Overview

Dataset statistics

Number of variables20
Number of observations10000
Missing cells99782
Missing cells (%)49.9%
Duplicate rows61
Duplicate rows (%)0.6%
Total size in memory1.6 MiB
Average record size in memory169.0 B

Variable types

Numeric1
DateTime4
Unsupported1
Boolean1
Text11
Categorical2

Dataset

Description23~25 공공데이터 중장기 개방계획에 의한 생활민원처리시스템 DB개방자료로서 발송이력정보,생활민원처리정보,이관정보,협조부서정보 등의 테이블을 합성하여 개방함
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=16&beforeMenuCd=DOM_000000201001001000&publicdatapk=15122610

Alerts

Dataset has 61 (0.6%) duplicate rowsDuplicates
생활민원번호 is highly overall correlated with 중장기연장처리예정일 and 1 other fieldsHigh correlation
중장기연장처리예정일 is highly overall correlated with 생활민원번호High correlation
중장기연장처리사유 is highly overall correlated with 생활민원번호High correlation
중장기연장처리예정일 is highly imbalanced (99.6%)Imbalance
중장기연장처리사유 is highly imbalanced (99.6%)Imbalance
이관요청일시 has 5650 (56.5%) missing valuesMissing
알림예약일시 has 9502 (95.0%) missing valuesMissing
배정자부서명 has 8749 (87.5%) missing valuesMissing
배정자팀명 has 8755 (87.5%) missing valuesMissing
이관처리부서명 has 5656 (56.6%) missing valuesMissing
이관처리팀명 has 5668 (56.7%) missing valuesMissing
이관시의견 has 7907 (79.1%) missing valuesMissing
이관부서명 has 7902 (79.0%) missing valuesMissing
협조요청일시 has 9983 (99.8%) missing valuesMissing
협조부서명 has 9983 (99.8%) missing valuesMissing
협조상위부서명 has 9995 (> 99.9%) missing valuesMissing
협조팀명 has 9983 (99.8%) missing valuesMissing
발송일시 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-01-09 22:41:42.577121
Analysis finished2024-01-09 22:41:44.811692
Duration2.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

생활민원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9182
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0193505 × 1010
Minimum2.0180428 × 1010
Maximum2.0210324 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:41:44.871950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0180428 × 1010
5-th percentile2.018062 × 1010
Q12.0190107 × 1010
median2.0191004 × 1010
Q32.0200804 × 1010
95-th percentile2.0210214 × 1010
Maximum2.0210324 × 1010
Range29896052
Interquartile range (IQR)10697225

Descriptive statistics

Standard deviation9265111.1
Coefficient of variation (CV)0.00045881638
Kurtosis-1.0247792
Mean2.0193505 × 1010
Median Absolute Deviation (MAD)9792023
Skewness0.070135375
Sum2.0193505 × 1014
Variance8.5842283 × 1013
MonotonicityNot monotonic
2024-01-10T07:41:44.986959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201027054 4
 
< 0.1%
20190606006 4
 
< 0.1%
20190509025 4
 
< 0.1%
20190627025 3
 
< 0.1%
20180619025 3
 
< 0.1%
20181012023 3
 
< 0.1%
20180509044 3
 
< 0.1%
20180814033 3
 
< 0.1%
20190520003 3
 
< 0.1%
20180514033 3
 
< 0.1%
Other values (9172) 9967
99.7%
ValueCountFrequency (%)
20180428028 1
< 0.1%
20180430020 1
< 0.1%
20180430032 1
< 0.1%
20180430034 1
< 0.1%
20180501011 1
< 0.1%
20180502039 1
< 0.1%
20180503039 1
< 0.1%
20180504008 1
< 0.1%
20180504012 1
< 0.1%
20180505007 1
< 0.1%
ValueCountFrequency (%)
20210324080 2
< 0.1%
20210324078 1
< 0.1%
20210324077 1
< 0.1%
20210324070 1
< 0.1%
20210324069 1
< 0.1%
20210324067 1
< 0.1%
20210324065 1
< 0.1%
20210324064 1
< 0.1%
20210324059 1
< 0.1%
20210324058 1
< 0.1%
Distinct8712
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-05-07 11:14:00
Maximum2021-04-01 09:00:00
2024-01-10T07:41:45.103429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:45.214089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

발송일시
Unsupported

REJECTED  UNSUPPORTED 

Missing23
Missing (%)0.2%
Memory size156.2 KiB
Distinct2
Distinct (%)< 0.1%
Missing26
Missing (%)0.3%
Memory size97.7 KiB
True
5024 
False
4950 
(Missing)
 
26
ValueCountFrequency (%)
True 5024
50.2%
False 4950
49.5%
(Missing) 26
 
0.3%
2024-01-10T07:41:45.296060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct3928
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:41:45.533640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length8.6405
Min length1

Characters and Unicode

Total characters86405
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3055 ?
Unique (%)30.6%

Sample

1st row2018-08-28 14:20
2nd row
3rd row2019-07-23 10:20
4th row
5th row2019-09-10 17:50
ValueCountFrequency (%)
10:00 621
 
