Overview

Dataset statistics

Number of variables11
Number of observations311
Missing cells90
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.8 KiB
Average record size in memory91.4 B

Variable types

Categorical2
Text5
Numeric3
DateTime1

Dataset

Description경기도 시흥시 아파트현황입니다.(경기도 시흥시 아파트현황은 관내 아파트 건물명, 지번주소, 도로명주소, 동수, 층수, 세대수, 연면적, 준공일자입니다.)
URLhttps://www.data.go.kr/data/15055106/fileData.do

Alerts

구분 has constant value ""Constant
데이터기준일자 has constant value ""Constant
동수 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 동수 and 1 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 동수 and 1 other fieldsHigh correlation
전화번호 has 90 (28.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 05:18:03.942043
Analysis finished2023-12-12 05:18:05.800650
Duration1.86 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
아파트
311 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 311
100.0%

Length

2023-12-12T14:18:05.865845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:18:05.959982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 311
100.0%

건물명
Text

Distinct293
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T14:18:06.209816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.8810289
Min length3

Characters and Unicode

Total characters2140
Distinct characters229
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

Unique282 ?
Unique (%)90.7%

Sample

1st row명성1차
2nd row명성2차
3rd row명성3차
4th row태광아파트
5th row제성아파트
ValueCountFrequency (%)
시화 14
 
3.6%
아파트 6
 
1.6%
휴먼시아 6
 
1.6%
동진아파트 5
 
1.3%
삼호아파트 5
 
1.3%
대우아파트 3
 
0.8%
목감 3
 
0.8%
시흥 3
 
0.8%
호반베르디움 3
 
0.8%
배곧 3
 
0.8%
Other values (306) 335
86.8%
2023-12-12T14:18:06.635685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 173
 
8.1%
트 168
 
7.9%
파 157
 
7.3%
차 88
 
4.1%
76
 
3.6%
시 43
 
2.0%
2 37
 
1.7%
1 36
 
1.7%
지 33
 
1.5%
동 33
 
1.5%
Other values (219) 1296
60.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1875
87.6%
Decimal Number 126
 
5.9%
Space Separator 76
 
3.6%
Uppercase Letter 26
 
1.2%
Close Punctuation 17
 
0.8%
Open Punctuation 17
 
0.8%
Dash Punctuation 1
 
< 0.1%
Lowercase Letter 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 173
 
9.2%
트 168
 
9.0%
파 157
 
8.4%
차 88
 
4.7%
시 43
 
2.3%
지 33
 
1.8%
동 33
 
1.8%
스 29
 
1.5%
우 28
 
1.5%
호 25
 
1.3%
Other values (200) 1098
58.6%
Decimal Number
ValueCountFrequency (%)
2 37
29.4%
1 36
28.6%
3 17
13.5%
4 14
 
11.1%
5 7
 
5.6%
6 7
 
5.6%
7 4
 
3.2%
9 2
 
1.6%
0 2
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
L 11
42.3%
H 11
42.3%
S 3
 
11.5%
K 1
 
3.8%
Space Separator
ValueCountFrequency (%)
76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1875
87.6%
Common 238
 
11.1%
Latin 27
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 173
 
9.2%
트 168
 
9.0%
파 157
 
8.4%
차 88
 
4.7%
시 43
 
2.3%
지 33
 
1.8%
동 33
 
1.8%
스 29
 
1.5%
우 28
 
1.5%
호 25
 
1.3%
Other values (200) 1098
58.6%
Common
ValueCountFrequency (%)
76
31.9%
2 37
15.5%
1 36
15.1%
) 17
 
