Overview

Dataset statistics

Number of variables14
Number of observations114
Missing cells125
Missing cells (%)7.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory126.1 B

Variable types

Categorical1
Numeric13

Dataset

Description경기주택도시공사의 사업지구별 민원 현황 정보에 대한 데이터로써 사업지구에 대한 연도별, 월별 민원 건수 정보를 포함합니다.
Author경기주택도시공사
URLhttps://www.data.go.kr/data/15061920/fileData.do

Alerts

1월 is highly overall correlated with 2월 and 9 other fieldsHigh correlation
2월 is highly overall correlated with 1월 and 9 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 1월 and 10 other fieldsHigh correlation
6월 is highly overall correlated with 1월 and 10 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 1월 and 10 other fieldsHigh correlation
12월 is highly overall correlated with 3월 and 8 other fieldsHigh correlation
1월 has 2 (1.8%) missing valuesMissing
3월 has 12 (10.5%) missing valuesMissing
4월 has 13 (11.4%) missing valuesMissing
5월 has 13 (11.4%) missing valuesMissing
6월 has 12 (10.5%) missing valuesMissing
7월 has 12 (10.5%) missing valuesMissing
8월 has 12 (10.5%) missing valuesMissing
9월 has 12 (10.5%) missing valuesMissing
10월 has 12 (10.5%) missing valuesMissing
11월 has 13 (11.4%) missing valuesMissing
12월 has 12 (10.5%) missing valuesMissing
1월 has 45 (39.5%) zerosZeros
2월 has 46 (40.4%) zerosZeros
3월 has 38 (33.3%) zerosZeros
4월 has 41 (36.0%) zerosZeros
5월 has 40 (35.1%) zerosZeros
6월 has 36 (31.6%) zerosZeros
7월 has 31 (27.2%) zerosZeros
8월 has 36 (31.6%) zerosZeros
9월 has 33 (28.9%) zerosZeros
10월 has 31 (27.2%) zerosZeros
11월 has 37 (32.5%) zerosZeros
12월 has 36 (31.6%) zerosZeros

Reproduction

Analysis started2024-03-14 14:09:25.164609
Analysis finished2024-03-14 14:10:03.736746
Duration38.57 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
기타
다산택지
동탄택지
광교택지
고양택지
Other values (18)
70 

Length

Max length5
Median length4
Mean length3.8684211
Min length2

Unique

Unique3 ?
Unique (%)2.6%

Sample

1st row도시재생
2nd row고덕택지
3rd row고양택지
4th row광교택지
5th row다산택지

Common Values

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

Length

2024-03-14T23:10:03.857285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 9
 
7.9%
동탄택지 9
 
7.9%
광교택지 9
 
7.9%
다산택지 9
 
7.9%
고양택지 8
 
7.0%
행복주택 7
 
6.1%
고덕택지 7
 
6.1%
다산주택 6
 
5.3%
분양주택 5
 
4.4%
동탄주택 5
 
4.4%
Other values (13) 40
35.1%

년도
Real number (ℝ)

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
2024-03-14T23:10:04.050673image/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
MonotonicityDecreasing
2024-03-14T23:10:04.258129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 16
14.0%
2019 14
12.3%
2018 14
12.3%
2017 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 (%)30.4%
Missing2
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean22.1875
Minimum0
Maximum1028
Zeros45
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:04.496876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q312.5
95-th percentile64.8
Maximum1028
Range1028
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation99.894497
Coefficient of variation (CV)4.5022872
Kurtosis94.763308
Mean22.1875
Median Absolute Deviation (MAD)2
Skewness9.4265436
Sum2485
Variance9978.9105
MonotonicityNot monotonic
2024-03-14T23:10:04.834289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 45
39.5%
1 9
 
7.9%
3 7
 
6.1%
2 6
 
5.3%
7 4
 
3.5%
4 4
 
3.5%
12 3
 
2.6%
11 2
 
1.8%
58 2
 
1.8%
15 2
 
1.8%
Other values (24) 28
24.6%
ValueCountFrequency (%)
0 45
39.5%
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  ZEROS 

