Overview

Dataset statistics

Number of variables10
Number of observations819
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.5 KiB
Average record size in memory83.2 B

Variable types

Numeric3
Categorical3
Text4

Dataset

Description인천광역시 민방위 주민대피시설 현황입니다.총 819개소, 정부지원 대피시설111개소, 공공용 대피시설 708개소입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15105989&srcSe=7661IVAWM27C61E190

Alerts

시도 has constant value ""Constant
시설종류 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
시군구 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 시군구 and 1 other fieldsHigh correlation
대피시설_확보면적(제곱미터) is highly overall correlated with 대피시설_대피 가능인원(명)High correlation
대피시설_대피 가능인원(명) is highly overall correlated with 대피시설_확보면적(제곱미터)High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-21 02:40:40.424431
Analysis finished2024-04-21 02:40:44.848140
Duration4.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct819
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410
Minimum1
Maximum819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T11:40:45.050941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41.9
Q1205.5
median410
Q3614.5
95-th percentile778.1
Maximum819
Range818
Interquartile range (IQR)409

Descriptive statistics

Standard deviation236.56923
Coefficient of variation (CV)0.57699812
Kurtosis-1.2
Mean410
Median Absolute Deviation (MAD)205
Skewness0
Sum335790
Variance55965
MonotonicityStrictly increasing
2024-04-21T11:40:45.508605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
552 1
 
0.1%
542 1
 
0.1%
543 1
 
0.1%
544 1
 
0.1%
545 1
 
0.1%
546 1
 
0.1%
547 1
 
0.1%
548 1
 
0.1%
549 1
 
0.1%
Other values (809) 809
98.8%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
819 1
0.1%
818 1
0.1%
817 1
0.1%
816 1
0.1%
815 1
0.1%
814 1
0.1%
813 1
0.1%
812 1
0.1%
811 1
0.1%
810 1
0.1%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
인천
819 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천 819
100.0%

Length

2024-04-21T11:40:45.924764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:40:46.224618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천 819
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
미추홀구
135 
남동구
132 
서구
108 
부평구
93 
강화군
83 
Other values (5)
268 

Length

Max length4
Median length3
Mean length2.9389499
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미추홀구 135
16.5%
남동구 132
16.1%
서구 108
13.2%
부평구 93
11.4%
강화군 83
10.1%
계양구 72
8.8%
연수구 70
8.5%
옹진군 49
 
6.0%
중구 45
 
5.5%
동구 32
 
3.9%

Length

2024-04-21T11:40:46.419719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:40:46.658130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미추홀구 135
16.5%
남동구 132
16.1%
서구 108
13.2%
부평구 93
11.4%
강화군 83
10.1%
계양구 72
8.8%
연수구 70
8.5%
옹진군 49
 
6.0%
중구 45
 
5.5%
동구 32
 
3.9%

읍면동
Text

Distinct137
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2024-04-21T11:40:47.726056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6300366
Min length2

Characters and Unicode

Total characters2973
Distinct characters103
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)2.1%

Sample

1st row신포동
2nd row신포동
3rd row신포동
4th row신포동
5th row신포동
ValueCountFrequency (%)
백령 30
 
3.7%
관교동 26
 
3.2%
강화읍 21
 
2.6%
산곡동 19
 
2.3%
부평동 18
 
2.2%
학익2동 17
 
2.1%
용현5동 16
 
2.0%
부개동 14
 
1.7%
갈산동 14
 
1.7%
논현고잔동 13
 
1.6%
Other values (127) 631
77.0%
2024-04-21T11:40:49.028498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 711
23.9%
2 129
 
4.3%
1 119
 
4.0%
3 76
 
2.6%
산 66
 
2.2%
현 65
 
2.2%
면 62
 
2.1%
도 59
 
2.0%
송 59
 
2.0%
수 53
 
1.8%
Other values (93) 1574
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2529
85.1%
Decimal Number 434
 
14.6%
Other Punctuation 10
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 711
28.1%
산 66
 
2.6%
현 65
 
2.6%
면 62
 
2.5%
도 59
 
2.3%
송 59
 
2.3%
수 53
 
2.1%
서 43
 
1.7%
학 41
 
1.6%
화 39
 
1.5%
Other values (84) 1331
52.6%
Decimal Number
ValueCountFrequency (%)
2 129
29.7%
1 119
27.4%
3 76
17.5%
4 43
 
