Overview

Dataset statistics

Number of variables6
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory53.7 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description부산광역시북구_공유재산현황_20230922
Author부산광역시 북구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15021099

Alerts

구분 is highly overall correlated with 면적 and 2 other fieldsHigh correlation
지목 is highly overall correlated with 개별공시지가 and 2 other fieldsHigh correlation
면적 is highly overall correlated with 재산가액 and 1 other fieldsHigh correlation
개별공시지가 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
재산가액 is highly overall correlated with 면적 and 1 other fieldsHigh correlation
구분 is highly imbalanced (85.6%)Imbalance
소재지 has unique valuesUnique
재산가액 has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:09:32.177297
Analysis finished2023-12-10 17:09:34.602176
Duration2.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
토지
48 
건물
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
토지 48
98.0%
건물 1
 
2.0%

Length

2023-12-11T02:09:34.709834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:09:34.847296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토지 48
98.0%
건물 1
 
2.0%

소재지
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-11T02:09:35.127897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.714286
Min length17

Characters and Unicode

Total characters966
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st row부산광역시 북구 구포동 1030-68
2nd row부산광역시 북구 구포동 1075-79
3rd row부산광역시 북구 구포동 1141-205
4th row부산광역시 북구 구포동 1141-61
5th row부산광역시 북구 구포동 1141-97
ValueCountFrequency (%)
부산광역시 49
25.0%
북구 49
25.0%
구포동 37
18.9%
만덕동 7
 
3.6%
덕천동 4
 
2.0%
310-17 1
 
0.5%
688-18 1
 
0.5%
688-19 1
 
0.5%
688-2 1
 
0.5%
688-21 1
 
0.5%
Other values (45) 45
23.0%
2023-12-11T02:09:35.603102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
195
20.2%
구 86
 
8.9%
1 54
 
5.6%
부 49
 
5.1%
광 49
 
5.1%
역 49
 
5.1%
시 49
 
5.1%
북 49
 
5.1%
동 49
 
5.1%
산 49
 
5.1%
Other values (18) 288
29.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 492
50.9%
Decimal Number 232
24.0%
Space Separator 195
 
20.2%
Dash Punctuation 47
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 86
17.5%
부 49
10.0%
광 49
10.0%
역 49
10.0%
시 49
10.0%
북 49
10.0%
동 49
10.0%
산 49
10.0%
포 37
7.5%
덕 11
 
2.2%
Other values (6) 15
 
3.0%
Decimal Number
ValueCountFrequency (%)
1 54
23.3%
8 35
15.1%
6 26
11.2%
3 21
 
9.1%
0 21
 
9.1%
4 20
 
8.6%
7 18
 
7.8%
9 13
 
5.6%
2 13
 
5.6%
5 11
 
4.7%
Space Separator
ValueCountFrequency (%)
195
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 492
50.9%
Common 474
49.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 86
17.5%
부 49
10.0%
광 49
10.0%
역 49
10.0%
시 49
10.0%
북 49
10.0%
동 49
10.0%
산 49
10.0%
포 37
7.5%
덕 11
 
2.2%
Other values (6) 15
 
3.0%
Common
ValueCountFrequency (%)
195
41.1%
1 54
 
11.4%
- 47
 
9.9%
8 35
 
7.4%
6 26
 
5.5%
3 21
 
4.4%
0 21
 
4.4%
4 20
 
4.2%
7 18
 
3.8%
9 13
 
2.7%
Other values (2) 24
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 492
50.9%
ASCII 474
49.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
195
41.1%
1 54
 
11.4%
- 47
 
9.9%
8 35
 
7.4%
6 26
 
5.5%
3 21
 
4.4%
0 21
 
4.4%
4 20
 
4.2%
7 18
 
3.8%
9 13
 
2.7%
Other values (2) 24
 
5.1%
Hangul
ValueCountFrequency (%)
구 86
17.5%
부 49
10.0%
광 49
10.0%
역 49
10.0%
시 49
10.0%
북 49
10.0%
동 49
10.0%
산 49
10.0%
포 37
7.5%
덕 11
 
