Overview

Dataset statistics

Number of variables5
Number of observations69
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory45.9 B

Variable types

Text1
Numeric3
Categorical1

Dataset

Description부산광역시북구_미분양현황_20230831
Author부산광역시 북구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15026677

Alerts

전용 85제곱미터 초과 has constant value ""Constant
합계 is highly overall correlated with 전용 60제곱미터 이하 and 1 other fieldsHigh correlation
전용 60제곱미터 이하 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
전용 60-85제곱미터 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
구분(월별) has unique valuesUnique
전용 60-85제곱미터 has 1 (1.4%) zerosZeros

Reproduction

Analysis started2023-12-10 16:36:41.535637
Analysis finished2023-12-10 16:36:42.423471
Duration0.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분(월별)
Text

UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-12-11T01:36:42.574602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters1104
Distinct characters20
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)100.0%

Sample

1st row민간분양 주택(2023-08)
2nd row민간분양 주택(2023-07)
3rd row민간분양 주택(2023-06)
4th row민간분양 주택(2023-05)
5th row민간분양 주택(2023-04)
ValueCountFrequency (%)
민간분양 69
50.0%
주택(2020-01 1
 
0.7%
주택(2019-07 1
 
0.7%
주택(2019-08 1
 
0.7%
주택(2019-09 1
 
0.7%
주택(2019-10 1
 
0.7%
주택(2019-11 1
 
0.7%
주택(2020-08 1
 
0.7%
주택(2020-02 1
 
0.7%
주택(2019-05 1
 
0.7%
Other values (60) 60
43.5%
2023-12-11T01:36:42.923147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 139
12.6%
2 137
12.4%
민 69
 
6.2%
분 69
 
6.2%
양 69
 
6.2%
69
 
6.2%
주 69
 
6.2%
택 69
 
6.2%
( 69
 
6.2%
간 69
 
6.2%
Other values (10) 276
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 414
37.5%
Other Letter 414
37.5%
Space Separator 69
 
6.2%
Open Punctuation 69
 
6.2%
Dash Punctuation 69
 
6.2%
Close Punctuation 69
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 139
33.6%
2 137
33.1%
1 64
15.5%
8 18
 
4.3%
9 17
 
4.1%
3 14
 
3.4%
7 7
 
1.7%
6 6
 
1.4%
5 6
 
1.4%
4 6
 
1.4%
Other Letter
ValueCountFrequency (%)
민 69
16.7%
분 69
16.7%
양 69
16.7%
주 69
16.7%
택 69
16.7%
간 69
16.7%
Space Separator
ValueCountFrequency (%)
69
100.0%
Open Punctuation
ValueCountFrequency (%)
( 69
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%
Close Punctuation
ValueCountFrequency (%)
) 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 690
62.5%
Hangul 414
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 139
20.1%
2 137
19.9%
69
10.0%
( 69
10.0%
- 69
10.0%
) 69
10.0%
1 64
9.3%
8 18
 
2.6%
9 17
 
2.5%
3 14
 
2.0%
Other values (4) 25
 
3.6%
Hangul
ValueCountFrequency (%)
민 69
16.7%
분 69
16.7%
양 69
16.7%
주 69
16.7%
택 69
16.7%
간 69
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 690
62.5%
Hangul 414
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 139
20.1%
2 137
19.9%
69
10.0%
( 69
10.0%
- 69
10.0%
) 69
10.0%
1 64
9.3%
8 18
 
2.6%
9 17
 
2.5%
3 14
 
2.0%
Other values (4) 25
 
3.6%
Hangul
ValueCountFrequency (%)
민 69
16.7%
분 69
16.7%
양 69
16.7%
주 69
16.7%
택 69
16.7%
간 69
16.7%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.26087
Minimum10
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-11T01:36:43.037564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q116
median52
Q3197
95-th percentile288.2
Maximum440
Range430
Interquartile range (IQR)181

Descriptive statistics

Standard deviation110.42573
Coefficient of variation (CV)1.0106613
Kurtosis0.28477581
Mean109.26087
Median Absolute Deviation (MAD)42
Skewness0.98824931
Sum7539
Variance12193.843
MonotonicityNot monotonic
2023-12-11T01:36:43.134675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 11
 
15.9%
16 7
 
10.1%
197 5
 
7.2%
209 3
 
4.3%
126 2
 
2.9%
31 2
 
2.9%
12 2
 
2.9%
13 2
 
2.9%
173 2
 
2.9%
203 2
 
2.9%
Other values (30) 31
44.9%
ValueCountFrequency (%)
10 11
15.9%
12 2
 
2.9%
13 2
 
2.9%
16 7
10.1%
18 1
 
1.4%
19 1
 
1.4%
21 1
 
1.4%
24 1
 
1.4%
28 1
 
1.4%
31 2
 
2.9%
ValueCountFrequency (%)
440 1
1.4%
426 1
1.4%
302 1
1.4%
289 1
1.4%
287 1
1.4%
285 1
1.4%
278 1
1.4%
262 1
1.4%
249 1
1.4%
231 1
1.4%

