Overview

Dataset statistics

Number of variables13
Number of observations28
Missing cells2
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory112.7 B

Variable types

Text6
Categorical3
Numeric4

Dataset

Description시설명,시설코드,시설종류명(시설유형),시설종류상세명(시설종류),자치구(시)구분,시설장명,시군구코드,시군구명,시설주소,정원(수용인원),현인원,전화번호,우편번호
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-20408/S/1/datasetView.do

Alerts

시설종류상세명(시설종류) has constant value ""Constant
자치구(시)구분 has constant value ""Constant
정원(수용인원) is highly overall correlated with 현인원High correlation
현인원 is highly overall correlated with 정원(수용인원)High correlation
현인원 has 2 (7.1%) missing valuesMissing
시설코드 has unique valuesUnique
전화번호 has unique valuesUnique
현인원 has 1 (3.6%) zerosZeros

Reproduction

Analysis started2024-05-11 04:31:29.781553
Analysis finished2024-05-11 04:31:36.866974
Duration7.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-11T04:31:37.158723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length7.6428571
Min length4

Characters and Unicode

Total characters214
Distinct characters95
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

Unique26 ?
Unique (%)92.9%

Sample

1st row늘푸른자활의집
2nd row서울특별시립24시간게스트하우스
3rd row그리스도의공동체겨자씨들의둥지
4th row마더테레사의집
5th row서울특별시립비전트레이닝센터
ValueCountFrequency (%)
십자가쉼터 2
 
6.7%
소중한사람들 1
 
3.3%
서울특별시립 1
 
3.3%
강동희망의집 1
 
3.3%
시립양평쉼터 1
 
3.3%
아가페의집 1
 
3.3%
희망나무 1
 
3.3%
수송보현의집 1
 
3.3%
목동의집 1
 
3.3%
구세군가재울쉼터 1
 
3.3%
Other values (19) 19
63.3%
2024-05-11T04:31:38.209157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
의 11
 
5.1%
집 10
 
4.7%
터 9
 
4.2%
시 8
 
3.7%
립 6
 
2.8%
울 6
 
2.8%
서 6
 
2.8%
자 5
 
2.3%
쉼 5
 
2.3%
가 5
 
2.3%
Other values (85) 143
66.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 210
98.1%
Space Separator 2
 
0.9%
Decimal Number 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 11
 
5.2%
집 10
 
4.8%
터 9
 
4.3%
시 8
 
3.8%
립 6
 
2.9%
울 6
 
2.9%
서 6
 
2.9%
자 5
 
2.4%
쉼 5
 
2.4%
가 5
 
2.4%
Other values (82) 139
66.2%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 210
98.1%
Common 4
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 11
 
5.2%
집 10
 
4.8%
터 9
 
4.3%
시 8
 
3.8%
립 6
 
2.9%
울 6
 
2.9%
서 6
 
2.9%
자 5
 
2.4%
쉼 5
 
2.4%
가 5
 
2.4%
Other values (82) 139
66.2%
Common
ValueCountFrequency (%)
2
50.0%
2 1
25.0%
4 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 210
98.1%
ASCII 4
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
의 11
 
5.2%
집 10
 
4.8%
터 9
 
4.3%
시 8
 
3.8%
립 6
 
2.9%
울 6
 
2.9%
서 6
 
2.9%
자 5
 
2.4%
쉼 5
 
2.4%
가 5
 
2.4%
Other values (82) 139
66.2%
ASCII
ValueCountFrequency (%)
2
50.0%
2 1
25.0%
4 1
25.0%

시설코드
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-11T04:31:38.896701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters140
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st rowE0011
2nd rowE0043
3rd rowE0044
4th rowE0045
5th rowE0063
ValueCountFrequency (%)
e0011 1
 
