Overview

Dataset statistics

Number of variables12
Number of observations110
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.6 KiB
Average record size in memory108.2 B

Variable types

Categorical3
Text1
Numeric8

Dataset

Description국민연금 장애심사의 접수지사별 심사결과(1급, 2급, 3급, 4급, 등급외, 결정보류, 자격미달, 확인불가, 인과관계) 현황입니다
Author국민연금공단
URLhttps://www.data.go.kr/data/15072708/fileData.do

Alerts

기준연도 has constant value ""Constant
합계 is highly overall correlated with 1급 and 5 other fieldsHigh correlation
1급 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
2급 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
3급 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
4급 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
등급외 is highly overall correlated with 합계 and 6 other fieldsHigh correlation
결정보류 is highly overall correlated with 등급외 and 1 other fieldsHigh correlation
자격미달 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
확인불가 is highly overall correlated with 결정보류High correlation
확인불가 is highly imbalanced (69.6%)Imbalance
인과관계 is highly imbalanced (50.3%)Imbalance
구분명 has unique valuesUnique
결정보류 has 33 (30.0%) zerosZeros
자격미달 has 12 (10.9%) zerosZeros

Reproduction

Analysis started2023-12-12 21:39:06.695452
Analysis finished2023-12-12 21:39:13.780440
Duration7.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1012.0 B
2021
110 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 110
100.0%

Length

2023-12-13T06:39:13.857071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:39:13.970389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 110
100.0%

구분명
Text

UNIQUE 

Distinct110
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1012.0 B
2023-12-13T06:39:14.299064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length3.2272727
Min length2

Characters and Unicode

Total characters355
Distinct characters110
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

Unique110 ?
Unique (%)100.0%

Sample

1st row국제협력센터
2nd row종로중구
3rd row동대문중랑
4th row성북강북
5th row도봉노원
ValueCountFrequency (%)
국제협력센터 1
 
0.9%
정읍 1
 
0.9%
북대전 1
 
0.9%
동대전 1
 
0.9%
서귀포 1
 
0.9%
제주 1
 
0.9%
나주 1
 
0.9%
여수 1
 
0.9%
순천 1
 
0.9%
해남 1
 
0.9%
Other values (100) 100
90.9%
2023-12-13T06:39:14.800180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 20
 
5.6%
부 20
 
5.6%
주 19
 
5.4%
천 16
 
4.5%
동 13
 
3.7%
서 11
 
3.1%
대 10
 
2.8%
남 10
 
2.8%
양 9
 
2.5%
구 9
 
2.5%
Other values (100) 218
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 355
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 20
 
5.6%
부 20
 
5.6%
주 19
 
5.4%
천 16
 
4.5%
동 13
 
3.7%
서 11
 
3.1%
대 10
 
2.8%
남 10
 
2.8%
양 9
 
2.5%
구 9
 
2.5%
Other values (100) 218
61.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 355
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 20
 
5.6%
부 20
 
5.6%
주 19
 
5.4%
천 16
 
4.5%
동 13
 
3.7%
서 11
 
3.1%
대 10
 
2.8%
남 10
 
2.8%
양 9
 
2.5%
구 9
 
2.5%
Other values (100) 218
61.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 355
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 20
 
5.6%
부 20
 
5.6%
주 19
 
5.4%
천 16
 
4.5%
동 13
 
3.7%
서 11
 
3.1%
대 10
 
2.8%
남 10
 
2.8%
양 9
 
2.5%
구 9
 
2.5%
Other values (100) 218
61.4%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.56364
Minimum22
Maximum593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:39:14.972505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile75.7
Q1162.5
median265.5
Q3376.25
95-th percentile524.75
Maximum593
Range571
Interquartile range (IQR)213.75

Descriptive statistics

Standard deviation140.4708
Coefficient of variation (CV)0.5024645
Kurtosis-0.63559794
Mean279.56364
Median Absolute Deviation (MAD)106.5
Skewness0.29914819
Sum30752
Variance19732.046
MonotonicityNot monotonic
2023-12-13T06:39:15.102151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118 3
 
2.7%
193 2
 
1.8%
139 2
 
1.8%
265 2
 
1.8%
233 2
 
1.8%
456 2
 
1.8%
342 2
 
1.8%
346 2
 
1.8%
79 2
 
1.8%
359 2
 
1.8%
Other values (88) 89
80.9%
ValueCountFrequency (%)
22 1
0.9%
54 1
0.9%
69 1
0.9%
71 1
0.9%
72 1
0.9%
73 1
0.9%
79 2
1.8%
82 1
0.9%
84 1
0.9%
89 1
0.9%
ValueCountFrequency (%)
593 1
0.9%
592 1
0.9%
584 1
0.9%
574 1
0.9%
569 1
0.9%
527 1
0.9%
522 1
0.9%
515 1
0.9%
498 1
0.9%
476 1
0.9%

