Overview

Dataset statistics

Number of variables8
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory73.1 B

Variable types

Numeric8

Dataset

Description사립학교교직원연금공단의 VOC 상위 5개 민원별 월별 발생 건수를 연도, 월, 심사 및 장수, 급여, 대여, 재해보상, 연금연계, 기타로 나타냅니다.※ VOC : Voice of Customer('고객의 소리'로써 민원 창구 역할을 하는 시스템)
Author사립학교교직원연금공단
URLhttps://www.data.go.kr/data/15124513/fileData.do

Alerts

연도 is highly overall correlated with 심사_징수 and 4 other fieldsHigh correlation
심사_징수 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
급여 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
대여 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
재해보상 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
기타 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
재해보상 has 3 (2.5%) zerosZeros

Reproduction

Analysis started2023-12-12 13:24:52.248386
Analysis finished2023-12-12 13:24:58.873815
Duration6.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.5
Minimum2013
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:24:58.921688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12015
median2017.5
Q32020
95-th percentile2022
Maximum2022
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8843245
Coefficient of variation (CV)0.0014296528
Kurtosis-1.2251099
Mean2017.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum242100
Variance8.3193277
MonotonicityIncreasing
2023-12-12T22:24:59.020673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2013 12
10.0%
2014 12
10.0%
2015 12
10.0%
2016 12
10.0%
2017 12
10.0%
2018 12
10.0%
2019 12
10.0%
2020 12
10.0%
2021 12
10.0%
2022 12
10.0%
ValueCountFrequency (%)
2013 12
10.0%
2014 12
10.0%
2015 12
10.0%
2016 12
10.0%
2017 12
10.0%
2018 12
10.0%
2019 12
10.0%
2020 12
10.0%
2021 12
10.0%
2022 12
10.0%
ValueCountFrequency (%)
2022 12
10.0%
2021 12
10.0%
2020 12
10.0%
2019 12
10.0%
2018 12
10.0%
2017 12
10.0%
2016 12
10.0%
2015 12
10.0%
2014 12
10.0%
2013 12
10.0%

월
Real number (ℝ)

Distinct12
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:24:59.124643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4665266
Coefficient of variation (CV)0.53331179
Kurtosis-1.2173303
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum780
Variance12.016807
MonotonicityNot monotonic
2023-12-12T22:24:59.229429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 10
8.3%
2 10
8.3%
3 10
8.3%
4 10
8.3%
5 10
8.3%
6 10
8.3%
7 10
8.3%
8 10
8.3%
9 10
8.3%
10 10
8.3%
Other values (2) 20
16.7%
ValueCountFrequency (%)
1 10
8.3%
2 10
8.3%
3 10
8.3%
4 10
8.3%
5 10
8.3%
6 10
8.3%
7 10
8.3%
8 10
8.3%
9 10
8.3%
10 10
8.3%
ValueCountFrequency (%)
12 10
8.3%
11 10
8.3%
10 10
8.3%
9 10
8.3%
8 10
8.3%
7 10
8.3%
6 10
8.3%
5 10
8.3%
4 10
8.3%
3 10
8.3%

심사_징수
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.625
Minimum28
Maximum684
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:24:59.363456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile40
Q159
median84.5
Q3340.5
95-th percentile477.3
Maximum684
Range656
Interquartile range (IQR)281.5

Descriptive statistics

Standard deviation166.15064
Coefficient of variation (CV)0.84501278
Kurtosis-0.70341993
Mean196.625
Median Absolute Deviation (MAD)44.5
Skewness0.73008199
Sum23595
Variance27606.035
MonotonicityNot monotonic
2023-12-12T22:24:59.528491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 4
 
3.3%
66 4
 
3.3%
347 3
 
2.5%
46 3
 
2.5%
325 3
 
2.5%
73 3
 
2.5%
68 2
 
1.7%
55 2
 
1.7%
59 2
 
1.7%
65 2
 
1.7%
Other values (77) 92
76.7%
ValueCountFrequency (%)
28 1
 
0.8%
32 1
 
0.8%
37 2
1.7%
40 4
3.3%
41 2
1.7%
42 1
 
0.8%
43 2
1.7%
46 3
2.5%
48 1
 
0.8%
50 2
1.7%
ValueCountFrequency (%)
684 1
0.8%
597 1
0.8%
572 1
0.8%
527 1
0.8%
492 1
0.8%
483 1
0.8%
477 1
0.8%
471 1
0.8%
462 1
0.8%
452 1
0.8%

급여
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean355.69167
Minimum93
Maximum1169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:24:59.699217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum93
5-th percentile118.9
Q1161.25
median237
Q3514.5
95-th percentile807.05
Maximum1169
Range1076
Interquartile range (IQR)353.25

