Overview

Dataset statistics

Number of variables10
Number of observations65
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory88.0 B

Variable types

Categorical6
Numeric4

Dataset

Description전남 영광군의 지방세 체납현황에 관련된 데이터로 체납액 규모별 체납 건수를 납세자 유형별로 제공하여 체납정책 수립 시 기초자료로 활용할 수 있음.
Author전라남도 영광군
URLhttps://www.data.go.kr/data/15079831/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수 is highly overall correlated with 체납금액 and 2 other fieldsHigh correlation
체납금액 is highly overall correlated with 체납건수 and 2 other fieldsHigh correlation
누적체납건수 is highly overall correlated with 체납건수 and 2 other fieldsHigh correlation
누적체납금액 is highly overall correlated with 체납건수 and 2 other fieldsHigh correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:54:48.869189
Analysis finished2023-12-12 21:54:51.058144
Duration2.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size652.0 B
전라남도
65 

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 65
100.0%

Length

2023-12-13T06:54:51.123109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:51.234274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 65
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size652.0 B
영광군
65 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영광군 65
100.0%

Length

2023-12-13T06:54:51.319715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:51.416677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영광군 65
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size652.0 B
46870
65 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46870
2nd row46870
3rd row46870
4th row46870
5th row46870

Common Values

ValueCountFrequency (%)
46870 65
100.0%

Length

2023-12-13T06:54:51.499084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:51.591894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46870 65
100.0%

과세년도
Categorical

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size652.0 B
2018
24 
2019
24 
2017
17 

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 (%)
2018 24
36.9%
2019 24
36.9%
2017 17
26.2%

Length

2023-12-13T06:54:51.683945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:51.783702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 24
36.9%
2019 24
36.9%
2017 17
26.2%

세목명
Categorical

Distinct6
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size652.0 B
재산세
19 
지방소득세
16 
취득세
11 
자동차세
9 
주민세
7 

Length

Max length5
Median length3
Mean length3.7230769
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 19
29.2%
지방소득세 16
24.6%
취득세 11
16.9%
자동차세 9
13.8%
주민세 7
 
10.8%
등록면허세 3
 
4.6%

Length

2023-12-13T06:54:51.893213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:52.027839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 19
29.2%
지방소득세 16
24.6%
취득세 11
16.9%
자동차세 9
13.8%
주민세 7
 
10.8%
등록면허세 3
 
4.6%

체납액구간
Categorical

Distinct8
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size652.0 B
10만원 미만
18 
10만원~30만원미만
15 
30만원~50만원미만
11 
1백만원~3백만원미만
7 
50만원~1백만원미만
7 
Other values (3)
7 

Length

Max length11
Median length11
Mean length9.8923077
Min length7

Unique

Unique1 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 18
27.7%
10만원~30만원미만 15
23.1%
30만원~50만원미만 11
16.9%
1백만원~3백만원미만 7
 
10.8%
50만원~1백만원미만 7
 
10.8%
3백만원~5백만원미만 4
 
6.2%
5백만원~1천만원미만 2
 
3.1%
1천만원~3천만원미만 1
 
1.5%

Length

2023-12-13T06:54:52.157377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:52.275617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 18
21.7%
미만 18
21.7%
10만원~30만원미만 15
18.1%
30만원~50만원미만 11
13.3%
1백만원~3백만원미만 7
 
8.4%
50만원~1백만원미만 7
 
8.4%
3백만원~5백만원미만 4
 
4.8%
5백만원~1천만원미만 2
 
2.4%
1천만원~3천만원미만 1
 
1.2%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.33846
Minimum1
Maximum2721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T06:54:52.408355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q363
95-th percentile1331
Maximum2721
Range2720
Interquartile range (IQR)61

Descriptive statistics

Standard deviation506.24507
Coefficient of variation (CV)2.6879537
Kurtosis13.217274
Mean188.33846
Median Absolute Deviation (MAD)6
Skewness3.5876567
Sum12242
Variance256284.07
MonotonicityNot monotonic
2023-12-13T06:54:52.536599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 12
18.5%
3 7
 
