Overview

Dataset statistics

Number of variables11
Number of observations1551
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory141.0 KiB
Average record size in memory93.1 B

Variable types

Numeric4
Categorical4
Text3

Dataset

Description피난처 ID,시도명,시군구명,시설명,상세주소,시설면적,경도,위도,구분,구분명,관리부서
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21060/S/1/datasetView.do

Alerts

시도명 has constant value ""Constant
구분 has constant value ""Constant
구분명 has constant value ""Constant
피난처 ID is highly overall correlated with 위도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 시군구명High correlation
위도 is highly overall correlated with 피난처 ID and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 피난처 ID and 2 other fieldsHigh correlation
피난처 ID has unique valuesUnique

Reproduction

Analysis started2024-05-17 21:57:34.332144
Analysis finished2024-05-17 21:57:42.294142
Duration7.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

피난처 ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1551
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean776
Minimum1
Maximum1551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-05-18T06:57:42.579849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile78.5
Q1388.5
median776
Q31163.5
95-th percentile1473.5
Maximum1551
Range1550
Interquartile range (IQR)775

Descriptive statistics

Standard deviation447.87945
Coefficient of variation (CV)0.57716424
Kurtosis-1.2
Mean776
Median Absolute Deviation (MAD)388
Skewness0
Sum1203576
Variance200596
MonotonicityStrictly increasing
2024-05-18T06:57:43.028492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1020 1
 
0.1%
1042 1
 
0.1%
1041 1
 
0.1%
1040 1
 
0.1%
1039 1
 
0.1%
1038 1
 
0.1%
1037 1
 
0.1%
1036 1
 
0.1%
1035 1
 
0.1%
Other values (1541) 1541
99.4%
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 (%)
1551 1
0.1%
1550 1
0.1%
1549 1
0.1%
1548 1
0.1%
1547 1
0.1%
1546 1
0.1%
1545 1
0.1%
1544 1
0.1%
1543 1
0.1%
1542 1
0.1%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
서울특별시
1551 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 1551
100.0%

Length

2024-05-18T06:57:43.474921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T06:57:43.883579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 1551
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
강남구
120 
강서구
116 
송파구
 
90
양천구
 
84
관악구
 
80
Other values (20)
1061 

Length

Max length4
Median length3
Mean length3.0960671
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동작구
2nd row동작구
3rd row도봉구
4th row도봉구
5th row도봉구

Common Values

ValueCountFrequency (%)
강남구 120
 
7.7%
강서구 116
 
7.5%
송파구 90
 
5.8%
양천구 84
 
5.4%
관악구 80
 
5.2%
노원구 79
 
5.1%
영등포구 71
 
4.6%
서초구 67
 
4.3%
마포구 65
 
4.2%
성북구 64
 
4.1%
Other values (15) 715
46.1%

Length

2024-05-18T06:57:44.369658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 120
 
7.7%
강서구 116
 
7.5%
송파구 90
 
5.8%
양천구 84
 
5.4%
관악구 80
 
5.2%
노원구 79
 
5.1%
영등포구 71
 
4.6%
서초구 67
 
4.3%
마포구 65
 
4.2%
성북구 64
 
4.1%
Other values (15) 715
46.1%

시설명
Text

Distinct1532
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
2024-05-18T06:57:45.150081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length9.0967118
Min length4

Characters and Unicode

Total characters14109
Distinct characters396
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

Unique1515 ?
Unique (%)97.7%

Sample

1st row동작고등학교 운동장
2nd row동작초등학교 운동장
3rd row창림초등학교 운동장
4th row창원초등학교 운동장
5th row창일중학교 운동장
ValueCountFrequency (%)
운동장 861
34.4%
공원 10
 
0.4%
주차장 5
 
0.2%
어린이 5
 
0.2%
어린이공원 5
 
0.2%
사범대학 3
 
0.1%
lh보금자리 3
 
0.1%
무지개어린이공원 3
 
0.1%
새싹어린이공원 3
 
0.1%
유수지 2
 
0.1%
Other values (1581) 1605
64.1%
2024-05-18T06:57:46.332858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1132
 
8.0%
장 1076
 
7.6%
학 1058
 
7.5%
교 1038
 
7.4%
운 1035
 
7.3%
957
 
6.8%
등 742
 
5.3%
원 570
 
4.0%
공 528
 
3.7%
초 518
 
3.7%
Other values (386) 5455
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13026
92.3%
Space Separator 957
 
