Overview

Dataset statistics

Number of variables14
Number of observations114
Missing cells343
Missing cells (%)21.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory126.2 B

Variable types

Categorical1
Numeric13

Dataset

Description경기주택도시공사 민원정보
Author경기주택도시공사
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=8OAAUJCSIZW3HDER62Q630746610&infSeq=1

Alerts

년도 is highly overall correlated with 5월 and 2 other fieldsHigh correlation
1월 is highly overall correlated with 2월 and 10 other fieldsHigh correlation
2월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
3월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
4월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
5월 is highly overall correlated with 년도 and 11 other fieldsHigh correlation
6월 is highly overall correlated with 년도 and 11 other fieldsHigh correlation
7월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
8월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
9월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
10월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
11월 is highly overall correlated with 년도 and 11 other fieldsHigh correlation
12월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
1월 has 21 (18.4%) missing valuesMissing
2월 has 25 (21.9%) missing valuesMissing
3월 has 33 (28.9%) missing valuesMissing
4월 has 35 (30.7%) missing valuesMissing
5월 has 36 (31.6%) missing valuesMissing
6월 has 34 (29.8%) missing valuesMissing
7월 has 30 (26.3%) missing valuesMissing
8월 has 29 (25.4%) missing valuesMissing
9월 has 29 (25.4%) missing valuesMissing
10월 has 22 (19.3%) missing valuesMissing
11월 has 23 (20.2%) missing valuesMissing
12월 has 26 (22.8%) missing valuesMissing
1월 has 26 (22.8%) zerosZeros
2월 has 21 (18.4%) zerosZeros
3월 has 17 (14.9%) zerosZeros
4월 has 19 (16.7%) zerosZeros
5월 has 17 (14.9%) zerosZeros
6월 has 14 (12.3%) zerosZeros
7월 has 13 (11.4%) zerosZeros
8월 has 19 (16.7%) zerosZeros
9월 has 16 (14.0%) zerosZeros
10월 has 21 (18.4%) zerosZeros
11월 has 27 (23.7%) zerosZeros
12월 has 22 (19.3%) zerosZeros

Reproduction

Analysis started2024-03-23 01:31:24.813238
Analysis finished2024-03-23 01:32:15.503441
Duration50.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

민원구분
Categorical

Distinct23
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
광교택지
동탄택지
다산택지
기타
고양택지
Other values (18)
70 

Length

Max length5
Median length4
Mean length3.8684211
Min length2

Unique

Unique3 ?
Unique (%)2.6%

Sample

1st row위례주택
2nd row동탄택지
3rd row광교택지
4th row따복하우스
5th row주거복지

Common Values

ValueCountFrequency (%)
광교택지 9
 
7.9%
동탄택지 9
 
7.9%
다산택지 9
 
7.9%
기타 9
 
7.9%
고양택지 8
 
7.0%
행복주택 7
 
6.1%
고덕택지 7
 
6.1%
다산주택 6
 
5.3%
산업단지 5
 
4.4%
동탄주택 5
 
4.4%
Other values (13) 40
35.1%

Length

2024-03-23T01:32:15.711212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광교택지 9
 
7.9%
다산택지 9
 
7.9%
기타 9
 
7.9%
동탄택지 9
 
7.9%
고양택지 8
 
7.0%
행복주택 7
 
6.1%
고덕택지 7
 
6.1%
다산주택 6
 
5.3%
분양주택 5
 
4.4%
임대주택 5
 
4.4%
Other values (13) 40
35.1%

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.0702
Minimum2016
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:16.181043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12018
median2020
Q32022
95-th percentile2024
Maximum2024
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4664808
Coefficient of variation (CV)0.0012209877
Kurtosis-1.1549511
Mean2020.0702
Median Absolute Deviation (MAD)2
Skewness0.025955393
Sum230288
Variance6.0835274
MonotonicityNot monotonic
2024-03-23T01:32:16.627605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 16
14.0%
2017 14
12.3%
2019 14
12.3%
2018 14
12.3%
2024 12
10.5%
2023 12
10.5%
2022 12
10.5%
2020 12
10.5%
2016 8
7.0%
ValueCountFrequency (%)
2016 8
7.0%
2017 14
12.3%
2018 14
12.3%
2019 14
12.3%
2020 12
10.5%
2021 16
14.0%
2022 12
10.5%
2023 12
10.5%
2024 12
10.5%
ValueCountFrequency (%)
2024 12
10.5%
2023 12
10.5%
2022 12
10.5%
2021 16
14.0%
2020 12
10.5%
2019 14
12.3%
2018 14
12.3%
2017 14
12.3%
2016 8
7.0%

