Overview

Dataset statistics

Number of variables6
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory53.2 B

Variable types

Numeric2
Text4

Dataset

Description요양보호사교육기관202001
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=203167

Alerts

번호 has unique valuesUnique
교육기관명 has unique valuesUnique
소 재 지 has unique valuesUnique
대표자 has unique valuesUnique

Reproduction

Analysis started2024-03-14 01:06:41.246629
Analysis finished2024-03-14 01:06:41.986432
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-03-14T10:06:42.041145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q331
95-th percentile39
Maximum41
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.979149
Coefficient of variation (CV)0.57043565
Kurtosis-1.2
Mean21
Median Absolute Deviation (MAD)10
Skewness0
Sum861
Variance143.5
MonotonicityStrictly increasing
2024-03-14T10:06:42.145672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 1
 
2.4%
32 1
 
2.4%
24 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%
32 1
2.4%

교육기관명
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2024-03-14T10:06:42.358326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length13.414634
Min length10

Characters and Unicode

Total characters550
Distinct characters89
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st row전주성모요양보호사교육원
2nd row전주성신간호학원부설요양보호사교육원
3rd row온누리요양보호사교육원
4th row전주요양보호사교육원
5th row전주여성인력개발센터요양보호사교육원
ValueCountFrequency (%)
전주성모요양보호사교육원 1
 
2.4%
중앙간호학원요양보호사교육원 1
 
2.4%
원광보건대학원평생교육원요양보호사교육원 1
 
2.4%
익산성모요양보호사교육원 1
 
2.4%
원광요양보호사교육원 1
 
2.4%
고려요양보호사교육원 1
 
2.4%
익산평화요양보호사교육원 1
 
2.4%
제이성모요양보호사교육원 1
 
2.4%
종로요양보호사교육원 1
 
2.4%
정읍간호학원부설요양보호사교육원 1
 
2.4%
Other values (31) 31
75.6%
2024-03-14T10:06:42.745757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 58
 
10.5%
호 56
 
10.2%
교 43
 
7.8%
보 42
 
7.6%
육 42
 
7.6%
요 41
 
7.5%
양 41
 
7.5%
사 41
 
7.5%
간 14
 
2.5%
학 14
 
2.5%
Other values (79) 158
28.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 545
99.1%
Uppercase Letter 3
 
0.5%
Lowercase Letter 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 58
 
10.6%
호 56
 
10.3%
교 43
 
7.9%
보 42
 
7.7%
육 42
 
7.7%
요 41
 
7.5%
양 41
 
7.5%
사 41
 
7.5%
간 14
 
2.6%
학 14
 
2.6%
Other values (74) 153
28.1%
Uppercase Letter
ValueCountFrequency (%)
T 1
33.3%
J 1
33.3%
K 1
33.3%
Lowercase Letter
ValueCountFrequency (%)
h 1
50.0%
e 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 545
99.1%
Latin 5
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 58
 
10.6%
호 56
 
10.3%
교 43
 
7.9%
보 42
 
7.7%
육 42
 
7.7%
요 41
 
7.5%
양 41
 
7.5%
사 41
 
7.5%
간 14
 
2.6%
학 14
 
2.6%
Other values (74) 153
28.1%
Latin
ValueCountFrequency (%)
T 1
20.0%
h 1
20.0%
e 1
20.0%
J 1
20.0%
K 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 545
99.1%
ASCII 5
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 58
 
10.6%
호 56
 
10.3%
교 43
 
7.9%
보 42
 
7.7%
육 42
 
7.7%
요 41
 
7.5%
양 41
 
7.5%
사 41
 
7.5%
간 14
 
2.6%
학 14
 
2.6%
Other values (74) 153
28.1%
ASCII
ValueCountFrequency (%)
T 1
20.0%
h 1
20.0%
e 1
20.0%
J 1
20.0%
K 1
20.0%

소 재 지
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2024-03-14T10:06:42.972554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length14.585366
Min length10

Characters and Unicode

Total characters598
Distinct characters95
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

Unique41 ?
Unique (%)100.0%

Sample

1st row전주시 완산구 팔달로 229
2nd row전주시 완산구 팔달로 250
3rd row전주시 완산구 팔달로 202-16
4th row전주시 덕진구 떡전 4길 8
5th row전주시 완산구 장승배기로 213
ValueCountFrequency (%)
전주시 18
 
11.8%
완산구 10
 
6.5%
덕진구 8
 
5.2%
익산시 7
 
4.6%
군산시 4
 
2.6%
무왕로 3
 
2.0%
익산대로 3
 
2.0%
팔달로 3
 
2.0%
정읍시 3
 
2.0%
3층 2
 
1.3%
Other values (86) 92
60.1%
2024-03-14T10:06:43.296743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
113
18.9%
로 38
 
6.4%
시 36
 
6.0%
1 30
 
5.0%
산 25
 
4.2%
2 22
 
3.7%
전 20
 
3.3%
주 19
 
3.2%
구 18
 
3.0%
4 14
 
2.3%
Other values (85) 263
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 342
57.2%
Decimal Number 127
 
