Overview

Dataset statistics

Number of variables13
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory116.8 B

Variable types

Numeric7
Text5
DateTime1

Dataset

Description전북특별자치도 시군별 사회복귀시설 현황(시설명, 시설종류, 정원, 위치 등)정신재활시설에 종사하는 사람의 현재 인원
Author전북특별자치도
URLhttps://www.data.go.kr/data/3081357/fileData.do

Alerts

종사자 정원 is highly overall correlated with 종사자 현원 and 4 other fieldsHigh correlation
종사자 현원 is highly overall correlated with 종사자 정원 and 4 other fieldsHigh correlation
생활인 정원 is highly overall correlated with 종사자 정원 and 2 other fieldsHigh correlation
생활인 현원 is highly overall correlated with 종사자 정원 and 2 other fieldsHigh correlation
이용인 정원 is highly overall correlated with 종사자 정원 and 2 other fieldsHigh correlation
이용인 현원 is highly overall correlated with 종사자 정원 and 2 other fieldsHigh correlation
연번 has unique valuesUnique
시설명 has unique valuesUnique
생활인 정원 has 3 (13.6%) zerosZeros
생활인 현원 has 3 (13.6%) zerosZeros
이용인 정원 has 11 (50.0%) zerosZeros
이용인 현원 has 11 (50.0%) zerosZeros

Reproduction

Analysis started2024-03-15 02:04:07.385039
Analysis finished2024-03-15 02:04:19.585900
Duration12.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T11:04:19.691840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2024-03-15T11:04:20.070447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

시설명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-15T11:04:20.901125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8.5
Mean length5.9090909
Min length2

Characters and Unicode

Total characters130
Distinct characters61
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

Unique22 ?
Unique (%)100.0%

Sample

1st row마음건강복지관
2nd row마음건강회복홈
3rd row마음건강힐링홈
4th row아름다운세상
5th row아름다운집
ValueCountFrequency (%)
마음건강복지관 1
 
3.7%
참마음 1
 
3.7%
복지센터 1
 
3.7%
장수보건 1
 
3.7%
소망의집 1
 
3.7%
한사랑 1
 
3.7%
마을 1
 
3.7%
서로돕는 1
 
3.7%
유엔아이 1
 
3.7%
성일 1
 
3.7%
Other values (17) 17
63.0%
2024-03-15T11:04:22.252314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 7
 
5.4%
음 6
 
4.6%
건 5
 
3.8%
의 5
 
3.8%
집 5
 
3.8%
5
 
3.8%
홈 4
 
3.1%
터 4
 
3.1%
망 4
 
3.1%
복 4
 
3.1%
Other values (51) 81
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 125
96.2%
Space Separator 5
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 7
 
5.6%
음 6
 
4.8%
건 5
 
4.0%
의 5
 
4.0%
집 5
 
4.0%
홈 4
 
3.2%
터 4
 
3.2%
망 4
 
3.2%
복 4
 
3.2%
강 4
 
3.2%
Other values (50) 77
61.6%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 125
96.2%
Common 5
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 7
 
5.6%
음 6
 
4.8%
건 5
 
4.0%
의 5
 
4.0%
집 5
 
4.0%
홈 4
 
3.2%
터 4
 
3.2%
망 4
 
3.2%
복 4
 
3.2%
강 4
 
3.2%
Other values (50) 77
61.6%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 125
96.2%
ASCII 5
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 7
 
5.6%
음 6
 
4.8%
건 5
 
4.0%
의 5
 
4.0%
집 5
 
4.0%
홈 4
 
3.2%
터 4
 
3.2%
망 4
 
3.2%
복 4
 
3.2%
강 4
 
3.2%
Other values (50) 77
61.6%
ASCII
ValueCountFrequency (%)
5
100.0%

시설신고일
Date

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size304.0 B
Minimum1999-12-31 00:00:00
Maximum2020-06-28 00:00:00
2024-03-15T11:04:22.632184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:23.040457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)

시설장
Text

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-15T11:04:23.699470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
Distinct characters13
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

