Overview

Dataset statistics

Number of variables13
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory115.0 B

Variable types

Numeric7
Categorical6

Dataset

Description평창군 지방세 징수현황에 대한 데이터로, 과세년도, 세목명, 부과금액, 수납급액, 환급금액, 결손금액, 미수납 금액, 징수율을 제공합니다.(2017~2021)
Author강원도 평창군
URLhttps://www.data.go.kr/data/15080513/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
순번 is highly overall correlated with 과세년도High correlation
부과금액 is highly overall correlated with 수납급액 and 5 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
결손금액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
과세년도 is highly overall correlated with 순번High correlation
세목명 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
순번 has unique valuesUnique
부과금액 has 15 (22.4%) zerosZeros
수납급액 has 15 (22.4%) zerosZeros
환급금액 has 19 (28.4%) zerosZeros
결손금액 has 25 (37.3%) zerosZeros
미수납 금액 has 22 (32.8%) zerosZeros
징수율 has 15 (22.4%) zerosZeros

Reproduction

Analysis started2023-12-12 16:28:37.097812
Analysis finished2023-12-12 16:28:43.635372
Duration6.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T01:28:43.752285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q117.5
median34
Q350.5
95-th percentile63.7
Maximum67
Range66
Interquartile range (IQR)33

Descriptive statistics

Standard deviation19.485037
Coefficient of variation (CV)0.57308932
Kurtosis-1.2
Mean34
Median Absolute Deviation (MAD)17
Skewness0
Sum2278
Variance379.66667
MonotonicityStrictly increasing
2023-12-13T01:28:43.914412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
44 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
45 1
 
1.5%
43 1
 
1.5%
2 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
강원도
67 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원도 67
100.0%

Length

2023-12-13T01:28:44.072091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:28:44.177989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 67
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
평창군
67 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
평창군 67
100.0%

Length

2023-12-13T01:28:44.282645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:28:44.376550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
평창군 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
42760
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42760
2nd row42760
3rd row42760
4th row42760
5th row42760

Common Values

ValueCountFrequency (%)
42760 67
100.0%

Length

2023-12-13T01:28:44.487541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:28:44.608191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
42760 67
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
2017
14 
2018
14 
2019
13 
2020
13 
2021
13 

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 (%)
2017 14
20.9%
2018 14
20.9%
2019 13
19.4%
2020 13
19.4%
2021 13
19.4%

Length

2023-12-13T01:28:44.754623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:28:44.900904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 14
20.9%
2018 14
20.9%
2019 13
19.4%
2020 13
19.4%
2021 13
19.4%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Memory size668.0 B
레저세
5 
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
Other values (9)
42 

Length

Max length7
Median length5
Mean length4.4179104
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도축세
2nd row레저세
3rd row재산세
4th row주민세
5th row취득세

Common Values

ValueCountFrequency (%)
레저세 5
 
7.5%
재산세 5
 
7.5%
주민세 5
 
7.5%
취득세 5
 
7.5%
자동차세 5
 
7.5%
과년도수입 5
 
7.5%
담배소비세 5
 
7.5%
도시계획세 5
 
7.5%
등록면허세 5
 
7.5%
지방교육세 5
 
7.5%
Other values (4) 17
25.4%

Length

2023-12-13T01:28:45.045725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 5
 
7.5%
재산세 5
 
7.5%
주민세 5
 
7.5%
취득세 5
 
7.5%
자동차세 5
 
7.5%
과년도수입 5
 
7.5%
담배소비세 5
 
7.5%
도시계획세 5
 
7.5%
등록면허세 5
 
7.5%
지방교육세 5
 
7.5%
Other values (4) 17
25.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3706016 × 109
Minimum0
Maximum3.8152991 × 1010
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T01:28:45.233066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.2745725 × 109
median3.497365 × 109
Q37.6303145 × 109
95-th percentile3.0526077 × 1010
Maximum3.8152991 × 1010
Range3.8152991 × 1010
Interquartile range (IQR)6.355742 × 109

Descriptive statistics

Standard deviation8.9541292 × 109
Coefficient of variation (CV)1.405539
Kurtosis5.6162423
Mean6.3706016 × 109
Median Absolute Deviation (MAD)3.163386 × 109
Skewness2.4141158
Sum4.268303 × 1011
Variance8.017643 × 1019
MonotonicityNot monotonic
2023-12-13T01:28:45.435607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
5298600000 1
 
