Overview

Dataset statistics

Number of variables11
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory98.5 B

Variable types

Categorical5
Numeric6

Dataset

Description지방세 부과액에 대한 세목별 징수현황을 제공(시도명,시군구명,자치단체코드,과세년도,세목명,부과금액,수납급액,환급금액,결손금액,미수납 금액,징수율)
URLhttps://www.data.go.kr/data/15079130/fileData.do

Alerts

시도명 has constant value ""Constant
과세년도 has constant value ""Constant
시군구명 is highly overall correlated with 자치단체코드High correlation
자치단체코드 is highly overall correlated with 시군구명High correlation
부과금액 is highly overall correlated with 수납급액 and 4 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 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 1 other fieldsHigh correlation
부과금액 has 19 (36.5%) zerosZeros
수납급액 has 19 (36.5%) zerosZeros
환급금액 has 23 (44.2%) zerosZeros
결손금액 has 32 (61.5%) zerosZeros
미수납 금액 has 25 (48.1%) zerosZeros
징수율 has 19 (36.5%) zerosZeros

Reproduction

Analysis started2023-12-12 23:20:19.744636
Analysis finished2023-12-12 23:20:23.725522
Duration3.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
경기도
52 

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 52
100.0%

Length

2023-12-13T08:20:23.788218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:20:23.897504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 52
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
고양시
13 
고양시덕양구
13 
고양시일산동구
13 
고양시일산서구
13 

Length

Max length7
Median length6.5
Mean length5.75
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고양시 13
25.0%
고양시덕양구 13
25.0%
고양시일산동구 13
25.0%
고양시일산서구 13
25.0%

Length

2023-12-13T08:20:24.011372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:20:24.132686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시 13
25.0%
고양시덕양구 13
25.0%
고양시일산동구 13
25.0%
고양시일산서구 13
25.0%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
41280
13 
41281
13 
41285
13 
41287
13 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41280
2nd row41280
3rd row41280
4th row41280
5th row41280

Common Values

ValueCountFrequency (%)
41280 13
25.0%
41281 13
25.0%
41285 13
25.0%
41287 13
25.0%

Length

2023-12-13T08:20:24.248028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:20:24.344891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41280 13
25.0%
41281 13
25.0%
41285 13
25.0%
41287 13
25.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
2022
52 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 52
100.0%

Length

2023-12-13T08:20:24.445091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:20:24.526432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 52
100.0%

세목명
Categorical

Distinct13
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
레저세
4 
재산세
4 
주민세
4 
취득세
4 
자동차세
4 
Other values (8)
32 

Length

Max length7
Median length5
Mean length4.4615385
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 4
 
7.7%
재산세 4
 
7.7%
주민세 4
 
7.7%
취득세 4
 
7.7%
자동차세 4
 
7.7%
과년도수입 4
 
7.7%
담배소비세 4
 
7.7%
도시계획세 4
 
7.7%
등록면허세 4
 
7.7%
지방교육세 4
 
7.7%
Other values (3) 12
23.1%

Length

2023-12-13T08:20:24.648793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 4
 
7.7%
재산세 4
 
7.7%
주민세 4
 
7.7%
취득세 4
 
7.7%
자동차세 4
 
7.7%
과년도수입 4
 
7.7%
담배소비세 4
 
7.7%
도시계획세 4
 
7.7%
등록면허세 4
 
7.7%
지방교육세 4
 
7.7%
Other values (3) 12
23.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1772904 × 1010
Minimum0
Maximum3.2298574 × 1011
Zeros19
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T08:20:24.810898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.1330795 × 1010
Q33.8281733 × 1010
95-th percentile1.2413136 × 1011
Maximum3.2298574 × 1011
Range3.2298574 × 1011
Interquartile range (IQR)3.8281733 × 1010

Descriptive statistics

Standard deviation5.5801864 × 1010
Coefficient of variation (CV)1.7562721
Kurtosis14.292686
Mean3.1772904 × 1010
Median Absolute Deviation (MAD)1.1330795 × 1010
Skewness3.3383612
Sum1.652191 × 1012
Variance3.113848 × 1021
MonotonicityNot monotonic
2023-12-13T08:20:25.021867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 19
36.5%
160816186000 1
 
