Overview

Dataset statistics

Number of variables11
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory99.1 B

Variable types

Text1
Numeric6
Categorical4

Dataset

Description2022년 하반기 부서별 민원접수건수, 완결건수, 불가건수, 반려건수, 취하건수, 이첩이송건수, 기타건수, 처리중건수, 민원접수처리기간을 제공합니다.
Author서울특별시 금천구
URLhttps://www.data.go.kr/data/3081190/fileData.do

Alerts

민원접수처리기간 has constant value ""Constant
데이터기준일자 has constant value ""Constant
민원접수건수 is highly overall correlated with 완결건수 and 3 other fieldsHigh correlation
완결건수 is highly overall correlated with 민원접수건수 and 3 other fieldsHigh correlation
반려건수 is highly overall correlated with 민원접수건수 and 2 other fieldsHigh correlation
취하건수 is highly overall correlated with 처리중건수High correlation
기타건수 is highly overall correlated with 민원접수건수 and 2 other fieldsHigh correlation
이첩이송건수 is highly overall correlated with 민원접수건수 and 1 other fieldsHigh correlation
처리중건수 is highly overall correlated with 취하건수High correlation
이첩이송건수 is highly imbalanced (62.0%)Imbalance
처리중건수 is highly imbalanced (71.9%)Imbalance
부서명 has unique valuesUnique
완결건수 has 1 (2.4%) zerosZeros
불가건수 has 29 (70.7%) zerosZeros
반려건수 has 17 (41.5%) zerosZeros
취하건수 has 26 (63.4%) zerosZeros
기타건수 has 10 (24.4%) zerosZeros

Reproduction

Analysis started2024-03-15 01:04:56.490906
Analysis finished2024-03-15 01:05:05.774560
Duration9.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

부서명
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size456.0 B
2024-03-15T10:05:06.464905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.8292683
Min length3

Characters and Unicode

Total characters198
Distinct characters77
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

Unique41 ?
Unique (%)100.0%

Sample

1st row가족정책과
2nd row건설행정과
3rd row건축과
4th row공원녹지과
5th row교통행정과
ValueCountFrequency (%)
보건소 4
 
8.9%
가족정책과 1
 
2.2%
가산동 1
 
2.2%
주민안전과 1
 
2.2%
주차관리과 1
 
2.2%
주택과 1
 
2.2%
지역경제과 1
 
2.2%
청소행정과 1
 
2.2%
치수과 1
 
2.2%
행정지원과 1
 
2.2%
Other values (32) 32
71.1%
2024-03-15T10:05:07.925834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 31
 
15.7%
동 12
 
6.1%
정 10
 
5.1%
건 8
 
4.0%
지 6
 
3.0%
보 6
 
3.0%
시 6
 
3.0%
산 6
 
3.0%
소 6
 
3.0%
행 5
 
2.5%
Other values (67) 102
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 184
92.9%
Decimal Number 10
 
5.1%
Space Separator 4
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 31
 
16.8%
동 12
 
6.5%
정 10
 
5.4%
건 8
 
4.3%
지 6
 
3.3%
보 6
 
3.3%
시 6
 
3.3%
산 6
 
3.3%
소 6
 
3.3%
행 5
 
2.7%
Other values (61) 88
47.8%
Decimal Number
ValueCountFrequency (%)
2 3
30.0%
4 2
20.0%
3 2
20.0%
1 2
20.0%
5 1
 
10.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 184
92.9%
Common 14
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 31
 
16.8%
동 12
 
6.5%
정 10
 
5.4%
건 8
 
4.3%
지 6
 
3.3%
보 6
 
3.3%
시 6
 
3.3%
산 6
 
3.3%
소 6
 
3.3%
행 5
 
2.7%
Other values (61) 88
47.8%
Common
ValueCountFrequency (%)
4
28.6%
2 3
21.4%
4 2
14.3%
3 2
14.3%
1 2
14.3%
5 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 184
92.9%
ASCII 14
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 31
 
16.8%
동 12
 
6.5%
정 10
 
5.4%
건 8
 
4.3%
지 6
 
3.3%
보 6
 
3.3%
시 6
 
3.3%
산 6
 
3.3%
소 6
 
3.3%
행 5
 
2.7%
Other values (61) 88
47.8%
ASCII
ValueCountFrequency (%)
4
28.6%
2 3
21.4%
4 2
14.3%
3 2
14.3%
1 2
14.3%
5 1
 
7.1%

민원접수건수
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4296.3171
Minimum1
Maximum47945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T10:05:08.317995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q146
median812
Q36269
95-th percentile17893
Maximum47945
Range47944
Interquartile range (IQR)6223

