Overview

Dataset statistics

Number of variables12
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory107.1 B

Variable types

Categorical7
Numeric5

Dataset

Description최근 3년간 지방세 부과 환급 자료에 따른 지방세 세목별 통계자료를 근거로 연도별 지방세 미환급 현황을 추출한 자료에 해당됩니다
Author충청북도 진천군
URLhttps://www.data.go.kr/data/15079490/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 (15.6%) zerosZeros

Reproduction

Analysis started2024-03-30 07:41:53.239097
Analysis finished2024-03-30 07:42:05.480709
Duration12.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
충청북도
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 32
100.0%

Length

2024-03-30T07:42:05.654501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:42:05.953804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 32
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
진천군
32 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진천군 32
100.0%

Length

2024-03-30T07:42:06.318056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:42:06.635752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진천군 32
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
43750
32 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43750
2nd row43750
3rd row43750
4th row43750
5th row43750

Common Values

ValueCountFrequency (%)
43750 32
100.0%

Length

2024-03-30T07:42:07.044886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:42:07.467694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43750 32
100.0%

세목명
Categorical

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size388.0 B
자동차세
8 
지방소득세
8 
재산세
7 
등록면허세
4 
주민세
4 

Length

Max length5
Median length4
Mean length4
Min length3

Unique

Unique1 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 8
25.0%
지방소득세 8
25.0%
재산세 7
21.9%
등록면허세 4
12.5%
주민세 4
12.5%
취득세 1
 
3.1%

Length

2024-03-30T07:42:07.995491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:42:08.558890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 8
25.0%
지방소득세 8
25.0%
재산세 7
21.9%
등록면허세 4
12.5%
주민세 4
12.5%
취득세 1
 
3.1%

과세년도
Categorical

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
2022
10 
2021
9 
2019
7 
2020
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2022 10
31.2%
2021 9
28.1%
2019 7
21.9%
2020 6
18.8%

Length

2024-03-30T07:42:09.236656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:42:09.684741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10
31.2%
2021 9
28.1%
2019 7
21.9%
2020 6
18.8%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
신규
32 

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 32
100.0%

Length

2024-03-30T07:42:10.010089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:42:10.323976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 32
100.0%

납세자유형
Categorical

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
개인
18 
법인
14 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 18
56.2%
법인 14
43.8%

Length

2024-03-30T07:42:10.656238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:42:11.047013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 18
56.2%
법인 14
43.8%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.5625
Minimum1
Maximum838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-03-30T07:42:11.549792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median11.5
Q389.5
95-th percentile463.4
Maximum838
Range837
Interquartile range (IQR)85.75

Descriptive statistics

Standard deviation184.67302
Coefficient of variation (CV)2.0619458
Kurtosis10.146556
Mean89.5625
Median Absolute Deviation (MAD)10
Skewness3.1419391
Sum2866
Variance34104.125
MonotonicityNot monotonic
2024-03-30T07:42:11.973141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 4
 
12.5%
4 4
 
12.5%
3 3
 
9.4%
94 1
 
3.1%
10 1
 
3.1%
161 1
 
3.1%
8 1
 
3.1%
139 1
 
3.1%
88 1
 
3.1%
624 1
 
3.1%
Other values (14) 14
43.8%
ValueCountFrequency (%)
1 4
12.5%
2 1
 
3.1%
3 3
9.4%
4 4
12.5%
5 1
 
3.1%
7 1
 
3.1%
8 1
 
3.1%
10 1
 
3.1%
13 1
 
3.1%
15 1
 
3.1%
ValueCountFrequency (%)
838 1
3.1%
624 1
3.1%
332 1
3.1%
161 1
3.1%
145 1
3.1%
139 1
3.1%
121 1
3.1%
94 1
3.1%
88 1
3.1%
75 1
3.1%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1507073.1
Minimum2210
Maximum12506960
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-03-30T07:42:12.392220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2210
5-th percentile9615.5
Q133142.5
median402135
Q31620847.5
95-th percentile6259753.5
Maximum12506960
Range12504750
Interquartile range (IQR)1587705

Descriptive statistics

Standard deviation2738228.1
Coefficient of variation (CV)1.8169179
Kurtosis9.8620553
Mean1507073.1
Median Absolute Deviation (MAD)390470
Skewness3.0474356
Sum48226340
Variance7.4978932 × 1012
MonotonicityNot monotonic
2024-03-30T07:42:12.953984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
12000 1
 