6.1%
14:00 535
 
5.3%
15:00 409
 
4.0%
09:00 356
 
3.5%
16:00 305
 
3.0%
11:00 297
 
2.9%
13:00 207
 
2.0%
14:30 156
 
1.5%
17:00 145
 
1.4%
15:30 136
 
1.3%
Other values (1048) 7020
68.9%
2024-01-10T07:41:45.901989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22494
26.0%
1 12916
14.9%
2 10310
11.9%
- 10182
11.8%
10000
11.6%
: 5101
 
5.9%
9 3245
 
3.8%
8 2785
 
3.2%
3 2599
 
3.0%
5 2092
 
2.4%
Other values (3) 4681
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61122
70.7%
Dash Punctuation 10182
 
11.8%
Space Separator 10000
 
11.6%
Other Punctuation 5101
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22494
36.8%
1 12916
21.1%
2 10310
16.9%
9 3245
 
5.3%
8 2785
 
4.6%
3 2599
 
4.3%
5 2092
 
3.4%
4 1744
 
2.9%
6 1563
 
2.6%
7 1374
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 10182
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 5101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 86405
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22494
26.0%
1 12916
14.9%
2 10310
11.9%
- 10182
11.8%
10000
11.6%
: 5101
 
5.9%
9 3245
 
3.8%
8 2785
 
3.2%
3 2599
 
3.0%
5 2092
 
2.4%
Other values (3) 4681
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22494
26.0%
1 12916
14.9%
2 10310
11.9%
- 10182
11.8%
10000
11.6%
: 5101
 
5.9%
9 3245
 
3.8%
8 2785
 
3.2%
3 2599
 
3.0%
5 2092
 
2.4%
Other values (3) 4681
 
5.4%
Distinct8983
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:41:46.152669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length15.9595
Min length1

Characters and Unicode

Total characters159595
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8075 ?
Unique (%)80.8%

Sample

1st row2018-08-28 17:56
2nd row2018-08-13 13:45
3rd row2019-07-01 09:39
4th row2020-09-11 12:12
5th row2019-09-10 19:32
ValueCountFrequency (%)
2020-08-10 59
 
0.3%
2019-09-09 48
 
0.2%
2020-08-12 44
 
0.2%
09:17 41
 
0.2%
2020-08-11 40
 
0.2%
2020-08-06 40
 
0.2%
09:16 38
 
0.2%
09:15 38
 
0.2%
2020-08-18 37
 
0.2%
09:13 37
 
0.2%
Other values (1751) 19524
97.9%
2024-01-10T07:41:46.521047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32175
20.2%
1 25710
16.1%
2 23010
14.4%
- 19946
12.5%
10000
 
6.3%
: 9973
 
6.2%
9 7762
 
4.9%
8 6919
 
4.3%
3 5657
 
3.5%
5 5006
 
3.1%
Other values (3) 13437
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119676
75.0%
Dash Punctuation 19946
 
12.5%
Space Separator 10000
 
6.3%
Other Punctuation 9973
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32175
26.9%
1 25710
21.5%
2 23010
19.2%
9 7762
 
6.5%
8 6919
 
5.8%
3 5657
 
4.7%
5 5006
 
4.2%
4 4679
 
3.9%
7 4638
 
3.9%
6 4120
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 19946
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 9973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 159595
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32175
20.2%
1 25710
16.1%
2 23010
14.4%
- 19946
12.5%
10000
 
6.3%
: 9973
 
6.2%
9 7762
 
4.9%
8 6919
 
4.3%
3 5657
 
3.5%
5 5006
 
3.1%
Other values (3) 13437
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32175
20.2%
1 25710
16.1%
2 23010
14.4%
- 19946
12.5%
10000
 
6.3%
: 9973
 
6.2%
9 7762
 
4.9%
8 6919
 
4.3%
3 5657
 
3.5%
5 5006
 
3.1%
Other values (3) 13437
8.4%

이관요청일시
Date

MISSING 

Distinct3912
Distinct (%)89.9%
Missing5650
Missing (%)56.5%
Memory size156.2 KiB
Minimum2019-12-31 00:27:00
Maximum2021-03-30 14:51:00
2024-01-10T07:41:46.642745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:46.759547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

알림예약일시
Date

MISSING 

Distinct249
Distinct (%)50.0%
Missing9502
Missing (%)95.0%
Memory size156.2 KiB
Minimum2019-12-31 09:00:00
Maximum2021-03-25 09:05:00
2024-01-10T07:41:46.873910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:46.991622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

배정자부서명
Text

MISSING 

Distinct61
Distinct (%)4.9%
Missing8749
Missing (%)87.5%
Memory size156.2 KiB
2024-01-10T07:41:47.180901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.4708233
Min length3

Characters and Unicode

Total characters5593
Distinct characters116
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

Unique2 ?
Unique (%)0.2%

Sample

1st row교통행정과
2nd row자원순환과
3rd row교통행정과
4th row민원봉사과
5th row미래전략과
ValueCountFrequency (%)
민원봉사과 199
 
15.9%
도로과 75
 
6.0%
허가담당관 72
 
5.8%
교통행정과 55
 
4.4%
자원순환과 51
 
4.1%
환경보전과 50
 
4.0%
기후변화대책과 35
 
2.8%
기업경제과 34
 
2.7%
건축과 33
 
2.6%
위생과 32
 
2.6%
Other values (51) 615
49.2%
2024-01-10T07:41:47.482424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1089
 
