Overview

Dataset statistics

Number of variables13
Number of observations28
Missing cells22
Missing cells (%)6.0%
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-20426/S/1/datasetView.do

Alerts

시설종류상세명(시설종류) has constant value ""Constant
자치구(시)구분 has constant value ""Constant
시군구코드 is highly overall correlated with 우편번호High correlation
현인원 is highly overall correlated with 우편번호High correlation
우편번호 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
정원(수용인원) has 8 (28.6%) missing valuesMissing
현인원 has 14 (50.0%) missing valuesMissing
시설명 has unique valuesUnique
시설코드 has unique valuesUnique
시설주소 has unique valuesUnique
정원(수용인원) has 2 (7.1%) zerosZeros
현인원 has 1 (3.6%) zerosZeros

Reproduction

Analysis started2024-05-18 05:46:52.665283
Analysis finished2024-05-18 05:47:00.922843
Duration8.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-18T14:47:01.266283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length16
Mean length13.892857
Min length7

Characters and Unicode

Total characters389
Distinct characters87
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
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 row영등포구 장애인사랑나눔의 집
2nd row마포점자도서실
3rd row사람희망 금천장애인자립생활센터
4th row해오름장애인자립생활센터
5th row이음장애인자립생활센터
ValueCountFrequency (%)
평생교육센터 3
 
7.3%
영등포구 2
 
4.9%
발달장애인평생교육센터 2
 
4.9%
발달장애인 2
 
4.9%
종로발달장애인평생교육센터 1
 
2.4%
용산구장애인가족지원센터 1
 
2.4%
서대문장애인가족지원센터 1
 
2.4%
노원발달장애인평생교육센터 1
 
2.4%
강남발달장애인평생교육센터 1
 
2.4%
장애인사랑나눔의 1
 
2.4%
Other values (26) 26
63.4%
2024-05-18T14:47:02.472014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 25
 
6.4%
애 25
 
6.4%
인 25
 
6.4%
터 24
 
6.2%
센 24
 
6.2%
생 22
 
5.7%
평 15
 
3.9%
발 14
 
3.6%
달 14
 
3.6%
교 14
 
3.6%
Other values (77) 187
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 361
92.8%
Space Separator 13
 
3.3%
Lowercase Letter 5
 
1.3%
Open Punctuation 3
 
0.8%
Close Punctuation 3
 
0.8%
Dash Punctuation 2
 
0.5%
Uppercase Letter 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 25
 
6.9%
애 25
 
6.9%
인 25
 
6.9%
터 24
 
6.6%
센 24
 
6.6%
생 22
 
6.1%
평 15
 
4.2%
발 14
 
3.9%
달 14
 
3.9%
교 14
 
3.9%
Other values (68) 159
44.0%
Lowercase Letter
ValueCountFrequency (%)
o 3
60.0%
b 1
 
20.0%
d 1
 
20.0%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
J 1
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 361
92.8%
Common 21
 
5.4%
Latin 7
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 25
 
6.9%
애 25
 
6.9%
인 25
 
6.9%
터 24
 
6.6%
센 24
 
6.6%
생 22
 
6.1%
평 15
 
4.2%
발 14
 
3.9%
달 14
 
3.9%
교 14
 
3.9%
Other values (68) 159
44.0%
Latin
ValueCountFrequency (%)
o 3
42.9%
b 1
 
14.3%
G 1
 
14.3%
d 1
 
14.3%
J 1
 
14.3%
Common
ValueCountFrequency (%)
13
61.9%
( 3
 
14.3%
) 3
 
14.3%
- 2
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 361
92.8%
ASCII 28
 
7.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 25
 
6.9%
애 25
 
6.9%
인 25
 
6.9%
터 24
 
6.6%
센 24
 
6.6%
생 22
 
6.1%
평 15
 
4.2%
발 14
 
3.9%
달 14
 
3.9%
교 14
 
3.9%
Other values (68) 159
44.0%
ASCII
ValueCountFrequency (%)
13
46.4%
( 3
 
10.7%
o 3
 
10.7%
) 3
 
10.7%
- 2
 
7.1%
b 1
 
3.6%
G 1
 
3.6%
d 1
 
3.6%
J 1
 
3.6%

시설코드
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-18T14:47:03.099600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.3571429
Min length5

