Overview

Dataset statistics

Number of variables11
Number of observations324
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.9 KiB
Average record size in memory91.4 B

Variable types

Numeric3
Categorical5
Text3

Dataset

Description특정소방대상물 중 다수의 인명피해 발생이 우려되는 시설로 화재예방 및 대응이 필요하여 소방본부장 또는 소방서장이 지정하는 대상
Author대구광역시
URLhttps://www.data.go.kr/data/15031865/fileData.do

Alerts

시도 has constant value ""Constant
중점관리대상 용도별 구분 is highly overall correlated with 선정기준 구분High correlation
선정기준 구분 is highly overall correlated with 중점관리대상 용도별 구분High correlation
선정기준 구분 is highly imbalanced (59.0%)Imbalance
연번 has unique valuesUnique
지상층수 has 4 (1.2%) zerosZeros
지하층수 has 48 (14.8%) zerosZeros

Reproduction

Analysis started2023-12-12 22:51:53.853867
Analysis finished2023-12-12 22:51:55.609007
Duration1.76 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct324
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.5
Minimum1
Maximum324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:51:56.029264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.15
Q181.75
median162.5
Q3243.25
95-th percentile307.85
Maximum324
Range323
Interquartile range (IQR)161.5

Descriptive statistics

Standard deviation93.67497
Coefficient of variation (CV)0.57646135
Kurtosis-1.2
Mean162.5
Median Absolute Deviation (MAD)81
Skewness0
Sum52650
Variance8775
MonotonicityStrictly increasing
2023-12-13T07:51:56.200463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
205 1
 
0.3%
223 1
 
0.3%
222 1
 
0.3%
221 1
 
0.3%
220 1
 
0.3%
219 1
 
0.3%
218 1
 
0.3%
217 1
 
0.3%
216 1
 
0.3%
Other values (314) 314
96.9%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
324 1
0.3%
323 1
0.3%
322 1
0.3%
321 1
0.3%
320 1
0.3%
319 1
0.3%
318 1
0.3%
317 1
0.3%
316 1
0.3%
315 1
0.3%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
대구
324 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구 324
100.0%

Length

2023-12-13T07:51:56.340618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:51:56.442636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구 324
100.0%

특정소방대상물 구분
Categorical

Distinct19
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
복합
105 
의료
66 
판매
45 
노유자
29 
공장
29 
Other values (14)
50 

Length

Max length4
Median length2
Mean length2.1419753
Min length2

Unique

Unique4 ?
Unique (%)1.2%

Sample

1st row의료
2nd row의료
3rd row의료
4th row의료
5th row노유자

Common Values

ValueCountFrequency (%)
복합 105
32.4%
의료 66
20.4%
판매 45
13.9%
노유자 29
 
9.0%
공장 29
 
9.0%
숙박 12
 
3.7%
근린 7
 
2.2%
지하가 6
 
1.9%
업무 5
 
1.5%
문화집회 4
 
1.2%
Other values (9) 16
 
4.9%

Length

2023-12-13T07:51:56.568031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
복합 105
32.4%
의료 66
20.4%
판매 45
13.9%
노유자 29
 
9.0%
공장 29
 
9.0%
숙박 12
 
3.7%
근린 7
 
2.2%
지하가 6
 
1.9%
업무 5
 
1.5%
문화집회 4
 
1.2%
Other values (9) 16
 
4.9%

중점관리대상 용도별 구분
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
기타 소방관서장 지정
174 
다중이용업소
60 
의료시설
33 
판매시설
23 
공장 및 창고
 
10
Other values (6)
24 

Length

Max length11
Median length11
Mean length8.2716049
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row의료시설
2nd row의료시설
3rd row의료시설
4th row의료시설
5th row노유자시설

Common Values

ValueCountFrequency (%)
기타 소방관서장 지정 174
53.7%
다중이용업소 60
 
18.5%
의료시설 33
 
10.2%
판매시설 23
 
7.1%
공장 및 창고 10
 
3.1%
복합건축물 6
 
1.9%
지하상가 6
 
1.9%
대형건축물 6
 
1.9%
노유자시설 3
 
0.9%
숙박시설 2
 
0.6%

Length

2023-12-13T07:51:56.748791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 174
25.1%
소방관서장 174
25.1%
지정 174
25.1%
다중이용업소 60
 
8.7%
의료시설 33
 
4.8%
판매시설 23
 
3.3%
공장 10
 
1.4%
및 10
 
1.4%
창고 10
 
1.4%
복합건축물 6
 
0.9%
Other values (5) 18
 
2.6%

선정기준 구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
심의
273 
필수
50 
 
1

Length

Max length2
Median length2
Mean length1.9969136
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
심의 273
84.3%
필수 50
 
