Overview

Dataset statistics

Number of variables11
Number of observations43
Missing cells15
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory96.1 B

Variable types

Text4
Categorical4
Numeric3

Dataset

Description대전광역시 재가노인복지시설(방문목욕) 현황에 대한 데이터로 시설명, 소재지, 시설종류, 전화번호 등의 항목을 제공합니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15063211/fileData.do

Alerts

시설종류(유형) has constant value ""Constant
종사자 정원 is highly overall correlated with 종사자(남) and 2 other fieldsHigh correlation
종사자(남) is highly overall correlated with 종사자 정원 and 2 other fieldsHigh correlation
종사자(여) is highly overall correlated with 종사자 정원 and 3 other fieldsHigh correlation
시설이용(남) is highly overall correlated with 시설이용(여) and 1 other fieldsHigh correlation
시설이용(여) is highly overall correlated with 종사자 정원 and 4 other fieldsHigh correlation
설치주체 is highly overall correlated with 종사자(여) and 2 other fieldsHigh correlation
종사자 정원 has 11 (25.6%) missing valuesMissing
종사자(남) has 3 (7.0%) missing valuesMissing
종사자(여) has 1 (2.3%) missing valuesMissing
시설명 has unique valuesUnique
전화번호 has unique valuesUnique
종사자 정원 has 3 (7.0%) zerosZeros
종사자(남) has 18 (41.9%) zerosZeros
종사자(여) has 14 (32.6%) zerosZeros

Reproduction

Analysis started2023-12-12 14:04:34.503238
Analysis finished2023-12-12 14:04:36.229976
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T23:04:36.358074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length10.023256
Min length6

Characters and Unicode

Total characters431
Distinct characters102
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

Unique43 ?
Unique (%)100.0%

Sample

1st row금성노인복지센터
2nd row대전재가노인복지센터
3rd row산내노인종합센터
4th row해드림노인복지센터
5th row예명 주야간보호센터
ValueCountFrequency (%)
주야간보호센터 2
 
4.2%
금성노인복지센터 1
 
2.1%
둔산부성'노인복지센터 1
 
2.1%
재가노인지원센터 1
 
2.1%
유앤아이주간보호센터 1
 
2.1%
해드림재가노인복지센터 1
 
2.1%
산마을노인데이케어센터 1
 
2.1%
이래노인복지센터 1
 
2.1%
a+부모사랑노인복지센터 1
 
2.1%
청춘재가노인복지센터 1
 
2.1%
Other values (37) 37
77.1%
2023-12-12T23:04:36.802582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
센 40
 
9.3%
터 40
 
9.3%
지 27
 
6.3%
노 26
 
6.0%
인 26
 
6.0%
복 24
 
5.6%
재 12
 
2.8%
가 12
 
2.8%
주 11
 
2.6%
간 11
 
2.6%
Other values (92) 202
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 415
96.3%
Space Separator 5
 
1.2%
Other Punctuation 4
 
0.9%
Decimal Number 3
 
0.7%
Math Symbol 1
 
0.2%
Uppercase Letter 1
 
0.2%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 40
 
9.6%
터 40
 
9.6%
지 27
 
6.5%
노 26
 
6.3%
인 26
 
6.3%
복 24
 
5.8%
재 12
 
2.9%
가 12
 
2.9%
주 11
 
2.7%
간 11
 
2.7%
Other values (82) 186
44.8%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
2 1
33.3%
0 1
33.3%
Other Punctuation
ValueCountFrequency (%)
" 2
50.0%
' 2
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 415
96.3%
Common 15
 
3.5%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 40
 
9.6%
터 40
 
9.6%
지 27
 
6.5%
노 26
 
6.3%
인 26
 
6.3%
복 24
 
5.8%
재 12
 
2.9%
가 12
 
2.9%
주 11
 
2.7%
간 11
 
2.7%
Other values (82) 186
44.8%
Common
ValueCountFrequency (%)
5
33.3%
" 2
 
13.3%
' 2
 
13.3%
1 1
 
6.7%
2 1
 
6.7%
+ 1
 
6.7%
0 1
 
6.7%
) 1
 
6.7%
( 1
 
6.7%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 415
96.3%
ASCII 16
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
센 40
 
