Overview

Dataset statistics

Number of variables10
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory90.9 B

Variable types

Text1
Numeric5
Categorical4

Dataset

Description인천광역시 서구의 부서별민원접수처리현황에 관한 데이터로, 처리부서, 접수, 완결, 불가, 반려, 취하, 이첩.이송, 기타, 진행중, 민원접수처리기간 항목을 제공합니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15105206&srcSe=7661IVAWM27C61E190

Alerts

민원접수처리기간 has constant value ""Constant
접수 is highly overall correlated with 완결 and 3 other fieldsHigh correlation
완결 is highly overall correlated with 접수 and 2 other fieldsHigh correlation
취하 is highly overall correlated with 접수 and 2 other fieldsHigh correlation
기타 is highly overall correlated with 접수 and 3 other fieldsHigh correlation
진행중 is highly overall correlated with 접수 and 4 other fieldsHigh correlation
불가 is highly overall correlated with 기타 and 1 other fieldsHigh correlation
반려 is highly overall correlated with 기타 and 2 other fieldsHigh correlation
이첩_이송 is highly overall correlated with 취하 and 1 other fieldsHigh correlation
반려 is highly imbalanced (67.2%)Imbalance
이첩_이송 is highly imbalanced (71.7%)Imbalance
처리부서 has unique valuesUnique
완결 has 3 (6.7%) zerosZeros
취하 has 27 (60.0%) zerosZeros
기타 has 26 (57.8%) zerosZeros
진행중 has 6 (13.3%) zerosZeros

Reproduction

Analysis started2024-01-28 16:00:44.045938
Analysis finished2024-01-28 16:00:46.283466
Duration2.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처리부서
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2024-01-29T01:00:46.421170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length10.755556
Min length5

Characters and Unicode

Total characters484
Distinct characters90
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

Unique45 ?
Unique (%)100.0%

Sample

1st row도시주택국 토지정보과
2nd row도시주택국 도시재생과
3rd row도시주택국 주택과
4th row도시주택국 건축과
5th row도시주택국 도시계획과
ValueCountFrequency (%)
복지문화국 9
 
10.2%
환경안전국 8
 
9.1%
경제교통국 7
 
8.0%
도시관리국 6
 
6.8%
도시주택국 6
 
6.8%
미래기획실 4
 
4.5%
건축과 3
 
3.4%
자치행정국 3
 
3.4%
도로과 2
 
2.3%
주택과 2
 
2.3%
Other values (36) 38
43.2%
2024-01-29T01:00:46.698454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43
 
8.9%
국 40
 
8.3%
과 39
 
8.1%
도 21
 
4.3%
시 20
 
4.1%
지 18
 
3.7%
경 18
 
3.7%
복 13
 
2.7%
관 12
 
2.5%
문 10
 
2.1%
Other values (80) 250
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 441
91.1%
Space Separator 43
 
8.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 40
 
9.1%
과 39
 
8.8%
도 21
 
4.8%
시 20
 
4.5%
지 18
 
4.1%
경 18
 
4.1%
복 13
 
2.9%
관 12
 
2.7%
문 10
 
2.3%
화 10
 
2.3%
Other values (79) 240
54.4%
Space Separator
ValueCountFrequency (%)
43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 441
91.1%
Common 43
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 40
 
9.1%
과 39
 
8.8%
도 21
 
4.8%
시 20
 
4.5%
지 18
 
4.1%
경 18
 
4.1%
복 13
 
2.9%
관 12
 
2.7%
문 10
 
2.3%
화 10
 
2.3%
Other values (79) 240
54.4%
Common
ValueCountFrequency (%)
43
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 441
91.1%
ASCII 43
 
8.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43
100.0%
Hangul
ValueCountFrequency (%)
국 40
 
9.1%
과 39
 
8.8%
도 21
 
4.8%
시 20
 
4.5%
지 18
 
4.1%
경 18
 
4.1%
복 13
 
2.9%
관 12
 
2.7%
문 10
 
2.3%
화 10
 
2.3%
Other values (79) 240
54.4%

접수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2732.4444
Minimum1
Maximum92394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T01:00:46.837009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q123
median119
Q3658
95-th percentile3168.2
Maximum92394
Range92393
Interquartile range (IQR)635

