Overview

Dataset statistics

Number of variables13
Number of observations31
Missing cells4
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory112.3 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-20424/S/1/datasetView.do

Alerts

시설종류명(시설유형) has constant value ""Constant
시설종류상세명(시설종류) has constant value ""Constant
자치구(시)구분 has constant value ""Constant
정원(수용인원) has 2 (6.5%) missing valuesMissing
현인원 has 2 (6.5%) missing valuesMissing
시설명 has unique valuesUnique
시설코드 has unique valuesUnique
시설장명 has unique valuesUnique
시설주소 has unique valuesUnique
전화번호 has unique valuesUnique
우편번호 has unique valuesUnique
정원(수용인원) has 9 (29.0%) zerosZeros
현인원 has 3 (9.7%) zerosZeros

Reproduction

Analysis started2024-05-11 01:11:53.056328
Analysis finished2024-05-11 01:12:04.461816
Duration11.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2024-05-11T01:12:04.852864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length10.064516
Min length8

Characters and Unicode

Total characters312
Distinct characters54
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row서울강서등촌지역자활센터
2nd row서울 영등포지역자활센터
3rd row서울관악봉천지역자활센터
4th row서울성동지역자활센터
5th row강남지역자활센터
ValueCountFrequency (%)
서울강서등촌지역자활센터 1
 
3.0%
서울강서지역자활센터 1
 
3.0%
서초지역자활센터 1
 
3.0%
서울광역자활센터 1
 
3.0%
금천지역자활센터 1
 
3.0%
서울동작지역자활센터 1
 
3.0%
성북지역자활센터 1
 
3.0%
서울노원남부지역자활센터 1
 
3.0%
노원지역자활센터 1
 
3.0%
양천지역자활센터 1
 
3.0%
Other values (23) 23
69.7%
2024-05-11T01:12:06.015904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
터 32
10.3%
자 32
10.3%
역 31
9.9%
센 31
9.9%
활 31
9.9%
지 30
9.6%
서 25
 
8.0%
울 21
 
6.7%
강 5
 
1.6%
동 4
 
1.3%
Other values (44) 70
22.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 310
99.4%
Space Separator 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
터 32
10.3%
자 32
10.3%
역 31
10.0%
센 31
10.0%
활 31
10.0%
지 30
9.7%
서 25
 
8.1%
울 21
 
6.8%
강 5
 
1.6%
동 4
 
1.3%
Other values (43) 68
21.9%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 310
99.4%
Common 2
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
터 32
10.3%
자 32
10.3%
역 31
10.0%
센 31
10.0%
활 31
10.0%
지 30
9.7%
서 25
 
8.1%
울 21
 
6.8%
강 5
 
1.6%
동 4
 
1.3%
Other values (43) 68
21.9%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 310
99.4%
ASCII 2
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
터 32
10.3%
자 32
10.3%
역 31
10.0%
센 31
10.0%
활 31
10.0%
지 30
9.7%
서 25
 
8.1%
울 21
 
6.8%
강 5
 
1.6%
동 4
 
1.3%
Other values (43) 68
21.9%
ASCII
ValueCountFrequency (%)
2
100.0%

시설코드
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2024-05-11T01:12:06.778435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters155
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

Unique31 ?
Unique (%)100.0%

Sample

1st rowZ0005
2nd rowZ0017
3rd rowZ0051
4th rowZ0065
5th rowZ0067
ValueCountFrequency (%)
z0005 1
 
3.2%
z0329 1
 
3.2%
z5994 1
 
3.2%
z5578 1
 
3.2%
z0607 1
 
3.2%
z0522 1
 
3.2%
z0492 1
 
3.2%
z0481 1
 
3.2%
z0476 1
 
3.2%
z0475 1
 
3.2%
Other values (21) 21
67.7%
2024-05-11T01:12:07.742626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 41
26.5%
Z 31
20.0%
3 14
 
9.0%
4 13
 
8.4%
7 12
 
7.7%
2 11
 
7.1%
5 10
 
6.5%
1 8
 
5.2%
6 5
 
3.2%
8 5
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124
80.0%
Uppercase Letter 31
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41
33.1%
3 14
 
11.3%
4 13
 
10.5%
7 12
 
9.7%
2 11
 
8.9%
5 10
 
8.1%
1 8
 
6.5%
6 5
 
4.0%
8 5
 
4.0%
9 5
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
Z 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 124
80.0%
Latin 31
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41
33.1%
3 14
 
