Overview

Dataset statistics

Number of variables9
Number of observations343
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.6 KiB
Average record size in memory76.4 B

Variable types

Categorical7
Numeric2

Dataset

Description연도별 지방세 체납액 규모별 체납 건수를 납세자 유형별로 제공합니다. (추후 체납정책 수립시 기초자료로 활용함으로써 효과적인 정책 수립 가능)
Author전라남도 광양시
URLhttps://www.data.go.kr/data/15078906/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 데이터기준일High correlation
체납건수 is highly overall correlated with 체납금액High correlation
체납금액 is highly overall correlated with 체납건수High correlation

Reproduction

Analysis started2023-12-12 14:33:27.730286
Analysis finished2023-12-12 14:33:28.730585
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
전라남도
343 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 343
100.0%

Length

2023-12-12T23:33:28.814799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:33:28.934094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 343
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
광양시
343 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광양시 343
100.0%

Length

2023-12-12T23:33:29.062980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:33:29.160473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광양시 343
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
46230
343 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46230
2nd row46230
3rd row46230
4th row46230
5th row46230

Common Values

ValueCountFrequency (%)
46230 343
100.0%

Length

2023-12-12T23:33:29.256404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:33:29.361758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46230 343
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2021
173 
2022
170 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 173
50.4%
2022 170
49.6%

Length

2023-12-12T23:33:29.474296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:33:29.622611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 173
50.4%
2022 170
49.6%

세목명
Categorical

Distinct47
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
지방소득세(종합소득)
 
18
재산세(주택)
 
18
지방소득세(양도소득)
 
18
재산세(건축물)
 
17
취득세(부동산)
 
17
Other values (42)
255 

Length

Max length11
Median length10
Mean length8.6239067
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row구)재산세(건축물)
2nd row구)재산세(건축물)
3rd row구)재산세(건축물)
4th row구)재산세(건축물)
5th row구)재산세(선박)

Common Values

ValueCountFrequency (%)
지방소득세(종합소득) 18
 
5.2%
재산세(주택) 18
 
5.2%
지방소득세(양도소득) 18
 
5.2%
재산세(건축물) 17
 
5.0%
취득세(부동산) 17
 
5.0%
재산세(토지) 17
 
5.0%
취득세(기타) 16
 
4.7%
지방소득세(법인소득) 16
 
4.7%
지방소득세(특별징수) 12
 
3.5%
주민세(종업원분) 11
 
3.2%
Other values (37) 183
53.4%

Length

2023-12-12T23:33:29.841381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지방소득세(종합소득 18
 
5.2%
지방소득세(양도소득 18
 
5.2%
재산세(주택 18
 
5.2%
재산세(건축물 17
 
5.0%
취득세(부동산 17
 
5.0%
재산세(토지 17
 
5.0%
취득세(기타 16
 
4.7%
지방소득세(법인소득 16
 
4.7%
지방소득세(특별징수 12
 
3.5%
주민세(종업원분 11
 
3.2%
Other values (37) 183
53.4%

체납액구간
Categorical

Distinct9
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
10만원 미만
83 
10~30만원 미만
66 
50~100만원 미만
48 
1~500만원 미만
41 
30~50만원 미만
40 
Other values (4)
65 

Length

Max length11
Median length10
Mean length9.2361516
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10만원 미만
2nd row30~50만원 미만
3rd row50~100만원 미만
4th row10~30만원 미만
5th row10만원 미만

Common Values

ValueCountFrequency (%)
10만원 미만 83
24.2%
10~30만원 미만 66
19.2%
50~100만원 미만 48
14.0%
1~500만원 미만 41
12.0%
30~50만원 미만 40
11.7%
1~3천만원 미만 20
 
5.8%
500~1천만원 미만 18
 
5.2%
5천만원 이상 16
 
4.7%
3~5천만원 미만 11
 
3.2%

Length

2023-12-12T23:33:30.077414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:33:30.321554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미만 327
47.7%
10만원 83
 
12.1%
10~30만원 66
 
9.6%
50~100만원 48
 
7.0%
1~500만원 41
 
6.0%
30~50만원 40
 
5.8%
1~3천만원 20
 
2.9%
500~1천만원 18
 
2.6%
5천만원 16
 
2.3%
이상 16
 
2.3%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343.92711
Minimum1
Maximum13807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-12T23:33:30.496926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q340.5
95-th percentile960.6
Maximum13807
Range13806
Interquartile range (IQR)38.5

Descriptive statistics

Standard deviation1613.694
Coefficient of variation (CV)4.6919651
Kurtosis41.755634
Mean343.92711
Median Absolute Deviation (MAD)5
Skewness6.3173327
Sum117967
Variance2604008.3
MonotonicityNot monotonic
2023-12-12T23:33:30.669284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 84
24.5%
2 27
 
7.9%
4 22
 
6.4%
3 19
 
5.5%
5 16
 
4.7%
6 9
 
2.6%
9 8
 
2.3%
7 8
 
2.3%
30 5
 
1.5%
10 5
 
1.5%
Other values (100) 140
40.8%
ValueCountFrequency (%)
1 84
24.5%
2 27
 
7.9%
3 19
 
5.5%
4 22
 
6.4%
5 16
 
4.7%
6 9
 
2.6%
7 8
 
2.3%
8 1
 
0.3%
9 8
 
2.3%
10 5
 
1.5%
ValueCountFrequency (%)
13807 1
0.3%
13675 1
0.3%
10440 1
0.3%
9863 1
0.3%
9658 1
0.3%
8844 1
0.3%
8053 1
0.3%
7184 1
0.3%
5019 1
0.3%
2664 1
0.3%

