Overview

Dataset statistics

Number of variables20
Number of observations43
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory182.1 B

Variable types

Text1
Numeric19

Dataset

Description공무원 직종별(정무직, 별정직, 일반직_국가,지방, 경찰직, 소방직, 교육직 등) 가입자 추이에 대한 데이터입니다. 1982년부터 시작됩니다.
URLhttps://www.data.go.kr/data/15054019/fileData.do

Alerts

is highly overall correlated with 정무직 and 10 other fieldsHigh correlation
정무직 is highly overall correlated with and 12 other fieldsHigh correlation
별정직(국가) is highly overall correlated with 정무직 and 11 other fieldsHigh correlation
별정직(지방) is highly overall correlated with 정무직 and 5 other fieldsHigh correlation
일반직(국가) is highly overall correlated with 별정직(국가) and 7 other fieldsHigh correlation
일반직(지방) is highly overall correlated with 별정직(지방) and 2 other fieldsHigh correlation
경찰소방 is highly overall correlated with and 11 other fieldsHigh correlation
교육직 is highly overall correlated with and 10 other fieldsHigh correlation
법관검사 is highly overall correlated with and 11 other fieldsHigh correlation
기능직 is highly overall correlated with 별정직(국가) and 6 other fieldsHigh correlation
고용직 is highly overall correlated with and 11 other fieldsHigh correlation
공안직 is highly overall correlated with and 13 other fieldsHigh correlation
군무원 is highly overall correlated with and 13 other fieldsHigh correlation
연구직 is highly overall correlated with and 13 other fieldsHigh correlation
지도직 is highly overall correlated with and 12 other fieldsHigh correlation
계약직 is highly overall correlated with and 13 other fieldsHigh correlation
공중보건의 is highly overall correlated with and 12 other fieldsHigh correlation
기타 is highly overall correlated with 별정직(지방)High correlation
공중보건의 has 1 (2.3%) missing valuesMissing
구분 has unique valuesUnique
has unique valuesUnique
별정직(국가) 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
전년대비증감율 has 3 (7.0%) zerosZeros
정무직 has 19 (44.2%) zerosZeros
별정직(지방) has 33 (76.7%) zerosZeros
일반직(지방) has 14 (32.6%) zerosZeros
고용직 has 17 (39.5%) 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
공중보건의 has 19 (44.2%) zerosZeros

Reproduction

Analysis started2023-12-12 18:56:26.016581
Analysis finished2023-12-12 18:57:23.141476
Duration57.12 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
2023-12-13T03:57:23.405144image/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%
2023-12-13T03:57:23.988013image/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%
Mean950559.98
Minimum623723
Maximum1280994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:24.238589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum623723
5-th percentile667771.9
Q1863955
median957882
Q31061215
95-th percentile1218694.9
Maximum1280994
Range657271
Interquartile range (IQR)197260

Descriptive statistics

Standard deviation170029.42
Coefficient of variation (CV)0.1788729
Kurtosis-0.51234863
Mean950559.98
Median Absolute Deviation (MAD)106590
Skewness-0.21464587
Sum40874079
Variance2.8910004 × 1010
MonotonicityNot monotonic
2023-12-13T03:57:24.526402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
667554 1
 
2.3%
669733 1
 
2.3%
1009145 1
 
2.3%
1021771 1
 
2.3%
1030256 1
 
2.3%
1047897 1
 
2.3%
1052407 1
 
2.3%
1057958 1
 
2.3%
1064472 1
 
2.3%
1072610 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
623723 1
2.3%
657271 1
2.3%
667554 1
2.3%
669733 1
2.3%
682281 1
2.3%
696951 1
2.3%
716629 1
2.3%
737688 1
2.3%
767123 1
2.3%
810069 1
2.3%
ValueCountFrequency (%)
1280994 1
2.3%
1261421 1
2.3%
1221322 1
2.3%
1195051 1
2.3%
1160586 1
2.3%
1120458 1
2.3%
1107972 1
2.3%
1093038 1
2.3%
1081147 1
2.3%
1072610 1
2.3%

전년대비증감율
Real number (ℝ)

ZEROS 

Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9127907
Minimum0
Maximum5.6
Zeros3
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:24.788913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.033
Q10.815
median1.79
Q32.88
95-th percentile4.217
Maximum5.6
Range5.6
Interquartile range (IQR)2.065

Descriptive statistics

Standard deviation1.3989066
Coefficient of variation (CV)0.73134326
Kurtosis-0.034011573
Mean1.9127907
Median Absolute Deviation (MAD)1.03
Skewness0.76115511
Sum82.25
Variance1.9569396
MonotonicityNot monotonic
2023-12-13T03:57:25.046132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 3
 
