Overview

Dataset statistics

Number of variables16
Number of observations88
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory136.5 B

Variable types

Categorical2
Text6
Numeric7
DateTime1

Dataset

Description장애유형별 거주시설 현황
Author경기복지재단(경기도장애인복지종합지원센터)
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=BR2UDZQFU1U6C02ZUKO325527797&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
기관구분명 is highly imbalanced (61.3%)Imbalance
기관명 has unique valuesUnique
소재지지번주소 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
WGS84위도 has unique valuesUnique
WGS84경도 has unique valuesUnique
전화번호 has unique valuesUnique
입소현원수(명) has 1 (1.1%) zerosZeros

Reproduction

Analysis started2024-03-12 23:14:21.208351
Analysis finished2024-03-12 23:14:25.886817
Duration4.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Memory size836.0 B
고양시
8 
포천시
8 
용인시
8 
남양주시
7 
양주시
6 
Other values (20)
51 

Length

Max length4
Median length3
Mean length3.1022727
Min length3

Unique

Unique6 ?
Unique (%)6.8%

Sample

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

Common Values

ValueCountFrequency (%)
고양시 8
 
9.1%
포천시 8
 
9.1%
용인시 8
 
9.1%
남양주시 7
 
8.0%
양주시 6
 
6.8%
광주시 5
 
5.7%
파주시 5
 
5.7%
성남시 5
 
5.7%
하남시 3
 
3.4%
김포시 3
 
3.4%
Other values (15) 30
34.1%

Length

2024-03-13T08:14:25.940517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 8
 
9.1%
포천시 8
 
9.1%
용인시 8
 
9.1%
남양주시 7
 
8.0%
양주시 6
 
6.8%
광주시 5
 
5.7%
파주시 5
 
5.7%
성남시 5
 
5.7%
가평군 3
 
3.4%
평택시 3
 
3.4%
Other values (15) 30
34.1%

기관구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size836.0 B
지적장애인시설
76 
지체장애인시설
 
6
시각장애인시설
 
4
청각·언어 장애인시설
 
2

Length

Max length11
Median length7
Mean length7.0909091
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지적장애인시설 76
86.4%
지체장애인시설 6
 
6.8%
시각장애인시설 4
 
4.5%
청각·언어 장애인시설 2
 
2.3%

Length

2024-03-13T08:14:26.038838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:14:26.126270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지적장애인시설 76
84.4%
지체장애인시설 6
 
6.7%
시각장애인시설 4
 
4.4%
청각·언어 2
 
2.2%
장애인시설 2
 
2.2%

기관명
Text

UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-13T08:14:26.303596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length5.2840909
Min length2

Characters and Unicode

Total characters465
Distinct characters152
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

Unique88 ?
Unique (%)100.0%

Sample

1st row가난한마음의집
2nd row꽃동네은총의집
3rd row성 빈센트 환경마을
4th row꿈나무의집
5th row늘사랑의집
ValueCountFrequency (%)
집 11
 
10.5%
가난한마음의집 1
 
1.0%
창인재활원 1
 
1.0%
해든솔 1
 
1.0%
하늘의별 1
 
1.0%
참사랑마을 1
 
1.0%
성가원 1
 
1.0%
생수사랑회장애인복지시설 1
 
1.0%
새빛요한의 1
 
1.0%
꽃동산 1
 
1.0%
Other values (85) 85
81.0%
2024-03-13T08:14:26.599746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
집 44
 
9.5%
의 41
 
8.8%
원 21
 
4.5%
17
 
3.7%
사 16
 
3.4%
랑 14
 
3.0%
동 13
 
2.8%
산 10
 
2.2%
나 9
 
1.9%
마 8
 
1.7%
Other values (142) 272
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 448
96.3%
Space Separator 17
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
집 44
 
9.8%
의 41
 
9.2%
원 21
 
4.7%
사 16
 
3.6%
랑 14
 
3.1%
동 13
 
2.9%
산 10
 
2.2%
나 9
 
2.0%
마 8
 
1.8%
을 7
 
1.6%
Other values (141) 265
59.2%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 448
96.3%
Common 17
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
집 44
 
9.8%
의 41
 
9.2%
원 21
 
4.7%
사 16
 
3.6%
랑 14
 
3.1%
동 13
 
2.9%
산 10
 
2.2%
나 9
 
2.0%
마 8
 
1.8%
을 7
 
1.6%
Other values (141) 265
59.2%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 448
96.3%
ASCII 17
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
집 44
 
9.8%
의 41
 
9.2%
원 21
 
4.7%
사 16
 
3.6%
랑 14
 
3.1%
동 13
 
2.9%
산 10
 
2.2%
나 9
 
2.0%
마 8
 
1.8%
을 7
 
1.6%
Other values (141) 265
59.2%
ASCII
ValueCountFrequency (%)
17
100.0%

소재지지번주소
Text

UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-13T08:14:26.837983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length22.306818
Min length16

Characters and Unicode

Total characters1963
Distinct characters144
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

