Overview

Dataset statistics

Number of variables6
Number of observations123
Missing cells61
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory50.0 B

Variable types

Numeric1
Categorical1
Text3
DateTime1

Dataset

Description인천광역시 서구 직업소개소 현황 데이터로 법인명(인력 사무소 ,직업소개소),사업소도로명주소 구성 되어 있습니다.
Author인천광역시 서구
URLhttps://www.data.go.kr/data/15091254/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
유무료구분 is highly imbalanced (83.5%)Imbalance
전화번호 has 61 (49.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 23:18:03.200217
Analysis finished2024-03-14 23:18:04.584088
Duration1.38 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T08:18:04.895500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.1
Q131.5
median62
Q392.5
95-th percentile116.9
Maximum123
Range122
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.651087
Coefficient of variation (CV)0.57501753
Kurtosis-1.2
Mean62
Median Absolute Deviation (MAD)31
Skewness0
Sum7626
Variance1271
MonotonicityStrictly increasing
2024-03-15T08:18:05.386945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
79 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
Other values (113) 113
91.9%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
123 1
0.8%
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%

유무료구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
유료
120 
무료
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유료 120
97.6%
무료 3
 
2.4%

Length

2024-03-15T08:18:05.850936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:18:06.252058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유료 120
97.6%
무료 3
 
2.4%

직업소개소명
Text

Distinct121
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-15T08:18:07.436484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length6.4390244
Min length2

Characters and Unicode

Total characters792
Distinct characters212
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

Unique119 ?
Unique (%)96.7%

Sample

1st row전국인력
2nd row토성인력개발
3rd row당하인력
4th row조은파출인력센터
5th row든든한파출부 인천서구점
ValueCountFrequency (%)
인력 4
 
2.8%
우리인력 2
 
1.4%
직업소개소 2
 
1.4%
인천 2
 
1.4%
믿음인력 2
 
1.4%
키움인력 1
 
0.7%
토성인력개발 1
 
0.7%
성강이지에스 1
 
0.7%
창조디자인 1
 
0.7%
대신맨인력 1
 
0.7%
Other values (128) 128
88.3%
2024-03-15T08:18:08.938637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 81
 
10.2%
력 69
 
8.7%
소 37
 
4.7%
개 29
 
3.7%
22
 
2.8%
사 16
 
2.0%
업 15
 
1.9%
) 14
 
1.8%
직 14
 
1.8%
( 14
 
1.8%
Other values (202) 481
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 717
90.5%
Space Separator 22
 
2.8%
Close Punctuation 14
 
1.8%
Open Punctuation 14
 
1.8%
Uppercase Letter 13
 
1.6%
Decimal Number 7
 
0.9%
Lowercase Letter 3
 
0.4%
Math Symbol 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 81
 
11.3%
력 69
 
9.6%
소 37
 
5.2%
개 29
 
4.0%
사 16
 
2.2%
업 15
 
2.1%
직 14
 
2.0%
파 14
 
2.0%
이 12
 
1.7%
성 12
 
1.7%
Other values (181) 418
58.3%
Uppercase Letter
ValueCountFrequency (%)
A 3
23.1%
B 3
23.1%
R 1
 
7.7%
H 1
 
7.7%
S 1
 
7.7%
J 1
 
7.7%
N 1
 
7.7%
O 1
 
7.7%
D 1
 
7.7%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
5 1
 
14.3%
6 1
 
14.3%
3 1
 
14.3%
4 1
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
o 2
66.7%
l 1
33.3%
Space Separator
ValueCountFrequency (%)
22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 717
90.5%
Common 59
 
7.4%
Latin 16
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 81
 
11.3%
력 69
 
9.6%
소 37
 
5.2%
개 29
 
4.0%
사 16
 
2.2%
업 15
 
2.1%
직 14
 
2.0%
파 14
 
2.0%
이 12
 
1.7%
성 12
 
1.7%
Other values (181) 418
58.3%
Latin
ValueCountFrequency (%)
A 3
18.8%
B 3
18.8%
o 2
12.5%
l 1
 
6.2%
R 1
 
6.2%
H 1
 
6.2%
S 1
 
6.2%
J 1
 
6.2%
N 1
 
6.2%
O 1
 
6.2%
Common
ValueCountFrequency (%)
22
37.3%
) 14
23.7%
( 14
23.7%
1 3
 
5.1%
5 1
 
1.7%
6 1
 
1.7%
3 1
 
1.7%
+ 1
 
1.7%
4 1
 
1.7%
& 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 717
90.5%
ASCII 75
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 81
 
