Overview

Dataset statistics

Number of variables10
Number of observations44
Missing cells16
Missing cells (%)3.6%
Duplicate rows1
Duplicate rows (%)2.3%
Total size in memory3.9 KiB
Average record size in memory91.0 B

Variable types

Text1
Numeric7
Categorical2

Dataset

Description인천광역시 서구의 부서별민원접수처리현황에 관한 데이터로, 처리부서, 접수, 완결, 불가, 반려, 취하, 이첩.이송, 기타, 진행중, 민원접수처리기간 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15105206/fileData.do

Alerts

Dataset has 1 (2.3%) duplicate rowsDuplicates
민원접수처리기간 is highly overall correlated with 접수 and 7 other fieldsHigh correlation
진행중 is highly overall correlated with 취하 and 1 other fieldsHigh correlation
접수 is highly overall correlated with 해결 and 5 other fieldsHigh correlation
해결 is highly overall correlated with 접수 and 5 other fieldsHigh correlation
불가 is highly overall correlated with 민원접수처리기간High correlation
이송이첩 is highly overall correlated with 접수 and 4 other fieldsHigh correlation
취하 is highly overall correlated with 접수 and 6 other fieldsHigh correlation
반려 is highly overall correlated with 접수 and 3 other fieldsHigh correlation
기타(착오_시스템장애 등) is highly overall correlated with 접수 and 4 other fieldsHigh correlation
진행중 is highly imbalanced (65.9%)Imbalance
민원접수처리기간 is highly imbalanced (73.3%)Imbalance
주관부서 has 2 (4.5%) missing valuesMissing
접수 has 2 (4.5%) missing valuesMissing
해결 has 2 (4.5%) missing valuesMissing
불가 has 2 (4.5%) missing valuesMissing
이송이첩 has 2 (4.5%) missing valuesMissing
취하 has 2 (4.5%) missing valuesMissing
반려 has 2 (4.5%) missing valuesMissing
기타(착오_시스템장애 등) has 2 (4.5%) missing valuesMissing
불가 has 21 (47.7%) zerosZeros
이송이첩 has 26 (59.1%) zerosZeros
취하 has 16 (36.4%) zerosZeros
반려 has 26 (59.1%) zerosZeros
기타(착오_시스템장애 등) has 15 (34.1%) zerosZeros

Reproduction

Analysis started2023-12-12 14:43:20.983603
Analysis finished2023-12-12 14:43:26.542074
Duration5.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주관부서
Text

MISSING 

Distinct42
Distinct (%)100.0%
Missing2
Missing (%)4.5%
Memory size484.0 B
2023-12-12T23:43:26.742663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length10.761905
Min length5

Characters and Unicode

Total characters452
Distinct characters88
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

Unique42 ?
Unique (%)100.0%

Sample

1st row감사담당관
2nd row소통협력담당관
3rd row자치행정국 공동체협치과
4th row자치행정국 민원봉사과
5th row자치행정국 세무1과
ValueCountFrequency (%)
복지문화국 8
 
9.8%
도시주택국 7
 
8.5%
환경안전국 7
 
8.5%
경제교통국 7
 
8.5%
자치행정국 6
 
7.3%
미래기획실 3
 
3.7%
주택과 2
 
2.4%
건축과 2
 
2.4%
민원봉사과 1
 
1.2%
토지정보과 1
 
1.2%
Other values (38) 38
46.3%
2023-12-12T23:43:27.154081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
 
8.8%
국 37
 
8.2%
과 37
 
8.2%
경 16
 
3.5%
지 16
 
3.5%
도 13
 
2.9%
정 13
 
2.9%
복 12
 
2.7%
시 12
 
2.7%
주 11
 
2.4%
Other values (78) 245
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 410
90.7%
Space Separator 40
 
8.8%
Decimal Number 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 37
 
9.0%
과 37
 
9.0%
경 16
 
3.9%
지 16
 
3.9%
도 13
 
3.2%
정 13
 
3.2%
복 12
 
2.9%
시 12
 
2.9%
주 11
 
2.7%
통 10
 
2.4%
Other values (75) 233
56.8%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Space Separator
ValueCountFrequency (%)
40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 410
90.7%
Common 42
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 37
 
