Overview

Dataset statistics

Number of variables5
Number of observations76
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory45.7 B

Variable types

Text1
Numeric3
Categorical1

Dataset

Description부산광역시 북구 관내에 있는 공동주택의 미분양현황 데이터로서 월별, 전용면적별 미분양세대 수 항목들을 제공하고 있습니다.
Author부산광역시 북구
URLhttps://www.data.go.kr/data/15026677/fileData.do

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 (1.3%) zerosZeros

Reproduction

Analysis started2024-04-21 01:02:32.868192
Analysis finished2024-04-21 01:02:35.291588
Duration2.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분(월별)
Text

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size740.0 B
2024-04-21T10:02:35.471691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters1216
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

Unique76 ?
Unique (%)100.0%

Sample

1st row민간분양 주택(2024-03)
2nd row민간분양 주택(2024-02)
3rd row민간분양 주택(2024-01)
4th row민간분양 주택(2023-12)
5th row민간분양 주택(2023-11)
ValueCountFrequency (%)
민간분양 76
50.0%
주택(2020-04 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(2019-12 1
 
0.7%
주택(2020-01 1
 
0.7%
주택(2020-02 1
 
0.7%
주택(2024-02 1
 
0.7%
Other values (67) 67
44.1%
2024-04-21T10:02:35.838739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 153
12.6%
0 151
12.4%
민 76
 
6.2%
분 76
 
6.2%
양 76
 
6.2%
76
 
6.2%
주 76
 
6.2%
택 76
 
6.2%
( 76
 
6.2%
간 76
 
6.2%
Other values (10) 304
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 456
37.5%
Other Letter 456
37.5%
Space Separator 76
 
6.2%
Open Punctuation 76
 
6.2%
Dash Punctuation 76
 
6.2%
Close Punctuation 76
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 153
33.6%
0 151
33.1%
1 69
15.1%
3 19
 
4.2%
9 18
 
3.9%
8 18
 
3.9%
4 9
 
2.0%
7 7
 
1.5%
6 6
 
1.3%
5 6
 
1.3%
Other Letter
ValueCountFrequency (%)
민 76
16.7%
분 76
16.7%
양 76
16.7%
주 76
16.7%
택 76
16.7%
간 76
16.7%
Space Separator
ValueCountFrequency (%)
76
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 760
62.5%
Hangul 456
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 153
20.1%
0 151
19.9%
76
10.0%
( 76
10.0%
- 76
10.0%
) 76
10.0%
1 69
9.1%
3 19
 
2.5%
9 18
 
2.4%
8 18
 
2.4%
Other values (4) 28
 
3.7%
Hangul
ValueCountFrequency (%)
민 76
16.7%
분 76
16.7%
양 76
16.7%
주 76
16.7%
택 76
16.7%
간 76
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 760
62.5%
Hangul 456
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 153
20.1%
0 151
19.9%
76
10.0%
( 76
10.0%
- 76
10.0%
) 76
10.0%
1 69
9.1%
3 19
 
2.5%
9 18
 
2.4%
8 18
 
2.4%
Other values (4) 28
 
3.7%
Hangul
ValueCountFrequency (%)
민 76
16.7%
분 76
16.7%
양 76
16.7%
주 76
16.7%
택 76
16.7%
간 76
16.7%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.57895
Minimum10
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2024-04-21T10:02:35.969503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q116
median67
Q3197
95-th percentile287.5
Maximum440
Range430
Interquartile range (IQR)181

Descriptive statistics

Standard deviation105.22893
Coefficient of variation (CV)0.95161809
Kurtosis0.55398241
Mean110.57895
Median Absolute Deviation (MAD)57
Skewness0.9942352
Sum8404
Variance11073.127
MonotonicityNot monotonic
2024-04-21T10:02:36.089180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
10 11
 
14.5%
16 7
 
9.2%
197 5
 
6.6%
124 4
 
5.3%
123 3
 
3.9%
209 3
 
3.9%
13 2
 
2.6%
203 2
 
2.6%
173 2
 
2.6%
12 2
 
2.6%
Other values (32) 35
46.1%
ValueCountFrequency (%)
10 11
14.5%
12 2
 
2.6%
13 2
 
2.6%
16 7
9.2%
18 1
 
1.3%
19 1
 
1.3%
21 1
 
1.3%
24 1
 
1.3%
28 1
 
1.3%
31 2
 
2.6%
ValueCountFrequency (%)
440 1
1.3%
426 1
1.3%
302 1
1.3%
289 1
1.3%
287 1
1.3%
285 1
1.3%
278 1
1.3%
262 1
1.3%
249 1
1.3%
231 1
1.3%

전용 60제곱미터 이하
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.315789
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2024-04-21T10:02:36.213930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median23
Q3115.5
95-th percentile172
Maximum255
Range254
Interquartile range (IQR)112.5