2.2%
Other values (6) 15
 
3.0%

지목
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
대
35 
잡종지
5 
답
4 
전
 
2
임야
 
2

Length

Max length5
Median length1
Mean length1.3265306
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row대
2nd row잡종지
3rd row대
4th row대
5th row대

Common Values

ValueCountFrequency (%)
대 35
71.4%
잡종지 5
 
10.2%
답 4
 
8.2%
전 2
 
4.1%
임야 2
 
4.1%
시멘트블럭 1
 
2.0%

Length

2023-12-11T02:09:35.834362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:09:35.985527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대 35
71.4%
잡종지 5
 
10.2%
답 4
 
8.2%
전 2
 
4.1%
임야 2
 
4.1%
시멘트블럭 1
 
2.0%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.800204
Minimum0.5
Maximum579
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-11T02:09:36.127527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q15.03
median24
Q363
95-th percentile464.84
Maximum579
Range578.5
Interquartile range (IQR)57.97

Descriptive statistics

Standard deviation139.2125
Coefficient of variation (CV)1.9122543
Kurtosis7.1836113
Mean72.800204
Median Absolute Deviation (MAD)20
Skewness2.8438109
Sum3567.21
Variance19380.121
MonotonicityNot monotonic
2023-12-11T02:09:36.312734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1.0 4
 
8.2%
4.0 4
 
8.2%
2.0 2
 
4.1%
18.0 2
 
4.1%
483.4 1
 
2.0%
21.0 1
 
2.0%
25.0 1
 
2.0%
579.0 1
 
2.0%
38.0 1
 
2.0%
38.1 1
 
2.0%
Other values (31) 31
63.3%
ValueCountFrequency (%)
0.5 1
 
2.0%
1.0 4
8.2%
2.0 2
4.1%
4.0 4
8.2%
5.0 1
 
2.0%
5.03 1
 
2.0%
11.0 1
 
2.0%
14.0 1
 
2.0%
15.0 1
 
2.0%
18.0 2
4.1%
ValueCountFrequency (%)
579.0 1
2.0%
551.48 1
2.0%
483.4 1
2.0%
437.0 1
2.0%
223.0 1
2.0%
119.8 1
2.0%
98.02 1
2.0%
89.2 1
2.0%
88.0 1
2.0%
76.0 1
2.0%

개별공시지가
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2430814.8
Minimum92100
Maximum52880323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-11T02:09:36.493611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92100
5-th percentile409860
Q1817400
median1020000
Q31511000
95-th percentile4277000
Maximum52880323
Range52788223
Interquartile range (IQR)693600

Descriptive statistics

Standard deviation7453066.9
Coefficient of variation (CV)3.0660777
Kurtosis46.371895
Mean2430814.8
Median Absolute Deviation (MAD)338000
Skewness6.7348656
Sum1.1910992 × 108
Variance5.5548206 × 1013
MonotonicityNot monotonic
2023-12-11T02:09:36.722504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
969600 3
 
6.1%
817400 2
 
4.1%
498600 2
 
4.1%
1020000 2
 
4.1%
1511000 2
 
4.1%
624100 2
 
4.1%
1009000 1
 
2.0%
350700 1
 
2.0%
1213000 1
 
2.0%
752400 1
 
2.0%
Other values (32) 32
65.3%
ValueCountFrequency (%)
92100 1
2.0%
285900 1
2.0%
350700 1
2.0%
498600 2
4.1%
589100 1
2.0%
624100 2
4.1%
632800 1
2.0%
734500 1
2.0%
752400 1
2.0%
797700 1
2.0%
ValueCountFrequency (%)
52880323 1
2.0%
7173000 1
2.0%
4657000 1
2.0%
3707000 1
2.0%
3319000 1
2.0%
2716000 1
2.0%
2048000 1
2.0%
2045000 1
2.0%
1672000 1
2.0%
1543000 1
2.0%

재산가액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5615002 × 108
Minimum350700
Maximum2.9162441 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-11T02:09:36.885706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum350700
5-th percentile1198780
Q113276000
median24240000
Q344182500
95-th percentile4.0340668 × 108
Maximum2.9162441 × 1010
Range2.916209 × 1010
Interquartile range (IQR)30906500

