Overview

Dataset statistics

Number of variables17
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory154.8 B

Variable types

Text1
Numeric16

Dataset

Description공무원의 직종별(정무직, 별정직, 일반직, 경찰,소방 기능직, 연구직 등) 공무원연금 가입자 수 데이터로 18세 이상부터 구분되고 있습니다.
URLhttps://www.data.go.kr/data/15053029/fileData.do

Alerts

is highly overall correlated with 별정직 and 11 other fieldsHigh correlation
별정직 is highly overall correlated with and 11 other fieldsHigh correlation
일반직 is highly overall correlated with and 11 other fieldsHigh correlation
경찰 is highly overall correlated with and 11 other fieldsHigh correlation
소방 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 10 other fieldsHigh correlation
기능직 is highly overall correlated with 교육직 and 4 other fieldsHigh correlation
공안직 is highly overall correlated with and 12 other fieldsHigh correlation
군무원 is highly overall correlated with and 11 other fieldsHigh correlation
연구직 is highly overall correlated with and 12 other fieldsHigh correlation
지도직 is highly overall correlated with and 10 other fieldsHigh correlation
계약직 is highly overall correlated with and 12 other fieldsHigh correlation
공중보건의 is highly overall correlated with 소방High correlation
기타 is highly overall correlated with and 12 other fieldsHigh correlation
구분 has unique valuesUnique
has unique valuesUnique
정무직 has 32 (66.7%) zerosZeros
별정직 has 4 (8.3%) zerosZeros
일반직 has 5 (10.4%) zerosZeros
경찰 has 7 (14.6%) zerosZeros
소방 has 6 (12.5%) zerosZeros
교육직 has 3 (6.2%) zerosZeros
법관검사 has 8 (16.7%) zerosZeros
기능직 has 29 (60.4%) zerosZeros
공안직 has 7 (14.6%) zerosZeros
군무원 has 6 (12.5%) zerosZeros
연구직 has 10 (20.8%) zerosZeros
지도직 has 9 (18.8%) zerosZeros
계약직 has 3 (6.2%) zerosZeros
공중보건의 has 35 (72.9%) zerosZeros
기타 has 2 (4.2%) zerosZeros

Reproduction

Analysis started2023-12-12 15:52:26.394740
Analysis finished2023-12-12 15:52:56.281372
Duration29.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-13T00:52:56.480203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0833333
Min length3

Characters and Unicode

Total characters148
Distinct characters13
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

Unique48 ?
Unique (%)100.0%

Sample

1st row18세이상
2nd row19세
3rd row20세
4th row21세
5th row22세
ValueCountFrequency (%)
18세이상 1
 
2.1%
19세 1
 
2.1%
53세 1
 
2.1%
44세 1
 
2.1%
45세 1
 
2.1%
46세 1
 
2.1%
47세 1
 
2.1%
48세 1
 
2.1%
49세 1
 
2.1%
50세 1
 
2.1%
Other values (38) 38
79.2%
2023-12-13T00:52:56.952553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48
32.4%
2 15
 
10.1%
3 15
 
10.1%
4 15
 
10.1%
5 15
 
10.1%
6 10
 
6.8%
1 7
 
4.7%
8 5
 
3.4%
9 5
 
3.4%
0 5
 
3.4%
Other values (3) 8
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
64.9%
Other Letter 52
35.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15
15.6%
3 15
15.6%
4 15
15.6%
5 15
15.6%
6 10
10.4%
1 7
7.3%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.2%
Other Letter
ValueCountFrequency (%)
48
92.3%
2
 
3.8%
2
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 96
64.9%
Hangul 52
35.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15
15.6%
3 15
15.6%
4 15
15.6%
5 15
15.6%
6 10
10.4%
1 7
7.3%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.2%
Hangul
ValueCountFrequency (%)
48
92.3%
2
 
3.8%
2
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96
64.9%
Hangul 52
35.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
48
92.3%
2
 
3.8%
2
 
3.8%
ASCII
ValueCountFrequency (%)
2 15
15.6%
3 15
15.6%
4 15
15.6%
5 15
15.6%
6 10
10.4%
1 7
7.3%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.2%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26687.375
Minimum15
Maximum44190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:52:57.153412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile738.45
Q111739.75
median34799.5
Q337867.5
95-th percentile43060.9
Maximum44190
Range44175
Interquartile range (IQR)26127.75

