Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells43
Missing cells (%)0.1%
Duplicate rows21
Duplicate rows (%)0.2%
Total size in memory703.1 KiB
Average record size in memory72.0 B

Variable types

Text4
Categorical3
DateTime1

Dataset

Description아산시 생활민원처리시스템을 통해 접수된 민원의 처리 담당자에 관한 정보(민원인의 개인정보 및 민원내용 제외)입니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=378&beforeMenuCd=DOM_000000201001001000&publicdatapk=15063409

Alerts

처리상태 has constant value ""Constant
Dataset has 21 (0.2%) duplicate rowsDuplicates
접수채널 is highly imbalanced (61.9%)Imbalance

Reproduction

Analysis started2024-01-09 20:14:14.081979
Analysis finished2024-01-09 20:14:15.192545
Duration1.11 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처리담당자
Text

Distinct605
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T05:14:15.513210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0001
Min length2

Characters and Unicode

Total characters30001
Distinct characters183
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

Unique189 ?
Unique (%)1.9%

Sample

1st row이용중
2nd row성현숙
3rd row이용중
4th row김정대
5th row김재우
ValueCountFrequency (%)
이용중 1204
 
12.0%
유상봉 452
 
4.5%
장하수 436
 
4.4%
임재성 298
 
3.0%
장선호 275
 
2.8%
김재우 255
 
2.5%
김은경 254
 
2.5%
배진찬 225
 
2.2%
박제형 169
 
1.7%
임동천 162
 
1.6%
Other values (595) 6270
62.7%
2024-01-10T05:14:16.121955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 2660
 
8.9%
김 1668
 
5.6%
용 1436
 
4.8%
중 1312
 
4.4%
재 968
 
3.2%
수 797
 
2.7%
성 782
 
2.6%
박 762
 
2.5%
장 759
 
2.5%
호 722
 
2.4%
Other values (173) 18135
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30001
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 2660
 
8.9%
김 1668
 
5.6%
용 1436
 
4.8%
중 1312
 
4.4%
재 968
 
3.2%
수 797
 
2.7%
성 782
 
2.6%
박 762
 
2.5%
장 759
 
2.5%
호 722
 
2.4%
Other values (173) 18135
60.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30001
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 2660
 
8.9%
김 1668
 
5.6%
용 1436
 
4.8%
중 1312
 
4.4%
재 968
 
3.2%
수 797
 
2.7%
성 782
 
2.6%
박 762
 
2.5%
장 759
 
2.5%
호 722
 
2.4%
Other values (173) 18135
60.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30001
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 2660
 
8.9%
김 1668
 
5.6%
용 1436
 
4.8%
중 1312
 
4.4%
재 968
 
3.2%
수 797
 
2.7%
성 782
 
2.6%
박 762
 
2.5%
장 759
 
2.5%
호 722
 
2.4%
Other values (173) 18135
60.4%

최종처리자
Text

Distinct624
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T05:14:17.032425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0003
Min length2

Characters and Unicode

Total characters30003
Distinct characters184
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

Unique191 ?
Unique (%)1.9%

Sample

1st row이경민
2nd row성현숙
3rd row민성재
4th row김정대
5th row백정호
ValueCountFrequency (%)
민성재 957
 
9.6%
유상봉 447
 
4.5%
장하수 430
 
4.3%
임재성 294
 
2.9%
백정호 250
 
2.5%
이경민 241
 
2.4%
배진찬 224
 
2.2%
박이응 223
 
2.2%
임동천 208
 
2.1%
박제형 181
 
1.8%
Other values (614) 6545
65.5%
2024-01-10T05:14:17.634273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 1931
 
6.4%
성 1679
 
5.6%
민 1620
 
5.4%
재 1550
 
5.2%
김 1340
 
4.5%
박 885
 
2.9%
호 789
 
2.6%
수 733
 
2.4%
정 724
 
2.4%
장 653
 
2.2%
Other values (174) 18099
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30003
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 1931
 
6.4%
성 1679
 
5.6%
민 1620
 
5.4%
재 1550
 
5.2%
김 1340
 
4.5%
박 885
 
2.9%
호 789
 
2.6%
수 733
 
2.4%
정 724
 
2.4%
장 653
 
2.2%
Other values (174) 18099
60.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30003
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 1931
 
