Overview

Dataset statistics

Number of variables15
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory129.5 B

Variable types

Categorical1
Text6
Numeric7
DateTime1

Dataset

Description중증장애인 거주시설 현황
Author경기복지재단(경기도장애인복지종합지원센터)
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=HNT7VFDH7NU7SN20W26T25533106&infSeq=1

Alerts

소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
입소정원수(명) is highly overall correlated with 입소현원수(명) and 2 other fieldsHigh correlation
입소현원수(명) is highly overall correlated with 입소정원수(명) and 2 other fieldsHigh correlation
종사자정원수(명) is highly overall correlated with 입소정원수(명) and 2 other fieldsHigh correlation
종사자현원수(명) is highly overall correlated with 입소정원수(명) and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
기관명 has unique valuesUnique
설치신고일 has unique valuesUnique

Reproduction

Analysis started2024-04-11 01:57:16.798606
Analysis finished2024-04-11 01:57:23.455053
Duration6.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Memory size548.0 B
이천시
7 
양평군
7 
화성시
4 
파주시
4 
안산시
3 
Other values (17)
27 

Length

Max length4
Median length3
Mean length3.0576923
Min length3

Unique

Unique8 ?
Unique (%)15.4%

Sample

1st row가평군
2nd row가평군
3rd row고양시
4th row광주시
5th row군포시

Common Values

ValueCountFrequency (%)
이천시 7
13.5%
양평군 7
13.5%
화성시 4
 
7.7%
파주시 4
 
7.7%
안산시 3
 
5.8%
용인시 3
 
5.8%
부천시 2
 
3.8%
양주시 2
 
3.8%
김포시 2
 
3.8%
남양주시 2
 
3.8%
Other values (12) 16
30.8%

Length

2024-04-11T10:57:23.518383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이천시 7
13.5%
양평군 7
13.5%
화성시 4
 
7.7%
파주시 4
 
7.7%
안산시 3
 
5.8%
용인시 3
 
5.8%
시흥시 2
 
3.8%
연천군 2
 
3.8%
안성시 2
 
3.8%
가평군 2
 
3.8%
Other values (12) 16
30.8%

기관명
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-11T10:57:23.724212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length5.0961538
Min length2

Characters and Unicode

Total characters265
Distinct characters115
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)100.0%

Sample

1st row가평꽃동네희망의집
2nd row성가정의집
3rd row홀트일산요양원
4th row품안의집
5th row양지의집
ValueCountFrequency (%)
가평꽃동네희망의집 1
 
1.9%
창인홈 1
 
1.9%
가람 1
 
1.9%
동트는마을 1
 
1.9%
성심요양원 1
 
1.9%
양지바른 1
 
1.9%
요한의집 1
 
1.9%
한울장애인공동체 1
 
1.9%
해밀 1
 
1.9%
베데스다 1
 
1.9%
Other values (43) 43
81.1%
2024-04-11T10:57:24.051885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
집 17
 
6.4%
의 16
 
6.0%
원 11
 
4.2%
양 10
 
3.8%
지 8
 
3.0%
가 6
 
2.3%
복 6
 
2.3%
아 6
 
2.3%
리 6
 
2.3%
요 6
 
2.3%
Other values (105) 173
65.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 261
98.5%
Space Separator 1
 
0.4%
Other Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
집 17
 
6.5%
의 16
 
6.1%
원 11
 
4.2%
양 10
 
3.8%
지 8
 
3.1%
가 6
 
2.3%
복 6
 
2.3%
아 6
 
2.3%
리 6
 
2.3%
요 6
 
2.3%
Other values (101) 169
64.8%
Space Separator
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 261
98.5%
Common 4
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
집 17
 
6.5%
의 16
 
6.1%
원 11
 
4.2%
양 10
 
3.8%
지 8
 
3.1%
가 6
 
2.3%
복 6
 
2.3%
아 6
 
2.3%
리 6
 
2.3%
요 6
 
2.3%
Other values (101) 169
64.8%
Common
ValueCountFrequency (%)
1
25.0%
. 1
25.0%
( 1
25.0%
) 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 261
98.5%
ASCII 4
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
집 17
 
6.5%
의 16
 
6.1%
원 11
 
4.2%
양 10
 
3.8%
지 8
 
3.1%
가 6
 
2.3%
복 6
 
2.3%
아 6
 
2.3%
리 6
 
2.3%
요 6
 
2.3%
Other values (101) 169
64.8%
ASCII
ValueCountFrequency (%)
1
25.0%
. 1
25.0%
( 1
25.0%
) 1
25.0%

소재지지번주소
Text

Distinct50
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-11T10:57:24.280461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length22.634615
Min length17

Characters and Unicode

Total characters1177
Distinct characters110
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

