Overview

Dataset statistics

Number of variables10
Number of observations110
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory87.2 B

Variable types

Categorical6
Numeric4

Dataset

Description경상남도 사천시 지방세 체납 현황(2018 ~ 2020년)에 대한 데이터로 체납액 규모별 체납 건수를 납세자 유형별로 제공합니다.
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079596

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 체납건수High correlation
누적체납금액 is highly overall correlated with 체납금액High correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:15:01.624117
Analysis finished2023-12-10 23:15:03.494706
Duration1.87 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1012.0 B
경상남도
110 

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 110
100.0%

Length

2023-12-11T08:15:03.571738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:03.679958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 110
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1012.0 B
사천시
110 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사천시 110
100.0%

Length

2023-12-11T08:15:03.766725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:03.857536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사천시 110
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1012.0 B
48240
110 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48240
2nd row48240
3rd row48240
4th row48240
5th row48240

Common Values

ValueCountFrequency (%)
48240 110
100.0%

Length

2023-12-11T08:15:03.958893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:04.052240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48240 110
100.0%

과세년도
Categorical

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1012.0 B
2020
38 
2018
36 
2019
36 

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 (%)
2020 38
34.5%
2018 36
32.7%
2019 36
32.7%

Length

2023-12-11T08:15:04.153347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:04.455263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 38
34.5%
2018 36
32.7%
2019 36
32.7%

세목명
Categorical

Distinct7
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size1012.0 B
재산세
26 
지방소득세
26 
취득세
22 
주민세
15 
자동차세
12 
Other values (2)
9 

Length

Max length7
Median length3
Mean length3.8545455
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 26
23.6%
지방소득세 26
23.6%
취득세 22
20.0%
주민세 15
13.6%
자동차세 12
10.9%
지역자원시설세 6
 
5.5%
등록면허세 3
 
2.7%

Length

2023-12-11T08:15:04.564193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:04.666573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 26
23.6%
지방소득세 26
23.6%
취득세 22
20.0%
주민세 15
13.6%
자동차세 12
10.9%
지역자원시설세 6
 
5.5%
등록면허세 3
 
2.7%

체납액구간
Categorical

Distinct10
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size1012.0 B
10만원 미만
21 
10만원~30만원미만
18 
30만원~50만원미만
15 
50만원~1백만원미만
14 
1백만원~3백만원미만
12 
Other values (5)
30 

Length

Max length11
Median length11
Mean length10.209091
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10만원 미만
2nd row10만원 미만
3rd row10만원~30만원미만
4th row30만원~50만원미만
5th row50만원~1백만원미만

Common Values

ValueCountFrequency (%)
10만원 미만 21
19.1%
10만원~30만원미만 18
16.4%
30만원~50만원미만 15
13.6%
50만원~1백만원미만 14
12.7%
1백만원~3백만원미만 12
10.9%
3백만원~5백만원미만 9
8.2%
5백만원~1천만원미만 9
8.2%
1천만원~3천만원미만 7
 
6.4%
5천만원~1억원미만 3
 
2.7%
3천만원~5천만원미만 2
 
1.8%

Length

2023-12-11T08:15:04.807553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:15:04.933307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 21
16.0%
미만 21
16.0%
10만원~30만원미만 18
13.7%
30만원~50만원미만 15
11.5%
50만원~1백만원미만 14
10.7%
1백만원~3백만원미만 12
9.2%
3백만원~5백만원미만 9
6.9%
5백만원~1천만원미만 9
6.9%
1천만원~3천만원미만 7
 
5.3%
5천만원~1억원미만 3
 
2.3%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.58182
Minimum1
Maximum4234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T08:15:05.084970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9.5
Q369.25
95-th percentile1478.75
Maximum4234
Range4233
Interquartile range (IQR)66.25

Descriptive statistics

Standard deviation624.46077
Coefficient of variation (CV)2.6965017
Kurtosis19.212426
Mean231.58182
Median Absolute Deviation (MAD)8.5
Skewness4.0428791
Sum25474
Variance389951.25
MonotonicityNot monotonic
2023-12-11T08:15:05.200302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14
 
