Overview

Dataset statistics

Number of variables14
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory128.7 B

Variable types

Text1
Categorical1
Numeric12

Dataset

Description장기요양 등급판정 결과 인정자 및 등급외자 현황 전체계,인정자소계(1등급, 2등급, 3등급, 4등급, 5등급, 인지지원등급), 등급외자소계 (등급외A, 등급외B, 등급외C)의 현황
URLhttps://www.data.go.kr/data/3051420/fileData.do

Alerts

전체계 is highly overall correlated with 인정자소계 and 10 other fieldsHigh correlation
인정자소계 is highly overall correlated with 전체계 and 11 other fieldsHigh correlation
1등급 is highly overall correlated with 전체계 and 8 other fieldsHigh correlation
2등급 is highly overall correlated with 전체계 and 8 other fieldsHigh correlation
3등급 is highly overall correlated with 전체계 and 8 other fieldsHigh correlation
4등급 is highly overall correlated with 전체계 and 3 other fieldsHigh correlation
5등급 is highly overall correlated with 전체계 and 3 other fieldsHigh correlation
인지지원등급 is highly overall correlated with 인정자소계 and 2 other fieldsHigh correlation
등급외자소계 is highly overall correlated with 전체계 and 8 other fieldsHigh correlation
등급외A is highly overall correlated with 전체계 and 8 other fieldsHigh correlation
등급외B is highly overall correlated with 전체계 and 8 other fieldsHigh correlation
등급외C is highly overall correlated with 전체계 and 8 other fieldsHigh correlation
구분2 is highly overall correlated with 전체계 and 8 other fieldsHigh correlation
2등급 has unique valuesUnique
3등급 has unique valuesUnique
등급외A has unique valuesUnique
4등급 has 10 (35.7%) zerosZeros
5등급 has 10 (35.7%) zerosZeros
인지지원등급 has 18 (64.3%) zerosZeros

Reproduction

Analysis started2023-12-12 09:09:07.276299
Analysis finished2023-12-12 09:09:23.746012
Duration16.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct14
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-12T18:09:23.872130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters140
Distinct characters11
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

Unique0 ?
Unique (%)0.0%

Sample

1st row2009년
2nd row2009년
3rd row2010년
4th row2010년
5th row2011년
ValueCountFrequency (%)
2009년 2
 
7.1%
2010년 2
 
7.1%
2011년 2
 
7.1%
2012년 2
 
7.1%
2013년 2
 
7.1%
2014년 2
 
7.1%
2015년 2
 
7.1%
2016년 2
 
7.1%
2017년 2
 
7.1%
2018년 2
 
7.1%
Other values (4) 8
28.6%
2023-12-12T18:09:24.235681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 38
27.1%
0 34
24.3%
28
20.0%
1 24
17.1%
9 4
 
2.9%
3 2
 
1.4%
4 2
 
1.4%
5 2
 
1.4%
6 2
 
1.4%
7 2
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112
80.0%
Other Letter 28
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 38
33.9%
0 34
30.4%
1 24
21.4%
9 4
 
3.6%
3 2
 
1.8%
4 2
 
1.8%
5 2
 
1.8%
6 2
 
1.8%
7 2
 
1.8%
8 2
 
1.8%
Other Letter
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 112
80.0%
Hangul 28
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 38
33.9%
0 34
30.4%
1 24
21.4%
9 4
 
3.6%
3 2
 
1.8%
4 2
 
1.8%
5 2
 
1.8%
6 2
 
1.8%
7 2
 
1.8%
8 2
 
1.8%
Hangul
ValueCountFrequency (%)
28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112
80.0%
Hangul 28
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 38
33.9%
0 34
30.4%
1 24
21.4%
9 4
 
3.6%
3 2
 
1.8%
4 2
 
1.8%
5 2
 
1.8%
6 2
 
1.8%
7 2
 
1.8%
8 2
 
1.8%
Hangul
ValueCountFrequency (%)
28
100.0%

구분2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
인원
14 
비율
14 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인원
2nd row비율
3rd row인원
4th row비율
5th row인원

Common Values

ValueCountFrequency (%)
인원 14
50.0%
비율 14
50.0%

Length

2023-12-12T18:09:24.408290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:09:24.537762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인원 14
50.0%
비율 14
50.0%

