Overview

Dataset statistics

Number of variables10
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory86.4 B

Variable types

Text6
Numeric3
Categorical1

Dataset

Description부산광역시 해운대구 지진겸용임시주거시설에 대한 정보로 지진겸용임시거주시설명, 지진겸용임시거주시설주소 등을 제공합니다.
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/3075739/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
지진겸용임시주거시설명 has unique valuesUnique
지진겸용임시주거시설상세시설명 has unique valuesUnique
도로명주소 has unique valuesUnique
지번주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
관리기관명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:08:48.098180
Analysis finished2023-12-12 13:08:49.387423
Duration1.29 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지진겸용임시주거시설명
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T22:08:49.543428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.2820513
Min length3

Characters and Unicode

Total characters245
Distinct characters65
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row강동초등학교
2nd row해강고등학교
3rd row해강중학교
4th row해림초등학교
5th row동백초등학교
ValueCountFrequency (%)
호텔 2
 
4.4%
모텔 2
 
4.4%
강동초등학교 1
 
2.2%
신곡중학교 1
 
2.2%
신도고등학교 1
 
2.2%
해운대여자중학교 1
 
2.2%
해운대초등학교 1
 
2.2%
go 1
 
2.2%
잉카 1
 
2.2%
로 1
 
2.2%
Other values (33) 33
73.3%
2023-12-12T22:08:49.939104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 25
 
10.2%
교 25
 
10.2%
등 19
 
7.8%
초 14
 
5.7%
센 10
 
4.1%
동 9
 
3.7%
송 7
 
2.9%
터 7
 
2.9%
민 7
 
2.9%
주 7
 
2.9%
Other values (55) 115
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 230
93.9%
Decimal Number 7
 
2.9%
Space Separator 6
 
2.4%
Uppercase Letter 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 25
 
10.9%
교 25
 
10.9%
등 19
 
8.3%
초 14
 
6.1%
센 10
 
4.3%
동 9
 
3.9%
송 7
 
3.0%
터 7
 
3.0%
민 7
 
3.0%
주 7
 
3.0%
Other values (48) 100
43.5%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
2 2
28.6%
4 1
 
14.3%
3 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
O 1
50.0%
G 1
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 230
93.9%
Common 13
 
5.3%
Latin 2
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 25
 
10.9%
교 25
 
10.9%
등 19
 
8.3%
초 14
 
6.1%
센 10
 
4.3%
동 9
 
3.9%
송 7
 
3.0%
터 7
 
3.0%
민 7
 
3.0%
주 7
 
3.0%
Other values (48) 100
43.5%
Common
ValueCountFrequency (%)
6
46.2%
1 3
23.1%
2 2
 
15.4%
4 1
 
7.7%
3 1
 
7.7%
Latin
ValueCountFrequency (%)
O 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 230
93.9%
ASCII 15
 
6.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 25
 
10.9%
교 25
 
10.9%
등 19
 
8.3%
초 14
 
6.1%
센 10
 
4.3%
동 9
 
3.9%
송 7
 
3.0%
터 7
 
3.0%
민 7
 
3.0%
주 7
 
3.0%
Other values (48) 100
43.5%
ASCII
ValueCountFrequency (%)
6
40.0%
1 3
20.0%
2 2
 
13.3%
4 1
 
6.7%
O 1
 
6.7%
G 1
 
6.7%
3 1
 
6.7%

지진겸용임시주거시설상세시설명
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T22:08:50.195353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length7.7435897
Min length3

Characters and Unicode

Total characters302
Distinct characters68
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row강동초등학교강당
2nd row해강고등학교강당
3rd row해강중학교강당
4th row해림초등학교강당
5th row동백초등학교강당
ValueCountFrequency (%)
주민센터 3
 
6.4%
호텔 2
 
4.3%
모텔 2
 
4.3%
강동초등학교강당 1
 
2.1%
버킹검 1
 
2.1%
신도고등학교강당 1
 
2.1%
해운대여자중학교강당 1
 
2.1%
해운대초등학교강당 1
 
2.1%
go 1
 
2.1%
잉카 1
 
2.1%
Other values (33) 33
70.2%
2023-12-12T22:08:50.607198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
강 26
 