7.0%
0.76 1
 
2.3%
2.25 1
 
2.3%
2.31 1
 
2.3%
1.25 1
 
2.3%
0.83 1
 
2.3%
1.71 1
 
2.3%
0.43 1
 
2.3%
0.53 1
 
2.3%
0.62 1
 
2.3%
Other values (31) 31
72.1%
ValueCountFrequency (%)
0.0 3
7.0%
0.33 1
 
2.3%
0.43 1
 
2.3%
0.44 1
 
2.3%
0.52 1
 
2.3%
0.53 1
 
2.3%
0.62 1
 
2.3%
0.76 1
 
2.3%
0.8 1
 
2.3%
0.83 1
 
2.3%
ValueCountFrequency (%)
5.6 1
2.3%
4.91 1
2.3%
4.23 1
2.3%
4.1 1
2.3%
4.02 1
2.3%
3.99 1
2.3%
3.58 1
2.3%
3.28 1
2.3%
3.02 1
2.3%
2.97 1
2.3%

정무직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean482.23256
Minimum0
Maximum2221
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:25.293722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median141
Q3176
95-th percentile1930.4
Maximum2221
Range2221
Interquartile range (IQR)176

Descriptive statistics

Standard deviation782.80484
Coefficient of variation (CV)1.6232932
Kurtosis-0.16502665
Mean482.23256
Median Absolute Deviation (MAD)141
Skewness1.3212649
Sum20736
Variance612783.42
MonotonicityNot monotonic
2023-12-13T03:57:25.538727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 19
44.2%
148 2
 
4.7%
144 2
 
4.7%
1694 1
 
2.3%
139 1
 
2.3%
17 1
 
2.3%
156 1
 
2.3%
173 1
 
2.3%
179 1
 
2.3%
145 1
 
2.3%
Other values (13) 13
30.2%
ValueCountFrequency (%)
0 19
44.2%
17 1
 
2.3%
139 1
 
2.3%
141 1
 
2.3%
142 1
 
2.3%
144 2
 
4.7%
145 1
 
2.3%
146 1
 
2.3%
148 2
 
4.7%
150 1
 
2.3%
ValueCountFrequency (%)
2221 1
2.3%
1938 1
2.3%
1932 1
2.3%
1916 1
2.3%
1906 1
2.3%
1905 1
2.3%
1871 1
2.3%
1703 1
2.3%
1694 1
2.3%
1678 1
2.3%

별정직(국가)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14865.93
Minimum119
Maximum37598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:25.779165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile1187.3
Q12048.5
median5027
Q331663
95-th percentile35970.1
Maximum37598
Range37479
Interquartile range (IQR)29614.5

Descriptive statistics

Standard deviation14718.153
Coefficient of variation (CV)0.99005934
Kurtosis-1.7256169
Mean14865.93
Median Absolute Deviation (MAD)3423
Skewness0.45712376
Sum639235
Variance2.1662403 × 108
MonotonicityNot monotonic
2023-12-13T03:57:26.063200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
119 1
 
2.3%
13707 1
 
2.3%
4919 1
 
2.3%
5036 1
 
2.3%
5027 1
 
2.3%
4332 1
 
2.3%
4220 1
 
2.3%
7676 1
 
2.3%
6007 1
 
2.3%
3060 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
119 1
2.3%
502 1
2.3%
1141 1
2.3%
1604 1
2.3%
1612 1
2.3%
1643 1
2.3%
1649 1
2.3%
1705 1
2.3%
1736 1
2.3%
1756 1
2.3%
ValueCountFrequency (%)
37598 1
2.3%
36624 1
2.3%
35994 1
2.3%
35755 1
2.3%
35679 1
2.3%
35627 1
2.3%
33405 1
2.3%
32599 1
2.3%
32588 1
2.3%
32325 1
2.3%

별정직(지방)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean876.34884
Minimum0
Maximum4655
Zeros33
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:26.301124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3798.4
Maximum4655
Range4655
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1619.1589
Coefficient of variation (CV)1.8476192
Kurtosis-0.095840414
Mean876.34884
Median Absolute Deviation (MAD)0
Skewness1.3535232
Sum37683
Variance2621675.6
MonotonicityNot monotonic
2023-12-13T03:57:26.511317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 33
76.7%
4655 1
 
2.3%
3995 1
 
2.3%
3806 1
 
2.3%
3730 1
 
2.3%
3626 1
 
2.3%
3586 1
 
2.3%
3665 1
 
2.3%
3651 1
 
2.3%
3648 1
 
2.3%
ValueCountFrequency (%)
0 33
76.7%
3321 1
 
2.3%
3586 1
 
2.3%
3626 1
 
2.3%
3648 1
 
2.3%
3651 1
 
2.3%
3665 1
 
2.3%
3730 1
 
2.3%
3806 1
 
2.3%
3995 1
 
2.3%
ValueCountFrequency (%)
4655 1
2.3%
3995 1
2.3%
3806 1
2.3%
3730 1
2.3%
3665 1
2.3%
3651 1
2.3%
3648 1
2.3%
3626 1
2.3%
3586 1
2.3%
3321 1
2.3%

일반직(국가)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188874.35
Minimum72901
Maximum515371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:26.751179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72901
5-th percentile73785.6
Q176872
median88248
Q3321706.5
95-th percentile478380.5
Maximum515371
Range442470
Interquartile range (IQR)244834.5

Descriptive statistics

Standard deviation161128.07
Coefficient of variation (CV)0.85309664
Kurtosis-0.79266406
Mean188874.35
Median Absolute Deviation (MAD)12886
Skewness1.0018119
Sum8121597
Variance2.5962256 × 1010
MonotonicityNot monotonic
2023-12-13T03:57:26.988683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
100100 1
 