전체계
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean358576.21
Minimum100
Maximum1160850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:24.661225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1100
median195315
Q3643319.25
95-th percentile1065948.3
Maximum1160850
Range1160750
Interquartile range (IQR)643219.25

Descriptive statistics

Standard deviation404517.57
Coefficient of variation (CV)1.1281216
Kurtosis-1.082496
Mean358576.21
Median Absolute Deviation (MAD)195215
Skewness0.58793727
Sum10040134
Variance1.6363446 × 1011
MonotonicityNot monotonic
2023-12-12T18:09:24.818773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
100 14
50.0%
390530 1
 
3.6%
465777 1
 
3.6%
478446 1
 
3.6%
495445 1
 
3.6%
535328 1
 
3.6%
585386 1
 
3.6%
630757 1
 
3.6%
681006 1
 
3.6%
749809 1
 
3.6%
Other values (5) 5
 
17.9%
ValueCountFrequency (%)
100 14
50.0%
390530 1
 
3.6%
465777 1
 
3.6%
478446 1
 
3.6%
495445 1
 
3.6%
535328 1
 
3.6%
585386 1
 
3.6%
630757 1
 
3.6%
681006 1
 
3.6%
749809 1
 
3.6%
ValueCountFrequency (%)
1160850 1
3.6%
1097462 1
3.6%
1007423 1
3.6%
929003 1
3.6%
831512 1
3.6%
749809 1
3.6%
681006 1
3.6%
630757 1
3.6%
585386 1
3.6%
535328 1
3.6%

인정자소계
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282848.91
Minimum67.8
Maximum1019130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:24.973913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67.8
5-th percentile68.22
Q175.775
median143497.4
Q3480776.5
95-th percentile920076.55
Maximum1019130
Range1019062.2
Interquartile range (IQR)480700.72

Descriptive statistics

Standard deviation336071.03
Coefficient of variation (CV)1.1881645
Kurtosis-0.5053591
Mean282848.91
Median Absolute Deviation (MAD)143429.6
Skewness0.84213552
Sum7919769.6
Variance1.1294374 × 1011
MonotonicityNot monotonic
2023-12-12T18:09:25.141103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
67.8 2
 
7.1%
286907.0 1
 
3.6%
585287.0 1
 
3.6%
87.8 1
 
3.6%
1019130.0 1
 
3.6%
86.9 1
 
3.6%
953511.0 1
 
3.6%
85.2 1
 
3.6%
857984.0 1
 
3.6%
83.1 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
67.8 2
7.1%
69.0 1
3.6%
70.7 1
3.6%
72.5 1
3.6%
73.5 1
3.6%
74.2 1
3.6%
76.3 1
3.6%
78.1 1
3.6%
80.7 1
3.6%
83.1 1
3.6%
ValueCountFrequency (%)
1019130.0 1
3.6%
953511.0 1
3.6%
857984.0 1
3.6%
772206.0 1
3.6%
670810.0 1
3.6%
585287.0 1
3.6%
519850.0 1
3.6%
467752.0 1
3.6%
424572.0 1
3.6%
378493.0 1
3.6%

1등급
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21735.846
Minimum4.3
Maximum54368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:25.299930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile4.335
Q16
median18648.45
Q343125.5
95-th percentile49194.9
Maximum54368
Range54363.7
Interquartile range (IQR)43119.5

Descriptive statistics

Standard deviation22408.029
Coefficient of variation (CV)1.0309251
Kurtosis-2.0253315
Mean21735.846
Median Absolute Deviation (MAD)18644.15
Skewness0.081158529
Sum608603.7
Variance5.0211976 × 108
MonotonicityNot monotonic
2023-12-12T18:09:25.435586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6.0 2
 
7.1%
4.3 2
 
7.1%
40917.0 1
 
3.6%
49946.0 1
 
3.6%
4.4 1
 
3.6%
47800.0 1
 
3.6%
43040.0 1
 
3.6%
4.8 1
 
3.6%
44504.0 1
 
3.6%
5.4 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
4.3 2
7.1%
4.4 1
3.6%
4.8 1
3.6%
5.4 1
3.6%
5.8 1
3.6%
6.0 2
7.1%
6.4 1
3.6%
7.0 1
3.6%
7.7 1
3.6%
8.6 1
3.6%
ValueCountFrequency (%)
54368.0 1
3.6%
49946.0 1
3.6%
47800.0 1
3.6%
46994.0 1
3.6%
45111.0 1
3.6%
44504.0 1
3.6%
43382.0 1
3.6%
43040.0 1
3.6%
41326.0 1
3.6%
40917.0 1
3.6%

