Overview

Dataset statistics

Number of variables9
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory81.1 B

Variable types

Categorical2
Numeric7

Dataset

Description-해당연도 17개 지역별, 환급사유별 환급결정 현황. 1)세법에 의한 환급(공제초과 등)과 납세자 착오납부 등에 의한 환급으로 구분 중간예납 등 각종 세법에 의하여 공제금액이 납부세액을 초과하여 발생하는 환급.
URLhttps://www.data.go.kr/data/15113684/fileData.do

Alerts

세법에 의한 환급-공제초과 is highly overall correlated with 세법에 의한 환급-부가가치세법59조 and 5 other fieldsHigh correlation
세법에 의한 환급-부가가치세법59조 is highly overall correlated with 세법에 의한 환급-공제초과 and 5 other fieldsHigh correlation
세법에 의한 환급-감면기타 is highly overall correlated with 세법에 의한 환급-공제초과 and 5 other fieldsHigh correlation
납세자 착오납부 등에 의한 환급-착오 이중납부 is highly overall correlated with 세법에 의한 환급-공제초과 and 5 other fieldsHigh correlation
납세자 착오납부 등에 의한 환급-직권경정 is highly overall correlated with 세법에 의한 환급-공제초과 and 5 other fieldsHigh correlation
납세자 착오납부 등에 의한 환급-경정청구 is highly overall correlated with 세법에 의한 환급-공제초과 and 6 other fieldsHigh correlation
불복환급 is highly overall correlated with 세법에 의한 환급-공제초과 and 5 other fieldsHigh correlation
구분2 is highly overall correlated with 납세자 착오납부 등에 의한 환급-경정청구High correlation
세법에 의한 환급-공제초과 has 16 (25.0%) zerosZeros
세법에 의한 환급-부가가치세법59조 has 16 (25.0%) zerosZeros
세법에 의한 환급-감면기타 has 16 (25.0%) zerosZeros
납세자 착오납부 등에 의한 환급-직권경정 has 16 (25.0%) zerosZeros
납세자 착오납부 등에 의한 환급-경정청구 has 16 (25.0%) zerosZeros
불복환급 has 16 (25.0%) zerosZeros

Reproduction

Analysis started2023-12-13 00:48:38.082832
Analysis finished2023-12-13 00:48:42.192096
Duration4.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분1
Categorical

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size644.0 B
발생액
16 
지급액
16 
충당액
16 
미처리
16 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
발생액 16
25.0%
지급액 16
25.0%
충당액 16
25.0%
미처리 16
25.0%

Length

2023-12-13T09:48:42.244166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:48:42.325635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
발생액 16
25.0%
지급액 16
25.0%
충당액 16
25.0%
미처리 16
25.0%

구분2
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
서울
 
4
인천
 
4
경기
 
4
강원
 
4
대전
 
4
Other values (11)
44 

Length

Max length5
Median length2
Mean length2.1875
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row인천
3rd row경기
4th row강원
5th row대전

Common Values

ValueCountFrequency (%)
서울 4
 
6.2%
인천 4
 
6.2%
경기 4
 
6.2%
강원 4
 
6.2%
대전 4
 
6.2%
충북 4
 
6.2%
충남 세종 4
 
6.2%
광주 4
 
6.2%
전북 4
 
6.2%
전남 4
 
6.2%
Other values (6) 24
37.5%

Length

2023-12-13T09:48:42.415257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 4
 
5.9%
전북 4
 
5.9%
경남 4
 
5.9%
울산 4
 
5.9%
부산 4
 
5.9%
경북 4
 
5.9%
대구 4
 
5.9%
전남 4
 
5.9%
광주 4
 
5.9%
인천 4
 
5.9%
Other values (7) 28
41.2%

세법에 의한 환급-공제초과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean275502.88
Minimum0
Maximum4725124
Zeros16
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:48:42.513551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12087.25
median61356.5
Q3160531.5
95-th percentile1247196.2
Maximum4725124
Range4725124
Interquartile range (IQR)158444.25

Descriptive statistics

Standard deviation840771.64
Coefficient of variation (CV)3.0517708
Kurtosis23.655849
Mean275502.88
Median Absolute Deviation (MAD)61356.5
Skewness4.8316515
Sum17632184
Variance7.0689695 × 1011
MonotonicityNot monotonic
2023-12-13T09:48:42.632817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 16
25.0%
4725124 1
 
