Overview

Dataset statistics

Number of variables11
Number of observations80
Missing cells16
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory97.7 B

Variable types

Categorical4
Numeric7

Dataset

Description2017년~2022년 통영시 지방세 징수 현황에 대한 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액, 징수율에 대한 정보
Author경상남도 통영시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15078234

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액(원) is highly overall correlated with 수납급액(원) and 3 other fieldsHigh correlation
수납급액(원) is highly overall correlated with 부과금액(원) and 2 other fieldsHigh correlation
환급금액(원) is highly overall correlated with 부과금액(원) and 4 other fieldsHigh correlation
결손금액(원) is highly overall correlated with 환급금액(원) and 2 other fieldsHigh correlation
미수납 금액(원) is highly overall correlated with 부과금액(원) and 4 other fieldsHigh correlation
징수율 is highly overall correlated with 환급금액(원) and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액(원) and 2 other fieldsHigh correlation
징수율 has 16 (20.0%) missing valuesMissing
부과금액(원) has 16 (20.0%) zerosZeros
수납급액(원) has 16 (20.0%) zerosZeros
환급금액(원) has 22 (27.5%) zerosZeros
결손금액(원) has 33 (41.2%) zerosZeros
미수납 금액(원) has 28 (35.0%) zerosZeros

Reproduction

Analysis started2024-04-20 18:37:28.723256
Analysis finished2024-04-20 18:37:34.101631
Duration5.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
경상남도
80 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 80
100.0%

Length

2024-04-21T03:37:34.169196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:37:34.257677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 80
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
통영시
80 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통영시 80
100.0%

Length

2024-04-21T03:37:34.337308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:37:34.413131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
48220
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48220
2nd row48220
3rd row48220
4th row48220
5th row48220

Common Values

ValueCountFrequency (%)
48220 80
100.0%

Length

2024-04-21T03:37:34.492690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:37:34.586145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48220 80
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.45
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T03:37:34.676103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7276603
Coefficient of variation (CV)0.00085551031
Kurtosis-1.2889692
Mean2019.45
Median Absolute Deviation (MAD)1.5
Skewness0.041238905
Sum161556
Variance2.9848101
MonotonicityIncreasing
2024-04-21T03:37:34.769100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2022 13
16.2%
2021 13
16.2%
2020 13
16.2%
2019 13
16.2%
2018 14
17.5%
2017 14
17.5%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
레저세
6 
재산세
6 
주민세
6 
취득세
6 
자동차세
6 
Other values (9)
50 

Length

Max length7
Median length5
Mean length4.425
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
과년도수입 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
등록면허세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

Length

2024-04-21T03:37:34.872472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
과년도수입 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
등록면허세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

부과금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5031134 × 109
Minimum0
Maximum4.2969276 × 1010
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T03:37:34.986592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.2645965 × 109
median6.858324 × 109
Q31.4789303 × 1010
95-th percentile2.6996129 × 1010
Maximum4.2969276 × 1010
Range4.2969276 × 1010
Interquartile range (IQR)1.2524706 × 1010

Descriptive statistics

Standard deviation9.0995953 × 109
Coefficient of variation (CV)0.95753834
Kurtosis1.3202969
Mean9.5031134 × 109
Median Absolute Deviation (MAD)6.7087035 × 109
Skewness1.116143
Sum7.6024907 × 1011
Variance8.2802635 × 1019
MonotonicityNot monotonic
2024-04-21T03:37:35.100811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
12187396000 1
 
1.2%
4681433000 1
 
1.2%
10744373000 1
 
1.2%
3244778000 1
 
1.2%
12146918000 1
 
1.2%
13014996000 1
 
1.2%
5430000000 1
 
1.2%
5056720000 1
 
1.2%
20632500000 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
0 16
20.0%
67432000 1
 
1.2%
2011936000 1
 
1.2%
2113367000 1
 
1.2%
2251581000 1
 
1.2%
2268935000 1
 
1.2%
2280887000 1
 
1.2%
2492014000 1
 
1.2%
2927221000 1
 
1.2%
3046957000 1
 
1.2%
ValueCountFrequency (%)
42969276000 1
1.2%
31771634000 1
1.2%
29599210000 1
1.2%
28038509000 1
1.2%
26941267000 1
1.2%
25958307000 1
1.2%
20915098000 1
1.2%
20802206000 1
1.2%
20632500000 1
1.2%
20530854000 1
1.2%

