Overview

Dataset statistics

Number of variables16
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory147.9 B

Variable types

Text1
Categorical3
Numeric12

Dataset

Description(주)한국가스기술공사 14개 사업장별 보유하고 있는 안전관리 장비 현황에 대한 정보(장비명)에 대한 현황자료입니다.
URLhttps://www.data.go.kr/data/15103281/fileData.do

Alerts

평택기지 is highly overall correlated with 인천기지 and 4 other fieldsHigh correlation
인천기지 is highly overall correlated with 평택기지 and 5 other fieldsHigh correlation
통영기지 is highly overall correlated with 평택기지 and 8 other fieldsHigh correlation
삼척기지 is highly overall correlated with 평택기지 and 4 other fieldsHigh correlation
서 울 is highly overall correlated with 통영기지 and 10 other fieldsHigh correlation
경 기 is highly overall correlated with 서 울 and 7 other fieldsHigh correlation
강 원 is highly overall correlated with 통영기지 and 9 other fieldsHigh correlation
대전충청 is highly overall correlated with 서 울 and 9 other fieldsHigh correlation
전 북 is highly overall correlated with 서 울 and 7 other fieldsHigh correlation
광주전남 is highly overall correlated with 서 울 and 8 other fieldsHigh correlation
대구경북 is highly overall correlated with 통영기지 and 10 other fieldsHigh correlation
부산경남 is highly overall correlated with 인천기지 and 12 other fieldsHigh correlation
본사 is highly overall correlated with 통영기지 and 5 other fieldsHigh correlation
제주LNG is highly overall correlated with 평택기지 and 13 other fieldsHigh correlation
인 천 is highly overall correlated with 평택기지 and 10 other fieldsHigh correlation
본사 is highly imbalanced (58.6%)Imbalance
장비명 has unique valuesUnique
평택기지 has 9 (33.3%) zerosZeros
인천기지 has 5 (18.5%) zerosZeros
통영기지 has 13 (48.1%) zerosZeros
삼척기지 has 11 (40.7%) zerosZeros
서 울 has 19 (70.4%) zerosZeros
경 기 has 19 (70.4%) zerosZeros
강 원 has 20 (74.1%) zerosZeros
대전충청 has 18 (66.7%) zerosZeros
전 북 has 14 (51.9%) zerosZeros
광주전남 has 17 (63.0%) zerosZeros
대구경북 has 18 (66.7%) zerosZeros
부산경남 has 17 (63.0%) zerosZeros

Reproduction

Analysis started2023-12-12 17:44:51.260327
Analysis finished2023-12-12 17:45:04.255954
Duration13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

장비명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T02:45:04.376196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length6.6296296
Min length2

Characters and Unicode

Total characters179
Distinct characters74
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row가스분석기
2nd row독성가스분석기
3rd row가스검지기
4th row가스검지기(레이져)
5th row가스검지기(FID)
ValueCountFrequency (%)
가스분석기 1
 
3.3%
독성가스분석기 1
 
3.3%
이동식 1
 
3.3%
방사선측정기 1
 
3.3%
들것 1
 
3.3%
구강기도유지기 1
 
3.3%
골절부목 1
 
3.3%
목고정장치 1
 
3.3%
자동심장충격기 1
 
3.3%
전격방지기 1
 
3.3%
Other values (20) 20
66.7%
2023-12-13T02:45:04.697215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25
 
14.0%
11
 
6.1%
7
 
3.9%
7
 
3.9%
6
 
3.4%
) 5
 
2.8%
5
 
2.8%
( 5
 
2.8%
4
 
2.2%
4
 
2.2%
Other values (64) 100
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 162
90.5%
Close Punctuation 5
 
2.8%
Open Punctuation 5
 
2.8%
Space Separator 3
 
1.7%
Uppercase Letter 3
 
1.7%
Other Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
25
 
15.4%
11
 
6.8%
7
 
4.3%
7
 
4.3%
6
 
3.7%
5
 
3.1%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (57) 87
53.7%
Uppercase Letter
ValueCountFrequency (%)
F 1
33.3%
D 1
33.3%
I 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 162
90.5%
Common 14
 
7.8%
Latin 3
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
25
 
15.4%
11
 
6.8%
7
 
4.3%
7
 
4.3%
6
 
3.7%
5
 
3.1%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (57) 87
53.7%
Common
ValueCountFrequency (%)
) 5
35.7%
( 5
35.7%
3
21.4%
, 1
 