Distinct37
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.859649
Minimum0
Maximum679
Zeros46
Zeros (%)40.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:05.212344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q311.75
95-th percentile60.35
Maximum679
Range679
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation78.65944
Coefficient of variation (CV)3.9607669
Kurtosis54.076462
Mean19.859649
Median Absolute Deviation (MAD)1
Skewness7.1414116
Sum2264
Variance6187.3076
MonotonicityNot monotonic
2024-03-14T23:10:05.625799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 46
40.4%
1 12
 
10.5%
2 8
 
7.0%
3 4
 
3.5%
5 3
 
2.6%
9 3
 
2.6%
6 3
 
2.6%
4 3
 
2.6%
19 2
 
1.8%
26 2
 
1.8%
Other values (27) 28
24.6%
ValueCountFrequency (%)
0 46
40.4%
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 (%)32.4%
Missing12
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean129.06863
Minimum0
Maximum11157
Zeros38
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:06.027951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q312
95-th percentile115.6
Maximum11157
Range11157
Interquartile range (IQR)12

Descriptive statistics

Standard deviation1104.1709
Coefficient of variation (CV)8.5549135
Kurtosis101.45605
Mean129.06863
Median Absolute Deviation (MAD)2
Skewness10.060131
Sum13165
Variance1219193.5
MonotonicityNot monotonic
2024-03-14T23:10:06.422513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 38
33.3%
1 9
 
7.9%
2 5
 
4.4%
5 4
 
3.5%
3 4
 
3.5%
4 4
 
3.5%
8 3
 
2.6%
6 3
 
2.6%
12 3
 
2.6%
23 3
 
2.6%
Other values (23) 26
22.8%
(Missing) 12
 
10.5%
ValueCountFrequency (%)
0 38
33.3%
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 (%)28.7%
Missing13
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean43.920792
Minimum0
Maximum1573
Zeros41
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:06.793858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q311
95-th percentile64
Maximum1573
Range1573
Interquartile range (IQR)11

Descriptive statistics

Standard deviation198.49875
Coefficient of variation (CV)4.5194711
Kurtosis41.697492
Mean43.920792
Median Absolute Deviation (MAD)2
Skewness6.2605778
Sum4436
Variance39401.754
MonotonicityNot monotonic
2024-03-14T23:10:07.201840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 41
36.0%
1 8
 
7.0%
2 7
 
6.1%
3 6
 
5.3%
4 4
 
3.5%
6 4
 
3.5%
16 3
 
2.6%
10 3
 
2.6%
19 3
 
2.6%
9 2
 
1.8%
Other values (19) 20
17.5%
(Missing) 13
 
11.4%
ValueCountFrequency (%)
0 41
36.0%
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 (%)33.7%
Missing13
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean56.950495
Minimum0
Maximum1947
Zeros40
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:07.597575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q312
95-th percentile246
Maximum1947
Range1947
Interquartile range (IQR)12

Descriptive statistics

Standard deviation231.71035
Coefficient of variation (CV)4.0686275
Kurtosis47.169228
Mean56.950495
Median Absolute Deviation (MAD)2
Skewness6.4467714
Sum5752
Variance53689.688
MonotonicityNot monotonic
2024-03-14T23:10:08.019308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 40
35.1%
1 9
 
7.9%
6 5
 
4.4%
2 4
 
3.5%
4 4
 
3.5%
14 4
 
3.5%
10 3
 
2.6%
3 2
 
1.8%
32 2
 
1.8%
11 2
 
1.8%
Other values (24) 26
22.8%
(Missing) 13
 
11.4%
ValueCountFrequency (%)
0 40
35.1%
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 (%)35.3%
Missing12
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean407.36275
Minimum0
Maximum23543
Zeros36
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:08.417994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q319
95-th percentile472.85
Maximum23543
Range23543
Interquartile range (IQR)19

Descriptive statistics

Standard deviation2571.8212
Coefficient of variation (CV)6.3133441
Kurtosis68.565727
Mean407.36275
Median Absolute Deviation (MAD)2.5
Skewness8.0363108
Sum41551
Variance6614264.3
MonotonicityNot monotonic
2024-03-14T23:10:08.808561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 36
31.6%
1 10
 