6.8%
Open Punctuation 41
 
0.3%
Close Punctuation 41
 
0.3%
Decimal Number 29
 
0.2%
Uppercase Letter 9
 
0.1%
Dash Punctuation 4
 
< 0.1%
Other Punctuation 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1132
 
8.7%
장 1076
 
8.3%
학 1058
 
8.1%
교 1038
 
8.0%
운 1035
 
7.9%
등 742
 
5.7%
원 570
 
4.4%
공 528
 
4.1%
초 518
 
4.0%
중 334
 
2.6%
Other values (366) 4995
38.3%
Decimal Number
ValueCountFrequency (%)
1 7
24.1%
2 6
20.7%
3 6
20.7%
5 3
10.3%
7 2
 
6.9%
4 2
 
6.9%
6 1
 
3.4%
8 1
 
3.4%
9 1
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
H 3
33.3%
L 3
33.3%
C 1
 
11.1%
M 1
 
11.1%
D 1
 
11.1%
Space Separator
ValueCountFrequency (%)
957
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13026
92.3%
Common 1074
 
7.6%
Latin 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1132
 
8.7%
장 1076
 
8.3%
학 1058
 
8.1%
교 1038
 
8.0%
운 1035
 
7.9%
등 742
 
5.7%
원 570
 
4.4%
공 528
 
4.1%
초 518
 
4.0%
중 334
 
2.6%
Other values (366) 4995
38.3%
Common
ValueCountFrequency (%)
957
89.1%
( 41
 
3.8%
) 41
 
3.8%
1 7
 
0.7%
2 6
 
0.6%
3 6
 
0.6%
- 4
 
0.4%
5 3
 
0.3%
7 2
 
0.2%
4 2
 
0.2%
Other values (5) 5
 
0.5%
Latin
ValueCountFrequency (%)
H 3
33.3%
L 3
33.3%
C 1
 
11.1%
M 1
 
11.1%
D 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13026
92.3%
ASCII 1083
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1132
 
8.7%
장 1076
 
8.3%
학 1058
 
8.1%
교 1038
 
8.0%
운 1035
 
7.9%
등 742
 
5.7%
원 570
 
4.4%
공 528
 
4.1%
초 518
 
4.0%
중 334
 
2.6%
Other values (366) 4995
38.3%
ASCII
ValueCountFrequency (%)
957
88.4%
( 41
 
3.8%
) 41
 
3.8%
1 7
 
0.6%
2 6
 
0.6%
3 6
 
0.6%
- 4
 
0.4%
5 3
 
0.3%
H 3
 
0.3%
L 3
 
0.3%
Other values (10) 12
 
1.1%

상세주소
Text

Distinct1521
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
2024-05-18T06:57:47.277511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length33
Mean length21.89942
Min length15

Characters and Unicode

Total characters33966
Distinct characters293
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

Unique1492 ?
Unique (%)96.2%

Sample

1st row서울특별시 동작구 솔밭로 51(사당동)
2nd row서울특별시 동작구 동작대로29길 214(사당동)
3rd row서울특별시 도봉구 덕릉로63길 46(창동)
4th row서울특별시 도봉구 해등로 103(창동)
5th row서울특별시 도봉구 노해로66길 60(창동)
ValueCountFrequency (%)
서울특별시 1551
 
24.7%
강남구 120
 
1.9%
강서구 116
 
1.8%
송파구 90
 
1.4%
양천구 84
 
1.3%
관악구 80
 
1.3%
노원구 79
 
1.3%
영등포구 71
 
1.1%
서초구 67
 
1.1%
마포구 65
 
1.0%
Other values (2364) 3968
63.1%
2024-05-18T06:57:48.800933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4740
 
14.0%
서 1849
 
5.4%
동 1831
 
5.4%
구 1649
 
4.9%
시 1588
 
4.7%
울 1554
 
4.6%
특 1551
 
4.6%
별 1551
 
4.6%
1 1078
 
3.2%
로 936
 
2.8%
Other values (283) 15639
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21004
61.8%
Decimal Number 5790
 
17.0%
Space Separator 4740
 
14.0%
Open Punctuation 874
 
2.6%
Close Punctuation 874
 
2.6%
Dash Punctuation 674
 
2.0%
Other Punctuation 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 1849
 
8.8%
동 1831
 
8.7%
구 1649
 
7.9%
시 1588
 
7.6%
울 1554
 
7.4%
특 1551
 
7.4%
별 1551
 
7.4%
로 936
 
4.5%
길 551
 
2.6%
강 371
 
1.8%
Other values (268) 7573
36.1%
Decimal Number
ValueCountFrequency (%)
1 1078
18.6%
2 833
14.4%
3 695
12.0%
4 592
10.2%
0 511
8.8%
6 494
8.5%
5 491
8.5%
7 395
 