1.5%
6660751000 1
 
1.5%
2048805000 1
 
1.5%
14726387000 1
 
1.5%
1283733000 1
 
1.5%
31815248000 1
 
1.5%
5966489000 1
 
1.5%
3473585000 1
 
1.5%
4519051000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
1233901000 1
 
1.5%
1265412000 1
 
1.5%
1283733000 1
 
1.5%
1304697000 1
 
1.5%
1353769000 1
 
1.5%
1650938000 1
 
1.5%
1694205000 1
 
1.5%
1753547000 1
 
1.5%
1861028000 1
 
1.5%
ValueCountFrequency (%)
38152991000 1
1.5%
37850329000 1
1.5%
35471099000 1
1.5%
31815248000 1
1.5%
27518011000 1
1.5%
15435079000 1
1.5%
14726387000 1
1.5%
14524002000 1
1.5%
13860476000 1
1.5%
13233479000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0167449 × 109
Minimum0
Maximum3.7882444 × 1010
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T01:28:45.611207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.187986 × 109
median2.097813 × 109
Q36.8506975 × 109
95-th percentile3.0126335 × 1010
Maximum3.7882444 × 1010
Range3.7882444 × 1010
Interquartile range (IQR)5.6627115 × 109

Descriptive statistics

Standard deviation8.8977842 × 109
Coefficient of variation (CV)1.4788369
Kurtosis5.765244
Mean6.0167449 × 109
Median Absolute Deviation (MAD)2.097813 × 109
Skewness2.454312
Sum4.0312191 × 1011
Variance7.9170563 × 1019
MonotonicityNot monotonic
2023-12-13T01:28:45.825585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
5298600000 1
 
1.5%
6294288000 1
 
1.5%
1968574000 1
 
1.5%
14291484000 1
 
1.5%
1238795000 1
 
1.5%
31445158000 1
 
1.5%
5474921000 1
 
1.5%
1184626000 1
 
1.5%
4519051000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
678489000 1
 
1.5%
1184626000 1
 
1.5%
1191346000 1
 
1.5%
1218928000 1
 
1.5%
1237114000 1
 
1.5%
1238795000 1
 
1.5%
1264057000 1
 
1.5%
1307008000 1
 
1.5%
1407442000 1
 
1.5%
ValueCountFrequency (%)
37882444000 1
1.5%
37501320000 1
1.5%
34821497000 1
1.5%
31445158000 1
1.5%
27049082000 1
1.5%
14993801000 1
1.5%
14291484000 1
1.5%
14043106000 1
1.5%
13388414000 1
1.5%
12786533000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76689955
Minimum0
Maximum9.02462 × 108
Zeros19
Zeros (%)28.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T01:28:46.029421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5467000
Q356485500
95-th percentile4.682149 × 108
Maximum9.02462 × 108
Range9.02462 × 108
Interquartile range (IQR)56485500

Descriptive statistics

Standard deviation1.6585713 × 108
Coefficient of variation (CV)2.1626969
Kurtosis11.214925
Mean76689955
Median Absolute Deviation (MAD)5467000
Skewness3.2268948
Sum5.138227 × 109
Variance2.7508588 × 1016
MonotonicityNot monotonic
2023-12-13T01:28:46.229030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 19
28.4%
554383000 1
 
1.5%
152530000 1
 
1.5%
1500000 1
 
1.5%
4821000 1
 
1.5%
520000 1
 
1.5%
104498000 1
 
1.5%
58318000 1
 
1.5%
627922000 1
 
1.5%
4909000 1
 
1.5%
Other values (39) 39
58.2%
ValueCountFrequency (%)
0 19
28.4%
25000 1
 
1.5%
70000 1
 
1.5%
130000 1
 
1.5%
371000 1
 
1.5%
441000 1
 
1.5%
483000 1
 
1.5%
520000 1
 
1.5%
662000 1
 
1.5%
1357000 1
 
1.5%
ValueCountFrequency (%)
902462000 1
1.5%
627922000 1
1.5%
554383000 1
1.5%
497482000 1
1.5%
399925000 1
1.5%
262980000 1
1.5%
213529000 1
1.5%
176503000 1
1.5%
157890000 1
1.5%
152804000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69774254
Minimum0
Maximum1.288463 × 109
Zeros25
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T01:28:46.408845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median588000
Q311521000
95-th percentile6.049076 × 108
Maximum1.288463 × 109
Range1.288463 × 109
Interquartile range (IQR)11521000