11.3%
4 13
 
10.5%
7 12
 
9.7%
2 11
 
8.9%
5 10
 
8.1%
1 8
 
6.5%
6 5
 
4.0%
8 5
 
4.0%
9 5
 
4.0%
Latin
ValueCountFrequency (%)
Z 31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41
26.5%
Z 31
20.0%
3 14
 
9.0%
4 13
 
8.4%
7 12
 
7.7%
2 11
 
7.1%
5 10
 
6.5%
1 8
 
5.2%
6 5
 
3.2%
8 5
 
3.2%

시설종류명(시설유형)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
(저소득) 지역자활센터
31 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(저소득) 지역자활센터
2nd row(저소득) 지역자활센터
3rd row(저소득) 지역자활센터
4th row(저소득) 지역자활센터
5th row(저소득) 지역자활센터

Common Values

ValueCountFrequency (%)
(저소득) 지역자활센터 31
100.0%

Length

2024-05-11T01:12:08.382618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:12:08.788352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
저소득 31
50.0%
지역자활센터 31
50.0%

시설종류상세명(시설종류)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
자활시설
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자활시설 31
100.0%

Length

2024-05-11T01:12:09.158533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:12:09.483558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자활시설 31
100.0%

자치구(시)구분
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
자치구
31 

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 31
100.0%

Length

2024-05-11T01:12:09.802652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:12:10.113451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자치구 31
100.0%

시설장명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2024-05-11T01:12:10.652835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9677419
Min length2

Characters and Unicode

Total characters92
Distinct characters54
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

Unique31 ?
Unique (%)100.0%

Sample

1st row장재승
2nd row김경미
3rd row김봉준
4th row이선화
5th row이경화
ValueCountFrequency (%)
장재승 1
 
3.2%
이용구 1
 
3.2%
전명재 1
 
3.2%
민경연 1
 
3.2%
이정일 1
 
3.2%
김경태 1
 
3.2%
이효삼 1
 
3.2%
임근형 1
 
3.2%
김영호 1
 
3.2%
정진아 1
 
3.2%
Other values (21) 21
67.7%
2024-05-11T01:12:11.883272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 7
 
7.6%
이 7
 
7.6%
김 7
 
7.6%
민 4
 
4.3%
정 4
 
4.3%
미 3
 
3.3%
재 3
 
3.3%
화 3
 
3.3%
최 2
 
2.2%
희 2
 
2.2%
Other values (44) 50
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 7
 
7.6%
이 7
 
7.6%
김 7
 
7.6%
민 4
 
4.3%
정 4
 
4.3%
미 3
 
3.3%
재 3
 
3.3%
화 3
 
3.3%
최 2
 
2.2%
희 2
 
2.2%
Other values (44) 50
54.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 7
 
7.6%
이 7
 
7.6%
김 7
 
7.6%
민 4
 
4.3%
정 4
 
4.3%
미 3
 
3.3%
재 3
 
3.3%
화 3
 
3.3%
최 2
 
2.2%
희 2
 
2.2%
Other values (44) 50
54.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 7
 
7.6%
이 7
 
7.6%
김 7
 
7.6%
민 4
 
4.3%
정 4
 
4.3%
미 3
 
3.3%
재 3
 
3.3%
화 3
 
3.3%
최 2
 
2.2%
희 2
 
2.2%
Other values (44) 50
54.3%

시군구코드
Real number (ℝ)

Distinct25
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1426935 × 109
Minimum1.111 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-05-11T01:12:12.404860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile1.1155 × 109
Q11.12975 × 109
median1.144 × 109
Q31.15525 × 109
95-th percentile1.1695 × 109
Maximum1.174 × 109
Range63000000
Interquartile range (IQR)25500000

Descriptive statistics

Standard deviation17679403
Coefficient of variation (CV)0.015471692
Kurtosis-0.99598991
Mean1.1426935 × 109
Median Absolute Deviation (MAD)13500000
Skewness-0.041681861
Sum3.54235 × 1010
Variance3.1256129 × 1014
MonotonicityNot monotonic
2024-05-11T01:12:12.829447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1135000000 3
 