1월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct34
Distinct (%)36.6%
Missing21
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean26.72043
Minimum0
Maximum1028
Zeros26
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:17.144608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q317
95-th percentile69
Maximum1028
Range1028
Interquartile range (IQR)17

Descriptive statistics

Standard deviation109.16654
Coefficient of variation (CV)4.0855084
Kurtosis79.094737
Mean26.72043
Median Absolute Deviation (MAD)3
Skewness8.621406
Sum2485
Variance11917.334
MonotonicityNot monotonic
2024-03-23T01:32:17.674213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 26
22.8%
1 9
 
7.9%
3 7
 
6.1%
2 6
 
5.3%
7 4
 
3.5%
4 4
 
3.5%
12 3
 
2.6%
17 2
 
1.8%
15 2
 
1.8%
11 2
 
1.8%
Other values (24) 28
24.6%
(Missing) 21
18.4%
ValueCountFrequency (%)
0 26
22.8%
1 9
 
7.9%
2 6
 
5.3%
3 7
 
6.1%
4 4
 
3.5%
5 1
 
0.9%
6 2
 
1.8%
7 4
 
3.5%
9 1
 
0.9%
11 2
 
1.8%
ValueCountFrequency (%)
1028 1
0.9%
197 1
0.9%
129 1
0.9%
107 1
0.9%
72 1
0.9%
67 1
0.9%
63 1
0.9%
58 2
1.8%
55 1
0.9%
45 1
0.9%

2월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct37
Distinct (%)41.6%
Missing25
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean25.438202
Minimum0
Maximum679
Zeros21
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:18.168726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q318
95-th percentile68.8
Maximum679
Range679
Interquartile range (IQR)17

Descriptive statistics

Standard deviation88.326352
Coefficient of variation (CV)3.4721932
Kurtosis42.064903
Mean25.438202
Median Absolute Deviation (MAD)3
Skewness6.318679
Sum2264
Variance7801.5444
MonotonicityNot monotonic
2024-03-23T01:32:18.724515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 21
18.4%
1 12
10.5%
2 8
 
7.0%
3 4
 
3.5%
6 3
 
2.6%
4 3
 
2.6%
5 3
 
2.6%
9 3
 
2.6%
19 2
 
1.8%
15 2
 
1.8%
Other values (27) 28
24.6%
(Missing) 25
21.9%
ValueCountFrequency (%)
0 21
18.4%
1 12
10.5%
2 8
 
7.0%
3 4
 
3.5%
4 3
 
2.6%
5 3
 
2.6%
6 3
 
2.6%
7 1
 
0.9%
9 3
 
2.6%
10 1
 
0.9%
ValueCountFrequency (%)
679 1
0.9%
482 1
0.9%
104 1
0.9%
88 1
0.9%
74 1
0.9%
61 1
0.9%
60 1
0.9%
58 1
0.9%
51 1
0.9%
48 1
0.9%

3월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)40.7%
Missing33
Missing (%)28.9%
Infinite0
Infinite (%)0.0%
Mean162.53086
Minimum0
Maximum11157
Zeros17
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:19.289273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q316
95-th percentile187
Maximum11157
Range11157
Interquartile range (IQR)15

Descriptive statistics

Standard deviation1238.4366
Coefficient of variation (CV)7.6197009
Kurtosis80.577407
Mean162.53086
Median Absolute Deviation (MAD)5
Skewness8.9657523
Sum13165
Variance1533725.1
MonotonicityNot monotonic
2024-03-23T01:32:20.031705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 17
14.9%
1 9
 
7.9%
2 5
 
4.4%
3 4
 
3.5%
4 4
 
3.5%
5 4
 
3.5%
7 3
 
2.6%
8 3
 
2.6%
6 3
 
2.6%
23 3
 
2.6%
Other values (23) 26
22.8%
(Missing) 33
28.9%
ValueCountFrequency (%)
0 17
14.9%
1 9
7.9%
2 5
 
