Overview

Dataset statistics

Number of variables17
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory154.9 B

Variable types

Text1
Categorical2
Numeric14

Dataset

Description공무원 직종별(정무직,일반직,경찰,소방 등) 지역별(서울,부산,대구,대전, 부산 등) 공무원연금 신규가입자 현황으로 남녀 구분도 포함됩니다.
URLhttps://www.data.go.kr/data/15053038/fileData.do

Alerts

is highly overall correlated with 일반직 and 5 other fieldsHigh correlation
정무직 is highly overall correlated with 군무원High correlation
별정직 is highly overall correlated with 경찰직 and 4 other fieldsHigh correlation
일반직 is highly overall correlated with and 4 other fieldsHigh correlation
경찰직 is highly overall correlated with and 5 other fieldsHigh correlation
소방직 is highly overall correlated with and 6 other fieldsHigh correlation
교육 is highly overall correlated with and 3 other fieldsHigh correlation
법관검사 is highly overall correlated with 공안직 and 2 other fieldsHigh correlation
공안직 is highly overall correlated with 법관검사 and 3 other fieldsHigh correlation
연구직 is highly overall correlated with 공안직 and 1 other fieldsHigh correlation
계약직 is highly overall correlated with and 6 other fieldsHigh correlation
공중보건의 is highly overall correlated with 별정직 and 2 other fieldsHigh correlation
기타 is highly overall correlated with and 6 other fieldsHigh correlation
남여구분 is highly overall correlated with 소방직 and 1 other fieldsHigh correlation
군무원 is highly overall correlated with 정무직 and 9 other fieldsHigh correlation
군무원 is highly imbalanced (75.9%)Imbalance
has unique valuesUnique
일반직 has unique valuesUnique
정무직 has 23 (67.6%) zerosZeros
별정직 has 2 (5.9%) zerosZeros
법관검사 has 22 (64.7%) zerosZeros
공안직 has 2 (5.9%) zerosZeros
연구직 has 1 (2.9%) zerosZeros
지도직 has 6 (17.6%) zerosZeros
공중보건의 has 17 (50.0%) zerosZeros

Reproduction

Analysis started2023-12-12 09:27:27.169763
Analysis finished2023-12-12 09:27:51.697357
Duration24.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T18:27:51.844005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters68
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row서울
3rd row부산
4th row부산
5th row대구
ValueCountFrequency (%)
서울 2
 
5.9%
강원 2
 
5.9%
전남 2
 
5.9%
전북 2
 
5.9%
경남 2
 
5.9%
경북 2
 
5.9%
충남 2
 
5.9%
충북 2
 
5.9%
경기 2
 
5.9%
부산 2
 
5.9%
Other values (7) 14
41.2%
2023-12-12T18:27:52.225126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

남여구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
남자
17 
여자
17 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남자
2nd row여자
3rd row남자
4th row여자
5th row남자

Common Values

ValueCountFrequency (%)
남자 17
50.0%
여자 17
50.0%

Length

2023-12-12T18:27:52.384930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:27:52.512455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남자 17
50.0%
여자 17
50.0%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2089.5294
Minimum517
Maximum7652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:52.642557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum517
5-th percentile603.65
Q11251.25
median1602.5
Q31987.75
95-th percentile7322.65
Maximum7652
Range7135
Interquartile range (IQR)736.5

Descriptive statistics

Standard deviation1914.8677
Coefficient of variation (CV)0.91641096
Kurtosis4.0930272
Mean2089.5294
Median Absolute Deviation (MAD)380
Skewness2.2394302
Sum71044
Variance3666718.1
MonotonicityNot monotonic
2023-12-12T18:27:52.810474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7652 1
 
2.9%
1864 1
 
2.9%
1358 1
 
2.9%
1305 1
 
2.9%
1821 1
 
2.9%
1828 1
 
2.9%
2220 1
 
2.9%
1972 1
 
2.9%
2081 1
 
2.9%
1749 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
517 1
2.9%
538 1
2.9%
639 1
2.9%
676 1
2.9%
679 1
2.9%
761 1
2.9%
887 1
2.9%
937 1
2.9%
1251 1
2.9%
1252 1
2.9%
ValueCountFrequency (%)
7652 1
2.9%
7582 1
2.9%
7183 1
2.9%
5630 1
2.9%
2395 1
2.9%
2220 1
2.9%
2191 1
2.9%
2081 1
2.9%
1993 1
2.9%
1972 1
2.9%