9.7%
1150000000 2
 
6.5%
1147000000 2
 
6.5%
1153000000 2
 
6.5%
1162000000 2
 
6.5%
1168000000 1
 
3.2%
1141000000 1
 
3.2%
1171000000 1
 
3.2%
1165000000 1
 
3.2%
1154500000 1
 
3.2%
Other values (15) 15
48.4%
ValueCountFrequency (%)
1111000000 1
3.2%
1114000000 1
3.2%
1117000000 1
3.2%
1120000000 1
3.2%
1121500000 1
3.2%
1123000000 1
3.2%
1126000000 1
3.2%
1129000000 1
3.2%
1130500000 1
3.2%
1132000000 1
3.2%
ValueCountFrequency (%)
1174000000 1
3.2%
1171000000 1
3.2%
1168000000 1
3.2%
1165000000 1
3.2%
1162000000 2
6.5%
1159000000 1
3.2%
1156000000 1
3.2%
1154500000 1
3.2%
1153000000 2
6.5%
1150000000 2
6.5%

시군구명
Text

Distinct25
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size380.0 B
2024-05-11T01:12:13.323161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0645161
Min length2

Characters and Unicode

Total characters95
Distinct characters36
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

Unique20 ?
Unique (%)64.5%

Sample

1st row강서구
2nd row영등포구
3rd row관악구
4th row성동구
5th row강남구
ValueCountFrequency (%)
노원구 3
 
9.7%
양천구 2
 
6.5%
구로구 2
 
6.5%
강서구 2
 
6.5%
관악구 2
 
6.5%
도봉구 1
 
3.2%
강동구 1
 
3.2%
서초구 1
 
3.2%
금천구 1
 
3.2%
동작구 1
 
3.2%
Other values (15) 15
48.4%
2024-05-11T01:12:14.219127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 33
34.7%
강 5
 
5.3%
서 4
 
4.2%
동 4
 
4.2%
노 3
 
3.2%
원 3
 
3.2%
천 3
 
3.2%
로 3
 
3.2%
성 2
 
2.1%
북 2
 
2.1%
Other values (26) 33
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 95
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 33
34.7%
강 5
 
5.3%
서 4
 
4.2%
동 4
 
4.2%
노 3
 
3.2%
원 3
 
3.2%
천 3
 
3.2%
로 3
 
3.2%
성 2
 
2.1%
북 2
 
2.1%
Other values (26) 33
34.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 95
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 33
34.7%
강 5
 
5.3%
서 4
 
4.2%
동 4
 
4.2%
노 3
 
3.2%
원 3
 
3.2%
천 3
 
3.2%
로 3
 
3.2%
성 2
 
2.1%
북 2
 
2.1%
Other values (26) 33
34.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 95
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 33
34.7%
강 5
 
5.3%
서 4
 
4.2%
동 4
 
4.2%
노 3
 
3.2%
원 3
 
3.2%
천 3
 
3.2%
로 3
 
3.2%
성 2
 
2.1%
북 2
 
2.1%
Other values (26) 33
34.7%

시설주소
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2024-05-11T01:12:14.932452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length30
Mean length26.419355
Min length17

Characters and Unicode

Total characters819
Distinct characters126
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

Unique31 ?
Unique (%)100.0%

Sample

1st row서울특별시 강서구 양천로 335
2nd row서울특별시 영등포구 영등포로65길 3-02층
3rd row서울특별시 관악구 봉천로 2794층 (봉천동)
4th row서울특별시 성동구 매봉길 21-0(옥수동)
5th row서울특별시 강남구 개포로38길 12 (개포동)
ValueCountFrequency (%)
서울특별시 31
 
21.2%
노원구 3
 
2.1%
관악구 2
 
1.4%
양천구 2
 
1.4%
어바니엘 2
 
1.4%
덕릉로 2
 
1.4%
3층 2
 
1.4%
구로구 2
 
1.4%
강서구 2
 
1.4%
동대문구 1
 
0.7%
Other values (97) 97
66.4%
2024-05-11T01:12:16.032956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
115
 
14.0%
구 38
 
4.6%
서 36
 
4.4%
로 36
 
4.4%
시 34
 
4.2%
동 32
 
3.9%
울 32
 
3.9%
특 31
 
3.8%
별 31
 
3.8%
3 21
 
2.6%
Other values (116) 413
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 517
63.1%
Decimal Number 133
 
16.2%
Space Separator 115
 
14.0%
Close Punctuation 20
 
2.4%
Open Punctuation 20
 
2.4%
Dash Punctuation 11
 
1.3%
Other Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 38
 
7.4%
서 36
 
7.0%
로 36
 
7.0%
시 34
 
6.6%
동 32
 
6.2%
울 32
 
6.2%
특 31
 
6.0%
별 31
 
6.0%
층 17
 
3.3%
길 13
 
2.5%
Other values (101) 217
42.0%
Decimal Number
ValueCountFrequency (%)
3 21
15.8%
1 20
15.0%
2 17
12.8%
4 13
9.8%
5 13
9.8%
6 12
9.0%
7 12
9.0%
0 11
8.3%
8 8
 
