Overview

Dataset statistics

Number of variables13
Number of observations397
Missing cells80
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.0 KiB
Average record size in memory108.3 B

Variable types

Numeric4
Text5
Categorical3
DateTime1

Dataset

Description남양주시 공동주택 정보 데이터로 단지명, 지역, 지번주소, 도로명주소, 세대수, 층, 동, 주택유형 등의 항목을 제공합니다.
Author경기도 남양주시
URLhttps://www.data.go.kr/data/3074494/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
세대수 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
층 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
동 is highly overall correlated with 세대수High correlation
지역 is highly overall correlated with 주택유형High correlation
주택유형 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
주택유형 is highly imbalanced (78.2%)Imbalance
관리실전화번호 has 32 (8.1%) missing valuesMissing
관리실팩스번호 has 48 (12.1%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 13:23:40.520836
Analysis finished2024-03-14 13:23:46.263754
Duration5.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct397
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199
Minimum1
Maximum397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-03-14T22:23:46.489812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.8
Q1100
median199
Q3298
95-th percentile377.2
Maximum397
Range396
Interquartile range (IQR)198

Descriptive statistics

Standard deviation114.74828
Coefficient of variation (CV)0.5766245
Kurtosis-1.2
Mean199
Median Absolute Deviation (MAD)99
Skewness0
Sum79003
Variance13167.167
MonotonicityStrictly increasing
2024-03-14T22:23:46.948867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
274 1
 
0.3%
272 1
 
0.3%
271 1
 
0.3%
270 1
 
0.3%
269 1
 
0.3%
268 1
 
0.3%
267 1
 
0.3%
266 1
 
0.3%
265 1
 
0.3%
Other values (387) 387
97.5%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
397 1
0.3%
396 1
0.3%
395 1
0.3%
394 1
0.3%
393 1
0.3%
392 1
0.3%
391 1
0.3%
390 1
0.3%
389 1
0.3%
388 1
0.3%

단지명
Text

Distinct392
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-03-14T22:23:47.880801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length8.7758186
Min length2

Characters and Unicode

Total characters3484
Distinct characters289
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique388 ?
Unique (%)97.7%

Sample

1st row우신연립주택
2nd row남양
3rd row남양
4th row인정연립맨숀
5th row원일연립주택
ValueCountFrequency (%)
평내마을 4
 
0.9%
신우가든아파트 3
 
0.6%
호평마을 3
 
0.6%
1단지 3
 
0.6%
2단지 3
 
0.6%
다산한양수자인 2
 
0.4%
신안인스빌 2
 
0.4%
다산신안인스빌 2
 
0.4%
경기행복주택 2
 
0.4%
다산펜테리움 2
 
0.4%
Other values (430) 442
94.4%
2024-03-14T22:23:48.965253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 172
 
4.9%
아 169
 
4.9%
파 158
 
4.5%
지 100
 
2.9%
단 85
 
2.4%
스 72
 
2.1%
71
 
2.0%
산 62
 
1.8%
신 61
 
1.8%
주 61
 
1.8%
Other values (279) 2473
71.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3170
91.0%
Decimal Number 159
 
4.6%
Space Separator 71
 
2.0%
Close Punctuation 20
 
0.6%
Open Punctuation 20
 
0.6%
Uppercase Letter 20
 
0.6%
Dash Punctuation 14
 
0.4%
Lowercase Letter 10
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 172
 
5.4%
아 169
 
5.3%
파 158
 
5.0%
지 100
 
3.2%
단 85
 
2.7%
스 72
 
2.3%
산 62
 
2.0%
신 61
 
1.9%
주 61
 
1.9%
마 60
 
1.9%
Other values (255) 2170
68.5%
Decimal Number
ValueCountFrequency (%)
2 45
28.3%
1 44
27.7%
3 23
14.5%
4 13
 
8.2%
5 9
 
5.7%
7 7
 
4.4%
6 7
 
4.4%
8 4
 
2.5%
9 4
 
2.5%
0 3
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
S 7
35.0%
L 3
15.0%
H 2
 
