Overview

Dataset statistics

Number of variables10
Number of observations21
Missing cells16
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory89.3 B

Variable types

Categorical2
Text5
Numeric3

Dataset

Description청년취업인턴제운영기관 현황
Author고용노동부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=X1BC80BO0JZ0LHG5NBWZ20893542&infSeq=1

Alerts

시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
관할고용센터명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84위도 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
소재지우편번호 has 4 (19.0%) missing valuesMissing
소재지도로명주소 has 4 (19.0%) missing valuesMissing
WGS84위도 has 4 (19.0%) missing valuesMissing
WGS84경도 has 4 (19.0%) missing valuesMissing
대표전화번호 has unique valuesUnique
팩스번호 has unique valuesUnique
소재지지번주소 has unique valuesUnique
운영기관명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:36:44.252738
Analysis finished2023-12-10 21:36:45.609247
Duration1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
수원시
5 
성남시
4 
고양시
3 
안산시
3 
부천시
2 
Other values (2)
4 

Length

Max length4
Median length3
Mean length3.0952381
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부천시
2nd row부천시
3rd row의정부시
4th row의정부시
5th row고양시

Common Values

ValueCountFrequency (%)
수원시 5
23.8%
성남시 4
19.0%
고양시 3
14.3%
안산시 3
14.3%
부천시 2
 
9.5%
의정부시 2
 
9.5%
안양시 2
 
9.5%

Length

2023-12-11T06:36:45.676044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:36:45.791273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수원시 5
23.8%
성남시 4
19.0%
고양시 3
14.3%
안산시 3
14.3%
부천시 2
 
9.5%
의정부시 2
 
9.5%
안양시 2
 
9.5%

관할고용센터명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
수원
5 
성남
4 
고양
3 
안산
3 
부천
2 
Other values (2)
4 

Length

Max length3
Median length2
Mean length2.0952381
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부천
2nd row부천
3rd row의정부
4th row의정부
5th row고양

Common Values

ValueCountFrequency (%)
수원 5
23.8%
성남 4
19.0%
고양 3
14.3%
안산 3
14.3%
부천 2
 
9.5%
의정부 2
 
9.5%
안양 2
 
9.5%

Length

2023-12-11T06:36:45.936352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:36:46.076143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수원 5
23.8%
성남 4
19.0%
고양 3
14.3%
안산 3
14.3%
부천 2
 
9.5%
의정부 2
 
9.5%
안양 2
 
9.5%

대표전화번호
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-11T06:36:46.271116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.142857
Min length12

Characters and Unicode

Total characters255
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

Unique21 ?
Unique (%)100.0%

Sample

1st row070-7094-5469
2nd row031-984-9001
3rd row031-879-5161
4th row031-592-3062
5th row031-8071-4242
ValueCountFrequency (%)
070-7094-5469 1
 
4.8%
031-336-2528 1
 
4.8%
031-482-1413 1
 
4.8%
031-410-3030 1
 
4.8%
031-447-9171 1
 
4.8%
031-463-4521 1
 
4.8%
031-761-9090 1
 
4.8%
031-781-7903 1
 
4.8%
031-717-2950 1
 
4.8%
031-628-9631 1
 
4.8%
Other values (11) 11
52.4%
2023-12-11T06:36:46.626338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 43
16.9%
- 42
16.5%
1 38
14.9%
3 32
12.5%
4 17
 
6.7%
2 16
 
6.3%
7 15
 
5.9%
9 15
 
5.9%
5 13
 
5.1%
6 13
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 213
83.5%
Dash Punctuation 42
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43
20.2%
1 38
17.8%
3 32
15.0%
4 17
 
8.0%
2 16
 
7.5%
7 15
 
7.0%
9 15
 
7.0%
5 13
 
6.1%
6 13
 
6.1%
8 11
 
5.2%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 255
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43
16.9%
- 42
16.5%
1 38
14.9%
3 32
12.5%
4 17
 
6.7%
2 16
 
6.3%
7 15
 
5.9%
9 15
 
5.9%
5 13
 
5.1%
6 13
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43
16.9%
- 42
16.5%
1 38
14.9%
3 32
12.5%
4 17
 
6.7%
2 16
 
6.3%
7 15
 
5.9%
9 15
 
5.9%
5 13
 
5.1%
6 13
 
5.1%

팩스번호
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-11T06:36:46.841563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.095238
Min length12