9.0%
과 37
 
9.0%
경 16
 
3.9%
지 16
 
3.9%
도 13
 
3.2%
정 13
 
3.2%
복 12
 
2.9%
시 12
 
2.9%
주 11
 
2.7%
통 10
 
2.4%
Other values (75) 233
56.8%
Common
ValueCountFrequency (%)
40
95.2%
2 1
 
2.4%
1 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 410
90.7%
ASCII 42
 
9.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
95.2%
2 1
 
2.4%
1 1
 
2.4%
Hangul
ValueCountFrequency (%)
국 37
 
9.0%
과 37
 
9.0%
경 16
 
3.9%
지 16
 
3.9%
도 13
 
3.2%
정 13
 
3.2%
복 12
 
2.9%
시 12
 
2.9%
주 11
 
2.7%
통 10
 
2.4%
Other values (75) 233
56.8%

접수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)95.2%
Missing2
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2775.7857
Minimum1
Maximum21664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:43:27.320138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.05
Q120.25
median671.5
Q33586.25
95-th percentile9785.35
Maximum21664
Range21663
Interquartile range (IQR)3566

Descriptive statistics

Standard deviation4765.7464
Coefficient of variation (CV)1.7169
Kurtosis8.4441118
Mean2775.7857
Median Absolute Deviation (MAD)667
Skewness2.7804403
Sum116583
Variance22712339
MonotonicityNot monotonic
2023-12-12T23:43:27.447089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5 3
 
6.8%
1811 1
 
2.3%
6271 1
 
2.3%
5957 1
 
2.3%
204 1
 
2.3%
20 1
 
2.3%
39 1
 
2.3%
4 1
 
2.3%
19658 1
 
2.3%
21 1
 
2.3%
Other values (30) 30
68.2%
(Missing) 2
 
4.5%
ValueCountFrequency (%)
1 1
 
2.3%
3 1
 
2.3%
4 1
 
2.3%
5 3
6.8%
7 1
 
2.3%
10 1
 
2.3%
14 1
 
2.3%
18 1
 
2.3%
20 1
 
2.3%
21 1
 
2.3%
ValueCountFrequency (%)
21664 1
2.3%
19658 1
2.3%
9894 1
2.3%
7721 1
2.3%
6271 1
2.3%
6174 1
2.3%
5957 1
2.3%
5953 1
2.3%
4214 1
2.3%
4134 1
2.3%

해결
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)92.9%
Missing2
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2690.2381
Minimum1
Maximum21414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:43:27.597545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q118
median657.5
Q33328.25
95-th percentile8520.85
Maximum21414
Range21413
Interquartile range (IQR)3310.25

Descriptive statistics

Standard deviation4658.464
Coefficient of variation (CV)1.7316177
Kurtosis8.9927568
Mean2690.2381
Median Absolute Deviation (MAD)655
Skewness2.8597809
Sum112990
Variance21701286
MonotonicityNot monotonic
2023-12-12T23:43:27.721588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
24 2
 
4.5%
1 2
 
4.5%
5 2
 
4.5%
16 1
 
2.3%
7587 1
 
2.3%
3 1
 
2.3%
19389 1
 
2.3%
21 1
 
2.3%
6269 1
 
2.3%
779 1
 
2.3%
Other values (29) 29
65.9%
(Missing) 2
 
4.5%
ValueCountFrequency (%)
1 2
4.5%
2 1
2.3%
3 1
2.3%
5 2
4.5%
6 1
2.3%
7 1
2.3%
8 1
2.3%
16 1
2.3%
17 1
2.3%
21 1
2.3%
ValueCountFrequency (%)
21414 1
2.3%
19389 1
2.3%
8570 1
2.3%
7587 1
2.3%
6269 1
2.3%
5917 1
2.3%
5754 1
2.3%
5715 1
2.3%
4131 1
2.3%
4116 1
2.3%