19.5%
287
 
5.1%
246
 
4.4%
204
 
3.6%
199
 
3.6%
146
 
2.6%
141
 
2.5%
138
 
2.5%
105
 
1.9%
103
 
1.8%
Other values (106) 2935
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5575
99.7%
Decimal Number 18
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1089
 
19.5%
287
 
5.1%
246
 
4.4%
204
 
3.7%
199
 
3.6%
146
 
2.6%
141
 
2.5%
138
 
2.5%
105
 
1.9%
103
 
1.8%
Other values (101) 2917
52.3%
Decimal Number
ValueCountFrequency (%)
6 7
38.9%
5 4
22.2%
2 3
16.7%
3 2
 
11.1%
1 2
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5575
99.7%
Common 18
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1089
 
19.5%
287
 
5.1%
246
 
4.4%
204
 
3.7%
199
 
3.6%
146
 
2.6%
141
 
2.5%
138
 
2.5%
105
 
1.9%
103
 
1.8%
Other values (101) 2917
52.3%
Common
ValueCountFrequency (%)
6 7
38.9%
5 4
22.2%
2 3
16.7%
3 2
 
11.1%
1 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5575
99.7%
ASCII 18
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1089
 
19.5%
287
 
5.1%
246
 
4.4%
204
 
3.7%
199
 
3.6%
146
 
2.6%
141
 
2.5%
138
 
2.5%
105
 
1.9%
103
 
1.8%
Other values (101) 2917
52.3%
ASCII
ValueCountFrequency (%)
6 7
38.9%
5 4
22.2%
2 3
16.7%
3 2
 
11.1%
1 2
 
11.1%

배정자팀명
Text

MISSING 

Distinct164
Distinct (%)13.2%
Missing8755
Missing (%)87.5%
Memory size156.2 KiB
2024-01-10T07:41:47.716731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.1373494
Min length3

Characters and Unicode

Total characters6396
Distinct characters174
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)2.2%

Sample

1st row교통지도팀
2nd row재활용팀
3rd row교통지도팀
4th row민원콜센터팀
5th row클린아산T/F팀
ValueCountFrequency (%)
민원콜센터팀 177
 
14.2%
도로관리팀 32
 
2.6%
청소행정팀 30
 
2.4%
산업개발팀 28
 
2.2%
건축지도팀 26
 
2.1%
총무팀 24
 
1.9%
환경지도팀 23
 
1.8%
위생지도팀 22
 
1.8%
주택개발팀 21
 
1.7%
민원행정팀 20
 
1.6%
Other values (154) 842
67.6%
2024-01-10T07:41:48.072009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1212
 
18.9%
242
 
3.8%
216
 
3.4%
212
 
3.3%
203
 
3.2%
178
 
2.8%
177
 
2.8%
177
 
2.8%
177
 
2.8%
168
 
2.6%
Other values (164) 3434
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6350
99.3%
Uppercase Letter 20
 
0.3%
Decimal Number 18
 
0.3%
Other Punctuation 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1212
 
19.1%
242
 
3.8%
216
 
3.4%
212
 
3.3%
203
 
3.2%
178
 
2.8%
177
 
2.8%
177
 
2.8%
177
 
2.8%
168
 
2.6%
Other values (159) 3388
53.4%
Decimal Number
ValueCountFrequency (%)
2 11
61.1%
1 7
38.9%
Uppercase Letter
ValueCountFrequency (%)
T 10
50.0%
F 10
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6350
99.3%
Common 26
 
0.4%
Latin 20
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1212
 
19.1%
242
 
3.8%
216
 
3.4%
212
 
3.3%
203
 
3.2%
178
 
2.8%
177
 
2.8%
177
 
2.8%
177
 
2.8%
168
 
2.6%
Other values (159) 3388
53.4%
Common
ValueCountFrequency (%)
2 11
42.3%
/ 8
30.8%
1 7
26.9%
Latin
ValueCountFrequency (%)
T 10
50.0%
F 10
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6350
99.3%
ASCII 46
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1212
 
19.1%
242
 
3.8%
216
 
3.4%
212
 
3.3%
203
 
3.2%
178
 
2.8%
177
 
2.8%
177
 
2.8%
177
 
2.8%
168
 
2.6%
Other values (159) 3388
53.4%
ASCII
ValueCountFrequency (%)
2 11
23.9%
T 10
21.7%
F 10
21.7%
/ 8
17.4%
1 7
15.2%

이관처리부서명
Text

MISSING 

Distinct66
Distinct (%)1.5%
Missing5656
Missing (%)56.6%
Memory size156.2 KiB
2024-01-10T07:41:48.269438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.3646409
Min length3

Characters and Unicode

Total characters18960
Distinct characters126
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row질병예방과
2nd row건강증진과
3rd row도로과
4th row도로과
5th row대중교통과
ValueCountFrequency (%)
도로과 618
 