Characters and Unicode

Total characters254
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

Unique21 ?
Unique (%)100.0%

Sample

1st row032-621-2088
2nd row031-984-9013
3rd row031-879-4429
4th row031-591-3054
5th row031-8071-4499
ValueCountFrequency (%)
032-621-2088 1
 
4.8%
031-336-2529 1
 
4.8%
031-697-2260 1
 
4.8%
031-410-3037 1
 
4.8%
031-443-9260 1
 
4.8%
031-444-2724 1
 
4.8%
031-761-8486 1
 
4.8%
031-781-7758 1
 
4.8%
031-786-1965 1
 
4.8%
031-360-6344 1
 
4.8%
Other values (11) 11
52.4%
2023-12-11T06:36:47.170913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 42
16.5%
0 41
16.1%
3 33
13.0%
1 28
11.0%
2 19
7.5%
4 19
7.5%
7 17
6.7%
9 16
 
6.3%
8 14
 
5.5%
6 13
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 212
83.5%
Dash Punctuation 42
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41
19.3%
3 33
15.6%
1 28
13.2%
2 19
9.0%
4 19
9.0%
7 17
8.0%
9 16
 
7.5%
8 14
 
6.6%
6 13
 
6.1%
5 12
 
5.7%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 254
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 42
16.5%
0 41
16.1%
3 33
13.0%
1 28
11.0%
2 19
7.5%
4 19
7.5%
7 17
6.7%
9 16
 
6.3%
8 14
 
5.5%
6 13
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 42
16.5%
0 41
16.1%
3 33
13.0%
1 28
11.0%
2 19
7.5%
4 19
7.5%
7 17
6.7%
9 16
 
6.3%
8 14
 
5.5%
6 13
 
5.1%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean14498.059
Minimum10380
Maximum18590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T06:36:47.299565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10380
5-th percentile10447.2
Q113567
median14502
Q316313
95-th percentile17363.6
Maximum18590
Range8210
Interquartile range (IQR)2746

Descriptive statistics

Standard deviation2251.3256
Coefficient of variation (CV)0.15528462
Kurtosis-0.14928113
Mean14498.059
Median Absolute Deviation (MAD)1016
Skewness-0.32298276
Sum246467
Variance5068466.8
MonotonicityNot monotonic
2023-12-11T06:36:47.406063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11652 1
 
4.8%
15073 1
 
4.8%
15361 1
 
4.8%
15357 1
 
4.8%
14092 1
 
4.8%
13916 1
 
4.8%
13567 1
 
4.8%
13615 1
 
4.8%
14502 1
 
4.8%
17057 1
 
4.8%
Other values (7) 7
33.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
10380 1
4.8%
10464 1
4.8%
11652 1
4.8%
13486 1
4.8%
13567 1
4.8%
13615 1
4.8%
13916 1
4.8%
14092 1
4.8%
14502 1
4.8%
15073 1
4.8%
ValueCountFrequency (%)
18590 1
4.8%
17057 1
4.8%
16571 1
4.8%
16471 1
4.8%
16313 1
4.8%
15361 1
4.8%
15357 1
4.8%
15073 1
4.8%
14502 1
4.8%
14092 1
4.8%

소재지지번주소
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-11T06:36:47.618093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length25
Mean length23.285714
Min length13

Characters and Unicode

Total characters489
Distinct characters102
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row경기도 부천시 약대동 193번지
2nd row경기도 김포시 김포대로 841
3rd row경기도 의정부시 의정부동 572-1번지 청홍빌딩 4층
4th row경기 남양주시 홍유릉로 248번길 39
5th row경기 파주시 금빛로 15
ValueCountFrequency (%)
경기도 18
 
16.4%
성남시 3
 
2.7%
분당구 3
 
2.7%
수원시 3
 
2.7%
경기 3
 
2.7%
안산시 2
 
1.8%
안양시 2
 
1.8%
고양시 2
 
1.8%
단원구 2
 
1.8%
고잔동 2
 
1.8%
Other values (70) 70
63.6%
2023-12-11T06:36:48.018043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
 
18.2%
시 24
 
4.9%
1 22
 
4.5%
경 21
 
4.3%
기 21
 
4.3%
도 19
 
3.9%
동 18
 
3.7%
번 16
 
3.3%
2 16
 
3.3%
지 15
 
3.1%
Other values (92) 228
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 299
61.1%
Decimal Number 90
 
