Overview

Dataset statistics

Number of variables9
Number of observations175
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.1 KiB
Average record size in memory76.8 B

Variable types

Numeric4
Categorical1
Text4

Dataset

Description경기도 안양시 관내 의무적관리대상 공동주택 현황(일련번호 구분 아파트명, 새주소, 준공년도, 동수, 세대수 ,관리사무소, 관리사무소 팩스번호 등) 정보 데이터 입니다.
Author경기도 안양시
URLhttps://www.data.go.kr/data/3045074/fileData.do

Alerts

일련번호 is highly overall correlated with 구분High correlation
동수 is highly overall correlated with 세대수High correlation
세대수 is highly overall correlated with 동수High correlation
구분 is highly overall correlated with 일련번호High correlation
일련번호 has unique valuesUnique

Reproduction

Analysis started2023-12-16 15:12:05.818404
Analysis finished2023-12-16 15:12:15.573752
Duration9.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct175
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88
Minimum1
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-16T15:12:15.957372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.7
Q144.5
median88
Q3131.5
95-th percentile166.3
Maximum175
Range174
Interquartile range (IQR)87

Descriptive statistics

Standard deviation50.662281
Coefficient of variation (CV)0.57570773
Kurtosis-1.2
Mean88
Median Absolute Deviation (MAD)44
Skewness0
Sum15400
Variance2566.6667
MonotonicityStrictly increasing
2023-12-16T15:12:17.100228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
2 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
120 1
 
0.6%
Other values (165) 165
94.3%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%

구분
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
호계2동
 
10
평안동
 
10
귀인동
 
9
석수1동
 
9
박달2동
 
9
Other values (26)
128 

Length

Max length4
Median length4
Mean length3.64
Min length3

Unique

Unique2 ?
Unique (%)1.1%

Sample

1st row안양1동
2nd row안양1동
3rd row안양1동
4th row안양2동
5th row안양2동

Common Values

ValueCountFrequency (%)
호계2동 10
 
5.7%
평안동 10
 
5.7%
귀인동 9
 
5.1%
석수1동 9
 
5.1%
박달2동 9
 
5.1%
비산2동 8
 
4.6%
부림동 8
 
4.6%
범계동 8
 
4.6%
안양9동 7
 
4.0%
신촌동 7
 
4.0%
Other values (21) 90
51.4%

Length

2023-12-16T15:12:17.892148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
호계2동 10
 
5.7%
평안동 10
 
5.7%
귀인동 9
 
5.1%
석수1동 9
 
5.1%
박달2동 9
 
5.1%
비산2동 8
 
4.6%
부림동 8
 
4.6%
범계동 8
 
4.6%
안양9동 7
 
4.0%
신촌동 7
 
4.0%
Other values (21) 90
51.4%

아파트명
Text

Distinct174
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-16T15:12:19.154780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.5485714
Min length2

Characters and Unicode

Total characters1146
Distinct characters196
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)98.9%

Sample

1st row삼성래미안
2nd row안양 주공뜨란채
3rd row안양역 한양수자인 리버파크
4th row안양동 삼성
5th row안양대우
ValueCountFrequency (%)
안양 3
 
1.6%
흥화 2
 
1.1%
삼성 2
 
1.1%
공작마을럭키 1
 
0.5%
평촌더샵센트럴시티 1
 
0.5%
목련7단지우성3차 1
 
0.5%
공작마을부영2차 1
 
0.5%
평촌삼성래미안 1
 
0.5%
한가람한양6차 1
 
0.5%
한가람삼성 1
 
0.5%
Other values (173) 173
92.5%
2023-12-16T15:12:20.791389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
한 33
 
2.9%
성 30
 
2.6%
차 30
 
2.6%
마 29
 
2.5%
대 25
 
2.2%
을 24
 
2.1%
수 24
 
2.1%
양 23
 
2.0%
산 23
 
2.0%
원 20
 
1.7%
Other values (186) 885
77.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1069
93.3%
Decimal Number 45
 