수납급액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7610439 × 109
Minimum-1.656383 × 109
Maximum4.2604895 × 1010
Zeros16
Zeros (%)20.0%
Negative2
Negative (%)2.5%
Memory size852.0 B
2024-04-21T03:37:35.218420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.656383 × 109
5-th percentile0
Q11.8462615 × 109
median4.817145 × 109
Q31.3635259 × 1010
95-th percentile2.6952327 × 1010
Maximum4.2604895 × 1010
Range4.4261278 × 1010
Interquartile range (IQR)1.1788998 × 1010

Descriptive statistics

Standard deviation9.0388736 × 109
Coefficient of variation (CV)1.0317119
Kurtosis1.6354149
Mean8.7610439 × 109
Median Absolute Deviation (MAD)4.817145 × 109
Skewness1.2202601
Sum7.0088351 × 1011
Variance8.1701236 × 1019
MonotonicityNot monotonic
2024-04-21T03:37:35.334130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
11665586000 1
 
1.2%
-647211000 1
 
1.2%
10744373000 1
 
1.2%
3230464000 1
 
1.2%
11662934000 1
 
1.2%
12156340000 1
 
1.2%
5430000000 1
 
1.2%
4937289000 1
 
1.2%
18648898000 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
-1656383000 1
 
1.2%
-647211000 1
 
1.2%
0 16
20.0%
67432000 1
 
1.2%
1668033000 1
 
1.2%
1905671000 1
 
1.2%
1964934000 1
 
1.2%
2004518000 1
 
1.2%
2101894000 1
 
1.2%
2124920000 1
 
1.2%
ValueCountFrequency (%)
42604895000 1
1.2%
31607361000 1
1.2%
29418802000 1
1.2%
27941977000 1
1.2%
26900240000 1
1.2%
25613451000 1
1.2%
19997351000 1
1.2%
19595154000 1
1.2%
18881854000 1
1.2%
18648898000 1
1.2%

환급금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.196718 × 108
Minimum0
Maximum4.388212 × 109
Zeros22
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T03:37:35.457213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8704000
Q376838250
95-th percentile1.1933693 × 109
Maximum4.388212 × 109
Range4.388212 × 109
Interquartile range (IQR)76838250

Descriptive statistics

Standard deviation7.0080875 × 108
Coefficient of variation (CV)3.1902536
Kurtosis26.300176
Mean2.196718 × 108
Median Absolute Deviation (MAD)8704000
Skewness4.963295
Sum1.7573744 × 1010
Variance4.9113291 × 1017
MonotonicityNot monotonic
2024-04-21T03:37:35.722134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
27.5%
2254000 1
 
1.2%
97913000 1
 
1.2%
4042504000 1
 
1.2%
11910000 1
 
1.2%
26979000 1
 
1.2%
45483000 1
 
1.2%
473329000 1
 
1.2%
2082000 1
 
1.2%
12773000 1
 
1.2%
Other values (49) 49
61.3%
ValueCountFrequency (%)
0 22
27.5%
10000 1
 
1.2%
66000 1
 
1.2%
77000 1
 
1.2%
120000 1
 
1.2%
405000 1
 
1.2%
593000 1
 
1.2%
658000 1
 
1.2%
1605000 1
 
1.2%
1618000 1
 
1.2%
ValueCountFrequency (%)
4388212000 1
1.2%
4042504000 1
1.2%
1392074000 1
1.2%
1226169000 1
1.2%
1191643000 1
1.2%
702418000 1
1.2%
629507000 1
1.2%
618598000 1
1.2%
506665000 1
1.2%
473329000 1
1.2%

결손금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97656250
Minimum0
Maximum1.36707 × 109
Zeros33
Zeros (%)41.2%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T03:37:35.828693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median173000
Q32377750
95-th percentile1.0712415 × 109
Maximum1.36707 × 109
Range1.36707 × 109
Interquartile range (IQR)2377750