21.2%
Space Separator 113
 
18.9%
Dash Punctuation 6
 
1.0%
Other Punctuation 6
 
1.0%
Close Punctuation 2
 
0.3%
Open Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 38
 
11.1%
시 36
 
10.5%
산 25
 
7.3%
전 20
 
5.8%
주 19
 
5.6%
구 18
 
5.3%
완 11
 
3.2%
익 10
 
2.9%
군 10
 
2.9%
읍 9
 
2.6%
Other values (70) 146
42.7%
Decimal Number
ValueCountFrequency (%)
1 30
23.6%
2 22
17.3%
4 14
11.0%
3 13
10.2%
7 12
 
9.4%
0 10
 
7.9%
9 7
 
5.5%
5 7
 
5.5%
6 7
 
5.5%
8 5
 
3.9%
Space Separator
ValueCountFrequency (%)
113
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 342
57.2%
Common 256
42.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 38
 
11.1%
시 36
 
10.5%
산 25
 
7.3%
전 20
 
5.8%
주 19
 
5.6%
구 18
 
5.3%
완 11
 
3.2%
익 10
 
2.9%
군 10
 
2.9%
읍 9
 
2.6%
Other values (70) 146
42.7%
Common
ValueCountFrequency (%)
113
44.1%
1 30
 
11.7%
2 22
 
8.6%
4 14
 
5.5%
3 13
 
5.1%
7 12
 
4.7%
0 10
 
3.9%
9 7
 
2.7%
5 7
 
2.7%
6 7
 
2.7%
Other values (5) 21
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 342
57.2%
ASCII 256
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113
44.1%
1 30
 
11.7%
2 22
 
8.6%
4 14
 
5.5%
3 13
 
5.1%
7 12
 
4.7%
0 10
 
3.9%
9 7
 
2.7%
5 7
 
2.7%
6 7
 
2.7%
Other values (5) 21
 
8.2%
Hangul
ValueCountFrequency (%)
로 38
 
11.1%
시 36
 
10.5%
산 25
 
7.3%
전 20
 
5.8%
주 19
 
5.6%
구 18
 
5.3%
완 11
 
3.2%
익 10
 
2.9%
군 10
 
2.9%
읍 9
 
2.6%
Other values (70) 146
42.7%

대표자
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2024-03-14T10:06:43.480180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123
Distinct characters52
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

Unique41 ?
Unique (%)100.0%

Sample

1st row김민지
2nd row이연주
3rd row박흥순
4th row신성호
5th row임경진
ValueCountFrequency (%)
김민지 1
 
2.4%
오규만 1
 
2.4%
김인종 1
 
2.4%
정선희 1
 
2.4%
장대희 1
 
2.4%
권선숙 1
 
2.4%
박희원 1
 
2.4%
이태영 1
 
2.4%
신정숙 1
 
2.4%
정희숙 1
 
2.4%
Other values (31) 31
75.6%
2024-03-14T10:06:43.758849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 8
 
6.5%
정 8
 
6.5%
박 7
 
5.7%
희 7
 
5.7%
성 6
 
4.9%
이 5
 
4.1%
숙 5
 
4.1%
선 4
 
3.3%
임 4
 
3.3%
경 4
 
3.3%
Other values (42) 65
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 8
 
6.5%
정 8
 
6.5%
박 7
 
5.7%
희 7
 
5.7%
성 6
 
4.9%
이 5
 
4.1%
숙 5
 
4.1%
선 4
 
3.3%
임 4
 
3.3%
경 4
 
3.3%
Other values (42) 65
52.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 123
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 8
 
6.5%
정 8
 
6.5%
박 7
 
5.7%
희 7
 
5.7%
성 6
 
4.9%
이 5
 
4.1%
숙 5
 
4.1%
선 4
 
3.3%
임 4
 
3.3%
경 4
 
3.3%
Other values (42) 65
52.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 123
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 8
 
6.5%
정 8
 
6.5%
박 7
 
5.7%
희 7
 
5.7%
성 6
 
4.9%
이 5
 
4.1%
숙 5
 
4.1%
선 4
 
3.3%
임 4
 
3.3%
경 4
 
3.3%
Other values (42) 65
52.8%

정원
Real number (ℝ)

Distinct9
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.365854
Minimum15
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-03-14T10:06:43.864965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile26
Q130
median32
Q340
95-th percentile40
Maximum40
Range25
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9989837
Coefficient of variation (CV)0.1797941
Kurtosis0.81682404
Mean33.365854
Median Absolute Deviation (MAD)3
Skewness-0.715206
Sum1368
Variance35.987805
MonotonicityNot monotonic
2024-03-14T10:06:43.988888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
30 16
39.0%
40 14
34.1%
35 3
 
