Overview

Dataset statistics

Number of variables6
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory57.5 B

Variable types

Text1
Numeric4
DateTime1

Dataset

Description부산광역시_서구_부동산거래현황_20230331
Author부산광역시 서구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3057415

Alerts

데이터기준일 has constant value ""Constant
물건수 is highly overall correlated with 토지면적 and 2 other fieldsHigh correlation
토지면적 is highly overall correlated with 물건수 and 2 other fieldsHigh correlation
건축물면적 is highly overall correlated with 물건수 and 2 other fieldsHigh correlation
금액 is highly overall correlated with 물건수 and 2 other fieldsHigh correlation
행정구역 has unique valuesUnique
물건수 has 4 (16.7%) zerosZeros
토지면적 has 4 (16.7%) zerosZeros
건축물면적 has 4 (16.7%) zerosZeros
금액 has 6 (25.0%) zerosZeros

Reproduction

Analysis started2023-12-10 17:23:42.605581
Analysis finished2023-12-10 17:23:47.435767
Duration4.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-11T02:23:47.741891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0416667
Min length3

Characters and Unicode

Total characters121
Distinct characters23
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

Unique24 ?
Unique (%)100.0%

Sample

1st row동대신동1가
2nd row동대신동2가
3rd row동대신동3가
4th row서대신동1가
5th row서대신동2가
ValueCountFrequency (%)
동대신동1가 1
 
4.2%
동대신동2가 1
 
4.2%
남부민동 1
 
4.2%
충무동3가 1
 
4.2%
충무동2가 1
 
4.2%
충무동1가 1
 
4.2%
초장동 1
 
4.2%
토성동5가 1
 
4.2%
토성동4가 1
 
4.2%
아미동2가 1
 
4.2%
Other values (14) 14
58.3%
2023-12-11T02:23:48.452239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
22.3%
가 21
17.4%
1 7
 
5.8%
2 7
 
5.8%
부 6
 
5.0%
신 6
 
5.0%
대 6
 
5.0%
성 5
 
4.1%
토 5
 
4.1%
3 5
 
4.1%
Other values (13) 26
21.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 100
82.6%
Decimal Number 21
 
17.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
27.0%
가 21
21.0%
부 6
 
6.0%
신 6
 
6.0%
대 6
 
6.0%
성 5
 
5.0%
토 5
 
5.0%
민 4
 
4.0%
서 3
 
3.0%
충 3
 
3.0%
Other values (8) 14
14.0%
Decimal Number
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 5
23.8%
4 1
 
4.8%
5 1
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 100
82.6%
Common 21
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
27.0%
가 21
21.0%
부 6
 
6.0%
신 6
 
6.0%
대 6
 
6.0%
성 5
 
5.0%
토 5
 
5.0%
민 4
 
4.0%
서 3
 
3.0%
충 3
 
3.0%
Other values (8) 14
14.0%
Common
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 5
23.8%
4 1
 
4.8%
5 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 100
82.6%
ASCII 21
 
17.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
27.0%
가 21
21.0%
부 6
 
6.0%
신 6
 
6.0%
대 6
 
6.0%
성 5
 
5.0%
토 5
 
5.0%
민 4
 
4.0%
서 3
 
3.0%
충 3
 
3.0%
Other values (8) 14
14.0%
ASCII
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 5
23.8%
4 1
 
4.8%
5 1
 
4.8%

물건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0833333
Minimum0
Maximum40
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T02:23:48.747558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median4
Q39.5
95-th percentile25.7
Maximum40
Range40
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation10.387772
Coefficient of variation (CV)1.2850851
Kurtosis2.7530611
Mean8.0833333
Median Absolute Deviation (MAD)3
Skewness1.765274
Sum194
Variance107.9058
MonotonicityNot monotonic
2023-12-11T02:23:49.034804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 4
16.7%
0 4
16.7%
4 2
 
8.3%
5 2
 
8.3%
1 2
 
8.3%
3 1
 
4.2%
24 1
 
4.2%
9 1
 
4.2%
26 1
 
4.2%
7 1
 
4.2%
Other values (5) 5
20.8%
ValueCountFrequency (%)
0 4
16.7%
1 2
8.3%
2 4
16.7%
3 1
 
4.2%
4 2
8.3%
5 2
8.3%
6 1
 
4.2%
7 1
 
4.2%
9 1
 
4.2%
11 1
 
4.2%
ValueCountFrequency (%)
40 1
4.2%
26 1
4.2%
24 1
4.2%
21 1
4.2%
19 1
4.2%
11 1
4.2%
9 1
4.2%
7 1
4.2%
6 1
4.2%
5 2
8.3%

토지면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5762.9
Minimum0
Maximum128095.02
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T02:23:49.325407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.465
median120.415
Q3286.23
95-th percentile4563.3285
Maximum128095.02
Range128095.02
Interquartile range (IQR)267.765

Descriptive statistics

Standard deviation26078.535
Coefficient of variation (CV)4.5252452
Kurtosis23.908285
Mean5762.9
Median Absolute Deviation (MAD)108.645
Skewness4.8857965
Sum138309.6
Variance6.8009 × 108
MonotonicityNot monotonic
2023-12-11T02:23:49.686432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 4
 