1급
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.890909
Minimum2
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:39:15.257959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.45
Q112
median20
Q327
95-th percentile45.55
Maximum52
Range50
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.033855
Coefficient of variation (CV)0.57603312
Kurtosis0.018374425
Mean20.890909
Median Absolute Deviation (MAD)8
Skewness0.63446606
Sum2298
Variance144.81368
MonotonicityNot monotonic
2023-12-13T06:39:15.390149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
5 5
 
4.5%
23 5
 
4.5%
15 5
 
4.5%
17 5
 
4.5%
18 5
 
4.5%
2 4
 
3.6%
24 4
 
3.6%
20 4
 
3.6%
28 4
 
3.6%
9 4
 
3.6%
Other values (31) 65
59.1%
ValueCountFrequency (%)
2 4
3.6%
3 1
 
0.9%
4 1
 
0.9%
5 5
4.5%
7 4
3.6%
8 4
3.6%
9 4
3.6%
10 1
 
0.9%
11 3
2.7%
12 2
 
1.8%
ValueCountFrequency (%)
52 1
0.9%
51 2
1.8%
48 1
0.9%
46 2
1.8%
45 1
0.9%
44 1
0.9%
41 1
0.9%
39 1
0.9%
38 2
1.8%
37 2
1.8%

2급
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.53636
Minimum10
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:39:15.518790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile29.9
Q160
median106.5
Q3149.75
95-th percentile215.8
Maximum262
Range252
Interquartile range (IQR)89.75

Descriptive statistics

Standard deviation58.995051
Coefficient of variation (CV)0.5289311
Kurtosis-0.46958708
Mean111.53636
Median Absolute Deviation (MAD)45.5
Skewness0.43984006
Sum12269
Variance3480.4161
MonotonicityNot monotonic
2023-12-13T06:39:15.673769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 3
 
2.7%
89 3
 
2.7%
142 3
 
2.7%
189 2
 
1.8%
107 2
 
1.8%
66 2
 
1.8%
60 2
 
1.8%
139 2
 
1.8%
54 2
 
1.8%
163 2
 
1.8%
Other values (80) 87
79.1%
ValueCountFrequency (%)
10 1
0.9%
19 1
0.9%
23 1
0.9%
24 1
0.9%
27 1
0.9%
29 1
0.9%
31 1
0.9%
32 1
0.9%
34 1
0.9%
35 1
0.9%
ValueCountFrequency (%)
262 1
0.9%
254 1
0.9%
245 1
0.9%
243 1
0.9%
225 1
0.9%
223 1
0.9%
207 1
0.9%
201 1
0.9%
196 1
0.9%
192 2
1.8%

3급
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.527273
Minimum7
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:39:15.821225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q131.25
median54
Q376.75
95-th percentile102
Maximum130
Range123
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation28.585205
Coefficient of variation (CV)0.51479576
Kurtosis-0.70348237
Mean55.527273
Median Absolute Deviation (MAD)23
Skewness0.33753851
Sum6108
Variance817.11393
MonotonicityNot monotonic
2023-12-13T06:39:15.995661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 4
 
3.6%
43 3
 
2.7%
21 3
 
2.7%
78 3
 
2.7%
36 3
 
2.7%
70 3
 
2.7%
54 3
 
2.7%
34 3
 
2.7%
22 3
 
2.7%
50 2
 
1.8%
Other values (58) 80
72.7%
ValueCountFrequency (%)
7 1
 
0.9%
8 1
 
0.9%
10 1
 
0.9%
11 1
 
0.9%
13 1
 
0.9%
16 2
1.8%
19 1
 
0.9%
20 2
1.8%
21 3
2.7%
22 3
2.7%
ValueCountFrequency (%)
130 1
0.9%
116 1
0.9%
112 1
0.9%
109 1
0.9%
104 1
0.9%
102 2
1.8%
101 2
1.8%
100 2
1.8%
97 2
1.8%
96 1
0.9%

4급
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.481818
Minimum1
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:39:16.160134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.45
Q121.75
median36.5
Q350
95-th percentile72.2
Maximum83
Range82
Interquartile range (IQR)28.25

