Overview

Dataset statistics

Number of variables9
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory84.4 B

Variable types

Numeric9

Dataset

Description직종별(정무직, 별정직, 국가직, 경찰, 소방 등) 공무상요양 승인건수에 대한 데이터입니다. 1983년부터 시작되며 연 단위로 구분됩니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15054918/fileData.do

Alerts

연도 is highly overall correlated with 별정직 and 6 other fieldsHigh correlation
별정직 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
일반직 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
경찰소방 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
교육 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
법관검사 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
기능직 is highly overall correlated with 별정직High correlation
고용직 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
기타 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
연도 has unique valuesUnique
법관검사 has 13 (34.2%) zerosZeros
기능직 has 2 (5.3%) zerosZeros
고용직 has 15 (39.5%) zerosZeros

Reproduction

Analysis started2024-04-21 01:00:44.319874
Analysis finished2024-04-21 01:00:56.771776
Duration12.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.5
Minimum1983
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-04-21T10:00:56.905858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile1984.85
Q11992.25
median2001.5
Q32010.75
95-th percentile2018.15
Maximum2020
Range37
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation11.113055
Coefficient of variation (CV)0.0055523634
Kurtosis-1.2
Mean2001.5
Median Absolute Deviation (MAD)9.5
Skewness0
Sum76057
Variance123.5
MonotonicityStrictly increasing
2024-04-21T10:00:57.364080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1983 1
 
2.6%
2012 1
 
2.6%
2005 1
 
2.6%
2006 1
 
2.6%
2007 1
 
2.6%
2008 1
 
2.6%
2009 1
 
2.6%
2010 1
 
2.6%
2011 1
 
2.6%
2013 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
1983 1
2.6%
1984 1
2.6%
1985 1
2.6%
1986 1
2.6%
1987 1
2.6%
1988 1
2.6%
1989 1
2.6%
1990 1
2.6%
1991 1
2.6%
1992 1
2.6%
ValueCountFrequency (%)
2020 1
2.6%
2019 1
2.6%
2018 1
2.6%
2017 1
2.6%
2016 1
2.6%
2015 1
2.6%
2014 1
2.6%
2013 1
2.6%
2012 1
2.6%
2011 1
2.6%

별정직
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.315789
Minimum2
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-04-21T10:00:57.609843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.85
Q111.25
median19
Q345.5
95-th percentile86.5
Maximum102
Range100
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation28.398806
Coefficient of variation (CV)0.93676617
Kurtosis0.18064943
Mean30.315789
Median Absolute Deviation (MAD)11
Skewness1.1633285
Sum1152
Variance806.49218
MonotonicityNot monotonic
2024-04-21T10:00:57.832808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
13 3
 
7.9%
20 2
 
5.3%
8 2
 
5.3%
3 2
 
5.3%
35 2
 
5.3%
19 2
 
5.3%
2 2
 
5.3%
11 2
 
5.3%
5 2
 
5.3%
12 2
 
5.3%
Other values (17) 17
44.7%
ValueCountFrequency (%)
2 2
5.3%
3 2
5.3%
5 2
5.3%
8 2
5.3%
11 2
5.3%
12 2
5.3%
13 3
7.9%
16 1
 
2.6%
17 1
 
2.6%
18 1
 
2.6%
ValueCountFrequency (%)
102 1
2.6%
95 1
2.6%
85 1
2.6%
77 1
2.6%
73 1
2.6%
66 1
2.6%
64 1
2.6%
62 1
2.6%
60 1
2.6%
49 1
2.6%

일반직
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean589.89474
Minimum239
Maximum1068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-04-21T10:00:58.068396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum239
5-th percentile361.8
Q1468.25
median536.5
Q3621.25
95-th percentile1037.05
Maximum1068
Range829
Interquartile range (IQR)153

Descriptive statistics

Standard deviation214.73237
Coefficient of variation (CV)0.36401811
Kurtosis0.28382948
Mean589.89474
Median Absolute Deviation (MAD)81.5
Skewness0.99663232
Sum22416
Variance46109.989
MonotonicityNot monotonic
2024-04-21T10:00:58.338832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
509 2
 