Descriptive statistics

Standard deviation13777.744
Coefficient of variation (CV)5.0422777
Kurtosis43.509532
Mean2732.4444
Median Absolute Deviation (MAD)118
Skewness6.5543919
Sum122960
Variance1.8982622 × 108
MonotonicityNot monotonic
2024-01-29T01:00:46.939804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 4
 
8.9%
2 2
 
4.4%
1369 1
 
2.2%
92394 1
 
2.2%
22 1
 
2.2%
823 1
 
2.2%
71 1
 
2.2%
57 1
 
2.2%
437 1
 
2.2%
248 1
 
2.2%
Other values (31) 31
68.9%
ValueCountFrequency (%)
1 4
8.9%
2 2
4.4%
3 1
 
2.2%
5 1
 
2.2%
6 1
 
2.2%
11 1
 
2.2%
22 1
 
2.2%
23 1
 
2.2%
24 1
 
2.2%
32 1
 
2.2%
ValueCountFrequency (%)
92394 1
2.2%
10932 1
2.2%
3193 1
2.2%
3069 1
2.2%
1957 1
2.2%
1446 1
2.2%
1369 1
2.2%
929 1
2.2%
823 1
2.2%
816 1
2.2%

완결
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2623.4667
Minimum0
Maximum92200
Zeros3
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T01:00:47.345576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q118
median96
Q3467
95-th percentile2721.6
Maximum92200
Range92200
Interquartile range (IQR)449

Descriptive statistics

Standard deviation13756.555
Coefficient of variation (CV)5.2436555
Kurtosis43.619857
Mean2623.4667
Median Absolute Deviation (MAD)96
Skewness6.5664165
Sum118056
Variance1.8924282 × 108
MonotonicityNot monotonic
2024-01-29T01:00:47.452714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2 3
 
6.7%
0 3
 
6.7%
96 2
 
4.4%
5 2
 
4.4%
1 2
 
4.4%
1317 1
 
2.2%
231 1
 
2.2%
43 1
 
2.2%
56 1
 
2.2%
380 1
 
2.2%
Other values (28) 28
62.2%
ValueCountFrequency (%)
0 3
6.7%
1 2
4.4%
2 3
6.7%
5 2
4.4%
15 1
 
2.2%
18 1
 
2.2%
20 1
 
2.2%
22 1
 
2.2%
32 1
 
2.2%
38 1
 
2.2%
ValueCountFrequency (%)
92200 1
2.2%
10808 1
2.2%
2985 1
2.2%
1668 1
2.2%
1317 1
2.2%
1157 1
2.2%
890 1
2.2%
721 1
2.2%
709 1
2.2%
656 1
2.2%

불가
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size492.0 B
0
34 
1
9 
43
 
1
2
 
1

Length

Max length2
Median length1
Mean length1.0222222
Min length1

Unique

Unique2 ?
Unique (%)4.4%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 34
75.6%
1 9
 
20.0%
43 1
 
2.2%
2 1
 
2.2%

Length

2024-01-29T01:00:47.552708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:00:47.652559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 34
75.6%
1 9
 
20.0%
43 1
 
2.2%
2 1
 
2.2%

반려
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size492.0 B
0
40 
1
 
3
6
 
1
17
 
1

Length

Max length2
Median length1
Mean length1.0222222
Min length1

Unique

Unique2 ?
Unique (%)4.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 40
88.9%
1 3
 
6.7%
6 1
 
2.2%
17 1
 
2.2%

Length

2024-01-29T01:00:47.763607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:00:47.882868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 40
88.9%
1 3
 
6.7%
6 1
 
2.2%
17 1
 
2.2%

취하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3333333
Minimum0
Maximum31
Zeros27
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T01:00:48.002686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile9.8
Maximum31
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.9122369
Coefficient of variation (CV)2.5338158
Kurtosis15.215039
Mean2.3333333
Median Absolute Deviation (MAD)0
Skewness3.7847582
Sum105
Variance34.954545
MonotonicityNot monotonic
2024-01-29T01:00:48.120364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 27
60.0%
1 6
 