8.6%
학 25
 
8.3%
교 25
 
8.3%
당 23
 
7.6%
등 19
 
6.3%
초 14
 
4.6%
센 10
 
3.3%
동 9
 
3.0%
8
 
2.6%
터 7
 
2.3%
Other values (58) 136
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 285
94.4%
Space Separator 8
 
2.6%
Decimal Number 7
 
2.3%
Uppercase Letter 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 26
 
9.1%
학 25
 
8.8%
교 25
 
8.8%
당 23
 
8.1%
등 19
 
6.7%
초 14
 
4.9%
센 10
 
3.5%
동 9
 
3.2%
터 7
 
2.5%
민 7
 
2.5%
Other values (51) 120
42.1%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
2 2
28.6%
4 1
 
14.3%
3 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
O 1
50.0%
G 1
50.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 285
94.4%
Common 15
 
5.0%
Latin 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 26
 
9.1%
학 25
 
8.8%
교 25
 
8.8%
당 23
 
8.1%
등 19
 
6.7%
초 14
 
4.9%
센 10
 
3.5%
동 9
 
3.2%
터 7
 
2.5%
민 7
 
2.5%
Other values (51) 120
42.1%
Common
ValueCountFrequency (%)
8
53.3%
1 3
 
20.0%
2 2
 
13.3%
4 1
 
6.7%
3 1
 
6.7%
Latin
ValueCountFrequency (%)
O 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 285
94.4%
ASCII 17
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
강 26
 
9.1%
학 25
 
8.8%
교 25
 
8.8%
당 23
 
8.1%
등 19
 
6.7%
초 14
 
4.9%
센 10
 
3.5%
동 9
 
3.2%
터 7
 
2.5%
민 7
 
2.5%
Other values (51) 120
42.1%
ASCII
ValueCountFrequency (%)
8
47.1%
1 3
 
17.6%
2 2
 
11.8%
4 1
 
5.9%
O 1
 
5.9%
G 1
 
5.9%
3 1
 
5.9%

도로명주소
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T22:08:50.860403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length25
Min length17

Characters and Unicode

Total characters975
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row부산광역시 해운대구 해운대로349번길 10(우동)
2nd row부산광역시 해운대구 해운대해변로 33(우동)
3rd row부산광역시 해운대구 해운대해변로 17(우동)
4th row부산광역시 해운대구 해운대로469번길 42(우동)
5th row부산광역시 해운대구 좌동순환로357번길 6(중동)
ValueCountFrequency (%)
부산광역시 39
23.9%
해운대구 39
23.9%
해운대로 4
 
2.5%
좌동순환로 4
 
2.5%
반여로 3
 
1.8%
해운대해변로 2
 
1.2%
반송동 2
 
1.2%
2-18(송정동 1
 
0.6%
재송1로 1
 
0.6%
30-8(재송동 1
 
0.6%
Other values (67) 67
41.1%
2023-12-12T22:08:51.320659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
124
 
12.7%
해 51
 
5.2%
대 50
 
5.1%
운 50
 
5.1%
구 40
 
4.1%
동 40
 
4.1%
산 40
 
4.1%
로 39
 
4.0%
부 39
 
4.0%
시 39
 
4.0%
Other values (46) 463
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 632
64.8%
Decimal Number 152
 
15.6%
Space Separator 124
 
12.7%
Open Punctuation 30
 
3.1%
Close Punctuation 30
 
3.1%
Dash Punctuation 7
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 51
 
8.1%
대 50
 
7.9%
운 50
 
7.9%
구 40
 
6.3%
동 40
 
6.3%
산 40
 
6.3%
로 39
 
6.2%
부 39
 
6.2%
시 39
 
6.2%
역 39
 
6.2%
Other values (32) 205
32.4%
Decimal Number
ValueCountFrequency (%)
1 32
21.1%
3 22
14.5%
2 21
13.8%
6 20
13.2%
4 15
9.9%
0 10
 
