Overview

Dataset statistics

Number of variables10
Number of observations429
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.7 KiB
Average record size in memory85.3 B

Variable types

Categorical5
Numeric4
Boolean1

Dataset

Description부산광역시동래구_지방세납세현황_20221231
Author부산광역시 동래구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15087054

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부건수 is highly overall correlated with 납부금액 and 1 other fieldsHigh correlation
납부금액 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부매체비율 is highly overall correlated with 납부건수 and 1 other fieldsHigh correlation
납부매체비율 has 18 (4.2%) zerosZeros

Reproduction

Analysis started2023-12-10 16:52:47.024263
Analysis finished2023-12-10 16:52:50.302536
Duration3.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
부산광역시
429 

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 429
100.0%

Length

2023-12-11T01:52:50.390824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:52:50.511547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 429
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
동래구
429 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동래구 429
100.0%

Length

2023-12-11T01:52:50.652485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:52:50.798216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동래구 429
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
26260
429 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26260
2nd row26260
3rd row26260
4th row26260
5th row26260

Common Values

ValueCountFrequency (%)
26260 429
100.0%

Length

2023-12-11T01:52:50.943331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:52:51.060184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26260 429
100.0%

납부년도
Real number (ℝ)

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5618
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T01:52:51.158338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.716885
Coefficient of variation (CV)0.00085012749
Kurtosis-1.2688386
Mean2019.5618
Median Absolute Deviation (MAD)1
Skewness-0.06367113
Sum866392
Variance2.9476941
MonotonicityIncreasing
2023-12-11T01:52:51.298650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 75
17.5%
2022 75
17.5%
2020 73
17.0%
2017 71
16.6%
2019 70
16.3%
2018 65
15.2%
ValueCountFrequency (%)
2017 71
16.6%
2018 65
15.2%
2019 70
16.3%
2020 73
17.0%
2021 75
17.5%
2022 75
17.5%
ValueCountFrequency (%)
2022 75
17.5%
2021 75
17.5%
2020 73
17.0%
2019 70
16.3%
2018 65
15.2%
2017 71
16.6%

세목명
Categorical

Distinct13
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
등록면허세
58 
자동차세
58 
재산세
58 
주민세
58 
지방소득세
52 
Other values (8)
145 

Length

Max length7
Median length5
Mean length4.1258741
Min length3

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
등록면허세 58
13.5%
자동차세 58
13.5%
재산세 58
13.5%
주민세 58
13.5%
지방소득세 52
12.1%
취득세 52
12.1%
지역자원시설세 41
9.6%
면허세 23
 
5.4%
종합토지세 17
 
4.0%
등록세 7
 
1.6%
Other values (3) 5
 
1.2%

Length

2023-12-11T01:52:51.523769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등록면허세 58
13.5%
자동차세 58
13.5%
재산세 58
13.5%
주민세 58
13.5%
지방소득세 52
12.1%
취득세 52
12.1%
지역자원시설세 41
9.6%
면허세 23
 
5.4%
종합토지세 17
 
4.0%
등록세 7
 
1.6%
Other values (3) 5
 
1.2%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
이택스
60 
은행창구
55 
기타
49 
위택스
46 
자동화기기
46 
Other values (5)
173 

Length

Max length5
Median length4
Mean length3.8811189
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
이택스 60
14.0%
은행창구 55
12.8%
기타 49
11.4%
위택스 46
10.7%
자동화기기 46
10.7%
인터넷지로 43
10.0%
가상계좌 42
9.8%
지자체방문 40
9.3%
자동이체 24
 
5.6%
페이사납부 24
 
5.6%

Length

2023-12-11T01:52:51.756113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:52:51.970427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이택스 60
14.0%
은행창구 55
12.8%
기타 49
11.4%
위택스 46
10.7%
자동화기기 46
10.7%
인터넷지로 43
10.0%
가상계좌 42
9.8%
지자체방문 40
9.3%
자동이체 24
 
5.6%
페이사납부 24
 
5.6%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size561.0 B
True
239 
False
190 
ValueCountFrequency (%)
True 239
55.7%
False 190
44.3%
2023-12-11T01:52:52.158955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct352
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9261.4476
Minimum1
Maximum126382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T01:52:52.297881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q185
median1871
Q38609
95-th percentile50074
Maximum126382
Range126381
Interquartile range (IQR)8524

Descriptive statistics

Standard deviation18844.136
Coefficient of variation (CV)2.0346858
Kurtosis13.583892
Mean9261.4476
Median Absolute Deviation (MAD)1865
Skewness3.4780041
Sum3973161
Variance3.5510145 × 108
MonotonicityNot monotonic
2023-12-11T01:52:52.491011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 16
 
3.7%
2 12
 
2.8%
15 6
 
1.4%
7 6
 
1.4%
10 5
 
1.2%
6 4
 
0.9%
17 4
 
0.9%
3 3
 
0.7%
16 3
 
0.7%
9 3
 
0.7%
Other values (342) 367
85.5%
ValueCountFrequency (%)
1 16
3.7%
2 12
2.8%
3 3
 
0.7%
4 2
 
0.5%
5 1
 
0.2%
6 4
 
0.9%
7 6
 
1.4%
8 2
 
0.5%
9 3
 
0.7%
10 5
 
1.2%
ValueCountFrequency (%)
126382 1
0.2%
118701 1
0.2%
112470 1
0.2%
104357 1
0.2%
98207 1
0.2%
87849 1
0.2%
87665 1
0.2%
87622 1
0.2%
87195 1
0.2%
84025 1
0.2%