Descriptive statistics

Standard deviation3.1366786 × 108
Coefficient of variation (CV)3.2119589
Kurtosis10.04223
Mean97656250
Median Absolute Deviation (MAD)173000
Skewness3.3695073
Sum7.8125 × 109
Variance9.8387528 × 1016
MonotonicityNot monotonic
2024-04-21T03:37:35.946358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 33
41.2%
4000 1
 
1.2%
577000 1
 
1.2%
1290968000 1
 
1.2%
265000 1
 
1.2%
171000 1
 
1.2%
41433000 1
 
1.2%
12073000 1
 
1.2%
715000 1
 
1.2%
419000 1
 
1.2%
Other values (38) 38
47.5%
ValueCountFrequency (%)
0 33
41.2%
4000 1
 
1.2%
23000 1
 
1.2%
48000 1
 
1.2%
114000 1
 
1.2%
159000 1
 
1.2%
160000 1
 
1.2%
171000 1
 
1.2%
175000 1
 
1.2%
227000 1
 
1.2%
ValueCountFrequency (%)
1367070000 1
1.2%
1290968000 1
1.2%
1289082000 1
1.2%
1223840000 1
1.2%
1063210000 1
1.2%
769705000 1
1.2%
228761000 1
1.2%
189756000 1
1.2%
119682000 1
1.2%
100855000 1
1.2%

미수납 금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3529875 × 108
Minimum0
Maximum4.037676 × 109
Zeros28
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T03:37:36.104205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.16136 × 108
Q37.9831775 × 108
95-th percentile3.6679247 × 109
Maximum4.037676 × 109
Range4.037676 × 109
Interquartile range (IQR)7.9831775 × 108

Descriptive statistics

Standard deviation1.052908 × 109
Coefficient of variation (CV)1.6573431
Kurtosis4.0790774
Mean6.3529875 × 108
Median Absolute Deviation (MAD)1.16136 × 108
Skewness2.1805201
Sum5.08239 × 1010
Variance1.1086152 × 1018
MonotonicityNot monotonic
2024-04-21T03:37:36.261266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
35.0%
12962000 1
 
1.2%
1273697000 1
 
1.2%
4037676000 1
 
1.2%
14049000 1
 
1.2%
483813000 1
 
1.2%
817223000 1
 
1.2%
119431000 1
 
1.2%
1971529000 1
 
1.2%
108134000 1
 
1.2%
Other values (43) 43
53.8%
ValueCountFrequency (%)
0 28
35.0%
10985000 1
 
1.2%
12962000 1
 
1.2%
14049000 1
 
1.2%
15649000 1
 
1.2%
17070000 1
 
1.2%
41027000 1
 
1.2%
95315000 1
 
1.2%
96532000 1
 
1.2%
100685000 1
 
1.2%
ValueCountFrequency (%)
4037676000 1
1.2%
4006062000 1
1.2%
3888545000 1
1.2%
3815264000 1
1.2%
3660170000 1
1.2%
3486709000 1
1.2%
2160269000 1
1.2%
2027721000 1
1.2%
1971529000 1
1.2%
1474861000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)29.7%
Missing16
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean88.265625
Minimum-50
Maximum100
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)2.5%
Memory size852.0 B
2024-04-21T03:37:36.360196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-50
5-th percentile31.4
Q192
median95.5
Q3100
95-th percentile100
Maximum100
Range150
Interquartile range (IQR)8

Descriptive statistics

Standard deviation26.707256
Coefficient of variation (CV)0.30257822
Kurtosis14.382325
Mean88.265625
Median Absolute Deviation (MAD)3.5
Skewness-3.6894397
Sum5649
Variance713.27753
MonotonicityNot monotonic
2024-04-21T03:37:36.452575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
100 17
21.2%
95 9
11.2%
96 6
 
