Overview

Dataset statistics

Number of variables13
Number of observations34
Missing cells2
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory111.9 B

Variable types

Categorical4
Text4
Numeric4
Boolean1

Dataset

Description부산광역시 동래구 관내 재난 이재민 수용시설 현황에 대한 데이터로 지역명, 시설구분, 시설명, 상세시설명, 상세주소, 위도, 경도, 수용가능면적, 수용가능인원, 대표전화, 내진설계, 사용여부, 지정일자 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15116998/fileData.do

Alerts

사용여부 has constant value ""Constant
수용가능면적(제곱미터) is highly overall correlated with 수용가능인원(명)High correlation
수용가능인원(명) is highly overall correlated with 수용가능면적(제곱미터) and 2 other fieldsHigh correlation
시설구분 is highly overall correlated with 수용가능인원(명) and 2 other fieldsHigh correlation
내진설계 is highly overall correlated with 시설구분 and 1 other fieldsHigh correlation
지정일자 is highly overall correlated with 수용가능인원(명) and 2 other fieldsHigh correlation
대표전화 has 2 (5.9%) missing valuesMissing
시설명 has unique valuesUnique
상세주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:56:50.147668
Analysis finished2023-12-12 21:56:52.855871
Duration2.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역명
Categorical

Distinct9
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size404.0 B
부산광역시 동래구 온천동
10 
부산광역시 동래구 명장동
7 
부산광역시 동래구 사직동
5 
부산광역시 동래구 명륜동
4 
부산광역시 동래구 안락동
4 
Other values (4)
4 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique4 ?
Unique (%)11.8%

Sample

1st row부산광역시 동래구 낙민동
2nd row부산광역시 동래구 수안동
3rd row부산광역시 동래구 복천동
4th row부산광역시 동래구 칠산동
5th row부산광역시 동래구 명륜동

Common Values

ValueCountFrequency (%)
부산광역시 동래구 온천동 10
29.4%
부산광역시 동래구 명장동 7
20.6%
부산광역시 동래구 사직동 5
14.7%
부산광역시 동래구 명륜동 4
 
11.8%
부산광역시 동래구 안락동 4
 
11.8%
부산광역시 동래구 낙민동 1
 
2.9%
부산광역시 동래구 수안동 1
 
2.9%
부산광역시 동래구 복천동 1
 
2.9%
부산광역시 동래구 칠산동 1
 
2.9%

Length

2023-12-13T06:56:52.922588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:56:53.040768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 34
33.3%
동래구 34
33.3%
온천동 10
 
9.8%
명장동 7
 
6.9%
사직동 5
 
4.9%
명륜동 4
 
3.9%
안락동 4
 
3.9%
낙민동 1
 
1.0%
수안동 1
 
1.0%
복천동 1
 
1.0%

시설구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
학교
20 
연수,숙박
10 
교회
4 

Length

Max length5
Median length2
Mean length2.8823529
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연수,숙박
2nd row연수,숙박
3rd row학교
4th row학교
5th row교회

Common Values

ValueCountFrequency (%)
학교 20
58.8%
연수,숙박 10
29.4%
교회 4
 
11.8%

Length

2023-12-13T06:56:53.194376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:56:53.300525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
학교 20
58.8%
연수,숙박 10
29.4%
교회 4
 
11.8%

시설명
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-13T06:56:53.514781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.8235294
Min length3

Characters and Unicode

Total characters198
Distinct characters68
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row스위트모텔
2nd row화성빌
3rd row내성초등학교
4th row동래고등학교
5th row새한교회
ValueCountFrequency (%)
스위트모텔 1
 
2.8%
동남장모텔 1
 
2.8%
동신중학교 1
 
2.8%
대명여자고등학교 1
 
2.8%
혜화초등학교 1
 
2.8%
충렬중학교 1
 
2.8%
충렬고등학교 1
 
2.8%
안락초등학교 1
 
2.8%
화성빌 1
 
2.8%
안락중학교 1
 
2.8%
Other values (26) 26
72.2%
2023-12-13T06:56:53.877280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 24
 
12.1%
학 20
 
10.1%
등 17
 
8.6%
초 11
 
5.6%
고 7
 
3.5%
텔 7
 
3.5%
동 6
 
3.0%
모 5
 
2.5%
회 4
 
2.0%
락 4
 
2.0%
Other values (58) 93
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 193
97.5%
Space Separator 2
 