1.9%
28396781000 1
 
1.9%
21473226000 1
 
1.9%
10914250000 1
 
1.9%
39482088000 1
 
1.9%
122605797000 1
 
1.9%
12302612000 1
 
1.9%
64239159000 1
 
1.9%
582962000 1
 
1.9%
Other values (24) 24
46.2%
ValueCountFrequency (%)
0 19
36.5%
582962000 1
 
1.9%
5494347000 1
 
1.9%
6791292000 1
 
1.9%
7692831000 1
 
1.9%
8732263000 1
 
1.9%
9760456000 1
 
1.9%
10914250000 1
 
1.9%
11747340000 1
 
1.9%
12083600000 1
 
1.9%
ValueCountFrequency (%)
322985739000 1
1.9%
160816186000 1
1.9%
125995940000 1
1.9%
122605797000 1
1.9%
97613057000 1
1.9%
92988434000 1
1.9%
85084052000 1
1.9%
64239159000 1
1.9%
64033723000 1
1.9%
61265090000 1
1.9%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0322925 × 1010
Minimum0
Maximum3.2107178 × 1011
Zeros19
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T08:20:25.209827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.0384355 × 109
Q33.5516717 × 1010
95-th percentile1.1961785 × 1011
Maximum3.2107178 × 1011
Range3.2107178 × 1011
Interquartile range (IQR)3.5516717 × 1010

Descriptive statistics

Standard deviation5.5369125 × 1010
Coefficient of variation (CV)1.8259823
Kurtosis14.712609
Mean3.0322925 × 1010
Median Absolute Deviation (MAD)8.0384355 × 109
Skewness3.3981917
Sum1.5767921 × 1012
Variance3.06574 × 1021
MonotonicityNot monotonic
2023-12-13T08:20:25.384695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 19
36.5%
160657659000 1
 
1.9%
25433797000 1
 
1.9%
6102268000 1
 
1.9%
10833216000 1
 
1.9%
38296351000 1
 
1.9%
116387661000 1
 
1.9%
12135613000 1
 
1.9%
62884633000 1
 
1.9%
582962000 1
 
1.9%
Other values (24) 24
46.2%
ValueCountFrequency (%)
0 19
36.5%
582962000 1
 
1.9%
5286961000 1
 
1.9%
5786763000 1
 
1.9%
5941086000 1
 
1.9%
6102268000 1
 
1.9%
6584574000 1
 
1.9%
7656315000 1
 
1.9%
8420556000 1
 
1.9%
9391055000 1
 
1.9%
ValueCountFrequency (%)
321071781000 1
1.9%
160657659000 1
1.9%
123565855000 1
1.9%
116387661000 1
1.9%
95888228000 1
1.9%
87728180000 1
1.9%
85005590000 1
1.9%
62884633000 1
1.9%
61265090000 1
1.9%
60543454000 1
1.9%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9037981 × 108
Minimum0
Maximum5.810895 × 109
Zeros23
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T08:20:25.553114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1760000
Q32.829395 × 108
95-th percentile3.3759567 × 109
Maximum5.810895 × 109
Range5.810895 × 109
Interquartile range (IQR)2.829395 × 108

Descriptive statistics

Standard deviation1.1701039 × 109
Coefficient of variation (CV)2.3861176
Kurtosis9.2305708
Mean4.9037981 × 108
Median Absolute Deviation (MAD)1760000
Skewness2.9891248
Sum2.549975 × 1010
Variance1.3691431 × 1018
MonotonicityNot monotonic
2023-12-13T08:20:25.702261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 23
44.2%
3276540000 1
 
1.9%
3137000 1
 
1.9%
2297151000 1
 
1.9%
120744000 1
 
1.9%
25933000 1
 
1.9%
1149221000 1
 
1.9%
340610000 1
 
1.9%
282571000 1
 
1.9%
1538000 1
 
1.9%
Other values (20) 20
38.5%
ValueCountFrequency (%)
0 23
44.2%
24000 1
 
1.9%
54000 1
 
1.9%
1538000 1
 
1.9%
1982000 1
 
1.9%
3137000 1
 
1.9%
6947000 1
 
1.9%
14318000 1
 
1.9%
23982000 1
 
1.9%
25933000 1
 
1.9%
ValueCountFrequency (%)
5810895000 1
1.9%
3619594000 1
1.9%
3497466000 1
1.9%
3276540000 1
1.9%
2297151000 1
1.9%
1683696000 1
1.9%
1501863000 1
1.9%
1149221000 1
1.9%
492530000 1
1.9%
378974000 1
1.9%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3589835 × 108
Minimum0
Maximum5.224577 × 109
Zeros32
Zeros (%)61.5%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T08:20:25.821129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3659000
95-th percentile1.0520213 × 109
Maximum5.224577 × 109
Range5.224577 × 109
Interquartile range (IQR)659000