10.8%
4 5
 
7.7%
2 5
 
7.7%
9 3
 
4.6%
7 2
 
3.1%
13 2
 
3.1%
5 2
 
3.1%
63 1
 
1.5%
24 1
 
1.5%
Other values (25) 25
38.5%
ValueCountFrequency (%)
1 12
18.5%
2 5
7.7%
3 7
10.8%
4 5
7.7%
5 2
 
3.1%
6 1
 
1.5%
7 2
 
3.1%
9 3
 
4.6%
10 1
 
1.5%
12 1
 
1.5%
ValueCountFrequency (%)
2721 1
1.5%
2098 1
1.5%
1710 1
1.5%
1448 1
1.5%
863 1
1.5%
571 1
1.5%
563 1
1.5%
441 1
1.5%
270 1
1.5%
269 1
1.5%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8725948.5
Minimum148560
Maximum97110220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T06:54:52.675239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum148560
5-th percentile312156
Q1913190
median3333880
Q312314360
95-th percentile32748886
Maximum97110220
Range96961660
Interquartile range (IQR)11401170

Descriptive statistics

Standard deviation14462806
Coefficient of variation (CV)1.657448
Kurtosis21.590028
Mean8725948.5
Median Absolute Deviation (MAD)2810060
Skewness4.0098936
Sum5.6718665 × 108
Variance2.0917274 × 1014
MonotonicityNot monotonic
2023-12-13T06:54:52.882011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1160130 1
 
1.5%
12171920 1
 
1.5%
4736740 1
 
1.5%
5230240 1
 
1.5%
312780 1
 
1.5%
568940 1
 
1.5%
442710 1
 
1.5%
523820 1
 
1.5%
3333880 1
 
1.5%
18336580 1
 
1.5%
Other values (55) 55
84.6%
ValueCountFrequency (%)
148560 1
1.5%
225620 1
1.5%
271660 1
1.5%
312000 1
1.5%
312780 1
1.5%
422590 1
1.5%
442710 1
1.5%
502880 1
1.5%
523820 1
1.5%
540560 1
1.5%
ValueCountFrequency (%)
97110220 1
1.5%
38761510 1
1.5%
33971650 1
1.5%
33739360 1
1.5%
28786990 1
1.5%
24683430 1
1.5%
24094700 1
1.5%
18933660 1
1.5%
18336580 1
1.5%
15541150 1
1.5%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean545.69231
Minimum1
Maximum9387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T06:54:53.055136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q15
median17
Q3139
95-th percentile3613.4
Maximum9387
Range9386
Interquartile range (IQR)134

Descriptive statistics

Standard deviation1595.7938
Coefficient of variation (CV)2.9243473
Kurtosis17.815856
Mean545.69231
Median Absolute Deviation (MAD)14
Skewness4.0651061
Sum35470
Variance2546557.9
MonotonicityNot monotonic
2023-12-13T06:54:53.184541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
17 5
 
7.7%
1 4
 
6.2%
2 4
 
6.2%
3 4
 
6.2%
4 3
 
4.6%
5 3
 
4.6%
6 2
 
3.1%
14 2
 
3.1%
19 2
 
3.1%
8 2
 
3.1%
Other values (31) 34
52.3%
ValueCountFrequency (%)
1 4
6.2%
2 4
6.2%
3 4
6.2%
4 3
4.6%
5 3
4.6%
6 2
3.1%
7 1
 
1.5%
8 2
3.1%
9 2
3.1%
10 2
3.1%
ValueCountFrequency (%)
9387 1
1.5%
6666 1
1.5%
4568 1
1.5%
3903 1
1.5%
2455 1
1.5%
1592 1
1.5%
1366 1
1.5%
1036 1
1.5%
925 1
1.5%
655 1
1.5%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18238301
Minimum350220
Maximum1.7092573 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T06:54:53.348594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum350220
5-th percentile884842
Q12203490
median6574380
Q321811380
95-th percentile71368186
Maximum1.7092573 × 108
Range1.7057551 × 108
Interquartile range (IQR)19607890

