Overview

Dataset statistics

Number of variables5
Number of observations86
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory43.5 B

Variable types

Numeric2
Text1
Boolean1
DateTime1

Dataset

Description전라남도 무안군 지방세 ARS 납부 시스템 데이터 베이스 정보로 (수납정보, 통신사정보, 세목정보) 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15040998/fileData.do

Alerts

삭제여부(flag) has constant value ""Constant
사용일자 has constant value ""Constant
시퀀스번호 is highly overall correlated with 세목코드High correlation
세목코드 is highly overall correlated with 시퀀스번호High correlation
시퀀스번호 has unique valuesUnique
세목코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:54:16.839782
Analysis finished2023-12-12 07:54:17.893463
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시퀀스번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct86
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.5
Minimum1
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T16:54:17.984411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.25
Q122.25
median43.5
Q364.75
95-th percentile81.75
Maximum86
Range85
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation24.969982
Coefficient of variation (CV)0.57402257
Kurtosis-1.2
Mean43.5
Median Absolute Deviation (MAD)21.5
Skewness0
Sum3741
Variance623.5
MonotonicityStrictly increasing
2023-12-12T16:54:18.122643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
56 1
 
1.2%
64 1
 
1.2%
63 1
 
1.2%
62 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
Other values (76) 76
88.4%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
86 1
1.2%
85 1
1.2%
84 1
1.2%
83 1
1.2%
82 1
1.2%
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%

세목코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct86
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137013.76
Minimum101000
Maximum910094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-12T16:54:18.260272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101000
5-th percentile101004.25
Q1102006.25
median105004.5
Q3112750
95-th percentile140003.75
Maximum910094
Range809094
Interquartile range (IQR)10743.75

Descriptive statistics

Standard deviation142266.3
Coefficient of variation (CV)1.0383359
Kurtosis25.227447
Mean137013.76
Median Absolute Deviation (MAD)3004
Skewness5.1297677
Sum11783183
Variance2.0239701 × 1010
MonotonicityStrictly increasing
2023-12-12T16:54:18.392903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101000 1
 
1.2%
106004 1
 
1.2%
112000 1
 
1.2%
111000 1
 
1.2%
110000 1
 
1.2%
109999 1
 
1.2%
109000 1
 
1.2%
108000 1
 
1.2%
107000 1
 
1.2%
106093 1
 
1.2%
Other values (76) 76
88.4%
ValueCountFrequency (%)
101000 1
1.2%
101001 1
1.2%
101002 1
1.2%
101003 1
1.2%
101004 1
1.2%
101005 1
1.2%
101006 1
1.2%
101099 1
1.2%
101501 1
1.2%
101502 1
1.2%
ValueCountFrequency (%)
910094 1
1.2%
910000 1
1.2%
810000 1
1.2%
140011 1
1.2%
140004 1
1.2%
140003 1
1.2%
140002 1
1.2%
140001 1
1.2%
140000 1
1.2%
135003 1
1.2%

세목명
Text

Distinct85
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-12T16:54:18.671114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.7209302
Min length3

Characters and Unicode

Total characters664
Distinct characters76
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)97.7%

Sample

1st row취득세
2nd row구)취득세(부동산)
3rd row구)취득세(차량)
4th row구)취득세(이륜차량)
5th row구)취득세(기계장비)
ValueCountFrequency (%)
지역자원시설세(특부 2
 
2.3%
구)재산세(기타 1
 
1.2%
담배소비세 1
 
1.2%
토지과다보유세 1
 
1.2%
레저세 1
 
1.2%
도축세 1
 
1.2%
농업소득세 1
 
1.2%
자동차세(징수촉탁 1
 
1.2%
자동차세(주행 1
 
1.2%
자동차세(기계장비 1
 
1.2%
Other values (75) 75
87.2%
2023-12-12T16:54:19.071450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 88
 
13.3%
) 77
 
11.6%
( 64
 
9.6%
득 27
 
4.1%
산 18
 
2.7%
지 17
 
2.6%
소 16
 
2.4%
재 15
 
2.3%
차 15
 
2.3%
주 15
 
2.3%
Other values (66) 312
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 523
78.8%
Close Punctuation 77
 
11.6%
Open Punctuation 64
 
9.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 88
 
16.8%
득 27
 
5.2%
산 18
 
3.4%
지 17
 
3.3%
소 16
 
3.1%
재 15
 
2.9%
차 15
 
2.9%
주 15
 
2.9%
취 15
 
2.9%
등 15
 
2.9%
Other values (64) 282
53.9%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 523
78.8%
Common 141
 
21.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 88
 
16.8%
득 27
 
5.2%
산 18
 
3.4%
지 17
 
3.3%
소 16
 
3.1%
재 15
 
2.9%
차 15
 
2.9%
주 15
 
2.9%
취 15
 
2.9%
등 15
 
2.9%
Other values (64) 282
53.9%
Common
ValueCountFrequency (%)
) 77
54.6%
( 64
45.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 523
78.8%
ASCII 141
 
21.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 88
 
16.8%
득 27
 
5.2%
산 18
 
3.4%
지 17
 
3.3%
소 16
 
3.1%
재 15
 
2.9%
차 15
 
2.9%
주 15
 
2.9%
취 15
 
2.9%
등 15
 
2.9%
Other values (64) 282
53.9%
ASCII
ValueCountFrequency (%)
) 77
54.6%
( 64
45.4%

삭제여부(flag)
Boolean

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size218.0 B
False
86 
ValueCountFrequency (%)
False 86
100.0%
2023-12-12T16:54:19.212967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

사용일자
Date

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size820.0 B
Minimum1900-01-01 00:00:00
Maximum1900-01-01 00:00:00
2023-12-12T16:54:19.294377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:19.371592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T16:54:17.152998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:16.971853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:17.245172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:17.048067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:54:19.437446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시퀀스번호세목코드세목명
시퀀스번호1.0000.3621.000
세목코드0.3621.0001.000
세목명1.0001.0001.000
2023-12-12T16:54:19.513731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시퀀스번호세목코드
시퀀스번호1.0001.000
세목코드1.0001.000

Missing values

2023-12-12T16:54:17.743670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:54:17.852889image/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

시퀀스번호세목코드세목명삭제여부(flag)사용일자
01101000취득세N1900-01-01
12101001구)취득세(부동산)N1900-01-01
23101002구)취득세(차량)N1900-01-01
34101003구)취득세(이륜차량)N1900-01-01
45101004구)취득세(기계장비)N1900-01-01
56101005구)취득세(선박)N1900-01-01
67101006구)취득세(항공기)N1900-01-01
78101099구)취득세(기타)N1900-01-01
89101501취득세(부동산)N1900-01-01
910101502취득세(차량)N1900-01-01
시퀀스번호세목코드세목명삭제여부(flag)사용일자
7677135003지역자원시설세(특부)N1900-01-01
7778140000지방소득세N1900-01-01
7879140001지방소득세(종합소득)N1900-01-01
7980140002지방소득세(양도소득)N1900-01-01
8081140003지방소득세(법인소득)N1900-01-01
8182140004지방소득세(특별징수)N1900-01-01
8283140011지방소득세(종업원분)N1900-01-01
8384810000농특세N1900-01-01
8485910000교육세N1900-01-01
8586910094교육세(징수촉탁)N1900-01-01