7.3%
26 2
 
4.9%
34 2
 
4.9%
15 1
 
2.4%
32 1
 
2.4%
20 1
 
2.4%
36 1
 
2.4%
ValueCountFrequency (%)
15 1
 
2.4%
20 1
 
2.4%
26 2
 
4.9%
30 16
39.0%
32 1
 
2.4%
34 2
 
4.9%
35 3
 
7.3%
36 1
 
2.4%
40 14
34.1%
ValueCountFrequency (%)
40 14
34.1%
36 1
 
2.4%
35 3
 
7.3%
34 2
 
4.9%
32 1
 
2.4%
30 16
39.0%
26 2
 
4.9%
20 1
 
2.4%
15 1
 
2.4%

전화번호
Text

Distinct39
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size460.0 B
2024-03-14T10:06:44.213570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters328
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

Unique37 ?
Unique (%)90.2%

Sample

1st row283-6662
2nd row285-1999
3rd row231-4554
4th row284-1199
5th row232-2346
ValueCountFrequency (%)
442-9895 2
 
4.9%
626-2233 2
 
4.9%
644-8885 1
 
2.4%
261-1125 1
 
2.4%
471-2970 1
 
2.4%
652-8001 1
 
2.4%
538-3663 1
 
2.4%
858-9840 1
 
2.4%
840-1532 1
 
2.4%
853-8331 1
 
2.4%
Other values (29) 29
70.7%
2024-03-14T10:06:44.470605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 57
17.4%
- 41
12.5%
3 33
10.1%
8 32
9.8%
5 29
8.8%
4 28
8.5%
1 25
7.6%
6 22
 
6.7%
7 22
 
6.7%
9 21
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 287
87.5%
Dash Punctuation 41
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 57
19.9%
3 33
11.5%
8 32
11.1%
5 29
10.1%
4 28
9.8%
1 25
8.7%
6 22
 
7.7%
7 22
 
7.7%
9 21
 
7.3%
0 18
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 57
17.4%
- 41
12.5%
3 33
10.1%
8 32
9.8%
5 29
8.8%
4 28
8.5%
1 25
7.6%
6 22
 
6.7%
7 22
 
6.7%
9 21
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 57
17.4%
- 41
12.5%
3 33
10.1%
8 32
9.8%
5 29
8.8%
4 28
8.5%
1 25
7.6%
6 22
 
6.7%
7 22
 
6.7%
9 21
 
6.4%

Interactions

2024-03-14T10:06:41.636796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:06:41.497627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:06:41.717014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:06:41.570116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T10:06:44.563095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호교육기관명소 재 지대표자정원전화번호
번호1.0001.0001.0001.0000.0000.967
교육기관명1.0001.0001.0001.0001.0001.000
소 재 지1.0001.0001.0001.0001.0001.000
대표자1.0001.0001.0001.0001.0001.000
정원0.0001.0001.0001.0001.0000.968
전화번호0.9671.0001.0001.0000.9681.000
2024-03-14T10:06:44.692292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호정원
번호1.0000.245
정원0.2451.000

Missing values

2024-03-14T10:06:41.854658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:06:41.953114image/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

번호교육기관명소 재 지대표자정원전화번호
01전주성모요양보호사교육원전주시 완산구 팔달로 229김민지35283-6662
12전주성신간호학원부설요양보호사교육원전주시 완산구 팔달로 250이연주40285-1999
23온누리요양보호사교육원전주시 완산구 팔달로 202-16박흥순30231-4554
34전주요양보호사교육원전주시 덕진구 떡전 4길 8신성호30284-1199
45전주여성인력개발센터요양보호사교육원전주시 완산구 장승배기로 213임경진26232-2346
56전주메디칼요양보호사교육원전주시 완산구 용머리로 57정미경40225-3910
67평화요양보호사교육원전주시 완산구 장승배기로 210박선애30225-1441
78송천탑클래스간호학원요양보호사교육원전주시 덕진구 천마산로 19정행순30277-7701
89미래요양보호사교육원전주시 덕진구 백제대로 545조희경30272-4747
910JK요양보호사교육원전주시 완산구 전라감영로 10김태우30273-8354
번호교육기관명소 재 지대표자정원전화번호
3132성모간호학원부설요양보호사교육원정읍시 충정로 175-1박미숙40535-0297
3233남원간호요양보호사교육원남원시 의총로 77, 3층박성희36534-1119
3334The드림요양보호사교육원남원시 시청 북1길 13, 2층조현진40626-2233
3435김제성모요양보호사교육원김제시 동서로 222-3박수임34626-2233
3536봉동간호학원부설요양보호사교육원완주군 봉동읍 봉동로 140허유미30545-9888
3637장수요양보호사교육원장수군 장수읍 싸리재로 16김은경35352-8001
3738임실효나눔요양보호사교육원임실군 임실읍 봉황11길(2층)최문성40261-1125
3839순창요양보호사교육원순창군 순창읍 순화로 2백기성30644-8885
3940고창성모요양보호사교육원고창군 고창읍 중앙로 180유춘옥40652-8001
4041부안요양보호사교육원부안군 부안읍 석정로 241김인수30582-6222