Overview

Dataset statistics

Number of variables12
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory108.3 B

Variable types

Categorical7
Numeric5

Dataset

Description전라남도 영광군의 지방세 미환급 현황과 관련된 데이터로 미환급 유형별 미환급금 현황 및 연간 누적률 제공하여 자치단체의 환급금 해소노력 확인 가능
Author전라남도 영광군
URLhttps://www.data.go.kr/data/15079833/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
미환급유형 has constant value ""Constant
당해미환급건수 is highly overall correlated with 당해미환급금액 and 2 other fieldsHigh correlation
당해미환급금액 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급건수 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급금액 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
당해미환급금액 has unique valuesUnique
누적미환급금액 has unique valuesUnique
누적미환급금액증감 has 5 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-12 06:34:04.448620
Analysis finished2023-12-12 06:34:08.226891
Duration3.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
전라남도
25 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 25
100.0%

Length

2023-12-12T15:34:08.317914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:34:08.442231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 25
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
영광군
25 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영광군 25
100.0%

Length

2023-12-12T15:34:08.553649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:34:08.662702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영광군 25
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
46870
25 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46870
2nd row46870
3rd row46870
4th row46870
5th row46870

Common Values

ValueCountFrequency (%)
46870 25
100.0%

Length

2023-12-12T15:34:08.772831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:34:08.871634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46870 25
100.0%

세목명
Categorical

Distinct5
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
자동차세
10 
지방소득세
7 
재산세
6 
등록면허세
 
1
주민세
 
1

Length

Max length5
Median length4
Mean length4.04
Min length3

Unique

Unique2 ?
Unique (%)8.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 10
40.0%
지방소득세 7
28.0%
재산세 6
24.0%
등록면허세 1
 
4.0%
주민세 1
 
4.0%

Length

2023-12-12T15:34:09.017008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:34:09.148992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 10
40.0%
지방소득세 7
28.0%
재산세 6
24.0%
등록면허세 1
 
4.0%
주민세 1
 
4.0%

과세년도
Categorical

Distinct5
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2021
7 
2019
5 
2020
5 
2017
4 
2018
4 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2018

Common Values

ValueCountFrequency (%)
2021 7
28.0%
2019 5
20.0%
2020 5
20.0%
2017 4
16.0%
2018 4
16.0%

Length

2023-12-12T15:34:09.270037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:34:09.366511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 7
28.0%
2019 5
20.0%
2020 5
20.0%
2017 4
16.0%
2018 4
16.0%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
신규
25 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신규 25
100.0%

Length

2023-12-12T15:34:09.473189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:34:09.558596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 25
100.0%

납세자유형
Categorical

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
개인
17 
법인
8 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 17
68.0%
법인 8
32.0%

Length

2023-12-12T15:34:09.645028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:34:09.732058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 17
68.0%
법인 8
32.0%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.64
Minimum1
Maximum264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T15:34:09.814235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q316
95-th percentile63.8
Maximum264
Range263
Interquartile range (IQR)15

Descriptive statistics

Standard deviation53.158474
Coefficient of variation (CV)2.5755074
Kurtosis20.148901
Mean20.64
Median Absolute Deviation (MAD)2
Skewness4.35188
Sum516
Variance2825.8233
MonotonicityNot monotonic
2023-12-12T15:34:09.923638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 7
28.0%
3 6
24.0%
4 2
 
8.0%
16 1
 
4.0%
13 1
 
4.0%
2 1
 
4.0%
25 1
 
4.0%
6 1
 
4.0%
27 1
 
4.0%
264 1
 
4.0%
Other values (3) 3
12.0%
ValueCountFrequency (%)
1 7
28.0%
2 1
 
4.0%
3 6
24.0%
4 2
 
8.0%
6 1
 
4.0%
13 1
 
4.0%
16 1
 
4.0%
21 1
 
4.0%
25 1
 
4.0%
27 1
 
4.0%
ValueCountFrequency (%)
264 1
4.0%
70 1
4.0%
39 1
4.0%
27 1
4.0%
25 1
4.0%
21 1
4.0%
16 1
4.0%
13 1
4.0%
6 1
4.0%
4 2
8.0%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean370020
Minimum1430
Maximum6269000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T15:34:10.049587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1430
5-th percentile2542
Q110580
median34150
Q3133050
95-th percentile739536
Maximum6269000
Range6267570
Interquartile range (IQR)122470