10.0%
G 2
 
10.0%
C 2
 
10.0%
I 2
 
10.0%
K 1
 
5.0%
A 1
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
e 8
80.0%
i 2
 
20.0%
Space Separator
ValueCountFrequency (%)
71
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3170
91.0%
Common 284
 
8.2%
Latin 30
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 172
 
5.4%
아 169
 
5.3%
파 158
 
5.0%
지 100
 
3.2%
단 85
 
2.7%
스 72
 
2.3%
산 62
 
2.0%
신 61
 
1.9%
주 61
 
1.9%
마 60
 
1.9%
Other values (255) 2170
68.5%
Common
ValueCountFrequency (%)
71
25.0%
2 45
15.8%
1 44
15.5%
3 23
 
8.1%
) 20
 
7.0%
( 20
 
7.0%
- 14
 
4.9%
4 13
 
4.6%
5 9
 
3.2%
7 7
 
2.5%
Other values (4) 18
 
6.3%
Latin
ValueCountFrequency (%)
e 8
26.7%
S 7
23.3%
L 3
 
10.0%
H 2
 
6.7%
G 2
 
6.7%
C 2
 
6.7%
i 2
 
6.7%
I 2
 
6.7%
K 1
 
3.3%
A 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3170
91.0%
ASCII 314
 
9.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 172
 
5.4%
아 169
 
5.3%
파 158
 
5.0%
지 100
 
3.2%
단 85
 
2.7%
스 72
 
2.3%
산 62
 
2.0%
신 61
 
1.9%
주 61
 
1.9%
마 60
 
1.9%
Other values (255) 2170
68.5%
ASCII
ValueCountFrequency (%)
71
22.6%
2 45
14.3%
1 44
14.0%
3 23
 
7.3%
) 20
 
6.4%
( 20
 
6.4%
- 14
 
4.5%
4 13
 
4.1%
5 9
 
2.9%
e 8
 
2.5%
Other values (14) 47
15.0%

지역
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
다산
62 
화도
56 
진접
49 
오남
43 
별내동
39 
Other values (7)
148 

Length

Max length3
Median length2
Mean length2.1687657
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row와부
2nd row호평
3rd row호평
4th row와부
5th row다산

Common Values

ValueCountFrequency (%)
다산 62
15.6%
화도 56
14.1%
진접 49
12.3%
오남 43
10.8%
별내동 39
9.8%
와부 34
8.6%
호평 28
7.1%
금곡 27
6.8%
평내 18
 
4.5%
퇴계원 17
 
4.3%
Other values (2) 24
 
6.0%

Length

2024-03-14T22:23:49.383280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
다산 62
15.6%
화도 56
14.1%
진접 49
12.3%
오남 43
10.8%
별내동 39
9.8%
와부 34
8.6%
호평 28
7.1%
금곡 27
6.8%
평내 18
 
4.5%
퇴계원 17
 
4.3%
Other values (2) 24
 
6.0%

지번주소
Text

Distinct392
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-03-14T22:23:50.209101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length19.297229
Min length15

Characters and Unicode

Total characters7661
Distinct characters70
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

Unique390 ?
Unique (%)98.2%

Sample

1st row경기도 남양주시 와부읍 덕소리 409-2
2nd row경기도 남양주시 호평동 196-1
3rd row경기도 남양주시 호평동 196-4
4th row경기도 남양주시 와부읍 덕소리 421
5th row경기도 남양주시 다산동 4113-39
ValueCountFrequency (%)
경기도 397
21.9%
남양주시 397
21.9%
다산동 62
 
3.4%
화도읍 56
 
3.1%
진접읍 49
 
2.7%
오남읍 43
 
2.4%
별내동 38
 
2.1%
와부읍 34
 
1.9%
오남리 33
 
1.8%
호평동 28
 
1.5%
Other values (402) 674
37.2%
2024-03-14T22:23:51.151243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1415
18.5%
남 473
 