7.1%
( 17
 
7.1%
3 17
 
7.1%
4 14
 
5.9%
5 7
 
2.9%
6 7
 
2.9%
7 4
 
1.7%
Other values (4) 6
 
2.5%
Latin
ValueCountFrequency (%)
L 11
40.7%
H 11
40.7%
S 3
 
11.1%
K 1
 
3.7%
e 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1875
87.6%
ASCII 265
 
12.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 173
 
9.2%
트 168
 
9.0%
파 157
 
8.4%
차 88
 
4.7%
시 43
 
2.3%
지 33
 
1.8%
동 33
 
1.8%
스 29
 
1.5%
우 28
 
1.5%
호 25
 
1.3%
Other values (200) 1098
58.6%
ASCII
ValueCountFrequency (%)
76
28.7%
2 37
14.0%
1 36
13.6%
) 17
 
6.4%
( 17
 
6.4%
3 17
 
6.4%
4 14
 
5.3%
L 11
 
4.2%
H 11
 
4.2%
5 7
 
2.6%
Other values (9) 22
 
8.3%

지번주소
Text

Distinct308
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T14:18:07.057254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length8.4790997
Min length4

Characters and Unicode

Total characters2637
Distinct characters59
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

Unique306 ?
Unique (%)98.4%

Sample

1st row신천동 69-9
2nd row신천동 69-4
3rd row신천동 882-5
4th row신천동 387
5th row신천동 26-3
ValueCountFrequency (%)
정왕동 75
 
11.8%
신천동 36
 
5.7%
은행동 34
 
5.4%
대야동 31
 
4.9%
조남동 19
 
3.0%
능곡동 17
 
2.7%
거모동 12
 
1.9%
매화동 11
 
1.7%
장곡동 10
 
1.6%
장현동 9
 
1.4%
Other values (318) 379
59.9%
2023-12-12T14:18:07.605721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
322
 
12.2%
동 304
 
11.5%
1 201
 
7.6%
2 155
 
5.9%
- 155
 
5.9%
8 150
 
5.7%
7 126
 
4.8%
6 118
 
4.5%
5 99
 
3.8%
4 91
 
3.5%
Other values (49) 916
34.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1179
44.7%
Other Letter 967
36.7%
Space Separator 322
 
12.2%
Dash Punctuation 155
 
5.9%
Open Punctuation 6
 
0.2%
Close Punctuation 6
 
0.2%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 304
31.4%
정 83
 
8.6%
왕 77
 
8.0%
신 36
 
3.7%
천 36
 
3.7%
행 35
 
3.6%
은 35
 
3.6%
곡 32
 
3.3%
대 31
 
3.2%
야 31
 
3.2%
Other values (34) 267
27.6%
Decimal Number
ValueCountFrequency (%)
1 201
17.0%
2 155
13.1%
8 150
12.7%
7 126
10.7%
6 118
10.0%
5 99
8.4%
4 91
7.7%
3 89
7.5%
9 76
 
6.4%
0 74
 
6.3%
Space Separator
ValueCountFrequency (%)
322
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 155
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1668
63.3%
Hangul 967
36.7%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 304
31.4%
정 83
 
8.6%
왕 77
 
8.0%
신 36
 
3.7%
천 36
 
3.7%
행 35
 
3.6%
은 35
 
3.6%
곡 32
 
3.3%
대 31
 
3.2%
야 31
 
3.2%
Other values (34) 267
27.6%
Common
ValueCountFrequency (%)
322
19.3%
1 201
12.1%
2 155
9.3%
- 155
9.3%
8 150
9.0%
7 126
 
7.6%
6 118
 
7.1%
5 99
 
5.9%
4 91
 
5.5%
3 89
 
5.3%
Other values (4) 162
9.7%
Latin
ValueCountFrequency (%)
B 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1670
63.3%
Hangul 967
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
322
19.3%
1 201
12.0%
2 155
9.3%
- 155
9.3%
8 150
9.0%
7 126
 
