Overview

Dataset statistics

Number of variables4
Number of observations21
Missing cells56
Missing cells (%)66.7%
Duplicate rows1
Duplicate rows (%)4.8%
Total size in memory867.0 B
Average record size in memory41.3 B

Variable types

Text1
Numeric3

Dataset

Description체납자 정보 및 압류공매에 관한 현황을 통계화 하여 공개 지방청별, 구분(부동산, 자동차, 동산·유가증권) 해당연도 12.31일 기준으로 작성함
URLhttps://www.data.go.kr/data/15070694/fileData.do

Alerts

Dataset has 1 (4.8%) duplicate rowsDuplicates
부동산 is highly overall correlated with 자동차 and 1 other fieldsHigh correlation
자동차 is highly overall correlated with 부동산 and 1 other fieldsHigh correlation
동산 유가증권 is highly overall correlated with 부동산 and 1 other fieldsHigh correlation
지방청별 has 14 (66.7%) missing valuesMissing
부동산 has 14 (66.7%) missing valuesMissing
자동차 has 14 (66.7%) missing valuesMissing
동산 유가증권 has 14 (66.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 15:05:49.885040
Analysis finished2023-12-12 15:05:51.185178
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지방청별
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing14
Missing (%)66.7%
Memory size300.0 B
2023-12-13T00:05:51.295603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters13
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

Unique7 ?
Unique (%)100.0%

Sample

1st row서울청
2nd row중부청
3rd row인천청
4th row대전청
5th row광주청
ValueCountFrequency (%)
서울청 1
14.3%
중부청 1
14.3%
인천청 1
14.3%
대전청 1
14.3%
광주청 1
14.3%
대구청 1
14.3%
부산청 1
14.3%
2023-12-13T00:05:51.646767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
청 7
33.3%
부 2
 
9.5%
대 2
 
9.5%
서 1
 
4.8%
울 1
 
4.8%
중 1
 
4.8%
인 1
 
4.8%
천 1
 
4.8%
전 1
 
4.8%
광 1
 
4.8%
Other values (3) 3
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 7
33.3%
부 2
 
9.5%
대 2
 
9.5%
서 1
 
4.8%
울 1
 
4.8%
중 1
 
4.8%
인 1
 
4.8%
천 1
 
4.8%
전 1
 
4.8%
광 1
 
4.8%
Other values (3) 3
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 7
33.3%
부 2
 
9.5%
대 2
 
9.5%
서 1
 
4.8%
울 1
 
4.8%
중 1
 
4.8%
인 1
 
4.8%
천 1
 
4.8%
전 1
 
4.8%
광 1
 
4.8%
Other values (3) 3
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
청 7
33.3%
부 2
 
9.5%
대 2
 
9.5%
서 1
 
4.8%
울 1
 
4.8%
중 1
 
4.8%
인 1
 
4.8%
천 1
 
4.8%
전 1
 
4.8%
광 1
 
4.8%
Other values (3) 3
14.3%

부동산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing14
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean16658.571
Minimum8371
Maximum29740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T00:05:51.765784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8371
5-th percentile8554.6
Q111045.5
median17975
Q319216.5
95-th percentile26826.1
Maximum29740
Range21369
Interquartile range (IQR)8171

Descriptive statistics

Standard deviation7387.6177
Coefficient of variation (CV)0.44347246
Kurtosis0.51773912
Mean16658.571
Median Absolute Deviation (MAD)4867
Skewness0.70498603
Sum116610
Variance54576895
MonotonicityNot monotonic
2023-12-13T00:05:51.887066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
18406 1
 
4.8%
29740 1
 
4.8%
20027 1
 
4.8%
13108 1
 
4.8%
8983 1
 
4.8%
8371 1
 
4.8%
17975 1
 
4.8%
(Missing) 14
66.7%
ValueCountFrequency (%)
8371 1
4.8%
8983 1
4.8%
13108 1
4.8%
17975 1
4.8%
18406 1
4.8%
20027 1
4.8%
29740 1
4.8%
ValueCountFrequency (%)
29740 1
4.8%
20027 1
4.8%
18406 1
4.8%
17975 1
4.8%
13108 1
4.8%
8983 1
4.8%
8371 1
4.8%