Descriptive statistics

Standard deviation4.1589271 × 109
Coefficient of variation (CV)6.3383783
Kurtosis48.911788
Mean6.5615002 × 108
Median Absolute Deviation (MAD)16761000
Skewness6.9908325
Sum3.2151351 × 1010
Variance1.7296674 × 1019
MonotonicityNot monotonic
2023-12-11T02:09:37.070386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
674291000 1
 
2.0%
27448780 1
 
2.0%
30057600 1
 
2.0%
21420000 1
 
2.0%
4036000 1
 
2.0%
4080000 1
 
2.0%
24240000 1
 
2.0%
350700 1
 
2.0%
2426000 1
 
2.0%
435639600 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
350700 1
2.0%
755500 1
2.0%
908100 1
2.0%
1634800 1
2.0%
2045000 1
2.0%
2426000 1
2.0%
2716000 1
2.0%
4036000 1
2.0%
4080000 1
2.0%
6116000 1
2.0%
ValueCountFrequency (%)
29162440528 1
2.0%
674291000 1
2.0%
435639600 1
2.0%
355057300 1
2.0%
176585200 1
2.0%
127720060 1
2.0%
126038000 1
2.0%
107129200 1
2.0%
100422000 1
2.0%
96748000 1
2.0%

Interactions

2023-12-11T02:09:33.530049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:32.590883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:33.070745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:33.691057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:32.751483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:33.219257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:33.833847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:32.911144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:09:33.362627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:09:37.174011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분소재지지목면적개별공시지가재산가액
구분1.0001.0001.0000.6011.0000.676
소재지1.0001.0001.0001.0001.0001.000
지목1.0001.0001.0000.5290.9601.000
면적0.6011.0000.5291.0000.7210.599
개별공시지가1.0001.0000.9600.7211.0001.000
재산가액0.6761.0001.0000.5991.0001.000
2023-12-11T02:09:37.275913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지목
구분1.0000.957
지목0.9571.000
2023-12-11T02:09:37.369348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적개별공시지가재산가액구분지목
면적1.000-0.2490.8740.6100.342
개별공시지가-0.2491.0000.1270.9890.713
재산가액0.8740.1271.0000.4730.957
구분0.6100.9890.4731.0000.957
지목0.3420.7130.9570.9571.000

Missing values

2023-12-11T02:09:34.380908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:09:34.537146image/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

구분소재지지목면적개별공시지가재산가액
0토지부산광역시 북구 구포동 1030-68대437.01543000674291000
1토지부산광역시 북구 구포동 1075-79잡종지223.09210020538300
2토지부산광역시 북구 구포동 1141-205대5.016720008360000
3토지부산광역시 북구 구포동 1141-61대18.0131700023706000
4토지부산광역시 북구 구포동 1141-97대76.0127300096748000
5토지부산광역시 북구 구포동 1144-12대11.0137300015103000
6토지부산광역시 북구 구포동 1166-29대1.027160002716000
7토지부산광역시 북구 구포동 117-38대119.81474000176585200
8토지부산광역시 북구 구포동 130-10대0.51511000755500
9토지부산광역시 북구 구포동 130-13대28.08135700038104560
구분소재지지목면적개별공시지가재산가액
39토지부산광역시 북구 덕천동 784잡종지483.4734500355057300
40토지부산광역시 북구 만덕동 376-37답88.028590025159200
41토지부산광역시 북구 만덕동 484-12임야63.062410039318300
42토지부산광역시 북구 만덕동 484-18임야68.062410042438800
43토지부산광역시 북구 만덕동 487-1대75.058910044182500
44토지부산광역시 북구 만덕동 907-4대89.21201000107129200
45토지부산광역시 북구 만덕동 97-4답24.079770019144800
46토지부산광역시 북구 만덕동 97-5답21.563280013605200
47토지부산광역시 북구 화명동 360-3대4.015290006116000
48건물부산광역시 북구 구포동 1030-68번지시멘트블럭551.485288032329162440528