Overview

Dataset statistics

Number of variables3
Number of observations86
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory27.5 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description한국자산관리공사 행복기금 은행 연합회 공공정보 변동 건수(변동년도, 변동일자, 건수) 데이터
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15069836/fileData.do

Alerts

변동년도 has constant value ""Constant
변동일자 has unique valuesUnique
건수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:33:08.225292
Analysis finished2023-12-12 16:33:08.470134
Duration0.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

변동년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size820.0 B
2013
86 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 86
100.0%

Length

2023-12-13T01:33:08.520755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:33:08.602750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 86
100.0%

변동일자
Text

UNIQUE 

Distinct86
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-13T01:33:08.825484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters430
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

Unique86 ?
Unique (%)100.0%

Sample

1st row05-09
2nd row05-10
3rd row05-11
4th row05-13
5th row05-14
ValueCountFrequency (%)
05-09 1
 
1.2%
07-04 1
 
1.2%
07-12 1
 
1.2%
07-11 1
 
1.2%
07-10 1
 
1.2%
07-09 1
 
1.2%
07-08 1
 
1.2%
07-07 1
 
1.2%
07-06 1
 
1.2%
07-18 1
 
1.2%
Other values (76) 76
88.4%
2023-12-13T01:33:09.196117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 119
27.7%
- 86
20.0%
7 39
 
9.1%
2 38
 
8.8%
1 38
 
8.8%
6 38
 
8.8%
5 29
 
6.7%
3 14
 
3.3%
8 12
 
2.8%
4 9
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 344
80.0%
Dash Punctuation 86
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 119
34.6%
7 39
 
11.3%
2 38
 
11.0%
1 38
 
11.0%
6 38
 
11.0%
5 29
 
8.4%
3 14
 
4.1%
8 12
 
3.5%
4 9
 
2.6%
9 8
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 119
27.7%
- 86
20.0%
7 39
 
9.1%
2 38
 
8.8%
1 38
 
8.8%
6 38
 
8.8%
5 29
 
6.7%
3 14
 
3.3%
8 12
 
2.8%
4 9
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 119
27.7%
- 86
20.0%
7 39
 
9.1%
2 38
 
8.8%
1 38
 
8.8%
6 38
 
8.8%
5 29
 
6.7%
3 14
 
3.3%
8 12
 
2.8%
4 9
 
2.1%

건수
Real number (ℝ)

UNIQUE 

Distinct86
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10989.593
Minimum1
Maximum213041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size906.0 B
2023-12-13T01:33:09.348861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.25
Q1365.25
median720
Q34076.25
95-th percentile46560
Maximum213041
Range213040
Interquartile range (IQR)3711

Descriptive statistics

Standard deviation34575.573
Coefficient of variation (CV)3.1462105
Kurtosis22.267315
Mean10989.593
Median Absolute Deviation (MAD)599
Skewness4.6565682
Sum945105
Variance1.1954703 × 109
MonotonicityNot monotonic
2023-12-13T01:33:09.527629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 1
 
1.2%
7928 1
 
1.2%
458 1
 
1.2%
29355 1
 
1.2%
4500 1
 
1.2%
27783 1
 
1.2%
772 1
 
1.2%
27509 1
 
1.2%
5215 1
 
1.2%
414 1
 
1.2%
Other values (76) 76
88.4%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
4 1
1.2%
7 1
1.2%
11 1
1.2%
16 1
1.2%
20 1
1.2%
25 1
1.2%
69 1
1.2%
110 1
1.2%
ValueCountFrequency (%)
213041 1
1.2%
175555 1
1.2%
157828 1
1.2%
55146 1
1.2%
49189 1
1.2%
38673 1
1.2%
29355 1
1.2%
27783 1
1.2%
27509 1
1.2%
20587 1
1.2%

Interactions

2023-12-13T01:33:08.292189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:33:09.620676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
변동일자건수
변동일자1.0001.000
건수1.0001.000

Missing values

2023-12-13T01:33:08.378982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:33:08.441240image/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

변동년도변동일자건수
0201305-0925
1201305-1069
2201305-1116
3201305-1320
4201305-14260
5201305-15240
6201305-16313
7201305-17132
8201305-20215
9201305-21411
변동년도변동일자건수
76201307-27500
77201307-28721
78201307-29250
79201307-307850
80201307-3110902
81201308-0111888
82201308-02460
83201308-03350
84201308-04325
85201308-057