Descriptive statistics

Standard deviation1.0033312 × 109
Coefficient of variation (CV)4.2532352
Kurtosis19.114619
Mean2.3589835 × 108
Median Absolute Deviation (MAD)0
Skewness4.4261735
Sum1.2266714 × 1010
Variance1.0066734 × 1018
MonotonicityNot monotonic
2023-12-13T08:20:25.931833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 32
61.5%
3413000 1
 
1.9%
605000 1
 
1.9%
100000 1
 
1.9%
2251936000 1
 
1.9%
312000 1
 
1.9%
62000 1
 
1.9%
1121000 1
 
1.9%
70273000 1
 
1.9%
1828000 1
 
1.9%
Other values (11) 11
 
21.2%
ValueCountFrequency (%)
0 32
61.5%
56000 1
 
1.9%
62000 1
 
1.9%
100000 1
 
1.9%
312000 1
 
1.9%
319000 1
 
1.9%
402000 1
 
1.9%
605000 1
 
1.9%
821000 1
 
1.9%
1121000 1
 
1.9%
ValueCountFrequency (%)
5224577000 1
1.9%
4675586000 1
1.9%
2251936000 1
1.9%
70273000 1
1.9%
21954000 1
1.9%
8338000 1
1.9%
3413000 1
1.9%
2388000 1
1.9%
1828000 1
1.9%
1373000 1
1.9%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2140808 × 109
Minimum0
Maximum1.0146381 × 1010
Zeros25
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T08:20:26.048537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median43088000
Q31.425483 × 109
95-th percentile6.2436009 × 109
Maximum1.0146381 × 1010
Range1.0146381 × 1010
Interquartile range (IQR)1.425483 × 109

Descriptive statistics

Standard deviation2.3708161 × 109
Coefficient of variation (CV)1.9527664
Kurtosis6.0982652
Mean1.2140808 × 109
Median Absolute Deviation (MAD)43088000
Skewness2.49208
Sum6.3132201 × 1010
Variance5.6207692 × 1018
MonotonicityNot monotonic
2023-12-13T08:20:26.165878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 25
48.1%
80213000 1
 
1.9%
206718000 1
 
1.9%
3489664000 1
 
1.9%
891902000 1
 
1.9%
36516000 1
 
1.9%
6360614000 1
 
1.9%
2339522000 1
 
1.9%
78462000 1
 
1.9%
207324000 1
 
1.9%
Other values (18) 18
34.6%
ValueCountFrequency (%)
0 25
48.1%
36516000 1
 
1.9%
49660000 1
 
1.9%
78462000 1
 
1.9%
80213000 1
 
1.9%
158527000 1
 
1.9%
165878000 1
 
1.9%
172259000 1
 
1.9%
206718000 1
 
1.9%
207324000 1
 
1.9%
ValueCountFrequency (%)
10146381000 1
1.9%
9715403000 1
1.9%
6360614000 1
1.9%
6147863000 1
1.9%
5238300000 1
1.9%
3771087000 1
1.9%
3489664000 1
1.9%
2961611000 1
1.9%
2426672000 1
1.9%
2339522000 1
1.9%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.018846
Minimum0
Maximum100
Zeros19
Zeros (%)36.5%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-13T08:20:26.278323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median94.445
Q398.5875
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)98.5875

Descriptive statistics

Standard deviation46.976833
Coefficient of variation (CV)0.8096823
Kurtosis-1.8686725
Mean58.018846
Median Absolute Deviation (MAD)5.555
Skewness-0.37735852
Sum3016.98
Variance2206.8228
MonotonicityNot monotonic
2023-12-13T08:20:26.382944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 19
36.5%
100.0 6
 