7.1%
Latin
ValueCountFrequency (%)
F 1
33.3%
D 1
33.3%
I 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 162
90.5%
ASCII 17
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
25
 
15.4%
11
 
6.8%
7
 
4.3%
7
 
4.3%
6
 
3.7%
5
 
3.1%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (57) 87
53.7%
ASCII
ValueCountFrequency (%)
) 5
29.4%
( 5
29.4%
3
17.6%
, 1
 
5.9%
F 1
 
5.9%
D 1
 
5.9%
I 1
 
5.9%

본사
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
23 
5
 
2
3
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)7.4%

Sample

1st row3
2nd row0
3rd row5
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23
85.2%
5 2
 
7.4%
3 1
 
3.7%
1 1
 
3.7%

Length

2023-12-13T02:45:04.800486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:45:04.877168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23
85.2%
5 2
 
7.4%
3 1
 
3.7%
1 1
 
3.7%

평택기지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6
Minimum0
Maximum102
Zeros9
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:04.949608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile12.1
Maximum102
Range102
Interquartile range (IQR)3

Descriptive statistics

Standard deviation19.438265
Coefficient of variation (CV)3.2397109
Kurtosis25.456788
Mean6
Median Absolute Deviation (MAD)2
Skewness4.9896554
Sum162
Variance377.84615
MonotonicityNot monotonic
2023-12-13T02:45:05.040159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 9
33.3%
2 6
22.2%
1 4
14.8%
3 2
 
7.4%
13 1
 
3.7%
4 1
 
3.7%
10 1
 
3.7%
6 1
 
3.7%
5 1
 
3.7%
102 1
 
3.7%
ValueCountFrequency (%)
0 9
33.3%
1 4
14.8%
2 6
22.2%
3 2
 
7.4%
4 1
 
3.7%
5 1
 
3.7%
6 1
 
3.7%
10 1
 
3.7%
13 1
 
3.7%
102 1
 
3.7%
ValueCountFrequency (%)
102 1
 
3.7%
13 1
 
3.7%
10 1
 
3.7%
6 1
 
3.7%
5 1
 
3.7%
4 1
 
3.7%
3 2
 
7.4%
2 6
22.2%
1 4
14.8%
0 9
33.3%

인천기지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.962963
Minimum0
Maximum13
Zeros5
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:05.123198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile10.1
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.3337607
Coefficient of variation (CV)1.1251442
Kurtosis2.7933424
Mean2.962963
Median Absolute Deviation (MAD)1
Skewness1.7568965
Sum80
Variance11.11396
MonotonicityNot monotonic
2023-12-13T02:45:05.218602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 9
33.3%
1 5
18.5%
0 5
18.5%
5 2
 
7.4%
4 2
 
7.4%
13 1
 
3.7%
8 1
 
3.7%
11 1
 
3.7%
7 1
 
3.7%
ValueCountFrequency (%)
0 5
18.5%
1 5
18.5%
2 9
33.3%
4 2
 
7.4%
5 2
 
7.4%
7 1
 
3.7%
8 1
 
3.7%
11 1
 
3.7%
13 1
 
3.7%
ValueCountFrequency (%)
13 1
 
3.7%
11 1
 
3.7%
8 1
 
3.7%
7 1
 
3.7%
5 2
 
7.4%
4 2
 
7.4%
2 9
33.3%
1 5
18.5%
0 5
18.5%

통영기지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7407407
Minimum0
Maximum43
Zeros13
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:05.333009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33.5
95-th percentile13
Maximum43
Range43
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation8.6317508
Coefficient of variation (CV)2.3074977
Kurtosis17.511032
Mean3.7407407
Median Absolute Deviation (MAD)1
Skewness3.9621144
Sum101
Variance74.507123
MonotonicityNot monotonic
2023-12-13T02:45:05.445362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 13
48.1%
1 4
 
14.8%
13 2
 
7.4%
3 2
 
7.4%
5 2
 
7.4%
2 1
 
3.7%
6 1
 
3.7%
4 1
 
3.7%
43 1
 
3.7%
ValueCountFrequency (%)
0 13
48.1%
1 4
 
14.8%
2 1
 
3.7%
3 2
 
7.4%
4 1
 
3.7%
5 2
 
7.4%
6 1
 
3.7%
13 2
 
7.4%
43 1
 
3.7%
ValueCountFrequency (%)
43 1
 
3.7%
13 2
 
7.4%
6 1
 
3.7%
5 2
 
7.4%
4 1
 
3.7%
3 2
 
7.4%
2 1
 
3.7%
1 4
 
14.8%
0 13
48.1%

삼척기지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2
Minimum0
Maximum10
Zeros11
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:05.537428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile8.7
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8147961
Coefficient of variation (CV)1.407398
Kurtosis2.8005203
Mean2
Median Absolute Deviation (MAD)1
Skewness1.8551069
Sum54
Variance7.9230769
MonotonicityNot monotonic
2023-12-13T02:45:05.629248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 11
40.7%
2 6
22.2%
1 4
 