1.0%
Open Punctuation 1
 
0.5%
Uppercase Letter 1
 
0.5%
Close Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 24
 
12.4%
학 20
 
10.4%
등 17
 
8.8%
초 11
 
5.7%
고 7
 
3.6%
텔 7
 
3.6%
동 6
 
3.1%
모 5
 
2.6%
회 4
 
2.1%
락 4
 
2.1%
Other values (54) 88
45.6%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 193
97.5%
Common 4
 
2.0%
Latin 1
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 24
 
12.4%
학 20
 
10.4%
등 17
 
8.8%
초 11
 
5.7%
고 7
 
3.6%
텔 7
 
3.6%
동 6
 
3.1%
모 5
 
2.6%
회 4
 
2.1%
락 4
 
2.1%
Other values (54) 88
45.6%
Common
ValueCountFrequency (%)
2
50.0%
( 1
25.0%
) 1
25.0%
Latin
ValueCountFrequency (%)
S 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 193
97.5%
ASCII 5
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 24
 
12.4%
학 20
 
10.4%
등 17
 
8.8%
초 11
 
5.7%
고 7
 
3.6%
텔 7
 
3.6%
동 6
 
3.1%
모 5
 
2.6%
회 4
 
2.1%
락 4
 
2.1%
Other values (54) 88
45.6%
ASCII
ValueCountFrequency (%)
2
40.0%
( 1
20.0%
S 1
20.0%
) 1
20.0%

상세시설명
Text

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-13T06:56:54.055790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12.5
Mean length8.9411765
Min length2

Characters and Unicode

Total characters304
Distinct characters69
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

Unique32 ?
Unique (%)94.1%

Sample

1st row2인실 27호
2nd row2인실 15호
3rd row2층 강당(참빛관)
4th row별관2층 체육관(군붕관)
5th row본관 1층
ValueCountFrequency (%)
2인실 10
 
13.7%
2층 6
 
8.2%
별관 4
 
5.5%
강당 3
 
4.1%
별관2층 3
 
4.1%
1층 3
 
4.1%
건물 2
 
2.7%
15호 2
 
2.7%
4층 2
 
2.7%
체육관 2
 
2.7%
Other values (35) 36
49.3%
2023-12-13T06:56:54.377148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39
 
12.8%
관 27
 
8.9%
2 24
 
7.9%
층 19
 
6.2%
강 17
 
5.6%
) 17
 
5.6%
당 16
 
5.3%
( 16
 
5.3%
실 10
 
3.3%
인 10
 
3.3%
Other values (59) 109
35.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 181
59.5%
Decimal Number 50
 
16.4%
Space Separator 39
 
12.8%
Close Punctuation 17
 
5.6%
Open Punctuation 16
 
5.3%
Math Symbol 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 27
14.9%
층 19
 
10.5%
강 17
 
9.4%
당 16
 
8.8%
실 10
 
5.5%
인 10
 
5.5%
호 10
 
5.5%
별 9
 
5.0%
육 5
 
2.8%
체 4
 
2.2%
Other values (45) 54
29.8%
Decimal Number
ValueCountFrequency (%)
2 24
48.0%
1 7
 
14.0%
3 6
 
12.0%
4 4
 
8.0%
5 4
 
8.0%
7 1
 
2.0%
9 1
 
2.0%
6 1
 
2.0%
0 1
 
2.0%
8 1
 
2.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 181
59.5%
Common 123
40.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 27
14.9%
층 19
 
10.5%
강 17
 
9.4%
당 16
 
8.8%
실 10
 
5.5%
인 10
 
5.5%
호 10
 
5.5%
별 9
 
5.0%
육 5
 
2.8%
체 4
 
2.2%
Other values (45) 54
29.8%
Common
ValueCountFrequency (%)
39
31.7%
2 24
19.5%
) 17
13.8%
( 16
13.0%
1 7
 
5.7%
3 6
 
4.9%
4 4
 
3.3%
5 4
 
3.3%
7 1
 
0.8%
9 1
 
0.8%
Other values (4) 4
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 181
59.5%
ASCII 123
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39
31.7%
2 24
19.5%
) 17
13.8%
( 16
13.0%
1 7
 
5.7%
3 6
 
4.9%
4 4
 
3.3%
5 4
 
3.3%
7 1
 
0.8%
9 1
 
0.8%
Other values (4) 4
 
3.3%
Hangul
ValueCountFrequency (%)
관 27
14.9%
층 19
 
10.5%
강 17
 
9.4%
당 16
 
8.8%
실 10
 
5.5%
인 10
 
5.5%
호 10
 
5.5%
별 9
 
5.0%
육 5
 
2.8%
체 4
 
2.2%
Other values (45) 54
29.8%

상세주소
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-13T06:56:54.878161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length24.823529
Min length21