6.8%
9 354
 
6.1%
8 347
 
6.0%
Space Separator
ValueCountFrequency (%)
4740
100.0%
Open Punctuation
ValueCountFrequency (%)
( 874
100.0%
Close Punctuation
ValueCountFrequency (%)
) 874
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 674
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21004
61.8%
Common 12962
38.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 1849
 
8.8%
동 1831
 
8.7%
구 1649
 
7.9%
시 1588
 
7.6%
울 1554
 
7.4%
특 1551
 
7.4%
별 1551
 
7.4%
로 936
 
4.5%
길 551
 
2.6%
강 371
 
1.8%
Other values (268) 7573
36.1%
Common
ValueCountFrequency (%)
4740
36.6%
1 1078
 
8.3%
( 874
 
6.7%
) 874
 
6.7%
2 833
 
6.4%
3 695
 
5.4%
- 674
 
5.2%
4 592
 
4.6%
0 511
 
3.9%
6 494
 
3.8%
Other values (5) 1597
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21004
61.8%
ASCII 12962
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4740
36.6%
1 1078
 
8.3%
( 874
 
6.7%
) 874
 
6.7%
2 833
 
6.4%
3 695
 
5.4%
- 674
 
5.2%
4 592
 
4.6%
0 511
 
3.9%
6 494
 
3.8%
Other values (5) 1597
 
12.3%
Hangul
ValueCountFrequency (%)
서 1849
 
8.8%
동 1831
 
8.7%
구 1649
 
7.9%
시 1588
 
7.6%
울 1554
 
7.4%
특 1551
 
7.4%
별 1551
 
7.4%
로 936
 
4.5%
길 551
 
2.6%
강 371
 
1.8%
Other values (268) 7573
36.1%

시설면적
Real number (ℝ)

Distinct1347
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5654.4426
Minimum165
Maximum378440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-05-18T06:57:49.476242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum165
5-th percentile635.5
Q11643
median3049
Q35086.5
95-th percentile14167
Maximum378440
Range378275
Interquartile range (IQR)3443.5

Descriptive statistics

Standard deviation16800.026
Coefficient of variation (CV)2.9711197
Kurtosis250.63471
Mean5654.4426
Median Absolute Deviation (MAD)1599
Skewness14.289043
Sum8770040.5
Variance2.8224086 × 108
MonotonicityNot monotonic
2024-05-18T06:57:50.017068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000.0 6
 
0.4%
400.0 5
 
0.3%
570.0 4
 
0.3%
2230.0 4
 
0.3%
3000.0 4
 
0.3%
1200.0 4
 
0.3%
4500.0 4
 
0.3%
2850.0 4
 
0.3%
500.0 3
 
0.2%
2360.0 3
 
0.2%
Other values (1337) 1510
97.4%
ValueCountFrequency (%)
165.0 1
0.1%
198.0 1
0.1%
230.0 1
0.1%
241.0 1
0.1%
245.0 1
0.1%
248.0 1
0.1%
250.0 1
0.1%
298.0 2
0.1%
300.0 2
0.1%
309.0 1
0.1%
ValueCountFrequency (%)
378440.0 1
0.1%
284421.0 1
0.1%
219167.0 1
0.1%
215706.0 1
0.1%
195308.0 1
0.1%
128954.0 1
0.1%
109653.0 1
0.1%
103713.0 1
0.1%
80595.0 1
0.1%
68623.0 1
0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct1212
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98711
Minimum126.7988
Maximum127.178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-05-18T06:57:50.480510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.7988
5-th percentile126.8386
Q1126.9117
median127.002
Q3127.0585
95-th percentile127.12935
Maximum127.178
Range0.3792
Interquartile range (IQR)0.1468

Descriptive statistics

Standard deviation0.090547193
Coefficient of variation (CV)0.00071304239
Kurtosis-1.0308678
Mean126.98711
Median Absolute Deviation (MAD)0.0736
Skewness-0.11466952
Sum196957.01
Variance0.0081987941
MonotonicityNot monotonic
2024-05-18T06:57:51.224421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0508 5
 