1.6%
5609 1
 
1.6%
147398 1
 
1.6%
313096 1
 
1.6%
159232 1
 
1.6%
241918 1
 
1.6%
59304 1
 
1.6%
60626 1
 
1.6%
17897 1
 
1.6%
Other values (39) 39
60.9%
ValueCountFrequency (%)
0 16
25.0%
2783 1
 
1.6%
3794 1
 
1.6%
4226 1
 
1.6%
4376 1
 
1.6%
5609 1
 
1.6%
5892 1
 
1.6%
6542 1
 
1.6%
6768 1
 
1.6%
7019 1
 
1.6%
ValueCountFrequency (%)
4725124 1
1.6%
4664498 1
1.6%
1499481 1
1.6%
1409731 1
1.6%
326166 1
1.6%
313096 1
1.6%
279148 1
1.6%
278229 1
1.6%
269512 1
1.6%
260333 1
1.6%

세법에 의한 환급-부가가치세법59조
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2574882.8
Minimum0
Maximum25468883
Zeros16
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:48:42.738963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13801
median233531.5
Q32568470.5
95-th percentile19952136
Maximum25468883
Range25468883
Interquartile range (IQR)2564669.5

Descriptive statistics

Standard deviation5801834.4
Coefficient of variation (CV)2.2532421
Kurtosis10.26816
Mean2574882.8
Median Absolute Deviation (MAD)233531.5
Skewness3.3111189
Sum1.647925 × 108
Variance3.3661283 × 1013
MonotonicityNot monotonic
2023-12-13T09:48:42.851660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 16
25.0%
25468883 1
 
1.6%
9348 1
 
1.6%
4095401 1
 
1.6%
2581855 1
 
1.6%
2558941 1
 
1.6%
3775863 1
 
1.6%
313013 1
 
1.6%
79762 1
 
1.6%
138201 1
 
1.6%
Other values (39) 39
60.9%
ValueCountFrequency (%)
0 16
25.0%
5068 1
 
1.6%
7482 1
 
1.6%
8141 1
 
1.6%
8505 1
 
1.6%
9348 1
 
1.6%
9925 1
 
1.6%
10404 1
 
1.6%
12762 1
 
1.6%
13791 1
 
1.6%
ValueCountFrequency (%)
25468883 1
1.6%
25389121 1
1.6%
22705241 1
1.6%
22551191 1
1.6%
5224160 1
1.6%
5207507 1
1.6%
4394614 1
1.6%
4256413 1
1.6%
4109192 1
1.6%
4095401 1
1.6%

세법에 의한 환급-감면기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213763.12
Minimum0
Maximum1432534
Zeros16
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:48:42.953937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11901.25
median91229.5
Q3314149.75
95-th percentile1071553.5
Maximum1432534
Range1432534
Interquartile range (IQR)312248.5

Descriptive statistics

Standard deviation327868.98
Coefficient of variation (CV)1.5337958
Kurtosis6.2647511
Mean213763.12
Median Absolute Deviation (MAD)91229.5
Skewness2.4435882
Sum13680840
Variance1.0749807 × 1011
MonotonicityNot monotonic
2023-12-13T09:48:43.292019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 16
25.0%
1253195 1
 
1.6%
4602 1
 
1.6%
434092 1
 
1.6%
413640 1
 
1.6%
110963 1
 
1.6%
490006 1
 
1.6%
101379 1
 
1.6%
81080 1
 
1.6%
10385 1
 
1.6%
Other values (39) 39
60.9%
ValueCountFrequency (%)
0 16
25.0%
2535 1
 
1.6%
3051 1
 
1.6%
3412 1
 
1.6%
3897 1
 
1.6%
4243 1
 
1.6%
4586 1
 
1.6%
4602 1
 
1.6%
4630 1
 
1.6%
7157 1
 
1.6%
ValueCountFrequency (%)
1432534 1
1.6%
1392269 1
1.6%
1253195 1
1.6%
1172114 1
1.6%
501711 1
1.6%
490006 1
1.6%
438334 1
1.6%
434092 1
1.6%
420797 1
1.6%
413640 1
1.6%

납세자 착오납부 등에 의한 환급-착오 이중납부
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24794.25
Minimum24
Maximum352028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:48:43.397024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile47.3
Q1632
median8449
Q320786.75
95-th percentile138361.1
Maximum352028
Range352004
Interquartile range (IQR)20154.75

Descriptive statistics

Standard deviation63948.014
Coefficient of variation (CV)2.579147
Kurtosis18.060739
Mean24794.25
Median Absolute Deviation (MAD)8316.5
Skewness4.2085798
Sum1586832
Variance4.0893485 × 109
MonotonicityNot monotonic
2023-12-13T09:48:43.506850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103 2
 
3.1%
352028 1
 
1.6%
2228 1
 
1.6%
21200 1
 
1.6%
1131 1
 
1.6%
883 1
 
1.6%
2182 1
 
1.6%
2237 1
 
1.6%
1456 1
 
1.6%
1211 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
24 1
1.6%
38 1
1.6%
40 1
1.6%
47 1
1.6%
49 1
1.6%
56 1
1.6%
57 1
1.6%
65 1
1.6%
70 1
1.6%
103 2
3.1%
ValueCountFrequency (%)
352028 1
1.6%
323782 1
1.6%
177850 1
1.6%
156086 1
1.6%
37920 1
1.6%
34138 1
1.6%
32935 1
1.6%
29939 1
1.6%
26955 1
1.6%
25895 1
1.6%

