Overview

Dataset statistics

Number of variables9
Number of observations50
Missing cells102
Missing cells (%)22.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory79.6 B

Variable types

Categorical2
Text2
DateTime1
Numeric4

Dataset

Description경상북도내 유원시설업 현황(종합유원시설 3개소, 일반유원시설업 30개소, 기타유원시설업 148개소)자료
Author경상북도
URLhttps://www.data.go.kr/data/3083299/fileData.do

Alerts

안전성 검사(대상)(기종) is highly overall correlated with 안전성 검사(대상)(대수) and 3 other fieldsHigh correlation
안전성 검사(대상)(대수) is highly overall correlated with 안전성 검사(대상)(기종) and 2 other fieldsHigh correlation
안전성 검사(비대상)(기종) is highly overall correlated with 안전성 검사(대상)(기종) and 2 other fieldsHigh correlation
안전성 검사(비대상)(대수) is highly overall correlated with 안전성 검사(대상)(기종) and 1 other fieldsHigh correlation
업종 is highly overall correlated with 안전성 검사(대상)(기종) and 2 other fieldsHigh correlation
기타 is highly overall correlated with 업종High correlation
기타 is highly imbalanced (68.9%)Imbalance
소 재 지 has 17 (34.0%) missing valuesMissing
최초 허가일 has 17 (34.0%) missing valuesMissing
안전성 검사(대상)(기종) has 18 (36.0%) missing valuesMissing
안전성 검사(대상)(대수) has 18 (36.0%) missing valuesMissing
안전성 검사(비대상)(기종) has 16 (32.0%) missing valuesMissing
안전성 검사(비대상)(대수) has 16 (32.0%) missing valuesMissing
상호명_대표 has unique valuesUnique
안전성 검사(비대상)(기종) has 2 (4.0%) zerosZeros
안전성 검사(비대상)(대수) has 2 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-12 06:17:25.548905
Analysis finished2023-12-12 06:17:28.024405
Duration2.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
일반
30 
기타
17 
종합
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종합
2nd row종합
3rd row종합
4th row일반
5th row일반

Common Values

ValueCountFrequency (%)
일반 30
60.0%
기타 17
34.0%
종합 3
 
6.0%

Length

2023-12-12T15:17:28.089733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:17:28.190284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 30
60.0%
기타 17
34.0%
종합 3
 
6.0%

상호명_대표
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-12T15:17:28.408059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length11.48
Min length4

Characters and Unicode

Total characters574
Distinct characters196
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row경주월드 ㈜삼봉개발_최건환
2nd row블루원워터파크_윤재연
3rd row금오랜드 구미산업개발(주)박창하
4th rowIN디팡_이규목,정의빈
5th row형산강야외물놀이장_포항시 생태하천과
ValueCountFrequency (%)
1개업체 2
 
3.2%
5개업체 2
 
3.2%
경주월드 1
 
1.6%
김천시(9개업체 1
 
1.6%
청도랜드_이동혁 1
 
1.6%
청도레일바이크_정현우 1
 
1.6%
예천군 1
 
1.6%
3개업체 1
 
1.6%
㈜군파크레져_이민형,이동군 1
 
1.6%
예마을물놀이장_김병환 1
 
1.6%
Other values (50) 50
80.6%
2023-12-12T15:17:28.834900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 29
 
5.1%
업 22
 
3.8%
개 21
 
3.7%
) 20
 
3.5%
( 20
 
3.5%
체 17
 
3.0%
이 13
 
2.3%
13
 
2.3%
시 13
 
2.3%
경 12
 
2.1%
Other values (186) 394
68.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 452
78.7%
Connector Punctuation 29
 
5.1%
Decimal Number 23
 
4.0%
Close Punctuation 20
 
3.5%
Open Punctuation 20
 
3.5%
Space Separator 13
 
2.3%
Other Symbol 7
 
1.2%
Uppercase Letter 5
 
0.9%
Lowercase Letter 3
 
0.5%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 22
 
4.9%
개 21
 
4.6%
체 17
 
3.8%
이 13
 
2.9%
시 13
 
2.9%
경 12
 
2.7%
주 12
 
2.7%
드 12
 
2.7%
군 10
 
2.2%
산 8
 
1.8%
Other values (165) 312
69.0%
Decimal Number
ValueCountFrequency (%)
1 10
43.5%
5 4
 