14.8%
4 2
 
7.4%
8 1
 
3.7%
10 1
 
3.7%
3 1
 
3.7%
9 1
 
3.7%
ValueCountFrequency (%)
0 11
40.7%
1 4
 
14.8%
2 6
22.2%
3 1
 
3.7%
4 2
 
7.4%
8 1
 
3.7%
9 1
 
3.7%
10 1
 
3.7%
ValueCountFrequency (%)
10 1
 
3.7%
9 1
 
3.7%
8 1
 
3.7%
4 2
 
7.4%
3 1
 
3.7%
2 6
22.2%
1 4
 
14.8%
0 11
40.7%

제주LNG
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
16 
1
2
3
10
 
1

Length

Max length2
Median length1
Mean length1.037037
Min length1

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row1
2nd row0
3rd row10
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16
59.3%
1 4
 
14.8%
2 4
 
14.8%
3 2
 
7.4%
10 1
 
3.7%

Length

2023-12-13T02:45:05.734886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:45:05.820548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16
59.3%
1 4
 
14.8%
2 4
 
14.8%
3 2
 
7.4%
10 1
 
3.7%

서 울
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.037037
Minimum0
Maximum29
Zeros19
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:05.915561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8.5
Maximum29
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.8341269
Coefficient of variation (CV)2.8640259
Kurtosis18.914598
Mean2.037037
Median Absolute Deviation (MAD)0
Skewness4.1791634
Sum55
Variance34.037037
MonotonicityNot monotonic
2023-12-13T02:45:06.013739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 19
70.4%
1 2
 
7.4%
29 1
 
3.7%
3 1
 
3.7%
10 1
 
3.7%
2 1
 
3.7%
5 1
 
3.7%
4 1
 
3.7%
ValueCountFrequency (%)
0 19
70.4%
1 2
 
7.4%
2 1
 
3.7%
3 1
 
3.7%
4 1
 
3.7%
5 1
 
3.7%
10 1
 
3.7%
29 1
 
3.7%
ValueCountFrequency (%)
29 1
 
3.7%
10 1
 
3.7%
5 1
 
3.7%
4 1
 
3.7%
3 1
 
3.7%
2 1
 
3.7%
1 2
 
7.4%
0 19
70.4%

인 천
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
17 
1
2
22
 
1
6
 
1

Length

Max length2
Median length1
Mean length1.037037
Min length1

Unique

Unique2 ?
Unique (%)7.4%

Sample

1st row0
2nd row0
3rd row22
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 17
63.0%
1 6
 
22.2%
2 2
 
7.4%
22 1
 
3.7%
6 1
 
3.7%

Length

2023-12-13T02:45:06.114271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:45:06.202282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17
63.0%
1 6
 
22.2%
2 2
 
7.4%
22 1
 
3.7%
6 1
 
3.7%

경 기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7037037
Minimum0
Maximum46
Zeros19
Zeros (%)70.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:06.297517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9.1
Maximum46
Range46
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.9692415
Coefficient of variation (CV)3.3173907
Kurtosis22.964925
Mean2.7037037
Median Absolute Deviation (MAD)0
Skewness4.6770732
Sum73
Variance80.447293
MonotonicityNot monotonic
2023-12-13T02:45:06.409455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 19
70.4%
3 2
 
7.4%
1 2
 
7.4%
46 1
 
3.7%
7 1
 
3.7%
10 1
 
3.7%
2 1
 
3.7%
ValueCountFrequency (%)
0 19
70.4%
1 2
 
7.4%
2 1
 
3.7%
3 2
 
7.4%
7 1
 
3.7%
10 1
 
3.7%
46 1
 
3.7%
ValueCountFrequency (%)
46 1
 
3.7%
10 1
 
3.7%
7 1
 
3.7%
3 2
 
7.4%
2 1
 
3.7%
1 2
 
7.4%
0 19
70.4%

강 원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2222222
Minimum0
Maximum37
Zeros20
Zeros (%)74.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:06.505129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6.1
Maximum37
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.1754567
Coefficient of variation (CV)3.2289555
Kurtosis23.381586
Mean2.2222222
Median Absolute Deviation (MAD)0
Skewness4.7183664
Sum60
Variance51.487179
MonotonicityNot monotonic
2023-12-13T02:45:06.598198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 20
74.1%
3 2
 