Unique88 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 상면 율길리 747번지
2nd row경기도 가평군 조종면 운악리 540-6번지
3rd row경기도 가평군 조종면 신하리 45-3번지
4th row경기도 고양시 일산동구 설문동 492번지
5th row경기도 고양시 덕양구 관산동 551-5번지
ValueCountFrequency (%)
경기도 88
 
20.5%
고양시 8
 
1.9%
포천시 8
 
1.9%
용인시 8
 
1.9%
남양주시 7
 
1.6%
처인구 7
 
1.6%
양주시 6
 
1.4%
파주시 5
 
1.2%
광주시 5
 
1.2%
성남시 5
 
1.2%
Other values (233) 283
65.8%
2024-03-13T08:14:27.160916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
342
 
17.4%
지 93
 
4.7%
도 91
 
4.6%
기 90
 
4.6%
경 88
 
4.5%
번 88
 
4.5%
시 83
 
4.2%
- 67
 
3.4%
1 60
 
3.1%
리 54
 
2.8%
Other values (134) 907
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1216
61.9%
Space Separator 342
 
17.4%
Decimal Number 338
 
17.2%
Dash Punctuation 67
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 93
 
7.6%
도 91
 
7.5%
기 90
 
7.4%
경 88
 
7.2%
번 88
 
7.2%
시 83
 
6.8%
리 54
 
4.4%
동 45
 
3.7%
양 36
 
3.0%
면 33
 
2.7%
Other values (122) 515
42.4%
Decimal Number
ValueCountFrequency (%)
1 60
17.8%
2 41
12.1%
5 40
11.8%
4 39
11.5%
3 34
10.1%
8 34
10.1%
0 26
7.7%
7 25
7.4%
9 22
 
6.5%
6 17
 
5.0%
Space Separator
ValueCountFrequency (%)
342
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1216
61.9%
Common 747
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 93
 
7.6%
도 91
 
7.5%
기 90
 
7.4%
경 88
 
7.2%
번 88
 
7.2%
시 83
 
6.8%
리 54
 
4.4%
동 45
 
3.7%
양 36
 
3.0%
면 33
 
2.7%
Other values (122) 515
42.4%
Common
ValueCountFrequency (%)
342
45.8%
- 67
 
9.0%
1 60
 
8.0%
2 41
 
5.5%
5 40
 
5.4%
4 39
 
5.2%
3 34
 
4.6%
8 34
 
4.6%
0 26
 
3.5%
7 25
 
3.3%
Other values (2) 39
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1216
61.9%
ASCII 747
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
342
45.8%
- 67
 
9.0%
1 60
 
8.0%
2 41
 
5.5%
5 40
 
5.4%
4 39
 
5.2%
3 34
 
4.6%
8 34
 
4.6%
0 26
 
3.5%
7 25
 
3.3%
Other values (2) 39
 
5.2%
Hangul
ValueCountFrequency (%)
지 93
 
7.6%
도 91
 
7.5%
기 90
 
7.4%
경 88
 
7.2%
번 88
 
7.2%
시 83
 
6.8%
리 54
 
4.4%
동 45
 
3.7%
양 36
 
3.0%
면 33
 
2.7%
Other values (122) 515
42.4%

소재지도로명주소
Text

UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-13T08:14:27.391010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length21.681818
Min length14

Characters and Unicode

Total characters1908
Distinct characters162
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

Unique88 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 상면 수역길 119
2nd row경기도 가평군 조종면 꽃동네길 53-13
3rd row경기도 가평군 조종면 연인산로 308
4th row경기도 고양시 일산동구 은마길79번길 113
5th row경기도 고양시 덕양구 통일로1018번길 112-6
ValueCountFrequency (%)
경기도 88
 
20.5%
고양시 8
 
1.9%
포천시 8
 
1.9%
용인시 8
 
1.9%
처인구 7
 
1.6%
남양주시 7
 
1.6%
양주시 6
 
1.4%
덕양구 5
 
1.2%
파주시 5
 
1.2%
성남시 5
 
1.2%
Other values (236) 283
65.8%
2024-03-13T08:14:27.744684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
342
 
17.9%
기 91
 
4.8%
경 90
 
4.7%
도 90
 
4.7%
시 83
 
4.4%
로 71
 
3.7%
1 62
 
3.2%
길 61
 
3.2%
3 53
 
2.8%
2 50
 
2.6%
Other values (152) 915
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1143
59.9%
Decimal Number 391
 
20.5%
Space Separator 342
 
17.9%
Dash Punctuation 32
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 91
 
8.0%
경 90
 
7.9%
도 90
 
7.9%
시 83
 
7.3%
로 71
 
6.2%
길 61
 
5.3%
번 41
 
3.6%
양 35
 
3.1%
면 33
 
2.9%
구 32
 
2.8%
Other values (140) 516
45.1%
Decimal Number
ValueCountFrequency (%)
1 62
15.9%
3 53
13.6%
2 50
12.8%
9 42
10.7%
4 38
9.7%
8 31
7.9%
5 30
7.7%
0 29
7.4%
6 28
7.2%
7 28
7.2%
Space Separator
ValueCountFrequency (%)
342
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1143
59.9%
Common 765
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 91
 
