Overview

Dataset statistics

Number of variables10
Number of observations432
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.0 KiB
Average record size in memory85.3 B

Variable types

Categorical6
Boolean1
Numeric3

Dataset

Description납부 매체별 지방세 납부 현황 제공
Author경상남도 창원시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15078693

Alerts

시도명 has constant value ""Constant
납부년도 has constant value ""Constant
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부매체 is highly overall correlated with 납부매체전자고지여부High correlation
시군구명 is highly overall correlated with 자치단체코드High correlation
자치단체코드 is highly overall correlated with 시군구명High 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 52 (12.0%) zerosZeros

Reproduction

Analysis started2023-12-11 00:04:02.788238
Analysis finished2023-12-11 00:04:04.147863
Duration1.36 second
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
창원시
432 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
창원시 432
100.0%

Length

2023-12-11T09:04:04.199016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:04:04.286439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창원시 432
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
의창구
91 
마산회원구
87 
성산구
87 
진해구
84 
마산합포구
83 

Length

Max length5
Median length3
Mean length3.787037
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의창구 91
21.1%
마산회원구 87
20.1%
성산구 87
20.1%
진해구 84
19.4%
마산합포구 83
19.2%

Length

2023-12-11T09:04:04.378131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:04:04.483683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의창구 91
21.1%
마산회원구 87
20.1%
성산구 87
20.1%
진해구 84
19.4%
마산합포구 83
19.2%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
48121
91 
48127
87 
48123
87 
48129
84 
48125
83 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48125
2nd row48125
3rd row48125
4th row48125
5th row48125

Common Values

ValueCountFrequency (%)
48121 91
21.1%
48127 87
20.1%
48123 87
20.1%
48129 84
19.4%
48125 83
19.2%

Length

2023-12-11T09:04:04.588517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:04:04.686485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48121 91
21.1%
48127 87
20.1%
48123 87
20.1%
48129 84
19.4%
48125 83
19.2%

납부년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2020
432 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 432
100.0%

Length

2023-12-11T09:04:04.782157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:04:04.860157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 432
100.0%

세목명
Categorical

Distinct15
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
등록면허세
55 
자동차세
55 
재산세
55 
주민세
55 
지방소득세
49 
Other values (10)
163 

Length

Max length7
Median length5
Mean length4.1273148
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
등록면허세 55
12.7%
자동차세 55
12.7%
재산세 55
12.7%
주민세 55
12.7%
지방소득세 49
11.3%
취득세 45
10.4%
지역자원시설세 41
9.5%
등록세 24
5.6%
면허세 17
 
3.9%
종합토지세 13
 
3.0%
Other values (5) 23
5.3%

Length

2023-12-11T09:04:04.946435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등록면허세 55
12.7%
자동차세 55
12.7%
재산세 55
12.7%
주민세 55
12.7%
지방소득세 49
11.3%
취득세 45
10.4%
지역자원시설세 41
9.5%
등록세 24
5.6%
면허세 17
 
3.9%
종합토지세 13
 
3.0%
Other values (5) 23
5.3%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
ARS
59 
기타
57 
가상계좌
51 
지자체방문
50 
위택스
43 
Other values (5)
172 

Length

Max length5
Median length4
Mean length3.8680556
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ARS 59
13.7%
기타 57
13.2%
가상계좌 51
11.8%
지자체방문 50
11.6%
위택스 43
10.0%
은행창구 43
10.0%
자동화기기 42
9.7%
인터넷지로 35
8.1%
페이사납부 32
7.4%
자동이체 20
 
4.6%

Length

2023-12-11T09:04:05.051998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:04:05.164022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ars 59
13.7%
기타 57
13.2%
가상계좌 51
11.8%
지자체방문 50
11.6%
위택스 43
10.0%
은행창구 43
10.0%
자동화기기 42
9.7%
인터넷지로 35
8.1%
페이사납부 32
7.4%
자동이체 20
 
4.6%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size564.0 B
False
227 
True
205 
ValueCountFrequency (%)
False 227
52.5%
True 205
47.5%
2023-12-11T09:04:05.277749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct327
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6668.0949
Minimum1
Maximum102389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T09:04:05.388422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q122
median1188
Q37120.75
95-th percentile32064.65
Maximum102389
Range102388
Interquartile range (IQR)7098.75

Descriptive statistics

Standard deviation13294.816
Coefficient of variation (CV)1.9937953
Kurtosis15.211342
Mean6668.0949
Median Absolute Deviation (MAD)1184.5
Skewness3.5592826
Sum2880617
Variance1.7675214 × 108
MonotonicityNot monotonic
2023-12-11T09:04:05.572524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 15
 
3.5%
1 14
 
3.2%
6 14
 
3.2%
8 9
 
2.1%
4 8
 
1.9%
5 8
 
1.9%
3 7
 
1.6%
9 4
 
0.9%
25 4
 
0.9%
10 4
 
0.9%
Other values (317) 345
79.9%
ValueCountFrequency (%)
1 14
3.2%
2 15
3.5%
3 7
1.6%
4 8
1.9%
5 8
1.9%
6 14
3.2%
7 3
 
0.7%
8 9
2.1%
9 4
 
0.9%
10 4
 
0.9%
ValueCountFrequency (%)
102389 1
0.2%
82308 1
0.2%
76959 1
0.2%
72658 1
0.2%
72282 1
0.2%
63969 1
0.2%
60669 1
0.2%
58360 1
0.2%
57734 1
0.2%
52652 1
0.2%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct432
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4712986 × 109
Minimum2830
Maximum6.7792176 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T09:04:05.721368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2830
5-th percentile68895.5
Q13899085
median2.3228378 × 108
Q32.8059507 × 109
95-th percentile1.7058579 × 1010
Maximum6.7792176 × 1010
Range6.7792173 × 1010
Interquartile range (IQR)2.8020516 × 109