3.9%
Space Separator 12
 
1.0%
Other Punctuation 6
 
0.5%
Lowercase Letter 5
 
0.4%
Uppercase Letter 4
 
0.3%
Dash Punctuation 3
 
0.3%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 33
 
3.1%
성 30
 
2.8%
차 30
 
2.8%
마 29
 
2.7%
대 25
 
2.3%
을 24
 
2.2%
수 24
 
2.2%
양 23
 
2.2%
산 23
 
2.2%
원 20
 
1.9%
Other values (168) 808
75.6%
Decimal Number
ValueCountFrequency (%)
2 14
31.1%
1 12
26.7%
3 6
13.3%
4 4
 
8.9%
5 4
 
8.9%
6 2
 
4.4%
7 1
 
2.2%
9 1
 
2.2%
8 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 4
66.7%
, 2
33.3%
Uppercase Letter
ValueCountFrequency (%)
G 2
50.0%
L 2
50.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1069
93.3%
Common 68
 
5.9%
Latin 9
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 33
 
3.1%
성 30
 
2.8%
차 30
 
2.8%
마 29
 
2.7%
대 25
 
2.3%
을 24
 
2.2%
수 24
 
2.2%
양 23
 
2.2%
산 23
 
2.2%
원 20
 
1.9%
Other values (168) 808
75.6%
Common
ValueCountFrequency (%)
2 14
20.6%
12
17.6%
1 12
17.6%
3 6
8.8%
. 4
 
5.9%
4 4
 
5.9%
5 4
 
5.9%
- 3
 
4.4%
, 2
 
2.9%
6 2
 
2.9%
Other values (5) 5
 
7.4%
Latin
ValueCountFrequency (%)
e 5
55.6%
G 2
 
22.2%
L 2
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1069
93.3%
ASCII 77
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
한 33
 
3.1%
성 30
 
2.8%
차 30
 
2.8%
마 29
 
2.7%
대 25
 
2.3%
을 24
 
2.2%
수 24
 
2.2%
양 23
 
2.2%
산 23
 
2.2%
원 20
 
1.9%
Other values (168) 808
75.6%
ASCII
ValueCountFrequency (%)
2 14
18.2%
12
15.6%
1 12
15.6%
3 6
7.8%
e 5
 
6.5%
. 4
 
5.2%
4 4
 
5.2%
5 4
 
5.2%
- 3
 
3.9%
, 2
 
2.6%
Other values (8) 11
14.3%

새주소
Text

Distinct173
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-16T15:12:21.806340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length12.697143
Min length6

Characters and Unicode

Total characters2222
Distinct characters80
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)97.7%

Sample

1st row만안구 안양천서로 311
2nd row만안구 안양천서로 289
3rd row만안구 안양천서로 357
4th row만안구 예술공원로 59
5th row만안구 경수대로1001번길 116
ValueCountFrequency (%)
동안구 106
 
20.0%
만안구 64
 
12.1%
동안로 10
 
1.9%
경수대로 9
 
1.7%
달안로 9
 
1.7%
귀인로 8
 
1.5%
12 7
 
1.3%
학의로 7
 
1.3%
부림로 6
 
1.1%
만안로 5
 
0.9%
Other values (201) 298
56.3%
2023-12-16T15:12:23.832120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
354
15.9%
안 222
 
10.0%
로 176
 
7.9%
구 171
 
7.7%
1 126
 
5.7%
동 125
 
5.6%
2 80
 
3.6%
만 69
 
3.1%
3 66
 
3.0%
길 66
 
3.0%
Other values (70) 767
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1257
56.6%
Decimal Number 605
27.2%
Space Separator 354
 
15.9%
Dash Punctuation 3
 
0.1%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 222
17.7%
로 176
14.0%
구 171
13.6%
동 125
9.9%
만 69
 
5.5%
길 66
 
5.3%
번 65
 
5.2%
대 50
 
4.0%
수 28
 
2.2%
경 27
 
2.1%
Other values (56) 258
20.5%
Decimal Number
ValueCountFrequency (%)
1 126
20.8%
2 80
13.2%
3 66
10.9%
4 57
9.4%
0 51
8.4%
5 51
8.4%
7 49
 
8.1%
6 45
 
7.4%
9 40
 
6.6%
8 40
 
6.6%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
354
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1257
56.6%
Common 965
43.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 222
17.7%
로 176
14.0%
구 171
13.6%
동 125
9.9%
만 69
 