Unique48 ?
Unique (%)92.3%

Sample

1st row경기도 가평군 조종면 운악리 540-3번지
2nd row경기도 가평군 조종면 마일리 296-14번지
3rd row경기도 고양시 일산서구 탄현동 41-1번지
4th row경기도 광주시 곤지암읍 연곡리 80-2번지
5th row경기도 군포시 당정동 982-1번지
ValueCountFrequency (%)
경기도 52
 
20.0%
양평군 7
 
2.7%
이천시 7
 
2.7%
화성시 4
 
1.5%
파주시 4
 
1.5%
안산시 3
 
1.2%
연곡리 3
 
1.2%
용인시 3
 
1.2%
단원구 3
 
1.2%
처인구 3
 
1.2%
Other values (145) 171
65.8%
2024-04-11T10:57:24.602442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
208
 
17.7%
지 57
 
4.8%
경 52
 
4.4%
도 52
 
4.4%
기 52
 
4.4%
번 52
 
4.4%
시 43
 
3.7%
리 42
 
3.6%
1 39
 
3.3%
- 36
 
3.1%
Other values (100) 544
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 741
63.0%
Space Separator 208
 
17.7%
Decimal Number 192
 
16.3%
Dash Punctuation 36
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 57
 
7.7%
경 52
 
7.0%
도 52
 
7.0%
기 52
 
7.0%
번 52
 
7.0%
시 43
 
5.8%
리 42
 
5.7%
면 26
 
3.5%
양 23
 
3.1%
동 17
 
2.3%
Other values (88) 325
43.9%
Decimal Number
ValueCountFrequency (%)
1 39
20.3%
3 27
14.1%
2 27
14.1%
4 19
9.9%
5 18
9.4%
6 15
 
7.8%
8 14
 
7.3%
9 12
 
6.2%
0 12
 
6.2%
7 9
 
4.7%
Space Separator
ValueCountFrequency (%)
208
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 741
63.0%
Common 436
37.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 57
 
7.7%
경 52
 
7.0%
도 52
 
7.0%
기 52
 
7.0%
번 52
 
7.0%
시 43
 
5.8%
리 42
 
5.7%
면 26
 
3.5%
양 23
 
3.1%
동 17
 
2.3%
Other values (88) 325
43.9%
Common
ValueCountFrequency (%)
208
47.7%
1 39
 
8.9%
- 36
 
8.3%
3 27
 
6.2%
2 27
 
6.2%
4 19
 
4.4%
5 18
 
4.1%
6 15
 
3.4%
8 14
 
3.2%
9 12
 
2.8%
Other values (2) 21
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 741
63.0%
ASCII 436
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
208
47.7%
1 39
 
8.9%
- 36
 
8.3%
3 27
 
6.2%
2 27
 
6.2%
4 19
 
4.4%
5 18
 
4.1%
6 15
 
3.4%
8 14
 
3.2%
9 12
 
2.8%
Other values (2) 21
 
4.8%
Hangul
ValueCountFrequency (%)
지 57
 
7.7%
경 52
 
7.0%
도 52
 
7.0%
기 52
 
7.0%
번 52
 
7.0%
시 43
 
5.8%
리 42
 
5.7%
면 26
 
3.5%
양 23
 
3.1%
동 17
 
2.3%
Other values (88) 325
43.9%

소재지도로명주소
Text

Distinct50
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-11T10:57:24.851139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length22.230769
Min length15

Characters and Unicode

Total characters1156
Distinct characters121
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

Unique48 ?
Unique (%)92.3%

Sample

1st row경기도 가평군 조종면 꽃동네길 35-25
2nd row경기도 가평군 조종면 연인산로474번길 139-37
3rd row경기도 고양시 일산서구 탄현로 42
4th row경기도 광주시 곤지암읍 광여로 555-40
5th row경기도 군포시 한세로70번길 14
ValueCountFrequency (%)
경기도 52
 
20.2%
양평군 7
 
2.7%
이천시 7
 
2.7%
파주시 4
 
1.6%
화성시 4
 
1.6%
안산시 3
 
1.2%
단원구 3
 
1.2%
처인구 3
 
1.2%
용인시 3
 
1.2%
남양주시 2
 
0.8%
Other values (142) 169
65.8%
2024-04-11T10:57:25.236660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
205
 
17.7%
기 54
 
4.7%
경 53
 
4.6%
도 52
 
4.5%
시 43
 
3.7%
2 42
 
3.6%
1 40
 
3.5%
로 37
 
3.2%
길 35
 
3.0%
3 35
 
3.0%
Other values (111) 560
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 683
59.1%
Decimal Number 241
 
20.8%
Space Separator 205
 
17.7%
Dash Punctuation 27
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 54
 