Unique5 ?
Unique (%)22.7%

Sample

1st row박**
2nd row최**
3rd row최**
4th row김**
5th row강**
ValueCountFrequency (%)
강 3
13.6%
윤 3
13.6%
이 3
13.6%
박 2
9.1%
최 2
9.1%
김 2
9.1%
한 2
9.1%
송 1
 
4.5%
손 1
 
4.5%
백 1
 
4.5%
Other values (2) 2
9.1%
2024-03-15T11:04:24.831225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 44
66.7%
강 3
 
4.5%
윤 3
 
4.5%
이 3
 
4.5%
박 2
 
3.0%
최 2
 
3.0%
김 2
 
3.0%
한 2
 
3.0%
송 1
 
1.5%
손 1
 
1.5%
Other values (3) 3
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 44
66.7%
Other Letter 22
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 3
13.6%
윤 3
13.6%
이 3
13.6%
박 2
9.1%
최 2
9.1%
김 2
9.1%
한 2
9.1%
송 1
 
4.5%
손 1
 
4.5%
백 1
 
4.5%
Other values (2) 2
9.1%
Other Punctuation
ValueCountFrequency (%)
* 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44
66.7%
Hangul 22
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 3
13.6%
윤 3
13.6%
이 3
13.6%
박 2
9.1%
최 2
9.1%
김 2
9.1%
한 2
9.1%
송 1
 
4.5%
손 1
 
4.5%
백 1
 
4.5%
Other values (2) 2
9.1%
Common
ValueCountFrequency (%)
* 44
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44
66.7%
Hangul 22
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 44
100.0%
Hangul
ValueCountFrequency (%)
강 3
13.6%
윤 3
13.6%
이 3
13.6%
박 2
9.1%
최 2
9.1%
김 2
9.1%
한 2
9.1%
송 1
 
4.5%
손 1
 
4.5%
백 1
 
4.5%
Other values (2) 2
9.1%

소 재 지
Text

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-15T11:04:25.749305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length16.818182
Min length11

Characters and Unicode

Total characters370
Distinct characters76
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)90.9%

Sample

1st row전주시 완산구 물왕멀2길20-29
2nd row전주시 완산구 물왕멀2길 25
3rd row전주시 완산구 물왕멀2길 20-17
4th row전주시 덕진구 아중7길 9-5
5th row전주시 덕진구 인교9길 11, 401호
ValueCountFrequency (%)
전주시 7
 
9.0%
익산시 5
 
6.4%
덕진구 4
 
5.1%
3층 3
 
3.8%
229 3
 
3.8%
완산구 3
 
3.8%
목천로 3
 
3.8%
군산시 3
 
3.8%
물왕멀2길 2
 
2.6%
둔배미길 2
 
2.6%
Other values (43) 43
55.1%
2024-03-15T11:04:26.977324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58
 
15.7%
2 26
 
7.0%
시 18
 
4.9%
1 17
 
4.6%
길 13
 
3.5%
산 12
 
3.2%
로 10
 
2.7%
- 10
 
2.7%
0 9
 
2.4%
3 9
 
2.4%
Other values (66) 188
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 195
52.7%
Decimal Number 93
25.1%
Space Separator 58
 
15.7%
Dash Punctuation 10
 
2.7%
Other Punctuation 8
 
2.2%
Open Punctuation 3
 
0.8%
Close Punctuation 3
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 18
 
9.2%
길 13
 
6.7%
산 12
 
6.2%
로 10
 
5.1%
주 8
 
4.1%
구 8
 
4.1%
전 7
 
3.6%
군 7
 
3.6%
진 6
 
3.1%
호 5
 
2.6%
Other values (51) 101
51.8%
Decimal Number
ValueCountFrequency (%)
2 26
28.0%
1 17
18.3%
0 9
 
9.7%
3 9
 
9.7%
6 8
 
8.6%
9 8
 
8.6%
5 7
 
7.5%
4 4
 
4.3%
7 3
 
3.2%
8 2
 
2.2%
Space Separator
ValueCountFrequency (%)
58
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 195
52.7%
Common 175
47.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 18
 