Characters and Unicode

Total characters844
Distinct characters57
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

Unique34 ?
Unique (%)100.0%

Sample

1st row부산광역시 동래구 충렬대로255번길 18(낙민동)
2nd row부산광역시 동래구 충렬대로237번가길 11(수안동)
3rd row부산광역시 동래구 동래로 173(복천동)
4th row부산광역시 동래구 충렬대로285번길 22(칠산동)
5th row부산광역시 동래구 명륜로218번길 6(명륜동)
ValueCountFrequency (%)
부산광역시 34
24.5%
동래구 34
24.5%
온천동 3
 
2.2%
금강로 2
 
1.4%
온천천로 2
 
1.4%
25(명장동 1
 
0.7%
명안로53번길 1
 
0.7%
26(명장동 1
 
0.7%
명장로106번길 1
 
0.7%
45(명장동 1
 
0.7%
Other values (59) 59
42.4%
2023-12-13T06:56:55.219112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
105
 
12.4%
동 70
 
8.3%
래 36
 
4.3%
1 35
 
4.1%
시 35
 
4.1%
산 35
 
4.1%
부 34
 
4.0%
) 34
 
4.0%
로 34
 
4.0%
( 34
 
4.0%
Other values (47) 392
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 535
63.4%
Decimal Number 133
 
15.8%
Space Separator 105
 
12.4%
Close Punctuation 34
 
4.0%
Open Punctuation 34
 
4.0%
Dash Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 70
13.1%
래 36
 
6.7%
시 35
 
6.5%
산 35
 
6.5%
부 34
 
6.4%
로 34
 
6.4%
구 34
 
6.4%
역 34
 
6.4%
광 34
 
6.4%
번 21
 
3.9%
Other values (33) 168
31.4%
Decimal Number
ValueCountFrequency (%)
1 35
26.3%
2 22
16.5%
3 18
13.5%
5 16
12.0%
6 10
 
7.5%
8 9
 
6.8%
4 8
 
6.0%
7 7
 
5.3%
9 6
 
4.5%
0 2
 
1.5%
Space Separator
ValueCountFrequency (%)
105
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 535
63.4%
Common 309
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 70
13.1%
래 36
 
6.7%
시 35
 
6.5%
산 35
 
6.5%
부 34
 
6.4%
로 34
 
6.4%
구 34
 
6.4%
역 34
 
6.4%
광 34
 
6.4%
번 21
 
3.9%
Other values (33) 168
31.4%
Common
ValueCountFrequency (%)
105
34.0%
1 35
 
11.3%
) 34
 
11.0%
( 34
 
11.0%
2 22
 
7.1%
3 18
 
5.8%
5 16
 
5.2%
6 10
 
3.2%
8 9
 
2.9%
4 8
 
2.6%
Other values (4) 18
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 535
63.4%
ASCII 309
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
105
34.0%
1 35
 
11.3%
) 34
 
11.0%
( 34
 
11.0%
2 22
 
7.1%
3 18
 
5.8%
5 16
 
5.2%
6 10
 
3.2%
8 9
 
2.9%
4 8
 
2.6%
Other values (4) 18
 
5.8%
Hangul
ValueCountFrequency (%)
동 70
13.1%
래 36
 
6.7%
시 35
 
6.5%
산 35
 
6.5%
부 34
 
6.4%
로 34
 
6.4%
구 34
 
6.4%
역 34
 
6.4%
광 34
 
6.4%
번 21
 
3.9%
Other values (33) 168
31.4%

위도
Real number (ℝ)

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.205255
Minimum35.191446
Maximum35.222957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T06:56:55.341101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.191446
5-th percentile35.195076
Q135.200846
median35.203011
Q335.210699
95-th percentile35.219011
Maximum35.222957
Range0.03151084
Interquartile range (IQR)0.009853445

Descriptive statistics

Standard deviation0.0075067938
Coefficient of variation (CV)0.00021322935
Kurtosis-0.094649117
Mean35.205255
Median Absolute Deviation (MAD)0.0033171
Skewness0.58621992
Sum1196.9787
Variance5.6351953 × 10-5
MonotonicityNot monotonic
2023-12-13T06:56:55.466478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
35.20137428 1
 
