Overview

Dataset statistics

Number of variables11
Number of observations226
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.3 KiB
Average record size in memory96.6 B

Variable types

Categorical4
Numeric7

Dataset

Description지방세 부과액에 대한 세목별 징수현황을 제공하여 지자체의 재정자주도·재정자립도 산출하는 기초 및 납세 협력도 및 조세 순응도를 확인하는 자료로 활용
URLhttps://www.data.go.kr/data/15080394/fileData.do

Alerts

시도명 has constant value ""Constant
자치단체코드 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 자치단체코드High correlation
부과금액 is highly overall correlated with 수납급액 and 3 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
결손금액 is highly overall correlated with 미수납 금액High correlation
미수납 금액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
부과금액 has 104 (46.0%) zerosZeros
수납급액 has 104 (46.0%) zerosZeros
환급금액 has 115 (50.9%) zerosZeros
결손금액 has 197 (87.2%) zerosZeros
미수납 금액 has 118 (52.2%) zerosZeros
징수율 has 104 (46.0%) zerosZeros

Reproduction

Analysis started2023-12-12 03:12:18.096428
Analysis finished2023-12-12 03:12:24.738199
Duration6.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
226 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 226
100.0%

Length

2023-12-12T12:12:24.829703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:12:24.949184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 226
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
안양시동안구
80 
안양시만안구
80 
안양시
66 

Length

Max length6
Median length6
Mean length5.1238938
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
안양시동안구 80
35.4%
안양시만안구 80
35.4%
안양시 66
29.2%

Length

2023-12-12T12:12:25.133886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:12:25.298632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안양시동안구 80
35.4%
안양시만안구 80
35.4%
안양시 66
29.2%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
41173
80 
41171
80 
41170
66 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41170
2nd row41170
3rd row41170
4th row41170
5th row41170

Common Values

ValueCountFrequency (%)
41173 80
35.4%
41171 80
35.4%
41170 66
29.2%

Length

2023-12-12T12:12:25.424018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:12:25.556062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41173 80
35.4%
41171 80
35.4%
41170 66
29.2%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5398
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T12:12:25.700668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7335149
Coefficient of variation (CV)0.00085837122
Kurtosis-1.2631566
Mean2019.5398
Median Absolute Deviation (MAD)1
Skewness-0.077937503
Sum456416
Variance3.0050737
MonotonicityIncreasing
2023-12-12T12:12:25.854215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 42
18.6%
2019 39
17.3%
2020 39
17.3%
2021 39
17.3%
2022 39
17.3%
2018 28
12.4%
ValueCountFrequency (%)
2017 42
18.6%
2018 28
12.4%
2019 39
17.3%
2020 39
17.3%
2021 39
17.3%
2022 39
17.3%
ValueCountFrequency (%)
2022 39
17.3%
2021 39
17.3%
2020 39
17.3%
2019 39
17.3%
2018 28
12.4%
2017 42
18.6%

세목명
Categorical

Distinct14
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
레저세
17 
재산세
17 
주민세
17 
취득세
17 
자동차세
17 
Other values (9)
141 

Length

Max length7
Median length5
Mean length4.4292035
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 17
 
7.5%
재산세 17
 
7.5%
주민세 17
 
7.5%
취득세 17
 
7.5%
자동차세 17
 
7.5%
과년도수입 17
 
7.5%
담배소비세 17
 
7.5%
도시계획세 17
 
7.5%
등록면허세 17
 
7.5%
지방교육세 17
 
7.5%
Other values (4) 56
24.8%

Length

2023-12-12T12:12:26.035252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 17
 
7.5%
재산세 17
 
7.5%
주민세 17
 
7.5%
취득세 17
 
7.5%
자동차세 17
 
7.5%
과년도수입 17
 
7.5%
담배소비세 17
 
7.5%
도시계획세 17
 
7.5%
등록면허세 17
 
7.5%
지방교육세 17
 
7.5%
Other values (4) 56
24.8%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2177958 × 1010
Minimum-1.9806667 × 1010
Maximum2.48 × 1011
Zeros104
Zeros (%)46.0%
Negative1
Negative (%)0.4%
Memory size2.1 KiB
2023-12-12T12:12:26.200642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.9806667 × 1010
5-th percentile0
Q10
median4.878555 × 109
Q33.0797549 × 1010
95-th percentile1.126605 × 1011
Maximum2.48 × 1011
Range2.6780667 × 1011
Interquartile range (IQR)3.0797549 × 1010

Descriptive statistics

Standard deviation3.9302197 × 1010
Coefficient of variation (CV)1.7721288
Kurtosis8.1992528
Mean2.2177958 × 1010
Median Absolute Deviation (MAD)4.878555 × 109
Skewness2.6545086
Sum5.0122185 × 1012
Variance1.5446627 × 1021
MonotonicityNot monotonic
2023-12-12T12:12:26.383049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104
46.0%
29387368000 1
 