6.4%
성 1679
 
5.6%
민 1620
 
5.4%
재 1550
 
5.2%
김 1340
 
4.5%
박 885
 
2.9%
호 789
 
2.6%
수 733
 
2.4%
정 724
 
2.4%
장 653
 
2.2%
Other values (174) 18099
60.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30003
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 1931
 
6.4%
성 1679
 
5.6%
민 1620
 
5.4%
재 1550
 
5.2%
김 1340
 
4.5%
박 885
 
2.9%
호 789
 
2.6%
수 733
 
2.4%
정 724
 
2.4%
장 653
 
2.2%
Other values (174) 18099
60.3%

처리부서
Text

Distinct89
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T05:14:17.956152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.5332
Min length3

Characters and Unicode

Total characters45332
Distinct characters138
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

Unique5 ?
Unique (%)< 0.1%

Sample

1st row도로관리과
2nd row온양4동
3rd row도로과
4th row배방읍
5th row도시계획과
ValueCountFrequency (%)
도로과 1447
 
14.5%
교통행정과 950
 
9.5%
기후변화대책과 837
 
8.4%
자원순환과 800
 
8.0%
대중교통과 732
 
7.3%
환경보전과 344
 
3.4%
기업경제과 308
 
3.1%
도로관리과 299
 
3.0%
공원녹지과 288
 
2.9%
도시계획과 272
 
2.7%
Other values (79) 3723
37.2%
2024-01-10T05:14:18.466089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 8821
 
19.5%
도 2778
 
6.1%
로 1907
 
4.2%
통 1715
 
3.8%
교 1692
 
3.7%
대 1569
 
3.5%
정 1312
 
2.9%
원 1156
 
2.6%
기 1150
 
2.5%
환 1144
 
2.5%
Other values (128) 22088
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45029
99.3%
Decimal Number 303
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 8821
19.6%
도 2778
 
6.2%
로 1907
 
4.2%
통 1715
 
3.8%
교 1692
 
3.8%
대 1569
 
3.5%
정 1312
 
2.9%
원 1156
 
2.6%
기 1150
 
2.6%
환 1144
 
2.5%
Other values (122) 21785
48.4%
Decimal Number
ValueCountFrequency (%)
3 73
24.1%
5 71
23.4%
6 54
17.8%
4 40
13.2%
1 39
12.9%
2 26
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45029
99.3%
Common 303
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 8821
19.6%
도 2778
 
6.2%
로 1907
 
4.2%
통 1715
 
3.8%
교 1692
 
3.8%
대 1569
 
3.5%
정 1312
 
2.9%
원 1156
 
2.6%
기 1150
 
2.6%
환 1144
 
2.5%
Other values (122) 21785
48.4%
Common
ValueCountFrequency (%)
3 73
24.1%
5 71
23.4%
6 54
17.8%
4 40
13.2%
1 39
12.9%
2 26
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45029
99.3%
ASCII 303
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 8821
19.6%
도 2778
 
6.2%
로 1907
 
4.2%
통 1715
 
3.8%
교 1692
 
3.8%
대 1569
 
3.5%
정 1312
 
2.9%
원 1156
 
2.6%
기 1150
 
2.6%
환 1144
 
2.5%
Other values (122) 21785
48.4%
ASCII
ValueCountFrequency (%)
3 73
24.1%
5 71
23.4%
6 54
17.8%
4 40
13.2%
1 39
12.9%
2 26
 
8.6%

처리팀
Text

Distinct201
Distinct (%)2.0%
Missing43
Missing (%)0.4%
Memory size156.2 KiB
2024-01-10T05:14:18.802906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length5
Mean length5.0794416
Min length2

Characters and Unicode

Total characters50576
Distinct characters187
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

Unique32 ?
Unique (%)0.3%

Sample

1st row총무
2nd row도로관리팀
3rd row산업개발팀
4th row가로등팀
5th row도로행정팀
ValueCountFrequency (%)
도로관리팀 1322
 
13.3%
생활환경팀 781
 
7.8%
청소행정팀 634
 
6.4%
운수지도팀 520
 
5.2%
산업개발팀 504
 
5.1%
교통지도팀 455
 
4.6%
교통시설팀 393
 
3.9%
가로등팀 306
 
3.1%
소상공인지원팀 293
 
2.9%
총무팀 290
 
2.9%
Other values (193) 4472
44.9%
2024-01-10T05:14:19.316528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
팀 9901
19.6%
도 3059
 
