Overview

Dataset statistics

Number of variables8
Number of observations8434
Missing cells569
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory535.5 KiB
Average record size in memory65.0 B

Variable types

Numeric1
Categorical1
Text2
Boolean2
DateTime2

Dataset

Description한국노인인력개발원에서 운영하는 노인일자리 취업연계에서 제공하는 시스템 관리 정보로 행정구역명, 시도명, 시군구명, 읍면동명 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15067126/fileData.do

Alerts

행정구역코드 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 행정구역코드High correlation
시도관리여부 is highly imbalanced (96.4%)Imbalance
읍명동명 has 515 (6.1%) missing valuesMissing
행정구역코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:30:38.472682
Analysis finished2023-12-12 23:30:39.422525
Duration0.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8434
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7599712 × 109
Minimum1.1 × 109
Maximum9.9101 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.3 KiB
2023-12-13T08:30:39.502282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile1.1470546 × 109
Q12.8140632 × 109
median4.2150605 × 109
Q34.67876 × 109
95-th percentile4.8710454 × 109
Maximum9.9101 × 109
Range8.8101 × 109
Interquartile range (IQR)1.8646968 × 109

Descriptive statistics

Standard deviation1.1735462 × 109
Coefficient of variation (CV)0.31211573
Kurtosis0.045018287
Mean3.7599712 × 109
Median Absolute Deviation (MAD)5.12998 × 108
Skewness-0.94916471
Sum3.1711597 × 1013
Variance1.3772106 × 1018
MonotonicityNot monotonic
2023-12-13T08:30:39.657654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1120087000 1
 
< 0.1%
2141069000 1
 
< 0.1%
2141064000 1
 
< 0.1%
2141062000 1
 
< 0.1%
2141059100 1
 
< 0.1%
2141057000 1
 
< 0.1%
2141055000 1
 
< 0.1%
2141052000 1
 
< 0.1%
2141000000 1
 
< 0.1%
2138062000 1
 
< 0.1%
Other values (8424) 8424
99.9%
ValueCountFrequency (%)
1100000000 1
< 0.1%
1100900000 1
< 0.1%
1111000000 1
< 0.1%
1111051000 1
< 0.1%
1111051500 1
< 0.1%
1111052000 1
< 0.1%
1111053000 1
< 0.1%
1111054000 1
< 0.1%
1111055000 1
< 0.1%
1111056000 1
< 0.1%
ValueCountFrequency (%)
9910100000 1
< 0.1%
9900000000 1
< 0.1%
9800900000 1
< 0.1%
9800000000 1
< 0.1%
5013062000 1
< 0.1%
5013061000 1
< 0.1%
5013060000 1
< 0.1%
5013059000 1
< 0.1%
5013058000 1
< 0.1%
5013057000 1
< 0.1%

시도명
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size66.0 KiB
경기도
1361 
경상남도
966 
경상북도
803 
서울특별시
745 
전라남도
573 
Other values (22)
3986 

Length

Max length7
Median length5
Mean length4.1247332
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 1361
16.1%
경상남도 966
11.5%
경상북도 803
9.5%
서울특별시 745
8.8%
전라남도 573
 
6.8%
충청남도 554
 
6.6%
전라북도 519
 
6.2%
강원도 448
 
5.3%
충청북도 353
 
4.2%
부산광역시 315
 
3.7%
Other values (17) 1797
21.3%

Length

2023-12-13T08:30:39.791295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1361
16.1%
경상남도 966
11.5%
경상북도 803
9.5%
서울특별시 745
8.8%
전라남도 573
 
6.8%
충청남도 554
 
6.6%
전라북도 519
 
6.2%
강원도 448
 
5.3%
충청북도 353
 
4.2%
부산광역시 315
 
3.7%
Other values (17) 1797
21.3%

시군구명
Text

Distinct395
Distinct (%)4.7%
Missing54
Missing (%)0.6%
Memory size66.0 KiB
2023-12-13T08:30:40.101178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3356802
Min length2

Characters and Unicode

Total characters27953
Distinct characters158
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

Unique50 ?
Unique (%)0.6%

Sample

1st row성동구
2nd row광진구
3rd row광진구
4th row광진구
5th row광진구
ValueCountFrequency (%)
북구 319
 
3.4%
동구 305
 
3.3%
중구 278
 
3.0%
서구 272
 
2.9%
남구 210
 
2.3%
포항시 123
 
1.3%
마산시 121
 
1.3%
수원시 120
 
1.3%
부천시 112
 
1.2%
창원시 111
 
1.2%
Other values (357) 7308
78.8%
2023-12-13T08:30:40.538088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 3652
 
