Overview

Dataset statistics

Number of variables14
Number of observations275
Missing cells60
Missing cells (%)1.6%
Duplicate rows1
Duplicate rows (%)0.4%
Total size in memory31.3 KiB
Average record size in memory116.5 B

Variable types

Categorical4
Text5
DateTime1
Numeric4

Dataset

Description양천구 공동주택(아파트, 주상복합, 연립)의 건물명, 소재지, 준공일자, 층수, 세대수 현황, 관리구분, 관리사무소 전화번호, 관리사무소 팩스번호 등의 정보를 제공합니다.
Author서울특별시 양천구
URLhttps://www.data.go.kr/data/15052389/fileData.do

Alerts

Dataset has 1 (0.4%) duplicate rowsDuplicates
난방 is highly overall correlated with 호수 and 1 other fieldsHigh correlation
형태 is highly overall correlated with 최소층수 and 2 other fieldsHigh correlation
동수 is highly overall correlated with 호수High correlation
호수 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
최소층수 is highly overall correlated with 최대층수 and 1 other fieldsHigh correlation
최대층수 is highly overall correlated with 호수 and 2 other fieldsHigh correlation
형태 is highly imbalanced (68.5%)Imbalance
건물명 has 6 (2.2%) missing valuesMissing
지번 주소 has 6 (2.2%) missing valuesMissing
도로명주소 has 6 (2.2%) missing valuesMissing
준공일자 has 6 (2.2%) missing valuesMissing
동수 has 6 (2.2%) missing valuesMissing
호수 has 6 (2.2%) missing valuesMissing
최소층수 has 6 (2.2%) missing valuesMissing
최대층수 has 6 (2.2%) missing valuesMissing
관리사무소연락처 has 6 (2.2%) missing valuesMissing
관리사무소 팩스 has 6 (2.2%) missing valuesMissing
최소층수 has 12 (4.4%) zerosZeros
최대층수 has 12 (4.4%) zerosZeros

Reproduction

Analysis started2024-04-06 08:14:10.878928
Analysis finished2024-04-06 08:14:16.467635
Duration5.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

형태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
아파트
247 
연립
 
12
주상복합
 
10
<NA>
 
6

Length

Max length4
Median length3
Mean length3.0145455
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 247
89.8%
연립 12
 
4.4%
주상복합 10
 
3.6%
<NA> 6
 
2.2%

Length

2024-04-06T17:14:16.620850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:14:16.865019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 247
89.8%
연립 12
 
4.4%
주상복합 10
 
3.6%
na 6
 
2.2%

관리구분
Categorical

Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
임의관리
135 
의무관리
97 
<NA>
18 
혼합
15 
임대아파트
 
10

Length

Max length5
Median length4
Mean length3.9272727
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
임의관리 135
49.1%
의무관리 97
35.3%
<NA> 18
 
6.5%
혼합 15
 
5.5%
임대아파트 10
 
3.6%

Length

2024-04-06T17:14:17.083075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:14:17.309570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임의관리 135
49.1%
의무관리 97
35.3%
na 18
 
6.5%
혼합 15
 
5.5%
임대아파트 10
 
3.6%

건물명
Text

MISSING 

Distinct260
Distinct (%)96.7%
Missing6
Missing (%)2.2%
Memory size2.3 KiB
2024-04-06T17:14:17.725586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length5.9665428
Min length2

Characters and Unicode

Total characters1605
Distinct characters236
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

Unique251 ?
Unique (%)93.3%

Sample

1st row목동1단지
2nd row목동2단지
3rd row목동3단지
4th row목동4단지
5th row목동5단지
ValueCountFrequency (%)
정은스카이빌 3
 
1.1%
신정대성유니드 2
 
0.7%
탑건위너빌 2
 
0.7%
신정뉴타운 2
 
0.7%
2단지 2
 
0.7%
신월동코아루 2
 
0.7%
명지해드는터 2
 
0.7%
동일하이빌 2
 
0.7%
목동삼성 2
 
0.7%
목동성원 2
 
0.7%
Other values (257) 259
92.5%
2024-04-06T17:14:18.382077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 97
 
