Overview

Dataset statistics

Number of variables9
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory82.7 B

Variable types

Text1
Numeric8

Dataset

Description공무원 연령별 연금지급 정지자수(공무원, 사립학교교직원, 일부정지, 고의, 중과실, 파면 등) 현황 데이터로 38세 미만부터 구분되고 있습니다.
URLhttps://www.data.go.kr/data/15052984/fileData.do

Alerts

계 is highly overall correlated with 국회의원 등 and 1 other fieldsHigh correlation
국회의원 등 is highly overall correlated with 계 and 3 other fieldsHigh correlation
전액(소계) is highly overall correlated with 국회의원 등 and 4 other fieldsHigh correlation
전액(공무원) is highly overall correlated with 국회의원 등 and 4 other fieldsHigh correlation
전액(사립학교교직원등) is highly overall correlated with 전액(소계) and 3 other fieldsHigh correlation
고의_증과실_파면등 is highly overall correlated with 전액(소계) and 3 other fieldsHigh correlation
기타제한사유 is highly overall correlated with 전액(소계) and 3 other fieldsHigh correlation
일부정지 is highly overall correlated with 계 and 1 other fieldsHigh correlation
구분 has unique valuesUnique
국회의원 등 has 24 (49.0%) zerosZeros
전액(소계) has 9 (18.4%) zerosZeros
전액(공무원) has 9 (18.4%) zerosZeros
전액(사립학교교직원등) has 25 (51.0%) zerosZeros
고의_증과실_파면등 has 20 (40.8%) zerosZeros
기타제한사유 has 24 (49.0%) zerosZeros
일부정지 has 8 (16.3%) zerosZeros

Reproduction

Analysis started2023-12-12 21:58:20.208910
Analysis finished2023-12-12 21:58:26.662047
Duration6.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-13T06:58:27.072220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1836735
Min length3

Characters and Unicode

Total characters156
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st row38세 미만
2nd row38세 이상
3rd row39세
4th row40세
5th row41세
ValueCountFrequency (%)
38세 2
 
3.8%
이상 2
 
3.8%
74세 1
 
1.9%
64세 1
 
1.9%
65세 1
 
1.9%
66세 1
 
1.9%
67세 1
 
1.9%
68세 1
 
1.9%
69세 1
 
1.9%
70세 1
 
1.9%
Other values (40) 40
76.9%
2023-12-13T06:58:27.361271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 49
31.4%
4 15
 
9.6%
5 15
 
9.6%
6 14
 
9.0%
7 14
 
9.0%
8 12
 
7.7%
3 8
 
5.1%
9 5
 
3.2%
0 5
 
3.2%
1 5
 
3.2%
Other values (6) 14
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98
62.8%
Other Letter 55
35.3%
Space Separator 3
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 15
15.3%
5 15
15.3%
6 14
14.3%
7 14
14.3%
8 12
12.2%
3 8
8.2%
9 5
 
5.1%
0 5
 
5.1%
1 5
 
5.1%
2 5
 
5.1%
Other Letter
ValueCountFrequency (%)
세 49
89.1%
이 2
 
3.6%
상 2
 
3.6%
미 1
 
1.8%
만 1
 
1.8%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 101
64.7%
Hangul 55
35.3%

Most frequent character per script

Common
ValueCountFrequency (%)
4 15
14.9%
5 15
14.9%
6 14
13.9%
7 14
13.9%
8 12
11.9%
3 8
7.9%
9 5
 
5.0%
0 5
 
5.0%
1 5
 
5.0%
2 5
 
5.0%
Hangul
ValueCountFrequency (%)
세 49
89.1%
이 2
 
3.6%
상 2
 
3.6%
미 1
 
1.8%
만 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101
64.7%
Hangul 55
35.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 49
89.1%
이 2
 
3.6%
상 2
 
3.6%
미 1
 
1.8%
만 1
 
1.8%
ASCII
ValueCountFrequency (%)
4 15
14.9%
5 15
14.9%
6 14
13.9%
7 14
13.9%
8 12
11.9%
3 8
7.9%
9 5
 
5.0%
0 5
 
5.0%
1 5
 
5.0%
2 5
 
5.0%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537.08163
Minimum6
Maximum2398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:27.482298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.4
Q124
median201
Q3852
95-th percentile1737.2
Maximum2398
Range2392
Interquartile range (IQR)828