Characters and Unicode

Total characters150
Distinct characters13
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 rowC0384
2nd rowC1764
3rd rowF04194
4th rowF04195
5th rowF04196
ValueCountFrequency (%)
c0384 1
 
3.6%
c1764 1
 
3.6%
z6399 1
 
3.6%
z6325 1
 
3.6%
z6300 1
 
3.6%
z6295 1
 
3.6%
z6291 1
 
3.6%
z6246 1
 
3.6%
z6232 1
 
3.6%
z6223 1
 
3.6%
Other values (18) 18
64.3%
2024-05-18T14:47:04.273685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 23
15.3%
0 19
12.7%
4 17
11.3%
1 16
10.7%
Z 16
10.7%
5 11
7.3%
F 10
6.7%
9 10
6.7%
2 10
6.7%
3 8
 
5.3%
Other values (3) 10
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 122
81.3%
Uppercase Letter 28
 
18.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 23
18.9%
0 19
15.6%
4 17
13.9%
1 16
13.1%
5 11
9.0%
9 10
8.2%
2 10
8.2%
3 8
 
6.6%
8 5
 
4.1%
7 3
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
Z 16
57.1%
F 10
35.7%
C 2
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 122
81.3%
Latin 28
 
18.7%

Most frequent character per script

Common
ValueCountFrequency (%)
6 23
18.9%
0 19
15.6%
4 17
13.9%
1 16
13.1%
5 11
9.0%
9 10
8.2%
2 10
8.2%
3 8
 
6.6%
8 5
 
4.1%
7 3
 
2.5%
Latin
ValueCountFrequency (%)
Z 16
57.1%
F 10
35.7%
C 2
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 23
15.3%
0 19
12.7%
4 17
11.3%
1 16
10.7%
Z 16
10.7%
5 11
7.3%
F 10
6.7%
9 10
6.7%
2 10
6.7%
3 8
 
5.3%
Other values (3) 10
6.7%

시설종류명(시설유형)
Categorical

Distinct3
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size356.0 B
(장애인) (기타)장애인복지시설
20 
(장애인) 중증장애인자립생활지원센터
6 
(장애인) 장애인자립지원센터
 
2

Length

Max length19
Median length17
Mean length17.285714
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(장애인) (기타)장애인복지시설
2nd row(장애인) (기타)장애인복지시설
3rd row(장애인) 중증장애인자립생활지원센터
4th row(장애인) 중증장애인자립생활지원센터
5th row(장애인) 중증장애인자립생활지원센터

Common Values

ValueCountFrequency (%)
(장애인) (기타)장애인복지시설 20
71.4%
(장애인) 중증장애인자립생활지원센터 6
 
21.4%
(장애인) 장애인자립지원센터 2
 
7.1%

Length

2024-05-18T14:47:04.923299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:47:05.395273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장애인 28
50.0%
기타)장애인복지시설 20
35.7%
중증장애인자립생활지원센터 6
 
10.7%
장애인자립지원센터 2
 
3.6%

시설종류상세명(시설종류)
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장애인기타 28
100.0%

Length

2024-05-18T14:47:05.851864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:47:06.234437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장애인기타 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

2024-05-18T14:47:06.550309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:47:06.853778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자치구 28
100.0%

시설장명
Text

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-18T14:47:07.253304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84
Distinct characters47
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

Unique26 ?
Unique (%)92.9%

Sample

1st row김금상
2nd row김옥숙
3rd row황백남
4th row안권수
5th row이규식
ValueCountFrequency (%)
오성섭 2
 
7.1%
김금상 1
 
3.6%
황숙현 1
 
3.6%
성효진 1
 
3.6%
김미현 1
 
3.6%
박미진 1
 
3.6%
신건철 1
 
3.6%
정정애 1
 
3.6%
정희경 1
 
3.6%
안보현 1
 
3.6%
Other values (17) 17
60.7%
2024-05-18T14:47:08.077297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 10
 
11.9%
정 5
 
6.0%
오 4
 
4.8%
현 3
 
3.6%
미 3
 
3.6%
진 3
 
3.6%
성 3
 
3.6%
재 3
 
3.6%
경 3
 
3.6%
숙 2
 
2.4%
Other values (37) 45
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 10
 
