Overview

Dataset statistics

Number of variables13
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory118.0 B

Variable types

Numeric6
Categorical7

Dataset

Description경상북도 문경시 미환급 유형별 미환급금 현황에 대한 자료로, 2019년부터 2021년까지 미환급건수, 미환급금액등을 제공합니다.
URLhttps://www.data.go.kr/data/15078719/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
당해미환급금액 has unique valuesUnique
누적미환급금액 has unique valuesUnique
누적미환급금액증감 has 3 (13.6%) zerosZeros

Reproduction

Analysis started2023-12-12 00:25:21.534066
Analysis finished2023-12-12 00:25:25.942263
Duration4.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:25:26.036650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2023-12-12T09:25:26.198938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
경상북도
22 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 22
100.0%

Length

2023-12-12T09:25:26.321047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:26.412746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 22
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
문경시
22 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
문경시 22
100.0%

Length

2023-12-12T09:25:26.522622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:26.615964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문경시 22
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
47280
22 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47280
2nd row47280
3rd row47280
4th row47280
5th row47280

Common Values

ValueCountFrequency (%)
47280 22
100.0%

Length

2023-12-12T09:25:26.711238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:26.822356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47280 22
100.0%

세목명
Categorical

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
자동차세
6 
재산세
5 
등록면허세
4 
지방소득세
4 
주민세
3 

Length

Max length5
Median length4
Mean length4
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록면허세
2nd row자동차세
3rd row자동차세
4th row재산세
5th row주민세

Common Values

ValueCountFrequency (%)
자동차세 6
27.3%
재산세 5
22.7%
등록면허세 4
18.2%
지방소득세 4
18.2%
주민세 3
13.6%

Length

2023-12-12T09:25:26.937382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:27.060615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 6
27.3%
재산세 5
22.7%
등록면허세 4
18.2%
지방소득세 4
18.2%
주민세 3
13.6%

과세년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
2021
8 
2019
7 
2020
7 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2021 8
36.4%
2019 7
31.8%
2020 7
31.8%

Length

2023-12-12T09:25:27.194421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:27.314374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 8
36.4%
2019 7
31.8%
2020 7
31.8%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
신규
22 

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 22
100.0%

Length

2023-12-12T09:25:27.422194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:27.515223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 22
100.0%

납세자유형
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
개인
13 
법인
9 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 13
59.1%
법인 9
40.9%

Length

2023-12-12T09:25:27.605970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:27.704576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 13
59.1%
법인 9
40.9%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.954545
Minimum1
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:25:27.790605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.25
median7.5
Q322
95-th percentile172.4
Maximum219
Range218
Interquartile range (IQR)20.75

Descriptive statistics

Standard deviation57.31407
Coefficient of variation (CV)1.9794498
Kurtosis7.1303247
Mean28.954545
Median Absolute Deviation (MAD)6.5
Skewness2.7798495
Sum637
Variance3284.9026
MonotonicityNot monotonic
2023-12-12T09:25:27.888999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 6
27.3%
4 2
 
9.1%
7 1
 
4.5%
2 1
 
4.5%
32 1
 
4.5%
31 1
 
4.5%
11 1
 
4.5%
219 1
 
4.5%
19 1
 
4.5%
23 1
 
4.5%
Other values (6) 6
27.3%
ValueCountFrequency (%)
1 6
27.3%
2 1
 
4.5%
4 2
 
9.1%
5 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%
9 1
 
4.5%
11 1
 
4.5%
13 1
 
4.5%
19 1
 
4.5%
ValueCountFrequency (%)
219 1
4.5%
178 1
4.5%
66 1
4.5%
32 1
4.5%
31 1
4.5%
23 1
4.5%
19 1
4.5%
13 1
4.5%
11 1
4.5%
9 1
4.5%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348365
Minimum200
Maximum2572000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:25:28.019625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile897.5
Q130655
median106220
Q3286605
95-th percentile1728841
Maximum2572000
Range2571800
Interquartile range (IQR)255950

Descriptive statistics

Standard deviation633845.48
Coefficient of variation (CV)1.8194867
Kurtosis7.830455
Mean348365
Median Absolute Deviation (MAD)92795
Skewness2.8044401
Sum7664030
Variance4.0176009 × 1011
MonotonicityNot monotonic
2023-12-12T09:25:28.159824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
340 1
 