정무직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2647059
Minimum0
Maximum30
Zeros23
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:52.987888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile14.05
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.016469
Coefficient of variation (CV)3.0981811
Kurtosis13.256828
Mean2.2647059
Median Absolute Deviation (MAD)0
Skewness3.7488989
Sum77
Variance49.230838
MonotonicityNot monotonic
2023-12-12T18:27:53.135403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 23
67.6%
1 5
 
14.7%
2 2
 
5.9%
29 1
 
2.9%
6 1
 
2.9%
3 1
 
2.9%
30 1
 
2.9%
ValueCountFrequency (%)
0 23
67.6%
1 5
 
14.7%
2 2
 
5.9%
3 1
 
2.9%
6 1
 
2.9%
29 1
 
2.9%
30 1
 
2.9%
ValueCountFrequency (%)
30 1
 
2.9%
29 1
 
2.9%
6 1
 
2.9%
3 1
 
2.9%
2 2
 
5.9%
1 5
 
14.7%
0 23
67.6%

별정직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.323529
Minimum0
Maximum277
Zeros2
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:53.291528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.65
Q14
median15
Q325.75
95-th percentile97.2
Maximum277
Range277
Interquartile range (IQR)21.75

Descriptive statistics

Standard deviation50.834086
Coefficient of variation (CV)1.8604509
Kurtosis18.556573
Mean27.323529
Median Absolute Deviation (MAD)11
Skewness4.0722008
Sum929
Variance2584.1043
MonotonicityNot monotonic
2023-12-12T18:27:53.449435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4 5
14.7%
3 4
 
11.8%
16 3
 
8.8%
12 2
 
5.9%
0 2
 
5.9%
21 2
 
5.9%
24 1
 
2.9%
30 1
 
2.9%
26 1
 
2.9%
28 1
 
2.9%
Other values (12) 12
35.3%
ValueCountFrequency (%)
0 2
 
5.9%
1 1
 
2.9%
3 4
11.8%
4 5
14.7%
7 1
 
2.9%
11 1
 
2.9%
12 2
 
5.9%
14 1
 
2.9%
16 3
8.8%
19 1
 
2.9%
ValueCountFrequency (%)
277 1
2.9%
131 1
2.9%
79 1
2.9%
44 1
2.9%
35 1
2.9%
32 1
2.9%
30 1
2.9%
28 1
2.9%
26 1
2.9%
25 1
2.9%

일반직
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean849.11765
Minimum160
Maximum3016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:53.844928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile173.75
Q1509.75
median685
Q3928.75
95-th percentile2283.45
Maximum3016
Range2856
Interquartile range (IQR)419

Descriptive statistics

Standard deviation638.39371
Coefficient of variation (CV)0.75183187
Kurtosis4.5291565
Mean849.11765
Median Absolute Deviation (MAD)218
Skewness2.0847032
Sum28870
Variance407546.53
MonotonicityNot monotonic
2023-12-12T18:27:54.020686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1799 1
 
2.9%
771 1
 
2.9%
561 1
 
2.9%
645 1
 
2.9%
779 1
 
2.9%
889 1
 
2.9%
942 1
 
2.9%
1067 1
 
2.9%
994 1
 
2.9%
672 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
160 1
2.9%
164 1
2.9%
179 1
2.9%
285 1
2.9%
383 1
2.9%
414 1
2.9%
448 1
2.9%
453 1
2.9%
499 1
2.9%
542 1
2.9%
ValueCountFrequency (%)
3016 1
2.9%
2600 1
2.9%
2113 1
2.9%
1799 1
2.9%
1067 1
2.9%
1044 1
2.9%
1014 1
2.9%
994 1
2.9%
942 1
2.9%
889 1
2.9%

경찰직
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.64706
Minimum8
Maximum1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:54.204114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile21.6
Q145
median80.5
Q3227.25
95-th percentile477.35
Maximum1011
Range1003
Interquartile range (IQR)182.25

Descriptive statistics

Standard deviation199.89086
Coefficient of variation (CV)1.2067275
Kurtosis9.1363314
Mean165.64706
Median Absolute Deviation (MAD)54.5
Skewness2.6794878
Sum5632
Variance39956.357
MonotonicityNot monotonic
2023-12-12T18:27:54.408943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
98 2
 
