Overview

Dataset statistics

Number of variables18
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory164.1 B

Variable types

Text1
Numeric17

Dataset

Description공무원 직종별(경찰소방,교육직,법관검사,고용직,공안직,군무원,연구직,지도직 등) 퇴직연금 수급자 추이 데이터로 1982년부터 2022년까지 연도로 구분되어 있습니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15052968/fileData.do

Alerts

is highly overall correlated with 정무직 and 12 other fieldsHigh correlation
정무직 is highly overall correlated with and 12 other fieldsHigh correlation
별정직(국가) is highly overall correlated with and 12 other fieldsHigh correlation
별정직(지방) is highly overall correlated with 일반직(지방)High correlation
일반직(국가) is highly overall correlated with and 12 other fieldsHigh correlation
일반직(지방) is highly overall correlated with 별정직(지방)High correlation
경찰소방 is highly overall correlated with and 12 other fieldsHigh correlation
교육직 is highly overall correlated with and 12 other fieldsHigh correlation
법관검사 is highly overall correlated with and 12 other fieldsHigh correlation
기능직 is highly overall correlated with and 12 other fieldsHigh correlation
공안직 is highly overall correlated with and 12 other fieldsHigh correlation
군무원 is highly overall correlated with and 12 other fieldsHigh correlation
연구직 is highly overall correlated with and 12 other fieldsHigh correlation
지도직 is highly overall correlated with and 12 other fieldsHigh correlation
계약직 is highly overall correlated with and 12 other fieldsHigh correlation
기타 is highly overall correlated with and 12 other fieldsHigh correlation
구분 has unique valuesUnique
has unique valuesUnique
일반직(국가) has unique valuesUnique
경찰소방 has unique valuesUnique
교육직 has unique valuesUnique
법관검사 has unique valuesUnique
기능직 has unique valuesUnique
기타 has unique valuesUnique
정무직 has 19 (44.2%) zerosZeros
별정직(지방) has 33 (76.7%) zerosZeros
일반직(지방) has 14 (32.6%) zerosZeros
기능직 has 1 (2.3%) zerosZeros
고용직 has 7 (16.3%) zerosZeros
공안직 has 19 (44.2%) zerosZeros
군무원 has 19 (44.2%) zerosZeros
연구직 has 19 (44.2%) zerosZeros
지도직 has 19 (44.2%) zerosZeros
계약직 has 19 (44.2%) zerosZeros

Reproduction

Analysis started2024-04-17 16:31:58.820756
Analysis finished2024-04-17 16:32:20.308949
Duration21.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-04-18T01:32:20.424428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8604651
Min length1

Characters and Unicode

Total characters166
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row1982
2nd row1983
3rd row1984
4th row1985
5th row1986
ValueCountFrequency (%)
1982 1
 
2.3%
2004 1
 
2.3%
2006 1
 
2.3%
2007 1
 
2.3%
2008 1
 
2.3%
2009 1
 
2.3%
2010 1
 
2.3%
2011 1
 
2.3%
2012 1
 
2.3%
2013 1
 
2.3%
Other values (33) 33
76.7%
2024-04-18T01:32:20.710821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37
22.3%
1 32
19.3%
9 32
19.3%
2 31
18.7%
8 12
 
7.2%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Other values (2) 2
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164
98.8%
Other Letter 2
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37
22.6%
1 32
19.5%
9 32
19.5%
2 31
18.9%
8 12
 
7.3%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 164
98.8%
Hangul 2
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37
22.6%
1 32
19.5%
9 32
19.5%
2 31
18.9%
8 12
 
7.3%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164
98.8%
Hangul 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37
22.6%
1 32
19.5%
9 32
19.5%
2 31
18.9%
8 12
 
7.3%
3 4
 
2.4%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 4
 
2.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193344.7
Minimum3556
Maximum546010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:20.827190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3556
5-th percentile7115.1
Q134735
median155863
Q3313840
95-th percentile491689.6
Maximum546010
Range542454
Interquartile range (IQR)279105

Descriptive statistics

Standard deviation168708.92
Coefficient of variation (CV)0.87258108
Kurtosis-0.93632238
Mean193344.7
Median Absolute Deviation (MAD)132019
Skewness0.57336896
Sum8313822
Variance2.8462701 × 1010
MonotonicityNot monotonic
2024-04-18T01:32:20.927662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3556 1
 
