Overview

Dataset statistics

Number of variables10
Number of observations159
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.5 KiB
Average record size in memory86.8 B

Variable types

Categorical5
Numeric5

Dataset

Description경상남도 거창군 지방세 체납현황에 대한 데이터로 과세년도, 세목명, 체납액구간, 체납건수, 체납금액, 누적체납건수, 누적체납금액 항목을 제공합니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079230

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수 is highly overall correlated with 누적체납건수High correlation
체납금액 is highly overall correlated with 누적체납금액High correlation
누적체납건수 is highly overall correlated with 체납건수 and 1 other fieldsHigh correlation
누적체납금액 is highly overall correlated with 체납금액 and 1 other fieldsHigh correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:16:22.856479
Analysis finished2023-12-10 23:16:25.134929
Duration2.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
경상남도
159 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 159
100.0%

Length

2023-12-11T08:16:25.209540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:25.291331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 159
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
거창군
159 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거창군 159
100.0%

Length

2023-12-11T08:16:25.375898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:25.456691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 159
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
48880
159 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 159
100.0%

Length

2023-12-11T08:16:25.537571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:25.635608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 159
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.8113
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T08:16:25.707655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018.5
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.6271813
Coefficient of variation (CV)0.00080561057
Kurtosis-1.124401
Mean2019.8113
Median Absolute Deviation (MAD)1
Skewness-0.19063544
Sum321150
Variance2.6477191
MonotonicityIncreasing
2023-12-11T08:16:25.794201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 32
20.1%
2020 31
19.5%
2021 29
18.2%
2019 27
17.0%
2018 24
15.1%
2017 16
10.1%
ValueCountFrequency (%)
2017 16
10.1%
2018 24
15.1%
2019 27
17.0%
2020 31
19.5%
2021 29
18.2%
2022 32
20.1%
ValueCountFrequency (%)
2022 32
20.1%
2021 29
18.2%
2020 31
19.5%
2019 27
17.0%
2018 24
15.1%
2017 16
10.1%

세목명
Categorical

Distinct7
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
지방소득세
43 
재산세
38 
취득세
25 
주민세
23 
자동차세
20 
Other values (2)
10 

Length

Max length7
Median length3
Mean length3.8427673
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 43
27.0%
재산세 38
23.9%
취득세 25
15.7%
주민세 23
14.5%
자동차세 20
12.6%
등록면허세 6
 
3.8%
지역자원시설세 4
 
2.5%

Length

2023-12-11T08:16:25.906470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:26.014383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 43
27.0%
재산세 38
23.9%
취득세 25
15.7%
주민세 23
14.5%
자동차세 20
12.6%
등록면허세 6
 
3.8%
지역자원시설세 4
 
2.5%

체납액구간
Categorical

Distinct8
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
10만원 미만
39 
10만원~30만원미만
26 
30만원~50만원미만
24 
50만원~1백만원미만
22 
1백만원~3백만원미만
17 
Other values (3)
31 

Length

Max length11
Median length11
Mean length10.018868
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10만원 미만
2nd row10만원 미만
3rd row10만원~30만원미만
4th row30만원~50만원미만
5th row10만원 미만

Common Values

ValueCountFrequency (%)
10만원 미만 39
24.5%
10만원~30만원미만 26
16.4%
30만원~50만원미만 24
15.1%
50만원~1백만원미만 22
13.8%
1백만원~3백만원미만 17
10.7%
3백만원~5백만원미만 12
 
7.5%
5백만원~1천만원미만 11
 
6.9%
1천만원~3천만원미만 8
 
5.0%

Length

2023-12-11T08:16:26.177207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:26.296851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 39
19.7%
미만 39
19.7%
10만원~30만원미만 26
13.1%
30만원~50만원미만 24
12.1%
50만원~1백만원미만 22
11.1%
1백만원~3백만원미만 17
8.6%
3백만원~5백만원미만 12
 
6.1%
5백만원~1천만원미만 11
 
5.6%
1천만원~3천만원미만 8
 
4.0%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.08805
Minimum1
Maximum2065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T08:16:26.440243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median7
Q328
95-th percentile539.2
Maximum2065
Range2064
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation332.03577
Coefficient of variation (CV)2.7881535
Kurtosis17.285957
Mean119.08805
Median Absolute Deviation (MAD)6
Skewness4.0684689
Sum18935
Variance110247.75
MonotonicityNot monotonic
2023-12-11T08:16:26.619380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 27
17.0%
2 13
 
