Overview

Dataset statistics

Number of variables11
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory98.0 B

Variable types

Categorical5
Numeric6

Dataset

Description지방세 부과액에 대한 세목별 징수현황에 대한 데이터로 시도명, 시군구명, 자치단체코드, 과세년도, 세목명, 부과금액, 수납금액, 환급금액 등의 항목을 제공합니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15079594/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 3 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
징수율 is highly overall correlated with 세목명High correlation
세목명 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
부과금액 has 10 (14.9%) zerosZeros
수납급액 has 10 (14.9%) zerosZeros
환급금액 has 20 (29.9%) zerosZeros
결손금액 has 31 (46.3%) zerosZeros
미수납 금액 has 22 (32.8%) zerosZeros
징수율 has 10 (14.9%) zerosZeros

Reproduction

Analysis started2023-12-12 23:01:31.792998
Analysis finished2023-12-12 23:01:35.378889
Duration3.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

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

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 67
100.0%

Length

2023-12-13T08:01:35.448369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:35.539466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 67
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
김해시 67
100.0%

Length

2023-12-13T08:01:35.633286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:35.742614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
김해시 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
48250
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48250
2nd row48250
3rd row48250
4th row48250
5th row48250

Common Values

ValueCountFrequency (%)
48250 67
100.0%

Length

2023-12-13T08:01:35.828707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:35.917250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48250 67
100.0%

과세년도
Categorical

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

2023-12-13T08:01:36.018378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:36.125818image/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

2023-12-13T08:01:36.273560image/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 

Distinct58
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0321002 × 1010
Minimum0
Maximum2.71345 × 1011
Zeros10
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T08:01:36.431840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.6503592 × 1010
median3.7765668 × 1010
Q39.4606057 × 1010
95-th percentile2.013885 × 1011
Maximum2.71345 × 1011
Range2.71345 × 1011
Interquartile range (IQR)7.8102466 × 1010

Descriptive statistics

Standard deviation6.4371705 × 1010
Coefficient of variation (CV)1.0671524
Kurtosis2.7458596
Mean6.0321002 × 1010
Median Absolute Deviation (MAD)3.7282991 × 1010
Skewness1.6343723
Sum4.0415071 × 1012
Variance4.1437164 × 1021
MonotonicityNot monotonic
2023-12-13T08:01:36.585176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
14.9%
20288700000 1
 
1.5%
105395000000 1
 
1.5%
16179940000 1
 
1.5%
9827264000 1
 
1.5%
122363000000 1
 
1.5%
17118896000 1
 
1.5%
200692000000 1
 
1.5%
78097902000 1
 
1.5%
32807711000 1
 
1.5%
Other values (48) 48
71.6%
ValueCountFrequency (%)
0 10
14.9%
6609117000 1
 
1.5%
9827264000 1
 
1.5%
14027153000 1
 
1.5%
15262235000 1
 
1.5%
15961688000 1
 
1.5%
16179940000 1
 
1.5%
16194768000 1
 
1.5%
16812415000 1
 
1.5%
16949969000 1
 
1.5%
ValueCountFrequency (%)
271345000000 1
1.5%
266224000000 1
1.5%
247711000000 1
1.5%
201687000000 1
1.5%
200692000000 1
1.5%
126818000000 1
1.5%
122363000000 1
1.5%
118478000000 1
1.5%
109630000000 1
1.5%
107920000000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6465762 × 1010
Minimum0
Maximum2.68712 × 1011
Zeros10
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T08:01:36.729898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1735621 × 1010
median2.02887 × 1010
Q39.0413044 × 1010
95-th percentile1.988211 × 1011
Maximum2.68712 × 1011
Range2.68712 × 1011
Interquartile range (IQR)7.8677423 × 1010

Descriptive statistics

Standard deviation6.4440675 × 1010
Coefficient of variation (CV)1.1412345
Kurtosis2.8476688
Mean5.6465762 × 1010
Median Absolute Deviation (MAD)2.02887 × 1010
Skewness1.6770581
Sum3.783206 × 1012
Variance4.1526006 × 1021
MonotonicityNot monotonic
2023-12-13T08:01:36.871451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
14.9%
20288700000 1
 
1.5%
99990583000 1
 
1.5%
15713031000 1
 
1.5%
9827264000 1
 
1.5%
118836000000 1
 
1.5%
16610822000 1
 
1.5%
198770000000 1
 
1.5%
71372468000 1
 
1.5%
725110000 1
 
1.5%
Other values (48) 48
71.6%
ValueCountFrequency (%)
0 10
14.9%
725110000 1
 
1.5%
6082321000 1
 
1.5%
6609117000 1
 
1.5%
7201259000 1
 
1.5%
9206446000 1
 
1.5%
9827264000 1
 
1.5%
9853427000 1
 
1.5%
13617815000 1
 
1.5%
14753470000 1
 
1.5%
ValueCountFrequency (%)
268712000000 1
1.5%
264529000000 1
1.5%
243788000000 1
1.5%
198843000000 1
1.5%
198770000000 1
1.5%
123642000000 1
1.5%
118836000000 1
1.5%
115218000000 1
1.5%
106110000000 1
1.5%
103605000000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0750718 × 109
Minimum0
Maximum1.411997 × 1010
Zeros20
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T08:01:37.380197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60052000
Q35.714745 × 108
95-th percentile6.407959 × 109
Maximum1.411997 × 1010
Range1.411997 × 1010
Interquartile range (IQR)5.714745 × 108

