Overview

Dataset statistics

Number of variables11
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory98.4 B

Variable types

Categorical5
Numeric6

Dataset

Description경상남도 거창군 지방세 징수현황에 대한 데이터로 과세년도, 세목명, 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액, 징수율 항목을 제공합니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079224

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
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 4 other fieldsHigh correlation
결손금액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
부과금액 has 13 (24.1%) zerosZeros
수납급액 has 13 (24.1%) zerosZeros
환급금액 has 17 (31.5%) zerosZeros
결손금액 has 24 (44.4%) zerosZeros
미수납 금액 has 18 (33.3%) zerosZeros
징수율 has 13 (24.1%) zerosZeros

Reproduction

Analysis started2023-12-11 00:10:26.340468
Analysis finished2023-12-11 00:10:30.296664
Duration3.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size564.0 B
경상남도
54 

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 54
100.0%

Length

2023-12-11T09:10:30.350432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:10:30.428749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 54
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size564.0 B
거창군
54 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거창군 54
100.0%

Length

2023-12-11T09:10:30.508267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:10:30.586550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 54
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size564.0 B
48880
54 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 54
100.0%

Length

2023-12-11T09:10:30.665986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:10:30.744688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 54
100.0%

과세년도
Categorical

Distinct4
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size564.0 B
2017
14 
2018
14 
2019
13 
2020
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
25.9%
2018 14
25.9%
2019 13
24.1%
2020 13
24.1%

Length

2023-12-11T09:10:30.827147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:10:30.913162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 14
25.9%
2018 14
25.9%
2019 13
24.1%
2020 13
24.1%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Memory size564.0 B
레저세
4 
재산세
4 
주민세
4 
취득세
4 
자동차세
4 
Other values (9)
34 

Length

Max length7
Median length5
Mean length4.4074074
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 4
 
7.4%
재산세 4
 
7.4%
주민세 4
 
7.4%
취득세 4
 
7.4%
자동차세 4
 
7.4%
과년도수입 4
 
7.4%
담배소비세 4
 
7.4%
도시계획세 4
 
7.4%
등록면허세 4
 
7.4%
지방교육세 4
 
7.4%
Other values (4) 14
25.9%

Length

2023-12-11T09:10:31.035862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 4
 
7.4%
재산세 4
 
7.4%
주민세 4
 
7.4%
취득세 4
 
7.4%
자동차세 4
 
7.4%
과년도수입 4
 
7.4%
담배소비세 4
 
7.4%
도시계획세 4
 
7.4%
등록면허세 4
 
7.4%
지방교육세 4
 
7.4%
Other values (4) 14
25.9%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0211456 × 109
Minimum0
Maximum1.9181784 × 1010
Zeros13
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T09:10:31.155370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.027435 × 108
median1.505269 × 109
Q35.8050415 × 109
95-th percentile1.4189293 × 1010
Maximum1.9181784 × 1010
Range1.9181784 × 1010
Interquartile range (IQR)5.002298 × 109

Descriptive statistics

Standard deviation4.7386358 × 109
Coefficient of variation (CV)1.1784293
Kurtosis2.1904882
Mean4.0211456 × 109
Median Absolute Deviation (MAD)1.505269 × 109
Skewness1.5634581
Sum2.1714186 × 1011
Variance2.245467 × 1019
MonotonicityNot monotonic
2023-12-11T09:10:31.284930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 13
24.1%
1190666000 1
 