8.8%
3 6
 
5.3%
2 5
 
4.4%
19 3
 
2.6%
5 3
 
2.6%
9 3
 
2.6%
18 2
 
1.8%
39 2
 
1.8%
25 2
 
1.8%
Other values (26) 30
26.3%
(Missing) 12
 
10.5%
ValueCountFrequency (%)
0 36
31.6%
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 (%)35.3%
Missing12
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean69.735294
Minimum0
Maximum1688
Zeros31
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:09.181157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q314.25
95-th percentile318.25
Maximum1688
Range1688
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation245.86328
Coefficient of variation (CV)3.5256649
Kurtosis25.513646
Mean69.735294
Median Absolute Deviation (MAD)2
Skewness4.883677
Sum7113
Variance60448.751
MonotonicityNot monotonic
2024-03-14T23:10:09.594891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 31
27.2%
1 12
 
10.5%
2 9
 
7.9%
4 5
 
4.4%
8 4
 
3.5%
3 3
 
2.6%
21 3
 
2.6%
15 2
 
1.8%
6 2
 
1.8%
9 2
 
1.8%
Other values (26) 29
25.4%
(Missing) 12
 
10.5%
ValueCountFrequency (%)
0 31
27.2%
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 (%)34.3%
Missing12
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean54.127451
Minimum0
Maximum1577
Zeros36
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:09.978536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile241.45
Maximum1577
Range1577
Interquartile range (IQR)9

Descriptive statistics

Standard deviation217.19183
Coefficient of variation (CV)4.0126004
Kurtosis37.175936
Mean54.127451
Median Absolute Deviation (MAD)2
Skewness5.9227857
Sum5521
Variance47172.291
MonotonicityNot monotonic
2024-03-14T23:10:10.392832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 36
31.6%
2 9
 
7.9%
5 7
 
6.1%
1 7
 
6.1%
3 5
 
4.4%
4 4
 
3.5%
9 3
 
2.6%
6 2
 
1.8%
8 2
 
1.8%
7 2
 
1.8%
Other values (25) 25
21.9%
(Missing) 12
 
10.5%
ValueCountFrequency (%)
0 36
31.6%
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 (%)32.4%
Missing12
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean89.421569
Minimum0
Maximum2969
Zeros33
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:10.995601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q311
95-th percentile318.95
Maximum2969
Range2969
Interquartile range (IQR)11

Descriptive statistics

Standard deviation368.05457
Coefficient of variation (CV)4.1159485
Kurtosis40.392107
Mean89.421569
Median Absolute Deviation (MAD)2.5
Skewness5.9725305
Sum9121
Variance135464.17
MonotonicityNot monotonic
2024-03-14T23:10:11.386503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 33
28.9%
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) 12
 
10.5%
ValueCountFrequency (%)
0 33
28.9%
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 (%)34.3%
Missing12
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean94.803922
Minimum0
Maximum4470
Zeros31
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:11.778491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5
Q314.75
95-th percentile123.7
Maximum4470
Range4470
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation541.7834
Coefficient of variation (CV)5.7147783
Kurtosis52.430152
Mean94.803922
Median Absolute Deviation (MAD)3.5
Skewness7.1629235
Sum9670
Variance293529.25
MonotonicityNot monotonic
2024-03-14T23:10:12.023121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 31
27.2%
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%
16 2
 
1.8%
Other values (25) 31
27.2%
(Missing) 12
 
10.5%
ValueCountFrequency (%)
0 31
27.2%
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 (%)38.6%
Missing13
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean81.356436
Minimum0
Maximum2664
Zeros37
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:12.243654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q319
95-th percentile337
Maximum2664
Range2664
Interquartile range (IQR)19

Descriptive statistics

Standard deviation327.9237
Coefficient of variation (CV)4.0307038
Kurtosis42.602183
Mean81.356436
Median Absolute Deviation (MAD)4
Skewness6.1731914
Sum8217
Variance107533.95
MonotonicityNot monotonic
2024-03-14T23:10:12.477760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 37
32.5%
7 6
 