6.0%
9 6
 
4.5%
Space Separator
ValueCountFrequency (%)
115
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 517
63.1%
Common 302
36.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 38
 
7.4%
서 36
 
7.0%
로 36
 
7.0%
시 34
 
6.6%
동 32
 
6.2%
울 32
 
6.2%
특 31
 
6.0%
별 31
 
6.0%
층 17
 
3.3%
길 13
 
2.5%
Other values (101) 217
42.0%
Common
ValueCountFrequency (%)
115
38.1%
3 21
 
7.0%
) 20
 
6.6%
1 20
 
6.6%
( 20
 
6.6%
2 17
 
5.6%
4 13
 
4.3%
5 13
 
4.3%
6 12
 
4.0%
7 12
 
4.0%
Other values (5) 39
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 517
63.1%
ASCII 302
36.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115
38.1%
3 21
 
7.0%
) 20
 
6.6%
1 20
 
6.6%
( 20
 
6.6%
2 17
 
5.6%
4 13
 
4.3%
5 13
 
4.3%
6 12
 
4.0%
7 12
 
4.0%
Other values (5) 39
 
12.9%
Hangul
ValueCountFrequency (%)
구 38
 
7.4%
서 36
 
7.0%
로 36
 
7.0%
시 34
 
6.6%
동 32
 
6.2%
울 32
 
6.2%
특 31
 
6.0%
별 31
 
6.0%
층 17
 
3.3%
길 13
 
2.5%
Other values (101) 217
42.0%

정원(수용인원)
Real number (ℝ)

MISSING  ZEROS 

Distinct19
Distinct (%)65.5%
Missing2
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean120.7931
Minimum0
Maximum1200
Zeros9
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-05-11T01:12:16.621999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median100
Q3134
95-th percentile221.6
Maximum1200
Range1200
Interquartile range (IQR)134

Descriptive statistics

Standard deviation219.26767
Coefficient of variation (CV)1.8152334
Kurtosis22.667872
Mean120.7931
Median Absolute Deviation (MAD)77
Skewness4.5187604
Sum3503
Variance48078.313
MonotonicityNot monotonic
2024-05-11T01:12:17.279406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 9
29.0%
130 2
 
6.5%
100 2
 
6.5%
114 1
 
3.2%
1200 1
 
3.2%
103 1
 
3.2%
76 1
 
3.2%
60 1
 
3.2%
180 1
 
3.2%
150 1
 
3.2%
Other values (9) 9
29.0%
(Missing) 2
 
6.5%
ValueCountFrequency (%)
0 9
29.0%
20 1
 
3.2%
55 1
 
3.2%
60 1
 
3.2%
76 1
 
3.2%
90 1
 
3.2%
100 2
 
6.5%
103 1
 
3.2%
110 1
 
3.2%
114 1
 
3.2%
ValueCountFrequency (%)
1200 1
3.2%
236 1
3.2%
200 1
3.2%
180 1
3.2%
177 1
3.2%
150 1
3.2%
138 1
3.2%
134 1
3.2%
130 2
6.5%
114 1
3.2%

현인원
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)72.4%
Missing2
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean152.75862
Minimum0
Maximum1200
Zeros3
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-05-11T01:12:17.842961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q182
median120
Q3140
95-th percentile244.4
Maximum1200
Range1200
Interquartile range (IQR)58

Descriptive statistics

Standard deviation210.67301
Coefficient of variation (CV)1.3791236
Kurtosis23.721926
Mean152.75862
Median Absolute Deviation (MAD)38
Skewness4.6598146
Sum4430
Variance44383.118
MonotonicityNot monotonic
2024-05-11T01:12:18.643199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3
 
9.7%
130 3
 
9.7%
100 3
 
9.7%
120 2
 
6.5%
200 2
 
6.5%
221 1
 
3.2%
76 1
 
3.2%
65 1
 
3.2%
260 1
 
3.2%
138 1
 
3.2%
Other values (11) 11
35.5%
(Missing) 2
 
6.5%
ValueCountFrequency (%)
0 3
9.7%
60 1
 
3.2%
65 1
 
3.2%
75 1
 
3.2%
76 1
 
3.2%
82 1
 
3.2%
90 1
 
3.2%
100 3
9.7%
103 1
 
3.2%
108 1
 
3.2%
ValueCountFrequency (%)
1200 1
 
3.2%
260 1
 
3.2%
221 1
 
3.2%
200 2
6.5%
178 1
 
3.2%
170 1
 
3.2%
140 1
 
3.2%
138 1
 
3.2%
134 1
 
3.2%
130 3
9.7%

전화번호
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2024-05-11T01:12:19.622958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.032258
Min length9

