Overview

Dataset statistics

Number of variables13
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory115.8 B

Variable types

Numeric6
Categorical7

Dataset

Description지방세 세목별 유형별 미환급금 현황 및 연간 누적 미환급액과 미환급 누적률을 제공하고 지방자치단체의 환급금 해소노력을 확인할 수 있는 자료로 활용할 예정
URLhttps://www.data.go.kr/data/15078936/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
미환급유형 has constant value ""Constant
연번 is highly overall correlated with 과세년도High correlation
당해미환급건수 is highly overall correlated with 당해미환급금액 and 2 other fieldsHigh correlation
당해미환급금액 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급건수 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급금액 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
과세년도 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
누적미환급금액 has unique valuesUnique
누적미환급금액증감 has 7 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-12 04:52:39.448017
Analysis finished2023-12-12 04:52:44.287450
Duration4.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T13:52:44.367775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.7
Q19.5
median18
Q326.5
95-th percentile33.3
Maximum35
Range34
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.246951
Coefficient of variation (CV)0.56927504
Kurtosis-1.2
Mean18
Median Absolute Deviation (MAD)9
Skewness0
Sum630
Variance105
MonotonicityStrictly increasing
2023-12-12T13:52:44.579462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 1
 
2.9%
2 1
 
2.9%
21 1
 
2.9%
22 1
 
2.9%
23 1
 
2.9%
24 1
 
2.9%
25 1
 
2.9%
26 1
 
2.9%
27 1
 
2.9%
28 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 1
2.9%
6 1
2.9%
7 1
2.9%
8 1
2.9%
9 1
2.9%
10 1
2.9%
ValueCountFrequency (%)
35 1
2.9%
34 1
2.9%
33 1
2.9%
32 1
2.9%
31 1
2.9%
30 1
2.9%
29 1
2.9%
28 1
2.9%
27 1
2.9%
26 1
2.9%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
전라남도
35 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 35
100.0%

Length

2023-12-12T13:52:44.775587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:52:44.893576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 35
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
영암군
35 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영암군 35
100.0%

Length

2023-12-12T13:52:45.013133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:52:45.148203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영암군 35
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
46830
35 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46830
2nd row46830
3rd row46830
4th row46830
5th row46830

Common Values

ValueCountFrequency (%)
46830 35
100.0%

Length

2023-12-12T13:52:45.300359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:52:45.436411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46830 35
100.0%

세목명
Categorical

Distinct6
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
자동차세
10 
지방소득세
9 
재산세
7 
주민세
6 
등록면허세
2 

Length

Max length5
Median length4
Mean length3.9142857
Min length3

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row자동차세
2nd row자동차세
3rd row주민세
4th row지방소득세
5th row지방소득세

Common Values

ValueCountFrequency (%)
자동차세 10
28.6%
지방소득세 9
25.7%
재산세 7
20.0%
주민세 6
17.1%
등록면허세 2
 
5.7%
취득세 1
 
2.9%

Length

2023-12-12T13:52:45.881188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:52:46.044835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 10
28.6%
지방소득세 9
25.7%
재산세 7
20.0%
주민세 6
17.1%
등록면허세 2
 
5.7%
취득세 1
 
2.9%

과세년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size412.0 B
2020
9 
2021
8 
2018
7 
2019
6 
2017
5 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2020 9
25.7%
2021 8
22.9%
2018 7
20.0%
2019 6
17.1%
2017 5
14.3%

Length

2023-12-12T13:52:46.229329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:52:46.359345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 9
25.7%
2021 8
22.9%
2018 7
20.0%
2019 6
17.1%
2017 5
14.3%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
신규
35 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신규 35
100.0%

Length

2023-12-12T13:52:46.497491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:52:46.610160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 35
100.0%

납세자유형
Categorical

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
개인
19 
법인
16 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row법인
3rd row법인
4th row개인
5th row법인

Common Values

ValueCountFrequency (%)
개인 19
54.3%
법인 16
45.7%

Length

2023-12-12T13:52:46.733814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:52:46.878548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 19
54.3%
법인 16
45.7%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.285714
Minimum1
Maximum271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T13:52:47.020941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q337.5
95-th percentile213.5
Maximum271
Range270
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation68.741019
Coefficient of variation (CV)1.8944375
Kurtosis6.3920594
Mean36.285714
Median Absolute Deviation (MAD)6
Skewness2.6291242
Sum1270
Variance4725.3277
MonotonicityNot monotonic
2023-12-12T13:52:47.193680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 10
28.6%
2 5
14.3%
8 2
 
