Overview

Dataset statistics

Number of variables6
Number of observations157
Missing cells125
Missing cells (%)13.3%
Duplicate rows1
Duplicate rows (%)0.6%
Total size in memory7.6 KiB
Average record size in memory49.8 B

Variable types

Numeric1
Categorical1
Text4

Dataset

Description매년 공표되는 직업안정기관 설치 현황- 연번, 청(지청), 센터명, 관할구역, 소재지(도로명), 대표전화- * "고용복지센터"는 5명 내외 소규모 센터로 국민취업지원제도 업무 수행
Author고용노동부
URLhttps://www.data.go.kr/data/15068740/fileData.do

Alerts

Dataset has 1 (0.6%) duplicate rowsDuplicates
연번 is highly overall correlated with 청(지청)High correlation
청(지청) is highly overall correlated with 연번High correlation
연번 has 25 (15.9%) missing valuesMissing
센터명 has 25 (15.9%) missing valuesMissing
관할구역 has 25 (15.9%) missing valuesMissing
소재지(도로명) has 25 (15.9%) missing valuesMissing
대표전화 has 25 (15.9%) missing valuesMissing

Reproduction

Analysis started2024-04-21 01:36:55.159116
Analysis finished2024-04-21 01:36:57.004100
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct132
Distinct (%)100.0%
Missing25
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean66.5
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-21T10:36:57.089212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.55
Q133.75
median66.5
Q399.25
95-th percentile125.45
Maximum132
Range131
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation38.249183
Coefficient of variation (CV)0.57517568
Kurtosis-1.2
Mean66.5
Median Absolute Deviation (MAD)33
Skewness0
Sum8778
Variance1463
MonotonicityStrictly increasing
2024-04-21T10:36:57.227165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 1
 
0.6%
99 1
 
0.6%
98 1
 
0.6%
97 1
 
0.6%
96 1
 
0.6%
95 1
 
0.6%
94 1
 
0.6%
93 1
 
0.6%
92 1
 
0.6%
91 1
 
0.6%
Other values (122) 122
77.7%
(Missing) 25
 
15.9%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
132 1
0.6%
131 1
0.6%
130 1
0.6%
129 1
0.6%
128 1
0.6%
127 1
0.6%
126 1
0.6%
125 1
0.6%
124 1
0.6%
123 1
0.6%

청(지청)
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
25 
의정부
 
6
성남
 
6
대전청
 
5
광주청
 
5
Other values (45)
110 

Length

Max length5
Median length2
Mean length2.7197452
Min length2

Unique

Unique10 ?
Unique (%)6.4%

Sample

1st row서울청
2nd row서울청
3rd row서울강남
4th row서울동부
5th row서울동부

Common Values

ValueCountFrequency (%)
<NA> 25
 
15.9%
의정부 6
 
3.8%
성남 6
 
3.8%
대전청 5
 
3.2%
광주청 5
 
3.2%
대구청 5
 
3.2%
안양 4
 
2.5%
창원 4
 
2.5%
진주 4
 
2.5%
목포 4
 
2.5%
Other values (40) 89
56.7%

Length

2024-04-21T10:36:57.364264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 25
 
15.9%
성남 6
 
3.8%
의정부 6
 
3.8%
대전청 5
 
3.2%
광주청 5
 
3.2%
대구청 5
 
3.2%
천안 4
 
2.5%
보령 4
 
2.5%
목포 4
 
2.5%
진주 4
 
2.5%
Other values (40) 89
56.7%

센터명
Text

MISSING 

Distinct132
Distinct (%)100.0%
Missing25
Missing (%)15.9%
Memory size1.4 KiB
2024-04-21T10:36:57.553615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.871212
Min length9

Characters and Unicode

Total characters1435
Distinct characters104
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

Unique132 ?
Unique (%)100.0%

Sample

1st row서울고용복지플러스센터
2nd row서초고용복지플러스센터
3rd row서울강남고용복지플러스센터
4th row서울동부고용복지플러스센터
5th row성동광진고용복지플러스센터
ValueCountFrequency (%)
진주고용복지플러스센터 1
 
0.8%
거창고용복지플러스센터 1
 
0.8%
익산고용복지플러스센터 1
 
0.8%
정읍고용복지플러스센터 1
 
0.8%
남원고용복지플러스센터 1
 
0.8%
전주고용복지플러스센터 1
 
0.8%
영광고용복지센터 1
 
0.8%
광주광산고용복지플러스센터 1
 
0.8%
화순고용복지센터 1
 
0.8%
나주고용복지센터 1
 
0.8%
Other values (122) 122
92.4%
2024-04-21T10:36:57.872624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 135
9.4%
용 133
9.3%
터 132
9.2%
복 132
9.2%
지 132
9.2%
센 132
9.2%
플 102
 
