Overview

Dataset statistics

Number of variables3
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 KiB
Average record size in memory28.7 B

Variable types

Text1
Numeric1
Categorical1

Dataset

Description용산구 법정동 기준 아파트 주택 수에 대한 데이터로 법정동, 아파트 주택수, 데이터기준일에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15108236/fileData.do

Alerts

데이터기준일 has constant value ""Constant
법정동 has unique valuesUnique
아파트 주택수 has 12 (33.3%) zerosZeros

Reproduction

Analysis started2023-12-12 21:07:11.104052
Analysis finished2023-12-12 21:07:11.685383
Duration0.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-13T06:07:11.831782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9722222
Min length3

Characters and Unicode

Total characters143
Distinct characters41
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row후암동
2nd row용산동2가
3rd row용산동4가
4th row갈월동
5th row남영동
ValueCountFrequency (%)
후암동 1
 
2.8%
용산동2가 1
 
2.8%
용산동3가 1
 
2.8%
효창동 1
 
2.8%
용문동 1
 
2.8%
도원동 1
 
2.8%
한강로1가 1
 
2.8%
한강로2가 1
 
2.8%
한강로3가 1
 
2.8%
용산동5가 1
 
2.8%
Other values (26) 26
72.2%
2023-12-13T06:07:12.211931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 31
21.7%
가 16
 
11.2%
로 7
 
4.9%
용 7
 
4.9%
산 7
 
4.9%
원 6
 
4.2%
효 5
 
3.5%
2 4
 
2.8%
청 4
 
2.8%
한 4
 
2.8%
Other values (31) 52
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127
88.8%
Decimal Number 16
 
11.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 31
24.4%
가 16
12.6%
로 7
 
5.5%
용 7
 
5.5%
산 7
 
5.5%
원 6
 
4.7%
효 5
 
3.9%
청 4
 
3.1%
한 4
 
3.1%
파 3
 
2.4%
Other values (25) 37
29.1%
Decimal Number
ValueCountFrequency (%)
2 4
25.0%
3 4
25.0%
1 4
25.0%
4 2
12.5%
6 1
 
6.2%
5 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 127
88.8%
Common 16
 
11.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 31
24.4%
가 16
12.6%
로 7
 
5.5%
용 7
 
5.5%
산 7
 
5.5%
원 6
 
4.7%
효 5
 
3.9%
청 4
 
3.1%
한 4
 
3.1%
파 3
 
2.4%
Other values (25) 37
29.1%
Common
ValueCountFrequency (%)
2 4
25.0%
3 4
25.0%
1 4
25.0%
4 2
12.5%
6 1
 
6.2%
5 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 127
88.8%
ASCII 16
 
11.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 31
24.4%
가 16
12.6%
로 7
 
5.5%
용 7
 
5.5%
산 7
 
5.5%
원 6
 
4.7%
효 5
 
3.9%
청 4
 
3.1%
한 4
 
3.1%
파 3
 
2.4%
Other values (25) 37
29.1%
ASCII
ValueCountFrequency (%)
2 4
25.0%
3 4
25.0%
1 4
25.0%
4 2
12.5%
6 1
 
6.2%
5 1
 
6.2%

아파트 주택수
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.27778
Minimum0
Maximum11614
Zeros12
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T06:07:12.378036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median266
Q31344.5
95-th percentile2248.25
Maximum11614
Range11614
Interquartile range (IQR)1344.5

Descriptive statistics

Standard deviation1987.2815
Coefficient of variation (CV)2.0651849
Kurtosis24.782562
Mean962.27778
Median Absolute Deviation (MAD)266
Skewness4.6427823
Sum34642
Variance3949287.9
MonotonicityNot monotonic
2023-12-13T06:07:12.532015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 12
33.3%
390 1
 
2.8%
195 1
 
2.8%
522 1
 
2.8%
1621 1
 
2.8%
2780 1
 
2.8%
1269 1
 
2.8%
11614 1
 
2.8%
888 1
 
2.8%
2071 1
 
2.8%
Other values (15) 15
41.7%
ValueCountFrequency (%)
0 12
33.3%
30 1
 
2.8%
83 1
 
2.8%
143 1
 
2.8%
170 1
 
2.8%
176 1
 
2.8%
195 1
 
2.8%
337 1
 
2.8%
390 1
 
2.8%
522 1
 
2.8%
ValueCountFrequency (%)
11614 1
2.8%
2780 1
2.8%
2071 1
2.8%
2027 1
2.8%
1826 1
2.8%
1754 1
2.8%
1701 1
2.8%
1621 1
2.8%
1571 1
2.8%
1269 1
2.8%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-05-26
36 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-26
2nd row2023-05-26
3rd row2023-05-26
4th row2023-05-26
5th row2023-05-26

Common Values

ValueCountFrequency (%)
2023-05-26 36
100.0%

Length

2023-12-13T06:07:12.689778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:07:12.792379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-26 36
100.0%

Interactions

2023-12-13T06:07:11.185726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:07:12.845257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동아파트 주택수
법정동\t\t1.0001.000
아파트 주택수1.0001.000

Missing values

2023-12-13T06:07:11.590212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:07:11.658494image/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

법정동아파트 주택수데이터기준일
0후암동3902023-05-26
1용산동2가1432023-05-26
2용산동4가02023-05-26
3갈월동02023-05-26
4남영동02023-05-26
5동자동3372023-05-26
6용산동1가02023-05-26
7서계동02023-05-26
8청파동1가02023-05-26
9청파동2가02023-05-26
법정동아파트 주택수데이터기준일
26용산동3가02023-05-26
27용산동5가8882023-05-26
28이촌동116142023-05-26
29이태원동12692023-05-26
30한남동27802023-05-26
31서빙고동16212023-05-26
32동빙고동02023-05-26
33주성동02023-05-26
34용산동6가02023-05-26
35보광동5222023-05-26