2.3%
94836 1
 
2.3%
75362 1
 
2.3%
79024 1
 
2.3%
82095 1
 
2.3%
85652 1
 
2.3%
90180 1
 
2.3%
315207 1
 
2.3%
328206 1
 
2.3%
392690 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
72901 1
2.3%
73725 1
2.3%
73741 1
2.3%
74187 1
2.3%
74274 1
2.3%
74905 1
2.3%
75362 1
2.3%
75364 1
2.3%
75688 1
2.3%
76133 1
2.3%
ValueCountFrequency (%)
515371 1
2.3%
500652 1
2.3%
479562 1
2.3%
467747 1
2.3%
455878 1
2.3%
444293 1
2.3%
436636 1
2.3%
429242 1
2.3%
424150 1
2.3%
392690 1
2.3%

일반직(지방)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114392.77
Minimum0
Maximum220164
Zeros14
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:27.217594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median143948
Q3185374
95-th percentile215723
Maximum220164
Range220164
Interquartile range (IQR)185374

Descriptive statistics

Standard deviation86364.15
Coefficient of variation (CV)0.75497911
Kurtosis-1.5730939
Mean114392.77
Median Absolute Deviation (MAD)53546
Skewness-0.39586719
Sum4918889
Variance7.4587664 × 109
MonotonicityNot monotonic
2023-12-13T03:57:27.418457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 14
32.6%
121695 1
 
2.3%
185386 1
 
2.3%
217734 1
 
2.3%
220164 1
 
2.3%
215873 1
 
2.3%
214373 1
 
2.3%
214333 1
 
2.3%
207654 1
 
2.3%
197494 1
 
2.3%
Other values (20) 20
46.5%
ValueCountFrequency (%)
0 14
32.6%
78087 1
 
2.3%
100704 1
 
2.3%
115637 1
 
2.3%
116718 1
 
2.3%
121695 1
 
2.3%
121775 1
 
2.3%
138603 1
 
2.3%
143948 1
 
2.3%
145159 1
 
2.3%
ValueCountFrequency (%)
220164 1
2.3%
217734 1
2.3%
215873 1
2.3%
214373 1
2.3%
214333 1
2.3%
207654 1
2.3%
197494 1
2.3%
196307 1
2.3%
191805 1
2.3%
187751 1
2.3%

경찰소방
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125615.67
Minimum26855
Maximum207885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:27.653099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26855
5-th percentile70158.4
Q1100029
median124525
Q3150030
95-th percentile193982.8
Maximum207885
Range181030
Interquartile range (IQR)50001

Descriptive statistics

Standard deviation40809.32
Coefficient of variation (CV)0.32487442
Kurtosis-0.25435857
Mean125615.67
Median Absolute Deviation (MAD)26517
Skewness0.03180975
Sum5401474
Variance1.6654006 × 109
MonotonicityNot monotonic
2023-12-13T03:57:27.894879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
77888 1
 
2.3%
69947 1
 
2.3%
131010 1
 
2.3%
132541 1
 
2.3%
135302 1
 
2.3%
140055 1
 
2.3%
143913 1
 
2.3%
145798 1
 
2.3%
149018 1
 
2.3%
151042 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
26855 1
2.3%
69045 1
2.3%
69947 1
2.3%
72061 1
2.3%
75323 1
2.3%
77307 1
2.3%
77888 1
2.3%
78511 1
2.3%
81163 1
2.3%
88677 1
2.3%
ValueCountFrequency (%)
207885 1
2.3%
202998 1
2.3%
194595 1
2.3%
188473 1
2.3%
181030 1
2.3%
177871 1
2.3%
171717 1
2.3%
167443 1
2.3%
161663 1
2.3%
155696 1
2.3%

교육직
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301924.26
Minimum103322
Maximum378709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:28.125507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103322
5-th percentile206908.7
Q1273862
median288708
Q3357399.5
95-th percentile375501.7
Maximum378709
Range275387
Interquartile range (IQR)83537.5

Descriptive statistics

Standard deviation61895.607
Coefficient of variation (CV)0.20500376
Kurtosis0.89277864
Mean301924.26
Median Absolute Deviation (MAD)59253
Skewness-0.85044356
Sum12982743
Variance3.8310661 × 109
MonotonicityNot monotonic
2023-12-13T03:57:28.338571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
197387 1
 
2.3%
206296 1
 
2.3%
340679 1
 
2.3%
347961 1
 
2.3%
350715 1
 
2.3%
354943 1
 
2.3%
356336 1
 
2.3%
357271 1
 
2.3%
357528 1
 
2.3%
360218 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
103322 1
2.3%
197387 1
2.3%
206296 1
2.3%
212423 1
2.3%
220190 1
2.3%
228283 1
2.3%
237625 1
2.3%
245071 1
2.3%
259266 1
2.3%
267337 1
2.3%
ValueCountFrequency (%)
378709 1
2.3%
377849 1
2.3%
375767 1
2.3%
373114 1
2.3%
371024 1
2.3%
367009 1
2.3%
365809 1
2.3%
363092 1
2.3%
362587 1
2.3%
360218 1
2.3%

