Overview

Dataset statistics

Number of variables11
Number of observations316
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.8 KiB
Average record size in memory93.4 B

Variable types

Categorical6
Boolean1
Numeric3
DateTime1

Dataset

Description신용카드, 가상계좌 등 지방세 납부매체별 납부현황을 제공하여 전자송달 시장 규모 및 편익 분석, 수수료 산정시 기초자료로 활용
Author충청북도 영동군
URLhttps://www.data.go.kr/data/15078773/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일 has constant value ""Constant
납부매체전자고지여부 is highly overall correlated with 납부매체High correlation
납부매체 is highly overall correlated with 납부매체전자고지여부High 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

Reproduction

Analysis started2023-12-12 02:03:02.396737
Analysis finished2023-12-12 02:03:04.510500
Duration2.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
충청북도
316 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 316
100.0%

Length

2023-12-12T11:03:04.619534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:04.734097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 316
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
영동군
316 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영동군 316
100.0%

Length

2023-12-12T11:03:04.875716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:04.983919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영동군 316
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
43740
316 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43740
2nd row43740
3rd row43740
4th row43740
5th row43740

Common Values

ValueCountFrequency (%)
43740 316
100.0%

Length

2023-12-12T11:03:05.098059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:05.215195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43740 316
100.0%

납부년도
Categorical

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2020
71 
2021
65 
2019
64 
2018
60 
2017
56 

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 (%)
2020 71
22.5%
2021 65
20.6%
2019 64
20.3%
2018 60
19.0%
2017 56
17.7%

Length

2023-12-12T11:03:05.358654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:05.477456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 71
22.5%
2021 65
20.6%
2019 64
20.3%
2018 60
19.0%
2017 56
17.7%

세목명
Categorical

Distinct12
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
자동차세
43 
재산세
43 
주민세
43 
등록면허세
42 
취득세
38 
Other values (7)
107 

Length

Max length7
Median length5
Mean length4.0348101
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 43
13.6%
재산세 43
13.6%
주민세 43
13.6%
등록면허세 42
13.3%
취득세 38
12.0%
지방소득세 37
11.7%
등록세 27
8.5%
지역자원시설세 22
7.0%
담배소비세 9
 
2.8%
종합토지세 7
 
2.2%
Other values (2) 5
 
1.6%

Length

2023-12-12T11:03:05.637370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세 43
13.6%
재산세 43
13.6%
주민세 43
13.6%
등록면허세 42
13.3%
취득세 38
12.0%
지방소득세 37
11.7%
등록세 27
8.5%
지역자원시설세 22
7.0%
담배소비세 9
 
2.8%
종합토지세 7
 
2.2%
Other values (2) 5
 
1.6%

납부매체
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
은행창구
46 
가상계좌
45 
위택스
41 
자동화기기
39 
인터넷지로
37 
Other values (4)
108 

Length

Max length5
Median length4
Mean length4.056962
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
은행창구 46
14.6%
가상계좌 45
14.2%
위택스 41
13.0%
자동화기기 39
12.3%
인터넷지로 37
11.7%
지자체방문 37
11.7%
기타 35
11.1%
자동이체 20
6.3%
페이사납부 16
 
5.1%

Length

2023-12-12T11:03:05.807307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:05.953675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
은행창구 46
14.6%
가상계좌 45
14.2%
위택스 41
13.0%
자동화기기 39
12.3%
인터넷지로 37
11.7%
지자체방문 37
11.7%
기타 35
11.1%
자동이체 20
6.3%
페이사납부 16
 
5.1%

납부매체전자고지여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size448.0 B
True
159 
False
157 
ValueCountFrequency (%)
True 159
50.3%
False 157
49.7%
2023-12-12T11:03:06.113604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

납부건수
Real number (ℝ)

HIGH CORRELATION 

Distinct263
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2385.3797
Minimum1
Maximum24545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T11:03:06.271009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q174.25
median470
Q32578
95-th percentile11123
Maximum24545
Range24544
Interquartile range (IQR)2503.75

Descriptive statistics

Standard deviation4164.6242
Coefficient of variation (CV)1.7458957
Kurtosis7.6730257
Mean2385.3797
Median Absolute Deviation (MAD)465
Skewness2.6758381
Sum753780
Variance17344094
MonotonicityNot monotonic
2023-12-12T11:03:06.449350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
3.2%
5 6
 
1.9%
4 5
 
1.6%
2 5
 
1.6%
7 4
 
1.3%
6 4
 
1.3%
58 3
 
0.9%
12 3
 
0.9%
327 2
 
0.6%
43 2
 
0.6%
Other values (253) 272
86.1%
ValueCountFrequency (%)
1 10
3.2%
2 5
1.6%
3 2
 
0.6%
4 5
1.6%
5 6
1.9%
6 4
 
1.3%
7 4
 
1.3%
10 1
 
0.3%
12 3
 
0.9%
13 1
 
0.3%
ValueCountFrequency (%)
24545 1
0.3%
22095 1
0.3%
20279 1
0.3%
20176 1
0.3%
18631 1
0.3%
17427 1
0.3%
16553 1
0.3%
16519 1
0.3%
15971 1
0.3%
15322 1
0.3%

납부금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct316
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8303881 × 108
Minimum1690
Maximum1.0888247 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T11:03:06.633451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1690
5-th percentile148612.5
Q18910700
median1.076949 × 108
Q35.4493854 × 108
95-th percentile2.875274 × 109
Maximum1.0888247 × 1010
Range1.0888245 × 1010
Interquartile range (IQR)5.3602784 × 108

