Overview

Dataset statistics

Number of variables7
Number of observations252
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.9 KiB
Average record size in memory60.5 B

Variable types

Categorical2
Text1
Numeric4

Dataset

Description한국가스안전공사에서 보유한 지역별(시/도, 군/구) 가스운반차량(탱크로리, 벌크로리, 전용운반차량, 튜브트레일러) 통계자료로 현황파악 통계자료로 활용가능한 데이터입니다.
Author한국가스안전공사
URLhttps://www.data.go.kr/data/15001490/fileData.do

Alerts

탱크로리 is highly overall correlated with 튜브트레일러 and 1 other fieldsHigh correlation
튜브트레일러 is highly overall correlated with 탱크로리High correlation
전용운반차량 is highly overall correlated with 탱크로리 and 1 other fieldsHigh correlation
벌크로리 is highly overall correlated with 전용운반차량High correlation
지사 is highly overall correlated with 시-도High correlation
시-도 is highly overall correlated with 지사High correlation
군-구 has unique valuesUnique
탱크로리 has 73 (29.0%) zerosZeros
튜브트레일러 has 192 (76.2%) zerosZeros
벌크로리 has 52 (20.6%) zerosZeros

Reproduction

Analysis started2023-12-12 15:31:33.145873
Analysis finished2023-12-12 15:31:35.470318
Duration2.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지사
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
대구광역본부
 
16
전북본부
 
15
대전광역본부
 
14
경남본부
 
12
경기광역본부
 
12
Other values (24)
183 

Length

Max length6
Median length6
Mean length5.5079365
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울광역본부
2nd row서울광역본부
3rd row서울광역본부
4th row서울광역본부
5th row서울서부지사

Common Values

ValueCountFrequency (%)
대구광역본부 16
 
6.3%
전북본부 15
 
6.0%
대전광역본부 14
 
5.6%
경남본부 12
 
4.8%
경기광역본부 12
 
4.8%
광주광역본부 12
 
4.8%
경기동부지사 11
 
4.4%
인천본부 10
 
4.0%
충북본부 10
 
4.0%
경남서부지사 10
 
4.0%
Other values (19) 130
51.6%

Length

2023-12-13T00:31:35.581620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대구광역본부 16
 
6.3%
전북본부 15
 
6.0%
대전광역본부 14
 
5.6%
경남본부 12
 
4.8%
경기광역본부 12
 
4.8%
광주광역본부 12
 
4.8%
경기동부지사 11
 
4.4%
인천본부 10
 
4.0%
충북본부 10
 
4.0%
경남서부지사 10
 
4.0%
Other values (19) 130
51.6%

시-도
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
경기
44 
서울
25 
경북
24 
전남
22 
경남
22 
Other values (11)
115 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row서울
3rd row서울
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
경기 44
17.5%
서울 25
9.9%
경북 24
9.5%
전남 22
8.7%
경남 22
8.7%
강원 18
7.1%
충남 17
 
6.7%
부산 16
 
6.3%
전북 15
 
6.0%
충북 14
 
5.6%
Other values (6) 35
13.9%

Length

2023-12-13T00:31:35.721406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 44
17.5%
서울 25
9.9%
경북 24
9.5%
전남 22
8.7%
경남 22
8.7%
강원 18
7.1%
충남 17
 
6.7%
부산 16
 
6.3%
전북 15
 
6.0%
충북 14
 
5.6%
Other values (6) 35
13.9%

군-구
Text

UNIQUE 

Distinct252
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-13T00:31:36.088215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length6
Mean length6.5
Min length3

Characters and Unicode

Total characters1638
Distinct characters143
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique252 ?
Unique (%)100.0%

Sample

1st row서울 강남구
2nd row서울 강동구
3rd row서울 서초구
4th row서울 송파구
5th row서울 마포구
ValueCountFrequency (%)
경기 44
 
8.2%
서울 25
 
4.6%
경북 24
 
4.5%
전남 22
 
4.1%
경남 22
 
4.1%
강원 18
 
3.3%
부산 16
 
3.0%
충남 16
 
3.0%
전북 15
 
2.8%
충북 14
 
2.6%
Other values (245) 322
59.9%
2023-12-13T00:31:36.662919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
287
17.5%
구 117
 
7.1%
시 101
 
6.2%
경 93
 
5.7%
군 85
 
5.2%
남 76
 
4.6%
북 61
 
3.7%
기 46
 
2.8%
산 44
 
2.7%
전 44
 
2.7%
Other values (133) 684
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1351
82.5%
Space Separator 287
 