14.2%
기후변화대책과 386
 
8.9%
자원순환과 341
 
7.8%
교통행정과 321
 
7.4%
대중교통과 310
 
7.1%
환경보전과 197
 
4.5%
위생과 178
 
4.1%
질병예방과 139
 
3.2%
배방읍 132
 
3.0%
주택과 125
 
2.9%
Other values (56) 1597
36.8%
2024-01-10T07:41:48.592816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3739
 
19.7%
967
 
5.1%
696
 
3.7%
642
 
3.4%
638
 
3.4%
632
 
3.3%
538
 
2.8%
513
 
2.7%
444
 
2.3%
437
 
2.3%
Other values (116) 9714
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18866
99.5%
Decimal Number 94
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3739
 
19.8%
967
 
5.1%
696
 
3.7%
642
 
3.4%
638
 
3.4%
632
 
3.3%
538
 
2.9%
513
 
2.7%
444
 
2.4%
437
 
2.3%
Other values (110) 9620
51.0%
Decimal Number
ValueCountFrequency (%)
6 28
29.8%
5 23
24.5%
3 16
17.0%
2 14
14.9%
4 7
 
7.4%
1 6
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18866
99.5%
Common 94
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3739
 
19.8%
967
 
5.1%
696
 
3.7%
642
 
3.4%
638
 
3.4%
632
 
3.3%
538
 
2.9%
513
 
2.7%
444
 
2.4%
437
 
2.3%
Other values (110) 9620
51.0%
Common
ValueCountFrequency (%)
6 28
29.8%
5 23
24.5%
3 16
17.0%
2 14
14.9%
4 7
 
7.4%
1 6
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18866
99.5%
ASCII 94
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3739
 
19.8%
967
 
5.1%
696
 
3.7%
642
 
3.4%
638
 
3.4%
632
 
3.3%
538
 
2.9%
513
 
2.7%
444
 
2.4%
437
 
2.3%
Other values (110) 9620
51.0%
ASCII
ValueCountFrequency (%)
6 28
29.8%
5 23
24.5%
3 16
17.0%
2 14
14.9%
4 7
 
7.4%
1 6
 
6.4%

이관처리팀명
Text

MISSING 

Distinct150
Distinct (%)3.5%
Missing5668
Missing (%)56.7%
Memory size156.2 KiB
2024-01-10T07:41:48.833849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length5
Mean length5.0413204
Min length2

Characters and Unicode

Total characters21839
Distinct characters169
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.8%

Sample

1st row감염병관리팀
2nd row건강증진팀
3rd row도로관리팀
4th row도로관리팀
5th row운수지도팀
ValueCountFrequency (%)
도로관리팀 479
 
11.1%
산업개발팀 320
 
7.4%
청소행정팀 302
 
7.0%
생활환경팀 245
 
5.7%
운수지도팀 240
 
5.5%
교통지도팀 176
 
4.1%
위생지도팀 163
 
3.8%
미세먼지대책팀 140
 
3.2%
총무팀 119
 
2.7%
가로등팀 112
 
2.6%
Other values (140) 2036
47.0%
2024-01-10T07:41:49.175413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4306
19.7%
1422
 
6.5%
1014
 
4.6%
962
 
4.4%
962
 
4.4%
685
 
3.1%
602
 
2.8%
473
 
2.2%
464
 
2.1%
441
 
2.0%
Other values (159) 10508
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21705
99.4%
Uppercase Letter 92
 
0.4%
Other Punctuation 35
 
0.2%
Decimal Number 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4306
19.8%
1422
 
6.6%
1014
 
4.7%
962
 
4.4%
962
 
4.4%
685
 
3.2%
602
 
2.8%
473
 
2.2%
464
 
2.1%
441
 
2.0%
Other values (154) 10374
47.8%
Uppercase Letter
ValueCountFrequency (%)
F 46
50.0%
T 46
50.0%
Decimal Number
ValueCountFrequency (%)
2 6
85.7%
1 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
/ 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21705
99.4%
Latin 92
 
0.4%
Common 42
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4306
19.8%
1422
 
6.6%
1014
 
4.7%
962
 
4.4%
962
 
4.4%
685
 
3.2%
602
 
2.8%
473
 
2.2%
464
 
2.1%
441
 
2.0%
Other values (154) 10374
47.8%
Common
ValueCountFrequency (%)
/ 35
83.3%
2 6
 
14.3%
1 1
 
2.4%
Latin
ValueCountFrequency (%)
F 46
50.0%
T 46
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21705
99.4%
ASCII 134
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4306
19.8%
1422
 
6.6%
1014
 
4.7%
962
 
4.4%
962
 
4.4%
685
 
3.2%
602
 
2.8%
473
 
2.2%
464
 
2.1%
441
 
2.0%
Other values (154) 10374
47.8%
ASCII
ValueCountFrequency (%)
F 46
34.3%
T 46
34.3%
/ 35
26.1%
2 6
 
4.5%
1 1
 
0.7%

이관시의견
Text

MISSING 

Distinct933
Distinct (%)44.6%
Missing7907
Missing (%)79.1%
Memory size156.2 KiB
2024-01-10T07:41:49.401619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length128
Median length74
Mean length11.634018
Min length1

Characters and Unicode

Total characters24350
Distinct characters473
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique708 ?
Unique (%)33.8%