7.1%
러 102
 
7.1%
스 102
 
7.1%
* 30
 
2.1%
Other values (94) 303
21.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1405
97.9%
Other Punctuation 30
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 135
9.6%
용 133
9.5%
터 132
9.4%
복 132
9.4%
지 132
9.4%
센 132
9.4%
플 102
 
7.3%
러 102
 
7.3%
스 102
 
7.3%
천 19
 
1.4%
Other values (93) 284
20.2%
Other Punctuation
ValueCountFrequency (%)
* 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1405
97.9%
Common 30
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 135
9.6%
용 133
9.5%
터 132
9.4%
복 132
9.4%
지 132
9.4%
센 132
9.4%
플 102
 
7.3%
러 102
 
7.3%
스 102
 
7.3%
천 19
 
1.4%
Other values (93) 284
20.2%
Common
ValueCountFrequency (%)
* 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1405
97.9%
ASCII 30
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 135
9.6%
용 133
9.5%
터 132
9.4%
복 132
9.4%
지 132
9.4%
센 132
9.4%
플 102
 
7.3%
러 102
 
7.3%
스 102
 
7.3%
천 19
 
1.4%
Other values (93) 284
20.2%
ASCII
ValueCountFrequency (%)
* 30
100.0%

관할구역
Text

MISSING 

Distinct132
Distinct (%)100.0%
Missing25
Missing (%)15.9%
Memory size1.4 KiB
2024-04-21T10:36:58.072866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length33
Mean length12.954545
Min length3

Characters and Unicode

Total characters1710
Distinct characters162
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)100.0%

Sample

1st row서울특별시 중구ㆍ종로구ㆍ동대문구
2nd row서울특별시 서초구
3rd row서울특별시 강남구
4th row서울특별시 송파구ㆍ강동구
5th row서울특별시 성동구ㆍ광진구
ValueCountFrequency (%)
경기도 30
 
10.4%
경상남도 14
 
4.8%
충청남도 14
 
4.8%
경상북도 14
 
4.8%
전라남도 12
 
4.2%
서울특별시 11
 
3.8%
강원특별자치도 10
 
3.5%
전북특별자치도 8
 
2.8%
충청북도 6
 
2.1%
대구광역시 5
 
1.7%
Other values (155) 165
57.1%
2024-04-21T10:36:58.396556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
158
 
9.2%
시 115
 
6.7%
도 114
 
6.7%
ㆍ 111
 
6.5%
군 108
 
6.3%
구 75
 
4.4%
경 61
 
3.6%
남 51
 
3.0%
북 33
 
1.9%
천 32
 
1.9%
Other values (152) 852
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1525
89.2%
Space Separator 158
 
9.2%
Other Punctuation 11
 
0.6%
Close Punctuation 5
 
0.3%
Open Punctuation 5
 
0.3%
Decimal Number 4
 
0.2%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 115
 
7.5%
도 114
 
7.5%
ㆍ 111
 
7.3%
군 108
 
7.1%
구 75
 
4.9%
경 61
 
4.0%
남 51
 
3.3%
북 33
 
2.2%
천 32
 
2.1%
기 31
 
2.0%
Other values (143) 794
52.1%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 1
25.0%
6 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 8
72.7%
· 3
 
27.3%
Space Separator
ValueCountFrequency (%)
158
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Math Symbol
ValueCountFrequency (%)
∼ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1525
89.2%
Common 185
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 115
 
7.5%
도 114
 
7.5%
ㆍ 111
 
7.3%
군 108
 
7.1%
구 75
 
4.9%
경 61
 
4.0%
남 51
 
3.3%
북 33
 
2.2%
천 32
 
2.1%
기 31
 
2.0%
Other values (143) 794
52.1%
Common
ValueCountFrequency (%)
158
85.4%
, 8
 
4.3%
) 5
 
2.7%
( 5
 
2.7%
· 3
 
1.6%
1 2
 
1.1%
∼ 2
 
1.1%
2 1
 
0.5%
6 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1414
82.7%
ASCII 180
 
10.5%
Compat Jamo 111
 
6.5%
None 3
 
0.2%
Math Operators 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
158
87.8%
, 8
 
4.4%
) 5
 
2.8%
( 5
 
2.8%
1 2
 
1.1%
2 1
 
0.6%
6 1
 
0.6%
Hangul
ValueCountFrequency (%)
시 115
 
8.1%
도 114
 
8.1%
군 108
 
7.6%
구 75
 
5.3%
경 61
 
4.3%
남 51
 
3.6%
북 33
 
2.3%
천 32
 
2.3%
기 31
 
2.2%
상 31
 
2.2%
Other values (142) 763
54.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 111
100.0%
None
ValueCountFrequency (%)
· 3
100.0%
Math Operators
ValueCountFrequency (%)
∼ 2
100.0%