4.5%
11490 1
 
4.5%
200 1
 
4.5%
482290 1
 
4.5%
74110 1
 
4.5%
162340 1
 
4.5%
107340 1
 
4.5%
2572000 1
 
4.5%
20140 1
 
4.5%
64540 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
200 1
4.5%
340 1
4.5%
11490 1
4.5%
11550 1
4.5%
15300 1
4.5%
20140 1
4.5%
62200 1
4.5%
64540 1
4.5%
74110 1
4.5%
91740 1
4.5%
ValueCountFrequency (%)
2572000 1
4.5%
1780560 1
4.5%
746180 1
4.5%
517260 1
4.5%
482290 1
4.5%
305340 1
4.5%
230400 1
4.5%
162340 1
4.5%
160210 1
4.5%
143400 1
4.5%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.454545
Minimum1
Maximum476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:25:28.272729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.05
Q13.5
median18
Q336.5
95-th percentile268.15
Maximum476
Range475
Interquartile range (IQR)33

Descriptive statistics

Standard deviation112.85926
Coefficient of variation (CV)1.9643225
Kurtosis9.4464146
Mean57.454545
Median Absolute Deviation (MAD)16
Skewness3.0127586
Sum1264
Variance12737.212
MonotonicityNot monotonic
2023-12-12T09:25:28.383065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 3
 
13.6%
1 2
 
9.1%
37 1
 
4.5%
3 1
 
4.5%
61 1
 
4.5%
8 1
 
4.5%
63 1
 
4.5%
33 1
 
4.5%
476 1
 
4.5%
6 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
1 2
9.1%
2 3
13.6%
3 1
 
4.5%
5 1
 
4.5%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%
14 1
 
4.5%
22 1
 
4.5%
23 1
 
4.5%
ValueCountFrequency (%)
476 1
4.5%
274 1
4.5%
157 1
4.5%
63 1
4.5%
61 1
4.5%
37 1
4.5%
35 1
4.5%
33 1
4.5%
32 1
4.5%
23 1
4.5%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean607765.91
Minimum1310
Maximum5021950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:25:28.509426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1310
5-th percentile2800.5
Q143300
median252145
Q3378735
95-th percentile2520718.5
Maximum5021950
Range5020640
Interquartile range (IQR)335435

Descriptive statistics

Standard deviation1146746.5
Coefficient of variation (CV)1.8868227
Kurtosis11.04523
Mean607765.91
Median Absolute Deviation (MAD)200985
Skewness3.2065578
Sum13370850
Variance1.3150276 × 1012
MonotonicityNot monotonic
2023-12-12T09:25:28.638626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2340 1
 
4.5%
23040 1
 
4.5%
1310 1
 
4.5%
806570 1
 
4.5%
379450 1
 
4.5%
344390 1
 
4.5%
311930 1
 
4.5%
5021950 1
 
4.5%
35440 1
 
4.5%
66880 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1310 1
4.5%
2340 1
4.5%
11550 1
4.5%
15300 1
4.5%
23040 1
4.5%
35440 1
4.5%
66880 1
4.5%
128510 1
4.5%
182050 1
4.5%
202320 1
4.5%
ValueCountFrequency (%)
5021950 1
4.5%
2580420 1
4.5%
1386390 1
4.5%
806570 1
4.5%
684790 1
4.5%
379450 1
4.5%
376590 1
4.5%
344390 1
4.5%
311930 1
4.5%
305340 1
4.5%

누적미환급금액증감
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.82818
Minimum0
Maximum588.24
Zeros3
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T09:25:28.800757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126.66
median89.15
Q3130.335
95-th percentile547.8505
Maximum588.24
Range588.24
Interquartile range (IQR)103.675

Descriptive statistics

Standard deviation168.47471
Coefficient of variation (CV)1.2779871
Kurtosis3.0771428
Mean131.82818
Median Absolute Deviation (MAD)60.58
Skewness1.9594169
Sum2900.22
Variance28383.728
MonotonicityNot monotonic
2023-12-12T09:25:28.936089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 3
 
13.6%
588.24 1
 
4.5%
32.39 1
 
4.5%
555.0 1
 
4.5%
67.24 1
 
4.5%
412.01 1
 
4.5%
112.14 1
 
4.5%
190.6 1
 
4.5%
95.25 1
 
4.5%
75.97 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
0.0 3
13.6%
3.63 1
 