18.4%
Space Separator 89
 
18.2%
Dash Punctuation 10
 
2.0%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 24
 
8.0%
경 21
 
7.0%
기 21
 
7.0%
도 19
 
6.4%
동 18
 
6.0%
번 16
 
5.4%
지 15
 
5.0%
구 13
 
4.3%
안 8
 
2.7%
양 7
 
2.3%
Other values (79) 137
45.8%
Decimal Number
ValueCountFrequency (%)
1 22
24.4%
2 16
17.8%
5 10
11.1%
3 7
 
7.8%
0 7
 
7.8%
9 7
 
7.8%
8 6
 
6.7%
7 6
 
6.7%
6 5
 
5.6%
4 4
 
4.4%
Space Separator
ValueCountFrequency (%)
89
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 299
61.1%
Common 189
38.7%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 24
 
8.0%
경 21
 
7.0%
기 21
 
7.0%
도 19
 
6.4%
동 18
 
6.0%
번 16
 
5.4%
지 15
 
5.0%
구 13
 
4.3%
안 8
 
2.7%
양 7
 
2.3%
Other values (79) 137
45.8%
Common
ValueCountFrequency (%)
89
47.1%
1 22
 
11.6%
2 16
 
8.5%
- 10
 
5.3%
5 10
 
5.3%
3 7
 
3.7%
0 7
 
3.7%
9 7
 
3.7%
8 6
 
3.2%
7 6
 
3.2%
Other values (2) 9
 
4.8%
Latin
ValueCountFrequency (%)
E 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 299
61.1%
ASCII 190
38.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
89
46.8%
1 22
 
11.6%
2 16
 
8.4%
- 10
 
5.3%
5 10
 
5.3%
3 7
 
3.7%
0 7
 
3.7%
9 7
 
3.7%
8 6
 
3.2%
7 6
 
3.2%
Other values (3) 10
 
5.3%
Hangul
ValueCountFrequency (%)
시 24
 
8.0%
경 21
 
7.0%
기 21
 
7.0%
도 19
 
6.4%
동 18
 
6.0%
번 16
 
5.4%
지 15
 
5.0%
구 13
 
4.3%
안 8
 
2.7%
양 7
 
2.3%
Other values (79) 137
45.8%

소재지도로명주소
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2023-12-11T06:36:48.282058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length18.529412
Min length15

Characters and Unicode

Total characters315
Distinct characters69
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

Unique17 ?
Unique (%)100.0%

Sample

1st row경기도 부천시 평천로 655
2nd row경기도 의정부시 신흥로 206
3rd row경기도 고양시 덕양구 호국로 790
4th row경기도 고양시 일산서구 주화로 180
5th row경기도 화성시 향남읍 토성로 14
ValueCountFrequency (%)
경기도 17
 
20.7%
성남시 3
 
3.7%
분당구 3
 
3.7%
수원시 3
 
3.7%
고양시 2
 
2.4%
단원구 2
 
2.4%
안산시 2
 
2.4%
안양시 2
 
2.4%
동안구 1
 
1.2%
81 1
 
1.2%
Other values (46) 46
56.1%
2023-12-11T06:36:48.728213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
65
20.6%
시 18
 
5.7%
기 18
 
5.7%
경 17
 
5.4%
도 17
 
5.4%
로 17
 
5.4%
구 13
 
4.1%
1 11
 
3.5%
안 8
 
2.5%
2 7
 
2.2%
Other values (59) 124
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 202
64.1%
Space Separator 65
 
20.6%
Decimal Number 48
 
15.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 18
 
8.9%
기 18
 
8.9%
경 17
 
8.4%
도 17
 
8.4%
로 17
 
8.4%
구 13
 
6.4%
안 8
 
4.0%
성 7
 
3.5%
양 7
 
3.5%
원 6
 
3.0%
Other values (48) 74
36.6%
Decimal Number
ValueCountFrequency (%)
1 11
22.9%
2 7
14.6%
0 6
12.5%
3 5
10.4%
5 5
10.4%
6 4
 
8.3%
7 3
 
6.2%
9 3
 
6.2%
4 2
 
4.2%
8 2
 
4.2%
Space Separator
ValueCountFrequency (%)
65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 202
64.1%
Common 113
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 18
 