7.9%
경 53
 
7.8%
도 52
 
7.6%
시 43
 
6.3%
로 37
 
5.4%
길 35
 
5.1%
면 26
 
3.8%
양 25
 
3.7%
번 20
 
2.9%
산 14
 
2.0%
Other values (99) 324
47.4%
Decimal Number
ValueCountFrequency (%)
2 42
17.4%
1 40
16.6%
3 35
14.5%
4 21
8.7%
7 20
8.3%
5 19
7.9%
9 18
7.5%
6 17
7.1%
0 15
 
6.2%
8 14
 
5.8%
Space Separator
ValueCountFrequency (%)
205
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 683
59.1%
Common 473
40.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 54
 
7.9%
경 53
 
7.8%
도 52
 
7.6%
시 43
 
6.3%
로 37
 
5.4%
길 35
 
5.1%
면 26
 
3.8%
양 25
 
3.7%
번 20
 
2.9%
산 14
 
2.0%
Other values (99) 324
47.4%
Common
ValueCountFrequency (%)
205
43.3%
2 42
 
8.9%
1 40
 
8.5%
3 35
 
7.4%
- 27
 
5.7%
4 21
 
4.4%
7 20
 
4.2%
5 19
 
4.0%
9 18
 
3.8%
6 17
 
3.6%
Other values (2) 29
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 683
59.1%
ASCII 473
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
205
43.3%
2 42
 
8.9%
1 40
 
8.5%
3 35
 
7.4%
- 27
 
5.7%
4 21
 
4.4%
7 20
 
4.2%
5 19
 
4.0%
9 18
 
3.8%
6 17
 
3.6%
Other values (2) 29
 
6.1%
Hangul
ValueCountFrequency (%)
기 54
 
7.9%
경 53
 
7.8%
도 52
 
7.6%
시 43
 
6.3%
로 37
 
5.4%
길 35
 
5.1%
면 26
 
3.8%
양 25
 
3.7%
번 20
 
2.9%
산 14
 
2.0%
Other values (99) 324
47.4%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14290.308
Minimum10021
Maximum18577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-11T10:57:25.361346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10021
5-th percentile10286.4
Q111897.25
median13953.5
Q317387.75
95-th percentile18386.4
Maximum18577
Range8556
Interquartile range (IQR)5490.5

Descriptive statistics

Standard deviation2879.1864
Coefficient of variation (CV)0.20147826
Kurtosis-1.5831934
Mean14290.308
Median Absolute Deviation (MAD)2972
Skewness0.072899587
Sum743096
Variance8289714.1
MonotonicityNot monotonic
2024-04-11T10:57:25.472507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
12528 2
 
3.8%
17159 2
 
3.8%
12540 2
 
3.8%
11502 2
 
3.8%
10806 2
 
3.8%
15646 2
 
3.8%
17415 1
 
1.9%
17028 1
 
1.9%
10317 1
 
1.9%
17408 1
 
1.9%
Other values (36) 36
69.2%
ValueCountFrequency (%)
10021 1
1.9%
10069 1
1.9%
10249 1
1.9%
10317 1
1.9%
10806 2
3.8%
10940 1
1.9%
10948 1
1.9%
11015 1
1.9%
11020 1
1.9%
11137 1
1.9%
ValueCountFrequency (%)
18577 1
1.9%
18556 1
1.9%
18547 1
1.9%
18255 1
1.9%
18102 1
1.9%
17814 1
1.9%
17509 1
1.9%
17501 1
1.9%
17415 1
1.9%
17413 1
1.9%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.464496
Minimum37.007874
Maximum38.119581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-11T10:57:25.586199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.007874
5-th percentile37.104927
Q137.230304
median37.415321
Q337.701993
95-th percentile37.915519
Maximum38.119581
Range1.1117064
Interquartile range (IQR)0.47168875

Descriptive statistics

Standard deviation0.28832135
Coefficient of variation (CV)0.0076958555
Kurtosis-0.89856296
Mean37.464496
Median Absolute Deviation (MAD)0.23474603
Skewness0.4265332
Sum1948.1538
Variance0.083129201
MonotonicityNot monotonic
2024-04-11T10:57:25.709521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.54527495 2
 