Descriptive statistics

Standard deviation8428.2608
Coefficient of variation (CV)1.9617409
Kurtosis18.153768
Mean4296.3171
Median Absolute Deviation (MAD)810
Skewness3.8513537
Sum176149
Variance71035580
MonotonicityNot monotonic
2024-03-15T10:05:08.652579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2 3
 
7.3%
28 2
 
4.9%
1 2
 
4.9%
17893 1
 
2.4%
3896 1
 
2.4%
6401 1
 
2.4%
323 1
 
2.4%
84 1
 
2.4%
580 1
 
2.4%
18334 1
 
2.4%
Other values (27) 27
65.9%
ValueCountFrequency (%)
1 2
4.9%
2 3
7.3%
9 1
 
2.4%
11 1
 
2.4%
12 1
 
2.4%
28 2
4.9%
46 1
 
2.4%
52 1
 
2.4%
84 1
 
2.4%
102 1
 
2.4%
ValueCountFrequency (%)
47945 1
2.4%
18334 1
2.4%
17893 1
2.4%
12522 1
2.4%
10873 1
2.4%
7152 1
2.4%
6737 1
2.4%
6672 1
2.4%
6647 1
2.4%
6401 1
2.4%

완결건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4243.6098
Minimum0
Maximum47823
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T10:05:08.919271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q143
median741
Q35773
95-th percentile17834
Maximum47823
Range47823
Interquartile range (IQR)5730

Descriptive statistics

Standard deviation8402.0137
Coefficient of variation (CV)1.9799214
Kurtosis18.28149
Mean4243.6098
Median Absolute Deviation (MAD)739
Skewness3.8698118
Sum173988
Variance70593835
MonotonicityNot monotonic
2024-03-15T10:05:09.178234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2 3
 
7.3%
3317 1
 
2.4%
17834 1
 
2.4%
26 1
 
2.4%
3788 1
 
2.4%
5767 1
 
2.4%
316 1
 
2.4%
81 1
 
2.4%
567 1
 
2.4%
18208 1
 
2.4%
Other values (29) 29
70.7%
ValueCountFrequency (%)
0 1
 
2.4%
1 1
 
2.4%
2 3
7.3%
8 1
 
2.4%
11 1
 
2.4%
12 1
 
2.4%
24 1
 
2.4%
26 1
 
2.4%
43 1
 
2.4%
46 1
 
2.4%
ValueCountFrequency (%)
47823 1
2.4%
18208 1
2.4%
17834 1
2.4%
12452 1
2.4%
10839 1
2.4%
7128 1
2.4%
6715 1
2.4%
6658 1
2.4%
6616 1
2.4%
5998 1
2.4%

불가건수
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58536585
Minimum0
Maximum5
Zeros29
Zeros (%)70.7%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T10:05:09.509370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1613701
Coefficient of variation (CV)1.9840072
Kurtosis5.788648
Mean0.58536585
Median Absolute Deviation (MAD)0
Skewness2.3998425
Sum24
Variance1.3487805
MonotonicityNot monotonic
2024-03-15T10:05:09.804983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 29
70.7%
1 6
 
14.6%
2 3
 
7.3%
5 1
 
2.4%
3 1
 
2.4%
4 1
 
2.4%
ValueCountFrequency (%)
0 29
70.7%
1 6
 
14.6%
2 3
 
7.3%
3 1
 
2.4%
4 1
 
2.4%
5 1
 
2.4%
ValueCountFrequency (%)
5 1
 
2.4%
4 1
 
2.4%
3 1
 
2.4%
2 3
 
7.3%
1 6
 
14.6%
0 29
70.7%

반려건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16
Minimum0
Maximum249
Zeros17
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T10:05:10.002259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile71
Maximum249
Range249
Interquartile range (IQR)8

Descriptive statistics

Standard deviation46.558028
Coefficient of variation (CV)2.9098768
Kurtosis18.261718
Mean16
Median Absolute Deviation (MAD)2
Skewness4.2091013
Sum656
Variance2167.65
MonotonicityNot monotonic
2024-03-15T10:05:10.313574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 17
41.5%
3 3
 