0.4%
49411144000 1
 
0.4%
11374684000 1
 
0.4%
141000000000 1
 
0.4%
42155144000 1
 
0.4%
16664536000 1
 
0.4%
-19806667000 1
 
0.4%
60780117000 1
 
0.4%
248000000000 1
 
0.4%
Other values (113) 113
50.0%
ValueCountFrequency (%)
-19806667000 1
 
0.4%
0 104
46.0%
297285000 1
 
0.4%
3923015000 1
 
0.4%
4111622000 1
 
0.4%
4179523000 1
 
0.4%
4239430000 1
 
0.4%
4476994000 1
 
0.4%
4673686000 1
 
0.4%
4772027000 1
 
0.4%
ValueCountFrequency (%)
248000000000 1
0.4%
195000000000 1
0.4%
180000000000 1
0.4%
153000000000 1
0.4%
147598000000 1
0.4%
147429000000 1
0.4%
141000000000 1
0.4%
132000000000 1
0.4%
122000000000 1
0.4%
120000000000 1
0.4%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1104543 × 1010
Minimum-3.2681708 × 1010
Maximum2.47 × 1011
Zeros104
Zeros (%)46.0%
Negative2
Negative (%)0.9%
Memory size2.1 KiB
2023-12-12T12:12:26.566360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.2681708 × 1010
5-th percentile0
Q10
median3.866792 × 109
Q33.0631605 × 1010
95-th percentile1.110847 × 1011
Maximum2.47 × 1011
Range2.7968171 × 1011
Interquartile range (IQR)3.0631605 × 1010

Descriptive statistics

Standard deviation3.909861 × 1010
Coefficient of variation (CV)1.8526158
Kurtosis8.4260495
Mean2.1104543 × 1010
Median Absolute Deviation (MAD)3.866792 × 109
Skewness2.6888979
Sum4.7696267 × 1012
Variance1.5287013 × 1021
MonotonicityNot monotonic
2023-12-12T12:12:26.732360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104
46.0%
28685941000 1
 
0.4%
48378785000 1
 
0.4%
11307820000 1
 
0.4%
138000000000 1
 
0.4%
41447759000 1
 
0.4%
16604576000 1
 
0.4%
-32681708000 1
 
0.4%
58929135000 1
 
0.4%
247000000000 1
 
0.4%
Other values (113) 113
50.0%
ValueCountFrequency (%)
-32681708000 1
 
0.4%
-4214987000 1
 
0.4%
0 104
46.0%
297285000 1
 
0.4%
2739658000 1
 
0.4%
2895863000 1
 
0.4%
3400993000 1
 
0.4%
3804033000 1
 
0.4%
3849240000 1
 
0.4%
3852180000 1
 
0.4%
ValueCountFrequency (%)
247000000000 1
0.4%
194000000000 1
0.4%
180000000000 1
0.4%
153000000000 1
0.4%
147564000000 1
0.4%
144203000000 1
0.4%
138000000000 1
0.4%
131000000000 1
0.4%
120000000000 1
0.4%
117000000000 1
0.4%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3690709 × 108
Minimum0
Maximum4.0950535 × 1010
Zeros115
Zeros (%)50.9%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T12:12:26.913217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.310525 × 108
95-th percentile2.673933 × 109
Maximum4.0950535 × 1010
Range4.0950535 × 1010
Interquartile range (IQR)1.310525 × 108

Descriptive statistics

Standard deviation2.9154249 × 109
Coefficient of variation (CV)5.4300362
Kurtosis166.05434
Mean5.3690709 × 108
Median Absolute Deviation (MAD)0
Skewness12.172266
Sum1.21341 × 1011
Variance8.4997024 × 1018
MonotonicityNot monotonic
2023-12-12T12:12:27.096279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115
50.9%
378670000 1
 