11.3%
력 69
 
9.6%
소 37
 
5.2%
개 29
 
4.0%
사 16
 
2.2%
업 15
 
2.1%
직 14
 
2.0%
파 14
 
2.0%
이 12
 
1.7%
성 12
 
1.7%
Other values (181) 418
58.3%
ASCII
ValueCountFrequency (%)
22
29.3%
) 14
18.7%
( 14
18.7%
1 3
 
4.0%
A 3
 
4.0%
B 3
 
4.0%
o 2
 
2.7%
5 1
 
1.3%
6 1
 
1.3%
3 1
 
1.3%
Other values (11) 11
14.7%

소재지(도로명)
Text

Distinct122
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-15T08:18:09.884946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length41
Mean length32.943089
Min length22

Characters and Unicode

Total characters4052
Distinct characters181
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

Unique121 ?
Unique (%)98.4%

Sample

1st row인천광역시 서구 승학로422번길 33-4. 101호 (검암동)
2nd row인천광역시 서구 승학로495번길 4-1. 다복빌딩 203-2호 (검암동)
3rd row인천광역시 서구 원당대로 660. 영프라자 303호 일부호 (당하동)
4th row인천광역시 서구 원창로 180. 1층 (신현동)
5th row인천광역시 서구 서곶로301번길 16. 태영프라자 613호 (심곡동)
ValueCountFrequency (%)
인천광역시 123
 
14.8%
서구 123
 
14.8%
석남동 28
 
3.4%
마전동 26
 
3.1%
3층 23
 
2.8%
가정로 20
 
2.4%
2층 16
 
1.9%
완정로 13
 
1.6%
가정동 12
 
1.4%
왕길동 9
 
1.1%
Other values (288) 437
52.7%
2024-03-15T08:18:11.217822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
708
 
17.5%
1 143
 
3.5%
서 134
 
3.3%
동 131
 
3.2%
로 128
 
3.2%
인 125
 
3.1%
. 125
 
3.1%
시 125
 
3.1%
광 124
 
3.1%
천 124
 
3.1%
Other values (171) 2185
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2226
54.9%
Decimal Number 721
 
17.8%
Space Separator 708
 
17.5%
Other Punctuation 125
 
3.1%
Open Punctuation 123
 
3.0%
Close Punctuation 123
 
3.0%
Dash Punctuation 22
 
0.5%
Uppercase Letter 3
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 134
 
6.0%
동 131
 
5.9%
로 128
 
5.8%
인 125
 
5.6%
시 125
 
5.6%
광 124
 
5.6%
천 124
 
5.6%
구 123
 
5.5%
역 123
 
5.5%
호 78
 
3.5%
Other values (152) 1011
45.4%
Decimal Number
ValueCountFrequency (%)
1 143
19.8%
0 103
14.3%
2 102
14.1%
3 102
14.1%
4 65
9.0%
6 50
 
6.9%
5 46
 
6.4%
9 41
 
5.7%
8 35
 
4.9%
7 34
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
L 1
33.3%
G 1
33.3%
A 1
33.3%
Space Separator
ValueCountFrequency (%)
708
100.0%
Other Punctuation
ValueCountFrequency (%)
. 125
100.0%
Open Punctuation
ValueCountFrequency (%)
( 123
100.0%
Close Punctuation
ValueCountFrequency (%)
) 123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2226
54.9%
Common 1822
45.0%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 134
 
6.0%
동 131
 
5.9%
로 128
 
5.8%
인 125
 
5.6%
시 125
 
5.6%
광 124
 
5.6%
천 124
 
5.6%
구 123
 
5.5%
역 123
 
5.5%
호 78
 
3.5%
Other values (152) 1011
45.4%
Common
ValueCountFrequency (%)
708
38.9%
1 143
 
7.8%
. 125
 
6.9%
( 123
 
6.8%
) 123
 
6.8%
0 103
 
5.7%
2 102
 
5.6%
3 102
 
5.6%
4 65
 
3.6%
6 50
 
2.7%
Other values (5) 178
 
9.8%
Latin
ValueCountFrequency (%)
e 1
25.0%
L 1
25.0%
G 1
25.0%
A 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2226
54.9%
ASCII 1826
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
708
38.8%
1 143
 