Descriptive statistics

Standard deviation71.925556
Coefficient of variation (CV)1.2333805
Kurtosis-0.21729138
Mean58.315789
Median Absolute Deviation (MAD)20
Skewness1.0761711
Sum4432
Variance5173.2856
MonotonicityNot monotonic
2024-04-21T10:02:36.348553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3 17
22.4%
4 8
 
10.5%
29 7
 
9.2%
158 6
 
7.9%
1 5
 
6.6%
16 3
 
3.9%
23 3
 
3.9%
171 2
 
2.6%
153 2
 
2.6%
164 2
 
2.6%
Other values (20) 21
27.6%
ValueCountFrequency (%)
1 5
 
6.6%
3 17
22.4%
4 8
10.5%
15 1
 
1.3%
16 3
 
3.9%
17 1
 
1.3%
21 1
 
1.3%
23 3
 
3.9%
29 7
9.2%
31 2
 
2.6%
ValueCountFrequency (%)
255 1
 
1.3%
252 1
 
1.3%
193 1
 
1.3%
175 1
 
1.3%
171 2
 
2.6%
170 1
 
1.3%
165 1
 
1.3%
164 2
 
2.6%
158 6
7.9%
153 2
 
2.6%

전용 60-85제곱미터
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.25
Minimum0
Maximum185
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size816.0 B
2024-04-21T10:02:36.450189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q113
median36
Q394
95-th percentile174
Maximum185
Range185
Interquartile range (IQR)81

Descriptive statistics

Standard deviation52.826288
Coefficient of variation (CV)1.0110294
Kurtosis0.73284359
Mean52.25
Median Absolute Deviation (MAD)25.5
Skewness1.336049
Sum3971
Variance2790.6167
MonotonicityNot monotonic
2024-04-21T10:02:36.588646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
39 8
 
10.5%
9 7
 
9.2%
7 6
 
7.9%
95 6
 
7.9%
13 6
 
7.9%
94 3
 
3.9%
20 3
 
3.9%
33 3
 
3.9%
38 2
 
2.6%
185 2
 
2.6%
Other values (25) 30
39.5%
ValueCountFrequency (%)
0 1
 
1.3%
7 6
7.9%
9 7
9.2%
10 2
 
2.6%
11 1
 
1.3%
12 1
 
1.3%
13 6
7.9%
14 1
 
1.3%
15 1
 
1.3%
16 1
 
1.3%
ValueCountFrequency (%)
185 2
 
2.6%
176 1
 
1.3%
174 2
 
2.6%
173 2
 
2.6%
171 1
 
1.3%
120 1
 
1.3%
101 1
 
1.3%
99 1
 
1.3%
97 1
 
1.3%
95 6
7.9%

전용 85제곱미터 초과
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
0
76 

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 76
100.0%

Length

2024-04-21T10:02:36.703660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:02:36.795115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 76
100.0%

Interactions

2024-04-21T10:02:34.787861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:02:34.225716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:02:34.526309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:02:34.883222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:02:34.357555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:02:34.616376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:02:35.000358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:02:34.441667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:02:34.692161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:02:36.849204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분(월별)합계전용 60제곱미터 이하전용 60-85제곱미터
구분(월별)1.0001.0001.0001.000
합계1.0001.0000.9850.907
전용 60제곱미터 이하1.0000.9851.0000.871
전용 60-85제곱미터1.0000.9070.8711.000
2024-04-21T10:02:36.936787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계전용 60제곱미터 이하전용 60-85제곱미터
합계1.0000.9590.854
전용 60제곱미터 이하0.9591.0000.743
전용 60-85제곱미터0.8540.7431.000

Missing values

2024-04-21T10:02:35.139488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:02:35.233787image/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(2024-03)12329940
1민간분양 주택(2024-02)12329940
2민간분양 주택(2024-01)12329940
3민간분양 주택(2023-12)12429950
4민간분양 주택(2023-11)12429950
5민간분양 주택(2023-10)12429950
6민간분양 주택(2023-09)12429950
7민간분양 주택(2023-08)12631950
8민간분양 주택(2023-07)12631950
9민간분양 주택(2023-06)12932970
구분(월별)합계전용 60제곱미터 이하전용 60-85제곱미터전용 85제곱미터 초과
66민간분양 주택(2018-09)209171380
67민간분양 주택(2018-08)198158400
68민간분양 주택(2018-07)197158390
69민간분양 주택(2018-06)197158390
70민간분양 주택(2018-05)197158390
71민간분양 주택(2018-04)197158390
72민간분양 주택(2018-03)197158390
73민간분양 주택(2018-02)231175560
74민간분양 주택(2018-01)262193690
75민간분양 주택(2017-12)16516500