7.3%
10 2
 
4.9%
5 2
 
4.9%
1 2
 
4.9%
2 2
 
4.9%
7 2
 
4.9%
8 1
 
2.4%
11 1
 
2.4%
4 1
 
2.4%
Other values (8) 8
19.5%
ValueCountFrequency (%)
0 17
41.5%
1 2
 
4.9%
2 2
 
4.9%
3 3
 
7.3%
4 1
 
2.4%
5 2
 
4.9%
6 1
 
2.4%
7 2
 
4.9%
8 1
 
2.4%
10 2
 
4.9%
ValueCountFrequency (%)
249 1
2.4%
167 1
2.4%
71 1
2.4%
22 1
2.4%
21 1
2.4%
20 1
2.4%
18 1
2.4%
11 1
2.4%
10 2
4.9%
8 1
2.4%

취하건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2439024
Minimum0
Maximum61
Zeros26
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T10:05:10.601774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile15
Maximum61
Range61
Interquartile range (IQR)1

Descriptive statistics

Standard deviation10.751234
Coefficient of variation (CV)3.3142901
Kurtosis22.488992
Mean3.2439024
Median Absolute Deviation (MAD)0
Skewness4.5925809
Sum133
Variance115.58902
MonotonicityNot monotonic
2024-03-15T10:05:10.971109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 26
63.4%
1 6
 
14.6%
2 3
 
7.3%
3 2
 
4.9%
15 1
 
2.4%
61 1
 
2.4%
32 1
 
2.4%
7 1
 
2.4%
ValueCountFrequency (%)
0 26
63.4%
1 6
 
14.6%
2 3
 
7.3%
3 2
 
4.9%
7 1
 
2.4%
15 1
 
2.4%
32 1
 
2.4%
61 1
 
2.4%
ValueCountFrequency (%)
61 1
 
2.4%
32 1
 
2.4%
15 1
 
2.4%
7 1
 
2.4%
3 2
 
4.9%
2 3
 
7.3%
1 6
 
14.6%
0 26
63.4%

이첩이송건수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size456.0 B
0
35 
1
 
2
2
 
2
6
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)4.9%

Sample

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

Common Values

ValueCountFrequency (%)
0 35
85.4%
1 2
 
4.9%
2 2
 
4.9%
6 1
 
2.4%
3 1
 
2.4%

Length

2024-03-15T10:05:11.359732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:05:11.703234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 35
85.4%
1 2
 
4.9%
2 2
 
4.9%
6 1
 
2.4%
3 1
 
2.4%

기타건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.414634
Minimum0
Maximum619
Zeros10
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T10:05:11.942028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q318
95-th percentile119
Maximum619
Range619
Interquartile range (IQR)17

Descriptive statistics

Standard deviation98.816996
Coefficient of variation (CV)3.0485304
Kurtosis32.882482
Mean32.414634
Median Absolute Deviation (MAD)7
Skewness5.5275392
Sum1329
Variance9764.7988
MonotonicityNot monotonic
2024-03-15T10:05:12.303192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 10
24.4%
1 3
 
7.3%
7 3
 
7.3%
2 3
 
7.3%
13 2
 
4.9%
18 2
 
4.9%
4 2
 
4.9%
111 1
 
2.4%
39 1
 
2.4%
9 1
 
2.4%
Other values (13) 13
31.7%
ValueCountFrequency (%)
0 10
24.4%
1 3
 
7.3%
2 3
 
7.3%
3 1
 
2.4%
4 2
 
4.9%
7 3
 
7.3%
9 1
 
2.4%
10 1
 
2.4%
11 1
 
2.4%
12 1
 
2.4%
ValueCountFrequency (%)
619 1
2.4%
120 1
2.4%
119 1
2.4%
111 1
2.4%
55 1
2.4%
43 1
2.4%
39 1
2.4%
26 1
2.4%
21 1
2.4%
18 2
4.9%

처리중건수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size456.0 B
0
38 
1
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.4%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38
92.7%
1 2
 
4.9%
2 1
 
2.4%

Length

2024-03-15T10:05:12.722380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:05:13.045038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38
92.7%
1 2
 
4.9%
2 1
 
2.4%

민원접수처리기간
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size456.0 B
2023-01-01 ~ 2023-12-31
41 

Length

Max length23
Median length23
Mean length23
Min length23

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-01-01 ~ 2023-12-31
2nd row2023-01-01 ~ 2023-12-31
3rd row2023-01-01 ~ 2023-12-31
4th row2023-01-01 ~ 2023-12-31
5th row2023-01-01 ~ 2023-12-31

Common Values

ValueCountFrequency (%)
2023-01-01 ~ 2023-12-31 41
100.0%

Length

2024-03-15T10:05:13.403944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:05:13.604667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-01 41
33.3%
41
33.3%
2023-12-31 41
33.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size456.0 B
2024-02-02
41 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-02-02
2nd row2024-02-02
3rd row2024-02-02
4th row2024-02-02
5th row2024-02-02

