Overview

Dataset statistics

Number of variables11
Number of observations35
Missing cells40
Missing cells (%)10.4%
Duplicate rows1
Duplicate rows (%)2.9%
Total size in memory3.3 KiB
Average record size in memory97.8 B

Variable types

Categorical6
Numeric4
DateTime1

Dataset

Description충청북도 증평군_지방세에 대한 자료입니다. 지방세에는 취득세, 재산세, 자동차세, 지방소득세, 등록면허세 등 다양한 자료가 있습니다.
URLhttps://www.data.go.kr/data/15080372/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (2.9%) duplicate rowsDuplicates
시군구명 is highly overall correlated with 체납건수 and 8 other fieldsHigh correlation
시도명 is highly overall correlated with 체납건수 and 8 other fieldsHigh correlation
자치단체코드 is highly overall correlated with 체납건수 and 8 other fieldsHigh correlation
체납액구간 is highly overall correlated with 시도명 and 3 other fieldsHigh correlation
과세년도 is highly overall correlated with 체납건수 and 8 other fieldsHigh correlation
세목명 is highly overall correlated with 시도명 and 3 other fieldsHigh correlation
체납건수 is highly overall correlated with 누적체납건수 and 4 other fieldsHigh correlation
체납금액 is highly overall correlated with 누적체납금액 and 4 other fieldsHigh correlation
누적체납건수 is highly overall correlated with 체납건수 and 4 other fieldsHigh correlation
누적체납금액 is highly overall correlated with 체납금액 and 4 other fieldsHigh correlation
체납건수 has 8 (22.9%) missing valuesMissing
체납금액 has 8 (22.9%) missing valuesMissing
누적체납건수 has 8 (22.9%) missing valuesMissing
누적체납금액 has 8 (22.9%) missing valuesMissing
데이터기준일자 has 8 (22.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 11:01:26.002946
Analysis finished2023-12-12 11:01:29.671784
Duration3.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
충청북도
27 
<NA>
8 

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 27
77.1%
<NA> 8
 
22.9%

Length

2023-12-12T20:01:29.792414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:01:29.937493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 27
77.1%
na 8
 
22.9%

시군구명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
증평군
27 
<NA>
8 

Length

Max length4
Median length3
Mean length3.2285714
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
증평군 27
77.1%
<NA> 8
 
22.9%

Length

2023-12-12T20:01:30.092295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:01:30.223943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
증평군 27
77.1%
na 8
 
22.9%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
43745
27 
<NA>
8 

Length

Max length5
Median length5
Mean length4.7714286
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43745
2nd row43745
3rd row43745
4th row43745
5th row43745

Common Values

ValueCountFrequency (%)
43745 27
77.1%
<NA> 8
 
22.9%

Length

2023-12-12T20:01:30.374479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:01:30.569727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43745 27
77.1%
na 8
 
22.9%

과세년도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
2022
27 
<NA>
8 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 27
77.1%
<NA> 8
 
22.9%

Length

2023-12-12T20:01:30.713264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:01:30.857569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 27
77.1%
na 8
 
22.9%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
지방소득세
8 
<NA>
8 
재산세
7 
주민세
4 
자동차세
3 
Other values (3)
5 

Length

Max length7
Median length5
Mean length3.9428571
Min length3

Unique

Unique2 ?
Unique (%)5.7%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 8
22.9%
<NA> 8
22.9%
재산세 7
20.0%
주민세 4
11.4%
자동차세 3
 
8.6%
취득세 3
 
8.6%
등록면허세 1
 
2.9%
지역자원시설세 1
 
2.9%

Length

2023-12-12T20:01:31.560877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:01:31.882581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 8
22.9%
na 8
22.9%
재산세 7
20.0%
주민세 4
11.4%
자동차세 3
 
8.6%
취득세 3
 
8.6%
등록면허세 1
 
2.9%
지역자원시설세 1
 
2.9%

체납액구간
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
8 
10만원 미만
7 
10만원~30만원미만
4 
30만원~50만원미만
4 
50만원~1백만원미만
4 
Other values (5)
8 

Length

Max length11
Median length11
Mean length8.5714286
Min length4

Unique

Unique2 ?
Unique (%)5.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8
22.9%
10만원 미만 7
20.0%
10만원~30만원미만 4
11.4%
30만원~50만원미만 4
11.4%
50만원~1백만원미만 4
11.4%
1백만원~3백만원미만 2
 
5.7%
1천만원~3천만원미만 2
 
5.7%
5백만원~1천만원미만 2
 
5.7%
3백만원~5백만원미만 1
 
2.9%
5천만원~1억원미만 1
 
2.9%

Length

2023-12-12T20:01:32.147655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:01:32.381544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8
19.0%
10만원 7
16.7%
미만 7
16.7%
10만원~30만원미만 4
9.5%
30만원~50만원미만 4
9.5%
50만원~1백만원미만 4
9.5%
1백만원~3백만원미만 2
 