13.3%
2 4
 
8.9%
4 2
 
4.4%
31 1
 
2.2%
23 1
 
2.2%
7 1
 
2.2%
9 1
 
2.2%
10 1
 
2.2%
3 1
 
2.2%
ValueCountFrequency (%)
0 27
60.0%
1 6
 
13.3%
2 4
 
8.9%
3 1
 
2.2%
4 2
 
4.4%
7 1
 
2.2%
9 1
 
2.2%
10 1
 
2.2%
23 1
 
2.2%
31 1
 
2.2%
ValueCountFrequency (%)
31 1
 
2.2%
23 1
 
2.2%
10 1
 
2.2%
9 1
 
2.2%
7 1
 
2.2%
4 2
 
4.4%
3 1
 
2.2%
2 4
 
8.9%
1 6
 
13.3%
0 27
60.0%

이첩_이송
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size492.0 B
0
41 
1
 
2
7
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)4.4%

Sample

1st row0
2nd row0
3rd row7
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 41
91.1%
1 2
 
4.4%
7 1
 
2.2%
5 1
 
2.2%

Length

2024-01-29T01:00:48.233763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:00:48.321651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 41
91.1%
1 2
 
4.4%
7 1
 
2.2%
5 1
 
2.2%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.244444
Minimum0
Maximum194
Zeros26
Zeros (%)57.8%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T01:00:48.431420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile104.6
Maximum194
Range194
Interquartile range (IQR)3

Descriptive statistics

Standard deviation39.778452
Coefficient of variation (CV)3.2486939
Kurtosis13.408243
Mean12.244444
Median Absolute Deviation (MAD)0
Skewness3.740971
Sum551
Variance1582.3253
MonotonicityNot monotonic
2024-01-29T01:00:48.517474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 26
57.8%
1 5
 
11.1%
3 4
 
8.9%
2 2
 
4.4%
13 1
 
2.2%
27 1
 
2.2%
149 1
 
2.2%
194 1
 
2.2%
12 1
 
2.2%
124 1
 
2.2%
Other values (2) 2
 
4.4%
ValueCountFrequency (%)
0 26
57.8%
1 5
 
11.1%
2 2
 
4.4%
3 4
 
8.9%
4 1
 
2.2%
7 1
 
2.2%
12 1
 
2.2%
13 1
 
2.2%
27 1
 
2.2%
124 1
 
2.2%
ValueCountFrequency (%)
194 1
 
2.2%
149 1
 
2.2%
124 1
 
2.2%
27 1
 
2.2%
13 1
 
2.2%
12 1
 
2.2%
7 1
 
2.2%
4 1
 
2.2%
3 4
8.9%
2 2
4.4%

진행중
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.311111
Minimum0
Maximum2784
Zeros6
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size537.0 B
2024-01-29T01:00:48.621022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q350
95-th percentile128
Maximum2784
Range2784
Interquartile range (IQR)47

Descriptive statistics

Standard deviation413.04451
Coefficient of variation (CV)4.4744831
Kurtosis43.765896
Mean92.311111
Median Absolute Deviation (MAD)10
Skewness6.5764897
Sum4154
Variance170605.76
MonotonicityNot monotonic
2024-01-29T01:00:48.720148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 6
 
13.3%
1 4
 
8.9%
10 3
 
6.7%
4 3
 
6.7%
5 3
 
6.7%
3 2
 
4.4%
92 1
 
2.2%
24 1
 
2.2%
50 1
 
2.2%
15 1
 
2.2%
Other values (20) 20
44.4%
ValueCountFrequency (%)
0 6
13.3%
1 4
8.9%
2 1
 
2.2%
3 2
 
4.4%
4 3
6.7%
5 3
6.7%
6 1
 
2.2%
7 1
 
2.2%
10 3
6.7%
11 1
 
2.2%
ValueCountFrequency (%)
2784 1
2.2%
237 1
2.2%
133 1
2.2%
108 1
2.2%
107 1
2.2%
92 1
2.2%
90 1
2.2%
77 1
2.2%
63 1
2.2%
60 1
2.2%

