Overview

Dataset statistics

Number of variables10
Number of observations251
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.5 KiB
Average record size in memory83.5 B

Variable types

Categorical6
Boolean1
Text2
Numeric1

Dataset

Description2017년부터 2019년 지방세 납부방법(신용카드, 가상계좌, ARS 등)의 데이터로 전자송달 시장규모 및 분석, 수수료 산정시 기초자료로 활용
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079426

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
납부매체비율 has 26 (10.4%) zerosZeros

Reproduction

Analysis started2023-12-11 00:40:42.951938
Analysis finished2023-12-11 00:40:43.573322
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
경상남도
251 

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 251
100.0%

Length

2023-12-11T09:40:43.629829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:40:43.714966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 251
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
양산시
251 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양산시 251
100.0%

Length

2023-12-11T09:40:43.818688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:40:43.917493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양산시 251
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
48330
251 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48330
2nd row48330
3rd row48330
4th row48330
5th row48330

Common Values

ValueCountFrequency (%)
48330 251
100.0%

Length

2023-12-11T09:40:44.010013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:40:44.097923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48330 251
100.0%

납부년도
Categorical

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2019
87 
2018
83 
2017
81 

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 87
34.7%
2018 83
33.1%
2017 81
32.3%

Length

2023-12-11T09:40:44.184815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:40:44.274932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 87
34.7%
2018 83
33.1%
2017 81
32.3%

세목명
Categorical

Distinct12
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
등록면허세
31 
자동차세
31 
재산세
31 
주민세
31 
지방소득세
28 
Other values (7)
99 

Length

Max length7
Median length5
Mean length4.0717131
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등록면허세 31
12.4%
자동차세 31
12.4%
재산세 31
12.4%
주민세 31
12.4%
지방소득세 28
11.2%
취득세 25
10.0%
지역자원시설세 20
8.0%
등록세 18
7.2%
면허세 14
5.6%
종합토지세 14
5.6%
Other values (2) 8
 
3.2%

Length

2023-12-11T09:40:44.383380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등록면허세 31
12.4%
자동차세 31
12.4%
재산세 31
12.4%
주민세 31
12.4%
지방소득세 28
11.2%
취득세 25
10.0%
지역자원시설세 20
8.0%
등록세 18
7.2%
면허세 14
5.6%
종합토지세 14
5.6%
Other values (2) 8
 
3.2%

납부매체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
ARS
35 
가상계좌
31 
은행창구
31 
자동화기기
31 
지자체방문
29 
Other values (5)
94 

Length

Max length5
Median length4
Mean length3.9043825
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ARS 35
13.9%
가상계좌 31
12.4%
은행창구 31
12.4%
자동화기기 31
12.4%
지자체방문 29
11.6%
기타 27
10.8%
위택스 25
10.0%
인터넷지로 24
9.6%
자동이체 12
 
4.8%
페이사납부 6
 
2.4%

Length

2023-12-11T09:40:44.513453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:40:44.623431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ars 35
13.9%
가상계좌 31
12.4%
은행창구 31
12.4%
자동화기기 31
12.4%
지자체방문 29
11.6%
기타 27
10.8%
위택스 25
10.0%
인터넷지로 24
9.6%
자동이체 12
 
4.8%
페이사납부 6
 
2.4%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size383.0 B
False
138 
True
113 
ValueCountFrequency (%)
False 138
55.0%
True 113
45.0%
2023-12-11T09:40:44.745921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Text

Distinct201
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-11T09:40:45.056544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.8406375
Min length3

Characters and Unicode

Total characters1466
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)74.5%

Sample

1st row 127
2nd row 7
3rd row 4,598
4th row 25
5th row 3,391
ValueCountFrequency (%)
1 9
 
