Overview

Dataset statistics

Number of variables12
Number of observations66
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory106.0 B

Variable types

Categorical5
Numeric6
DateTime1

Dataset

Description인천광역시 남동구 지방세 징수 현황(과세년도, 세목명, 부과금액, 수납금액, 환급금액, 결손금액, 미수납 금액, 징수율, 데이터기준일)등을 제공합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15079469&srcSe=7661IVAWM27C61E190

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 4 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 4 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 1 other fieldsHigh correlation
부과금액 has 17 (25.8%) zerosZeros
수납급액 has 17 (25.8%) zerosZeros
환급금액 has 21 (31.8%) zerosZeros
결손금액 has 28 (42.4%) zerosZeros
미수납 금액 has 21 (31.8%) zerosZeros
징수율 has 17 (25.8%) zerosZeros

Reproduction

Analysis started2024-03-18 02:01:44.731479
Analysis finished2024-03-18 02:01:48.015496
Duration3.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
인천광역시
66 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 66
100.0%

Length

2024-03-18T11:01:48.088088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:01:48.160786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 66
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
남동구
66 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남동구 66
100.0%

Length

2024-03-18T11:01:48.237469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:01:48.310898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남동구 66
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
28200
66 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28200
2nd row28200
3rd row28200
4th row28200
5th row28200

Common Values

ValueCountFrequency (%)
28200 66
100.0%

Length

2024-03-18T11:01:48.384466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:01:48.457800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28200 66
100.0%

과세년도
Categorical

Distinct5
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size660.0 B
2018
14 
2019
13 
2020
13 
2021
13 
2022
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 14
21.2%
2019 13
19.7%
2020 13
19.7%
2021 13
19.7%
2022 13
19.7%

Length

2024-03-18T11:01:48.532765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:01:48.642528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 14
21.2%
2019 13
19.7%
2020 13
19.7%
2021 13
19.7%
2022 13
19.7%

세목명
Categorical

Distinct14
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size660.0 B
레저세
5 
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
Other values (9)
41 

Length

Max length7
Median length5
Mean length4.4393939
Min length3

Unique

Unique1 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 5
 
7.6%
재산세 5
 
7.6%
주민세 5
 
7.6%
취득세 5
 
7.6%
자동차세 5
 
7.6%
과년도수입 5
 
7.6%
담배소비세 5
 
7.6%
도시계획세 5
 
7.6%
등록면허세 5
 
7.6%
지방교육세 5
 
7.6%
Other values (4) 16
24.2%

Length

2024-03-18T11:01:48.748726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 5
 
7.6%
재산세 5
 
7.6%
주민세 5
 
7.6%
취득세 5
 
7.6%
자동차세 5
 
7.6%
과년도수입 5
 
7.6%
담배소비세 5
 
7.6%
도시계획세 5
 
7.6%
등록면허세 5
 
7.6%
지방교육세 5
 
7.6%
Other values (4) 16
24.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3257078 × 1010
Minimum0
Maximum3.27 × 1011
Zeros17
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-03-18T11:01:48.881623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q169895750
median1.6562587 × 1010
Q36.1529465 × 1010
95-th percentile2.52 × 1011
Maximum3.27 × 1011
Range3.27 × 1011
Interquartile range (IQR)6.1459569 × 1010

Descriptive statistics

Standard deviation7.8931054 × 1010
Coefficient of variation (CV)1.4820763
Kurtosis3.5508999
Mean5.3257078 × 1010
Median Absolute Deviation (MAD)1.6562587 × 1010
Skewness2.0148458
Sum3.5149672 × 1012
Variance6.2301113 × 1021
MonotonicityNot monotonic
2024-03-18T11:01:48.991449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
25.8%
15906060000 1
 