7.5%
6 118
 
7.1%
5 99
 
5.9%
4 91
 
5.4%
3 89
 
5.3%
Other values (5) 164
9.8%
Hangul
ValueCountFrequency (%)
동 304
31.4%
정 83
 
8.6%
왕 77
 
8.0%
신 36
 
3.7%
천 36
 
3.7%
행 35
 
3.6%
은 35
 
3.6%
곡 32
 
3.3%
대 31
 
3.2%
야 31
 
3.2%
Other values (34) 267
27.6%

도로명주소
Text

Distinct309
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T14:18:07.901274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length9.5659164
Min length5

Characters and Unicode

Total characters2975
Distinct characters117
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique307 ?
Unique (%)98.7%

Sample

1st row수인로3325번길 49
2nd row수인로3325번길 48-1
3rd row시흥대로 1083-6
4th row수인로3409번길 39
5th row신천로80번길 10-1
ValueCountFrequency (%)
중심상가로 13
 
2.1%
은행로 11
 
1.8%
동서로 10
 
1.6%
9 10
 
1.6%
은계중앙로 9
 
1.4%
8 8
 
1.3%
7 8
 
1.3%
36 8
 
1.3%
수인로3325번길 7
 
1.1%
4 7
 
1.1%
Other values (298) 532
85.4%
2023-12-12T14:18:08.345512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
320
 
10.8%
로 287
 
9.6%
1 243
 
8.2%
3 159
 
5.3%
길 159
 
5.3%
2 147
 
4.9%
4 126
 
4.2%
번 125
 
4.2%
5 86
 
2.9%
0 85
 
2.9%
Other values (107) 1238
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1456
48.9%
Decimal Number 1137
38.2%
Space Separator 320
 
10.8%
Dash Punctuation 60
 
2.0%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 287
19.7%
길 159
 
10.9%
번 125
 
8.6%
중 42
 
2.9%
대 39
 
2.7%
은 34
 
2.3%
신 30
 
2.1%
천 27
 
1.9%
정 27
 
1.9%
왕 27
 
1.9%
Other values (93) 659
45.3%
Decimal Number
ValueCountFrequency (%)
1 243
21.4%
3 159
14.0%
2 147
12.9%
4 126
11.1%
5 86
 
7.6%
0 85
 
7.5%
7 83
 
7.3%
9 80
 
7.0%
8 70
 
6.2%
6 58
 
5.1%
Space Separator
ValueCountFrequency (%)
320
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1519
51.1%
Hangul 1456
48.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 287
19.7%
길 159
 
10.9%
번 125
 
8.6%
중 42
 
2.9%
대 39
 
2.7%
은 34
 
2.3%
신 30
 
2.1%
천 27
 
1.9%
정 27
 
1.9%
왕 27
 
1.9%
Other values (93) 659
45.3%
Common
ValueCountFrequency (%)
320
21.1%
1 243
16.0%
3 159
10.5%
2 147
9.7%
4 126
 
8.3%
5 86
 
5.7%
0 85
 
5.6%
7 83
 
5.5%
9 80
 
5.3%
8 70
 
4.6%
Other values (4) 120
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1519
51.1%
Hangul 1456
48.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
320
21.1%
1 243
16.0%
3 159
10.5%
2 147
9.7%
4 126
 
8.3%
5 86
 
5.7%
0 85
 
5.6%
7 83
 
5.5%
9 80
 
5.3%
8 70
 
4.6%
Other values (4) 120
 
7.9%
Hangul
ValueCountFrequency (%)
로 287
19.7%
길 159
 
10.9%
번 125
 
8.6%
중 42
 
2.9%
대 39
 
2.7%
은 34
 
2.3%
신 30
 
2.1%
천 27
 
1.9%
정 27
 
1.9%
왕 27
 
1.9%
Other values (93) 659
45.3%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9292605
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T14:18:08.492639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q310
95-th percentile18
Maximum25
Range24
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.5603561
Coefficient of variation (CV)0.80244583
Kurtosis0.18291244
Mean6.9292605
Median Absolute Deviation (MAD)4
Skewness0.97691996
Sum2155
Variance30.91756
MonotonicityNot monotonic
2023-12-12T14:18:08.612930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 47
15.1%
1 47
15.1%
7 25
 
