Overview

Dataset statistics

Number of variables10
Number of observations189
Missing cells24
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.4 KiB
Average record size in memory83.7 B

Variable types

Categorical2
Text5
Numeric3

Dataset

Description경기도사회서비스원 노인일자리 현황
Author경기도사회서비스원
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=9Y7Z16WPRD9Y83KBULMD31572281&infSeq=1

Alerts

정제우편번호 is highly overall correlated with 정제WGS84위도 and 1 other fieldsHigh correlation
정제WGS84위도 is highly overall correlated with 정제우편번호 and 1 other fieldsHigh correlation
정제WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 정제우편번호 and 2 other fieldsHigh correlation
팩스번호 has 8 (4.2%) missing valuesMissing
정제도로명주소 has 8 (4.2%) missing valuesMissing
정제지번주소 has 2 (1.1%) missing valuesMissing
정제우편번호 has 2 (1.1%) missing valuesMissing
정제WGS84위도 has 2 (1.1%) missing valuesMissing
정제WGS84경도 has 2 (1.1%) missing valuesMissing
수행기관명 has unique valuesUnique

Reproduction

Analysis started2024-03-13 00:01:46.208475
Analysis finished2024-03-13 00:01:47.481110
Duration1.27 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
수원시
14 
부천시
 
12
성남시
 
12
용인시
 
12
고양시
 
12
Other values (26)
127 

Length

Max length4
Median length3
Mean length3.0740741
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
수원시 14
 
7.4%
부천시 12
 
6.3%
성남시 12
 
6.3%
용인시 12
 
6.3%
고양시 12
 
6.3%
안양시 9
 
4.8%
포천시 8
 
4.2%
의정부시 7
 
3.7%
안산시 7
 
3.7%
평택시 7
 
3.7%
Other values (21) 89
47.1%

Length

2024-03-13T09:01:47.533920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 14
 
7.4%
성남시 12
 
6.3%
용인시 12
 
6.3%
고양시 12
 
6.3%
부천시 12
 
6.3%
안양시 9
 
4.8%
포천시 8
 
4.2%
평택시 7
 
3.7%
광명시 7
 
3.7%
안산시 7
 
3.7%
Other values (21) 89
47.1%

기관유형
Categorical

Distinct10
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
노인복지관
51 
대한노인회
39 
시니어클럽
25 
기타
23 
실버인력뱅크
15 
Other values (5)
36 

Length

Max length7
Median length5
Mean length4.7195767
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row대한노인회
2nd row노인복지관
3rd row기타
4th row기타
5th row실버인력뱅크

Common Values

ValueCountFrequency (%)
노인복지관 51
27.0%
대한노인회 39
20.6%
시니어클럽 25
13.2%
기타 23
12.2%
실버인력뱅크 15
 
7.9%
종합사회복지관 15
 
7.9%
지자체 15
 
7.9%
노인복지센터 3
 
1.6%
종합복지관 2
 
1.1%
복지관 1
 
0.5%

Length

2024-03-13T09:01:47.647617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:01:47.761339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노인복지관 51
27.0%
대한노인회 39
20.6%
시니어클럽 25
13.2%
기타 23
12.2%
실버인력뱅크 15
 
7.9%
종합사회복지관 15
 
7.9%
지자체 15
 
7.9%
노인복지센터 3
 
1.6%
종합복지관 2
 
1.1%
복지관 1
 
0.5%

수행기관명
Text

UNIQUE 

Distinct189
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-03-13T09:01:47.958486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length10.767196
Min length4

Characters and Unicode

Total characters2035
Distinct characters187
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique189 ?
Unique (%)100.0%

Sample

1st row대한노인회 고양시 일산서구지회
2nd row덕양노인종합복지관
3rd row고양실버인력뱅크
4th row효샘재가노인지원 서비스센터
5th row과천시실버인력뱅크
ValueCountFrequency (%)
대한노인회 38
 