법관검사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3220.7674
Minimum1022
Maximum5430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:28.545856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1022
5-th percentile1174.9
Q11928
median3204
Q34524
95-th percentile5313.1
Maximum5430
Range4408
Interquartile range (IQR)2596

Descriptive statistics

Standard deviation1427.485
Coefficient of variation (CV)0.44321269
Kurtosis-1.4009352
Mean3220.7674
Median Absolute Deviation (MAD)1328
Skewness0.082747122
Sum138493
Variance2037713.4
MonotonicityNot monotonic
2023-12-13T03:57:28.749944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1026 1
 
2.3%
1022 1
 
2.3%
3798 1
 
2.3%
3935 1
 
2.3%
4057 1
 
2.3%
4177 1
 
2.3%
4336 1
 
2.3%
4442 1
 
2.3%
4606 1
 
2.3%
4717 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1022 1
2.3%
1026 1
2.3%
1157 1
2.3%
1336 1
2.3%
1468 1
2.3%
1506 1
2.3%
1633 1
2.3%
1720 1
2.3%
1816 1
2.3%
1871 1
2.3%
ValueCountFrequency (%)
5430 1
2.3%
5429 1
2.3%
5326 1
2.3%
5197 1
2.3%
5157 1
2.3%
5144 1
2.3%
5025 1
2.3%
4933 1
2.3%
4837 1
2.3%
4717 1
2.3%

기능직
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101253.19
Minimum30
Maximum197907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:28.963694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile73.4
Q122326
median135240
Q3157541.5
95-th percentile196515.8
Maximum197907
Range197877
Interquartile range (IQR)135215.5

Descriptive statistics

Standard deviation73534.587
Coefficient of variation (CV)0.72624467
Kurtosis-1.568673
Mean101253.19
Median Absolute Deviation (MAD)61415
Skewness-0.22963304
Sum4353887
Variance5.4073355 × 109
MonotonicityNot monotonic
2023-12-13T03:57:29.171982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
66204 1
 
2.3%
59395 1
 
2.3%
145568 1
 
2.3%
143354 1
 
2.3%
140462 1
 
2.3%
139677 1
 
2.3%
135240 1
 
2.3%
131336 1
 
2.3%
120128 1
 
2.3%
37521 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
30 1
2.3%
34 1
2.3%
64 1
2.3%
158 1
2.3%
353 1
2.3%
674 1
2.3%
991 1
2.3%
3741 1
2.3%
4637 1
2.3%
6326 1
2.3%
ValueCountFrequency (%)
197907 1
2.3%
197112 1
2.3%
196655 1
2.3%
195263 1
2.3%
195259 1
2.3%
190278 1
2.3%
183865 1
2.3%
181356 1
2.3%
169580 1
2.3%
162159 1
2.3%

고용직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20819.767
Minimum0
Maximum110988
Zeros17
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:29.377596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3427
Q311147.5
95-th percentile104432.3
Maximum110988
Range110988
Interquartile range (IQR)11147.5

Descriptive statistics

Standard deviation37382.549
Coefficient of variation (CV)1.7955315
Kurtosis1.3501088
Mean20819.767
Median Absolute Deviation (MAD)3427
Skewness1.7527955
Sum895250
Variance1.397455 × 109
MonotonicityNot monotonic
2023-12-13T03:57:29.544737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 17
39.5%
95938 1
 
2.3%
97317 1
 
2.3%
1 1
 
2.3%
3 1
 
2.3%
56 1
 
2.3%
1863 1
 
2.3%
3427 1
 
2.3%
4071 1
 
2.3%
4496 1
 
2.3%
Other values (17) 17
39.5%
ValueCountFrequency (%)
0 17
39.5%
1 1
 
2.3%
3 1
 
2.3%
56 1
 
2.3%
1863 1
 
2.3%
3427 1
 
2.3%
4071 1
 
2.3%
4496 1
 
2.3%
5533 1
 
2.3%
6413 1
 
2.3%
ValueCountFrequency (%)
110988 1
2.3%
107501 1
2.3%
104648 1
2.3%
102491 1
2.3%
99546 1
2.3%
97317 1
2.3%
95938 1
2.3%
39972 1
2.3%
28672 1
2.3%
14126 1
2.3%

공안직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17859.674
Minimum0
Maximum37662
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:29.718460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26935
Q334324.5
95-th percentile36849.5
Maximum37662
Range37662
Interquartile range (IQR)34324.5

Descriptive statistics

Standard deviation16667.986
Coefficient of variation (CV)0.93327491
Kurtosis-1.9695293
Mean17859.674
Median Absolute Deviation (MAD)10431
Skewness-0.086183068
Sum767966
Variance2.7782176 × 108
MonotonicityNot monotonic
2023-12-13T03:57:29.909710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 19
44.2%
26414 1
 
2.3%
10231 1
 
2.3%
27431 1
 
2.3%
37662 1
 
2.3%
37366 1
 
2.3%
36856 1
 
2.3%
36791 1
 
2.3%
35970 1
 
2.3%
35029 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 19
44.2%
10231 1
 