13.1%
시 3341
 
12.0%
군 2492
 
8.9%
주 992
 
3.5%
산 968
 
3.5%
899
 
3.2%
천 794
 
2.8%
동 729
 
2.6%
원 593
 
2.1%
성 581
 
2.1%
Other values (148) 12912
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27054
96.8%
Space Separator 899
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3652
 
13.5%
시 3341
 
12.3%
군 2492
 
9.2%
주 992
 
3.7%
산 968
 
3.6%
천 794
 
2.9%
동 729
 
2.7%
원 593
 
2.2%
성 581
 
2.1%
남 570
 
2.1%
Other values (147) 12342
45.6%
Space Separator
ValueCountFrequency (%)
899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27054
96.8%
Common 899
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3652
 
13.5%
시 3341
 
12.3%
군 2492
 
9.2%
주 992
 
3.7%
산 968
 
3.6%
천 794
 
2.9%
동 729
 
2.7%
원 593
 
2.2%
성 581
 
2.1%
남 570
 
2.1%
Other values (147) 12342
45.6%
Common
ValueCountFrequency (%)
899
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27054
96.8%
ASCII 899
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 3652
 
13.5%
시 3341
 
12.3%
군 2492
 
9.2%
주 992
 
3.7%
산 968
 
3.6%
천 794
 
2.9%
동 729
 
2.7%
원 593
 
2.2%
성 581
 
2.1%
남 570
 
2.1%
Other values (147) 12342
45.6%
ASCII
ValueCountFrequency (%)
899
100.0%

읍명동명
Text

MISSING 

Distinct4747
Distinct (%)59.9%
Missing515
Missing (%)6.1%
Memory size66.0 KiB
2023-12-13T08:30:40.856044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length3
Mean length3.6830408
Min length2

Characters and Unicode

Total characters29166
Distinct characters366
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2678 ?
Unique (%)33.8%

Sample

1st row구의제3동
2nd row화양동
3rd row중곡제1동
4th row중곡제2동
5th row중곡제4동
ValueCountFrequency (%)
중앙동 57
 
0.7%
남면 24
 
0.3%
동면 23
 
0.3%
북면 21
 
0.3%
서면 18
 
0.2%
신흥동 17
 
0.2%
중동 12
 
0.2%
산내면 12
 
0.2%
신안동 11
 
0.1%
금곡동 10
 
0.1%
Other values (4737) 7714
97.4%
2023-12-13T08:30:41.292377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 5169
 
17.7%
면 2528
 
8.7%
제 917
 
3.1%
1 870
 
3.0%
2 856
 
2.9%
산 682
 
2.3%
장 507
 
1.7%
읍 506
 
1.7%
소 426
 
1.5%
3 398
 
1.4%
Other values (356) 16307
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26563
91.1%
Decimal Number 2490
 
8.5%
Other Punctuation 95
 
0.3%
Close Punctuation 9
 
< 0.1%
Open Punctuation 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 5169
 
19.5%
면 2528
 
9.5%
제 917
 
3.5%
산 682
 
2.6%
장 507
 
1.9%
읍 506
 
1.9%
소 426
 
1.6%
성 389
 
1.5%
천 356
 
1.3%
남 346
 
1.3%
Other values (342) 14737
55.5%
Decimal Number
ValueCountFrequency (%)
1 870
34.9%
2 856
34.4%
3 398
16.0%
4 183
 
7.3%
5 73
 
2.9%
6 50
 
2.0%
7 26
 
1.0%
8 18
 
0.7%
9 11
 
0.4%
0 5
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 92
96.8%
, 3
 
3.2%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26563
91.1%
Common 2603
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 5169
 
19.5%
면 2528
 
9.5%
제 917
 
3.5%
산 682
 
2.6%
장 507
 
1.9%
읍 506
 
1.9%
소 426
 
1.6%
성 389
 
1.5%
천 356
 
1.3%
남 346
 
1.3%
Other values (342) 14737
55.5%
Common
ValueCountFrequency (%)
1 870
33.4%
2 856
32.9%
3 398
15.3%
4 183
 
7.0%
. 92
 
3.5%
5 73
 
2.8%
6 50
 
1.9%
7 26
 
1.0%
8 18
 
0.7%
9 11
 
0.4%
Other values (4) 26
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26563
91.1%
ASCII 2603
 
8.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 5169
 
19.5%
면 2528
 
9.5%
제 917
 
3.5%
산 682
 
2.6%
장 507
 
1.9%
읍 506
 
1.9%
소 426
 
1.6%
성 389
 
1.5%
천 356
 
1.3%
남 346
 
1.3%
Other values (342) 14737
55.5%
ASCII
ValueCountFrequency (%)
1 870
33.4%
2 856
32.9%
3 398
15.3%
4 183
 
