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 대해 각 세목별로 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액, 징수율을 확인할 수 있습니다.
URLhttps://www.data.go.kr/data/15078483/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 4 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 3 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 수납급액 and 1 other fieldsHigh correlation
부과금액 has 20 (29.9%) zerosZeros
수납급액 has 20 (29.9%) zerosZeros
환급금액 has 22 (32.8%) zerosZeros
결손금액 has 24 (35.8%) zerosZeros
미수납 금액 has 22 (32.8%) zerosZeros
징수율 has 20 (29.9%) zerosZeros

Reproduction

Analysis started2023-12-12 22:28:32.901052
Analysis finished2023-12-12 22:28:36.932684
Duration4.03 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
대전광역시
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 67
100.0%

Length

2023-12-13T07:28:37.020486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:37.150869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 67
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 67
100.0%

Length

2023-12-13T07:28:37.267724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:37.388437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
30140
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30140
2nd row30140
3rd row30140
4th row30140
5th row30140

Common Values

ValueCountFrequency (%)
30140 67
100.0%

Length

2023-12-13T07:28:37.481308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:37.595917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30140 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

2023-12-13T07:28:37.722369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:37.851096image/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

2023-12-13T07:28:38.001955image/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 

Distinct48
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3377541 × 1010
Minimum0
Maximum7.7150644 × 1010
Zeros20
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T07:28:38.156122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.818212 × 109
Q31.8643017 × 1010
95-th percentile4.2595467 × 1010
Maximum7.7150644 × 1010
Range7.7150644 × 1010
Interquartile range (IQR)1.8643017 × 1010

Descriptive statistics

Standard deviation1.6381221 × 1010
Coefficient of variation (CV)1.2245316
Kurtosis2.6336894
Mean1.3377541 × 1010
Median Absolute Deviation (MAD)6.818212 × 109
Skewness1.6008522
Sum8.9629523 × 1011
Variance2.6834441 × 1020
MonotonicityNot monotonic
2023-12-13T07:28:38.316896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 20
29.9%
14081077000 1
 
1.5%
4159209000 1
 
1.5%
32778111000 1
 
1.5%
6821179000 1
 
1.5%
56506306000 1
 
1.5%
18701664000 1
 
1.5%
6647907000 1
 
1.5%
5244631000 1
 
1.5%
15556387000 1
 
1.5%
Other values (38) 38
56.7%
ValueCountFrequency (%)
0 20
29.9%
3994593000 1
 
1.5%
4042975000 1
 
1.5%
4159209000 1
 
1.5%
4188084000 1
 
1.5%
4212136000 1
 
1.5%
4273069000 1
 
1.5%
4636294000 1
 
1.5%
5244631000 1
 
1.5%
5339829000 1
 
1.5%
ValueCountFrequency (%)
77150644000 1
1.5%
56506306000 1
1.5%
46335421000 1
1.5%
43311177000 1
1.5%
40925476000 1
1.5%
38309490000 1
1.5%
36635890000 1
1.5%
35229461000 1
1.5%
33538903000 1
1.5%
33450382000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.260467 × 1010
Minimum0
Maximum7.7095496 × 1010
Zeros20
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T07:28:38.481853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.316493 × 109
Q31.6385178 × 1010
95-th percentile4.1933818 × 1010
Maximum7.7095496 × 1010
Range7.7095496 × 1010
Interquartile range (IQR)1.6385178 × 1010

Descriptive statistics

Standard deviation1.6138202 × 1010
Coefficient of variation (CV)1.2803352
Kurtosis3.1006973
Mean1.260467 × 1010
Median Absolute Deviation (MAD)5.316493 × 109
Skewness1.7101129
Sum8.4451286 × 1011
Variance2.6044156 × 1020
MonotonicityNot monotonic
2023-12-13T07:28:38.666789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 20
29.9%
13270638000 1
 
1.5%
4074309000 1
 
1.5%
32049866000 1
 
1.5%
6630104000 1
 
1.5%
56313323000 1
 
1.5%
16483148000 1
 
1.5%
2960525000 1
 
1.5%
5214812000 1
 
1.5%
14778269000 1
 
1.5%
Other values (38) 38
56.7%
ValueCountFrequency (%)
0 20
29.9%
2960525000 1
 
1.5%
3644143000 1
 
1.5%
3870815000 1
 
1.5%
3921306000 1
 
1.5%
4074309000 1
 
1.5%
4087600000 1
 
1.5%
4099011000 1
 
1.5%
4115076000 1
 
1.5%
4248409000 1
 
1.5%
ValueCountFrequency (%)
77095496000 1
1.5%
56313323000 1
1.5%
43419896000 1
1.5%
43192548000 1
1.5%
38996781000 1
1.5%
38167849000 1
1.5%
34484901000 1
1.5%
33800592000 1
1.5%
33357782000 1
1.5%
32569911000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.758753 × 108
Minimum0
Maximum1.300167 × 109
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T07:28:38.850996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9282000
Q31.33882 × 108
95-th percentile1.0642388 × 109
Maximum1.300167 × 109
Range1.300167 × 109
Interquartile range (IQR)1.33882 × 108