9.2%
길 13
 
6.7%
산 12
 
6.2%
로 10
 
5.1%
주 8
 
4.1%
구 8
 
4.1%
전 7
 
3.6%
군 7
 
3.6%
진 6
 
3.1%
호 5
 
2.6%
Other values (51) 101
51.8%
Common
ValueCountFrequency (%)
58
33.1%
2 26
14.9%
1 17
 
9.7%
- 10
 
5.7%
0 9
 
5.1%
3 9
 
5.1%
6 8
 
4.6%
9 8
 
4.6%
, 8
 
4.6%
5 7
 
4.0%
Other values (5) 15
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 195
52.7%
ASCII 175
47.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
58
33.1%
2 26
14.9%
1 17
 
9.7%
- 10
 
5.7%
0 9
 
5.1%
3 9
 
5.1%
6 8
 
4.6%
9 8
 
4.6%
, 8
 
4.6%
5 7
 
4.0%
Other values (5) 15
 
8.6%
Hangul
ValueCountFrequency (%)
시 18
 
9.2%
길 13
 
6.7%
산 12
 
6.2%
로 10
 
5.1%
주 8
 
4.1%
구 8
 
4.1%
전 7
 
3.6%
군 7
 
3.6%
진 6
 
3.1%
호 5
 
2.6%
Other values (51) 101
51.8%

연락처
Text

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-15T11:04:27.632925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.227273
Min length12

Characters and Unicode

Total characters269
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

Unique18 ?
Unique (%)81.8%

Sample

1st row063-232-5558
2nd row063-224-7032
3rd row063-904-4334
4th row063-244-2816
5th row070-8201-2816
ValueCountFrequency (%)
070-4099-3934 2
 
9.1%
063-442-4599 2
 
9.1%
063-837-6446 1
 
4.5%
063-351-7130 1
 
4.5%
063-432-2194 1
 
4.5%
063-232-7567 1
 
4.5%
063-544-3380 1
 
4.5%
063-634-2344 1
 
4.5%
063-553-8233 1
 
4.5%
063-857-4031 1
 
4.5%
Other values (10) 10
45.5%
2024-03-15T11:04:28.758728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 44
16.4%
3 40
14.9%
0 38
14.1%
4 37
13.8%
6 28
10.4%
2 19
7.1%
7 15
 
5.6%
5 14
 
5.2%
9 13
 
4.8%
1 12
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 225
83.6%
Dash Punctuation 44
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 40
17.8%
0 38
16.9%
4 37
16.4%
6 28
12.4%
2 19
8.4%
7 15
 
6.7%
5 14
 
6.2%
9 13
 
5.8%
1 12
 
5.3%
8 9
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 269
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 44
16.4%
3 40
14.9%
0 38
14.1%
4 37
13.8%
6 28
10.4%
2 19
7.1%
7 15
 
5.6%
5 14
 
5.2%
9 13
 
4.8%
1 12
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 44
16.4%
3 40
14.9%
0 38
14.1%
4 37
13.8%
6 28
10.4%
2 19
7.1%
7 15
 
5.6%
5 14
 
5.2%
9 13
 
4.8%
1 12
 
4.5%

종사자 정원
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0454545
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T11:04:29.127401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q310.5
95-th percentile11
Maximum23
Range22
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation5.4639709
Coefficient of variation (CV)0.90381474
Kurtosis2.916656
Mean6.0454545
Median Absolute Deviation (MAD)3.5
Skewness1.47757
Sum133
Variance29.854978
MonotonicityNot monotonic
2024-03-15T11:04:29.310916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 6
27.3%
11 5
22.7%
1 4
18.2%
5 3
13.6%
23 1
 
4.5%
7 1
 
4.5%
9 1
 
4.5%
8 1
 
4.5%
ValueCountFrequency (%)
1 4
18.2%
2 6
27.3%
5 3
13.6%
7 1
 
4.5%
8 1
 
4.5%
9 1
 
4.5%
11 5
22.7%
23 1
 
4.5%
ValueCountFrequency (%)
23 1
 
4.5%
11 5
22.7%
9 1
 
4.5%
8 1
 
4.5%
7 1
 
4.5%
5 3
13.6%
2 6
27.3%
1 4
18.2%

종사자 현원
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0454545
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T11:04:29.511979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q310.5
95-th percentile11
Maximum23
Range22
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation5.4639709
Coefficient of variation (CV)0.90381474
Kurtosis2.916656
Mean6.0454545
Median Absolute Deviation (MAD)3.5
Skewness1.47757
Sum133
Variance29.854978
MonotonicityNot monotonic
2024-03-15T11:04:29.691236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 6
27.3%
11 5
22.7%
1 4
18.2%
5 3
13.6%
23 1
 