Descriptive statistics

Standard deviation1.2353803 × 109
Coefficient of variation (CV)2.1188646
Kurtosis27.362279
Mean5.8303881 × 108
Median Absolute Deviation (MAD)1.0696924 × 108
Skewness4.4557838
Sum1.8424027 × 1011
Variance1.5261645 × 1018
MonotonicityNot monotonic
2023-12-12T11:03:06.832855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57414620 1
 
0.3%
303180 1
 
0.3%
1224680 1
 
0.3%
10072400000 1
 
0.3%
1125836620 1
 
0.3%
220616650 1
 
0.3%
78270 1
 
0.3%
1546201710 1
 
0.3%
3407007140 1
 
0.3%
196602650 1
 
0.3%
Other values (306) 306
96.8%
ValueCountFrequency (%)
1690 1
0.3%
7620 1
0.3%
12600 1
0.3%
14930 1
0.3%
18540 1
0.3%
21620 1
0.3%
34500 1
0.3%
45860 1
0.3%
78270 1
0.3%
79720 1
0.3%
ValueCountFrequency (%)
10888247000 1
0.3%
10072400000 1
0.3%
4705105630 1
0.3%
4606378760 1
0.3%
4565423720 1
0.3%
4537323700 1
0.3%
4519633570 1
0.3%
4513938680 1
0.3%
4505694810 1
0.3%
3987597810 1
0.3%

납부매체비율
Real number (ℝ)

HIGH CORRELATION 

Distinct262
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.607437
Minimum0
Maximum85.2
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T11:03:06.987192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q11.2475
median8.975
Q319.9175
95-th percentile48.495
Maximum85.2
Range85.2
Interquartile range (IQR)18.67

Descriptive statistics

Standard deviation16.145569
Coefficient of variation (CV)1.1865254
Kurtosis4.219196
Mean13.607437
Median Absolute Deviation (MAD)8.465
Skewness1.875957
Sum4299.95
Variance260.6794
MonotonicityNot monotonic
2023-12-12T11:03:07.207097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 11
 
3.5%
0.04 5
 
1.6%
0.05 5
 
1.6%
0.18 4
 
1.3%
0.03 4
 
1.3%
0.09 3
 
0.9%
0.11 3
 
0.9%
0.12 3
 
0.9%
0.08 3
 
0.9%
2.34 2
 
0.6%
Other values (252) 273
86.4%
ValueCountFrequency (%)
0.0 1
 
0.3%
0.01 11
3.5%
0.02 2
 
0.6%
0.03 4
 
1.3%
0.04 5
1.6%
0.05 5
1.6%
0.06 2
 
0.6%
0.07 2
 
0.6%
0.08 3
 
0.9%
0.09 3
 
0.9%
ValueCountFrequency (%)
85.2 1
0.3%
83.92 1
0.3%
83.78 1
0.3%
83.26 1
0.3%
73.44 1
0.3%
60.76 1
0.3%
53.78 1
0.3%
52.51 1
0.3%
52.5 1
0.3%
52.42 1
0.3%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum2022-07-13 00:00:00
Maximum2022-07-13 00:00:00
2023-12-12T11:03:07.337408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:07.796859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T11:03:03.724858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:02.943659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:03.324171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:03.848905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:03.068961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:03.452141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:03.980769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:03.202581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:03.596381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:03:07.910684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율
납부년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0000.0000.2800.8050.628
납부매체0.0000.0001.0001.0000.4540.3840.522
납부매체전자고지여부0.0000.0001.0001.0000.1900.1710.328
납부건수0.0000.2800.4540.1901.0000.4930.756
납부금액0.0000.8050.3840.1710.4931.0000.247
납부매체비율0.0000.6280.5220.3280.7560.2471.000
2023-12-12T11:03:08.080743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부년도납부매체전자고지여부세목명납부매체
납부년도1.0000.0000.0000.000
납부매체전자고지여부0.0001.0000.0000.989
세목명0.0000.0001.0000.000
납부매체0.0000.9890.0001.000
2023-12-12T11:03:08.201667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부건수납부금액납부매체비율납부년도세목명납부매체납부매체전자고지여부
납부건수1.0000.7420.8130.0000.1200.2250.144
납부금액0.7421.0000.5600.0000.4450.1980.122
납부매체비율0.8130.5601.0000.0000.3220.2690.250
납부년도0.0000.0000.0001.0000.0000.0000.000
세목명0.1200.4450.3220.0001.0000.0000.000
납부매체0.2250.1980.2690.0000.0001.0000.989
납부매체전자고지여부0.1440.1220.2500.0000.0000.9891.000

Missing values

2023-12-12T11:03:04.141597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:03:04.403780image/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

시도명시군구명자치단체코드납부년도세목명납부매체납부매체전자고지여부납부건수납부금액납부매체비율데이터기준일
0충청북도영동군437402017등록면허세가상계좌Y3045574146208.322022-07-13
1충청북도영동군437402017등록세가상계좌Y410032700.012022-07-13
2충청북도영동군437402017자동차세가상계좌Y11035157402558030.172022-07-13
3충청북도영동군437402017재산세가상계좌Y1521289722923041.592022-07-13
4충청북도영동군437402017주민세가상계좌Y572714646141015.662022-07-13
5충청북도영동군437402017지방소득세가상계좌Y12259128641603.352022-07-13
6충청북도영동군437402017지역자원시설세가상계좌Y4815141600.132022-07-13
7충청북도영동군437402017취득세가상계좌Y2832305994200.772022-07-13
8충청북도영동군437402017등록면허세기타N8811732002.312022-07-13
9충청북도영동군437402017자동차세기타N274193542907.22022-07-13
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