4.4%
3 4
 
3.5%
4 4
 
3.5%
5 4
 
3.5%
6 3
 
2.6%
7 3
 
2.6%
8 3
 
2.6%
9 1
 
0.9%
ValueCountFrequency (%)
11157 1
0.9%
423 1
0.9%
230 1
0.9%
194 1
0.9%
187 1
0.9%
116 1
0.9%
108 1
0.9%
89 1
0.9%
80 1
0.9%
69 1
0.9%

4월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct29
Distinct (%)36.7%
Missing35
Missing (%)30.7%
Infinite0
Infinite (%)0.0%
Mean56.151899
Minimum0
Maximum1573
Zeros19
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:20.404881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q317.5
95-th percentile195.4
Maximum1573
Range1573
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation223.20274
Coefficient of variation (CV)3.9749812
Kurtosis32.121239
Mean56.151899
Median Absolute Deviation (MAD)3
Skewness5.5107074
Sum4436
Variance49819.464
MonotonicityNot monotonic
2024-03-23T01:32:21.164491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 19
16.7%
1 8
 
7.0%
2 7
 
6.1%
3 6
 
5.3%
4 4
 
3.5%
6 4
 
3.5%
19 3
 
2.6%
16 3
 
2.6%
10 3
 
2.6%
23 2
 
1.8%
Other values (19) 20
17.5%
(Missing) 35
30.7%
ValueCountFrequency (%)
0 19
16.7%
1 8
7.0%
2 7
 
6.1%
3 6
 
5.3%
4 4
 
3.5%
6 4
 
3.5%
9 2
 
1.8%
10 3
 
2.6%
11 1
 
0.9%
12 1
 
0.9%
ValueCountFrequency (%)
1573 1
0.9%
999 1
0.9%
750 1
0.9%
199 1
0.9%
195 1
0.9%
64 1
0.9%
58 1
0.9%
57 1
0.9%
48 1
0.9%
38 1
0.9%

5월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct34
Distinct (%)43.6%
Missing36
Missing (%)31.6%
Infinite0
Infinite (%)0.0%
Mean73.74359
Minimum0
Maximum1947
Zeros17
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:21.884716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q314.75
95-th percentile275.9
Maximum1947
Range1947
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation261.67232
Coefficient of variation (CV)3.5484076
Kurtosis36.181738
Mean73.74359
Median Absolute Deviation (MAD)6
Skewness5.6533177
Sum5752
Variance68472.401
MonotonicityNot monotonic
2024-03-23T01:32:22.491322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 17
14.9%
1 9
 
7.9%
6 5
 
4.4%
2 4
 
3.5%
14 4
 
3.5%
4 4
 
3.5%
10 3
 
2.6%
32 2
 
1.8%
9 2
 
1.8%
11 2
 
1.8%
Other values (24) 26
22.8%
(Missing) 36
31.6%
ValueCountFrequency (%)
0 17
14.9%
1 9
7.9%
2 4
 
3.5%
3 2
 
1.8%
4 4
 
3.5%
5 1
 
0.9%
6 5
 
4.4%
7 2
 
1.8%
8 1
 
0.9%
9 2
 
1.8%
ValueCountFrequency (%)
1947 1
0.9%
904 1
0.9%
828 1
0.9%
366 1
0.9%
260 1
0.9%
246 1
0.9%
244 1
0.9%
231 1
0.9%
87 1
0.9%
86 1
0.9%

6월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct36
Distinct (%)45.0%
Missing34
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean519.3875
Minimum0
Maximum23543
Zeros14
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:22.991413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5.5
Q328
95-th percentile519.1
Maximum23543
Range23543
Interquartile range (IQR)27

Descriptive statistics

Standard deviation2897.8079
Coefficient of variation (CV)5.5792792
Kurtosis53.503548
Mean519.3875
Median Absolute Deviation (MAD)5.5
Skewness7.1038223
Sum41551
Variance8397290.5
MonotonicityNot monotonic
2024-03-23T01:32:23.934667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 14
12.3%
1 10
 
8.8%
3 6
 
5.3%
2 5
 
4.4%
5 3
 
2.6%
19 3
 
2.6%
9 3
 
2.6%
35 2
 
1.8%
18 2
 
1.8%
6 2
 
1.8%
Other values (26) 30
26.3%
(Missing) 34
29.8%
ValueCountFrequency (%)
0 14
12.3%
1 10
8.8%
2 5
 
