Overview

Dataset statistics

Number of variables9
Number of observations222
Missing cells423
Missing cells (%)21.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.0 KiB
Average record size in memory73.6 B

Variable types

Text7
Categorical1
Numeric1

Dataset

Description한국청소년상담복지개발원에서 관리하고 있는 전국의 꿈드림 센터 목록입니다.각 꿈드림 센터의 이름, 시도, 구군, 주소, 우편번호, 전화번호 등의 센터에 관련한 정보를 담고 있습니다.
Author한국청소년상담복지개발원
URLhttps://www.data.go.kr/data/15088280/fileData.do

Alerts

우편번호 is highly overall correlated with 시도High correlation
시도 is highly overall correlated with 우편번호High correlation
주소2 has 4 (1.8%) missing valuesMissing
FAX번호 has 206 (92.8%) missing valuesMissing
홈페이지 has 211 (95.0%) missing valuesMissing
센터명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:55:00.094735
Analysis finished2023-12-12 07:55:01.646295
Duration1.55 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

센터명
Text

UNIQUE 

Distinct222
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T16:55:01.865804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length13.720721
Min length13

Characters and Unicode

Total characters3046
Distinct characters141
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

Unique222 ?
Unique (%)100.0%

Sample

1st row서울특별시학교밖청소년지원센터
2nd row서울종로구학교밖청소년지원센터
3rd row서울중구학교밖청소년지원센터
4th row서울용산구학교밖청소년지원센터
5th row서울성동구학교밖청소년지원센터
ValueCountFrequency (%)
서울특별시학교밖청소년지원센터 1
 
0.5%
익산시학교밖청소년지원센터 1
 
0.5%
남원시학교밖청소년지원센터 1
 
0.5%
금산군학교밖청소년지원센터 1
 
0.5%
부여군학교밖청소년지원센터 1
 
0.5%
서천군학교밖청소년지원센터 1
 
0.5%
청양군학교밖청소년지원센터 1
 
0.5%
홍성군학교밖청소년지원센터 1
 
0.5%
예산군학교밖청소년지원센터 1
 
0.5%
태안군학교밖청소년지원센터 1
 
0.5%
Other values (212) 212
95.5%
2023-12-12T16:55:02.340122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
232
 
7.6%
228
 
7.5%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
Other values (131) 810
26.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3046
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
232
 
7.6%
228
 
7.5%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
Other values (131) 810
26.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3046
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
232
 
7.6%
228
 
7.5%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
Other values (131) 810
26.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3046
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
232
 
7.6%
228
 
7.5%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
222
 
7.3%
Other values (131) 810
26.6%

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
32 
서울특별시
26 
전라남도
23 
경상남도
21 
부산광역시
17 
Other values (12)
103 

Length

Max length7
Median length5
Mean length4.4234234
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 32
14.4%
서울특별시 26
11.7%
전라남도 23
10.4%
경상남도 21
9.5%
부산광역시 17
7.7%
충청남도 16
7.2%
경상북도 15
6.8%
충청북도 13
 
5.9%
강원특별자치도 13
 
5.9%
전라북도 10
 
4.5%
Other values (7) 36
16.2%

Length

2023-12-12T16:55:02.531289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 32
14.4%
서울특별시 26
11.7%
전라남도 23
10.4%
경상남도 21
9.5%
부산광역시 17
7.7%
충청남도 16
7.2%
경상북도 15
6.8%
강원특별자치도 13
 
5.9%
충청북도 13
 
5.9%
전라북도 10
 
4.5%
Other values (7) 36
16.2%
Distinct191
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T16:55:02.906415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.2297297
Min length1

Characters and Unicode

Total characters717
Distinct characters133
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

Unique178 ?
Unique (%)80.2%

Sample

1st row중구
2nd row종로구
3rd row중구
4th row용산구
5th row성동구
ValueCountFrequency (%)
서구 7
 