6.6%
5 9
 
5.9%
7 8
 
5.3%
9 8
 
5.3%
8 7
 
4.6%
Space Separator
ValueCountFrequency (%)
124
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 632
64.8%
Common 343
35.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 51
 
8.1%
대 50
 
7.9%
운 50
 
7.9%
구 40
 
6.3%
동 40
 
6.3%
산 40
 
6.3%
로 39
 
6.2%
부 39
 
6.2%
시 39
 
6.2%
역 39
 
6.2%
Other values (32) 205
32.4%
Common
ValueCountFrequency (%)
124
36.2%
1 32
 
9.3%
( 30
 
8.7%
) 30
 
8.7%
3 22
 
6.4%
2 21
 
6.1%
6 20
 
5.8%
4 15
 
4.4%
0 10
 
2.9%
5 9
 
2.6%
Other values (4) 30
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 632
64.8%
ASCII 343
35.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124
36.2%
1 32
 
9.3%
( 30
 
8.7%
) 30
 
8.7%
3 22
 
6.4%
2 21
 
6.1%
6 20
 
5.8%
4 15
 
4.4%
0 10
 
2.9%
5 9
 
2.6%
Other values (4) 30
 
8.7%
Hangul
ValueCountFrequency (%)
해 51
 
8.1%
대 50
 
7.9%
운 50
 
7.9%
구 40
 
6.3%
동 40
 
6.3%
산 40
 
6.3%
로 39
 
6.2%
부 39
 
6.2%
시 39
 
6.2%
역 39
 
6.2%
Other values (32) 205
32.4%

지번주소
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T22:08:51.595757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length24.25641
Min length18

Characters and Unicode

Total characters946
Distinct characters57
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row부산광역시 해운대구 우동 1113 강동초등학교
2nd row부산광역시 해운대구 우동 1417-1 해강고등학교
3rd row부산광역시 해운대구 우동 1417-1 해강중학교
4th row부산광역시 해운대구 우동 1025-11 해림초등학교
5th row부산광역시 해운대구 중동 1518-1 동백초등학교
ValueCountFrequency (%)
해운대구 40
22.0%
부산광역시 39
21.4%
재송동 10
 
5.5%
좌동 8
 
4.4%
우동 7
 
3.8%
반여동 7
 
3.8%
반송동 3
 
1.6%
1417-1 2
 
1.1%
송정동 2
 
1.1%
중동 2
 
1.1%
Other values (62) 62
34.1%
2023-12-12T22:08:52.028549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
143
 
15.1%
1 58
 
6.1%
해 45
 
4.8%
운 43
 
4.5%
대 42
 
4.4%
동 41
 
4.3%
부 40
 
4.2%
구 40
 
4.2%
산 40
 
4.2%
광 39
 
4.1%
Other values (47) 415
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 605
64.0%
Decimal Number 175
 
18.5%
Space Separator 143
 
15.1%
Dash Punctuation 23
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 45
 
7.4%
운 43
 
7.1%
대 42
 
6.9%
동 41
 
6.8%
부 40
 
6.6%
구 40
 
6.6%
산 40
 
6.6%
광 39
 
6.4%
역 39
 
6.4%
시 39
 
6.4%
Other values (35) 197
32.6%
Decimal Number
ValueCountFrequency (%)
1 58
33.1%
2 25
14.3%
3 16
 
9.1%
4 15
 
8.6%
0 14
 
8.0%
5 11
 
6.3%
6 10
 
5.7%
7 9
 
5.1%
8 9
 
5.1%
9 8
 
4.6%
Space Separator
ValueCountFrequency (%)
143
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 605
64.0%
Common 341
36.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 45
 
7.4%
운 43
 
7.1%
대 42
 
6.9%
동 41
 
6.8%
부 40
 
6.6%
구 40
 
6.6%
산 40
 
6.6%
광 39
 
6.4%
역 39
 
6.4%
시 39
 
6.4%
Other values (35) 197
32.6%
Common
ValueCountFrequency (%)
143
41.9%
1 58
17.0%
2 25
 