소재지(도로명)
Text

MISSING 

Distinct132
Distinct (%)100.0%
Missing25
Missing (%)15.9%
Memory size1.4 KiB
2024-04-21T10:36:58.642529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length34
Mean length24.492424
Min length14

Characters and Unicode

Total characters3233
Distinct characters264
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

Unique132 ?
Unique (%)100.0%

Sample

1st row서울 중구 삼일대로 363 장교빌딩, 1~4층, 10층
2nd row서울 서초구 반포대로 43 코스모빌딩
3rd row서울 강남구 테헤란로 410 금강타워 3, 7~10층
4th row서울 송파구 중대로 135 IT벤처타워 3~5층, 17층
5th row서울 성동구 연무장길 76 성수AK밸리 1, 2층
ValueCountFrequency (%)
경기 29
 
4.0%
경남 14
 
1.9%
충남 13
 
1.8%
경북 13
 
1.8%
2층 12
 
1.6%
서울 11
 
1.5%
전남 10
 
1.4%
1층 9
 
1.2%
강원 9
 
1.2%
전북 8
 
1.1%
Other values (544) 605
82.5%
2024-04-21T10:36:59.022353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
607
 
18.8%
로 116
 
3.6%
1 113
 
3.5%
동 87
 
2.7%
층 87
 
2.7%
시 81
 
2.5%
3 75
 
2.3%
경 69
 
2.1%
2 68
 
2.1%
) 68
 
2.1%
Other values (254) 1862
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1870
57.8%
Space Separator 607
 
18.8%
Decimal Number 519
 
16.1%
Close Punctuation 68
 
2.1%
Open Punctuation 68
 
2.1%
Other Punctuation 60
 
1.9%
Math Symbol 18
 
0.6%
Dash Punctuation 13
 
0.4%
Uppercase Letter 10
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 116
 
6.2%
동 87
 
4.7%
층 87
 
4.7%
시 81
 
4.3%
경 69
 
3.7%
남 59
 
3.2%
구 50
 
2.7%
북 38
 
2.0%
산 37
 
2.0%
대 36
 
1.9%
Other values (231) 1210
64.7%
Decimal Number
ValueCountFrequency (%)
1 113
21.8%
3 75
14.5%
2 68
13.1%
4 54
10.4%
5 47
9.1%
6 45
 
8.7%
0 34
 
6.6%
8 32
 
6.2%
9 32
 
6.2%
7 19
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
K 3
30.0%
T 3
30.0%
A 1
 
10.0%
I 1
 
10.0%
H 1
 
10.0%
M 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 59
98.3%
. 1
 
1.7%
Space Separator
ValueCountFrequency (%)
607
100.0%
Close Punctuation
ValueCountFrequency (%)
) 68
100.0%
Open Punctuation
ValueCountFrequency (%)
( 68
100.0%
Math Symbol
ValueCountFrequency (%)
~ 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1870
57.8%
Common 1353
41.8%
Latin 10
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 116
 
6.2%
동 87
 
4.7%
층 87
 
4.7%
시 81
 
4.3%
경 69
 
3.7%
남 59
 
3.2%
구 50
 
2.7%
북 38
 
2.0%
산 37
 
2.0%
대 36
 
1.9%
Other values (231) 1210
64.7%
Common
ValueCountFrequency (%)
607
44.9%
1 113
 
8.4%
3 75
 
5.5%
2 68
 
5.0%
) 68
 
5.0%
( 68
 
5.0%
, 59
 
4.4%
4 54
 
4.0%
5 47
 
3.5%
6 45
 
3.3%
Other values (7) 149
 
11.0%
Latin
ValueCountFrequency (%)
K 3
30.0%
T 3
30.0%
A 1
 
10.0%
I 1
 
10.0%
H 1
 
10.0%
M 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1870
57.8%
ASCII 1363
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
607
44.5%
1 113
 
8.3%
3 75
 
5.5%
2 68
 
5.0%
) 68
 
5.0%
( 68
 
5.0%
, 59
 
4.3%
4 54
 
4.0%
5 47
 
3.4%
6 45
 
3.3%
Other values (13) 159
 
11.7%
Hangul
ValueCountFrequency (%)
로 116
 
6.2%
동 87
 
4.7%
층 87
 
4.7%
시 81
 
4.3%
경 69
 
3.7%
남 59
 
3.2%
구 50
 
2.7%
북 38
 
2.0%
산 37
 
2.0%
대 36
 
1.9%
Other values (231) 1210
64.7%

대표전화
Text

MISSING 

Distinct131
Distinct (%)99.2%
Missing25
Missing (%)15.9%
Memory size1.4 KiB
2024-04-21T10:36:59.221496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.992424
Min length11