Descriptive statistics

Standard deviation15536.131
Coefficient of variation (CV)0.58215285
Kurtosis-1.0632425
Mean26687.375
Median Absolute Deviation (MAD)5758.5
Skewness-0.77326555
Sum1280994
Variance2.4137138 × 108
MonotonicityNot monotonic
2023-12-13T00:52:57.389190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
15 1
 
2.1%
43063 1
 
2.1%
38190 1
 
2.1%
36196 1
 
2.1%
35247 1
 
2.1%
38289 1
 
2.1%
38458 1
 
2.1%
37481 1
 
2.1%
39132 1
 
2.1%
37760 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
15 1
2.1%
528 1
2.1%
652 1
2.1%
899 1
2.1%
1005 1
2.1%
1079 1
2.1%
1301 1
2.1%
1645 1
2.1%
2288 1
2.1%
4647 1
2.1%
ValueCountFrequency (%)
44190 1
2.1%
43953 1
2.1%
43063 1
2.1%
43057 1
2.1%
42465 1
2.1%
41606 1
2.1%
39132 1
2.1%
38458 1
2.1%
38368 1
2.1%
38359 1
2.1%

정무직
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6041667
Minimum0
Maximum39
Zeros32
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:52:57.551590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.5
95-th percentile15.95
Maximum39
Range39
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation7.2837318
Coefficient of variation (CV)2.0209198
Kurtosis11.278521
Mean3.6041667
Median Absolute Deviation (MAD)0
Skewness2.988033
Sum173
Variance53.052748
MonotonicityNot monotonic
2023-12-13T00:52:57.779414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 32
66.7%
3 2
 
4.2%
6 2
 
4.2%
14 2
 
4.2%
1 1
 
2.1%
13 1
 
2.1%
17 1
 
2.1%
18 1
 
2.1%
7 1
 
2.1%
11 1
 
2.1%
Other values (4) 4
 
8.3%
ValueCountFrequency (%)
0 32
66.7%
1 1
 
2.1%
3 2
 
4.2%
4 1
 
2.1%
6 2
 
4.2%
7 1
 
2.1%
8 1
 
2.1%
9 1
 
2.1%
11 1
 
2.1%
13 1
 
2.1%
ValueCountFrequency (%)
39 1
2.1%
18 1
2.1%
17 1
2.1%
14 2
4.2%
13 1
2.1%
11 1
2.1%
9 1
2.1%
8 1
2.1%
7 1
2.1%
6 2
4.2%

별정직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.229167
Minimum0
Maximum68
Zeros4
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:52:57.957380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.25
median38
Q348.25
95-th percentile60.3
Maximum68
Range68
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.510215
Coefficient of variation (CV)0.56998802
Kurtosis-0.96138491
Mean34.229167
Median Absolute Deviation (MAD)14
Skewness-0.35824227
Sum1643
Variance380.64849
MonotonicityNot monotonic
2023-12-13T00:52:58.134549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 4
 
8.3%
12 3
 
6.2%
37 3
 
6.2%
55 2
 
4.2%
11 2
 
4.2%
33 2
 
4.2%
44 2
 
4.2%
42 2
 
4.2%
41 2
 
4.2%
56 1
 
2.1%
Other values (25) 25
52.1%
ValueCountFrequency (%)
0 4
8.3%
4 1
 
2.1%
5 1
 
2.1%
8 1
 
2.1%
11 2
4.2%
12 3
6.2%
19 1
 
2.1%
22 1
 
2.1%
24 1
 
2.1%
26 1
 
2.1%
ValueCountFrequency (%)
68 1
2.1%
64 1
2.1%
61 1
2.1%
59 1
2.1%
58 1
2.1%
57 1
2.1%
56 1
2.1%
55 2
4.2%
52 1
2.1%
50 1
2.1%

일반직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10736.896
Minimum0
Maximum18840
Zeros5
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:52:58.307856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13640.25
median13172
Q315557
95-th percentile18219.4
Maximum18840
Range18840
Interquartile range (IQR)11916.75

Descriptive statistics

Standard deviation6582.6072
Coefficient of variation (CV)0.6130829
Kurtosis-1.1268035
Mean10736.896
Median Absolute Deviation (MAD)3537
Skewness-0.66194492
Sum515371
Variance43330717
MonotonicityNot monotonic
2023-12-13T00:52:58.510772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 5
 