납부금액
Real number (ℝ)

HIGH CORRELATION 

Distinct428
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.495559 × 109
Minimum3190
Maximum7.7354213 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T01:52:52.685040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3190
5-th percentile107064
Q19841820
median4.7910794 × 108
Q32.8536427 × 109
95-th percentile1.4736814 × 1010
Maximum7.7354213 × 1010
Range7.735421 × 1010
Interquartile range (IQR)2.8438008 × 109

Descriptive statistics

Standard deviation8.2240736 × 109
Coefficient of variation (CV)2.3527206
Kurtosis37.597416
Mean3.495559 × 109
Median Absolute Deviation (MAD)4.7883451 × 108
Skewness5.3158497
Sum1.4995948 × 1012
Variance6.7635386 × 1019
MonotonicityNot monotonic
2023-12-11T01:52:52.904006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27810 2
 
0.5%
169550270 1
 
0.2%
509098450 1
 
0.2%
3957351000 1
 
0.2%
7137575980 1
 
0.2%
135336150 1
 
0.2%
759260 1
 
0.2%
314893530 1
 
0.2%
67877130 1
 
0.2%
46350 1
 
0.2%
Other values (418) 418
97.4%
ValueCountFrequency (%)
3190 1
0.2%
4840 1
0.2%
5150 1
0.2%
10490 1
0.2%
12360 1
0.2%
18540 1
0.2%
18900 1
0.2%
19040 1
0.2%
20550 1
0.2%
22520 1
0.2%
ValueCountFrequency (%)
77354212700 1
0.2%
74277468740 1
0.2%
66066772930 1
0.2%
46406240910 1
0.2%
39139662390 1
0.2%
37547355490 1
0.2%
32124065120 1
0.2%
26441488300 1
0.2%
24379404060 1
0.2%
21773721240 1
0.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct323
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.519674
Minimum0
Maximum89.06
Zeros18
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T01:52:53.140286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.37
median9.05
Q320.35
95-th percentile42.946
Maximum89.06
Range89.06
Interquartile range (IQR)19.98

Descriptive statistics

Standard deviation15.911752
Coefficient of variation (CV)1.1769331
Kurtosis3.8076828
Mean13.519674
Median Absolute Deviation (MAD)8.97
Skewness1.7256114
Sum5799.94
Variance253.18384
MonotonicityNot monotonic
2023-12-11T01:52:53.332513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 22
 
5.1%
0.0 18
 
4.2%
0.02 17
 
4.0%
0.03 5
 
1.2%
0.07 5
 
1.2%
0.21 5
 
1.2%
0.04 5
 
1.2%
0.05 4
 
0.9%
0.08 4
 
0.9%
0.06 4
 
0.9%
Other values (313) 340
79.3%
ValueCountFrequency (%)
0.0 18
4.2%
0.01 22
5.1%
0.02 17
4.0%
0.03 5
 
1.2%
0.04 5
 
1.2%
0.05 4
 
0.9%
0.06 4
 
0.9%
0.07 5
 
1.2%
0.08 4
 
0.9%
0.09 3
 
0.7%
ValueCountFrequency (%)
89.06 1
0.2%
88.83 1
0.2%
88.08 1
0.2%
79.0 1
0.2%
64.51 1
0.2%
62.54 1
0.2%
61.32 1
0.2%
61.04 2
0.5%
59.67 1
0.2%
55.18 1
0.2%

Interactions

2023-12-11T01:52:49.470662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:47.535774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:48.089048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:48.960384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:49.603648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:47.673393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:48.226292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:49.060735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:49.724370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:47.821848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:48.734183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:49.167524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:49.841416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:47.970218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:48.868149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:49.328463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:52:53.480608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.1570.0820.3340.2980.655
납부매체0.0000.1571.0001.0000.3800.3100.601
납부매체전자고지여부0.0000.0821.0001.0000.1280.1150.272
납부건수0.0000.3340.3800.1281.0000.6560.589
납부금액0.0000.2980.3100.1150.6561.0000.416
납부매체비율0.0000.6550.6010.2720.5890.4161.000
2023-12-11T01:52:53.653499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납부매체납부매체전자고지여부
세목명1.0000.0640.075
납부매체0.0641.0000.991
납부매체전자고지여부0.0750.9911.000
2023-12-11T01:52:53.795463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부년도1.0000.0240.0420.0230.0000.0000.000
납부건수0.0241.0000.8120.7850.1490.1830.127
납부금액0.0420.8121.0000.5970.1380.1530.085
납부매체비율0.0230.7850.5971.0000.3410.2230.207
세목명0.0000.1490.1380.3411.0000.0640.075
납부매체0.0000.1830.1530.2230.0641.0000.991
납부매체전자고지여부0.0000.1270.0850.2070.0750.9911.000

Missing values

2023-12-11T01:52:49.999029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:52:50.222667image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
0부산광역시동래구262602017등록면허세가상계좌Y669916955027021.98
1부산광역시동래구262602017자동차세가상계좌Y6145100453227020.16
2부산광역시동래구262602017재산세가상계좌Y9470211042027031.07
3부산광역시동래구262602017주민세가상계좌Y458729703736015.05
4부산광역시동래구262602017지방소득세가상계좌Y3489274401187011.45
5부산광역시동래구262602017지역자원시설세가상계좌Y168968700.05
6부산광역시동래구262602017취득세가상계좌Y7611199946000.25
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