2.9%
35.19144632 1
 
2.9%
35.19570734 1
 
2.9%
35.20047538 1
 
2.9%
35.19790721 1
 
2.9%
35.19934354 1
 
2.9%
35.19468277 1
 
2.9%
35.19528744 1
 
2.9%
35.20199789 1
 
2.9%
35.20083075 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
35.19144632 1
2.9%
35.19468277 1
2.9%
35.19528744 1
2.9%
35.19570734 1
2.9%
35.19790721 1
2.9%
35.19934354 1
2.9%
35.20047538 1
2.9%
35.20081401 1
2.9%
35.20083075 1
2.9%
35.20089022 1
2.9%
ValueCountFrequency (%)
35.22295716 1
2.9%
35.21996261 1
2.9%
35.21849864 1
2.9%
35.2151854 1
2.9%
35.21491533 1
2.9%
35.21332638 1
2.9%
35.21268222 1
2.9%
35.21254481 1
2.9%
35.21152805 1
2.9%
35.2082121 1
2.9%

경도
Real number (ℝ)

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.08479
Minimum129.0541
Maximum129.11333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T06:56:55.574591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.0541
5-th percentile129.05912
Q1129.07405
median129.08258
Q3129.09741
95-th percentile129.10957
Maximum129.11333
Range0.0592327
Interquartile range (IQR)0.023360025

Descriptive statistics

Standard deviation0.016062901
Coefficient of variation (CV)0.00012443682
Kurtosis-0.78978322
Mean129.08479
Median Absolute Deviation (MAD)0.0126335
Skewness-0.0063890316
Sum4388.8828
Variance0.00025801678
MonotonicityNot monotonic
2023-12-13T06:56:55.708045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
129.0881812 1
 
2.9%
129.1095221 1
 
2.9%
129.057469 1
 
2.9%
129.0784338 1
 
2.9%
129.0695563 1
 
2.9%
129.0985242 1
 
2.9%
129.0993835 1
 
2.9%
129.1060929 1
 
2.9%
129.1032665 1
 
2.9%
129.0624269 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
129.0541011 1
2.9%
129.057469 1
2.9%
129.0600152 1
2.9%
129.0624269 1
2.9%
129.0645117 1
2.9%
129.0695563 1
2.9%
129.070337 1
2.9%
129.0733436 1
2.9%
129.0737556 1
2.9%
129.0749169 1
2.9%
ValueCountFrequency (%)
129.1133338 1
2.9%
129.109663 1
2.9%
129.1095221 1
2.9%
129.1083192 1
2.9%
129.1060929 1
2.9%
129.1032665 1
2.9%
129.0993835 1
2.9%
129.0985242 1
2.9%
129.0977115 1
2.9%
129.0964893 1
2.9%

수용가능면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean966.46
Minimum165
Maximum2987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T06:56:55.840199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum165
5-th percentile319.7
Q1692.25
median845.625
Q3988.4825
95-th percentile2010.2155
Maximum2987
Range2822
Interquartile range (IQR)296.2325

Descriptive statistics

Standard deviation570.09433
Coefficient of variation (CV)0.58987887
Kurtosis4.0823541
Mean966.46
Median Absolute Deviation (MAD)151.5
Skewness1.7719991
Sum32859.64
Variance325007.55
MonotonicityNot monotonic
2023-12-13T06:56:55.956182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
720.0 2
 
5.9%
165.0 2
 
5.9%
924.35 1
 
2.9%
589.0 1
 
2.9%
1258.67 1
 
2.9%
588.0 1
 
2.9%
776.0 1
 
2.9%
991.0 1
 
2.9%
825.0 1
 
2.9%
980.93 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
165.0 2
5.9%
403.0 1
2.9%
471.0 1
2.9%
527.0 1
2.9%
588.0 1
2.9%
589.0 1
2.9%
616.0 1
2.9%
688.0 1
2.9%
705.0 1
2.9%
720.0 2
5.9%
ValueCountFrequency (%)
2987.0 1
2.9%
2066.33 1
2.9%
1980.0 1
2.9%
1973.0 1
2.9%
1537.0 1
2.9%
1258.67 1
2.9%
1177.02 1
2.9%
1141.66 1
2.9%
991.0 1
2.9%
980.93 1
2.9%

수용가능인원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.08824
Minimum30
Maximum794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T06:56:56.098215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile35.2
Q166.25
median270
Q3367
95-th percentile521.45
Maximum794
Range764
Interquartile range (IQR)300.75