7.4%
4 2
 
7.4%
37 1
 
3.7%
7 1
 
3.7%
2 1
 
3.7%
ValueCountFrequency (%)
0 20
74.1%
2 1
 
3.7%
3 2
 
7.4%
4 2
 
7.4%
7 1
 
3.7%
37 1
 
3.7%
ValueCountFrequency (%)
37 1
 
3.7%
7 1
 
3.7%
4 2
 
7.4%
3 2
 
7.4%
2 1
 
3.7%
0 20
74.1%

대전충청
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2222222
Minimum0
Maximum30
Zeros18
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:06.706513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8.4
Maximum30
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.9893067
Coefficient of variation (CV)2.695188
Kurtosis19.158101
Mean2.2222222
Median Absolute Deviation (MAD)0
Skewness4.1869474
Sum60
Variance35.871795
MonotonicityNot monotonic
2023-12-13T02:45:06.804628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 18
66.7%
3 3
 
11.1%
2 2
 
7.4%
30 1
 
3.7%
7 1
 
3.7%
9 1
 
3.7%
1 1
 
3.7%
ValueCountFrequency (%)
0 18
66.7%
1 1
 
3.7%
2 2
 
7.4%
3 3
 
11.1%
7 1
 
3.7%
9 1
 
3.7%
30 1
 
3.7%
ValueCountFrequency (%)
30 1
 
3.7%
9 1
 
3.7%
7 1
 
3.7%
3 3
 
11.1%
2 2
 
7.4%
1 1
 
3.7%
0 18
66.7%

전 북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1851852
Minimum0
Maximum29
Zeros14
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:06.907671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6.8
Maximum29
Range29
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.6571061
Coefficient of variation (CV)2.5888451
Kurtosis21.153773
Mean2.1851852
Median Absolute Deviation (MAD)0
Skewness4.4351876
Sum59
Variance32.002849
MonotonicityNot monotonic
2023-12-13T02:45:06.997669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 14
51.9%
1 5
 
18.5%
3 3
 
11.1%
2 2
 
7.4%
4 1
 
3.7%
29 1
 
3.7%
8 1
 
3.7%
ValueCountFrequency (%)
0 14
51.9%
1 5
 
18.5%
2 2
 
7.4%
3 3
 
11.1%
4 1
 
3.7%
8 1
 
3.7%
29 1
 
3.7%
ValueCountFrequency (%)
29 1
 
3.7%
8 1
 
3.7%
4 1
 
3.7%
3 3
 
11.1%
2 2
 
7.4%
1 5
 
18.5%
0 14
51.9%

광주전남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2962963
Minimum0
Maximum33
Zeros17
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:07.091884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6.7
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.414016
Coefficient of variation (CV)2.7932005
Kurtosis22.10016
Mean2.2962963
Median Absolute Deviation (MAD)0
Skewness4.5485787
Sum62
Variance41.139601
MonotonicityNot monotonic
2023-12-13T02:45:07.193813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 17
63.0%
2 5
 
18.5%
3 2
 
7.4%
33 1
 
3.7%
7 1
 
3.7%
6 1
 
3.7%
ValueCountFrequency (%)
0 17
63.0%
2 5
 
18.5%
3 2
 
7.4%
6 1
 
3.7%
7 1
 
3.7%
33 1
 
3.7%
ValueCountFrequency (%)
33 1
 
3.7%
7 1
 
3.7%
6 1
 
3.7%
3 2
 
7.4%
2 5
 
18.5%
0 17
63.0%

대구경북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6666667
Minimum0
Maximum44
Zeros18
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:07.300401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile7.7
Maximum44
Range44
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation8.5304883
Coefficient of variation (CV)3.1989331
Kurtosis23.350947
Mean2.6666667
Median Absolute Deviation (MAD)0
Skewness4.7175645
Sum72
Variance72.769231
MonotonicityNot monotonic
2023-12-13T02:45:07.670566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 18
66.7%
1 2
 