15.4%
1
 
0.3%

Length

2023-12-13T07:51:56.892077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:51:56.995063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
심의 273
84.5%
필수 50
 
15.5%

화재위험등급
Categorical

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Ⅱ
182 
Ⅰ
109 
Ⅲ
33 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowⅠ
2nd rowⅢ
3rd rowⅡ
4th rowⅡ
5th rowⅠ

Common Values

ValueCountFrequency (%)
Ⅱ 182
56.2%
Ⅰ 109
33.6%
Ⅲ 33
 
10.2%

Length

2023-12-13T07:51:57.096654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:51:57.213343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ⅱ 182
56.2%
ⅰ 109
33.6%
ⅲ 33
 
10.2%

대상명
Text

Distinct322
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-13T07:51:57.451205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length17
Mean length7.787037
Min length2

Characters and Unicode

Total characters2523
Distinct characters383
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

Unique320 ?
Unique (%)98.8%

Sample

1st row경북대학교병원
2nd row대구가톨릭대학교병원
3rd row성심요양병원
4th row영남대학교병원
5th row무량수전노인전문요양원
ValueCountFrequency (%)
건물 3
 
0.8%
홈플러스 3
 
0.8%
서문시장 3
 
0.8%
롯데백화점 2
 
0.5%
롯데시네마 2
 
0.5%
이마트 2
 
0.5%
엘앤에프 2
 
0.5%
계명대학교 2
 
0.5%
대동병원 2
 
0.5%
성서점 2
 
0.5%
Other values (345) 345
93.8%
2023-12-13T07:51:57.864955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 119
 
4.7%
병 88
 
3.5%
대 63
 
2.5%
) 61
 
2.4%
( 61
 
2.4%
스 57
 
2.3%
양 54
 
2.1%
요 51
 
2.0%
46
 
1.8%
구 45
 
1.8%
Other values (373) 1878
74.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2278
90.3%
Close Punctuation 61
 
2.4%
Open Punctuation 61
 
2.4%
Uppercase Letter 48
 
1.9%
Space Separator 46
 
1.8%
Decimal Number 10
 
0.4%
Other Symbol 7
 
0.3%
Lowercase Letter 6
 
0.2%
Other Punctuation 5
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 119
 
5.2%
병 88
 
3.9%
대 63
 
2.8%
스 57
 
2.5%
양 54
 
2.4%
요 51
 
2.2%
구 45
 
2.0%
시 44
 
1.9%
성 43
 
1.9%
점 33
 
1.4%
Other values (334) 1681
73.8%
Uppercase Letter
ValueCountFrequency (%)
S 7
14.6%
M 6
12.5%
K 5
10.4%
L 5
10.4%
B 4
8.3%
C 4
8.3%
G 3
 
6.2%
P 2
 
4.2%
T 2
 
4.2%
V 2
 
4.2%
Other values (8) 8
16.7%
Decimal Number
ValueCountFrequency (%)
1 3
30.0%
2 2
20.0%
3 1
 
10.0%
5 1
 
10.0%
9 1
 
10.0%
7 1
 
10.0%
6 1
 
10.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
16.7%
r 1
16.7%
w 1
16.7%
o 1
16.7%
l 1
16.7%
d 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
, 1
 
20.0%
& 1
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 61
100.0%
Open Punctuation
ValueCountFrequency (%)
( 61
100.0%
Space Separator
ValueCountFrequency (%)
46
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2285
90.6%
Common 184
 
7.3%
Latin 54
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 119
 
5.2%
병 88
 
3.9%
대 63
 
2.8%
스 57
 
2.5%
양 54
 
2.4%
요 51
 
2.2%
구 45
 
2.0%
시 44
 
1.9%
성 43
 
1.9%
점 33
 
1.4%
Other values (335) 1688
73.9%
Latin
ValueCountFrequency (%)
S 7
13.0%
M 6
11.1%
K 5
 
9.3%
L 5
 
9.3%
B 4
 
7.4%
C 4
 
7.4%
G 3
 
5.6%
P 2
 
3.7%
T 2
 
3.7%
V 2
 
3.7%
Other values (14) 14
25.9%
Common
ValueCountFrequency (%)
) 61
33.2%
( 61
33.2%
46
25.0%
. 3
 