2등급
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39387.271
Minimum8.1
Maximum94233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:25.563600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.1
5-th percentile8.47
Q111.2
median35318.6
Q373958.25
95-th percentile90548.95
Maximum94233
Range94224.9
Interquartile range (IQR)73947.05

Descriptive statistics

Standard deviation40534.222
Coefficient of variation (CV)1.0291198
Kurtosis-2.0438622
Mean39387.271
Median Absolute Deviation (MAD)35310.35
Skewness0.070232111
Sum1102843.6
Variance1.6430232 × 109
MonotonicityNot monotonic
2023-12-12T18:09:25.707025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
71093.0 1
 
3.6%
10.9 1
 
3.6%
8.1 1
 
3.6%
94233.0 1
 
3.6%
8.4 1
 
3.6%
92461.0 1
 
3.6%
8.6 1
 
3.6%
86998.0 1
 
3.6%
9.3 1
 
3.6%
86678.0 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
8.1 1
3.6%
8.4 1
3.6%
8.6 1
3.6%
9.3 1
3.6%
10.2 1
3.6%
10.6 1
3.6%
10.9 1
3.6%
11.3 1
3.6%
12.3 1
3.6%
13.4 1
3.6%
ValueCountFrequency (%)
94233.0 1
3.6%
92461.0 1
3.6%
86998.0 1
3.6%
86678.0 1
3.6%
84751.0 1
3.6%
79853.0 1
3.6%
74334.0 1
3.6%
73833.0 1
3.6%
72640.0 1
3.6%
72100.0 1
3.6%

3등급
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107642.3
Minimum23.7
Maximum278520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:25.846646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.7
5-th percentile23.87
Q127.825
median80748.15
Q3210609
95-th percentile266467.35
Maximum278520
Range278496.3
Interquartile range (IQR)210581.17

Descriptive statistics

Standard deviation112603.88
Coefficient of variation (CV)1.0460932
Kurtosis-1.8864157
Mean107642.3
Median Absolute Deviation (MAD)80724.4
Skewness0.16843388
Sum3013984.4
Variance1.2679634 × 1010
MonotonicityNot monotonic
2023-12-12T18:09:25.981328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
161446.0 1
 
3.6%
27.3 1
 
3.6%
24.0 1
 
3.6%
278520.0 1
 
3.6%
23.8 1
 
3.6%
261047.0 1
 
3.6%
23.7 1
 
3.6%
238697.0 1
 
3.6%
24.3 1
 
3.6%
226182.0 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
23.7 1
3.6%
23.8 1
3.6%
24.0 1
3.6%
24.3 1
3.6%
25.4 1
3.6%
26.2 1
3.6%
27.3 1
3.6%
28.0 1
3.6%
29.1 1
3.6%
41.4 1
3.6%
ValueCountFrequency (%)
278520.0 1
3.6%
269386.0 1
3.6%
261047.0 1
3.6%
238697.0 1
3.6%
232907.0 1
3.6%
226182.0 1
3.6%
211098.0 1
3.6%
210446.0 1
3.6%
196167.0 1
3.6%
195167.0 1
3.6%

4등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91481.246
Minimum0
Maximum459316
Zeros10
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:26.120356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30.85
Q3169294.25
95-th percentile407680.85
Maximum459316
Range459316
Interquartile range (IQR)169294.25

Descriptive statistics

Standard deviation149763.13
Coefficient of variation (CV)1.637091
Kurtosis0.58099741
Mean91481.246
Median Absolute Deviation (MAD)30.85
Skewness1.3913815
Sum2561474.9
Variance2.2428995 × 1010
MonotonicityNot monotonic
2023-12-12T18:09:26.615581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 10
35.7%
134032.0 1
 
3.6%
39.6 1
 
3.6%
459316.0 1
 
3.6%
38.6 1
 
3.6%
423595.0 1
 
3.6%
37.5 1
 
3.6%
378126.0 1
 
3.6%
35.1 1
 
3.6%
325901.0 1
 
3.6%
Other values (9) 9
32.1%
ValueCountFrequency (%)
0.0 10
35.7%
22.9 1
 
3.6%
25.8 1
 
3.6%
27.7 1
 
3.6%
29.9 1
 
3.6%
31.8 1
 
3.6%
35.1 1
 
3.6%
37.5 1
 
3.6%
38.6 1
 
3.6%
39.6 1
 
3.6%
ValueCountFrequency (%)
459316.0 1
3.6%
423595.0 1
3.6%
378126.0 1
3.6%
325901.0 1
3.6%
264681.0 1
3.6%
223884.0 1
3.6%
188888.0 1
3.6%
162763.0 1
3.6%
134032.0 1
3.6%
39.6 1
3.6%