Characters and Unicode

Total characters342
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

Unique31 ?
Unique (%)100.0%

Sample

1st row02-3664-5920
2nd row02-848-0600
3rd row02-876-6730
4th row02-2299-6658
5th row02-445-1801
ValueCountFrequency (%)
02-3664-5920 1
 
3.2%
02-2605-1222 1
 
3.2%
0220580790 1
 
3.2%
023184140 1
 
3.2%
028061577 1
 
3.2%
02-822-7707 1
 
3.2%
02-927-2420 1
 
3.2%
02-941-6594 1
 
3.2%
02-939-3538 1
 
3.2%
02-2601-6039 1
 
3.2%
Other values (21) 21
67.7%
2024-05-11T01:12:21.310372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 57
16.7%
- 56
16.4%
0 53
15.5%
8 25
7.3%
7 25
7.3%
6 23
6.7%
4 22
 
6.4%
1 22
 
6.4%
3 20
 
5.8%
5 20
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 286
83.6%
Dash Punctuation 56
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 57
19.9%
0 53
18.5%
8 25
8.7%
7 25
8.7%
6 23
8.0%
4 22
 
7.7%
1 22
 
7.7%
3 20
 
7.0%
5 20
 
7.0%
9 19
 
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 57
16.7%
- 56
16.4%
0 53
15.5%
8 25
7.3%
7 25
7.3%
6 23
6.7%
4 22
 
6.4%
1 22
 
6.4%
3 20
 
5.8%
5 20
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 57
16.7%
- 56
16.4%
0 53
15.5%
8 25
7.3%
7 25
7.3%
6 23
6.7%
4 22
 
6.4%
1 22
 
6.4%
3 20
 
5.8%
5 20
 
5.8%

우편번호
Real number (ℝ)

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23081.968
Minimum1111
Maximum157904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-05-11T01:12:22.114639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1111
5-th percentile1467
Q13266.5
median5516
Q38125
95-th percentile143971
Maximum157904
Range156793
Interquartile range (IQR)4858.5

Descriptive statistics

Standard deviation47877.909
Coefficient of variation (CV)2.0742559
Kurtosis3.8226227
Mean23081.968
Median Absolute Deviation (MAD)2452
Skewness2.3442214
Sum715541
Variance2.2922942 × 109
MonotonicityNot monotonic
2024-05-11T01:12:22.640397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7523 1
 
3.2%
7318 1
 
3.2%
5516 1
 
3.2%
6802 1
 
3.2%
7968 1
 
3.2%
8624 1
 
3.2%
6948 1
 
3.2%
136041 1
 
3.2%
1848 1
 
3.2%
1648 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1111 1
3.2%
1330 1
3.2%
1604 1
3.2%
1648 1
3.2%
1848 1
3.2%
2069 1
3.2%
2589 1
3.2%
3110 1
3.2%
3423 1
3.2%
3701 1
3.2%
ValueCountFrequency (%)
157904 1
3.2%
151901 1
3.2%
136041 1
3.2%
133845 1
3.2%
8720 1
3.2%
8624 1
3.2%
8311 1
3.2%
8282 1
3.2%
7968 1
3.2%
7930 1
3.2%

Interactions

2024-05-11T01:12:01.632409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:11:58.437409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:11:59.512187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:12:00.505018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:12:01.934536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:11:58.744807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:11:59.745110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:12:00.857624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:12:02.206133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:11:59.007201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:12:00.009611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:12:01.112071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:12:02.483502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:11:59.249617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:12:00.251224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:12:01.357740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T01:12:22.983949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명시설코드시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시설코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시설장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구코드1.0001.0001.0001.0001.0001.0000.6200.5041.0000.000
시군구명1.0001.0001.0001.0001.0001.0000.9910.9901.0000.000
시설주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
정원(수용인원)1.0001.0001.0000.6200.9911.0001.0000.7351.0000.000
현인원1.0001.0001.0000.5040.9901.0000.7351.0001.0000.411
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
우편번호1.0001.0001.0000.0000.0001.0000.0000.4111.0001.000
2024-05-11T01:12:23.481496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드정원(수용인원)현인원우편번호
시군구코드1.000-0.129-0.1300.451
정원(수용인원)-0.1291.0000.401-0.151
현인원-0.1300.4011.000-0.214
우편번호0.451-0.151-0.2141.000