4.5%
7 1
 
4.5%
9 1
 
4.5%
8 1
 
4.5%
ValueCountFrequency (%)
1 4
18.2%
2 6
27.3%
5 3
13.6%
7 1
 
4.5%
8 1
 
4.5%
9 1
 
4.5%
11 5
22.7%
23 1
 
4.5%
ValueCountFrequency (%)
23 1
 
4.5%
11 5
22.7%
9 1
 
4.5%
8 1
 
4.5%
7 1
 
4.5%
5 3
13.6%
2 6
27.3%
1 4
18.2%

생활인 정원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.090909
Minimum0
Maximum30
Zeros3
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T11:04:30.000326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median10
Q320
95-th percentile24.75
Maximum30
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9703697
Coefficient of variation (CV)0.80880382
Kurtosis-0.94346711
Mean11.090909
Median Absolute Deviation (MAD)6
Skewness0.48222026
Sum244
Variance80.467532
MonotonicityNot monotonic
2024-03-15T11:04:30.267158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 6
27.3%
20 5
22.7%
10 3
13.6%
0 3
13.6%
15 2
 
9.1%
5 1
 
4.5%
30 1
 
4.5%
25 1
 
4.5%
ValueCountFrequency (%)
0 3
13.6%
4 6
27.3%
5 1
 
4.5%
10 3
13.6%
15 2
 
9.1%
20 5
22.7%
25 1
 
4.5%
30 1
 
4.5%
ValueCountFrequency (%)
30 1
 
4.5%
25 1
 
4.5%
20 5
22.7%
15 2
 
9.1%
10 3
13.6%
5 1
 
4.5%
4 6
27.3%
0 3
13.6%

생활인 현원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.318182
Minimum0
Maximum30
Zeros3
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T11:04:30.466060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median7.5
Q317
95-th percentile24.75
Maximum30
Range30
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation8.8446183
Coefficient of variation (CV)0.85718768
Kurtosis-0.68634766
Mean10.318182
Median Absolute Deviation (MAD)7.5
Skewness0.60991008
Sum227
Variance78.227273
MonotonicityNot monotonic
2024-03-15T11:04:30.685220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4 4
18.2%
3 3
13.6%
0 3
13.6%
17 2
9.1%
10 2
9.1%
19 1
 
4.5%
5 1
 
4.5%
18 1
 
4.5%
30 1
 
4.5%
15 1
 
4.5%
Other values (3) 3
13.6%
ValueCountFrequency (%)
0 3
13.6%
3 3
13.6%
4 4
18.2%
5 1
 
4.5%
10 2
9.1%
15 1
 
4.5%
16 1
 
4.5%
17 2
9.1%
18 1
 
4.5%
19 1
 
4.5%
ValueCountFrequency (%)
30 1
4.5%
25 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 2
9.1%
16 1
4.5%
15 1
4.5%
10 2
9.1%
5 1
4.5%

이용인 정원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.545455
Minimum0
Maximum115
Zeros11
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T11:04:30.881959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q330
95-th percentile30.95
Maximum115
Range115
Interquartile range (IQR)30

Descriptive statistics

Standard deviation26.015979
Coefficient of variation (CV)1.482776
Kurtosis9.1762091
Mean17.545455
Median Absolute Deviation (MAD)5
Skewness2.6228338
Sum386
Variance676.83117
MonotonicityNot monotonic
2024-03-15T11:04:31.077889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 11
50.0%
30 7
31.8%
115 1
 
