Overview

Dataset statistics

Number of variables11
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory99.2 B

Variable types

Categorical5
Numeric6

Dataset

Description산청군_지방세 징수현황(과세년도, 세목명, 부과금액, 수납급액, 환급금액, 결손금액, 미수납 금액, 징수율 등) 자료입니다.
Author경상남도 산청군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15078825

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 5 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
결손금액 is highly overall correlated with 부과금액 and 3 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
세목명 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
부과금액 has 11 (26.8%) zerosZeros
수납급액 has 11 (26.8%) zerosZeros
환급금액 has 13 (31.7%) zerosZeros
결손금액 has 15 (36.6%) zerosZeros
미수납 금액 has 14 (34.1%) zerosZeros
징수율 has 11 (26.8%) zerosZeros

Reproduction

Analysis started2023-12-11 00:13:13.928018
Analysis finished2023-12-11 00:13:17.665238
Duration3.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
경상남도
41 

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 41
100.0%

Length

2023-12-11T09:13:17.730996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:13:17.860175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 41
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
산청군
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
산청군 41
100.0%

Length

2023-12-11T09:13:17.984023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:13:18.125041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산청군 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
48860
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48860
2nd row48860
3rd row48860
4th row48860
5th row48860

Common Values

ValueCountFrequency (%)
48860 41
100.0%

Length

2023-12-11T09:13:18.257831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:13:18.383883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48860 41
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
2017
14 
2018
14 
2019
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
34.1%
2018 14
34.1%
2019 13
31.7%

Length

2023-12-11T09:13:18.511894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:13:18.692572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 14
34.1%
2018 14
34.1%
2019 13
31.7%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Memory size460.0 B
레저세
3 
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
Other values (9)
26 

Length

Max length7
Median length5
Mean length4.3902439
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 3
 
7.3%
재산세 3
 
7.3%
주민세 3
 
7.3%
취득세 3
 
7.3%
자동차세 3
 
7.3%
과년도수입 3
 
7.3%
담배소비세 3
 
7.3%
도시계획세 3
 
7.3%
등록면허세 3
 
7.3%
지방교육세 3
 
7.3%
Other values (4) 11
26.8%

Length

2023-12-11T09:13:18.871594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 3
 
7.3%
재산세 3
 
7.3%
주민세 3
 
7.3%
취득세 3
 
7.3%
자동차세 3
 
7.3%
과년도수입 3
 
7.3%
담배소비세 3
 
7.3%
도시계획세 3
 
7.3%
등록면허세 3
 
7.3%
지방교육세 3
 
7.3%
Other values (4) 11
26.8%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5674345 × 109
Minimum0
Maximum1.120733 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T09:13:19.037278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.106272 × 109
Q33.510788 × 109
95-th percentile1.0015464 × 1010
Maximum1.120733 × 1010
Range1.120733 × 1010
Interquartile range (IQR)3.510788 × 109

Descriptive statistics

Standard deviation3.0838308 × 109
Coefficient of variation (CV)1.2011332
Kurtosis1.3471679
Mean2.5674345 × 109
Median Absolute Deviation (MAD)1.106272 × 109
Skewness1.4443402
Sum1.0526481 × 1011
Variance9.5100124 × 1018
MonotonicityNot monotonic
2023-12-11T09:13:19.184269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
3208121000 1
 
2.4%
720255000 1
 
2.4%
4375830000 1
 
2.4%
3298894000 1
 
2.4%
1106272000 1
 
2.4%
2368631000 1
 
2.4%
1128238000 1
 
2.4%
8132639000 1
 
2.4%
10108621000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
558378000 1
 
2.4%
618253000 1
 
2.4%
627656000 1
 
2.4%
638786000 1
 
2.4%
645616000 1
 
2.4%
720255000 1
 
2.4%
966211000 1
 
2.4%
968160000 1
 
2.4%
1009382000 1
 
2.4%
ValueCountFrequency (%)
11207330000 1
2.4%
10108621000 1
2.4%
10015464000 1
2.4%
8132639000 1
2.4%
7585691000 1
2.4%
6016978000 1
2.4%
5064357000 1
2.4%
4543925000 1
2.4%
4375830000 1
2.4%
3826541000 1
2.4%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4275052 × 109
Minimum0
Maximum1.0998948 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T09:13:19.370753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9.63301 × 108
Q33.342315 × 109
95-th percentile9.937327 × 109
Maximum1.0998948 × 1010
Range1.0998948 × 1010
Interquartile range (IQR)3.342315 × 109

Descriptive statistics

Standard deviation3.0385512 × 109
Coefficient of variation (CV)1.2517177
Kurtosis1.4904635
Mean2.4275052 × 109
Median Absolute Deviation (MAD)9.63301 × 108
Skewness1.4866083
Sum9.9527714 × 1010
Variance9.2327933 × 1018
MonotonicityNot monotonic
2023-12-11T09:13:19.539649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
3102205000 1
 