6.0%
목 65
 
4.0%
신 56
 
3.5%
지 49
 
3.1%
단 42
 
2.6%
아 39
 
2.4%
트 36
 
2.2%
이 35
 
2.2%
스 34
 
2.1%
빌 34
 
2.1%
Other values (226) 1118
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1448
90.2%
Decimal Number 101
 
6.3%
Open Punctuation 15
 
0.9%
Close Punctuation 15
 
0.9%
Uppercase Letter 12
 
0.7%
Space Separator 11
 
0.7%
Lowercase Letter 2
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 97
 
6.7%
목 65
 
4.5%
신 56
 
3.9%
지 49
 
3.4%
단 42
 
2.9%
아 39
 
2.7%
트 36
 
2.5%
이 35
 
2.4%
스 34
 
2.3%
빌 34
 
2.3%
Other values (200) 961
66.4%
Uppercase Letter
ValueCountFrequency (%)
S 2
16.7%
C 1
8.3%
G 1
8.3%
B 1
8.3%
A 1
8.3%
M 1
8.3%
W 1
8.3%
E 1
8.3%
I 1
8.3%
V 1
8.3%
Decimal Number
ValueCountFrequency (%)
1 32
31.7%
2 31
30.7%
3 14
13.9%
0 8
 
7.9%
4 7
 
6.9%
5 3
 
3.0%
6 3
 
3.0%
7 1
 
1.0%
9 1
 
1.0%
8 1
 
1.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1448
90.2%
Common 143
 
8.9%
Latin 14
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 97
 
6.7%
목 65
 
4.5%
신 56
 
3.9%
지 49
 
3.4%
단 42
 
2.9%
아 39
 
2.7%
트 36
 
2.5%
이 35
 
2.4%
스 34
 
2.3%
빌 34
 
2.3%
Other values (200) 961
66.4%
Common
ValueCountFrequency (%)
1 32
22.4%
2 31
21.7%
( 15
10.5%
) 15
10.5%
3 14
9.8%
11
 
7.7%
0 8
 
5.6%
4 7
 
4.9%
5 3
 
2.1%
6 3
 
2.1%
Other values (4) 4
 
2.8%
Latin
ValueCountFrequency (%)
S 2
14.3%
e 2
14.3%
C 1
7.1%
G 1
7.1%
B 1
7.1%
A 1
7.1%
M 1
7.1%
W 1
7.1%
E 1
7.1%
I 1
7.1%
Other values (2) 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1448
90.2%
ASCII 157
 
9.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 97
 
6.7%
목 65
 
4.5%
신 56
 
3.9%
지 49
 
3.4%
단 42
 
2.9%
아 39
 
2.7%
트 36
 
2.5%
이 35
 
2.4%
스 34
 
2.3%
빌 34
 
2.3%
Other values (200) 961
66.4%
ASCII
ValueCountFrequency (%)
1 32
20.4%
2 31
19.7%
( 15
9.6%
) 15
9.6%
3 14
8.9%
11
 
7.0%
0 8
 
5.1%
4 7
 
4.5%
5 3
 
1.9%
6 3
 
1.9%
Other values (16) 18
11.5%

행정동
Categorical

Distinct19
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
신정3동
34 
신월2동
27 
신월4동
26 
목4동
24 
목1동
19 
Other values (14)
145 

Length

Max length4
Median length4
Mean length3.7309091
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row목5동
2nd row목5동
3rd row목5동
4th row목5동
5th row목5동

Common Values

ValueCountFrequency (%)
신정3동 34
12.4%
신월2동 27
9.8%
신월4동 26
 
9.5%
목4동 24
 
8.7%
목1동 19
 
6.9%
신정4동 18
 
6.5%
목2동 17
 
6.2%
신정2동 16
 
5.8%
신월1동 15
 
5.5%
신월5동 14
 
5.1%
Other values (9) 65
23.6%

Length

2024-04-06T17:14:18.669086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신정3동 34
12.4%
신월2동 27
9.8%
신월4동 26
 
