Overview

Dataset statistics

Number of variables6
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory56.3 B

Variable types

Text3
Numeric2
Categorical1

Dataset

Description서울특별시 성동구 임시 주거시설 현황 정보입니다. 시설이 위치한 행정동명, 시설명, 주소, 면적, 수용인원, 비고 등의 정보를 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15084643/fileData.do

Alerts

면적 is highly overall correlated with 수용인원High correlation
수용인원 is highly overall correlated with 면적High correlation
시설명 has unique valuesUnique
면적 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:08:56.321875
Analysis finished2023-12-12 16:08:57.026603
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T01:08:57.122535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.6666667
Min length3

Characters and Unicode

Total characters98
Distinct characters27
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

Unique9 ?
Unique (%)42.9%

Sample

1st row왕십리2동
2nd row왕십리도선동
3rd row마장동
4th row사근동
5th row사근동
ValueCountFrequency (%)
사근동 2
 
9.5%
행당2동 2
 
9.5%
금호2-3가동 2
 
9.5%
금호4가동 2
 
9.5%
성수1가2동 2
 
9.5%
성수2가1동 2
 
9.5%
왕십리2동 1
 
4.8%
왕십리도선동 1
 
4.8%
마장동 1
 
4.8%
행당동 1
 
4.8%
Other values (5) 5
23.8%
2023-12-13T01:08:57.426534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
21.4%
2 9
 
9.2%
가 9
 
9.2%
1 7
 
7.1%
수 6
 
6.1%
성 5
 
5.1%
행 4
 
4.1%
당 4
 
4.1%
금 4
 
4.1%
호 4
 
4.1%
Other values (17) 25
25.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76
77.6%
Decimal Number 20
 
20.4%
Dash Punctuation 2
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
27.6%
가 9
11.8%
수 6
 
7.9%
성 5
 
6.6%
행 4
 
5.3%
당 4
 
5.3%
금 4
 
5.3%
호 4
 
5.3%
리 2
 
2.6%
십 2
 
2.6%
Other values (12) 15
19.7%
Decimal Number
ValueCountFrequency (%)
2 9
45.0%
1 7
35.0%
4 2
 
10.0%
3 2
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76
77.6%
Common 22
 
22.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
27.6%
가 9
11.8%
수 6
 
7.9%
성 5
 
6.6%
행 4
 
5.3%
당 4
 
5.3%
금 4
 
5.3%
호 4
 
5.3%
리 2
 
2.6%
십 2
 
2.6%
Other values (12) 15
19.7%
Common
ValueCountFrequency (%)
2 9
40.9%
1 7
31.8%
4 2
 
9.1%
3 2
 
9.1%
- 2
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76
77.6%
ASCII 22
 
22.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
27.6%
가 9
11.8%
수 6
 
7.9%
성 5
 
6.6%
행 4
 
5.3%
당 4
 
5.3%
금 4
 
5.3%
호 4
 
5.3%
리 2
 
2.6%
십 2
 
2.6%
Other values (12) 15
19.7%
ASCII
ValueCountFrequency (%)
2 9
40.9%
1 7
31.8%
4 2
 
9.1%
3 2
 
9.1%
- 2
 
9.1%

시설명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T01:08:57.621912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length10.095238
Min length6

Characters and Unicode

Total characters212
Distinct characters64
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

Unique21 ?
Unique (%)100.0%

Sample

1st row무학초 다목적강당
2nd row숭신초등학교 교사동
3rd row동마중학교 다목적실-강당
4th row4차산업혁명 체험센터
5th row사근초등학교 사근동11길 16
ValueCountFrequency (%)
본관동 5
 
11.9%
체육관 4
 
9.5%
교사동 2
 
4.8%
무학초 1
 
2.4%
경일중학교 1
 
2.4%
금옥초등학교 1
 
2.4%
옥수초등학교 1
 
2.4%
옥정초등학교 1
 
2.4%
교실동 1
 
2.4%
성수고등학교 1
 
2.4%
Other values (24) 24
57.1%
2023-12-13T01:08:57.902467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
10.4%
교 17
 
8.0%
학 16
 
7.5%
동 13
 
6.1%
관 11
 
5.2%
등 10
 
4.7%
초 9
 
4.2%
체 6
 
2.8%
본 6
 
2.8%
성 5
 
2.4%
Other values (54) 97
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 184
86.8%
Space Separator 22
 
10.4%
Decimal Number 5
 
2.4%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 17
 
9.2%
학 16
 
8.7%
동 13
 
7.1%
관 11
 
6.0%
등 10
 
5.4%
초 9
 
4.9%
체 6
 
3.3%
본 6
 
3.3%
성 5
 
2.7%
당 5
 
2.7%
Other values (49) 86
46.7%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
6 1
 