9.9%
5 39
 
9.0%
6 21
 
4.8%
8 5
 
1.2%
7 2
 
0.5%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2529
85.1%
Common 444
 
14.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 711
28.1%
산 66
 
2.6%
현 65
 
2.6%
면 62
 
2.5%
도 59
 
2.3%
송 59
 
2.3%
수 53
 
2.1%
서 43
 
1.7%
학 41
 
1.6%
화 39
 
1.5%
Other values (84) 1331
52.6%
Common
ValueCountFrequency (%)
2 129
29.1%
1 119
26.8%
3 76
17.1%
4 43
 
9.7%
5 39
 
8.8%
6 21
 
4.7%
, 10
 
2.3%
8 5
 
1.1%
7 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2529
85.1%
ASCII 444
 
14.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 711
28.1%
산 66
 
2.6%
현 65
 
2.6%
면 62
 
2.5%
도 59
 
2.3%
송 59
 
2.3%
수 53
 
2.1%
서 43
 
1.7%
학 41
 
1.6%
화 39
 
1.5%
Other values (84) 1331
52.6%
ASCII
ValueCountFrequency (%)
2 129
29.1%
1 119
26.8%
3 76
17.1%
4 43
 
9.7%
5 39
 
8.8%
6 21
 
4.7%
, 10
 
2.3%
8 5
 
1.1%
7 2
 
0.5%

시설종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
공공용
708 
정부지원
111 

Length

Max length4
Median length3
Mean length3.1355311
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공용 708
86.4%
정부지원 111
 
13.6%

Length

2024-04-21T11:40:49.273839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:40:49.465104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공용 708
86.4%
정부지원 111
 
13.6%

민방위대피시설명칭
Text

Distinct765
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2024-04-21T11:40:50.374434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length8.4542125
Min length3

Characters and Unicode

Total characters6924
Distinct characters370
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

Unique738 ?
Unique (%)90.1%

Sample

1st row중구청 대피호
2nd row중구청 지하주차장
3rd row신포지하상가
4th row민방위교육장
5th row신생삼성아파트(지하주차장)
ValueCountFrequency (%)
지하 25
 
2.5%
지하주차장 24
 
2.4%
주차장 12
 
1.2%
지하대피소 11
 
1.1%
더월드아파트 8
 
0.8%
현대아파트 7
 
0.7%
한국아파트 6
 
0.6%
풍림아이원 6
 
0.6%
아파트 5
 
0.5%
신동아3차 5
 
0.5%
Other values (791) 896
89.2%
2024-04-21T11:40:51.605946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 505
 
7.3%
트 453
 
6.5%
파 442
 
6.4%
지 247
 
3.6%
193
 
2.8%
주 191
 
2.8%
차 179
 
2.6%
대 157
 
2.3%
동 154
 
2.2%
1 148
 
2.1%
Other values (360) 4255
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6106
88.2%
Decimal Number 440
 
6.4%
Space Separator 193
 
2.8%
Open Punctuation 51
 
0.7%
Close Punctuation 50
 
0.7%
Uppercase Letter 42
 
0.6%
Other Punctuation 22
 
0.3%
Lowercase Letter 16
 
0.2%
Dash Punctuation 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 505
 
8.3%
트 453
 
7.4%
파 442
 
7.2%
지 247
 
4.0%
주 191
 
3.1%
차 179
 
2.9%
대 157
 
2.6%
동 154
 
2.5%
하 146
 
2.4%
장 103
 
1.7%
Other values (325) 3529
57.8%
Decimal Number
ValueCountFrequency (%)
1 148
33.6%
2 118
26.8%
3 47
 
10.7%
0 32
 
7.3%
4 24
 
5.5%
5 17
 
3.9%
7 14
 
3.2%
6 14
 
3.2%
8 14
 
3.2%
9 12
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
L 10
23.8%
H 8
19.0%
S 5
11.9%
K 5
11.9%
A 4
 
9.5%
T 4
 
9.5%
G 2
 
4.8%
I 2
 
4.8%
P 1
 
2.4%
V 1
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
a 11
68.8%
w 1
 
6.2%
e 1
 
6.2%
i 1
 
6.2%
y 1
 
6.2%
k 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
@ 16
72.7%
· 3
 
13.6%
, 2
 
9.1%
& 1
 
4.5%
Space Separator
ValueCountFrequency (%)
193
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51
100.0%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6106
88.2%
Common 760
 
