Overview

Dataset statistics

Number of variables10
Number of observations390
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.9 KiB
Average record size in memory86.3 B

Variable types

Categorical6
Numeric4

Dataset

Description울산광역시 구군별(중구, 남구, 북구, 동구, 울주군) 지방세 체납 현황 정보(자치단체코드, 세목명, 체납건수, 체납금액 등)를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15091281/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-12 21:07:30.783498
Analysis finished2023-12-12 21:07:32.744312
Duration1.96 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
울산광역시
390 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울산광역시 390
100.0%

Length

2023-12-13T06:07:32.824214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:07:32.942895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산광역시 390
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
울주군
89 
남구
81 
동구
74 
북구
74 
중구
72 

Length

Max length3
Median length2
Mean length2.2282051
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울주군 89
22.8%
남구 81
20.8%
동구 74
19.0%
북구 74
19.0%
중구 72
18.5%

Length

2023-12-13T06:07:33.048973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:07:33.175034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울주군 89
22.8%
남구 81
20.8%
동구 74
19.0%
북구 74
19.0%
중구 72
18.5%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
31710
89 
31140
81 
31170
74 
31200
74 
31110
72 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31110
2nd row31110
3rd row31110
4th row31110
5th row31110

Common Values

ValueCountFrequency (%)
31710 89
22.8%
31140 81
20.8%
31170 74
19.0%
31200 74
19.0%
31110 72
18.5%

Length

2023-12-13T06:07:33.288269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:07:33.410490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31710 89
22.8%
31140 81
20.8%
31170 74
19.0%
31200 74
19.0%
31110 72
18.5%

과세년도
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2020
195 
2021
195 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 195
50.0%
2021 195
50.0%

Length

2023-12-13T06:07:33.527924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:07:33.622023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 195
50.0%
2021 195
50.0%

세목명
Categorical

Distinct7
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
지방소득세
97 
재산세
91 
취득세
83 
주민세
50 
자동차세
40 
Other values (2)
29 

Length

Max length7
Median length3
Mean length3.8205128
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 97
24.9%
재산세 91
23.3%
취득세 83
21.3%
주민세 50
12.8%
자동차세 40
10.3%
등록면허세 15
 
3.8%
지역자원시설세 14
 
3.6%

Length

2023-12-13T06:07:33.752662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:07:33.878088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 97
24.9%
재산세 91
23.3%
취득세 83
21.3%
주민세 50
12.8%
자동차세 40
10.3%
등록면허세 15
 
3.8%
지역자원시설세 14
 
3.6%

체납액구간
Categorical

Distinct12
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
10만원 미만
69 
10만원~30만원미만
57 
30만원~50만원미만
49 
50만원~1백만원미만
47 
1백만원~3백만원미만
38 
Other values (7)
130 

Length

Max length11
Median length11
Mean length10.2
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 69
17.7%
10만원~30만원미만 57
14.6%
30만원~50만원미만 49
12.6%
50만원~1백만원미만 47
12.1%
1백만원~3백만원미만 38
9.7%
3백만원~5백만원미만 30
7.7%
5백만원~1천만원미만 29
7.4%
1천만원~3천만원미만 28
7.2%
3천만원~5천만원미만 17
 
4.4%
5천만원~1억원미만 16
 
4.1%
Other values (2) 10
 
2.6%

Length

2023-12-13T06:07:34.046196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 69
15.0%
미만 69
15.0%
10만원~30만원미만 57
12.4%
30만원~50만원미만 49
10.7%
50만원~1백만원미만 47
10.2%
1백만원~3백만원미만 38
8.3%
3백만원~5백만원미만 30
6.5%
5백만원~1천만원미만 29
6.3%
1천만원~3천만원미만 28
6.1%
3천만원~5천만원미만 17
 
3.7%
Other values (3) 26
 
5.7%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean479.53846
Minimum1
Maximum13873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-13T06:07:34.196125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median16
Q3149
95-th percentile2158.65
Maximum13873
Range13872
Interquartile range (IQR)146

Descriptive statistics

Standard deviation1471.5861
Coefficient of variation (CV)3.0687551
Kurtosis34.462089
Mean479.53846
Median Absolute Deviation (MAD)15
Skewness5.3561721
Sum187020
Variance2165565.7
MonotonicityNot monotonic
2023-12-13T06:07:34.365362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 55
 
14.1%
2 31
 
7.9%
3 24
 
6.2%
4 19
 
4.9%
6 10
 
2.6%
5 9
 
2.3%
7 8
 
2.1%
15 6
 
1.5%
9 6
 
1.5%
16 6
 
1.5%
Other values (158) 216
55.4%
ValueCountFrequency (%)
1 55
14.1%
2 31
7.9%
3 24
6.2%
4 19
 