4.4%
3 6
5.3%
4 2
 
1.8%
5 3
 
2.6%
6 2
 
1.8%
7 1
 
0.9%
8 1
 
0.9%
9 3
 
2.6%
ValueCountFrequency (%)
23543 1
0.9%
10823 1
0.9%
3467 1
0.9%
825 1
0.9%
503 1
0.9%
479 1
0.9%
356 1
0.9%
342 1
0.9%
280 1
0.9%
168 1
0.9%

7월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct36
Distinct (%)42.9%
Missing30
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean84.678571
Minimum0
Maximum1688
Zeros13
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:24.445198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4.5
Q320.25
95-th percentile504.45
Maximum1688
Range1688
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation268.84475
Coefficient of variation (CV)3.1748852
Kurtosis20.504542
Mean84.678571
Median Absolute Deviation (MAD)4.5
Skewness4.3977878
Sum7113
Variance72277.498
MonotonicityNot monotonic
2024-03-23T01:32:25.127725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 13
11.4%
1 12
 
10.5%
2 9
 
7.9%
4 5
 
4.4%
8 4
 
3.5%
21 3
 
2.6%
3 3
 
2.6%
7 2
 
1.8%
10 2
 
1.8%
5 2
 
1.8%
Other values (26) 29
25.4%
(Missing) 30
26.3%
ValueCountFrequency (%)
0 13
11.4%
1 12
10.5%
2 9
7.9%
3 3
 
2.6%
4 5
 
4.4%
5 2
 
1.8%
6 2
 
1.8%
7 2
 
1.8%
8 4
 
3.5%
9 2
 
1.8%
ValueCountFrequency (%)
1688 1
0.9%
1195 1
0.9%
1145 1
0.9%
621 1
0.9%
537 1
0.9%
320 1
0.9%
285 1
0.9%
282 1
0.9%
149 1
0.9%
143 1
0.9%

8월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct35
Distinct (%)41.2%
Missing29
Missing (%)25.4%
Infinite0
Infinite (%)0.0%
Mean64.952941
Minimum0
Maximum1577
Zeros19
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:25.688176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q313
95-th percentile282.8
Maximum1577
Range1577
Interquartile range (IQR)12

Descriptive statistics

Standard deviation236.65913
Coefficient of variation (CV)3.6435476
Kurtosis30.603046
Mean64.952941
Median Absolute Deviation (MAD)4
Skewness5.3885896
Sum5521
Variance56007.545
MonotonicityNot monotonic
2024-03-23T01:32:26.459415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 19
16.7%
2 9
 
7.9%
1 7
 
6.1%
5 7
 
6.1%
3 5
 
4.4%
4 4
 
3.5%
9 3
 
2.6%
8 2
 
1.8%
6 2
 
1.8%
7 2
 
1.8%
Other values (25) 25
21.9%
(Missing) 29
25.4%
ValueCountFrequency (%)
0 19
16.7%
1 7
 
6.1%
2 9
7.9%
3 5
 
4.4%
4 4
 
3.5%
5 7
 
6.1%
6 2
 
1.8%
7 2
 
1.8%
8 2
 
1.8%
9 3
 
2.6%
ValueCountFrequency (%)
1577 1
0.9%
1380 1
0.9%
477 1
0.9%
435 1
0.9%
293 1
0.9%
242 1
0.9%
231 1
0.9%
143 1
0.9%
110 1
0.9%
103 1
0.9%

9월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)38.8%
Missing29
Missing (%)25.4%
Infinite0
Infinite (%)0.0%
Mean107.30588
Minimum0
Maximum2969
Zeros16
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:27.320894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q315
95-th percentile563
Maximum2969
Range2969
Interquartile range (IQR)14

Descriptive statistics

Standard deviation401.17028
Coefficient of variation (CV)3.7385675
Kurtosis33.350902
Mean107.30588
Median Absolute Deviation (MAD)4
Skewness5.4331408
Sum9121
Variance160937.6
MonotonicityNot monotonic
2024-03-23T01:32:28.061616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 16
14.0%
1 11
 