1.5%
15674354000 1
 
1.5%
110000000000 1
 
1.5%
18619930000 1
 
1.5%
327000000000 1
 
1.5%
61690180000 1
 
1.5%
13265616000 1
 
1.5%
15626804000 1
 
1.5%
50381429000 1
 
1.5%
Other values (40) 40
60.6%
ValueCountFrequency (%)
0 17
25.8%
279583000 1
 
1.5%
2360000000 1
 
1.5%
2520000000 1
 
1.5%
7428555000 1
 
1.5%
10351588000 1
 
1.5%
13265616000 1
 
1.5%
13792053000 1
 
1.5%
15162670000 1
 
1.5%
15332896000 1
 
1.5%
ValueCountFrequency (%)
327000000000 1
1.5%
304000000000 1
1.5%
266000000000 1
1.5%
253000000000 1
1.5%
249000000000 1
1.5%
185000000000 1
1.5%
152000000000 1
1.5%
130000000000 1
1.5%
123000000000 1
1.5%
121000000000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1164506 × 1010
Minimum0
Maximum3.26 × 1011
Zeros17
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-03-18T11:01:49.101825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q169895750
median1.5510652 × 1010
Q35.6470445 × 1010
95-th percentile2.52 × 1011
Maximum3.26 × 1011
Range3.26 × 1011
Interquartile range (IQR)5.6400549 × 1010

Descriptive statistics

Standard deviation7.8613107 × 1010
Coefficient of variation (CV)1.5364774
Kurtosis3.8014637
Mean5.1164506 × 1010
Median Absolute Deviation (MAD)1.5510652 × 1010
Skewness2.0717446
Sum3.3768574 × 1012
Variance6.1800205 × 1021
MonotonicityNot monotonic
2024-03-18T11:01:49.212933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 17
25.8%
116000000000 2
 
3.0%
2360000000 1
 
1.5%
15210085000 1
 
1.5%
108000000000 1
 
1.5%
18015164000 1
 
1.5%
326000000000 1
 
1.5%
56846735000 1
 
1.5%
6640701000 1
 
1.5%
15548968000 1
 
1.5%
Other values (39) 39
59.1%
ValueCountFrequency (%)
0 17
25.8%
279583000 1
 
1.5%
2360000000 1
 
1.5%
2520000000 1
 
1.5%
3598532000 1
 
1.5%
3649288000 1
 
1.5%
3983392000 1
 
1.5%
6640701000 1
 
1.5%
6826066000 1
 
1.5%
7428555000 1
 
1.5%
ValueCountFrequency (%)
326000000000 1
1.5%
304000000000 1
1.5%
266000000000 1
1.5%
253000000000 1
1.5%
249000000000 1
1.5%
174000000000 1
1.5%
146000000000 1
1.5%
126000000000 1
1.5%
118000000000 1
1.5%
116000000000 2
3.0%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1799962 × 108
Minimum0
Maximum8.219699 × 109
Zeros21
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-03-18T11:01:49.323668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median58294000
Q37.9136025 × 108
95-th percentile4.552161 × 109
Maximum8.219699 × 109
Range8.219699 × 109
Interquartile range (IQR)7.9136025 × 108

Descriptive statistics

Standard deviation1.8362555 × 109
Coefficient of variation (CV)2.0002791
Kurtosis5.4955564
Mean9.1799962 × 108
Median Absolute Deviation (MAD)58294000
Skewness2.4428682
Sum6.0587975 × 1010
Variance3.3718342 × 1018
MonotonicityNot monotonic
2024-03-18T11:01:49.425959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 21
31.8%
3974559000 1
 
1.5%
3813532000 1
 
1.5%
40645000 1
 
1.5%
77861000 1
 
1.5%
45215000 1
 
1.5%
709369000 1
 
1.5%
858148000 1
 
1.5%
3915311000 1
 
1.5%
60417000 1
 
1.5%
Other values (36) 36
54.5%
ValueCountFrequency (%)
0 21
31.8%
12819000 1
 
1.5%
15234000 1
 
1.5%
16876000 1
 
1.5%
20247000 1
 
1.5%
23154000 1
 
1.5%
26416000 1
 
1.5%
29172000 1
 
1.5%
38896000 1
 
1.5%
40645000 1
 
1.5%
ValueCountFrequency (%)
8219699000 1
1.5%
7000539000 1
1.5%
6481214000 1
1.5%
4638303000 1
1.5%
4293735000 1
1.5%
3974559000 1
1.5%
3915311000 1
1.5%
3813532000 1
1.5%
3492328000 1
1.5%
3080753000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9742094 × 108
Minimum0
Maximum4.690163 × 109
Zeros28
Zeros (%)42.4%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-03-18T11:01:49.531313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median158500
Q323456750
95-th percentile1.6491035 × 109
Maximum4.690163 × 109
Range4.690163 × 109
Interquartile range (IQR)23456750