9.6%
터 40
 
9.6%
지 27
 
6.5%
노 26
 
6.3%
인 26
 
6.3%
복 24
 
5.8%
재 12
 
2.9%
가 12
 
2.9%
주 11
 
2.7%
간 11
 
2.7%
Other values (82) 186
44.8%
ASCII
ValueCountFrequency (%)
5
31.2%
" 2
 
12.5%
' 2
 
12.5%
1 1
 
6.2%
2 1
 
6.2%
+ 1
 
6.2%
A 1
 
6.2%
0 1
 
6.2%
) 1
 
6.2%
( 1
 
6.2%

소재지
Text

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T23:04:37.103511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length31
Mean length26.465116
Min length16

Characters and Unicode

Total characters1138
Distinct characters123
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)95.3%

Sample

1st row대전광역시 동구 우암로139-3, 3·4층 (삼성동)
2nd row대전광역시 동구 충무로 250 (신흥동)
3rd row대전광역시 동구 산내로 1287번길 73 (낭월동)
4th row대전광역시 동구 동구청로 95, 8층 (가오동)
5th row대전광역시 동구 동부로73번길 6, 5층(판암동)
ValueCountFrequency (%)
대전광역시 43
 
19.9%
서구 12
 
5.6%
대덕구 11
 
5.1%
동구 8
 
3.7%
중구 7
 
3.2%
유성구 4
 
1.9%
송촌동 3
 
1.4%
6 3
 
1.4%
청사서로 2
 
0.9%
8,2층 2
 
0.9%
Other values (115) 121
56.0%
2023-12-12T23:04:37.569195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
175
 
15.4%
대 64
 
5.6%
동 54
 
4.7%
전 44
 
3.9%
구 44
 
3.9%
역 43
 
3.8%
광 43
 
3.8%
시 43
 
3.8%
로 43
 
3.8%
1 38
 
3.3%
Other values (113) 547
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 665
58.4%
Decimal Number 189
 
16.6%
Space Separator 175
 
15.4%
Open Punctuation 37
 
3.3%
Close Punctuation 37
 
3.3%
Other Punctuation 29
 
2.5%
Dash Punctuation 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 64
 
9.6%
동 54
 
8.1%
전 44
 
6.6%
구 44
 
6.6%
역 43
 
6.5%
광 43
 
6.5%
시 43
 
6.5%
로 43
 
6.5%
층 18
 
2.7%
서 18
 
2.7%
Other values (96) 251
37.7%
Decimal Number
ValueCountFrequency (%)
1 38
20.1%
2 25
13.2%
6 20
10.6%
0 20
10.6%
3 19
10.1%
5 17
9.0%
4 16
8.5%
7 16
8.5%
9 11
 
5.8%
8 7
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 26
89.7%
. 2
 
6.9%
· 1
 
3.4%
Space Separator
ValueCountFrequency (%)
175
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 665
58.4%
Common 473
41.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 64
 
9.6%
동 54
 
8.1%
전 44
 
6.6%
구 44
 
6.6%
역 43
 
6.5%
광 43
 
6.5%
시 43
 
6.5%
로 43
 
6.5%
층 18
 
2.7%
서 18
 
2.7%
Other values (96) 251
37.7%
Common
ValueCountFrequency (%)
175
37.0%
1 38
 
8.0%
( 37
 
7.8%
) 37
 
7.8%
, 26
 
5.5%
2 25
 
5.3%
6 20
 
4.2%
0 20
 
4.2%
3 19
 
4.0%
5 17
 
3.6%
Other values (7) 59
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 665
58.4%
ASCII 472
41.5%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
175
37.1%
1 38
 