3.8%
37.24881774 2
 
3.8%
38.11958083 1
 
1.9%
37.17644515 1
 
1.9%
37.2390792 1
 
1.9%
37.28921243 1
 
1.9%
37.24419197 1
 
1.9%
37.68598339 1
 
1.9%
37.17142111 1
 
1.9%
37.12618379 1
 
1.9%
Other values (40) 40
76.9%
ValueCountFrequency (%)
37.00787445 1
1.9%
37.05210281 1
1.9%
37.10423494 1
1.9%
37.10549384 1
1.9%
37.12618379 1
1.9%
37.13993915 1
1.9%
37.14452997 1
1.9%
37.16217226 1
1.9%
37.17142111 1
1.9%
37.17644515 1
1.9%
ValueCountFrequency (%)
38.11958083 1
1.9%
38.03316994 1
1.9%
37.95368811 1
1.9%
37.8842897 1
1.9%
37.88291767 1
1.9%
37.88156152 1
1.9%
37.84434522 1
1.9%
37.81747132 1
1.9%
37.8128924 1
1.9%
37.778086 1
1.9%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.1339
Minimum126.55269
Maximum127.77595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-11T10:57:25.826809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.55269
5-th percentile126.57044
Q1126.83403
median127.13146
Q3127.41297
95-th percentile127.7285
Maximum127.77595
Range1.223262
Interquartile range (IQR)0.57894103

Descriptive statistics

Standard deviation0.36566467
Coefficient of variation (CV)0.002876217
Kurtosis-1.1739996
Mean127.1339
Median Absolute Deviation (MAD)0.3038434
Skewness0.15808533
Sum6610.9626
Variance0.13371065
MonotonicityNot monotonic
2024-04-11T10:57:25.951141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.6826533 2
 
3.8%
126.5696537 2
 
3.8%
127.0697189 1
 
1.9%
127.0172615 1
 
1.9%
127.2673705 1
 
1.9%
127.2419016 1
 
1.9%
127.2531051 1
 
1.9%
126.8147854 1
 
1.9%
127.4956027 1
 
1.9%
127.554094 1
 
1.9%
Other values (40) 40
76.9%
ValueCountFrequency (%)
126.5526868 1
1.9%
126.5696537 2
3.8%
126.5710794 1
1.9%
126.6380806 1
1.9%
126.6941807 1
1.9%
126.7330935 1
1.9%
126.7708219 1
1.9%
126.7728476 1
1.9%
126.7844929 1
1.9%
126.7886575 1
1.9%
ValueCountFrequency (%)
127.7759488 1
1.9%
127.775042 1
1.9%
127.752956 1
1.9%
127.7084887 1
1.9%
127.6826533 2
3.8%
127.5847008 1
1.9%
127.554094 1
1.9%
127.541779 1
1.9%
127.5146475 1
1.9%
127.4956027 1
1.9%

전화번호
Text

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-11T10:57:26.158293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters624
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

Unique50 ?
Unique (%)96.2%

Sample

1st row031-589-0265
2nd row031-585-7906
3rd row031-914-6633
4th row031-762-8585
5th row031-451-8799
ValueCountFrequency (%)
032-886-0533 2
 
3.8%
031-642-5182 1
 
1.9%
031-775-2050 1
 
1.9%
031-832-7107 1
 
1.9%
031-370-7150 1
 
1.9%
031-338-8855 1
 
1.9%
031-339-0606 1
 
1.9%
031-334-0626 1
 
1.9%
031-965-0028 1
 
1.9%
031-631-8311 1
 
1.9%
Other values (41) 41
78.8%
2024-04-11T10:57:26.451548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 104
16.7%
3 94
15.1%
0 89
14.3%
1 74
11.9%
8 45
7.2%
6 45
7.2%
5 42
6.7%
7 40
 
6.4%
2 34
 
5.4%
9 30
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 520
83.3%
Dash Punctuation 104
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 94
18.1%
0 89
17.1%
1 74
14.2%
8 45
8.7%
6 45
8.7%
5 42
8.1%
7 40
7.7%
2 34
 
6.5%
9 30
 
5.8%
4 27
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 104
16.7%
3 94
15.1%
0 89
14.3%
1 74
11.9%
8 45
7.2%
6 45
7.2%
5 42
6.7%
7 40
 
6.4%
2 34
 
5.4%
9 30
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 104
16.7%
3 94
15.1%
0 89
14.3%
1 74
11.9%
8 45
7.2%
6 45
7.2%
5 42
6.7%
7 40
 
6.4%
2 34
 
5.4%
9 30
 
4.8%

홈페이지URL
Text

Distinct42
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-11T10:57:26.643522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length29
Mean length18.230769
Min length4

Characters and Unicode

Total characters948
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)75.0%

Sample

1st rowwww.
2nd rowwww.
3rd rowwww.holtilsan.or.kr
4th rowhttp://hyanglim.or.kr
5th rowhttp://cphouse.or.kr/
ValueCountFrequency (%)
www 9
 