2.3%
5390 1
 
2.3%
212560 1
 
2.3%
229157 1
 
2.3%
250476 1
 
2.3%
260910 1
 
2.3%
276188 1
 
2.3%
287980 1
 
2.3%
306582 1
 
2.3%
321098 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
3556 1
2.3%
5390 1
2.3%
6940 1
2.3%
8691 1
2.3%
10435 1
2.3%
14196 1
2.3%
17186 1
2.3%
20023 1
2.3%
23844 1
2.3%
27691 1
2.3%
ValueCountFrequency (%)
546010 1
2.3%
521486 1
2.3%
494417 1
2.3%
467143 1
2.3%
442241 1
2.3%
422985 1
2.3%
419968 1
2.3%
396743 1
2.3%
373529 1
2.3%
346781 1
2.3%

정무직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean578.30233
Minimum0
Maximum1306
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:21.021507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median873
Q31096
95-th percentile1194
Maximum1306
Range1306
Interquartile range (IQR)1096

Descriptive statistics

Standard deviation551.00015
Coefficient of variation (CV)0.9527891
Kurtosis-1.9985979
Mean578.30233
Median Absolute Deviation (MAD)412
Skewness-0.075485659
Sum24867
Variance303601.17
MonotonicityNot monotonic
2024-04-18T01:32:21.110712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 19
44.2%
851 1
 
2.3%
21 1
 
2.3%
1285 1
 
2.3%
1306 1
 
2.3%
1196 1
 
2.3%
1176 1
 
2.3%
1166 1
 
2.3%
1150 1
 
2.3%
1141 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 19
44.2%
21 1
 
2.3%
851 1
 
2.3%
873 1
 
2.3%
914 1
 
2.3%
956 1
 
2.3%
965 1
 
2.3%
1025 1
 
2.3%
1037 1
 
2.3%
1055 1
 
2.3%
ValueCountFrequency (%)
1306 1
2.3%
1285 1
2.3%
1196 1
2.3%
1176 1
2.3%
1166 1
2.3%
1150 1
2.3%
1141 1
2.3%
1115 1
2.3%
1100 1
2.3%
1099 1
2.3%

별정직(국가)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6816.1395
Minimum186
Maximum11636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:21.203034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum186
5-th percentile270.9
Q13484.5
median7955
Q39758
95-th percentile11478.5
Maximum11636
Range11450
Interquartile range (IQR)6273.5

Descriptive statistics

Standard deviation3749.1605
Coefficient of variation (CV)0.55004164
Kurtosis-1.0562256
Mean6816.1395
Median Absolute Deviation (MAD)2486
Skewness-0.55563274
Sum293094
Variance14056205
MonotonicityNot monotonic
2024-04-18T01:32:21.301565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
7955 2
 
4.7%
7961 2
 
4.7%
10920 1
 
2.3%
7949 1
 
2.3%
7939 1
 
2.3%
7893 1
 
2.3%
11594 1
 
2.3%
11636 1
 
2.3%
11483 1
 
2.3%
11155 1
 
2.3%
Other values (31) 31
72.1%
ValueCountFrequency (%)
186 1
2.3%
207 1
2.3%
222 1
2.3%
711 1
2.3%
848 1
2.3%
1025 1
2.3%
1272 1
2.3%
2029 1
2.3%
2297 1
2.3%
2696 1
2.3%
ValueCountFrequency (%)
11636 1
2.3%
11594 1
2.3%
11483 1
2.3%
11438 1
2.3%
11155 1
2.3%
10920 1
2.3%
10707 1
2.3%
10441 1
2.3%
10435 1
2.3%
10180 1
2.3%

별정직(지방)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean916.16279
Minimum0
Maximum3963
Zeros33
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:21.385995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3950.7
Maximum3963
Range3963
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1684.0049
Coefficient of variation (CV)1.8381066
Kurtosis-0.29334533
Mean916.16279
Median Absolute Deviation (MAD)0
Skewness1.3124087
Sum39395
Variance2835872.5
MonotonicityNot monotonic
2024-04-18T01:32:21.469862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 33
76.7%
3940 2
 