8.1%
( 37
 
7.8%
) 37
 
7.8%
, 26
 
5.5%
2 25
 
5.3%
6 20
 
4.2%
0 20
 
4.2%
3 19
 
4.0%
5 17
 
3.6%
Other values (6) 58
 
12.3%
Hangul
ValueCountFrequency (%)
대 64
 
9.6%
동 54
 
8.1%
전 44
 
6.6%
구 44
 
6.6%
역 43
 
6.5%
광 43
 
6.5%
시 43
 
6.5%
로 43
 
6.5%
층 18
 
2.7%
서 18
 
2.7%
Other values (96) 251
37.7%
None
ValueCountFrequency (%)
· 1
100.0%

시설종류(유형)
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
방문목욕
43 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
방문목욕 43
100.0%

Length

2023-12-12T23:04:37.724136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:04:37.816386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
방문목욕 43
100.0%

시설이용(남)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
0
30 
1
6 
<NA>
4 
13
 
1
2
 
1

Length

Max length4
Median length1
Mean length1.3023256
Min length1

Unique

Unique3 ?
Unique (%)7.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
69.8%
1 6
 
14.0%
<NA> 4
 
9.3%
13 1
 
2.3%
2 1
 
2.3%
3 1
 
2.3%

Length

2023-12-12T23:04:37.978590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:04:38.128613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
69.8%
1 6
 
14.0%
na 4
 
9.3%
13 1
 
2.3%
2 1
 
2.3%
3 1
 
2.3%

시설이용(여)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
0
26 
<NA>
6 
1
5 
2
4 
21
 
1

Length

Max length4
Median length1
Mean length1.4651163
Min length1

Unique

Unique2 ?
Unique (%)4.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26
60.5%
<NA> 6
 
14.0%
1 5
 
11.6%
2 4
 
9.3%
21 1
 
2.3%
10 1
 
2.3%

Length

2023-12-12T23:04:38.312994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:04:38.470596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26
60.5%
na 6
 
14.0%
1 5
 
11.6%
2 4
 
9.3%
21 1
 
2.3%
10 1
 
2.3%

종사자 정원
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct14
Distinct (%)43.8%
Missing11
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean13.28125
Minimum0
Maximum69
Zeros3
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:04:38.597127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median3
Q316
95-th percentile51.65
Maximum69
Range69
Interquartile range (IQR)13

Descriptive statistics

Standard deviation18.023702
Coefficient of variation (CV)1.3570787
Kurtosis2.9111253
Mean13.28125
Median Absolute Deviation (MAD)2.5
Skewness1.8670067
Sum425
Variance324.85383
MonotonicityNot monotonic
2023-12-12T23:04:38.732457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 10
23.3%
16 4
 
9.3%
0 3
 
7.0%
2 3
 
7.0%
13 2
 
4.7%
4 2
 
4.7%
40 1
 
2.3%
44 1
 
2.3%
34 1
 
2.3%
69 1
 
2.3%
Other values (4) 4
 
9.3%
(Missing) 11
25.6%
ValueCountFrequency (%)
0 3
 
7.0%
1 1
 
2.3%
2 3
 
7.0%
3 10
23.3%
4 2
 
4.7%
12 1
 
2.3%
13 2
 
4.7%
16 4
 
9.3%
30 1
 
2.3%
34 1
 
2.3%
ValueCountFrequency (%)
69 1
 
2.3%
61 1
 
2.3%
44 1
 
2.3%
40 1
 
2.3%
34 1
 
2.3%
30 1
 
2.3%
16 4
9.3%
13 2
4.7%
12 1
 
2.3%
4 2
4.7%

종사자(남)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)20.0%
Missing3
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean1.7
Minimum0
Maximum15
Zeros18
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:04:38.884413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5.2
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8572894
Coefficient of variation (CV)1.6807585
Kurtosis12.223901
Mean1.7
Median Absolute Deviation (MAD)1
Skewness3.1513658
Sum68
Variance8.1641026
MonotonicityNot monotonic
2023-12-12T23:04:39.014956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 18
41.9%
1 9
20.9%
3 6
 