6.2%
도 461
 
6.0%
양 407
 
5.3%
경 397
 
5.2%
기 397
 
5.2%
주 397
 
5.2%
시 397
 
5.2%
1 233
 
3.0%
리 223
 
2.9%
Other values (60) 2861
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4691
61.2%
Decimal Number 1421
 
18.5%
Space Separator 1415
 
18.5%
Dash Punctuation 134
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 473
10.1%
도 461
 
9.8%
양 407
 
8.7%
경 397
 
8.5%
기 397
 
8.5%
주 397
 
8.5%
시 397
 
8.5%
리 223
 
4.8%
읍 212
 
4.5%
동 174
 
3.7%
Other values (48) 1153
24.6%
Decimal Number
ValueCountFrequency (%)
1 233
16.4%
6 191
13.4%
0 143
10.1%
7 143
10.1%
2 141
9.9%
5 137
9.6%
4 124
8.7%
3 114
8.0%
9 98
6.9%
8 97
6.8%
Space Separator
ValueCountFrequency (%)
1415
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4691
61.2%
Common 2970
38.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 473
10.1%
도 461
 
9.8%
양 407
 
8.7%
경 397
 
8.5%
기 397
 
8.5%
주 397
 
8.5%
시 397
 
8.5%
리 223
 
4.8%
읍 212
 
4.5%
동 174
 
3.7%
Other values (48) 1153
24.6%
Common
ValueCountFrequency (%)
1415
47.6%
1 233
 
7.8%
6 191
 
6.4%
0 143
 
4.8%
7 143
 
4.8%
2 141
 
4.7%
5 137
 
4.6%
- 134
 
4.5%
4 124
 
4.2%
3 114
 
3.8%
Other values (2) 195
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4691
61.2%
ASCII 2970
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1415
47.6%
1 233
 
7.8%
6 191
 
6.4%
0 143
 
4.8%
7 143
 
4.8%
2 141
 
4.7%
5 137
 
4.6%
- 134
 
4.5%
4 124
 
4.2%
3 114
 
3.8%
Other values (2) 195
 
6.6%
Hangul
ValueCountFrequency (%)
남 473
10.1%
도 461
 
9.8%
양 407
 
8.7%
경 397
 
8.5%
기 397
 
8.5%
주 397
 
8.5%
시 397
 
8.5%
리 223
 
4.8%
읍 212
 
4.5%
동 174
 
3.7%
Other values (48) 1153
24.6%

도로명주소
Text

Distinct382
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-03-14T22:23:52.083335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length11.783375
Min length5

Characters and Unicode

Total characters4678
Distinct characters101
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

Unique377 ?
Unique (%)95.0%

Sample

1st row수레로9번길 53
2nd row호평로69번길 10
3rd row호평로69번길 10
4th row와부읍 수레로9번길 38
5th row경춘로 414-26
ValueCountFrequency (%)
화도읍 56
 
5.5%
진접읍 48
 
4.8%
오남읍 43
 
4.3%
와부읍 31
 
3.1%
진건오남로 27
 
2.7%
덕소로 19
 
1.9%
퇴계원읍 16
 
1.6%
7 12
 
1.2%
별내면 11
 
1.1%
경춘로 11
 
1.1%
Other values (394) 736
72.9%
2024-03-14T22:23:53.303026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
613
 
13.1%
로 392
 
8.4%
1 361
 
7.7%
읍 205
 
4.4%
3 191
 
4.1%
2 187
 
4.0%
길 170
 
3.6%
번 155
 
3.3%
5 149
 
3.2%
8 128
 
2.7%
Other values (91) 2127
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2415
51.6%
Decimal Number 1553
33.2%
Space Separator 613
 
13.1%
Dash Punctuation 97
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 392
 
16.2%
읍 205
 
8.5%
길 170
 
7.0%
번 155
 
6.4%
진 108
 
4.5%
남 90
 
3.7%
오 90
 
3.7%
도 69
 
2.9%
화 61
 
2.5%
건 58
 
2.4%
Other values (79) 1017
42.1%
Decimal Number
ValueCountFrequency (%)
1 361
23.2%
3 191
12.3%
2 187
12.0%
5 149
9.6%
8 128
 
