Overview

Dataset statistics

Number of variables10
Number of observations75
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory87.8 B

Variable types

Categorical8
Numeric2

Dataset

Description부산광역시_지방세체납현황_20191231
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15079162

Alerts

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

Reproduction

Analysis started2024-03-13 13:16:02.471208
Analysis finished2024-03-13 13:16:03.711337
Duration1.24 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
부산광역시
75 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 75
100.0%

Length

2024-03-13T22:16:03.783361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:16:03.884742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 75
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
부산광역시
75 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 75
100.0%

Length

2024-03-13T22:16:04.001638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:16:04.108125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 75
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
26000
75 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26000
2nd row26000
3rd row26000
4th row26000
5th row26000

Common Values

ValueCountFrequency (%)
26000 75
100.0%

Length

2024-03-13T22:16:04.224058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:16:04.339825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26000 75
100.0%

과세년도
Categorical

Distinct3
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
2019
27 
2017
25 
2018
23 

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 27
36.0%
2017 25
33.3%
2018 23
30.7%

Length

2024-03-13T22:16:04.438678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:16:04.568250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 27
36.0%
2017 25
33.3%
2018 23
30.7%

세목명
Categorical

Distinct6
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
지방소득세
33 
취득세
22 
자동차세
11 
주민세
4 
지역자원시설세
4 

Length

Max length7
Median length5
Mean length4.2666667
Min length3

Unique

Unique1 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 33
44.0%
취득세 22
29.3%
자동차세 11
 
14.7%
주민세 4
 
5.3%
지역자원시설세 4
 
5.3%
등록면허세 1
 
1.3%

Length

2024-03-13T22:16:04.738580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:16:04.880331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 33
44.0%
취득세 22
29.3%
자동차세 11
 
14.7%
주민세 4
 
5.3%
지역자원시설세 4
 
5.3%
등록면허세 1
 
1.3%

체납액구간
Categorical

Distinct13
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
10만원 미만
11 
30만원~50만원미만
10 
10만원~30만원미만
8 
50만원~1백만원미만
7 
1천만원~3천만원미만
6 
Other values (8)
33 

Length

Max length11
Median length11
Mean length10.173333
Min length7

Unique

Unique2 ?
Unique (%)2.7%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 11
14.7%
30만원~50만원미만 10
13.3%
10만원~30만원미만 8
10.7%
50만원~1백만원미만 7
9.3%
1천만원~3천만원미만 6
8.0%
5백만원~1천만원미만 6
8.0%
1백만원~3백만원미만 5
6.7%
1억원~3억원미만 5
6.7%
3백만원~5백만원미만 5
6.7%
3천만원~5천만원미만 5
6.7%
Other values (3) 7
9.3%

Length

2024-03-13T22:16:05.037671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 11
12.8%
미만 11
12.8%
30만원~50만원미만 10
11.6%
10만원~30만원미만 8
9.3%
50만원~1백만원미만 7
8.1%
1천만원~3천만원미만 6
7.0%
5백만원~1천만원미만 6
7.0%
1백만원~3백만원미만 5
5.8%
1억원~3억원미만 5
5.8%
3백만원~5백만원미만 5
5.8%
Other values (4) 12
14.0%

체납건수
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
0
75 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75
100.0%

Length

2024-03-13T22:16:05.176790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:16:05.274073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 75
100.0%

체납금액
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
0
75 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75
100.0%

Length

2024-03-13T22:16:05.374083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:16:05.490823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 75
100.0%

누적체납건수
Real number (ℝ)

Distinct64
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean459.85333
Minimum1
Maximum2736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2024-03-13T22:16:05.614831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q122
median96
Q3574
95-th percentile2212.5
Maximum2736
Range2735
Interquartile range (IQR)552

Descriptive statistics

Standard deviation720.02169
Coefficient of variation (CV)1.5657638
Kurtosis2.5898936
Mean459.85333
Median Absolute Deviation (MAD)94
Skewness1.9232487
Sum34489
Variance518431.23
MonotonicityNot monotonic
2024-03-13T22:16:05.764586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5
 
6.7%
7 3
 
4.0%
1 3
 
4.0%
30 2
 
2.7%
15 2
 
2.7%
4 2
 
2.7%
9 1
 
1.3%
69 1
 
1.3%
2434 1
 
1.3%
569 1
 
1.3%
Other values (54) 54
72.0%
ValueCountFrequency (%)
1 3
4.0%
2 5
6.7%
4 2
 
2.7%
7 3
4.0%
8 1
 
1.3%
9 1
 
1.3%
15 2
 
2.7%
17 1
 
1.3%
19 1
 
1.3%
25 1
 
1.3%
ValueCountFrequency (%)
2736 1
1.3%
2494 1
1.3%
2434 1
1.3%
2314 1
1.3%
2169 1
1.3%
2150 1
1.3%
2111 1
1.3%
1947 1
1.3%
1770 1
1.3%
1234 1
1.3%