9.6%
3 8
 
7.0%
2 7
 
6.1%
5 5
 
4.4%
11 4
 
3.5%
4 3
 
2.6%
15 3
 
2.6%
27 2
 
1.8%
9 2
 
1.8%
Other values (23) 24
21.1%
(Missing) 29
25.4%
ValueCountFrequency (%)
0 16
14.0%
1 11
9.6%
2 7
6.1%
3 8
7.0%
4 3
 
2.6%
5 5
 
4.4%
6 2
 
1.8%
7 1
 
0.9%
8 1
 
0.9%
9 2
 
1.8%
ValueCountFrequency (%)
2969 1
0.9%
1509 1
0.9%
1342 1
0.9%
959 1
0.9%
624 1
0.9%
319 1
0.9%
318 1
0.9%
231 1
0.9%
114 1
0.9%
74 1
0.9%

10월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct35
Distinct (%)38.0%
Missing22
Missing (%)19.3%
Infinite0
Infinite (%)0.0%
Mean105.1087
Minimum0
Maximum4470
Zeros21
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:28.792217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q316
95-th percentile127.6
Maximum4470
Range4470
Interquartile range (IQR)15

Descriptive statistics

Standard deviation569.81592
Coefficient of variation (CV)5.4212063
Kurtosis47.074946
Mean105.1087
Median Absolute Deviation (MAD)5
Skewness6.793447
Sum9670
Variance324690.19
MonotonicityNot monotonic
2024-03-23T01:32:29.363103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 21
18.4%
1 8
 
7.0%
2 8
 
7.0%
5 6
 
5.3%
7 4
 
3.5%
3 4
 
3.5%
9 3
 
2.6%
10 3
 
2.6%
28 2
 
1.8%
15 2
 
1.8%
Other values (25) 31
27.2%
(Missing) 22
19.3%
ValueCountFrequency (%)
0 21
18.4%
1 8
 
7.0%
2 8
 
7.0%
3 4
 
3.5%
4 2
 
1.8%
5 6
 
5.3%
6 1
 
0.9%
7 4
 
3.5%
8 2
 
1.8%
9 3
 
2.6%
ValueCountFrequency (%)
4470 1
0.9%
3164 1
0.9%
674 1
0.9%
226 1
0.9%
132 1
0.9%
124 1
0.9%
118 1
0.9%
67 1
0.9%
42 1
0.9%
38 1
0.9%

11월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct39
Distinct (%)42.9%
Missing23
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean90.296703
Minimum0
Maximum2664
Zeros27
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:30.024444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q326
95-th percentile347.5
Maximum2664
Range2664
Interquartile range (IQR)26

Descriptive statistics

Standard deviation344.4792
Coefficient of variation (CV)3.8149699
Kurtosis38.229108
Mean90.296703
Median Absolute Deviation (MAD)7
Skewness5.8519672
Sum8217
Variance118665.92
MonotonicityNot monotonic
2024-03-23T01:32:30.478997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 27
23.7%
7 6
 
5.3%
1 5
 
4.4%
4 4
 
3.5%
2 4
 
3.5%
8 4
 
3.5%
11 3
 
2.6%
3 3
 
2.6%
68 2
 
1.8%
13 2
 
1.8%
Other values (29) 31
27.2%
(Missing) 23
20.2%
ValueCountFrequency (%)
0 27
23.7%
1 5
 
4.4%
2 4
 
3.5%
3 3
 
2.6%
4 4
 
3.5%
5 1
 
0.9%
6 1
 
0.9%
7 6
 
5.3%
8 4
 
3.5%
9 1
 
0.9%
ValueCountFrequency (%)
2664 1
0.9%
1527 1
0.9%
946 1
0.9%
840 1
0.9%
358 1
0.9%
337 1
0.9%
263 1
0.9%
140 1
0.9%
139 1
0.9%
101 1
0.9%

12월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct34
Distinct (%)38.6%
Missing26
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean58.852273
Minimum0
Maximum3243
Zeros22
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-23T01:32:30.872686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median4
Q318
95-th percentile99.35
Maximum3243
Range3243
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation349.81107
Coefficient of variation (CV)5.9438837
Kurtosis81.486922
Mean58.852273
Median Absolute Deviation (MAD)4
Skewness8.9023486
Sum5179
Variance122367.78
MonotonicityNot monotonic
2024-03-23T01:32:31.358326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 22
19.3%
1 9
 