14.3%
노인장애인과 4
 
1.5%
용인시 3
 
1.1%
고양시 3
 
1.1%
복지국 2
 
0.8%
사회복지과 2
 
0.8%
안양시 2
 
0.8%
㈜지엔그린 1
 
0.4%
평택시니어클럽 1
 
0.4%
의왕시니어클럽 1
 
0.4%
Other values (208) 208
78.5%
2024-03-13T09:01:48.271050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 147
 
7.2%
인 144
 
7.1%
시 116
 
5.7%
회 110
 
5.4%
노 109
 
5.4%
복 98
 
4.8%
관 77
 
3.8%
76
 
3.7%
합 45
 
2.2%
사 45
 
2.2%
Other values (177) 1068
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1938
95.2%
Space Separator 76
 
3.7%
Close Punctuation 9
 
0.4%
Open Punctuation 8
 
0.4%
Uppercase Letter 2
 
0.1%
Other Symbol 1
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 147
 
7.6%
인 144
 
7.4%
시 116
 
6.0%
회 110
 
5.7%
노 109
 
5.6%
복 98
 
5.1%
관 77
 
4.0%
합 45
 
2.3%
사 45
 
2.3%
대 42
 
2.2%
Other values (170) 1005
51.9%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1939
95.3%
Common 94
 
4.6%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 147
 
7.6%
인 144
 
7.4%
시 116
 
6.0%
회 110
 
5.7%
노 109
 
5.6%
복 98
 
5.1%
관 77
 
4.0%
합 45
 
2.3%
사 45
 
2.3%
대 42
 
2.2%
Other values (171) 1006
51.9%
Common
ValueCountFrequency (%)
76
80.9%
) 9
 
9.6%
( 8
 
8.5%
1 1
 
1.1%
Latin
ValueCountFrequency (%)
S 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1938
95.2%
ASCII 96
 
4.7%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 147
 
7.6%
인 144
 
7.4%
시 116
 
6.0%
회 110
 
5.7%
노 109
 
5.6%
복 98
 
5.1%
관 77
 
4.0%
합 45
 
2.3%
사 45
 
2.3%
대 42
 
2.2%
Other values (170) 1005
51.9%
ASCII
ValueCountFrequency (%)
76
79.2%
) 9
 
9.4%
( 8
 
8.3%
S 1
 
1.0%
1 1
 
1.0%
K 1
 
1.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

전화번호
Text

Distinct185
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-03-13T09:01:48.485136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.010582
Min length9

Characters and Unicode

Total characters2270
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

Unique181 ?
Unique (%)95.8%

Sample

1st row031-917-1781
2nd row031-969-7781
3rd row1644-5104
4th row031-970-0361
5th row02-509-7610
ValueCountFrequency (%)
031-826-0742 2
 
1.1%
031-532-3515 2
 
1.1%
031-455-0551 2
 
1.1%
031-454-2077 2
 
1.1%
031-8059-4348 1
 
0.5%
031-426-7988 1
 
0.5%
031-917-1781 1
 
0.5%
032-677-0151 1
 
0.5%
031-633-2034 1
 
0.5%
031-943-0731 1
 
0.5%
Other values (175) 175
92.6%
2024-03-13T09:01:49.071757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 377
16.6%
0 364
16.0%
3 312
13.7%
1 292
12.9%
2 150
 
6.6%
5 150
 
6.6%
8 144
 
6.3%
7 129
 
5.7%
6 123
 
5.4%
4 121
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1893
83.4%
Dash Punctuation 377
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 364
19.2%
3 312
16.5%
1 292
15.4%
2 150
7.9%
5 150
7.9%
8 144
 
7.6%
7 129
 
6.8%
6 123
 
6.5%
4 121
 
6.4%
9 108
 
5.7%
Dash Punctuation
ValueCountFrequency (%)
- 377
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 377
16.6%
0 364
16.0%
3 312
13.7%
1 292
12.9%
2 150
 
6.6%
5 150
 
6.6%
8 144
 
6.3%
7 129
 
5.7%
6 123
 
5.4%
4 121
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 377
16.6%
0 364
16.0%
3 312
13.7%
1 292
12.9%
2 150
 