5.9%
24 2
 
5.9%
229 1
 
2.9%
75 1
 
2.9%
28 1
 
2.9%
235 1
 
2.9%
72 1
 
2.9%
292 1
 
2.9%
80 1
 
2.9%
1011 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
8 1
2.9%
19 1
2.9%
23 1
2.9%
24 2
5.9%
28 1
2.9%
31 1
2.9%
34 1
2.9%
41 1
2.9%
57 1
2.9%
59 1
2.9%
ValueCountFrequency (%)
1011 1
2.9%
556 1
2.9%
435 1
2.9%
348 1
2.9%
335 1
2.9%
292 1
2.9%
276 1
2.9%
235 1
2.9%
229 1
2.9%
222 1
2.9%

소방직
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.41176
Minimum3
Maximum674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:54.557793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.3
Q117.5
median52.5
Q3138
95-th percentile385.55
Maximum674
Range671
Interquartile range (IQR)120.5

Descriptive statistics

Standard deviation148.67743
Coefficient of variation (CV)1.2882346
Kurtosis5.1122769
Mean115.41176
Median Absolute Deviation (MAD)44
Skewness2.1092892
Sum3924
Variance22104.977
MonotonicityNot monotonic
2023-12-12T18:27:54.723854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
17 2
 
5.9%
13 2
 
5.9%
374 1
 
2.9%
216 1
 
2.9%
23 1
 
2.9%
251 1
 
2.9%
19 1
 
2.9%
301 1
 
2.9%
31 1
 
2.9%
209 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
3 1
2.9%
5 1
2.9%
7 1
2.9%
8 1
2.9%
13 2
5.9%
16 1
2.9%
17 2
5.9%
19 1
2.9%
23 1
2.9%
24 1
2.9%
ValueCountFrequency (%)
674 1
2.9%
407 1
2.9%
374 1
2.9%
301 1
2.9%
276 1
2.9%
251 1
2.9%
216 1
2.9%
209 1
2.9%
139 1
2.9%
135 1
2.9%

교육
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean397.35294
Minimum27
Maximum2811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:54.877762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile77.95
Q1162.5
median240.5
Q3472
95-th percentile880.65
Maximum2811
Range2784
Interquartile range (IQR)309.5

Descriptive statistics

Standard deviation491.36143
Coefficient of variation (CV)1.2365869
Kurtosis18.174395
Mean397.35294
Median Absolute Deviation (MAD)128
Skewness3.8879583
Sum13510
Variance241436.05
MonotonicityNot monotonic
2023-12-12T18:27:55.033068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
113 2
 
5.9%
237 1
 
2.9%
417 1
 
2.9%
264 1
 
2.9%
577 1
 
2.9%
275 1
 
2.9%
576 1
 
2.9%
242 1
 
2.9%
605 1
 
2.9%
416 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
27 1
2.9%
63 1
2.9%
86 1
2.9%
109 1
2.9%
112 1
2.9%
113 2
5.9%
133 1
2.9%
162 1
2.9%
164 1
2.9%
187 1
2.9%
ValueCountFrequency (%)
2811 1
2.9%
1179 1
2.9%
720 1
2.9%
682 1
2.9%
652 1
2.9%
605 1
2.9%
577 1
2.9%
576 1
2.9%
477 1
2.9%
457 1
2.9%

법관검사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8529412
Minimum0
Maximum76
Zeros22
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:55.196624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.75
95-th percentile30.1
Maximum76
Range76
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation17.593271
Coefficient of variation (CV)3.0058854
Kurtosis13.616896
Mean5.8529412
Median Absolute Deviation (MAD)0
Skewness3.8042412
Sum199
Variance309.52317
MonotonicityNot monotonic
2023-12-12T18:27:55.360106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22
64.7%
5 3
 
8.8%
7 2
 
5.9%
6 2
 
5.9%
73 1
 
2.9%
76 1
 
2.9%
2 1
 
2.9%
4 1
 
2.9%
3 1
 
2.9%
ValueCountFrequency (%)
0 22
64.7%
2 1
 
2.9%
3 1
 
2.9%
4 1
 
2.9%
5 3
 
8.8%
6 2
 
5.9%
7 2
 
5.9%
73 1
 
2.9%
76 1
 
2.9%
ValueCountFrequency (%)
76 1
 
2.9%
73 1
 
2.9%
7 2
 
5.9%
6 2
 
5.9%
5 3
 
8.8%
4 1
 
2.9%
3 1
 
2.9%
2 1
 
2.9%
0 22
64.7%

공안직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.5
Minimum0
Maximum383
Zeros2
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:55.534509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3
Q18.25
median16
Q369.25
95-th percentile172.1
Maximum383
Range383
Interquartile range (IQR)61