5.3%
239 1
 
2.6%
623 1
 
2.6%
616 1
 
2.6%
575 1
 
2.6%
610 1
 
2.6%
580 1
 
2.6%
529 1
 
2.6%
687 1
 
2.6%
724 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
239 1
2.6%
270 1
2.6%
378 1
2.6%
389 1
2.6%
396 1
2.6%
398 1
2.6%
417 1
2.6%
444 1
2.6%
453 1
2.6%
467 1
2.6%
ValueCountFrequency (%)
1068 1
2.6%
1060 1
2.6%
1033 1
2.6%
994 1
2.6%
948 1
2.6%
942 1
2.6%
837 1
2.6%
724 1
2.6%
687 1
2.6%
623 1
2.6%

경찰소방
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1568.1842
Minimum198
Maximum3095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-04-21T10:00:58.564560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum198
5-th percentile307.1
Q1851.25
median1417.5
Q32446
95-th percentile2866.65
Maximum3095
Range2897
Interquartile range (IQR)1594.75

Descriptive statistics

Standard deviation886.89257
Coefficient of variation (CV)0.56555382
Kurtosis-1.3995971
Mean1568.1842
Median Absolute Deviation (MAD)897
Skewness0.044550464
Sum59591
Variance786578.42
MonotonicityNot monotonic
2024-04-21T10:00:58.801387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
474 2
 
5.3%
2479 2
 
5.3%
198 1
 
2.6%
2524 1
 
2.6%
1948 1
 
2.6%
2128 1
 
2.6%
2148 1
 
2.6%
2302 1
 
2.6%
2327 1
 
2.6%
2510 1
 
2.6%
Other values (26) 26
68.4%
ValueCountFrequency (%)
198 1
2.6%
285 1
2.6%
311 1
2.6%
328 1
2.6%
436 1
2.6%
474 2
5.3%
780 1
2.6%
839 1
2.6%
849 1
2.6%
858 1
2.6%
ValueCountFrequency (%)
3095 1
2.6%
2927 1
2.6%
2856 1
2.6%
2643 1
2.6%
2583 1
2.6%
2524 1
2.6%
2510 1
2.6%
2487 1
2.6%
2479 2
5.3%
2347 1
2.6%

교육
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean462.5
Minimum68
Maximum982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-04-21T10:00:59.047426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile128.85
Q1259
median437.5
Q3574
95-th percentile960
Maximum982
Range914
Interquartile range (IQR)315

Descriptive statistics

Standard deviation260.49317
Coefficient of variation (CV)0.56322847
Kurtosis-0.59507639
Mean462.5
Median Absolute Deviation (MAD)166
Skewness0.50568684
Sum17575
Variance67856.689
MonotonicityNot monotonic
2024-04-21T10:00:59.271924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
960 2
 
5.3%
140 2
 
5.3%
224 2
 
5.3%
68 1
 
2.6%
669 1
 
2.6%
436 1
 
2.6%
515 1
 
2.6%
576 1
 
2.6%
485 1
 
2.6%
568 1
 
2.6%
Other values (25) 25
65.8%
ValueCountFrequency (%)
68 1
2.6%
94 1
2.6%
135 1
2.6%
140 2
5.3%
159 1
2.6%
174 1
2.6%
224 2
5.3%
244 1
2.6%
304 1
2.6%
319 1
2.6%
ValueCountFrequency (%)
982 1
2.6%
960 2
5.3%
870 1
2.6%
858 1
2.6%
828 1
2.6%
812 1
2.6%
672 1
2.6%
669 1
2.6%
576 1
2.6%
568 1
2.6%

법관검사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5526316
Minimum0
Maximum6
Zeros13
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-04-21T10:00:59.459075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4.15
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6056887
Coefficient of variation (CV)1.0341724
Kurtosis0.38158291
Mean1.5526316
Median Absolute Deviation (MAD)1
Skewness0.99531182
Sum59
Variance2.5782361
MonotonicityNot monotonic
2024-04-21T10:00:59.766759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 13
34.2%
2 9
23.7%
1 8
21.1%
4 4
 