17.4%
2 3
 
13.0%
3 2
 
8.7%
9 2
 
8.7%
0 1
 
4.3%
7 1
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
D 1
20.0%
O 1
20.0%
M 1
20.0%
N 1
20.0%
I 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
x 1
33.3%
t 1
33.3%
s 1
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 459
80.0%
Common 107
 
18.6%
Latin 8
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 22
 
4.8%
개 21
 
4.6%
체 17
 
3.7%
이 13
 
2.8%
시 13
 
2.8%
경 12
 
2.6%
주 12
 
2.6%
드 12
 
2.6%
군 10
 
2.2%
산 8
 
1.7%
Other values (166) 319
69.5%
Common
ValueCountFrequency (%)
_ 29
27.1%
) 20
18.7%
( 20
18.7%
13
12.1%
1 10
 
9.3%
5 4
 
3.7%
2 3
 
2.8%
3 2
 
1.9%
9 2
 
1.9%
, 2
 
1.9%
Other values (2) 2
 
1.9%
Latin
ValueCountFrequency (%)
x 1
12.5%
t 1
12.5%
s 1
12.5%
D 1
12.5%
O 1
12.5%
M 1
12.5%
N 1
12.5%
I 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 452
78.7%
ASCII 115
 
20.0%
None 7
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 29
25.2%
) 20
17.4%
( 20
17.4%
13
11.3%
1 10
 
8.7%
5 4
 
3.5%
2 3
 
2.6%
3 2
 
1.7%
9 2
 
1.7%
, 2
 
1.7%
Other values (10) 10
 
8.7%
Hangul
ValueCountFrequency (%)
업 22
 
4.9%
개 21
 
4.6%
체 17
 
3.8%
이 13
 
2.9%
시 13
 
2.9%
경 12
 
2.7%
주 12
 
2.7%
드 12
 
2.7%
군 10
 
2.2%
산 8
 
1.8%
Other values (165) 312
69.0%
None
ValueCountFrequency (%)
㈜ 7
100.0%

소 재 지
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing17
Missing (%)34.0%
Memory size532.0 B
2023-12-12T15:17:29.225472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length18
Mean length16.121212
Min length9

Characters and Unicode

Total characters532
Distinct characters116
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row경주시보문로544(천군동)
2nd row경주시보불로391
3rd row구미시 금오산로341(남통동)
4th row포항시 북구 남빈동 580
5th row포항시 남구 연일읍 중단리 58-1번지 외 49필지
ValueCountFrequency (%)
경주시 8
 
6.8%
보문로 3
 
2.5%
문경시 3
 
2.5%
청도군 3
 
2.5%
경산시 3
 
2.5%
포항시 2
 
1.7%
북군동 2
 
1.7%
구미시 2
 
1.7%
칠곡군 2
 
1.7%
석적읍 2
 
1.7%
Other values (84) 88
74.6%
2023-12-12T15:17:29.724348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87
 
16.4%
시 24
 
4.5%
로 22
 
4.1%
1 18
 
3.4%
경 17
 
3.2%
0 17
 
3.2%
3 17
 
3.2%
2 14
 
2.6%
군 13
 
2.4%
주 13
 
2.4%
Other values (106) 290
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 307
57.7%
Decimal Number 117
 
22.0%
Space Separator 87
 
16.4%
Dash Punctuation 12
 
2.3%
Close Punctuation 4
 
0.8%
Open Punctuation 4
 
0.8%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 24
 
7.8%
로 22
 
7.2%
경 17
 
5.5%
군 13
 
4.2%
주 13
 
4.2%
면 11
 
3.6%
읍 10
 
3.3%
남 10
 
3.3%
동 9
 
2.9%
문 8
 
2.6%
Other values (91) 170
55.4%
Decimal Number
ValueCountFrequency (%)
1 18
15.4%
0 17
14.5%
3 17
14.5%
2 14
12.0%
5 13
11.1%
4 11
9.4%
9 8
6.8%
6 7
 