8.2%
4 127
 
8.2%
7 119
 
7.7%
6 115
 
7.4%
0 98
 
6.3%
9 78
 
5.0%
Space Separator
ValueCountFrequency (%)
613
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2415
51.6%
Common 2263
48.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 392
 
16.2%
읍 205
 
8.5%
길 170
 
7.0%
번 155
 
6.4%
진 108
 
4.5%
남 90
 
3.7%
오 90
 
3.7%
도 69
 
2.9%
화 61
 
2.5%
건 58
 
2.4%
Other values (79) 1017
42.1%
Common
ValueCountFrequency (%)
613
27.1%
1 361
16.0%
3 191
 
8.4%
2 187
 
8.3%
5 149
 
6.6%
8 128
 
5.7%
4 127
 
5.6%
7 119
 
5.3%
6 115
 
5.1%
0 98
 
4.3%
Other values (2) 175
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2415
51.6%
ASCII 2263
48.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
613
27.1%
1 361
16.0%
3 191
 
8.4%
2 187
 
8.3%
5 149
 
6.6%
8 128
 
5.7%
4 127
 
5.6%
7 119
 
5.3%
6 115
 
5.1%
0 98
 
4.3%
Other values (2) 175
 
7.7%
Hangul
ValueCountFrequency (%)
로 392
 
16.2%
읍 205
 
8.5%
길 170
 
7.0%
번 155
 
6.4%
진 108
 
4.5%
남 90
 
3.7%
오 90
 
3.7%
도 69
 
2.9%
화 61
 
2.5%
건 58
 
2.4%
Other values (79) 1017
42.1%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct310
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean519.86902
Minimum18
Maximum2894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-03-14T22:23:53.547384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile53.4
Q1228
median440
Q3708
95-th percentile1262
Maximum2894
Range2876
Interquartile range (IQR)480

Descriptive statistics

Standard deviation402.02392
Coefficient of variation (CV)0.77331771
Kurtosis3.8215319
Mean519.86902
Median Absolute Deviation (MAD)226
Skewness1.5067726
Sum206388
Variance161623.23
MonotonicityNot monotonic
2024-03-14T22:23:53.883416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198 5
 
1.3%
60 4
 
1.0%
96 4
 
1.0%
24 3
 
0.8%
240 3
 
0.8%
388 3
 
0.8%
330 3
 
0.8%
352 3
 
0.8%
291 3
 
0.8%
499 3
 
0.8%
Other values (300) 363
91.4%
ValueCountFrequency (%)
18 1
 
0.3%
22 1
 
0.3%
24 3
0.8%
29 1
 
0.3%
30 2
0.5%
33 2
0.5%
39 2
0.5%
42 1
 
0.3%
45 2
0.5%
48 3
0.8%
ValueCountFrequency (%)
2894 1
0.3%
2078 1
0.3%
2075 1
0.3%
1997 1
0.3%
1685 1
0.3%
1620 1
0.3%
1615 1
0.3%
1614 1
0.3%
1488 1
0.3%
1484 1
0.3%

층
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.337531
Minimum3
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-03-14T22:23:54.327493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q115
median19
Q321
95-th percentile30
Maximum46
Range43
Interquartile range (IQR)6

Descriptive statistics

Standard deviation8.0664492
Coefficient of variation (CV)0.46525938
Kurtosis-0.080002574
Mean17.337531
Median Absolute Deviation (MAD)4
Skewness-0.075921719
Sum6883
Variance65.067603
MonotonicityNot monotonic
2024-03-14T22:23:54.537514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20 75
18.9%
15 72
18.1%
5 30
 