3.1%
190910 1
 
3.1%
304430 1
 
3.1%
21560 1
 
3.1%
2528300 1
 
3.1%
75900 1
 
3.1%
33990 1
 
3.1%
125510 1
 
3.1%
1244910 1
 
3.1%
941430 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
2210 1
3.1%
7520 1
3.1%
11330 1
3.1%
12000 1
3.1%
18120 1
3.1%
21560 1
3.1%
27000 1
3.1%
30600 1
3.1%
33990 1
3.1%
55080 1
3.1%
ValueCountFrequency (%)
12506960 1
3.1%
9547340 1
3.1%
3569910 1
3.1%
2864690 1
3.1%
2808260 1
3.1%
2731040 1
3.1%
2528300 1
3.1%
2409810 1
3.1%
1357860 1
3.1%
1312870 1
3.1%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187
Minimum1
Maximum1538
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-03-30T07:42:13.409704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median33
Q3161.25
95-th percentile969.4
Maximum1538
Range1537
Interquartile range (IQR)157.25

Descriptive statistics

Standard deviation369.82271
Coefficient of variation (CV)1.9776616
Kurtosis8.7178447
Mean187
Median Absolute Deviation (MAD)32
Skewness2.9712009
Sum5984
Variance136768.84
MonotonicityNot monotonic
2024-03-30T07:42:14.024021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4 4
 
12.5%
1 3
 
9.4%
18 2
 
6.2%
3 2
 
6.2%
87 1
 
3.1%
141 1
 
3.1%
35 1
 
3.1%
384 1
 
3.1%
16 1
 
3.1%
205 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
1 3
9.4%
3 2
6.2%
4 4
12.5%
5 1
 
3.1%
8 1
 
3.1%
12 1
 
3.1%
16 1
 
3.1%
18 2
6.2%
31 1
 
3.1%
35 1
 
3.1%
ValueCountFrequency (%)
1538 1
3.1%
1427 1
3.1%
595 1
3.1%
458 1
3.1%
384 1
3.1%
283 1
3.1%
205 1
3.1%
165 1
3.1%
160 1
3.1%
148 1
3.1%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3180297.2
Minimum11330
Maximum20118730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-03-30T07:42:14.664760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11330
5-th percentile27115.5
Q176642.5
median1704585
Q33307212.5
95-th percentile13354362
Maximum20118730
Range20107400
Interquartile range (IQR)3230570

Descriptive statistics

Standard deviation4876127.7
Coefficient of variation (CV)1.5332302
Kurtosis6.6792981
Mean3180297.2
Median Absolute Deviation (MAD)1628190
Skewness2.5454352
Sum1.0176951 × 108
Variance2.3776621 × 1013
MonotonicityNot monotonic
2024-03-30T07:42:15.265995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
45950 1
 
3.1%
3106200 1
 
3.1%
304430 1
 
3.1%
1822910 1
 
3.1%
8914950 1
 
3.1%
75900 1
 
3.1%
38370 1
 
3.1%
202400 1
 
3.1%
3219410 1
 
3.1%
2470140 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
11330 1
3.1%
27000 1
3.1%
27210 1
3.1%
38370 1
3.1%
39690 1
3.1%
45950 1
3.1%
55080 1
3.1%
75900 1
3.1%
76890 1
3.1%
158930 1
3.1%
ValueCountFrequency (%)
20118730 1
3.1%
18780310 1
3.1%
8914950 1
3.1%
7422580 1
3.1%
6376620 1
3.1%
5627520 1
3.1%
4888710 1
3.1%
3570620 1
3.1%
3219410 1
3.1%
3106200 1
3.1%

누적미환급금액증감
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1145.6013
Minimum0
Maximum20596.68
Zeros5
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-03-30T07:42:15.821910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125.505
median102.51
Q3171.835
95-th percentile5618.3315
Maximum20596.68
Range20596.68
Interquartile range (IQR)146.33

Descriptive statistics

Standard deviation3878.7585
Coefficient of variation (CV)3.3857841
Kurtosis21.926018
Mean1145.6013
Median Absolute Deviation (MAD)71.13
Skewness4.5474257
Sum36659.24
Variance15044768
MonotonicityNot monotonic
2024-03-30T07:42:16.294159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 5
 