Missing values

2024-05-11T01:12:02.956779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T01:12:03.604219image/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.
2024-05-11T01:12:04.284842image/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
0서울강서등촌지역자활센터Z0005(저소득) 지역자활센터자활시설자치구장재승1150000000강서구서울특별시 강서구 양천로 335<NA>002-3664-59207523
1서울 영등포지역자활센터Z0017(저소득) 지역자활센터자활시설자치구김경미1156000000영등포구서울특별시 영등포구 영등포로65길 3-02층012002-848-06007318
2서울관악봉천지역자활센터Z0051(저소득) 지역자활센터자활시설자치구김봉준1162000000관악구서울특별시 관악구 봉천로 2794층 (봉천동)010002-876-67308720
3서울성동지역자활센터Z0065(저소득) 지역자활센터자활시설자치구이선화1120000000성동구서울특별시 성동구 매봉길 21-0(옥수동)15010002-2299-6658133845
4강남지역자활센터Z0067(저소득) 지역자활센터자활시설자치구이경화1168000000강남구서울특별시 강남구 개포로38길 12 (개포동)177<NA>02-445-18016309
5이화여자대학교서대문지역자활센터Z0073(저소득) 지역자활센터자활시설자치구현리사1141000000서대문구서울특별시 서대문구 연희로11마길 86-77(연희동)017002-324-10273701
6서울노원북부지역자활센터Z0102(저소득) 지역자활센터자활시설자치구최경식1135000000노원구서울특별시 노원구 동일로245길 564층13013002-952-71841604
7서울관악지역자활센터Z0157(저소득) 지역자활센터자활시설자치구김승오1162000000관악구서울특별시 관악구 문성로 236평희빌딩 5층11413002-867-8381151901
8서울은평지역자활센터Z0244(저소득) 지역자활센터자활시설자치구최민준1138000000은평구서울특별시 은평구 갈현로 86 층 (신사동)1200120002-307-11863423
9서울마포지역자활센터Z0283(저소득) 지역자활센터자활시설자치구김선희1144000000마포구서울특별시 마포구 매봉산로 18창업복지관 3층(상암동)10310302-312-79423911
시설명시설코드시설종류명(시설유형)시설종류상세명(시설종류)자치구(시)구분시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
21동대문지역자활센터Z0473(저소득) 지역자활센터자활시설자치구변미숙1123000000동대문구서울특별시 동대문구 청계천로 5215층 (용두동)0002-2242-75782589
22양천지역자활센터Z0475(저소득) 지역자활센터자활시설자치구정진아1147000000양천구서울특별시 양천구 오목로 41명수프라자 5층(신월동)10010002-2601-60397930
23노원지역자활센터Z0476(저소득) 지역자활센터자활시설자치구김영호1135000000노원구서울특별시 노원구 덕릉로 730303호 (상계동, 상계불암대림아파트상가)13413402-939-35381648
24서울노원남부지역자활센터Z0481(저소득) 지역자활센터자활시설자치구임근형1135000000노원구서울특별시 노원구 동일로178길 19-38302호(공릉동, 연세빌딩) (공릉동)13813802-941-65941848
25성북지역자활센터Z0492(저소득) 지역자활센터자활시설자치구이효삼1129000000성북구서울특별시 성북구 삼선교로4길 47-1지층 1호026002-927-2420136041
26서울동작지역자활센터Z0522(저소득) 지역자활센터자활시설자치구김경태1159000000동작구서울특별시 동작구 등용로 474층(대방동)0002-822-77076948
27금천지역자활센터Z0607(저소득) 지역자활센터자활시설자치구이정일1154500000금천구서울특별시 금천구 시흥대로 2725층 (시흥동)(시흥동)01300280615778624
28서울광역자활센터Z5578(저소득) 지역자활센터자활시설자치구민경연1147000000양천구서울특별시 양천구 공항대로 630목동 어바니엘 3층 서울광역자활센터 (목동, 어바니엘)20<NA>0231841407968
29서초지역자활센터Z5994(저소득) 지역자활센터자활시설자치구전명재1165000000서초구서울특별시 서초구 청계산로9길1-3 (신원동)556502205807906802
30서울송파지역자활센터Z6080(저소득) 지역자활센터자활시설자치구박민수1171000000송파구서울특별시 송파구 풍성로5길 16 (풍납동)1007602-416-71195516