Descriptive statistics

Standard deviation7.9808277 × 109
Coefficient of variation (CV)2.29909
Kurtosis23.326579
Mean3.4712986 × 109
Median Absolute Deviation (MAD)2.3215744 × 108
Skewness4.2320327
Sum1.499601 × 1012
Variance6.3693611 × 1019
MonotonicityNot monotonic
2023-12-11T09:04:05.843209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123120 1
 
0.2%
786090 1
 
0.2%
154608650 1
 
0.2%
246351220 1
 
0.2%
3180490 1
 
0.2%
7889449060 1
 
0.2%
2621120 1
 
0.2%
175359540 1
 
0.2%
68945770 1
 
0.2%
302220 1
 
0.2%
Other values (422) 422
97.7%
ValueCountFrequency (%)
2830 1
0.2%
3060 1
0.2%
6180 1
0.2%
7410 1
0.2%
7720 1
0.2%
10300 1
0.2%
12150 1
0.2%
22480 1
0.2%
26770 1
0.2%
27000 1
0.2%
ValueCountFrequency (%)
67792175640 1
0.2%
60668352870 1
0.2%
58606309950 1
0.2%
39550877260 1
0.2%
38424237410 1
0.2%
34493385730 1
0.2%
34281423610 1
0.2%
30721113000 1
0.2%
30522943300 1
0.2%
27513293160 1
0.2%

납부매체비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct248
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3145833
Minimum0
Maximum17.48
Zeros52
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T09:04:05.970964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03
median1.065
Q33.555
95-th percentile8.7225
Maximum17.48
Range17.48
Interquartile range (IQR)3.525

Descriptive statistics

Standard deviation3.1157241
Coefficient of variation (CV)1.3461274
Kurtosis4.4890606
Mean2.3145833
Median Absolute Deviation (MAD)1.055
Skewness1.9379664
Sum999.9
Variance9.7077367
MonotonicityNot monotonic
2023-12-11T09:04:06.095353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 52
 
12.0%
0.01 37
 
8.6%
0.02 16
 
3.7%
0.03 15
 
3.5%
0.04 9
 
2.1%
0.08 7
 
1.6%
0.06 6
 
1.4%
0.07 5
 
1.2%
0.09 5
 
1.2%
0.45 3
 
0.7%
Other values (238) 277
64.1%
ValueCountFrequency (%)
0.0 52
12.0%
0.01 37
8.6%
0.02 16
 
3.7%
0.03 15
 
3.5%
0.04 9
 
2.1%
0.05 2
 
0.5%
0.06 6
 
1.4%
0.07 5
 
1.2%
0.08 7
 
1.6%
0.09 5
 
1.2%
ValueCountFrequency (%)
17.48 1
0.2%
16.81 1
0.2%
16.58 1
0.2%
15.76 1
0.2%
14.76 1
0.2%
13.5 1
0.2%
12.41 1
0.2%
11.58 1
0.2%
11.53 1
0.2%
11.31 1
0.2%

Interactions

2023-12-11T09:04:03.675416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:03.152698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:03.386491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:03.754932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:03.223752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:03.462811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:03.857336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:03.305668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:03.556271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:04:06.187306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
시군구명1.0001.0000.0000.0000.0000.0000.0880.240
자치단체코드1.0001.0000.0000.0000.0000.0000.0880.240
세목명0.0000.0001.0000.2950.1290.1830.3660.557
납부매체0.0000.0000.2951.0000.9940.5010.2430.380
납부매체전자고지여부0.0000.0000.1290.9941.0000.2240.0330.000
납부건수0.0000.0000.1830.5010.2241.0000.4390.733
납부금액0.0880.0880.3660.2430.0330.4391.0000.212
납부매체비율0.2400.2400.5570.3800.0000.7330.2121.000
2023-12-11T09:04:06.289995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체전자고지여부납부매체시군구명자치단체코드세목명
납부매체전자고지여부1.0000.9210.0000.0000.115
납부매체0.9211.0000.0000.0000.113
시군구명0.0000.0001.0001.0000.000
자치단체코드0.0000.0001.0001.0000.000
세목명0.1150.1130.0000.0001.000
2023-12-11T09:04:06.634086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율시군구명자치단체코드세목명납부매체납부매체전자고지여부
납부건수1.0000.8210.8820.0000.0000.0680.1740.170
납부금액0.8211.0000.7050.0540.0540.1650.1180.027
납부매체비율0.8820.7051.0000.1010.1010.2420.1250.000
시군구명0.0000.0540.1011.0001.0000.0000.0000.000
자치단체코드0.0000.0540.1011.0001.0000.0000.0000.000
세목명0.0680.1650.2420.0000.0001.0000.1130.115
납부매체0.1740.1180.1250.0000.0000.1131.0000.921
납부매체전자고지여부0.1700.0270.0000.0000.0000.1150.9211.000

Missing values

2023-12-11T09:04:03.973322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:04:04.097910image/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
0창원시마산합포구481252020등록면허세ARSY41231200.01
1창원시마산합포구481252020자동차세ARSN26015054512009.58
2창원시마산합포구481252020자동차세ARSY2625644300.1
3창원시마산합포구481252020재산세ARSN19053560363107.02
4창원시마산합포구481252020재산세ARSY85486700.03
5창원시마산합포구481252020주민세ARSN681111630602.51
6창원시마산합포구481252020주민세ARSY415776200.15
7창원시마산합포구481252020지방소득세ARSN90167807900.33
8창원시마산합포구481252020지방소득세ARSY2516800.01
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