5등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19321.389
Minimum0
Maximum113842
Zeros10
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:26.769195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.05
Q322081.75
95-th percentile101155.55
Maximum113842
Range113842
Interquartile range (IQR)22081.75

Descriptive statistics

Standard deviation35301.207
Coefficient of variation (CV)1.8270533
Kurtosis1.9157026
Mean19321.389
Median Absolute Deviation (MAD)6.05
Skewness1.7726541
Sum540998.9
Variance1.2461752 × 109
MonotonicityNot monotonic
2023-12-12T18:09:26.923264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 10
35.7%
10456.0 1
 
3.6%
9.8 1
 
3.6%
113842.0 1
 
3.6%
9.7 1
 
3.6%
106107.0 1
 
3.6%
9.1 1
 
3.6%
91960.0 1
 
3.6%
7.9 1
 
3.6%
73294.0 1
 
3.6%
Other values (9) 9
32.1%
ValueCountFrequency (%)
0.0 10
35.7%
1.8 1
 
3.6%
3.1 1
 
3.6%
4.4 1
 
3.6%
5.6 1
 
3.6%
6.5 1
 
3.6%
7.9 1
 
3.6%
9.1 1
 
3.6%
9.7 1
 
3.6%
9.8 1
 
3.6%
ValueCountFrequency (%)
113842.0 1
3.6%
106107.0 1
3.6%
91960.0 1
3.6%
73294.0 1
3.6%
53898.0 1
3.6%
42001.0 1
3.6%
29911.0 1
3.6%
19472.0 1
3.6%
10456.0 1
3.6%
9.8 1
3.6%

인지지원등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3280.8607
Minimum0
Maximum23273
Zeros18
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:27.081982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.925
95-th percentile21332.7
Maximum23273
Range23273
Interquartile range (IQR)1.925

Descriptive statistics

Standard deviation7417.7436
Coefficient of variation (CV)2.2609139
Kurtosis2.5949979
Mean3280.8607
Median Absolute Deviation (MAD)0
Skewness2.0215481
Sum91864.1
Variance55022920
MonotonicityNot monotonic
2023-12-12T18:09:27.262979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 18
64.3%
11271.0 1
 
3.6%
1.4 1
 
3.6%
15647.0 1
 
3.6%
1.7 1
 
3.6%
19163.0 1
 
3.6%
1.9 1
 
3.6%
22501.0 1
 
3.6%
2.1 1
 
3.6%
23273.0 1
 
3.6%
ValueCountFrequency (%)
0.0 18
64.3%
1.4 1
 
3.6%
1.7 1
 
3.6%
1.9 1
 
3.6%
2.0 1
 
3.6%
2.1 1
 
3.6%
11271.0 1
 
3.6%
15647.0 1
 
3.6%
19163.0 1
 
3.6%
22501.0 1
 
3.6%
ValueCountFrequency (%)
23273.0 1
3.6%
22501.0 1
3.6%
19163.0 1
3.6%
15647.0 1
3.6%
11271.0 1
3.6%
2.1 1
3.6%
2.0 1
3.6%
1.9 1
3.6%
1.7 1
3.6%
1.4 1
3.6%

등급외자소계
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75727.3
Minimum12.2
Maximum164522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:27.397026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.2
5-th percentile13.695
Q125.275
median51827.6
Q3154724.75
95-th percentile162357.85
Maximum164522
Range164509.8
Interquartile range (IQR)154699.48

Descriptive statistics

Standard deviation77830.963
Coefficient of variation (CV)1.0277795
Kurtosis-2.1042162
Mean75727.3
Median Absolute Deviation (MAD)51814.95
Skewness0.050502384
Sum2120364.4
Variance6.0576588 × 109
MonotonicityNot monotonic
2023-12-12T18:09:27.555843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
32.2 2
 