7.5%
99 5
 
6.2%
90 4
 
5.0%
93 4
 
5.0%
92 4
 
5.0%
98 2
 
2.5%
89 2
 
2.5%
97 2
 
2.5%
Other values (9) 9
11.2%
(Missing) 16
20.0%
ValueCountFrequency (%)
-50 1
 
1.2%
-14 1
 
1.2%
24 1
 
1.2%
29 1
 
1.2%
45 1
 
1.2%
48 1
 
1.2%
88 1
 
1.2%
89 2
2.5%
90 4
5.0%
91 1
 
1.2%
ValueCountFrequency (%)
100 17
21.2%
99 5
 
6.2%
98 2
 
2.5%
97 2
 
2.5%
96 6
 
7.5%
95 9
11.2%
94 1
 
1.2%
93 4
 
5.0%
92 4
 
5.0%
91 1
 
1.2%

Interactions

2024-04-21T03:37:33.345306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.008068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.669846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.204464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.691683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.164097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.671443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:33.428443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.122700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.735906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.288015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.756805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.248045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.743600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:33.509819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.295544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.795813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.353875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.817607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.314264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.811900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:33.577857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.359540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.855541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.413816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.878357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.379259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.879117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:33.641609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.432328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.931799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.475652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.938488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.446039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.955604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:33.717487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.510463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.020947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.549535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.009683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.521248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:33.031254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:33.794520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:30.592927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.116154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:31.624171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.083822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:32.597844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:33.262114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T03:37:36.527003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액(원)수납급액(원)환급금액(원)결손금액(원)미수납 금액(원)징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9010.9050.7080.5990.8340.434
부과금액(원)0.0000.9011.0000.9870.0000.0000.7590.000
수납급액(원)0.0000.9050.9871.0000.0000.0000.6690.000
환급금액(원)0.0000.7080.0000.0001.0000.9000.7200.891
결손금액(원)0.0000.5990.0000.0000.9001.0000.6610.982
미수납 금액(원)0.0000.8340.7590.6690.7200.6611.0000.772
징수율0.0000.4340.0000.0000.8910.9820.7721.000
2024-04-21T03:37:36.627532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액(원)수납급액(원)환급금액(원)결손금액(원)미수납 금액(원)징수율세목명
과세년도1.0000.0880.0850.0440.0670.0550.0720.000
부과금액(원)0.0881.0000.9650.6590.3980.602-0.0530.664
수납급액(원)0.0850.9651.0000.5170.2700.4630.0890.671
환급금액(원)0.0440.6590.5171.0000.7680.821-0.5390.436
결손금액(원)0.0670.3980.2700.7681.0000.843-0.8090.324
미수납 금액(원)0.0550.6020.4630.8210.8431.000-0.8770.542
징수율0.072-0.0530.089-0.539-0.809-0.8771.0000.166
세목명0.0000.6640.6710.4360.3240.5420.1661.000

Missing values

2024-04-21T03:37:33.894034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T03:37:34.034434image/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

시도명시군구명자치단체코드과세년도세목명부과금액(원)수납급액(원)환급금액(원)결손금액(원)미수납 금액(원)징수율
0경상남도통영시482202017도축세00000<NA>
1경상남도통영시482202017레저세00000<NA>
2경상남도통영시482202017재산세188425720001827557100022540002300056697800097
3경상남도통영시482202017주민세249201400023716570003547000250420009531500095
4경상남도통영시482202017취득세295992100002941880200056325000018040800099
5경상남도통영시482202017자동차세1682029100015400642000786810001705000141794400092
6경상남도통영시482202017과년도수입6818713000196493400012261690001367070000348670900029
7경상남도통영시482202017담배소비세1210692700012106927000000100
8경상남도통영시482202017도시계획세00000<NA>
9경상남도통영시482202017등록면허세29272210002916188000158630004800010985000100
시도명시군구명자치단체코드과세년도세목명부과금액(원)수납급액(원)환급금액(원)결손금액(원)미수납 금액(원)징수율
70경상남도통영시482202022취득세269412670002690024000053996000041027000100
71경상남도통영시482202022자동차세1473857500013603489000920780003947000113113900092
72경상남도통영시482202022과년도수입85644950004134620000702418000769705000366017000048
73경상남도통영시482202022담배소비세10627151000106271510001000000100
74경상남도통영시482202022도시계획세00000<NA>
75경상남도통영시482202022등록면허세33613640003345462000865300025300015649000100
76경상남도통영시482202022지방교육세121567010001165925900030186000188100049556100096
77경상남도통영시482202022지방소득세154813320001466143600061859800018975600063014000095
78경상남도통영시482202022지방소비세1086842300010868423000000100
79경상남도통영시482202022지역자원시설세483150100043400100001605000261700048887400090