7.9%
2 8
 
7.0%
4 7
 
6.1%
3 3
 
2.6%
18 3
 
2.6%
12 3
 
2.6%
22 2
 
1.8%
9 2
 
1.8%
47 2
 
1.8%
Other values (24) 27
23.7%
(Missing) 26
22.8%
ValueCountFrequency (%)
0 22
19.3%
1 9
7.9%
2 8
 
7.0%
3 3
 
2.6%
4 7
 
6.1%
5 1
 
0.9%
6 2
 
1.8%
7 1
 
0.9%
8 2
 
1.8%
9 2
 
1.8%
ValueCountFrequency (%)
3243 1
0.9%
554 1
0.9%
223 1
0.9%
186 1
0.9%
106 1
0.9%
87 1
0.9%
71 1
0.9%
48 1
0.9%
47 2
1.8%
40 1
0.9%

Interactions

2024-03-23T01:32:10.234248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:25.916690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:29.572698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:33.040156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:36.943969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:40.170918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:44.241076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:48.612257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:52.477352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:56.062527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:59.682579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:03.134839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:06.666673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:10.519237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:26.242482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:29.837087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:33.324064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:37.184239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:40.424676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:44.599012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:48.889653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:52.805287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:56.353120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:59.919927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:03.457967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:06.939014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:10.776433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:26.590922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:30.074226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:33.577968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:37.448591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:40.665943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:45.051613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:49.204215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:53.067451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:56.650384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:00.144981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:03.726357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:07.411412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:11.041401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:26.898770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:30.340484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:33.941038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:37.728659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:40.982872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:45.398973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:49.517797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:53.356956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:56.943186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:00.413374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:04.031498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:07.673962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:11.369027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:27.136371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:30.609884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:34.178997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:37.975040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:41.233471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:45.750758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:49.767394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:53.613295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:57.250962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:00.638209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:04.310806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:07.912161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:11.719497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:27.391179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:30.886732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:34.441243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:38.229720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:41.504071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:46.104294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:50.056636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:53.882728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:57.568948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:00.898062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:04.562205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:08.199691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:12.024769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:27.680361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:31.158254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:35.036604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:38.505402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:41.778115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:46.406771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:50.367280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:54.172824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:57.877602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:01.156246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:04.828407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:08.465110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:12.256867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:27.958598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:31.433093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:35.290336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:38.736927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:42.040115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:46.776392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:50.691532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:54.426517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:58.108442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:01.431938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:05.120788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:08.697797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:12.535312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:28.212505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:31.698235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:35.578818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:38.978941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:42.508832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:47.078049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:50.968709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:54.717823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:58.378730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:01.691829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:05.392058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:08.971590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:12.801120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:28.513090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:31.948323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:35.838992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:39.236625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:42.877577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:47.374681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:51.549408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:55.026898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:58.645276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:01.959679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:05.658318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:09.227235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:13.137475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:28.782696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:32.213394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:36.193221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:39.465125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:43.205946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:47.673853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:51.803000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:55.278207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:58.894177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:02.208828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:05.895870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:09.502716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:13.390007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:29.047619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:32.510381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:36.433563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:39.670501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:43.551674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:47.959802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:52.011328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:55.532429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:59.179916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:02.444875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:06.104834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:09.748811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:13.653557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:29.306037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:32.765499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:36.688879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:39.924460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:43.867092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:48.303915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:52.223826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:55.794006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:59.438913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:02.767355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:06.449036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:32:09.995758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T01:32:31.636978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
민원구분년도1월2월3월4월5월6월7월8월9월10월11월12월
민원구분1.0000.0000.0000.0000.0000.0000.0000.2060.0000.0000.0000.0000.0000.000
년도0.0001.0000.3150.1980.0000.2080.3160.1600.3240.2260.2960.3160.1270.153
1월0.0000.3151.0000.6940.0000.8260.3310.0000.0000.0000.9940.7170.4120.000
2월0.0000.1980.6941.0001.0000.7900.6870.8810.4570.4870.8220.3680.2940.000
3월0.0000.0000.0001.0001.0000.0000.0001.0000.4420.4410.0000.0000.0000.000
4월0.0000.2080.8260.7900.0001.0000.6810.6160.8430.6240.8280.7030.9071.000
5월0.0000.3160.3310.6870.0000.6811.0000.7220.4560.3410.5830.4050.5010.881
6월0.2060.1600.0000.8811.0000.6160.7221.0000.6430.3340.0000.0000.0001.000
7월0.0000.3240.0000.4570.4420.8430.4560.6431.0000.9410.6960.7900.6320.565
8월0.0000.2260.0000.4870.4410.6240.3410.3340.9411.0000.8460.9150.7110.000
9월0.0000.2960.9940.8220.0000.8280.5830.0000.6960.8461.0001.0000.7170.000
10월0.0000.3160.7170.3680.0000.7030.4050.0000.7900.9151.0001.0000.7440.000
11월0.0000.1270.4120.2940.0000.9070.5010.0000.6320.7110.7170.7441.0000.521
12월0.0000.1530.0000.0000.0001.0000.8811.0000.5650.0000.0000.0000.5211.000
2024-03-23T01:32:32.035480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도1월2월3월4월5월6월7월8월9월10월11월12월민원구분
년도1.000-0.456-0.481-0.433-0.442-0.511-0.602-0.471-0.429-0.442-0.495-0.517-0.4100.000
1월-0.4561.0000.8330.8070.8590.8360.7930.7940.7570.8200.8080.7800.6190.000
2월-0.4810.8331.0000.8670.8650.7910.7930.7940.8000.8060.7910.7930.5700.000
3월-0.4330.8070.8671.0000.8890.8360.7800.8020.8000.7630.7920.7670.5710.000
4월-0.4420.8590.8650.8891.0000.9070.7980.8140.8280.7850.8140.8220.6860.000
5월-0.5110.8360.7910.8360.9071.0000.7930.8240.8160.7870.8410.8010.7030.000
6월-0.6020.7930.7930.7800.7980.7931.0000.8480.8490.8250.8700.8150.6880.078
7월-0.4710.7940.7940.8020.8140.8240.8481.0000.8320.7920.8210.7880.6750.000
8월-0.4290.7570.8000.8000.8280.8160.8490.8321.0000.8590.8300.7850.6610.000
9월-0.4420.8200.8060.7630.7850.7870.8250.7920.8591.0000.9110.8230.7000.000
10월-0.4950.8080.7910.7920.8140.8410.8700.8210.8300.9111.0000.8680.7150.000
11월-0.5170.7800.7930.7670.8220.8010.8150.7880.7850.8230.8681.0000.7490.000
12월-0.4100.6190.5700.5710.6860.7030.6880.6750.6610.7000.7150.7491.0000.000
민원구분0.0000.0000.0000.0000.0000.0000.0780.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-23T01:32:14.043604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T01:32:14.685179image/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.
2024-03-23T01:32:15.109253image/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