7.1%
103623.0 1
 
3.6%
164522.0 1
 
3.6%
12.2 1
 
3.6%
141720.0 1
 
3.6%
13.1 1
 
3.6%
143951.0 1
 
3.6%
14.8 1
 
3.6%
149439.0 1
 
3.6%
16.9 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
12.2 1
3.6%
13.1 1
3.6%
14.8 1
3.6%
16.9 1
3.6%
19.3 1
3.6%
21.9 1
3.6%
23.7 1
3.6%
25.8 1
3.6%
26.5 1
3.6%
27.5 1
3.6%
ValueCountFrequency (%)
164522.0 1
3.6%
163005.0 1
3.6%
161156.0 1
3.6%
160814.0 1
3.6%
160702.0 1
3.6%
156835.0 1
3.6%
156797.0 1
3.6%
154034.0 1
3.6%
153657.0 1
3.6%
149783.0 1
3.6%

등급외A
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40629.475
Minimum6.5
Maximum95890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:27.719602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile7.08
Q112.7
median30260.55
Q378151.5
95-th percentile93048.9
Maximum95890
Range95883.5
Interquartile range (IQR)78138.8

Descriptive statistics

Standard deviation41876.647
Coefficient of variation (CV)1.0306962
Kurtosis-2.0483554
Mean40629.475
Median Absolute Deviation (MAD)30253.9
Skewness0.075069584
Sum1137625.3
Variance1.7536535 × 109
MonotonicityNot monotonic
2023-12-12T18:09:27.892159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
60501.0 1
 
3.6%
11.5 1
 
3.6%
6.5 1
 
3.6%
74878.0 1
 
3.6%
6.8 1
 
3.6%
74838.0 1
 
3.6%
7.6 1
 
3.6%
76481.0 1
 
3.6%
8.4 1
 
3.6%
78462.0 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
6.5 1
3.6%
6.8 1
3.6%
7.6 1
3.6%
8.4 1
3.6%
9.4 1
3.6%
10.3 1
3.6%
11.5 1
3.6%
13.1 1
3.6%
14.6 1
3.6%
15.5 1
3.6%
ValueCountFrequency (%)
95890.0 1
3.6%
93422.0 1
3.6%
92356.0 1
3.6%
89731.0 1
3.6%
85263.0 1
3.6%
82553.0 1
3.6%
78462.0 1
3.6%
78048.0 1
3.6%
77779.0 1
3.6%
77244.0 1
3.6%

등급외B
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27600.018
Minimum4.3
Maximum72491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:28.051307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile5.215
Q18.775
median13717.25
Q354939.75
95-th percentile69244.1
Maximum72491
Range72486.7
Interquartile range (IQR)54930.975

Descriptive statistics

Standard deviation29543.082
Coefficient of variation (CV)1.0704008
Kurtosis-1.8034134
Mean27600.018
Median Absolute Deviation (MAD)13712.65
Skewness0.26453541
Sum772800.5
Variance8.7279371 × 108
MonotonicityNot monotonic
2023-12-12T18:09:28.190784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
10.5 2
 
7.1%
7.0 2
 
7.1%
27424.0 1
 
3.6%
72491.0 1
 
3.6%
4.3 1
 
3.6%
50385.0 1
 
3.6%
4.9 1
 
3.6%
53700.0 1
 
3.6%
5.8 1
 
3.6%
58659.0 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
4.3 1
3.6%
4.9 1
3.6%
5.8 1
3.6%
7.0 2
7.1%
8.4 1
3.6%
8.7 1
3.6%
8.8 1
3.6%
9.0 1
3.6%
9.7 1
3.6%
9.8 1
3.6%
ValueCountFrequency (%)
72491.0 1
3.6%
69529.0 1
3.6%
68715.0 1
3.6%
65949.0 1
3.6%
64927.0 1
3.6%
61209.0 1
3.6%
58659.0 1
3.6%
53700.0 1
3.6%
52390.0 1
3.6%
50385.0 1
3.6%

등급외C
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7497.8143
Minimum1.4
Maximum17131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T18:09:28.358368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile1.4
Q12.25
median6699
Q314732.25
95-th percentile16257.15
Maximum17131
Range17129.6
Interquartile range (IQR)14730

Descriptive statistics

Standard deviation7669.6052
Coefficient of variation (CV)1.0229121
Kurtosis-2.1120982
Mean7497.8143
Median Absolute Deviation (MAD)6697.6
Skewness0.03050678
Sum209938.8
Variance58822844
MonotonicityNot monotonic
2023-12-12T18:09:28.528599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1.4 4
 
