Overview

Dataset statistics

Number of variables12
Number of observations113
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.5 KiB
Average record size in memory104.2 B

Variable types

Categorical6
Numeric6

Dataset

Description지방세 미환급 현황 자료는 미환급 유형별 미환급금 현황 및 연간 누적률을 제공하여 자치단체의 환급금 해소노력 확인 가능하도록 함.
URLhttps://www.data.go.kr/data/15080542/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 is highly overall correlated with 자치단체코드High correlation
자치단체코드 is highly overall correlated with 시군구명High correlation
당해미환급건수 is highly overall correlated with 당해미환급금액High correlation
당해미환급금액 is highly overall correlated with 당해미환급건수 and 1 other fieldsHigh correlation
누적미환급건수 is highly overall correlated with 누적미환급금액High correlation
누적미환급금액 is highly overall correlated with 누적미환급건수High correlation
누적미환급금액증감 is highly overall correlated with 당해미환급금액High correlation
미환급유형 is highly overall correlated with 납세자유형High correlation
납세자유형 is highly overall correlated with 미환급유형High correlation
누적미환급금액증감 has 8 (7.1%) zerosZeros

Reproduction

Analysis started2023-12-11 23:39:53.901283
Analysis finished2023-12-11 23:39:58.945429
Duration5.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
경기도
113 

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 113
100.0%

Length

2023-12-12T08:39:58.999954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:39:59.083935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 113
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
안양시만안구
73 
안양시동안구
40 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
안양시만안구 73
64.6%
안양시동안구 40
35.4%

Length

2023-12-12T08:39:59.186110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:39:59.289163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안양시만안구 73
64.6%
안양시동안구 40
35.4%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
41171
73 
41173
40 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41171
2nd row41171
3rd row41171
4th row41171
5th row41171

Common Values

ValueCountFrequency (%)
41171 73
64.6%
41173 40
35.4%

Length

2023-12-12T08:39:59.404480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:39:59.488942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41171 73
64.6%
41173 40
35.4%

세목명
Categorical

Distinct6
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
지방소득세
45 
자동차세
41 
주민세
10 
재산세
9 
등록면허세
6 

Length

Max length5
Median length4
Mean length4.2654867
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 45
39.8%
자동차세 41
36.3%
주민세 10
 
8.8%
재산세 9
 
8.0%
등록면허세 6
 
5.3%
취득세 2
 
1.8%

Length

2023-12-12T08:39:59.588712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:39:59.691440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 45
39.8%
자동차세 41
36.3%
주민세 10
 
8.8%
재산세 9
 
8.0%
등록면허세 6
 
5.3%
취득세 2
 
1.8%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5929
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T08:39:59.787785image/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 deviation1.6777811
Coefficient of variation (CV)0.00083075212
Kurtosis-1.1723238
Mean2019.5929
Median Absolute Deviation (MAD)1
Skewness-0.16426483
Sum228214
Variance2.8149494
MonotonicityIncreasing
2023-12-12T08:39:59.883929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 23
20.4%
2020 21
18.6%
2019 20
17.7%
2017 19
16.8%
2022 17
15.0%
2018 13
11.5%
ValueCountFrequency (%)
2017 19
16.8%
2018 13
11.5%
2019 20
17.7%
2020 21
18.6%
2021 23
20.4%
2022 17
15.0%
ValueCountFrequency (%)
2022 17
15.0%
2021 23
20.4%
2020 21
18.6%
2019 20
17.7%
2018 13
11.5%
2017 19
16.8%

미환급유형
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
신규
65 
사망
13 
폐업 또는 부도
11 
국외이주
9 
주소불명
8 

Length

Max length8
Median length2
Mean length3.1946903
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국외이주
2nd row사망
3rd row송달분 미수령
4th row신규
5th row주소불명

Common Values

ValueCountFrequency (%)
신규 65
57.5%
사망 13
 
11.5%
폐업 또는 부도 11
 
9.7%
국외이주 9
 
8.0%
주소불명 8
 
7.1%
송달분 미수령 7
 
6.2%

Length

2023-12-12T08:39:59.995963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:40:00.107049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 65
45.8%
사망 13
 