7.4%
2 2
 
7.4%
44 1
 
3.7%
4 1
 
3.7%
8 1
 
3.7%
7 1
 
3.7%
3 1
 
3.7%
ValueCountFrequency (%)
0 18
66.7%
1 2
 
7.4%
2 2
 
7.4%
3 1
 
3.7%
4 1
 
3.7%
7 1
 
3.7%
8 1
 
3.7%
44 1
 
3.7%
ValueCountFrequency (%)
44 1
 
3.7%
8 1
 
3.7%
7 1
 
3.7%
4 1
 
3.7%
3 1
 
3.7%
2 2
 
7.4%
1 2
 
7.4%
0 18
66.7%

부산경남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8888889
Minimum0
Maximum39
Zeros17
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:45:07.761920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5
95-th percentile10.5
Maximum39
Range39
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation7.7376867
Coefficient of variation (CV)2.67843
Kurtosis19.722296
Mean2.8888889
Median Absolute Deviation (MAD)0
Skewness4.2597397
Sum78
Variance59.871795
MonotonicityNot monotonic
2023-12-13T02:45:07.867899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 17
63.0%
2 2
 
7.4%
3 2
 
7.4%
39 1
 
3.7%
12 1
 
3.7%
5 1
 
3.7%
7 1
 
3.7%
1 1
 
3.7%
4 1
 
3.7%
ValueCountFrequency (%)
0 17
63.0%
1 1
 
3.7%
2 2
 
7.4%
3 2
 
7.4%
4 1
 
3.7%
5 1
 
3.7%
7 1
 
3.7%
12 1
 
3.7%
39 1
 
3.7%
ValueCountFrequency (%)
39 1
 
3.7%
12 1
 
3.7%
7 1
 
3.7%
5 1
 
3.7%
4 1
 
3.7%
3 2
 
7.4%
2 2
 
7.4%
1 1
 
3.7%
0 17
63.0%

Interactions

2023-12-13T02:45:03.153593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:51.764452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.671767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.627272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.631102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:55.844625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.378404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.461533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.423572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.313725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:01.168545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.392190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.218931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:51.842900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.743286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.702006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.731073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:55.949967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.476279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.550909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.491317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.380656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:01.246568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.461729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.289645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:51.918255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.815038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.782512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.835251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:56.059104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.556642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.651370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.564907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.456509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:01.364176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.527184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.355874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:51.984833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.885626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.858549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.929399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:56.157405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.641102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.725600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.628961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.521272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:01.448696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.585728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.425313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.054430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.961892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.946579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:55.032333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:56.254864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.744216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.804583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.700171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.587736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:01.524283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.645260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.494424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.126301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.049229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.026492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:55.125381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:56.676783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.825576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.886873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.774077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.658884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:01.593459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.709481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.557885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.195169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.123443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.121245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:55.236434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:56.784143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.941234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.963353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.843503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.723801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:01.676916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.766464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.630278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.287519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.203053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.207316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:55.351046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:56.890861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.033671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.041041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.919410