2.9%
동구 7
 
2.9%
중구 6
 
2.5%
남구 4
 
1.7%
북구 4
 
1.7%
창원시 4
 
1.7%
청주시 3
 
1.3%
강서구 2
 
0.8%
전주시 2
 
0.8%
부산진구 2
 
0.8%
Other values (190) 198
82.8%
2023-12-12T16:55:03.442209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
93
 
13.0%
90
 
12.6%
62
 
8.6%
23
 
3.2%
22
 
3.1%
20
 
2.8%
19
 
2.6%
17
 
2.4%
17
 
2.4%
15
 
2.1%
Other values (123) 339
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 699
97.5%
Space Separator 17
 
2.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
93
 
13.3%
90
 
12.9%
62
 
8.9%
23
 
3.3%
22
 
3.1%
20
 
2.9%
19
 
2.7%
17
 
2.4%
15
 
2.1%
14
 
2.0%
Other values (121) 324
46.4%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Punctuation
ValueCountFrequency (%)
* 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 699
97.5%
Common 18
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
93
 
13.3%
90
 
12.9%
62
 
8.9%
23
 
3.3%
22
 
3.1%
20
 
2.9%
19
 
2.7%
17
 
2.4%
15
 
2.1%
14
 
2.0%
Other values (121) 324
46.4%
Common
ValueCountFrequency (%)
17
94.4%
* 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 699
97.5%
ASCII 18
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
93
 
13.3%
90
 
12.9%
62
 
8.9%
23
 
3.3%
22
 
3.1%
20
 
2.9%
19
 
2.7%
17
 
2.4%
15
 
2.1%
14
 
2.0%
Other values (121) 324
46.4%
ASCII
ValueCountFrequency (%)
17
94.4%
* 1
 
5.6%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct221
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34437.144
Minimum1012
Maximum63590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T16:55:03.578296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1012
5-th percentile4545.65
Q117961.25
median34398
Q351081.5
95-th percentile59446.7
Maximum63590
Range62578
Interquartile range (IQR)33120.25

Descriptive statistics

Standard deviation18349.492
Coefficient of variation (CV)0.53284013
Kurtosis-1.2278367
Mean34437.144
Median Absolute Deviation (MAD)16627
Skewness-0.17189255
Sum7645046
Variance3.3670387 × 108
MonotonicityNot monotonic
2023-12-12T16:55:03.750717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36708 2
 
0.9%
4543 1
 
0.5%
57636 1
 
0.5%
32726 1
 
0.5%
33149 1
 
0.5%
33644 1
 
0.5%
33339 1
 
0.5%
32272 1
 
0.5%
32424 1
 
0.5%
32140 1
 
0.5%
Other values (211) 211
95.0%
ValueCountFrequency (%)
1012 1
0.5%
1412 1
0.5%
1616 1
0.5%
2254 1
0.5%
2586 1
0.5%
2840 1
0.5%
3065 1
0.5%
3476 1
0.5%
3676 1
0.5%
4016 1
0.5%
ValueCountFrequency (%)
63590 1
0.5%
63218 1
0.5%
63097 1
0.5%
62363 1
0.5%
62024 1
0.5%
61966 1
0.5%
61727 1
0.5%
61502 1
0.5%
61132 1
0.5%
59761 1
0.5%
Distinct214
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T16:55:04.200312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length8
Mean length9.981982
Min length7

Characters and Unicode

Total characters2216
Distinct characters175
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique206 ?
Unique (%)92.8%

Sample

1st row서울특별시 중구
2nd row서울특별시 종로구
3rd row서울특별시 중구
4th row서울특별시 용산구
5th row서울특별시 성동구
ValueCountFrequency (%)
경기도 32
 
6.1%
서울특별시 26
 
5.0%
전라남도 23
 
4.4%
경상남도 21
 
4.0%
부산광역시 17
 
3.3%
충청남도 16
 
3.1%
경상북도 15
 
2.9%
충청북도 13
 
2.5%
강원특별자치도 13
 
2.5%
전라북도 10
 
1.9%
Other values (266) 335
64.3%
2023-12-12T16:55:04.781123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/