2.3%
26414 1
 
2.3%
26935 1
 
2.3%
27402 1
 
2.3%
27431 1
 
2.3%
28331 1
 
2.3%
28834 1
 
2.3%
30391 1
 
2.3%
31415 1
 
2.3%
ValueCountFrequency (%)
37662 1
2.3%
37366 1
2.3%
36856 1
2.3%
36791 1
2.3%
36400 1
2.3%
36228 1
2.3%
35970 1
2.3%
35666 1
2.3%
35657 1
2.3%
35029 1
2.3%

군무원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12411.395
Minimum0
Maximum34911
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:30.115206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19744
Q321205
95-th percentile28591.8
Maximum34911
Range34911
Interquartile range (IQR)21205

Descriptive statistics

Standard deviation11804.531
Coefficient of variation (CV)0.95110426
Kurtosis-1.5742826
Mean12411.395
Median Absolute Deviation (MAD)10843
Skewness0.090488538
Sum533690
Variance1.3934695 × 108
MonotonicityNot monotonic
2023-12-13T03:57:30.368112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 19
44.2%
19732 1
 
2.3%
8901 1
 
2.3%
26010 1
 
2.3%
34911 1
 
2.3%
33976 1
 
2.3%
28856 1
 
2.3%
26214 1
 
2.3%
24147 1
 
2.3%
22158 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 19
44.2%
8901 1
 
2.3%
19732 1
 
2.3%
19744 1
 
2.3%
19751 1
 
2.3%
19780 1
 
2.3%
20047 1
 
2.3%
20187 1
 
2.3%
20220 1
 
2.3%
20310 1
 
2.3%
ValueCountFrequency (%)
34911 1
2.3%
33976 1
2.3%
28856 1
2.3%
26214 1
2.3%
26010 1
2.3%
24147 1
2.3%
22158 1
2.3%
22024 1
2.3%
21712 1
2.3%
21423 1
2.3%

연구직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4330.8837
Minimum0
Maximum11008
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:30.561708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5597
Q37811
95-th percentile10105.4
Maximum11008
Range11008
Interquartile range (IQR)7811

Descriptive statistics

Standard deviation4095.5079
Coefficient of variation (CV)0.94565177
Kurtosis-1.7207729
Mean4330.8837
Median Absolute Deviation (MAD)4551
Skewness0.047829661
Sum186228
Variance16773185
MonotonicityNot monotonic
2023-12-13T03:57:31.303764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 19
44.2%
5493 1
 
2.3%
5411 1
 
2.3%
5597 1
 
2.3%
11008 1
 
2.3%
10694 1
 
2.3%
10148 1
 
2.3%
9722 1
 
2.3%
9391 1
 
2.3%
9106 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 19
44.2%
5411 1
 
2.3%
5493 1
 
2.3%
5597 1
 
2.3%
5670 1
 
2.3%
5788 1
 
2.3%
6024 1
 
2.3%
6340 1
 
2.3%
6874 1
 
2.3%
7208 1
 
2.3%
ValueCountFrequency (%)
11008 1
2.3%
10694 1
2.3%
10148 1
2.3%
9722 1
2.3%
9391 1
2.3%
9106 1
2.3%
8851 1
2.3%
8657 1
2.3%
8340 1
2.3%
8198 1
2.3%

지도직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2576.7907
Minimum0
Maximum5149
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:31.541585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4519
Q34750.5
95-th percentile5088.3
Maximum5149
Range5149
Interquartile range (IQR)4750.5

Descriptive statistics

Standard deviation2377.675
Coefficient of variation (CV)0.92272725
Kurtosis-2.0074028
Mean2576.7907
Median Absolute Deviation (MAD)630
Skewness-0.133863
Sum110802
Variance5653338.4
MonotonicityNot monotonic
2023-12-13T03:57:31.800488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 19
44.2%
4613 3
 
7.0%
5089 1
 
2.3%
5080 1
 
2.3%
2212 1
 
2.3%
2666 1
 
2.3%
4878 1
 
2.3%
4861 1
 
2.3%
4779 1
 
2.3%
4715 1
 
2.3%
Other values (13) 13
30.2%
ValueCountFrequency (%)
0 19
44.2%
2212 1
 
2.3%
2666 1
 
2.3%
4519 1
 
2.3%
4548 1
 
2.3%
4604 1
 
2.3%
4613 3
 
7.0%
4648 1
 
2.3%
4650 1
 
2.3%
4661 1
 
2.3%
ValueCountFrequency (%)
5149 1
2.3%
5109 1
2.3%
5089 1
2.3%
5082 1
2.3%
5080 1
2.3%
5059 1
2.3%
5028 1
2.3%
4904 1
2.3%
4878 1
2.3%
4861 1
2.3%

계약직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4433.5814
Minimum0
Maximum15878
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:32.023392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2806
Q38090.5
95-th percentile14620.4
Maximum15878
Range15878
Interquartile range (IQR)8090.5