납세자 착오납부 등에 의한 환급-직권경정
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14354.031
Minimum0
Maximum252267
Zeros16
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:48:43.643342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1115.5
median1926
Q38339
95-th percentile45994.2
Maximum252267
Range252267
Interquartile range (IQR)8223.5

Descriptive statistics

Standard deviation43571.911
Coefficient of variation (CV)3.0355173
Kurtosis24.90273
Mean14354.031
Median Absolute Deviation (MAD)1926
Skewness4.9404119
Sum918658
Variance1.8985114 × 109
MonotonicityNot monotonic
2023-12-13T09:48:43.756920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 16
25.0%
252267 1
 
1.6%
13037 1
 
1.6%
5049 1
 
1.6%
20815 1
 
1.6%
2597 1
 
1.6%
15090 1
 
1.6%
1954 1
 
1.6%
12505 1
 
1.6%
592 1
 
1.6%
Other values (39) 39
60.9%
ValueCountFrequency (%)
0 16
25.0%
154 1
 
1.6%
214 1
 
1.6%
292 1
 
1.6%
342 1
 
1.6%
381 1
 
1.6%
493 1
 
1.6%
589 1
 
1.6%
592 1
 
1.6%
638 1
 
1.6%
ValueCountFrequency (%)
252267 1
1.6%
239763 1
1.6%
48578 1
1.6%
46083 1
1.6%
45491 1
1.6%
42958 1
1.6%
27475 1
1.6%
21404 1
1.6%
20815 1
1.6%
15728 1
1.6%

납세자 착오납부 등에 의한 환급-경정청구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111005.25
Minimum0
Maximum1499565
Zeros16
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:48:43.859589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12235
median28288
Q378494.5
95-th percentile706889.35
Maximum1499565
Range1499565
Interquartile range (IQR)76259.5

Descriptive statistics

Standard deviation287221.17
Coefficient of variation (CV)2.5874557
Kurtosis16.008735
Mean111005.25
Median Absolute Deviation (MAD)28288
Skewness4.0109568
Sum7104336
Variance8.2496001 × 1010
MonotonicityNot monotonic
2023-12-13T09:48:43.975737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 16
25.0%
1499565 1
 
1.6%
2980 1
 
1.6%
68952 1
 
1.6%
116125 1
 
1.6%
64235 1
 
1.6%
85741 1
 
1.6%
21745 1
 
1.6%
55678 1
 
1.6%
11553 1
 
1.6%
Other values (39) 39
60.9%
ValueCountFrequency (%)
0 16
25.0%
2980 1
 
1.6%
3623 1
 
1.6%
3704 1
 
1.6%
4025 1
 
1.6%
5338 1
 
1.6%
5484 1
 
1.6%
6806 1
 
1.6%
6908 1
 
1.6%
7126 1
 
1.6%
ValueCountFrequency (%)
1499565 1
1.6%
1443887 1
1.6%
883919 1
1.6%
805252 1
1.6%
149501 1
1.6%
148358 1
1.6%
136805 1
1.6%
132341 1
1.6%
125284 1
1.6%
116125 1
1.6%

불복환급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39285.594
Minimum0
Maximum778603
Zeros16
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T09:48:44.088699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.25
median2450
Q311768
95-th percentile173503.55
Maximum778603
Range778603
Interquartile range (IQR)11762.75

Descriptive statistics

Standard deviation137655.24
Coefficient of variation (CV)3.5039624
Kurtosis24.897697
Mean39285.594
Median Absolute Deviation (MAD)2450
Skewness4.966181
Sum2514278
Variance1.8948966 × 1010
MonotonicityNot monotonic
2023-12-13T09:48:44.200771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 16
25.0%
778603 1
 
1.6%
102 1
 
1.6%
14553 1
 
1.6%
63572 1
 
1.6%
19462 1
 
1.6%
46331 1
 
1.6%
524 1
 
1.6%
13979 1
 
1.6%
113 1
 
1.6%
Other values (39) 39
60.9%
ValueCountFrequency (%)
0 16
25.0%
7 1
 
1.6%
64 1
 
1.6%
76 1
 
1.6%
80 1
 
1.6%
85 1
 
1.6%
102 1
 
1.6%
113 1
 
1.6%
148 1
 
1.6%
152 1
 
1.6%
ValueCountFrequency (%)
778603 1
1.6%
764624 1
1.6%
195849 1
1.6%
190379 1
1.6%
77876 1
1.6%
77763 1
1.6%
65547 1
1.6%
63572 1
1.6%
46407 1
1.6%
46331 1
1.6%