6.0%
관 2507
 
5.0%
리 2476
 
4.9%
로 2015
 
4.0%
지 1950
 
3.9%
정 1352
 
2.7%
환 1240
 
2.5%
경 1210
 
2.4%
수 1050
 
2.1%
Other values (177) 23816
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50032
98.9%
Uppercase Letter 363
 
0.7%
Other Punctuation 110
 
0.2%
Decimal Number 58
 
0.1%
Space Separator 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
팀 9901
19.8%
도 3059
 
6.1%
관 2507
 
5.0%
리 2476
 
4.9%
로 2015
 
4.0%
지 1950
 
3.9%
정 1352
 
2.7%
환 1240
 
2.5%
경 1210
 
2.4%
수 1050
 
2.1%
Other values (169) 23272
46.5%
Uppercase Letter
ValueCountFrequency (%)
T 177
48.8%
F 177
48.8%
S 6
 
1.7%
N 3
 
0.8%
Decimal Number
ValueCountFrequency (%)
2 34
58.6%
1 24
41.4%
Other Punctuation
ValueCountFrequency (%)
/ 110
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50032
98.9%
Latin 363
 
0.7%
Common 181
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
팀 9901
19.8%
도 3059
 
6.1%
관 2507
 
5.0%
리 2476
 
4.9%
로 2015
 
4.0%
지 1950
 
3.9%
정 1352
 
2.7%
환 1240
 
2.5%
경 1210
 
2.4%
수 1050
 
2.1%
Other values (169) 23272
46.5%
Latin
ValueCountFrequency (%)
T 177
48.8%
F 177
48.8%
S 6
 
1.7%
N 3
 
0.8%
Common
ValueCountFrequency (%)
/ 110
60.8%
2 34
 
18.8%
1 24
 
13.3%
13
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50032
98.9%
ASCII 544
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
팀 9901
19.8%
도 3059
 
6.1%
관 2507
 
5.0%
리 2476
 
4.9%
로 2015
 
4.0%
지 1950
 
3.9%
정 1352
 
2.7%
환 1240
 
2.5%
경 1210
 
2.4%
수 1050
 
2.1%
Other values (169) 23272
46.5%
ASCII
ValueCountFrequency (%)
T 177
32.5%
F 177
32.5%
/ 110
20.2%
2 34
 
6.2%
1 24
 
4.4%
13
 
2.4%
S 6
 
1.1%
N 3
 
0.6%

민원유형
Categorical

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
도로관리
3102 
환경관리
1853 
교통관리
1766 
상하수도
590 
건축건설
490 
Other values (15)
2199 

Length

Max length6
Median length4
Mean length4.0541
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도로관리
2nd row환경관리
3rd row도로관리
4th row교통관리
5th row도로관리

Common Values

ValueCountFrequency (%)
도로관리 3102
31.0%
환경관리 1853
18.5%
교통관리 1766
17.7%
상하수도 590
 
5.9%
건축건설 490
 
4.9%
사회복지 449
 
4.5%
공원/녹지 407
 
4.1%
보건의료 379
 
3.8%
행정관리 166
 
1.7%
농축수산업 156
 
1.6%
Other values (10) 642
 
6.4%

Length

2024-01-10T05:14:19.514871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도로관리 3102
31.0%
환경관리 1853
18.5%
교통관리 1766
17.7%
상하수도 590
 
5.9%
건축건설 490
 
4.9%
사회복지 449
 
4.5%
공원/녹지 407
 
4.1%
보건의료 379
 
3.8%
행정관리 166
 
1.7%
농축수산업 156
 
1.6%
Other values (10) 642
 
6.4%

처리상태
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
처리완료
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
처리완료 10000
100.0%

Length

2024-01-10T05:14:19.676040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:14:19.820837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처리완료 10000
100.0%

접수채널
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
콜센터
8641 
당직실
1310 
기타
 
49

Length

Max length3
Median length3
Mean length2.9951
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row당직실
2nd row콜센터
3rd row기타
4th row당직실
5th row당직실