민원구분년도1월2월3월4월5월6월7월8월9월10월11월12월
0위례주택20171735624435630138015093164447
1동탄택지2017362769323244282435634235847
2광교택지20171071916258111896249684
3따복하우스2017422123235613643582
4주거복지201743737<NA>925<NA>43
5고덕택지20173<NA>21<NA>3331<NA>21
6광교주택20173<NA>2<NA>132<NA><NA><NA><NA><NA>
7산업단지2017<NA><NA><NA>12<NA>411<NA><NA><NA>
8고덕주택2017<NA>23<NA><NA><NA><NA><NA><NA><NA><NA><NA>
9동탄주택2017<NA><NA><NA>21<NA><NA><NA><NA><NA>1<NA>
민원구분년도1월2월3월4월5월6월7월8월9월10월11월12월
104고덕택지2018<NA><NA><NA><NA><NA><NA><NA><NA><NA>23<NA>
105고덕주택2018<NA><NA><NA><NA><NA><NA><NA><NA><NA>14<NA><NA>
106고양택지20181<NA>13<NA><NA>1<NA>3133
107행복주택2018<NA><NA><NA>191919184172
108주거복지201811111819129854368
109산업단지2018<NA><NA><NA><NA>13<NA>1<NA>3<NA><NA>
110기타20181117631014961515111
111다산택지20171028482230999260503149110959313934
112고양택지2017<NA><NA><NA><NA><NA>76114515771342674946<NA>
113다산주택201719774116750231825320772969447015279