Overview

Dataset statistics

Number of variables10
Number of observations387
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.3 KiB
Average record size in memory85.3 B

Variable types

Categorical5
Numeric4
Boolean1

Dataset

Description본 데이터는 경상남도 합천군의 년도별, 지방세 납부현황(세목명, 납부매체, 납무매체전자고지여부, 납부건수, 납무금액, 납부매체비율)에 대한 정보를 제공하고 있습니다.
Author경상남도 합천군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15089293

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
납부금액 has unique valuesUnique
납부매체비율 has 9 (2.3%) zerosZeros

Reproduction

Analysis started2023-12-11 00:21:57.421979
Analysis finished2023-12-11 00:21:59.525566
Duration2.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
경상남도
387 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 387
100.0%

Length

2023-12-11T09:21:59.579589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:21:59.654546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 387
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
합천군
387 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
합천군 387
100.0%

Length

2023-12-11T09:21:59.730068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:21:59.809289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
합천군 387
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
48890
387 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48890
2nd row48890
3rd row48890
4th row48890
5th row48890

Common Values

ValueCountFrequency (%)
48890 387
100.0%

Length

2023-12-11T09:21:59.894317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:21:59.976911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48890 387
100.0%

납부년도
Real number (ℝ)

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5736
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T09:22:00.066116image/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 deviation1.6977974
Coefficient of variation (CV)0.00084067119
Kurtosis-1.2452718
Mean2019.5736
Median Absolute Deviation (MAD)1
Skewness-0.061644355
Sum781575
Variance2.882516
MonotonicityIncreasing
2023-12-11T09:22:00.158915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 68
17.6%
2021 67
17.3%
2022 67
17.3%
2019 64
16.5%
2018 61
15.8%
2017 60
15.5%
ValueCountFrequency (%)
2017 60
15.5%
2018 61
15.8%
2019 64
16.5%
2020 68
17.6%
2021 67
17.3%
2022 67
17.3%
ValueCountFrequency (%)
2022 67
17.3%
2021 67
17.3%
2020 68
17.6%
2019 64
16.5%
2018 61
15.8%
2017 60
15.5%

세목명
Categorical

Distinct14
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
자동차세
52 
재산세
52 
주민세
52 
등록면허세
51 
취득세
45 
Other values (9)
135 

Length

Max length7
Median length5
Mean length4.0671835
Min length3

Unique

Unique3 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 52
13.4%
재산세 52
13.4%
주민세 52
13.4%
등록면허세 51
13.2%
취득세 45
11.6%
지방소득세 44
11.4%
등록세 35
9.0%
지역자원시설세 32
8.3%
담배소비세 13
 
3.4%
종합토지세 5
 
1.3%
Other values (4) 6
 
1.6%

Length

2023-12-11T09:22:00.267529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 52
13.4%
재산세 52
13.4%
주민세 52
13.4%
등록면허세 51
13.2%
취득세 45
11.6%
지방소득세 44
11.4%
등록세 35
9.0%
지역자원시설세 32
8.3%
담배소비세 13
 
3.4%
종합토지세 5
 
1.3%
Other values (4) 6
 
1.6%

납부매체
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
가상계좌
55 
위택스
52 
은행창구
51 
지자체방문
49 
자동화기기
47 
Other values (4)
133 

Length

Max length5
Median length4
Mean length4.0465116
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가상계좌 55
14.2%
위택스 52
13.4%
은행창구 51
13.2%
지자체방문 49
12.7%
자동화기기 47
12.1%
기타 45
11.6%
인터넷지로 43
11.1%
자동이체 24
6.2%
페이사납부 21
 
5.4%

Length

2023-12-11T09:22:00.378551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:22:00.491814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가상계좌 55
14.2%
위택스 52
13.4%
은행창구 51
13.2%
지자체방문 49
12.7%
자동화기기 47
12.1%
기타 45
11.6%
인터넷지로 43
11.1%
자동이체 24
6.2%
페이사납부 21
 
5.4%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size519.0 B
True
195 
False
192 
ValueCountFrequency (%)
True 195
50.4%
False 192
49.6%
2023-12-11T09:22:00.614948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct319
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2761.3385
Minimum1
Maximum34347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T09:22:00.743802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q169.5
median649
Q32386
95-th percentile12958.5
Maximum34347
Range34346
Interquartile range (IQR)2316.5

Descriptive statistics

Standard deviation5126.9874
Coefficient of variation (CV)1.8567037
Kurtosis10.427512
Mean2761.3385
Median Absolute Deviation (MAD)643
Skewness3.0092244
Sum1068638
Variance26286000
MonotonicityNot monotonic
2023-12-11T09:22:00.869645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
2.8%
2 11
 
2.8%
4 9
 
2.3%
3 7
 
1.8%
9 5
 
1.3%
5 4
 
1.0%
7 4
 
1.0%
89 3
 
0.8%
11 3
 
0.8%
167 2
 
0.5%
Other values (309) 328
84.8%
ValueCountFrequency (%)
1 11
2.8%
2 11
2.8%
3 7
1.8%
4 9
2.3%
5 4
 