6.6%
5 150
 
6.6%
8 144
 
6.3%
7 129
 
5.7%
6 123
 
5.4%
4 121
 
5.3%

팩스번호
Text

MISSING 

Distinct179
Distinct (%)98.9%
Missing8
Missing (%)4.2%
Memory size1.6 KiB
2024-03-13T09:01:49.285933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.071823
Min length11

Characters and Unicode

Total characters2185
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

Unique177 ?
Unique (%)97.8%

Sample

1st row031-917-1783
2nd row031-969-7784
3rd row0303-3442-5104
4th row031-970-0414
5th row02-502-8522
ValueCountFrequency (%)
070-7469-8616 2
 
1.1%
031-454-2079 2
 
1.1%
031-387-9973 1
 
0.6%
031-657-2477 1
 
0.6%
031-266-0886 1
 
0.6%
031-334-9600 1
 
0.6%
031-536-2027 1
 
0.6%
031-917-1783 1
 
0.6%
032-667-0152 1
 
0.6%
031-426-7981 1
 
0.6%
Other values (169) 169
93.4%
2024-03-13T09:01:49.589623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 362
16.6%
0 317
14.5%
3 294
13.5%
1 263
12.0%
2 146
6.7%
4 141
 
6.5%
6 140
 
6.4%
5 140
 
6.4%
7 135
 
6.2%
8 126
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1823
83.4%
Dash Punctuation 362
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 317
17.4%
3 294
16.1%
1 263
14.4%
2 146
8.0%
4 141
7.7%
6 140
7.7%
5 140
7.7%
7 135
7.4%
8 126
 
6.9%
9 121
 
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2185
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 362
16.6%
0 317
14.5%
3 294
13.5%
1 263
12.0%
2 146
6.7%
4 141
 
6.5%
6 140
 
6.4%
5 140
 
6.4%
7 135
 
6.2%
8 126
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 362
16.6%
0 317
14.5%
3 294
13.5%
1 263
12.0%
2 146
6.7%
4 141
 
6.5%
6 140
 
6.4%
5 140
 
6.4%
7 135
 
6.2%
8 126
 
5.8%

정제도로명주소
Text

MISSING 

Distinct155
Distinct (%)85.6%
Missing8
Missing (%)4.2%
Memory size1.6 KiB
2024-03-13T09:01:49.836835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length18.78453
Min length13

Characters and Unicode

Total characters3400
Distinct characters177
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

Unique130 ?
Unique (%)71.8%

Sample

1st row경기도 고양시 일산서구 일산로 778
2nd row경기도 고양시 덕양구 어울림로 49
3rd row경기도 고양시 일산동구 무궁화로 106
4th row경기도 고양시 덕양구 능곡로 7
5th row경기도 과천시 문원로 57
ValueCountFrequency (%)
경기도 181
 
22.2%
수원시 13
 
1.6%
부천시 12
 
1.5%
용인시 12
 
1.5%
고양시 12
 
1.5%
성남시 10
 
1.2%
포천시 8
 
1.0%
안양시 8
 
1.0%
50 7
 
0.9%
평택시 7
 
0.9%
Other values (323) 544
66.8%
2024-03-13T09:01:50.194062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
633
18.6%
경 191
 
5.6%
기 189
 
5.6%
도 182
 
5.4%
시 180
 
5.3%
로 169
 
5.0%
1 117
 
3.4%
2 81
 
2.4%
길 71
 
2.1%
구 70
 
2.1%
Other values (167) 1517
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2120
62.4%
Space Separator 633
 
18.6%
Decimal Number 624
 
18.4%
Dash Punctuation 23
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 191
 
9.0%
기 189
 
8.9%
도 182
 
8.6%
시 180
 
8.5%
로 169
 
8.0%
길 71
 
3.3%
구 70
 
3.3%
번 57
 
2.7%
양 48
 
2.3%
안 42
 
2.0%
Other values (155) 921
43.4%
Decimal Number
ValueCountFrequency (%)
1 117
18.8%
2 81
13.0%
3 69
11.1%
4 68
10.9%
6 51
8.2%
0 51
8.2%
5 51
8.2%
8 47
7.5%
7 45
 