Descriptive statistics

Standard deviation236.69662
Coefficient of variation (CV)0.6654545
Kurtosis0.16500482
Mean355.69167
Median Absolute Deviation (MAD)117
Skewness0.94533771
Sum42683
Variance56025.291
MonotonicityNot monotonic
2023-12-12T22:24:59.857767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 3
 
2.5%
199 2
 
1.7%
213 2
 
1.7%
145 2
 
1.7%
155 2
 
1.7%
134 2
 
1.7%
93 2
 
1.7%
176 2
 
1.7%
522 2
 
1.7%
514 2
 
1.7%
Other values (96) 99
82.5%
ValueCountFrequency (%)
93 2
1.7%
96 1
0.8%
114 1
0.8%
116 1
0.8%
117 1
0.8%
119 1
0.8%
121 2
1.7%
122 1
0.8%
131 1
0.8%
132 1
0.8%
ValueCountFrequency (%)
1169 1
0.8%
965 1
0.8%
828 1
0.8%
818 1
0.8%
810 1
0.8%
808 1
0.8%
807 1
0.8%
805 1
0.8%
799 1
0.8%
797 1
0.8%

대여
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.61667
Minimum17
Maximum971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:25:00.017549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile39.95
Q165.75
median97
Q3437
95-th percentile677.65
Maximum971
Range954
Interquartile range (IQR)371.25

Descriptive statistics

Standard deviation233.50429
Coefficient of variation (CV)0.95849064
Kurtosis-0.020351167
Mean243.61667
Median Absolute Deviation (MAD)55
Skewness1.0388758
Sum29234
Variance54524.255
MonotonicityNot monotonic
2023-12-12T22:25:00.162078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 4
 
3.3%
53 3
 
2.5%
62 3
 
2.5%
76 3
 
2.5%
78 3
 
2.5%
87 2
 
1.7%
89 2
 
1.7%
258 2
 
1.7%
73 2
 
1.7%
41 2
 
1.7%
Other values (87) 94
78.3%
ValueCountFrequency (%)
17 1
0.8%
24 1
0.8%
27 1
0.8%
34 1
0.8%
36 1
0.8%
39 1
0.8%
40 1
0.8%
41 2
1.7%
43 1
0.8%
45 1
0.8%
ValueCountFrequency (%)
971 1
0.8%
887 1
0.8%
807 1
0.8%
725 1
0.8%
715 1
0.8%
690 1
0.8%
677 1
0.8%
660 1
0.8%
647 1
0.8%
646 1
0.8%

재해보상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.6
Minimum0
Maximum127
Zeros3
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:25:00.289016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q39.5
95-th percentile97.35
Maximum127
Range127
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation30.092407
Coefficient of variation (CV)1.7097958
Kurtosis4.0289103
Mean17.6
Median Absolute Deviation (MAD)2
Skewness2.2047388
Sum2112
Variance905.55294
MonotonicityNot monotonic
2023-12-12T22:25:00.409742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
4 23
19.2%
1 17
14.2%
3 15
12.5%
2 12
10.0%
6 6
 
5.0%
5 6
 
5.0%
7 4
 
3.3%
0 3
 
2.5%
8 3
 
2.5%
23 2
 
1.7%
Other values (27) 29
24.2%
ValueCountFrequency (%)
0 3
 
2.5%
1 17
14.2%
2 12
10.0%
3 15
12.5%
4 23
19.2%
5 6
 
5.0%
6 6
 
5.0%
7 4
 
3.3%
8 3
 
2.5%
9 1
 
0.8%
ValueCountFrequency (%)
127 1
0.8%
126 1
0.8%
112 1
0.8%
110 1
0.8%
108 1
0.8%
104 1
0.8%
97 1
0.8%
80 1
0.8%
73 1
0.8%
71 1
0.8%

연금연계
Real number (ℝ)

Distinct68
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.833333
Minimum9
Maximum505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:25:00.575998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile16
Q130
median44
Q361
95-th percentile241.5
Maximum505
Range496
Interquartile range (IQR)31

Descriptive statistics

Standard deviation80.693146
Coefficient of variation (CV)1.1391974
Kurtosis7.6777559
Mean70.833333
Median Absolute Deviation (MAD)15.5
Skewness2.5919133
Sum8500
Variance6511.3838
MonotonicityNot monotonic
2023-12-12T22:25:00.737270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 7
 