0.4%
131218000 1
 
0.4%
8523000 1
 
0.4%
5392985000 1
 
0.4%
200258000 1
 
0.4%
80419000 1
 
0.4%
40950535000 1
 
0.4%
391498000 1
 
0.4%
485075000 1
 
0.4%
Other values (102) 102
45.1%
ValueCountFrequency (%)
0 115
50.9%
29000 1
 
0.4%
1086000 1
 
0.4%
1802000 1
 
0.4%
1813000 1
 
0.4%
1956000 1
 
0.4%
2275000 1
 
0.4%
3120000 1
 
0.4%
3991000 1
 
0.4%
4363000 1
 
0.4%
ValueCountFrequency (%)
40950535000 1
0.4%
9440467000 1
0.4%
5732596000 1
0.4%
5392985000 1
0.4%
5187937000 1
0.4%
4955635000 1
0.4%
3760142000 1
0.4%
3338125000 1
0.4%
3219441000 1
0.4%
2825277000 1
0.4%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8685849 × 108
Minimum0
Maximum8.295186 × 109
Zeros197
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T12:12:27.259392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.4942972 × 109
Maximum8.295186 × 109
Range8.295186 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.8752957 × 108
Coefficient of variation (CV)4.7497417
Kurtosis39.895039
Mean1.8685849 × 108
Median Absolute Deviation (MAD)0
Skewness5.8517185
Sum4.2230019 × 1010
Variance7.8770873 × 1017
MonotonicityNot monotonic
2023-12-12T12:12:27.435841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 197
87.2%
2744821000 1
 
0.4%
335000 1
 
0.4%
15914000 1
 
0.4%
1609000 1
 
0.4%
501000 1
 
0.4%
2085735000 1
 
0.4%
1946000 1
 
0.4%
1484000 1
 
0.4%
7037000 1
 
0.4%
Other values (20) 20
 
8.8%
ValueCountFrequency (%)
0 197
87.2%
4000 1
 
0.4%
10000 1
 
0.4%
14000 1
 
0.4%
54000 1
 
0.4%
56000 1
 
0.4%
70000 1
 
0.4%
210000 1
 
0.4%
335000 1
 
0.4%
501000 1
 
0.4%
ValueCountFrequency (%)
8295186000 1
0.4%
4965292000 1
0.4%
4755079000 1
0.4%
4262650000 1
0.4%
3491409000 1
0.4%
2744821000 1
0.4%
2555862000 1
0.4%
2377943000 1
0.4%
2371351000 1
0.4%
2257638000 1
0.4%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct109
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8003873 × 108
Minimum0
Maximum1.125586 × 1010
Zeros118
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T12:12:28.050881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36.9691175 × 108
95-th percentile7.0296202 × 109
Maximum1.125586 × 1010
Range1.125586 × 1010
Interquartile range (IQR)6.9691175 × 108

Descriptive statistics

Standard deviation2.1668129 × 109
Coefficient of variation (CV)2.462179
Kurtosis11.010439
Mean8.8003873 × 108
Median Absolute Deviation (MAD)0
Skewness3.3519019
Sum1.9888875 × 1011
Variance4.6950781 × 1018
MonotonicityNot monotonic
2023-12-12T12:12:28.271945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
52.2%
27578000 1
 
0.4%
2547519000 1
 
0.4%
707331000 1
 
0.4%
59904000 1
 
0.4%
10130220000 1
 
0.4%
1850772000 1
 
0.4%
569367000 1
 
0.4%
253627000 1
 
0.4%
855197000 1
 
0.4%
Other values (99) 99
43.8%
ValueCountFrequency (%)
0 118
52.2%
23254000 1
 
0.4%
26638000 1
 
0.4%
26640000 1
 
0.4%
27578000 1
 
0.4%
27646000 1
 
0.4%
28185000 1
 
0.4%
33978000 1
 
0.4%
46740000 1
 
0.4%
49203000 1
 
0.4%
ValueCountFrequency (%)
11255860000 1
0.4%
11185512000 1
0.4%
10826767000 1
0.4%
10130220000 1
0.4%
10058880000 1
0.4%
9168566000 1
0.4%
9018598000 1
0.4%
8081830000 1
0.4%
7860609000 1
0.4%
7294848000 1
0.4%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.249779
Minimum-48.89
Maximum165
Zeros104
Zeros (%)46.0%
Negative1
Negative (%)0.4%
Memory size2.1 KiB
2023-12-12T12:12:28.494016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-48.89
5-th percentile0
Q10
median29.635
Q398.18
95-th percentile100
Maximum165
Range213.89
Interquartile range (IQR)98.18

Descriptive statistics

Standard deviation49.135416
Coefficient of variation (CV)0.99767791
Kurtosis-1.8535702
Mean49.249779
Median Absolute Deviation (MAD)29.635
Skewness0.029897888
Sum11130.45
Variance2414.2891
MonotonicityNot monotonic
2023-12-12T12:12:28.715452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 104
46.0%
100.0 14
 