1.9%
9527448000 1
 
1.9%
1607907000 1
 
1.9%
3961047000 1
 
1.9%
1185022000 1
 
1.9%
4813112000 1
 
1.9%
6483608000 1
 
1.9%
913142000 1
 
1.9%
6175053000 1
 
1.9%
Other values (32) 32
59.3%
ValueCountFrequency (%)
0 13
24.1%
765944000 1
 
1.9%
913142000 1
 
1.9%
943961000 1
 
1.9%
945587000 1
 
1.9%
985619000 1
 
1.9%
1091524000 1
 
1.9%
1116565000 1
 
1.9%
1139731000 1
 
1.9%
1185022000 1
 
1.9%
ValueCountFrequency (%)
19181784000 1
1.9%
17574816000 1
1.9%
16491397000 1
1.9%
12949698000 1
1.9%
10771066000 1
1.9%
10564068000 1
1.9%
10168298000 1
1.9%
9527448000 1
1.9%
7175553000 1
1.9%
6527288000 1
1.9%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8771829 × 109
Minimum0
Maximum1.912377 × 1010
Zeros13
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T09:10:31.407898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.121645 × 108
median1.2091295 × 109
Q35.647171 × 109
95-th percentile1.3992335 × 1010
Maximum1.912377 × 1010
Range1.912377 × 1010
Interquartile range (IQR)4.9350065 × 109

Descriptive statistics

Standard deviation4.6949015 × 109
Coefficient of variation (CV)1.2109053
Kurtosis2.4205797
Mean3.8771829 × 109
Median Absolute Deviation (MAD)1.2091295 × 109
Skewness1.6120078
Sum2.0936788 × 1011
Variance2.20421 × 1019
MonotonicityNot monotonic
2023-12-11T09:10:31.531985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 13
24.1%
1156445000 1
 
1.9%
9054232000 1
 
1.9%
775759000 1
 
1.9%
3961047000 1
 
1.9%
1183224000 1
 
1.9%
4644368000 1
 
1.9%
6332823000 1
 
1.9%
873510000 1
 
1.9%
6070159000 1
 
1.9%
Other values (32) 32
59.3%
ValueCountFrequency (%)
0 13
24.1%
704052000 1
 
1.9%
736502000 1
 
1.9%
747765000 1
 
1.9%
775759000 1
 
1.9%
782760000 1
 
1.9%
873510000 1
 
1.9%
914412000 1
 
1.9%
922910000 1
 
1.9%
952049000 1
 
1.9%
ValueCountFrequency (%)
19123770000 1
1.9%
17554453000 1
1.9%
16338236000 1
1.9%
12729157000 1
1.9%
10333887000 1
1.9%
10145380000 1
1.9%
9678424000 1
1.9%
9054232000 1
1.9%
6676372000 1
1.9%
6332823000 1
1.9%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37666370
Minimum0
Maximum2.56045 × 108
Zeros17
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T09:10:31.650792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3181000
Q354876000
95-th percentile1.5932145 × 108
Maximum2.56045 × 108
Range2.56045 × 108
Interquartile range (IQR)54876000

Descriptive statistics

Standard deviation61839321
Coefficient of variation (CV)1.6417648
Kurtosis3.3684275
Mean37666370
Median Absolute Deviation (MAD)3181000
Skewness1.9340049
Sum2.033984 × 109
Variance3.8241017 × 1015
MonotonicityNot monotonic
2023-12-11T09:10:31.778936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 17
31.5%
2620000 1
 
1.9%
93405000 1
 
1.9%
150471000 1
 
1.9%
16360000 1
 
1.9%
30352000 1
 
1.9%
125128000 1
 
1.9%
424000 1
 
1.9%
1955000 1
 
1.9%
53052000 1
 
1.9%
Other values (28) 28
51.9%
ValueCountFrequency (%)
0 17
31.5%
349000 1
 
1.9%
424000 1
 
1.9%
460000 1
 
1.9%
490000 1
 
1.9%
879000 1
 
1.9%
1783000 1
 
1.9%
1955000 1
 
1.9%
2620000 1
 
1.9%
2991000 1
 
1.9%
ValueCountFrequency (%)
256045000 1
1.9%
230477000 1
1.9%
175758000 1
1.9%
150471000 1
1.9%
150027000 1
1.9%
125128000 1
1.9%
100316000 1
1.9%
100201000 1
1.9%
100099000 1
1.9%
95938000 1
1.9%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22524019
Minimum0
Maximum2.61875 × 108
Zeros24
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T09:10:31.902380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median182500
Q31649000
95-th percentile1.8913875 × 108
Maximum2.61875 × 108
Range2.61875 × 108
Interquartile range (IQR)1649000