1.6%
1 3
 
1.6%
2 2
 
1.1%
, 1
 
0.5%
3 1
 
0.5%
- 1
 
0.5%
& 1
 
0.5%
Other values (4) 4
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2278
90.3%
ASCII 238
 
9.4%
None 7
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 119
 
5.2%
병 88
 
3.9%
대 63
 
2.8%
스 57
 
2.5%
양 54
 
2.4%
요 51
 
2.2%
구 45
 
2.0%
시 44
 
1.9%
성 43
 
1.9%
점 33
 
1.4%
Other values (334) 1681
73.8%
ASCII
ValueCountFrequency (%)
) 61
25.6%
( 61
25.6%
46
19.3%
S 7
 
2.9%
M 6
 
2.5%
K 5
 
2.1%
L 5
 
2.1%
B 4
 
1.7%
C 4
 
1.7%
. 3
 
1.3%
Other values (28) 36
15.1%
None
ValueCountFrequency (%)
㈜ 7
100.0%

위치
Text

Distinct322
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-13T07:51:58.067109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length18.92284
Min length14

Characters and Unicode

Total characters6131
Distinct characters165
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

Unique320 ?
Unique (%)98.8%

Sample

1st row대구광역시 중구 동덕로 130
2nd row대구광역시 남구 두류공원로17길 33
3rd row대구광역시 남구 대명로 14
4th row대구광역시 남구 현충로 170
5th row대구광역시 남구 중앙대로 122
ValueCountFrequency (%)
대구광역시 324
23.9%
북구 60
 
4.4%
달서구 55
 
4.1%
달성군 48
 
3.5%
동구 45
 
3.3%
중구 41
 
3.0%
수성구 33
 
2.4%
달구벌대로 27
 
2.0%
서구 21
 
1.6%
남구 17
 
1.3%
Other values (434) 682
50.4%
2023-12-13T07:51:58.407681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1040
17.0%
구 645
 
10.5%
대 406
 
6.6%
시 328
 
5.3%
역 324
 
5.3%
광 324
 
5.3%
로 314
 
5.1%
1 203
 
3.3%
2 155
 
2.5%
달 140
 
2.3%
Other values (155) 2252
36.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3995
65.2%
Space Separator 1040
 
17.0%
Decimal Number 1006
 
16.4%
Open Punctuation 34
 
0.6%
Close Punctuation 34
 
0.6%
Dash Punctuation 21
 
0.3%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 645
16.1%
대 406
 
10.2%
시 328
 
8.2%
역 324
 
8.1%
광 324
 
8.1%
로 314
 
7.9%
달 140
 
3.5%
동 126
 
3.2%
성 107
 
2.7%
서 91
 
2.3%
Other values (140) 1190
29.8%
Decimal Number
ValueCountFrequency (%)
1 203
20.2%
2 155
15.4%
5 98
9.7%
0 97
9.6%
3 95
9.4%
4 91
9.0%
6 78
 
7.8%
7 69
 
6.9%
9 63
 
6.3%
8 57
 
5.7%
Space Separator
ValueCountFrequency (%)
1040
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3995
65.2%
Common 2136
34.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 645
16.1%
대 406
 
10.2%
시 328
 
8.2%
역 324
 
8.1%
광 324
 
8.1%
로 314
 
7.9%
달 140
 
3.5%
동 126
 
3.2%
성 107
 
2.7%
서 91
 
2.3%
Other values (140) 1190
29.8%
Common
ValueCountFrequency (%)
1040
48.7%
1 203
 
9.5%
2 155
 
7.3%
5 98
 
4.6%
0 97
 
4.5%
3 95
 
4.4%
4 91
 
4.3%
6 78
 
3.7%
7 69
 
3.2%
9 63
 
2.9%
Other values (5) 147
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3995
65.2%
ASCII 2136
34.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1040
48.7%
1 203
 
9.5%
2 155
 
7.3%
5 98
 
4.6%
0 97
 
4.5%
3 95
 
4.4%
4 91
 
4.3%
6 78
 
3.7%
7 69
 
3.2%
9 63
 
2.9%
Other values (5) 147
 
6.9%
Hangul
ValueCountFrequency (%)
구 645
16.1%
대 406
 
10.2%
시 328
 
8.2%
역 324
 
8.1%
광 324
 
8.1%
로 314
 
7.9%
달 140
 
3.5%
동 126
 
3.2%
성 107
 
2.7%
서 91
 
2.3%
Other values (140) 1190
29.8%

지상층수
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8302469
Minimum0
Maximum57
Zeros4
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:51:58.520944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q310
95-th percentile20
Maximum57
Range57
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.000589
Coefficient of variation (CV)0.89404448
Kurtosis16.943929
Mean7.8302469
Median Absolute Deviation (MAD)3
Skewness3.4152006
Sum2537
Variance49.008246
MonotonicityNot monotonic
2023-12-13T07:51:58.628937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 46
14.2%
3 39
12.0%
4 36
11.1%
10 26
8.0%
6 24
 