6.0%
8 7
 
6.0%
7 5
 
4.3%
Space Separator
ValueCountFrequency (%)
87
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 307
57.7%
Common 225
42.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 24
 
7.8%
로 22
 
7.2%
경 17
 
5.5%
군 13
 
4.2%
주 13
 
4.2%
면 11
 
3.6%
읍 10
 
3.3%
남 10
 
3.3%
동 9
 
2.9%
문 8
 
2.6%
Other values (91) 170
55.4%
Common
ValueCountFrequency (%)
87
38.7%
1 18
 
8.0%
0 17
 
7.6%
3 17
 
7.6%
2 14
 
6.2%
5 13
 
5.8%
- 12
 
5.3%
4 11
 
4.9%
9 8
 
3.6%
6 7
 
3.1%
Other values (5) 21
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 307
57.7%
ASCII 225
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
87
38.7%
1 18
 
8.0%
0 17
 
7.6%
3 17
 
7.6%
2 14
 
6.2%
5 13
 
5.8%
- 12
 
5.3%
4 11
 
4.9%
9 8
 
3.6%
6 7
 
3.1%
Other values (5) 21
 
9.3%
Hangul
ValueCountFrequency (%)
시 24
 
7.8%
로 22
 
7.2%
경 17
 
5.5%
군 13
 
4.2%
주 13
 
4.2%
면 11
 
3.6%
읍 10
 
3.3%
남 10
 
3.3%
동 9
 
2.9%
문 8
 
2.6%
Other values (91) 170
55.4%

최초 허가일
Date

MISSING 

Distinct33
Distinct (%)100.0%
Missing17
Missing (%)34.0%
Memory size532.0 B
Minimum1985-05-20 00:00:00
Maximum2021-04-12 00:00:00
2023-12-12T15:17:30.147951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:30.292064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

안전성 검사(대상)(기종)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)37.5%
Missing18
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean4.59375
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:17:30.399987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile13.35
Maximum38
Range37
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.1068317
Coefficient of variation (CV)1.5470654
Kurtosis16.100468
Mean4.59375
Median Absolute Deviation (MAD)1
Skewness3.6840435
Sum147
Variance50.507056
MonotonicityNot monotonic
2023-12-12T15:17:30.514492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 15
30.0%
3 4
 
8.0%
4 2
 
4.0%
5 2
 
4.0%
2 2
 
4.0%
38 1
 
2.0%
15 1
 
2.0%
11 1
 
2.0%
7 1
 
2.0%
6 1
 
2.0%
Other values (2) 2
 
4.0%
(Missing) 18
36.0%
ValueCountFrequency (%)
1 15
30.0%
2 2
 
4.0%
3 4
 
8.0%
4 2
 
4.0%
5 2
 
4.0%
6 1
 
2.0%
7 1
 
2.0%
9 1
 
2.0%
11 1
 
2.0%
12 1
 
2.0%
ValueCountFrequency (%)
38 1
 
2.0%
15 1
 
2.0%
12 1
 
2.0%
11 1
 
2.0%
9 1
 
2.0%
7 1
 
2.0%
6 1
 
2.0%
5 2
4.0%
4 2
4.0%
3 4
8.0%

안전성 검사(대상)(대수)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)40.6%
Missing18
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean6.53125
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:17:30.665167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2.5
Q35.25
95-th percentile25.35
Maximum62
Range61
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation12.401832
Coefficient of variation (CV)1.8988451
Kurtosis14.017563
Mean6.53125
Median Absolute Deviation (MAD)1.5
Skewness3.609746
Sum209
Variance153.80544
MonotonicityNot monotonic
2023-12-12T15:17:30.771956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 15
30.0%
3 4
 