8.9%
기 18
 
8.9%
경 17
 
8.4%
도 17
 
8.4%
로 17
 
8.4%
구 13
 
6.4%
안 8
 
4.0%
성 7
 
3.5%
양 7
 
3.5%
원 6
 
3.0%
Other values (48) 74
36.6%
Common
ValueCountFrequency (%)
65
57.5%
1 11
 
9.7%
2 7
 
6.2%
0 6
 
5.3%
3 5
 
4.4%
5 5
 
4.4%
6 4
 
3.5%
7 3
 
2.7%
9 3
 
2.7%
4 2
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 202
64.1%
ASCII 113
35.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
65
57.5%
1 11
 
9.7%
2 7
 
6.2%
0 6
 
5.3%
3 5
 
4.4%
5 5
 
4.4%
6 4
 
3.5%
7 3
 
2.7%
9 3
 
2.7%
4 2
 
1.8%
Hangul
ValueCountFrequency (%)
시 18
 
8.9%
기 18
 
8.9%
경 17
 
8.4%
도 17
 
8.4%
로 17
 
8.4%
구 13
 
6.4%
안 8
 
4.0%
성 7
 
3.5%
양 7
 
3.5%
원 6
 
3.0%
Other values (48) 74
36.6%

운영기관명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-11T06:36:48.957247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.9047619
Min length5

Characters and Unicode

Total characters166
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row(재)부천산업진흥재단
2nd row㈜미래고용정보
3rd row스탭스㈜의정부센터
4th row경기동부상공회의소
5th row파주상공회의소
ValueCountFrequency (%)
재)부천산업진흥재단 1
 
4.5%
㈜미래고용정보 1
 
4.5%
스탭스㈜안산센터 1
 
4.5%
안산상공회의소 1
 
4.5%
안양상공회의소 1
 
4.5%
대림대학교 1
 
4.5%
하광상공회의소 1
 
4.5%
성남상공회의소 1
 
4.5%
성남지점 1
 
4.5%
주)제니엘 1
 
4.5%
Other values (12) 12
54.5%
2023-12-11T06:36:49.383495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 13
 
7.8%
의 11
 
6.6%
소 11
 
6.6%
상 10
 
6.0%
공 10
 
6.0%
스 8
 
4.8%
센 5
 
3.0%
터 5
 
3.0%
기 5
 
3.0%
탭 4
 
2.4%
Other values (55) 84
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 157
94.6%
Other Symbol 4
 
2.4%
Open Punctuation 2
 
1.2%
Close Punctuation 2
 
1.2%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 13
 
8.3%
의 11
 
7.0%
소 11
 
7.0%
상 10
 
6.4%
공 10
 
6.4%
스 8
 
5.1%
센 5
 
3.2%
터 5
 
3.2%
기 5
 
3.2%
탭 4
 
2.5%
Other values (51) 75
47.8%
Other Symbol
ValueCountFrequency (%)
㈜ 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 161
97.0%
Common 5
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 13
 
8.1%
의 11
 
6.8%
소 11
 
6.8%
상 10
 
6.2%
공 10
 
6.2%
스 8
 
5.0%
센 5
 
3.1%
터 5
 
3.1%
기 5
 
3.1%
탭 4
 
2.5%
Other values (52) 79
49.1%
Common
ValueCountFrequency (%)
( 2
40.0%
) 2
40.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 157
94.6%
ASCII 5
 
3.0%
None 4
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 13
 
8.3%
의 11
 
7.0%
소 11
 
7.0%
상 10
 
6.4%
공 10
 
6.4%
스 8
 
5.1%
센 5
 
3.2%
터 5
 
3.2%
기 5
 
3.2%
탭 4
 
2.5%
Other values (51) 75
47.8%
None
ValueCountFrequency (%)
㈜ 4
100.0%
ASCII
ValueCountFrequency (%)
( 2
40.0%
) 2
40.0%
1
20.0%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean37.393529
Minimum37.128813
Maximum37.734347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T06:36:49.544393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.128813
5-th percentile37.208734
Q137.292013
median37.350676
Q337.404282
95-th percentile37.686463
Maximum37.734347
Range0.6055341
Interquartile range (IQR)0.1122696