Descriptive statistics

Standard deviation1244568.4
Coefficient of variation (CV)3.3635167
Kurtosis23.605056
Mean370020
Median Absolute Deviation (MAD)28580
Skewness4.8071728
Sum9250500
Variance1.5489506 × 1012
MonotonicityNot monotonic
2023-12-12T15:34:10.166142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
125250 1
 
4.0%
1430 1
 
4.0%
795220 1
 
4.0%
33510 1
 
4.0%
5570 1
 
4.0%
453640 1
 
4.0%
516800 1
 
4.0%
6269000 1
 
4.0%
9320 1
 
4.0%
61490 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1430 1
4.0%
2050 1
4.0%
4510 1
4.0%
5570 1
4.0%
7350 1
4.0%
9320 1
4.0%
10580 1
4.0%
11960 1
4.0%
13810 1
4.0%
22230 1
4.0%
ValueCountFrequency (%)
6269000 1
4.0%
795220 1
4.0%
516800 1
4.0%
453640 1
4.0%
306060 1
4.0%
204950 1
4.0%
133050 1
4.0%
125250 1
4.0%
100660 1
4.0%
61490 1
4.0%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.72
Minimum1
Maximum335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T15:34:10.272228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q326
95-th percentile75.8
Maximum335
Range334
Interquartile range (IQR)22

Descriptive statistics

Standard deviation67.237465
Coefficient of variation (CV)2.1887196
Kurtosis19.074845
Mean30.72
Median Absolute Deviation (MAD)6
Skewness4.1832958
Sum768
Variance4520.8767
MonotonicityNot monotonic
2023-12-12T15:34:10.378425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4 3
 
12.0%
1 3
 
12.0%
3 2
 
8.0%
6 2
 
8.0%
11 2
 
8.0%
20 1
 
4.0%
71 1
 
4.0%
50 1
 
4.0%
77 1
 
4.0%
26 1
 
4.0%
Other values (8) 8
32.0%
ValueCountFrequency (%)
1 3
12.0%
2 1
 
4.0%
3 2
8.0%
4 3
12.0%
6 2
8.0%
7 1
 
4.0%
8 1
 
4.0%
9 1
 
4.0%
11 2
8.0%
17 1
 
4.0%
ValueCountFrequency (%)
335 1
4.0%
77 1
4.0%
71 1
4.0%
58 1
4.0%
50 1
4.0%
33 1
4.0%
26 1
4.0%
20 1
4.0%
17 1
4.0%
11 2
8.0%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean459060
Minimum2050
Maximum6949670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T15:34:10.490235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2050
5-th percentile4506
Q133510
median66960
Q3248120
95-th percentile888614
Maximum6949670
Range6947620
Interquartile range (IQR)214610

Descriptive statistics

Standard deviation1374578.7
Coefficient of variation (CV)2.9943334
Kurtosis23.20143
Mean459060
Median Absolute Deviation (MAD)61390
Skewness4.7494657
Sum11476500
Variance1.8894665 × 1012
MonotonicityNot monotonic
2023-12-12T15:34:10.643136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
147460 1
 
4.0%
4240 1
 
4.0%
940600 1
 
4.0%
33510 1
 
4.0%
5570 1
 
4.0%
520600 1
 
4.0%
546560 1
 
4.0%
6949670 1
 
4.0%
9320 1
 
4.0%
63540 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
2050 1
4.0%
4240 1
4.0%
5570 1
4.0%
9320 1
4.0%
18050 1
4.0%
30010 1
4.0%
33510 1
4.0%
34150 1
4.0%
49000 1
4.0%
56380 1
4.0%
ValueCountFrequency (%)
6949670 1
4.0%
940600 1
4.0%
680670 1
4.0%
546560 1
4.0%
520600 1
4.0%
453070 1
4.0%
248120 1
4.0%
228050 1
4.0%
147460 1
4.0%
145380 1
4.0%

누적미환급금액증감
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.404
Minimum0
Maximum1250.11
Zeros5
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T15:34:10.791107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.76
median58.32
Q3150.92
95-th percentile1060.594
Maximum1250.11
Range1250.11
Interquartile range (IQR)145.16

Descriptive statistics

Standard deviation335.26616
Coefficient of variation (CV)1.911394
Kurtosis6.8815466
Mean175.404
Median Absolute Deviation (MAD)58.32
Skewness2.7385393
Sum4385.1
Variance112403.4
MonotonicityNot monotonic
2023-12-12T15:34:11.055547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 5
20.0%
17.73 1
 