4.8%
1천만원~3천만원미만 2
 
4.8%
5백만원~1천만원미만 2
 
4.8%
3백만원~5백만원미만 1
 
2.4%

체납건수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)74.1%
Missing8
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean111.44444
Minimum1
Maximum1205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T20:01:32.619065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median12
Q354
95-th percentile601.2
Maximum1205
Range1204
Interquartile range (IQR)52.5

Descriptive statistics

Standard deviation267.76272
Coefficient of variation (CV)2.4026565
Kurtosis11.323858
Mean111.44444
Median Absolute Deviation (MAD)11
Skewness3.2773102
Sum3009
Variance71696.872
MonotonicityNot monotonic
2023-12-12T20:01:32.847321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 7
20.0%
5 2
 
5.7%
1205 1
 
2.9%
14 1
 
2.9%
2 1
 
2.9%
21 1
 
2.9%
12 1
 
2.9%
8 1
 
2.9%
30 1
 
2.9%
78 1
 
2.9%
Other values (10) 10
28.6%
(Missing) 8
22.9%
ValueCountFrequency (%)
1 7
20.0%
2 1
 
2.9%
3 1
 
2.9%
5 2
 
5.7%
6 1
 
2.9%
8 1
 
2.9%
12 1
 
2.9%
13 1
 
2.9%
14 1
 
2.9%
15 1
 
2.9%
ValueCountFrequency (%)
1205 1
2.9%
702 1
2.9%
366 1
2.9%
304 1
2.9%
110 1
2.9%
87 1
2.9%
78 1
2.9%
30 1
2.9%
21 1
2.9%
16 1
2.9%

체납금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)100.0%
Missing8
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean16290787
Minimum65250
Maximum84327600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T20:01:33.100405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65250
5-th percentile160254
Q11800510
median12690030
Q317954320
95-th percentile72199003
Maximum84327600
Range84262350
Interquartile range (IQR)16153810

Descriptive statistics

Standard deviation22281715
Coefficient of variation (CV)1.3677495
Kurtosis4.6664914
Mean16290787
Median Absolute Deviation (MAD)8424660
Skewness2.2273277
Sum4.3985125 × 108
Variance4.9647484 × 1014
MonotonicityNot monotonic
2023-12-12T20:01:33.308422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
16373950 1
 
2.9%
84327600 1
 
2.9%
616530 1
 
2.9%
65250 1
 
2.9%
149520 1
 
2.9%
14124520 1
 
2.9%
16047320 1
 
2.9%
21452070 1
 
2.9%
4465830 1
 
2.9%
80717380 1
 
2.9%
Other values (17) 17
48.6%
(Missing) 8
22.9%
ValueCountFrequency (%)
65250 1
2.9%
149520 1
2.9%
185300 1
2.9%
319490 1
2.9%
616530 1
2.9%
815150 1
2.9%
1055240 1
2.9%
2545780 1
2.9%
4265370 1
2.9%
4465830 1
2.9%
ValueCountFrequency (%)
84327600 1
2.9%
80717380 1
2.9%
52322790 1
2.9%
28299160 1
2.9%
21452070 1
2.9%
21007610 1
2.9%
18117000 1
2.9%
17791640 1
2.9%
16373950 1
2.9%
16047320 1
2.9%

누적체납건수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)88.9%
Missing8
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean381.85185
Minimum1
Maximum4150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T20:01:33.524581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.6
Q19.5
median40
Q3179.5
95-th percentile1671.1
Maximum4150
Range4149
Interquartile range (IQR)170

Descriptive statistics

Standard deviation901.31411
Coefficient of variation (CV)2.3603764
Kurtosis11.934625
Mean381.85185
Median Absolute Deviation (MAD)34
Skewness3.2897212
Sum10310
Variance812367.13
MonotonicityNot monotonic
2023-12-12T20:01:33.742041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
41 2
 
5.7%
6 2
 
5.7%
7 2
 
5.7%
125 1
 
2.9%
1 1
 
2.9%
3 1
 
2.9%
34 1
 
2.9%
14 1
 
2.9%
70 1
 
2.9%
17 1
 
2.9%
Other values (14) 14
40.0%
(Missing) 8
22.9%
ValueCountFrequency (%)
1 1
2.9%
3 1
2.9%
5 1
2.9%
6 2
5.7%
7 2
5.7%
12 1
2.9%
14 1
2.9%
15 1
2.9%
17 1
2.9%
20 1
2.9%
ValueCountFrequency (%)
4150 1
2.9%
1684 1
2.9%
1641 1
2.9%
1436 1
2.9%
297 1
2.9%
249 1
2.9%
234 1
2.9%
125 1
2.9%
81 1
2.9%
74 1
2.9%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)100.0%
Missing8
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean56124649
Minimum265390
Maximum2.7566218 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T20:01:33.957822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum265390
5-th percentile511039
Q18331605
median39142750
Q372052800
95-th percentile2.2073831 × 108
Maximum2.7566218 × 108
Range2.7539679 × 108
Interquartile range (IQR)63721195

