Overview

Dataset statistics

Number of variables3
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory938.0 B
Average record size in memory30.3 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description한국자산관리공사 행복기금 채무불이행 등록 수(입금년도, 입금일자, 입금건수) 데이터
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15069825/fileData.do

Alerts

입금년도 has constant value ""Constant
입금일자 has unique valuesUnique
입금건수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:01:08.882475
Analysis finished2023-12-12 20:01:09.262326
Duration0.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

입금년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2012
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012
2nd row2012
3rd row2012
4th row2012
5th row2012

Common Values

ValueCountFrequency (%)
2012 31
100.0%

Length

2023-12-13T05:01:09.336302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:01:09.462092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012 31
100.0%

입금일자
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-13T05:01:09.678978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters155
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

Unique31 ?
Unique (%)100.0%

Sample

1st row03-01
2nd row03-02
3rd row03-03
4th row03-04
5th row03-05
ValueCountFrequency (%)
03-01 1
 
3.2%
03-17 1
 
3.2%
03-30 1
 
3.2%
03-29 1
 
3.2%
03-28 1
 
3.2%
03-27 1
 
3.2%
03-26 1
 
3.2%
03-25 1
 
3.2%
03-24 1
 
3.2%
03-23 1
 
3.2%
Other values (21) 21
67.7%
2023-12-13T05:01:10.057715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 43
27.7%
3 36
23.2%
- 31
20.0%
1 14
 
9.0%
2 13
 
8.4%
4 3
 
1.9%
5 3
 
1.9%
6 3
 
1.9%
7 3
 
1.9%
8 3
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124
80.0%
Dash Punctuation 31
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43
34.7%
3 36
29.0%
1 14
 
11.3%
2 13
 
10.5%
4 3
 
2.4%
5 3
 
2.4%
6 3
 
2.4%
7 3
 
2.4%
8 3
 
2.4%
9 3
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 155
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43
27.7%
3 36
23.2%
- 31
20.0%
1 14
 
9.0%
2 13
 
8.4%
4 3
 
1.9%
5 3
 
1.9%
6 3
 
1.9%
7 3
 
1.9%
8 3
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43
27.7%
3 36
23.2%
- 31
20.0%
1 14
 
9.0%
2 13
 
8.4%
4 3
 
1.9%
5 3
 
1.9%
6 3
 
1.9%
7 3
 
1.9%
8 3
 
1.9%

입금건수
Real number (ℝ)

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2786.4194
Minimum74
Maximum11250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T05:01:10.220728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile107
Q1379.5
median2089
Q33552.5
95-th percentile7573.5
Maximum11250
Range11176
Interquartile range (IQR)3173

Descriptive statistics

Standard deviation2770.5395
Coefficient of variation (CV)0.99430097
Kurtosis1.6743918
Mean2786.4194
Median Absolute Deviation (MAD)1682
Skewness1.351435
Sum86379
Variance7675889
MonotonicityNot monotonic
2023-12-13T05:01:10.342572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
182 1
 
3.2%
2089 1
 
3.2%
1193 1
 
3.2%
6121 1
 
3.2%
3333 1
 
3.2%
6258 1
 
3.2%
11250 1
 
3.2%
8348 1
 
3.2%
352 1
 
3.2%
407 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
74 1
3.2%
102 1
3.2%
112 1
3.2%
153 1
3.2%
182 1
3.2%
187 1
3.2%
255 1
3.2%
352 1
3.2%
407 1
3.2%
1193 1
3.2%
ValueCountFrequency (%)
11250 1
3.2%
8348 1
3.2%
6799 1
3.2%
6486 1
3.2%
6258 1
3.2%
6121 1
3.2%
4253 1
3.2%
3644 1
3.2%
3461 1
3.2%
3333 1
3.2%

Interactions

2023-12-13T05:01:08.960180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:01:10.470574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
입금일자입금건수
입금일자1.0001.000
입금건수1.0001.000

Missing values

2023-12-13T05:01:09.102882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:01:09.222030image/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

입금년도입금일자입금건수
0201203-01182
1201203-022089
2201203-03102
3201203-0474
4201203-052554
5201203-061345
6201203-074253
7201203-081675
8201203-091272
9201203-10255
입금년도입금일자입금건수
21201203-226799
22201203-233644
23201203-24407
24201203-25352
25201203-268348
26201203-2711250
27201203-286258
28201203-293333
29201203-306121
30201203-311193