Descriptive statistics

Standard deviation19.688273
Coefficient of variation (CV)0.5252753
Kurtosis-0.60857733
Mean37.481818
Median Absolute Deviation (MAD)13.5
Skewness0.26979435
Sum4123
Variance387.62811
MonotonicityNot monotonic
2023-12-13T06:39:16.300970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 5
 
4.5%
28 4
 
3.6%
19 4
 
3.6%
45 4
 
3.6%
17 4
 
3.6%
30 4
 
3.6%
62 3
 
2.7%
34 3
 
2.7%
51 3
 
2.7%
78 3
 
2.7%
Other values (50) 73
66.4%
ValueCountFrequency (%)
1 1
0.9%
3 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 2
1.8%
11 2
1.8%
12 1
0.9%
ValueCountFrequency (%)
83 1
 
0.9%
82 1
 
0.9%
78 3
2.7%
74 1
 
0.9%
70 1
 
0.9%
69 2
1.8%
65 1
 
0.9%
64 1
 
0.9%
63 1
 
0.9%
62 3
2.7%

등급외
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.545455
Minimum1
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:39:16.436913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q128
median45
Q366
95-th percentile93.1
Maximum105
Range104
Interquartile range (IQR)38

Descriptive statistics

Standard deviation25.238908
Coefficient of variation (CV)0.51990261
Kurtosis-0.71437288
Mean48.545455
Median Absolute Deviation (MAD)20
Skewness0.31337282
Sum5340
Variance637.0025
MonotonicityNot monotonic
2023-12-13T06:39:16.558157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 6
 
5.5%
40 5
 
4.5%
63 4
 
3.6%
83 3
 
2.7%
36 3
 
2.7%
51 3
 
2.7%
41 3
 
2.7%
69 3
 
2.7%
21 3
 
2.7%
42 3
 
2.7%
Other values (53) 74
67.3%
ValueCountFrequency (%)
1 1
 
0.9%
11 1
 
0.9%
12 6
5.5%
13 1
 
0.9%
14 1
 
0.9%
15 1
 
0.9%
16 2
 
1.8%
18 1
 
0.9%
19 2
 
1.8%
20 1
 
0.9%
ValueCountFrequency (%)
105 1
0.9%
104 1
0.9%
101 1
0.9%
99 1
0.9%
96 1
0.9%
94 1
0.9%
92 1
0.9%
90 2
1.8%
88 1
0.9%
86 1
0.9%

결정보류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8181818
Minimum0
Maximum8
Zeros33
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:39:16.666793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7774418
Coefficient of variation (CV)0.977593
Kurtosis0.64878569
Mean1.8181818
Median Absolute Deviation (MAD)1.5
Skewness0.9892478
Sum200
Variance3.1592994
MonotonicityNot monotonic
2023-12-13T06:39:16.780333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 33
30.0%
2 24
21.8%
1 22
20.0%
4 11
 
10.0%
3 10
 
9.1%
5 7
 
6.4%
7 1
 
0.9%
6 1
 
0.9%
8 1
 
0.9%
ValueCountFrequency (%)
0 33
30.0%
1 22
20.0%
2 24
21.8%
3 10
 
9.1%
4 11
 
10.0%
5 7
 
6.4%
6 1
 
0.9%
7 1
 
0.9%
8 1
 
0.9%
ValueCountFrequency (%)
8 1
 
0.9%
7 1
 
0.9%
6 1
 
0.9%
5 7
 
6.4%
4 11
 
10.0%
3 10
 
9.1%
2 24
21.8%
1 22
20.0%
0 33
30.0%

자격미달
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5545455
Minimum0
Maximum17
Zeros12
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:39:16.902497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile8
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0457863
Coefficient of variation (CV)0.85687082
Kurtosis4.60579
Mean3.5545455
Median Absolute Deviation (MAD)2
Skewness1.6482524
Sum391
Variance9.276814
MonotonicityNot monotonic
2023-12-13T06:39:17.000476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 20
18.2%
2 18
16.4%
5 15
13.6%
0 12
10.9%
3 12
10.9%
6 10
9.1%
4 9
8.2%
7 7
 
6.4%
8 3
 
2.7%
11 2
 
1.8%
Other values (2) 2
 
1.8%
ValueCountFrequency (%)
0 12
10.9%
1 20
18.2%
2 18
16.4%
3 12
10.9%
4 9
8.2%
5 15
13.6%
6 10
9.1%
7 7
 