Descriptive statistics

Standard deviation78.274672
Coefficient of variation (CV)1.5813065
Kurtosis9.6792386
Mean49.5
Median Absolute Deviation (MAD)11
Skewness2.8572053
Sum1683
Variance6126.9242
MonotonicityNot monotonic
2023-12-12T18:27:55.722245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
17 2
 
5.9%
11 2
 
5.9%
5 2
 
5.9%
0 2
 
5.9%
15 2
 
5.9%
84 1
 
2.9%
10 1
 
2.9%
2 1
 
2.9%
4 1
 
2.9%
12 1
 
2.9%
Other values (19) 19
55.9%
ValueCountFrequency (%)
0 2
5.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 2
5.9%
7 1
2.9%
8 1
2.9%
9 1
2.9%
10 1
2.9%
11 2
5.9%
ValueCountFrequency (%)
383 1
2.9%
215 1
2.9%
149 1
2.9%
147 1
2.9%
118 1
2.9%
91 1
2.9%
84 1
2.9%
74 1
2.9%
72 1
2.9%
61 1
2.9%

군무원
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
0
32 
1762
 
1
821
 
1

Length

Max length4
Median length1
Mean length1.1470588
Min length1

Unique

Unique2 ?
Unique (%)5.9%

Sample

1st row1762
2nd row821
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32
94.1%
1762 1
 
2.9%
821 1
 
2.9%

Length

2023-12-12T18:27:55.940981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:27:56.081407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
94.1%
1762 1
 
2.9%
821 1
 
2.9%

연구직
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.029412
Minimum0
Maximum52
Zeros1
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:56.228802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.65
Q18
median16
Q324.25
95-th percentile46.45
Maximum52
Range52
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation13.703539
Coefficient of variation (CV)0.7600658
Kurtosis0.47088001
Mean18.029412
Median Absolute Deviation (MAD)8.5
Skewness0.97651673
Sum613
Variance187.78699
MonotonicityNot monotonic
2023-12-12T18:27:56.418768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
17 3
 
8.8%
5 2
 
5.9%
8 2
 
5.9%
16 2
 
5.9%
22 2
 
5.9%
2 2
 
5.9%
13 2
 
5.9%
25 1
 
2.9%
11 1
 
2.9%
7 1
 
2.9%
Other values (16) 16
47.1%
ValueCountFrequency (%)
0 1
2.9%
1 1
2.9%
2 2
5.9%
5 2
5.9%
6 1
2.9%
7 1
2.9%
8 2
5.9%
9 1
2.9%
11 1
2.9%
12 1
2.9%
ValueCountFrequency (%)
52 1
2.9%
51 1
2.9%
44 1
2.9%
35 1
2.9%
34 1
2.9%
33 1
2.9%
31 1
2.9%
27 1
2.9%
25 1
2.9%
22 2
5.9%

지도직
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7058824
Minimum0
Maximum19
Zeros6
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:56.547065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2.5
Q312.75
95-th percentile19
Maximum19
Range19
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation6.9741054
Coefficient of variation (CV)1.0399982
Kurtosis-1.2757145
Mean6.7058824
Median Absolute Deviation (MAD)2.5
Skewness0.60739041
Sum228
Variance48.638146
MonotonicityNot monotonic
2023-12-12T18:27:56.690513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 8
23.5%
0 6
17.6%
2 3
 
8.8%
19 3
 
8.8%
17 2
 
5.9%
10 2
 
5.9%
12 1
 
2.9%
9 1
 
2.9%
11 1
 
2.9%
4 1
 
2.9%
Other values (6) 6
17.6%
ValueCountFrequency (%)
0 6
17.6%
1 8
23.5%
2 3
 
8.8%
3 1
 
2.9%
4 1
 
2.9%
6 1
 
2.9%
9 1
 
2.9%
10 2
 
5.9%
11 1
 
2.9%
12 1
 
2.9%
ValueCountFrequency (%)
19 3
8.8%
17 2
5.9%
16 1
 
2.9%
15 1
 
2.9%
14 1
 
2.9%
13 1
 
2.9%
12 1
 
2.9%
11 1
 
2.9%
10 2
5.9%
9 1
 
2.9%

계약직
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.35294
Minimum23
Maximum630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:56.870020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile26.6
Q148
median66.5
Q384.75
95-th percentile361.85
Maximum630
Range607
Interquartile range (IQR)36.75