8.0%
4 2
 
4.0%
5 2
 
4.0%
38 1
 
2.0%
15 1
 
2.0%
11 1
 
2.0%
7 1
 
2.0%
2 1
 
2.0%
6 1
 
2.0%
Other values (3) 3
 
6.0%
(Missing) 18
36.0%
ValueCountFrequency (%)
1 15
30.0%
2 1
 
2.0%
3 4
 
8.0%
4 2
 
4.0%
5 2
 
4.0%
6 1
 
2.0%
7 1
 
2.0%
9 1
 
2.0%
11 1
 
2.0%
14 1
 
2.0%
ValueCountFrequency (%)
62 1
2.0%
38 1
2.0%
15 1
2.0%
14 1
2.0%
11 1
2.0%
9 1
2.0%
7 1
2.0%
6 1
2.0%
5 2
4.0%
4 2
4.0%

안전성 검사(비대상)(기종)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)52.9%
Missing16
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean10.823529
Minimum0
Maximum85
Zeros2
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:17:30.916311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.65
Q11.25
median5
Q312.75
95-th percentile41.4
Maximum85
Range85
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation16.755769
Coefficient of variation (CV)1.5480873
Kurtosis11.633671
Mean10.823529
Median Absolute Deviation (MAD)4
Skewness3.1405182
Sum368
Variance280.75579
MonotonicityNot monotonic
2023-12-12T15:17:31.064420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 7
14.0%
3 3
 
6.0%
2 3
 
6.0%
6 2
 
4.0%
9 2
 
4.0%
0 2
 
4.0%
20 2
 
4.0%
4 2
 
4.0%
13 2
 
4.0%
10 1
 
2.0%
Other values (8) 8
16.0%
(Missing) 16
32.0%
ValueCountFrequency (%)
0 2
 
4.0%
1 7
14.0%
2 3
6.0%
3 3
6.0%
4 2
 
4.0%
6 2
 
4.0%
7 1
 
2.0%
8 1
 
2.0%
9 2
 
4.0%
10 1
 
2.0%
ValueCountFrequency (%)
85 1
2.0%
44 1
2.0%
40 1
2.0%
22 1
2.0%
20 2
4.0%
14 1
2.0%
13 2
4.0%
12 1
2.0%
10 1
2.0%
9 2
4.0%

안전성 검사(비대상)(대수)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)67.6%
Missing16
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean17.058824
Minimum0
Maximum117
Zeros2
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:17:31.214974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.65
Q12.25
median8
Q317.75
95-th percentile65.6
Maximum117
Range117
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation25.366336
Coefficient of variation (CV)1.4869921
Kurtosis7.1281163
Mean17.058824
Median Absolute Deviation (MAD)7
Skewness2.5539589
Sum580
Variance643.45098
MonotonicityNot monotonic
2023-12-12T15:17:31.376376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 6
 
12.0%
3 4
 
8.0%
0 2
 
4.0%
13 2
 
4.0%
8 2
 
4.0%
26 1
 
2.0%
23 1
 
2.0%
2 1
 
2.0%
31 1
 
2.0%
16 1
 
2.0%
Other values (13) 13
26.0%
(Missing) 16
32.0%
ValueCountFrequency (%)
0 2
 
4.0%
1 6
12.0%
2 1
 
2.0%
3 4
8.0%
4 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
8 2
 
4.0%
9 1
 
2.0%
10 1
 
2.0%
ValueCountFrequency (%)
117 1
2.0%
76 1
2.0%
60 1
2.0%
57 1
2.0%
31 1
2.0%
29 1
2.0%
26 1
2.0%
23 1
2.0%
18 1
2.0%
17 1
2.0%

기타
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
45 
2
 
2
4
 
2
1
 
1

Length

Max length4
Median length4
Mean length3.7
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row2
2nd row<NA>
3rd row2
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 45
90.0%
2 2
 
4.0%
4 2
 
4.0%
1 1
 
2.0%

Length

2023-12-12T15:17:31.569769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:17:31.677392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 45
90.0%
2 2
 