Descriptive statistics

Standard deviation2.1696767 × 108
Coefficient of variation (CV)3.1095663
Kurtosis16.972825
Mean69774254
Median Absolute Deviation (MAD)588000
Skewness3.9687418
Sum4.674875 × 109
Variance4.7074969 × 1016
MonotonicityNot monotonic
2023-12-13T01:28:46.604460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 25
37.3%
37045000 1
 
1.5%
58689000 1
 
1.5%
2289000 1
 
1.5%
2809000 1
 
1.5%
227000 1
 
1.5%
4587000 1
 
1.5%
570112000 1
 
1.5%
226000 1
 
1.5%
1346000 1
 
1.5%
Other values (33) 33
49.3%
ValueCountFrequency (%)
0 25
37.3%
155000 1
 
1.5%
173000 1
 
1.5%
201000 1
 
1.5%
206000 1
 
1.5%
226000 1
 
1.5%
227000 1
 
1.5%
249000 1
 
1.5%
515000 1
 
1.5%
588000 1
 
1.5%
ValueCountFrequency (%)
1288463000 1
1.5%
740102000 1
1.5%
661928000 1
1.5%
619820000 1
1.5%
570112000 1
1.5%
164197000 1
1.5%
162513000 1
1.5%
116367000 1
1.5%
58689000 1
1.5%
41233000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8408245 × 108
Minimum0
Maximum1.800144 × 109
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T01:28:46.765871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median51294000
Q34.293605 × 108
95-th percentile1.4053108 × 109
Maximum1.800144 × 109
Range1.800144 × 109
Interquartile range (IQR)4.293605 × 108

Descriptive statistics

Standard deviation4.3668233 × 108
Coefficient of variation (CV)1.5371676
Kurtosis4.4388177
Mean2.8408245 × 108
Median Absolute Deviation (MAD)51294000
Skewness2.1606866
Sum1.9033524 × 1010
Variance1.9069146 × 1017
MonotonicityNot monotonic
2023-12-13T01:28:46.956805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
215844000 1
 
1.5%
307774000 1
 
1.5%
77942000 1
 
1.5%
432094000 1
 
1.5%
44711000 1
 
1.5%
370090000 1
 
1.5%
486981000 1
 
1.5%
1718847000 1
 
1.5%
6229000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
6229000 1
 
1.5%
6519000 1
 
1.5%
6924000 1
 
1.5%
7572000 1
 
1.5%
11493000 1
 
1.5%
39835000 1
 
1.5%
40485000 1
 
1.5%
41770000 1
 
1.5%
44711000 1
 
1.5%
ValueCountFrequency (%)
1800144000 1
1.5%
1718847000 1
1.5%
1670116000 1
1.5%
1470103000 1
1.5%
1254129000 1
1.5%
1009998000 1
1.5%
640169000 1
1.5%
607576000 1
1.5%
592502000 1
1.5%
583647000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351791
Minimum0
Maximum100
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T01:28:47.156512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q135.96
median96.33
Q397.84
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)61.88

Descriptive statistics

Standard deviation41.532138
Coefficient of variation (CV)0.5903494
Kurtosis-0.89837095
Mean70.351791
Median Absolute Deviation (MAD)3.67
Skewness-1.0013309
Sum4713.57
Variance1724.9185
MonotonicityNot monotonic
2023-12-13T01:28:47.339384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 15
22.4%
100.0 7
 
10.4%
96.33 2
 
3.0%
96.08 2
 
3.0%
96.55 2
 
3.0%
40.24 1
 
1.5%
91.76 1
 
1.5%
96.32 1
 
1.5%
90.02 1
 
1.5%
94.5 1
 
1.5%
Other values (34) 34
50.7%
ValueCountFrequency (%)
0.0 15
22.4%
21.08 1
 
1.5%
34.1 1
 
1.5%
37.82 1
 
1.5%
39.23 1
 
1.5%
40.24 1
 
1.5%
85.69 1
 
1.5%
89.28 1
 
1.5%
89.89 1
 
1.5%
90.02 1
 
1.5%
ValueCountFrequency (%)
100.0 7
10.4%
99.65 1
 
1.5%
99.6 1
 
1.5%
99.59 1
 
1.5%
99.58 1
 
1.5%
99.31 1
 
1.5%
99.29 1
 
1.5%
99.08 1
 
1.5%
98.84 1
 
1.5%
98.3 1
 
1.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
2022-09-21
67 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-21
2nd row2022-09-21
3rd row2022-09-21
4th row2022-09-21
5th row2022-09-21