1.0%
6 2
 
0.5%
7 4
 
1.0%
8 1
 
0.3%
9 5
1.3%
10 2
 
0.5%
ValueCountFrequency (%)
34347 1
0.3%
31264 1
0.3%
27858 1
0.3%
24573 1
0.3%
24290 1
0.3%
22866 1
0.3%
22031 1
0.3%
21821 1
0.3%
20534 1
0.3%
19615 1
0.3%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3650079 × 108
Minimum3140
Maximum1.35987 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T09:22:01.081869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3140
5-th percentile181522
Q111102035
median90952680
Q35.2243975 × 108
95-th percentile3.1375999 × 109
Maximum1.35987 × 1010
Range1.3598697 × 1010
Interquartile range (IQR)5.1133772 × 108

Descriptive statistics

Standard deviation1.3709068 × 109
Coefficient of variation (CV)2.1538179
Kurtosis31.464018
Mean6.3650079 × 108
Median Absolute Deviation (MAD)90363630
Skewness4.7438354
Sum2.463258 × 1011
Variance1.8793854 × 1018
MonotonicityNot monotonic
2023-12-11T09:22:01.233404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44735700 1
 
0.3%
9266440 1
 
0.3%
18374270 1
 
0.3%
3562336890 1
 
0.3%
913320 1
 
0.3%
76860990 1
 
0.3%
466276870 1
 
0.3%
171611640 1
 
0.3%
1408023240 1
 
0.3%
186137250 1
 
0.3%
Other values (377) 377
97.4%
ValueCountFrequency (%)
3140 1
0.3%
6180 1
0.3%
6410 1
0.3%
8880 1
0.3%
9140 1
0.3%
9540 1
0.3%
14030 1
0.3%
16770 1
0.3%
16980 1
0.3%
19010 1
0.3%
ValueCountFrequency (%)
13598699790 1
0.3%
9641700000 1
0.3%
9574777000 1
0.3%
7598326660 1
0.3%
5358492320 1
0.3%
5147201010 1
0.3%
4772700520 1
0.3%
4533441630 1
0.3%
4152575240 1
0.3%
4029520290 1
0.3%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct323
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.436718
Minimum0
Maximum85.92
Zeros9
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-11T09:22:01.376166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q11.225
median7.49
Q316.975
95-th percentile56.439
Maximum85.92
Range85.92
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation17.686141
Coefficient of variation (CV)1.3162545
Kurtosis4.0089137
Mean13.436718
Median Absolute Deviation (MAD)7.18
Skewness2.0587598
Sum5200.01
Variance312.7996
MonotonicityNot monotonic
2023-12-11T09:22:01.500779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 10
 
2.6%
0.0 9
 
2.3%
0.09 8
 
2.1%
0.04 4
 
1.0%
0.1 4
 
1.0%
0.06 4
 
1.0%
0.03 4
 
1.0%
0.16 3
 
0.8%
0.02 3
 
0.8%
0.14 3
 
0.8%
Other values (313) 335
86.6%
ValueCountFrequency (%)
0.0 9
2.3%
0.01 10
2.6%
0.02 3
 
0.8%
0.03 4
 
1.0%
0.04 4
 
1.0%
0.05 1
 
0.3%
0.06 4
 
1.0%
0.07 2
 
0.5%
0.08 1
 
0.3%
0.09 8
2.1%
ValueCountFrequency (%)
85.92 1
0.3%
83.44 1
0.3%
80.94 1
0.3%
79.5 1
0.3%
78.25 1
0.3%
74.43 1
0.3%
74.4 1
0.3%
69.37 1
0.3%
67.62 1
0.3%
67.46 1
0.3%

Interactions

2023-12-11T09:21:58.969772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:57.752043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:58.123800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:58.512107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:59.070074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:57.830453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:58.213030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:58.632980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:59.156205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:57.919979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:58.320046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:58.750156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:59.252799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:58.034858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:58.427299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:58.854950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:22:01.585261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0600.0000.3800.8090.600
납부매체0.0000.0601.0001.0000.5050.3140.499
납부매체전자고지여부0.0000.0001.0001.0000.2670.1140.356
납부건수0.0000.3800.5050.2671.0000.3420.865
납부금액0.0000.8090.3140.1140.3421.0000.146
납부매체비율0.0000.6000.4990.3560.8650.1461.000
2023-12-11T09:22:01.691610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체납부매체전자고지여부세목명
납부매체1.0000.9910.023
납부매체전자고지여부0.9911.0000.000
세목명0.0230.0001.000
2023-12-11T09:22:01.796750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도납부건수납부금액납부매체비율세목명납부매체납부매체전자고지여부
납부년도1.0000.0190.0280.0330.0000.0000.000
납부건수0.0191.0000.7250.8270.1620.2570.203
납부금액0.0280.7251.0000.5850.4360.1690.120
납부매체비율0.0330.8270.5851.0000.2900.2530.271
세목명0.0000.1620.4360.2901.0000.0230.000
납부매체0.0000.2570.1690.2530.0231.0000.991
납부매체전자고지여부0.0000.2030.1200.2710.0000.9911.000

Missing values

2023-12-11T09:21:59.357553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:21:59.478430image/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
0경상남도합천군488902017등록면허세가상계좌Y2713447357007.46
1경상남도합천군488902017등록세가상계좌Y1943400.0
2경상남도합천군488902017자동차세가상계좌Y8165122583918022.45
3경상남도합천군488902017재산세가상계좌Y1961586728115053.93
4경상남도합천군488902017종합토지세가상계좌Y2690400.01
5경상남도합천군488902017주민세가상계좌Y449811809629012.37
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