Descriptive statistics

Standard deviation70580491
Coefficient of variation (CV)1.2575667
Kurtosis4.6614425
Mean56124649
Median Absolute Deviation (MAD)31372930
Skewness2.1302298
Sum1.5153655 × 109
Variance4.9816057 × 1015
MonotonicityNot monotonic
2023-12-12T20:01:34.151314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
68058010 1
 
2.9%
84327600 1
 
2.9%
1894370 1
 
2.9%
267280 1
 
2.9%
265390 1
 
2.9%
98399710 1
 
2.9%
51892770 1
 
2.9%
65878650 1
 
2.9%
16256380 1
 
2.9%
258617270 1
 
2.9%
Other values (17) 17
48.6%
(Missing) 8
22.9%
ValueCountFrequency (%)
265390 1
2.9%
267280 1
2.9%
1079810 1
2.9%
1894370 1
2.9%
2574280 1
2.9%
3646850 1
2.9%
7769820 1
2.9%
8893390 1
2.9%
14855920 1
2.9%
15351440 1
2.9%
ValueCountFrequency (%)
275662180 1
2.9%
258617270 1
2.9%
132354060 1
2.9%
98399710 1
2.9%
94536130 1
2.9%
84327600 1
2.9%
76047590 1
2.9%
68058010 1
2.9%
65878650 1
2.9%
56073250 1
2.9%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)3.7%
Missing8
Missing (%)22.9%
Memory size412.0 B
Minimum2022-12-31 00:00:00
Maximum2022-12-31 00:00:00
2023-12-12T20:01:34.328673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:34.491350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T20:01:28.419199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:26.769671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:27.330598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:27.902731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:28.545688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:26.919127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:27.509026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:28.028290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:28.678128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:27.049261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:27.638594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:28.170998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:28.810519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:27.170509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:27.775707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:01:28.292871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:01:34.638221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간체납건수체납금액누적체납건수누적체납금액
세목명1.0000.0000.2340.0000.4860.000
체납액구간0.0001.0000.0000.6680.0000.665
체납건수0.2340.0001.0000.6121.0000.470
체납금액0.0000.6680.6121.0000.5280.918
누적체납건수0.4860.0001.0000.5281.0000.422
누적체납금액0.0000.6650.4700.9180.4221.000
2023-12-12T20:01:34.827102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명시도명자치단체코드체납액구간과세년도세목명
시군구명1.0001.0001.0001.0001.0001.000
시도명1.0001.0001.0001.0001.0001.000
자치단체코드1.0001.0001.0001.0001.0001.000
체납액구간1.0001.0001.0001.0001.0000.000
과세년도1.0001.0001.0001.0001.0001.000
세목명1.0001.0001.0000.0001.0001.000
2023-12-12T20:01:35.034110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액시도명시군구명자치단체코드과세년도세목명체납액구간
체납건수1.0000.2980.9650.2051.0001.0001.0001.0000.1050.000
체납금액0.2981.0000.1950.8721.0001.0001.0001.0000.0000.371
누적체납건수0.9650.1951.0000.1851.0001.0001.0001.0000.3180.000
누적체납금액0.2050.8720.1851.0001.0001.0001.0001.0000.0000.366
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
자치단체코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
과세년도1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
세목명0.1050.0000.3180.0001.0001.0001.0001.0001.0000.000
체납액구간0.0000.3710.0000.3661.0001.0001.0001.0000.0001.000

Missing values

2023-12-12T20:01:29.014309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:01:29.261561image/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.
2023-12-12T20:01:29.480714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액데이터기준일자
0충청북도증평군437452022등록면허세10만원 미만87105524029736468502022-12-31
1충청북도증평군437452022자동차세10만원 미만366163739501436680580102022-12-31
2충청북도증평군437452022자동차세10만원~30만원미만3045232279016412756621802022-12-31
3충청북도증평군437452022자동차세30만원~50만원미만16538516081270768002022-12-31
4충청북도증평군437452022재산세10만원 미만702177916401684471288702022-12-31
5충청북도증평군437452022재산세10만원~30만원미만11018117000234391427502022-12-31
6충청북도증평군437452022재산세1백만원~3백만원미만152829916041760475902022-12-31
7충청북도증평군437452022재산세1천만원~3천만원미만1127071405945361302022-12-31
8충청북도증평군437452022재산세30만원~50만원미만13514676041153514402022-12-31
9충청북도증평군437452022재산세50만원~1백만원미만6426537020148559202022-12-31
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액데이터기준일자
25충청북도증평군437452022취득세50만원~1백만원미만1616530318943702022-12-31
26충청북도증평군437452022취득세5천만원~1억원미만1843276001843276002022-12-31
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33<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
34<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액데이터기준일자# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8