6.4%
8 3
 
2.7%
11 2
 
1.8%
ValueCountFrequency (%)
17 1
 
0.9%
16 1
 
0.9%
11 2
 
1.8%
8 3
 
2.7%
7 7
 
6.4%
6 10
9.1%
5 15
13.6%
4 9
8.2%
3 12
10.9%
2 18
16.4%

확인불가
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1012.0 B
0
100 
1
 
9
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 100
90.9%
1 9
 
8.2%
2 1
 
0.9%

Length

2023-12-13T06:39:17.114956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:39:17.203202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
90.9%
1 9
 
8.2%
2 1
 
0.9%

인과관계
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1012.0 B
0
98 
1
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 98
89.1%
1 12
 
10.9%

Length

2023-12-13T06:39:17.292485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:39:17.381828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 98
89.1%
1 12
 
10.9%

Interactions

2023-12-13T06:39:12.282383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:07.146931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:07.918487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.601349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:09.327713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.110332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.824027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.526870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:12.391234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:07.257240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.008277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.685272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:09.433534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.190614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.904655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.607632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:12.498279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:07.356824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.092651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.787246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:09.515031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.288339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.999436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.699398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:12.598305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:07.444193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.167632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.904281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:09.603898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.383245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.092346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.785411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:12.720827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:07.547212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.247896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.997142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:09.699964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.460063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.185165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.869373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:12.832592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:07.631111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.331034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:09.078837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:09.797516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.535303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.267120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.949552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:13.234385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:07.712801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.411889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:09.157859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:09.913704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.625991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.338798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:12.077779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:13.356831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:07.806247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:08.507132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:09.248804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.020026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:10.728735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:11.430961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:39:12.190853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:39:17.451191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계1급2급3급4급등급외결정보류자격미달확인불가인과관계
합계1.0000.8380.9460.9380.8910.8980.4120.4690.2340.000
1급0.8381.0000.8560.8340.7570.8280.4910.4880.4400.000
2급0.9460.8561.0000.9040.8270.8500.0000.4860.2970.000
3급0.9380.8340.9041.0000.8380.8410.3210.4400.2240.090
4급0.8910.7570.8270.8381.0000.8410.4120.4480.1640.261
등급외0.8980.8280.8500.8410.8411.0000.5050.4910.2430.214
결정보류0.4120.4910.0000.3210.4120.5051.0000.4080.9310.243
자격미달0.4690.4880.4860.4400.4480.4910.4081.0000.2100.000
확인불가0.2340.4400.2970.2240.1640.2430.9310.2101.0000.157
인과관계0.0000.0000.0000.0900.2610.2140.2430.0000.1571.000
2023-12-13T06:39:17.559818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인과관계확인불가
인과관계1.0000.258
확인불가0.2581.000
2023-12-13T06:39:17.641342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계1급2급3급4급등급외결정보류자격미달확인불가인과관계
합계1.0000.8930.9790.9500.9130.9480.4580.6200.1360.000
1급0.8931.0000.8590.8400.7850.8460.4530.6240.2730.000
2급0.9790.8591.0000.9210.8630.8990.4120.5650.1780.000
3급0.9500.8400.9211.0000.8320.8690.4040.5780.1290.060
4급0.9130.7850.8630.8321.0000.8680.4270.5420.0910.191
등급외0.9480.8460.8990.8690.8681.0000.5370.6510.1420.156
결정보류0.4580.4530.4120.4040.4270.5371.0000.4280.6730.233
자격미달0.6200.6240.5650.5780.5420.6510.4281.0000.1390.000
확인불가0.1360.2730.1780.1290.0910.1420.6730.1391.0000.258
인과관계0.0000.0000.0000.0600.1910.1560.2330.0000.2581.000

Missing values

2023-12-13T06:39:13.534576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:39:13.722942image/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

기준연도구분명합계1급2급3급4급등급외결정보류자격미달확인불가인과관계
02021국제협력센터222108110000
12021종로중구13212522617231100
22021동대문중랑395311657245731800
32021성북강북359211407849644210
42021도봉노원460372079750670200
52021성동광진333291166841664801
62021서울북부지역본부346171497046552700
72021은평252121255424320500
82021용산1097433011160110
92021김포강화359251425950770600
기준연도구분명합계1급2급3급4급등급외결정보류자격미달확인불가인과관계
1002021평택안성464391898262834410
1012021안산4564617910464562500
1022021광명20815894320400100
1032021부천52244196102701012610
1042021용인5744824511669865500
1052021화성오산59246262100789231100
1062021시흥361201637763380000
1072021군포의왕27420995544513200
1082021경기광주24619904638502100
1092021북수원316321074945708500