Common Values

ValueCountFrequency (%)
2024-02-02 41
100.0%

Length

2024-03-15T10:05:13.831938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:05:14.144134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-02-02 41
100.0%

Interactions

2024-03-15T10:05:03.948017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:57.019656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:58.395790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:59.807829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:01.067539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:02.634951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:04.096092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:57.264689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:58.579799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:00.051657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:01.311966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:02.888718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:04.239444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:57.507388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:58.818872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:00.299897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:01.553557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:03.131685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:04.394137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:57.756768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:59.065695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:00.437033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:01.800610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:03.315190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:04.536773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:57.999279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:59.306973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:00.575657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:02.044143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:03.568119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:04.705989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:58.249672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:59.558924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:00.826022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:02.380580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:05:03.788467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:05:14.347671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부서명민원접수건수완결건수불가건수반려건수취하건수이첩이송건수기타건수처리중건수
부서명1.0001.0001.0001.0001.0001.0001.0001.0001.000
민원접수건수1.0001.0001.0000.0000.0000.0000.8980.5200.286
완결건수1.0001.0001.0000.0000.0000.0000.8980.5200.286
불가건수1.0000.0000.0001.0000.0000.5290.0000.9250.000
반려건수1.0000.0000.0000.0001.0000.5860.5760.3180.000
취하건수1.0000.0000.0000.5290.5861.0000.4310.6910.690
이첩이송건수1.0000.8980.8980.0000.5760.4311.0000.3350.474
기타건수1.0000.5200.5200.9250.3180.6910.3351.0000.000
처리중건수1.0000.2860.2860.0000.0000.6900.4740.0001.000
2024-03-15T10:05:14.580047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이첩이송건수처리중건수
이첩이송건수1.0000.393
처리중건수0.3931.000
2024-03-15T10:05:14.747588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
민원접수건수완결건수불가건수반려건수취하건수기타건수이첩이송건수처리중건수
민원접수건수1.0000.9990.1760.7080.1620.8360.5620.214
완결건수0.9991.0000.1710.7100.1570.8290.5620.214
불가건수0.1760.1711.0000.1230.2070.2060.0000.000
반려건수0.7080.7100.1231.0000.2710.5500.4940.000
취하건수0.1620.1570.2070.2711.0000.2940.1650.651
기타건수0.8360.8290.2060.5500.2941.0000.2570.000
이첩이송건수0.5620.5620.0000.4940.1650.2571.0000.393
처리중건수0.2140.2140.0000.0000.6510.0000.3931.000

Missing values

2024-03-15T10:05:05.068889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:05:05.576874image/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

부서명민원접수건수완결건수불가건수반려건수취하건수이첩이송건수기타건수처리중건수민원접수처리기간데이터기준일자
0가족정책과3342331712210102023-01-01 ~ 2023-12-312024-02-02
1건설행정과1150112602150702023-01-01 ~ 2023-12-312024-02-02
2건축과920845026101022023-01-01 ~ 2023-12-312024-02-02
3공원녹지과52462010302023-01-01 ~ 2023-12-312024-02-02
4교통행정과11821150018301102023-01-01 ~ 2023-12-312024-02-02
5도로과3042932011702023-01-01 ~ 2023-12-312024-02-02
6도시계획과980100002023-01-01 ~ 2023-12-312024-02-02
7문화체육과43542000201302023-01-01 ~ 2023-12-312024-02-02
8민원여권과4794547823032611102023-01-01 ~ 2023-12-312024-02-02
9보건소 건강증진과220000002023-01-01 ~ 2023-12-312024-02-02
부서명민원접수건수완결건수불가건수반려건수취하건수이첩이송건수기타건수처리중건수민원접수처리기간데이터기준일자
31가산동1833418208070011902023-01-01 ~ 2023-12-312024-02-02
32독산1동1789317834020003902023-01-01 ~ 2023-12-312024-02-02
33독산2동6737671524001602023-01-01 ~ 2023-12-312024-02-02
34독산3동108731083917002602023-01-01 ~ 2023-12-312024-02-02
35독산4동578057730300402023-01-01 ~ 2023-12-312024-02-02
36시흥1동1252212452011035512023-01-01 ~ 2023-12-312024-02-02
37시흥2동66476616010002102023-01-01 ~ 2023-12-312024-02-02
38시흥3동5636561308001502023-01-01 ~ 2023-12-312024-02-02
39시흥4동667266584100902023-01-01 ~ 2023-12-312024-02-02
40시흥5동7152712805001812023-01-01 ~ 2023-12-312024-02-02