3.6%
e0043 1
 
3.6%
e0413 1
 
3.6%
e0405 1
 
3.6%
e0397 1
 
3.6%
e0396 1
 
3.6%
e0394 1
 
3.6%
e0368 1
 
3.6%
e0357 1
 
3.6%
e0355 1
 
3.6%
Other values (18) 18
64.3%
2024-05-11T04:31:39.932313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 40
28.6%
E 28
20.0%
1 16
 
11.4%
3 16
 
11.4%
4 12
 
8.6%
5 7
 
5.0%
9 6
 
4.3%
8 5
 
3.6%
6 4
 
2.9%
7 3
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112
80.0%
Uppercase Letter 28
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
35.7%
1 16
 
14.3%
3 16
 
14.3%
4 12
 
10.7%
5 7
 
6.2%
9 6
 
5.4%
8 5
 
4.5%
6 4
 
3.6%
7 3
 
2.7%
2 3
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
E 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 112
80.0%
Latin 28
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40
35.7%
1 16
 
14.3%
3 16
 
14.3%
4 12
 
10.7%
5 7
 
6.2%
9 6
 
5.4%
8 5
 
4.5%
6 4
 
3.6%
7 3
 
2.7%
2 3
 
2.7%
Latin
ValueCountFrequency (%)
E 28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40
28.6%
E 28
20.0%
1 16
 
11.4%
3 16
 
11.4%
4 12
 
8.6%
5 7
 
5.0%
9 6
 
4.3%
8 5
 
3.6%
6 4
 
2.9%
7 3
 
2.1%

시설종류명(시설유형)
Categorical

Distinct3
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size356.0 B
(노숙인등) 노숙인자활시설
14 
(노숙인등) 노숙인재활시설
9 
(노숙인등) 노숙인요양시설
5 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(노숙인등) 노숙인재활시설
2nd row(노숙인등) 노숙인자활시설
3rd row(노숙인등) 노숙인재활시설
4th row(노숙인등) 노숙인요양시설
5th row(노숙인등) 노숙인재활시설

Common Values

ValueCountFrequency (%)
(노숙인등) 노숙인자활시설 14
50.0%
(노숙인등) 노숙인재활시설 9
32.1%
(노숙인등) 노숙인요양시설 5
 
17.9%

Length

2024-05-11T04:31:40.526674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:31:41.015739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노숙인등 28
50.0%
노숙인자활시설 14
25.0%
노숙인재활시설 9
 
16.1%
노숙인요양시설 5
 
8.9%

시설종류상세명(시설종류)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
노숙인등생활시설
28 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노숙인등생활시설 28
100.0%

Length

2024-05-11T04:31:41.633791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:31:41.998747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노숙인등생활시설 28
100.0%

자치구(시)구분
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
자치구
28 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자치구 28
100.0%

Length

2024-05-11T04:31:42.306430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:31:42.620717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자치구 28
100.0%

시설장명
Text

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-11T04:31:42.994150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9285714
Min length2

Characters and Unicode

Total characters82
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)85.7%

Sample

1st row조창운
2nd row고민수
3rd row양재교
4th row조성제
5th row한명섭
ValueCountFrequency (%)
한명섭 2
 
7.1%
안치영 2
 
7.1%
조창운 1
 
3.6%
임정백 1
 
3.6%
염원숙 1
 
3.6%
이범승 1
 
3.6%
신용삼 1
 
3.6%
홍근표 1
 
3.6%
이영식 1
 
3.6%
고훈 1
 
3.6%
Other values (16) 16
57.1%
2024-05-11T04:31:44.117230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 4
 
4.9%
영 4
 
4.9%
정 4
 
4.9%
이 3
 
3.7%
안 3
 
3.7%
고 3
 
3.7%
승 3
 
3.7%
석 2
 
2.4%
순 2
 
2.4%
명 2
 
2.4%
Other values (41) 52
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 4
 
4.9%
영 4
 
4.9%
정 4
 
4.9%
이 3
 
3.7%
안 3
 
3.7%
고 3
 
3.7%
승 3
 
3.7%
석 2
 
2.4%
순 2
 
2.4%
명 2
 
2.4%
Other values (41) 52
63.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 4
 