Characters and Unicode

Total characters1583
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

Unique130 ?
Unique (%)98.5%

Sample

1st row02-2004-7301
2nd row02-580-4900
3rd row02-3468-4794
4th row02-403-0009
5th row02-2047-9900
ValueCountFrequency (%)
032-540-2001 2
 
1.5%
063-580-0501 1
 
0.8%
061-280-0183 1
 
0.8%
054-559-8280 1
 
0.8%
054-851-8061 1
 
0.8%
054-851-8150 1
 
0.8%
054-851-8180 1
 
0.8%
062-609-8500 1
 
0.8%
061-280-0155 1
 
0.8%
054-639-1122 1
 
0.8%
Other values (121) 121
91.7%
2024-04-21T10:36:59.578269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 403
25.5%
- 264
16.7%
1 139
 
8.8%
5 129
 
8.1%
3 124
 
7.8%
6 111
 
7.0%
9 95
 
6.0%
2 94
 
5.9%
4 89
 
5.6%
8 75
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1319
83.3%
Dash Punctuation 264
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 403
30.6%
1 139
 
10.5%
5 129
 
9.8%
3 124
 
9.4%
6 111
 
8.4%
9 95
 
7.2%
2 94
 
7.1%
4 89
 
6.7%
8 75
 
5.7%
7 60
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1583
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 403
25.5%
- 264
16.7%
1 139
 
8.8%
5 129
 
8.1%
3 124
 
7.8%
6 111
 
7.0%
9 95
 
6.0%
2 94
 
5.9%
4 89
 
5.6%
8 75
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1583
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 403
25.5%
- 264
16.7%
1 139
 
8.8%
5 129
 
8.1%
3 124
 
7.8%
6 111
 
7.0%
9 95
 
6.0%
2 94
 
5.9%
4 89
 
5.6%
8 75
 
4.7%

Interactions

2024-04-21T10:36:56.528527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:36:59.672559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번청(지청)
연번1.0000.994
청(지청)0.9941.000
2024-04-21T10:36:59.753278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번청(지청)
연번1.0000.768
청(지청)0.7681.000

Missing values

2024-04-21T10:36:56.692312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:36:56.802941image/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.
2024-04-21T10:36:56.922652image/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

연번청(지청)센터명관할구역소재지(도로명)대표전화
01서울청서울고용복지플러스센터서울특별시 중구ㆍ종로구ㆍ동대문구서울 중구 삼일대로 363 장교빌딩, 1~4층, 10층02-2004-7301
12서울청서초고용복지플러스센터서울특별시 서초구서울 서초구 반포대로 43 코스모빌딩02-580-4900
23서울강남서울강남고용복지플러스센터서울특별시 강남구서울 강남구 테헤란로 410 금강타워 3, 7~10층02-3468-4794
34서울동부서울동부고용복지플러스센터서울특별시 송파구ㆍ강동구서울 송파구 중대로 135 IT벤처타워 3~5층, 17층02-403-0009
45서울동부성동광진고용복지플러스센터서울특별시 성동구ㆍ광진구서울 성동구 연무장길 76 성수AK밸리 1, 2층02-2047-9900
56서울서부서울서부고용복지플러스센터서울특별시 용산구ㆍ마포구ㆍ서대문구ㆍ은평구서울 마포구 마포대로 63-8 삼창플라자 1층, 4층, 5층, 8층02-2077-6000
67서울남부서울남부고용복지플러스센터서울특별시 영등포구ㆍ양천구서울 영등포구 선유로 12002-2639-2300
78서울남부서울강서고용복지플러스센터서울특별시 강서구서울 강서구 양천로57길 10-10 탐라영재관 2,3층02-2063-6700
89서울북부서울북부고용복지플러스센터서울특별시 중랑구ㆍ노원구ㆍ도봉구서울 노원구 노해로 450 해청빌딩02-2171-1700
910서울북부강북성북고용복지플러스센터서울특별시 강북구ㆍ성북구서울 강북구 도봉로 136 풍양빌딩 2,3,6,8,10층02-3406-0900
연번청(지청)센터명관할구역소재지(도로명)대표전화
147<NA><NA><NA><NA><NA><NA>
148<NA><NA><NA><NA><NA><NA>
149<NA><NA><NA><NA><NA><NA>
150<NA><NA><NA><NA><NA><NA>
151<NA><NA><NA><NA><NA><NA>
152<NA><NA><NA><NA><NA><NA>
153<NA><NA><NA><NA><NA><NA>
154<NA><NA><NA><NA><NA><NA>
155<NA><NA><NA><NA><NA><NA>
156<NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

연번청(지청)센터명관할구역소재지(도로명)대표전화# duplicates
0<NA><NA><NA><NA><NA><NA>25