10.4%
15 1
 
2.1%
17179 1
 
2.1%
18253 1
 
2.1%
15332 1
 
2.1%
15181 1
 
2.1%
13622 1
 
2.1%
12972 1
 
2.1%
14382 1
 
2.1%
15034 1
 
2.1%
Other values (34) 34
70.8%
ValueCountFrequency (%)
0 5
10.4%
15 1
 
2.1%
505 1
 
2.1%
617 1
 
2.1%
765 1
 
2.1%
1054 1
 
2.1%
1772 1
 
2.1%
3281 1
 
2.1%
3760 1
 
2.1%
6688 1
 
2.1%
ValueCountFrequency (%)
18840 1
2.1%
18559 1
2.1%
18253 1
2.1%
18157 1
2.1%
18066 1
2.1%
17275 1
2.1%
17179 1
2.1%
16821 1
2.1%
16597 1
2.1%
16467 1
2.1%

경찰
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2995.0208
Minimum0
Maximum5612
Zeros7
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:52:58.729532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1944.25
median3653
Q34472.75
95-th percentile5294.45
Maximum5612
Range5612
Interquartile range (IQR)3528.5

Descriptive statistics

Standard deviation1918.9836
Coefficient of variation (CV)0.64072463
Kurtosis-1.1899162
Mean2995.0208
Median Absolute Deviation (MAD)977.5
Skewness-0.56808581
Sum143761
Variance3682498.1
MonotonicityNot monotonic
2023-12-13T00:52:58.936688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 7
 
14.6%
4732 1
 
2.1%
3710 1
 
2.1%
3048 1
 
2.1%
3005 1
 
2.1%
2757 1
 
2.1%
3093 1
 
2.1%
4032 1
 
2.1%
4402 1
 
2.1%
4364 1
 
2.1%
Other values (32) 32
66.7%
ValueCountFrequency (%)
0 7
14.6%
2 1
 
2.1%
24 1
 
2.1%
121 1
 
2.1%
363 1
 
2.1%
783 1
 
2.1%
998 1
 
2.1%
1801 1
 
2.1%
2219 1
 
2.1%
2627 1
 
2.1%
ValueCountFrequency (%)
5612 1
2.1%
5546 1
2.1%
5425 1
2.1%
5052 1
2.1%
5033 1
2.1%
5004 1
2.1%
4864 1
2.1%
4732 1
2.1%
4570 1
2.1%
4530 1
2.1%

소방
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1335.9167
Minimum0
Maximum3302
Zeros6
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:52:59.154398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1356.75
median1333.5
Q32000
95-th percentile2958.25
Maximum3302
Range3302
Interquartile range (IQR)1643.25

Descriptive statistics

Standard deviation959.43055
Coefficient of variation (CV)0.71818143
Kurtosis-0.81625819
Mean1335.9167
Median Absolute Deviation (MAD)706.5
Skewness0.08612238
Sum64124
Variance920506.97
MonotonicityNot monotonic
2023-12-13T00:52:59.368170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 6
 
12.5%
1570 2
 
4.2%
275 1
 
2.1%
1257 1
 
2.1%
2036 1
 
2.1%
1859 1
 
2.1%
1519 1
 
2.1%
1535 1
 
2.1%
1327 1
 
2.1%
1141 1
 
2.1%
Other values (32) 32
66.7%
ValueCountFrequency (%)
0 6
12.5%
3 1
 
2.1%
12 1
 
2.1%
54 1
 
2.1%
102 1
 
2.1%
168 1
 
2.1%
275 1
 
2.1%
384 1
 
2.1%
682 1
 
2.1%
761 1
 
2.1%
ValueCountFrequency (%)
3302 1
2.1%
3235 1
2.1%
3037 1
2.1%
2812 1
2.1%
2592 1
2.1%
2545 1
2.1%
2296 1
2.1%
2263 1
2.1%
2101 1
2.1%
2086 1
2.1%

교육직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7889.7708
Minimum0
Maximum13199
Zeros3
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:52:59.558374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.1
Q15644.25
median9101.5
Q311226.5
95-th percentile12956.65
Maximum13199
Range13199
Interquartile range (IQR)5582.25

Descriptive statistics

Standard deviation4331.4538
Coefficient of variation (CV)0.54899614
Kurtosis-0.69480478
Mean7889.7708
Median Absolute Deviation (MAD)2411.5
Skewness-0.76848235
Sum378709
Variance18761492
MonotonicityNot monotonic
2023-12-13T00:53:00.090824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 3
 