11.0%
Latin 58
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 505
 
8.3%
트 453
 
7.4%
파 442
 
7.2%
지 247
 
4.0%
주 191
 
3.1%
차 179
 
2.9%
대 157
 
2.6%
동 154
 
2.5%
하 146
 
2.4%
장 103
 
1.7%
Other values (325) 3529
57.8%
Common
ValueCountFrequency (%)
193
25.4%
1 148
19.5%
2 118
15.5%
( 51
 
6.7%
) 50
 
6.6%
3 47
 
6.2%
0 32
 
4.2%
4 24
 
3.2%
5 17
 
2.2%
@ 16
 
2.1%
Other values (9) 64
 
8.4%
Latin
ValueCountFrequency (%)
a 11
19.0%
L 10
17.2%
H 8
13.8%
S 5
8.6%
K 5
8.6%
A 4
 
6.9%
T 4
 
6.9%
G 2
 
3.4%
I 2
 
3.4%
P 1
 
1.7%
Other values (6) 6
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6106
88.2%
ASCII 815
 
11.8%
None 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 505
 
8.3%
트 453
 
7.4%
파 442
 
7.2%
지 247
 
4.0%
주 191
 
3.1%
차 179
 
2.9%
대 157
 
2.6%
동 154
 
2.5%
하 146
 
2.4%
장 103
 
1.7%
Other values (325) 3529
57.8%
ASCII
ValueCountFrequency (%)
193
23.7%
1 148
18.2%
2 118
14.5%
( 51
 
6.3%
) 50
 
6.1%
3 47
 
5.8%
0 32
 
3.9%
4 24
 
2.9%
5 17
 
2.1%
@ 16
 
2.0%
Other values (24) 119
14.6%
None
ValueCountFrequency (%)
· 3
100.0%

도로명 주소
Text

Distinct777
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2024-04-21T11:40:52.867733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length36
Mean length22.338217
Min length5

Characters and Unicode

Total characters18295
Distinct characters333
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique757 ?
Unique (%)92.4%

Sample

1st row인천시 중구 신포로27번길 80(관동1가, 중구청 )
2nd row인천시 중구 신포로27번길 80(관동1가, 중구청)
3rd row인천시 중구 우현로371(신포동, 정안경,허형범치과)
4th row인천시 중구 우현로20번길 46
5th row인천시 중구 인중로 111(신생동, 삼성아파트)
ValueCountFrequency (%)
인천광역시 611
 
17.1%
미추홀구 135
 
3.8%
남동구 131
 
3.7%
서구 109
 
3.1%
부평구 93
 
2.6%
연수구 71
 
2.0%
계양구 70
 
2.0%
옹진군 49
 
1.4%
인천시 46
 
1.3%
중구 45
 
1.3%
Other values (1166) 2206
61.9%
2024-04-21T11:40:54.397921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2809
 
15.4%
로 776
 
4.2%
인 728
 
4.0%
구 692
 
3.8%
천 690
 
3.8%
시 677
 
3.7%
역 631
 
3.4%
광 620
 
3.4%
1 576
 
3.1%
동 536
 
2.9%
Other values (323) 9560
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11702
64.0%
Decimal Number 2838
 
15.5%
Space Separator 2809
 
15.4%
Open Punctuation 333
 
1.8%
Close Punctuation 330
 
1.8%
Other Punctuation 204
 
1.1%
Dash Punctuation 68
 
0.4%
Uppercase Letter 6
 
< 0.1%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 776
 
6.6%
인 728
 
6.2%
구 692
 
5.9%
천 690
 
5.9%
시 677
 
5.8%
역 631
 
5.4%
광 620
 
5.3%
동 536
 
4.6%
길 319
 
2.7%
번 267
 
2.3%
Other values (304) 5766
49.3%
Decimal Number
ValueCountFrequency (%)
1 576
20.3%
2 379
13.4%
3 345
12.2%
4 271
9.5%
8 240
8.5%
0 215
 