8.0%
경 90
 
7.9%
도 90
 
7.9%
시 83
 
7.3%
로 71
 
6.2%
길 61
 
5.3%
번 41
 
3.6%
양 35
 
3.1%
면 33
 
2.9%
구 32
 
2.8%
Other values (140) 516
45.1%
Common
ValueCountFrequency (%)
342
44.7%
1 62
 
8.1%
3 53
 
6.9%
2 50
 
6.5%
9 42
 
5.5%
4 38
 
5.0%
- 32
 
4.2%
8 31
 
4.1%
5 30
 
3.9%
0 29
 
3.8%
Other values (2) 56
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1143
59.9%
ASCII 765
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
342
44.7%
1 62
 
8.1%
3 53
 
6.9%
2 50
 
6.5%
9 42
 
5.5%
4 38
 
5.0%
- 32
 
4.2%
8 31
 
4.1%
5 30
 
3.9%
0 29
 
3.8%
Other values (2) 56
 
7.3%
Hangul
ValueCountFrequency (%)
기 91
 
8.0%
경 90
 
7.9%
도 90
 
7.9%
시 83
 
7.3%
로 71
 
6.2%
길 61
 
5.3%
번 41
 
3.6%
양 35
 
3.1%
면 33
 
2.9%
구 32
 
2.8%
Other values (140) 516
45.1%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13129.091
Minimum10001
Maximum18034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:27.854462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10254.8
Q111182
median12484.5
Q315025.5
95-th percentile17514.75
Maximum18034
Range8033
Interquartile range (IQR)3843.5

Descriptive statistics

Standard deviation2445.704
Coefficient of variation (CV)0.18628129
Kurtosis-0.84157413
Mean13129.091
Median Absolute Deviation (MAD)1346
Skewness0.70720013
Sum1155360
Variance5981468
MonotonicityNot monotonic
2024-03-13T08:14:27.963547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12073 2
 
2.3%
12729 2
 
2.3%
12029 2
 
2.3%
11405 2
 
2.3%
13019 1
 
1.1%
11140 1
 
1.1%
11606 1
 
1.1%
16000 1
 
1.1%
16805 1
 
1.1%
17183 1
 
1.1%
Other values (74) 74
84.1%
ValueCountFrequency (%)
10001 1
1.1%
10016 1
1.1%
10022 1
1.1%
10249 1
1.1%
10252 1
1.1%
10260 1
1.1%
10265 1
1.1%
10267 1
1.1%
10271 1
1.1%
10273 1
1.1%
ValueCountFrequency (%)
18034 1
1.1%
17721 1
1.1%
17714 1
1.1%
17556 1
1.1%
17520 1
1.1%
17505 1
1.1%
17413 1
1.1%
17183 1
1.1%
17182 1
1.1%
17169 1
1.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.54414
Minimum36.989716
Maximum38.020973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:28.088544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.989716
5-th percentile37.086267
Q137.340927
median37.572833
Q337.758771
95-th percentile37.899515
Maximum38.020973
Range1.0312571
Interquartile range (IQR)0.41784417

Descriptive statistics

Standard deviation0.26957934
Coefficient of variation (CV)0.0071803305
Kurtosis-1.0341521
Mean37.54414
Median Absolute Deviation (MAD)0.19698754
Skewness-0.29813737
Sum3303.8844
Variance0.072673019
MonotonicityNot monotonic
2024-03-13T08:14:28.196681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.82391965 1
 
1.1%
37.4019979 1
 
1.1%
37.4057181 1
 
1.1%
37.3169591 1
 
1.1%
37.1012653 1
 
1.1%
37.1570587 1
 
1.1%
37.3258728 1
 
1.1%
37.120704 1
 
1.1%
37.1407036 1
 
1.1%
37.19163624 1
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
36.9897159 1
1.1%
37.04396581 1
1.1%
37.0606875 1
1.1%
37.08121452 1
1.1%
37.08332529 1
1.1%
37.0917304 1
1.1%
37.1012653 1
1.1%
37.1127341 1
1.1%
37.120704 1
1.1%
37.1261837 1
1.1%
ValueCountFrequency (%)
38.02097299 1
1.1%
37.93487578 1
1.1%
37.93210315 1
1.1%
37.91640727 1
1.1%
37.90475987 1
1.1%
37.8897744 1
1.1%
37.88695761 1
1.1%
37.88372962 1
1.1%
37.8831034 1
1.1%
37.87907955 1
1.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.11155
Minimum126.5594
Maximum127.68265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:28.300023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5594
5-th percentile126.77296
Q1126.90853
median127.13006
Q3127.28659
95-th percentile127.4967
Maximum127.68265
Range1.1232562
Interquartile range (IQR)0.378063