17.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 117
 
8.7%
시 101
 
7.5%
경 93
 
6.9%
군 85
 
6.3%
남 76
 
5.6%
북 61
 
4.5%
기 46
 
3.4%
산 44
 
3.3%
전 44
 
3.3%
서 41
 
3.0%
Other values (132) 643
47.6%
Space Separator
ValueCountFrequency (%)
287
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1351
82.5%
Common 287
 
17.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 117
 
8.7%
시 101
 
7.5%
경 93
 
6.9%
군 85
 
6.3%
남 76
 
5.6%
북 61
 
4.5%
기 46
 
3.4%
산 44
 
3.3%
전 44
 
3.3%
서 41
 
3.0%
Other values (132) 643
47.6%
Common
ValueCountFrequency (%)
287
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1351
82.5%
ASCII 287
 
17.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
287
100.0%
Hangul
ValueCountFrequency (%)
구 117
 
8.7%
시 101
 
7.5%
경 93
 
6.9%
군 85
 
6.3%
남 76
 
5.6%
북 61
 
4.5%
기 46
 
3.4%
산 44
 
3.3%
전 44
 
3.3%
서 41
 
3.0%
Other values (132) 643
47.6%

탱크로리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.313492
Minimum0
Maximum161
Zeros73
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T00:31:36.850314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile46.9
Maximum161
Range161
Interquartile range (IQR)10

Descriptive statistics

Standard deviation21.437785
Coefficient of variation (CV)2.0786155
Kurtosis17.687184
Mean10.313492
Median Absolute Deviation (MAD)2
Skewness3.8501211
Sum2599
Variance459.57862
MonotonicityNot monotonic
2023-12-13T00:31:37.028928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 73
29.0%
1 34
13.5%
2 27
 
10.7%
4 14
 
5.6%
5 10
 
4.0%
3 10
 
4.0%
6 6
 
2.4%
7 5
 
2.0%
8 5
 
2.0%
12 4
 
1.6%
Other values (39) 64
25.4%
ValueCountFrequency (%)
0 73
29.0%
1 34
13.5%
2 27
 
10.7%
3 10
 
4.0%
4 14
 
5.6%
5 10
 
4.0%
6 6
 
2.4%
7 5
 
2.0%
8 5
 
2.0%
9 3
 
1.2%
ValueCountFrequency (%)
161 1
0.4%
126 1
0.4%
113 1
0.4%
108 1
0.4%
92 1
0.4%
90 1
0.4%
88 1
0.4%
76 1
0.4%
74 1
0.4%
55 1
0.4%

튜브트레일러
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3253968
Minimum0
Maximum452
Zeros192
Zeros (%)76.2%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T00:31:37.190337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile52.45
Maximum452
Range452
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.016795
Coefficient of variation (CV)4.2911627
Kurtosis70.13197
Mean9.3253968
Median Absolute Deviation (MAD)0
Skewness7.5615144
Sum2350
Variance1601.3439
MonotonicityNot monotonic
2023-12-13T00:31:37.312833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 192
76.2%
1 10
 
4.0%
2 7
 
2.8%
7 4
 
1.6%
8 3
 
1.2%
3 2
 
0.8%
5 2
 
0.8%
4 2
 
0.8%
41 1
 
0.4%
20 1
 
0.4%
Other values (28) 28
 
11.1%
ValueCountFrequency (%)
0 192
76.2%
1 10
 
4.0%
2 7
 
2.8%
3 2
 
0.8%
4 2
 
0.8%
5 2
 
0.8%
6 1
 
0.4%
7 4
 
1.6%
8 3
 
1.2%
9 1
 
0.4%
ValueCountFrequency (%)
452 1
0.4%
287 1
0.4%
164 1
0.4%
162 1
0.4%
132 1
0.4%
118 1
0.4%
88 1
0.4%
87 1
0.4%
82 1
0.4%
74 1
0.4%

전용운반차량
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.456349
Minimum1
Maximum398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T00:31:37.459261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.55
Q116
median29
Q357
95-th percentile105.25
Maximum398
Range397
Interquartile range (IQR)41

Descriptive statistics

Standard deviation43.47223
Coefficient of variation (CV)1.0486266
Kurtosis24.076477
Mean41.456349
Median Absolute Deviation (MAD)15
Skewness3.8792876
Sum10447
Variance1889.8347
MonotonicityNot monotonic
2023-12-13T00:31:37.646492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 9
 