7.6%
5 215
 
7.6%
6 212
 
7.5%
7 203
 
7.2%
9 182
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 199
97.5%
. 5
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
L 3
50.0%
H 3
50.0%
Space Separator
ValueCountFrequency (%)
2809
100.0%
Open Punctuation
ValueCountFrequency (%)
( 333
100.0%
Close Punctuation
ValueCountFrequency (%)
) 330
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11702
64.0%
Common 6587
36.0%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 776
 
6.6%
인 728
 
6.2%
구 692
 
5.9%
천 690
 
5.9%
시 677
 
5.8%
역 631
 
5.4%
광 620
 
5.3%
동 536
 
4.6%
길 319
 
2.7%
번 267
 
2.3%
Other values (304) 5766
49.3%
Common
ValueCountFrequency (%)
2809
42.6%
1 576
 
8.7%
2 379
 
5.8%
3 345
 
5.2%
( 333
 
5.1%
) 330
 
5.0%
4 271
 
4.1%
8 240
 
3.6%
0 215
 
3.3%
5 215
 
3.3%
Other values (7) 874
 
13.3%
Latin
ValueCountFrequency (%)
L 3
50.0%
H 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11702
64.0%
ASCII 6593
36.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2809
42.6%
1 576
 
8.7%
2 379
 
5.7%
3 345
 
5.2%
( 333
 
5.1%
) 330
 
5.0%
4 271
 
4.1%
8 240
 
3.6%
0 215
 
3.3%
5 215
 
3.3%
Other values (9) 880
 
13.3%
Hangul
ValueCountFrequency (%)
로 776
 
6.6%
인 728
 
6.2%
구 692
 
5.9%
천 690
 
5.9%
시 677
 
5.8%
역 631
 
5.4%
광 620
 
5.3%
동 536
 
4.6%
길 319
 
2.7%
번 267
 
2.3%
Other values (304) 5766
49.3%

지번 주소
Text

Distinct757
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2024-04-21T11:40:55.357451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length28
Mean length16.598291
Min length5

Characters and Unicode

Total characters13594
Distinct characters170
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique726 ?
Unique (%)88.6%

Sample

1st row인천시 중구 관동 1가 9-1
2nd row인천시 중구 관동 1가 9-1
3rd row인천시 중구 신포동 16-1
4th row인천시 중구 신생동 25-1
5th row인천시 중구 신생동 38-5
ValueCountFrequency (%)
인천광역시 499
 
16.4%
미추홀구 135
 
4.4%
서구 108
 
3.6%
부평구 93
 
3.1%
계양구 72
 
2.4%
연수구 70
 
2.3%
남동구 63
 
2.1%
인천시 55
 
1.8%
옹진군 49
 
1.6%
중구 45
 
1.5%
Other values (922) 1848
60.8%
2024-04-21T11:40:57.000036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2226
 
16.4%
동 771
 
5.7%
1 637
 
4.7%
구 627
 
4.6%
인 606
 
4.5%
천 605
 
4.5%
시 555
 
4.1%
광 499
 
3.7%
역 499
 
3.7%
- 398
 
2.9%
Other values (160) 6171
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7856
57.8%
Decimal Number 3091
 
22.7%
Space Separator 2226
 
16.4%
Dash Punctuation 398
 
2.9%
Other Punctuation 14
 
0.1%
Close Punctuation 4
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 771
 
9.8%
구 627
 
8.0%
인 606
 
7.7%
천 605
 
7.7%
시 555
 
7.1%
광 499
 
6.4%
역 499
 
6.4%
지 196
 
2.5%
번 195
 
2.5%
서 135
 
1.7%
Other values (143) 3168
40.3%
Decimal Number
ValueCountFrequency (%)
1 637
20.6%
3 355
11.5%
2 355
11.5%
6 285
9.2%
4 285
9.2%
5 279
9.0%
7 238
 
7.7%
8 224
 
7.2%
0 221
 
7.1%
9 212
 
6.9%
Other Punctuation
ValueCountFrequency (%)
, 13
92.9%
/ 1
 
7.1%
Space Separator
ValueCountFrequency (%)
2226
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 398
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7856
57.8%
Common 5738
42.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 771
 
9.8%
구 627
 
8.0%
인 606
 
7.7%
천 605
 
7.7%
시 555
 
7.1%
광 499
 
6.4%
역 499
 
6.4%
지 196
 
2.5%
번 195
 
2.5%
서 135
 
1.7%
Other values (143) 3168
40.3%
Common
ValueCountFrequency (%)
2226
38.8%
1 637
 