Descriptive statistics

Standard deviation4944.6573
Coefficient of variation (CV)1.1152738
Kurtosis-0.43334903
Mean4433.5814
Median Absolute Deviation (MAD)2806
Skewness0.80054794
Sum190644
Variance24449636
MonotonicityNot monotonic
2023-12-13T03:57:32.200750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 19
44.2%
8849 2
 
4.7%
8463 1
 
2.3%
7029 1
 
2.3%
15878 1
 
2.3%
15112 1
 
2.3%
14743 1
 
2.3%
13517 1
 
2.3%
11291 1
 
2.3%
9754 1
 
2.3%
Other values (14) 14
32.6%
ValueCountFrequency (%)
0 19
44.2%
2334 1
 
2.3%
2544 1
 
2.3%
2806 1
 
2.3%
3469 1
 
2.3%
4100 1
 
2.3%
4758 1
 
2.3%
5650 1
 
2.3%
6010 1
 
2.3%
6297 1
 
2.3%
ValueCountFrequency (%)
15878 1
2.3%
15112 1
2.3%
14743 1
2.3%
13517 1
2.3%
11291 1
2.3%
9754 1
2.3%
9308 1
2.3%
8849 2
4.7%
8611 1
2.3%
8463 1
2.3%

공중보건의
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)54.8%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean2283.5
Minimum0
Maximum5281
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:32.390928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3432
Q34018.75
95-th percentile5171.4
Maximum5281
Range5281
Interquartile range (IQR)4018.75

Descriptive statistics

Standard deviation2162.5233
Coefficient of variation (CV)0.94702137
Kurtosis-1.8517272
Mean2283.5
Median Absolute Deviation (MAD)1745.5
Skewness-0.019139692
Sum95907
Variance4676507
MonotonicityNot monotonic
2023-12-13T03:57:32.606107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 19
44.2%
3371 2
 
4.7%
3691 1
 
2.3%
3525 1
 
2.3%
3494 1
 
2.3%
3547 1
 
2.3%
3544 1
 
2.3%
3625 1
 
2.3%
3493 1
 
2.3%
3634 1
 
2.3%
Other values (13) 13
30.2%
ValueCountFrequency (%)
0 19
44.2%
3371 2
 
4.7%
3493 1
 
2.3%
3494 1
 
2.3%
3525 1
 
2.3%
3544 1
 
2.3%
3547 1
 
2.3%
3625 1
 
2.3%
3634 1
 
2.3%
3691 1
 
2.3%
ValueCountFrequency (%)
5281 1
2.3%
5182 1
2.3%
5173 1
2.3%
5141 1
2.3%
5027 1
2.3%
5020 1
2.3%
4760 1
2.3%
4642 1
2.3%
4556 1
2.3%
4077 1
2.3%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34391.977
Minimum2744
Maximum67017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T03:57:32.831038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2744
5-th percentile5355.6
Q116129.5
median28165
Q355835
95-th percentile63957.1
Maximum67017
Range64273
Interquartile range (IQR)39705.5

Descriptive statistics

Standard deviation20646.58
Coefficient of variation (CV)0.6003313
Kurtosis-1.5550234
Mean34391.977
Median Absolute Deviation (MAD)14429
Skewness0.08904993
Sum1478855
Variance4.2628126 × 108
MonotonicityNot monotonic
2023-12-13T03:57:33.129884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
7197 1
 
2.3%
26509 1
 
2.3%
16096 1
 
2.3%
16163 1
 
2.3%
16460 1
 
2.3%
16577 1
 
2.3%
16737 1
 
2.3%
18243 1
 
2.3%
18359 1
 
2.3%
40987 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
2744 1
2.3%
3382 1
2.3%
5151 1
2.3%
7197 1
2.3%
11348 1
2.3%
11751 1
2.3%
13736 1
2.3%
15538 1
2.3%
15690 1
2.3%
15864 1
2.3%
ValueCountFrequency (%)
67017 1
2.3%
65086 1
2.3%
64012 1
2.3%
63463 1
2.3%
58772 1
2.3%
57873 1
2.3%
57784 1
2.3%
56805 1
2.3%
56712 1
2.3%
56112 1
2.3%

