Overview

Dataset statistics

Number of variables2
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory489.0 B
Average record size in memory23.3 B

Variable types

Text1
Numeric1

Dataset

Description인천광역시 본청, 군·구별 지방세(보통/목적, 광역시/군·구세, 레저/주민/지방소비/지방교육/도시계획세 등) 징수 현황에 대한 데이터 현황 정보를 제공합니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15066223&srcSe=7661IVAWM27C61E190

Alerts

2020 has unique valuesUnique

Reproduction

Analysis started2024-03-18 05:44:47.245436
Analysis finished2024-03-18 05:44:47.841441
Duration0.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지방세별
Text

Distinct14
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-03-18T14:44:47.931216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5
Min length3

Characters and Unicode

Total characters105
Distinct characters34
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

Unique8 ?
Unique (%)38.1%

Sample

1st row취득세
2nd row등록면허세
3rd row레저세
4th row주민세(균등분)
5th row자동차세
ValueCountFrequency (%)
지방소비세 3
14.3%
등록면허세 2
9.5%
자동차세 2
9.5%
지방소득세 2
9.5%
담배소비세 2
9.5%
재산세 2
9.5%
취득세 1
 
4.8%
레저세 1
 
4.8%
주민세(균등분 1
 
4.8%
주민세(재산분 1
 
4.8%
Other values (4) 4
19.0%
2024-03-18T14:44:48.221173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 21
20.0%
지 7
 
6.7%
소 7
 
6.7%
방 6
 
5.7%
비 5
 
4.8%
민 4
 
3.8%
주 4
 
3.8%
재 3
 
2.9%
등 3
 
2.9%
) 3
 
2.9%
Other values (24) 42
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
94.3%
Close Punctuation 3
 
2.9%
Open Punctuation 3
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 21
21.2%
지 7
 
7.1%
소 7
 
7.1%
방 6
 
6.1%
비 5
 
5.1%
민 4
 
4.0%
주 4
 
4.0%
재 3
 
3.0%
등 3
 
3.0%
분 3
 
3.0%
Other values (22) 36
36.4%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
94.3%
Common 6
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 21
21.2%
지 7
 
7.1%
소 7
 
7.1%
방 6
 
6.1%
비 5
 
5.1%
민 4
 
4.0%
주 4
 
4.0%
재 3
 
3.0%
등 3
 
3.0%
분 3
 
3.0%
Other values (22) 36
36.4%
Common
ValueCountFrequency (%)
) 3
50.0%
( 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
94.3%
ASCII 6
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 21
21.2%
지 7
 
7.1%
소 7
 
7.1%
방 6
 
6.1%
비 5
 
5.1%
민 4
 
4.0%
주 4
 
4.0%
재 3
 
3.0%
등 3
 
3.0%
분 3
 
3.0%
Other values (22) 36
36.4%
ASCII
ValueCountFrequency (%)
) 3
50.0%
( 3
50.0%

2020
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246220.24
Minimum1907
Maximum1848007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-18T14:44:48.335118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1907
5-th percentile3422
Q110201
median20866
Q3396670
95-th percentile723677
Maximum1848007
Range1846100
Interquartile range (IQR)386469

Descriptive statistics

Standard deviation433417.61
Coefficient of variation (CV)1.7602843
Kurtosis9.3122142
Mean246220.24
Median Absolute Deviation (MAD)18959
Skewness2.8167163
Sum5170625
Variance1.8785083 × 1011
MonotonicityNot monotonic
2024-03-18T14:44:48.435166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1848007 1
 
4.8%
4777 1
 
4.8%
396670 1
 
4.8%
112341 1
 
4.8%
5934 1
 
4.8%
13815 1
 
4.8%
7445 1
 
4.8%
13744 1
 
4.8%
20866 1
 
4.8%
1907 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1907 1
4.8%
3422 1
4.8%
4777 1
4.8%
5934 1
4.8%
7445 1
4.8%
10201 1
4.8%
13744 1
4.8%
13815 1
4.8%
18610 1
4.8%
19751 1
4.8%
ValueCountFrequency (%)
1848007 1
4.8%
723677 1
4.8%
624391 1
4.8%
495589 1
4.8%
479532 1
4.8%
396670 1
4.8%
195171 1
4.8%
112341 1
4.8%
106757 1
4.8%
68018 1
4.8%

Interactions

2024-03-18T14:44:47.630583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:44:48.510189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지방세별2020
지방세별1.0000.113
20200.1131.000

Missing values

2024-03-18T14:44:47.762826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:44:47.816851image/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

지방세별2020
0취득세1848007
1등록면허세4777
2레저세3422
3주민세(균등분)19751
4자동차세479532
5지방소득세624391
6지방소비세495589
7담배소비세195171
8등록면허세106757
9재산세723677
지방세별2020
11주민세(종업원분)68018
12지방소비세18610
13주민세1907
14재산세20866
15자동차세13744
16담배소비세7445
17지방소득세13815
18지방소비세5934
19지역자원시설세112341
20지방교육세396670