5.5%
길 66
 
5.3%
번 65
 
5.2%
대 50
 
4.0%
수 28
 
2.2%
경 27
 
2.1%
Other values (56) 258
20.5%
Common
ValueCountFrequency (%)
354
36.7%
1 126
 
13.1%
2 80
 
8.3%
3 66
 
6.8%
4 57
 
5.9%
0 51
 
5.3%
5 51
 
5.3%
7 49
 
5.1%
6 45
 
4.7%
9 40
 
4.1%
Other values (4) 46
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1257
56.6%
ASCII 965
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
354
36.7%
1 126
 
13.1%
2 80
 
8.3%
3 66
 
6.8%
4 57
 
5.9%
0 51
 
5.3%
5 51
 
5.3%
7 49
 
5.1%
6 45
 
4.7%
9 40
 
4.1%
Other values (4) 46
 
4.8%
Hangul
ValueCountFrequency (%)
안 222
17.7%
로 176
14.0%
구 171
13.6%
동 125
9.9%
만 69
 
5.5%
길 66
 
5.3%
번 65
 
5.2%
대 50
 
4.0%
수 28
 
2.2%
경 27
 
2.1%
Other values (56) 258
20.5%

준공년도
Real number (ℝ)

Distinct37
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2000.7657
Minimum1979
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-16T15:12:25.230679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1979
5-th percentile1990.7
Q11993
median2000
Q32006
95-th percentile2021
Maximum2023
Range44
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.7589906
Coefficient of variation (CV)0.0048776279
Kurtosis-0.35311201
Mean2000.7657
Median Absolute Deviation (MAD)7
Skewness0.66078282
Sum350134
Variance95.237898
MonotonicityNot monotonic
2023-12-16T15:12:26.423372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1992 28
 
16.0%
1993 21
 
12.0%
2003 10
 
5.7%
1996 8
 
4.6%
2000 8
 
4.6%
2001 7
 
4.0%
2002 6
 
3.4%
2021 6
 
3.4%
2006 5
 
2.9%
1999 5
 
2.9%
Other values (27) 71
40.6%
ValueCountFrequency (%)
1979 1
 
0.6%
1985 3
 
1.7%
1986 1
 
0.6%
1987 3
 
1.7%
1990 1
 
0.6%
1991 3
 
1.7%
1992 28
16.0%
1993 21
12.0%
1994 5
 
2.9%
1995 4
 
2.3%
ValueCountFrequency (%)
2023 1
 
0.6%
2022 4
2.3%
2021 6
3.4%
2020 2
 
1.1%
2019 3
1.7%
2018 1
 
0.6%
2017 2
 
1.1%
2016 2
 
1.1%
2015 2
 
1.1%
2014 1
 
0.6%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0342857
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-16T15:12:27.626801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile20
Maximum44
Range43
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.8815071
Coefficient of variation (CV)0.85651759
Kurtosis9.5321141
Mean8.0342857
Median Absolute Deviation (MAD)3
Skewness2.560781
Sum1406
Variance47.35514
MonotonicityNot monotonic
2023-12-16T15:12:28.562111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
6 21
12.0%
1 19
10.9%
10 16
9.1%
8 15
 
8.6%
2 14
 
8.0%
7 13
 
7.4%
5 11
 
6.3%
4 10
 
5.7%
3 9
 
5.1%
9 9
 
5.1%
Other values (15) 38
21.7%
ValueCountFrequency (%)
1 19
10.9%
2 14
8.0%
3 9
5.1%
4 10
5.7%
5 11
6.3%
6 21
12.0%
7 13
7.4%
8 15
8.6%
9 9
5.1%
10 16
9.1%
ValueCountFrequency (%)
44 2
1.1%
35 1
 
0.6%
34 1
 
0.6%
25 1
 
0.6%
24 2
1.1%
21 1
 
0.6%
20 3
1.7%
18 1
 
0.6%
17 2
1.1%
16 1
 
0.6%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean700.91429
Minimum152
Maximum4250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-16T15:12:29.814899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum152
5-th percentile175.9
Q1289
median498
Q3827.5
95-th percentile1929
Maximum4250
Range4098
Interquartile range (IQR)538.5