Descriptive statistics

Standard deviation0.16468611
Coefficient of variation (CV)0.0044041339
Kurtosis0.18525647
Mean37.393529
Median Absolute Deviation (MAD)0.0586636
Skewness0.83736903
Sum635.68999
Variance0.027121514
MonotonicityNot monotonic
2023-12-11T06:36:49.724655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
37.7343471 1
 
4.8%
37.3400819 1
 
4.8%
37.3170384 1
 
4.8%
37.3187044 1
 
4.8%
37.3861923 1
 
4.8%
37.4035812 1
 
4.8%
37.3983318 1
 
4.8%
37.3506762 1
 
4.8%
37.5164643 1
 
4.8%
37.2287141 1
 
4.8%
Other values (7) 7
33.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
37.128813 1
4.8%
37.2287141 1
4.8%
37.2625196 1
4.8%
37.2773501 1
4.8%
37.2920126 1
4.8%
37.3170384 1
4.8%
37.3187044 1
4.8%
37.3400819 1
4.8%
37.3506762 1
4.8%
37.3861923 1
4.8%
ValueCountFrequency (%)
37.7343471 1
4.8%
37.6744917 1
4.8%
37.6563844 1
4.8%
37.5164643 1
4.8%
37.4042822 1
4.8%
37.4035812 1
4.8%
37.3983318 1
4.8%
37.3861923 1
4.8%
37.3506762 1
4.8%
37.3400819 1
4.8%

WGS84경도
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean126.95126
Minimum126.73356
Maximum127.1884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T06:36:49.861382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.73356
5-th percentile126.74606
Q1126.83632
median126.93124
Q3127.04365
95-th percentile127.13828
Maximum127.1884
Range0.4548413
Interquartile range (IQR)0.2073363

Descriptive statistics

Standard deviation0.14214353
Coefficient of variation (CV)0.0011196701
Kurtosis-1.1831603
Mean126.95126
Median Absolute Deviation (MAD)0.1048771
Skewness-0.054304003
Sum2158.1715
Variance0.020204783
MonotonicityNot monotonic
2023-12-11T06:36:50.004447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
127.0436535 1
 
4.8%
126.7335596 1
 
4.8%
126.839517 1
 
4.8%
126.8263591 1
 
4.8%
126.9312362 1
 
4.8%
126.9302822 1
 
4.8%
127.1257439 1
 
4.8%
127.1082401 1
 
4.8%
126.7639651 1
 
4.8%
127.1884009 1
 
4.8%
Other values (7) 7
33.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
126.7335596 1
4.8%
126.7491883 1
4.8%
126.7639651 1
4.8%
126.8263591 1
4.8%
126.8363172 1
4.8%
126.839517 1
4.8%
126.9302822 1
4.8%
126.9311869 1
4.8%
126.9312362 1
4.8%
127.0058705 1
4.8%
ValueCountFrequency (%)
127.1884009 1
4.8%
127.1257439 1
4.8%
127.1082401 1
4.8%
127.1026114 1
4.8%
127.0436535 1
4.8%
127.0287477 1
4.8%
127.0265895 1
4.8%
127.0058705 1
4.8%
126.9312362 1
4.8%
126.9311869 1
4.8%

Interactions

2023-12-11T06:36:45.040789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:36:44.618756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:36:44.822137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:36:45.107191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:36:44.679876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:36:44.899329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:36:45.191352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:36:44.752387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:36:44.976227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:36:50.093548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명관할고용센터명대표전화번호팩스번호소재지우편번호소재지지번주소소재지도로명주소운영기관명WGS84위도WGS84경도
시군명1.0001.0001.0001.0000.9481.0001.0001.0000.8320.855
관할고용센터명1.0001.0001.0001.0000.9481.0001.0001.0000.8320.855
대표전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
팩스번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지우편번호0.9480.9481.0001.0001.0001.0001.0001.0000.9510.931
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
운영기관명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
WGS84위도0.8320.8321.0001.0000.9511.0001.0001.0001.0000.706
WGS84경도0.8550.8551.0001.0000.9311.0001.0001.0000.7061.000
2023-12-11T06:36:50.526485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명관할고용센터명
시군명1.0001.000
관할고용센터명1.0001.000
2023-12-11T06:36:50.624903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시군명관할고용센터명
소재지우편번호1.000-0.9440.1100.7890.789
WGS84위도-0.9441.000-0.2330.5870.587
WGS84경도0.110-0.2331.0000.4340.434
시군명0.7890.5870.4341.0001.000
관할고용센터명0.7890.5870.4341.0001.000