12.7%
2 11
 
10.0%
8 7
 
6.4%
5 6
 
5.5%
3 5
 
4.5%
4 5
 
4.5%
7 4
 
3.6%
11 3
 
2.7%
9 3
 
2.7%
12 3
 
2.7%
Other values (47) 49
44.5%
ValueCountFrequency (%)
1 14
12.7%
2 11
10.0%
3 5
 
4.5%
4 5
 
4.5%
5 6
5.5%
7 4
 
3.6%
8 7
6.4%
9 3
 
2.7%
10 1
 
0.9%
11 3
 
2.7%
ValueCountFrequency (%)
4234 1
0.9%
3057 1
0.9%
2168 1
0.9%
1664 1
0.9%
1626 1
0.9%
1535 1
0.9%
1410 1
0.9%
1168 1
0.9%
1154 1
0.9%
1078 1
0.9%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct110
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34041712
Minimum21840
Maximum2.3979323 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T08:15:05.346099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21840
5-th percentile346252.5
Q13358467.5
median15909130
Q347592990
95-th percentile1.2091103 × 108
Maximum2.3979323 × 108
Range2.3977139 × 108
Interquartile range (IQR)44234522

Descriptive statistics

Standard deviation45181326
Coefficient of variation (CV)1.3272342
Kurtosis5.8378509
Mean34041712
Median Absolute Deviation (MAD)15263835
Skewness2.2376649
Sum3.7445883 × 109
Variance2.0413522 × 1015
MonotonicityNot monotonic
2023-12-11T08:15:05.486380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2679990 1
 
0.9%
10515560 1
 
0.9%
54532630 1
 
0.9%
124328830 1
 
0.9%
162679180 1
 
0.9%
188800540 1
 
0.9%
89619770 1
 
0.9%
1037210 1
 
0.9%
32829390 1
 
0.9%
203206970 1
 
0.9%
Other values (100) 100
90.9%
ValueCountFrequency (%)
21840 1
0.9%
55730 1
0.9%
120770 1
0.9%
121930 1
0.9%
302790 1
0.9%
337410 1
0.9%
357060 1
0.9%
361440 1
0.9%
472890 1
0.9%
496210 1
0.9%
ValueCountFrequency (%)
239793230 1
0.9%
203206970 1
0.9%
188800540 1
0.9%
162679180 1
0.9%
141056150 1
0.9%
124328830 1
0.9%
116733720 1
0.9%
95067590 1
0.9%
91914620 1
0.9%
89737380 1
0.9%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean743.93636
Minimum1
Maximum11701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T08:15:05.610086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111.25
median33.5
Q3194.25
95-th percentile5349.05
Maximum11701
Range11700
Interquartile range (IQR)183

Descriptive statistics

Standard deviation1952.721
Coefficient of variation (CV)2.6248495
Kurtosis12.153626
Mean743.93636
Median Absolute Deviation (MAD)29
Skewness3.3648542
Sum81833
Variance3813119.4
MonotonicityNot monotonic
2023-12-11T08:15:05.723414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 4
 
3.6%
17 4
 
3.6%
3 4
 
3.6%
4 4
 
3.6%
1 4
 
3.6%
2 3
 
2.7%
5 3
 
2.7%
26 3
 
2.7%
37 3
 
2.7%
9 3
 
2.7%
Other values (66) 75
68.2%
ValueCountFrequency (%)
1 4
3.6%
2 3
2.7%
3 4
3.6%
4 4
3.6%
5 3
2.7%
6 1
 
0.9%
7 2
1.8%
8 1
 
0.9%
9 3
2.7%
11 3
2.7%
ValueCountFrequency (%)
11701 1
0.9%
8447 1
0.9%
7467 1
0.9%
5980 1
0.9%
5724 1
0.9%
5390 1
0.9%
5299 1
0.9%
4812 1
0.9%
4189 1
0.9%
3764 1
0.9%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct110
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89296385
Minimum242400
Maximum1.0036876 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T08:15:05.841033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum242400
5-th percentile1169148
Q111798652
median43920240
Q31.0810572 × 108
95-th percentile2.9611325 × 108
Maximum1.0036876 × 109
Range1.0034452 × 109
Interquartile range (IQR)96307065