7.2%
9 44
 
7.1%
Space Separator
ValueCountFrequency (%)
633
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2120
62.4%
Common 1280
37.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 191
 
9.0%
기 189
 
8.9%
도 182
 
8.6%
시 180
 
8.5%
로 169
 
8.0%
길 71
 
3.3%
구 70
 
3.3%
번 57
 
2.7%
양 48
 
2.3%
안 42
 
2.0%
Other values (155) 921
43.4%
Common
ValueCountFrequency (%)
633
49.5%
1 117
 
9.1%
2 81
 
6.3%
3 69
 
5.4%
4 68
 
5.3%
6 51
 
4.0%
0 51
 
4.0%
5 51
 
4.0%
8 47
 
3.7%
7 45
 
3.5%
Other values (2) 67
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2120
62.4%
ASCII 1280
37.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
633
49.5%
1 117
 
9.1%
2 81
 
6.3%
3 69
 
5.4%
4 68
 
5.3%
6 51
 
4.0%
0 51
 
4.0%
5 51
 
4.0%
8 47
 
3.7%
7 45
 
3.5%
Other values (2) 67
 
5.2%
Hangul
ValueCountFrequency (%)
경 191
 
9.0%
기 189
 
8.9%
도 182
 
8.6%
시 180
 
8.5%
로 169
 
8.0%
길 71
 
3.3%
구 70
 
3.3%
번 57
 
2.7%
양 48
 
2.3%
안 42
 
2.0%
Other values (155) 921
43.4%

정제지번주소
Text

MISSING 

Distinct161
Distinct (%)86.1%
Missing2
Missing (%)1.1%
Memory size1.6 KiB
2024-03-13T09:01:50.424662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length30
Mean length20.967914
Min length15

Characters and Unicode

Total characters3921
Distinct characters171
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

Unique136 ?
Unique (%)72.7%

Sample

1st row경기도 고양시 일산서구 대화동 2237번지
2nd row경기도 고양시 덕양구 화정동 846번지
3rd row경기도 고양시 일산동구 정발산동 816번지
4th row경기도 고양시 덕양구 토당동 343-1번지
5th row경기도 과천시 문원동 15-168번지
ValueCountFrequency (%)
경기도 187
 
21.9%
수원시 14
 
1.6%
고양시 12
 
1.4%
부천시 12
 
1.4%
용인시 12
 
1.4%
성남시 11
 
1.3%
안양시 8
 
0.9%
포천시 8
 
0.9%
평택시 7
 
0.8%
의정부시 7
 
0.8%
Other values (352) 577
67.5%
2024-03-13T09:01:50.759841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
668
 
17.0%
지 199
 
5.1%
기 192
 
4.9%
도 190
 
4.8%
경 188
 
4.8%
번 187
 
4.8%
시 186
 
4.7%
동 174
 
4.4%
1 119
 
3.0%
- 100
 
2.6%
Other values (161) 1718
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2448
62.4%
Decimal Number 705
 
18.0%
Space Separator 668
 
17.0%
Dash Punctuation 100
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 199
 
8.1%
기 192
 
7.8%
도 190
 
7.8%
경 188
 
7.7%
번 187
 
7.6%
시 186
 
7.6%
동 174
 
7.1%
구 72
 
2.9%
양 48
 
2.0%
안 42
 
1.7%
Other values (149) 970
39.6%
Decimal Number
ValueCountFrequency (%)
1 119
16.9%
2 89
12.6%
5 72
10.2%
3 68
9.6%
8 65
9.2%
4 64
9.1%
0 62
8.8%
7 60
8.5%
6 60
8.5%
9 46
 
6.5%
Space Separator
ValueCountFrequency (%)
668
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2448
62.4%
Common 1473
37.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 199
 
