Overview

Dataset statistics

Number of variables7
Number of observations106
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory59.2 B

Variable types

Categorical2
Text3
Numeric2

Dataset

Description울산광역시 북구에 등록 관리되고 있는 아파트 정보에 대한 데이터로서 아파트명, 세대수, 동수, 준공연도, 주소 에 대한 등록현황을 제공합니다.
Author울산광역시 북구
URLhttps://www.data.go.kr/data/15106220/fileData.do

Alerts

지자체 구분 has constant value ""Constant
데이터기준일자 has constant value ""Constant
아파트명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:54:06.509656
Analysis finished2023-12-12 08:54:07.526652
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지자체 구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size980.0 B
울산북구
106 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울산북구 106
100.0%

Length

2023-12-12T17:54:07.606291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:54:07.720517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산북구 106
100.0%

아파트명
Text

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size980.0 B
2023-12-12T17:54:07.923622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.8962264
Min length3

Characters and Unicode

Total characters1049
Distinct characters180
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)100.0%

Sample

1st row이화제일그린파크
2nd row약수제일그린파크아파트
3rd row신동아파트
4th row대하그린파크아파트
5th row한라신천지타운
ValueCountFrequency (%)
이화제일그린파크 1
 
0.9%
에일린의뜰2차 1
 
0.9%
약수제일그린파크아파트 1
 
0.9%
블루마시티효성해링턴플레이스2단지 1
 
0.9%
오토밸리로줌파크 1
 
0.9%
호계한양수자인1차 1
 
0.9%
오토밸리로효성해링턴플레이스 1
 
0.9%
강동서희스타힐스 1
 
0.9%
강동블루마시티푸르지오2차 1
 
0.9%
프리드 1
 
0.9%
Other values (104) 104
91.2%
2023-12-12T17:54:08.310222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 56
 
5.3%
파 50
 
4.8%
트 43
 
4.1%
차 29
 
2.8%
지 29
 
2.8%
2 24
 
2.3%
) 21
 
2.0%
( 21
 
2.0%
대 21
 
2.0%
1 20
 
1.9%
Other values (170) 735
70.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 924
88.1%
Decimal Number 58
 
5.5%
Close Punctuation 21
 
2.0%
Open Punctuation 21
 
2.0%
Uppercase Letter 15
 
1.4%
Space Separator 8
 
0.8%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 56
 
6.1%
파 50
 
5.4%
트 43
 
4.7%
차 29
 
3.1%
지 29
 
3.1%
대 21
 
2.3%
스 19
 
2.1%
단 19
 
2.1%
린 17
 
1.8%
이 16
 
1.7%
Other values (154) 625
67.6%
Uppercase Letter
ValueCountFrequency (%)
L 4
26.7%
B 3
20.0%
H 3
20.0%
C 2
13.3%
K 1
 
6.7%
D 1
 
6.7%
G 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
2 24
41.4%
1 20
34.5%
3 8
 
13.8%
8 3
 
5.2%
4 3
 
5.2%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 924
88.1%
Common 110
 
10.5%
Latin 15
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 56
 
6.1%
파 50
 
5.4%
트 43
 
4.7%
차 29
 
3.1%
지 29
 
3.1%
대 21
 
2.3%
스 19
 
2.1%
단 19
 
2.1%
린 17
 
1.8%
이 16
 
1.7%
Other values (154) 625
67.6%
Common
ValueCountFrequency (%)
2 24
21.8%
) 21
19.1%
( 21
19.1%
1 20
18.2%
3 8
 
7.3%
8
 
7.3%
8 3
 
2.7%
4 3
 
2.7%
, 2
 
1.8%
Latin
ValueCountFrequency (%)
L 4
26.7%
B 3
20.0%
H 3
20.0%
C 2
13.3%
K 1
 
6.7%
D 1
 
6.7%
G 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 924
88.1%
ASCII 125
 
11.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 56
 
6.1%
파 50
 
5.4%
트 43
 
4.7%
차 29
 
3.1%
지 29
 
3.1%
대 21
 
2.3%
스 19
 
2.1%
단 19
 
2.1%
린 17
 
1.8%
이 16
 
1.7%
Other values (154) 625
67.6%
ASCII
ValueCountFrequency (%)
2 24
19.2%
) 21
16.8%
( 21
16.8%
1 20
16.0%
3 8
 
6.4%
8
 
6.4%
L 4
 
3.2%
B 3
 
2.4%
8 3
 
2.4%
4 3
 
2.4%
Other values (6) 10
8.0%

세대수
Text

Distinct99
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size980.0 B
2023-12-12T17:54:08.755847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.2358491
Min length4