17.3%
www.changinwon.or.kr 2
 
3.8%
http://roundworld.or.kr 2
 
3.8%
http://beautifulnury.modoo.at 1
 
1.9%
www.han-wool.net 1
 
1.9%
http://happylog.naver.com 1
 
1.9%
https://happylog.naver.com/hlog/sgwonbokjitown/home 1
 
1.9%
http://happylog.naver.com/buliwon.do 1
 
1.9%
http://lel.or.kr 1
 
1.9%
http://happylog.naver.com/peniel4539.do 1
 
1.9%
Other values (32) 32
61.5%
2024-04-11T10:57:26.941862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 119
12.6%
. 113
 
11.9%
o 86
 
9.1%
r 71
 
7.5%
h 46
 
4.9%
t 45
 
4.7%
e 45
 
4.7%
n 44
 
4.6%
/ 41
 
4.3%
a 41
 
4.3%
Other values (27) 297
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 760
80.2%
Other Punctuation 170
 
17.9%
Decimal Number 16
 
1.7%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 119
15.7%
o 86
11.3%
r 71
 
9.3%
h 46
 
6.1%
t 45
 
5.9%
e 45
 
5.9%
n 44
 
5.8%
a 41
 
5.4%
p 34
 
4.5%
k 31
 
4.1%
Other values (14) 198
26.1%
Decimal Number
ValueCountFrequency (%)
4 3
18.8%
0 3
18.8%
1 2
12.5%
3 2
12.5%
5 2
12.5%
6 1
 
6.2%
9 1
 
6.2%
7 1
 
6.2%
8 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 113
66.5%
/ 41
 
24.1%
: 16
 
9.4%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 760
80.2%
Common 188
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 119
15.7%
o 86
11.3%
r 71
 
9.3%
h 46
 
6.1%
t 45
 
5.9%
e 45
 
5.9%
n 44
 
5.8%
a 41
 
5.4%
p 34
 
4.5%
k 31
 
4.1%
Other values (14) 198
26.1%
Common
ValueCountFrequency (%)
. 113
60.1%
/ 41
 
21.8%
: 16
 
8.5%
4 3
 
1.6%
0 3
 
1.6%
1 2
 
1.1%
3 2
 
1.1%
5 2
 
1.1%
- 2
 
1.1%
6 1
 
0.5%
Other values (3) 3
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 119
12.6%
. 113
 
11.9%
o 86
 
9.1%
r 71
 
7.5%
h 46
 
4.9%
t 45
 
4.7%
e 45
 
4.7%
n 44
 
4.6%
/ 41
 
4.3%
a 41
 
4.3%
Other values (27) 297
31.3%

입소정원수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.096154
Minimum10
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-11T10:57:27.052237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.1
Q130
median35
Q350.75
95-th percentile94.9
Maximum180
Range170
Interquartile range (IQR)20.75

Descriptive statistics

Standard deviation31.596264
Coefficient of variation (CV)0.68544253
Kurtosis7.5903023
Mean46.096154
Median Absolute Deviation (MAD)6
Skewness2.4834579
Sum2397
Variance998.32391
MonotonicityNot monotonic
2024-04-11T10:57:27.148965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
30 12
23.1%
40 7
13.5%
29 5
9.6%
70 3
 
5.8%
50 3
 
5.8%
60 3
 
5.8%
35 3
 
5.8%
31 2
 
3.8%
10 2
 
3.8%
180 1
 
1.9%
Other values (11) 11
21.2%
ValueCountFrequency (%)
10 2
 
3.8%
13 1
 
1.9%
15 1
 
1.9%
25 1
 
1.9%
29 5
9.6%
30 12
23.1%
31 2
 
3.8%
35 3
 
5.8%
36 1
 
1.9%
40 7
13.5%
ValueCountFrequency (%)
180 1
 
1.9%
155 1
 
1.9%
96 1
 
1.9%
94 1
 
1.9%
90 1
 
1.9%
80 1
 
1.9%
70 3
5.8%
60 3
5.8%
53 1
 
1.9%
50 3
5.8%

입소현원수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.423077
Minimum4
Maximum166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-11T10:57:27.252692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10.55
Q129
median32
Q347
95-th percentile92
Maximum166
Range162
Interquartile range (IQR)18

Descriptive statistics

Standard deviation27.909761
Coefficient of variation (CV)0.67377324
Kurtosis7.4848306
Mean41.423077
Median Absolute Deviation (MAD)6.5
Skewness2.3677926
Sum2154
Variance778.95475
MonotonicityNot monotonic
2024-04-11T10:57:27.375842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
30 8
15.4%
29 7
 