6.2%
8754 1
 
2.1%
12974 1
 
2.1%
12271 1
 
2.1%
12060 1
 
2.1%
11183 1
 
2.1%
10162 1
 
2.1%
9537 1
 
2.1%
9280 1
 
2.1%
9196 1
 
2.1%
Other values (36) 36
75.0%
ValueCountFrequency (%)
0 3
6.2%
6 1
 
2.1%
211 1
 
2.1%
212 1
 
2.1%
694 1
 
2.1%
724 1
 
2.1%
1853 1
 
2.1%
2001 1
 
2.1%
3913 1
 
2.1%
5345 1
 
2.1%
ValueCountFrequency (%)
13199 1
2.1%
12974 1
2.1%
12964 1
2.1%
12943 1
2.1%
12858 1
2.1%
12702 1
2.1%
12271 1
2.1%
12060 1
2.1%
12059 1
2.1%
11930 1
2.1%

법관검사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.10417
Minimum0
Maximum296
Zeros8
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:53:00.256165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.75
median76.5
Q3203.75
95-th percentile273
Maximum296
Range296
Interquartile range (IQR)190

Descriptive statistics

Standard deviation102.28418
Coefficient of variation (CV)0.90433606
Kurtosis-1.4409216
Mean113.10417
Median Absolute Deviation (MAD)76.5
Skewness0.36963573
Sum5429
Variance10462.053
MonotonicityNot monotonic
2023-12-13T00:53:00.385029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 8
 
16.7%
238 2
 
4.2%
13 2
 
4.2%
20 2
 
4.2%
45 2
 
4.2%
67 2
 
4.2%
273 2
 
4.2%
182 1
 
2.1%
165 1
 
2.1%
144 1
 
2.1%
Other values (25) 25
52.1%
ValueCountFrequency (%)
0 8
16.7%
7 1
 
2.1%
9 1
 
2.1%
13 2
 
4.2%
14 1
 
2.1%
20 2
 
4.2%
28 1
 
2.1%
29 1
 
2.1%
33 1
 
2.1%
45 2
 
4.2%
ValueCountFrequency (%)
296 1
2.1%
274 1
2.1%
273 2
4.2%
272 1
2.1%
264 1
2.1%
255 1
2.1%
238 2
4.2%
227 1
2.1%
225 1
2.1%
209 1
2.1%

기능직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3333333
Minimum0
Maximum10
Zeros29
Zeros (%)60.4%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:53:00.523993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5.65
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2249392
Coefficient of variation (CV)1.6687044
Kurtosis4.4198008
Mean1.3333333
Median Absolute Deviation (MAD)0
Skewness2.0511112
Sum64
Variance4.9503546
MonotonicityNot monotonic
2023-12-13T00:53:00.671812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 29
60.4%
2 5
 
10.4%
1 4
 
8.3%
3 3
 
6.2%
4 2
 
4.2%
5 2
 
4.2%
7 1
 
2.1%
10 1
 
2.1%
6 1
 
2.1%
ValueCountFrequency (%)
0 29
60.4%
1 4
 
8.3%
2 5
 
10.4%
3 3
 
6.2%
4 2
 
4.2%
5 2
 
4.2%
6 1
 
2.1%
7 1
 
2.1%
10 1
 
2.1%
ValueCountFrequency (%)
10 1
 
2.1%
7 1
 
2.1%
6 1
 
2.1%
5 2
 
4.2%
4 2
 
4.2%
3 3
 
6.2%
2 5
 
10.4%
1 4
 
8.3%
0 29
60.4%

공안직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean784.625
Minimum0
Maximum1695
Zeros7
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:53:00.844723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1137.5
median966.5
Q31220.75
95-th percentile1604.45
Maximum1695
Range1695
Interquartile range (IQR)1083.25

Descriptive statistics

Standard deviation559.11361
Coefficient of variation (CV)0.71258704
Kurtosis-1.3205344
Mean784.625
Median Absolute Deviation (MAD)376.5
Skewness-0.21398621
Sum37662
Variance312608.03
MonotonicityNot monotonic
2023-12-13T00:53:01.003487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 7
 
14.6%
1213 1
 
2.1%
1583 1
 
2.1%
1293 1
 
2.1%
1330 1
 
2.1%
1244 1
 
2.1%
1336 1
 
2.1%
1532 1
 
2.1%
1406 1
 
2.1%
1350 1
 
2.1%
Other values (32) 32
66.7%
ValueCountFrequency (%)
0 7
14.6%
2 1
 
2.1%
4 1
 
2.1%
24 1
 
2.1%
55 1
 
2.1%
130 1
 
2.1%
140 1
 
2.1%
276 1
 
2.1%
362 1
 
2.1%
458 1
 
2.1%
ValueCountFrequency (%)
1695 1
2.1%
1623 1
2.1%
1616 1
2.1%
1583 1
2.1%
1532 1
2.1%
1406 1
2.1%
1350 1
2.1%
1336 1
2.1%
1334 1
2.1%
1330 1
2.1%