Common Values

ValueCountFrequency (%)
콜센터 8641
86.4%
당직실 1310
 
13.1%
기타 49
 
0.5%

Length

2024-01-10T05:14:19.938275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:14:20.069376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
콜센터 8641
86.4%
당직실 1310
 
13.1%
기타 49
 
0.5%

접수일시
Date

Distinct9732
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-03-01 10:26:00
Maximum2023-03-27 14:54:00
2024-01-10T05:14:20.235188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:14:20.412835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Correlations

2024-01-10T05:14:20.530564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리부서민원유형접수채널
처리부서1.0000.9910.262
민원유형0.9911.0000.174
접수채널0.2620.1741.000
2024-01-10T05:14:20.646742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
민원유형접수채널
민원유형1.0000.092
접수채널0.0921.000
2024-01-10T05:14:20.759089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
민원유형접수채널
민원유형1.0000.092
접수채널0.0921.000

Missing values

2024-01-10T05:14:14.937204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:14:15.115304image/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

처리담당자최종처리자처리부서처리팀민원유형처리상태접수채널접수일시
3612이용중이경민도로관리과<NA>도로관리처리완료당직실2023-01-04 20:38
15065성현숙성현숙온양4동총무환경관리처리완료콜센터2022-06-19 10:21
9976이용중민성재도로과도로관리팀도로관리처리완료기타2022-09-15 13:30
19608김정대김정대배방읍산업개발팀교통관리처리완료당직실2022-03-28 20:15
8578김재우백정호도시계획과가로등팀도로관리처리완료당직실2022-10-04 06:21
2390김응구김응구도로시설과도로행정팀도로관리처리완료콜센터2023-02-02 15:42
8259이용중민성재도로과도로관리팀도로관리처리완료콜센터2022-10-08 18:24
10619정찬희정찬희교통행정과자동차관리팀교통관리처리완료콜센터2022-09-02 17:10
15724이은경이은경산업입지과에너지팀지역경제처리완료당직실2022-06-07 23:33
4720김진혁김진혁염치읍산업개발팀도로관리처리완료콜센터2022-12-19 18:06
처리담당자최종처리자처리부서처리팀민원유형처리상태접수채널접수일시
3961배진찬배진찬대중교통과운수지도팀교통관리처리완료콜센터2022-12-30 10:58
1733백정호백정호도로시설과가로등팀도로관리처리완료당직실2023-02-19 09:28
19710민성재민성재도로과도로관리팀도로관리처리완료콜센터2022-03-29 09:36
465이용중이경민도로관리과도로관리팀도로관리처리완료콜센터2023-03-16 17:51
20289민성재민성재도로과도로관리팀도로관리처리완료콜센터2022-03-16 11:42
8093강다빈강다빈배방읍공원관리팀공원/녹지처리완료콜센터2022-10-14 13:37
15607이용중민성재도로과도로관리팀도로관리처리완료당직실2022-05-31 07:59
10028김진철김진철대중교통과운수지도팀교통관리처리완료당직실2022-09-07 19:22
16210노수혁노수혁산림과산림복지팀산림처리완료콜센터2022-05-25 13:45
9082최정숙최정숙세정과재산세팀행정관리처리완료콜센터2022-09-29 14:55

Duplicate rows

Most frequently occurring

처리담당자최종처리자처리부서처리팀민원유형처리상태접수채널접수일시# duplicates
0김윤정김윤정온양3동총무팀도로관리처리완료콜센터2022-06-30 09:142
1김은경김은경기업경제과소상공인지원팀사회복지처리완료콜센터2022-04-25 09:372
2위태만위태만둔포면산업개발팀도로관리처리완료콜센터2022-05-18 10:572
3이다은이다은위생과위생지도팀식품공중위생처리완료콜센터2022-09-13 10:012
4이용중민성재도로과도로관리팀도로관리처리완료콜센터2022-08-03 17:392
5이용중민성재도로과도로관리팀도로관리처리완료콜센터2022-12-06 08:392
6이용중민성재도로과도로관리팀도로관리처리완료콜센터2022-12-15 16:042
7이용중민성재도로과도로관리팀도로관리처리완료콜센터2022-12-15 17:562
8이용중민성재도로과도로관리팀도로관리처리완료콜센터2022-12-16 15:132
9이용중민성재도로과도로관리팀도로관리처리완료콜센터2022-12-17 09:072