Descriptive statistics

Standard deviation665.44717
Coefficient of variation (CV)0.94939879
Kurtosis9.9955181
Mean700.91429
Median Absolute Deviation (MAD)252
Skewness2.8101934
Sum122660
Variance442819.94
MonotonicityNot monotonic
2023-12-16T15:12:30.650331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 3
 
1.7%
386 3
 
1.7%
392 2
 
1.1%
271 2
 
1.1%
436 2
 
1.1%
200 2
 
1.1%
370 2
 
1.1%
536 2
 
1.1%
203 2
 
1.1%
752 2
 
1.1%
Other values (150) 153
87.4%
ValueCountFrequency (%)
152 1
0.6%
153 1
0.6%
154 1
0.6%
159 1
0.6%
160 1
0.6%
162 1
0.6%
167 1
0.6%
168 1
0.6%
171 1
0.6%
178 1
0.6%
ValueCountFrequency (%)
4250 1
0.6%
3850 1
0.6%
3806 1
0.6%
3227 1
0.6%
2637 1
0.6%
2047 1
0.6%
1998 1
0.6%
1996 1
0.6%
1978 1
0.6%
1908 1
0.6%

관리사무소 전화번호
Text

Distinct174
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-16T15:12:31.835698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length12.034286
Min length12

Characters and Unicode

Total characters2106
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

Unique173 ?
Unique (%)98.9%

Sample

1st row031-466-2831
2nd row031-441-6202
3rd row031-468-9912
4th row031-471-5898
5th row031-473-3582
ValueCountFrequency (%)
031-341-2141 2
 
1.1%
031-381-1667 1
 
0.6%
031-383-4491 1
 
0.6%
031-382-6816 1
 
0.6%
031-424-6487 1
 
0.6%
031-422-9550 1
 
0.6%
031-386-0193 1
 
0.6%
031-385-8467 1
 
0.6%
031-382-3282 1
 
0.6%
031-387-1353 1
 
0.6%
Other values (164) 164
93.7%
2023-12-16T15:12:33.713918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 351
16.7%
3 320
15.2%
1 303
14.4%
0 268
12.7%
4 227
10.8%
2 132
 
6.3%
8 120
 
5.7%
7 115
 
5.5%
6 105
 
5.0%
9 83
 
3.9%
Other values (2) 82
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1754
83.3%
Dash Punctuation 351
 
16.7%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 320
18.2%
1 303
17.3%
0 268
15.3%
4 227
12.9%
2 132
7.5%
8 120
 
6.8%
7 115
 
6.6%
6 105
 
6.0%
9 83
 
4.7%
5 81
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 351
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2106
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 351
16.7%
3 320
15.2%
1 303
14.4%
0 268
12.7%
4 227
10.8%
2 132
 
6.3%
8 120
 
5.7%
7 115
 
5.5%
6 105
 
5.0%
9 83
 
3.9%
Other values (2) 82
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 351
16.7%
3 320
15.2%
1 303
14.4%
0 268
12.7%
4 227
10.8%
2 132
 
6.3%
8 120
 
5.7%
7 115
 
5.5%
6 105
 
5.0%
9 83
 
3.9%
Other values (2) 82
 
3.9%

관리사무소 팩스번호
Text

Distinct174
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-16T15:12:34.673107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.005714
Min length12

Characters and Unicode

Total characters2101
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)98.9%

Sample

1st row031-466-2832
2nd row031-441-6203
3rd row031-468-9914
4th row031-347-2593
5th row031-341-3582
ValueCountFrequency (%)
031-455-2141 2
 
1.1%
031-383-0248 1
 
0.6%
031-383-4494 1
 
0.6%
031-387-5694 1
 
0.6%
031-424-6488 1
 
0.6%
031-422-9552 1
 
0.6%
031-386-0194 1
 
0.6%
031-386-8857 1
 
0.6%
031-383-5258 1
 
0.6%
031-387-4463 1
 
0.6%
Other values (164) 164
93.7%
2023-12-16T15:12:36.490128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 350
16.7%
3 327
15.6%
1 275
13.1%
4 254
12.1%
0 252
12.0%
2 128
 
