Overview

Dataset statistics

Number of variables7
Number of observations30
Missing cells30
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory64.4 B

Variable types

Categorical4
Text2
Unsupported1

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://www.bigdata-region.kr/#/dataset/7bdcdffc-e7a1-4964-8d68-707dbbd2fc94

Alerts

시도명 has constant value ""Constant
기준년도 has constant value ""Constant
주택가격지수(현황) is highly overall correlated with 아파트가격지수(현황)High correlation
아파트가격지수(현황) is highly overall correlated with 주택가격지수(현황)High correlation
주택가격지수(현황) is highly imbalanced (64.7%)Imbalance
건물지수(현황) has 30 (100.0%) missing valuesMissing
건물지수(현황) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 14:14:38.753699
Analysis finished2023-12-10 14:14:39.628471
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T23:14:39.741681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:39.905627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:14:40.158497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.7
Min length3

Characters and Unicode

Total characters111
Distinct characters33
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

Unique9 ?
Unique (%)30.0%

Sample

1st row광주시
2nd row광명시
3rd row군포시
4th row동두천시
5th row군포시
ValueCountFrequency (%)
안성시 5
14.3%
여주시 4
 
11.4%
성남시 3
 
8.6%
파주시 2
 
5.7%
군포시 2
 
5.7%
부천시 2
 
5.7%
수정구 2
 
5.7%
양주시 2
 
5.7%
양평군 2
 
5.7%
의왕시 1
 
2.9%
Other values (10) 10
28.6%
2023-12-10T23:14:40.721650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 27
24.3%
주 9
 
8.1%
성 8
 
7.2%
안 5
 
4.5%
구 5
 
4.5%
군 5
 
4.5%
천 5
 
4.5%
5
 
4.5%
여 4
 
3.6%
양 4
 
3.6%
Other values (23) 34
30.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106
95.5%
Space Separator 5
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 27
25.5%
주 9
 
8.5%
성 8
 
7.5%
안 5
 
4.7%
구 5
 
4.7%
군 5
 
4.7%
천 5
 
4.7%
여 4
 
3.8%
양 4
 
3.8%
남 3
 
2.8%
Other values (22) 31
29.2%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106
95.5%
Common 5
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 27
25.5%
주 9
 
8.5%
성 8
 
7.5%
안 5
 
4.7%
구 5
 
4.7%
군 5
 
4.7%
천 5
 
4.7%
여 4
 
3.8%
양 4
 
3.8%
남 3
 
2.8%
Other values (22) 31
29.2%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106
95.5%
ASCII 5
 
4.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 27
25.5%
주 9
 
8.5%
성 8
 
7.5%
안 5
 
4.7%
구 5
 
4.7%
군 5
 
4.7%
천 5
 
4.7%
여 4
 
3.8%
양 4
 
3.8%
남 3
 
2.8%
Other values (22) 31
29.2%
ASCII
ValueCountFrequency (%)
5
100.0%

행정동명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:14:41.060665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9333333
Min length2

Characters and Unicode

Total characters88
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)93.3%

Sample

1st row중대동
2nd row하안동
3rd row당동
4th row지행동
5th row둔대동
ValueCountFrequency (%)
성남동 2
 
6.7%
중대동 1
 
3.3%
남방동 1
 
3.3%
서패동 1
 
3.3%
부발읍 1
 
3.3%
구갈동 1
 
3.3%
이동 1
 
3.3%
군남면 1
 
3.3%
흥천면 1
 
3.3%
오금동 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T23:14:41.541272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 25
28.4%
면 5
 
5.7%
남 4
 
4.5%
대 3
 
3.4%
성 2
 
2.3%
안 2
 
2.3%
연 2
 
2.3%
강 2
 
2.3%
흥 2
 
2.3%
신 2
 
2.3%
Other values (35) 39
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 25
28.4%
면 5
 
5.7%
남 4
 
4.5%
대 3
 
3.4%
성 2
 
2.3%
안 2
 
2.3%
연 2
 
2.3%
강 2
 
2.3%
흥 2
 
2.3%
신 2
 
2.3%
Other values (35) 39
44.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 25
28.4%
면 5
 
5.7%
남 4
 
4.5%
대 3
 
3.4%
성 2
 
2.3%
안 2
 
2.3%
연 2
 
2.3%
강 2
 
2.3%
흥 2
 
2.3%
신 2
 
2.3%
Other values (35) 39
44.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 25
28.4%
면 5
 
5.7%
남 4
 
4.5%
대 3
 
3.4%
성 2
 
2.3%
안 2
 
2.3%
연 2
 
2.3%
강 2
 
2.3%
흥 2
 
2.3%
신 2
 
2.3%
Other values (35) 39
44.3%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2019
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 30
100.0%

Length

2023-12-10T23:14:41.770160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:41.905128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 30
100.0%

건물지수(현황)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

주택가격지수(현황)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
28 
0
 
2

Length

Max length4
Median length4
Mean length3.8
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 28
93.3%
0 2
 
6.7%

Length

2023-12-10T23:14:42.066278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:42.234294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
93.3%
0 2
 
6.7%

아파트가격지수(현황)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
18 
<NA>
12 

Length

Max length4
Median length1
Mean length2.2
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 18
60.0%
<NA> 12
40.0%

Length

2023-12-10T23:14:42.405843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:14:42.565680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18
60.0%
na 12
40.0%

Correlations

2023-12-10T23:14:42.673867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명
시군구명1.0000.951
행정동명0.9511.000
2023-12-10T23:14:42.794454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택가격지수(현황)아파트가격지수(현황)
주택가격지수(현황)1.0001.000
아파트가격지수(현황)1.0001.000
2023-12-10T23:14:42.946079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택가격지수(현황)아파트가격지수(현황)
주택가격지수(현황)1.0001.000
아파트가격지수(현황)1.0001.000

Missing values

2023-12-10T23:14:39.245475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:14:39.486118image/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

시도명시군구명행정동명기준년도건물지수(현황)주택가격지수(현황)아파트가격지수(현황)
0경기도광주시중대동2019<NA><NA>0
1경기도광명시하안동2019<NA><NA><NA>
2경기도군포시당동2019<NA><NA><NA>
3경기도동두천시지행동2019<NA><NA><NA>
4경기도군포시둔대동2019<NA>00
5경기도부천시괴안동2019<NA><NA><NA>
6경기도부천시소사동2019<NA><NA><NA>
7경기도성남시 수정구오야동2019<NA><NA>0
8경기도성남시 수정구창곡동2019<NA><NA><NA>
9경기도성남시 중원구성남동2019<NA><NA><NA>
시도명시군구명행정동명기준년도건물지수(현황)주택가격지수(현황)아파트가격지수(현황)
20경기도여주시대신면2019<NA><NA>0
21경기도여주시연라동2019<NA><NA>0
22경기도여주시오금동2019<NA><NA>0
23경기도여주시흥천면2019<NA><NA>0
24경기도연천군군남면2019<NA><NA>0
25경기도의왕시이동2019<NA><NA>0
26경기도용인시 기흥구구갈동2019<NA><NA><NA>
27경기도이천시부발읍2019<NA><NA><NA>
28경기도파주시서패동2019<NA><NA>0
29경기도파주시연다산동2019<NA><NA>0