Descriptive statistics

Standard deviation652.82932
Coefficient of variation (CV)1.2155123
Kurtosis0.1421722
Mean537.08163
Median Absolute Deviation (MAD)190
Skewness1.1675628
Sum26317
Variance426186.12
MonotonicityNot monotonic
2023-12-13T06:58:27.624063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
11 2
 
4.1%
7 2
 
4.1%
10 2
 
4.1%
15 2
 
4.1%
104 2
 
4.1%
320 1
 
2.0%
1118 1
 
2.0%
836 1
 
2.0%
659 1
 
2.0%
649 1
 
2.0%
Other values (34) 34
69.4%
ValueCountFrequency (%)
6 1
2.0%
7 2
4.1%
8 1
2.0%
10 2
4.1%
11 2
4.1%
15 2
4.1%
16 1
2.0%
21 1
2.0%
24 1
2.0%
26 1
2.0%
ValueCountFrequency (%)
2398 1
2.0%
1758 1
2.0%
1752 1
2.0%
1715 1
2.0%
1628 1
2.0%
1607 1
2.0%
1568 1
2.0%
1529 1
2.0%
1362 1
2.0%
1333 1
2.0%

국회의원 등
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7346939
Minimum0
Maximum23
Zeros24
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:27.747932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile19.8
Maximum23
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.3730406
Coefficient of variation (CV)1.7064426
Kurtosis2.7242052
Mean3.7346939
Median Absolute Deviation (MAD)1
Skewness1.935113
Sum183
Variance40.615646
MonotonicityNot monotonic
2023-12-13T06:58:27.857032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 24
49.0%
2 7
 
14.3%
1 4
 
8.2%
3 2
 
4.1%
10 2
 
4.1%
5 1
 
2.0%
11 1
 
2.0%
13 1
 
2.0%
23 1
 
2.0%
21 1
 
2.0%
Other values (5) 5
 
10.2%
ValueCountFrequency (%)
0 24
49.0%
1 4
 
8.2%
2 7
 
14.3%
3 2
 
4.1%
4 1
 
2.0%
5 1
 
2.0%
7 1
 
2.0%
10 2
 
4.1%
11 1
 
2.0%
13 1
 
2.0%
ValueCountFrequency (%)
23 1
2.0%
22 1
2.0%
21 1
2.0%
18 1
2.0%
15 1
2.0%
13 1
2.0%
11 1
2.0%
10 2
4.1%
7 1
2.0%
5 1
2.0%

전액(소계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.122449
Minimum0
Maximum50
Zeros9
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:27.951584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q312
95-th percentile38.6
Maximum50
Range50
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.255326
Coefficient of variation (CV)1.2107077
Kurtosis3.3911404
Mean10.122449
Median Absolute Deviation (MAD)6
Skewness1.8711464
Sum496
Variance150.19303
MonotonicityNot monotonic
2023-12-13T06:58:28.054173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 9
18.4%
1 6
12.2%
9 4
 
8.2%
7 3
 
6.1%
8 3
 
6.1%
5 2
 
4.1%
2 2
 
4.1%
12 2
 
4.1%
4 2
 
4.1%
6 2
 
4.1%
Other values (14) 14
28.6%
ValueCountFrequency (%)
0 9
18.4%
1 6
12.2%
2 2
 
4.1%
4 2
 
4.1%
5 2
 
4.1%
6 2
 
4.1%
7 3
 
6.1%
8 3
 
6.1%
9 4
8.2%
10 1
 
2.0%
ValueCountFrequency (%)
50 1
2.0%
47 1
2.0%
45 1
2.0%
29 1
2.0%
26 1
2.0%
25 1
2.0%
22 1
2.0%
21 1
2.0%
20 1
2.0%
18 1
2.0%

전액(공무원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6530612
Minimum0
Maximum46
Zeros9
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:28.155581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q310
95-th percentile31.4
Maximum46
Range46
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.804226
Coefficient of variation (CV)1.2486016
Kurtosis4.3641522
Mean8.6530612
Median Absolute Deviation (MAD)5
Skewness2.0596267
Sum424
Variance116.73129
MonotonicityNot monotonic
2023-12-13T06:58:28.256006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 9
18.4%
1 6
12.2%
4 5
10.2%
9 4
 