7.3%
- 23
 
6.7%
3 16
 
4.7%
4 15
 
4.4%
0 14
 
4.1%
5 11
 
3.2%
6 10
 
2.9%
7 9
 
2.6%
Other values (2) 17
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 605
64.0%
ASCII 341
36.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
143
41.9%
1 58
17.0%
2 25
 
7.3%
- 23
 
6.7%
3 16
 
4.7%
4 15
 
4.4%
0 14
 
4.1%
5 11
 
3.2%
6 10
 
2.9%
7 9
 
2.6%
Other values (2) 17
 
5.0%
Hangul
ValueCountFrequency (%)
해 45
 
7.4%
운 43
 
7.1%
대 42
 
6.9%
동 41
 
6.8%
부 40
 
6.6%
구 40
 
6.6%
산 40
 
6.6%
광 39
 
6.4%
역 39
 
6.4%
시 39
 
6.4%
Other values (35) 197
32.6%

위도
Real number (ℝ)

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.184137
Minimum35.161684
Maximum35.229278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T22:08:52.184326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.161684
5-th percentile35.164021
Q135.168713
median35.180903
Q335.19716
95-th percentile35.223751
Maximum35.229278
Range0.06759356
Interquartile range (IQR)0.02844626

Descriptive statistics

Standard deviation0.018437417
Coefficient of variation (CV)0.00052402642
Kurtosis-0.032422425
Mean35.184137
Median Absolute Deviation (MAD)0.01274417
Skewness0.86487025
Sum1372.1813
Variance0.00033993835
MonotonicityNot monotonic
2023-12-12T22:08:52.344748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
35.17232538 1
 
2.6%
35.1641154 1
 
2.6%
35.17207839 1
 
2.6%
35.16804692 1
 
2.6%
35.16490245 1
 
2.6%
35.18214896 1
 
2.6%
35.16168422 1
 
2.6%
35.18463876 1
 
2.6%
35.18683065 1
 
2.6%
35.18462826 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
35.16168422 1
2.6%
35.16401546 1
2.6%
35.16402129 1
2.6%
35.1641154 1
2.6%
35.16482125 1
2.6%
35.16490245 1
2.6%
35.1659698 1
2.6%
35.16804692 1
2.6%
35.16806448 1
2.6%
35.16815888 1
2.6%
ValueCountFrequency (%)
35.22927778 1
2.6%
35.22533054 1
2.6%
35.22357514 1
2.6%
35.20865566 1
2.6%
35.2041199 1
2.6%
35.20295954 1
2.6%
35.2028828 1
2.6%
35.20287398 1
2.6%
35.2027251 1
2.6%
35.20115808 1
2.6%

경도
Real number (ℝ)

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.14719
Minimum129.11341
Maximum129.20553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T22:08:52.515125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.11341
5-th percentile129.1189
Q1129.12534
median129.13754
Q3129.16742
95-th percentile129.18621
Maximum129.20553
Range0.0921198
Interquartile range (IQR)0.0420893

Descriptive statistics

Standard deviation0.025954645
Coefficient of variation (CV)0.00020096949
Kurtosis-0.67946507
Mean129.14719
Median Absolute Deviation (MAD)0.0158608
Skewness0.67210426
Sum5036.7404
Variance0.00067364357
MonotonicityNot monotonic
2023-12-12T22:08:52.681226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
129.136752 1
 
2.6%
129.1375376 1
 
2.6%
129.1675844 1
 
2.6%
129.1535642 1
 
2.6%
129.1656489 1
 
2.6%
129.2055332 1
 
2.6%
129.157027 1
 
2.6%
129.1228363 1
 
2.6%
129.1213771 1
 
2.6%
129.1217694 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
129.1134134 1
2.6%
129.1159306 1
2.6%
129.1192299 1
2.6%
129.1213771 1
2.6%
129.1216768 1
2.6%
129.1217694 1
2.6%
129.1228363 1
2.6%
129.1237637 1
2.6%
129.1251201 1
2.6%
129.1252957 1
2.6%
ValueCountFrequency (%)
129.2055332 1
2.6%
129.2048929 1
2.6%
129.1841326 1
2.6%
129.1826364 1
2.6%
129.1816328 1
2.6%
129.181223 1
2.6%
129.1786262 1
2.6%
129.1762227 1
2.6%
129.1742683 1
2.6%
129.1675844 1
2.6%

