Overview

Dataset statistics

Number of variables4
Number of observations50
Missing cells1
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory35.6 B

Variable types

Numeric1
Text3

Dataset

Description내부시스템에서 사용하는 관리용 코드매핑 데이터이며, 지방관서별 관할관서에 대한 데이터입니다. * (항목명) 지방관서코드, 지방관서명칭, 관할청, 관할검찰청 ** 내부 통계관리용 데이터이므로, 타시스템의 데이터와 다소 차이가 있을 수 있습니다.
URLhttps://www.data.go.kr/data/15049594/fileData.do

Alerts

관할검찰청 has 1 (2.0%) missing valuesMissing
지방관서코드 has unique valuesUnique
관할청 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:24:41.717078
Analysis finished2023-12-12 21:24:42.454419
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지방관서코드
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4244.2
Minimum1000
Maximum7230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-13T06:24:42.546013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2014.5
Q12362.5
median4125
Q35795
95-th percentile7169.5
Maximum7230
Range6230
Interquartile range (IQR)3432.5

Descriptive statistics

Standard deviation1818.939
Coefficient of variation (CV)0.42857052
Kurtosis-1.2467092
Mean4244.2
Median Absolute Deviation (MAD)1925
Skewness0.11792237
Sum212210
Variance3308539.1
MonotonicityStrictly increasing
2023-12-13T06:24:42.737712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 1
 
2.0%
6110 1
 
2.0%
5010 1
 
2.0%
5110 1
 
2.0%
5120 1
 
2.0%
5130 1
 
2.0%
5140 1
 
2.0%
5150 1
 
2.0%
5160 1
 
2.0%
5170 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1000 1
2.0%
2000 1
2.0%
2010 1
2.0%
2020 1
2.0%
2030 1
2.0%
2040 1
2.0%
2050 1
2.0%
2060 1
2.0%
2110 1
2.0%
2120 1
2.0%
ValueCountFrequency (%)
7230 1
2.0%
7220 1
2.0%
7210 1
2.0%
7120 1
2.0%
7110 1
2.0%
7000 1
2.0%
6310 1
2.0%
6220 1
2.0%
6210 1
2.0%
6130 1
2.0%

지방관서명칭
Text

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-13T06:24:42.962106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length12.78
Min length7

Characters and Unicode

Total characters639
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)96.0%

Sample

1st row고용노동부본부
2nd row서울지방고용노동청
3rd row서울지방고용노동청서울강남지청
4th row서울지방고용노동청서울동부지청
5th row서울지방고용노동청서울서부지청
ValueCountFrequency (%)
광주지방고용노동청 2
 
4.0%
대구지방고용노동청영주지청 1
 
2.0%
중부지방고용노동청 1
 
2.0%
중부지방고용노동청인천북부지청 1
 
2.0%
중부지방고용노동청경기지청 1
 
2.0%
중부지방고용노동청부천지청 1
 
2.0%
중부지방고용노동청안양지청 1
 
2.0%
중부지방고용노동청안산지청 1
 
2.0%
중부지방고용노동청의정부지청 1
 
2.0%
중부지방고용노동청성남지청 1
 
2.0%
Other values (39) 39
78.0%
2023-12-13T06:24:43.355903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
청 90
14.1%
지 89
13.9%
동 53
 
8.3%
고 51
 
8.0%
용 50
 
7.8%
노 50
 
7.8%
방 49
 
7.7%
부 37
 
5.8%
서 16
 
2.5%
산 16
 
2.5%
Other values (47) 138
21.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 639
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 90
14.1%
지 89
13.9%
동 53
 
8.3%
고 51
 
8.0%
용 50
 
7.8%
노 50
 
7.8%
방 49
 
7.7%
부 37
 
5.8%
서 16
 
2.5%
산 16
 
2.5%
Other values (47) 138
21.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 639
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 90
14.1%
지 89
13.9%
동 53
 
8.3%
고 51
 
8.0%
용 50
 
7.8%
노 50
 
7.8%
방 49
 
7.7%
부 37
 
5.8%
서 16
 
2.5%
산 16
 
2.5%
Other values (47) 138
21.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 639
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
청 90
14.1%
지 89
13.9%
동 53
 
8.3%
고 51
 
8.0%
용 50
 
7.8%
노 50
 
7.8%
방 49
 
7.7%
부 37
 
5.8%
서 16
 
2.5%
산 16
 
2.5%
Other values (47) 138
21.6%

관할청
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-13T06:24:43.627498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.5
Min length4

Characters and Unicode

Total characters275
Distinct characters52
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 (%)100.0%

Sample

1st row본 부
2nd row서 울 청
3rd row서울강남
4th row서울동부
5th row서울서부
ValueCountFrequency (%)
주 8
 
8.2%
산 7
 
7.2%
청 7
 
7.2%
부 5
 
5.2%
안 4
 
4.1%
원 3
 
3.1%
영 3
 
3.1%
양 3
 
3.1%
강 2
 
2.1%
울 2
 
2.1%
Other values (47) 53
54.6%
2023-12-13T06:24:44.007535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
148
53.8%
부 15
 
5.5%
서 10
 
3.6%
산 9
 
3.3%
주 8
 
2.9%
울 8
 
2.9%
청 7
 
2.5%
안 4
 
1.5%
북 3
 
1.1%
대 3
 
1.1%
Other values (42) 60
21.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator 148
53.8%
Other Letter 127
46.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 15
 