Descriptive statistics

Standard deviation0.23973207
Coefficient of variation (CV)0.0018859976
Kurtosis-0.14396714
Mean127.11155
Median Absolute Deviation (MAD)0.16532545
Skewness-0.031991461
Sum11185.816
Variance0.057471465
MonotonicityNot monotonic
2024-03-13T08:14:28.404132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.2869853 1
 
1.1%
126.9083394 1
 
1.1%
127.0283626 1
 
1.1%
127.0558364 1
 
1.1%
127.4077194 1
 
1.1%
127.2864586 1
 
1.1%
127.2558952 1
 
1.1%
127.3794227 1
 
1.1%
127.2030988 1
 
1.1%
127.3028656 1
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
126.559397 1
1.1%
126.5651719 1
1.1%
126.5848091 1
1.1%
126.7114942 1
1.1%
126.7728475 1
1.1%
126.7731633 1
1.1%
126.7886574 1
1.1%
126.8025988 1
1.1%
126.8050783 1
1.1%
126.8194335 1
1.1%
ValueCountFrequency (%)
127.6826532 1
1.1%
127.6426692 1
1.1%
127.6381205 1
1.1%
127.554094 1
1.1%
127.5412519 1
1.1%
127.4139685 1
1.1%
127.4077194 1
1.1%
127.3958848 1
1.1%
127.3794227 1
1.1%
127.3786255 1
1.1%

전화번호
Text

UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-13T08:14:28.617759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1056
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

Unique88 ?
Unique (%)100.0%

Sample

1st row031-585-3210
2nd row031-589-0188
3rd row031-585-9066
4th row031-977-3452
5th row031-962-5391
ValueCountFrequency (%)
031-585-3210 1
 
1.1%
031-589-0188 1
 
1.1%
031-261-0863 1
 
1.1%
031-323-3340 1
 
1.1%
031-321-4982 1
 
1.1%
031-333-1267 1
 
1.1%
031-333-6287 1
 
1.1%
031-321-6112 1
 
1.1%
031-321-9862 1
 
1.1%
031-339-1403 1
 
1.1%
Other values (78) 78
88.6%
2024-03-13T08:14:28.915760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 176
16.7%
3 161
15.2%
1 144
13.6%
0 138
13.1%
6 75
7.1%
7 65
 
6.2%
5 64
 
6.1%
2 63
 
6.0%
4 60
 
5.7%
9 58
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 880
83.3%
Dash Punctuation 176
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 161
18.3%
1 144
16.4%
0 138
15.7%
6 75
8.5%
7 65
7.4%
5 64
 
7.3%
2 63
 
7.2%
4 60
 
6.8%
9 58
 
6.6%
8 52
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1056
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 176
16.7%
3 161
15.2%
1 144
13.6%
0 138
13.1%
6 75
7.1%
7 65
 
6.2%
5 64
 
6.1%
2 63
 
6.0%
4 60
 
5.7%
9 58
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 176
16.7%
3 161
15.2%
1 144
13.6%
0 138
13.1%
6 75
7.1%
7 65
 
6.2%
5 64
 
6.1%
2 63
 
6.0%
4 60
 
5.7%
9 58
 
5.5%

홈페이지URL
Text

Distinct75
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-13T08:14:29.098775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length32
Mean length20.965909
Min length4

Characters and Unicode

Total characters1845
Distinct characters78
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)84.1%

Sample

1st rowhttp://www.마음의집.com/
2nd rowwww.kkotgp.or.kr
3rd rowwww.vincentvillage.co.kr
4th rowhttp://꿈나무의집.kr/
5th rowhttp://ver2.iloveall.or.kr/
ValueCountFrequency (%)
www 14
 
15.9%
https://milalhouse.modoo.at/?pc=1 1
 
1.1%
https://cafe.naver.com/saebityohan 1
 
1.1%
www.heoorum.org 1
 
1.1%
www.haedunsol.or.kr 1
 
1.1%
http://www.haedunsol.or.kr 1
 
1.1%
www.chamtown.co.kr 1
 
1.1%
https://cafe.daum.net/sunggawon84 1
 
1.1%
http://cafe.naver.com/slove21 1
 
1.1%
www.osunjel.org 1
 
1.1%
Other values (65) 65
73.9%
2024-03-13T08:14:29.392072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 198
 
10.7%
. 187
 
10.1%
/ 133
 
7.2%
o 127
 
6.9%
t 117
 
6.3%
r 87
 
4.7%
a 86
 
4.7%
h 83
 
4.5%
e 76
 
4.1%
n 73
 
4.0%
Other values (68) 678
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1344
72.8%
Other Punctuation 370
 
20.1%
Decimal Number 83
 
4.5%
Other Letter 32
 
1.7%
Uppercase Letter 6
 
0.3%
Math Symbol 5
 
0.3%
Dash Punctuation 3
 
0.2%
Connector Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
꿈 2
 
6.2%
의 2
 
6.2%
이 2
 
6.2%
집 2
 
6.2%
곳 1
 
3.1%
은 1
 
3.1%
좋 1
 
3.1%
원 1
 
3.1%
없 1
 
3.1%
가 1
 
3.1%
Other values (18) 18
56.2%
Lowercase Letter
ValueCountFrequency (%)
w 198
14.7%
o 127
 