Characters and Unicode

Total characters555
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)86.8%

Sample

1st row 180
2nd row 364
3rd row 330
4th row 404
5th row 239
ValueCountFrequency (%)
245 2
 
1.9%
404 2
 
1.9%
946 2
 
1.9%
464 2
 
1.9%
211 2
 
1.9%
468 2
 
1.9%
298 2
 
1.9%
1,059 1
 
0.9%
180 1
 
0.9%
890 1
 
0.9%
Other values (89) 89
84.0%
2023-12-12T17:54:09.265173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
212
38.2%
4 51
 
9.2%
2 42
 
7.6%
1 42
 
7.6%
6 36
 
6.5%
8 32
 
5.8%
3 30
 
5.4%
7 28
 
5.0%
5 24
 
4.3%
9 24
 
4.3%
Other values (2) 34
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
59.5%
Space Separator 212
38.2%
Other Punctuation 13
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 51
15.5%
2 42
12.7%
1 42
12.7%
6 36
10.9%
8 32
9.7%
3 30
9.1%
7 28
8.5%
5 24
7.3%
9 24
7.3%
0 21
6.4%
Space Separator
ValueCountFrequency (%)
212
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 555
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
212
38.2%
4 51
 
9.2%
2 42
 
7.6%
1 42
 
7.6%
6 36
 
6.5%
8 32
 
5.8%
3 30
 
5.4%
7 28
 
5.0%
5 24
 
4.3%
9 24
 
4.3%
Other values (2) 34
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
212
38.2%
4 51
 
9.2%
2 42
 
7.6%
1 42
 
7.6%
6 36
 
6.5%
8 32
 
5.8%
3 30
 
5.4%
7 28
 
5.0%
5 24
 
4.3%
9 24
 
4.3%
Other values (2) 34
 
6.1%

동수
Real number (ℝ)

Distinct19
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2641509
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T17:54:09.440642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5.5
Q311
95-th percentile15.75
Maximum23
Range22
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.0072365
Coefficient of variation (CV)0.68930788
Kurtosis0.65499267
Mean7.2641509
Median Absolute Deviation (MAD)3.5
Skewness0.95735681
Sum770
Variance25.072417
MonotonicityNot monotonic
2023-12-12T17:54:09.579713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5 14
13.2%
4 11
10.4%
3 11
10.4%
2 9
8.5%
11 9
8.5%
1 8
7.5%
10 8
7.5%
7 7
 
6.6%
13 7
 
6.6%
6 4
 
3.8%
Other values (9) 18
17.0%
ValueCountFrequency (%)
1 8
7.5%
2 9
8.5%
3 11
10.4%
4 11
10.4%
5 14
13.2%
6 4
 
3.8%
7 7
6.6%
8 3
 
2.8%
9 2
 
1.9%
10 8
7.5%
ValueCountFrequency (%)
23 1
 
0.9%
22 2
 
1.9%
19 1
 
0.9%
16 2
 
1.9%
15 2
 
1.9%
14 2
 
1.9%
13 7
6.6%
12 3
 
2.8%
11 9
8.5%
10 8
7.5%

준공연도
Real number (ℝ)

Distinct26
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.3396
Minimum1992
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T17:54:09.705294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1993
Q11996
median2007
Q32016.75
95-th percentile2019
Maximum2021
Range29
Interquartile range (IQR)20.75

Descriptive statistics

Standard deviation9.6814863
Coefficient of variation (CV)0.0048254474
Kurtosis-1.5023065
Mean2006.3396
Median Absolute Deviation (MAD)10
Skewness-0.018287598
Sum212672
Variance93.731177
MonotonicityIncreasing
2023-12-12T17:54:09.835182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1996 9
 
8.5%
2019 9
 
8.5%
2018 8
 
7.5%
1993 8
 
7.5%
1994 7
 
6.6%
2008 6
 
5.7%
2017 6
 
5.7%
2004 5
 
4.7%
1998 5
 
4.7%
2009 4
 
3.8%
Other values (16) 39
36.8%
ValueCountFrequency (%)
1992 2
 
1.9%
1993 8
7.5%
1994 7
6.6%
1995 4
3.8%
1996 9
8.5%
1997 1
 
0.9%
1998 5
4.7%
2000 1
 
0.9%
2002 2
 
1.9%
2003 4
3.8%
ValueCountFrequency (%)
2021 2
 
1.9%
2020 2
 
1.9%
2019 9
8.5%
2018 8
7.5%
2017 6
5.7%
2016 4
3.8%
2015 2
 
1.9%
2014 2
 
1.9%
2013 2
 
1.9%
2011 2
 
1.9%

주소
Text

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size980.0 B
2023-12-12T17:54:10.201081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.2830189
Min length5