Sample

1st row자전거관리 관련부서 업무
2nd row기초 현황조사 부서 이관
3rd row담당자변경
4th row담당자 재지정
5th row담당자 변경
ValueCountFrequency (%)
담당자 513
 
8.8%
변경 445
 
7.6%
이관 426
 
7.3%
담당부서 271
 
4.6%
재지정 133
 
2.3%
담당자에게 107
 
1.8%
관련 90
 
1.5%
담당부서로 67
 
1.1%
담당자변경 54
 
0.9%
업무 47
 
0.8%
Other values (1708) 3709
63.3%
2024-01-10T07:41:49.756186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3850
 
15.8%
1240
 
5.1%
1207
 
5.0%
994
 
4.1%
826
 
3.4%
696
 
2.9%
648
 
2.7%
612
 
2.5%
600
 
2.5%
541
 
2.2%
Other values (463) 13136
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19702
80.9%
Space Separator 3850
 
15.8%
Decimal Number 254
 
1.0%
Other Punctuation 234
 
1.0%
Open Punctuation 133
 
0.5%
Close Punctuation 133
 
0.5%
Dash Punctuation 26
 
0.1%
Uppercase Letter 13
 
0.1%
Lowercase Letter 4
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1240
 
6.3%
1207
 
6.1%
994
 
5.0%
826
 
4.2%
696
 
3.5%
648
 
3.3%
612
 
3.1%
600
 
3.0%
541
 
2.7%
506
 
2.6%
Other values (432) 11832
60.1%
Decimal Number
ValueCountFrequency (%)
1 56
22.0%
2 48
18.9%
0 31
12.2%
4 26
10.2%
3 22
 
8.7%
5 21
 
8.3%
9 15
 
5.9%
6 14
 
5.5%
8 11
 
4.3%
7 10
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
C 4
30.8%
T 3
23.1%
V 2
15.4%
F 1
 
7.7%
L 1
 
7.7%
E 1
 
7.7%
D 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 182
77.8%
, 37
 
15.8%
: 7
 
3.0%
/ 6
 
2.6%
" 2
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
r 1
25.0%
c 1
25.0%
p 1
25.0%
o 1
25.0%
Space Separator
ValueCountFrequency (%)
3850
100.0%
Open Punctuation
ValueCountFrequency (%)
( 133
100.0%
Close Punctuation
ValueCountFrequency (%)
) 133
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19702
80.9%
Common 4631
 
19.0%
Latin 17
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1240
 
6.3%
1207
 
6.1%
994
 
5.0%
826
 
4.2%
696
 
3.5%
648
 
3.3%
612
 
3.1%
600
 
3.0%
541
 
2.7%
506
 
2.6%
Other values (432) 11832
60.1%
Common
ValueCountFrequency (%)
3850
83.1%
. 182
 
3.9%
( 133
 
2.9%
) 133
 
2.9%
1 56
 
1.2%
2 48
 
1.0%
, 37
 
0.8%
0 31
 
0.7%
4 26
 
0.6%
- 26
 
0.6%
Other values (10) 109
 
2.4%
Latin
ValueCountFrequency (%)
C 4
23.5%
T 3
17.6%
V 2
11.8%
r 1
 
5.9%
F 1
 
5.9%
c 1
 
5.9%
p 1
 
5.9%
L 1
 
5.9%
E 1
 
5.9%
D 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19702
80.9%
ASCII 4648
 
19.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3850
82.8%
. 182
 
3.9%
( 133
 
2.9%
) 133
 
2.9%
1 56
 
1.2%
2 48
 
1.0%
, 37
 
0.8%
0 31
 
0.7%
4 26
 
0.6%
- 26
 
0.6%
Other values (21) 126
 
2.7%
Hangul
ValueCountFrequency (%)
1240
 
6.3%
1207
 
6.1%
994
 
5.0%
826
 
4.2%
696
 
3.5%
648
 
3.3%
612
 
3.1%
600
 
3.0%
541
 
2.7%
506
 
2.6%
Other values (432) 11832
60.1%

이관부서명
Text

MISSING 

Distinct63
Distinct (%)3.0%
Missing7902
Missing (%)79.0%
Memory size156.2 KiB
2024-01-10T07:41:49.939376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.42898
Min length3

Characters and Unicode

Total characters9292
Distinct characters115
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

Unique11 ?
Unique (%)0.5%

Sample

1st row교통행정과
2nd row건축과
3rd row대중교통과
4th row민원봉사담당관
5th row민원봉사과
ValueCountFrequency (%)
민원봉사과 477
22.7%
도로과 205
 
9.8%
자원순환과 101
 
4.8%
공원녹지과 94
 
4.5%
허가담당관 87
 
4.1%
상수도과 86
 
4.1%
환경보전과 82
 
3.9%
교통행정과 80
 
3.8%
하수도과 65
 
3.1%
건축과 62
 
3.0%
Other values (53) 759
36.2%
2024-01-10T07:41:50.235014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1721
18.5%
740
 
8.0%
558
 
6.0%
549
 
5.9%
536
 
5.8%
383
 
4.1%
205
 
2.2%
183
 
2.0%
172
 
1.9%
164
 
1.8%
Other values (105) 4081
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9218
99.2%
Decimal Number 74
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1721
18.7%
740
 
