 1
 
3.7%
4 1
 
3.7%
5 1
 
3.7%
10 1
 
3.7%
12 1
 
3.7%
13 1
 
3.7%
29 1
 
3.7%
41 1
 
3.7%
ValueCountFrequency (%)
41 1
 
3.7%
29 1
 
3.7%
13 1
 
3.7%
12 1
 
3.7%
10 1
 
3.7%
5 1
 
3.7%
4 1
 
3.7%
3 1
 
3.7%
2 3
 
11.1%
0 16
59.3%

삼척기지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8888889
Minimum0
Maximum36
Zeros18
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-23T07:06:58.255801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11.8
Maximum36
Range36
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.3658951
Coefficient of variation (CV)2.5497329
Kurtosis16.626441
Mean2.8888889
Median Absolute Deviation (MAD)0
Skewness3.864541
Sum78
Variance54.25641
MonotonicityNot monotonic
2023-12-23T07:06:59.016785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 18
66.7%
6 2
 
7.4%
2 2
 
7.4%
1 1
 
3.7%
9 1
 
3.7%
3 1
 
3.7%
36 1
 
3.7%
13 1
 
3.7%
ValueCountFrequency (%)
0 18
66.7%
1 1
 
3.7%
2 2
 
7.4%
3 1
 
3.7%
6 2
 
7.4%
9 1
 
3.7%
13 1
 
3.7%
36 1
 
3.7%
ValueCountFrequency (%)
36 1
 
3.7%
13 1
 
3.7%
9 1
 
3.7%
6 2
 
7.4%
3 1
 
3.7%
2 2
 
7.4%
1 1
 
3.7%
0 18
66.7%

제주기지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81481481
Minimum0
Maximum6
Zeros19
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-23T07:06:59.933230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5941488
Coefficient of variation (CV)1.9564554
Kurtosis3.8377325
Mean0.81481481
Median Absolute Deviation (MAD)0
Skewness2.1109428
Sum22
Variance2.5413105
MonotonicityNot monotonic
2023-12-23T07:07:00.515914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19
70.4%
1 3
 
11.1%
4 2
 
7.4%
2 1
 
3.7%
6 1
 
3.7%
3 1
 
3.7%
ValueCountFrequency (%)
0 19
70.4%
1 3
 
11.1%
2 1
 
3.7%
3 1
 
3.7%
4 2
 
7.4%
6 1
 
3.7%
ValueCountFrequency (%)
6 1
 
3.7%
4 2
 
7.4%
3 1
 
3.7%
2 1
 
3.7%
1 3
 
11.1%
0 19
70.4%

Interactions

2023-12-23T07:06:46.280971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:30.815257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:34.540417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:37.851705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:40.692758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:43.898127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:46.599756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:31.450844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:35.147215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:38.437827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:41.088820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:44.297038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:46.910368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:32.085428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:35.633006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:39.052865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:41.583260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:44.585668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:47.249245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:32.526863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:36.111077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:39.445910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:42.073382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:45.001260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:47.723305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:33.198226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:36.586822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:39.738294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:42.682227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:45.532878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:48.620001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:33.870733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:37.286214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:40.214667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:43.377358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:06:45.865638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:07:01.218872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분용량단위평택기지인천기지통영기지삼척기지제주기지
구 분1.0000.0001.0000.6110.6430.3330.5100.572
용량0.0001.0000.8970.0000.0000.3810.4040.405
단위1.0000.8971.0000.0000.0000.0000.0000.496
평택기지0.6110.0000.0001.0000.9070.8770.9720.000
인천기지0.6430.0000.0000.9071.0000.9680.8910.000
통영기지0.3330.3810.0000.8770.9681.0000.8950.000
삼척기지0.5100.4040.0000.9720.8910.8951.0000.000
제주기지0.5720.4050.4960.0000.0000.0000.0001.000
2023-12-23T07:07:01.907765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단위구 분
단위1.0000.860
구 분0.8601.000
2023-12-23T07:07:02.802319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용량평택기지인천기지통영기지삼척기지제주기지구 분단위
용량1.0000.4140.3430.2450.385-0.3220.0000.571
평택기지0.4141.0000.8390.7240.632-0.5600.2350.000
인천기지0.3430.8391.0000.7150.638-0.5190.3480.000
통영기지0.2450.7240.7151.0000.652-0.4170.1070.000
삼척기지0.3850.6320.6380.6521.000-0.3440.1690.000
제주기지-0.322-0.560-0.519-0.417-0.3441.0000.2880.315
구 분0.0000.2350.3480.1070.1690.2881.0000.860
단위0.5710.0000.0000.0000.0000.3150.8601.000

Missing values

2023-12-23T07:06:49.251641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:06:50.327605image/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

구 분용량단위평택기지인천기지통영기지삼척기지제주기지
0하역설비<NA>선좌22211
1저장탱크45000.0㎘00002
2저장탱크100000.0㎘1010000
3저장탱크140000.0㎘421300
4저장탱크200000.0㎘911490
5저장탱크270000.0㎘00030
6송출설비(저압펌프)32.0T/H00006
7송출설비(저압펌프)150.0T/H595241360
8송출설비(고압펌프)25.0T/H00004
9송출설비(고압펌프)110.0T/H435829130
구 분용량단위평택기지인천기지통영기지삼척기지제주기지
17재액화설비5.0T/H00001
18재액화설비17.0T/H00200
19재액화설비21.0T/H00200
20재액화설비26.1T/H01000
21재액화설비30.0T/H24000
22재액화설비40.0T/H11020
23재액화설비60.0T/H10000
24BOG압축기3500.0Nm3/H00003
25BOG압축기12000.0Nm3/H11121060
26BOG압축기24000.0Nm3/H20000