Descriptive statistics

Standard deviation3.4280242 × 108
Coefficient of variation (CV)1.9491221
Kurtosis4.0122961
Mean1.758753 × 108
Median Absolute Deviation (MAD)9282000
Skewness2.2458026
Sum1.1783645 × 1010
Variance1.175135 × 1017
MonotonicityNot monotonic
2023-12-13T07:28:39.004956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
1300167000 1
 
1.5%
1113782000 1
 
1.5%
594000 1
 
1.5%
21515000 1
 
1.5%
3196000 1
 
1.5%
198198000 1
 
1.5%
294963000 1
 
1.5%
1245610000 1
 
1.5%
24346000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
85000 1
 
1.5%
451000 1
 
1.5%
594000 1
 
1.5%
1173000 1
 
1.5%
1372000 1
 
1.5%
3196000 1
 
1.5%
3523000 1
 
1.5%
3810000 1
 
1.5%
4997000 1
 
1.5%
ValueCountFrequency (%)
1300167000 1
1.5%
1259723000 1
1.5%
1245610000 1
1.5%
1113782000 1
1.5%
948638000 1
1.5%
905458000 1
1.5%
762663000 1
1.5%
681153000 1
1.5%
584504000 1
1.5%
415177000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3529778 × 108
Minimum0
Maximum1.337089 × 109
Zeros24
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T07:28:39.171108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median871000
Q312047000
95-th percentile9.821812 × 108
Maximum1.337089 × 109
Range1.337089 × 109
Interquartile range (IQR)12047000

Descriptive statistics

Standard deviation3.324829 × 108
Coefficient of variation (CV)2.4574158
Kurtosis4.609802
Mean1.3529778 × 108
Median Absolute Deviation (MAD)871000
Skewness2.4223527
Sum9.064951 × 109
Variance1.1054488 × 1017
MonotonicityNot monotonic
2023-12-13T07:28:39.644520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 24
35.8%
1038328000 1
 
1.5%
11295000 1
 
1.5%
641927000 1
 
1.5%
8155000 1
 
1.5%
3046000 1
 
1.5%
839000 1
 
1.5%
12799000 1
 
1.5%
4382000 1
 
1.5%
778054000 1
 
1.5%
Other values (34) 34
50.7%
ValueCountFrequency (%)
0 24
35.8%
125000 1
 
1.5%
308000 1
 
1.5%
390000 1
 
1.5%
426000 1
 
1.5%
464000 1
 
1.5%
575000 1
 
1.5%
762000 1
 
1.5%
782000 1
 
1.5%
839000 1
 
1.5%
ValueCountFrequency (%)
1337089000 1
1.5%
1221072000 1
1.5%
1038328000 1
1.5%
1003984000 1
1.5%
931308000 1
1.5%
850541000 1
1.5%
778054000 1
1.5%
667531000 1
1.5%
641927000 1
1.5%
307028000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.375734 × 108
Minimum0
Maximum3.437295 × 109
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T07:28:39.787763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.19876 × 108
Q38.72032 × 108
95-th percentile2.5953647 × 109
Maximum3.437295 × 109
Range3.437295 × 109
Interquartile range (IQR)8.72032 × 108

Descriptive statistics

Standard deviation9.3301863 × 108
Coefficient of variation (CV)1.4633901
Kurtosis0.95289936
Mean6.375734 × 108
Median Absolute Deviation (MAD)1.19876 × 108
Skewness1.4784266
Sum4.2717418 × 1010
Variance8.7052376 × 1017
MonotonicityNot monotonic
2023-12-13T07:28:39.930936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
1078154000 1
 
1.5%
786942000 1
 
1.5%
76745000 1
 
1.5%
725199000 1
 
1.5%
190236000 1
 
1.5%
180184000 1
 
1.5%
2214134000 1
 
1.5%
2909328000 1
 
1.5%
28948000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
22910000 1
 
1.5%
23616000 1
 
1.5%
24085000 1
 
1.5%
27523000 1
 
1.5%
28948000 1
 
1.5%
47362000 1
 
1.5%
55148000 1
 
1.5%
72618000 1
 
1.5%
76745000 1
 
1.5%
ValueCountFrequency (%)
3437295000 1
1.5%
2909328000 1
1.5%
2649101000 1
1.5%
2608535000 1
1.5%
2564634000 1
1.5%
2498696000 1
1.5%
2461494000 1
1.5%
2277784000 1
1.5%
2247994000 1
1.5%
2214134000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.029104
Minimum0
Maximum100
Zeros20
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T07:28:40.059743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median94.13
Q397.53
95-th percentile99.709
Maximum100
Range100
Interquartile range (IQR)97.53