4.9%
영 4
 
4.9%
정 4
 
4.9%
이 3
 
3.7%
안 3
 
3.7%
고 3
 
3.7%
승 3
 
3.7%
석 2
 
2.4%
순 2
 
2.4%
명 2
 
2.4%
Other values (41) 52
63.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 4
 
4.9%
영 4
 
4.9%
정 4
 
4.9%
이 3
 
3.7%
안 3
 
3.7%
고 3
 
3.7%
승 3
 
3.7%
석 2
 
2.4%
순 2
 
2.4%
명 2
 
2.4%
Other values (41) 52
63.4%

시군구코드
Real number (ℝ)

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1334286 × 109
Minimum1.1 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-05-11T04:31:44.605598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile1.11205 × 109
Q11.123 × 109
median1.129 × 109
Q31.141 × 109
95-th percentile1.1638 × 109
Maximum1.174 × 109
Range74000000
Interquartile range (IQR)18000000

Descriptive statistics

Standard deviation16298554
Coefficient of variation (CV)0.014379869
Kurtosis0.83889139
Mean1.1334286 × 109
Median Absolute Deviation (MAD)9000000
Skewness0.61428361
Sum3.1736 × 1010
Variance2.6564286 × 1014
MonotonicityNot monotonic
2024-05-11T04:31:45.485548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1129000000 6
21.4%
1138000000 4
14.3%
1141000000 3
10.7%
1120000000 3
10.7%
1123000000 2
 
7.1%
1130500000 1
 
3.6%
1168000000 1
 
3.6%
1156000000 1
 
3.6%
1126000000 1
 
3.6%
1174000000 1
 
3.6%
Other values (5) 5
17.9%
ValueCountFrequency (%)
1100000000 1
 
3.6%
1111000000 1
 
3.6%
1114000000 1
 
3.6%
1120000000 3
10.7%
1123000000 2
 
7.1%
1126000000 1
 
3.6%
1129000000 6
21.4%
1130500000 1
 
3.6%
1138000000 4
14.3%
1141000000 3
10.7%
ValueCountFrequency (%)
1174000000 1
 
3.6%
1168000000 1
 
3.6%
1156000000 1
 
3.6%
1154500000 1
 
3.6%
1147000000 1
 
3.6%
1141000000 3
10.7%
1138000000 4
14.3%
1130500000 1
 
3.6%
1129000000 6
21.4%
1126000000 1
 
3.6%

시군구명
Text

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-11T04:31:46.004686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.25
Min length2

Characters and Unicode

Total characters91
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)35.7%

Sample

1st row서대문구
2nd row성동구
3rd row강북구
4th row성북구
5th row성동구
ValueCountFrequency (%)
성북구 6
21.4%
은평구 4
14.3%
서대문구 3
10.7%
성동구 3
10.7%
동대문구 2
 
7.1%
강북구 1
 
3.6%
강남구 1
 
3.6%
영등포구 1
 
3.6%
중랑구 1
 
3.6%
강동구 1
 
3.6%
Other values (5) 5
17.9%
2024-05-11T04:31:46.787392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 27
29.7%
성 9
 
9.9%
북 7
 
7.7%
동 6
 
6.6%
대 5
 
5.5%
문 5
 
5.5%
은 4
 
4.4%
평 4
 
4.4%
서 4
 
4.4%
강 3
 
3.3%
Other values (15) 17
18.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 27
29.7%
성 9
 
9.9%
북 7
 
7.7%
동 6
 
6.6%
대 5
 
5.5%
문 5
 
5.5%
은 4
 
4.4%
평 4
 
4.4%
서 4
 
4.4%
강 3
 
3.3%
Other values (15) 17
18.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 27
29.7%
성 9
 