9.2%
폐업 11
 
7.7%
또는 11
 
7.7%
부도 11
 
7.7%
국외이주 9
 
6.3%
주소불명 8
 
5.6%
송달분 7
 
4.9%
미수령 7
 
4.9%

납세자유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
개인
75 
법인
38 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 75
66.4%
법인 38
33.6%

Length

2023-12-12T08:40:00.226758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:40:00.326743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 75
66.4%
법인 38
33.6%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.176991
Minimum1
Maximum765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T08:40:00.445936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q324
95-th percentile327.2
Maximum765
Range764
Interquartile range (IQR)23

Descriptive statistics

Standard deviation123.73396
Coefficient of variation (CV)2.4659502
Kurtosis15.056653
Mean50.176991
Median Absolute Deviation (MAD)3
Skewness3.6717358
Sum5670
Variance15310.093
MonotonicityNot monotonic
2023-12-12T08:40:00.586467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 32
28.3%
2 11
 
9.7%
3 8
 
7.1%
4 7
 
6.2%
5 6
 
5.3%
8 4
 
3.5%
12 2
 
1.8%
7 2
 
1.8%
10 2
 
1.8%
9 2
 
1.8%
Other values (36) 37
32.7%
ValueCountFrequency (%)
1 32
28.3%
2 11
 
9.7%
3 8
 
7.1%
4 7
 
6.2%
5 6
 
5.3%
6 1
 
0.9%
7 2
 
1.8%
8 4
 
3.5%
9 2
 
1.8%
10 2
 
1.8%
ValueCountFrequency (%)
765 1
0.9%
655 1
0.9%
399 1
0.9%
397 1
0.9%
355 1
0.9%
329 1
0.9%
326 1
0.9%
263 1
0.9%
261 1
0.9%
250 1
0.9%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean998155.31
Minimum130
Maximum11662900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T08:40:00.720123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile550
Q118600
median73240
Q3500400
95-th percentile6394830
Maximum11662900
Range11662770
Interquartile range (IQR)481800

Descriptive statistics

Standard deviation2202390.5
Coefficient of variation (CV)2.2064607
Kurtosis8.0587462
Mean998155.31
Median Absolute Deviation (MAD)72560
Skewness2.8536132
Sum1.1279155 × 108
Variance4.8505238 × 1012
MonotonicityNot monotonic
2023-12-12T08:40:00.855922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 2
 
1.8%
68520 2
 
1.8%
12240 1
 
0.9%
168490 1
 
0.9%
796270 1
 
0.9%
60210 1
 
0.9%
1221260 1
 
0.9%
23150 1
 
0.9%
11662900 1
 
0.9%
1994570 1
 
0.9%
Other values (101) 101
89.4%
ValueCountFrequency (%)
130 1
0.9%
150 1
0.9%
250 1
0.9%
390 1
0.9%
410 1
0.9%
460 1
0.9%
610 1
0.9%
680 1
0.9%
1160 1
0.9%
1210 1
0.9%
ValueCountFrequency (%)
11662900 1
0.9%
9935380 1
0.9%
7950340 1
0.9%
7041480 1
0.9%
6850280 1
0.9%
6845040 1
0.9%
6094690 1
0.9%
6075520 1
0.9%
5448850 1
0.9%
5339410 1
0.9%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.27434
Minimum1
Maximum2493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T08:40:00.985256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q142
median101
Q3540
95-th percentile1619
Maximum2493
Range2492
Interquartile range (IQR)498

Descriptive statistics

Standard deviation560.17357
Coefficient of variation (CV)1.4730775
Kurtosis4.1668273
Mean380.27434
Median Absolute Deviation (MAD)88
Skewness2.0658894
Sum42971
Variance313794.43
MonotonicityNot monotonic
2023-12-12T08:40:01.122245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
144 7
 
6.2%
92 6
 
5.3%
880 6
 
5.3%
540 5
 
4.4%
1619 5
 
4.4%
1 5
 
4.4%
271 5
 
4.4%
679 5
 
4.4%
101 4
 
3.5%
369 4
 
3.5%
Other values (26) 61
54.0%
ValueCountFrequency (%)
1 5
4.4%
2 1
 
0.9%
3 3
2.7%
4 3
2.7%
5 2
 
1.8%
8 1
 
0.9%
9 1
 
0.9%
13 3
2.7%
18 2
 
1.8%
24 2
 
1.8%
ValueCountFrequency (%)
2493 3
2.7%
1619 5
4.4%
1206 3
2.7%
1175 4
3.5%
880 6
5.3%
679 5
4.4%
540 5
4.4%
369 4
3.5%
271 5
4.4%
173 2
 