Descriptive statistics

Standard deviation43.826505
Coefficient of variation (CV)0.68447787
Kurtosis-1.4133119
Mean64.029104
Median Absolute Deviation (MAD)5.53
Skewness-0.7102125
Sum4289.95
Variance1920.7625
MonotonicityNot monotonic
2023-12-13T07:28:40.189099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 20
29.9%
100.0 2
 
3.0%
99.46 2
 
3.0%
97.96 1
 
1.5%
97.78 1
 
1.5%
97.2 1
 
1.5%
99.66 1
 
1.5%
88.14 1
 
1.5%
44.53 1
 
1.5%
99.43 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0.0 20
29.9%
44.53 1
 
1.5%
45.48 1
 
1.5%
50.63 1
 
1.5%
56.02 1
 
1.5%
57.69 1
 
1.5%
85.9 1
 
1.5%
86.03 1
 
1.5%
87.64 1
 
1.5%
88.14 1
 
1.5%
ValueCountFrequency (%)
100.0 2
3.0%
99.93 1
1.5%
99.73 1
1.5%
99.66 1
1.5%
99.63 1
1.5%
99.56 1
1.5%
99.49 1
1.5%
99.46 2
3.0%
99.43 1
1.5%
99.42 1
1.5%

Interactions

2023-12-13T07:28:35.992201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:33.213366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:33.756528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.471975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.958029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.486157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:36.076674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:33.315289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:33.840813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.553408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.040897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.572254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:36.174779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:33.406637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.157465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.629195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.134601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.655596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:36.314359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:33.498214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.236071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.703128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.233399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.732689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:36.400507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:33.594878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.315917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.798870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.328905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.820050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:36.526495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:33.680114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.398816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:34.888026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.411064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:28:35.916034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:28:40.274498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8110.8620.6900.6530.7870.932
부과금액0.0000.8111.0000.9650.7000.5760.8270.714
수납급액0.0000.8620.9651.0000.6460.0000.6610.624
환급금액0.0000.6900.7000.6461.0000.9240.8640.930
결손금액0.0000.6530.5760.0000.9241.0000.8860.732
미수납 금액0.0000.7870.8270.6610.8640.8861.0000.869
징수율0.0000.9320.7140.6240.9300.7320.8691.000
2023-12-13T07:28:40.375802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-13T07:28:40.469918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9780.8360.7290.7560.6280.0000.491
수납급액0.9781.0000.7550.6380.6640.7080.0000.580
환급금액0.8360.7551.0000.8390.8940.3920.0000.347
결손금액0.7290.6380.8391.0000.8740.3560.0000.277
미수납 금액0.7560.6640.8940.8741.0000.2730.0000.459
징수율0.6280.7080.3920.3560.2731.0000.0000.751
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.4910.5800.3470.2770.4590.7510.0001.000

Missing values

2023-12-13T07:28:36.665553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:28:36.869015image/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
0대전광역시중구301402017도축세000000.0
1대전광역시중구301402017레저세000000.0
2대전광역시중구301402017재산세29188050000282937430005147000523200088907500096.94
3대전광역시중구301402017주민세615351800059023450001372000582000024535300095.92
4대전광역시중구301402017취득세3830949000038167849000786650002176500011987600099.63
5대전광역시중구301402017자동차세18397646000158271350002443340005877000256463400086.03
6대전광역시중구301402017과년도수입845832700047385590004151770001221072000249869600056.02
7대전광역시중구301402017담배소비세000000.0
8대전광역시중구301402017도시계획세000000.0
9대전광역시중구301402017등록면허세463629400046114600001678600012180002361600099.46
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
57대전광역시중구301402021취득세771506440007709549600013647700005514800099.93
58대전광역시중구301402021자동차세1877498500016689438000280052000782000208476500088.89
59대전광역시중구301402021과년도수입80127460003644143000762663000931308000343729500045.48
60대전광역시중구301402021담배소비세000000.0
61대전광역시중구301402021도시계획세000000.0
62대전광역시중구301402021등록면허세53747430005347095000320840001250002752300099.49
63대전광역시중구301402021지방교육세16895353000161813390009702400046400071355000095.77
64대전광역시중구301402021지방소득세46335421000434198960001259723000667531000224799400093.71
65대전광역시중구301402021지방소비세71886080007188608000000100.0
66대전광역시중구301402021지역자원시설세4188084000411507600011730003900007261800098.26