Overview

Dataset statistics

Number of variables6
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory59.3 B

Variable types

Text1
Numeric5

Dataset

Description대구광역시 동구의 동별 등급별 빈집현황 데이터입니다. 등급은 빈집상태에 따라 1, 2, 3, 4등급으로 나누어집니다. 1등급이 가장 상태가 좋으며, 등급 숫자가 올라갈 수록 상태가 안 좋은 집입니다.
URLhttps://www.data.go.kr/data/15114165/fileData.do

Alerts

합계 is highly overall correlated with 2등급 and 2 other fieldsHigh correlation
2등급 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
3등급 is highly overall correlated with 합계 and 2 other fieldsHigh correlation
4등급 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
구분 has unique valuesUnique
1등급 has 4 (19.0%) zerosZeros
2등급 has 2 (9.5%) zerosZeros
3등급 has 2 (9.5%) zerosZeros
4등급 has 3 (14.3%) zerosZeros

Reproduction

Analysis started2023-12-12 01:24:36.434638
Analysis finished2023-12-12 01:24:40.250107
Duration3.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T10:24:40.425365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.9047619
Min length3

Characters and Unicode

Total characters82
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row총합계
2nd row신암1동
3rd row신암2동
4th row신암3동
5th row신암4동
ValueCountFrequency (%)
총합계 1
 
4.8%
도평동 1
 
4.8%
안심3.4동 1
 
4.8%
안심2동 1
 
4.8%
안심1동 1
 
4.8%
해안동 1
 
4.8%
방촌동 1
 
4.8%
동촌동 1
 
4.8%
지저동 1
 
4.8%
불로봉무동 1
 
4.8%
Other values (11) 11
52.4%
2023-12-12T10:24:40.882620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
25.6%
신 8
 
9.8%
암 5
 
6.1%
1 4
 
4.9%
2 4
 
4.9%
안 4
 
4.9%
심 3
 
3.7%
3 3
 
3.7%
4 3
 
3.7%
천 3
 
3.7%
Other values (20) 24
29.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65
79.3%
Decimal Number 15
 
18.3%
Other Punctuation 2
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
32.3%
신 8
 
12.3%
암 5
 
7.7%
안 4
 
6.2%
심 3
 
4.6%
천 3
 
4.6%
촌 2
 
3.1%
효 2
 
3.1%
목 2
 
3.1%
해 1
 
1.5%
Other values (14) 14
21.5%
Decimal Number
ValueCountFrequency (%)
1 4
26.7%
2 4
26.7%
3 3
20.0%
4 3
20.0%
5 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65
79.3%
Common 17
 
20.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
32.3%
신 8
 
12.3%
암 5
 
7.7%
안 4
 
6.2%
심 3
 
4.6%
천 3
 
4.6%
촌 2
 
3.1%
효 2
 
3.1%
목 2
 
3.1%
해 1
 
1.5%
Other values (14) 14
21.5%
Common
ValueCountFrequency (%)
1 4
23.5%
2 4
23.5%
3 3
17.6%
4 3
17.6%
. 2
11.8%
5 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65
79.3%
ASCII 17
 
20.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
32.3%
신 8
 
12.3%
암 5
 
7.7%
안 4
 
6.2%
심 3
 
4.6%
천 3
 
4.6%
촌 2
 
3.1%
효 2
 
3.1%
목 2
 
3.1%
해 1
 
1.5%
Other values (14) 14
21.5%
ASCII
ValueCountFrequency (%)
1 4
23.5%
2 4
23.5%
3 3
17.6%
4 3
17.6%
. 2
11.8%
5 1
 
5.9%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.47619
Minimum1
Maximum698
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:24:41.041410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q122
median33
Q357
95-th percentile66
Maximum698
Range697
Interquartile range (IQR)35

Descriptive statistics

Standard deviation146.21204
Coefficient of variation (CV)2.1994649
Kurtosis20.010948
Mean66.47619
Median Absolute Deviation (MAD)18
Skewness4.4269849
Sum1396
Variance21377.962
MonotonicityNot monotonic
2023-12-12T10:24:41.178640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
49 2
 