Descriptive statistics

Standard deviation62863981
Coefficient of variation (CV)2.7909754
Kurtosis7.4750996
Mean22524019
Median Absolute Deviation (MAD)182500
Skewness2.9213277
Sum1.216297 × 109
Variance3.9518801 × 1015
MonotonicityNot monotonic
2023-12-11T09:10:32.025753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 24
44.4%
874000 1
 
1.9%
146693000 1
 
1.9%
944000 1
 
1.9%
42000 1
 
1.9%
225009000 1
 
1.9%
2974000 1
 
1.9%
505000 1
 
1.9%
442000 1
 
1.9%
37706000 1
 
1.9%
Other values (21) 21
38.9%
ValueCountFrequency (%)
0 24
44.4%
42000 1
 
1.9%
73000 1
 
1.9%
96000 1
 
1.9%
269000 1
 
1.9%
401000 1
 
1.9%
432000 1
 
1.9%
442000 1
 
1.9%
505000 1
 
1.9%
687000 1
 
1.9%
ValueCountFrequency (%)
261875000 1
1.9%
238236000 1
1.9%
225009000 1
1.9%
169824000 1
1.9%
146693000 1
1.9%
93824000 1
1.9%
37706000 1
1.9%
15088000 1
1.9%
5194000 1
1.9%
3470000 1
1.9%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2143865 × 108
Minimum0
Maximum5.70273 × 108
Zeros18
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T09:10:32.160507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31186000
Q31.5230575 × 108
95-th percentile5.062158 × 108
Maximum5.70273 × 108
Range5.70273 × 108
Interquartile range (IQR)1.5230575 × 108

Descriptive statistics

Standard deviation1.7527234 × 108
Coefficient of variation (CV)1.4432995
Kurtosis1.0094395
Mean1.2143865 × 108
Median Absolute Deviation (MAD)31186000
Skewness1.5169365
Sum6.557687 × 109
Variance3.0720392 × 1016
MonotonicityNot monotonic
2023-12-11T09:10:32.313823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 18
33.3%
104452000 1
 
1.9%
126717000 1
 
1.9%
471012000 1
 
1.9%
570273000 1
 
1.9%
1725000 1
 
1.9%
163550000 1
 
1.9%
113079000 1
 
1.9%
39632000 1
 
1.9%
33716000 1
 
1.9%
Other values (27) 27
50.0%
ValueCountFrequency (%)
0 18
33.3%
1498000 1
 
1.9%
1714000 1
 
1.9%
1725000 1
 
1.9%
2080000 1
 
1.9%
17445000 1
 
1.9%
20363000 1
 
1.9%
21051000 1
 
1.9%
27657000 1
 
1.9%
29595000 1
 
1.9%
ValueCountFrequency (%)
570273000 1
1.9%
567069000 1
1.9%
539670000 1
1.9%
488202000 1
1.9%
486378000 1
1.9%
471012000 1
1.9%
434205000 1
1.9%
416473000 1
1.9%
352488000 1
1.9%
240602000 1
1.9%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.640185
Minimum0
Maximum100
Zeros13
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T09:10:32.482060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147.3575
median96.645
Q398.22
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)50.8625

Descriptive statistics

Standard deviation42.103644
Coefficient of variation (CV)0.59602963
Kurtosis-0.85776535
Mean70.640185
Median Absolute Deviation (MAD)3.19
Skewness-1.0176547
Sum3814.57
Variance1772.7168
MonotonicityNot monotonic
2023-12-11T09:10:32.615258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 13
24.1%
100.0 5
 