Interactions

2023-12-13T03:57:18.882790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:26.895119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T03:56:43.428396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T03:56:37.287312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:39.962651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:42.094855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:44.507134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:47.128946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:50.012880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:53.412382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:56.422345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:59.832333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:02.706826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:06.012477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:08.360423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:10.845195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:14.581947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:17.505758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:20.640649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:28.633297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:31.277696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:34.529352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:37.456624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:40.106339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:42.206193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:44.641858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:47.281564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:50.160878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:53.562580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:56.610328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:00.009213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:03.356857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:06.141707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:08.483437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:10.976699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:14.773224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:17.651203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:20.781911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:28.768077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:31.410395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:34.682739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:37.591949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:40.239882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:42.298827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:44.748539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:47.437140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:50.300547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:53.712358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:56.784894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:00.184055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:03.504028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:06.252658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:08.598706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:11.095232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:14.950733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:17.788423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:20.928634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:28.901799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:31.566079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:34.847424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:37.733577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:40.360389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:42.399497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:44.882847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:47.575798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:50.467302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:53.849579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:56.957858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:00.377978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:03.667794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:06.394559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:08.730742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:11.236210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:15.145677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:17.924699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:21.074441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:29.055959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:31.715176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:35.016345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:37.891649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:40.484718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:42.502158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:45.013097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:47.719055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:50.617963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:53.993516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:57.153573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:00.532185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:03.829440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:06.514215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:08.844226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:11.415670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:15.343919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:18.060332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:21.230236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:29.195766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:31.849119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:35.178661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:38.050745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:40.601473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:42.596020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:45.135850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:47.831075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:50.779796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:54.124693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:57.329717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:00.671508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:03.987149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:06.635251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:08.958487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:11.626494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:15.539882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:18.177775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:21.391230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:29.322654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:31.985513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:35.317813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:38.203608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:40.718535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:42.683867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:45.244092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:47.981537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:50.950908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:54.258341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:57.509675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:00.802493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:04.173172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:06.753890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:09.093469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:12.285039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:15.685211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:18.345665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:21.641469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:29.470910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:32.148162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:35.470814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:38.358199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:40.826857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:42.793759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:45.407773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:48.164191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:51.153347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:54.433054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:57.698539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:00.951406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:04.333410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:06.913031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:09.265856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:12.459194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:15.869022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:18.570507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:22.297619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:29.595894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:32.281121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:35.596122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:38.505785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:40.923257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:42.897685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:45.515410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:48.325067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:51.304458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:54.585330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:56:57.851022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:01.073148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:04.479001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:07.039448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:09.403825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:12.608268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:16.023611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:57:18.716867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:57:33.359523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분전년대비증감율정무직별정직(국가)별정직(지방)일반직(국가)일반직(지방)경찰소방교육직법관검사기능직고용직공안직군무원연구직지도직계약직공중보건의기타
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1.0001.0000.8490.3030.6770.0000.8030.8280.9120.9040.8770.7450.8200.7780.7840.8060.7490.9260.8090.751
전년대비증감율1.0000.8491.0000.0000.0000.3290.0000.5200.2830.5640.4230.0000.7790.0000.0000.2930.0000.7800.4170.377
정무직1.0000.3030.0001.0000.5580.9600.0000.8260.6460.7690.7890.8180.0000.6430.5630.7030.3500.9210.6550.546
별정직(국가)1.0000.6770.0000.5581.0000.6640.7030.9610.7970.7390.8330.8970.5610.7310.3660.6570.4780.4620.7590.653
별정직(지방)1.0000.0000.3290.9600.6641.0000.0000.8870.4810.7170.7780.6090.0000.6890.5630.8440.3500.8190.6460.614
일반직(국가)1.0000.8030.0000.0000.7030.0001.0000.6770.7880.5740.6800.7380.0000.6980.9280.9450.7560.8040.7220.719
일반직(지방)1.0000.8280.5200.8260.9610.8870.6771.0000.8440.8970.8040.9010.8140.7360.5350.6530.5270.5290.7160.756
경찰소방1.0000.9120.2830.6460.7970.4810.7880.8441.0000.7550.8660.9350.6980.9280.8460.8180.9240.8490.7050.819
교육직1.0000.9040.5640.7690.7390.7170.5740.8970.7551.0000.8590.8130.7600.7340.7500.8670.6680.7300.7380.670
법관검사1.0000.8770.4230.7890.8330.7780.6800.8040.8660.8591.0000.7900.2080.9830.7130.8870.8910.8590.9550.764
기능직1.0000.7450.0000.8180.8970.6090.7380.9010.9350.8130.7901.0000.7950.7160.6230.6450.5980.7110.7530.828
고용직1.0000.8200.7790.0000.5610.0000.0000.8140.6980.7600.2080.7951.0000.0000.0000.0000.0000.0000.0000.794
공안직1.0000.7780.0000.6430.7310.6890.6980.7360.9280.7340.9830.7160.0001.0000.8940.9170.9730.9660.7810.733
군무원1.0000.7840.0000.5630.3660.5630.9280.5350.8460.7500.7130.6230.0000.8941.0000.9600.9230.8720.7600.719
연구직1.0000.8060.2930.7030.6570.8440.9450.6530.8180.8670.8870.6450.0000.9170.9601.0000.8070.9010.8280.719
지도직1.0000.7490.0000.3500.4780.3500.7560.5270.9240.6680.8910.5980.0000.9730.9230.8071.0000.9270.7110.719
계약직1.0000.9260.7800.9210.4620.8190.8040.5290.8490.7300.8590.7110.0000.9660.8720.9010.9271.0000.9540.794
공중보건의1.0000.8090.4170.6550.7590.6460.7220.7160.7050.7380.9550.7530.0000.7810.7600.8280.7110.9541.0000.769
기타1.0000.7510.3770.5460.6530.6140.7190.7560.8190.6700.7640.8280.7940.7330.7190.7190.7190.7940.7691.000
2023-12-13T03:57:33.673739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전년대비증감율정무직별정직(국가)별정직(지방)일반직(국가)일반직(지방)경찰소방교육직법관검사기능직고용직공안직군무원연구직지도직계약직공중보건의기타
1.0000.0260.522-0.3580.0770.412-0.1750.8550.9750.921-0.251-0.7560.8400.7470.8480.5630.7500.5720.458
전년대비증감율0.0261.000-0.2300.312-0.119-0.0610.050-0.132-0.033-0.0990.1380.334-0.152-0.161-0.140-0.147-0.205-0.2700.159
정무직0.522-0.2301.000-0.5950.7430.0080.1840.6600.6170.684-0.268-0.7140.7330.7240.7310.9450.6900.950-0.251
별정직(국가)-0.3580.312-0.5951.000-0.165-0.5530.478-0.596-0.474-0.5870.7950.672-0.751-0.804-0.750-0.620-0.812-0.5600.078
별정직(지방)0.077-0.1190.743-0.1651.000-0.5450.6710.1450.1780.1680.243-0.1850.1800.1740.1860.7390.1000.721-0.559
일반직(국가)0.412-0.0610.008-0.553-0.5451.000-0.8350.4450.3980.443-0.831-0.4580.5860.5640.5830.0120.6400.0040.316
일반직(지방)-0.1750.0500.1840.4780.671-0.8351.000-0.283-0.192-0.2630.7540.277-0.394-0.433-0.3920.117-0.4840.190-0.292
경찰소방0.855-0.1320.660-0.5960.1450.445-0.2831.0000.8680.967-0.416-0.8740.9000.9130.8980.6670.8780.6400.381
교육직0.975-0.0330.617-0.4740.1780.398-0.1920.8681.0000.950-0.312-0.8250.8940.8090.9040.6760.8170.6500.371
법관검사0.921-0.0990.684-0.5870.1680.443-0.2630.9670.9501.000-0.415-0.9140.9390.9070.9410.7040.9030.6810.379
기능직-0.2510.138-0.2680.7950.243-0.8310.754-0.416-0.312-0.4151.0000.477-0.635-0.659-0.628-0.265-0.717-0.239-0.023
고용직-0.7560.334-0.7140.672-0.185-0.4580.277-0.874-0.825-0.9140.4771.000-0.900-0.883-0.895-0.680-0.920-0.743-0.270
공안직0.840-0.1520.733-0.7510.1800.586-0.3940.9000.8940.939-0.635-0.9001.0000.9610.9970.7490.9710.7170.197
군무원0.747-0.1610.724-0.8040.1740.564-0.4330.9130.8090.907-0.659-0.8830.9611.0000.9600.7470.9720.6770.202
연구직0.848-0.1400.731-0.7500.1860.583-0.3920.8980.9040.941-0.628-0.8950.9970.9601.0000.7580.9710.7190.198
지도직0.563-0.1470.945-0.6200.7390.0120.1170.6670.6760.704-0.265-0.6800.7490.7470.7581.0000.6990.903-0.218
계약직0.750-0.2050.690-0.8120.1000.640-0.4840.8780.8170.903-0.717-0.9200.9710.9720.9710.6991.0000.6830.239
공중보건의0.572-0.2700.950-0.5600.7210.0040.1900.6400.6500.681-0.239-0.7430.7170.6770.7190.9030.6831.000-0.296
기타0.4580.159-0.2510.078-0.5590.316-0.2920.3810.3710.379-0.023-0.2700.1970.2020.198-0.2180.239-0.2961.000