7.6%
3 26
 
6.5%
18 24
 
6.0%
29 21
 
5.3%
22 18
 
4.5%
25 16
 
4.0%
21 16
 
4.0%
19 13
 
3.3%
Other values (22) 86
21.7%
ValueCountFrequency (%)
3 26
6.5%
4 10
 
2.5%
5 30
7.6%
6 6
 
1.5%
7 1
 
0.3%
8 4
 
1.0%
9 2
 
0.5%
10 1
 
0.3%
11 2
 
0.5%
12 5
 
1.3%
ValueCountFrequency (%)
46 1
 
0.3%
40 2
 
0.5%
35 2
 
0.5%
33 5
 
1.3%
32 1
 
0.3%
30 11
2.8%
29 21
5.3%
28 3
 
0.8%
27 4
 
1.0%
26 1
 
0.3%

동
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.02267
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-03-14T22:23:54.758688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile16
Maximum49
Range48
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.4152786
Coefficient of variation (CV)0.77111392
Kurtosis16.449157
Mean7.02267
Median Absolute Deviation (MAD)3
Skewness2.9501365
Sum2788
Variance29.325242
MonotonicityNot monotonic
2024-03-14T22:23:55.053308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5 45
11.3%
4 40
10.1%
7 40
10.1%
2 39
9.8%
3 37
9.3%
6 33
8.3%
8 25
 
6.3%
1 23
 
5.8%
9 23
 
5.8%
10 22
 
5.5%
Other values (17) 70
17.6%
ValueCountFrequency (%)
1 23
5.8%
2 39
9.8%
3 37
9.3%
4 40
10.1%
5 45
11.3%
6 33
8.3%
7 40
10.1%
8 25
6.3%
9 23
5.8%
10 22
5.5%
ValueCountFrequency (%)
49 1
 
0.3%
46 1
 
0.3%
33 1
 
0.3%
25 2
0.5%
23 1
 
0.3%
22 1
 
0.3%
21 1
 
0.3%
20 1
 
0.3%
19 1
 
0.3%
18 3
0.8%

주택유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
<NA>
353 
연립
37 
 
2
건축법
 
2
도시형
 
1
Other values (2)
 
2

Length

Max length13
Median length4
Mean length3.8337531
Min length1

Unique

Unique3 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 353
88.9%
연립 37
 
9.3%
2
 
0.5%
건축법 2
 
0.5%
도시형 1
 
0.3%
도시 및 주거환경정비법 1
 
0.3%
도시 및 주거환경정비법 1
 
0.3%

Length

2024-03-14T22:23:55.276435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:23:55.484423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 353
88.5%
연립 37
 
9.3%
건축법 2
 
0.5%
도시 2
 
0.5%
및 2
 
0.5%
주거환경정비법 2
 
0.5%
도시형 1
 
0.3%

준공일자
Date

Distinct347
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Minimum1984-06-20 00:00:00
Maximum2023-12-13 00:00:00
2024-03-14T22:23:55.719507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:56.009815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관리실전화번호
Text

MISSING 

Distinct350
Distinct (%)95.9%
Missing32
Missing (%)8.1%
Memory size3.2 KiB
2024-03-14T22:23:57.039589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.994521
Min length9

Characters and Unicode

Total characters4378
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

Unique343 ?
Unique (%)94.0%

Sample

1st row031-591-7751
2nd row031-591-7751
3rd row031-567-5878
4th row1899-8997
5th row070-8987-4466
ValueCountFrequency (%)
031-573-0077 9
 
2.5%
031-568-1060 3
 
0.8%
031-577-9443 2
 
0.5%
031-512-2296 2
 
0.5%
031-527-6381 2
 
0.5%
031-568-8260 2
 
0.5%
031-591-7751 2
 
0.5%
031-567-1975 1
 
0.3%
031-595-0620 1
 
0.3%
031-575-3503 1
 
0.3%
Other values (340) 340
93.2%
2024-03-14T22:23:58.539951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 729
16.7%
1 634
14.5%
5 604
13.8%
0 573
13.1%
3 548
12.5%
7 296
6.8%
2 253
 