10.5%
3 2
 
5.3%
5 1
 
2.6%
6 1
 
2.6%
ValueCountFrequency (%)
0 13
34.2%
1 8
21.1%
2 9
23.7%
3 2
 
5.3%
4 4
 
10.5%
5 1
 
2.6%
6 1
 
2.6%
ValueCountFrequency (%)
6 1
 
2.6%
5 1
 
2.6%
4 4
 
10.5%
3 2
 
5.3%
2 9
23.7%
1 8
21.1%
0 13
34.2%

기능직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean619.55263
Minimum0
Maximum1081
Zeros2
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-04-21T10:01:00.141551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.55
Q1253
median840
Q3935
95-th percentile1042.15
Maximum1081
Range1081
Interquartile range (IQR)682

Descriptive statistics

Standard deviation393.65348
Coefficient of variation (CV)0.63538344
Kurtosis-1.4926114
Mean619.55263
Median Absolute Deviation (MAD)187.5
Skewness-0.50040797
Sum23543
Variance154963.06
MonotonicityNot monotonic
2024-04-21T10:01:00.548464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 2
 
5.3%
280 2
 
5.3%
884 2
 
5.3%
935 2
 
5.3%
232 1
 
2.6%
855 1
 
2.6%
995 1
 
2.6%
900 1
 
2.6%
934 1
 
2.6%
942 1
 
2.6%
Other values (24) 24
63.2%
ValueCountFrequency (%)
0 2
5.3%
3 1
2.6%
14 1
2.6%
21 1
2.6%
25 1
2.6%
56 1
2.6%
231 1
2.6%
232 1
2.6%
252 1
2.6%
256 1
2.6%
ValueCountFrequency (%)
1081 1
2.6%
1043 1
2.6%
1042 1
2.6%
1013 1
2.6%
1003 1
2.6%
995 1
2.6%
992 1
2.6%
983 1
2.6%
942 1
2.6%
935 2
5.3%

고용직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.131579
Minimum0
Maximum313
Zeros15
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-04-21T10:01:00.907421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17.5
Q377.25
95-th percentile288.75
Maximum313
Range313
Interquartile range (IQR)77.25

Descriptive statistics

Standard deviation105.27266
Coefficient of variation (CV)1.5010736
Kurtosis0.29436063
Mean70.131579
Median Absolute Deviation (MAD)17.5
Skewness1.3760597
Sum2665
Variance11082.334
MonotonicityNot monotonic
2024-04-21T10:01:01.284656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 15
39.5%
30 2
 
5.3%
245 2
 
5.3%
25 2
 
5.3%
23 1
 
2.6%
1 1
 
2.6%
14 1
 
2.6%
15 1
 
2.6%
32 1
 
2.6%
16 1
 
2.6%
Other values (11) 11
28.9%
ValueCountFrequency (%)
0 15
39.5%
1 1
 
2.6%
14 1
 
2.6%
15 1
 
2.6%
16 1
 
2.6%
19 1
 
2.6%
23 1
 
2.6%
25 2
 
5.3%
30 2
 
5.3%
32 1
 
2.6%
ValueCountFrequency (%)
313 1
2.6%
310 1
2.6%
285 1
2.6%
270 1
2.6%
245 2
5.3%
235 1
2.6%
175 1
2.6%
151 1
2.6%
80 1
2.6%
69 1
2.6%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.10526
Minimum4
Maximum1173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.0 B
2024-04-21T10:01:01.664683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.85
Q184.75
median172
Q3274.75
95-th percentile1087
Maximum1173
Range1169
Interquartile range (IQR)190

Descriptive statistics

Standard deviation321.04212
Coefficient of variation (CV)1.1300112
Kurtosis1.8066588
Mean284.10526
Median Absolute Deviation (MAD)97
Skewness1.6452148
Sum10796
Variance103068.04
MonotonicityNot monotonic
2024-04-21T10:01:02.078364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
71 2
 