0.3%
126.9545 4
 
0.3%
127.0323 4
 
0.3%
126.842 4
 
0.3%
127.0151 4
 
0.3%
127.0572 3
 
0.2%
127.0545 3
 
0.2%
127.0585 3
 
0.2%
126.9314 3
 
0.2%
126.9437 3
 
0.2%
Other values (1202) 1515
97.7%
ValueCountFrequency (%)
126.7988 1
0.1%
126.8013 1
0.1%
126.8044 1
0.1%
126.8067 2
0.1%
126.8068 1
0.1%
126.8084 1
0.1%
126.8088 1
0.1%
126.8093 1
0.1%
126.8102 1
0.1%
126.8107 1
0.1%
ValueCountFrequency (%)
127.178 1
0.1%
127.1779 1
0.1%
127.1773 1
0.1%
127.1751 1
0.1%
127.1734 1
0.1%
127.1723 1
0.1%
127.1701 1
0.1%
127.1665 1
0.1%
127.1655 1
0.1%
127.1649 1
0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct1090
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.54877
Minimum37.4348
Maximum37.6889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2024-05-18T06:57:52.146899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.4348
5-th percentile37.4716
Q137.5036
median37.5447
Q337.58435
95-th percentile37.65
Maximum37.6889
Range0.2541
Interquartile range (IQR)0.08075

Descriptive statistics

Standard deviation0.05455764
Coefficient of variation (CV)0.0014529807
Kurtosis-0.6176206
Mean37.54877
Median Absolute Deviation (MAD)0.0403
Skewness0.38891844
Sum58238.142
Variance0.0029765361
MonotonicityNot monotonic
2024-05-18T06:57:52.958925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4927 5
 
0.3%
37.565 5
 
0.3%
37.5204 5
 
0.3%
37.5576 4
 
0.3%
37.5603 4
 
0.3%
37.5459 4
 
0.3%
37.4983 4
 
0.3%
37.5569 4
 
0.3%
37.6116 4
 
0.3%
37.5476 4
 
0.3%
Other values (1080) 1508
97.2%
ValueCountFrequency (%)
37.4348 1
0.1%
37.4396 1
0.1%
37.4415 1
0.1%
37.4418 1
0.1%
37.4432 1
0.1%
37.4449 1
0.1%
37.447 1
0.1%
37.4472 1
0.1%
37.4474 1
0.1%
37.4481 1
0.1%
ValueCountFrequency (%)
37.6889 1
0.1%
37.6856 1
0.1%
37.6849 1
0.1%
37.6838 1
0.1%
37.6837 1
0.1%
37.6826 1
0.1%
37.6811 1
0.1%
37.6805 1
0.1%
37.6803 1
0.1%
37.6743 1
0.1%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
1
1551 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1551
100.0%

Length

2024-05-18T06:57:53.639016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T06:57:54.033643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1551
100.0%

구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
지진실내구호소
1551 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지진실내구호소 1551
100.0%

Length

2024-05-18T06:57:54.394293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T06:57:54.708969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지진실내구호소 1551
100.0%

관리부서
Text

Distinct645
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
2024-05-18T06:57:55.504328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.535783
Min length8

Characters and Unicode

Total characters17892
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

Unique533 ?
Unique (%)34.4%

Sample

1st row070-4629-6407
2nd row02-537-1773
3rd row02-2091-5718
4th row02-2091-5779
5th row02-2091-5678
ValueCountFrequency (%)
02-2147-3101 89
 
5.7%
02-2670-3064 70
 
4.5%
02-2155-7112 67
 
4.3%
02-2600-6439 63
 
4.1%
02-2600-4112 53
 
3.4%
02-2094-0022 52
 
3.4%
02-901-5903 51
 
3.3%
02-860-3347 47
 
3.0%
02-2148-3007 45
 
2.9%
02-2620-3586 33
 
2.1%
Other values (635) 981
63.2%
2024-05-18T06:57:56.437058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3132
17.5%
- 3037
17.0%
0 3030
16.9%
1 1409
7.9%
3 1329
7.4%
4 1228
 
6.9%
5 1109
 
6.2%
7 1052
 
5.9%
6 1031
 
5.8%
9 874
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14855
83.0%
Dash Punctuation 3037
 
17.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3132
21.1%
0 3030
20.4%
1 1409
9.5%
3 1329
8.9%
4 1228
 
8.3%
5 1109
 
7.5%
7 1052
 
7.1%
6 1031
 
6.9%
9 874
 
5.9%
8 661
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 3037
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3132
17.5%
- 3037
17.0%
0 3030
16.9%
1 1409
7.9%
3 1329
7.4%
4 1228
 
6.9%
5 1109
 
6.2%
7 1052
 
5.9%
6 1031
 
5.8%
9 874
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3132
17.5%
- 3037
17.0%
0 3030
16.9%
1 1409
7.9%
3 1329
7.4%
4 1228
 