20.0%
4 1
 
20.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 184
86.8%
Common 28
 
13.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 17
 
9.2%
학 16
 
8.7%
동 13
 
7.1%
관 11
 
6.0%
등 10
 
5.4%
초 9
 
4.9%
체 6
 
3.3%
본 6
 
3.3%
성 5
 
2.7%
당 5
 
2.7%
Other values (49) 86
46.7%
Common
ValueCountFrequency (%)
22
78.6%
1 3
 
10.7%
6 1
 
3.6%
- 1
 
3.6%
4 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 184
86.8%
ASCII 28
 
13.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
78.6%
1 3
 
10.7%
6 1
 
3.6%
- 1
 
3.6%
4 1
 
3.6%
Hangul
ValueCountFrequency (%)
교 17
 
9.2%
학 16
 
8.7%
동 13
 
7.1%
관 11
 
6.0%
등 10
 
5.4%
초 9
 
4.9%
체 6
 
3.3%
본 6
 
3.3%
성 5
 
2.7%
당 5
 
2.7%
Other values (49) 86
46.7%

주소
Text

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T01:08:58.086226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length17.904762
Min length16

Characters and Unicode

Total characters376
Distinct characters55
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

Unique19 ?
Unique (%)90.5%

Sample

1st row서울특별시 성동구 무학봉15길 21
2nd row서울특별시 성동구 마장로 161
3rd row서울특별시 성동구 마조로11길 18
4th row서울특별시 성동구 살곶이길 327
5th row서울특별시 성동구 사근동11길 16
ValueCountFrequency (%)
서울특별시 21
26.2%
성동구 21
26.2%
서울숲길18 2
 
2.5%
독서당로 2
 
2.5%
자동차시장길 1
 
1.2%
69 1
 
1.2%
251 1
 
1.2%
금호로1길 1
 
1.2%
25 1
 
1.2%
한림말길43 1
 
1.2%
Other values (28) 28
35.0%
2023-12-13T01:08:58.395549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61
16.2%
서 25
 
6.6%
동 25
 
6.6%
성 23
 
6.1%
울 23
 
6.1%
시 22
 
5.9%
특 21
 
5.6%
별 21
 
5.6%
구 21
 
5.6%
1 17
 
4.5%
Other values (45) 117
31.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 253
67.3%
Space Separator 61
 
16.2%
Decimal Number 61
 
16.2%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 25
9.9%
동 25
9.9%
성 23
9.1%
울 23
9.1%
시 22
8.7%
특 21
8.3%
별 21
8.3%
구 21
8.3%
길 15
 
5.9%
로 10
 
4.0%
Other values (33) 47
18.6%
Decimal Number
ValueCountFrequency (%)
1 17
27.9%
2 9
14.8%
3 7
11.5%
6 6
 
9.8%
4 5
 
8.2%
9 4
 
6.6%
5 4
 
6.6%
8 4
 
6.6%
7 3
 
4.9%
0 2
 
3.3%
Space Separator
ValueCountFrequency (%)
61
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 253
67.3%
Common 123
32.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 25
9.9%
동 25
9.9%
성 23
9.1%
울 23
9.1%
시 22
8.7%
특 21
8.3%
별 21
8.3%
구 21
8.3%
길 15
 
5.9%
로 10
 
4.0%
Other values (33) 47
18.6%
Common
ValueCountFrequency (%)
61
49.6%
1 17
 
13.8%
2 9
 
7.3%
3 7
 
5.7%
6 6
 
4.9%
4 5
 
4.1%
9 4
 
3.3%
5 4
 
3.3%
8 4
 
3.3%
7 3
 
2.4%
Other values (2) 3
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 253
67.3%
ASCII 123
32.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61
49.6%
1 17
 
13.8%
2 9
 
7.3%
3 7
 
5.7%
6 6
 
4.9%
4 5
 
4.1%
9 4
 
3.3%
5 4
 
3.3%
8 4
 
3.3%
7 3
 
2.4%
Other values (2) 3
 
2.4%
Hangul
ValueCountFrequency (%)
서 25
9.9%
동 25
9.9%
성 23
9.1%
울 23
9.1%
시 22
8.7%
특 21
8.3%
별 21
8.3%
구 21
8.3%
길 15
 
5.9%
로 10
 
4.0%
Other values (33) 47
18.6%

면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1177.3333
Minimum529
Maximum2722
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:08:58.516317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum529
5-th percentile554
Q1630
median855
Q31568
95-th percentile2680
Maximum2722
Range2193
Interquartile range (IQR)938