9.9%
북 7
 
7.7%
동 6
 
6.6%
대 5
 
5.5%
문 5
 
5.5%
은 4
 
4.4%
평 4
 
4.4%
서 4
 
4.4%
강 3
 
3.3%
Other values (15) 17
18.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 27
29.7%
성 9
 
9.9%
북 7
 
7.7%
동 6
 
6.6%
대 5
 
5.5%
문 5
 
5.5%
은 4
 
4.4%
평 4
 
4.4%
서 4
 
4.4%
강 3
 
3.3%
Other values (15) 17
18.7%

시설주소
Text

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-11T04:31:47.486749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length30.5
Mean length27.071429
Min length18

Characters and Unicode

Total characters758
Distinct characters120
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row경기도 파주시 문산읍 바리골길 421
2nd row서울특별시 성동구 가람길 125시립게스트하우스 (송정동)
3rd row서울특별시 강북구 수유로21길 10(수유3동)
4th row서울특별시 성북구 삼선교로2길 20　
5th row서울특별시 성동구 자동차시장길 48 (용답동)
ValueCountFrequency (%)
서울특별시 25
 
18.7%
성북구 6
 
4.5%
은평구 4
 
3.0%
경기도 3
 
2.2%
성동구 3
 
2.2%
정릉로6가길 2
 
1.5%
동대문구 2
 
1.5%
장위동 2
 
1.5%
서대문구 2
 
1.5%
용답동 2
 
1.5%
Other values (81) 83
61.9%
2024-05-11T04:31:48.868276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
107
 
14.1%
시 33
 
4.4%
서 30
 
4.0%
동 30
 
4.0%
1 28
 
3.7%
울 27
 
3.6%
특 27
 
3.6%
별 27
 
3.6%
구 27
 
3.6%
로 23
 
3.0%
Other values (110) 399
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 476
62.8%
Decimal Number 118
 
15.6%
Space Separator 110
 
14.5%
Open Punctuation 19
 
2.5%
Close Punctuation 19
 
2.5%
Dash Punctuation 11
 
1.5%
Other Punctuation 5
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 33
 
6.9%
서 30
 
6.3%
동 30
 
6.3%
울 27
 
5.7%
특 27
 
5.7%
별 27
 
5.7%
구 27
 
5.7%
로 23
 
4.8%
길 21
 
4.4%
성 9
 
1.9%
Other values (93) 222
46.6%
Decimal Number
ValueCountFrequency (%)
1 28
23.7%
2 21
17.8%
4 13
11.0%
3 13
11.0%
7 10
 
8.5%
0 10
 
8.5%
6 7
 
5.9%
8 6
 
5.1%
5 5
 
4.2%
9 5
 
4.2%
Space Separator
ValueCountFrequency (%)
107
97.3%
　 3
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 476
62.8%
Common 282
37.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 33
 
6.9%
서 30
 
6.3%
동 30
 
6.3%
울 27
 
5.7%
특 27
 
5.7%
별 27
 
5.7%
구 27
 
5.7%
로 23
 
4.8%
길 21
 
4.4%
성 9
 
1.9%
Other values (93) 222
46.6%
Common
ValueCountFrequency (%)
107
37.9%
1 28
 
9.9%
2 21
 
7.4%
( 19
 
6.7%
) 19
 
6.7%
4 13
 
4.6%
3 13
 
4.6%
- 11
 
3.9%
7 10
 
3.5%
0 10
 
3.5%
Other values (7) 31
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 476
62.8%
ASCII 279
36.8%
None 3
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
107
38.4%
1 28
 