11.9%
정 5
 
6.0%
오 4
 
4.8%
현 3
 
3.6%
미 3
 
3.6%
진 3
 
3.6%
성 3
 
3.6%
재 3
 
3.6%
경 3
 
3.6%
숙 2
 
2.4%
Other values (37) 45
53.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 84
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 10
 
11.9%
정 5
 
6.0%
오 4
 
4.8%
현 3
 
3.6%
미 3
 
3.6%
진 3
 
3.6%
성 3
 
3.6%
재 3
 
3.6%
경 3
 
3.6%
숙 2
 
2.4%
Other values (37) 45
53.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 84
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 10
 
11.9%
정 5
 
6.0%
오 4
 
4.8%
현 3
 
3.6%
미 3
 
3.6%
진 3
 
3.6%
성 3
 
3.6%
재 3
 
3.6%
경 3
 
3.6%
숙 2
 
2.4%
Other values (37) 45
53.6%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1490893 × 109
Minimum1.111 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-05-18T14:47:08.415918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile1.11505 × 109
Q11.13725 × 109
median1.156 × 109
Q31.168 × 109
95-th percentile1.174 × 109
Maximum1.174 × 109
Range63000000
Interquartile range (IQR)30750000

Descriptive statistics

Standard deviation19762504
Coefficient of variation (CV)0.017198406
Kurtosis-0.80122431
Mean1.1490893 × 109
Median Absolute Deviation (MAD)12000000
Skewness-0.59114669
Sum3.21745 × 1010
Variance3.9055655 × 1014
MonotonicityNot monotonic
2024-05-18T14:47:08.841911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1156000000 5
17.9%
1168000000 5
17.9%
1174000000 3
10.7%
1141000000 2
 
7.1%
1117000000 2
 
7.1%
1154500000 1
 
3.6%
1120000000 1
 
3.6%
1159000000 1
 
3.6%
1162000000 1
 
3.6%
1114000000 1
 
3.6%
Other values (6) 6
21.4%
ValueCountFrequency (%)
1111000000 1
3.6%
1114000000 1
3.6%
1117000000 2
7.1%
1120000000 1
3.6%
1132000000 1
3.6%
1135000000 1
3.6%
1138000000 1
3.6%
1141000000 2
7.1%
1144000000 1
3.6%
1147000000 1
3.6%
ValueCountFrequency (%)
1174000000 3
10.7%
1168000000 5
17.9%
1162000000 1
 
3.6%
1159000000 1
 
3.6%
1156000000 5
17.9%
1154500000 1
 
3.6%
1147000000 1
 
3.6%
1144000000 1
 
3.6%
1141000000 2
 
7.1%
1138000000 1
 
3.6%

시군구명
Text

Distinct16
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-18T14:47:09.256437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2142857
Min length2

Characters and Unicode

Total characters90
Distinct characters29
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

Unique11 ?
Unique (%)39.3%

Sample

1st row영등포구
2nd row마포구
3rd row금천구
4th row영등포구
5th row영등포구
ValueCountFrequency (%)
영등포구 5
17.9%
강남구 5
17.9%
강동구 3
10.7%
서대문구 2
 
7.1%
용산구 2
 
7.1%
마포구 1
 
3.6%
금천구 1
 
3.6%
성동구 1
 
3.6%
동작구 1
 
3.6%
관악구 1
 
3.6%
Other values (6) 6
21.4%
2024-05-18T14:47:10.173546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 28
31.1%
강 8
 
8.9%
포 6
 
6.7%
영 5
 
5.6%
남 5
 
5.6%
동 5
 
5.6%
등 5
 
5.6%
용 2
 
2.2%
산 2
 
2.2%
천 2
 
2.2%
Other values (19) 22
24.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 28
31.1%
강 8
 
8.9%
포 6
 
6.7%
영 5
 
5.6%
남 5
 
5.6%
동 5
 
5.6%
등 5
 
5.6%
용 2
 
2.2%
산 2
 
2.2%
천 2
 
2.2%
Other values (19) 22
24.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 28
31.1%
강 8
 
8.9%
포 6
 
6.7%
영 5
 
5.6%
남 5
 
5.6%
동 5
 
5.6%
등 5
 
5.6%
용 2
 
2.2%
산 2
 
2.2%
천 2
 
2.2%
Other values (19) 22
24.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 28
31.1%
강 8
 