9.4%
t 117
 
8.7%
r 87
 
6.5%
a 86
 
6.4%
h 83
 
6.2%
e 76
 
5.7%
n 73
 
5.4%
p 69
 
5.1%
m 57
 
4.2%
Other values (16) 371
27.6%
Decimal Number
ValueCountFrequency (%)
1 13
15.7%
0 12
14.5%
6 12
14.5%
2 11
13.3%
3 9
10.8%
7 7
8.4%
4 6
7.2%
9 6
7.2%
5 5
 
6.0%
8 2
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
M 1
16.7%
P 1
16.7%
I 1
16.7%
N 1
16.7%
V 1
16.7%
H 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 187
50.5%
/ 133
35.9%
: 44
 
11.9%
? 4
 
1.1%
& 2
 
0.5%
Math Symbol
ValueCountFrequency (%)
= 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1350
73.2%
Common 463
 
25.1%
Hangul 32
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 198
14.7%
o 127
 
9.4%
t 117
 
8.7%
r 87
 
6.4%
a 86
 
6.4%
h 83
 
6.1%
e 76
 
5.6%
n 73
 
5.4%
p 69
 
5.1%
m 57
 
4.2%
Other values (22) 377
27.9%
Hangul
ValueCountFrequency (%)
꿈 2
 
6.2%
의 2
 
6.2%
이 2
 
6.2%
집 2
 
6.2%
곳 1
 
3.1%
은 1
 
3.1%
좋 1
 
3.1%
원 1
 
3.1%
없 1
 
3.1%
가 1
 
3.1%
Other values (18) 18
56.2%
Common
ValueCountFrequency (%)
. 187
40.4%
/ 133
28.7%
: 44
 
9.5%
1 13
 
2.8%
0 12
 
2.6%
6 12
 
2.6%
2 11
 
2.4%
3 9
 
1.9%
7 7
 
1.5%
4 6
 
1.3%
Other values (8) 29
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1813
98.3%
Hangul 32
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 198
 
10.9%
. 187
 
10.3%
/ 133
 
7.3%
o 127
 
7.0%
t 117
 
6.5%
r 87
 
4.8%
a 86
 
4.7%
h 83
 
4.6%
e 76
 
4.2%
n 73
 
4.0%
Other values (40) 646
35.6%
Hangul
ValueCountFrequency (%)
꿈 2
 
6.2%
의 2
 
6.2%
이 2
 
6.2%
집 2
 
6.2%
곳 1
 
3.1%
은 1
 
3.1%
좋 1
 
3.1%
원 1
 
3.1%
없 1
 
3.1%
가 1
 
3.1%
Other values (18) 18
56.2%

입소정원수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.647727
Minimum7
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:29.496481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10.7
Q125
median29
Q330.25
95-th percentile76.5
Maximum135
Range128
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation20.922978
Coefficient of variation (CV)0.62182442
Kurtosis7.1557035
Mean33.647727
Median Absolute Deviation (MAD)4
Skewness2.3422734
Sum2961
Variance437.77103
MonotonicityNot monotonic
2024-03-13T08:14:29.808982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
29 24
27.3%
30 15
17.0%
20 4
 
4.5%
15 4
 
4.5%
25 3
 
3.4%
16 3
 
3.4%
10 3
 
3.4%
26 2
 
2.3%
55 2
 
2.3%
50 2
 
2.3%
Other values (26) 26
29.5%
ValueCountFrequency (%)
7 1
 
1.1%
9 1
 
1.1%
10 3
3.4%
12 1
 
1.1%
15 4
4.5%
16 3
3.4%
17 1
 
1.1%
18 1
 
1.1%
20 4
4.5%
21 1
 
1.1%
ValueCountFrequency (%)
135 1
1.1%
100 1
1.1%
94 1
1.1%
89 1
1.1%
80 1
1.1%
70 1
1.1%
65 1
1.1%
60 1
1.1%
56 1
1.1%
55 2
2.3%

입소현원수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.693182
Minimum0
Maximum119
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:29.909644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.35
Q116
median25.5
Q330.25
95-th percentile63.3
Maximum119
Range119
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation19.069961
Coefficient of variation (CV)0.66461645
Kurtosis6.3888429
Mean28.693182
Median Absolute Deviation (MAD)7
Skewness2.112514
Sum2525
Variance363.6634
MonotonicityNot monotonic
2024-03-13T08:14:30.018257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
29 8
 