최대수용인원수
Real number (ℝ)

Distinct36
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.38462
Minimum40
Maximum1038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T22:08:52.830558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile56.8
Q1117.5
median187
Q3263
95-th percentile354.6
Maximum1038
Range998
Interquartile range (IQR)145.5

Descriptive statistics

Standard deviation164.41389
Coefficient of variation (CV)0.78522431
Kurtosis16.887989
Mean209.38462
Median Absolute Deviation (MAD)76
Skewness3.4681265
Sum8166
Variance27031.927
MonotonicityNot monotonic
2023-12-12T22:08:52.977066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
263 2
 
5.1%
121 2
 
5.1%
76 2
 
5.1%
58 1
 
2.6%
137 1
 
2.6%
246 1
 
2.6%
180 1
 
2.6%
100 1
 
2.6%
46 1
 
2.6%
40 1
 
2.6%
Other values (26) 26
66.7%
ValueCountFrequency (%)
40 1
2.6%
46 1
2.6%
58 1
2.6%
76 2
5.1%
85 1
2.6%
93 1
2.6%
100 1
2.6%
101 1
2.6%
114 1
2.6%
121 2
5.1%
ValueCountFrequency (%)
1038 1
2.6%
405 1
2.6%
349 1
2.6%
334 1
2.6%
299 1
2.6%
291 1
2.6%
285 1
2.6%
276 1
2.6%
266 1
2.6%
263 2
5.1%

관리기관명
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T22:08:53.202050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.3333333
Min length3

Characters and Unicode

Total characters247
Distinct characters65
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row강동초등학교
2nd row해강고등학교
3rd row해강중학교
4th row해림초등학교
5th row동백초등학교
ValueCountFrequency (%)
주민센터 3
 
6.4%
호텔 2
 
4.3%
모텔 2
 
4.3%
강동초등학교 1
 
2.1%
버킹검 1
 
2.1%
신도고등학교 1
 
2.1%
해운대여자중학교 1
 
2.1%
해운대초등학교 1
 
2.1%
go 1
 
2.1%
잉카 1
 
2.1%
Other values (33) 33
70.2%
2023-12-12T22:08:53.635717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 25
 
10.1%
교 25
 
10.1%
등 19
 
7.7%
초 14
 
5.7%
센 10
 
4.0%
동 9
 
3.6%
8
 
3.2%
송 7
 
2.8%
터 7
 
2.8%
민 7
 
2.8%
Other values (55) 116
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 230
93.1%
Space Separator 8
 
3.2%
Decimal Number 7
 
2.8%
Uppercase Letter 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 25
 
10.9%
교 25
 
10.9%
등 19
 
8.3%
초 14
 
6.1%
센 10
 
4.3%
동 9
 
3.9%
송 7
 
3.0%
터 7
 
3.0%
민 7
 
3.0%
주 7
 
3.0%
Other values (48) 100
43.5%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
2 2
28.6%
4 1
 
14.3%
3 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
O 1
50.0%
G 1
50.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 230
93.1%
Common 15
 
6.1%
Latin 2
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 25
 
10.9%
교 25
 
10.9%
등 19
 
8.3%
초 14
 
6.1%
센 10
 
4.3%
동 9
 
3.9%
송 7
 
3.0%
터 7
 
3.0%
민 7
 
3.0%
주 7
 
3.0%
Other values (48) 100
43.5%
Common
ValueCountFrequency (%)
8
53.3%
1 3
 
20.0%
2 2
 
13.3%
4 1
 
6.7%
3 1
 
6.7%
Latin
ValueCountFrequency (%)
O 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 230
93.1%
ASCII 17
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 25
 