Missing values

2023-12-13T03:57:22.549354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:57:22.963457image/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

구분전년대비증감율정무직별정직(국가)별정직(지방)일반직(국가)일반직(지방)경찰소방교육직법관검사기능직고용직공안직군무원연구직지도직계약직공중보건의기타
019826675540.00119010010012169577888197387102666204959380000007197
119836697330.33013707094836100704699472062961022593959731700000026509
219846822811.870238610127588780876904521242311575587810249100000011751
319856969512.15027501097381115637720612201901336554531046480000002744
419867166292.820292240878031167187730722828314685458910750100000013736
519877376882.940308770827481217757532323762515064868111098800000028165
619887671233.99031335088248143948785112450711633674839954600000011348
719898100695.60319910906831386038116325926617201407013997200000025970
819908432624.10325880942281451598867726733718161580902867200000026695
919918846484.910325990756881584649621227233718711813561412600000051995
구분전년대비증감율정무직별정직(국가)별정직(지방)일반직(국가)일반직(지방)경찰소방교육직법관검사기능직고용직공안직군무원연구직지도직계약직공중보건의기타
33201510930381.91481942042924201616633630924933632603640021712865745488611363442130
34201611079721.371391705043663601674433658095025463703622822024885146049308349342070
35201711204581.131421649044429301717173670095144374103502922158910646139754362542478
36201811605863.581461756045587801778713710245157991035970241479391464811291354458772
37201911950512.971411736046774701884733731145197674036791262149722471513517354763463
38202012213222.214516120479562019459537576753263530368562885610148477914743349465086
39202112614213.2817916040500652020299837784954301580373663397610694486115112352567017
40202212809941.551731643051537102078853787095429640376623491111008487815878337164012
416572710.015611410251703018103010332235533402743126010559726668849337142408
426237230.01750202636680268552753871876300102318901541122127029<NA>21604