Overview

Dataset statistics

Number of variables3
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory964.0 B
Average record size in memory30.1 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description한국자산관리공사 공사채권 물건 세부내역 등록현황("인수년도","용도","등록건수") 데이터 제공
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15074434/fileData.do

Alerts

인수년도 has constant value ""Constant
용도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:33:49.933638
Analysis finished2023-12-12 11:33:50.735447
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

인수년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
2018
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 32
100.0%

Length

2023-12-12T20:33:50.892049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:33:51.139043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 32
100.0%

용도
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T20:33:52.109290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length2.9375
Min length1

Characters and Unicode

Total characters94
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row
2nd row
3rd row건물
4th row공장
5th row기타
ValueCountFrequency (%)
1
 
3.1%
1
 
3.1%
집합건물 1
 
3.1%
점포상가 1
 
3.1%
오피스텔 1
 
3.1%
연립주택 1
 
3.1%
목장용지 1
 
3.1%
단독주택 1
 
3.1%
농가주택 1
 
3.1%
기계기구 1
 
3.1%
Other values (22) 22
68.8%
2023-12-12T20:33:52.916952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
 
5.3%
4
 
4.3%
4
 
4.3%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.1%
Other values (53) 60
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 94
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
 
5.3%
4
 
4.3%
4
 
4.3%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.1%
Other values (53) 60
63.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 94
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5
 
5.3%
4
 
4.3%
4
 
4.3%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.1%
Other values (53) 60
63.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 94
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
5
 
5.3%
4
 
4.3%
4
 
4.3%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.1%
Other values (53) 60
63.8%

등록건수
Real number (ℝ)

Distinct15
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.03125
Minimum1
Maximum1274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T20:33:53.212891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2.5
Q312
95-th percentile442.4
Maximum1274
Range1273
Interquartile range (IQR)11

Descriptive statistics

Standard deviation258.18791
Coefficient of variation (CV)3.3087758
Kurtosis16.717883
Mean78.03125
Median Absolute Deviation (MAD)1.5
Skewness4.056855
Sum2497
Variance66660.999
MonotonicityNot monotonic
2023-12-12T20:33:53.514474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 12
37.5%
12 4
 
12.5%
2 4
 
12.5%
11 1
 
3.1%
768 1
 
3.1%
1274 1
 
3.1%
8 1
 
3.1%
28 1
 
3.1%
176 1
 
3.1%
124 1
 
3.1%
Other values (5) 5
15.6%
ValueCountFrequency (%)
1 12
37.5%
2 4
 
12.5%
3 1
 
3.1%
4 1
 
3.1%
6 1
 
3.1%
7 1
 
3.1%
8 1
 
3.1%
11 1
 
3.1%
12 4
 
12.5%
20 1
 
3.1%
ValueCountFrequency (%)
1274 1
 
3.1%
768 1
 
3.1%
176 1
 
3.1%
124 1
 
3.1%
28 1
 
3.1%
20 1
 
3.1%
12 4
12.5%
11 1
 
3.1%
8 1
 
3.1%
7 1
 
3.1%

Interactions

2023-12-12T20:33:50.126238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:33:53.721136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도등록건수
용도1.0001.000
등록건수1.0001.000

Missing values

2023-12-12T20:33:50.439756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:33:50.650485image/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

인수년도용도등록건수
0201812
1201811
22018건물768
32018공장2
42018기타1
52018대지1274
62018도로8
72018묘지1
82018빌라1
92018수목1
인수년도용도등록건수
222018근저당권1
232018기계기구12
242018농가주택1
252018단독주택2
262018목장용지7
272018연립주택2
282018오피스텔3
292018점포상가6
302018집합건물1
312018근린생활시설4