4.9%
5 9
 
2.3%
6 10
 
2.6%
7 8
 
2.1%
8 4
 
1.0%
9 6
 
1.5%
10 6
 
1.5%
ValueCountFrequency (%)
13873 1
0.3%
11898 1
0.3%
9214 1
0.3%
7613 1
0.3%
7082 1
0.3%
6896 1
0.3%
6634 1
0.3%
6280 1
0.3%
6249 1
0.3%
6237 1
0.3%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86873503
Minimum16680
Maximum7.2935168 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-13T06:07:34.567494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16680
5-th percentile403861
Q15436517.5
median47819760
Q31.2323089 × 108
95-th percentile3.1199413 × 108
Maximum7.2935168 × 108
Range7.29335 × 108
Interquartile range (IQR)1.1779438 × 108

Descriptive statistics

Standard deviation1.118068 × 108
Coefficient of variation (CV)1.2870069
Kurtosis6.5428434
Mean86873503
Median Absolute Deviation (MAD)45744915
Skewness2.2296943
Sum3.3880666 × 1010
Variance1.250076 × 1016
MonotonicityNot monotonic
2023-12-13T06:07:34.753754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13736440 1
 
0.3%
88132450 1
 
0.3%
10170760 1
 
0.3%
1694230 1
 
0.3%
494110 1
 
0.3%
110350 1
 
0.3%
129200 1
 
0.3%
50003490 1
 
0.3%
243218980 1
 
0.3%
82052780 1
 
0.3%
Other values (380) 380
97.4%
ValueCountFrequency (%)
16680 1
0.3%
27260 1
0.3%
40810 1
0.3%
93750 1
0.3%
98680 1
0.3%
110350 1
0.3%
129200 1
0.3%
131090 1
0.3%
146910 1
0.3%
151280 1
0.3%
ValueCountFrequency (%)
729351680 1
0.3%
706138100 1
0.3%
528952330 1
0.3%
499919350 1
0.3%
487750000 1
0.3%
465782820 1
0.3%
457662350 1
0.3%
421849150 1
0.3%
416322360 1
0.3%
402258280 1
0.3%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct224
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1557.6744
Minimum1
Maximum42666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-13T06:07:34.930483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.45
Q116
median60
Q3438.75
95-th percentile9425.3
Maximum42666
Range42665
Interquartile range (IQR)422.75

Descriptive statistics

Standard deviation4539.3089
Coefficient of variation (CV)2.9141578
Kurtosis28.742911
Mean1557.6744
Median Absolute Deviation (MAD)57
Skewness4.7860796
Sum607493
Variance20605325
MonotonicityNot monotonic
2023-12-13T06:07:35.084488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
5.1%
3 11
 
2.8%
23 10
 
2.6%
2 9
 
2.3%
7 8
 
2.1%
4 8
 
2.1%
6 8
 
2.1%
14 7
 
1.8%
5 7
 
1.8%
20 6
 
1.5%
Other values (214) 296
75.9%
ValueCountFrequency (%)
1 20
5.1%
2 9
2.3%
3 11
2.8%
4 8
 
2.1%
5 7
 
1.8%
6 8
 
2.1%
7 8
 
2.1%
8 3
 
0.8%
9 5
 
1.3%
10 5
 
1.3%
ValueCountFrequency (%)
42666 1
0.3%
33944 1
0.3%
23078 1
0.3%
22735 1
0.3%
19755 1
0.3%
19682 1
0.3%
19270 1
0.3%
18793 1
0.3%
18587 1
0.3%
17914 1
0.3%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4780976 × 108
Minimum100950
Maximum2.2523356 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-13T06:07:35.549276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100950
5-th percentile1856674
Q129680448
median1.2914268 × 108
Q33.2832643 × 108
95-th percentile9.2115202 × 108
Maximum2.2523356 × 109
Range2.2522346 × 109
Interquartile range (IQR)2.9864598 × 108

Descriptive statistics

Standard deviation3.4953575 × 108
Coefficient of variation (CV)1.4105003
Kurtosis8.8629481
Mean2.4780976 × 108
Median Absolute Deviation (MAD)1.1300658 × 108
Skewness2.7240189
Sum9.6645806 × 1010
Variance1.2217524 × 1017
MonotonicityNot monotonic
2023-12-13T06:07:35.700656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31115990 1
 