8.2%
3 11
 
6.9%
6 10
 
6.3%
5 8
 
5.0%
4 8
 
5.0%
13 5
 
3.1%
7 4
 
2.5%
17 3
 
1.9%
8 3
 
1.9%
Other values (54) 67
42.1%
ValueCountFrequency (%)
1 27
17.0%
2 13
8.2%
3 11
6.9%
4 8
 
5.0%
5 8
 
5.0%
6 10
 
6.3%
7 4
 
2.5%
8 3
 
1.9%
9 2
 
1.3%
10 2
 
1.3%
ValueCountFrequency (%)
2065 1
0.6%
1802 1
0.6%
1730 1
0.6%
1444 1
0.6%
1442 1
0.6%
1297 1
0.6%
799 1
0.6%
694 1
0.6%
522 1
0.6%
511 1
0.6%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct159
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12248073
Minimum28420
Maximum92594940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T08:16:26.751364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28420
5-th percentile199653
Q11451720
median6360330
Q317943990
95-th percentile44094230
Maximum92594940
Range92566520
Interquartile range (IQR)16492270

Descriptive statistics

Standard deviation15677705
Coefficient of variation (CV)1.280014
Kurtosis5.327352
Mean12248073
Median Absolute Deviation (MAD)5607420
Skewness2.0942952
Sum1.9474436 × 109
Variance2.4579043 × 1014
MonotonicityNot monotonic
2023-12-11T08:16:26.876830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202370 1
 
0.6%
5434810 1
 
0.6%
37841410 1
 
0.6%
34105180 1
 
0.6%
37571360 1
 
0.6%
9432240 1
 
0.6%
11822140 1
 
0.6%
18050150 1
 
0.6%
16947530 1
 
0.6%
19543630 1
 
0.6%
Other values (149) 149
93.7%
ValueCountFrequency (%)
28420 1
0.6%
53010 1
0.6%
70640 1
0.6%
85410 1
0.6%
91930 1
0.6%
101170 1
0.6%
187320 1
0.6%
190500 1
0.6%
200670 1
0.6%
202370 1
0.6%
ValueCountFrequency (%)
92594940 1
0.6%
71863430 1
0.6%
60123010 1
0.6%
52262900 1
0.6%
51537360 1
0.6%
50344750 1
0.6%
50069590 1
0.6%
46699730 1
0.6%
43804730 1
0.6%
43017380 1
0.6%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.67925
Minimum1
Maximum4416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T08:16:27.280737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median13
Q365.5
95-th percentile1584.6
Maximum4416
Range4415
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation780.6159
Coefficient of variation (CV)2.7324908
Kurtosis15.291243
Mean285.67925
Median Absolute Deviation (MAD)12
Skewness3.8239629
Sum45423
Variance609361.18
MonotonicityNot monotonic
2023-12-11T08:16:27.400398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
7.5%
2 11
 
6.9%
3 10
 
6.3%
7 10
 
6.3%
8 9
 
5.7%
5 7
 
4.4%
11 7
 
4.4%
9 5
 
3.1%
36 4
 
2.5%
13 4
 
2.5%
Other values (66) 80
50.3%
ValueCountFrequency (%)
1 12
7.5%
2 11
6.9%
3 10
6.3%
4 4
 
2.5%
5 7
4.4%
7 10
6.3%
8 9
5.7%
9 5
3.1%
10 2
 
1.3%
11 7
4.4%
ValueCountFrequency (%)
4416 1
0.6%
4341 1
0.6%
4305 1
0.6%
3320 1
0.6%
3138 1
0.6%
2643 1
0.6%
2351 1
0.6%
1878 1
0.6%
1552 1
0.6%
1302 1
0.6%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct159
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26182821
Minimum85410
Maximum1.8095162 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T08:16:27.530478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum85410
5-th percentile285989
Q13071005
median12243190
Q333265815
95-th percentile89723637
Maximum1.8095162 × 108
Range1.8086621 × 108
Interquartile range (IQR)30194810