8.9%
포 6
 
6.7%
영 5
 
5.6%
남 5
 
5.6%
동 5
 
5.6%
등 5
 
5.6%
용 2
 
2.2%
산 2
 
2.2%
천 2
 
2.2%
Other values (19) 22
24.4%

시설주소
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-18T14:47:10.846935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length34.5
Mean length31.321429
Min length17

Characters and Unicode

Total characters877
Distinct characters141
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

Unique28 ?
Unique (%)100.0%

Sample

1st row서울특별시 영등포구 신길로 55
2nd row서울특별시 마포구 망원2동 451-1
3rd row서울특별시 금천구 독산로 70C동 603호(현대지식산업센터) (시흥동)
4th row서울특별시 영등포구 국회대로62길 14한국스카우트연맹 302호 (여의도동)
5th row서울특별시 영등포구 선유로 1461213호 (양평동3가)
ValueCountFrequency (%)
서울특별시 28
 
18.5%
영등포구 5
 
3.3%
강남구 5
 
3.3%
1층 3
 
2.0%
강동구 3
 
2.0%
수서동 2
 
1.3%
서대문구 2
 
1.3%
용산구 2
 
1.3%
원효로1가 2
 
1.3%
역삼동 2
 
1.3%
Other values (95) 97
64.2%
2024-05-18T14:47:11.918888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
 
14.0%
1 37
 
4.2%
서 35
 
4.0%
동 34
 
3.9%
울 31
 
3.5%
로 31
 
3.5%
시 30
 
3.4%
특 29
 
3.3%
별 29
 
3.3%
) 29
 
3.3%
Other values (131) 469
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 521
59.4%
Decimal Number 149
 
17.0%
Space Separator 123
 
14.0%
Close Punctuation 29
 
3.3%
Open Punctuation 29
 
3.3%
Other Punctuation 13
 
1.5%
Dash Punctuation 8
 
0.9%
Math Symbol 4
 
0.5%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 35
 
6.7%
동 34
 
6.5%
울 31
 
6.0%
로 31
 
6.0%
시 30
 
5.8%
특 29
 
5.6%
별 29
 
5.6%
구 28
 
5.4%
층 15
 
2.9%
길 15
 
2.9%
Other values (114) 244
46.8%
Decimal Number
ValueCountFrequency (%)
1 37
24.8%
4 19
12.8%
2 17
11.4%
6 14
 
9.4%
0 14
 
9.4%
3 13
 
8.7%
5 12
 
8.1%
8 9
 
6.0%
7 8
 
5.4%
9 6
 
4.0%
Space Separator
ValueCountFrequency (%)
123
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 521
59.4%
Common 355
40.5%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 35
 
6.7%
동 34
 
6.5%
울 31
 
6.0%
로 31
 
6.0%
시 30
 
5.8%
특 29
 
5.6%
별 29
 
5.6%
구 28
 
5.4%
층 15
 
2.9%
길 15
 
2.9%
Other values (114) 244
46.8%
Common
ValueCountFrequency (%)
123
34.6%
1 37
 
10.4%
) 29
 
8.2%
( 29
 
8.2%
4 19
 
5.4%
2 17
 
4.8%
6 14
 
3.9%
0 14
 
3.9%
3 13
 
3.7%
, 13
 
3.7%
Other values (6) 47
 
13.2%
Latin
ValueCountFrequency (%)
C 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 521
59.4%
ASCII 356
40.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
34.6%
1 37
 
10.4%
) 29
 
8.1%
( 29
 
8.1%
4 19
 
5.3%
2 17
 
4.8%
6 14
 
3.9%
0 14
 
3.9%
3 13
 
3.7%
, 13
 
3.7%
Other values (7) 48
 
13.5%
Hangul
ValueCountFrequency (%)
서 35
 
6.7%
동 34
 
6.5%
울 31
 
6.0%
로 31
 
6.0%
시 30
 
5.8%
특 29
 
5.6%
별 29
 
5.6%
구 28
 
5.4%
층 15
 
2.9%
길 15
 
2.9%
Other values (114) 244
46.8%

정원(수용인원)
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)50.0%
Missing8
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Mean38
Minimum0
Maximum300
Zeros2
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-05-18T14:47:12.480785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median30
Q330
95-th percentile110
Maximum300
Range300
Interquartile range (IQR)22