14.3%
15698.0 1
 
3.6%
4.0 1
 
3.6%
16457.0 1
 
3.6%
15413.0 1
 
3.6%
14299.0 1
 
3.6%
13408.0 1
 
3.6%
1.6 1
 
3.6%
13394.0 1
 
3.6%
2.0 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
1.4 4
14.3%
1.6 1
 
3.6%
2.0 1
 
3.6%
2.1 1
 
3.6%
2.3 1
 
3.6%
2.4 1
 
3.6%
2.7 1
 
3.6%
3.1 1
 
3.6%
3.3 1
 
3.6%
3.7 1
 
3.6%
ValueCountFrequency (%)
17131.0 1
3.6%
16457.0 1
3.6%
15886.0 1
3.6%
15698.0 1
3.6%
15481.0 1
3.6%
15413.0 1
3.6%
14787.0 1
3.6%
14714.0 1
3.6%
14503.0 1
3.6%
14393.0 1
3.6%

Interactions

2023-12-12T18:09:22.017225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:07.808450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:09.124655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:10.511556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:11.795250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.262068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.363010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.384176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:16.613376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:17.915253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:19.411348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:20.689416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:22.140907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:07.928407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:09.229313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:10.609782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:11.885476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.360863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.455765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.464958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:16.730434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:18.020325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:19.505985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:20.798843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:22.259824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:08.043222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:09.330327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:10.717746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:12.011471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.466172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.543358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.544784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:16.867682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:18.119883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:19.626827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:20.905394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:22.373896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:08.162958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:09.427568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:10.809625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:12.417058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.550952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.636145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.625104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:16.964291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:18.225342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:19.741420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:20.998160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:22.473961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:08.281683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:09.528742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:10.918484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:12.504198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.628004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.719239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.729046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:17.066872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:18.329291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:19.826560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:21.099272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:22.571124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:08.396904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:09.651494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:11.023171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:12.603534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.710241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.803094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.839246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:17.171023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:18.438210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:19.920314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:21.206220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:22.662419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:08.505169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:09.752316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:11.122065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:12.686818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.801932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.874066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.952054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:17.266217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:18.549254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:20.000874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:21.310224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:22.777837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:08.595613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:09.863132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:11.232391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:12.795868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.898151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.965935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:16.055705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:17.370415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:18.665418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:20.095339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:21.396700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:22.889827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:08.687054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:09.981053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:11.341558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:12.881839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.997791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.051794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:16.179640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:17.476133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:18.764947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:20.199073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:21.507970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:23.023484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:08.798686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:10.090258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:11.434979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:12.974309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.083862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.133902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:16.303426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:17.580644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:18.847522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:20.326742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:21.597972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:23.131025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:08.917189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:10.228426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:11.553758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.055295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.162910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.215228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:16.401839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:17.689517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:18.943328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:20.457649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:21.699143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:23.251817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:09.030752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:10.365576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:11.673088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:13.142340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:14.273541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:15.298311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:16.499179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:17.817152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:19.048179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:20.559306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:09:21.858683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:09:28.665029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1구분2전체계인정자소계1등급2등급3등급4등급5등급인지지원등급등급외자소계등급외A등급외B등급외C
구분11.0000.0000.0000.0000.0000.0000.0000.1340.2560.2380.0000.0000.0000.000
구분20.0001.0001.0001.0001.0001.0001.0000.7140.5910.2411.0001.0001.0001.000
전체계0.0001.0001.0000.9770.8171.0000.9580.9650.9740.8941.0000.9650.8820.981
인정자소계0.0001.0000.9771.0000.9050.9510.9640.9660.9451.0001.0001.0000.8770.937
1등급0.0001.0000.8170.9051.0000.6650.7830.7360.6430.7220.7790.9010.7380.784
2등급0.0001.0001.0000.9510.6651.0000.8670.9600.9770.7020.9310.8050.7910.913
3등급0.0001.0000.9580.9640.7830.8671.0000.7620.7480.6270.9840.8600.9060.789
4등급0.1340.7140.9650.9660.7360.9600.7621.0000.9961.0000.8870.7410.6880.941
5등급0.2560.5910.9740.9450.6430.9770.7480.9961.0001.0000.8620.6870.5820.915
인지지원등급0.2380.2410.8941.0000.7220.7020.6271.0001.0001.0000.6120.8250.5350.625
등급외자소계0.0001.0001.0001.0000.7790.9310.9840.8870.8620.6121.0000.9120.9040.890
등급외A0.0001.0000.9651.0000.9010.8050.8600.7410.6870.8250.9121.0000.8980.746
등급외B0.0001.0000.8820.8770.7380.7910.9060.6880.5820.5350.9040.8981.0000.856
등급외C0.0001.0000.9810.9370.7840.9130.7890.9410.9150.6250.8900.7460.8561.000
2023-12-12T18:09:28.901577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전체계인정자소계1등급2등급3등급4등급5등급인지지원등급등급외자소계등급외A등급외B등급외C구분2
전체계1.0000.9360.8340.9180.8800.6250.6250.3800.8000.7290.8740.7500.877
인정자소계0.9361.0000.6660.7430.7060.7350.7350.5260.6240.5590.7450.5880.855
1등급0.8340.6661.0000.9360.8750.2240.2240.0900.7850.7860.7740.9180.941
2등급0.9180.7430.9361.0000.9290.3880.3880.1840.8410.8050.8490.8520.961
3등급0.8800.7060.8750.9291.0000.2340.2340.1250.8220.8570.8140.8690.920
4등급0.6250.7350.2240.3880.2341.0001.0000.7260.217-0.0050.4470.0160.472
5등급0.6250.7350.2240.3880.2341.0001.0000.7260.217-0.0050.4470.0160.382
인지지원등급0.3800.5260.0900.1840.1250.7260.7261.000-0.183-0.220-0.004-0.1370.268
등급외자소계0.8000.6240.7850.8410.8220.2170.217-0.1831.0000.9190.9220.8030.961
등급외A0.7290.5590.7860.8050.857-0.005-0.005-0.2200.9191.0000.7970.8820.941
등급외B0.8740.7450.7740.8490.8140.4470.447-0.0040.9220.7971.0000.7140.899
등급외C0.7500.5880.9180.8520.8690.0160.016-0.1370.8030.8820.7141.0000.961
구분20.8770.8550.9410.9610.9200.4720.3820.2680.9610.9410.8990.9611.000