14.0%
4 2
 
4.7%
2 2
 
4.7%
5 1
 
2.3%
9 1
 
2.3%
15 1
 
2.3%
(Missing) 3
 
7.0%
ValueCountFrequency (%)
0 18
41.9%
1 9
20.9%
2 2
 
4.7%
3 6
 
14.0%
4 2
 
4.7%
5 1
 
2.3%
9 1
 
2.3%
15 1
 
2.3%
ValueCountFrequency (%)
15 1
 
2.3%
9 1
 
2.3%
5 1
 
2.3%
4 2
 
4.7%
3 6
 
14.0%
2 2
 
4.7%
1 9
20.9%
0 18
41.9%

종사자(여)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)40.5%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean10.714286
Minimum0
Maximum60
Zeros14
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T23:04:39.131594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313.75
95-th percentile56.1
Maximum60
Range60
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation17.206473
Coefficient of variation (CV)1.6059375
Kurtosis2.4680905
Mean10.714286
Median Absolute Deviation (MAD)2
Skewness1.8478207
Sum450
Variance296.06272
MonotonicityNot monotonic
2023-12-12T23:04:39.240206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 14
32.6%
2 7
16.3%
1 3
 
7.0%
4 3
 
7.0%
16 2
 
4.7%
29 2
 
4.7%
57 1
 
2.3%
6 1
 
2.3%
15 1
 
2.3%
59 1
 
2.3%
Other values (7) 7
16.3%
ValueCountFrequency (%)
0 14
32.6%
1 3
 
7.0%
2 7
16.3%
4 3
 
7.0%
6 1
 
2.3%
8 1
 
2.3%
9 1
 
2.3%
10 1
 
2.3%
15 1
 
2.3%
16 2
 
4.7%
ValueCountFrequency (%)
60 1
2.3%
59 1
2.3%
57 1
2.3%
39 1
2.3%
37 1
2.3%
31 1
2.3%
29 2
4.7%
16 2
4.7%
15 1
2.3%
10 1
2.3%

전화번호
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T23:04:39.474195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.976744
Min length11

Characters and Unicode

Total characters515
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row042-626-7530
2nd row042-625-0991
3rd row042-285-6800
4th row042-282-5055
5th row042-283-2015
ValueCountFrequency (%)
042-626-7530 1
 
2.3%
042-623-6595 1
 
2.3%
042-349-3300 1
 
2.3%
042-934-9988 1
 
2.3%
042-624-0188 1
 
2.3%
042-621-8662 1
 
2.3%
042-622-0288 1
 
2.3%
042-636-0333 1
 
2.3%
042-632-7576 1
 
2.3%
042-483-1290 1
 
2.3%
Other values (33) 33
76.7%
2023-12-12T23:04:40.049046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 94
18.3%
- 83
16.1%
0 75
14.6%
4 61
11.8%
8 38
7.4%
5 34
 
6.6%
3 34
 
6.6%
6 32
 
6.2%
1 23
 
4.5%
9 21
 
4.1%
Other values (2) 20
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 430
83.5%
Dash Punctuation 83
 
16.1%
Close Punctuation 2
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 94
21.9%
0 75
17.4%
4 61
14.2%
8 38
8.8%
5 34
 
7.9%
3 34
 
7.9%
6 32
 
7.4%
1 23
 
5.3%
9 21
 
4.9%
7 18
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 515
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 94
18.3%
- 83
16.1%
0 75
14.6%
4 61
11.8%
8 38
7.4%
5 34
 
6.6%
3 34
 
6.6%
6 32
 
6.2%
1 23
 
4.5%
9 21
 
4.1%
Other values (2) 20
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 94
18.3%
- 83
16.1%
0 75
14.6%
4 61
11.8%
8 38
7.4%
5 34
 