Descriptive statistics

Standard deviation133.87757
Coefficient of variation (CV)1.2829305
Kurtosis10.505015
Mean104.35294
Median Absolute Deviation (MAD)18.5
Skewness3.2660378
Sum3548
Variance17923.205
MonotonicityNot monotonic
2023-12-12T18:27:57.048386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
48 3
 
8.8%
66 2
 
5.9%
81 2
 
5.9%
57 2
 
5.9%
630 1
 
2.9%
73 1
 
2.9%
24 1
 
2.9%
42 1
 
2.9%
87 1
 
2.9%
67 1
 
2.9%
Other values (19) 19
55.9%
ValueCountFrequency (%)
23 1
 
2.9%
24 1
 
2.9%
28 1
 
2.9%
37 1
 
2.9%
40 1
 
2.9%
42 1
 
2.9%
43 1
 
2.9%
48 3
8.8%
49 1
 
2.9%
56 1
 
2.9%
ValueCountFrequency (%)
630 1
2.9%
564 1
2.9%
253 1
2.9%
225 1
2.9%
117 1
2.9%
98 1
2.9%
89 1
2.9%
87 1
2.9%
86 1
2.9%
81 2
5.9%

공중보건의
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.852941
Minimum0
Maximum264
Zeros17
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:57.218801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q325.75
95-th percentile139
Maximum264
Range264
Interquartile range (IQR)25.75

Descriptive statistics

Standard deviation59.713017
Coefficient of variation (CV)1.9354076
Kurtosis7.3171262
Mean30.852941
Median Absolute Deviation (MAD)1
Skewness2.6242712
Sum1049
Variance3565.6444
MonotonicityNot monotonic
2023-12-12T18:27:57.400342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 17
50.0%
6 2
 
5.9%
2 1
 
2.9%
60 1
 
2.9%
22 1
 
2.9%
264 1
 
2.9%
101 1
 
2.9%
111 1
 
2.9%
191 1
 
2.9%
75 1
 
2.9%
Other values (7) 7
20.6%
ValueCountFrequency (%)
0 17
50.0%
2 1
 
2.9%
3 1
 
2.9%
6 2
 
5.9%
12 1
 
2.9%
14 1
 
2.9%
18 1
 
2.9%
22 1
 
2.9%
27 1
 
2.9%
49 1
 
2.9%
ValueCountFrequency (%)
264 1
2.9%
191 1
2.9%
111 1
2.9%
101 1
2.9%
88 1
2.9%
75 1
2.9%
60 1
2.9%
49 1
2.9%
27 1
2.9%
22 1
2.9%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240.76471
Minimum30
Maximum1371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:27:57.601254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile37.2
Q179.5
median133.5
Q3197
95-th percentile1110.45
Maximum1371
Range1341
Interquartile range (IQR)117.5

Descriptive statistics

Standard deviation343.92327
Coefficient of variation (CV)1.4284622
Kurtosis5.9450254
Mean240.76471
Median Absolute Deviation (MAD)57.5
Skewness2.6125375
Sum8186
Variance118283.22
MonotonicityNot monotonic
2023-12-12T18:27:57.804857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
152 2
 
5.9%
143 2
 
5.9%
201 1
 
2.9%
102 1
 
2.9%
172 1
 
2.9%
91 1
 
2.9%
129 1
 
2.9%
120 1
 
2.9%
231 1
 
2.9%
1349 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
30 1
2.9%
32 1
2.9%
40 1
2.9%
50 1
2.9%
55 1
2.9%
59 1
2.9%
73 1
2.9%
74 1
2.9%
78 1
2.9%
84 1
2.9%
ValueCountFrequency (%)
1371 1
2.9%
1349 1
2.9%
982 1
2.9%
801 1
2.9%
264 1
2.9%
238 1
2.9%
231 1
2.9%
203 1
2.9%
201 1
2.9%
185 1
2.9%