Interactions

2023-12-13T09:48:41.513179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:38.359652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:38.816127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.493879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.997944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.502141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.983014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.578318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:38.423203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:38.874180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.567316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.063879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.562668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.057402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.645346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:38.484052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:38.934520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.637269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.132162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.631174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.124199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.720332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:38.552358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.006292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.710613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.209883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.700451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.200994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.795209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:38.618219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.073395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.789201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.282467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.771695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.287213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.865323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:38.679634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.349230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.853816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.348419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.833559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.352395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.942307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:38.746878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.422705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:39.927078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.423623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:40.903766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:48:41.426966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:48:44.279522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1구분2세법에 의한 환급-공제초과세법에 의한 환급-부가가치세법59조세법에 의한 환급-감면기타납세자 착오납부 등에 의한 환급-착오 이중납부납세자 착오납부 등에 의한 환급-직권경정납세자 착오납부 등에 의한 환급-경정청구불복환급
구분11.0000.0000.0000.3240.6130.0000.1450.0000.000
구분20.0001.0000.6240.7730.7600.5170.7090.7500.769
세법에 의한 환급-공제초과0.0000.6241.0000.8320.9121.0000.7671.0000.980
세법에 의한 환급-부가가치세법59조0.3240.7730.8321.0000.8840.9540.8091.0000.842
세법에 의한 환급-감면기타0.6130.7600.9120.8841.0000.8040.9831.0000.926
납세자 착오납부 등에 의한 환급-착오 이중납부0.0000.5171.0000.9540.8041.0000.7991.0000.832
납세자 착오납부 등에 의한 환급-직권경정0.1450.7090.7670.8090.9830.7991.0000.9800.817
납세자 착오납부 등에 의한 환급-경정청구0.0000.7501.0001.0001.0001.0000.9801.0001.000
불복환급0.0000.7690.9800.8420.9260.8320.8171.0001.000
2023-12-13T09:48:44.374783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2구분1
구분21.0000.000
구분10.0001.000
2023-12-13T09:48:44.440316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세법에 의한 환급-공제초과세법에 의한 환급-부가가치세법59조세법에 의한 환급-감면기타납세자 착오납부 등에 의한 환급-착오 이중납부납세자 착오납부 등에 의한 환급-직권경정납세자 착오납부 등에 의한 환급-경정청구불복환급구분1구분2
세법에 의한 환급-공제초과1.0000.9690.9600.9270.9140.9760.9490.0000.297
세법에 의한 환급-부가가치세법59조0.9691.0000.9770.9260.8960.9750.9390.2650.477
세법에 의한 환급-감면기타0.9600.9771.0000.9370.8980.9560.9320.4350.447
납세자 착오납부 등에 의한 환급-착오 이중납부0.9270.9260.9371.0000.9490.9390.9370.0000.259
납세자 착오납부 등에 의한 환급-직권경정0.9140.8960.8980.9491.0000.9100.9250.1330.459
납세자 착오납부 등에 의한 환급-경정청구0.9760.9750.9560.9390.9101.0000.9450.0000.503
불복환급0.9490.9390.9320.9370.9250.9451.0000.0000.414
구분10.0000.2650.4350.0000.1330.0000.0001.0000.000
구분20.2970.4770.4470.2590.4590.5030.4140.0001.000

Missing values

2023-12-13T09:48:42.040525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:48:42.147500image/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

구분1구분2세법에 의한 환급-공제초과세법에 의한 환급-부가가치세법59조세법에 의한 환급-감면기타납세자 착오납부 등에 의한 환급-착오 이중납부납세자 착오납부 등에 의한 환급-직권경정납세자 착오납부 등에 의한 환급-경정청구불복환급
0발생액서울47251242546888312531953520282522671499565778603
1발생액인천2782294394614366382341384608314835877876
2발생액경기149948122705241143253417785048578883919195849
3발생액강원132077772223254765113753743345117711
4발생액대전154406891768191599902651487452811774
5발생액충북12042118785242485162589527475673458684
6발생액충남 세종279148522416034160421662119181495018884
7발생액광주166749933431255865157154433681776421
8발생액전북1286501122350330525137232279532133005
9발생액전남1448663379736310220106861462984925164
구분1구분2세법에 의한 환급-공제초과세법에 의한 환급-부가가치세법59조세법에 의한 환급-감면기타납세자 착오납부 등에 의한 환급-착오 이중납부납세자 착오납부 등에 의한 환급-직권경정납세자 착오납부 등에 의한 환급-경정청구불복환급
54미처리충남 세종000103000
55미처리광주000122000
56미처리전북00038000
57미처리전남00065000
58미처리대구00070000
59미처리경북00056000
60미처리부산000103000
61미처리울산00040000
62미처리경남00057000
63미처리제주00024000