누적체납금액
Real number (ℝ)

UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0713738 × 109
Minimum7400
Maximum1.420123 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2024-03-13T22:16:05.912384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7400
5-th percentile755619
Q171599675
median2.491316 × 108
Q36.9656998 × 108
95-th percentile5.0984014 × 109
Maximum1.420123 × 1010
Range1.4201222 × 1010
Interquartile range (IQR)6.249703 × 108

Descriptive statistics

Standard deviation2.4283419 × 109
Coefficient of variation (CV)2.2665683
Kurtosis16.420285
Mean1.0713738 × 109
Median Absolute Deviation (MAD)2.399447 × 108
Skewness3.9240108
Sum8.0353035 × 1010
Variance5.8968442 × 1018
MonotonicityNot monotonic
2024-03-13T22:16:06.085078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37080 1
 
1.3%
103985670 1
 
1.3%
755744160 1
 
1.3%
101811870 1
 
1.3%
43422410 1
 
1.3%
22847340 1
 
1.3%
76160000 1
 
1.3%
246474940 1
 
1.3%
392074120 1
 
1.3%
509029410 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
7400 1
1.3%
37080 1
1.3%
237310 1
1.3%
542420 1
1.3%
846990 1
1.3%
6416490 1
1.3%
9186900 1
1.3%
14279100 1
1.3%
20152090 1
1.3%
22102720 1
1.3%
ValueCountFrequency (%)
14201229530 1
1.3%
11542940050 1
1.3%
8883077110 1
1.3%
6144022150 1
1.3%
4650278160 1
1.3%
3247188730 1
1.3%
3222533820 1
1.3%
2034949150 1
1.3%
1867956580 1
1.3%
1859459400 1
1.3%

Interactions

2024-03-13T22:16:03.186189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:02.999226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:03.276766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:03.088085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:16:06.192843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0000.0000.6770.000
체납액구간0.0000.0001.0000.0000.389
누적체납건수0.0000.6770.0001.0000.383
누적체납금액0.0000.0000.3890.3831.000
2024-03-13T22:16:06.302432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간
과세년도1.0000.0000.000
세목명0.0001.0000.000
체납액구간0.0000.0001.000
2024-03-13T22:16:06.418108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
누적체납건수누적체납금액과세년도세목명체납액구간
누적체납건수1.0000.0380.0000.4050.000
누적체납금액0.0381.0000.0000.0000.180
과세년도0.0000.0001.0000.0000.000
세목명0.4050.0000.0001.0000.000
체납액구간0.0000.1800.0000.0001.000

Missing values

2024-03-13T22:16:03.450835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:16:03.641752image/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
0부산광역시부산광역시260002017등록면허세10만원 미만00237080
1부산광역시부산광역시260002017자동차세10만원 미만00215092172350
2부산광역시부산광역시260002017자동차세10만원~30만원미만001770327625190
3부산광역시부산광역시260002017자동차세30만원~50만원미만00571225768210
4부산광역시부산광역시260002017자동차세50만원~1백만원미만00204136425000
5부산광역시부산광역시260002017주민세10만원 미만00216954339010
6부산광역시부산광역시260002017지방소득세10만원 미만0076528578470
7부산광역시부산광역시260002017지방소득세10만원~30만원미만0035963412990
8부산광역시부산광역시260002017지방소득세1백만원~3백만원미만00274492356210
9부산광역시부산광역시260002017지방소득세1억원~3억원미만002249131600
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
65부산광역시부산광역시260002019취득세1억원~3억원미만0071093451740
66부산광역시부산광역시260002019취득세1천만원~3천만원미만001221859459400
67부산광역시부산광역시260002019취득세30만원~50만원미만00259186900
68부산광역시부산광역시260002019취득세3백만원~5백만원미만0048190567590
69부산광역시부산광역시260002019취득세3억원~5억원미만002721392070
70부산광역시부산광역시260002019취득세3천만원~5천만원미만0015552785880
71부산광역시부산광역시260002019취득세50만원~1백만원미만003022102720
72부산광역시부산광역시260002019취득세5백만원~1천만원미만0089671747890
73부산광역시부산광역시260002019취득세5억원~10억원미만001645754290
74부산광역시부산광역시260002019취득세5천만원~1억원미만008592832670