1.8%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7043786.8
Minimum680
Maximum31263030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T08:40:01.252311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum680
5-th percentile30530
Q1904160
median3234660
Q310519640
95-th percentile27808120
Maximum31263030
Range31262350
Interquartile range (IQR)9615480

Descriptive statistics

Standard deviation8503189.7
Coefficient of variation (CV)1.2071901
Kurtosis1.1530376
Mean7043786.8
Median Absolute Deviation (MAD)2899380
Skewness1.4424295
Sum7.9594791 × 108
Variance7.2304235 × 1013
MonotonicityNot monotonic
2023-12-12T08:40:01.373189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1589840 7
 
6.2%
626190 6
 
5.3%
16257700 6
 
5.3%
10519640 5
 
4.4%
27808120 5
 
4.4%
3255340 5
 
4.4%
9584760 5
 
4.4%
3234660 4
 
3.5%
22673870 4
 
3.5%
4383130 4
 
3.5%
Other values (34) 62
54.9%
ValueCountFrequency (%)
680 1
0.9%
7200 1
0.9%
8410 1
0.9%
13130 1
0.9%
26700 1
0.9%
30530 2
1.8%
34420 1
0.9%
42220 1
0.9%
70650 1
0.9%
220120 1
0.9%
ValueCountFrequency (%)
31263030 3
2.7%
27808120 5
4.4%
22673870 4
3.5%
16257700 6
5.3%
14945650 3
2.7%
14845570 2
 
1.8%
14082180 2
 
1.8%
10519640 5
4.4%
9584760 5
4.4%
8356150 2
 
1.8%

누적미환급금액증감
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198956.38
Minimum0
Maximum9388020
Zeros8
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T08:40:01.497026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1144.04
median1715.31
Q317173.87
95-th percentile412862.43
Maximum9388020
Range9388020
Interquartile range (IQR)17029.83

Descriptive statistics

Standard deviation1067409.9
Coefficient of variation (CV)5.3650449
Kurtosis54.623326
Mean198956.38
Median Absolute Deviation (MAD)1714.7
Skewness7.1235912
Sum22482071
Variance1.1393639 × 1012
MonotonicityNot monotonic
2023-12-12T08:40:01.613912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
7.1%
4620.75 2
 
1.8%
5015.93 1
 
0.9%
1077.34 1
 
0.9%
1715.31 1
 
0.9%
164.86 1
 
0.9%
13872.61 1
 
0.9%
94.41 1
 
0.9%
1036.78 1
 
0.9%
125796.0 1
 
0.9%
Other values (95) 95
84.1%
ValueCountFrequency (%)
0.0 8
7.1%
0.61 1
 
0.9%
3.22 1
 
0.9%
5.48 1
 
0.9%
12.73 1
 
0.9%
26.05 1
 
0.9%
37.26 1
 
0.9%
67.31 1
 
0.9%
71.7 1
 
0.9%
73.15 1
 
0.9%
ValueCountFrequency (%)
9388020.0 1
0.9%
5190876.92 1
0.9%
3965192.68 1
0.9%
634197.44 1
0.9%
437877.86 1
0.9%
424004.42 1
0.9%
405434.43 1
0.9%
361564.0 1
0.9%
254326.97 1
0.9%
196456.52 1
0.9%