11.8%
서 10
 
7.9%
산 9
 
7.1%
주 8
 
6.3%
울 8
 
6.3%
청 7
 
5.5%
안 4
 
3.1%
북 3
 
2.4%
대 3
 
2.4%
구 3
 
2.4%
Other values (41) 57
44.9%
Space Separator
ValueCountFrequency (%)
148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 148
53.8%
Hangul 127
46.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 15
 
11.8%
서 10
 
7.9%
산 9
 
7.1%
주 8
 
6.3%
울 8
 
6.3%
청 7
 
5.5%
안 4
 
3.1%
북 3
 
2.4%
대 3
 
2.4%
구 3
 
2.4%
Other values (41) 57
44.9%
Common
ValueCountFrequency (%)
148
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148
53.8%
Hangul 127
46.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
148
100.0%
Hangul
ValueCountFrequency (%)
부 15
 
11.8%
서 10
 
7.9%
산 9
 
7.1%
주 8
 
6.3%
울 8
 
6.3%
청 7
 
5.5%
안 4
 
3.1%
북 3
 
2.4%
대 3
 
2.4%
구 3
 
2.4%
Other values (41) 57
44.9%

관할검찰청
Text

MISSING 

Distinct33
Distinct (%)67.3%
Missing1
Missing (%)2.0%
Memory size532.0 B
2023-12-13T06:24:44.230947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.1836735
Min length7

Characters and Unicode

Total characters450
Distinct characters46
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

Unique22 ?
Unique (%)44.9%

Sample

1st row서울중앙지방검찰청
2nd row서울중앙지방검찰청
3rd row서울동부지방검찰청
4th row서울서부지방검찰청
5th row서울남부지방검찰청
ValueCountFrequency (%)
대구지방검찰청 6
 
9.0%
수원지방검찰청 5
 
7.5%
춘천지방검찰청 5
 
7.5%
대전지방검찰청 4
 
6.0%
인천지방검찰청 3
 
4.5%
서울중앙지방검찰청 3
 
4.5%
창원지방검찰청 3
 
4.5%
부산지방검찰청 3
 
4.5%
전주지방검찰청 3
 
4.5%
광주지방검찰청 3
 
4.5%
Other values (23) 29
43.3%
2023-12-13T06:24:44.595124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
청 69
15.3%
지 67
14.9%
방 49
10.9%
검 49
10.9%
찰 49
10.9%
19
 
4.2%
천 11
 
2.4%
주 11
 
2.4%
대 10
 
2.2%
서 10
 
2.2%
Other values (36) 106
23.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 431
95.8%
Space Separator 19
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 69
16.0%
지 67
15.5%
방 49
11.4%
검 49
11.4%
찰 49
11.4%
천 11
 
2.6%
주 11
 
2.6%
대 10
 
2.3%
서 10
 
2.3%
부 9
 
2.1%
Other values (35) 97
22.5%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 431
95.8%
Common 19
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 69
16.0%
지 67
15.5%
방 49
11.4%
검 49
11.4%
찰 49
11.4%
천 11
 
2.6%
주 11
 
2.6%
대 10
 
2.3%
서 10
 
2.3%
부 9
 
2.1%
Other values (35) 97
22.5%
Common
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 431
95.8%
ASCII 19
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
청 69
16.0%
지 67
15.5%
방 49
11.4%
검 49
11.4%
찰 49
11.4%
천 11
 
2.6%
주 11
 
2.6%
대 10
 
2.3%
서 10
 
2.3%
부 9
 
2.1%
Other values (35) 97
22.5%
ASCII
ValueCountFrequency (%)
19
100.0%

Interactions

2023-12-13T06:24:41.959523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:24:44.706920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지방관서코드지방관서명칭관할청관할검찰청
지방관서코드1.0001.0001.0001.000
지방관서명칭1.0001.0001.0000.945
관할청1.0001.0001.0001.000
관할검찰청1.0000.9451.0001.000

Missing values

2023-12-13T06:24:42.332448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:24:42.413774image/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

지방관서코드지방관서명칭관할청관할검찰청
01000고용노동부본부본 부<NA>
12000서울지방고용노동청서 울 청서울중앙지방검찰청
22010서울지방고용노동청서울강남지청서울강남서울중앙지방검찰청
32020서울지방고용노동청서울동부지청서울동부서울동부지방검찰청
42030서울지방고용노동청서울서부지청서울서부서울서부지방검찰청
52040서울지방고용노동청서울남부지청서울남부서울남부지방검찰청
62050서울지방고용노동청서울북부지청서울북부서울북부지방검찰청
72060서울지방고용노동청서울관악지청서울관악서울중앙지방검찰청
82110중부지방고용노동청강원지청강 원춘천지방검찰청
92120중부지방고용노동청태백지청태 백춘천지방검찰청 영월지청
지방관서코드지방관서명칭관할청관할검찰청
406130광주지방고용노동청군산지청군 산전주지방검찰청 군산지청
416210광주지방고용노동청목포지청목 포광주지방검찰청 목포지청
426220광주지방고용노동청여수지청여 수광주지방검찰청 순천지청
436310광주지방고용노동청제 주제주지방검찰청
447000대전지방고용노동청대 전 청대전지방검찰청
457110대전지방고용노동청청주지청청 주청주지방검찰청
467120대전지방고용노동청충주지청충 주청주지방검찰청 충주지청
477210대전지방고용노동청천안지청천 안대전지방검찰청 천안지청
487220대전지방고용노동청보령지청보 령대전지방검찰청 서산지청
497230대전지방고용노동청서산출장소서 산대전지방검찰청 서산지청