8.0%
558
 
6.1%
549
 
6.0%
536
 
5.8%
383
 
4.2%
205
 
2.2%
183
 
2.0%
172
 
1.9%
164
 
1.8%
Other values (99) 4007
43.5%
Decimal Number
ValueCountFrequency (%)
6 21
28.4%
3 18
24.3%
1 13
17.6%
5 10
13.5%
4 9
12.2%
2 3
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9218
99.2%
Common 74
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1721
18.7%
740
 
8.0%
558
 
6.1%
549
 
6.0%
536
 
5.8%
383
 
4.2%
205
 
2.2%
183
 
2.0%
172
 
1.9%
164
 
1.8%
Other values (99) 4007
43.5%
Common
ValueCountFrequency (%)
6 21
28.4%
3 18
24.3%
1 13
17.6%
5 10
13.5%
4 9
12.2%
2 3
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9218
99.2%
ASCII 74
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1721
18.7%
740
 
8.0%
558
 
6.1%
549
 
6.0%
536
 
5.8%
383
 
4.2%
205
 
2.2%
183
 
2.0%
172
 
1.9%
164
 
1.8%
Other values (99) 4007
43.5%
ASCII
ValueCountFrequency (%)
6 21
28.4%
3 18
24.3%
1 13
17.6%
5 10
13.5%
4 9
12.2%
2 3
 
4.1%

협조요청일시
Date

MISSING 

Distinct15
Distinct (%)88.2%
Missing9983
Missing (%)99.8%
Memory size156.2 KiB
Minimum2018-11-05 11:16:00
Maximum2020-10-23 14:29:00
2024-01-10T07:41:50.331425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:41:50.429544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)

협조부서명
Text

MISSING 

Distinct10
Distinct (%)58.8%
Missing9983
Missing (%)99.8%
Memory size156.2 KiB
2024-01-10T07:41:50.558512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.1176471
Min length3

Characters and Unicode

Total characters70
Distinct characters30
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

Unique7 ?
Unique (%)41.2%

Sample

1st row주택과
2nd row주택과
3rd row교통행정과
4th row민원봉사담당관
5th row교통행정과
ValueCountFrequency (%)
주택과 4
23.5%
교통행정과 4
23.5%
농정과 2
11.8%
민원봉사담당관 1
 
5.9%
축수산과 1
 
5.9%
자원순환과 1
 
5.9%
건설과 1
 
5.9%
도로과 1
 
5.9%
허가담당관 1
 
5.9%
환경보전과 1
 
5.9%
2024-01-10T07:41:50.802109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
21.4%
6
 
8.6%
4
 
5.7%
4
 
5.7%
4
 
5.7%
4
 
5.7%
4
 
5.7%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (20) 23
32.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
15
21.4%
6
 
8.6%
4
 
5.7%
4
 
5.7%
4
 
5.7%
4
 
5.7%
4
 
5.7%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (20) 23
32.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
15
21.4%
6
 
8.6%
4
 
5.7%
4
 
5.7%
4
 
5.7%
4
 
5.7%
4
 
5.7%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (20) 23
32.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
15
21.4%
6
 
8.6%
4
 
5.7%
4
 
5.7%
4
 
5.7%
4
 
5.7%
4
 
5.7%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (20) 23
32.9%

협조상위부서명
Text

MISSING 

Distinct4
Distinct (%)80.0%
Missing9995
Missing (%)> 99.9%
Memory size156.2 KiB
2024-01-10T07:41:50.915910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.6
Min length5

Characters and Unicode

Total characters28
Distinct characters20
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

Unique3 ?
Unique (%)60.0%

Sample

1st row도시개발국
2nd row시민행복기획실
3rd row농업기술센터
4th row도시개발국
5th row건설교통국
ValueCountFrequency (%)
도시개발국 2
40.0%
시민행복기획실 1
20.0%
농업기술센터 1
20.0%
건설교통국 1
20.0%
2024-01-10T07:41:51.198690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3
 
10.7%
3
 
10.7%
2
 
7.1%
2
 
7.1%
2
 
7.1%
2
 
7.1%
1
 
3.6%
1
 
3.6%
1
 
3.6%
1
 
3.6%
Other values (10) 10
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3
 
10.7%
3
 
10.7%
2
 
7.1%
2
 
7.1%
2
 
7.1%
2
 
7.1%
1
 
3.6%
1
 
3.6%
1
 
3.6%
1
 
3.6%
Other values (10) 10
35.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3
 
10.7%
3
 
10.7%
2
 
7.1%
2
 
7.1%
2
 
7.1%
2
 
7.1%
1
 
3.6%
1
 
3.6%
1
 
3.6%
1
 
3.6%
Other values (10) 10
35.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3
 
10.7%
3
 
10.7%
2
 
7.1%
2
 
7.1%
2
 
7.1%
2
 
7.1%
1
 
3.6%
1
 
3.6%
1
 
3.6%
1
 
3.6%
Other values (10) 10
35.7%

협조팀명
Text

MISSING 

Distinct12
Distinct (%)70.6%
Missing9983
Missing (%)99.8%
Memory size156.2 KiB
2024-01-10T07:41:51.345611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.2352941
Min length4

Characters and Unicode

Total characters89
Distinct characters43
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