군무원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean727.3125
Minimum0
Maximum1523
Zeros6
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:53:01.172279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1281
median832.5
Q31014
95-th percentile1341.8
Maximum1523
Range1523
Interquartile range (IQR)733

Descriptive statistics

Standard deviation461.65342
Coefficient of variation (CV)0.63473874
Kurtosis-0.99956124
Mean727.3125
Median Absolute Deviation (MAD)263
Skewness-0.42653104
Sum34911
Variance213123.88
MonotonicityNot monotonic
2023-12-13T00:53:01.353859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 6
 
12.5%
641 2
 
4.2%
1351 1
 
2.1%
906 1
 
2.1%
889 1
 
2.1%
754 1
 
2.1%
902 1
 
2.1%
971 1
 
2.1%
1032 1
 
2.1%
1346 1
 
2.1%
Other values (32) 32
66.7%
ValueCountFrequency (%)
0 6
12.5%
17 1
 
2.1%
20 1
 
2.1%
26 1
 
2.1%
72 1
 
2.1%
156 1
 
2.1%
173 1
 
2.1%
317 1
 
2.1%
570 1
 
2.1%
641 2
 
4.2%
ValueCountFrequency (%)
1523 1
2.1%
1351 1
2.1%
1346 1
2.1%
1334 1
2.1%
1325 1
2.1%
1301 1
2.1%
1236 1
2.1%
1159 1
2.1%
1156 1
2.1%
1145 1
2.1%

연구직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.33333
Minimum0
Maximum485
Zeros10
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:53:01.520648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127
median286
Q3361.75
95-th percentile448.95
Maximum485
Range485
Interquartile range (IQR)334.75

Descriptive statistics

Standard deviation163.76713
Coefficient of variation (CV)0.71410087
Kurtosis-1.420425
Mean229.33333
Median Absolute Deviation (MAD)97
Skewness-0.33682778
Sum11008
Variance26819.674
MonotonicityNot monotonic
2023-12-13T00:53:01.709289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 10
 
20.8%
3 1
 
2.1%
370 1
 
2.1%
385 1
 
2.1%
375 1
 
2.1%
378 1
 
2.1%
381 1
 
2.1%
372 1
 
2.1%
357 1
 
2.1%
322 1
 
2.1%
Other values (29) 29
60.4%
ValueCountFrequency (%)
0 10
20.8%
3 1
 
2.1%
12 1
 
2.1%
32 1
 
2.1%
68 1
 
2.1%
85 1
 
2.1%
113 1
 
2.1%
168 1
 
2.1%
203 1
 
2.1%
207 1
 
2.1%
ValueCountFrequency (%)
485 1
2.1%
458 1
2.1%
450 1
2.1%
447 1
2.1%
397 1
2.1%
396 1
2.1%
385 1
2.1%
381 1
2.1%
378 1
2.1%
375 1
2.1%

지도직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.625
Minimum0
Maximum209
Zeros9
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:53:01.881716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121.25
median108.5
Q3155
95-th percentile205.65
Maximum209
Range209
Interquartile range (IQR)133.75

Descriptive statistics

Standard deviation69.427331
Coefficient of variation (CV)0.68317177
Kurtosis-1.1355079
Mean101.625
Median Absolute Deviation (MAD)49.5
Skewness-0.27267867
Sum4878
Variance4820.1543
MonotonicityNot monotonic
2023-12-13T00:53:02.081201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 9
 
18.8%
133 3
 
6.2%
155 2
 
4.2%
162 2
 
4.2%
105 2
 
4.2%
7 1
 
2.1%
196 1
 
2.1%
103 1
 
2.1%
149 1
 
2.1%
178 1
 
2.1%
Other values (25) 25
52.1%
ValueCountFrequency (%)
0 9
18.8%
1 1
 
2.1%
7 1
 
2.1%
19 1
 
2.1%
22 1
 
2.1%
78 1
 
2.1%
81 1
 
2.1%
83 1
 
2.1%
86 1
 
2.1%
89 1
 
2.1%
ValueCountFrequency (%)
209 1
2.1%
208 1
2.1%
206 1
2.1%
205 1
2.1%
196 1
2.1%
187 1
2.1%
178 1
2.1%
171 1
2.1%
162 2
4.2%
161 1
2.1%