9.5%
1 2
 
9.5%
22 2
 
9.5%
25 2
 
9.5%
63 2
 
9.5%
698 1
 
4.8%
51 1
 
4.8%
27 1
 
4.8%
66 1
 
4.8%
24 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
1 2
9.5%
5 1
4.8%
13 1
4.8%
22 2
9.5%
24 1
4.8%
25 2
9.5%
27 1
4.8%
33 1
4.8%
41 1
4.8%
49 2
9.5%
ValueCountFrequency (%)
698 1
4.8%
66 1
4.8%
63 2
9.5%
61 1
4.8%
57 1
4.8%
51 1
4.8%
49 2
9.5%
41 1
4.8%
33 1
4.8%
27 1
4.8%

1등급
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7619048
Minimum0
Maximum71
Zeros4
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:24:41.311488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile14
Maximum71
Range71
Interquartile range (IQR)4

Descriptive statistics

Standard deviation15.102665
Coefficient of variation (CV)2.2334926
Kurtosis18.618883
Mean6.7619048
Median Absolute Deviation (MAD)2
Skewness4.2244954
Sum142
Variance228.09048
MonotonicityNot monotonic
2023-12-12T10:24:41.432885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 5
23.8%
0 4
19.0%
8 2
 
9.5%
2 2
 
9.5%
5 2
 
9.5%
1 2
 
9.5%
71 1
 
4.8%
14 1
 
4.8%
6 1
 
4.8%
4 1
 
4.8%
ValueCountFrequency (%)
0 4
19.0%
1 2
 
9.5%
2 2
 
9.5%
3 5
23.8%
4 1
 
4.8%
5 2
 
9.5%
6 1
 
4.8%
8 2
 
9.5%
14 1
 
4.8%
71 1
 
4.8%
ValueCountFrequency (%)
71 1
 
4.8%
14 1
 
4.8%
8 2
 
9.5%
6 1
 
4.8%
5 2
 
9.5%
4 1
 
4.8%
3 5
23.8%
2 2
 
9.5%
1 2
 
9.5%
0 4
19.0%

2등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.761905
Minimum0
Maximum302
Zeros2
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:24:41.562509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median12
Q329
95-th percentile45
Maximum302
Range302
Interquartile range (IQR)21

Descriptive statistics

Standard deviation63.884196
Coefficient of variation (CV)2.2211393
Kurtosis19.109782
Mean28.761905
Median Absolute Deviation (MAD)8
Skewness4.2930204
Sum604
Variance4081.1905
MonotonicityNot monotonic
2023-12-12T10:24:41.708339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
12 4
19.0%
0 2
 
9.5%
10 2
 
9.5%
15 2
 
9.5%
302 1
 
4.8%
2 1
 
4.8%
29 1
 
4.8%
45 1
 
4.8%
8 1
 
4.8%
31 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
0 2
9.5%
2 1
 
4.8%
3 1
 
4.8%
4 1
 
4.8%
8 1
 
4.8%
10 2
9.5%
11 1
 
4.8%
12 4
19.0%
15 2
9.5%
29 1
 
4.8%
ValueCountFrequency (%)
302 1
 
4.8%
45 1
 
4.8%
37 1
 
4.8%
34 1
 
4.8%
31 1
 
4.8%
29 1
 
4.8%
15 2
9.5%
12 4
19.0%
11 1
 
4.8%
10 2
9.5%

3등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.904762
Minimum0
Maximum188
Zeros2
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:24:41.828280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median7
Q316
95-th percentile39
Maximum188
Range188
Interquartile range (IQR)12

Descriptive statistics

Standard deviation40.178234
Coefficient of variation (CV)2.2439971
Kurtosis18.209645
Mean17.904762
Median Absolute Deviation (MAD)4
Skewness4.16575
Sum376
Variance1614.2905
MonotonicityNot monotonic
2023-12-12T10:24:41.966781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4 4
19.0%
0 2
 