13.5%
32 3
 
5.8%
39 3
 
5.8%
52 2
 
3.8%
47 2
 
3.8%
10 2
 
3.8%
27 2
 
3.8%
35 2
 
3.8%
92 2
 
3.8%
Other values (19) 19
36.5%
ValueCountFrequency (%)
4 1
 
1.9%
10 2
 
3.8%
11 1
 
1.9%
12 1
 
1.9%
24 1
 
1.9%
25 1
 
1.9%
27 2
 
3.8%
28 1
 
1.9%
29 7
13.5%
30 8
15.4%
ValueCountFrequency (%)
166 1
1.9%
113 1
1.9%
92 2
3.8%
87 1
1.9%
80 1
1.9%
69 1
1.9%
55 1
1.9%
53 1
1.9%
52 2
3.8%
51 1
1.9%

종사자정원수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.711538
Minimum3
Maximum117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-11T10:57:27.494280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.55
Q129.75
median33
Q343
95-th percentile68.45
Maximum117
Range114
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation20.008171
Coefficient of variation (CV)0.54501043
Kurtosis4.6022565
Mean36.711538
Median Absolute Deviation (MAD)5
Skewness1.4426903
Sum1909
Variance400.32692
MonotonicityNot monotonic
2024-04-11T10:57:27.602359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
33 5
 
9.6%
32 4
 
7.7%
24 3
 
5.8%
38 3
 
5.8%
43 3
 
5.8%
36 3
 
5.8%
31 3
 
5.8%
46 2
 
3.8%
30 2
 
3.8%
3 2
 
3.8%
Other values (19) 22
42.3%
ValueCountFrequency (%)
3 2
3.8%
4 1
 
1.9%
5 1
 
1.9%
6 1
 
1.9%
19 1
 
1.9%
21 1
 
1.9%
24 3
5.8%
28 1
 
1.9%
29 2
3.8%
30 2
3.8%
ValueCountFrequency (%)
117 1
1.9%
85 1
1.9%
69 1
1.9%
68 1
1.9%
66 1
1.9%
65 1
1.9%
58 1
1.9%
51 1
1.9%
47 1
1.9%
46 2
3.8%

종사자현원수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.826923
Minimum2
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-04-11T10:57:27.713714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.1
Q126
median32
Q336
95-th percentile65
Maximum93
Range91
Interquartile range (IQR)10

Descriptive statistics

Standard deviation18.438792
Coefficient of variation (CV)0.54509221
Kurtosis2.7010943
Mean33.826923
Median Absolute Deviation (MAD)5.5
Skewness1.1838548
Sum1759
Variance339.98906
MonotonicityNot monotonic
2024-04-11T10:57:27.815162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
34 5
 
9.6%
33 4
 
7.7%
29 4
 
7.7%
32 3
 
5.8%
31 3
 
5.8%
63 2
 
3.8%
28 2
 
3.8%
24 2
 
3.8%
65 2
 
3.8%
27 2
 
3.8%
Other values (19) 23
44.2%
ValueCountFrequency (%)
2 1
1.9%
3 2
3.8%
5 1
1.9%
6 1
1.9%
17 1
1.9%
18 1
1.9%
21 1
1.9%
23 1
1.9%
24 2
3.8%
25 1
1.9%
ValueCountFrequency (%)
93 1
1.9%
91 1
1.9%
65 2
3.8%
63 2
3.8%
54 1
1.9%
46 1
1.9%
44 1
1.9%
43 2
3.8%
42 1
1.9%
36 2
3.8%

설치신고일
Date

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
Minimum1984-04-01 00:00:00
Maximum2021-07-31 00:00:00
2024-04-11T10:57:27.919462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:28.045632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

법인명정보
Text

Distinct45
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-04-11T10:57:28.454301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length6.4038462
Min length2

Characters and Unicode

Total characters333
Distinct characters117
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

Unique41 ?
Unique (%)78.8%

Sample

1st row예수의꽃동네유지재단
2nd row작은예수수녀회
3rd row홀트아동복지회
4th row사회복지법인향림원
5th row씨피재활원
ValueCountFrequency (%)
개인 5
 
8.5%
사회복지법인 5
 
8.5%
창인원 2
 
3.4%
한국미래복지재단 2
 
3.4%
주내자육원 2
 
3.4%
평화의집 2
 
3.4%
한울공동체 1
 
1.7%
엘리엘동산 1
 
1.7%
김옥이재단 1
 
1.7%
연꽃마을 1
 
1.7%
Other values (37) 37
62.7%
2024-04-11T10:57:28.755196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 24
 
7.2%
복 23
 
6.9%
회 19
 
5.7%
인 16
 
4.8%
사 14
 
4.2%
재 13
 
3.9%
단 12
 
3.6%
원 11
 
3.3%
법 8
 
2.4%
7
 
2.1%
Other values (107) 186
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 326
97.9%
Space Separator 7
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 24
 