3.6%
3 9
 
3.6%
4 9
 
3.6%
2 8
 
3.2%
5 4
 
1.6%
15 3
 
1.2%
11 3
 
1.2%
6 3
 
1.2%
7 3
 
1.2%
20 3
 
1.2%
Other values (191) 197
78.5%
2023-12-11T09:40:45.525444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
502
34.2%
1 141
 
9.6%
, 137
 
9.3%
2 117
 
8.0%
4 85
 
5.8%
3 84
 
5.7%
6 83
 
5.7%
5 72
 
4.9%
8 67
 
4.6%
7 60
 
4.1%
Other values (2) 118
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 827
56.4%
Space Separator 502
34.2%
Other Punctuation 137
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 141
17.0%
2 117
14.1%
4 85
10.3%
3 84
10.2%
6 83
10.0%
5 72
8.7%
8 67
8.1%
7 60
7.3%
0 59
7.1%
9 59
7.1%
Space Separator
ValueCountFrequency (%)
502
100.0%
Other Punctuation
ValueCountFrequency (%)
, 137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1466
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
502
34.2%
1 141
 
9.6%
, 137
 
9.3%
2 117
 
8.0%
4 85
 
5.8%
3 84
 
5.7%
6 83
 
5.7%
5 72
 
4.9%
8 67
 
4.6%
7 60
 
4.1%
Other values (2) 118
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
502
34.2%
1 141
 
9.6%
, 137
 
9.3%
2 117
 
8.0%
4 85
 
5.8%
3 84
 
5.7%
6 83
 
5.7%
5 72
 
4.9%
8 67
 
4.6%
7 60
 
4.1%
Other values (2) 118
 
8.0%

납부금액
Text

Distinct250
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-11T09:40:45.836167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length12.749004
Min length8

Characters and Unicode

Total characters3200
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique249 ?
Unique (%)99.2%

Sample

1st row 1,696,720
2nd row 95,790
3rd row 832,398,030
4th row 2,225,640
5th row 646,922,520
ValueCountFrequency (%)
15,450 2
 
0.8%
1,110,630 1
 
0.4%
1,139,437,940 1
 
0.4%
11,102,526,740 1
 
0.4%
414,290 1
 
0.4%
199,880 1
 
0.4%
74,817,300 1
 
0.4%
312,325,340 1
 
0.4%
615,110 1
 
0.4%
5,607,190,020 1
 
0.4%
Other values (240) 240
95.6%
2023-12-11T09:40:46.226547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 554
17.3%
502
15.7%
0 409
12.8%
1 235
7.3%
2 220
 
6.9%
4 212
 
6.6%
6 194
 
6.1%
5 191
 
6.0%
3 191
 
6.0%
7 182
 
5.7%
Other values (2) 310
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2144
67.0%
Other Punctuation 554
 
17.3%
Space Separator 502
 
15.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 409
19.1%
1 235
11.0%
2 220
10.3%
4 212
9.9%
6 194
9.0%
5 191
8.9%
3 191
8.9%
7 182
8.5%
9 159
 
7.4%
8 151
 
7.0%
Other Punctuation
ValueCountFrequency (%)
, 554
100.0%
Space Separator
ValueCountFrequency (%)
502
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 554
17.3%
502
15.7%
0 409
12.8%
1 235
7.3%
2 220
 
6.9%
4 212
 
6.6%
6 194
 
6.1%
5 191
 
6.0%
3 191
 
6.0%
7 182
 
5.7%
Other values (2) 310
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 554
17.3%
502
15.7%
0 409
12.8%
1 235
7.3%
2 220
 
6.9%
4 212
 
6.6%
6 194
 
6.1%
5 191
 
6.0%
3 191
 
6.0%
7 182
 
5.7%
Other values (2) 310
9.7%

납부매체비율
Real number (ℝ)

ZEROS 

Distinct178
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.155538
Minimum0
Maximum83.73
Zeros26
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-11T09:40:46.355909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.06
median4.13
Q317.33
95-th percentile39.36
Maximum83.73
Range83.73
Interquartile range (IQR)17.27