Descriptive statistics

Standard deviation29075145
Coefficient of variation (CV)1.5941805
Kurtosis12.146837
Mean18238301
Median Absolute Deviation (MAD)5113770
Skewness3.0932891
Sum1.1854896 × 109
Variance8.4536403 × 1014
MonotonicityNot monotonic
2023-12-13T06:54:53.499423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1992720 1
 
1.5%
31730940 1
 
1.5%
4736740 1
 
1.5%
6591570 1
 
1.5%
917610 1
 
1.5%
2443610 1
 
1.5%
876650 1
 
1.5%
2145860 1
 
1.5%
7154210 1
 
1.5%
54909690 1
 
1.5%
Other values (55) 55
84.6%
ValueCountFrequency (%)
350220 1
1.5%
540560 1
1.5%
604830 1
1.5%
876650 1
1.5%
917610 1
1.5%
1024470 1
1.5%
1066170 1
1.5%
1299240 1
1.5%
1375820 1
1.5%
1460610 1
1.5%
ValueCountFrequency (%)
170925730 1
1.5%
107557220 1
1.5%
73815510 1
1.5%
73585570 1
1.5%
62498650 1
1.5%
54909690 1
1.5%
49490870 1
1.5%
47417050 1
1.5%
47122850 1
1.5%
40076150 1
1.5%

Interactions

2023-12-13T06:54:50.513457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:49.185756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:49.539105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:50.223187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:50.588405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:49.296767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:49.618885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:50.285771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:50.665067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:49.377741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:49.999951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:50.355901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:50.742142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:49.455390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:50.111706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:50.429833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:54:53.614919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.0320.0000.000
세목명0.0001.0000.0000.1730.2330.2920.231
체납액구간0.0000.0001.0000.0000.1680.0000.000
체납건수0.0000.1730.0001.0000.7511.0000.874
체납금액0.0320.2330.1680.7511.0000.6430.857
누적체납건수0.0000.2920.0001.0000.6431.0000.823
누적체납금액0.0000.2310.0000.8740.8570.8231.000
2023-12-13T06:54:53.762186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.0000.000
체납액구간0.0001.0000.000
세목명0.0000.0001.000
2023-12-13T06:54:53.868962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.6070.9670.6320.0000.0850.000
체납금액0.6071.0000.5000.9670.0000.1620.089
누적체납건수0.9670.5001.0000.5760.0000.1030.000
누적체납금액0.6320.9670.5761.0000.0000.1220.000
과세년도0.0000.0000.0000.0001.0000.0000.000
세목명0.0850.1620.1030.1220.0001.0000.000
체납액구간0.0000.0890.0000.0000.0000.0001.000

Missing values

2023-12-13T06:54:50.850478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:54:51.002500image/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
0전라남도영광군468702017등록면허세10만원 미만11111601301911992720
1전라남도영광군468702017자동차세10만원 미만216859943065525215210
2전라남도영광군468702017자동차세10만원~30만원미만1001518527026340076150
3전라남도영광군468702017자동차세30만원~50만원미만3103394051728550
4전라남도영광군468702017재산세10만원 미만171018933660456849490870
5전라남도영광군468702017재산세10만원~30만원미만131859690284325510
6전라남도영광군468702017재산세1백만원~3백만원미만1133281022794710
7전라남도영광군468702017재산세30만원~50만원미만3125217062456960
8전라남도영광군468702017재산세3백만원~5백만원미만1326183026914760
9전라남도영광군468702017주민세10만원 미만5719630460159223089450
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
55전라남도영광군468702019지방소득세10만원~30만원미만2442632705910837650
56전라남도영광군468702019지방소득세1백만원~3백만원미만9138797701726273140
57전라남도영광군468702019지방소득세1천만원~3천만원미만112314360112314360
58전라남도영광군468702019지방소득세30만원~50만원미만93631290145834780
59전라남도영광군468702019지방소득세50만원~1백만원미만1068819401913473510
60전라남도영광군468702019취득세10만원 미만3148560181066170
61전라남도영광군468702019취득세10만원~30만원미만3699580173143190
62전라남도영광군468702019취득세1백만원~3백만원미만1101615022059260
63전라남도영광군468702019취득세30만원~50만원미만142259031299240
64전라남도영광군468702019취득세50만원~1백만원미만178530042931160