4.0%
122.4 1
 
4.0%
18.28 1
 
4.0%
14.76 1
 
4.0%
5.76 1
 
4.0%
10.86 1
 
4.0%
3.33 1
 
4.0%
214.88 1
 
4.0%
532.89 1
 
4.0%
Other values (11) 11
44.0%
ValueCountFrequency (%)
0.0 5
20.0%
3.33 1
 
4.0%
5.76 1
 
4.0%
10.86 1
 
4.0%
14.76 1
 
4.0%
17.73 1
 
4.0%
18.28 1
 
4.0%
30.7 1
 
4.0%
58.32 1
 
4.0%
71.4 1
 
4.0%
ValueCountFrequency (%)
1250.11 1
4.0%
1192.52 1
4.0%
532.89 1
4.0%
214.88 1
4.0%
196.5 1
4.0%
153.62 1
4.0%
150.92 1
4.0%
146.49 1
4.0%
122.4 1
4.0%
121.06 1
4.0%

Interactions

2023-12-12T15:34:07.321740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:04.875439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:05.436971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:05.950943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:06.765377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:07.433632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:04.975006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:05.532128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:06.064259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:06.868388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:07.557466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:05.092166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:05.636389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:06.171051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:06.977018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:07.669593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:05.214371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:05.735589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:06.258802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:07.084839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:07.768095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:05.338902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:05.844962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:06.366088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:07.201626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:34:11.204443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
세목명1.0000.0000.0000.0000.0000.0000.0000.000
과세년도0.0001.0000.0000.0000.0000.0000.0000.164
납세자유형0.0000.0001.0000.0000.0000.0000.0000.000
당해미환급건수0.0000.0000.0001.0001.0000.9731.0000.000
당해미환급금액0.0000.0000.0001.0001.0000.7801.0000.000
누적미환급건수0.0000.0000.0000.9730.7801.0000.7800.000
누적미환급금액0.0000.0000.0001.0001.0000.7801.0000.000
누적미환급금액증감0.0000.1640.0000.0000.0000.0000.0001.000
2023-12-12T15:34:11.360621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형
세목명1.0000.0000.000
과세년도0.0001.0000.000
납세자유형0.0000.0001.000
2023-12-12T15:34:11.479537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명과세년도납세자유형
당해미환급건수1.0000.9030.9050.911-0.0640.0000.0000.000
당해미환급금액0.9031.0000.8130.879-0.1950.0000.0000.000
누적미환급건수0.9050.8131.0000.9690.2820.0000.0000.000
누적미환급금액0.9110.8790.9691.0000.1750.0000.0000.000
누적미환급금액증감-0.064-0.1950.2820.1751.0000.0000.0990.000
세목명0.0000.0000.0000.0000.0001.0000.0000.000
과세년도0.0000.0000.0000.0000.0990.0001.0000.000
납세자유형0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T15:34:07.950835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:34:08.140738image/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
0전라남도영광군46870자동차세2017신규개인161252502014746017.73
1전라남도영광군46870자동차세2017신규법인1143024240196.5
2전라남도영광군46870재산세2017신규개인4341504341500.0
3전라남도영광군46870지방소득세2017신규개인35079088765072.57
4전라남도영광군46870자동차세2018신규개인1310066033248120146.49
5전라남도영광군46870자동차세2018신규법인11381031805030.7
6전라남도영광군46870재산세2018신규개인222230656380153.62
7전라남도영광군46870지방소득세2018신규개인3735011950001192.52
8전라남도영광군46870자동차세2019신규개인2520495058453070121.06
9전라남도영광군46870자동차세2019신규법인111960430010150.92
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
15전라남도영광군46870재산세2020신규개인110580766960532.89
16전라남도영광군46870지방소득세2020신규개인44617011145380214.88
17전라남도영광군46870지방소득세2020신규법인3614904635403.33
18전라남도영광군46870등록면허세2021신규개인19320193200.0
19전라남도영광군46870자동차세2021신규개인2646269000335694967010.86
20전라남도영광군46870자동차세2021신규법인21516800265465605.76
21전라남도영광군46870재산세2021신규개인704536407752060014.76
22전라남도영광군46870재산세2021신규법인15570155700.0
23전라남도영광군46870주민세2021신규개인3335103335100.0
24전라남도영광군46870지방소득세2021신규개인397952205094060018.28