Characters and Unicode

Total characters772
Distinct characters69
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

Unique106 ?
Unique (%)100.0%

Sample

1st row이화5길 63
2nd row약수9길 20
3rd row호계로 371
4th row매곡본길 28
5th row갈밭길 1
ValueCountFrequency (%)
달천로 7
 
3.3%
화산로 6
 
2.8%
신천로 6
 
2.8%
호계로 6
 
2.8%
천곡길 5
 
2.4%
천곡남로 5
 
2.4%
이화5길 4
 
1.9%
상방로 4
 
1.9%
아진로 4
 
1.9%
박상진3로 4
 
1.9%
Other values (128) 160
75.8%
2023-12-12T17:54:10.767971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
105
 
13.6%
로 74
 
9.6%
1 60
 
7.8%
2 41
 
5.3%
3 39
 
5.1%
5 38
 
4.9%
길 32
 
4.1%
천 24
 
3.1%
0 24
 
3.1%
7 21
 
2.7%
Other values (59) 314
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 360
46.6%
Decimal Number 295
38.2%
Space Separator 105
 
13.6%
Dash Punctuation 12
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 74
20.6%
길 32
 
8.9%
천 24
 
6.7%
산 19
 
5.3%
곡 18
 
5.0%
화 16
 
4.4%
진 11
 
3.1%
상 11
 
3.1%
호 10
 
2.8%
동 10
 
2.8%
Other values (47) 135
37.5%
Decimal Number
ValueCountFrequency (%)
1 60
20.3%
2 41
13.9%
3 39
13.2%
5 38
12.9%
0 24
 
8.1%
7 21
 
7.1%
9 21
 
7.1%
6 21
 
7.1%
4 17
 
5.8%
8 13
 
4.4%
Space Separator
ValueCountFrequency (%)
105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 412
53.4%
Hangul 360
46.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 74
20.6%
길 32
 
8.9%
천 24
 
6.7%
산 19
 
5.3%
곡 18
 
5.0%
화 16
 
4.4%
진 11
 
3.1%
상 11
 
3.1%
호 10
 
2.8%
동 10
 
2.8%
Other values (47) 135
37.5%
Common
ValueCountFrequency (%)
105
25.5%
1 60
14.6%
2 41
 
10.0%
3 39
 
9.5%
5 38
 
9.2%
0 24
 
5.8%
7 21
 
5.1%
9 21
 
5.1%
6 21
 
5.1%
4 17
 
4.1%
Other values (2) 25
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 412
53.4%
Hangul 360
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
105
25.5%
1 60
14.6%
2 41
 
10.0%
3 39
 
9.5%
5 38
 
9.2%
0 24
 
5.8%
7 21
 
5.1%
9 21
 
5.1%
6 21
 
5.1%
4 17
 
4.1%
Other values (2) 25
 
6.1%
Hangul
ValueCountFrequency (%)
로 74
20.6%
길 32
 
8.9%
천 24
 
6.7%
산 19
 
5.3%
곡 18
 
5.0%
화 16
 
4.4%
진 11
 
3.1%
상 11
 
3.1%
호 10
 
2.8%
동 10
 
2.8%
Other values (47) 135
37.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size980.0 B
2022-08-30
106 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-30
2nd row2022-08-30
3rd row2022-08-30
4th row2022-08-30
5th row2022-08-30

Common Values

ValueCountFrequency (%)
2022-08-30 106
100.0%

Length

2023-12-12T17:54:10.918298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:54:11.021581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-30 106
100.0%

Interactions

2023-12-12T17:54:06.881652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:06.735244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:06.952994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:54:06.807033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:54:11.095473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수동수준공연도
세대수1.0000.9960.939
동수0.9961.0000.359
준공연도0.9390.3591.000
2023-12-12T17:54:11.209544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동수준공연도
동수1.0000.309
준공연도0.3091.000

Missing values

2023-12-12T17:54:07.306592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:54:07.459792image/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

지자체 구분아파트명세대수동수준공연도주소데이터기준일자
0울산북구이화제일그린파크18011992이화5길 632022-08-30
1울산북구약수제일그린파크아파트36431992약수9길 202022-08-30
2울산북구신동아파트33021993호계로 3712022-08-30
3울산북구대하그린파크아파트40431993매곡본길 282022-08-30
4울산북구한라신천지타운23911993갈밭길 12022-08-30
5울산북구일지리버타워22511993천곡길 51-22022-08-30
6울산북구성우현대아파트49931993호계로 3132022-08-30
7울산북구백산그린타워27421993화정1길 12-22022-08-30
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