4.5%
31 1
 
4.5%
10 1
 
4.5%
20 1
 
4.5%
ValueCountFrequency (%)
0 11
50.0%
10 1
 
4.5%
20 1
 
4.5%
30 7
31.8%
31 1
 
4.5%
115 1
 
4.5%
ValueCountFrequency (%)
115 1
 
4.5%
31 1
 
4.5%
30 7
31.8%
20 1
 
4.5%
10 1
 
4.5%
0 11
50.0%

이용인 현원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.409091
Minimum0
Maximum115
Zeros11
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T11:04:31.365722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q330
95-th percentile30.95
Maximum115
Range115
Interquartile range (IQR)30

Descriptive statistics

Standard deviation26.010363
Coefficient of variation (CV)1.4940678
Kurtosis9.2539843
Mean17.409091
Median Absolute Deviation (MAD)5
Skewness2.6417897
Sum383
Variance676.53896
MonotonicityNot monotonic
2024-03-15T11:04:31.571406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 11
50.0%
30 7
31.8%
115 1
 
4.5%
31 1
 
4.5%
10 1
 
4.5%
17 1
 
4.5%
ValueCountFrequency (%)
0 11
50.0%
10 1
 
4.5%
17 1
 
4.5%
30 7
31.8%
31 1
 
4.5%
115 1
 
4.5%
ValueCountFrequency (%)
115 1
 
4.5%
31 1
 
4.5%
30 7
31.8%
17 1
 
4.5%
10 1
 
4.5%
0 11
50.0%

법인
Text

Distinct13
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-15T11:04:32.087293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.5
Min length3

Characters and Unicode

Total characters121
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)40.9%

Sample

1st row마음건강
2nd row마음건강
3rd row마음건강
4th row인산의료재단
5th row인산의료재단
ValueCountFrequency (%)
인산의료재단 4
18.2%
마음건강 3
13.6%
규란복지재단 3
13.6%
삼동회 3
13.6%
제은복지재단 1
 
4.5%
보배복지재단 1
 
4.5%
샘골복지재단 1
 
4.5%
성일의료법인 1
 
4.5%
일봉복지재단 1
 
4.5%
한국장로교복지재단 1
 
4.5%
Other values (3) 3
13.6%
2024-03-15T11:04:33.001768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
재 15
 
12.4%
단 15
 
12.4%
지 11
 
9.1%
복 11
 
9.1%
인 5
 
4.1%
료 5
 
4.1%
의 5
 
4.1%
산 4
 
3.3%
마 3
 
2.5%
동 3
 
2.5%
Other values (28) 44
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
재 15
 
12.4%
단 15
 
12.4%
지 11
 
9.1%
복 11
 
9.1%
인 5
 
4.1%
료 5
 
4.1%
의 5
 
4.1%
산 4
 
3.3%
마 3
 
2.5%
동 3
 
2.5%
Other values (28) 44
36.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
재 15
 
12.4%
단 15
 
12.4%
지 11
 
9.1%
복 11
 
9.1%
인 5
 
4.1%
료 5
 
4.1%
의 5
 
4.1%
산 4
 
3.3%
마 3
 
2.5%
동 3
 
2.5%
Other values (28) 44
36.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
재 15
 