민원접수처리기간
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size492.0 B
2021-01-01~2021-12-31
45 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-01~2021-12-31
2nd row2021-01-01~2021-12-31
3rd row2021-01-01~2021-12-31
4th row2021-01-01~2021-12-31
5th row2021-01-01~2021-12-31

Common Values

ValueCountFrequency (%)
2021-01-01~2021-12-31 45
100.0%

Length

2024-01-29T01:00:48.811312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:00:48.901394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-01~2021-12-31 45
100.0%

Interactions

2024-01-29T01:00:45.753528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.385961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.687291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.995881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.378086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.821052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.441984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.746419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.069905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.450091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.883372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.502640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.803907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.147511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.514342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.947760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.563082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.864187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.221430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.594582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:46.024063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.630880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:44.932381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.301113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:00:45.674829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:00:48.960756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리부서접수완결불가반려취하이첩_이송기타진행중
처리부서1.0001.0001.0001.0001.0001.0001.0001.0001.000
접수1.0001.0001.0000.0000.0000.0000.0001.0000.000
완결1.0001.0001.0000.0000.0000.0000.0001.0000.000
불가1.0000.0000.0001.0000.8730.0000.0000.5980.000
반려1.0000.0000.0000.8731.0000.0000.0000.5930.725
취하1.0000.0000.0000.0000.0001.0000.8090.1830.000
이첩_이송1.0000.0000.0000.0000.0000.8091.0000.0000.870
기타1.0001.0001.0000.5980.5930.1830.0001.0000.000
진행중1.0000.0000.0000.0000.7250.0000.8700.0001.000
2024-01-29T01:00:49.051564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
반려이첩_이송불가
반려1.0000.0000.538
이첩_이송0.0001.0000.000
불가0.5380.0001.000
2024-01-29T01:00:49.126918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
접수완결취하기타진행중불가반려이첩_이송
접수1.0000.9890.5030.6080.6290.0000.0000.000
완결0.9891.0000.4950.6170.5880.0000.0000.000
취하0.5030.4951.0000.2720.5580.0000.0000.642
기타0.6080.6170.2721.0000.2520.5180.5130.000
진행중0.6290.5880.5580.2521.0000.0000.5060.656
불가0.0000.0000.0000.5180.0001.0000.5380.000
반려0.0000.0000.0000.5130.5060.5381.0000.000
이첩_이송0.0000.0000.6420.0000.6560.0000.0001.000

Missing values

2024-01-29T01:00:46.117700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:00:46.229438image/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

처리부서접수완결불가반려취하이첩_이송기타진행중민원접수처리기간
0도시주택국 토지정보과1369131710201482021-01-01~2021-12-31
1도시주택국 도시재생과104960040042021-01-01~2021-12-31
2도시주택국 주택과1446115710317132372021-01-01~2021-12-31
3도시주택국 건축과6274671023031332021-01-01~2021-12-31
4도시주택국 도시계획과23180000052021-01-01~2021-12-31
5도시주택국 도로과32631210200112021-01-01~2021-12-31
6도시관리국 도로과1179600001202021-01-01~2021-12-31
7경제교통국 식품산업위생과92989000702752021-01-01~2021-12-31
8경제교통국 경제정책과1957166843610149902021-01-01~2021-12-31
9환경안전국 기후에너지정책과87770020172021-01-01~2021-12-31
처리부서접수완결불가반려취하이첩_이송기타진행중민원접수처리기간
35자치행정국 총무과550000002021-01-01~2021-12-31
36복지문화국 아동행복과65864601100102021-01-01~2021-12-31
37복지문화국 교육혁신과620000132021-01-01~2021-12-31
38복지문화국 장애인복지과41838911000272021-01-01~2021-12-31
39복지문화국 문화관광체육과1191110010432021-01-01~2021-12-31
40복지문화국 인재육성과110000002021-01-01~2021-12-31
41복지문화국 가정보육과30692985017007602021-01-01~2021-12-31
42복지문화국 노인복지과816709000001072021-01-01~2021-12-31
43복지문화국 복지정책과31934060111027842021-01-01~2021-12-31
44복지문화국100000012021-01-01~2021-12-31