Interactions

2023-12-12T18:27:49.290299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:27.928978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:29.918118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.405905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:32.722731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:34.413362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:36.371544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:37.832534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:39.311699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:40.736604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:42.769452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:44.390325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:46.192672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:47.759329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:49.414123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:28.039292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:30.036433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.499292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:32.843271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:34.524590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:36.474436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:37.943330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:39.426845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:40.866394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:42.907131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:44.553175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:46.313376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:47.866131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:49.537101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:28.157293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:30.158152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.601582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:32.964574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:34.639336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:36.574200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:38.036014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:39.528197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:40.975139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:43.004858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:44.682038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:46.424236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:47.999358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:49.661276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:28.280641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:30.245621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.682824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:33.087125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:34.755388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:36.677608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:38.123834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:39.631999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:41.071164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:43.122258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:44.794919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:46.548710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:48.115691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:50.094495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:28.409877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:30.361763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.766692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:33.201916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:34.865378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:36.784398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:38.218006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:39.721971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:41.167902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:43.236142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:44.916405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:46.663120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:48.221256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:50.230460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:28.849791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:30.466768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.852257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:33.333064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:34.963324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:36.882561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:38.327459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:39.824109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:41.292538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:43.354878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:45.048453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:46.770996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:48.312690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:50.371437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:28.966705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:30.576652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.945205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:33.451622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:35.070381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:36.986882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:38.441653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:39.945393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:41.443452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:43.464184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:45.172737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:46.887194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:48.404791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:50.506549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:29.070604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:30.684663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:32.033266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:33.558328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:35.173609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:37.089425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:38.542511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:40.050171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:41.570739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:43.572903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:45.293160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:46.983931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:48.497247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:50.640185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:29.181504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:30.799788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:32.132094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:33.683612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:35.314528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:37.203781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:38.666454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:40.162954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:41.703140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:43.697953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:45.425271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:47.099502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:48.608120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:50.745697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:29.319180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:30.915221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:32.229560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:33.820555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:35.429838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