Descriptive statistics

Standard deviation723.61007
Coefficient of variation (CV)0.61461784
Kurtosis0.039407332
Mean1177.3333
Median Absolute Deviation (MAD)301
Skewness1.085823
Sum24724
Variance523611.53
MonotonicityNot monotonic
2023-12-13T01:08:58.644238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
855 1
 
4.8%
1923 1
 
4.8%
1630 1
 
4.8%
680 1
 
4.8%
1568 1
 
4.8%
567 1
 
4.8%
1386 1
 
4.8%
2381 1
 
4.8%
632 1
 
4.8%
2722 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
529 1
4.8%
554 1
4.8%
561 1
4.8%
567 1
4.8%
576 1
4.8%
630 1
4.8%
632 1
4.8%
659 1
4.8%
660 1
4.8%
680 1
4.8%
ValueCountFrequency (%)
2722 1
4.8%
2680 1
4.8%
2381 1
4.8%
1923 1
4.8%
1630 1
4.8%
1568 1
4.8%
1386 1
4.8%
1263 1
4.8%
1178 1
4.8%
1090 1
4.8%

수용인원
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.33333
Minimum203
Maximum1046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:08:58.756647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum203
5-th percentile213
Q1242
median328
Q3603
95-th percentile1030
Maximum1046
Range843
Interquartile range (IQR)361

Descriptive statistics

Standard deviation278.18345
Coefficient of variation (CV)0.61499658
Kurtosis0.037552394
Mean452.33333
Median Absolute Deviation (MAD)115
Skewness1.0851237
Sum9499
Variance77386.033
MonotonicityNot monotonic
2023-12-13T01:08:58.874314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
253 2
 
9.5%
328 1
 
4.8%
221 1
 
4.8%
626 1
 
4.8%
261 1
 
4.8%
603 1
 
4.8%
218 1
 
4.8%
533 1
 
4.8%
915 1
 
4.8%
243 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
203 1
4.8%
213 1
4.8%
215 1
4.8%
218 1
4.8%
221 1
4.8%
242 1
4.8%
243 1
4.8%
253 2
9.5%
261 1
4.8%
328 1
4.8%
ValueCountFrequency (%)
1046 1
4.8%
1030 1
4.8%
915 1
4.8%
739 1
4.8%
626 1
4.8%
603 1
4.8%
533 1
4.8%
485 1
4.8%
453 1
4.8%
419 1
4.8%

비고
Categorical

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
학교
16 
공공시설
4 
관공서
 
1

Length

Max length4
Median length2
Mean length2.4285714
Min length2

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row학교
2nd row학교
3rd row학교
4th row공공시설
5th row학교

Common Values

ValueCountFrequency (%)
학교 16
76.2%
공공시설 4
 
19.0%
관공서 1
 
4.8%

Length

2023-12-13T01:08:59.032319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:08:59.145646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
학교 16
76.2%
공공시설 4
 
19.0%
관공서 1
 
4.8%

Interactions

2023-12-13T01:08:56.734615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:08:56.588730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:08:56.804239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:08:56.657834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:08:59.219682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동시설명주소면적수용인원비고
행정동1.0001.0001.0000.8400.8400.811
시설명1.0001.0001.0001.0001.0001.000
주소1.0001.0001.0000.5480.5481.000
면적0.8401.0000.5481.0001.0000.127
수용인원0.8401.0000.5481.0001.0000.127
비고0.8111.0001.0000.1270.1271.000
2023-12-13T01:08:59.312291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적수용인원비고
면적1.0001.0000.000
수용인원1.0001.0000.000
비고0.0000.0001.000

Missing values

2023-12-13T01:08:56.896848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:08:56.991269image/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

행정동시설명주소면적수용인원비고
0왕십리2동무학초 다목적강당서울특별시 성동구 무학봉15길 21855328학교
1왕십리도선동숭신초등학교 교사동서울특별시 성동구 마장로 1611923739학교
2마장동동마중학교 다목적실-강당서울특별시 성동구 마조로11길 18659253학교
3사근동4차산업혁명 체험센터서울특별시 성동구 살곶이길 3271263485공공시설
4사근동사근초등학교 사근동11길 16서울특별시 성동구 사근동11길 16630242학교
5행당동성동구청 대강당서울특별시 성동구 고산자로 270529203관공서
6행당1동행당초등학교 체육관서울특별시 성동구 고산자로8길6554213학교
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8행당2동무학여고 식당동서울특별시 성동구 행당동 322-11178453학교
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