Descriptive statistics

Standard deviation65.499699
Coefficient of variation (CV)1.7236763
Kurtosis15.053335
Mean38
Median Absolute Deviation (MAD)6
Skewness3.730257
Sum760
Variance4290.2105
MonotonicityNot monotonic
2024-05-18T14:47:12.920863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
30 8
28.6%
2 3
 
10.7%
0 2
 
7.1%
300 1
 
3.6%
10 1
 
3.6%
11 1
 
3.6%
100 1
 
3.6%
36 1
 
3.6%
33 1
 
3.6%
24 1
 
3.6%
(Missing) 8
28.6%
ValueCountFrequency (%)
0 2
 
7.1%
2 3
 
10.7%
10 1
 
3.6%
11 1
 
3.6%
24 1
 
3.6%
30 8
28.6%
33 1
 
3.6%
36 1
 
3.6%
100 1
 
3.6%
300 1
 
3.6%
ValueCountFrequency (%)
300 1
 
3.6%
100 1
 
3.6%
36 1
 
3.6%
33 1
 
3.6%
30 8
28.6%
24 1
 
3.6%
11 1
 
3.6%
10 1
 
3.6%
2 3
 
10.7%
0 2
 
7.1%

현인원
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)71.4%
Missing14
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean46.928571
Minimum0
Maximum300
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-05-18T14:47:13.333863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3
Q110.25
median30
Q339.5
95-th percentile170
Maximum300
Range300
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation77.119751
Coefficient of variation (CV)1.6433433
Kurtosis10.436524
Mean46.928571
Median Absolute Deviation (MAD)14.5
Skewness3.1204219
Sum657
Variance5947.456
MonotonicityNot monotonic
2024-05-18T14:47:13.847361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
30 3
 
10.7%
40 2
 
7.1%
2 2
 
7.1%
300 1
 
3.6%
10 1
 
3.6%
38 1
 
3.6%
11 1
 
3.6%
0 1
 
3.6%
100 1
 
3.6%
24 1
 
3.6%
(Missing) 14
50.0%
ValueCountFrequency (%)
0 1
 
3.6%
2 2
7.1%
10 1
 
3.6%
11 1
 
3.6%
24 1
 
3.6%
30 3
10.7%
38 1
 
3.6%
40 2
7.1%
100 1
 
3.6%
300 1
 
3.6%
ValueCountFrequency (%)
300 1
 
3.6%
100 1
 
3.6%
40 2
7.1%
38 1
 
3.6%
30 3
10.7%
24 1
 
3.6%
11 1
 
3.6%
10 1
 
3.6%
2 2
7.1%
0 1
 
3.6%

전화번호
Text

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-05-18T14:47:14.575944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.928571
Min length9

Characters and Unicode

Total characters306
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

Unique26 ?
Unique (%)92.9%

Sample

1st row02-846-0042
2nd row02-338-0180
3rd row070-4035-4340
4th row02-786-8482
5th row02-6081-5900
ValueCountFrequency (%)
02-442-9664 2
 
7.1%
02-846-0042 1
 
3.6%
0234128551 1
 
3.6%
029557979 1
 
3.6%
07043544701 1
 
3.6%
01031800216 1
 
3.6%
029422223 1
 
3.6%
02-798-9935 1
 
3.6%
023944430 1
 
3.6%
029352999 1
 
3.6%
Other values (17) 17
60.7%
2024-05-18T14:47:15.969292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 58
19.0%
2 41
13.4%
- 38
12.4%
4 27
8.8%
5 25
8.2%
3 23
 
7.5%
8 21
 
6.9%
7 21
 
6.9%
9 20
 
6.5%
1 17
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 268
87.6%
Dash Punctuation 38
 
12.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58
21.6%
2 41
15.3%
4 27
10.1%
5 25
9.3%
3 23
 
8.6%
8 21
 
7.8%
7 21
 
7.8%
9 20
 
7.5%
1 17
 
6.3%
6 15
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 306
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58
19.0%
2 41
13.4%
- 38
12.4%
4 27
8.8%
5 25
8.2%
3 23
 
7.5%
8 21
 
6.9%
7 21
 
6.9%
9 20
 
6.5%
1 17
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58
19.0%
2 41
13.4%
- 38
12.4%
4 27
8.8%
5 25
8.2%
3 23
 