5.7%
19 2
 
5.7%
47 1
 
2.9%
259 1
 
2.9%
85 1
 
2.9%
40 1
 
2.9%
194 1
 
2.9%
271 1
 
2.9%
Other values (10) 10
28.6%
ValueCountFrequency (%)
1 10
28.6%
2 5
14.3%
3 1
 
2.9%
5 1
 
2.9%
7 1
 
2.9%
8 2
 
5.7%
9 1
 
2.9%
15 1
 
2.9%
19 2
 
5.7%
24 1
 
2.9%
ValueCountFrequency (%)
271 1
2.9%
259 1
2.9%
194 1
2.9%
91 1
2.9%
85 1
2.9%
60 1
2.9%
51 1
2.9%
47 1
2.9%
40 1
2.9%
35 1
2.9%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean869182.86
Minimum130
Maximum9173250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T13:52:47.335321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile7477
Q119045
median113840
Q3979070
95-th percentile4225058
Maximum9173250
Range9173120
Interquartile range (IQR)960025

Descriptive statistics

Standard deviation1796738.4
Coefficient of variation (CV)2.0671582
Kurtosis13.632196
Mean869182.86
Median Absolute Deviation (MAD)104740
Skewness3.4412938
Sum30421400
Variance3.2282691 × 1012
MonotonicityNot monotonic
2023-12-12T13:52:47.478704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
11000 2
 
5.7%
54720 1
 
2.9%
1172650 1
 
2.9%
158140 1
 
2.9%
12560 1
 
2.9%
9173250 1
 
2.9%
1630500 1
 
2.9%
21880 1
 
2.9%
9100 1
 
2.9%
1250900 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
130 1
2.9%
3690 1
2.9%
9100 1
2.9%
10700 1
2.9%
11000 2
5.7%
11330 1
2.9%
12560 1
2.9%
17880 1
2.9%
20210 1
2.9%
21880 1
2.9%
ValueCountFrequency (%)
9173250 1
2.9%
4247360 1
2.9%
4215500 1
2.9%
2045570 1
2.9%
1833120 1
2.9%
1630500 1
2.9%
1308780 1
2.9%
1250900 1
2.9%
1172650 1
2.9%
785490 1
2.9%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.914286
Minimum1
Maximum479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T13:52:47.629312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median11
Q362.5
95-th percentile389.9
Maximum479
Range478
Interquartile range (IQR)60

Descriptive statistics

Standard deviation130.4457
Coefficient of variation (CV)1.7648239
Kurtosis3.5581086
Mean73.914286
Median Absolute Deviation (MAD)10
Skewness2.1548616
Sum2587
Variance17016.081
MonotonicityNot monotonic
2023-12-12T13:52:47.792913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 5
 
14.3%
1 4
 
11.4%
3 4
 
11.4%
11 2
 
5.7%
78 1
 
2.9%
68 1
 
2.9%
10 1
 
2.9%
350 1
 
2.9%
49 1
 
2.9%
57 1
 
2.9%
Other values (14) 14
40.0%
ValueCountFrequency (%)
1 4
11.4%
2 5
14.3%
3 4
11.4%
5 1
 
2.9%
9 1
 
2.9%
10 1
 
2.9%
11 2
 
5.7%
14 1
 
2.9%
24 1
 
2.9%
27 1
 
2.9%
ValueCountFrequency (%)
479 1
2.9%
420 1
2.9%
377 1
2.9%
350 1
2.9%
220 1
2.9%
129 1
2.9%
106 1
2.9%
78 1
2.9%
68 1
2.9%
57 1
2.9%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1798276
Minimum9100
Maximum11905800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T13:52:47.931519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9100
5-th percentile15816
Q131950
median440810
Q32037480
95-th percentile8894622
Maximum11905800
Range11896700
Interquartile range (IQR)2005530

Descriptive statistics

Standard deviation3077993.1
Coefficient of variation (CV)1.7116356
Kurtosis4.2229396
Mean1798276
Median Absolute Deviation (MAD)419110
Skewness2.2401118
Sum62939660
Variance9.4740417 × 1012
MonotonicityNot monotonic
2023-12-12T13:52:48.110588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2003650 1
 
2.9%
309910 1
 
2.9%
1726720 1
 
2.9%
705290 1
 
2.9%
30570 1
 
2.9%
33330 1
 
2.9%
11905800 1
 
2.9%
2071310 1
 
2.9%
21880 1
 
2.9%
9100 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
9100 1
2.9%
11000 1
2.9%
17880 1
2.9%
18010 1
2.9%
20210 1
2.9%
21700 1
2.9%
21880 1
2.9%
22330 1
2.9%
30570 1
2.9%
33330 1
2.9%
ValueCountFrequency (%)
11905800 1
2.9%
10250760 1
2.9%
8313420 1
2.9%
8191940 1
2.9%
4097920 1
2.9%
2789140 1
2.9%
2732550 1
2.9%
2088080 1
2.9%
2071310 1
2.9%
2003650 1
2.9%

누적미환급금액증감
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1149.0243
Minimum0
Maximum14727.91
Zeros7
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T13:52:48.244334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.94
median97.09
Q3252.2
95-th percentile9431.868
Maximum14727.91
Range14727.91
Interquartile range (IQR)236.26

Descriptive statistics

Standard deviation3508.6399
Coefficient of variation (CV)3.053582
Kurtosis10.839042
Mean1149.0243
Median Absolute Deviation (MAD)97.09
Skewness3.4206977
Sum40215.85
Variance12310554
MonotonicityNot monotonic
2023-12-12T13:52:48.382511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 7
 