6.1%
6 117
 
5.6%
8 114
 
5.4%
7 103
 
4.9%
5 97
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1751
83.3%
Dash Punctuation 350
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 327
18.7%
1 275
15.7%
4 254
14.5%
0 252
14.4%
2 128
 
7.3%
6 117
 
6.7%
8 114
 
6.5%
7 103
 
5.9%
5 97
 
5.5%
9 84
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 350
16.7%
3 327
15.6%
1 275
13.1%
4 254
12.1%
0 252
12.0%
2 128
 
6.1%
6 117
 
5.6%
8 114
 
5.4%
7 103
 
4.9%
5 97
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 350
16.7%
3 327
15.6%
1 275
13.1%
4 254
12.1%
0 252
12.0%
2 128
 
6.1%
6 117
 
5.6%
8 114
 
5.4%
7 103
 
4.9%
5 97
 
4.6%

Interactions

2023-12-16T15:12:12.048146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:07.117026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:08.806098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:10.380911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:12.552463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:07.780660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:09.329382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:10.876099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:13.121414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:08.133519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:09.632877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:11.314774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:13.691278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:08.438546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:10.012067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:12:11.613201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:12:37.050680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호구분준공년도동수세대수
일련번호1.0000.9790.5650.3820.216
구분0.9791.0000.7950.5150.441
준공년도0.5650.7951.0000.3630.340
동수0.3820.5150.3631.0000.816
세대수0.2160.4410.3400.8161.000
2023-12-16T15:12:37.703142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호준공년도동수세대수구분
일련번호1.000-0.2060.3380.2620.802
준공년도-0.2061.000-0.085-0.1210.384
동수0.338-0.0851.0000.8940.209
세대수0.262-0.1210.8941.0000.168
구분0.8020.3840.2090.1681.000

Missing values

2023-12-16T15:12:14.356494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:12:15.121848image/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

일련번호구분아파트명새주소준공년도동수세대수관리사무소 전화번호관리사무소 팩스번호
01안양1동삼성래미안만안구 안양천서로 3112002171998031-466-2831031-466-2832
12안양1동안양 주공뜨란채만안구 안양천서로 2892004111093031-441-6202031-441-6203
23안양1동안양역 한양수자인 리버파크만안구 안양천서로 35720194419031-468-9912031-468-9914
34안양2동안양동 삼성만안구 예술공원로 5919944366031-471-5898031-347-2593
45안양2동안양대우만안구 경수대로1001번길 11619915498031-473-3582031-341-3582
56안양2동경남아너스빌만안구 태평로 21420012248031-464-2134031-342-8596
67안양2동미래엠피아만안구 만안로 27220031167031-466-0036031-441-0036
78안양3동흥화만안구 장내로 5619961152031-448-7697031-448-7697
89안양3동성원1차만안구 병목안로 8119958934031-441-4668031-464-5554
910안양3동진흥5차만안구 양화로71번길 2419875432031-443-2295031-443-2291
일련번호구분아파트명새주소준공년도동수세대수관리사무소 전화번호관리사무소 팩스번호
165166갈산동샘마을대우동안구 흥안대로223번길 48199310536031-341-2141031-455-2141
166167비산2동평촌래미안푸르지오동안구 경수대로884번길 122021101199031-360-5016031-360-5026
167168비산2동한양수자인평촌리버뷰동안구 경수대로813번길 14-1120214304031-442-9963031-442-9964
168169비산1동평촌자이아이파크동안구 비산로 222021212637031-464-2226031-464-2227
169170호계3동평촌두산위브리버뷰동안구 엘에스로45번길 2020228855031-429-7713031-429-7714
170171비산1동힐스테이트비산파크뷰동안구 임곡로 6020225303031-465-9001031-465-9002
171172안양7동광신프로그래스리버뷰만안구 전파로61번길 2020213230031-447-2800031-447-2801
172173안양2동두산위브더아티움아파트만안구 예술공원로 5120226558031-471-2822031-471-2823
173174안양2동아르테자이아파트만안구 경수대로1166번길102022121021031-471-9181031-471-9182
174175비산1동비산한신더휴아파트동안구 비산로 920232230031-468-9490031-468-9491