12.4%
단 15
 
12.4%
지 11
 
9.1%
복 11
 
9.1%
인 5
 
4.1%
료 5
 
4.1%
의 5
 
4.1%
산 4
 
3.3%
마 3
 
2.5%
동 3
 
2.5%
Other values (28) 44
36.4%

Interactions

2024-03-15T11:04:17.375426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:08.126845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:09.806343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:11.467630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:13.082984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:14.709563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:16.129421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:17.514883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:08.353797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:10.036109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:11.692762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:13.319645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:14.944853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:16.287807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:17.652830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:08.615106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:10.263925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:11.924285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:13.558313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:15.226559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:16.425718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:17.824539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:08.844076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:10.492684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:12.149336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:13.796729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:15.434081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:16.563370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:18.035223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:09.084810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:10.731567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:12.395131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:14.051911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:15.586292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:16.731754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:18.343088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:09.325005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:10.974817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:12.636992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:14.303623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:15.735600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:16.991293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:18.851411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:09.566692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:11.225364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:12.836732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:14.506562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:15.980184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:04:17.223601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T11:04:33.275762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설명시설신고일시설장소 재 지연락처종사자 정원종사자 현원생활인 정원생활인 현원이용인 정원이용인 현원법인
연번1.0001.0000.9020.8341.0001.0000.0000.0000.6550.7900.0000.0000.893
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시설신고일0.9021.0001.0000.8940.9830.9490.9210.9211.0001.0000.9250.9250.663
시설장0.8341.0000.8941.0001.0001.0000.6830.6830.6200.6940.6910.6910.957
소 재 지1.0001.0000.9831.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
연락처1.0001.0000.9491.0001.0001.0000.7220.7221.0001.0000.9880.9881.000
종사자 정원0.0001.0000.9210.6831.0000.7221.0001.0000.7610.7670.8480.8480.668
종사자 현원0.0001.0000.9210.6831.0000.7221.0001.0000.7610.7670.8480.8480.668
생활인 정원0.6551.0001.0000.6201.0001.0000.7610.7611.0000.9930.7600.7600.905
생활인 현원0.7901.0001.0000.6941.0001.0000.7670.7670.9931.0000.7860.7860.905
이용인 정원0.0001.0000.9250.6911.0000.9880.8480.8480.7600.7861.0001.0000.593
이용인 현원0.0001.0000.9250.6911.0000.9880.8480.8480.7600.7861.0001.0000.593
법인0.8931.0000.6630.9571.0001.0000.6680.6680.9050.9050.5930.5931.000
2024-03-15T11:04:33.628999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번종사자 정원종사자 현원생활인 정원생활인 현원이용인 정원이용인 현원
연번1.0000.3200.3200.2500.2450.1260.126
종사자 정원0.3201.0001.0000.7530.7540.7830.783
종사자 현원0.3201.0001.0000.7530.7540.7830.783
생활인 정원0.2500.7530.7531.0000.9750.2980.298
생활인 현원0.2450.7540.7540.9751.0000.2870.287
이용인 정원0.1260.7830.7830.2980.2871.0001.000
이용인 현원0.1260.7830.7830.2980.2871.0001.000

Missing values

2024-03-15T11:04:19.075173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T11:04:19.432049image/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

연번시설명시설신고일시설장소 재 지연락처종사자 정원종사자 현원생활인 정원생활인 현원이용인 정원이용인 현원법인
01마음건강복지관2000-12-08박**전주시 완산구 물왕멀2길20-29063-232-555823232017115115마음건강
12마음건강회복홈2011-07-04최**전주시 완산구 물왕멀2길 25063-224-7032114400마음건강
23마음건강힐링홈2015-09-11최**전주시 완산구 물왕멀2길 20-17063-904-4334114300마음건강
34아름다운세상2002-11-21김**전주시 덕진구 아중7길 9-5063-244-2816111120193030인산의료재단
45아름다운집2011-05-26강**전주시 덕진구 인교9길 11, 401호070-8201-2816224400인산의료재단
56꿈이있는집2012-05-08강**전주시 덕진구 아중1길23-3, 402호070-7561-3714224400인산의료재단
67행복한집2014-05-27강**전주시 덕진구 인교로35-25,501호070-4141-00522210400인산의료재단
78희망의쉼터1999-12-31윤**군산시 둔배미길 6-2063-442-45997710103131규란복지재단
89희망의그루터기2000-12-30윤**군산시 둔배미길 6-6063-442-459922101000규란복지재단
910희망의 샘2020-06-28윤**군산시 영명길6 101동206호063-442-4597225500규란복지재단
연번시설명시설신고일시설장소 재 지연락처종사자 정원종사자 현원생활인 정원생활인 현원이용인 정원이용인 현원법인
1213둥근나래꿈남성홈2014-12-02한**익산시 목천로 229, (3층)070-4099-3934114300삼동회
1314참마음2006-10-31이**익산시 황등면 황등중앙로112063-837-644622001010제은복지재단
1415보배정신건강상담센터2003-08-07송**익산시 인북로2길53063-857-403155003030보배복지재단
1516마음사랑 의집2006-10-31손**정읍시 수성택지3길28063-553-823355003030샘골복지재단
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