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.804210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:01.995919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.831619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.698097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.373697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.283922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.291698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:55.465020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.000631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.134201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.113821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.002889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.878887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.066557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.895405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.777340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.451107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.378335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.373719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:55.552323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.092393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.217152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.194461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.088466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.949486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.149629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.960517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.847979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.525706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.461725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.467981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:55.646244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.177039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.298677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.269094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.172240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:01.026716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.227430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.025975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.915154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:52.593187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:53.538604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:54.539551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:55.751141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:57.264958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:58.375857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:59.340214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:00.237013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:01.089699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:02.306275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:45:03.084145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:45:07.961231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장비명본사평택기지인천기지통영기지삼척기지제주LNG서 울인 천경 기강 원대전충청전 북광주전남대구경북부산경남
장비명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
본사1.0001.0000.3940.5980.8510.1460.6100.8500.4820.8480.4900.6520.7950.5530.5320.850
평택기지1.0000.3941.0000.7170.7830.3370.6790.6420.6770.6400.9320.6730.6420.6400.9310.642
인천기지1.0000.5980.7171.0000.7740.9040.7760.8990.7040.8750.9010.7600.6630.6520.8720.932
통영기지1.0000.8510.7830.7741.0000.3980.6230.9210.5680.9090.6670.5670.7020.7770.5980.921
삼척기지1.0000.1460.3370.9040.3981.0000.6930.6000.1360.3730.6900.4320.4980.7340.7160.681
제주LNG1.0000.6100.6790.7760.6230.6931.0000.7530.8860.7540.8700.9320.7590.7061.0000.807
서 울1.0000.8500.6420.8990.9210.6000.7531.0000.7350.9770.8540.8800.9450.9500.8250.992
인 천1.0000.4820.6770.7040.5680.1360.8860.7351.0000.8930.7160.9100.6590.6410.7560.704
경 기1.0000.8480.6400.8750.9090.3730.7540.9770.8931.0000.7360.8170.8960.8490.7830.977
강 원1.0000.4900.9320.9010.6670.6900.8700.8540.7160.7361.0000.7860.7230.7360.9930.854
대전충청1.0000.6520.6730.7600.5670.4320.9320.8800.9100.8170.7861.0001.0000.8670.8500.827
전 북1.0000.7950.6420.6630.7020.4980.7590.9450.6590.8960.7231.0001.0000.9860.7770.903
광주전남1.0000.5530.6400.6520.7770.7340.7060.9500.6410.8490.7360.8670.9861.0000.7830.910
대구경북1.0000.5320.9310.8720.5980.7161.0000.8250.7560.7830.9930.8500.7770.7831.0000.825
부산경남1.0000.8500.6420.9320.9210.6810.8070.9920.7040.9770.8540.8270.9030.9100.8251.000
2023-12-13T02:45:08.097202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제주LNG본사인 천
제주LNG1.0000.5220.538
본사0.5221.0000.395
인 천0.5380.3951.000
2023-12-13T02:45:08.189864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평택기지인천기지통영기지삼척기지서 울경 기강 원대전충청전 북광주전남대구경북부산경남본사제주LNG인 천
평택기지1.0000.7720.7570.5020.3970.3430.3360.3590.3830.3930.3590.4210.3710.6230.622
인천기지0.7721.0000.6530.6030.4350.3990.3580.3650.3200.3480.4340.5370.4170.6110.521
통영기지0.7570.6531.0000.5620.5890.4490.5170.4600.4330.3710.5130.6770.5030.5350.479
삼척기지0.5020.6030.5621.0000.3770.3040.3610.3180.3900.2510.4310.5470.0000.5750.373
서 울0.3970.4350.5890.3771.0000.6990.9280.9060.6520.8510.8950.8720.5010.6820.659
경 기0.3430.3990.4490.3040.6991.0000.5990.6380.3810.7090.6830.6230.4980.6830.872
강 원0.3360.3580.5170.3610.9280.5991.0000.8500.5850.8090.8360.8150.4740.8740.673
대전충청0.3590.3650.4600.3180.9060.6380.8501.0000.7850.7780.8140.7650.5210.7030.725
전 북0.3830.3200.4330.3900.6520.3810.5850.7851.0000.5200.5610.5770.4300.6890.573
광주전남0.3930.3480.3710.2510.8510.7090.8090.7780.5201.0000.7480.7100.2330.6260.555
대구경북0.3590.4340.5130.4310.8950.6830.8360.8140.5610.7481.0000.7710.5210.9570.725
부산경남0.4210.5370.6770.5470.8720.6230.8150.7650.5770.7100.7711.0000.5010.7500.623
본사0.3710.4170.5030.0000.5010.4980.4740.5210.4300.2330.5210.5011.0000.5220.395
제주LNG0.6230.6110.5350.5750.6820.6830.8740.7030.6890.6260.9570.7500.5221.0000.538
인 천0.6220.5210.4790.3730.6590.8720.6730.7250.5730.5550.7250.6230.3950.5381.000

Missing values

2023-12-13T02:45:04.033998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:45:04.192432image/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

장비명본사평택기지인천기지통영기지삼척기지제주LNG서 울인 천경 기강 원대전충청전 북광주전남대구경북부산경남
0가스분석기322011000034000
1독성가스분석기001000000000000
2가스검지기5131313810292246373029334439
3가스검지기(레이져)012020000000040
4가스검지기(FID)000000310378712
5복합가스검지기0483100000002000
6초음파가스누설검지기000000000001000
7산소농도 측정기51011131310274933812
8안전대(맨홀구조기)022111000000000
9안전블럭062220000001000
장비명본사평택기지인천기지통영기지삼척기지제주LNG서 울인 천경 기강 원대전충청전 북광주전남대구경북부산경남
17검전기024492000001004
18방독면010224300000000000
19전격방지기010000000001000
20자동심장충격기122121113310213
21목고정장치001040000000000
22골절부목002000021000000
23구강기도유지기001000000000000
24들것021010011000300
25방사선측정기000000010000000
26이동식 국소배기장치002000000000000