Interactions

2023-12-12T08:39:57.979880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:54.476679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:55.147635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:55.872828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:56.512881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:57.087506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:58.097542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:54.570854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:55.241521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:55.978017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:56.598354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:57.192462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:58.218845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:54.686968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:55.400900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:56.098613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:56.713788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:57.555445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:58.343967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:54.829463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:55.506047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:56.223358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:56.817488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:57.658429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:58.461787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:54.943225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:55.622999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:56.323431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:56.903129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:57.760525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:58.569144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:55.047543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:55.760917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:56.430287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:56.995873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:39:57.880256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:40:01.692612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
시군구명1.0001.0000.0000.0000.6840.2000.1810.4310.0000.0000.000
자치단체코드1.0001.0000.0000.0000.6840.2000.1810.4310.0000.0000.000
세목명0.0000.0001.0000.0000.0000.0000.0000.0000.2930.4170.000
과세년도0.0000.0000.0001.0000.0820.0000.0000.1010.6000.6460.087
미환급유형0.6840.6840.0000.0821.0000.7280.0000.0000.0950.0000.221
납세자유형0.2000.2000.0000.0000.7281.0000.1360.0000.4540.3700.176
당해미환급건수0.1810.1810.0000.0000.0000.1361.0000.9490.6560.6200.000
당해미환급금액0.4310.4310.0000.1010.0000.0000.9491.0000.5070.5960.000
누적미환급건수0.0000.0000.2930.6000.0950.4540.6560.5071.0000.9660.084
누적미환급금액0.0000.0000.4170.6460.0000.3700.6200.5960.9661.0000.415
누적미환급금액증감0.0000.0000.0000.0870.2210.1760.0000.0000.0840.4151.000
2023-12-12T08:40:01.818165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명시군구명납세자유형자치단체코드미환급유형
세목명1.0000.0000.0000.0000.000
시군구명0.0001.0000.1280.9800.493
납세자유형0.0000.1281.0000.1280.528
자치단체코드0.0000.9800.1281.0000.493
미환급유형0.0000.4930.5280.4931.000
2023-12-12T08:40:01.935592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감시군구명자치단체코드세목명미환급유형납세자유형
과세년도1.0000.2450.1960.2250.330-0.0420.0000.0000.0000.0590.000
당해미환급건수0.2451.0000.8420.4500.363-0.4720.1300.1300.0000.0000.096
당해미환급금액0.1960.8421.0000.3190.346-0.6230.3170.3170.0000.0000.000
누적미환급건수0.2250.4500.3191.0000.7980.3320.0000.0000.1760.0520.475
누적미환급금액0.3300.3630.3460.7981.0000.3880.0000.0000.2210.0000.354
누적미환급금액증감-0.042-0.472-0.6230.3320.3881.0000.0000.0000.0000.1410.114
시군구명0.0000.1300.3170.0000.0000.0001.0000.9800.0000.4930.128
자치단체코드0.0000.1300.3170.0000.0000.0000.9801.0000.0000.4930.128
세목명0.0000.0000.0000.1760.2210.0000.0000.0001.0000.0000.000
미환급유형0.0590.0000.0000.0520.0000.1410.4930.4930.0001.0000.528
납세자유형0.0000.0960.0000.4750.3540.1140.1280.1280.0000.5281.000

Missing values

2023-12-12T08:39:58.718156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:39:58.886035image/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

시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
0경기도안양시만안구41171자동차세2017국외이주개인112240926261905015.93
1경기도안양시만안구41171자동차세2017사망개인531550926261901884.75
2경기도안양시만안구41171자동차세2017송달분 미수령개인19030926261906834.55
3경기도안양시만안구41171자동차세2017신규개인849940926261901153.88
4경기도안양시만안구41171자동차세2017주소불명개인135409262619017588.98
5경기도안양시동안구41173자동차세2017신규개인4625696092626190143.69
6경기도안양시만안구41171자동차세2017폐업 또는 부도법인121020283352801495.05
7경기도안양시동안구41173자동차세2017신규법인81709702833528096.1
8경기도안양시동안구41173자동차세2017폐업 또는 부도법인16530283352805034.46
9경기도안양시만안구41171지방소득세2017국외이주개인37324014415898402070.73
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
103경기도안양시동안구41173재산세2022신규개인5733855701736776540100.16
104경기도안양시만안구41171재산세2022신규법인32229208643470188.66
105경기도안양시만안구41171주민세2022신규개인638956701141931490115.65
106경기도안양시동안구41173주민세2022신규개인225067701141931490281.14
107경기도안양시만안구41171지방소득세2022신규개인7656845040249331263030356.73
108경기도안양시만안구41171지방소득세2022주소불명개인219150249331263030163153.42
109경기도안양시동안구41173지방소득세2022신규개인6559935380249331263030214.66
110경기도안양시만안구41171지방소득세2022신규법인51037090179130017173.87
111경기도안양시동안구41173지방소득세2022신규법인28615910901791300190.84
112경기도안양시만안구41171취득세2022신규개인568056800.0