9.5%
목4동 24
 
8.7%
목1동 19
 
6.9%
신정4동 18
 
6.5%
목2동 17
 
6.2%
신정2동 16
 
5.8%
신월1동 15
 
5.5%
신월5동 14
 
5.1%
Other values (9) 65
23.6%

지번 주소
Text

MISSING 

Distinct268
Distinct (%)99.6%
Missing6
Missing (%)2.2%
Memory size2.3 KiB
2024-04-06T17:14:19.439642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length17.412639
Min length12

Characters and Unicode

Total characters4684
Distinct characters30
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

Unique267 ?
Unique (%)99.3%

Sample

1st row서울시 양천구 목5동 901
2nd row서울시 양천구 목5동 902
3rd row서울시 양천구 목5동 903
4th row서울시 양천구 목5동 904
5th row서울시 양천구 목5동 912
ValueCountFrequency (%)
양천구 269
25.0%
서울시 247
23.0%
신정3동 32
 
3.0%
신월2동 27
 
2.5%
신월4동 26
 
2.4%
목4동 23
 
2.1%
서울특별시 22
 
2.0%
목1동 19
 
1.8%
신정4동 18
 
1.7%
신정2동 16
 
1.5%
Other values (282) 375
34.9%
2024-04-06T17:14:20.501010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
806
17.2%
1 279
 
6.0%
서 269
 
5.7%
시 269
 
5.7%
양 269
 
5.7%
천 269
 
5.7%
구 269
 
5.7%
울 269
 
5.7%
동 268
 
5.7%
신 194
 
4.1%
Other values (20) 1523
32.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2400
51.2%
Decimal Number 1344
28.7%
Space Separator 806
 
17.2%
Dash Punctuation 126
 
2.7%
Other Punctuation 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 269
11.2%
시 269
11.2%
양 269
11.2%
천 269
11.2%
구 269
11.2%
울 269
11.2%
동 268
11.2%
신 194
8.1%
월 104
 
4.3%
정 90
 
3.8%
Other values (6) 130
5.4%
Decimal Number
ValueCountFrequency (%)
1 279
20.8%
2 179
13.3%
3 162
12.1%
4 161
12.0%
5 115
8.6%
0 113
8.4%
7 111
 
8.3%
9 103
 
7.7%
8 61
 
4.5%
6 60
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 7
87.5%
. 1
 
12.5%
Space Separator
ValueCountFrequency (%)
806
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2400
51.2%
Common 2284
48.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 269
11.2%
시 269
11.2%
양 269
11.2%
천 269
11.2%
구 269
11.2%
울 269
11.2%
동 268
11.2%
신 194
8.1%
월 104
 
4.3%
정 90
 
3.8%
Other values (6) 130
5.4%
Common
ValueCountFrequency (%)
806
35.3%
1 279
 
12.2%
2 179
 
7.8%
3 162
 
7.1%
4 161
 
7.0%
- 126
 
5.5%
5 115
 
5.0%
0 113
 
4.9%
7 111
 
4.9%
9 103
 
4.5%
Other values (4) 129
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2400
51.2%
ASCII 2284
48.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
806
35.3%
1 279
 
12.2%
2 179
 
7.8%
3 162
 
7.1%
4 161
 
7.0%
- 126
 
5.5%
5 115
 
5.0%
0 113
 
4.9%
7 111
 
4.9%
9 103
 
4.5%
Other values (4) 129
 
5.6%
Hangul
ValueCountFrequency (%)
서 269
11.2%
시 269
11.2%
양 269
11.2%
천 269
11.2%
구 269
11.2%
울 269
11.2%
동 268
11.2%
신 194
8.1%
월 104
 
4.3%
정 90
 
3.8%
Other values (6) 130
5.4%

도로명주소
Text

MISSING 

Distinct266
Distinct (%)98.9%
Missing6
Missing (%)2.2%
Memory size2.3 KiB
2024-04-06T17:14:21.085296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length175
Median length23
Mean length17.583643
Min length13