자동차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing14
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean930.28571
Minimum632
Maximum1296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T00:05:52.006891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum632
5-th percentile654.2
Q1783
median965
Q31026.5
95-th percentile1225.2
Maximum1296
Range664
Interquartile range (IQR)243.5

Descriptive statistics

Standard deviation223.5805
Coefficient of variation (CV)0.2403353
Kurtosis-0.047687272
Mean930.28571
Median Absolute Deviation (MAD)105
Skewness0.27588811
Sum6512
Variance49988.238
MonotonicityNot monotonic
2023-12-13T00:05:52.136331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
993 1
 
4.8%
1296 1
 
4.8%
860 1
 
4.8%
965 1
 
4.8%
706 1
 
4.8%
632 1
 
4.8%
1060 1
 
4.8%
(Missing) 14
66.7%
ValueCountFrequency (%)
632 1
4.8%
706 1
4.8%
860 1
4.8%
965 1
4.8%
993 1
4.8%
1060 1
4.8%
1296 1
4.8%
ValueCountFrequency (%)
1296 1
4.8%
1060 1
4.8%
993 1
4.8%
965 1
4.8%
860 1
4.8%
706 1
4.8%
632 1
4.8%

동산 유가증권
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing14
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean1835.5714
Minimum852
Maximum4006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T00:05:52.246752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum852
5-th percentile893.1
Q11186
median1390
Q32114.5
95-th percentile3599.2
Maximum4006
Range3154
Interquartile range (IQR)928.5

Descriptive statistics

Standard deviation1120.0695
Coefficient of variation (CV)0.61020206
Kurtosis1.7653874
Mean1835.5714
Median Absolute Deviation (MAD)401
Skewness1.495836
Sum12849
Variance1254555.6
MonotonicityNot monotonic
2023-12-13T00:05:52.388091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4006 1
 
4.8%
2650 1
 
4.8%
1383 1
 
4.8%
1390 1
 
4.8%
989 1
 
4.8%
852 1
 
4.8%
1579 1
 
4.8%
(Missing) 14
66.7%
ValueCountFrequency (%)
852 1
4.8%
989 1
4.8%
1383 1
4.8%
1390 1
4.8%
1579 1
4.8%
2650 1
4.8%
4006 1
4.8%
ValueCountFrequency (%)
4006 1
4.8%
2650 1
4.8%
1579 1
4.8%
1390 1
4.8%
1383 1
4.8%
989 1
4.8%
852 1
4.8%

Interactions

2023-12-13T00:05:50.559476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:49.999680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.287088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.656457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.094015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.371760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.751950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.204444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:05:50.458743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:05:52.490872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지방청별부동산자동차동산 유가증권
지방청별1.0001.0001.0001.000
부동산1.0001.0000.9420.859
자동차1.0000.9421.0000.942
동산 유가증권1.0000.8590.9421.000
2023-12-13T00:05:52.597294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부동산자동차동산 유가증권
부동산1.0000.7500.714
자동차0.7501.0000.893
동산 유가증권0.7140.8931.000

Missing values

2023-12-13T00:05:50.895688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:05:51.004494image/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.
2023-12-13T00:05:51.110618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

지방청별부동산자동차동산 유가증권
0서울청184069934006
1중부청2974012962650
2인천청200278601383
3대전청131089651390
4광주청8983706989
5대구청8371632852
6부산청1797510601579
7<NA><NA><NA><NA>
8<NA><NA><NA><NA>
9<NA><NA><NA><NA>
지방청별부동산자동차동산 유가증권
11<NA><NA><NA><NA>
12<NA><NA><NA><NA>
13<NA><NA><NA><NA>
14<NA><NA><NA><NA>
15<NA><NA><NA><NA>
16<NA><NA><NA><NA>
17<NA><NA><NA><NA>
18<NA><NA><NA><NA>
19<NA><NA><NA><NA>
20<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

지방청별부동산자동차동산 유가증권# duplicates
0<NA><NA><NA><NA>14