10.0%
2 21
 
7.5%
( 19
 
6.8%
) 19
 
6.8%
4 13
 
4.7%
3 13
 
4.7%
- 11
 
3.9%
7 10
 
3.6%
0 10
 
3.6%
Other values (6) 28
 
10.0%
Hangul
ValueCountFrequency (%)
시 33
 
6.9%
서 30
 
6.3%
동 30
 
6.3%
울 27
 
5.7%
특 27
 
5.7%
별 27
 
5.7%
구 27
 
5.7%
로 23
 
4.8%
길 21
 
4.4%
성 9
 
1.9%
Other values (93) 222
46.6%
None
ValueCountFrequency (%)
　 3
100.0%

정원(수용인원)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.28571
Minimum6
Maximum1100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-05-11T04:31:49.494265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.05
Q114.25
median33
Q3115.25
95-th percentile716.15
Maximum1100
Range1094
Interquartile range (IQR)101

Descriptive statistics

Standard deviation267.64125
Coefficient of variation (CV)2.1193312
Kurtosis10.018398
Mean126.28571
Median Absolute Deviation (MAD)24
Skewness3.262531
Sum3536
Variance71631.841
MonotonicityNot monotonic
2024-05-11T04:31:49.952653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
15 3
 
10.7%
9 3
 
10.7%
6 2
 
7.1%
189 2
 
7.1%
43 2
 
7.1%
131 1
 
3.6%
30 1
 
3.6%
25 1
 
3.6%
20 1
 
3.6%
36 1
 
3.6%
Other values (11) 11
39.3%
ValueCountFrequency (%)
6 2
7.1%
9 3
10.7%
10 1
 
3.6%
12 1
 
3.6%
15 3
10.7%
20 1
 
3.6%
23 1
 
3.6%
25 1
 
3.6%
30 1
 
3.6%
36 1
 
3.6%
ValueCountFrequency (%)
1100 1
3.6%
1000 1
3.6%
189 2
7.1%
180 1
3.6%
134 1
3.6%
131 1
3.6%
110 1
3.6%
73 1
3.6%
60 1
3.6%
44 1
3.6%

현인원
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)84.6%
Missing2
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean70.269231
Minimum0
Maximum760
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-05-11T04:31:50.489082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110.25
median27
Q370.75
95-th percentile164.75
Maximum760
Range760
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation148.065
Coefficient of variation (CV)2.10711
Kurtosis20.591866
Mean70.269231
Median Absolute Deviation (MAD)19
Skewness4.3623965
Sum1827
Variance21923.245
MonotonicityNot monotonic
2024-05-11T04:31:51.043975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6 3
 
10.7%
100 2
 
7.1%
34 2
 
7.1%
13 1
 
3.6%
77 1
 
3.6%
23 1
 
3.6%
20 1
 
3.6%
15 1
 
3.6%
0 1
 
3.6%
31 1
 
3.6%
Other values (12) 12
42.9%
(Missing) 2
 
7.1%
ValueCountFrequency (%)
0 1
 
3.6%
6 3
10.7%
7 1
 
3.6%
9 1
 
3.6%
10 1
 
3.6%
11 1
 
3.6%
13 1
 
3.6%
15 1
 
3.6%
16 1
 
3.6%
20 1
 
3.6%
ValueCountFrequency (%)
760 1
3.6%
166 1
3.6%
161 1
3.6%
100 2
7.1%
91 1
3.6%
77 1
3.6%
52 1
3.6%
44 1
3.6%
35 1
3.6%
34 2
7.1%

전화번호
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-11T04:31:51.864873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.035714
Min length9

Characters and Unicode

Total characters309
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row031-953-3491
2nd row02-2215-9251
3rd row02-999-3932
4th row02-3216-2431
5th row02-2243-9183
ValueCountFrequency (%)
031-953-3491 1
 
3.6%
02-2215-9251 1
 
3.6%
0222439183 1
 
3.6%
0317754940 1
 
3.6%
029429193 1
 
3.6%
028463070 1
 
3.6%
027374894 1
 
3.6%
070-4688-3432 1
 
3.6%
02-309-3009 1
 
3.6%
02-2213-8004 1
 
3.6%
Other values (18) 18
64.3%
2024-05-11T04:31:53.105139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 51
16.5%
0 50
16.2%
- 41
13.3%
3 40
12.9%
1 26
8.4%
9 26
8.4%
4 22
7.1%
7 15
 