7.5%
8 21
 
6.9%
7 21
 
6.9%
9 20
 
6.5%
1 17
 
5.6%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15279.107
Minimum1327
Maximum150851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-05-18T14:47:16.389025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1327
5-th percentile2153.3
Q14226.25
median5771.5
Q37264.25
95-th percentile91272.3
Maximum150851
Range149524
Interquartile range (IQR)3038

Descriptive statistics

Standard deviation36256.36
Coefficient of variation (CV)2.3729371
Kurtosis11.311878
Mean15279.107
Median Absolute Deviation (MAD)1502
Skewness3.5220067
Sum427815
Variance1.3145236 × 109
MonotonicityNot monotonic
2024-05-18T14:47:17.241748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4316 2
 
7.1%
150851 1
 
3.6%
3957 1
 
3.6%
1327 1
 
3.6%
6220 1
 
3.6%
7292 1
 
3.6%
3140 1
 
3.6%
3618 1
 
3.6%
1622 1
 
3.6%
6369 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
1327 1
3.6%
1622 1
3.6%
3140 1
3.6%
3337 1
3.6%
3618 1
3.6%
3663 1
3.6%
3957 1
3.6%
4316 2
7.1%
4506 1
3.6%
4796 1
3.6%
ValueCountFrequency (%)
150851 1
3.6%
135660 1
3.6%
8838 1
3.6%
8573 1
3.6%
8106 1
3.6%
7317 1
3.6%
7292 1
3.6%
7255 1
3.6%
7235 1
3.6%
7005 1
3.6%

Interactions

2024-05-18T14:46:57.850961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:53.784719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:55.350352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:56.435775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:58.216994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:54.097459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:55.647427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:56.814598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:58.571111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:54.392010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:55.916670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:57.198809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:58.942756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:55.005328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:56.166857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:46:57.507230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T14:47:17.783055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명시설코드시설종류명(시설유형)시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시설코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시설종류명(시설유형)1.0001.0001.0001.0000.0000.1761.0000.3750.1071.0000.773
시설장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구코드1.0001.0000.0001.0001.0001.0001.0000.0000.0001.0000.000
시군구명1.0001.0000.1761.0001.0001.0001.0000.0000.0001.0000.000
시설주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
정원(수용인원)1.0001.0000.3751.0000.0000.0001.0001.0000.9741.0001.000
현인원1.0001.0000.1071.0000.0000.0001.0000.9741.0001.0000.632
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
우편번호1.0001.0000.7731.0000.0000.0001.0001.0000.6321.0001.000
2024-05-18T14:47:18.286267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드정원(수용인원)현인원우편번호시설종류명(시설유형)
시군구코드1.000-0.307-0.3360.5480.000
정원(수용인원)-0.3071.0000.417-0.1340.000
현인원-0.3360.4171.0000.5950.000
우편번호0.548-0.1340.5951.0000.432
시설종류명(시설유형)0.0000.0000.0000.4321.000

Missing values

2024-05-18T14:46:59.604771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T14:47:00.332046image/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.
2024-05-18T14:47:00.776626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시설명시설코드시설종류명(시설유형)시설종류상세명(시설종류)자치구(시)구분시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
0영등포구 장애인사랑나눔의 집C0384(장애인) (기타)장애인복지시설장애인기타자치구김금상1156000000영등포구서울특별시 영등포구 신길로 5530030002-846-0042150851
1마포점자도서실C1764(장애인) (기타)장애인복지시설장애인기타자치구김옥숙1144000000마포구서울특별시 마포구 망원2동 451-1101002-338-01803957
2사람희망 금천장애인자립생활센터F04194(장애인) 중증장애인자립생활지원센터장애인기타자치구황백남1154500000금천구서울특별시 금천구 독산로 70C동 603호(현대지식산업센터) (시흥동)038070-4035-43408573
3해오름장애인자립생활센터F04195(장애인) 중증장애인자립생활지원센터장애인기타자치구안권수1156000000영등포구서울특별시 영등포구 국회대로62길 14한국스카우트연맹 302호 (여의도동)111102-786-84827235
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24영등포구 발달장애인평생교육센터Z6300(장애인) (기타)장애인복지시설장애인기타자치구박미진1156000000영등포구서울특별시 영등포구 문래북로 1055~7층, 어울림센터 (당산동1가)<NA><NA>010318002167292
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