15.6%
50.16 1
 
3.1%
8355.06 1
 
3.1%
252.61 1
 
3.1%
12.89 1
 
3.1%
61.26 1
 
3.1%
158.61 1
 
3.1%
162.38 1
 
3.1%
110.73 1
 
3.1%
29.71 1
 
3.1%
Other values (18) 18
56.2%
ValueCountFrequency (%)
0.0 5
15.6%
4.16 1
 
3.1%
5.02 1
 
3.1%
12.89 1
 
3.1%
29.71 1
 
3.1%
50.16 1
 
3.1%
50.17 1
 
3.1%
50.26 1
 
3.1%
61.26 1
 
3.1%
74.08 1
 
3.1%
ValueCountFrequency (%)
20596.68 1
3.1%
8355.06 1
3.1%
3379.19 1
3.1%
1527.05 1
3.1%
494.88 1
3.1%
282.92 1
3.1%
252.61 1
3.1%
171.97 1
3.1%
171.79 1
3.1%
162.38 1
3.1%

Interactions

2024-03-30T07:42:01.935195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:53.999549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:55.916768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:57.781892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:59.881564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:42:02.291395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:54.267867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:56.282790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:58.167251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:42:00.258854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:42:02.716063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:54.652774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:56.734492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:58.657050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:42:00.661942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:42:03.144714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:54.993890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:57.177711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:59.138489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:42:01.172590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:42:03.549387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:55.445607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:57.458456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:41:59.461363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:42:01.541668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T07:42:16.611024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
세목명1.0000.0000.0000.0000.0000.0000.0000.000
과세년도0.0001.0000.0000.0000.3690.0000.0000.403
납세자유형0.0000.0001.0000.0000.2520.2180.5710.312
당해미환급건수0.0000.0000.0001.0000.9630.9770.7580.000
당해미환급금액0.0000.3690.2520.9631.0000.8830.7400.000
누적미환급건수0.0000.0000.2180.9770.8831.0000.8530.000
누적미환급금액0.0000.0000.5710.7580.7400.8531.0000.000
누적미환급금액증감0.0000.4030.3120.0000.0000.0000.0001.000
2024-03-30T07:42:16.987509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명납세자유형
과세년도1.0000.0000.000
세목명0.0001.0000.000
납세자유형0.0000.0001.000
2024-03-30T07:42:17.374696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명과세년도납세자유형
당해미환급건수1.0000.8670.9760.9450.3650.0000.0000.000
당해미환급금액0.8671.0000.8300.871-0.0220.0000.2650.307
누적미환급건수0.9760.8301.0000.9610.4700.0000.0000.245
누적미환급금액0.9450.8710.9611.0000.4010.0000.0000.334
누적미환급금액증감0.365-0.0220.4700.4011.0000.0000.1560.193
세목명0.0000.0000.0000.0000.0001.0000.0000.000
과세년도0.0000.2650.0000.0000.1560.0001.0000.000
납세자유형0.0000.3070.2450.3340.1930.0000.0001.000

Missing values

2024-03-30T07:42:04.209005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T07:42:05.291500image/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
0충청북도진천군43750등록면허세2019신규개인112000445950282.92
1충청북도진천군43750자동차세2019신규개인14524098104585627520133.53
2충청북도진천군43750자동차세2019신규법인19713660871586260122.27
3충청북도진천군43750재산세2019신규개인47290012158930118.01
4충청북도진천군43750재산세2019신규법인448977055101504.16
5충청북도진천군43750지방소득세2019신규개인5213128701483570620171.97
6충청북도진천군43750지방소득세2019신규법인7128900018193691050.26
7충청북도진천군43750자동차세2020신규개인3323569910595637662078.62
8충청북도진천군43750자동차세2020신규법인20606970471280340110.94
9충청북도진천군43750재산세2020신규개인7528646908330084905.02
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
22충청북도진천군43750등록면허세2022신규개인33060043969029.71
23충청북도진천군43750자동차세2022신규개인6249547340153820118730110.73
24충청북도진천군43750자동차세2022신규법인889414301602470140162.38
25충청북도진천군43750재산세2022신규개인13912449102053219410158.61
26충청북도진천군43750재산세2022신규법인81255101620240061.26
27충청북도진천군43750주민세2022신규개인33399043837012.89
28충청북도진천군43750주민세2022신규법인3759003759000.0
29충청북도진천군43750지방소득세2022신규개인16125283003848914950252.61
30충청북도진천군43750지방소득세2022신규법인10215603518229108355.06
31충청북도진천군43750취득세2022신규개인430443043044300.0