Missing values

2023-12-11T06:36:45.301357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:36:45.437989image/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.
2023-12-11T06:36:45.546151image/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

시군명관할고용센터명대표전화번호팩스번호소재지우편번호소재지지번주소소재지도로명주소운영기관명WGS84위도WGS84경도
0부천시부천070-7094-5469032-621-208814502경기도 부천시 약대동 193번지경기도 부천시 평천로 655(재)부천산업진흥재단37.516464126.763965
1부천시부천031-984-9001031-984-9013<NA>경기도 김포시 김포대로 841<NA>㈜미래고용정보<NA><NA>
2의정부시의정부031-879-5161031-879-442911652경기도 의정부시 의정부동 572-1번지 청홍빌딩 4층경기도 의정부시 신흥로 206스탭스㈜의정부센터37.734347127.043654
3의정부시의정부031-592-3062031-591-3054<NA>경기 남양주시 홍유릉로 248번길 39<NA>경기동부상공회의소<NA><NA>
4고양시고양031-8071-4242031-8071-4499<NA>경기 파주시 금빛로 15<NA>파주상공회의소<NA><NA>
5고양시고양031-818-75000505-720-180010464경기도 고양시 덕양구 성사동 698번지 88경기도 고양시 덕양구 호국로 790명은취업센터37.656384126.836317
6고양시고양070-8611-345602-6280-830510380경기도 고양시 일산서구 대화동 2239-1번지경기도 고양시 일산서구 주화로 180스탭스주식회사고양센터37.674492126.749188
7수원시수원031-350-7900031-350-799018590경기도 화성시 향남읍 행정리 327번지경기도 화성시 향남읍 토성로 14화성상공회의소37.128813126.931187
8수원시수원031-244-3454031-244-347716313경기도 수원시 장안구 정자동 80-17번지경기도 수원시 장안구 수성로 311수원상공회의소37.292013127.005871
9수원시수원031-222-9619031-697-239716471경기도 수원시 팔달구 인계동 206번지 한라시그마팰리스 305호경기도 수원시 팔달구 중부대로 110스탭스㈜경기센터37.27735127.028748
시군명관할고용센터명대표전화번호팩스번호소재지우편번호소재지지번주소소재지도로명주소운영기관명WGS84위도WGS84경도
11수원시수원031-336-2528031-336-252917057경기도 용인시 처인구 역북동 597경기도 용인시 처인구 명지로 81용인상공회의소37.228714127.188401
12성남시성남031-628-9631031-360-634413486경기도 성남시 분당구 삼평동 622번지 판교이노밸리 E동 202호경기도 성남시 분당구 판교로 255중소기업기술혁신협회37.404282127.102611
13성남시성남031-717-2950031-786-196513615경기도 성남시 분당구 금곡동 161번지 천사의도시오피스텔 1차 301-2호경기도 성남시 분당구 성남대로 165(주)제니엘 성남지점37.350676127.10824
14성남시성남031-781-7903031-781-775813567경기도 성남시 분당구 이매동 113번지경기도 성남시 분당구 양현로 164성남상공회의소37.398332127.125744
15성남시성남031-761-9090031-761-8486<NA>경기 광주시 중앙로 197<NA>하광상공회의소<NA><NA>
16안양시안양031-463-4521031-444-272413916경기도 안양시 동안구 비산동 526-7번지경기도 안양시 동안구 임곡로 29대림대학교37.403581126.930282
17안양시안양031-447-9171031-443-926014092경기도 안양시 만안구 안양동 505-2번지경기도 안양시 만안구 안양로 133안양상공회의소37.386192126.931236
18안산시안산031-410-3030031-410-303715357경기도 안산시 단원구 고잔동 519-1번지경기도 안산시 단원구 적금로 120안산상공회의소37.318704126.826359
19안산시안산031-482-1413031-697-226015361경기도 안산시 단원구 고잔동 541-2번지경기도 안산시 단원구 중앙대로 927스탭스㈜안산센터37.317038126.839517
20안산시안산031-501-5700031-501-220015073경기도 시흥시 정왕동 2121-1번지경기도 시흥시 산기대학로 237시흥상공회의소37.340082126.73356