Characters and Unicode

Total characters4730
Distinct characters51
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

Unique263 ?
Unique (%)97.8%

Sample

1st row서울시 양천구 목동서로 38
2nd row서울시 양천구 목동서로 70
3rd row서울시 양천구 목동서로 100
4th row서울시 양천구 목동서로 130
5th row서울시 양천구 목동동로 350
ValueCountFrequency (%)
양천구 269
25.0%
서울시 247
23.0%
서울특별시 22
 
2.0%
목동동로 19
 
1.8%
오목로 13
 
1.2%
목동서로 10
 
0.9%
11 9
 
0.8%
16 9
 
0.8%
월정로 8
 
0.7%
중앙로29길 8
 
0.7%
Other values (257) 462
42.9%
2024-04-06T17:14:21.966437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
963
20.4%
서 284
 
6.0%
양 276
 
5.8%
로 273
 
5.8%
시 269
 
5.7%
천 269
 
5.7%
구 269
 
5.7%
울 269
 
5.7%
1 195
 
4.1%
길 163
 
3.4%
Other values (41) 1500
31.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2866
60.6%
Space Separator 963
 
20.4%
Decimal Number 873
 
18.5%
Dash Punctuation 28
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 284
9.9%
양 276
9.6%
로 273
9.5%
시 269
9.4%
천 269
9.4%
구 269
9.4%
울 269
9.4%
길 163
 
5.7%
목 139
 
4.8%
동 123
 
4.3%
Other values (29) 532
18.6%
Decimal Number
ValueCountFrequency (%)
1 195
22.3%
2 128
14.7%
3 96
11.0%
5 89
10.2%
0 83
9.5%
7 72
 
8.2%
6 65
 
7.4%
4 54
 
6.2%
9 53
 
6.1%
8 38
 
4.4%
Space Separator
ValueCountFrequency (%)
963
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2866
60.6%
Common 1864
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 284
9.9%
양 276
9.6%
로 273
9.5%
시 269
9.4%
천 269
9.4%
구 269
9.4%
울 269
9.4%
길 163
 
5.7%
목 139
 
4.8%
동 123
 
4.3%
Other values (29) 532
18.6%
Common
ValueCountFrequency (%)
963
51.7%
1 195
 
10.5%
2 128
 
6.9%
3 96
 
5.2%
5 89
 
4.8%
0 83
 
4.5%
7 72
 
3.9%
6 65
 
3.5%
4 54
 
2.9%
9 53
 
2.8%
Other values (2) 66
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2866
60.6%
ASCII 1864
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
963
51.7%
1 195
 
10.5%
2 128
 
6.9%
3 96
 
5.2%
5 89
 
4.8%
0 83
 
4.5%
7 72
 
3.9%
6 65
 
3.5%
4 54
 
2.9%
9 53
 
2.8%
Other values (2) 66
 
3.5%
Hangul
ValueCountFrequency (%)
서 284
9.9%
양 276
9.6%
로 273
9.5%
시 269
9.4%
천 269
9.4%
구 269
9.4%
울 269
9.4%
길 163
 
5.7%
목 139
 
4.8%
동 123
 
4.3%
Other values (29) 532
18.6%

준공일자
Date

MISSING 

Distinct240
Distinct (%)89.2%
Missing6
Missing (%)2.2%
Memory size2.3 KiB
Minimum1981-09-17 00:00:00
Maximum2022-12-13 00:00:00
2024-04-06T17:14:22.592304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:22.878760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

동수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)9.7%
Missing6
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean4.3494424
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-06T17:14:23.104149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile19.6
Maximum37
Range36
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.8570566
Coefficient of variation (CV)1.5765369
Kurtosis10.163325
Mean4.3494424
Median Absolute Deviation (MAD)1
Skewness3.1547406
Sum1170
Variance47.019225
MonotonicityNot monotonic
2024-04-06T17:14:23.377226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 127
46.2%
2 42
 