8.1%
기 192
 
7.8%
도 190
 
7.8%
경 188
 
7.7%
번 187
 
7.6%
시 186
 
7.6%
동 174
 
7.1%
구 72
 
2.9%
양 48
 
2.0%
안 42
 
1.7%
Other values (149) 970
39.6%
Common
ValueCountFrequency (%)
668
45.3%
1 119
 
8.1%
- 100
 
6.8%
2 89
 
6.0%
5 72
 
4.9%
3 68
 
4.6%
8 65
 
4.4%
4 64
 
4.3%
0 62
 
4.2%
7 60
 
4.1%
Other values (2) 106
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2448
62.4%
ASCII 1473
37.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
668
45.3%
1 119
 
8.1%
- 100
 
6.8%
2 89
 
6.0%
5 72
 
4.9%
3 68
 
4.6%
8 65
 
4.4%
4 64
 
4.3%
0 62
 
4.2%
7 60
 
4.1%
Other values (2) 106
 
7.2%
Hangul
ValueCountFrequency (%)
지 199
 
8.1%
기 192
 
7.8%
도 190
 
7.8%
경 188
 
7.7%
번 187
 
7.6%
시 186
 
7.6%
동 174
 
7.1%
구 72
 
2.9%
양 48
 
2.0%
안 42
 
1.7%
Other values (149) 970
39.6%

정제우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct154
Distinct (%)82.4%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean14152.642
Minimum10032
Maximum18595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-13T09:01:50.879508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10032
5-th percentile10407.7
Q111934.5
median14120
Q316380.5
95-th percentile18111.1
Maximum18595
Range8563
Interquartile range (IQR)4446

Descriptive statistics

Standard deviation2510.0013
Coefficient of variation (CV)0.17735214
Kurtosis-1.2388145
Mean14152.642
Median Absolute Deviation (MAD)2220
Skewness0.081189158
Sum2646544
Variance6300106.4
MonotonicityNot monotonic
2024-03-13T09:01:50.987262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16835 3
 
1.6%
10382 2
 
1.1%
16253 2
 
1.1%
16959 2
 
1.1%
16077 2
 
1.1%
15858 2
 
1.1%
12129 2
 
1.1%
11652 2
 
1.1%
17882 2
 
1.1%
11147 2
 
1.1%
Other values (144) 166
87.8%
ValueCountFrequency (%)
10032 1
0.5%
10101 1
0.5%
10109 1
0.5%
10111 1
0.5%
10222 1
0.5%
10382 2
1.1%
10400 2
1.1%
10405 1
0.5%
10414 1
0.5%
10470 2
1.1%
ValueCountFrequency (%)
18595 1
0.5%
18590 1
0.5%
18427 2
1.1%
18316 1
0.5%
18274 1
0.5%
18139 1
0.5%
18136 1
0.5%
18130 1
0.5%
18112 1
0.5%
18109 1
0.5%

정제WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct161
Distinct (%)86.1%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean37.455608
Minimum36.957641
Maximum38.158137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-13T09:01:51.089513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.957641
5-th percentile37.004993
Q137.290369
median37.428064
Q337.630327
95-th percentile37.902234
Maximum38.158137
Range1.2004954
Interquartile range (IQR)0.33995781

Descriptive statistics

Standard deviation0.24859803
Coefficient of variation (CV)0.0066371378
Kurtosis-0.027892841
Mean37.455608
Median Absolute Deviation (MAD)0.14761337
Skewness0.38431539
Sum7004.1987
Variance0.061800983
MonotonicityNot monotonic
2024-03-13T09:01:51.198838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3219745051 3
 