Missing values

2023-12-12T18:09:23.414687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:09:23.653141image/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

구분1구분2전체계인정자소계1등급2등급3등급4등급5등급인지지원등급등급외자소계등급외A등급외B등급외C
02009년인원390530286907.054368.071093.0161446.00.00.00.0103623.060501.027424.015698.0
12009년비율10073.513.918.241.40.00.00.026.515.57.04.0
22010년인원465777315994.046994.073833.0195167.00.00.00.0149783.092356.040296.017131.0
32010년비율10067.810.115.841.90.00.00.032.219.88.73.7
42011년인원478446324412.041326.072640.0210446.00.00.00.0154034.095890.042258.015886.0
52011년비율10067.88.615.244.00.00.00.032.220.18.83.3
62012년인원495445341788.038262.070619.0232907.00.00.00.0153657.093422.044754.015481.0
72012년비율10069.07.714.347.00.00.00.031.018.99.03.1
82013년인원535328378493.037283.071824.0269386.00.00.00.0156835.089731.052390.014714.0
92013년비율10070.77.013.450.30.00.00.029.316.89.82.7
구분1구분2전체계인정자소계1등급2등급3등급4등급5등급인지지원등급등급외자소계등급외A등급외B등급외C
182018년인원831512670810.045111.084751.0211098.0264681.053898.011271.0160702.077779.069529.013394.0
192018년비율10080.75.410.225.431.86.51.419.39.48.41.6
202019년인원929003772206.044504.086678.0226182.0325901.073294.015647.0156797.078462.064927.013408.0
212019년비율10083.14.89.324.335.17.91.716.98.47.01.4
222020년인원1007423857984.043040.086998.0238697.0378126.091960.019163.0149439.076481.058659.014299.0
232020년비율10085.24.38.623.737.59.11.914.87.65.81.4
242021년인원1097462953511.047800.092461.0261047.0423595.0106107.022501.0143951.074838.053700.015413.0
252021년비율10086.94.48.423.838.69.72.113.16.84.91.4
262022년인원11608501019130.049946.094233.0278520.0459316.0113842.023273.0141720.074878.050385.016457.0
272022년비율10087.84.38.124.039.69.82.012.26.54.31.4