Descriptive statistics

Standard deviation2.5277303 × 109
Coefficient of variation (CV)2.3512199
Kurtosis12.771342
Mean1.0750718 × 109
Median Absolute Deviation (MAD)60052000
Skewness3.4205514
Sum7.2029812 × 1010
Variance6.3894204 × 1018
MonotonicityNot monotonic
2023-12-13T08:01:37.527779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 20
29.9%
287383000 1
 
1.5%
4655000 1
 
1.5%
53260000 1
 
1.5%
19774000 1
 
1.5%
2340468000 1
 
1.5%
629308000 1
 
1.5%
14119970000 1
 
1.5%
42144000 1
 
1.5%
145108000 1
 
1.5%
Other values (38) 38
56.7%
ValueCountFrequency (%)
0 20
29.9%
3745000 1
 
1.5%
4655000 1
 
1.5%
5713000 1
 
1.5%
6952000 1
 
1.5%
7964000 1
 
1.5%
8297000 1
 
1.5%
8994000 1
 
1.5%
19774000 1
 
1.5%
22068000 1
 
1.5%
ValueCountFrequency (%)
14119970000 1
1.5%
9490515000 1
1.5%
8816610000 1
1.5%
6891823000 1
1.5%
5278943000 1
1.5%
3371055000 1
1.5%
3229422000 1
1.5%
2953313000 1
1.5%
2795324000 1
1.5%
2340468000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8857615 × 108
Minimum0
Maximum8.348837 × 109
Zeros31
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T08:01:37.673864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median53000
Q36837000
95-th percentile4.3445876 × 109
Maximum8.348837 × 109
Range8.348837 × 109
Interquartile range (IQR)6837000

Descriptive statistics

Standard deviation1.7261258 × 109
Coefficient of variation (CV)3.5329719
Kurtosis13.915618
Mean4.8857615 × 108
Median Absolute Deviation (MAD)53000
Skewness3.8625263
Sum3.2734602 × 1010
Variance2.9795102 × 1018
MonotonicityNot monotonic
2023-12-13T08:01:37.852691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 31
46.3%
191000 1
 
1.5%
5127000 1
 
1.5%
8015622000 1
 
1.5%
366000 1
 
1.5%
7261000 1
 
1.5%
630715000 1
 
1.5%
1939000 1
 
1.5%
21256000 1
 
1.5%
63179000 1
 
1.5%
Other values (27) 27
40.3%
ValueCountFrequency (%)
0 31
46.3%
21000 1
 
1.5%
40000 1
 
1.5%
53000 1
 
1.5%
59000 1
 
1.5%
75000 1
 
1.5%
100000 1
 
1.5%
191000 1
 
1.5%
205000 1
 
1.5%
366000 1
 
1.5%
ValueCountFrequency (%)
8348837000 1
1.5%
8015622000 1
1.5%
6492609000 1
1.5%
5799122000 1
1.5%
950674000 1
1.5%
907276000 1
1.5%
726631000 1
1.5%
630715000 1
1.5%
456723000 1
1.5%
173797000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3666459 × 109
Minimum0
Maximum2.8707609 × 1010
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T08:01:38.042440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.27524 × 108
Q33.509283 × 109
95-th percentile2.2867631 × 1010
Maximum2.8707609 × 1010
Range2.8707609 × 1010
Interquartile range (IQR)3.509283 × 109

Descriptive statistics

Standard deviation6.4068666 × 109
Coefficient of variation (CV)1.9030414
Kurtosis7.3939089
Mean3.3666459 × 109
Median Absolute Deviation (MAD)5.27524 × 108
Skewness2.8185981
Sum2.2556527 × 1011
Variance4.1047939 × 1019
MonotonicityNot monotonic
2023-12-13T08:01:38.205093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
3640660000 1
 
1.5%
4774037000 1
 
1.5%
464970000 1
 
1.5%
3505016000 1
 
1.5%
507883000 1
 
1.5%
1858957000 1
 
1.5%
6720549000 1
 
1.5%
23733764000 1
 
1.5%
32314000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
32314000 1
 
1.5%
34300000 1
 
1.5%
35009000 1
 
1.5%
37857000 1
 
1.5%
52376000 1
 
1.5%
409279000 1
 
1.5%
464970000 1
 
1.5%
480457000 1
 
1.5%
480737000 1
 
1.5%
ValueCountFrequency (%)
28707609000 1
1.5%
24364977000 1
1.5%
24318163000 1
1.5%
23733764000 1
1.5%
20846653000 1
1.5%
6993542000 1
1.5%
6941917000 1
1.5%
6734625000 1
1.5%
6720549000 1
1.5%
6071918000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.943731
Minimum0
Maximum100
Zeros10
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T08:01:38.385470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q191.685
median97
Q399.515
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)7.83