1.6%
37.6744730961 2
 
1.1%
37.648470362 2
 
1.1%
37.2936231233 2
 
1.1%
37.2878686917 2
 
1.1%
37.3427653881 2
 
1.1%
37.352356887 2
 
1.1%
37.7336792838 2
 
1.1%
36.998843304 2
 
1.1%
37.3642976294 2
 
1.1%
Other values (151) 166
87.8%
ValueCountFrequency (%)
36.9576412931 1
0.5%
36.9857568173 1
0.5%
36.9912208605 1
0.5%
36.992297194 1
0.5%
36.998843304 2
1.1%
37.0024275967 1
0.5%
37.0035096548 1
0.5%
37.004372045 2
1.1%
37.0064435119 1
0.5%
37.0083795168 1
0.5%
ValueCountFrequency (%)
38.1581367057 1
0.5%
38.1035057227 2
1.1%
38.0965166652 1
0.5%
37.9370912337 1
0.5%
37.9336369166 1
0.5%
37.9059223773 1
0.5%
37.9051489094 1
0.5%
37.9050478228 1
0.5%
37.9047627073 1
0.5%
37.896334695 1
0.5%

정제WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct161
Distinct (%)86.1%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean127.04453
Minimum126.6069
Maximum127.64064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-13T09:01:51.314440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.6069
5-th percentile126.76045
Q1126.86736
median127.05321
Q3127.1721
95-th percentile127.49002
Maximum127.64064
Range1.0337346
Interquartile range (IQR)0.30474049

Descriptive statistics

Standard deviation0.21585471
Coefficient of variation (CV)0.0016990477
Kurtosis0.45166556
Mean127.04453
Median Absolute Deviation (MAD)0.14552204
Skewness0.69559452
Sum23757.326
Variance0.046593257
MonotonicityNot monotonic
2024-03-13T09:01:51.425239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0973688191 3
 
1.6%
126.7481458171 2
 
1.1%
126.8368835541 2
 
1.1%
127.6406364705 2
 
1.1%
127.1116977163 2
 
1.1%
126.9725060266 2
 
1.1%
126.93936712 2
 
1.1%
127.0456120226 2
 
1.1%
127.1021583874 2
 
1.1%
126.9331688141 2
 
1.1%
Other values (151) 166
87.8%
ValueCountFrequency (%)
126.6069018961 1
0.5%
126.7043866355 1
0.5%
126.715694858 1
0.5%
126.7226202082 1
0.5%
126.7365612783 1
0.5%
126.7423886716 1
0.5%
126.7455759437 1
0.5%
126.7481458171 2
1.1%
126.7604466371 2
1.1%
126.7739395836 1
0.5%
ValueCountFrequency (%)
127.6406364705 2
1.1%
127.6402231432 1
0.5%
127.6383689142 1
0.5%
127.6366282067 1
0.5%
127.5922768609 1
0.5%
127.5892402505 1
0.5%
127.5177596264 1
0.5%
127.5112051744 1
0.5%
127.4912334922 1
0.5%
127.4871719211 1
0.5%

Interactions

2024-03-13T09:01:47.005590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:46.601931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:46.798079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:47.067843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:46.659375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:46.871637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:47.133561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:46.729182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:46.941067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T09:01:51.496220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명기관유형정제우편번호정제WGS84위도정제WGS84경도
시군명1.0000.0000.9900.9760.956
기관유형0.0001.0000.0000.3970.104
정제우편번호0.9900.0001.0000.9230.855
정제WGS84위도0.9760.3970.9231.0000.683
정제WGS84경도0.9560.1040.8550.6831.000
2024-03-13T09:01:51.569611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명기관유형
시군명1.0000.000
기관유형0.0001.000
2024-03-13T09:01:51.632526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정제우편번호정제WGS84위도정제WGS84경도시군명기관유형
정제우편번호1.000-0.9080.1030.8640.000
정제WGS84위도-0.9081.000-0.1470.7890.130
정제WGS84경도0.103-0.1471.0000.7110.028
시군명0.8640.7890.7111.0000.000
기관유형0.0000.1300.0280.0001.000

Missing values

2024-03-13T09:01:47.225046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:01:47.334608image/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.
2024-03-13T09:01:47.424459image/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