5.8%
22 5
 
4.2%
46 5
 
4.2%
37 5
 
4.2%
50 4
 
3.3%
40 4
 
3.3%
52 4
 
3.3%
16 3
 
2.5%
25 3
 
2.5%
30 3
 
2.5%
Other values (58) 77
64.2%
ValueCountFrequency (%)
9 1
 
0.8%
10 1
 
0.8%
14 1
 
0.8%
15 2
1.7%
16 3
2.5%
17 1
 
0.8%
18 2
1.7%
19 1
 
0.8%
20 1
 
0.8%
21 2
1.7%
ValueCountFrequency (%)
505 1
0.8%
330 1
0.8%
280 1
0.8%
274 1
0.8%
261 1
0.8%
251 1
0.8%
241 1
0.8%
234 1
0.8%
226 1
0.8%
224 1
0.8%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.916667
Minimum3
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:25:00.886361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q113
median20
Q354.25
95-th percentile127.6
Maximum222
Range219
Interquartile range (IQR)41.25

Descriptive statistics

Standard deviation41.025015
Coefficient of variation (CV)1.0277665
Kurtosis4.3973907
Mean39.916667
Median Absolute Deviation (MAD)11.5
Skewness1.9890608
Sum4790
Variance1683.0518
MonotonicityNot monotonic
2023-12-12T22:25:01.346451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 8
 
6.7%
20 7
 
5.8%
15 6
 
5.0%
11 6
 
5.0%
9 5
 
4.2%
13 5
 
4.2%
14 4
 
3.3%
23 3
 
2.5%
18 3
 
2.5%
6 3
 
2.5%
Other values (50) 70
58.3%
ValueCountFrequency (%)
3 1
 
0.8%
5 3
2.5%
6 3
2.5%
7 2
 
1.7%
8 2
 
1.7%
9 5
4.2%
10 2
 
1.7%
11 6
5.0%
12 2
 
1.7%
13 5
4.2%
ValueCountFrequency (%)
222 1
0.8%
192 1
0.8%
158 1
0.8%
144 1
0.8%
141 1
0.8%
139 1
0.8%
127 2
1.7%
115 1
0.8%
102 1
0.8%
97 1
0.8%

Interactions

2023-12-12T22:24:58.094074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:52.518203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.272168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.922276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:54.729064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:55.579282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:56.655904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.436354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:58.161282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:52.627513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.342439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:54.018544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:54.833619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:55.670852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:56.738819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.525946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:58.233516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:52.716185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.427499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:54.104450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:54.941136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:56.115661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:56.834843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.624596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:58.316779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:52.815842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.519485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:54.195163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:55.061844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:56.209244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:56.931767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.722294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:58.385987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:52.902727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.604672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:54.292413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:55.165057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:56.309842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.023158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.798312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:58.453330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:52.998860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.687973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:54.397498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:55.267823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:56.396502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.151983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.874826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:58.522098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.103368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.770102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:54.537370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:55.372060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:56.490876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.254508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.951569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:58.604803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.198730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:53.847327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:54.639920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:55.486122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:56.571984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:57.348878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:58.023654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:25:01.451955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월심사_징수급여대여재해보상연금연계기타
연도1.0000.0000.5960.5890.7040.6490.5290.669
월0.0001.0000.5010.2540.0000.0000.0000.287
심사_징수0.5960.5011.0000.8260.8970.7090.3140.875
급여0.5890.2540.8261.0000.7960.6320.1400.787
대여0.7040.0000.8970.7961.0000.8380.0000.864
재해보상0.6490.0000.7090.6320.8381.0000.0000.912
연금연계0.5290.0000.3140.1400.0000.0001.0000.000
기타0.6690.2870.8750.7870.8640.9120.0001.000
2023-12-12T22:25:01.582119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월심사_징수급여대여재해보상연금연계기타
연도1.0000.000-0.749-0.716-0.729-0.6550.125-0.746
월0.0001.000-0.243-0.279-0.1820.027-0.115-0.114
심사_징수-0.749-0.2431.0000.8670.7890.6120.1700.768
급여-0.716-0.2790.8671.0000.8430.6060.3360.780
대여-0.729-0.1820.7890.8431.0000.6110.2910.756
재해보상-0.6550.0270.6120.6060.6111.0000.1280.604
연금연계0.125-0.1150.1700.3360.2910.1281.0000.225
기타-0.746-0.1140.7680.7800.7560.6040.2251.000

Missing values

2023-12-12T22:24:58.710647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:24:58.831689image/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

연도월심사_징수급여대여재해보상연금연계기타
020131398818637975688
1201323478088871045572
220133572116960312749192
32013442379753012644222
42013538463352511251115
52013646249445211039127
62013738361247610845144
720138284514646483737
8201393054954367331158
9201310325522660805086
연도월심사_징수급여대여재해보상연금연계기타
110202231693389329220
111202241023129232079
11220225662131161559
1132022668175844465
1142022762173785399
11520228561215351515
11620229621218142416
117202210661564035211
11820221163213734669
119202212401421773120