6.9%
5 1109
 
6.2%
7 1052
 
5.9%
6 1031
 
5.8%
9 874
 
4.9%

Interactions

2024-05-18T06:57:39.809406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:35.715690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:36.917697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:38.462886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:40.111657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:36.002244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:37.218583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:38.839920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:40.441717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:36.322943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:37.689119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:39.156114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:40.842045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:36.622795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:38.087042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T06:57:39.483080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T06:57:56.722261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
피난처 ID시군구명시설면적경도위도
피난처 ID1.0000.9730.0000.8730.817
시군구명0.9731.0000.0630.9380.922
시설면적0.0000.0631.0000.0000.000
경도0.8730.9380.0001.0000.619
위도0.8170.9220.0000.6191.000
2024-05-18T06:57:56.991870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
피난처 ID시설면적경도위도시군구명
피난처 ID1.0000.156-0.062-0.5560.815
시설면적0.1561.0000.157-0.0980.026
경도-0.0620.1571.0000.1660.687
위도-0.556-0.0980.1661.0000.645
시군구명0.8150.0260.6870.6451.000

Missing values

2024-05-18T06:57:41.373565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T06:57:42.036953image/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

피난처 ID시도명시군구명시설명상세주소시설면적경도위도구분구분명관리부서
01서울특별시동작구동작고등학교 운동장서울특별시 동작구 솔밭로 51(사당동)3246.0126.965537.4821지진실내구호소070-4629-6407
12서울특별시동작구동작초등학교 운동장서울특별시 동작구 동작대로29길 214(사당동)2763.0126.976837.4941지진실내구호소02-537-1773
23서울특별시도봉구창림초등학교 운동장서울특별시 도봉구 덕릉로63길 46(창동)2584.0127.041737.64311지진실내구호소02-2091-5718
34서울특별시도봉구창원초등학교 운동장서울특별시 도봉구 해등로 103(창동)2704.0127.040737.65211지진실내구호소02-2091-5779
45서울특별시도봉구창일중학교 운동장서울특별시 도봉구 노해로66길 60(창동)4800.0127.047137.64841지진실내구호소02-2091-5678
56서울특별시도봉구갈대밭어린이공원서울특별시 도봉구 도봉동 625-123433.0127.045737.67431지진실내구호소02-2091-5830
67서울특별시도봉구누원어린이공원서울특별시 도봉구 도봉동 653400.0127.0537.68031지진실내구호소02-2091-5830
78서울특별시도봉구무수어린이공원서울특별시 도봉구 도봉동 585-2350.0127.042637.68051지진실내구호소02-2091-5806
89서울특별시도봉구무지개어린이공원서울특별시 도봉구 도봉동 570-13385.0127.04537.68561지진실내구호소02-2091-5806
910서울특별시도봉구새동네어린이공원서울특별시 도봉구 도봉동 289-5670.0127.043137.68891지진실내구호소02-2091-5806
피난처 ID시도명시군구명시설명상세주소시설면적경도위도구분구분명관리부서
15411542서울특별시구로구신미림초등학교운동장서울특별시 구로구 신도림로 18(신도림동)2104.0126.879537.50641지진실내구호소02-860-3347
15421543서울특별시구로구구로근린공원서울특별시 구로구 구로동 102-01853.0126.889637.49671지진실내구호소02-860-3347
15431544서울특별시구로구삼각(어린이)공원서울특별시 구로구 구로동 471205.0126.89237.50141지진실내구호소02-860-3347
15441545서울특별시구로구화원어린이공원서울특별시 구로구 구로동 478-13759.0126.880237.49771지진실내구호소02-860-3347
15451546서울특별시구로구구로구시설관리공단서울특별시 구로구 구로동로26길 54(구로동)1574.0126.886937.48971지진실내구호소02-860-3347
15461547서울특별시구로구구로고등학교운동장서울특별시 구로구 구로동 105-14854.0126.8937.49831지진실내구호소02-860-3347
15471548서울특별시구로구구로남초등학교운동장서울특별시 구로구 구로동 253-03934.0126.890937.48461지진실내구호소02-860-3347
15481549서울특별시구로구구로중학교운동장서울특별시 구로구 구로동 905715.0126.890737.49431지진실내구호소02-860-3347
15491550서울특별시구로구구로초등학교운동장서울특별시 구로구 구로동 4433657.0126.88637.49681지진실내구호소02-860-3347
15501551서울특별시구로구구일고등학교운동장서울특별시 구로구 구일로 90-51 (구로동)4618.0126.87437.49381지진실내구호소02-860-3347