4.9%
5 14
 
4.5%
6 12
 
3.9%
Other values (2) 12
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 267
86.4%
Dash Punctuation 41
 
13.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 51
19.1%
0 50
18.7%
3 40
15.0%
1 26
9.7%
9 26
9.7%
4 22
8.2%
7 15
 
5.6%
5 14
 
5.2%
6 12
 
4.5%
8 11
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 309
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 51
16.5%
0 50
16.2%
- 41
13.3%
3 40
12.9%
1 26
8.4%
9 26
8.4%
4 22
7.1%
7 15
 
4.9%
5 14
 
4.5%
6 12
 
3.9%
Other values (2) 12
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 51
16.5%
0 50
16.2%
- 41
13.3%
3 40
12.9%
1 26
8.4%
9 26
8.4%
4 22
7.1%
7 15
 
4.9%
5 14
 
4.5%
6 12
 
3.9%
Other values (2) 12
 
3.9%

우편번호
Real number (ℝ)

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52240.286
Minimum2044
Maximum449833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-05-11T04:31:53.676658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2044
5-th percentile2555.4
Q13238
median5071.5
Q3106294.25
95-th percentile150008.8
Maximum449833
Range447789
Interquartile range (IQR)103056.25

Descriptive statistics

Standard deviation95862.368
Coefficient of variation (CV)1.8350276
Kurtosis10.563236
Mean52240.286
Median Absolute Deviation (MAD)2434
Skewness2.9204641
Sum1462728
Variance9.1895936 × 109
MonotonicityNot monotonic
2024-05-11T04:31:54.178440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4809 2
 
7.1%
122600 2
 
7.1%
2812 2
 
7.1%
10833 1
 
3.6%
5334 1
 
3.6%
12524 1
 
3.6%
2755 1
 
3.6%
7948 1
 
3.6%
3673 1
 
3.6%
2558 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
2044 1
3.6%
2554 1
3.6%
2558 1
3.6%
2717 1
3.6%
2755 1
3.6%
2812 2
7.1%
3380 1
3.6%
3428 1
3.6%
3673 1
3.6%
3746 1
3.6%
ValueCountFrequency (%)
449833 1
3.6%
153842 1
3.6%
142890 1
3.6%
136853 1
3.6%
136041 1
3.6%
122600 2
7.1%
100859 1
3.6%
12524 1
3.6%
10833 1
3.6%
7948 1
3.6%

Interactions

2024-05-11T04:31:34.585979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:30.713640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:32.064067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:33.116352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:34.964871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:30.999422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:32.365338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:33.531231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:35.285315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:31.370457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:32.614658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:33.825013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:35.562932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:31.690243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:32.885436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:31:34.196685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T04:31:54.441512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명시설코드시설종류명(시설유형)시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
시설명1.0001.0001.0001.0001.0001.0000.9891.0001.0001.0001.000
시설코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시설종류명(시설유형)1.0001.0001.0001.0000.1220.4121.0000.6950.2961.0000.228
시설장명1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.000
시군구코드1.0001.0000.1221.0001.0001.0001.0000.6070.4441.0000.373
시군구명1.0001.0000.4121.0001.0001.0001.0000.9560.3411.0000.849
시설주소0.9891.0001.0001.0001.0001.0001.0001.0000.0001.0001.000
정원(수용인원)1.0001.0000.6951.0000.6070.9561.0001.0000.8721.0000.456
현인원1.0001.0000.2960.0000.4440.3410.0000.8721.0001.0000.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
우편번호1.0001.0000.2281.0000.3730.8491.0000.4560.0001.0001.000
2024-05-11T04:31:54.868636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드정원(수용인원)현인원우편번호시설종류명(시설유형)
시군구코드1.000-0.285-0.2150.1200.208
정원(수용인원)-0.2851.0000.974-0.0190.349
현인원-0.2150.9741.000-0.2500.270
우편번호0.120-0.019-0.2501.0000.208
시설종류명(시설유형)0.2080.3490.2700.2081.000