불가
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)26.2%
Missing2
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean11.309524
Minimum0
Maximum307
Zeros21
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:43:27.845578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q31.75
95-th percentile10.8
Maximum307
Range307
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation49.810611
Coefficient of variation (CV)4.4043067
Kurtosis32.305789
Mean11.309524
Median Absolute Deviation (MAD)0.5
Skewness5.5597153
Sum475
Variance2481.097
MonotonicityNot monotonic
2023-12-12T23:43:28.281578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 21
47.7%
1 10
22.7%
3 2
 
4.5%
5 2
 
4.5%
112 1
 
2.3%
307 1
 
2.3%
6 1
 
2.3%
2 1
 
2.3%
11 1
 
2.3%
4 1
 
2.3%
(Missing) 2
 
4.5%
ValueCountFrequency (%)
0 21
47.7%
1 10
22.7%
2 1
 
2.3%
3 2
 
4.5%
4 1
 
2.3%
5 2
 
4.5%
6 1
 
2.3%
7 1
 
2.3%
11 1
 
2.3%
112 1
 
2.3%
ValueCountFrequency (%)
307 1
 
2.3%
112 1
 
2.3%
11 1
 
2.3%
7 1
 
2.3%
6 1
 
2.3%
5 2
 
4.5%
4 1
 
2.3%
3 2
 
4.5%
2 1
 
2.3%
1 10
22.7%

이송이첩
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)21.4%
Missing2
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2.4285714
Minimum0
Maximum40
Zeros26
Zeros (%)59.1%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:43:28.403152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile11.65
Maximum40
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.1606746
Coefficient of variation (CV)2.9485131
Kurtosis20.191805
Mean2.4285714
Median Absolute Deviation (MAD)0
Skewness4.3419145
Sum102
Variance51.275261
MonotonicityNot monotonic
2023-12-12T23:43:28.530628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 26
59.1%
1 6
 
13.6%
2 3
 
6.8%
3 2
 
4.5%
12 1
 
2.3%
23 1
 
2.3%
5 1
 
2.3%
40 1
 
2.3%
4 1
 
2.3%
(Missing) 2
 
4.5%
ValueCountFrequency (%)
0 26
59.1%
1 6
 
13.6%
2 3
 
6.8%
3 2
 
4.5%
4 1
 
2.3%
5 1
 
2.3%
12 1
 
2.3%
23 1
 
2.3%
40 1
 
2.3%
ValueCountFrequency (%)
40 1
 
2.3%
23 1
 
2.3%
12 1
 
2.3%
5 1
 
2.3%
4 1
 
2.3%
3 2
 
4.5%
2 3
 
6.8%
1 6
 
13.6%
0 26
59.1%

취하
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)47.6%
Missing2
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean20.97619
Minimum0
Maximum212
Zeros16
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:43:28.684402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q316.75
95-th percentile139.85
Maximum212
Range212
Interquartile range (IQR)16.75

Descriptive statistics

Standard deviation46.601313
Coefficient of variation (CV)2.221629
Kurtosis9.0429257
Mean20.97619
Median Absolute Deviation (MAD)2
Skewness3.046351
Sum881
Variance2171.6823
MonotonicityNot monotonic
2023-12-12T23:43:28.797257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 16
36.4%
1 4
 
9.1%
2 3
 
6.8%
18 2
 
4.5%
39 2
 
4.5%
9 1
 
2.3%
15 1
 
2.3%
212 1
 
2.3%
80 1
 
2.3%
17 1
 
2.3%
Other values (10) 10
22.7%
(Missing) 2
 
4.5%
ValueCountFrequency (%)
0 16
36.4%
1 4
 
9.1%
2 3
 
6.8%
3 1
 
2.3%
5 1
 
2.3%
6 1
 
2.3%
7 1
 
2.3%
8 1
 
2.3%
9 1
 
2.3%
15 1
 
2.3%
ValueCountFrequency (%)
212 1
2.3%
169 1
2.3%
143 1
2.3%
80 1
2.3%
43 1
2.3%
39 2
4.5%
24 1
2.3%
18 2
4.5%
17 1
2.3%
16 1
2.3%

반려
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)23.8%
Missing2
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean7.6190476
Minimum0
Maximum201
Zeros26
Zeros (%)59.1%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:43:28.916626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile33.9
Maximum201
Range201
Interquartile range (IQR)2

