Overview

Dataset statistics

Number of variables5
Number of observations227
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 KiB
Average record size in memory41.6 B

Variable types

Numeric1
DateTime1
Categorical2
Text1

Dataset

Description경상남도 밀양시 지방세ARS시스템에서 수집한 과오납 및 환급현황입니다. 연번 등록일자 은행명 과오납금액 과오납환급여부
Author경상남도 밀양시
URLhttps://www.data.go.kr/data/15042696/fileData.do

Alerts

과오납환급여부 is highly imbalanced (95.9%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:08:13.267417
Analysis finished2023-12-12 22:08:13.654547
Duration0.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114
Minimum1
Maximum227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-13T07:08:13.755989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.3
Q157.5
median114
Q3170.5
95-th percentile215.7
Maximum227
Range226
Interquartile range (IQR)113

Descriptive statistics

Standard deviation65.673435
Coefficient of variation (CV)0.57608276
Kurtosis-1.2
Mean114
Median Absolute Deviation (MAD)57
Skewness0
Sum25878
Variance4313
MonotonicityStrictly increasing
2023-12-13T07:08:13.907867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
144 1
 
0.4%
146 1
 
0.4%
147 1
 
0.4%
148 1
 
0.4%
149 1
 
0.4%
150 1
 
0.4%
151 1
 
0.4%
152 1
 
0.4%
153 1
 
0.4%
Other values (217) 217
95.6%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
227 1
0.4%
226 1
0.4%
225 1
0.4%
224 1
0.4%
223 1
0.4%
222 1
0.4%
221 1
0.4%
220 1
0.4%
219 1
0.4%
218 1
0.4%

등록일자
Date

Distinct129
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Minimum2017-01-02 00:00:00
Maximum2019-09-23 00:00:00
2023-12-13T07:08:14.064082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:08:14.486254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

은행명
Categorical

Distinct13
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
단위농협
78 
농협중앙회
65 
국민은행
23 
우리은행
15 
경남은행
11 
Other values (8)
35 

Length

Max length8
Median length4
Mean length4.2951542
Min length3

Unique

Unique3 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
단위농협 78
34.4%
농협중앙회 65
28.6%
국민은행 23
 
10.1%
우리은행 15
 
6.6%
경남은행 11
 
4.8%
기업은행 11
 
4.8%
부산은행 9
 
4.0%
우체국 6
 
2.6%
신한은행 4
 
1.8%
하나(서울)은행 2
 
0.9%
Other values (3) 3
 
1.3%

Length

2023-12-13T07:08:14.604795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단위농협 78
34.4%
농협중앙회 65
28.6%
국민은행 23
 
10.1%
우리은행 15
 
6.6%
경남은행 11
 
4.8%
기업은행 11
 
4.8%
부산은행 9
 
4.0%
우체국 6
 
2.6%
신한은행 4
 
1.8%
하나(서울)은행 2
 
0.9%
Other values (3) 3
 
1.3%

과오납금액
Text

Distinct218
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T07:08:14.966965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.7444934
Min length2

Characters and Unicode

Total characters1077
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)92.5%

Sample

1st row3660
2nd row2280
3rd row6140
4th row31910
5th row9950
ValueCountFrequency (%)
3
 
1.3%
2000 2
 
0.9%
230 2
 
0.9%
48100 2
 
0.9%
14650 2
 
0.9%
27880 2
 
0.9%
3560 2
 
0.9%
9020 2
 
0.9%
13890 1
 
0.4%
2640 1
 
0.4%
Other values (208) 208
91.6%
2023-12-13T07:08:15.482377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 290
26.9%
1 152
14.1%
2 128
11.9%
5 83
 
7.7%
4 81
 
7.5%
9 73
 
6.8%
3 70
 
6.5%
6 70
 
6.5%
8 64
 
5.9%
7 60
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1071
99.4%
Other Punctuation 6
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290
27.1%
1 152
14.2%
2 128
12.0%
5 83
 
7.7%
4 81
 
7.6%
9 73
 
6.8%
3 70
 
6.5%
6 70
 
6.5%
8 64
 
6.0%
7 60
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1077
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290
26.9%
1 152
14.1%
2 128
11.9%
5 83
 
7.7%
4 81
 
7.5%
9 73
 
6.8%
3 70
 
6.5%
6 70
 
6.5%
8 64
 
5.9%
7 60
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1077
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290
26.9%
1 152
14.1%
2 128
11.9%
5 83
 
7.7%
4 81
 
7.5%
9 73
 
6.8%
3 70
 
6.5%
6 70
 
6.5%
8 64
 
5.9%
7 60
 
5.6%

과오납환급여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
환급
226 
삭제
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
환급 226
99.6%
삭제 1
 
0.4%

Length

2023-12-13T07:08:15.608190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:08:15.694795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
환급 226
99.6%
삭제 1
 
0.4%

Interactions

2023-12-13T07:08:13.413053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:08:15.765345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번은행명과오납환급여부
연번1.0000.2740.000
은행명0.2741.0000.484
과오납환급여부0.0000.4841.000
2023-12-13T07:08:15.876851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과오납환급여부은행명
과오납환급여부1.0000.441
은행명0.4411.000
2023-12-13T07:08:15.984223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번은행명과오납환급여부
연번1.0000.1190.000
은행명0.1191.0000.441
과오납환급여부0.0000.4411.000

Missing values

2023-12-13T07:08:13.523135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:08:13.611045image/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

연번등록일자은행명과오납금액과오납환급여부
012017-01-02농협중앙회3660환급
122017-01-02농협중앙회2280환급
232017-01-04농협중앙회6140환급
342017-01-10농협중앙회31910환급
452017-01-12농협중앙회9950환급
562017-01-12하나(서울)은행2530환급
672017-01-17농협중앙회4570환급
782017-01-17국민은행17060환급
892017-02-01농협중앙회4640환급
9102017-02-07농협중앙회24740환급
연번등록일자은행명과오납금액과오납환급여부
2172182019-06-07농협중앙회82400환급
2182192019-06-07농협중앙회12100환급
2192202019-06-07국민은행10120환급
2202212019-06-09우체국11280환급
2212222019-06-10농협중앙회14650환급
2222232019-06-10우리은행13530환급
2232242019-06-11농협중앙회14650환급
2242252019-06-11농협중앙회1610환급
2252262019-08-20기업은행21640환급
2262272019-09-23농협중앙회11890환급