Overview

Dataset statistics

Number of variables10
Number of observations86
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory87.5 B

Variable types

Categorical6
Numeric4

Dataset

Description2017~2021년도 충청남도 보령시 지방세 관련 체납액 규모별 체납 건수를 납세자 유형별 현황항목에 대해 정리한 자료를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=351&beforeMenuCd=DOM_000000201001001000&publicdatapk=15079141

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 누적체납금액 and 1 other fieldsHigh correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액 is highly overall correlated with 체납금액High correlation
체납액구간 is highly overall correlated with 체납금액High correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2024-01-09 23:18:17.099280
Analysis finished2024-01-09 23:18:19.188631
Duration2.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size820.0 B
충청남도
86 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 86
100.0%

Length

2024-01-10T08:18:19.239734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:18:19.317135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 86
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size820.0 B
보령시
86 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
보령시 86
100.0%

Length

2024-01-10T08:18:19.397204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:18:19.476501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보령시 86
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size820.0 B
44180
86 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44180
2nd row44180
3rd row44180
4th row44180
5th row44180

Common Values

ValueCountFrequency (%)
44180 86
100.0%

Length

2024-01-10T08:18:19.554081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:18:19.632595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44180 86
100.0%

과세년도
Categorical

Distinct3
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size820.0 B
2019
35 
2018
29 
2017
22 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2019 35
40.7%
2018 29
33.7%
2017 22
25.6%

Length

2024-01-10T08:18:19.712059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:18:19.796577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 35
40.7%
2018 29
33.7%
2017 22
25.6%

세목명
Categorical

Distinct6
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size820.0 B
지방소득세
24 
재산세
20 
취득세
15 
자동차세
12 
주민세
11 

Length

Max length5
Median length3
Mean length3.7906977
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 24
27.9%
재산세 20
23.3%
취득세 15
17.4%
자동차세 12
14.0%
주민세 11
12.8%
등록면허세 4
 
4.7%

Length

2024-01-10T08:18:19.901173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:18:20.028165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 24
27.9%
재산세 20
23.3%
취득세 15
17.4%
자동차세 12
14.0%
주민세 11
12.8%
등록면허세 4
 
4.7%

체납액구간
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
10만원 미만
16 
10만원~30만원미만
16 
30만원~50만원미만
12 
50만원~1백만원미만
12 
1백만원~3백만원미만
9 
Other values (6)
21 

Length

Max length11
Median length11
Mean length10.22093
Min length7

Unique

Unique2 ?
Unique (%)2.3%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 16
18.6%
10만원~30만원미만 16
18.6%
30만원~50만원미만 12
14.0%
50만원~1백만원미만 12
14.0%
1백만원~3백만원미만 9
10.5%
3백만원~5백만원미만 7
8.1%
5백만원~1천만원미만 6
 
7.0%
1천만원~3천만원미만 4
 
4.7%
3천만원~5천만원미만 2
 
2.3%
5천만원~1억원미만 1
 
1.2%

Length

2024-01-10T08:18:20.183858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 16
15.7%
미만 16
15.7%
10만원~30만원미만 16
15.7%
30만원~50만원미만 12
11.8%
50만원~1백만원미만 12
11.8%
1백만원~3백만원미만 9
8.8%
3백만원~5백만원미만 7
6.9%
5백만원~1천만원미만 6
 
5.9%
1천만원~3천만원미만 4
 
3.9%
3천만원~5천만원미만 2
 
2.0%
Other values (2) 2
 
2.0%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.22093
Minimum1
Maximum1861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2024-01-10T08:18:20.312528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median9
Q352.25
95-th percentile1018
Maximum1861
Range1860
Interquartile range (IQR)50.25

Descriptive statistics

Standard deviation368.26622
Coefficient of variation (CV)2.2562439
Kurtosis7.5080825
Mean163.22093
Median Absolute Deviation (MAD)8
Skewness2.7589577
Sum14037
Variance135620.01
MonotonicityNot monotonic
2024-01-10T08:18:20.439420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 16
18.6%
2 10
 