:37.312312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:38.780958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:40.263722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:41.845479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:43.825765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:45.569440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:47.223244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:48.732817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:50.853522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:29.421596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.032283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:32.315733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:33.934560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:35.919558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:37.412113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:38.867628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:40.358488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:41.942494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:43.935229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:45.693997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:47.322733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:48.826702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:50.967231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:29.538406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.137342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:32.443727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:34.073359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:36.021832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:37.529545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:38.987306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:40.451254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:42.394917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:44.060863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:45.824481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:47.448606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:48.936874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:51.068488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:29.676253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.230945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:32.544789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:34.194920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:36.133397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:37.626866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:39.095345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:40.541269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:42.508500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:44.179152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:45.938957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:47.560181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:49.044092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:51.183018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:29.782444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:31.317929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:32.631681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:34.306170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:36.234526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:37.719741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:39.196244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:40.635639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:42.643038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:44.279715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:46.062825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:47.648649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:27:49.165181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:27:58.305940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역남여구분정무직별정직일반직경찰직소방직교육법관검사공안직군무원연구직지도직계약직공중보건의기타
지역1.0000.0000.9060.3250.0640.6680.0000.0000.0001.0000.4580.3990.3070.7430.8170.0000.786
남여구분0.0001.0000.0000.2870.3470.2490.4150.8180.4640.0000.1490.0000.2100.3980.0600.4400.000
0.9060.0001.0000.3190.9040.9100.7140.6960.8510.6430.6320.5380.5040.7610.8700.0000.948
정무직0.3250.2870.3191.0000.7380.9150.3150.0000.5860.9690.5620.7880.0000.0000.7190.0000.507
별정직0.0640.3470.9040.7381.0000.9070.8250.8220.8541.0000.7651.0000.0000.0000.9820.0000.943
일반직0.6680.2490.9100.9150.9071.0000.7530.8380.8501.0000.7671.0000.4200.0190.9080.0000.909
경찰직0.0000.4150.7140.3150.8250.7531.0000.9150.0000.6310.5980.7570.4600.4530.7760.8450.715
소방직0.0000.8180.6960.0000.8220.8380.9151.0000.0000.7810.6520.7250.0000.4840.7680.8190.712
교육0.0000.4640.8510.5860.8540.8500.0000.0001.0000.5380.4470.6870.4140.6110.9080.0000.923
법관검사1.0000.0000.6430.9691.0001.0000.6310.7810.5381.0000.7091.0000.2720.0001.0000.0001.000
공안직0.4580.1490.6320.5620.7650.7670.5980.6520.4470.7091.0000.8490.5520.0000.6500.0000.735
군무원0.3990.0000.5380.7881.0001.0000.7570.7250.6871.0000.8491.0000.5900.0001.0000.0000.682
연구직0.3070.2100.5040.0000.0000.4200.4600.0000.4140.2720.5520.5901.0000.4350.4160.3720.300
지도직0.7430.3980.7610.0000.0000.0190.4530.4840.6110.0000.0000.0000.4351.0000.0000.7830.587
계약직0.8170.0600.8700.7190.9820.9080.7760.7680.9081.0000.6501.0000.4160.0001.0000.0000.952
공중보건의0.0000.4400.0000.0000.0000.0000.8450.8190.0000.0000.0000.0000.3720.7830.0001.0000.000
기타0.7860.0000.9480.5070.9430.9090.7150.7120.9231.0000.7350.6820.3000.5870.9520.0001.000
2023-12-12T18:27:58.508317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
군무원남여구분
군무원1.0000.000
남여구분0.0001.000
2023-12-12T18:27:58.643122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정무직별정직일반직경찰직소방직교육법관검사공안직연구직지도직계약직공중보건의기타남여구분군무원
1.0000.1050.3240.9350.6920.5020.7380.2730.1690.3470.4610.6960.1170.6120.0000.463
정무직0.1051.0000.3440.223-0.117-0.0390.0110.4210.2480.034-0.2690.314-0.2760.3010.1720.852
별정직0.3240.3441.0000.1220.5320.662-0.1530.4570.224-0.133-0.2970.7480.5680.4070.4000.967
일반직0.9350.2230.1221.0000.4940.2680.8230.3080.1840.3960.4670.590-0.1630.6000.1260.916
경찰직0.692-0.1170.5320.4941.0000.8030.3450.046-0.005-0.1010.2450.6130.5920.3000.4030.643
소방직0.502-0.0390.6620.2680.8031.0000.0090.1220.061-0.1260.1770.5770.8210.1930.5720.563
교육0.7380.011-0.1530.8230.3450.0091.0000.0740.0550.3380.4960.295-0.3720.3860.5350.642
법관검사0.2730.4210.4570.3080.0460.1220.0741.0000.7960.411-0.3720.423-0.1500.7040.0000.984
공안직0.1690.2480.2240.184-0.0050.0610.0550.7961.0000.505-0.2610.253-0.1320.7340.0740.513
연구직0.3470.034-0.1330.396-0.101-0.1260.3380.4110.5051.0000.1510.131-0.3430.5180.1650.273
지도직0.461-0.269-0.2970.4670.2450.1770.496-0.372-0.2610.1511.000-0.0270.062-0.0020.2500.000
계약직0.6960.3140.7480.5900.6130.5770.2950.4230.2530.131-0.0271.0000.3470.6320.0340.967
공중보건의0.117-0.2760.568-0.1630.5920.821-0.372-0.150-0.132-0.3430.0620.3471.000-0.0770.4290.000
기타0.6120.3010.4070.6000.3000.1930.3860.7040.7340.518-0.0020.632-0.0771.0000.0000.635
남여구분0.0000.1720.4000.1260.4030.5720.5350.0000.0740.1650.2500.0340.4290.0001.0000.000
군무원0.4630.8520.9670.9160.6430.5630.6420.9840.5130.2730.0000.9670.0000.6350.0001.000

Missing values

2023-12-12T18:27:51.352707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:27:51.612422image/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

지역남여구분정무직별정직일반직경찰직소방직교육법관검사공안직군무원연구직지도직계약직공중보건의기타
0서울남자765229277179910113742377384176225063021349
1서울여자718361312600276511179767482134056401371
2부산남자17010356862221352715310201866203
3부산여자2191016104470176527240331890238
4대구남자12670324487110010952150606927185
5대구여자12511466824252212720121780143
6인천남자1425019542348139162013081664978
7인천여자16260081476134770150161630138
8광주남자937016383416363611803124814152
9광주여자88704499197113561052328096
지역남여구분정무직별정직일반직경찰직소방직교육법관검사공안직군무원연구직지도직계약직공중보건의기타
24경북남자222001194229230127504013198119191
25경북여자1972011067803157601001619430129
26경남남자18640287712292162420120144117111120
27경남여자208104994811660502502717810231
28전북남자17210266421872091870150441198101201
29전북여자1579048045924457080359670112
30전남남자23950308304354072360307128726484
31전남여자19930010149855682070171942059
32제주남자6390121641129611209050572250
33제주여자51704160311323901105024030