Overview

Dataset statistics

Number of variables8
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory71.3 B

Variable types

Categorical6
Numeric2

Dataset

Description2017년부터 체납액 규모별 체납 건수를 납세자 금액 구간 유형별로 제공하여 체납정책 수립시 기초자료 활용할 수 있습니다.
URLhttps://www.data.go.kr/data/15079370/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-11 22:49:03.769836
Analysis finished2023-12-11 22:49:04.667581
Duration0.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
대전광역시
40 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 40
100.0%

Length

2023-12-12T07:49:04.733483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:49:04.840384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 40
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
대전광역시
40 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 40
100.0%

Length

2023-12-12T07:49:04.932992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:49:05.022386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 40
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
30000
40 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30000
2nd row30000
3rd row30000
4th row30000
5th row30000

Common Values

ValueCountFrequency (%)
30000 40
100.0%

Length

2023-12-12T07:49:05.161883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:49:05.304586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30000 40
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2019
16 
2017
12 
2018
12 

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 16
40.0%
2017 12
30.0%
2018 12
30.0%

Length

2023-12-12T07:49:05.427083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:49:05.521285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 16
40.0%
2017 12
30.0%
2018 12
30.0%

세목명
Categorical

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
지방소득세
27 
취득세
13 

Length

Max length5
Median length5
Mean length4.35
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 27
67.5%
취득세 13
32.5%

Length

2023-12-12T07:49:05.632824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:49:05.756804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 27
67.5%
취득세 13
32.5%

체납액구간
Categorical

Distinct11
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
1백만원~3백만원미만
6 
3백만원~5백만원미만
6 
1천만원~3천만원미만
5 
50만원~1백만원미만
4 
5백만원~1천만원미만
4 
Other values (6)
15 

Length

Max length11
Median length11
Mean length10.65
Min length7

Unique

Unique1 ?
Unique (%)2.5%

Sample

1st row10만원 미만
2nd row10만원~30만원미만
3rd row1백만원~3백만원미만
4th row1천만원~3천만원미만
5th row30만원~50만원미만

Common Values

ValueCountFrequency (%)
1백만원~3백만원미만 6
15.0%
3백만원~5백만원미만 6
15.0%
1천만원~3천만원미만 5
12.5%
50만원~1백만원미만 4
10.0%
5백만원~1천만원미만 4
10.0%
5천만원~1억원미만 4
10.0%
10만원~30만원미만 3
7.5%
3천만원~5천만원미만 3
7.5%
10만원 미만 2
 
5.0%
30만원~50만원미만 2
 
5.0%

Length

2023-12-12T07:49:05.874113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1백만원~3백만원미만 6
14.3%
3백만원~5백만원미만 6
14.3%
1천만원~3천만원미만 5
11.9%
50만원~1백만원미만 4
9.5%
5백만원~1천만원미만 4
9.5%
5천만원~1억원미만 4
9.5%
10만원~30만원미만 3
7.1%
3천만원~5천만원미만 3
7.1%
10만원 2
 
4.8%
미만 2
 
4.8%
Other values (2) 3
7.1%

누적체납건수
Real number (ℝ)

Distinct26
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.5
Minimum1
Maximum368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T07:49:05.995297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18.25
median16.5
Q357.5
95-th percentile141.6
Maximum368
Range367
Interquartile range (IQR)49.25

Descriptive statistics

Standard deviation72.199155
Coefficient of variation (CV)1.5199822
Kurtosis9.8031808
Mean47.5
Median Absolute Deviation (MAD)14.5
Skewness2.8354412
Sum1900
Variance5212.7179
MonotonicityNot monotonic
2023-12-12T07:49:06.179538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2 5
 
12.5%
12 4
 
10.0%
10 3
 
7.5%
1 3
 
7.5%
16 2
 
5.0%
56 2
 
5.0%
3 2
 
5.0%
20 1
 
2.5%
13 1
 
2.5%
32 1
 
2.5%
Other values (16) 16
40.0%
ValueCountFrequency (%)
1 3
7.5%
2 5
12.5%
3 2
 
5.0%
10 3
7.5%
12 4
10.0%
13 1
 
2.5%
16 2
 
5.0%
17 1
 
2.5%
20 1
 
2.5%
21 1
 
2.5%
ValueCountFrequency (%)
368 1
2.5%
229 1
2.5%
137 1
2.5%
119 1
2.5%
118 1
2.5%
116 1
2.5%
106 1
2.5%
97 1
2.5%
84 1
2.5%
62 1
2.5%