4.7%
3926 1
 
2.3%
3948 1
 
2.3%
3953 1
 
2.3%
3937 1
 
2.3%
3907 1
 
2.3%
3963 1
 
2.3%
3930 1
 
2.3%
3951 1
 
2.3%
ValueCountFrequency (%)
0 33
76.7%
3907 1
 
2.3%
3926 1
 
2.3%
3930 1
 
2.3%
3937 1
 
2.3%
3940 2
 
4.7%
3948 1
 
2.3%
3951 1
 
2.3%
3953 1
 
2.3%
3963 1
 
2.3%
ValueCountFrequency (%)
3963 1
 
2.3%
3953 1
 
2.3%
3951 1
 
2.3%
3948 1
 
2.3%
3940 2
 
4.7%
3937 1
 
2.3%
3930 1
 
2.3%
3926 1
 
2.3%
3907 1
 
2.3%
0 33
76.7%

일반직(국가)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51157.209
Minimum1620
Maximum194078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:21.572399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1620
5-th percentile2639.8
Q18060.5
median23600
Q387021
95-th percentile169277.3
Maximum194078
Range192458
Interquartile range (IQR)78960.5

Descriptive statistics

Standard deviation59195.779
Coefficient of variation (CV)1.1571346
Kurtosis0.011554285
Mean51157.209
Median Absolute Deviation (MAD)17350
Skewness1.1969845
Sum2199760
Variance3.5041403 × 109
MonotonicityNot monotonic
2024-04-18T01:32:21.693619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1620 1
 
2.3%
2244 1
 
2.3%
30064 1
 
2.3%
31220 1
 
2.3%
32507 1
 
2.3%
33330 1
 
2.3%
34327 1
 
2.3%
80550 1
 
2.3%
85200 1
 
2.3%
88842 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1620 1
2.3%
2244 1
2.3%
2639 1
2.3%
2647 1
2.3%
3097 1
2.3%
4132 1
2.3%
4881 1
2.3%
5504 1
2.3%
6250 1
2.3%
6751 1
2.3%
ValueCountFrequency (%)
194078 1
2.3%
182839 1
2.3%
169738 1
2.3%
165131 1
2.3%
156201 1
2.3%
144060 1
2.3%
133237 1
2.3%
121732 1
2.3%
111045 1
2.3%
99875 1
2.3%

일반직(지방)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9994.3488
Minimum0
Maximum44292
Zeros14
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:21.792812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2235
Q319400
95-th percentile39433.8
Maximum44292
Range44292
Interquartile range (IQR)19400

Descriptive statistics

Standard deviation13785.234
Coefficient of variation (CV)1.3793029
Kurtosis0.20719664
Mean9994.3488
Median Absolute Deviation (MAD)2235
Skewness1.2592853
Sum429757
Variance1.9003267 × 108
MonotonicityNot monotonic
2024-04-18T01:32:22.109021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 14
32.6%
497 1
 
2.3%
12051 1
 
2.3%
44292 1
 
2.3%
41680 1
 
2.3%
39800 1
 
2.3%
36138 1
 
2.3%
32966 1
 
2.3%
29566 1
 
2.3%
27803 1
 
2.3%
Other values (20) 20
46.5%
ValueCountFrequency (%)
0 14
32.6%
497 1
 
2.3%
786 1
 
2.3%
1109 1
 
2.3%
1272 1
 
2.3%
1461 1
 
2.3%
1646 1
 
2.3%
1870 1
 
2.3%
2235 1
 
2.3%
2803 1
 
2.3%
ValueCountFrequency (%)
44292 1
2.3%
41680 1
2.3%
39800 1
2.3%
36138 1
2.3%
32966 1
2.3%
29566 1
2.3%
27803 1
2.3%
25711 1
2.3%
24093 1
2.3%
22925 1
2.3%

경찰소방
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19864.209
Minimum360
Maximum56083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:22.200844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum360
5-th percentile707.2
Q13502.5
median16861
Q330937
95-th percentile52986.9
Maximum56083
Range55723
Interquartile range (IQR)27434.5

Descriptive statistics

Standard deviation17746.244
Coefficient of variation (CV)0.89337782
Kurtosis-0.81069442
Mean19864.209
Median Absolute Deviation (MAD)13607
Skewness0.64498517
Sum854161
Variance3.1492918 × 108
MonotonicityNot monotonic
2024-04-18T01:32:22.294499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
360 1
 
2.3%
678 1
 
2.3%
21622 1
 
2.3%
22919 1
 
2.3%
25196 1
 
2.3%
25529 1
 
2.3%
26950 1
 
2.3%
28694 1
 
2.3%
30468 1
 
2.3%
31406 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
360 1
2.3%
589 1
2.3%
678 1
2.3%
970 1
2.3%
1268 1
2.3%
1397 1
2.3%
1522 1
2.3%
1806 1
2.3%
2176 1
2.3%
2720 1
2.3%
ValueCountFrequency (%)
56083 1
2.3%
55494 1
2.3%
53282 1
2.3%
50331 1
2.3%
47767 1
2.3%
45592 1
2.3%
43351 1
2.3%
40707 1
2.3%
37895 1
2.3%
35075 1
2.3%