Descriptive statistics

Standard deviation1.4348243 × 108
Coefficient of variation (CV)1.6068112
Kurtosis20.215398
Mean89296385
Median Absolute Deviation (MAD)37783540
Skewness4.0049996
Sum9.8226024 × 109
Variance2.0587209 × 1016
MonotonicityNot monotonic
2023-12-11T08:15:05.968878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7770190 1
 
0.9%
33947360 1
 
0.9%
74554130 1
 
0.9%
172998140 1
 
0.9%
323951500 1
 
0.9%
332079560 1
 
0.9%
207615900 1
 
0.9%
28686610 1
 
0.9%
115665990 1
 
0.9%
1003687620 1
 
0.9%
Other values (100) 100
90.9%
ValueCountFrequency (%)
242400 1
0.9%
363170 1
0.9%
676550 1
0.9%
724610 1
0.9%
1013960 1
0.9%
1021620 1
0.9%
1349460 1
0.9%
1587340 1
0.9%
1822350 1
0.9%
1863920 1
0.9%
ValueCountFrequency (%)
1003687620 1
0.9%
800480650 1
0.9%
560687420 1
0.9%
332079560 1
0.9%
323951500 1
0.9%
317297270 1
0.9%
270221660 1
0.9%
249874730 1
0.9%
247256390 1
0.9%
222229680 1
0.9%

Interactions

2023-12-11T08:15:02.907474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:01.933923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.260761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.598603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.978962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.005171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.357382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.666094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:03.052963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.098884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.433156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.742249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:03.153025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.185568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.517978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:15:02.831433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:15:06.062856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.2670.1800.307
세목명0.0001.0000.0000.4270.2990.3770.375
체납액구간0.0000.0001.0000.0000.2370.0000.000
체납건수0.0000.4270.0001.0000.5850.9760.789
체납금액0.2670.2990.2370.5851.0000.7550.918
누적체납건수0.1800.3770.0000.9760.7551.0000.784
누적체납금액0.3070.3750.0000.7890.9180.7841.000
2023-12-11T08:15:06.168974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명과세년도
체납액구간1.0000.0000.000
세목명0.0001.0000.000
과세년도0.0000.0001.000
2023-12-11T08:15:06.259557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.4510.9390.4960.0000.1590.000
체납금액0.4511.0000.3100.9460.1620.1560.074
누적체납건수0.9390.3101.0000.4280.0730.2050.000
누적체납금액0.4960.9460.4281.0000.2150.1400.000
과세년도0.0000.1620.0730.2151.0000.0000.000
세목명0.1590.1560.2050.1400.0001.0000.000
체납액구간0.0000.0740.0000.0000.0000.0001.000

Missing values

2023-12-11T08:15:03.277349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:15:03.430641image/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
0경상남도사천시482402018등록면허세10만원 미만18126799905377770190
1경상남도사천시482402018자동차세10만원 미만667310933903316150511030
2경상남도사천시482402018자동차세10만원~30만원미만8411410561503402560687420
3경상남도사천시482402018자동차세30만원~50만원미만612120607013948720750
4경상남도사천시482402018자동차세50만원~1백만원미만844875304125522370
5경상남도사천시482402018재산세10만원 미만115422763010376480138290
6경상남도사천시482402018재산세10만원~30만원미만1902979960038959218240
7경상남도사천시482402018재산세1백만원~3백만원미만23382657404476148740
8경상남도사천시482402018재산세1천만원~3천만원미만111880010336128170
9경상남도사천시482402018재산세30만원~50만원미만1243801702910916020
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
100경상남도사천시482402020지역자원시설세10만원 미만1836144041724610
101경상남도사천시482402020지역자원시설세10만원~30만원미만5784510173099820
102경상남도사천시482402020취득세10만원 미만10496210452083550
103경상남도사천시482402020취득세10만원~30만원미만91519150387353170
104경상남도사천시482402020취득세1백만원~3백만원미만8156662602649395610
105경상남도사천시482402020취득세30만원~50만원미만284996051863920
106경상남도사천시482402020취득세3백만원~5백만원미만277966601141744020
107경상남도사천시482402020취득세50만원~1백만원미만756085902115122130
108경상남도사천시482402020취득세5백만원~1천만원미만212165550534866060
109경상남도사천시482402020취득세5천만원~1억원미만171007770171007770