15.3%
3 20
 
7.3%
4 15
 
5.5%
5 12
 
4.4%
6 10
 
3.6%
7 7
 
2.5%
8 6
 
2.2%
34 4
 
1.5%
12 4
 
1.5%
Other values (16) 22
 
8.0%
(Missing) 6
 
2.2%
ValueCountFrequency (%)
1 127
46.2%
2 42
 
15.3%
3 20
 
7.3%
4 15
 
5.5%
5 12
 
4.4%
6 10
 
3.6%
7 7
 
2.5%
8 6
 
2.2%
9 2
 
0.7%
10 1
 
0.4%
ValueCountFrequency (%)
37 1
 
0.4%
36 1
 
0.4%
34 4
1.5%
33 1
 
0.4%
32 1
 
0.4%
30 1
 
0.4%
26 1
 
0.4%
23 1
 
0.4%
22 1
 
0.4%
20 2
0.7%

호수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct187
Distinct (%)69.5%
Missing6
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean327.71747
Minimum24
Maximum3100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-06T17:14:23.651192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile32.4
Q149
median120
Q3353
95-th percentile1506
Maximum3100
Range3076
Interquartile range (IQR)304

Descriptive statistics

Standard deviation511.53125
Coefficient of variation (CV)1.560891
Kurtosis9.1732123
Mean327.71747
Median Absolute Deviation (MAD)81
Skewness2.897805
Sum88156
Variance261664.22
MonotonicityNot monotonic
2024-04-06T17:14:23.897964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 13
 
4.7%
39 7
 
2.5%
49 6
 
2.2%
99 4
 
1.5%
36 4
 
1.5%
41 3
 
1.1%
55 3
 
1.1%
94 3
 
1.1%
40 3
 
1.1%
35 3
 
1.1%
Other values (177) 220
80.0%
(Missing) 6
 
2.2%
ValueCountFrequency (%)
24 2
0.7%
25 2
0.7%
26 1
 
0.4%
27 2
0.7%
28 2
0.7%
30 3
1.1%
31 1
 
0.4%
32 1
 
0.4%
33 1
 
0.4%
34 1
 
0.4%
ValueCountFrequency (%)
3100 1
0.4%
2998 1
0.4%
2550 1
0.4%
2280 1
0.4%
2256 1
0.4%
2160 1
0.4%
2030 1
0.4%
1882 1
0.4%
1860 1
0.4%
1848 1
0.4%

최소층수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct29
Distinct (%)10.8%
Missing6
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean12.591078
Minimum0
Maximum69
Zeros12
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-06T17:14:24.243567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median12
Q315
95-th percentile22
Maximum69
Range69
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.4760859
Coefficient of variation (CV)0.59376059
Kurtosis15.823686
Mean12.591078
Median Absolute Deviation (MAD)3
Skewness2.7478282
Sum3387
Variance55.89186
MonotonicityNot monotonic
2024-04-06T17:14:24.506857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
15 41
14.9%
12 28
10.2%
10 22
 
8.0%
7 22
 
8.0%
5 19
 
6.9%
14 19
 
6.9%
8 14
 
5.1%
11 13
 
4.7%
13 12
 
4.4%
0 12
 
4.4%
Other values (19) 67
24.4%
ValueCountFrequency (%)
0 12
4.4%
3 1
 
0.4%
5 19
6.9%
6 5
 
1.8%
7 22
8.0%
8 14
5.1%
9 7
 
2.5%
10 22
8.0%
11 13
4.7%
12 28
10.2%
ValueCountFrequency (%)
69 1
 
0.4%
49 1
 
0.4%
48 1
 
0.4%
41 1
 
0.4%
39 1
 
0.4%
32 1
 
0.4%
31 1
 
0.4%
26 1
 
0.4%
25 1
 
0.4%
24 3
1.1%

최대층수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct31
Distinct (%)11.5%
Missing6
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean14.200743
Minimum0
Maximum69
Zeros12
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-06T17:14:24.748371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median15
Q316
95-th percentile24
Maximum69
Range69
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.5000398
Coefficient of variation (CV)0.52814416
Kurtosis13.456788
Mean14.200743
Median Absolute Deviation (MAD)3
Skewness2.2958
Sum3820
Variance56.250596
MonotonicityNot monotonic
2024-04-06T17:14:25.005348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15 64
23.3%
12 25
 