교육직
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56906.581
Minimum369
Maximum169906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:22.399767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum369
5-th percentile844.3
Q16662
median47059
Q390997
95-th percentile152070.9
Maximum169906
Range169537
Interquartile range (IQR)84335

Descriptive statistics

Standard deviation52534.417
Coefficient of variation (CV)0.92316944
Kurtosis-0.82954217
Mean56906.581
Median Absolute Deviation (MAD)41683
Skewness0.60439591
Sum2446983
Variance2.7598649 × 109
MonotonicityNot monotonic
2024-04-18T01:32:22.505728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
369 1
 
2.3%
586 1
 
2.3%
56540 1
 
2.3%
62458 1
 
2.3%
70540 1
 
2.3%
75201 1
 
2.3%
80661 1
 
2.3%
86147 1
 
2.3%
93252 1
 
2.3%
99968 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
369 1
2.3%
586 1
2.3%
819 1
2.3%
1072 1
2.3%
1361 1
2.3%
1704 1
2.3%
2269 1
2.3%
2931 1
2.3%
3736 1
2.3%
4824 1
2.3%
ValueCountFrequency (%)
169906 1
2.3%
161127 1
2.3%
152899 1
2.3%
144618 1
2.3%
136452 1
2.3%
129644 1
2.3%
123527 1
2.3%
117208 1
2.3%
107409 1
2.3%
99968 1
2.3%

법관검사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean723.60465
Minimum42
Maximum2082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:22.611329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile69
Q1201
median507
Q31147
95-th percentile1918.9
Maximum2082
Range2040
Interquartile range (IQR)946

Descriptive statistics

Standard deviation614.89535
Coefficient of variation (CV)0.849767
Kurtosis-0.57922778
Mean723.60465
Median Absolute Deviation (MAD)390
Skewness0.77740504
Sum31115
Variance378096.29
MonotonicityNot monotonic
2024-04-18T01:32:22.716660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
42 1
 
2.3%
58 1
 
2.3%
748 1
 
2.3%
796 1
 
2.3%
852 1
 
2.3%
945 1
 
2.3%
1002 1
 
2.3%
1057 1
 
2.3%
1111 1
 
2.3%
1183 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
42 1
2.3%
58 1
2.3%
68 1
2.3%
78 1
2.3%
87 1
2.3%
104 1
2.3%
117 1
2.3%
135 1
2.3%
141 1
2.3%
153 1
2.3%
ValueCountFrequency (%)
2082 1
2.3%
2004 1
2.3%
1931 1
2.3%
1810 1
2.3%
1694 1
2.3%
1588 1
2.3%
1499 1
2.3%
1385 1
2.3%
1293 1
2.3%
1231 1
2.3%

기능직
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29803.628
Minimum0
Maximum60308
Zeros1
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:22.822489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile690.9
Q16118.5
median26298
Q355811.5
95-th percentile59870.5
Maximum60308
Range60308
Interquartile range (IQR)49693

Descriptive statistics

Standard deviation23778.709
Coefficient of variation (CV)0.79784614
Kurtosis-1.7686584
Mean29803.628
Median Absolute Deviation (MAD)22977
Skewness0.089504105
Sum1281556
Variance5.6542702 × 108
MonotonicityNot monotonic
2024-04-18T01:32:22.924931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 1
 
2.3%
518 1
 
2.3%
42751 1
 
2.3%
45730 1
 
2.3%
49275 1
 
2.3%
50431 1
 
2.3%
53454 1
 
2.3%
54673 1
 
2.3%
57888 1
 
2.3%
59455 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
0 1
2.3%
518 1
2.3%
664 1
2.3%
933 1
2.3%
1253 1
2.3%
2975 1
2.3%
3730 1
2.3%
4158 1
2.3%
5139 1
2.3%
5646 1
2.3%
ValueCountFrequency (%)
60308 1
2.3%
60230 1
2.3%
59914 1
2.3%
59479 1
2.3%
59455 1
2.3%
59447 1
2.3%
59316 1
2.3%
58716 1
2.3%
57928 1
2.3%
57888 1
2.3%