5.3%
1 5
 
4.4%
2 4
 
3.5%
8 4
 
3.5%
4 4
 
3.5%
11 3
 
2.6%
3 3
 
2.6%
16 2
 
1.8%
13 2
 
1.8%
Other values (29) 31
27.2%
(Missing) 13
 
11.4%
ValueCountFrequency (%)
0 37
32.5%
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 (%)33.3%
Missing12
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean50.77451
Minimum0
Maximum3243
Zeros36
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T23:10:12.712087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313.5
95-th percentile86.2
Maximum3243
Range3243
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation325.30004
Coefficient of variation (CV)6.4067589
Kurtosis94.406334
Mean50.77451
Median Absolute Deviation (MAD)2
Skewness9.5804828
Sum5179
Variance105820.12
MonotonicityNot monotonic
2024-03-14T23:10:13.004874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 36
31.6%
1 9
 
7.9%
2 8
 
7.0%
4 7
 
6.1%
12 3
 
2.6%
3 3
 
2.6%
18 3
 
2.6%
6 2
 
1.8%
11 2
 
1.8%
47 2
 
1.8%
Other values (24) 27
23.7%
(Missing) 12
 
10.5%
ValueCountFrequency (%)
0 36
31.6%
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

2024-03-14T23:09:59.860593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:25.855215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:28.062587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:30.344935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:32.525404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:35.498081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:39.037812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:41.896549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:44.598529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:47.917646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:51.440930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:54.693672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:57.864299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:00.074178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:26.068234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:28.516621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:30.512632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:32.693950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:35.762734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:39.256770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:42.152940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:44.864252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:48.188573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:51.702141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:54.947720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:58.022262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:00.315984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:26.227949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:28.652384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:30.745706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:32.834001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:36.009801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:39.407779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:42.393487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:45.111905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:48.438512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:51.945547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:55.182565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:58.161047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:00.571817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:26.393940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:28.803988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:30.902193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:33.040117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:36.270619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:39.569404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:42.649330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:45.371056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:48.701106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:52.199720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:55.434969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:58.314680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:00.811990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:26.544341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:28.941194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:31.050854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:33.276681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:36.521466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:39.718334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:42.826072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:45.619914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:48.947578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:52.441019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:55.671989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:58.453915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:01.296664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:26.710945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:29.099507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:31.214880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:33.534672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:36.787172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:39.890306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:42.992599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:45.886317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:49.214084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:52.707794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:55.932991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:58.611219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:01.473285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:26.880209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:29.256321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:31.382139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:33.792264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:37.055254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:40.107576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:43.163888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:46.155352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:49.486406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:52.971232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:56.190042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:58.769822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:01.609077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:27.091212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:29.440857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:31.520860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:34.024139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:37.296120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:40.351753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:43.289408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:46.394117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:49.916592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:53.204899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:56.420587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:58.989345