Descriptive statistics

Standard deviation31.568704
Coefficient of variation (CV)4.1433923
Kurtosis36.563767
Mean7.6190476
Median Absolute Deviation (MAD)0
Skewness5.9112158
Sum320
Variance996.58304
MonotonicityNot monotonic
2023-12-12T23:43:29.077810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 26
59.1%
1 4
 
9.1%
2 4
 
9.1%
3 2
 
4.5%
13 1
 
2.3%
36 1
 
2.3%
12 1
 
2.3%
5 1
 
2.3%
201 1
 
2.3%
35 1
 
2.3%
(Missing) 2
 
4.5%
ValueCountFrequency (%)
0 26
59.1%
1 4
 
9.1%
2 4
 
9.1%
3 2
 
4.5%
5 1
 
2.3%
12 1
 
2.3%
13 1
 
2.3%
35 1
 
2.3%
36 1
 
2.3%
201 1
 
2.3%
ValueCountFrequency (%)
201 1
 
2.3%
36 1
 
2.3%
35 1
 
2.3%
13 1
 
2.3%
12 1
 
2.3%
5 1
 
2.3%
3 2
 
4.5%
2 4
 
9.1%
1 4
 
9.1%
0 26
59.1%

기타(착오_시스템장애 등)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)35.7%
Missing2
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean42.52381
Minimum0
Maximum942
Zeros15
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T23:43:29.228421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile129.1
Maximum942
Range942
Interquartile range (IQR)10

Descriptive statistics

Standard deviation157.19729
Coefficient of variation (CV)3.6966887
Kurtosis27.932857
Mean42.52381
Median Absolute Deviation (MAD)2
Skewness5.1287279
Sum1786
Variance24710.987
MonotonicityNot monotonic
2023-12-12T23:43:29.362313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 15
34.1%
10 4
 
9.1%
1 4
 
9.1%
2 4
 
9.1%
7 2
 
4.5%
16 2
 
4.5%
6 2
 
4.5%
4 2
 
4.5%
942 1
 
2.3%
415 1
 
2.3%
Other values (5) 5
 
11.4%
(Missing) 2
 
4.5%
ValueCountFrequency (%)
0 15
34.1%
1 4
 
9.1%
2 4
 
9.1%
4 2
 
4.5%
5 1
 
2.3%
6 2
 
4.5%
7 2
 
4.5%
10 4
 
9.1%
16 2
 
4.5%
35 1
 
2.3%
ValueCountFrequency (%)
942 1
 
2.3%
415 1
 
2.3%
132 1
 
2.3%
74 1
 
2.3%
65 1
 
2.3%
35 1
 
2.3%
16 2
4.5%
10 4
9.1%
7 2
4.5%
6 2
4.5%

진행중
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size484.0 B
0
38 
<NA>
 
2
20
 
1
2
 
1
4
 
1

Length

Max length4
Median length1
Mean length1.1590909
Min length1

Unique

Unique4 ?
Unique (%)9.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 38
86.4%
<NA> 2
 
4.5%
20 1
 
2.3%
2 1
 
2.3%
4 1
 
2.3%
3 1
 
2.3%

Length

2023-12-12T23:43:29.497643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:43:29.665296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38
86.4%
na 2
 
4.5%
20 1
 
2.3%
2 1
 
2.3%
4 1
 
2.3%
3 1
 
2.3%

민원접수처리기간
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
2022-01-01~2022-12-31
42 
<NA>
 
2

Length

Max length21
Median length21
Mean length20.227273
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-01-01~2022-12-31
2nd row2022-01-01~2022-12-31
3rd row2022-01-01~2022-12-31
4th row2022-01-01~2022-12-31
5th row2022-01-01~2022-12-31

Common Values

ValueCountFrequency (%)
2022-01-01~2022-12-31 42
95.5%
<NA> 2
 
4.5%

Length

2023-12-12T23:43:29.814304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:43:29.929887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-01-01~2022-12-31 42
95.5%
na 2
 