8.0%
5 25
 
8.0%
3 22
 
7.1%
6 20
 
6.4%
9 17
 
5.5%
8 15
 
4.8%
10 14
 
4.5%
11 11
 
3.5%
Other values (13) 68
21.9%
ValueCountFrequency (%)
1 47
15.1%
2 47
15.1%
3 22
7.1%
4 10
 
3.2%
5 25
8.0%
6 20
6.4%
7 25
8.0%
8 15
 
4.8%
9 17
 
5.5%
10 14
 
4.5%
ValueCountFrequency (%)
25 1
 
0.3%
22 1
 
0.3%
21 6
1.9%
20 5
1.6%
19 2
 
0.6%
18 7
2.3%
17 7
2.3%
16 4
1.3%
15 5
1.6%
14 7
2.3%

층수
Text

Distinct58
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T14:18:08.807375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.2893891
Min length1

Characters and Unicode

Total characters712
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

Unique29 ?
Unique (%)9.3%

Sample

1st row5
2nd row6
3rd row6
4th row5
5th row4
ValueCountFrequency (%)
10 59
19.0%
5 42
13.5%
20 34
10.9%
6 32
 
10.3%
15 15
 
4.8%
25 13
 
4.2%
18 11
 
3.5%
19 11
 
3.5%
12 7
 
2.3%
23~25 6
 
1.9%
Other values (48) 81
26.0%
2023-12-12T14:18:09.223039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 153
21.5%
1 149
20.9%
0 105
14.7%
5 103
14.5%
~ 55
 
7.7%
6 42
 
5.9%
9 40
 
5.6%
3 22
 
3.1%
4 18
 
2.5%
8 14
 
2.0%
Other values (2) 11
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 656
92.1%
Math Symbol 55
 
7.7%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 153
23.3%
1 149
22.7%
0 105
16.0%
5 103
15.7%
6 42
 
6.4%
9 40
 
6.1%
3 22
 
3.4%
4 18
 
2.7%
8 14
 
2.1%
7 10
 
1.5%
Math Symbol
ValueCountFrequency (%)
~ 55
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 712
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 153
21.5%
1 149
20.9%
0 105
14.7%
5 103
14.5%
~ 55
 
7.7%
6 42
 
5.9%
9 40
 
5.6%
3 22
 
3.1%
4 18
 
2.5%
8 14
 
2.0%
Other values (2) 11
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 153
21.5%
1 149
20.9%
0 105
14.7%
5 103
14.5%
~ 55
 
7.7%
6 42
 
5.9%
9 40
 
5.6%
3 22
 
3.1%
4 18
 
2.5%
8 14
 
2.0%
Other values (2) 11
 
1.5%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct236
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean476.35691
Minimum20
Maximum2701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T14:18:09.368284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile52.5
Q1168
median386
Q3657.5
95-th percentile1282
Maximum2701
Range2681
Interquartile range (IQR)489.5

Descriptive statistics

Standard deviation426.05109
Coefficient of variation (CV)0.89439467
Kurtosis6.2072364
Mean476.35691
Median Absolute Deviation (MAD)234
Skewness2.0052264
Sum148147
Variance181519.53
MonotonicityNot monotonic
2023-12-12T14:18:09.524993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 6
 