4.5%
13.63 1
 
4.5%
24.75 1
 
4.5%
32.39 1
 
4.5%
44.92 1
 
4.5%
67.24 1
 
4.5%
75.97 1
 
4.5%
85.8 1
 
4.5%
92.5 1
 
4.5%
ValueCountFrequency (%)
588.24 1
4.5%
555.0 1
4.5%
412.01 1
4.5%
190.6 1
4.5%
162.62 1
4.5%
136.4 1
4.5%
112.14 1
4.5%
106.61 1
4.5%
100.52 1
4.5%
95.25 1
4.5%

Interactions

2023-12-12T09:25:24.962783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:21.898147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.452107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.003343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.550607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.113322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.059040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:21.980875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.533192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.092320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.639275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.209247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.151453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.056598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.615038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.190101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.734355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.280362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.235512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.143778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.727524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.289546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.825092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.365759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.344826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.240219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.813450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.374526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.921097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.449906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:25.473567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.342480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:22.916954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:23.462918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.012182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:24.561876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:25:29.018909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세목명과세년도납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
연번1.0000.0000.9500.0000.0000.0000.0000.0000.144
세목명0.0001.0000.0000.0000.0000.0000.0000.6430.000
과세년도0.9500.0001.0000.0000.0380.0000.2910.0000.338
납세자유형0.0000.0000.0001.0000.2520.0000.1330.0390.000
당해미환급건수0.0000.0000.0380.2521.0000.9690.9980.9890.000
당해미환급금액0.0000.0000.0000.0000.9691.0000.9730.9850.000
누적미환급건수0.0000.0000.2910.1330.9980.9731.0000.9920.000
누적미환급금액0.0000.6430.0000.0390.9890.9850.9921.0000.000
누적미환급금액증감0.1440.0000.3380.0000.0000.0000.0000.0001.000
2023-12-12T09:25:29.131892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형
세목명1.0000.0000.000
과세년도0.0001.0000.000
납세자유형0.0000.0001.000
2023-12-12T09:25:29.254675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명과세년도납세자유형
연번1.0000.0990.0720.1790.1910.1780.0000.7470.000
당해미환급건수0.0991.0000.8730.9570.806-0.0390.0000.0000.269
당해미환급금액0.0720.8731.0000.8520.918-0.2650.0000.0000.000
누적미환급건수0.1790.9570.8521.0000.8900.1480.0000.1950.117
누적미환급금액0.1910.8060.9180.8901.0000.0030.1680.0000.117
누적미환급금액증감0.178-0.039-0.2650.1480.0031.0000.0000.0840.000
세목명0.0000.0000.0000.0000.1680.0001.0000.0000.000
과세년도0.7470.0000.0000.1950.0000.0840.0001.0000.000
납세자유형0.0000.2690.0000.1170.1170.0000.0000.0001.000

Missing values

2023-12-12T09:25:25.602190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:25:25.850821image/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
01경상북도문경시47280등록면허세2019신규개인134022340588.24
12경상북도문경시47280자동차세2019신규개인66746180157138639085.8
23경상북도문경시47280자동차세2019신규법인91051002220232092.5
34경상북도문경시47280재산세2019신규개인56220014128510106.61
45경상북도문경시47280주민세2019신규개인1115501115500.0
56경상북도문경시47280주민세2019신규법인4230400528742024.75
67경상북도문경시47280지방소득세2019신규개인1314340035376590162.62
78경상북도문경시47280등록면허세2020신규법인1153001153000.0
89경상북도문경시47280자동차세2020신규개인1781780560274258042044.92
910경상북도문경시47280자동차세2020신규법인89174023216870136.4
연번시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
1213경상북도문경시47280주민세2020신규개인111490223040100.52
1314경상북도문경시47280지방소득세2020신규개인195172603768479032.39
1415경상북도문경시47280등록면허세2021신규개인4645406668803.63
1516경상북도문경시47280등록면허세2021신규법인12014023544075.97
1617경상북도문경시47280자동차세2021신규개인2192572000476502195095.25
1718경상북도문경시47280자동차세2021신규법인1110734033311930190.6
1819경상북도문경시47280재산세2021신규개인3116234063344390112.14
1920경상북도문경시47280재산세2021신규법인1741108379450412.01
2021경상북도문경시47280지방소득세2021신규개인324822906180657067.24
2122경상북도문경시47280지방소득세2021신규법인220031310555.0