9.1%
14 21
 
7.6%
7 18
 
6.5%
10 14
 
5.1%
18 12
 
4.4%
0 12
 
4.4%
20 12
 
4.4%
19 10
 
3.6%
8 10
 
3.6%
Other values (21) 71
25.8%
ValueCountFrequency (%)
0 12
4.4%
5 8
 
2.9%
6 2
 
0.7%
7 18
6.5%
8 10
 
3.6%
9 6
 
2.2%
10 14
5.1%
11 7
 
2.5%
12 25
9.1%
13 9
 
3.3%
ValueCountFrequency (%)
69 1
0.4%
49 1
0.4%
48 1
0.4%
41 1
0.4%
39 1
0.4%
32 1
0.4%
31 1
0.4%
27 1
0.4%
26 1
0.4%
25 2
0.7%

난방
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
개별
168 
지역
76 
<NA>
28 
중앙→개별
 
2
중앙→지역
 
1

Length

Max length5
Median length2
Mean length2.2363636
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
개별 168
61.1%
지역 76
27.6%
<NA> 28
 
10.2%
중앙→개별 2
 
0.7%
중앙→지역 1
 
0.4%

Length

2024-04-06T17:14:25.256220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:14:25.474160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개별 168
61.1%
지역 76
27.6%
na 28
 
10.2%
중앙→개별 2
 
0.7%
중앙→지역 1
 
0.4%

관리사무소연락처
Text

MISSING 

Distinct235
Distinct (%)87.4%
Missing6
Missing (%)2.2%
Memory size2.3 KiB
2024-04-06T17:14:26.005357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.022305
Min length12

Characters and Unicode

Total characters3234
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

Unique228 ?
Unique (%)84.8%

Sample

1st row02-2648-3110
2nd row02-2647-0539
3rd row02-2647-0337
4th row02-2647-0898
5th row02-2647-0049
ValueCountFrequency (%)
02-0000-0000 26
 
9.7%
02-2699-8010 4
 
1.5%
02-2603-3149 3
 
1.1%
02-2651-0211 2
 
0.7%
02-2625-0273 2
 
0.7%
02-2696-4581 2
 
0.7%
02-2061-1584 2
 
0.7%
02-2636-2382 1
 
0.4%
02-2065-1522 1
 
0.4%
02-2645-6333 1
 
0.4%
Other values (225) 225
83.6%
2024-04-06T17:14:26.863796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 685
21.2%
2 626
19.4%
- 538
16.6%
6 335
10.4%
4 198
 
6.1%
9 181
 
5.6%
5 151
 
4.7%
1 137
 
4.2%
3 135
 
4.2%
8 125
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2696
83.4%
Dash Punctuation 538
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 685
25.4%
2 626
23.2%
6 335
12.4%
4 198
 
7.3%
9 181
 
6.7%
5 151
 
5.6%
1 137
 
5.1%
3 135
 
5.0%
8 125
 
4.6%
7 123
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 685
21.2%
2 626
19.4%
- 538
16.6%
6 335
10.4%
4 198
 
6.1%
9 181
 
5.6%
5 151
 
4.7%
1 137
 
4.2%
3 135
 
4.2%
8 125
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 685
21.2%
2 626
19.4%
- 538
16.6%
6 335
10.4%
4 198
 
6.1%
9 181
 
5.6%
5 151
 
4.7%
1 137
 
4.2%
3 135
 
4.2%
8 125
 
3.9%

관리사무소 팩스
Text

MISSING 

Distinct200
Distinct (%)74.3%
Missing6
Missing (%)2.2%
Memory size2.3 KiB
2024-04-06T17:14:27.472034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.01487
Min length11