3.6%
21 9
 
3.6%
17 8
 
3.2%
8 7
 
2.8%
10 7
 
2.8%
24 7
 
2.8%
4 7
 
2.8%
15 6
 
2.4%
41 6
 
2.4%
33 6
 
2.4%
Other values (84) 180
71.4%
ValueCountFrequency (%)
1 3
1.2%
2 1
 
0.4%
3 2
 
0.8%
4 7
2.8%
5 3
1.2%
6 2
 
0.8%
7 3
1.2%
8 7
2.8%
10 7
2.8%
11 3
1.2%
ValueCountFrequency (%)
398 1
0.4%
299 1
0.4%
265 1
0.4%
135 1
0.4%
134 1
0.4%
130 1
0.4%
126 1
0.4%
125 1
0.4%
114 1
0.4%
110 1
0.4%

벌크로리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7380952
Minimum0
Maximum84
Zeros52
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T00:31:37.865232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q39
95-th percentile24.45
Maximum84
Range84
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.7160307
Coefficient of variation (CV)1.4419551
Kurtosis21.027049
Mean6.7380952
Median Absolute Deviation (MAD)4
Skewness3.7000065
Sum1698
Variance94.401252
MonotonicityNot monotonic
2023-12-13T00:31:38.028195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 52
20.6%
1 28
11.1%
2 26
10.3%
4 21
8.3%
9 16
 
6.3%
5 15
 
6.0%
3 15
 
6.0%
6 11
 
4.4%
13 8
 
3.2%
7 8
 
3.2%
Other values (22) 52
20.6%
ValueCountFrequency (%)
0 52
20.6%
1 28
11.1%
2 26
10.3%
3 15
 
6.0%
4 21
8.3%
5 15
 
6.0%
6 11
 
4.4%
7 8
 
3.2%
8 5
 
2.0%
9 16
 
6.3%
ValueCountFrequency (%)
84 1
0.4%
65 1
0.4%
44 1
0.4%
33 1
0.4%
32 1
0.4%
31 2
0.8%
30 2
0.8%
28 2
0.8%
26 1
0.4%
25 1
0.4%

Interactions

2023-12-13T00:31:34.752145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:33.483986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:33.923153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:34.284281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:34.870476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:33.594708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:34.022357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:34.390494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:34.962607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:33.696680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:34.126243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:34.481690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:35.090211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:33.833728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:34.217531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:31:34.629500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:31:38.145930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지사시-도탱크로리튜브트레일러전용운반차량벌크로리
지사1.0000.9920.2620.2180.5680.589
시-도0.9921.0000.4250.3160.5420.537
탱크로리0.2620.4251.0000.7190.4320.543
튜브트레일러0.2180.3160.7191.0000.5290.506
전용운반차량0.5680.5420.4320.5291.0000.953
벌크로리0.5890.5370.5430.5060.9531.000
2023-12-13T00:31:38.296627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지사시-도
지사1.0000.888
시-도0.8881.000
2023-12-13T00:31:38.426026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
탱크로리튜브트레일러전용운반차량벌크로리지사시-도
탱크로리1.0000.5030.5470.4590.0940.187
튜브트레일러0.5031.0000.4240.4130.0910.153
전용운반차량0.5470.4241.0000.7010.2620.278
벌크로리0.4590.4130.7011.0000.2760.275
지사0.0940.0910.2620.2761.0000.888
시-도0.1870.1530.2780.2750.8881.000

Missing values

2023-12-13T00:31:35.273951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:31:35.415179image/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

지사시-도군-구탱크로리튜브트레일러전용운반차량벌크로리
0서울광역본부서울서울 강남구94180
1서울광역본부서울서울 강동구2070
2서울광역본부서울서울 서초구10080
3서울광역본부서울서울 송파구70170
4서울서부지사서울서울 마포구00340
5서울서부지사서울서울 서대문구2080
6서울서부지사서울서울 용산구0040
7서울서부지사서울서울 은평구1040
8서울서부지사서울서울 종로구0080
9서울서부지사서울서울 중구0060
지사시-도군-구탱크로리튜브트레일러전용운반차량벌크로리
242경기중부지사경기경기 고양시 덕양구20122
243경기중부지사경기경기 고양시 일산동구10155
244경기중부지사경기경기 고양시 일산서구0044
245경기중부지사경기경기 김포시13010911
246경기중부지사경기경기 양주시1908831
247경기중부지사경기경기 파주시88213532
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