8.2%
8 3
 
6.1%
16 3
 
6.1%
7 3
 
6.1%
6 2
 
4.1%
2 2
 
4.1%
10 2
 
4.1%
Other values (9) 10
20.4%
ValueCountFrequency (%)
0 9
18.4%
1 6
12.2%
2 2
 
4.1%
3 1
 
2.0%
4 5
10.2%
5 1
 
2.0%
6 2
 
4.1%
7 3
 
6.1%
8 3
 
6.1%
9 4
8.2%
ValueCountFrequency (%)
46 1
 
2.0%
44 1
 
2.0%
37 1
 
2.0%
23 2
4.1%
21 1
 
2.0%
20 1
 
2.0%
16 3
6.1%
11 1
 
2.0%
10 2
4.1%
9 4
8.2%

전액(사립학교교직원등)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4693878
Minimum0
Maximum8
Zeros25
Zeros (%)51.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:28.345926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5.6
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.102598
Coefficient of variation (CV)1.4309348
Kurtosis2.3853602
Mean1.4693878
Median Absolute Deviation (MAD)0
Skewness1.6709515
Sum72
Variance4.4209184
MonotonicityNot monotonic
2023-12-13T06:58:28.446615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 25
51.0%
2 8
 
16.3%
1 6
 
12.2%
4 3
 
6.1%
5 2
 
4.1%
3 2
 
4.1%
8 2
 
4.1%
6 1
 
2.0%
ValueCountFrequency (%)
0 25
51.0%
1 6
 
12.2%
2 8
 
16.3%
3 2
 
4.1%
4 3
 
6.1%
5 2
 
4.1%
6 1
 
2.0%
8 2
 
4.1%
ValueCountFrequency (%)
8 2
 
4.1%
6 1
 
2.0%
5 2
 
4.1%
4 3
 
6.1%
3 2
 
4.1%
2 8
 
16.3%
1 6
 
12.2%
0 25
51.0%

고의_증과실_파면등
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7142857
Minimum0
Maximum17
Zeros20
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:28.560084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8188131
Coefficient of variation (CV)1.4069311
Kurtosis3.2978806
Mean2.7142857
Median Absolute Deviation (MAD)1
Skewness1.7921353
Sum133
Variance14.583333
MonotonicityNot monotonic
2023-12-13T06:58:28.666697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 20
40.8%
1 10
20.4%
4 6
 
12.2%
7 3
 
6.1%
3 2
 
4.1%
10 2
 
4.1%
8 2
 
4.1%
17 1
 
2.0%
12 1
 
2.0%
5 1
 
2.0%
ValueCountFrequency (%)
0 20
40.8%
1 10
20.4%
2 1
 
2.0%
3 2
 
4.1%
4 6
 
12.2%
5 1
 
2.0%
7 3
 
6.1%
8 2
 
4.1%
10 2
 
4.1%
12 1
 
2.0%
ValueCountFrequency (%)
17 1
 
2.0%
12 1
 
2.0%
10 2
 
4.1%
8 2
 
4.1%
7 3
 
6.1%
5 1
 
2.0%
4 6
12.2%
3 2
 
4.1%
2 1
 
2.0%
1 10
20.4%

기타제한사유
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9591837
Minimum0
Maximum43
Zeros24
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:28.770345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11
Maximum43
Range43
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.689417
Coefficient of variation (CV)2.2605616
Kurtosis27.367609
Mean2.9591837
Median Absolute Deviation (MAD)1
Skewness4.7825922
Sum145
Variance44.748299
MonotonicityNot monotonic
2023-12-13T06:58:28.877544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 24
49.0%
2 7
 
14.3%
1 5
 
10.2%
3 2
 
4.1%
5 2
 
4.1%
6 2
 
4.1%
11 2
 
4.1%
8 2
 
4.1%
13 1
 
2.0%
43 1
 
2.0%
ValueCountFrequency (%)
0 24
49.0%
1 5
 
10.2%
2 7
 
14.3%
3 2
 
4.1%
4 1
 
2.0%
5 2
 
4.1%
6 2
 
4.1%
8 2
 
4.1%
11 2
 
4.1%
13 1
 
2.0%
ValueCountFrequency (%)
43 1
 
2.0%
13 1
 
2.0%
11 2
 
4.1%
8 2
 
4.1%
6 2
 
4.1%
5 2
 
4.1%
4 1
 
2.0%
3 2
 
4.1%
2 7
14.3%
1 5
10.2%

일부정지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean517.55102
Minimum0
Maximum2285
Zeros8
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T06:58:28.976384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median201
Q3822
95-th percentile1662.2
Maximum2285
Range2285
Interquartile range (IQR)815