시군명기관유형수행기관명전화번호팩스번호정제도로명주소정제지번주소정제우편번호정제WGS84위도정제WGS84경도
0고양시대한노인회대한노인회 고양시 일산서구지회031-917-1781031-917-1783경기도 고양시 일산서구 일산로 778경기도 고양시 일산서구 대화동 2237번지1038237.674473126.748146
1고양시노인복지관덕양노인종합복지관031-969-7781031-969-7784경기도 고양시 덕양구 어울림로 49경기도 고양시 덕양구 화정동 846번지1047037.64847126.836884
2고양시기타고양실버인력뱅크1644-51040303-3442-5104경기도 고양시 일산동구 무궁화로 106경기도 고양시 일산동구 정발산동 816번지1040537.666914126.774573
3고양시기타효샘재가노인지원 서비스센터031-970-0361031-970-0414경기도 고양시 덕양구 능곡로 7경기도 고양시 덕양구 토당동 343-1번지1050837.621452126.820137
4과천시실버인력뱅크과천시실버인력뱅크02-509-761002-502-8522경기도 과천시 문원로 57경기도 과천시 문원동 15-168번지1382837.428064127.004199
5광명시기타한국지역복지봉사회02-2618-045302-2618-2500경기도 광명시 소하로 88경기도 광명시 소하동 1341-3번지1431637.446947126.883345
6광주시실버인력뱅크광주시노인복지관(광주시실버인력뱅크)031-769-9129031-763-9266경기도 광주시 파발로 202경기도 광주시 탄벌동 18-1번지1273937.416897127.250011
7구리시종합사회복지관구리시종합사회복지관031-556-8100031-556-6052경기도 구리시 벌말로129번길 50경기도 구리시 토평동 984번지1194637.589198127.146384
8군포시노인복지관군포시노인복지관031-399-2270031-399-2271경기도 군포시 고산로 223경기도 군포시 당동 887번지1587637.345518126.947502
9남양주시노인복지관남양주시동부노인복지관031-559-5880031-559-5883경기도 남양주시 수동면 비룡로 801-47경기도 남양주시 수동면 운수리 361번지1203137.709704127.32054
시군명기관유형수행기관명전화번호팩스번호정제도로명주소정제지번주소정제우편번호정제WGS84위도정제WGS84경도
179수원시노인복지관수원시광교노인복지관031-8006-7400031-8006-7459경기도 수원시 영통구 센트럴타운로 22경기도 수원시 영통구 이의동 1328-1번지1650537.289416127.057041
180수원시종합사회복지관우만종합사회복지관031-254-1992031-254-1434<NA>경기도 수원시 팔달구 우만동 301번지 주공3단지아파트1623037.291803127.035218
181시흥시노인복지관시흥시노인종합복지관031-404-3100031-404-3122경기도 시흥시 장현능곡로 214경기도 시흥시 능곡동 765번지1499537.368674126.813334
182안산시대한노인회대한노인회 안산단원구지회031-403-8787031-484-3727경기도 안산시 단원구 선부광장1로 134경기도 안산시 단원구 선부동 1077-9번지1536837.332252126.810595
183안성시대한노인회대한노인회 안성시지회031-673-5393031-676-3102경기도 안성시 장기로 109경기도 안성시 낙원동 68-24번지1759137.004372127.275084
184안성시기타안성시서부무한돌봄네트워크팀031-657-2472031-657-2477<NA>경기도 안성시 공도읍 만정리 788-4번지 경기도시공사참아름1756037.002428127.175049
185안양시대한노인회대한노인회 안양시동안구지회031-388-9078031-387-9973경기도 안양시 동안구 동안로 151경기도 안양시 동안구 비산동 1106번지1404837.391462126.949627
186양평군종합사회복지관양평군종합사회복지관031-775-7741031-775-7745경기도 양평군 용문면 용문역길 67경기도 양평군 용문면 다문리 793-8번지1252037.480512127.58924
187여주시지자체여주시청 사회복지과031-887-2260031-885-3121경기도 여주시 세종로 1경기도 여주시 홍문동 4번지1261937.298216127.636628
188여주시실버인력뱅크여주시노인복지관(여주실버인력뱅크)031-881-0050031-881-0041경기도 여주시 여흥로160번길 27경기도 여주시 상동 352번지1262937.293623127.640636