고용직
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48
Minimum0
Maximum299
Zeros7
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:23.037773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median30
Q359.5
95-th percentile170.1
Maximum299
Range299
Interquartile range (IQR)48.5

Descriptive statistics

Standard deviation61.670785
Coefficient of variation (CV)1.284808
Kurtosis7.8457905
Mean48
Median Absolute Deviation (MAD)26
Skewness2.6425464
Sum2064
Variance3803.2857
MonotonicityNot monotonic
2024-04-18T01:32:23.146719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 7
16.3%
11 4
 
9.3%
31 2
 
4.7%
77 2
 
4.7%
24 2
 
4.7%
20 2
 
4.7%
4 2
 
4.7%
53 2
 
4.7%
29 2
 
4.7%
64 1
 
2.3%
Other values (17) 17
39.5%
ValueCountFrequency (%)
0 7
16.3%
4 2
 
4.7%
11 4
9.3%
20 2
 
4.7%
23 1
 
2.3%
24 2
 
4.7%
28 1
 
2.3%
29 2
 
4.7%
30 1
 
2.3%
31 2
 
4.7%
ValueCountFrequency (%)
299 1
2.3%
245 1
2.3%
175 1
2.3%
126 1
2.3%
81 1
2.3%
77 2
4.7%
75 1
2.3%
65 1
2.3%
64 1
2.3%
60 1
2.3%

공안직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4647.4419
Minimum0
Maximum14492
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:23.242299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3638
Q38435
95-th percentile13590.1
Maximum14492
Range14492
Interquartile range (IQR)8435

Descriptive statistics

Standard deviation5046.419
Coefficient of variation (CV)1.0858488
Kurtosis-1.154432
Mean4647.4419
Median Absolute Deviation (MAD)3638
Skewness0.56002367
Sum199840
Variance25466345
MonotonicityNot monotonic
2024-04-18T01:32:23.330782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 19
44.2%
3413 1
 
2.3%
439 1
 
2.3%
14053 1
 
2.3%
14492 1
 
2.3%
13667 1
 
2.3%
12898 1
 
2.3%
12282 1
 
2.3%
11729 1
 
2.3%
11281 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 19
44.2%
439 1
 
2.3%
3413 1
 
2.3%
3638 1
 
2.3%
4063 1
 
2.3%
4653 1
 
2.3%
4979 1
 
2.3%
5595 1
 
2.3%
6163 1
 
2.3%
6758 1
 
2.3%
ValueCountFrequency (%)
14492 1
2.3%
14053 1
2.3%
13667 1
2.3%
12898 1
2.3%
12282 1
2.3%
11729 1
2.3%
11281 1
2.3%
10690 1
2.3%
10136 1
2.3%
9432 1
2.3%

군무원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4048.4884
Minimum0
Maximum14377
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:23.417021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1086
Q38048
95-th percentile13264
Maximum14377
Range14377
Interquartile range (IQR)8048

Descriptive statistics

Standard deviation5005.8185
Coefficient of variation (CV)1.2364661
Kurtosis-0.86009383
Mean4048.4884
Median Absolute Deviation (MAD)1086
Skewness0.83988306
Sum174085
Variance25058218
MonotonicityNot monotonic
2024-04-18T01:32:23.517359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 19
44.2%
369 1
 
2.3%
1086 1
 
2.3%
13291 1
 
2.3%
14377 1
 
2.3%
13777 1
 
2.3%
13021 1
 
2.3%
12357 1
 
2.3%
11631 1
 
2.3%
11052 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 19
44.2%
369 1
 
2.3%
743 1
 
2.3%
1086 1
 
2.3%
1259 1
 
2.3%
1893 1
 
2.3%
2369 1
 
2.3%
3146 1
 
2.3%
4009 1
 
2.3%
5036 1
 
2.3%
ValueCountFrequency (%)
14377 1
2.3%
13777 1
2.3%
13291 1
2.3%
13021 1
2.3%
12357 1
2.3%
11631 1
2.3%
11052 1
2.3%
10378 1
2.3%
9796 1
2.3%
8970 1
2.3%

연구직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean982.74419
Minimum0
Maximum3270
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:23.604135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median892
Q31687
95-th percentile2956.2
Maximum3270
Range3270
Interquartile range (IQR)1687

Descriptive statistics

Standard deviation1069.0789
Coefficient of variation (CV)1.0878507
Kurtosis-0.85557463
Mean982.74419
Median Absolute Deviation (MAD)892
Skewness0.65919172
Sum42258
Variance1142929.8
MonotonicityNot monotonic
2024-04-18T01:32:23.695220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 19
44.2%
834 1
 