9.1%
30 8
 
9.1%
13 6
 
6.8%
21 5
 
5.7%
22 4
 
4.5%
24 4
 
4.5%
15 3
 
3.4%
16 3
 
3.4%
28 2
 
2.3%
26 2
 
2.3%
Other values (35) 43
48.9%
ValueCountFrequency (%)
0 1
 
1.1%
3 1
 
1.1%
7 1
 
1.1%
8 2
 
2.3%
9 1
 
1.1%
10 1
 
1.1%
11 2
 
2.3%
12 1
 
1.1%
13 6
6.8%
14 1
 
1.1%
ValueCountFrequency (%)
119 1
1.1%
92 1
1.1%
80 1
1.1%
76 1
1.1%
64 1
1.1%
62 1
1.1%
55 1
1.1%
52 2
2.3%
51 1
1.1%
49 1
1.1%

종사자정원수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.840909
Minimum2
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:30.126571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median18
Q331.25
95-th percentile49.65
Maximum87
Range85
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation16.94405
Coefficient of variation (CV)0.77579418
Kurtosis1.7306952
Mean21.840909
Median Absolute Deviation (MAD)11
Skewness1.2423942
Sum1922
Variance287.10084
MonotonicityNot monotonic
2024-03-13T08:14:30.247975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
8 8
 
9.1%
21 6
 
6.8%
5 6
 
6.8%
9 5
 
5.7%
6 5
 
5.7%
28 4
 
4.5%
13 4
 
4.5%
33 4
 
4.5%
7 4
 
4.5%
12 3
 
3.4%
Other values (27) 39
44.3%
ValueCountFrequency (%)
2 1
 
1.1%
3 1
 
1.1%
4 2
 
2.3%
5 6
6.8%
6 5
5.7%
7 4
4.5%
8 8
9.1%
9 5
5.7%
12 3
 
3.4%
13 4
4.5%
ValueCountFrequency (%)
87 1
1.1%
65 2
2.3%
60 1
1.1%
50 1
1.1%
49 1
1.1%
48 1
1.1%
46 1
1.1%
45 1
1.1%
43 2
2.3%
42 1
1.1%

종사자현원수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.625
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-13T08:14:30.367596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median16
Q328.25
95-th percentile45.65
Maximum73
Range72
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation15.151581
Coefficient of variation (CV)0.77205508
Kurtosis0.99897935
Mean19.625
Median Absolute Deviation (MAD)10
Skewness1.0955019
Sum1727
Variance229.5704
MonotonicityNot monotonic
2024-03-13T08:14:30.496456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
8 7
 
8.0%
6 7
 
8.0%
7 6
 
6.8%
27 5
 
5.7%
5 5
 
5.7%
4 3
 
3.4%
28 3
 
3.4%
20 3
 
3.4%
30 3
 
3.4%
11 3
 
3.4%
Other values (31) 43
48.9%
ValueCountFrequency (%)
1 1
 
1.1%
2 1
 
1.1%
3 2
 
2.3%
4 3
3.4%
5 5
5.7%
6 7
8.0%
7 6
6.8%
8 7
8.0%
9 1
 
1.1%
10 1
 
1.1%
ValueCountFrequency (%)
73 1
1.1%
62 1
1.1%
54 1
1.1%
51 1
1.1%
46 1
1.1%
45 1
1.1%
43 1
1.1%
42 2
2.3%
41 1
1.1%
39 2
2.3%

설치신고일
Date

Distinct83
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
Minimum1960-12-15 00:00:00
Maximum2022-04-08 00:00:00
2024-03-13T08:14:30.606769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:30.710873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

법인명정보
Text

Distinct52
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-13T08:14:30.872066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length6.1704545
Min length2

Characters and Unicode

Total characters543
Distinct characters132
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

Unique50 ?
Unique (%)56.8%

Sample

1st row마음의집
2nd row(재)예수의꽃동네유지재단
3rd row서울가톨릭사회복지회
4th row개인
5th row개인
ValueCountFrequency (%)
개인 36
33.3%
사회복지법인 19
17.6%
무지개동산 2
 
1.9%
바다의별 2
 
1.9%
대한불교조계종사회복지재단 1
 
0.9%
의정부밀알복지재단 1
 
0.9%
마음의집 1
 
0.9%
주내자육원 1
 
0.9%
천사재단 1
 
0.9%
창인원 1
 
0.9%
Other values (43) 43
39.8%
2024-03-13T08:14:31.134100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 61
 
11.2%
지 43
 
7.9%
복 41
 
7.6%
회 40
 
7.4%
개 38
 
7.0%
사 33
 
6.1%
법 23
 
4.2%
20
 
3.7%
재 16
 
2.9%
단 13
 
2.4%
Other values (122) 215
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 515
94.8%
Space Separator 20
 
3.7%
Close Punctuation 4
 
0.7%
Open Punctuation 4
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 61
 
11.8%
지 43
 
8.3%
복 41
 
8.0%
회 40
 
7.8%
개 38
 
7.4%
사 33
 
6.4%
법 23
 
4.5%
재 16
 
3.1%
단 13
 
2.5%
의 8
 
1.6%
Other values (119) 199
38.6%
Space Separator
ValueCountFrequency (%)
20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 515
94.8%
Common 28
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 61
 
