Overview

Dataset statistics

Number of variables7
Number of observations261
Missing cells57
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.7 KiB
Average record size in memory57.5 B

Variable types

Numeric1
Categorical2
Text3
DateTime1

Dataset

Description경상남도 양산시 평생학습센터에서 시행중인 배달강좌 현황으로 강의종류, 강의명, 출강가능지역, 출강가능시간, 전화번호, 데이터기준일자를 제공합니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15106286

Alerts

데이터기준일자 has constant value ""Constant
종류 is highly imbalanced (78.9%)Imbalance
출강가능지역 is highly imbalanced (60.4%)Imbalance
출강가능시간 has 57 (21.8%) missing valuesMissing
연번 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:41:16.166416
Analysis finished2023-12-10 23:41:16.761380
Duration0.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct261
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131
Minimum1
Maximum261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T08:41:16.827752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q166
median131
Q3196
95-th percentile248
Maximum261
Range260
Interquartile range (IQR)130

Descriptive statistics

Standard deviation75.48841
Coefficient of variation (CV)0.5762474
Kurtosis-1.2
Mean131
Median Absolute Deviation (MAD)65
Skewness0
Sum34191
Variance5698.5
MonotonicityStrictly increasing
2023-12-11T08:41:16.948292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
165 1
 
0.4%
167 1
 
0.4%
168 1
 
0.4%
169 1
 
0.4%
170 1
 
0.4%
171 1
 
0.4%
172 1
 
0.4%
173 1
 
0.4%
174 1
 
0.4%
Other values (251) 251
96.2%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
261 1
0.4%
260 1
0.4%
259 1
0.4%
258 1
0.4%
257 1
0.4%
256 1
0.4%
255 1
0.4%
254 1
0.4%
253 1
0.4%
252 1
0.4%

종류
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
취미
248 
직무교육
 
7
자격증
 
6

Length

Max length4
Median length2
Mean length2.0766284
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취미 248
95.0%
직무교육 7
 
2.7%
자격증 6
 
2.3%

Length

2023-12-11T08:41:17.064771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:41:17.155769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
취미 248
95.0%
직무교육 7
 
2.7%
자격증 6
 
2.3%

강의명
Text

Distinct245
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-11T08:41:17.387333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length25
Mean length9.7279693
Min length2

Characters and Unicode

Total characters2539
Distinct characters379
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique235 ?
Unique (%)90.0%

Sample

1st row반려동물 관리사 및 행동교정 강의실운영
2nd row반려동물 행동교정
3rd row명상지도사 자격증 취득 및 정신건강, 심신치유
4th row서비스 및 매너교육
5th row다도교육
ValueCountFrequency (%)
만들기 11
 
2.1%
및 7
 
1.3%
캘리그라피 7
 
1.3%
우쿨렐레 6
 
1.1%
통기타 5
 
0.9%
오카리나 5
 
0.9%
배우는 5
 
0.9%
영어회화 4
 
0.8%
소품 4
 
0.8%
요가 4
 
0.8%
Other values (394) 474
89.1%
2023-12-11T08:41:17.735813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
271
 
10.7%
, 68
 
2.7%
기 59
 
2.3%
리 44
 
1.7%
어 42
 
1.7%
스 40
 
1.6%
화 32
 
1.3%
라 29
 
1.1%
급 27
 
1.1%
트 25
 
1.0%
Other values (369) 1902
74.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2094
82.5%
Space Separator 271
 
10.7%
Other Punctuation 71
 
2.8%
Uppercase Letter 34
 
1.3%
Close Punctuation 21
 
0.8%
Open Punctuation 21
 
0.8%
Dash Punctuation 9
 
0.4%
Decimal Number 9
 
0.4%
Lowercase Letter 9
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 59
 
2.8%
리 44
 
2.1%
어 42
 
2.0%
스 40
 
1.9%
화 32
 
1.5%
라 29
 
1.4%
급 27
 
1.3%
트 25
 
1.2%
이 25
 
1.2%
초 25
 
1.2%
Other values (338) 1746
83.4%
Uppercase Letter
ValueCountFrequency (%)
S 7
20.6%
D 5
14.7%
P 4
11.8%
Y 3
8.8%
I 3
8.8%
O 3
8.8%
K 2
 
5.9%
H 2
 
5.9%
W 1
 
2.9%
N 1
 
2.9%
Other values (3) 3
8.8%
Lowercase Letter
ValueCountFrequency (%)
d 3
33.3%
r 1
 
11.1%
y 1
 
11.1%
a 1
 
11.1%
p 1
 
11.1%
e 1
 
11.1%
o 1
 
11.1%
Decimal Number
ValueCountFrequency (%)
3 4
44.4%
1 2
22.2%
2 2
22.2%
9 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 68
95.8%
. 2
 