11.6%
3 7
 
8.1%
9 3
 
3.5%
19 3
 
3.5%
4 3
 
3.5%
7 2
 
2.3%
13 2
 
2.3%
31 2
 
2.3%
23 2
 
2.3%
Other values (34) 36
41.9%
ValueCountFrequency (%)
1 16
18.6%
2 10
11.6%
3 7
8.1%
4 3
 
3.5%
5 1
 
1.2%
6 2
 
2.3%
7 2
 
2.3%
8 1
 
1.2%
9 3
 
3.5%
10 1
 
1.2%
ValueCountFrequency (%)
1861 1
1.2%
1450 1
1.2%
1344 1
1.2%
1140 1
1.2%
1036 1
1.2%
964 1
1.2%
914 1
1.2%
705 1
1.2%
632 1
1.2%
589 1
1.2%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct86
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19602763
Minimum168590
Maximum1.7714544 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2024-01-10T08:18:20.564898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168590
5-th percentile304220
Q12872770
median9363080
Q322917048
95-th percentile86261768
Maximum1.7714544 × 108
Range1.7697685 × 108
Interquartile range (IQR)20044278

Descriptive statistics

Standard deviation30968756
Coefficient of variation (CV)1.5798158
Kurtosis12.934577
Mean19602763
Median Absolute Deviation (MAD)7841140
Skewness3.3937049
Sum1.6858377 × 109
Variance9.5906387 × 1014
MonotonicityNot monotonic
2024-01-10T08:18:20.686533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3779610 1
 
1.2%
14188090 1
 
1.2%
20899430 1
 
1.2%
11600260 1
 
1.2%
8932210 1
 
1.2%
12105320 1
 
1.2%
33017180 1
 
1.2%
28658460 1
 
1.2%
29550280 1
 
1.2%
1236640 1
 
1.2%
Other values (76) 76
88.4%
ValueCountFrequency (%)
168590 1
1.2%
200530 1
1.2%
206030 1
1.2%
290520 1
1.2%
301960 1
1.2%
311000 1
1.2%
371740 1
1.2%
466380 1
1.2%
835980 1
1.2%
1183390 1
1.2%
ValueCountFrequency (%)
177145440 1
1.2%
161521640 1
1.2%
105127840 1
1.2%
104805380 1
1.2%
96510550 1
1.2%
55515420 1
1.2%
46544820 1
1.2%
38810700 1
1.2%
37835900 1
1.2%
35513870 1
1.2%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean737.52326
Minimum1
Maximum7637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2024-01-10T08:18:20.808920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110.25
median30
Q3195.75
95-th percentile4717.25
Maximum7637
Range7636
Interquartile range (IQR)185.5

Descriptive statistics

Standard deviation1648.0379
Coefficient of variation (CV)2.2345572
Kurtosis4.9514371
Mean737.52326
Median Absolute Deviation (MAD)28
Skewness2.4119234
Sum63427
Variance2716028.9
MonotonicityNot monotonic
2024-01-10T08:18:20.933079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
7.0%
2 4
 
4.7%
6 3
 
3.5%
8 3
 
3.5%
31 3
 
3.5%
10 2
 
2.3%
22 2
 
2.3%
17 2
 
2.3%
46 2
 
2.3%
27 2
 
2.3%
Other values (55) 57
66.3%
ValueCountFrequency (%)
1 6
7.0%
2 4
4.7%
3 1
 
1.2%
4 1
 
1.2%
5 1
 
1.2%
6 3
3.5%
7 1
 
1.2%
8 3
3.5%
10 2
 
2.3%
11 2
 
2.3%
ValueCountFrequency (%)
7637 1
1.2%
5776 1
1.2%
5339 1
1.2%
5069 1
1.2%
4835 1
1.2%
4364 1
1.2%
4326 1
1.2%
3995 1
1.2%
3871 1
1.2%
3805 1
1.2%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct86
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66946989
Minimum200530
Maximum7.8828042 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2024-01-10T08:18:21.057685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200530
5-th percentile3202365
Q110523100
median35113085
Q365214810
95-th percentile1.8441852 × 108
Maximum7.8828042 × 108
Range7.8807989 × 108
Interquartile range (IQR)54691710