Descriptive statistics

Standard deviation36831743
Coefficient of variation (CV)1.4067141
Kurtosis6.428747
Mean26182821
Median Absolute Deviation (MAD)10798250
Skewness2.4230906
Sum4.1630685 × 109
Variance1.3565773 × 1015
MonotonicityNot monotonic
2023-12-11T08:16:27.694612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
582840 1
 
0.6%
16208910 1
 
0.6%
76149350 1
 
0.6%
61263830 1
 
0.6%
65772740 1
 
0.6%
16691630 1
 
0.6%
18974520 1
 
0.6%
25982470 1
 
0.6%
32162280 1
 
0.6%
45626090 1
 
0.6%
Other values (149) 149
93.7%
ValueCountFrequency (%)
85410 1
0.6%
101170 1
0.6%
215300 1
0.6%
224980 1
0.6%
243720 1
0.6%
268310 1
0.6%
269780 1
0.6%
285170 1
0.6%
286080 1
0.6%
315620 1
0.6%
ValueCountFrequency (%)
180951620 1
0.6%
177773800 1
0.6%
172238720 1
0.6%
171468650 1
0.6%
159778090 1
0.6%
133754050 1
0.6%
117650790 1
0.6%
99907740 1
0.6%
88592070 1
0.6%
82697400 1
0.6%

Interactions

2023-12-11T08:16:24.528553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.108915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.473710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.879841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.202981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.599253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.182525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.590417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.944867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.264990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.668375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.250206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.676550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.010231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.324856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.747908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.320252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.743997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.068578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.385976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.819054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.391421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:23.807142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.128576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:24.445774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:16:27.803096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.1470.0000.168
세목명0.0001.0000.2760.4620.2750.3770.228
체납액구간0.0000.2761.0000.2900.4450.1590.452
체납건수0.0000.4620.2901.0000.5390.9040.647
체납금액0.1470.2750.4450.5391.0000.4340.871
누적체납건수0.0000.3770.1590.9040.4341.0000.644
누적체납금액0.1680.2280.4520.6470.8710.6441.000
2023-12-11T08:16:27.901739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명
체납액구간1.0000.150
세목명0.1501.000
2023-12-11T08:16:27.989190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수체납금액누적체납건수누적체납금액세목명체납액구간
과세년도1.0000.1330.2320.1760.2600.0000.000
체납건수0.1331.0000.4330.9630.4690.1750.158
체납금액0.2320.4331.0000.4080.9590.1490.236
누적체납건수0.1760.9630.4081.0000.5060.2110.051
누적체납금액0.2600.4690.9590.5061.0000.1180.233
세목명0.0000.1750.1490.2110.1181.0000.150
체납액구간0.0000.1580.2360.0510.2330.1501.000

Missing values

2023-12-11T08:16:24.958206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:16:25.087064image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
0경상남도거창군488802017등록면허세10만원 미만2020237058582840
1경상남도거창군488802017자동차세10만원 미만117543481034716208910
2경상남도거창군488802017자동차세10만원~30만원미만871451899022637850170
3경상남도거창군488802017자동차세30만원~50만원미만5171271093172220
4경상남도거창군488802017재산세10만원 미만3585134060105614767010
5경상남도거창군488802017재산세10만원~30만원미만152583980387013920
6경상남도거창군488802017재산세50만원~1백만원미만16222401622240
7경상남도거창군488802017주민세10만원 미만302440891071310342100
8경상남도거창군488802017주민세50만원~1백만원미만16962301696230
9경상남도거창군488802017지방소득세10만원 미만14484050271195970
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
149경상남도거창군488802022지방소득세50만원~1백만원미만17122685402921404040
150경상남도거창군488802022지방소득세5백만원~1천만원미만6430173801399907740
151경상남도거창군488802022지역자원시설세10만원 미만59193011269780
152경상남도거창군488802022취득세10만원 미만51905008315620
153경상남도거창군488802022취득세10만원~30만원미만101644800162721400
154경상남도거창군488802022취득세1백만원~3백만원미만4653794058390370
155경상남도거창군488802022취득세30만원~50만원미만7242131072421310
156경상남도거창군488802022취득세3백만원~5백만원미만1348115013481150
157경상남도거창군488802022취득세50만원~1백만원미만5313330074411510
158경상남도거창군488802022취득세5백만원~1천만원미만1616041016160410