전용 60제곱미터 이하
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.289855
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-11T01:36:43.227064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median17
Q3153
95-th percentile173.4
Maximum255
Range254
Interquartile range (IQR)150

Descriptive statistics

Standard deviation74.889154
Coefficient of variation (CV)1.221885
Kurtosis-0.55784688
Mean61.289855
Median Absolute Deviation (MAD)14
Skewness0.93803184
Sum4229
Variance5608.3853
MonotonicityNot monotonic
2023-12-11T01:36:43.323828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3 17
24.6%
4 8
11.6%
158 6
 
8.7%
1 5
 
7.2%
16 3
 
4.3%
23 3
 
4.3%
164 2
 
2.9%
153 2
 
2.9%
171 2
 
2.9%
31 2
 
2.9%
Other values (19) 19
27.5%
ValueCountFrequency (%)
1 5
 
7.2%
3 17
24.6%
4 8
11.6%
15 1
 
1.4%
16 3
 
4.3%
17 1
 
1.4%
21 1
 
1.4%
23 3
 
4.3%
31 2
 
2.9%
32 1
 
1.4%
ValueCountFrequency (%)
255 1
 
1.4%
252 1
 
1.4%
193 1
 
1.4%
175 1
 
1.4%
171 2
 
2.9%
170 1
 
1.4%
165 1
 
1.4%
164 2
 
2.9%
158 6
8.7%
153 2
 
2.9%

전용 60-85제곱미터
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.956522
Minimum0
Maximum185
Zeros1
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-11T01:36:43.425445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q112
median33
Q340
95-th percentile174
Maximum185
Range185
Interquartile range (IQR)28

Descriptive statistics

Standard deviation53.616952
Coefficient of variation (CV)1.1180325
Kurtosis1.3652682
Mean47.956522
Median Absolute Deviation (MAD)20
Skewness1.6090052
Sum3309
Variance2874.7775
MonotonicityNot monotonic
2023-12-11T01:36:43.526295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
39 8
 
11.6%
9 7
 
10.1%
7 6
 
8.7%
13 6
 
8.7%
33 3
 
4.3%
20 3
 
4.3%
38 2
 
2.9%
185 2
 
2.9%
174 2
 
2.9%
173 2
 
2.9%
Other values (24) 28
40.6%
ValueCountFrequency (%)
0 1
 
1.4%
7 6
8.7%
9 7
10.1%
10 2
 
2.9%
11 1
 
1.4%
12 1
 
1.4%
13 6
8.7%
14 1
 
1.4%
15 1
 
1.4%
16 1
 
1.4%
ValueCountFrequency (%)
185 2
2.9%
176 1
1.4%
174 2
2.9%
173 2
2.9%
171 1
1.4%
120 1
1.4%
101 1
1.4%
99 1
1.4%
97 1
1.4%
95 2
2.9%

전용 85제곱미터 초과
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
0
69 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69
100.0%

Length

2023-12-11T01:36:43.636769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:36:43.710304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 69
100.0%

Interactions

2023-12-11T01:36:42.090075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:36:41.666623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:36:41.896059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:36:42.153302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:36:41.739589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:36:41.954640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:36:42.218906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:36:41.820464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:36:42.017433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:36:43.764971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분(월별)합계전용 60제곱미터 이하전용 60-85제곱미터
구분(월별)1.0001.0001.0001.000
합계1.0001.0000.9840.906
전용 60제곱미터 이하1.0000.9841.0000.869
전용 60-85제곱미터1.0000.9060.8691.000
2023-12-11T01:36:43.859580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계전용 60제곱미터 이하전용 60-85제곱미터
합계1.0000.9450.896
전용 60제곱미터 이하0.9451.0000.773
전용 60-85제곱미터0.8960.7731.000

Missing values

2023-12-11T01:36:42.313225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:36:42.391524image/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

구분(월별)합계전용 60제곱미터 이하전용 60-85제곱미터전용 85제곱미터 초과
0민간분양 주택(2023-08)12631950
1민간분양 주택(2023-07)12631950
2민간분양 주택(2023-06)12932970
3민간분양 주택(2023-05)13435990
4민간분양 주택(2023-04)137361010
5민간분양 주택(2023-03)3115160
6민간분양 주택(2023-02)2817110
7민간분양 주택(2023-01)10370
8민간분양 주택(2022-12)10370
9민간분양 주택(2022-11)10370
구분(월별)합계전용 60제곱미터 이하전용 60-85제곱미터전용 85제곱미터 초과
59민간분양 주택(2018-09)209171380
60민간분양 주택(2018-08)198158400
61민간분양 주택(2018-07)197158390
62민간분양 주택(2018-06)197158390
63민간분양 주택(2018-05)197158390
64민간분양 주택(2018-04)197158390
65민간분양 주택(2018-03)197158390
66민간분양 주택(2018-02)231175560
67민간분양 주택(2018-01)262193690
68민간분양 주택(2017-12)16516500