Common Values

ValueCountFrequency (%)
2022-09-21 67
100.0%

Length

2023-12-13T01:28:47.487918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:28:47.613118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-21 67
100.0%

Interactions

2023-12-13T01:28:42.247245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:37.557023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:38.354893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:39.161024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:39.964349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:40.625629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:41.411107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:42.338048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:37.648490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:38.495389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:39.268214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:40.049109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:40.719946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:41.503661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:42.449744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:37.752612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:38.622914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:39.398445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:40.157978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:40.833066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:41.609364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:42.549759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:37.886063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:38.736196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:39.525989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:40.266075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:40.952782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:41.739475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:42.641276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:37.995013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:38.824832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:39.649136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:40.344294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:41.087038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:41.845875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:42.746690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:38.117341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:38.937955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:39.762912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:40.438258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:41.202203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:41.988246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:42.868580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:38.241364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:39.054199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:39.868698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:40.541245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:41.320577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:28:42.123233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:28:47.690591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
순번1.0000.9980.0000.0000.0000.0000.0000.0000.000
과세년도0.9981.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0000.0001.0000.8470.9600.7460.6740.8300.793
부과금액0.0000.0000.8471.0000.9750.3910.0000.7960.000
수납급액0.0000.0000.9600.9751.0000.5670.0000.6690.127
환급금액0.0000.0000.7460.3910.5671.0000.8330.8160.793
결손금액0.0000.0000.6740.0000.0000.8331.0000.7720.740
미수납 금액0.0000.0000.8300.7960.6690.8160.7721.0000.873
징수율0.0000.0000.7930.0000.1270.7930.7400.8731.000
2023-12-13T01:28:47.851626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-13T01:28:47.948941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
순번1.0000.0970.1160.0930.0160.0210.1840.9030.000
부과금액0.0971.0000.9770.6760.5290.6920.5330.0000.556
수납급액0.1160.9771.0000.5810.4400.5840.6200.0000.663
환급금액0.0930.6760.5811.0000.7710.8720.1950.0000.349
결손금액0.0160.5290.4400.7711.0000.8380.0020.0000.397
미수납 금액0.0210.6920.5840.8720.8381.0000.0430.0000.530
징수율0.1840.5330.6200.1950.0020.0431.0000.0000.509
과세년도0.9030.0000.0000.0000.0000.0000.0001.0000.000
세목명0.0000.5560.6630.3490.3970.5300.5090.0001.000

Missing values

2023-12-13T01:28:43.347465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:28:43.555477image/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

순번시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일자
01강원도평창군427602017도축세000000.02022-09-21
12강원도평창군427602017레저세000000.02022-09-21
23강원도평창군427602017재산세132334790001278653300013570003704500040990100096.622022-09-21
34강원도평창군427602017주민세13046970001264057000700001550004048500096.892022-09-21
45강원도평창군427602017취득세3547109900034821497000497482000943300064016900098.172022-09-21
56강원도평창군427602017자동차세5794887000520875900041696000248100058364700089.892022-09-21
67강원도평창군427602017과년도수입31531710001237114000399925000661928000125412900039.232022-09-21
78강원도평창군427602017담배소비세48692250004869225000000100.02022-09-21
89강원도평창군427602017도시계획세000000.02022-09-21
910강원도평창군427602017등록면허세1650938000164421800012238000201000651900099.592022-09-21
순번시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일자
5758강원도평창군427602021취득세3785032900037501320000608210003426300031474600099.082022-09-21
5859강원도평창군427602021자동차세546486100049877330005210200090400047622400091.272022-09-21
5960강원도평창군427602021과년도수입34973650001407442000262980000619820000147010300040.242022-09-21
6061강원도평창군427602021담배소비세4248529000424852900066200000100.02022-09-21
6162강원도평창군427602021도시계획세000000.02022-09-21
6263강원도평창군427602021등록면허세1943745000193600000082979000173000757200099.62022-09-21
6364강원도평창군427602021지방교육세8883909000864374000035048000437200023579700097.32022-09-21
6465강원도평창군427602021지방소득세7730450000695867700015789000016419700060757600090.022022-09-21
6566강원도평창군427602021지방소비세53641260005364126000000100.02022-09-21
6667강원도평창군427602021지역자원시설세2027890000195344200037100038510007059700096.332022-09-21