1.9%
70 5
 
1.6%
330 5
 
1.6%
580 4
 
1.3%
690 4
 
1.3%
40 3
 
1.0%
480 3
 
1.0%
75 3
 
1.0%
168 3
 
1.0%
84 3
 
1.0%
Other values (226) 272
87.5%
ValueCountFrequency (%)
20 1
 
0.3%
24 1
 
0.3%
30 2
0.6%
32 1
 
0.3%
36 2
0.6%
40 3
1.0%
46 1
 
0.3%
48 3
1.0%
50 2
0.6%
55 1
 
0.3%
ValueCountFrequency (%)
2701 1
0.3%
2695 1
0.3%
2560 1
0.3%
2003 1
0.3%
1725 1
0.3%
1719 1
0.3%
1647 1
0.3%
1594 1
0.3%
1445 1
0.3%
1442 1
0.3%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct308
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56671.413
Minimum1462
Maximum450728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T14:18:09.709812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1462
5-th percentile3286
Q114726.5
median44091
Q372225
95-th percentile164424
Maximum450728
Range449266
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation60883.03
Coefficient of variation (CV)1.0743164
Kurtosis12.308032
Mean56671.413
Median Absolute Deviation (MAD)29279
Skewness2.7850395
Sum17624809
Variance3.7067434 × 109
MonotonicityNot monotonic
2023-12-12T14:18:09.910829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15582.0 2
 
0.6%
159868.0 2
 
0.6%
2064.0 2
 
0.6%
2020.0 1
 
0.3%
48421.0 1
 
0.3%
68466.0 1
 
0.3%
101235.0 1
 
0.3%
78303.0 1
 
0.3%
182734.0 1
 
0.3%
260818.0 1
 
0.3%
Other values (298) 298
95.8%
ValueCountFrequency (%)
1462.0 1
0.3%
1558.0 1
0.3%
1958.0 1
0.3%
2020.0 1
0.3%
2041.0 1
0.3%
2064.0 2
0.6%
2124.0 1
0.3%
2385.0 1
0.3%
2764.0 1
0.3%
2793.0 1
0.3%
ValueCountFrequency (%)
450728.0 1
0.3%
446889.0 1
0.3%
353325.0 1
0.3%
260818.0 1
0.3%
238153.0 1
0.3%
235323.27 1
0.3%
219748.0 1
0.3%
205093.0 1
0.3%
202037.0 1
0.3%
197333.0 1
0.3%

준공일자
Date

Distinct274
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum1984-01-23 00:00:00
Maximum2023-02-23 00:00:00
2023-12-12T14:18:10.099971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:10.303185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

MISSING 

Distinct216
Distinct (%)97.7%
Missing90
Missing (%)28.9%
Memory size2.6 KiB
2023-12-12T14:18:10.614220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.004525
Min length12

Characters and Unicode

Total characters2653
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

Unique211 ?
Unique (%)95.5%

Sample

1st row031-498-3665
2nd row031-433-7133
3rd row031-497-3285
4th row031-497-3249
5th row031-497-3357
ValueCountFrequency (%)
031-504-7501 2
 
0.9%
031-318-6007 2
 
0.9%
031-314-0432 2
 
0.9%
031-404-6397 2
 
0.9%
031-431-3377 2
 
0.9%
031-317-4225 1
 
0.5%
031-317-7720 1
 
0.5%
031-503-0023 1
 
0.5%
031-498-7684 1
 
0.5%
031-317-3153 1
 
0.5%
Other values (206) 206
93.2%
2023-12-12T14:18:11.076823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 492
18.5%
- 442
16.7%
1 440
16.6%
0 350
13.2%
4 225
8.5%
9 150
 
5.7%
7 148
 
5.6%
8 129
 
4.9%
2 98
 
3.7%
5 97
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2211
83.3%
Dash Punctuation 442
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 492
22.3%
1 440
19.9%
0 350
15.8%
4 225
10.2%
9 150
 
6.8%
7 148
 
6.7%
8 129
 
5.8%
2 98
 
4.4%
5 97
 
4.4%
6 82
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 442
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2653
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 492
18.5%
- 442
16.7%
1 440
16.6%
0 350
13.2%
4 225
8.5%
9 150
 
5.7%
7 148
 
5.6%
8 129
 
4.9%
2 98
 
3.7%
5 97
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2653
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 492
18.5%
- 442
16.7%
1 440
16.6%
0 350
13.2%
4 225
8.5%
9 150
 