계약직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.79167
Minimum0
Maximum628
Zeros3
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:53:02.277456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7
Q1106
median413
Q3485.75
95-th percentile609.85
Maximum628
Range628
Interquartile range (IQR)379.75

Descriptive statistics

Standard deviation208.78442
Coefficient of variation (CV)0.6311659
Kurtosis-1.3258178
Mean330.79167
Median Absolute Deviation (MAD)141
Skewness-0.32630982
Sum15878
Variance43590.934
MonotonicityNot monotonic
2023-12-13T00:53:02.540141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 3
 
6.2%
416 2
 
4.2%
479 2
 
4.2%
438 2
 
4.2%
555 1
 
2.1%
591 1
 
2.1%
548 1
 
2.1%
550 1
 
2.1%
520 1
 
2.1%
428 1
 
2.1%
Other values (33) 33
68.8%
ValueCountFrequency (%)
0 3
6.2%
2 1
 
2.1%
7 1
 
2.1%
21 1
 
2.1%
30 1
 
2.1%
70 1
 
2.1%
73 1
 
2.1%
85 1
 
2.1%
101 1
 
2.1%
103 1
 
2.1%
ValueCountFrequency (%)
628 1
2.1%
627 1
2.1%
620 1
2.1%
591 1
2.1%
590 1
2.1%
555 1
2.1%
554 1
2.1%
551 1
2.1%
550 1
2.1%
548 1
2.1%

공중보건의
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.229167
Minimum0
Maximum564
Zeros35
Zeros (%)72.9%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:53:02.817450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile444
Maximum564
Range564
Interquartile range (IQR)5

Descriptive statistics

Standard deviation154.54563
Coefficient of variation (CV)2.2005903
Kurtosis3.3596363
Mean70.229167
Median Absolute Deviation (MAD)0
Skewness2.1546043
Sum3371
Variance23884.351
MonotonicityNot monotonic
2023-12-13T00:53:02.971163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 35
72.9%
17 1
 
2.1%
169 1
 
2.1%
405 1
 
2.1%
564 1
 
2.1%
518 1
 
2.1%
465 1
 
2.1%
342 1
 
2.1%
388 1
 
2.1%
273 1
 
2.1%
Other values (4) 4
 
8.3%
ValueCountFrequency (%)
0 35
72.9%
1 1
 
2.1%
17 1
 
2.1%
39 1
 
2.1%
62 1
 
2.1%
128 1
 
2.1%
169 1
 
2.1%
273 1
 
2.1%
342 1
 
2.1%
388 1
 
2.1%
ValueCountFrequency (%)
564 1
2.1%
518 1
2.1%
465 1
2.1%
405 1
2.1%
388 1
2.1%
342 1
2.1%
273 1
2.1%
169 1
2.1%
128 1
2.1%
62 1
2.1%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1333.5833
Minimum0
Maximum2398
Zeros2
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T00:53:03.153437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.25
Q1563.5
median1575
Q31986.75
95-th percentile2363.15
Maximum2398
Range2398
Interquartile range (IQR)1423.25

Descriptive statistics

Standard deviation800.23289
Coefficient of variation (CV)0.60006215
Kurtosis-1.1760126
Mean1333.5833
Median Absolute Deviation (MAD)518
Skewness-0.47564581
Sum64012
Variance640372.67
MonotonicityNot monotonic
2023-12-13T00:53:03.389638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 2
 
4.2%
2 1
 
2.1%
1964 1
 
2.1%
1959 1
 
2.1%
2106 1
 
2.1%
2367 1
 
2.1%
2356 1
 
2.1%
2398 1
 
2.1%
2391 1
 
2.1%
2350 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
0 2
4.2%
2 1
2.1%
17 1
2.1%
49 1
2.1%
96 1
2.1%
216 1
2.1%
238 1
2.1%
250 1
2.1%
287 1
2.1%
304 1
2.1%
ValueCountFrequency (%)
2398 1
2.1%
2391 1
2.1%
2367 1
2.1%
2356 1
2.1%
2350 1
2.1%
2250 1
2.1%
2118 1
2.1%
2106 1
2.1%
2080 1
2.1%
2066 1
2.1%