체납금액
Real number (ℝ)

HIGH CORRELATION 

Distinct312
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59855747
Minimum4470
Maximum2.3477964 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-12T23:33:30.834114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4470
5-th percentile123029
Q1766205
median6625150
Q342018680
95-th percentile2.0353317 × 108
Maximum2.3477964 × 109
Range2.3477919 × 109
Interquartile range (IQR)41252475

Descriptive statistics

Standard deviation2.1916198 × 108
Coefficient of variation (CV)3.6615028
Kurtosis77.382879
Mean59855747
Median Absolute Deviation (MAD)6398550
Skewness8.2921267
Sum2.0530521 × 1010
Variance4.8031975 × 1016
MonotonicityNot monotonic
2023-12-12T23:33:31.008299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
395430 2
 
0.6%
895910 2
 
0.6%
604090 2
 
0.6%
250460 2
 
0.6%
4470 2
 
0.6%
570770 2
 
0.6%
192080 2
 
0.6%
1846680 2
 
0.6%
1469000 2
 
0.6%
305090 2
 
0.6%
Other values (302) 323
94.2%
ValueCountFrequency (%)
4470 2
0.6%
6380 1
0.3%
11370 2
0.6%
26480 1
0.3%
30810 1
0.3%
40010 2
0.6%
40340 1
0.3%
43060 2
0.6%
72890 1
0.3%
102510 1
0.3%
ValueCountFrequency (%)
2347796350 1
0.3%
2333775990 1
0.3%
1618814550 1
0.3%
1270790280 1
0.3%
477970660 1
0.3%
469407420 1
0.3%
442367190 1
0.3%
404761630 1
0.3%
404005490 1
0.3%
401365260 1
0.3%

데이터기준일
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2021-12-30
173 
2022-12-30
170 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-12-30
2nd row2021-12-30
3rd row2021-12-30
4th row2021-12-30
5th row2021-12-30

Common Values

ValueCountFrequency (%)
2021-12-30 173
50.4%
2022-12-30 170
49.6%

Length

2023-12-12T23:33:31.170660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:33:31.298046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-30 173
50.4%
2022-12-30 170
49.6%

Interactions

2023-12-12T23:33:28.237298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:28.061281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:28.335638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:28.152259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:33:31.369843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액데이터기준일
과세년도1.0000.0000.0000.0000.0001.000
세목명0.0001.0000.0000.7570.2560.000
체납액구간0.0000.0001.0000.0000.2270.000
체납건수0.0000.7570.0001.0000.7310.000
체납금액0.0000.2560.2270.7311.0000.000
데이터기준일1.0000.0000.0000.0000.0001.000
2023-12-12T23:33:31.489257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터기준일체납액구간과세년도세목명
데이터기준일1.0000.0000.9940.000
체납액구간0.0001.0000.0000.000
과세년도0.9940.0001.0000.000
세목명0.0000.0000.0001.000
2023-12-12T23:33:31.582936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액과세년도세목명체납액구간데이터기준일
체납건수1.0000.5070.0000.3990.0000.000
체납금액0.5071.0000.0000.1070.1140.000
과세년도0.0000.0001.0000.0000.0000.994
세목명0.3990.1070.0001.0000.0000.000
체납액구간0.0000.1140.0000.0001.0000.000
데이터기준일0.0000.0000.9940.0000.0001.000

Missing values

2023-12-12T23:33:28.471230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:33:28.652196image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액데이터기준일
0전라남도광양시462302021구)재산세(건축물)10만원 미만216668402021-12-30
1전라남도광양시462302021구)재산세(건축물)30~50만원 미만13954302021-12-30
2전라남도광양시462302021구)재산세(건축물)50~100만원 미만15850502021-12-30
3전라남도광양시462302021구)재산세(건축물)10~30만원 미만22176702021-12-30
4전라남도광양시462302021구)재산세(선박)10만원 미만135521502021-12-30
5전라남도광양시462302021구)재산세(주택)10~30만원 미만11101402021-12-30
6전라남도광양시462302021구)재산세(주택)10만원 미만20940977702021-12-30
7전라남도광양시462302021구)재산세(토지)10만원 미만40471820302021-12-30
8전라남도광양시462302021구)재산세(토지)10~30만원 미만46991102021-12-30
9전라남도광양시462302021구)취득세(기계장비)10~30만원 미만12327302021-12-30
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액데이터기준일
333전라남도광양시462302022취득세(부동산)5천만원 이상12079742202022-12-30
334전라남도광양시462302022취득세(부동산)10만원 미만168091502022-12-30
335전라남도광양시462302022취득세(선박)50~100만원 미만16144502022-12-30
336전라남도광양시462302022취득세(이륜차량)10~30만원 미만11290502022-12-30
337전라남도광양시462302022취득세(이륜차량)10만원 미만218957002022-12-30
338전라남도광양시462302022취득세(차량)30~50만원 미만626123902022-12-30
339전라남도광양시462302022취득세(차량)10만원 미만138808402022-12-30
340전라남도광양시462302022취득세(차량)10~30만원 미만1223898802022-12-30
341전라남도광양시462302022취득세(차량)1~500만원 미만10229959102022-12-30
342전라남도광양시462302022취득세(차량)50~100만원 미만14101928102022-12-30