Unique7 ?
Unique (%)41.2%

Sample

1st row옥외광고물팀
2nd row주택개발팀
3rd row교통시설팀
4th row민원콜센터팀
5th row교통지도팀
ValueCountFrequency (%)
옥외광고물팀 2
11.8%
주택개발팀 2
11.8%
교통시설팀 2
11.8%
교통지도팀 2
11.8%
농촌관리 2
11.8%
민원콜센터팀 1
5.9%
축산유통팀 1
5.9%
청소행정팀 1
5.9%
농촌지역개발팀 1
5.9%
자전거문화팀 1
5.9%
Other values (2) 2
11.8%
2024-01-10T07:41:51.600515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
 
16.9%
5
 
5.6%
4
 
4.5%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
Other values (33) 44
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
15
 
16.9%
5
 
5.6%
4
 
4.5%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
Other values (33) 44
49.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
15
 
16.9%
5
 
5.6%
4
 
4.5%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
Other values (33) 44
49.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
15
 
16.9%
5
 
5.6%
4
 
4.5%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
3
 
3.4%
Other values (33) 44
49.4%

중장기연장처리예정일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9995 
2020-06-30
 
3
2019-10-25
 
2

Length

Max length10
Median length4
Mean length4.003
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9995
> 99.9%
2020-06-30 3
 
< 0.1%
2019-10-25 2
 
< 0.1%

Length

2024-01-10T07:41:51.711888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:41:51.790586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9995
> 99.9%
2020-06-30 3
 
< 0.1%
2019-10-25 2
 
< 0.1%

중장기연장처리사유
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9995 
추경예산확보 후 처리예정(배방읍 박병훈 3월2일요청)
 
3
잡초제거 용역 진행 중
 
2

Length

Max length29
Median length4
Mean length4.0091
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9995
> 99.9%
추경예산확보 후 처리예정(배방읍 박병훈 3월2일요청) 3
 
< 0.1%
잡초제거 용역 진행 중 2
 
< 0.1%

Length

2024-01-10T07:41:51.874201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:41:51.955005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9995
99.8%
추경예산확보 3
 
< 0.1%
3
 
< 0.1%
처리예정(배방읍 3
 
< 0.1%
박병훈 3
 
< 0.1%
3월2일요청 3
 
< 0.1%
잡초제거 2
 
< 0.1%
용역 2
 
< 0.1%
진행 2
 
< 0.1%
2
 
< 0.1%

Interactions

2024-01-10T07:41:43.893348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:41:52.017992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생활민원번호출동처리여부배정자부서명이관처리부서명이관부서명협조요청일시협조부서명협조상위부서명협조팀명중장기연장처리예정일중장기연장처리사유
생활민원번호1.0000.2310.3230.3110.5321.0000.8961.0001.000NaNNaN
출동처리여부0.2311.0000.2100.6230.3381.0000.4050.0000.5680.6110.611
배정자부서명0.3230.2101.0000.8570.996NaNNaNNaNNaNNaNNaN
이관처리부서명0.3110.6230.8571.0000.9731.0000.8031.0000.979NaNNaN
이관부서명0.5320.3380.9960.9731.0001.0001.000NaN1.000NaNNaN
협조요청일시1.0001.000NaN1.0001.0001.0001.0001.0001.000NaNNaN
협조부서명0.8960.405NaN0.8031.0001.0001.0001.0001.000NaNNaN
협조상위부서명1.0000.000NaN1.000NaN1.0001.0001.0001.000NaNNaN
협조팀명1.0000.568NaN0.9791.0001.0001.0001.0001.000NaNNaN
중장기연장처리예정일NaN0.611NaNNaNNaNNaNNaNNaNNaN1.0000.611
중장기연장처리사유NaN0.611NaNNaNNaNNaNNaNNaNNaN0.6111.000
2024-01-10T07:41:52.127379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출동처리여부중장기연장처리예정일중장기연장처리사유
출동처리여부1.0000.3470.347
중장기연장처리예정일0.3471.0000.347
중장기연장처리사유0.3470.3471.000
2024-01-10T07:41:52.206890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생활민원번호출동처리여부중장기연장처리예정일중장기연장처리사유
생활민원번호1.0000.1541.0001.000
출동처리여부0.1541.0000.3470.347
중장기연장처리예정일1.0000.3471.0000.347
중장기연장처리사유1.0000.3470.3471.000

Missing values

2024-01-10T07:41:44.021306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:41:44.251714image/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.
2024-01-10T07:41:44.676125image/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