Descriptive statistics

Standard deviation1.2188743 × 108
Coefficient of variation (CV)1.8206559
Kurtosis20.526606
Mean66946989
Median Absolute Deviation (MAD)24938865
Skewness4.2990736
Sum5.757441 × 109
Variance1.4856545 × 1016
MonotonicityNot monotonic
2024-01-10T08:18:21.186693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11709630 1
 
1.2%
59256700 1
 
1.2%
55850940 1
 
1.2%
51300260 1
 
1.2%
27173240 1
 
1.2%
12105320 1
 
1.2%
81092720 1
 
1.2%
85857320 1
 
1.2%
114638610 1
 
1.2%
10023050 1
 
1.2%
Other values (76) 76
88.4%
ValueCountFrequency (%)
200530 1
1.2%
921080 1
1.2%
2344510 1
1.2%
2694110 1
1.2%
3180490 1
1.2%
3267990 1
1.2%
4349300 1
1.2%
4678270 1
1.2%
4702920 1
1.2%
4815680 1
1.2%
ValueCountFrequency (%)
788280420 1
1.2%
626758780 1
1.2%
521630940 1
1.2%
217786860 1
1.2%
186842880 1
1.2%
177145440 1
1.2%
162819710 1
1.2%
162313790 1
1.2%
132263340 1
1.2%
124756010 1
1.2%

Interactions

2024-01-10T08:18:18.664392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:17.408820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:17.739483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:18.347498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:18.745690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:17.480854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:17.821360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:18.426105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:18.832143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:17.558414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:17.901845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:18.505418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:18.918747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:17.640307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:17.980831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:18.583921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T08:18:21.280327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.3260.4370.213
세목명0.0001.0000.0000.6610.0000.6850.398
체납액구간0.0000.0001.0000.0000.8070.0000.115
체납건수0.0000.6610.0001.0000.5580.9830.778
체납금액0.3260.0000.8070.5581.0000.3580.912
누적체납건수0.4370.6850.0000.9830.3581.0000.725
누적체납금액0.2130.3980.1150.7780.9120.7251.000
2024-01-10T08:18:21.373897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.0000.000
체납액구간0.0001.0000.000
세목명0.0000.0001.000
2024-01-10T08:18:21.451223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.3750.9400.4980.0000.3920.000
체납금액0.3751.0000.2230.9040.1460.0000.566
누적체납건수0.9400.2231.0000.4390.2040.4140.000
누적체납금액0.4980.9040.4391.0000.0730.1560.031
과세년도0.0000.1460.2040.0731.0000.0000.000
세목명0.3920.0000.4140.1560.0001.0000.000
체납액구간0.0000.5660.0000.0310.0000.0001.000

Missing values

2024-01-10T08:18:19.026153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T08:18:19.141812image/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
0충청남도보령시441802017등록면허세10만원 미만247377961083911709630
1충청남도보령시441802017자동차세10만원 미만511219657103805162819710
2충청남도보령시441802017자동차세10만원~30만원미만589965105503239521630940
3충청남도보령시441802017자동차세30만원~50만원미만21697510010535274660
4충청남도보령시441802017자동차세50만원~1백만원미만31633600117025850
5충청남도보령시441802017재산세10만원 미만91418149410295963677730
6충청남도보령시441802017재산세10만원~30만원미만56889166026142067870
7충청남도보령시441802017재산세1백만원~3백만원미만474770102539749010
8충청남도보령시441802017재산세30만원~50만원미만832655602710325390
9충청남도보령시441802017재산세3백만원~5백만원미만28360600727847820
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
76충청남도보령시441802019지방소득세5백만원~1천만원미만6378359001061416100
77충청남도보령시441802019지방소득세5천만원~1억원미만155515420155515420
78충청남도보령시441802019취득세10만원 미만320603031921080
79충청남도보령시441802019취득세10만원~30만원미만2290520265106200
80충청남도보령시441802019취득세1백만원~3백만원미만125078002034830580
81충청남도보령시441802019취득세1억원~3억원미만11771454401177145440
82충청남도보령시441802019취득세1천만원~3천만원미만235513870235513870
83충청남도보령시441802019취득세3천만원~5천만원미만146544820281939340
84충청남도보령시441802019취득세50만원~1백만원미만213495801913655470
85충청남도보령시441802019취득세5백만원~1천만원미만427363180646738730