Descriptive statistics

Standard deviation8.6050591 × 108
Coefficient of variation (CV)2.8932257
Kurtosis14.12584
Mean2.9742094 × 108
Median Absolute Deviation (MAD)158500
Skewness3.6817212
Sum1.9629782 × 1010
Variance7.4047042 × 1017
MonotonicityNot monotonic
2024-03-18T11:01:49.632965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 28
42.4%
108000 1
 
1.5%
23252000 1
 
1.5%
1150515000 1
 
1.5%
66206000 1
 
1.5%
174992000 1
 
1.5%
2392000 1
 
1.5%
9034000 1
 
1.5%
9961000 1
 
1.5%
968315000 1
 
1.5%
Other values (29) 29
43.9%
ValueCountFrequency (%)
0 28
42.4%
5000 1
 
1.5%
6000 1
 
1.5%
44000 1
 
1.5%
56000 1
 
1.5%
108000 1
 
1.5%
209000 1
 
1.5%
1432000 1
 
1.5%
2159000 1
 
1.5%
2199000 1
 
1.5%
ValueCountFrequency (%)
4690163000 1
1.5%
3490354000 1
1.5%
3266746000 1
1.5%
1722773000 1
1.5%
1428095000 1
1.5%
1150515000 1
1.5%
1004024000 1
1.5%
968315000 1
1.5%
739554000 1
1.5%
475961000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8202509 × 109
Minimum0
Maximum1.2260414 × 1010
Zeros21
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-03-18T11:01:49.738699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.072535 × 108
Q32.855513 × 109
95-th percentile6.5698275 × 109
Maximum1.2260414 × 1010
Range1.2260414 × 1010
Interquartile range (IQR)2.855513 × 109

Descriptive statistics

Standard deviation2.6575606 × 109
Coefficient of variation (CV)1.4599968
Kurtosis3.5098976
Mean1.8202509 × 109
Median Absolute Deviation (MAD)5.072535 × 108
Skewness1.848142
Sum1.2013656 × 1011
Variance7.0626285 × 1018
MonotonicityNot monotonic
2024-03-18T11:01:49.850635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 21
31.8%
3805630000 1
 
1.5%
3071556000 1
 
1.5%
398063000 1
 
1.5%
2200910000 1
 
1.5%
602374000 1
 
1.5%
579881000 1
 
1.5%
4833484000 1
 
1.5%
5656600000 1
 
1.5%
77627000 1
 
1.5%
Other values (36) 36
54.5%
ValueCountFrequency (%)
0 21
31.8%
64072000 1
 
1.5%
77107000 1
 
1.5%
77627000 1
 
1.5%
83078000 1
 
1.5%
113905000 1
 
1.5%
181584000 1
 
1.5%
196527000 1
 
1.5%
281679000 1
 
1.5%
336039000 1
 
1.5%
ValueCountFrequency (%)
12260414000 1
1.5%
9876852000 1
1.5%
7721497000 1
1.5%
6659341000 1
1.5%
6301287000 1
1.5%
6013502000 1
1.5%
5660641000 1
1.5%
5656600000 1
1.5%
4965695000 1
1.5%
4833484000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.153485
Minimum0
Maximum100
Zeros17
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-03-18T11:01:49.971543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.3625
median96.105
Q397.4425
95-th percentile99.9825
Maximum100
Range100
Interquartile range (IQR)92.08

Descriptive statistics

Standard deviation43.37087
Coefficient of variation (CV)0.6458469
Kurtosis-1.2762349
Mean67.153485
Median Absolute Deviation (MAD)3.8
Skewness-0.81639803
Sum4432.13
Variance1881.0324
MonotonicityNot monotonic
2024-03-18T11:01:50.166442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 17
25.8%
100.0 4
 