7.8%
. 125
 
6.8%
( 123
 
6.7%
) 123
 
6.7%
0 103
 
5.6%
2 102
 
5.6%
3 102
 
5.6%
4 65
 
3.6%
6 50
 
2.7%
Other values (9) 182
 
10.0%
Hangul
ValueCountFrequency (%)
서 134
 
6.0%
동 131
 
5.9%
로 128
 
5.8%
인 125
 
5.6%
시 125
 
5.6%
광 124
 
5.6%
천 124
 
5.6%
구 123
 
5.5%
역 123
 
5.5%
호 78
 
3.5%
Other values (152) 1011
45.4%

전화번호
Text

MISSING 

Distinct62
Distinct (%)100.0%
Missing61
Missing (%)49.6%
Memory size1.1 KiB
2024-03-15T08:18:12.132850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.919355
Min length9

Characters and Unicode

Total characters739
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

Unique62 ?
Unique (%)100.0%

Sample

1st row1522-6326
2nd row032-214-2000
3rd row032-583-5533
4th row032-563-0085
5th row032-578-1666
ValueCountFrequency (%)
032 2
 
3.1%
032-577-0225 1
 
1.5%
032-567-4954 1
 
1.5%
032-568-4380 1
 
1.5%
032-573-9117 1
 
1.5%
032-561-3551 1
 
1.5%
032-572-7277 1
 
1.5%
032-548-3579 1
 
1.5%
032-567-9447 1
 
1.5%
032-881-1173 1
 
1.5%
Other values (54) 54
83.1%
2024-03-15T08:18:13.452010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 119
16.1%
2 103
13.9%
0 98
13.3%
3 95
12.9%
5 80
10.8%
6 57
7.7%
7 47
 
6.4%
8 47
 
6.4%
1 43
 
5.8%
4 25
 
3.4%
Other values (2) 25
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 617
83.5%
Dash Punctuation 119
 
16.1%
Space Separator 3
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 103
16.7%
0 98
15.9%
3 95
15.4%
5 80
13.0%
6 57
9.2%
7 47
7.6%
8 47
7.6%
1 43
7.0%
4 25
 
4.1%
9 22
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 119
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 739
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 119
16.1%
2 103
13.9%
0 98
13.3%
3 95
12.9%
5 80
10.8%
6 57
7.7%
7 47
 
6.4%
8 47
 
6.4%
1 43
 
5.8%
4 25
 
3.4%
Other values (2) 25
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 119
16.1%
2 103
13.9%
0 98
13.3%
3 95
12.9%
5 80
10.8%
6 57
7.7%
7 47
 
6.4%
8 47
 
6.4%
1 43
 
5.8%
4 25
 
3.4%
Other values (2) 25
 
3.4%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2023-12-01 00:00:00
Maximum2023-12-01 00:00:00
2024-03-15T08:18:13.649947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:18:13.818829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T08:18:03.669230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:18:14.019406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번유무료구분전화번호
연번1.0000.0001.000
유무료구분0.0001.0001.000
전화번호1.0001.0001.000
2024-03-15T08:18:14.259600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번유무료구분
연번1.0000.000
유무료구분0.0001.000

Missing values

2024-03-15T08:18:04.004406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:18:04.417123image/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

연번유무료구분직업소개소명소재지(도로명)전화번호데이터기준일자
01유료전국인력인천광역시 서구 승학로422번길 33-4. 101호 (검암동)1522-63262023-12-01
12유료토성인력개발인천광역시 서구 승학로495번길 4-1. 다복빌딩 203-2호 (검암동)032-214-20002023-12-01
23유료당하인력인천광역시 서구 원당대로 660. 영프라자 303호 일부호 (당하동)<NA>2023-12-01
34유료조은파출인력센터인천광역시 서구 원창로 180. 1층 (신현동)032-583-55332023-12-01
45유료든든한파출부 인천서구점인천광역시 서구 서곶로301번길 16. 태영프라자 613호 (심곡동)<NA>2023-12-01
56유료글로벌인재교류센터(JOBBAND)인천광역시 서구 완정로165번안길 1. 206호호 (왕길동)<NA>2023-12-01
67유료대성종합직업소개소인천광역시 서구 서곶로 345. 서광빌딩 306호 (연희동)<NA>2023-12-01
78유료우주인력인천광역시 서구 검단로 492. 3층 (마전동)<NA>2023-12-01
89유료럭키인력인천광역시 서구 완정로 146. 208호 (마전동. 리더스빌)<NA>2023-12-01
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