2.3%
304 1
 
2.3%
2966 1
 
2.3%
3270 1
 
2.3%
3064 1
 
2.3%
2868 1
 
2.3%
2654 1
 
2.3%
2481 1
 
2.3%
2334 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 19
44.2%
304 1
 
2.3%
834 1
 
2.3%
892 1
 
2.3%
940 1
 
2.3%
1017 1
 
2.3%
1054 1
 
2.3%
1135 1
 
2.3%
1189 1
 
2.3%
1339 1
 
2.3%
ValueCountFrequency (%)
3270 1
2.3%
3064 1
2.3%
2966 1
2.3%
2868 1
2.3%
2654 1
2.3%
2481 1
2.3%
2334 1
2.3%
2159 1
2.3%
2006 1
2.3%
1887 1
2.3%

지도직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1880.1163
Minimum0
Maximum4613
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:23.794181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2392
Q33477.5
95-th percentile4455.1
Maximum4613
Range4613
Interquartile range (IQR)3477.5

Descriptive statistics

Standard deviation1837.7438
Coefficient of variation (CV)0.97746285
Kurtosis-1.7981917
Mean1880.1163
Median Absolute Deviation (MAD)2136
Skewness0.093150381
Sum80845
Variance3377302.3
MonotonicityNot monotonic
2024-04-18T01:32:23.883167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 19
44.2%
2327 1
 
2.3%
256 1
 
2.3%
4357 1
 
2.3%
4613 1
 
2.3%
4593 1
 
2.3%
4466 1
 
2.3%
4304 1
 
2.3%
4149 1
 
2.3%
4046 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 19
44.2%
256 1
 
2.3%
2327 1
 
2.3%
2392 1
 
2.3%
2498 1
 
2.3%
2624 1
 
2.3%
2713 1
 
2.3%
2919 1
 
2.3%
3076 1
 
2.3%
3242 1
 
2.3%
ValueCountFrequency (%)
4613 1
2.3%
4593 1
2.3%
4466 1
2.3%
4357 1
2.3%
4304 1
2.3%
4149 1
2.3%
4046 1
2.3%
3901 1
2.3%
3780 1
2.3%
3655 1
2.3%

계약직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean409.04651
Minimum0
Maximum2444
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:23.969942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median55
Q3530
95-th percentile1908.4
Maximum2444
Range2444
Interquartile range (IQR)530

Descriptive statistics

Standard deviation649.26995
Coefficient of variation (CV)1.5872766
Kurtosis2.3807952
Mean409.04651
Median Absolute Deviation (MAD)55
Skewness1.8010222
Sum17589
Variance421551.47
MonotonicityNot monotonic
2024-04-18T01:32:24.056651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 19
44.2%
4 1
 
2.3%
523 1
 
2.3%
1921 1
 
2.3%
2444 1
 
2.3%
2070 1
 
2.3%
1795 1
 
2.3%
1460 1
 
2.3%
1264 1
 
2.3%
1062 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 19
44.2%
4 1
 
2.3%
26 1
 
2.3%
55 1
 
2.3%
85 1
 
2.3%
111 1
 
2.3%
139 1
 
2.3%
175 1
 
2.3%
228 1
 
2.3%
286 1
 
2.3%
ValueCountFrequency (%)
2444 1
2.3%
2070 1
2.3%
1921 1
2.3%
1795 1
2.3%
1460 1
2.3%
1264 1
2.3%
1062 1
2.3%
839 1
2.3%
695 1
2.3%
628 1
2.3%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4568.6744
Minimum162
Maximum17266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T01:32:24.147294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile226.6
Q1565.5
median2816
Q36643.5
95-th percentile14978.3
Maximum17266
Range17104
Interquartile range (IQR)6078

Descriptive statistics

Standard deviation4949.5597
Coefficient of variation (CV)1.0833689
Kurtosis0.63973937
Mean4568.6744
Median Absolute Deviation (MAD)2334
Skewness1.2961932
Sum196453
Variance24498142
MonotonicityNot monotonic
2024-04-18T01:32:24.271921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
482 1
 