Interactions

2023-12-13T00:52:54.632467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:27.063251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:28.924876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.483757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:32.512603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:34.212765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:35.974038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:37.714258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:39.652010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.252873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:42.853792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:44.756704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:47.320097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:49.371256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:51.258765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:53.049711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.701793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:27.190575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:29.037095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.572904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:32.623181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:34.289743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:36.061909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:37.799416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:39.748932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.331935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:42.962183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:44.870862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:47.424246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:49.472679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:51.371160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:53.139941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.778358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:27.316092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:29.150591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.682404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:32.745908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:34.389338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:36.176883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:37.924005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:39.854134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.441001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:43.090425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:45.018146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:47.542816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:49.585800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:51.510058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:53.243032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.859286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:27.434113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:29.276357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.772100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:32.870176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:34.545549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:36.286835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:38.036832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:39.964744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.530770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:43.229238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:45.157652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:47.673411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:49.720942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:51.665581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:53.350111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.935966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:27.566581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:29.386090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.876898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:32.973478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:34.674133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:36.383189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:38.139220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:40.064731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.618817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:43.360207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:45.687766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:47.820633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:49.851351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:51.815744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:53.747729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.005085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:27.688539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:29.467220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.961954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:33.097271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:34.777983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:36.491496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:38.231618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:40.168527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.701036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:43.474968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:45.806309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:47.925456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:49.970316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:51.926099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:53.823645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.081328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:27.821862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:29.546354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:31.054317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:33.217262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:34.873590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:36.603103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:38.340363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:40.284053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.801662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:43.597071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:45.960047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:48.035457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:50.093970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:52.021066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:53.907432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.165196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:27.949666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:29.627009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:31.178097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:33.334278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:34.989111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:36.701550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:38.777478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:40.389183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.898669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:43.726979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:46.103263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:48.145838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:50.191820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:52.116166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:53.987725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.243634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:28.071704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:29.723890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:31.264890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:33.440472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:35.103510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:36.818844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:38.862657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:40.492804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.996532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:43.856615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:46.221732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:48.268228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:50.296963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:52.232299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.069903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.315302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:28.175863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:29.812887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:31.364702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:33.533799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:35.227422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:36.955645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:38.965053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:40.588573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:42.078713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:43.978960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:46.375347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:48.398843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:50.407693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:52.330860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.136004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.405934