20.0%
60.18 1
 
2.9%
47.25 1
 
2.9%
7579.59 1
 
2.9%
459.2 1
 
2.9%
206.53 1
 
2.9%
102.8 1
 
2.9%
37.88 1
 
2.9%
249.32 1
 
2.9%
92.87 1
 
2.9%
Other values (19) 19
54.3%
ValueCountFrequency (%)
0.0 7
20.0%
4.81 1
 
2.9%
4.84 1
 
2.9%
27.04 1
 
2.9%
29.79 1
 
2.9%
33.58 1
 
2.9%
37.88 1
 
2.9%
47.25 1
 
2.9%
59.15 1
 
2.9%
60.18 1
 
2.9%
ValueCountFrequency (%)
14727.91 1
2.9%
13753.85 1
2.9%
7579.59 1
2.9%
607.26 1
2.9%
459.2 1
2.9%
345.99 1
2.9%
301.06 1
2.9%
292.34 1
2.9%
255.08 1
2.9%
249.32 1
2.9%

Interactions

2023-12-12T13:52:43.117509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:39.817763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.283263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.756488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:41.457314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:42.366117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:43.225714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:39.901307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.361329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.857787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:41.590067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:42.499355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:43.346956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:39.975304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.435470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.964730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:41.729732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:42.607644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:43.471834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.047272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.509833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:41.083601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:41.895733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:42.713638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:43.612127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.120292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.584502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:41.188843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:42.063372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:42.868483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:43.750901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.201119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:40.666474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:41.320481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:42.250270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:42.986733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:52:48.497583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세목명과세년도납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
연번1.0000.3370.9890.0000.0000.0000.3880.0000.643
세목명0.3371.0000.0000.0000.0000.0000.0000.0000.341
과세년도0.9890.0001.0000.0000.0000.0000.0000.0000.370
납세자유형0.0000.0000.0001.0000.4240.0000.2100.3000.000
당해미환급건수0.0000.0000.0000.4241.0000.8760.9130.8540.000
당해미환급금액0.0000.0000.0000.0000.8761.0000.8250.9120.000
누적미환급건수0.3880.0000.0000.2100.9130.8251.0000.9870.000
누적미환급금액0.0000.0000.0000.3000.8540.9120.9871.0000.000
누적미환급금액증감0.6430.3410.3700.0000.0000.0000.0000.0001.000
2023-12-12T13:52:48.634039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형
세목명1.0000.0000.000
과세년도0.0001.0000.000
납세자유형0.0000.0001.000
2023-12-12T13:52:48.749759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명과세년도납세자유형
연번1.000-0.0290.0100.0720.1540.1700.1550.8120.000
당해미환급건수-0.0291.0000.8490.9490.8580.2260.0000.0000.279
당해미환급금액0.0100.8491.0000.8160.865-0.0400.0000.0000.017
누적미환급건수0.0720.9490.8161.0000.9050.4230.0000.0000.195
누적미환급금액0.1540.8580.8650.9051.0000.3630.0000.0000.270
누적미환급금액증감0.1700.226-0.0400.4230.3631.0000.1270.2860.000
세목명0.1550.0000.0000.0000.0000.1271.0000.0000.000
과세년도0.8120.0000.0000.0000.0000.2860.0001.0000.000
납세자유형0.0000.2790.0170.1950.2700.0000.0000.0001.000

Missing values

2023-12-12T13:52:43.958603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:52:44.186441image/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

연번시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
01전라남도영암군46830자동차세2017신규개인47125090078200365060.18
12전라남도영암군46830자동차세2017신규법인729561093099104.84
23전라남도영암군46830주민세2017신규법인211384021138400.0
34전라남도영암군46830지방소득세2017신규개인88999027255330183.73
45전라남도영암군46830지방소득세2017신규법인2202102202100.0
56전라남도영암군46830자동차세2018신규개인517854901292789140255.08
67전라남도영암군46830자동차세2018신규법인1510601024415920292.34
78전라남도영암군46830재산세2018신규개인57684011543460607.26
89전라남도영암군46830재산세2018신규법인2178802178800.0
910전라남도영암군46830주민세2018신규개인1110001110000.0
연번시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
2526전라남도영암군46830지방소득세2020신규법인8163050011207131027.04
2627전라남도영암군46830취득세2020신규법인1218801218800.0
2728전라남도영암군46830등록면허세2021신규개인19100191000.0
2829전라남도영암군46830자동차세2021신규개인1944247360420819194092.87
2930전라남도영암군46830자동차세2021신규법인19404180571411890249.32
3031전라남도영암군46830재산세2021신규개인402567604935403037.88
3132전라남도영암군46830주민세2021신규개인110700221700102.8
3233전라남도영암군46830주민세2021신규법인1551203168960206.53
3334전라남도영암군46830지방소득세2021신규개인85183312035010250760459.2
3435전라남도영암군46830지방소득세2021신규법인2271901020880807579.59