4.5%

Interactions

2023-12-12T23:43:25.341040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:21.380790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:22.323232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:22.824281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.421576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.075354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.625384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:25.432989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:21.470496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:22.395530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:22.891909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.513411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.145577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.757239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:25.509833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:21.581935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:22.458658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:22.964858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.595170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.219296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.870295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:25.587678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:21.688935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:22.530200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.040575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.683007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.298416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.953383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:25.701628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:21.782967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:22.609134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.125955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.784080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.400795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:25.043801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:25.806146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:21.854823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:22.673005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.201002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.879456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.473276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:25.128096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:25.925964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:21.942641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:22.756467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.332005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:23.974247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:24.551248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:43:25.256915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:43:30.032533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주관부서접수해결불가이송이첩취하반려기타(착오_시스템장애 등)진행중
주관부서1.0001.0001.0001.0001.0001.0001.0001.0001.000
접수1.0001.0001.0001.0000.8650.6310.8640.8530.093
해결1.0001.0001.0001.0000.8650.6310.8640.8530.093
불가1.0001.0001.0001.0001.0000.2120.0000.6540.000
이송이첩1.0000.8650.8651.0001.0000.7760.4750.8990.000
취하1.0000.6310.6310.2120.7761.0000.7250.7940.898
반려1.0000.8640.8640.0000.4750.7251.0000.4390.475
기타(착오_시스템장애 등)1.0000.8530.8530.6540.8990.7940.4391.0000.000
진행중1.0000.0930.0930.0000.0000.8980.4750.0001.000
2023-12-12T23:43:30.172672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
민원접수처리기간진행중
민원접수처리기간1.0001.000
진행중1.0001.000
2023-12-12T23:43:30.259089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
접수해결불가이송이첩취하반려기타(착오_시스템장애 등)진행중민원접수처리기간
접수1.0000.9980.1580.5470.6370.6060.7160.0251.000
해결0.9981.0000.1450.5400.6360.6110.7020.0251.000
불가0.1580.1451.0000.2140.2270.3620.3420.0001.000
이송이첩0.5470.5400.2141.0000.5640.3740.5500.0001.000
취하0.6370.6360.2270.5641.0000.6030.5930.8191.000
반려0.6060.6110.3620.3740.6031.0000.4950.3941.000
기타(착오_시스템장애 등)0.7160.7020.3420.5500.5930.4951.0000.0001.000
진행중0.0250.0250.0000.0000.8190.3940.0001.0001.000
민원접수처리기간1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T23:43:26.067713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:43:26.222053image/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.
2023-12-12T23:43:26.380646image/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

주관부서접수해결불가이송이첩취하반려기타(착오_시스템장애 등)진행중민원접수처리기간
0감사담당관362410101002022-01-01~2022-12-31
1소통협력담당관523000002022-01-01~2022-12-31
2자치행정국 공동체협치과770000002022-01-01~2022-12-31
3자치행정국 민원봉사과7721758711212001002022-01-01~2022-12-31
4자치행정국 세무1과18161000102022-01-01~2022-12-31
5자치행정국 세무2과311000102022-01-01~2022-12-31
6자치행정국 재무과1461000702022-01-01~2022-12-31
7자치행정국 총무과32241000702022-01-01~2022-12-31
8경제교통국 경제정책과9894857030723391394202022-01-01~2022-12-31
9경제교통국 교통정책과42144116108011602022-01-01~2022-12-31
주관부서접수해결불가이송이첩취하반려기타(착오_시스템장애 등)진행중민원접수처리기간
34복지문화국 장애인복지과245024470003002022-01-01~2022-12-31
35환경안전국 공원녹지과1201133130002022-01-01~2022-12-31
36환경안전국 기후에너지정책과5505460020202022-01-01~2022-12-31
37환경안전국 생태하천과1131110771601002022-01-01~2022-12-31
38환경안전국 안전총괄과550000002022-01-01~2022-12-31
39환경안전국 자원순환과2235220104240602022-01-01~2022-12-31
40환경안전국 클린도시과197219670050002022-01-01~2022-12-31
41환경안전국 환경관리과181117971081402022-01-01~2022-12-31
42<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
43<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

주관부서접수해결불가이송이첩취하반려기타(착오_시스템장애 등)진행중민원접수처리기간# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2