11.1%
- 398
 
6.9%
3 355
 
6.2%
2 355
 
6.2%
6 285
 
5.0%
4 285
 
5.0%
5 279
 
4.9%
7 238
 
4.1%
8 224
 
3.9%
Other values (7) 456
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7856
57.8%
ASCII 5738
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2226
38.8%
1 637
 
11.1%
- 398
 
6.9%
3 355
 
6.2%
2 355
 
6.2%
6 285
 
5.0%
4 285
 
5.0%
5 279
 
4.9%
7 238
 
4.1%
8 224
 
3.9%
Other values (7) 456
 
7.9%
Hangul
ValueCountFrequency (%)
동 771
 
9.8%
구 627
 
8.0%
인 606
 
7.7%
천 605
 
7.7%
시 555
 
7.1%
광 499
 
6.4%
역 499
 
6.4%
지 196
 
2.5%
번 195
 
2.5%
서 135
 
1.7%
Other values (143) 3168
40.3%

대피시설_확보면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct693
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6716.9182
Minimum89
Maximum160879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T11:40:57.403471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile181
Q1859
median3074
Q36549.5
95-th percentile26732.7
Maximum160879
Range160790
Interquartile range (IQR)5690.5

Descriptive statistics

Standard deviation12751.419
Coefficient of variation (CV)1.8984032
Kurtosis42.183309
Mean6716.9182
Median Absolute Deviation (MAD)2413
Skewness5.3586657
Sum5501156
Variance1.6259869 × 108
MonotonicityNot monotonic
2024-04-21T11:40:57.846973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181 12
 
1.5%
166 7
 
0.9%
660 6
 
0.7%
495 5
 
0.6%
205 4
 
0.5%
248 3
 
0.4%
860 3
 
0.4%
825 3
 
0.4%
331 3
 
0.4%
189 3
 
0.4%
Other values (683) 770
94.0%
ValueCountFrequency (%)
89 1
0.1%
100 1
0.1%
101 2
0.2%
104 1
0.1%
112 1
0.1%
130 2
0.2%
133 1
0.1%
134 1
0.1%
142 1
0.1%
155 1
0.1%
ValueCountFrequency (%)
160879 1
0.1%
120369 1
0.1%
96695 1
0.1%
85693 1
0.1%
69976 1
0.1%
68722 1
0.1%
67128 1
0.1%
65492 1
0.1%
61849 1
0.1%
60416 1
0.1%

대피시설_대피 가능인원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct697
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8125.072
Minimum62
Maximum195004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-04-21T11:40:58.277918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile126
Q11040.5
median3726
Q37938.5
95-th percentile32403.2
Maximum195004
Range194942
Interquartile range (IQR)6898

Descriptive statistics

Standard deviation15464.54
Coefficient of variation (CV)1.9033111
Kurtosis42.109745
Mean8125.072
Median Absolute Deviation (MAD)2926
Skewness5.3524107
Sum6654434
Variance2.39152 × 108
MonotonicityNot monotonic
2024-04-21T11:40:58.729182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 12
 
1.5%
116 7
 
0.9%
800 6
 
0.7%
600 5
 
0.6%
143 4
 
0.5%
465 4
 
0.5%
118 4
 
0.5%
120 4
 
0.5%
162 4
 
0.5%
173 3
 
0.4%
Other values (687) 766
93.5%
ValueCountFrequency (%)
62 1
0.1%
69 1
0.1%
70 2
0.2%
72 1
0.1%
90 2
0.2%
93 2
0.2%
108 1
0.1%
109 1
0.1%
110 1
0.1%
112 1
0.1%
ValueCountFrequency (%)
195004 1
0.1%
145902 1
0.1%
117206 1
0.1%
103870 1
0.1%
84819 1
0.1%
83299 1
0.1%
81367 1
0.1%
79384 1
0.1%
74968 1
0.1%
73231 1
0.1%