5.8%
9 202
 
4.6%
4 186
 
4.2%
8 178
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3649
83.3%
Dash Punctuation 729
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 634
17.4%
5 604
16.6%
0 573
15.7%
3 548
15.0%
7 296
8.1%
2 253
 
6.9%
9 202
 
5.5%
4 186
 
5.1%
8 178
 
4.9%
6 175
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 729
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 729
16.7%
1 634
14.5%
5 604
13.8%
0 573
13.1%
3 548
12.5%
7 296
6.8%
2 253
 
5.8%
9 202
 
4.6%
4 186
 
4.2%
8 178
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 729
16.7%
1 634
14.5%
5 604
13.8%
0 573
13.1%
3 548
12.5%
7 296
6.8%
2 253
 
5.8%
9 202
 
4.6%
4 186
 
4.2%
8 178
 
4.1%

관리실팩스번호
Text

MISSING 

Distinct335
Distinct (%)96.0%
Missing48
Missing (%)12.1%
Memory size3.2 KiB
2024-03-14T22:23:59.490068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.002865
Min length12

Characters and Unicode

Total characters4189
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

Unique329 ?
Unique (%)94.3%

Sample

1st row031-591-7751
2nd row031-591-7751
3rd row031-592-3859
4th row031-591-1038
5th row031-571-2692
ValueCountFrequency (%)
031-573-1555 9
 
2.6%
031-563-1060 3
 
0.9%
031-513-6383 2
 
0.6%
031-558-8261 2
 
0.6%
031-512-2297 2
 
0.6%
031-591-7751 2
 
0.6%
031-515-9097 1
 
0.3%
031-527-2832 1
 
0.3%
031-571-1728 1
 
0.3%
031-595-0621 1
 
0.3%
Other values (325) 325
93.1%
2024-03-14T22:24:00.679487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 698
16.7%
1 606
14.5%
5 588
14.0%
3 527
12.6%
0 494
11.8%
2 268
 
6.4%
7 247
 
5.9%
9 215
 
5.1%
4 189
 
4.5%
6 179
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3491
83.3%
Dash Punctuation 698
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 606
17.4%
5 588
16.8%
3 527
15.1%
0 494
14.2%
2 268
7.7%
7 247
7.1%
9 215
 
6.2%
4 189
 
5.4%
6 179
 
5.1%
8 178
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 698
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4189
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 698
16.7%
1 606
14.5%
5 588
14.0%
3 527
12.6%
0 494
11.8%
2 268
 
6.4%
7 247
 
5.9%
9 215
 
5.1%
4 189
 
4.5%
6 179
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 698
16.7%
1 606
14.5%
5 588
14.0%
3 527
12.6%
0 494
11.8%
2 268
 
6.4%
7 247
 
5.9%
9 215
 
5.1%
4 189
 
4.5%
6 179
 
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2024-02-01
397 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-02-01
2nd row2024-02-01
3rd row2024-02-01
4th row2024-02-01
5th row2024-02-01

Common Values

ValueCountFrequency (%)
2024-02-01 397
100.0%

Length

2024-03-14T22:24:00.907610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:24:01.070343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-02-01 397
100.0%

Interactions

2024-03-14T22:23:44.080506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:41.315431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:42.294585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:43.110915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:44.344947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:41.591729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:42.478865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:43.375117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:44.599061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:41.853289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:42.631918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:43.618256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:44.830461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:42.122469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:42.860345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:43.842941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T22:24:01.179525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지역세대수층동주택유형
연번1.0000.6110.4850.8040.3210.868
지역0.6111.0000.3900.5620.2680.816
세대수0.4850.3901.0000.5710.6880.903
층0.8040.5620.5711.0000.2590.953
동0.3210.2680.6880.2591.0000.557
주택유형0.8680.8160.9030.9530.5571.000
2024-03-14T22:24:01.349831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역주택유형
지역1.0000.574
주택유형0.5741.000
2024-03-14T22:24:01.495374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세대수층동지역주택유형
연번1.0000.5510.6470.4370.3090.697
세대수0.5511.0000.6970.7880.1760.765
층0.6470.6971.0000.3840.2750.897
동0.4370.7880.3841.0000.1170.411
지역0.3090.1760.2750.1171.0000.574
주택유형0.6970.7650.8970.4110.5741.000