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:01.763662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:27.277373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:29.627214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:31.681148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:34.278235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:37.563076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:40.615269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:43.435657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:46.653316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:50.177510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:53.459311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:56.685379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:59.143564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:01.947443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:27.444031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:29.786037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:31.933609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:34.536726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:37.844296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:40.887067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:43.648104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:46.918596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:50.440865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:53.719965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:56.939069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:59.301232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:02.230319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:27.601196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:29.929354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:32.089597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:34.780101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:38.289770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:41.141858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:43.889858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:47.179375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:50.697985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:53.965732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:57.182647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:59.448704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:02.471702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:27.757652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:30.061235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:32.227185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:35.011744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:38.529551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:41.390804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:44.120736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:47.417987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:50.938729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:54.204005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:57.409348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:59.579949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:10:02.713445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:27.907957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:30.198921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:32.373544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:35.249519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:38.778284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:41.638738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:44.354931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:47.665069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:51.187579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:54.446435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:57.648358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:09:59.717805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:10:13.289407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
민원구분년도1월2월3월4월5월6월7월8월9월10월11월12월
민원구분1.0000.0000.0000.0000.0000.0000.0000.4070.0000.0000.0000.0000.0000.000
년도0.0001.0000.1740.0000.0950.0000.1900.1170.2230.1140.0000.0000.0000.000
1월0.0000.1741.0000.6970.0000.7900.3230.0000.0000.0000.9020.3810.4280.000
2월0.0000.0000.6971.0001.0000.7940.7100.8850.5600.5900.7890.6240.3240.000
3월0.0000.0950.0001.0001.0000.0000.0001.0000.7300.7300.0000.0000.0000.000
4월0.0000.0000.7900.7940.0001.0000.6880.6250.6200.3950.8320.6250.8790.707
5월0.0000.1900.3230.7100.0000.6881.0000.7360.5600.4310.5640.6720.3930.477
6월0.4070.1170.0000.8851.0000.6250.7361.0000.6570.3780.0000.0000.0000.668
7월0.0000.2230.0000.5600.7300.6200.5600.6571.0000.9430.7070.7960.5950.778
8월0.0000.1140.0000.5900.7300.3950.4310.3780.9431.0000.8500.9170.5970.000
9월0.0000.0000.9020.7890.0000.8320.5640.0000.7070.8501.0001.0000.7250.000
10월0.0000.0000.3810.6240.0000.6250.6720.0000.7960.9171.0001.0000.7460.000
11월0.0000.0000.4280.3240.0000.8790.3930.0000.5950.5970.7250.7461.0000.527
12월0.0000.0000.0000.0000.0000.7070.4770.6680.7780.0000.0000.0000.5271.000
2024-03-14T23:10:13.646947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도1월2월3월4월5월6월7월8월9월10월11월12월민원구분
년도1.000-0.0760.0210.004-0.003-0.018-0.042-0.042-0.042-0.040-0.314-0.376-0.2080.000
1월-0.0761.0000.8370.8540.8020.7940.7320.7990.7530.8030.6320.6160.4970.000
2월0.0210.8371.0000.8830.8170.7880.7520.7590.7390.7740.6290.6240.5000.000
3월0.0040.8540.8831.0000.8470.8250.7450.7890.7400.7390.6080.5960.5010.000
4월-0.0030.8020.8170.8471.0000.9210.7670.7710.7740.7650.6160.6280.5380.000
5월-0.0180.7940.7880.8250.9211.0000.7780.7870.7730.7560.6300.5950.5210.000
6월-0.0420.7320.7520.7450.7670.7781.0000.8350.8490.7890.7060.6550.5290.200
7월-0.0420.7990.7590.7890.7710.7870.8351.0000.8450.8010.6250.6270.5080.000
8월-0.0420.7530.7390.7400.7740.7730.8490.8451.0000.8550.6670.6440.5230.000
9월-0.0400.8030.7740.7390.7650.7560.7890.8010.8551.0000.7120.6680.5310.000
10월-0.3140.6320.6290.6080.6160.6300.7060.6250.6670.7121.0000.8520.6910.000
11월-0.3760.6160.6240.5960.6280.5950.6550.6270.6440.6680.8521.0000.7410.000
12월-0.2080.4970.5000.5010.5380.5210.5290.5080.5230.5310.6910.7411.0000.000
민원구분0.0000.0000.0000.0000.0000.0000.2000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-14T23:10:02.935929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:10:03.251713image/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.
2024-03-14T23:10:03.525349image/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
0도시재생202431<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1고덕택지202422<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2고양택지202400<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
3광교택지202400<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4다산택지202414<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
5동탄택지202400<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
6용인택지202400<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7주택건설202491<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8분양주택202475<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9임대주택20246751<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
민원구분년도1월2월3월4월5월6월7월8월9월10월11월12월
104동탄주택2017000210000010
105기타201711974172884111678
106광교주택20160000000001<NA>3
107광교택지20160000000002726318
108기타2016000000000161611
109다산주택201600000000010748
110다산택지20160000000008337554
111동탄택지2016000000000972
112위례주택201600000000012124
113주거복지2016000000000224