생활민원번호발송요청일시발송일시출동처리여부출동예정일시처리완료일시이관요청일시알림예약일시배정자부서명배정자팀명이관처리부서명이관처리팀명이관시의견이관부서명협조요청일시협조부서명협조상위부서명협조팀명중장기연장처리예정일중장기연장처리사유
11732201808260092018-08-26 17:522.02E+13N2018-08-28 14:202018-08-28 17:56<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
10501201808130112018-08-13 13:362.02E+13Y2018-08-13 13:45<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
35733201906200422019-06-28 09:0020200000000000.0N2019-07-23 10:202019-07-01 09:39<NA><NA><NA><NA><NA><NA>자전거관리 관련부서 업무교통행정과<NA><NA><NA><NA><NA><NA>
73691202009070522020-09-11 12:1220200000000000.0Y2020-09-11 12:122020-09-07 17:23<NA><NA><NA>질병예방과감염병관리팀<NA><NA><NA><NA><NA><NA><NA><NA>
43259201909090242019-09-09 09:4320200000000000.0N2019-09-10 17:502019-09-10 19:32<NA><NA><NA><NA><NA><NA>기초 현황조사 부서 이관건축과<NA><NA><NA><NA><NA><NA>
23390201901180012019-01-22 16:572.02E+13Y2019-01-22 16:57<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
91422202103170592021-03-17 15:1820200000000000.0N2021-03-17 16:302021-03-17 17:132021-03-17 15:18<NA><NA><NA>건강증진과건강증진팀<NA><NA><NA><NA><NA><NA><NA><NA>
37325201907080592019-07-08 15:4020200000000000.0Y2019-07-08 15:40<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
68598202008035272020-08-03 18:1620200000000000.0Y2020-08-27 06:062020-08-03 18:16<NA><NA><NA>도로과도로관리팀<NA><NA><NA><NA><NA><NA><NA><NA>
72487202008280202020-08-28 10:4120200000000000.0Y2020-09-21 11:052020-08-28 10:41<NA><NA><NA>도로과도로관리팀<NA><NA><NA><NA><NA><NA><NA><NA>
생활민원번호발송요청일시발송일시출동처리여부출동예정일시처리완료일시이관요청일시알림예약일시배정자부서명배정자팀명이관처리부서명이관처리팀명이관시의견이관부서명협조요청일시협조부서명협조상위부서명협조팀명중장기연장처리예정일중장기연장처리사유
80998202011290022020-11-29 07:2920200000000000.0N2020-12-02 16:002020-12-03 17:482020-11-30 13:22<NA>도로과도로구조물팀교통행정과교통시설팀교통표지판으로 교통행정과로 이관도로과<NA><NA><NA><NA><NA><NA>
15771201810050142018-10-11 11:392.02E+13Y2018-10-11 11:39<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
35396201906180052019-06-18 09:3220200000000000.0N2019-06-18 14:302019-06-18 15:54<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
57205202004010042020-04-01 08:4320200000000000.0Y2020-04-02 09:002020-04-01 08:43<NA><NA><NA>도로과도로관리팀<NA><NA><NA><NA><NA><NA><NA><NA>
41953201908270462019-08-27 16:4020200000000000.0Y2019-08-29 09:07<NA><NA><NA><NA><NA><NA>담당부서 변경민원봉사과<NA><NA><NA><NA><NA><NA>
18840201811090152018-11-15 17:372.02E+13N2018-11-14 15:002018-11-15 17:37<NA><NA><NA><NA><NA><NA>환경미화원담당자에게 이관자원순환과<NA><NA><NA><NA><NA><NA>
75596202009240422020-10-06 09:0020200000000000.0Y2020-10-07 09:192020-09-24 18:02<NA><NA><NA>교통행정과교통지도팀<NA><NA><NA><NA><NA><NA><NA><NA>
79045202011040362020-11-04 15:4820200000000000.0N2020-11-04 17:002020-11-09 09:052020-11-04 17:24<NA>민원봉사과민원콜센터팀기후변화대책과생활환경팀담당자 변경민원봉사과<NA><NA><NA><NA><NA><NA>
74244202009110362020-09-11 11:4820200000000000.0N2020-09-16 12:002020-09-21 09:432020-09-11 11:51<NA>온양6동총무팀온양5동총무팀초사동 (온양5동)온양6동<NA><NA><NA><NA><NA><NA>
48801201911110072019-11-18 07:5320200000000000.0Y2019-11-18 07:53<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

생활민원번호발송요청일시출동처리여부출동예정일시처리완료일시이관요청일시알림예약일시배정자부서명배정자팀명이관처리부서명이관처리팀명이관시의견이관부서명협조요청일시협조부서명협조상위부서명협조팀명중장기연장처리예정일중장기연장처리사유# duplicates
0201805090442018-05-10 09:00N2018-05-10 11:002018-05-10 13:11<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
1201805100212018-05-10 14:12N2018-05-11 11:302018-05-11 18:17<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
2201806200512018-06-20 17:21N2018-06-21 10:002018-06-28 17:00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
3201806260652018-06-26 16:37N2018-06-28 10:002018-07-04 17:24<NA><NA><NA><NA><NA><NA>어린이보호구역 관련도로과<NA><NA><NA><NA><NA><NA>2
4201807110412018-07-11 17:35Y2018-07-12 10:12<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
5201808130342018-08-14 09:00N2018-08-16 09:002018-08-16 17:51<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
6201808220352018-08-22 14:26N2018-08-24 10:002018-08-24 16:06<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
7201809010192018-09-03 09:00N2018-09-03 13:002018-09-04 10:23<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
8201810040402018-10-04 14:37Y2018-10-12 10:19<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2
9201810110162018-10-11 12:40Y2018-10-16 13:22<NA><NA><NA><NA><NA><NA>시설관리공단민원봉사담당관<NA><NA><NA><NA><NA><NA>2