7.4%
7 24
 
7.4%
8 21
 
6.5%
9 20
 
6.2%
2 19
 
5.9%
11 9
 
2.8%
Other values (21) 60
18.5%
ValueCountFrequency (%)
0 4
 
1.2%
1 7
 
2.2%
2 19
5.9%
3 39
12.0%
4 36
11.1%
5 46
14.2%
6 24
7.4%
7 24
7.4%
8 21
6.5%
9 20
6.2%
ValueCountFrequency (%)
57 1
0.3%
54 1
0.3%
45 1
0.3%
42 1
0.3%
31 2
0.6%
29 1
0.3%
28 1
0.3%
26 1
0.3%
24 1
0.3%
23 2
0.6%

지하층수
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6419753
Minimum0
Maximum9
Zeros48
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:51:58.723194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.522249
Coefficient of variation (CV)0.92708396
Kurtosis4.5018967
Mean1.6419753
Median Absolute Deviation (MAD)1
Skewness1.9082716
Sum532
Variance2.3172419
MonotonicityNot monotonic
2023-12-13T07:51:58.820346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 154
47.5%
2 65
20.1%
0 48
 
14.8%
3 23
 
7.1%
5 12
 
3.7%
4 11
 
3.4%
6 6
 
1.9%
7 3
 
0.9%
9 2
 
0.6%
ValueCountFrequency (%)
0 48
 
14.8%
1 154
47.5%
2 65
20.1%
3 23
 
7.1%
4 11
 
3.4%
5 12
 
3.7%
6 6
 
1.9%
7 3
 
0.9%
9 2
 
0.6%
ValueCountFrequency (%)
9 2
 
0.6%
7 3
 
0.9%
6 6
 
1.9%
5 12
 
3.7%
4 11
 
3.4%
3 23
 
7.1%
2 65
20.1%
1 154
47.5%
0 48
 
14.8%

연면적(제곱미터)
Text

Distinct322
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-13T07:51:59.116397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.2006173
Min length3

Characters and Unicode

Total characters1685
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique320 ?
Unique (%)98.8%

Sample

1st row22072
2nd row32653
3rd row8445
4th row43489
5th row3771
ValueCountFrequency (%)
2995 2
 
0.6%
4998 2
 
0.6%
34865 1
 
0.3%
63553 1
 
0.3%
22072 1
 
0.3%
8751.49 1
 
0.3%
119669 1
 
0.3%
4034 1
 
0.3%
6054 1
 
0.3%
24857 1
 
0.3%
Other values (312) 312
96.3%
2023-12-13T07:51:59.539924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 214
12.7%
2 182
10.8%
4 176
10.4%
3 163
9.7%
8 158
9.4%
6 155
9.2%
9 153
9.1%
7 142
8.4%
5 141
8.4%
0 130
7.7%
Other values (2) 71
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1614
95.8%
Other Punctuation 71
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 214
13.3%
2 182
11.3%
4 176
10.9%
3 163
10.1%
8 158
9.8%
6 155
9.6%
9 153
9.5%
7 142
8.8%
5 141
8.7%
0 130
8.1%
Other Punctuation
ValueCountFrequency (%)
. 67
94.4%
, 4
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1685
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 214
12.7%
2 182
10.8%
4 176
10.4%
3 163
9.7%
8 158
9.4%
6 155
9.2%
9 153
9.1%
7 142
8.4%
5 141
8.4%
0 130
7.7%
Other values (2) 71
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 214
12.7%
2 182
10.8%
4 176
10.4%
3 163
9.7%
8 158
9.4%
6 155
9.2%
9 153
9.1%
7 142
8.4%
5 141
8.4%
0 130
7.7%
Other values (2) 71
 