11.8%
지 43
 
8.3%
복 41
 
8.0%
회 40
 
7.8%
개 38
 
7.4%
사 33
 
6.4%
법 23
 
4.5%
재 16
 
3.1%
단 13
 
2.5%
의 8
 
1.6%
Other values (119) 199
38.6%
Common
ValueCountFrequency (%)
20
71.4%
) 4
 
14.3%
( 4
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 515
94.8%
ASCII 28
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 61
 
11.8%
지 43
 
8.3%
복 41
 
8.0%
회 40
 
7.8%
개 38
 
7.4%
사 33
 
6.4%
법 23
 
4.5%
재 16
 
3.1%
단 13
 
2.5%
의 8
 
1.6%
Other values (119) 199
38.6%
ASCII
ValueCountFrequency (%)
20
71.4%
) 4
 
14.3%
( 4
 
14.3%

Interactions

2024-03-13T08:14:25.098657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.123452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.611682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.087114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.561699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.020249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.664142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:25.187584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.195423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.678364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.163854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.632185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.095154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.733365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:25.259164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.259696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.738324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.236048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.699608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.160573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.792922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:25.332109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.325903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.799674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.296150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.764413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.225295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.854547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:25.397102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.394334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.863000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.359109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.828717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.478761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.922782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:25.462569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.477574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.939356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.424950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.891329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.539124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.981322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:25.523083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:22.539106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.008496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.490548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:23.952737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:24.597439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:14:25.036152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:14:31.216456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명기관구분명기관명소재지지번주소소재지도로명주소소재지우편번호WGS84위도WGS84경도전화번호홈페이지URL입소정원수(명)입소현원수(명)종사자정원수(명)종사자현원수(명)설치신고일법인명정보
시군명1.0000.0001.0001.0001.0000.9920.9110.9381.0000.9280.5700.5480.5970.5530.9730.911
기관구분명0.0001.0001.0001.0001.0000.0000.4560.0001.0000.8470.0000.0000.0000.0001.0000.822
기관명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지우편번호0.9920.0001.0001.0001.0001.0000.8970.6981.0000.8070.1640.0000.3520.4460.0000.163
WGS84위도0.9110.4561.0001.0001.0000.8971.0000.2551.0000.9030.3410.3210.3390.2980.9610.800
WGS84경도0.9380.0001.0001.0001.0000.6980.2551.0001.0000.6440.4380.4440.3570.5430.9220.889
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
홈페이지URL0.9280.8471.0001.0001.0000.8070.9030.6441.0001.0000.9700.9790.9830.9890.9910.997
입소정원수(명)0.5700.0001.0001.0001.0000.1640.3410.4381.0000.9701.0000.9800.9430.8840.9910.979
입소현원수(명)0.5480.0001.0001.0001.0000.0000.3210.4441.0000.9790.9801.0000.9610.8880.9900.960
종사자정원수(명)0.5970.0001.0001.0001.0000.3520.3390.3571.0000.9830.9430.9611.0000.9520.9970.991
종사자현원수(명)0.5530.0001.0001.0001.0000.4460.2980.5431.0000.9890.8840.8880.9521.0000.9970.993
설치신고일0.9731.0001.0001.0001.0000.0000.9610.9221.0000.9910.9910.9900.9970.9971.0000.998
법인명정보0.9110.8221.0001.0001.0000.1630.8000.8891.0000.9970.9790.9600.9910.9930.9981.000
2024-03-13T08:14:31.328197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관구분명시군명
기관구분명1.0000.000
시군명0.0001.000
2024-03-13T08:14:31.392813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도입소정원수(명)입소현원수(명)종사자정원수(명)종사자현원수(명)시군명기관구분명
소재지우편번호1.000-0.8530.3610.0750.0330.1140.1080.8270.000
WGS84위도-0.8531.000-0.200-0.094-0.043-0.101-0.1100.5540.277
WGS84경도0.361-0.2001.0000.0910.0480.0820.0720.6220.000
입소정원수(명)0.075-0.0940.0911.0000.9010.8820.8610.2230.000
입소현원수(명)0.033-0.0430.0480.9011.0000.8930.9030.2110.000
종사자정원수(명)0.114-0.1010.0820.8820.8931.0000.9680.2290.000
종사자현원수(명)0.108-0.1100.0720.8610.9030.9681.0000.1980.000
시군명0.8270.5540.6220.2230.2110.2290.1981.0000.000
기관구분명0.0000.2770.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-13T08:14:25.631444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:14:25.819170image/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