4.0%
4 2
 
4.0%
1 1
 
2.0%

Interactions

2023-12-12T15:17:27.262600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.044003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.475398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.879484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.351685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.160373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.567463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.986814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.432499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.269183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.678367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.074048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.514637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.361886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:26.761957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:17:27.165254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:17:31.754026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종상호명_대표소 재 지최초 허가일안전성 검사(대상)(기종)안전성 검사(대상)(대수)안전성 검사(비대상)(기종)안전성 검사(비대상)(대수)기타
업종1.0001.0001.0001.0000.6850.6380.6460.4651.000
상호명_대표1.0001.0001.0001.0001.0001.0001.0001.0001.000
소 재 지1.0001.0001.0001.0001.0001.0001.0001.0001.000
최초 허가일1.0001.0001.0001.0001.0001.0001.0001.0001.000
안전성 검사(대상)(기종)0.6851.0001.0001.0001.0000.9630.7600.6830.261
안전성 검사(대상)(대수)0.6381.0001.0001.0000.9631.0000.7670.7670.751
안전성 검사(비대상)(기종)0.6461.0001.0001.0000.7600.7671.0000.9370.827
안전성 검사(비대상)(대수)0.4651.0001.0001.0000.6830.7670.9371.0000.827
기타1.0001.0001.0001.0000.2610.7510.8270.8271.000
2023-12-12T15:17:31.903650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기타업종
기타1.0000.816
업종0.8161.000
2023-12-12T15:17:32.007396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
안전성 검사(대상)(기종)안전성 검사(대상)(대수)안전성 검사(비대상)(기종)안전성 검사(비대상)(대수)업종기타
안전성 검사(대상)(기종)1.0000.9480.6940.6300.6370.000
안전성 검사(대상)(대수)0.9481.0000.5020.4400.5770.000
안전성 검사(비대상)(기종)0.6940.5021.0000.9490.3150.000
안전성 검사(비대상)(대수)0.6300.4400.9491.0000.3220.000
업종0.6370.5770.3150.3221.0000.816
기타0.0000.0000.0000.0000.8161.000

Missing values

2023-12-12T15:17:27.663906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:17:27.794492image/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.
2023-12-12T15:17:27.926715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

업종상호명_대표소 재 지최초 허가일안전성 검사(대상)(기종)안전성 검사(대상)(대수)안전성 검사(비대상)(기종)안전성 검사(비대상)(대수)기타
0종합경주월드 ㈜삼봉개발_최건환경주시보문로544(천군동)1985-05-20383813132
1종합블루원워터파크_윤재연경주시보불로3912011-06-1715151313<NA>
2종합금오랜드 구미산업개발(주)박창하구미시 금오산로341(남통동)1993-05-13111120292
3일반IN디팡_이규목,정의빈포항시 북구 남빈동 5802018-05-0411<NA><NA><NA>
4일반형산강야외물놀이장_포항시 생태하천과포항시 남구 연일읍 중단리 58-1번지 외 49필지2020-08-031111<NA>
5일반㈜MOD경주시 양남면 동남로 8962005-02-031111<NA>
6일반㈜소노인터네셔널_경주지점 민병소경주시 보문로 402-12(신평동)2006-12-294411<NA>
7일반한화호텔앤드리조트경주_문석경주시 북군동 30-32007-01-161133<NA>
8일반우양산업개발㈜힐튼경주_조영준경주시 보문로 484-72014-09-171133<NA>
9일반히어로플레이파크_박진용경주시 강변로 200(노서동)2017-07-261166<NA>
업종상호명_대표소 재 지최초 허가일안전성 검사(대상)(기종)안전성 검사(대상)(대수)안전성 검사(비대상)(기종)안전성 검사(비대상)(대수)기타
40기타영천시( 5개업체)<NA><NA><NA><NA>68<NA>
41기타문경시( 5개업체)<NA><NA><NA><NA>1216<NA>
42기타경산시(10개업체)<NA><NA><NA><NA>2231<NA>
43기타군위시(1개업체)<NA><NA><NA><NA><NA><NA><NA>
44기타영양군(1개업체)<NA><NA><NA><NA>11<NA>
45기타영덕군(1개업체)<NA><NA><NA><NA>11<NA>
46기타성주군( 1개업체)<NA><NA><NA><NA>22<NA>
47기타칠곡군(11개업체)<NA><NA><NA><NA>823<NA>
48기타예천군( 3개업체)<NA><NA>55<NA><NA><NA>
49기타울진군( 1개업체)<NA><NA><NA><NA>11<NA>