4.2%

Interactions

2023-12-13T07:51:55.071751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:51:54.488173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:51:54.789839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:51:55.168166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:51:54.599674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:51:54.896988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:51:55.256824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:51:54.701641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:51:54.990252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:51:59.671391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번특정소방대상물 구분중점관리대상 용도별 구분선정기준 구분화재위험등급지상층수지하층수
연번1.0000.5160.3270.2500.3880.3450.245
특정소방대상물 구분0.5161.0000.8150.4440.4360.4400.347
중점관리대상 용도별 구분0.3270.8151.0000.7830.3000.6620.392
선정기준 구분0.2500.4440.7831.0000.2480.2930.000
화재위험등급0.3880.4360.3000.2481.0000.2300.436
지상층수0.3450.4400.6620.2930.2301.0000.514
지하층수0.2450.3470.3920.0000.4360.5141.000
2023-12-13T07:51:59.793957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중점관리대상 용도별 구분선정기준 구분특정소방대상물 구분화재위험등급
중점관리대상 용도별 구분1.0000.6440.4630.180
선정기준 구분0.6441.0000.2580.080
특정소방대상물 구분0.4630.2581.0000.252
화재위험등급0.1800.0800.2521.000
2023-12-13T07:51:59.978094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지상층수지하층수특정소방대상물 구분중점관리대상 용도별 구분선정기준 구분화재위험등급
연번1.000-0.091-0.2310.2170.1440.1520.250
지상층수-0.0911.0000.3870.1970.3870.1920.148
지하층수-0.2310.3871.0000.1410.1880.0000.212
특정소방대상물 구분0.2170.1970.1411.0000.4630.2580.252
중점관리대상 용도별 구분0.1440.3870.1880.4631.0000.6440.180
선정기준 구분0.1520.1920.0000.2580.6441.0000.080
화재위험등급0.2500.1480.2120.2520.1800.0801.000

Missing values

2023-12-13T07:51:55.382093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:51:55.535640image/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

연번시도특정소방대상물 구분중점관리대상 용도별 구분선정기준 구분화재위험등급대상명위치지상층수지하층수연면적(제곱미터)
01대구의료의료시설필수Ⅰ경북대학교병원대구광역시 중구 동덕로 1309122072
12대구의료의료시설필수Ⅲ대구가톨릭대학교병원대구광역시 남구 두류공원로17길 3313232653
23대구의료의료시설필수Ⅱ성심요양병원대구광역시 남구 대명로 141428445
34대구의료의료시설필수Ⅱ영남대학교병원대구광역시 남구 현충로 17015343489
45대구노유자노유자시설필수Ⅰ무량수전노인전문요양원대구광역시 남구 중앙대로 122623771
56대구판매다중이용업소필수Ⅱ더현대 대구점(현대백화점)대구광역시 중구 달구벌대로 2077106118206
67대구의료의료시설필수Ⅰ뉴영대요양병원대구광역시 남구 대명로 275814989.33
78대구복합기타 소방관서장 지정심의Ⅲ대구시티센터(노보텔)대구광역시 중구 국채보상로 61123986892
89대구판매판매시설심의Ⅱ대구백화점 프라자점대구광역시 중구 명덕로 33313491456.5
910대구복합판매시설심의Ⅱ반월당효성해링턴플레이스(탑마트)대구광역시 중구 중앙대로66길 2029040129
연번시도특정소방대상물 구분중점관리대상 용도별 구분선정기준 구분화재위험등급대상명위치지상층수지하층수연면적(제곱미터)
314315대구판매기타 소방관서장 지정심의Ⅲ동아아울렛강북점대구광역시 북구 칠곡중앙대로 416(읍내동)8522340
315316대구노유자기타 소방관서장 지정심의Ⅱ사회복지법인복음재단(복음양로원)대구광역시 북구 칠곡중앙대로77길 14(태전동)419848
316317대구복합다중이용업소심의Ⅱ네오시티프라자대구광역시 북구 칠곡중앙대로 412(태전동)10623284
317318대구판매판매시설심의Ⅰ농수산물시장대구광역시 북구 매천로18길 34(매천동)5197858
318319대구의료기타 소방관서장 지정심의Ⅱ문요양병원대구광역시 북구 칠곡중앙대로 215(태전동)725586
319320대구의료기타 소방관서장 지정심의Ⅲk병원대구광역시 북구 칠곡중앙대로 35710220704
320321대구복합다중이용업소심의Ⅱ연경그루시티대구광역시 북구 동화천로 290(연경동)5112533
321322대구판매기타 소방관서장 지정심의Ⅱ농협 군위 종합유통센터대구광역시 군위군 효령면 경북대로 21852128114
322323대구동식물기타 소방관서장 지정심의Ⅱ㈜민속LPC대구광역시 군위군 군위읍 경북대로 40522117750.57
323324대구공장기타 소방관서장 지정심의Ⅱ대구경북 능금농협음료가공공장대구광역시 군위군 의흥면 동부로 935-14317103.87