6.1%
96.75 2
 
3.0%
96.35 1
 
1.5%
97.04 1
 
1.5%
97.84 1
 
1.5%
99.82 1
 
1.5%
92.15 1
 
1.5%
50.06 1
 
1.5%
99.5 1
 
1.5%
Other values (36) 36
54.5%
ValueCountFrequency (%)
0.0 17
25.8%
21.45 1
 
1.5%
25.98 1
 
1.5%
33.98 1
 
1.5%
34.76 1
 
1.5%
50.06 1
 
1.5%
89.02 1
 
1.5%
90.65 1
 
1.5%
92.07 1
 
1.5%
92.09 1
 
1.5%
ValueCountFrequency (%)
100.0 4
6.1%
99.93 1
 
1.5%
99.92 1
 
1.5%
99.89 1
 
1.5%
99.82 1
 
1.5%
99.77 1
 
1.5%
99.61 1
 
1.5%
99.5 1
 
1.5%
99.49 1
 
1.5%
99.44 1
 
1.5%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
Minimum2024-01-08 00:00:00
Maximum2024-01-08 00:00:00
2024-03-18T11:01:50.299759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:50.562273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-18T11:01:47.128154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:44.992390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.397406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.900545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.309031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.712585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:47.204588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.060476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.479618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.965925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.375670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.788777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:47.284235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.127879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.547088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.031384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.447071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.861411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:47.352295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.189551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.608767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.090433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.517803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.925529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:47.583364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.257555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.696806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.178113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.584730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.990260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:47.655242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.323536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:45.819007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.248607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:46.648942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:01:47.060217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T11:01:50.622732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8130.8130.6600.6080.7930.761
부과금액0.0000.8131.0001.0000.5260.6570.8930.000
수납급액0.0000.8131.0001.0000.5260.6570.8930.000
환급금액0.0000.6600.5260.5261.0000.9400.8680.783
결손금액0.0000.6080.6570.6570.9401.0000.8680.721
미수납 금액0.0000.7930.8930.8930.8680.8681.0000.847
징수율0.0000.7610.0000.0000.7830.7210.8471.000
2024-03-18T11:01:50.731307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-03-18T11:01:50.806323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9880.7890.6100.7350.5580.0000.494
수납급액0.9881.0000.7390.5640.6800.6040.0000.494
환급금액0.7890.7391.0000.8020.9080.2450.0000.341
결손금액0.6100.5640.8021.0000.7880.1850.0000.247
미수납 금액0.7350.6800.9080.7881.0000.1420.0000.468
징수율0.5580.6040.2450.1850.1421.0000.0000.469
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.4940.4940.3410.2470.4680.4690.0001.000

Missing values

2024-03-18T11:01:47.744744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:01:47.932098image/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 금액징수율데이터기준일자
0인천광역시남동구282002018도축세000000.02024-01-08
1인천광역시남동구282002018레저세000000.02024-01-08
2인천광역시남동구282002018재산세98008118000950379760001165670000297014200096.972024-01-08
3인천광역시남동구282002018주민세17612132000169756640001687600010800063636000096.392024-01-08
4인천광역시남동구282002018취득세266000000000266000000000505735000028167900099.892024-01-08
5인천광역시남동구282002018자동차세57455430000511467740008086620007369000630128700089.022024-01-08
6인천광역시남동구282002018과년도수입200905040006826066000463830300010040240001226041400033.982024-01-08
7인천광역시남동구282002018담배소비세000000.02024-01-08
8인천광역시남동구282002018도시계획세000000.02024-01-08
9인천광역시남동구282002018등록면허세151626700001508550700085609000560007710700099.492024-01-08
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일자
56인천광역시남동구282002022취득세253000000000253000000000739455000058154600099.772024-01-08
57인천광역시남동구282002022자동차세58897910000542271050009128270000467080500092.072024-01-08
58인천광역시남동구282002022과년도수입1035158800035985320006481214000739554000601350200034.762024-01-08
59인천광역시남동구282002022담배소비세000000.02024-01-08
60인천광역시남동구282002022도시계획세000000.02024-01-08
61인천광역시남동구282002022등록면허세137920530001367814800058658000011390500099.172024-01-08
62인천광역시남동구282002022지방교육세43764074000419197990002960220006000184426900095.792024-01-08
63인천광역시남동구282002022지방소득세18500000000017400000000042937350003266746000772149700094.072024-01-08
64인천광역시남동구282002022지방소비세74285550007428555000000100.02024-01-08
65인천광역시남동구282002022지역자원시설세162137380001568185800023154000500053187500096.722024-01-08