6.6%
3 34
 
6.6%
6 32
 
6.2%
1 23
 
4.5%
9 21
 
4.1%
Other values (2) 20
 
3.9%

FAX번호
Text

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T23:04:40.375545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.046512
Min length12

Characters and Unicode

Total characters518
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

Unique41 ?
Unique (%)95.3%

Sample

1st row042-626-7532
2nd row042-625-0992
3rd row042-285-6805
4th row042-284-1234
5th row042-271-2016
ValueCountFrequency (%)
042-483-5003 2
 
4.7%
042-627-2247 1
 
2.3%
042-535-4202 1
 
2.3%
070-7016-1290 1
 
2.3%
070-4009-7828 1
 
2.3%
042-349-3305 1
 
2.3%
042-934-9986 1
 
2.3%
042-624-0184 1
 
2.3%
042-621-8661 1
 
2.3%
042-622-0288 1
 
2.3%
Other values (32) 32
74.4%
2023-12-12T23:04:40.898030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 95
18.3%
- 86
16.6%
0 77
14.9%
4 60
11.6%
8 34
 
6.6%
3 34
 
6.6%
6 34
 
6.6%
5 31
 
6.0%
1 24
 
4.6%
9 23
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 432
83.4%
Dash Punctuation 86
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 95
22.0%
0 77
17.8%
4 60
13.9%
8 34
 
7.9%
3 34
 
7.9%
6 34
 
7.9%
5 31
 
7.2%
1 24
 
5.6%
9 23
 
5.3%
7 20
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 518
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 95
18.3%
- 86
16.6%
0 77
14.9%
4 60
11.6%
8 34
 
6.6%
3 34
 
6.6%
6 34
 
6.6%
5 31
 
6.0%
1 24
 
4.6%
9 23
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 95
18.3%
- 86
16.6%
0 77
14.9%
4 60
11.6%
8 34
 
6.6%
3 34
 
6.6%
6 34
 
6.6%
5 31
 
6.0%
1 24
 
4.6%
9 23
 
4.4%

설치주체
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
개인
32 
사회복지법인
8 
그외법인
 
3

Length

Max length6
Median length2
Mean length2.8837209
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사회복지법인
2nd row개인
3rd row사회복지법인
4th row개인
5th row개인

Common Values

ValueCountFrequency (%)
개인 32
74.4%
사회복지법인 8
 
18.6%
그외법인 3
 
7.0%

Length

2023-12-12T23:04:41.450942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:04:41.609937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 32
74.4%
사회복지법인 8
 
18.6%
그외법인 3
 
7.0%

Interactions

2023-12-12T23:04:35.562704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:34.997513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:35.295550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:35.644289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:35.089679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:35.381732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:35.741027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:35.204486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:35.467182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:04:41.705068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명소재지시설이용(남)시설이용(여)종사자 정원종사자(남)종사자(여)전화번호FAX번호설치주체
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0000.8381.0001.0001.0001.0001.0001.000
시설이용(남)1.0001.0001.0000.8430.7740.6590.5371.0001.0000.581
시설이용(여)1.0000.8380.8431.0000.8160.8130.8031.0000.8380.584
종사자 정원1.0001.0000.7740.8161.0000.9270.9161.0001.0000.379
종사자(남)1.0001.0000.6590.8130.9271.0000.6951.0001.0000.760
종사자(여)1.0001.0000.5370.8030.9160.6951.0001.0001.0000.702
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
FAX번호1.0001.0001.0000.8381.0001.0001.0001.0001.0001.000
설치주체1.0001.0000.5810.5840.3790.7600.7021.0001.0001.000
2023-12-12T23:04:41.861967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설이용(여)설치주체시설이용(남)
시설이용(여)1.0000.5150.805
설치주체0.5151.0000.513
시설이용(남)0.8050.5131.000
2023-12-12T23:04:41.978184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종사자 정원종사자(남)종사자(여)시설이용(남)시설이용(여)설치주체
종사자 정원1.0000.6770.8350.4000.6400.222
종사자(남)0.6771.0000.7020.4610.7080.417
종사자(여)0.8350.7021.0000.3700.6980.581
시설이용(남)0.4000.4610.3701.0000.8050.513
시설이용(여)0.6400.7080.6980.8051.0000.515
설치주체0.2220.4170.5810.5130.5151.000