Interactions

2024-04-21T11:40:43.221456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:41.521053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:42.358749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:43.505646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:41.781950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:42.647266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:43.794812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:42.070608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:40:42.931716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:40:59.007345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구시설종류대피시설_확보면적(제곱미터)대피시설_대피 가능인원(명)
연번1.0000.9840.9410.0830.085
시군구0.9841.0000.9890.1280.128
시설종류0.9410.9891.0000.0810.083
대피시설_확보면적(제곱미터)0.0830.1280.0811.0001.000
대피시설_대피 가능인원(명)0.0850.1280.0831.0001.000
2024-04-21T11:40:59.264945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류시군구
시설종류1.0000.902
시군구0.9021.000
2024-04-21T11:40:59.507293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번대피시설_확보면적(제곱미터)대피시설_대피 가능인원(명)시군구시설종류
연번1.000-0.241-0.2480.7760.790
대피시설_확보면적(제곱미터)-0.2411.0000.9930.0580.081
대피시설_대피 가능인원(명)-0.2480.9931.0000.0580.082
시군구0.7760.0580.0581.0000.902
시설종류0.7900.0810.0820.9021.000

Missing values

2024-04-21T11:40:44.181273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:40:44.659627image/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

연번시도시군구읍면동시설종류민방위대피시설명칭도로명 주소지번 주소대피시설_확보면적(제곱미터)대피시설_대피 가능인원(명)
01인천중구신포동공공용중구청 대피호인천시 중구 신포로27번길 80(관동1가, 중구청 )인천시 중구 관동 1가 9-1571692
12인천중구신포동공공용중구청 지하주차장인천시 중구 신포로27번길 80(관동1가, 중구청)인천시 중구 관동 1가 9-112531518
23인천중구신포동공공용신포지하상가인천시 중구 우현로371(신포동, 정안경,허형범치과)인천시 중구 신포동 16-149455993
34인천중구신포동공공용민방위교육장인천시 중구 우현로20번길 46인천시 중구 신생동 25-1733888
45인천중구신포동공공용신생삼성아파트(지하주차장)인천시 중구 인중로 111(신생동, 삼성아파트)인천시 중구 신생동 38-510691295
56인천중구신포동공공용한중문화관(지하주차장)인천시 중구 제물량로238(항동1가, 한중문화관 지하주차장)인천시 중구 항동 1가 1-213241604
67인천중구신포동공공용하버파크호텔(지하주차장)인천시 중구 제물량로 217(항동3가, 하버파크호텔)인천시 중구 항동 3가 532693962
78인천중구연안동공공용연안부두해양광장인천시 중구 연안부두로 36인천시 중구 항동7가 58-162897623
89인천중구신흥동공공용경남아너스빌아파트(지하주차장)인천시 중구 서해대로439(신흥동1가, 경남아너스빌아파트)인천시 중구 신흥동 1가34-149395986
910인천중구신흥동공공용대림아파트(지하주차장)인천시 중구 인중로 114(신흥동1가, 인천신흥대림아파트)인천시 중구 신흥동 1가34-244575402
연번시도시군구읍면동시설종류민방위대피시설명칭도로명 주소지번 주소대피시설_확보면적(제곱미터)대피시설_대피 가능인원(명)
809810인천옹진군연평정부지원연평2호옹진군 연평면 연평로 829-1옹진군 연평면 연평리 325-165275192
810811인천옹진군연평정부지원연평3호옹진군 연평면 연평중앙로6번길 6옹진군 연평면 연평리 284169118
811812인천옹진군연평정부지원연평4호옹진군 연평면 연평로 196옹진군 연평면 연평리 250169118
812813인천옹진군연평정부지원연평5호옹진군 연평면 연평로 314-7옹진군 연평면 연평리 산3-1248173
813814인천옹진군연평정부지원연평6호옹진군 연평면 소연평로19번길 28옹진군 연평면 연평리 1012232162
814815인천옹진군연평정부지원연평7호옹진군 연평면 연평중앙로24번길 3옹진군 연평면 연평리 411-3331231
815816인천옹진군연평정부지원연평8호옹진군 연평면 연평리 산3-51옹진군 연평면 연평리 산3-51370258
816817인천옹진군연평공공용연평 바다역 지하대피소옹진군 연평면 연평로 8옹진군 연평면 연평리 산16-1197238
817818인천옹진군연평공공용연평 안보교육장 지하대피소옹진군 연평면 연평중앙로12번길 25옹진군 연평면 연평리 174215260
818819인천옹진군백령공공용융기포 신항 대합실 지하대피소옹진군 백령면 백령로 68-85옹진군 백령면 진촌리 2618201243