Descriptive statistics

Standard deviation38.821405
Coefficient of variation (CV)0.5045428
Kurtosis0.0063753935
Mean76.943731
Median Absolute Deviation (MAD)2.82
Skewness-1.3890835
Sum5155.23
Variance1507.1015
MonotonicityNot monotonic
2023-12-13T08:01:38.543541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
100.0 12
 
17.9%
0.0 10
 
14.9%
97.24 2
 
3.0%
99.82 2
 
3.0%
97.19 1
 
1.5%
97.3 1
 
1.5%
94.87 1
 
1.5%
97.11 1
 
1.5%
97.12 1
 
1.5%
97.03 1
 
1.5%
Other values (35) 35
52.2%
ValueCountFrequency (%)
0.0 10
14.9%
2.21 1
 
1.5%
17.02 1
 
1.5%
21.28 1
 
1.5%
22.98 1
 
1.5%
23.36 1
 
1.5%
91.31 1
 
1.5%
91.39 1
 
1.5%
91.98 1
 
1.5%
92.61 1
 
1.5%
ValueCountFrequency (%)
100.0 12
17.9%
99.82 2
 
3.0%
99.81 1
 
1.5%
99.79 1
 
1.5%
99.67 1
 
1.5%
99.36 1
 
1.5%
99.04 1
 
1.5%
99.03 1
 
1.5%
98.59 1
 
1.5%
98.42 1
 
1.5%

Interactions

2023-12-13T08:01:34.665609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.134918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.632631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.168923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.716878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.185772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.754921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.214184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.712770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.252225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.807347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.261820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.835972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.298512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.785391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.330778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.892839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.335059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.916596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.385629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.888313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.443277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.971884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.417998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.983459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.468124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.986726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.536784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.039678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.487179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:35.057202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:32.549543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.085452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:33.625556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.115197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:34.567461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:01:38.637517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9750.9700.7670.4520.8470.834
부과금액0.0000.9751.0000.9960.6340.3570.6200.443
수납급액0.0000.9700.9961.0000.0230.0000.4670.094
환급금액0.0000.7670.6340.0231.0000.8790.8090.767
결손금액0.0000.4520.3570.0000.8791.0000.8560.709
미수납 금액0.0000.8470.6200.4670.8090.8561.0000.888
징수율0.0000.8340.4430.0940.7670.7090.8881.000
2023-12-13T08:01:38.742068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-13T08:01:38.845138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9490.6680.4290.6420.2960.0000.716
수납급액0.9491.0000.5010.2690.4670.4390.0000.695
환급금액0.6680.5011.0000.7180.907-0.1690.0000.368
결손금액0.4290.2690.7181.0000.767-0.2280.0000.230
미수납 금액0.6420.4670.9070.7671.000-0.3010.0000.585
징수율0.2960.439-0.169-0.228-0.3011.0000.0000.592
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.7160.6950.3680.2300.5850.5920.0001.000

Missing values

2023-12-13T08:01:35.165577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:01:35.317231image/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
0경상남도김해시482502017도축세000000.0
1경상남도김해시482502017레저세7424320800074243208000000100.0
2경상남도김해시482502017재산세1011880000009839289100084019000205000279497900097.24
3경상남도김해시482502017주민세1619476800015616539000899400090000057732900096.43
4경상남도김해시482502017취득세247711000000243788000000106477000087205000383605600098.42
5경상남도김해시482502017자동차세8396579200077230399000430057000768000673462500091.98
6경상남도김해시482502017과년도수입338470340007201259000881661000057991220002084665300021.28
7경상남도김해시482502017담배소비세3912606300039126063000000100.0
8경상남도김해시482502017도시계획세000000.0
9경상남도김해시482502017등록면허세185237550001848870600060052000400003500900099.81
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
57경상남도김해시482502021취득세26622400000026452900000012291180000169430600099.36
58경상남도김해시482502021자동차세88479646000824077280006491220000607191800093.14
59경상남도김해시482502021과년도수입35740604000608232100094905150009506740002870760900017.02
60경상남도김해시482502021담배소비세3870342400038703424000571300000100.0
61경상남도김해시482502021도시계획세000000.0
62경상남도김해시482502021등록면허세18272012000182341550008908400003785700099.79
63경상남도김해시482502021지방교육세75048659000727474230003258530000230123600096.93
64경상남도김해시482502021지방소득세10792000000010360500000033710550000431434100096.0
65경상남도김해시482502021지방소비세1801177000018011770000000100.0
66경상남도김해시482502021지역자원시설세17080229000165994920007964000048073700097.19