16.7%
130.87 1
 
4.2%
284.45 1
 
4.2%
128095.02 1
 
4.2%
1372.63 1
 
4.2%
13.29 1
 
4.2%
179.89 1
 
4.2%
375.54 1
 
4.2%
31.77 1
 
4.2%
291.57 1
 
4.2%
Other values (11) 11
45.8%
ValueCountFrequency (%)
0.0 4
16.7%
10.25 1
 
4.2%
13.29 1
 
4.2%
20.19 1
 
4.2%
24.48 1
 
4.2%
31.77 1
 
4.2%
68.4 1
 
4.2%
92.56 1
 
4.2%
109.96 1
 
4.2%
130.87 1
 
4.2%
ValueCountFrequency (%)
128095.02 1
4.2%
5085.15 1
4.2%
1606.34 1
4.2%
1372.63 1
4.2%
375.54 1
4.2%
291.57 1
4.2%
284.45 1
4.2%
223.45 1
4.2%
179.89 1
4.2%
147.68 1
4.2%

건축물면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean869.06208
Minimum0
Maximum6400.22
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T02:23:50.024406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170.055
median239.735
Q3607.8125
95-th percentile5178.7845
Maximum6400.22
Range6400.22
Interquartile range (IQR)537.7575

Descriptive statistics

Standard deviation1687.8879
Coefficient of variation (CV)1.9421949
Kurtosis6.8676363
Mean869.06208
Median Absolute Deviation (MAD)199.19
Skewness2.7317245
Sum20857.49
Variance2848965.6
MonotonicityNot monotonic
2023-12-11T02:23:50.283744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 4
 
16.7%
240.87 1
 
4.2%
324.5 1
 
4.2%
6400.22 1
 
4.2%
2350.86 1
 
4.2%
77.98 1
 
4.2%
1334.48 1
 
4.2%
360.46 1
 
4.2%
178.45 1
 
4.2%
238.6 1
 
4.2%
Other values (11) 11
45.8%
ValueCountFrequency (%)
0.0 4
16.7%
34.81 1
 
4.2%
46.28 1
 
4.2%
77.98 1
 
4.2%
117.86 1
 
4.2%
142.14 1
 
4.2%
178.45 1
 
4.2%
210.1 1
 
4.2%
238.6 1
 
4.2%
240.87 1
 
4.2%
ValueCountFrequency (%)
6400.22 1
4.2%
5677.83 1
4.2%
2350.86 1
4.2%
1334.48 1
4.2%
1323.43 1
4.2%
639.35 1
4.2%
597.3 1
4.2%
360.46 1
4.2%
324.5 1
4.2%
288.42 1
4.2%

금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2147.5417
Minimum0
Maximum17313
Zeros6
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T02:23:50.568806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145
median533
Q32492.75
95-th percentile8870.5
Maximum17313
Range17313
Interquartile range (IQR)2447.75

Descriptive statistics

Standard deviation3920.9539
Coefficient of variation (CV)1.8257871
Kurtosis9.9851262
Mean2147.5417
Median Absolute Deviation (MAD)533
Skewness3.0074053
Sum51541
Variance15373879
MonotonicityNot monotonic
2023-12-11T02:23:50.838565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 6
25.0%
517 1
 
4.2%
60 1
 
4.2%
17313 1
 
4.2%
3154 1
 
4.2%
310 1
 
4.2%
4205 1
 
4.2%
630 1
 
4.2%
480 1
 
4.2%
503 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
0 6
25.0%
60 1
 
4.2%
310 1
 
4.2%
480 1
 
4.2%
503 1
 
4.2%
517 1
 
4.2%
528 1
 
4.2%
538 1
 
4.2%
630 1
 
4.2%
731 1
 
4.2%
ValueCountFrequency (%)
17313 1
4.2%
9535 1
4.2%
5105 1
4.2%
4205 1
4.2%
3154 1
4.2%
2717 1
4.2%
2418 1
4.2%
1872 1
4.2%
925 1
4.2%
731 1
4.2%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2023-03-31 00:00:00
Maximum2023-03-31 00:00:00
2023-12-11T02:23:51.656617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:51.892334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T02:23:45.899433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:42.926586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:43.797791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:44.747825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:46.145378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:43.112857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:44.018701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:45.027977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:46.435782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:43.320567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:44.244597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:45.313668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:46.714391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:43.555541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:44.503978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:23:45.616389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:23:52.069483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역물건수토지면적건축물면적금액
행정구역1.0001.0001.0001.0001.000
물건수1.0001.0001.0000.9110.843
토지면적1.0001.0001.0001.0001.000
건축물면적1.0000.9111.0001.0000.992
금액1.0000.8431.0000.9921.000
2023-12-11T02:23:52.326479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
물건수토지면적건축물면적금액
물건수1.0000.9490.9380.906
토지면적0.9491.0000.9340.848
건축물면적0.9380.9341.0000.913
금액0.9060.8480.9131.000

Missing values

2023-12-11T02:23:47.054006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:23:47.335924image/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

행정구역물건수토지면적건축물면적금액데이터기준일
0동대신동1가3130.87240.875172023-03-31
1동대신동2가4284.45324.59252023-03-31
2동대신동3가241606.341323.4351052023-03-31
3서대신동1가5147.68288.4218722023-03-31
4서대신동2가9223.45639.3527172023-03-31
5서대신동3가265085.155677.8395352023-03-31
6부용동1가220.19117.865382023-03-31
7부용동2가268.434.8102023-03-31
8부민동1가224.48142.145282023-03-31
9부민동2가292.56273.5502023-03-31
행정구역물건수토지면적건축물면적금액데이터기준일
14아미동1가00.00.002023-03-31
15아미동2가11291.57238.65032023-03-31
16토성동4가00.00.002023-03-31
17토성동5가431.77178.454802023-03-31
18초장동6375.54360.466302023-03-31
19충무동1가21179.891334.4842052023-03-31
20충무동2가00.00.002023-03-31
21충무동3가113.2977.983102023-03-31
22남부민동191372.632350.8631542023-03-31
23암남동40128095.026400.22173132023-03-31