Descriptive statistics

Standard deviation183.63986
Coefficient of variation (CV)0.73430027
Kurtosis0.7505589
Mean250.08824
Median Absolute Deviation (MAD)141.5
Skewness0.76410426
Sum8503
Variance33723.598
MonotonicityNot monotonic
2023-12-13T06:56:56.242419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
30 2
 
5.9%
63 2
 
5.9%
381 1
 
2.9%
226 1
 
2.9%
276 1
 
2.9%
484 1
 
2.9%
38 1
 
2.9%
298 1
 
2.9%
54 1
 
2.9%
155 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
30 2
5.9%
38 1
2.9%
40 1
2.9%
44 1
2.9%
52 1
2.9%
54 1
2.9%
63 2
5.9%
76 1
2.9%
86 1
2.9%
90 1
2.9%
ValueCountFrequency (%)
794 1
2.9%
591 1
2.9%
484 1
2.9%
452 1
2.9%
438 1
2.9%
381 1
2.9%
377 1
2.9%
376 1
2.9%
371 1
2.9%
355 1
2.9%

대표전화
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing2
Missing (%)5.9%
Memory size404.0 B
2023-12-13T06:56:56.471273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters384
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

Unique32 ?
Unique (%)100.0%

Sample

1st row051-552-9798
2nd row051-550-8606
3rd row051-555-0151
4th row051-557-5291
5th row051-558-1212
ValueCountFrequency (%)
051-790-0505 1
 
3.1%
051-550-8606 1
 
3.1%
051-500-3200 1
 
3.1%
051-500-0406 1
 
3.1%
051-559-3900 1
 
3.1%
051-590-0500 1
 
3.1%
051-522-4228 1
 
3.1%
051-520-1600 1
 
3.1%
051-552-9798 1
 
3.1%
051-502-4700 1
 
3.1%
Other values (22) 22
68.8%
2023-12-13T06:56:56.914590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 92
24.0%
5 86
22.4%
- 64
16.7%
1 46
12.0%
2 22
 
5.7%
4 15
 
3.9%
9 13
 
3.4%
3 13
 
3.4%
7 12
 
3.1%
6 12
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320
83.3%
Dash Punctuation 64
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92
28.7%
5 86
26.9%
1 46
14.4%
2 22
 
6.9%
4 15
 
4.7%
9 13
 
4.1%
3 13
 
4.1%
7 12
 
3.8%
6 12
 
3.8%
8 9
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 384
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92
24.0%
5 86
22.4%
- 64
16.7%
1 46
12.0%
2 22
 
5.7%
4 15
 
3.9%
9 13
 
3.4%
3 13
 
3.4%
7 12
 
3.1%
6 12
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92
24.0%
5 86
22.4%
- 64
16.7%
1 46
12.0%
2 22
 
5.7%
4 15
 
3.9%
9 13
 
3.4%
3 13
 
3.4%
7 12
 
3.1%
6 12
 
3.1%

내진설계
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
적용
23 
미적용
11 

Length

Max length3
Median length2
Mean length2.3235294
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row미적용
2nd row미적용
3rd row미적용
4th row적용
5th row미적용

Common Values

ValueCountFrequency (%)
적용 23
67.6%
미적용 11
32.4%

Length

2023-12-13T06:56:57.066991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:56:57.199337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적용 23
67.6%
미적용 11
32.4%

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size166.0 B
True
34 
ValueCountFrequency (%)
True 34
100.0%
2023-12-13T06:56:57.289438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

지정일자
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
2019-01-01
12 
2021-03-11
11 
2019-08-09
10 
2021-01-26
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row2019-08-09
2nd row2019-08-09
3rd row2019-01-01
4th row2021-03-11
5th row2019-01-01

Common Values

ValueCountFrequency (%)
2019-01-01 12
35.3%
2021-03-11 11
32.4%
2019-08-09 10
29.4%
2021-01-26 1
 
2.9%

Length

2023-12-13T06:56:57.388749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:56:57.520862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-01-01 12
35.3%
2021-03-11 11
32.4%
2019-08-09 10
29.4%
2021-01-26 1
 