Descriptive statistics

Standard deviation632.07733
Coefficient of variation (CV)1.2212851
Kurtosis0.017829321
Mean517.55102
Median Absolute Deviation (MAD)200
Skewness1.1281448
Sum25360
Variance399521.75
MonotonicityNot monotonic
2023-12-13T06:58:29.093868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 8
 
16.3%
249 1
 
2.0%
822 1
 
2.0%
652 1
 
2.0%
645 1
 
2.0%
372 1
 
2.0%
379 1
 
2.0%
366 1
 
2.0%
353 1
 
2.0%
319 1
 
2.0%
Other values (32) 32
65.3%
ValueCountFrequency (%)
0 8
16.3%
1 1
 
2.0%
2 1
 
2.0%
3 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
12 1
 
2.0%
17 1
 
2.0%
35 1
 
2.0%
77 1
 
2.0%
ValueCountFrequency (%)
2285 1
2.0%
1674 1
2.0%
1667 1
2.0%
1655 1
2.0%
1575 1
2.0%
1557 1
2.0%
1536 1
2.0%
1493 1
2.0%
1321 1
2.0%
1310 1
2.0%

Interactions

2023-12-13T06:58:25.934003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:20.766550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.449210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:22.158352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:22.947966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.722071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:24.517724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.351369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.994270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:20.858128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.527158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:22.256757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.041470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.806991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:24.620430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.429683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:26.051320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:20.935527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.602194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:22.367099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.135740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.894844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:24.722637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.499025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:26.115575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.020985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.685056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:22.488677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.228541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.993240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:24.832965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.585205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:26.176022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.104807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.765002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:22.567410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.334426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:24.089292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:24.934774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.659559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:26.248138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.205189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.898175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:22.671582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.461940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:24.205943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.048111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.741340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:26.321117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.288349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.999090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:22.781961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.561903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:24.315481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.159497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.815658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:26.390695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:21.367268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:22.079981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:22.862289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:23.652421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:24.409909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.261580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:58:25.876536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:58:29.175821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분계국회의원 등전액(소계)전액(공무원)전액(사립학교교직원등)고의_증과실_파면등기타제한사유일부정지
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
계1.0001.0000.9510.8320.7920.5980.7130.8031.000
국회의원 등1.0000.9511.0000.8020.8160.4430.6130.7870.951
전액(소계)1.0000.8320.8021.0000.9850.8670.7440.8040.832
전액(공무원)1.0000.7920.8160.9851.0000.8610.7710.8440.792
전액(사립학교교직원등)1.0000.5980.4430.8670.8611.0000.7400.6930.598
고의_증과실_파면등1.0000.7130.6130.7440.7710.7401.0000.7620.713
기타제한사유1.0000.8030.7870.8040.8440.6930.7621.0000.803
일부정지1.0001.0000.9510.8320.7920.5980.7130.8031.000
2023-12-13T06:58:29.284561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계국회의원 등전액(소계)전액(공무원)전액(사립학교교직원등)고의_증과실_파면등기타제한사유일부정지
계1.0000.7140.3240.3620.1340.3880.2550.989
국회의원 등0.7141.0000.5700.6040.2950.4370.4950.719
전액(소계)0.3240.5701.0000.9890.8050.6620.8070.294
전액(공무원)0.3620.6040.9891.0000.7230.6550.7890.340
전액(사립학교교직원등)0.1340.2950.8050.7231.0000.5990.7270.081
고의_증과실_파면등0.3880.4370.6620.6550.5991.0000.7410.354
기타제한사유0.2550.4950.8070.7890.7270.7411.0000.228
일부정지0.9890.7190.2940.3400.0810.3540.2281.000

Missing values

2023-12-13T06:58:26.487103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:58:26.607097image/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

구분계국회의원 등전액(소계)전액(공무원)전액(사립학교교직원등)고의_증과실_파면등기타제한사유일부정지
038세 미만110981020
138세 이상81642010
239세70770000
340세60550001
441세101734110
542세1501495100
643세110972020
744세100541410
845세151862330
946세71220013
구분계국회의원 등전액(소계)전액(공무원)전액(사립학교교직원등)고의_증과실_파면등기타제한사유일부정지
3976세249000000249
4077세166011000165
4178세156000000156
4279세183011010181
4380세201000000201
4481세133000000133
4582세104000010103
4683세127000000127
4784세104000000104
4885세 이상392000000392