2.3%
277 1
 
2.3%
1978 1
 
2.3%
2327 1
 
2.3%
2656 1
 
2.3%
2922 1
 
2.3%
3105 1
 
2.3%
3835 1
 
2.3%
4143 1
 
2.3%
5025 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
162 1
2.3%
170 1
2.3%
221 1
2.3%
277 1
2.3%
282 1
2.3%
343 1
2.3%
382 1
2.3%
419 1
2.3%
482 1
2.3%
491 1
2.3%
ValueCountFrequency (%)
17266 1
2.3%
16663 1
2.3%
15037 1
2.3%
14450 1
2.3%
13371 1
2.3%
11998 1
2.3%
10515 1
2.3%
9330 1
2.3%
8159 1
2.3%
7170 1
2.3%

Interactions

2024-04-18T01:32:18.878391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:31:59.216632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:00.344877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:01.699353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:02.863584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:04.107781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:05.600659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:06.692612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:07.857507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:09.005845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:10.483259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:11.630023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:12.788158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:14.191062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:15.344238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:16.476927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:17.560717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:18.941377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:31:59.276084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:00.412565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:01.759858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:02.928623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:04.187480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:05.657811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:06.755932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:07.920842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:09.071324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:10.543749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:11.690271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:12.850805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:14.252901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:15.404538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:16.540502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:17.623732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:19.009164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:31:59.337777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:00.474911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:01.829679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-04-18T01:32:18.630385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:19.836467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:00.162592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:01.284805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:02.670844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:03.886749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:05.147256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:06.502431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:07.664688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:08.819978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:10.264281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:11.410774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:12.595551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:13.974447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:15.150829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:16.275923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:17.383699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:18.697005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:19.894364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:00.218986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:01.566262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:02.732580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:03.952366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:05.229084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:06.570984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:07.719891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:08.877864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:10.342811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:11.474282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:12.660831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:14.052778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:15.211266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:16.334106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:17.441417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:18.755132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:19.958896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:00.280250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:01.626977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:02.792880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:04.038941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:05.306454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:06.629495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:07.790934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:08.940064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:10.410340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:11.554319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:12.720813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:14.119489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:15.277389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:16.411983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:17.498145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:18.814431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T01:32:24.371119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분정무직별정직(국가)별정직(지방)일반직(국가)일반직(지방)경찰소방교육직법관검사기능직고용직공안직군무원연구직지도직계약직기타
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1.0001.0000.9840.7270.9380.9170.8890.9920.9730.9350.8940.2070.9620.9780.8600.8710.8370.920
정무직1.0000.9841.0000.8570.6890.8110.9350.9720.9760.9830.9850.3970.9630.9850.9810.9060.7580.813
별정직(국가)1.0000.7270.8571.0000.9920.7060.5300.8730.7910.8970.7770.7130.7020.5110.7330.6840.3390.699
별정직(지방)1.0000.9380.6890.9921.0000.9931.0000.9900.9740.9780.9950.0000.9000.8460.9000.9770.0000.350
일반직(국가)1.0000.9170.8110.7060.9931.0000.0000.9430.9520.8820.4700.2210.9380.9190.8910.8780.8970.897
일반직(지방)1.0000.8890.9350.5301.0000.0001.0000.8820.8730.8800.9710.0000.7630.8490.6860.7740.0000.544
경찰소방1.0000.9920.9720.8730.9900.9430.8821.0000.9860.9640.8990.2980.9690.9740.9020.8930.8300.938
교육직1.0000.9730.9760.7910.9740.9520.8730.9861.0000.9630.9170.1560.9450.9690.8990.9090.8530.939
법관검사1.0000.9350.9830.8970.9780.8820.8800.9640.9631.0000.8970.4020.9020.9580.9770.9510.8400.947
기능직1.0000.8940.9850.7770.9950.4700.9710.8990.9170.8971.0000.1640.7910.8900.8090.8870.0000.617
고용직1.0000.2070.3970.7130.0000.2210.0000.2980.1560.4020.1641.0000.2930.0310.4560.2440.0000.000
공안직1.0000.9620.9630.7020.9000.9380.7630.9690.9450.9020.7910.2931.0000.9430.9840.9500.9380.788
군무원1.0000.9780.9850.5110.8460.9190.8490.9740.9690.9580.8900.0310.9431.0000.9090.9430.8830.901
연구직1.0000.8600.9810.7330.9000.8910.6860.9020.8990.9770.8090.4560.9840.9091.0000.9890.9290.844
지도직1.0000.8710.9060.6840.9770.8780.7740.8930.9090.9510.8870.2440.9500.9430.9891.0000.8130.798
계약직1.0000.8370.7580.3390.0000.8970.0000.8300.8530.8400.0000.0000.9380.8830.9290.8131.0000.893
기타1.0000.9200.8130.6990.3500.8970.5440.9380.9390.9470.6170.0000.7880.9010.8440.7980.8931.000
2024-04-18T01:32:24.503017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정무직별정직(국가)별정직(지방)일반직(국가)일반직(지방)경찰소방교육직법관검사기능직고용직공안직군무원연구직지도직계약직기타
1.0000.9400.8300.1580.996-0.3070.9750.9870.9800.9630.1700.9530.9530.9530.9540.9390.798
정무직0.9401.0000.6630.2380.940-0.3340.9150.9210.9180.8800.3040.9870.9860.9860.9860.9710.662
별정직(국가)0.8300.6631.0000.0210.808-0.2470.8490.8070.8500.8850.1490.7050.7000.7060.7060.6640.745
별정직(지방)0.1580.2380.0211.0000.1370.7410.1580.1140.1580.162-0.0840.1650.1560.1650.1650.119-0.283
일반직(국가)0.9960.9400.8080.1371.000-0.3330.9580.9920.9640.9500.1570.9530.9550.9530.9540.9510.806
일반직(지방)-0.307-0.334-0.2470.741-0.3331.000-0.264-0.359-0.267-0.276-0.427-0.417-0.426-0.417-0.417-0.467-0.439
경찰소방0.9750.9150.8490.1580.958-0.2641.0000.9301.0000.9580.1910.9280.9210.9270.9270.8810.781
교육직0.9870.9210.8070.1140.992-0.3590.9301.0000.9390.9490.1710.9390.9430.9400.9420.9480.800
법관검사0.9800.9180.8500.1580.964-0.2671.0000.9391.0000.9600.1850.9310.9250.9310.9300.8880.786
기능직0.9630.8800.8850.1620.950-0.2760.9580.9490.9601.0000.2280.9050.9020.9060.9070.8760.760
고용직0.1700.3040.149-0.0840.157-0.4270.1910.1710.1850.2281.0000.3380.3380.3400.3420.312-0.023
공안직0.9530.9870.7050.1650.953-0.4170.9280.9390.9310.9050.3381.0000.9991.0001.0000.9870.687
군무원0.9530.9860.7000.1560.955-0.4260.9210.9430.9250.9020.3380.9991.0001.0000.9990.9910.693
연구직0.9530.9860.7060.1650.953-0.4170.9270.9400.9310.9060.3401.0001.0001.0001.0000.9870.687
지도직0.9540.9860.7060.1650.954-0.4170.9270.9420.9300.9070.3421.0000.9991.0001.0000.9870.687
계약직0.9390.9710.6640.1190.951-0.4670.8810.9480.8880.8760.3120.9870.9910.9870.9871.0000.696
기타0.7980.6620.745-0.2830.806-0.4390.7810.8000.7860.760-0.0230.6870.6930.6870.6870.6961.000