10.9%
교 25
 
10.9%
등 19
 
8.3%
초 14
 
6.1%
센 10
 
4.3%
동 9
 
3.9%
송 7
 
3.0%
터 7
 
3.0%
민 7
 
3.0%
주 7
 
3.0%
Other values (48) 100
43.5%
ASCII
ValueCountFrequency (%)
8
47.1%
1 3
 
17.6%
2 2
 
11.8%
4 1
 
5.9%
O 1
 
5.9%
G 1
 
5.9%
3 1
 
5.9%

관리기관전화번호
Text

Distinct38
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T22:08:53.907309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters468
Distinct characters11
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

Unique37 ?
Unique (%)94.9%

Sample

1st row051-740-2405
2nd row051-749-8705
3rd row051-740-2703
4th row051-740-1507
5th row051-747-6400
ValueCountFrequency (%)
051-749-6832 2
 
5.1%
051-746-5765 1
 
2.6%
051-749-6871 1
 
2.6%
051-784-7111 1
 
2.6%
051-746-2952 1
 
2.6%
051-709-8403 1
 
2.6%
051-746-2948 1
 
2.6%
051-746-5462 1
 
2.6%
051-703-9290 1
 
2.6%
051-784-3432 1
 
2.6%
Other values (28) 28
71.8%
2023-12-12T22:08:54.320858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 85
18.2%
- 78
16.7%
5 62
13.2%
1 54
11.5%
7 52
11.1%
4 37
7.9%
2 28
 
6.0%
9 21
 
4.5%
6 21
 
4.5%
8 20
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 390
83.3%
Dash Punctuation 78
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85
21.8%
5 62
15.9%
1 54
13.8%
7 52
13.3%
4 37
9.5%
2 28
 
7.2%
9 21
 
5.4%
6 21
 
5.4%
8 20
 
5.1%
3 10
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 85
18.2%
- 78
16.7%
5 62
13.2%
1 54
11.5%
7 52
11.1%
4 37
7.9%
2 28
 
6.0%
9 21
 
4.5%
6 21
 
4.5%
8 20
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85
18.2%
- 78
16.7%
5 62
13.2%
1 54
11.5%
7 52
11.1%
4 37
7.9%
2 28
 
6.0%
9 21
 
4.5%
6 21
 
4.5%
8 20
 
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
2022-09-15
39 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-15
2nd row2022-09-15
3rd row2022-09-15
4th row2022-09-15
5th row2022-09-15

Common Values

ValueCountFrequency (%)
2022-09-15 39
100.0%

Length

2023-12-12T22:08:54.472918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:08:54.584884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-15 39
100.0%

Interactions

2023-12-12T22:08:48.953126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:48.487269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:48.733573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:49.031217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:48.570309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:48.816463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:49.103073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:48.641505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:08:48.885262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:08:54.650782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지진겸용임시주거시설명지진겸용임시주거시설상세시설명도로명주소지번주소위도경도최대수용인원수관리기관명관리기관전화번호
지진겸용임시주거시설명1.0001.0001.0001.0001.0001.0001.0001.0001.000
지진겸용임시주거시설상세시설명1.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0001.0001.0000.5390.2461.0000.892
경도1.0001.0001.0001.0000.5391.0000.0001.0000.864
최대수용인원수1.0001.0001.0001.0000.2460.0001.0001.0001.000
관리기관명1.0001.0001.0001.0001.0001.0001.0001.0001.000
관리기관전화번호1.0001.0001.0001.0000.8920.8641.0001.0001.000
2023-12-12T22:08:54.777515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도최대수용인원수
위도1.000-0.4460.068
경도-0.4461.000-0.001
최대수용인원수0.068-0.0011.000

Missing values

2023-12-12T22:08:49.204448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:08:49.333230image/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