2.9%

Interactions

2023-12-13T06:56:52.020005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:50.735413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:51.191620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:51.607957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:52.136641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:50.835116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:51.300992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:51.717064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:52.237535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:50.944746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:51.403843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:51.815631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:52.352674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:51.054912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:51.488060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:56:51.909083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:56:57.615698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명시설구분시설명상세시설명상세주소위도경도수용가능면적(제곱미터)수용가능인원(명)대표전화내진설계지정일자
지역명1.0000.5931.0000.0001.0000.4420.7610.5210.5621.0000.3730.216
시설구분0.5931.0001.0001.0001.0000.1750.6220.3860.7351.0000.3430.745
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
상세시설명0.0001.0001.0001.0001.0000.9690.9260.9751.0001.0001.0001.000
상세주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도0.4420.1751.0000.9691.0001.0000.0000.0000.1341.0000.0000.292
경도0.7610.6221.0000.9261.0000.0001.0000.0000.6511.0000.5060.068
수용가능면적(제곱미터)0.5210.3861.0000.9751.0000.0000.0001.0000.8901.0000.1530.391
수용가능인원(명)0.5620.7351.0001.0001.0000.1340.6510.8901.0001.0000.5180.895
대표전화1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
내진설계0.3730.3431.0001.0001.0000.0000.5060.1530.5181.0001.0000.753
지정일자0.2160.7451.0001.0001.0000.2920.0680.3910.8951.0000.7531.000
2023-12-13T06:56:57.781022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명지정일자내진설계시설구분
지역명1.0000.0930.3200.276
지정일자0.0931.0000.5260.780
내진설계0.3200.5261.0000.538
시설구분0.2760.7800.5381.000
2023-12-13T06:56:57.907180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도수용가능면적(제곱미터)수용가능인원(명)지역명시설구분내진설계지정일자
위도1.000-0.1870.153-0.1680.1890.0140.0000.130
경도-0.1871.0000.1920.2350.4810.4170.3510.086
수용가능면적(제곱미터)0.1530.1921.0000.5380.3020.0000.0000.330
수용가능인원(명)-0.1680.2350.5381.0000.2980.5760.3440.546
지역명0.1890.4810.3020.2981.0000.2760.3200.093
시설구분0.0140.4170.0000.5760.2761.0000.5380.780
내진설계0.0000.3510.0000.3440.3200.5381.0000.526
지정일자0.1300.0860.3300.5460.0930.7800.5261.000

Missing values

2023-12-13T06:56:52.569803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:56:52.777878image/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

지역명시설구분시설명상세시설명상세주소위도경도수용가능면적(제곱미터)수용가능인원(명)대표전화내진설계사용여부지정일자
0부산광역시 동래구 낙민동연수,숙박스위트모텔2인실 27호부산광역시 동래구 충렬대로255번길 18(낙민동)35.201374129.088181825.054051-552-9798미적용Y2019-08-09
1부산광역시 동래구 수안동연수,숙박화성빌2인실 15호부산광역시 동래구 충렬대로237번가길 11(수안동)35.202511129.085973471.030<NA>미적용Y2019-08-09
2부산광역시 동래구 복천동학교내성초등학교2층 강당(참빛관)부산광역시 동래구 동래로 173(복천동)35.204371129.089688527.0202051-550-8606미적용Y2019-01-01
3부산광역시 동래구 칠산동학교동래고등학교별관2층 체육관(군붕관)부산광역시 동래구 충렬대로285번길 22(칠산동)35.20089129.0927451537.0591051-555-0151적용Y2021-03-11
4부산광역시 동래구 명륜동교회새한교회본관 1층부산광역시 동래구 명륜로218번길 6(명륜동)35.212682129.08363797.0306051-557-5291미적용Y2019-01-01
5부산광역시 동래구 명륜동연수,숙박락모텔2인실 22호부산광역시 동래구 명륜로 125(명륜동)35.204826129.082205940.044051-558-1212미적용Y2019-08-09
6부산광역시 동래구 명륜동연수,숙박브이모텔2인실 43호부산광역시 동래구 명륜로112번가길 36(명륜동)35.205078129.0829561980.086051-552-3420적용Y2019-08-09
7부산광역시 동래구 명륜동학교부산중앙여자고등학교강당부산광역시 동래구 온천천로 131(명륜동)35.208118129.079629760.23292051-550-0370적용Y2021-03-11
8부산광역시 동래구 온천동연수,숙박노블래스2인실 26호부산광역시 동래구 금강공원로 41-2 (온천동)35.218499129.079909720.052<NA>미적용Y2019-08-09
9부산광역시 동래구 온천동연수,숙박동남장모텔2인실 20호부산광역시 동래구 금강로 161-3 (온천동)35.222957129.081006705.040051-550-4314미적용Y2019-08-09
지역명시설구분시설명상세시설명상세주소위도경도수용가능면적(제곱미터)수용가능인원(명)대표전화내진설계사용여부지정일자
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