Missing values

2024-05-11T04:31:36.001652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T04:31:36.656747image/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

시설명시설코드시설종류명(시설유형)시설종류상세명(시설종류)자치구(시)구분시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
0늘푸른자활의집E0011(노숙인등) 노숙인재활시설노숙인등생활시설자치구조창운1141000000서대문구경기도 파주시 문산읍 바리골길 4216044031-953-349110833
1서울특별시립24시간게스트하우스E0043(노숙인등) 노숙인자활시설노숙인등생활시설자치구고민수1120000000성동구서울특별시 성동구 가람길 125시립게스트하우스 (송정동)1349102-2215-92514806
2그리스도의공동체겨자씨들의둥지E0044(노숙인등) 노숙인재활시설노숙인등생활시설자치구양재교1130500000강북구서울특별시 강북구 수유로21길 10(수유3동)9902-999-3932142890
3마더테레사의집E0045(노숙인등) 노숙인요양시설노숙인등생활시설자치구조성제1129000000성북구서울특별시 성북구 삼선교로2길 2010702-3216-2431136041
4서울특별시립비전트레이닝센터E0063(노숙인등) 노숙인재활시설노숙인등생활시설자치구한명섭1120000000성동구서울특별시 성동구 자동차시장길 48 (용답동)18916602-2243-91834809
5시립은평의마을E0081(노숙인등) 노숙인요양시설노숙인등생활시설자치구장경환1138000000은평구서울특별시 은평구 갈현로15길 27-1시립은평의 마을110076002-3156-63003428
6가나안쉼터E0083(노숙인등) 노숙인자활시설노숙인등생활시설자치구김정재1123000000동대문구서울특별시 동대문구 왕산로 256-1죽정빌딩 (전농동)11010002-964-15582554
7서울특별시여성보호센터E0088(노숙인등) 노숙인요양시설노숙인등생활시설자치구이운승1168000000강남구서울특별시 강남구 광평로34길 124(수서동)18016102-3412-45026362
8구세군서대문사랑방E0095(노숙인등) 노숙인자활시설노숙인등생활시설자치구김욱1141000000서대문구서울특별시 서대문구 경기대로 81433502-312-72253746
9우리집공동체E0109(노숙인등) 노숙인재활시설노숙인등생활시설자치구김승현1129000000성북구서울특별시 성북구 보국문로29바길 14-1(정릉3동)9602)918-3569136853
시설명시설코드시설종류명(시설유형)시설종류상세명(시설종류)자치구(시)구분시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
18천애원 희망의집E0350(노숙인등) 노숙인자활시설노숙인등생활시설자치구안주석1138000000은평구서울특별시 은평구 녹번로 7-7 (녹번동)15602-952-45643380
19다일작은천국E0355(노숙인등) 노숙인요양시설노숙인등생활시설자치구고훈1123000000동대문구서울특별시 동대문구 서울특별시립대로 57전농동 497-77433402-2213-80042558
20구세군가재울쉼터E0357(노숙인등) 노숙인자활시설노숙인등생활시설자치구이영식1141000000서대문구서울특별시 서대문구 명지대1다길 2101호 (남가좌동)363402-309-30093673
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23희망나무E0396(노숙인등) 노숙인자활시설노숙인등생활시설자치구이범승1138000000은평구서울특별시 은평구 통일로92길37-4 (불광동)2520028463070122600
24아가페의집E0397(노숙인등) 노숙인재활시설노숙인등생활시설자치구염원숙1129000000성북구서울특별시 성북구 장위로17길 6-0 (장위동, 예담빌)6 (장위동, 예담빌)30230294291932755
25시립양평쉼터E0405(노숙인등) 노숙인자활시설노숙인등생활시설자치구김도진1111000000종로구경기도 양평군 용문면 화전로 26413177031775494012524
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