2.8%
& 1
 
1.4%
Space Separator
ValueCountFrequency (%)
271
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2094
82.5%
Common 402
 
15.8%
Latin 43
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 59
 
2.8%
리 44
 
2.1%
어 42
 
2.0%
스 40
 
1.9%
화 32
 
1.5%
라 29
 
1.4%
급 27
 
1.3%
트 25
 
1.2%
이 25
 
1.2%
초 25
 
1.2%
Other values (338) 1746
83.4%
Latin
ValueCountFrequency (%)
S 7
16.3%
D 5
11.6%
P 4
 
9.3%
d 3
 
7.0%
Y 3
 
7.0%
I 3
 
7.0%
O 3
 
7.0%
K 2
 
4.7%
H 2
 
4.7%
r 1
 
2.3%
Other values (10) 10
23.3%
Common
ValueCountFrequency (%)
271
67.4%
, 68
 
16.9%
) 21
 
5.2%
( 21
 
5.2%
- 9
 
2.2%
3 4
 
1.0%
. 2
 
0.5%
1 2
 
0.5%
2 2
 
0.5%
& 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2094
82.5%
ASCII 445
 
17.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
271
60.9%
, 68
 
15.3%
) 21
 
4.7%
( 21
 
4.7%
- 9
 
2.0%
S 7
 
1.6%
D 5
 
1.1%
3 4
 
0.9%
P 4
 
0.9%
d 3
 
0.7%
Other values (21) 32
 
7.2%
Hangul
ValueCountFrequency (%)
기 59
 
2.8%
리 44
 
2.1%
어 42
 
2.0%
스 40
 
1.9%
화 32
 
1.5%
라 29
 
1.4%
급 27
 
1.3%
트 25
 
1.2%
이 25
 
1.2%
초 25
 
1.2%
Other values (338) 1746
83.4%

출강가능지역
Categorical

IMBALANCE 

Distinct42
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
양산 전지역
195 
웅상
 
4
웅상 전지역
 
4
중부동
 
2
양산,부산 전지역
 
2
Other values (37)
54 

Length

Max length21
Median length6
Mean length7.0114943
Min length2

Unique

Unique20 ?
Unique (%)7.7%

Sample

1st row멍스타그램 반려동물교육센터 내 강의실
2nd row양산 전지역
3rd row양산,부산 전지역
4th row양산,부산 전지역
5th row양산 전지역

Common Values

ValueCountFrequency (%)
양산 전지역 195
74.7%
웅상 4
 
1.5%
웅상 전지역 4
 
1.5%
중부동 2
 
0.8%
양산,부산 전지역 2
 
0.8%
범어,석산,증산 2
 
0.8%
양주동,동면,석산,물금 2
 
0.8%
물금,동면,중앙동,양주동,삼성동 2
 
0.8%
남부동,중부동,중앙동 2
 
0.8%
웅상 덕계동 2
 
0.8%
Other values (32) 44
 
16.9%

Length

2023-12-11T08:41:17.859989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전지역 206
43.2%
양산 196
41.1%
웅상 11
 
2.3%
협의 4
 
0.8%
웅상제외 2
 
0.4%
물금,양주동,삼성동,상북면,중앙동,동면 2
 
0.4%
물금,양주동,석산 2
 
0.4%
물금,범어,중부동 2
 
0.4%
물금,범어,남부동,중부동 2
 
0.4%
na 2
 
0.4%
Other values (36) 48
 
10.1%

출강가능시간
Text

MISSING 

Distinct74
Distinct (%)36.3%
Missing57
Missing (%)21.8%
Memory size2.2 KiB
2023-12-11T08:41:18.041223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length39
Mean length7.9509804
Min length2

Characters and Unicode

Total characters1622
Distinct characters40
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 (%)20.1%

Sample

1st row주말(10시-19시)
2nd row평일(19시-22시)
3rd row협의
4th row협의
5th row협의
ValueCountFrequency (%)
협의 95
40.4%
2시간 8
 
3.4%
이후 6
 
2.6%
오전 4
 
1.7%
이전 4
 
1.7%
평일 4
 
1.7%
18시 3
 
1.3%
평일(10시-18시 3
 
1.3%
평일(저녁 2
 
0.9%
화(10시-12시,13시-15시,19시-21시 2
 
0.9%
Other values (71) 104
44.3%
2023-12-11T08:41:18.350025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 231
14.2%
1 201
12.4%
협 103
 
6.4%
의 103
 
6.4%
( 102
 
6.3%
) 102
 
6.3%
, 98
 
6.0%
- 98
 
6.0%
0 69
 
4.3%
2 50
 
3.1%
Other values (30) 465
28.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 736
45.4%
Decimal Number 455
28.1%
Open Punctuation 102
 