5.3%
265 2
 
5.3%
6 1
 
2.6%
449 1
 
2.6%
244 1
 
2.6%
186 1
 
2.6%
217 1
 
2.6%
221 1
 
2.6%
278 1
 
2.6%
208 1
 
2.6%
Other values (26) 26
68.4%
ValueCountFrequency (%)
4 1
2.6%
5 1
2.6%
6 1
2.6%
8 1
2.6%
21 1
2.6%
29 1
2.6%
35 1
2.6%
71 2
5.3%
81 1
2.6%
96 1
2.6%
ValueCountFrequency (%)
1173 1
2.6%
1104 1
2.6%
1084 1
2.6%
788 1
2.6%
752 1
2.6%
678 1
2.6%
662 1
2.6%
449 1
2.6%
328 1
2.6%
278 1
2.6%

Interactions

2024-04-21T10:00:55.105098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:44.695006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:46.025336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:47.269304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:48.483221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:50.012923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:51.294848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:52.571177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:53.809135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:55.258103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:44.849768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:46.177043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:47.415996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:48.637439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:50.170409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:51.451513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:52.720750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:53.965633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:55.399505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:44.995712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:46.311619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:47.550577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:48.773707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:50.303678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:51.589143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:52.858769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:54.105868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:55.529914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:45.133651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:46.440899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:47.673655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:48.915508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:50.435542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:51.723610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:52.985851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:54.253967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:55.672857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:45.281827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:46.579173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:47.810590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:49.066185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:50.586748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:51.862537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:53.123296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:54.393269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:55.803873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:45.419464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:46.707965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:47.935609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:49.199931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:50.718562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:51.997615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:53.250978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:54.526685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:55.952380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:45.574599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:46.849668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:48.076324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:49.355155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:50.870193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:52.141094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:53.395053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:54.670065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:56.089462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:45.718206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:46.984251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:48.207337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:49.492735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:51.012901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:52.282983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:53.526900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:54.808837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:56.236023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:45.872420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:47.125280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:48.346936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:49.639183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:51.158546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:52.426483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:53.669904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:00:54.960434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:01:02.348567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별정직일반직경찰소방교육법관검사기능직고용직기타
연도1.0000.8150.7550.8280.8600.4570.7820.7070.833
별정직0.8151.0000.0000.6800.5890.0000.5430.6340.000
일반직0.7550.0001.0000.7460.8960.4990.5820.5320.694
경찰소방0.8280.6800.7461.0000.9340.6080.7510.6160.835
교육0.8600.5890.8960.9341.0000.6040.7630.6210.740
법관검사0.4570.0000.4990.6080.6041.0000.4790.0000.898
기능직0.7820.5430.5820.7510.7630.4791.0000.5850.797
고용직0.7070.6340.5320.6160.6210.0000.5851.0000.000
기타0.8330.0000.6940.8350.7400.8980.7970.0001.000
2024-04-21T10:01:02.652560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별정직일반직경찰소방교육법관검사기능직고용직기타
연도1.000-0.6490.9100.9860.9790.596-0.172-0.9330.963
별정직-0.6491.000-0.722-0.634-0.642-0.3820.5860.612-0.626
일반직0.910-0.7221.0000.9130.8850.531-0.298-0.7910.857
경찰소방0.986-0.6340.9131.0000.9730.595-0.140-0.9230.942
교육0.979-0.6420.8850.9731.0000.601-0.131-0.9070.953
법관검사0.596-0.3820.5310.5950.6011.0000.137-0.6110.563
기능직-0.1720.586-0.298-0.140-0.1310.1371.0000.057-0.131
고용직-0.9330.612-0.791-0.923-0.907-0.6110.0571.000-0.899
기타0.963-0.6260.8570.9420.9530.563-0.131-0.8991.000

Missing values

2024-04-21T10:00:56.432923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:00:56.676734image/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

연도별정직일반직경찰소방교육법관검사기능직고용직기타
01983132391986802321516
11984172702859402561758
2198520378328135023123521
3198623398311159025227029
4198728472436174129831335
5198835509474140028024571
619892054493024415683104
719901950785822405232855
819916039683922408428097
919926241778030408275781
연도별정직일반직경찰소방교육법관검사기능직고용직기타
28201113687251055028380265
29201218724252466958840265
30201311623248785868630449
312014283724796724560662
322015299423478284210678
3320163106025838122140752
342017894226439600250788
352018394828569601301084
3620198103329279821001104
3720205106830958702001173