누적체납금액
Real number (ℝ)

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8119743 × 108
Minimum985080
Maximum9.7603187 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T07:49:06.302480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum985080
5-th percentile2390609
Q116237125
median71753420
Q32.8806077 × 108
95-th percentile6.7298501 × 108
Maximum9.7603187 × 108
Range9.7504679 × 108
Interquartile range (IQR)2.7182365 × 108

Descriptive statistics

Standard deviation2.3044463 × 108
Coefficient of variation (CV)1.2717875
Kurtosis2.7616286
Mean1.8119743 × 108
Median Absolute Deviation (MAD)64735245
Skewness1.696656
Sum7.2478973 × 109
Variance5.3104725 × 1016
MonotonicityNot monotonic
2023-12-12T07:49:06.424481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
985080 1
 
2.5%
43323729 1
 
2.5%
149004560 1
 
2.5%
988580 1
 
2.5%
12709550 1
 
2.5%
695566610 1
 
2.5%
132390220 1
 
2.5%
976031870 1
 
2.5%
4713220 1
 
2.5%
439842230 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
985080 1
2.5%
988580 1
2.5%
2464400 1
2.5%
3928630 1
2.5%
4713220 1
2.5%
9323130 1
2.5%
10941960 1
2.5%
12477920 1
2.5%
12709550 1
2.5%
16125120 1
2.5%
ValueCountFrequency (%)
976031870 1
2.5%
695566610 1
2.5%
671796510 1
2.5%
499944820 1
2.5%
445144710 1
2.5%
439842230 1
2.5%
429694870 1
2.5%
361289740 1
2.5%
341991690 1
2.5%
316613280 1
2.5%

Interactions

2023-12-12T07:49:04.250287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:04.029373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:04.370516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:49:04.141119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:49:06.504266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.400
세목명0.0001.0000.0000.4840.315
체납액구간0.0000.0001.0000.0000.000
누적체납건수0.0000.4840.0001.0000.847
누적체납금액0.4000.3150.0000.8471.000
2023-12-12T07:49:06.593656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.0000.000
체납액구간0.0001.0000.000
세목명0.0000.0001.000
2023-12-12T07:49:06.672691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
누적체납건수누적체납금액과세년도세목명체납액구간
누적체납건수1.0000.3230.0000.3260.000
누적체납금액0.3231.0000.1580.2380.000
과세년도0.0000.1581.0000.0000.000
세목명0.3260.2380.0001.0000.000
체납액구간0.0000.0000.0000.0001.000

Missing values

2023-12-12T07:49:04.487374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:49:04.618724image/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

시도명시군구명자치단체코드과세년도세목명체납액구간누적체납건수누적체납금액
0대전광역시대전광역시300002017지방소득세10만원 미만16985080
1대전광역시대전광역시300002017지방소득세10만원~30만원미만10610941960
2대전광역시대전광역시300002017지방소득세1백만원~3백만원미만137273643320
3대전광역시대전광역시300002017지방소득세1천만원~3천만원미만22361289740
4대전광역시대전광역시300002017지방소득세30만원~50만원미만103928630
5대전광역시대전광역시300002017지방소득세3백만원~5백만원미만56211075850
6대전광역시대전광역시300002017지방소득세3천만원~5천만원미만276421250
7대전광역시대전광역시300002017지방소득세50만원~1백만원미만129323130
8대전광역시대전광역시300002017지방소득세5백만원~1천만원미만41278543270
9대전광역시대전광역시300002017취득세1백만원~3백만원미만1226707640
시도명시군구명자치단체코드과세년도세목명체납액구간누적체납건수누적체납금액
30대전광역시대전광역시300002019지방소득세3백만원~5백만원미만116439842230
31대전광역시대전광역시300002019지방소득세3천만원~5천만원미만10341991690
32대전광역시대전광역시300002019지방소득세50만원~1백만원미만2619537130
33대전광역시대전광역시300002019지방소득세5백만원~1천만원미만97671796510
34대전광역시대전광역시300002019지방소득세5천만원~1억원미만3176574580
35대전광역시대전광역시300002019취득세1백만원~3백만원미만3266133210
36대전광역시대전광역시300002019취득세1천만원~3천만원미만239382150
37대전광역시대전광역시300002019취득세3백만원~5백만원미만1657114719
38대전광역시대전광역시300002019취득세50만원~1백만원미만32464400
39대전광역시대전광역시300002019취득세5백만원~1천만원미만1382120010