Descriptive statistics

Standard deviation14.909768
Coefficient of variation (CV)1.3365351
Kurtosis4.4701426
Mean11.155538
Median Absolute Deviation (MAD)4.13
Skewness1.8523872
Sum2800.04
Variance222.30118
MonotonicityNot monotonic
2023-12-11T09:40:46.473599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
 
10.4%
0.01 10
 
4.0%
0.02 8
 
3.2%
0.09 8
 
3.2%
0.05 7
 
2.8%
0.04 7
 
2.8%
0.06 6
 
2.4%
0.03 4
 
1.6%
0.1 3
 
1.2%
0.07 3
 
1.2%
Other values (168) 169
67.3%
ValueCountFrequency (%)
0.0 26
10.4%
0.01 10
 
4.0%
0.02 8
 
3.2%
0.03 4
 
1.6%
0.04 7
 
2.8%
0.05 7
 
2.8%
0.06 6
 
2.4%
0.07 3
 
1.2%
0.08 1
 
0.4%
0.09 8
 
3.2%
ValueCountFrequency (%)
83.73 1
0.4%
80.92 1
0.4%
76.45 1
0.4%
48.23 1
0.4%
47.25 1
0.4%
46.53 1
0.4%
45.84 1
0.4%
45.46 1
0.4%
43.82 1
0.4%
43.46 1
0.4%

Interactions

2023-12-11T09:40:43.225448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:40:46.549624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부매체비율
납부년도1.0000.0000.0000.0000.000
세목명0.0001.0000.0000.0400.600
납부매체0.0000.0001.0000.9930.457
납부매체전자고지여부0.0000.0400.9931.0000.182
납부매체비율0.0000.6000.4570.1821.000
2023-12-11T09:40:46.691444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체납부매체전자고지여부납부년도세목명
납부매체1.0000.9110.0000.000
납부매체전자고지여부0.9111.0000.0000.028
납부년도0.0000.0001.0000.000
세목명0.0000.0280.0001.000
2023-12-11T09:40:46.775958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부매체비율1.0000.0000.3420.2500.193
납부년도0.0001.0000.0000.0000.000
세목명0.3420.0001.0000.0000.028
납부매체0.2500.0000.0001.0000.911
납부매체전자고지여부0.1930.0000.0280.9111.000

Missing values

2023-12-11T09:40:43.336873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:40:43.515603image/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
0경상남도양산시483302017등록면허세ARSN1271,696,7201.29
1경상남도양산시483302017등록면허세ARSY795,7900.07
2경상남도양산시483302017자동차세ARSN4,598832,398,03046.53
3경상남도양산시483302017자동차세ARSY252,225,6400.25
4경상남도양산시483302017재산세ARSN3,391646,922,52034.31
5경상남도양산시483302017재산세ARSY9573,5300.09
6경상남도양산시483302017주민세ARSN1,45822,025,31014.75
7경상남도양산시483302017주민세ARSY48733,1400.49
8경상남도양산시483302017지방소득세ARSN13454,567,4301.36
9경상남도양산시483302017지방소득세ARSY568,8700.05
시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
241경상남도양산시483302019주민세지자체방문N3,74677,334,87016.93
242경상남도양산시483302019지방소득세지자체방문N834268,441,7003.77
243경상남도양산시483302019지역자원시설세지자체방문N8697,6700.04
244경상남도양산시483302019취득세지자체방문N1,5915,637,545,4007.19
245경상남도양산시483302019등록면허세페이사납부Y451,0000.09
246경상남도양산시483302019자동차세페이사납부Y1,226186,325,68029.04
247경상남도양산시483302019재산세페이사납부Y1,606271,155,78038.04
248경상남도양산시483302019주민세페이사납부Y1,37818,736,90032.64
249경상남도양산시483302019지방소득세페이사납부Y4368,3000.09
250경상남도양산시483302019취득세페이사납부Y47,645,0400.09