시군명기관구분명기관명소재지지번주소소재지도로명주소소재지우편번호WGS84위도WGS84경도전화번호홈페이지URL입소정원수(명)입소현원수(명)종사자정원수(명)종사자현원수(명)설치신고일법인명정보
0가평군지적장애인시설가난한마음의집경기도 가평군 상면 율길리 747번지경기도 가평군 상면 수역길 1191244137.82392127.286985031-585-3210http://www.마음의집.com/292921192013-02-06마음의집
1가평군지적장애인시설꽃동네은총의집경기도 가평군 조종면 운악리 540-6번지경기도 가평군 조종면 꽃동네길 53-131243237.88373127.350383031-589-0188www.kkotgp.or.kr303033292012-04-04(재)예수의꽃동네유지재단
2가평군지적장애인시설성 빈센트 환경마을경기도 가평군 조종면 신하리 45-3번지경기도 가평군 조종면 연인산로 3081243637.838791127.367802031-585-9066www.vincentvillage.co.kr292921212005-02-03서울가톨릭사회복지회
3고양시지적장애인시설꿈나무의집경기도 고양시 일산동구 설문동 492번지경기도 고양시 일산동구 은마길79번길 1131025237.717039126.802599031-977-3452http://꿈나무의집.kr/2625882007-04-09개인
4고양시지적장애인시설늘사랑의집경기도 고양시 덕양구 관산동 551-5번지경기도 고양시 덕양구 통일로1018번길 112-61026537.70979126.861397031-962-5391http://ver2.iloveall.or.kr/2922872012-01-20개인
5고양시지적장애인시설벧엘의집경기도 고양시 일산동구 사리현동 444-2번지경기도 고양시 일산동구 공릉천로71번길 107-291026037.69904126.839204031-962-2788www.bethelhouse.co.kr292921212006-01-31한국벧엘복지재단
6고양시지적장애인시설사랑의동산경기도 고양시 덕양구 행주외동 178-5번지경기도 고양시 덕양구 행주산성로116번길 81044037.600391126.819434031-970-2754http://lovegardens.modoo.at2923892011-09-15개인
7고양시시각장애인시설소망복지원경기도 고양시 덕양구 벽제동 548-1번지경기도 고양시 덕양구 보광로 1301026737.726557126.90835031-963-7862http://www.smolove.com/2921982006-11-14개인
8고양시지적장애인시설애덕의 집경기도 고양시 덕양구 벽제동 486번지경기도 고양시 덕양구 혜음로 2841027137.724593126.895397031-962-4450www.aduck.or.kr555143421991-09-14사회복지법인애덕의집
9고양시지적장애인시설천사의집경기도 고양시 덕양구 고양동 421-1번지경기도 고양시 덕양구 동헌로 363-181027337.70828126.893336031-963-6506www.031-963-6506.kti114.net292912122009-03-26개인
시군명기관구분명기관명소재지지번주소소재지도로명주소소재지우편번호WGS84위도WGS84경도전화번호홈페이지URL입소정원수(명)입소현원수(명)종사자정원수(명)종사자현원수(명)설치신고일법인명정보
78포천시지적장애인시설나눔의 집경기도 포천시 내촌면 진목리 369-3번지경기도 포천시 내촌면 내진로 274-201119037.90476127.206126031-532-1111http://www.pcnanum.com/292619182018-04-02아름다운사람들
79포천시지적장애인시설남사랑의 집경기도 포천시 신북면 기지리 934-9번지경기도 포천시 신북면 호국로 2039-1571113937.932103127.222707031-535-0035http://namsarang.com/292721201990-06-04물댄동산
80포천시시각장애인시설소망원경기도 포천시 이동면 노곡리 1094-1번지경기도 포천시 이동면 새낭로 5001111238.020973127.32958031-536-6292https://happylog.naver.com/hlog/ihope/info?basic302119152002-12-26소망원
81포천시청각·언어 장애인시설운보원경기도 포천시 내촌면 마명리 120-8번지경기도 포천시 내촌면 부마로282번길 991119137.769357127.202223031-531-2161http://cafe.daum.net/MakeHappiness563428251993-10-19한국청각장애인복지회
82포천시지체장애인시설유일사랑의집경기도 포천시 자작동 209-3번지경기도 포천시 자작로3길 111115837.871502127.171578031-531-9154www.1615972009-05-08개인
83포천시지적장애인시설임마누엘의 집경기도 포천시 군내면 상성북리 429-4번지경기도 포천시 군내면 반월산성로375번길 491115237.886958127.231141031-535-8796http://www.im21.org/main/main.php2918782003-06-02개인
84포천시지적장애인시설해뜨는 집경기도 포천시 신북면 가채리 486번지경기도 포천시 신북면 중앙로335번길 571114037.916407127.208161031-535-5076http://www.sunrisehouse.co.kr/303024222007-12-11푸른나무
85하남시지적장애인시설나그네집경기도 하남시 하사창동 179번지경기도 하남시 춘궁로37번길 49-431301937.513249127.205141031-791-9049www.nagnezip.com2929772010-07-13개인
86하남시지적장애인시설소망의집경기도 하남시 항동 258번지경기도 하남시 고골로242번길 931302137.502576127.189121031-791-4972www.2019872007-06-20개인
87하남시지적장애인시설작은프란치스코의집경기도 하남시 상산곡동 124-18번지경기도 하남시 하남대로249번길 121302737.497826127.231087031-793-5159www.151413132008-12-24영보사회복지회법인