0.3%
174357790 1
 
0.3%
27868110 1
 
0.3%
6453460 1
 
0.3%
1886770 1
 
0.3%
1205830 1
 
0.3%
304710 1
 
0.3%
124132390 1
 
0.3%
1272340140 1
 
0.3%
418932980 1
 
0.3%
Other values (380) 380
97.4%
ValueCountFrequency (%)
100950 1
0.3%
131090 1
0.3%
173670 1
0.3%
175510 1
0.3%
191290 1
0.3%
304710 1
0.3%
455530 1
0.3%
500580 1
0.3%
559570 1
0.3%
566770 1
0.3%
ValueCountFrequency (%)
2252335600 1
0.3%
2144723800 1
0.3%
1813591810 1
0.3%
1793279360 1
0.3%
1777456230 1
0.3%
1570480530 1
0.3%
1504067980 1
0.3%
1480565920 1
0.3%
1401731630 1
0.3%
1380554740 1
0.3%

Interactions

2023-12-13T06:07:32.157155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:31.177884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:31.524241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:31.862567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:32.239084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:31.273901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:31.616943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:31.935309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:32.324093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:31.359147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:31.704562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:32.009614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:32.408910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:31.438679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:31.782512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:07:32.077315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:07:35.809540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
시군구명1.0001.0000.0000.0000.0000.0740.2360.1430.309
자치단체코드1.0001.0000.0000.0000.0000.0740.2360.1430.309
과세년도0.0000.0001.0000.0000.0000.0000.0000.0000.000
세목명0.0000.0000.0001.0000.3580.3590.3440.4590.381
체납액구간0.0000.0000.0000.3581.0000.2390.4860.3160.186
체납건수0.0740.0740.0000.3590.2391.0000.5100.9540.403
체납금액0.2360.2360.0000.3440.4860.5101.0000.3550.799
누적체납건수0.1430.1430.0000.4590.3160.9540.3551.0000.523
누적체납금액0.3090.3090.0000.3810.1860.4030.7990.5231.000
2023-12-13T06:07:35.960161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간시군구명과세년도자치단체코드
세목명1.0000.1810.0000.0000.000
체납액구간0.1811.0000.0000.0000.000
시군구명0.0000.0001.0000.0001.000
과세년도0.0000.0000.0001.0000.000
자치단체코드0.0000.0001.0000.0001.000
2023-12-13T06:07:36.093783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액시군구명자치단체코드과세년도세목명체납액구간
체납건수1.0000.4750.9480.5470.0420.0420.0000.1980.103
체납금액0.4751.0000.3320.9450.1390.1390.0000.1890.229
누적체납건수0.9480.3321.0000.4700.0870.0870.0000.2660.138
누적체납금액0.5470.9450.4701.0000.1330.1330.0000.2030.078
시군구명0.0420.1390.0870.1331.0001.0000.0000.0000.000
자치단체코드0.0420.1390.0870.1331.0001.0000.0000.0000.000
과세년도0.0000.0000.0000.0000.0000.0001.0000.0000.000
세목명0.1980.1890.2660.2030.0000.0000.0001.0000.181
체납액구간0.1030.2290.1380.0780.0000.0000.0000.1811.000

Missing values

2023-12-13T06:07:32.537153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:07:32.688578image/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
0울산광역시중구311102020등록면허세10만원 미만4061373644093931115990
1울산광역시중구311102020자동차세10만원 미만1831777717008309367968360
2울산광역시중구311102020자동차세10만원~30만원미만135323141257082271380554740
3울산광역시중구311102020자동차세30만원~50만원미만7325009320405145825910
4울산광역시중구311102020자동차세50만원~1백만원미만317866303823300800
5울산광역시중구311102020재산세10만원 미만1470658076904577181379410
6울산광역시중구311102020재산세10만원~30만원미만14682517702202588435603810
7울산광역시중구311102020재산세1백만원~3백만원미만457054072090144287990
8울산광역시중구311102020재산세1천만원~3천만원미만112518140112518140
9울산광역시중구311102020재산세30만원~50만원미만17967954230315118164630
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
380울산광역시울주군317102021취득세10만원 미만12387770541832050
381울산광역시울주군317102021취득세10만원~30만원미만1122099005410449450
382울산광역시울주군317102021취득세1백만원~3백만원미만479280102948300690
383울산광역시울주군317102021취득세1억원~3억원미만22776945604515631030
384울산광역시울주군317102021취득세1천만원~3천만원미만57988411011174081840
385울산광역시울주군317102021취득세30만원~50만원미만41656300217666280
386울산광역시울주군317102021취득세3백만원~5백만원미만3129622701452511980
387울산광역시울주군317102021취득세50만원~1백만원미만749630303725992190
388울산광역시울주군317102021취득세5백만원~1천만원미만3222947101069934530
389울산광역시울주군317102021취득세5천만원~1억원미만1575969503168800840