지진겸용임시주거시설명지진겸용임시주거시설상세시설명도로명주소지번주소위도경도최대수용인원수관리기관명관리기관전화번호데이터기준일자
0강동초등학교강동초등학교강당부산광역시 해운대구 해운대로349번길 10(우동)부산광역시 해운대구 우동 1113 강동초등학교35.172325129.136752263강동초등학교051-740-24052022-09-15
1해강고등학교해강고등학교강당부산광역시 해운대구 해운대해변로 33(우동)부산광역시 해운대구 우동 1417-1 해강고등학교35.164115129.137538349해강고등학교051-749-87052022-09-15
2해강중학교해강중학교강당부산광역시 해운대구 해운대해변로 17(우동)부산광역시 해운대구 우동 1417-1 해강중학교35.164015129.135703266해강중학교051-740-27032022-09-15
3해림초등학교해림초등학교강당부산광역시 해운대구 해운대로469번길 42(우동)부산광역시 해운대구 우동 1025-11 해림초등학교35.164821129.143936125해림초등학교051-740-15072022-09-15
4동백초등학교동백초등학교강당부산광역시 해운대구 좌동순환로357번길 6(중동)부산광역시 해운대구 중동 1518-1 동백초등학교35.164021129.181223285동백초등학교051-747-64002022-09-15
5해운대구 보훈회관해운대구 보훈회관강당부산광역시 해운대구 좌동순환로 18(좌동)부산광역시 해운대구 좌동 1347-3 해운대구 보훈회관35.168159129.167265114해운대구 보훈회관051-749-43122022-09-15
6송정초등학교송정초등학교강당부산광역시 해운대구 해운대로 1164-9 (송정동)부산광역시 해운대구 송정동 935 송정초등학교35.189594129.204893263송정초등학교051-709-67082022-09-15
7반여고등학교반여고등학교강당부산광역시 해운대구 반여로 136(반여동)부산광역시 해운대구 반여동 1622 반여고등학교35.20296129.12656334반여고등학교051-520-17072022-09-15
8반여초등학교반여초등학교강당부산광역시 해운대구 재반로 301(반여동)부산광역시 해운대구 반여동 1600-12 반여초등학교35.202725129.129467207반여초등학교051-520-04722022-09-15
9인지중학교인지중학교체육관부산광역시 해운대구 반여로 142(반여동)부산광역시 해운대구 반여동 1621 인지중학교35.20412129.125851258인지중학교051-520-07622022-09-15
지진겸용임시주거시설명지진겸용임시주거시설상세시설명도로명주소지번주소위도경도최대수용인원수관리기관명관리기관전화번호데이터기준일자
29로망스 모텔로망스 모텔부산광역시 해운대구 해운대로143번길 12(재송동)부산광역시 해운대구 재송동 1098-1235.184628129.12176940로망스 모텔051-784-77252022-09-15
30좌산초등학교좌산초등학교강당부산광역시 해운대구 좌동순환로311번길 7부산광역시 해운대구 좌동 144435.169339129.184133249좌산초등학교051-709-17042022-09-15
31장산노인복지관장산노인복지관부산광역시 해운대구 좌동로 126 장산노인복지관부산광역시 해운대구 좌동 1305-135.173975129.17862685장산노인복지관051-704-91412022-09-15
32좌1동주민센터좌1동주민센터부산광역시 해운대구 양운로 91부산광역시 해운대구 좌동 1458-235.171065129.17426876좌1동주민센터051-749-67412022-09-15
33좌2동주민센터좌2동 주민센터부산광역시 해운대구 좌동순환로 302부산광역시 해운대구 좌동 1327-235.169268129.18263693좌2동 주민센터051-749-67512022-09-15
34재송2동주민센터재송2동주민센터부산광역시 해운대구 재반로125번길 26부산광역시 해운대구 재송동 104635.189814129.125375130재송2동주민센터051-749-69252022-09-15
35재송1동주민센터재송1동주민센터부산광역시 해운대구 재반로63번길 23부산광역시 해운대구 재송동 274-235.184824129.12529676재송1동주민센터051-749-68322022-09-15
36좌4동주민센터좌4동 주민센터부산광역시 해운대구 좌동순환로 183부산광역시 해운대구 좌동 1407-635.178066129.176223121좌4동 주민센터051-749-68322022-09-15
37반송1동 주민센터반송1동 주민센터부산광역시 해운대구 아랫반송로 16부산광역시 해운대구 반송동 717-535.225331129.147936123반송1동 주민센터051-749-68712022-09-15
38반여3동주민센터반여3동주민센터부산광역시 해운대구 재반로256번길 29-70부산광역시 해운대구 반여동 1591-435.201158129.133481101반여3동주민센터051-749-64122022-09-15