11.5%
96.96 1
 
1.9%
94.55 1
 
1.9%
95.93 1
 
1.9%
99.53 1
 
1.9%
40.82 1
 
1.9%
90.7 1
 
1.9%
99.91 1
 
1.9%
96.23 1
 
1.9%
Other values (19) 19
36.5%
ValueCountFrequency (%)
0.0 19
36.5%
28.42 1
 
1.9%
28.68 1
 
1.9%
40.82 1
 
1.9%
89.57 1
 
1.9%
90.17 1
 
1.9%
90.7 1
 
1.9%
94.34 1
 
1.9%
94.55 1
 
1.9%
94.93 1
 
1.9%
ValueCountFrequency (%)
100.0 6
11.5%
99.91 1
 
1.9%
99.9 1
 
1.9%
99.67 1
 
1.9%
99.53 1
 
1.9%
99.41 1
 
1.9%
99.26 1
 
1.9%
98.64 1
 
1.9%
98.57 1
 
1.9%
98.23 1
 
1.9%

Interactions

2023-12-13T08:20:22.875238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.046033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.583488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:21.120686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:21.951619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.381776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.987647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.121342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.689158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:21.202491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.023849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.461720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:23.090171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.195266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.777123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:21.605439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.100097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.539552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:23.168831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.322544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.868904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:21.711786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.173698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.638911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:23.265271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.399487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.944743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:21.802457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.240878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.711064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:23.353994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:20.492820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:21.043926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:21.878624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.311358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:20:22.789555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:20:26.464605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명부과금액수납급액환급금액결손금액미수납 금액징수율
시군구명1.0001.0000.0000.0000.0000.0000.0510.0000.019
자치단체코드1.0001.0000.0000.0000.0000.0000.0510.0000.019
세목명0.0000.0001.0000.4880.3250.6210.2290.6370.579
부과금액0.0000.0000.4881.0000.9980.6550.0000.6520.000
수납급액0.0000.0000.3250.9981.0000.6550.0000.6440.000
환급금액0.0000.0000.6210.6550.6551.0001.0000.9410.797
결손금액0.0510.0510.2290.0000.0001.0001.0000.7700.829
미수납 금액0.0000.0000.6370.6520.6440.9410.7701.0000.783
징수율0.0190.0190.5790.0000.0000.7970.8290.7831.000
2023-12-13T08:20:26.563897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명세목명자치단체코드
시군구명1.0000.0001.000
세목명0.0001.0000.000
자치단체코드1.0000.0001.000
2023-12-13T08:20:26.665504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율시군구명자치단체코드세목명
부과금액1.0000.9860.8170.5400.7260.7300.0000.0000.236
수납급액0.9861.0000.7590.4710.6610.7810.0000.0000.137
환급금액0.8170.7591.0000.7710.9400.4210.0000.0000.317
결손금액0.5400.4710.7711.0000.8560.1620.0000.0000.101
미수납 금액0.7260.6610.9400.8561.0000.2970.0000.0000.330
징수율0.7300.7810.4210.1620.2971.0000.0000.0000.323
시군구명0.0000.0000.0000.0000.0000.0001.0001.0000.000
자치단체코드0.0000.0000.0000.0000.0000.0001.0001.0000.000
세목명0.2360.1370.3170.1010.3300.3230.0000.0001.000

Missing values

2023-12-13T08:20:23.475538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:20:23.652542image/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
0경기도고양시412802022레저세582962000582962000000100.0
1경기도고양시412802022재산세000000.0
2경기도고양시412802022주민세000000.0
3경기도고양시412802022취득세000000.0
4경기도고양시412802022자동차세3824918800038249188000000100.0
5경기도고양시412802022과년도수입000000.0
6경기도고양시412802022담배소비세61265090000612650900005400000100.0
7경기도고양시412802022도시계획세000000.0
8경기도고양시412802022등록면허세000000.0
9경기도고양시412802022지방교육세27183697000271836970002400000100.0
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
42경기도고양시일산서구412872022취득세850840520008500559000028257100007846200099.91
43경기도고양시일산서구412872022자동차세2516686600022827032000340610000312000233952200090.7
44경기도고양시일산서구412872022과년도수입14553636000594108600011492210002251936000636061400040.82
45경기도고양시일산서구412872022담배소비세000000.0
46경기도고양시일산서구412872022도시계획세000000.0
47경기도고양시일산서구412872022등록면허세769283100076563150002593300003651600099.53
48경기도고양시일산서구412872022지방교육세219113340002101933200012074400010000089190200095.93
49경기도고양시일산서구412872022지방소득세64033723000605434540002297151000605000348966400094.55
50경기도고양시일산서구412872022지방소비세000000.0
51경기도고양시일산서구412872022지역자원시설세679129200065845740003137000020671800096.96