Missing values

2023-12-12T23:04:35.857815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:04:35.998304image/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.
2023-12-12T23:04:36.150083image/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

시설명소재지시설종류(유형)시설이용(남)시설이용(여)종사자 정원종사자(남)종사자(여)전화번호FAX번호설치주체
0금성노인복지센터대전광역시 동구 우암로139-3, 3·4층 (삼성동)방문목욕003157042-626-7530042-626-7532사회복지법인
1대전재가노인복지센터대전광역시 동구 충무로 250 (신흥동)방문목욕00302042-625-0991042-625-0992개인
2산내노인종합센터대전광역시 동구 산내로 1287번길 73 (낭월동)방문목욕00300042-285-6800042-285-6805사회복지법인
3해드림노인복지센터대전광역시 동구 동구청로 95, 8층 (가오동)방문목욕00300042-282-5055042-284-1234개인
4예명 주야간보호센터대전광역시 동구 동부로73번길 6, 5층(판암동)방문목욕00300042-283-2015042-271-2016개인
5백세실버케어대전광역시 동구 계족로 10, 5층 (효동)방문목욕00300042-286-1008042-284-1008그외법인
6새봄실버주간보호센터대전광역시 동구 동서대로1572번길 157, 3층 (성남동)방문목욕00301042-321-0082042-321-0083개인
7다송실버케어대전광역시 동구 태전로 151 (삼성동)방문목욕00310042-636-7711042-636-7722개인
8남대전노인복지센터대전광역시 중구 보문로 190, 201호(대흥동)방문목욕1240337042-273-0002042-273-0066개인
9한빛재가노인복지센터대전광역시 중구 보문로 90-7(부사동)방문목욕0244539042-254-8866042-254-8867사회복지법인
시설명소재지시설종류(유형)시설이용(남)시설이용(여)종사자 정원종사자(남)종사자(여)전화번호FAX번호설치주체
33유앤아이주간보호센터대전광역시 서구 복수동로 104-7 (복수동)방문목욕00111042-581-6200042-581-8898개인
34청남실버케어재가복지센터대전광역시 서구 청사서로 8,2층방문목욕00434042)489-3344042-483-5003개인
35청남주간보호센터대전광역시 서구 청사서로 8,2층방문목욕02434042)489-5858042-483-5003개인
36대전재가노인지원센터대전광역시 서구 도솔로 528(용문동)방문목욕310161529042-525-6176042-537-2707그외법인
37대전효주간보호센터대전광역시 서구 정림로65번길 6, 4층(정림동)방문목욕00<NA>00042-581-7352042-864-0600개인
38경산재가노인지원센터(본점)대전광역시 서구 동서대로1016방문목욕1<NA><NA>22042-525-3164042-526-3164개인
39샤론주간보호센터대전광역시 서구 도산로 219 제이엠빌딩방문목욕<NA><NA><NA>00042-525-9839042-525-9639개인
40마실어르신주야간보호센터대전광역시 서구 계룡로 616. 409호(괴정동. 오렌지타운)방문목욕<NA><NA><NA><NA><NA>042-527-6401042-527-6402개인
41어울림노인복지센터대전광역시 서구 도솔로 513방문목욕<NA><NA><NA><NA>0042-5354201042-535-4202개인
42골든데일리케어대전광역시 서구 동서대로696 에프엠프라임2차 403호방문목욕00<NA>11042-531-5233042-531-5232개인