2.4%
696579000 1
 
2.4%
4122435000 1
 
2.4%
3176859000 1
 
2.4%
1103160000 1
 
2.4%
2368631000 1
 
2.4%
440981000 1
 
2.4%
7825448000 1
 
2.4%
9970617000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
333681000 1
 
2.4%
376674000 1
 
2.4%
440981000 1
 
2.4%
536730000 1
 
2.4%
601459000 1
 
2.4%
605171000 1
 
2.4%
609341000 1
 
2.4%
622868000 1
 
2.4%
696579000 1
 
2.4%
ValueCountFrequency (%)
10998948000 1
2.4%
9970617000 1
2.4%
9937327000 1
2.4%
7825448000 1
2.4%
7229949000 1
2.4%
5662103000 1
2.4%
4862695000 1
2.4%
4306494000 1
2.4%
4122435000 1
2.4%
3681367000 1
2.4%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40834537
Minimum0
Maximum3.23954 × 108
Zeros13
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T09:13:19.674264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1209000
Q330208000
95-th percentile2.7164 × 108
Maximum3.23954 × 108
Range3.23954 × 108
Interquartile range (IQR)30208000

Descriptive statistics

Standard deviation86096767
Coefficient of variation (CV)2.1084301
Kurtosis5.0611942
Mean40834537
Median Absolute Deviation (MAD)1209000
Skewness2.4721684
Sum1.674216 × 109
Variance7.4126534 × 1015
MonotonicityNot monotonic
2023-12-11T09:13:19.822330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 13
31.7%
1209000 1
 
2.4%
36000 1
 
2.4%
271640000 1
 
2.4%
11198000 1
 
2.4%
4136000 1
 
2.4%
301280000 1
 
2.4%
30208000 1
 
2.4%
37933000 1
 
2.4%
1556000 1
 
2.4%
Other values (19) 19
46.3%
ValueCountFrequency (%)
0 13
31.7%
5000 1
 
2.4%
9000 1
 
2.4%
30000 1
 
2.4%
36000 1
 
2.4%
51000 1
 
2.4%
694000 1
 
2.4%
818000 1
 
2.4%
1209000 1
 
2.4%
1556000 1
 
2.4%
ValueCountFrequency (%)
323954000 1
2.4%
301280000 1
2.4%
271640000 1
2.4%
244512000 1
2.4%
120014000 1
2.4%
91360000 1
2.4%
69549000 1
2.4%
54553000 1
2.4%
37933000 1
2.4%
32789000 1
2.4%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42376585
Minimum0
Maximum8.36717 × 108
Zeros15
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T09:13:20.033682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median803000
Q37956000
95-th percentile1.71307 × 108
Maximum8.36717 × 108
Range8.36717 × 108
Interquartile range (IQR)7956000

Descriptive statistics

Standard deviation1.3542179 × 108
Coefficient of variation (CV)3.1956748
Kurtosis31.218441
Mean42376585
Median Absolute Deviation (MAD)803000
Skewness5.3423154
Sum1.73744 × 109
Variance1.833906 × 1016
MonotonicityNot monotonic
2023-12-11T09:13:20.475915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 15
36.6%
1592000 1
 
2.4%
871000 1
 
2.4%
73208000 1
 
2.4%
7956000 1
 
2.4%
65000 1
 
2.4%
179332000 1
 
2.4%
4372000 1
 
2.4%
80647000 1
 
2.4%
803000 1
 
2.4%
Other values (17) 17
41.5%
ValueCountFrequency (%)
0 15
36.6%
56000 1
 
2.4%
65000 1
 
2.4%
83000 1
 
2.4%
247000 1
 
2.4%
466000 1
 
2.4%
803000 1
 
2.4%
871000 1
 
2.4%
1592000 1
 
2.4%
2149000 1
 
2.4%
ValueCountFrequency (%)
836717000 1
2.4%
179332000 1
2.4%
171307000 1
2.4%
138490000 1
2.4%
88633000 1
2.4%
80647000 1
2.4%
73208000 1
2.4%
55829000 1
2.4%
55553000 1
2.4%
16679000 1
2.4%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97552659
Minimum0
Maximum5.07925 × 108
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T09:13:20.624891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21682000
Q31.42843 × 108
95-th percentile4.43871 × 108
Maximum5.07925 × 108
Range5.07925 × 108
Interquartile range (IQR)1.42843 × 108

Descriptive statistics

Standard deviation1.4560823 × 108
Coefficient of variation (CV)1.4926116
Kurtosis2.0688907
Mean97552659
Median Absolute Deviation (MAD)21682000
Skewness1.7208348
Sum3.999659 × 109
Variance2.1201758 × 1016
MonotonicityNot monotonic
2023-12-11T09:13:20.783779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
2910000 1
 
2.4%
22805000 1
 
2.4%
180187000 1
 
2.4%
114079000 1
 
2.4%
3047000 1
 
2.4%
507925000 1
 
2.4%
302819000 1
 
2.4%
57357000 1
 
2.4%
21682000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
1892000 1
 
2.4%
2910000 1
 
2.4%
3047000 1
 
2.4%
16328000 1
 
2.4%
20121000 1
 
2.4%
21401000 1
 
2.4%
21682000 1
 
2.4%
22308000 1
 
2.4%
22805000 1
 
2.4%
ValueCountFrequency (%)
507925000 1
2.4%
504394000 1
2.4%
443871000 1
2.4%
352726000 1
2.4%
351372000 1
2.4%
302819000 1
2.4%
180187000 1
2.4%
161915000 1
2.4%
148798000 1
2.4%
146109000 1
2.4%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.223171
Minimum0
Maximum100
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-11T09:13:20.935327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median95.8
Q396.71
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)96.71