9.3%
98.3 2
 
3.7%
99.88 2
 
3.7%
95.66 1
 
1.9%
95.03 1
 
1.9%
48.25 1
 
1.9%
99.85 1
 
1.9%
96.49 1
 
1.9%
97.67 1
 
1.9%
Other values (26) 26
48.1%
ValueCountFrequency (%)
0.0 13
24.1%
47.06 1
 
1.9%
48.25 1
 
1.9%
48.63 1
 
1.9%
54.4 1
 
1.9%
93.04 1
 
1.9%
95.03 1
 
1.9%
95.18 1
 
1.9%
95.66 1
 
1.9%
95.72 1
 
1.9%
ValueCountFrequency (%)
100.0 5
9.3%
99.88 2
 
3.7%
99.85 1
 
1.9%
99.84 1
 
1.9%
99.83 1
 
1.9%
99.7 1
 
1.9%
99.07 1
 
1.9%
98.3 2
 
3.7%
97.98 1
 
1.9%
97.77 1
 
1.9%

Interactions

2023-12-11T09:10:29.531949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:26.632541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:27.166700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:27.773997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:28.264791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:28.741542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:29.614932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:26.736762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:27.310077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:27.863451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:28.356394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:29.121329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:29.716789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:26.823023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:27.406539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:27.957502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:28.442561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:29.206283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:29.790796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:26.909223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:27.483350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:28.037433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:28.514650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:29.293134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:29.876688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:26.998482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:27.567239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:28.115884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:28.591261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:29.366840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:29.956355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:27.075433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:27.662835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:28.188190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:28.664514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:10:29.457766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:10:32.699592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8580.8360.8610.5630.8130.882
부과금액0.0000.8581.0001.0000.8560.5530.8630.116
수납급액0.0000.8361.0001.0000.8500.5770.8760.116
환급금액0.0000.8610.8560.8501.0000.8860.9150.898
결손금액0.0000.5630.5530.5770.8861.0000.8040.863
미수납 금액0.0000.8130.8630.8760.9150.8041.0000.932
징수율0.0000.8820.1160.1160.8980.8630.9321.000
2023-12-11T09:10:32.825532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-11T09:10:32.920558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9710.7280.5020.6820.5680.0000.564
수납급액0.9711.0000.6330.4000.5780.6470.0000.529
환급금액0.7280.6331.0000.7910.9020.2110.0000.569
결손금액0.5020.4000.7911.0000.8650.0120.0000.213
미수납 금액0.6820.5780.9020.8651.0000.0690.0000.496
징수율0.5680.6470.2110.0120.0691.0000.0000.647
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.5640.5290.5690.2130.4960.6470.0001.000

Missing values

2023-12-11T09:10:30.081311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:10:30.244454image/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

시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
0경상남도거창군488802017도축세000000.0
1경상남도거창군488802017레저세000000.0
2경상남도거창군488802017재산세50944200004967990000300700087400012555600097.52
3경상남도거창군488802017주민세98561900095204900045630007930003277700096.59
4경상남도거창군488802017취득세164913970001633823600055811000015316100099.07
5경상남도거창군488802017자동차세105640680001014538000083372000221500041647300096.04
6경상남도거창군488802017과년도수입143896200078276000015002700016982400048637800054.4
7경상남도거창군488802017담배소비세44301210004430121000000100.0
8경상남도거창군488802017도시계획세000000.0
9경상남도거창군488802017등록면허세11165650001114755000365100096000171400099.84
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
44경상남도거창군488802020취득세17574816000175544530005305200002036300099.88
45경상남도거창군488802020자동차세107710660001033388700095938000297400043420500095.94
46경상남도거창군488802020과년도수입149613000070405200025604500022500900056706900047.06
47경상남도거창군488802020담배소비세41386940004138694000484400000100.0
48경상남도거창군488802020도시계획세000000.0
49경상남도거창군488802020등록면허세12834500001281328000374800042000208000099.83
50경상남도거창군488802020지방교육세537754900052330570003092100094400014354800097.31
51경상남도거창군488802020지방소득세7175553000667637200023047700014669300035248800093.04
52경상남도거창군488802020지방소비세53766000005376600000000100.0
53경상남도거창군488802020지역자원시설세94396100092291000049000002105100097.77