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:28.286170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:29.926203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:31.469658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:33.646034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:35.336806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:37.067741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:39.071520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:40.687458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:42.169544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:44.100375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:46.530512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:48.526259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:50.536136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:52.457733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.208863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.491699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:28.376478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.032007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:31.987769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:33.753949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:35.453268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:37.186046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:39.177395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:40.781760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:42.265602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:44.215189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:46.673851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:48.667670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:50.664464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:52.566109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.275337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.576269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:28.468873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.137210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:32.075639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:33.844367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:35.552191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:37.286883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:39.262853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:40.859222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:42.356403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:44.307521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:46.779183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:48.821825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:50.778797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:52.664171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.336567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.658966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:28.562162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.219218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:32.157908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:33.941765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:35.655412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:37.373819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:39.362535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:40.945469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:42.452087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:44.413428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:46.892652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:48.969568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:50.885342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:52.747787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.405180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.738653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:28.687260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.308432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:32.277184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:34.028527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:35.773228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:37.498327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:39.461573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.039690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:42.617375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:44.532158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:47.018572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:49.117944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:51.006897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:52.833306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.496128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:55.815560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:28.815640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:30.388577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:32.401695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:34.114849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:35.877995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:37.616609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:39.553766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:41.133134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:42.728754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:44.650098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:47.182871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:49.248253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:51.134850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:52.937661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:52:54.564907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:53:03.556899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1.0001.0000.5780.8480.9660.8940.8570.8400.6570.0000.9500.9010.8830.7180.8690.4590.896
정무직1.0000.5781.0000.5390.5460.3050.0000.4830.4670.0000.5320.5600.0000.0000.7070.0000.543
별정직1.0000.8480.5391.0000.7490.7110.8540.7240.7350.0000.8630.7070.8080.5690.8460.7020.884
일반직1.0000.9660.5460.7491.0000.9630.7960.8990.5790.0000.8480.8050.7720.8890.7390.5180.740
경찰1.0000.8940.3050.7110.9631.0000.8250.8750.7110.3160.8040.7460.6480.8790.6700.5400.690
소방1.0000.8570.0000.8540.7960.8251.0000.7180.8760.0000.9030.8940.8460.8660.7520.7910.804
교육직1.0000.8400.4830.7240.8990.8750.7181.0000.5660.0000.7970.8260.7190.8850.8130.3430.798
법관검사1.0000.6570.4670.7350.5790.7110.8760.5661.0000.7130.7660.8110.7890.5580.8050.4590.688
기능직1.0000.0000.0000.0000.0000.3160.0000.0000.7131.0000.2880.1880.0000.6940.0000.0000.351
공안직1.0000.9500.5320.8630.8480.8040.9030.7970.7660.2881.0000.8570.9140.6670.8890.6020.882
군무원1.0000.9010.5600.7070.8050.7460.8940.8260.8110.1880.8571.0000.7230.8270.8430.0000.860
연구직1.0000.8830.0000.8080.7720.6480.8460.7190.7890.0000.9140.7231.0000.6420.9360.6770.945
지도직1.0000.7180.0000.5690.8890.8790.8660.8850.5580.6940.6670.8270.6421.0000.5580.3840.640
계약직1.0000.8690.7070.8460.7390.6700.7520.8130.8050.0000.8890.8430.9360.5581.0000.4010.924
공중보건의1.0000.4590.0000.7020.5180.5400.7910.3430.4590.0000.6020.0000.6770.3840.4011.0000.614
기타1.0000.8960.5430.8840.7400.6900.8040.7980.6880.3510.8820.8600.9450.6400.9240.6141.000
2023-12-13T00:53:03.761427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
1.000-0.2790.8650.9570.8310.8110.8470.7470.4840.8800.8540.8220.8320.8510.1670.823
정무직-0.2791.000-0.257-0.302-0.236-0.456-0.350-0.2120.116-0.391-0.314-0.230-0.127-0.246-0.413-0.028
별정직0.865-0.2571.0000.8260.8770.8500.7800.7720.2560.7770.7210.7130.7140.7670.3200.689
일반직0.957-0.3020.8261.0000.8620.8420.7590.6670.4800.8240.8470.7800.8760.7560.2090.771
경찰0.831-0.2360.8770.8621.0000.8980.6190.6150.2190.6740.8280.6170.8650.6170.4140.665
소방0.811-0.4560.8500.8420.8981.0000.6720.6360.0660.6790.7490.6070.7570.6420.5550.523
교육직0.847-0.3500.7800.7590.6190.6721.0000.9240.5370.9490.6640.9330.5690.967-0.0260.842
법관검사0.747-0.2120.7720.6670.6150.6360.9241.0000.4560.8910.5220.8960.4530.933-0.1190.817
기능직0.4840.1160.2560.4800.2190.0660.5370.4561.0000.5890.4630.6440.4540.564-0.4660.753
공안직0.880-0.3910.7770.8240.6740.6790.9490.8910.5891.0000.7470.9440.6410.940-0.0330.887
군무원0.854-0.3140.7210.8470.8280.7490.6640.5220.4630.7471.0000.6460.9300.6570.3370.753
연구직0.822-0.2300.7130.7800.6170.6070.9330.8960.6440.9440.6461.0000.6050.924-0.1440.896
지도직0.832-0.1270.7140.8760.8650.7570.5690.4530.4540.6410.9300.6051.0000.5730.3510.711
계약직0.851-0.2460.7670.7560.6170.6420.9670.9330.5640.9400.6570.9240.5731.000-0.1100.881
공중보건의0.167-0.4130.3200.2090.4140.555-0.026-0.119-0.466-0.0330.337-0.1440.351-0.1101.000-0.205
기타0.823-0.0280.6890.7710.6650.5230.8420.8170.7530.8870.7530.8960.7110.881-0.2051.000

Missing values

2023-12-13T00:52:55.951964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:52:56.197389image/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

구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
018세이상150015000000000000
119세528005050300002000000
220세6520061721200021700002
321세899007652454600426012017
422세1645041054121102212002472007049
523세4647051772363168200100551563721096
624세9855012376099838439130014031712223017250
725세1676901966881801761574400276641327873169487
826세2320702494712627133669437045888868109103405768
927세296260261211235961909814614061211451131552025641032
구분정무직별정직일반직경찰소방교육직법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
3856세2948113371249935511315809167178974228513032801633
3957세2860117331135934851122889163066869626010832101578
4058세25441143010180272497383326714976412228325501422
4159세2146718378546221968272264503625702039116201306
4260세123687223281783275682328213017385191510589
4361세57931112000534520000001010304
4462세2288912000185320000001070287
4563세10798110007241300000850238
4664세1005480006941300000700216
4765세이상130139110002119000002910740