6.2%
99.54 4
 
1.8%
98.02 3
 
1.3%
98.15 2
 
0.9%
99.53 2
 
0.9%
99.57 2
 
0.9%
96.85 2
 
0.9%
97.56 2
 
0.9%
97.98 2
 
0.9%
Other values (88) 89
39.4%
ValueCountFrequency (%)
-48.89 1
 
0.4%
0.0 104
46.0%
14.78 1
 
0.4%
20.07 1
 
0.4%
20.7 1
 
0.4%
21.32 1
 
0.4%
23.52 1
 
0.4%
23.7 1
 
0.4%
26.61 1
 
0.4%
27.32 1
 
0.4%
ValueCountFrequency (%)
165.0 1
 
0.4%
100.0 14
6.2%
99.98 1
 
0.4%
99.97 1
 
0.4%
99.94 1
 
0.4%
99.93 1
 
0.4%
99.92 1
 
0.4%
99.9 1
 
0.4%
99.89 2
 
0.9%
99.8 1
 
0.4%

Interactions

2023-12-12T12:12:23.548141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:18.683343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:19.801497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:20.547178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:21.337525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:22.026313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:22.700132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:23.663665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:18.781785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:19.912716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:20.659466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:21.446093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:22.127923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:22.811569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:23.790217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:18.889195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:20.007209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:20.775351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:21.563725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:22.225346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:22.963241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:23.908993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:18.994348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:20.108832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:20.896699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:21.656598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:22.306445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:23.078739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:24.017646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:19.093930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:20.217481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:21.007570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:21.742018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:22.390779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:23.179848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:24.115331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:19.192400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:20.335546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:21.139421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:21.824203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:22.480707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:23.307901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:24.219142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:19.304367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:20.450250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:21.249162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:21.923075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:22.583262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:12:23.431367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:12:28.875137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
시군구명1.0001.0000.1560.0000.5750.5980.1150.2450.3540.803
자치단체코드1.0001.0000.1560.0000.5750.5980.1150.2450.3540.803
과세년도0.1560.1561.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0000.0000.0001.0000.5980.5660.2720.5270.6800.647
부과금액0.5750.5750.0000.5981.0000.9810.4990.0040.5600.758
수납급액0.5980.5980.0000.5660.9811.0000.6150.0000.4980.421
환급금액0.1150.1150.0000.2720.4990.6151.0000.7040.9600.922
결손금액0.2450.2450.0000.5270.0040.0000.7041.0000.8100.909
미수납 금액0.3540.3540.0000.6800.5600.4980.9600.8101.0000.852
징수율0.8030.8030.0000.6470.7580.4210.9220.9090.8521.000
2023-12-12T12:12:29.061158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치단체코드세목명시군구명
자치단체코드1.0000.0001.000
세목명0.0001.0000.000
시군구명1.0000.0001.000
2023-12-12T12:12:29.192823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액수납급액환급금액결손금액미수납 금액징수율시군구명자치단체코드세목명
과세년도1.000-0.011-0.0170.0160.111-0.0310.0240.0840.0840.000
부과금액-0.0111.0000.9790.8630.2580.8200.7860.4070.4070.286
수납급액-0.0170.9791.0000.7990.1500.7520.8300.3180.3180.260
환급금액0.0160.8630.7991.0000.4890.9500.6180.1080.1080.153
결손금액0.1110.2580.1500.4891.0000.5110.1280.1030.1030.286
미수납 금액-0.0310.8200.7520.9500.5111.0000.5370.2380.2380.377
징수율0.0240.7860.8300.6180.1280.5371.0000.4820.4820.381
시군구명0.0840.4070.3180.1080.1030.2380.4821.0001.0000.000
자치단체코드0.0840.4070.3180.1080.1030.2380.4821.0001.0000.000
세목명0.0000.2860.2600.1530.2860.3770.3810.0000.0001.000

Missing values

2023-12-12T12:12:24.379501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:12:24.653387image/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
0경기도안양시411702017도축세000000.0
1경기도안양시411702017레저세000000.0
2경기도안양시411702017재산세000000.0
3경기도안양시411702017주민세000000.0
4경기도안양시411702017취득세000000.0
5경기도안양시411702017자동차세000000.0
6경기도안양시411702017과년도수입000000.0
7경기도안양시411702017담배소비세000000.0
8경기도안양시411702017도시계획세000000.0
9경기도안양시411702017등록면허세000000.0
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
216경기도안양시만안구411712022취득세9944861100099338808000833485000010980300099.89
217경기도안양시만안구411712022자동차세32135672000306197710002191720001946000151395500095.28
218경기도안양시만안구411712022과년도수입12781576000340099300013528220002085735000729484800026.61
219경기도안양시만안구411712022담배소비세30635550000306355500002900000100.0
220경기도안양시만안구411712022도시계획세000000.0
221경기도안양시만안구411712022등록면허세60830680006054921000210660005010002764600099.54
222경기도안양시만안구411712022지방교육세3312083200032469080000170261000160900065014300098.03
223경기도안양시만안구411712022지방소득세4683333800044049937000282527700015914000276748700094.06
224경기도안양시만안구411712022지방소비세1371720400013717204000000100.0
225경기도안양시만안구411712022지역자원시설세4772027000468664700043630003350008504500098.21