6.3%
Close Punctuation 102
 
6.3%
Other Punctuation 98
 
6.0%
Dash Punctuation 98
 
6.0%
Space Separator 31
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 231
31.4%
협 103
14.0%
의 103
14.0%
일 37
 
5.0%
평 30
 
4.1%
수 23
 
3.1%
금 19
 
2.6%
목 18
 
2.4%
화 17
 
2.3%
월 17
 
2.3%
Other values (15) 138
18.8%
Decimal Number
ValueCountFrequency (%)
1 201
44.2%
0 69
 
15.2%
2 50
 
11.0%
3 33
 
7.3%
9 32
 
7.0%
8 23
 
5.1%
5 18
 
4.0%
6 13
 
2.9%
4 10
 
2.2%
7 6
 
1.3%
Open Punctuation
ValueCountFrequency (%)
( 102
100.0%
Close Punctuation
ValueCountFrequency (%)
) 102
100.0%
Other Punctuation
ValueCountFrequency (%)
, 98
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%
Space Separator
ValueCountFrequency (%)
31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 886
54.6%
Hangul 736
45.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 231
31.4%
협 103
14.0%
의 103
14.0%
일 37
 
5.0%
평 30
 
4.1%
수 23
 
3.1%
금 19
 
2.6%
목 18
 
2.4%
화 17
 
2.3%
월 17
 
2.3%
Other values (15) 138
18.8%
Common
ValueCountFrequency (%)
1 201
22.7%
( 102
11.5%
) 102
11.5%
, 98
11.1%
- 98
11.1%
0 69
 
7.8%
2 50
 
5.6%
3 33
 
3.7%
9 32
 
3.6%
31
 
3.5%
Other values (5) 70
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 886
54.6%
Hangul 736
45.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 231
31.4%
협 103
14.0%
의 103
14.0%
일 37
 
5.0%
평 30
 
4.1%
수 23
 
3.1%
금 19
 
2.6%
목 18
 
2.4%
화 17
 
2.3%
월 17
 
2.3%
Other values (15) 138
18.8%
ASCII
ValueCountFrequency (%)
1 201
22.7%
( 102
11.5%
) 102
11.5%
, 98
11.1%
- 98
11.1%
0 69
 
7.8%
2 50
 
5.6%
3 33
 
3.7%
9 32
 
3.6%
31
 
3.5%
Other values (5) 70
 
7.9%

전화번호
Text

UNIQUE 

Distinct261
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-11T08:41:18.583721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters3132
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

Unique261 ?
Unique (%)100.0%

Sample

1st row055-392-3144
2nd row055-392-3145
3rd row055-392-3146
4th row055-392-3147
5th row055-392-3148
ValueCountFrequency (%)
055-392-3144 1
 
0.4%
055-392-3276 1
 
0.4%
055-392-3310 1
 
0.4%
055-392-3311 1
 
0.4%
055-392-3312 1
 
0.4%
055-392-3313 1
 
0.4%
055-392-3314 1
 
0.4%
055-392-3315 1
 
0.4%
055-392-3316 1
 
0.4%
055-392-3317 1
 
0.4%
Other values (251) 251
96.2%
2023-12-11T08:41:18.931866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 668
21.3%
5 578
18.5%
- 522
16.7%
2 407
13.0%
9 317
10.1%
0 312
10.0%
1 102
 
3.3%
4 58
 
1.9%
7 56
 
1.8%
6 56
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2610
83.3%
Dash Punctuation 522
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 668
25.6%
5 578
22.1%
2 407
15.6%
9 317
12.1%
0 312
12.0%
1 102
 
3.9%
4 58
 
2.2%
7 56
 
2.1%
6 56
 
2.1%
8 56
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 668
21.3%
5 578
18.5%
- 522
16.7%
2 407
13.0%
9 317
10.1%
0 312
10.0%
1 102
 
3.3%
4 58
 
1.9%
7 56
 
1.8%
6 56
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 668
21.3%
5 578
18.5%
- 522
16.7%
2 407
13.0%
9 317
10.1%
0 312
10.0%
1 102
 
3.3%
4 58
 
1.9%
7 56
 
1.8%
6 56
 
1.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Minimum2022-08-30 00:00:00
Maximum2022-08-30 00:00:00
2023-12-11T08:41:19.060277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:41:19.148571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T08:41:16.540018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:41:19.212791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번종류출강가능지역출강가능시간
연번1.0000.0000.6880.856
종류0.0001.0000.0000.395
출강가능지역0.6880.0001.0000.987
출강가능시간0.8560.3950.9871.000
2023-12-11T08:41:19.293841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종류출강가능지역
종류1.0000.000
출강가능지역0.0001.000
2023-12-11T08:41:19.362243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번종류출강가능지역
연번1.0000.0000.292
종류0.0001.0000.000
출강가능지역0.2920.0001.000

Missing values

2023-12-11T08:41:16.628608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:41:16.723893image/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

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