7.4%
복 23
 
7.1%
회 19
 
5.8%
인 16
 
4.9%
사 14
 
4.3%
재 13
 
4.0%
단 12
 
3.7%
원 11
 
3.4%
법 8
 
2.5%
한 6
 
1.8%
Other values (106) 180
55.2%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 326
97.9%
Common 7
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 24
 
7.4%
복 23
 
7.1%
회 19
 
5.8%
인 16
 
4.9%
사 14
 
4.3%
재 13
 
4.0%
단 12
 
3.7%
원 11
 
3.4%
법 8
 
2.5%
한 6
 
1.8%
Other values (106) 180
55.2%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 326
97.9%
ASCII 7
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 24
 
7.4%
복 23
 
7.1%
회 19
 
5.8%
인 16
 
4.9%
사 14
 
4.3%
재 13
 
4.0%
단 12
 
3.7%
원 11
 
3.4%
법 8
 
2.5%
한 6
 
1.8%
Other values (106) 180
55.2%
ASCII
ValueCountFrequency (%)
7
100.0%

Interactions

2024-04-11T10:57:22.441917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.016275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.593382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.119762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.718396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.299624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.877008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:22.512347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.135201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.659299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.192873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.793646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.367880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.947150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:22.589403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.205667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.729166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.283165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.887643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.447903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:22.021909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:22.662162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.275606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.800974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.378114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.971027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.531438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:22.099190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:22.732549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.348059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.876234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.468075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.042911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.618973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:22.170732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:23.008256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.440556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.954415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.556725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.152312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.721958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:22.267426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:23.076835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:19.518133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.040013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:20.645675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.232985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:21.805984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T10:57:22.358818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-11T10:57:28.846845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명기관명소재지지번주소소재지도로명주소소재지우편번호WGS84위도WGS84경도전화번호홈페이지URL입소정원수(명)입소현원수(명)종사자정원수(명)종사자현원수(명)설치신고일법인명정보
시군명1.0001.0001.0001.0000.9920.9510.9611.0000.7470.6500.7880.7460.6371.0000.984
기관명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0000.9950.0000.9300.9490.9291.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0000.9950.0000.9300.9490.9291.0001.000
소재지우편번호0.9921.0001.0001.0001.0000.6650.8201.0000.9020.5380.0000.2990.4211.0000.904
WGS84위도0.9511.0001.0001.0000.6651.0000.7711.0000.0000.0460.3870.0000.0001.0000.592
WGS84경도0.9611.0001.0001.0000.8200.7711.0001.0000.9170.3170.0000.1550.0001.0000.949
전화번호1.0001.0001.0001.0001.0001.0001.0001.0000.9900.9751.0001.0000.9831.0001.000
홈페이지URL0.7471.0000.9950.9950.9020.0000.9170.9901.0000.0000.4290.0000.4851.0000.942
입소정원수(명)0.6501.0000.0000.0000.5380.0460.3170.9750.0001.0000.9740.9740.9471.0000.662
입소현원수(명)0.7881.0000.9300.9300.0000.3870.0001.0000.4290.9741.0000.9880.9701.0000.958
종사자정원수(명)0.7461.0000.9490.9490.2990.0000.1551.0000.0000.9740.9881.0000.9771.0000.968
종사자현원수(명)0.6371.0000.9290.9290.4210.0000.0000.9830.4850.9470.9700.9771.0001.0000.967
설치신고일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
법인명정보0.9841.0001.0001.0000.9040.5920.9491.0000.9420.6620.9580.9680.9671.0001.000
2024-04-11T10:57:28.979377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도입소정원수(명)입소현원수(명)종사자정원수(명)종사자현원수(명)시군명
소재지우편번호1.000-0.9260.107-0.012-0.064-0.0080.0100.794
WGS84위도-0.9261.000-0.0690.0170.0780.0380.0300.644
WGS84경도0.107-0.0691.0000.0890.1070.1660.0700.675
입소정원수(명)-0.0120.0170.0891.0000.9550.9410.9330.259
입소현원수(명)-0.0640.0780.1070.9551.0000.9490.9390.379
종사자정원수(명)-0.0080.0380.1660.9410.9491.0000.9570.337
종사자현원수(명)0.0100.0300.0700.9330.9390.9571.0000.250
시군명0.7940.6440.6750.2590.3790.3370.2501.000

Missing values

2024-04-11T10:57:23.203176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-11T10:57:23.388447image/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