Missing values

2024-03-14T22:23:45.194430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:23:45.751272image/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.
2024-03-14T22:23:46.108960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번단지명지역지번주소도로명주소세대수층동주택유형준공일자관리실전화번호관리실팩스번호데이터기준일자
01우신연립주택와부경기도 남양주시 와부읍 덕소리 409-2수레로9번길 532431연립1984-06-20<NA><NA>2024-02-01
12남양호평경기도 남양주시 호평동 196-1호평로69번길 108052<NA>1984-12-31031-591-7751031-591-77512024-02-01
23남양호평경기도 남양주시 호평동 196-4호평로69번길 1014054<NA>1984-12-31031-591-7751031-591-77512024-02-01
34인정연립맨숀와부경기도 남양주시 와부읍 덕소리 421와부읍 수레로9번길 386032연립1985-01-10<NA><NA>2024-02-01
45원일연립주택다산경기도 남양주시 다산동 4113-39경춘로 414-265734연립1985-06-17031-567-5878<NA>2024-02-01
56대복연립주택화도경기도 남양주시 화도읍 마석우리 373-3화도읍 비룡로87번길 72431연립1985-06-20<NA><NA>2024-02-01
67세림아파트금곡경기도 남양주시 금곡동 161-6사릉로34번안길 7-134842연립1985-06-28<NA><NA>2024-02-01
78일성맨션아파트와부경기도 남양주시 와부읍 덕소리 191-1와부읍 수레로 575052<NA>1985-07-181899-8997<NA>2024-02-01
89동부주택진접경기도 남양주시 진접읍 장현리 593진접읍 장현로 43-2917139연립1985-09-14070-8987-4466<NA>2024-02-01
910인정빌라금곡경기도 남양주시 금곡동 164-1사릉로34번길 41-296034연립1985-09-27<NA>031-592-38592024-02-01
연번단지명지역지번주소도로명주소세대수층동주택유형준공일자관리실전화번호관리실팩스번호데이터기준일자
387388e편한세상 평내메트로원평내경기도 남양주시 평내동 667평내로 14511082715도시 및 주거환경정비법2022-06-24031-595-7533031-595-75352024-02-01
388389다산지금데시앙다산경기도 남양주시 다산동 6109다산중앙로82번길 105961296<NA>2022-08-19031-554-5262031-554-52632024-02-01
389390다산진건데시앙다산경기도 남양주시 다산동 6026다산중앙로171번길 21-10651294<NA>2022-08-19031-554-5253031-554-52542024-02-01
390391LH 남양주금곡 홍유마을금곡경기도 남양주시 금곡동 778경춘로 89935293<NA>2022-12-15031-591-6438031-591-64392024-02-01
391392진접유승한내들 더테라스진접경기도 남양주시 진접읍 금곡리 968진접읍 해밀예당1로 83236417연립2023-02-08031-574-2091031-574-17382024-02-01
392393진접삼부르네상스 더퍼스트진접경기도 남양주시 진접읍 금곡리 527-4진접읍 주곡로 133348273<NA>2023-03-31031-527-0627031-527-06282024-02-01
393394나라키움 복합청사 행복주택다산경기도 남양주시 다산동 672가운로2길 77452연립2023-07-13031-554-2539031-510-59122024-02-01
394395다산포레스트2단지아파트다산경기도 남양주시 다산동 6013다산순환로 311928295<NA>2023-10-04031-554-4431031-554-44322024-02-01
395396별내 별헤임별내동경기도 남양주시 별내동 805-2덕송3로 45-13576208<NA>2023-10-23031-529-7490031-529-74912024-02-01
396397별내자이 더 스타별내동경기도 남양주시 별내동 999별내1로 13-21740465<NA>2023-12-13031-522-6230031-522-62312024-02-01