5.7%
7 148
 
5.6%
8 129
 
4.9%
2 98
 
3.7%
5 97
 
3.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-07-04
311 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-04
2nd row2023-07-04
3rd row2023-07-04
4th row2023-07-04
5th row2023-07-04

Common Values

ValueCountFrequency (%)
2023-07-04 311
100.0%

Length

2023-12-12T14:18:11.230522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:18:11.410835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-04 311
100.0%

Interactions

2023-12-12T14:18:04.966470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:04.376474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:04.694052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:05.067282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:04.487737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:04.787238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:05.162790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:04.594628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:04.864357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:18:11.496074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동수층수세대수연면적(제곱미터)
동수1.0000.6810.6840.617
층수0.6811.0000.9260.947
세대수0.6840.9261.0000.909
연면적(제곱미터)0.6170.9470.9091.000
2023-12-12T14:18:11.610096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동수세대수연면적(제곱미터)
동수1.0000.8160.788
세대수0.8161.0000.940
연면적(제곱미터)0.7880.9401.000

Missing values

2023-12-12T14:18:05.557244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:18:05.740485image/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

구분건물명지번주소도로명주소동수층수세대수연면적(제곱미터)준공일자전화번호데이터기준일자
0아파트명성1차신천동 69-9수인로3325번길 4925402020.01984-01-23<NA>2023-07-04
1아파트명성2차신천동 69-4수인로3325번길 48-116362385.01984-01-23<NA>2023-07-04
2아파트명성3차신천동 882-5시흥대로 1083-616302124.01984-11-19<NA>2023-07-04
3아파트태광아파트신천동 387수인로3409번길 393518010075.01986-08-13<NA>2023-07-04
4아파트제성아파트신천동 26-3신천로80번길 10-124321958.01986-10-13<NA>2023-07-04
5아파트장미아파트신천동 375-3신천3길 3125905583.01986-12-29<NA>2023-07-04
6아파트연희아파트신천동 277-4신천천동로25번길 176524516520.01987-03-18<NA>2023-07-04
7아파트제성아파트신천동 749-1수인로3413번길 2525603132.01987-08-04<NA>2023-07-04
8아파트동경1차신천동 37-3수인로3325번길 47-716482954.01987-09-12<NA>2023-07-04
9아파트무지개신천동 892-1두문로 815402064.01987-11-16<NA>2023-07-04
구분건물명지번주소도로명주소동수층수세대수연면적(제곱미터)준공일자전화번호데이터기준일자
301아파트장현 서희스타힐스(장현지구 지번 확정 전)장곡남로 35925887138162.672022-04-13<NA>2023-07-04
302아파트장현 엘에이치 트리플센텀장현동160시청로 7982054671290.582022-04-15031-315-09232023-07-04
303아파트시흥 센트럴 헤센월곶동 1064-1월곶중앙로 5572549471627.12022-09-27031-434-83782023-07-04
304아파트포레미엘 더파크장현동 588장현순환로 20710~2541355099.132022-11-29<NA>2023-07-04
305아파트유승한내들퍼스트파크장곡동 936장현장곡로 10725676127126.682022-11-23031-435-92272023-07-04
306아파트호반써밋더퍼스트오션정왕동 2712거북섬남로 45624~2957887469.52022-12-23031-497-66682023-07-04
307아파트영무예다음장곡동924장현장곡로 15825747118906.822022-12-29031-315-81112023-07-04
308아파트호반써밋 더프라임정왕동 2732거북섬남로 77829826124579.82023-01-26<NA>2023-07-04
309아파트금강펜테리움 오션베이정왕동 2737거북섬남로 90628~30930130893.352023-02-22031-431-19712023-07-04
310아파트파라곤 센트럴 오션시티정왕동 2711거북섬남로 11629656101112.222023-02-23<NA>2023-07-04