Descriptive statistics

Standard deviation44.07851
Coefficient of variation (CV)0.66560554
Kurtosis-1.3780615
Mean66.223171
Median Absolute Deviation (MAD)3.42
Skewness-0.77757747
Sum2715.15
Variance1942.915
MonotonicityNot monotonic
2023-12-11T09:13:21.083538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 11
26.8%
100.0 3
 
7.3%
99.7 1
 
2.4%
96.71 1
 
2.4%
94.21 1
 
2.4%
96.3 1
 
2.4%
99.72 1
 
2.4%
39.09 1
 
2.4%
96.22 1
 
2.4%
98.63 1
 
2.4%
Other values (19) 19
46.3%
ValueCountFrequency (%)
0.0 11
26.8%
22.73 1
 
2.4%
33.06 1
 
2.4%
39.09 1
 
2.4%
94.1 1
 
2.4%
94.21 1
 
2.4%
94.77 1
 
2.4%
95.2 1
 
2.4%
95.31 1
 
2.4%
95.39 1
 
2.4%
ValueCountFrequency (%)
100.0 3
7.3%
99.8 1
 
2.4%
99.72 1
 
2.4%
99.7 1
 
2.4%
99.22 1
 
2.4%
98.63 1
 
2.4%
98.14 1
 
2.4%
97.28 1
 
2.4%
96.71 1
 
2.4%
96.7 1
 
2.4%

Interactions

2023-12-11T09:13:16.897537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:14.241416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:14.746111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.293798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.888298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.411083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.975084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:14.334927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:14.824129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.412002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.965197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.512640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:17.057486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:14.420841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:14.907347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.506747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.058858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.596172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:17.138978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:14.500072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.008439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.639864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.166942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.681533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:17.219703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:14.575152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.098157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.720470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.248557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.756975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:17.309759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:14.661613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.201458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:15.801969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.320495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:16.826541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:13:21.198446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.1690.0000.0000.000
세목명0.0001.0000.8480.8350.7640.6290.8370.876
부과금액0.0000.8481.0000.9610.8030.6880.9300.636
수납급액0.0000.8350.9611.0000.6380.0000.8560.000
환급금액0.1690.7640.8030.6381.0000.9500.8460.868
결손금액0.0000.6290.6880.0000.9501.0000.9870.979
미수납 금액0.0000.8370.9300.8560.8460.9871.0000.985
징수율0.0000.8760.6360.0000.8680.9790.9851.000
2023-12-11T09:13:21.341128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-11T09:13:21.465205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9730.7680.7460.7270.5540.0000.513
수납급액0.9731.0000.6790.6450.6310.6340.0000.501
환급금액0.7680.6791.0000.9150.8900.3000.0890.338
결손금액0.7460.6450.9151.0000.9050.2150.0000.340
미수납 금액0.7270.6310.8900.9051.0000.1450.0000.511
징수율0.5540.6340.3000.2150.1451.0000.0000.608
과세년도0.0000.0000.0890.0000.0000.0001.0000.000
세목명0.5130.5010.3380.3400.5110.6080.0001.000

Missing values

2023-12-11T09:13:17.440085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:13:17.605741image/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
0경상남도산청군488602017도축세000000.0
1경상남도산청군488602017레저세000000.0
2경상남도산청군488602017재산세320812100031022050001209000159200010432400096.7
3경상남도산청군488602017주민세558378000536730000300002470002140100096.12
4경상남도산청군488602017취득세1120733000010998948000695490001384900006989200098.14
5경상남도산청군488602017자동차세7585691000722994900025774000437000035137200095.31
6경상남도산청군488602017과년도수입165726200037667400024451200083671700044387100022.73
7경상남도산청군488602017담배소비세24580100002458010000000100.0
8경상남도산청군488602017도시계획세000000.0
9경상남도산청군488602017등록면허세9681600009662120002009500056000189200099.8
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
31경상남도산청군488602019취득세10108621000997061700037933000806470005735700098.63
32경상남도산청군488602019자동차세8132639000782544800030208000437200030281900096.22
33경상남도산청군488602019과년도수입112823800044098100030128000017933200050792500039.09
34경상남도산청군488602019담배소비세23686310002368631000000100.0
35경상남도산청군488602019도시계획세000000.0
36경상남도산청군488602019등록면허세11062720001103160000413600065000304700099.72
37경상남도산청군488602019지방교육세3298894000317685900011198000795600011407900096.3
38경상남도산청군488602019지방소득세437583000041224350002716400007320800018018700094.21
39경상남도산청군488602019지방소비세000000.0
40경상남도산청군488602019지역자원시설세720255000696579000360008710002280500096.71