7.0%
. 92
 
3.5%
5 73
 
2.8%
6 50
 
1.9%
7 26
 
1.0%
8 18
 
0.7%
9 11
 
0.4%
Other values (4) 26
 
1.0%

시도관리여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
False
8402 
True
 
32
ValueCountFrequency (%)
False 8402
99.6%
True 32
 
0.4%
2023-12-13T08:30:41.425715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

사용여부
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
False
4498 
True
3936 
ValueCountFrequency (%)
False 4498
53.3%
True 3936
46.7%
2023-12-13T08:30:41.525759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

등록일
Date

Distinct32
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size66.0 KiB
Minimum2017-03-27 14:25:06
Maximum2021-07-12 10:30:02
2023-12-13T08:30:41.628279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:41.765727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)

수정일
Date

Distinct42
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size66.0 KiB
Minimum2017-03-27 14:25:06
Maximum2021-07-12 10:30:09
2023-12-13T08:30:41.887043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:30:42.012516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)

Interactions

2023-12-13T08:30:39.057121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:30:42.127370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역코드시도명시도관리여부사용여부등록일수정일
행정구역코드1.0000.9990.0610.2410.2560.277
시도명0.9991.0000.0600.4340.4730.493
시도관리여부0.0610.0601.0000.0990.0000.000
사용여부0.2410.4340.0991.0000.1250.126
등록일0.2560.4730.0000.1251.0001.000
수정일0.2770.4930.0000.1261.0001.000
2023-12-13T08:30:42.234426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도관리여부사용여부시도명
시도관리여부1.0000.0630.051
사용여부0.0631.0000.374
시도명0.0510.3741.000
2023-12-13T08:30:42.314427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역코드시도명시도관리여부사용여부
행정구역코드1.0000.9820.0440.173
시도명0.9821.0000.0510.374
시도관리여부0.0440.0511.0000.063
사용여부0.1730.3740.0631.000

Missing values

2023-12-13T08:30:39.161598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:30:39.274523image/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.
2023-12-13T08:30:39.367429image/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

행정구역코드시도명시군구명읍명동명시도관리여부사용여부등록일수정일
01120087000서울특별시성동구구의제3동NN2017-03-27 14:25:062017-03-27 14:25:06
11121571000서울특별시광진구화양동NY2017-03-27 14:25:062017-03-27 14:25:06
21121574000서울특별시광진구중곡제1동NY2017-03-27 14:25:062017-03-27 14:25:06
31121575000서울특별시광진구중곡제2동NY2017-03-27 14:25:062017-03-27 14:25:06
41121577000서울특별시광진구중곡제4동NY2017-03-27 14:25:062017-03-27 14:25:06
51121581000서울특별시광진구광장동NY2017-03-27 14:25:062017-03-27 14:25:06
61121583000서울특별시광진구자양제2동NY2017-03-27 14:25:062017-03-27 14:25:06
71121584500서울특별시광진구노유제1동NN2017-03-27 14:25:062017-03-27 14:25:06
81121584700서울특별시광진구자양제4동NY2017-03-27 14:25:062017-03-27 14:25:06
91121585000서울특별시광진구구의제1동NY2017-03-27 14:25:062017-03-27 14:25:06
행정구역코드시도명시군구명읍명동명시도관리여부사용여부등록일수정일
84244511366500전라북도전주시 덕진구여의동NY2020-11-27 15:51:162020-11-27 15:51:16
84254161055000경기도광주시탄벌동NY2020-12-03 17:34:392020-12-03 17:34:39
84264161056000경기도광주시광남1동NY2020-12-03 17:34:392020-12-03 17:34:39
84274161057000경기도광주시광남2동NY2020-12-03 17:34:392020-12-03 17:34:39
84284679041500전라남도화순군사평면NY2020-12-10 13:35:262020-12-10 13:35:26
84294679039500전라남도화순군백아면NY2020-12-10 13:35:272020-12-10 13:35:27
84302818586000인천광역시연수구송도5동NY2020-12-16 15:27:462020-12-16 15:27:46
84313611011800세종특별자치시<NA>집현동NY2021-05-06 13:28:392021-05-06 13:28:39
84323611011600세종특별자치시<NA>해밀동NY2021-05-06 13:28:392021-05-06 13:28:39
84332811061500인천광역시중구개항동NY2021-07-12 10:30:022021-07-12 10:30:09