시군명기관명소재지지번주소소재지도로명주소소재지우편번호WGS84위도WGS84경도전화번호홈페이지URL입소정원수(명)입소현원수(명)종사자정원수(명)종사자현원수(명)설치신고일법인명정보
0가평군가평꽃동네희망의집경기도 가평군 조종면 운악리 540-3번지경기도 가평군 조종면 꽃동네길 35-251243237.881562127.350037031-589-0265www.180166117931995-03-27예수의꽃동네유지재단
1가평군성가정의집경기도 가평군 조종면 마일리 296-14번지경기도 가평군 조종면 연인산로474번길 139-371243337.844345127.387343031-585-7906www.292419182008-08-04작은예수수녀회
2고양시홀트일산요양원경기도 고양시 일산서구 탄현동 41-1번지경기도 고양시 일산서구 탄현로 421024937.695677126.772848031-914-6633www.holtilsan.or.kr15511385911999-01-01홀트아동복지회
3광주시품안의집경기도 광주시 곤지암읍 연곡리 80-2번지경기도 광주시 곤지암읍 광여로 555-401272237.371065127.395516031-762-8585http://hyanglim.or.kr705243321993-12-27사회복지법인향림원
4군포시양지의집경기도 군포시 당정동 982-1번지경기도 군포시 한세로70번길 141585337.347119126.956565031-451-8799http://cphouse.or.kr/313032312007-10-27씨피재활원
5김포시가연마을경기도 김포시 월곶면 고막리 382번지경기도 김포시 월곶면 용강로37번길 76-251002137.723832126.552687031-983-0108www.504643362008-02-15대한불교조계종 석왕사룸비니
6김포시소망의집경기도 김포시 양촌읍 구래리 668-2번지경기도 김포시 양촌읍 김포한강8로 100-911006937.63135126.638081031-985-8858www.1512662011-12-19개인
7남양주시참누리경기도 남양주시 수동면 입석리 522-2번지경기도 남양주시 수동면 비룡로972번길 171202937.720941127.314852031-594-6655www.shma.kr605551431989-02-02신망애복지재단
8남양주시호세아동산경기도 남양주시 수동면 송천리 452-1번지경기도 남양주시 수동면 소래비로 470-191203437.687661127.32741031-591-6644www.lovepeace.kr303033322006-12-12사랑과평화복지재단
9부천시로뎀나무경기도 부천시 소사구 송내동 612-26번지경기도 부천시 성주로 1321474637.479231126.770822032-652-5002www.koreacare.org2911552006-03-14개인
시군명기관명소재지지번주소소재지도로명주소소재지우편번호WGS84위도WGS84경도전화번호홈페이지URL입소정원수(명)입소현원수(명)종사자정원수(명)종사자현원수(명)설치신고일법인명정보
42파주시금빛사랑의집경기도 파주시 조리읍 장곡리 166-6번지경기도 파주시 조리읍 기곡길 191094037.752171126.840449031-949-0636www.newdreamer.net104332014-08-11개인
43파주시아름다운누리경기도 파주시 법원읍 금곡리 428-3번지경기도 파주시 법원읍 술이홀로1333번길 631080637.88429126.875481031-959-7008http://beautifulnury.modoo.at504736331992-06-01주내자육원
44파주시조이빌리지경기도 파주시 광탄면 신산리 73번지 조이빌리지경기도 파주시 광탄면 심궁로 76-251094837.778086126.860185031-947-2720www.302730292019-05-20사회복지법인 대건카리타스
45파주시큰나무경기도 파주시 법원읍 금곡리 428-1번지경기도 파주시 법원읍 술이홀로1333번길 691080637.882918126.876157031-959-7003https://lovenamu.modoo.at/303029272010-04-22사회복지법인 주내자육원
46평택시아나율의집경기도 평택시 포승읍 석정리 1-6번지경기도 평택시 포승읍 절골길 43-11781437.007874126.897134031-683-1377www.303029282013-10-11연꽃마을
47포천시노아의집경기도 포천시 신북면 갈월리 441번지경기도 포천시 신북면 청신로 10951113737.953688127.141056031-534-3884http://happylog.naver.com705346441997-12-26김옥이재단
48화성시둘다섯해누리경기도 화성시 서신면 백미리 588-1번지경기도 화성시 서신면 밸미길 87-311855637.139939126.694181031-357-1945www.haenuri.or.kr808066632008-09-08천주교수원교구사회복지회
49화성시불이원경기도 화성시 남양읍 북양리 620번지경기도 화성시 남양읍 현대기아로 6481825537.205412126.854796031-357-6268http://happylog.naver.com/buliwon.do484743432009-02-18자제공덕회
50화성시브니엘복지원경기도 화성시 팔탄면 덕천리 375-23번지경기도 화성시 팔탄면 온천로237번길 651857737.14453126.863357031-352-4539http://happylog.naver.com/peniel4539.do1310432005-12-29개인
51화성시화성아름마을경기도 화성시 송산면 쌍정리 1000번지경기도 화성시 송산면 공룡로264번길 161854737.233679126.733093031-355-7522www.arumvillage.com353534342003-08-06세종복지회