9.5%
1 2
 
9.5%
7 2
 
9.5%
188 1
 
4.8%
39 1
 
4.8%
25 1
 
4.8%
8 1
 
4.8%
5 1
 
4.8%
3 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
0 2
9.5%
1 2
9.5%
3 1
 
4.8%
4 4
19.0%
5 1
 
4.8%
7 2
9.5%
8 1
 
4.8%
9 1
 
4.8%
11 1
 
4.8%
16 1
 
4.8%
ValueCountFrequency (%)
188 1
4.8%
39 1
4.8%
25 1
4.8%
22 1
4.8%
18 1
4.8%
16 1
4.8%
11 1
4.8%
9 1
4.8%
8 1
4.8%
7 2
9.5%

4등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.047619
Minimum0
Maximum137
Zeros3
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:24:42.091802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q312
95-th percentile22
Maximum137
Range137
Interquartile range (IQR)11

Descriptive statistics

Standard deviation29.011164
Coefficient of variation (CV)2.2234833
Kurtosis19.017679
Mean13.047619
Median Absolute Deviation (MAD)5
Skewness4.2765917
Sum274
Variance841.64762
MonotonicityNot monotonic
2023-12-12T10:24:42.223990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 3
14.3%
0 3
14.3%
4 3
14.3%
12 2
9.5%
14 2
9.5%
137 1
 
4.8%
7 1
 
4.8%
3 1
 
4.8%
8 1
 
4.8%
11 1
 
4.8%
Other values (3) 3
14.3%
ValueCountFrequency (%)
0 3
14.3%
1 3
14.3%
3 1
 
4.8%
4 3
14.3%
7 1
 
4.8%
8 1
 
4.8%
9 1
 
4.8%
10 1
 
4.8%
11 1
 
4.8%
12 2
9.5%
ValueCountFrequency (%)
137 1
 
4.8%
22 1
 
4.8%
14 2
9.5%
12 2
9.5%
11 1
 
4.8%
10 1
 
4.8%
9 1
 
4.8%
8 1
 
4.8%
7 1
 
4.8%
4 3
14.3%

Interactions

2023-12-12T10:24:39.445502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:36.646386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:37.305974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:37.943372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:38.486079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:39.559993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:36.767465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:37.426730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:38.056207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:38.608016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:39.682397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:36.950030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:37.549069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:38.167980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:38.743277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:39.786933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:37.054895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:37.688942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:38.262825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:39.198738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:39.898893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:37.184959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:37.817748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:38.376699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:24:39.324615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:24:42.330334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분합계1등급2등급3등급4등급
구분1.0001.0001.0001.0001.0001.000
합계1.0001.0001.0001.0001.0001.000
1등급1.0001.0001.0000.9300.6370.932
2등급1.0001.0000.9301.0000.6420.934
3등급1.0001.0000.6370.6421.0000.819
4등급1.0001.0000.9320.9340.8191.000
2023-12-12T10:24:42.436313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계1등급2등급3등급4등급
합계1.0000.3350.8040.8770.789
1등급0.3351.0000.3950.3100.138
2등급0.8040.3951.0000.5450.439
3등급0.8770.3100.5451.0000.817
4등급0.7890.1380.4390.8171.000

Missing values

2023-12-12T10:24:40.054749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:24:40.202143image/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

구분합계1등급2등급3등급4등급
0총합계69871302188137
1신암1동630123912
2신암2동10001
3신암3동2281040
4신암4동52210
5신암5동10001
6신천1.2동573152514
7신천3동2551271
8신천4동33141540
9효목1동4112974
구분합계1등급2등급3등급4등급
11도평동2231243
12불로봉무동2521058
13지저동130814
14동촌동49331411
15방촌동2461134
16해안동66837912
17안심1동495121814
18안심2동2743119
19안심3.4동633341610
20공산동51342222