Missing values

2024-04-18T01:32:20.066007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T01:32:20.239466image/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

구분정무직별정직(국가)별정직(지방)일반직(국가)일반직(지방)경찰소방교육직법관검사기능직고용직공안직군무원연구직지도직계약직기타
019823556018601620497360369420000000482
119835390020702244786678586585183600000277
2198469400222026471109970819686645900000382
319858691084802639127212681072879338100000491
41986104350102503097146113971361104125312600000611
51987141960127204132164615221704117297517500000653
61988171860202904881187018062269135373024500000221
71989200230229705504223521762931141415829900000282
819902384402696062502803272037361535139400000343
9199127691031780675133083360482419456461100000419
구분정무직별정직(국가)별정직(지방)일반직(국가)일반직(지방)경찰소방교육직법관검사기능직고용직공안직군무원연구직지도직계약직기타
332015373529108510920011104503789511720812935944764101369796200637806958159
3420163967431098107070121732040707123527138560230601069010378215939018399330
352017419968114110441013323704335112964414996030857112811105223344046106210515
362018442241115010180014406004559213645215885991453117291163124814149126411998
37201946714311669900015620104776714461816945931653122821235726544304146013371
38202049441711769616016973805033115289918105871646128981302128684466179515037
39202152148611969306018283905328216112719315792843136671377730644593207016663
40202254601013069119019407805608316990620825695024144921437732704613244417266
414229851285840801651310554948874220045086320140531329129664357192114450
4212302521711028947058981164786087443910863042565232816