Characters and Unicode

Total characters3232
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

Unique193 ?
Unique (%)71.7%

Sample

1st row02-6739-3110
2nd row02-2647-0540
3rd row02-2648-3377
4th row02-2642-9810
5th row02-2647-1449
ValueCountFrequency (%)
02-0000-0000 61
 
22.7%
02-2699-8019 4
 
1.5%
02-2690-2948 3
 
1.1%
02-2061-1586 2
 
0.7%
02-2651-4591 2
 
0.7%
02-6737-4581 2
 
0.7%
02-2625-0274 2
 
0.7%
02-2644-4606 1
 
0.4%
02-2690-4324 1
 
0.4%
02-2648-0433 1
 
0.4%
Other values (190) 190
70.6%
2024-04-06T17:14:28.511170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 922
28.5%
2 543
16.8%
- 538
16.6%
6 304
 
9.4%
4 170
 
5.3%
9 165
 
5.1%
5 134
 
4.1%
3 128
 
4.0%
1 125
 
3.9%
7 105
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2694
83.4%
Dash Punctuation 538
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 922
34.2%
2 543
20.2%
6 304
 
11.3%
4 170
 
6.3%
9 165
 
6.1%
5 134
 
5.0%
3 128
 
4.8%
1 125
 
4.6%
7 105
 
3.9%
8 98
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3232
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 922
28.5%
2 543
16.8%
- 538
16.6%
6 304
 
9.4%
4 170
 
5.3%
9 165
 
5.1%
5 134
 
4.1%
3 128
 
4.0%
1 125
 
3.9%
7 105
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 922
28.5%
2 543
16.8%
- 538
16.6%
6 304
 
9.4%
4 170
 
5.3%
9 165
 
5.1%
5 134
 
4.1%
3 128
 
4.0%
1 125
 
3.9%
7 105
 
3.2%

Interactions

2024-04-06T17:14:14.209531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:11.976485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:12.740443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:13.430079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:14.382866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:12.204591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:12.904241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:13.606557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:14.752307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:12.388824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:13.073014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:13.807132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:15.017698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:12.562806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:13.259879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:14:14.007394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:14:28.726340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
형태관리구분행정동동수호수최소층수최대층수난방
형태1.0000.3530.6390.0000.0000.9460.968NaN
관리구분0.3531.0000.6710.6120.6170.2710.4720.735
행정동0.6390.6711.0000.6390.6620.5120.6380.680
동수0.0000.6120.6391.0000.8110.1540.0000.549
호수0.0000.6170.6620.8111.0000.3880.3220.714
최소층수0.9460.2710.5120.1540.3881.0000.9960.264
최대층수0.9680.4720.6380.0000.3220.9961.0000.568
난방NaN0.7350.6800.5490.7140.2640.5681.000
2024-04-06T17:14:28.954830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리구분난방행정동형태
관리구분1.0000.3730.4230.235
난방0.3731.0000.4321.000
행정동0.4230.4321.0000.364
형태0.2351.0000.3641.000
2024-04-06T17:14:29.146622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동수호수최소층수최대층수형태관리구분행정동난방
동수1.0000.8200.0280.4010.0000.4410.2740.416
호수0.8201.0000.3790.7060.0000.4140.3190.511
최소층수0.0280.3791.0000.7930.7180.1740.1900.106
최대층수0.4010.7060.7931.0000.7760.3180.2600.251
형태0.0000.0000.7180.7761.0000.2350.3641.000
관리구분0.4410.4140.1740.3180.2351.0000.4230.373
행정동0.2740.3190.1900.2600.3640.4231.0000.432
난방0.4160.5110.1060.2511.0000.3730.4321.000

Missing values

2024-04-06T17:14:15.359436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:14:15.754057image/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.
2024-04-06T17:14:16.129753image/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

형태관리구분건물명행정동지번 주소도로명주소준공일자동수호수최소층수최대층수난방관리사무소연락처관리사무소 팩스
0아파트의무관리목동1단지목5동서울시 양천구 목5동 901서울시 양천구 목동서로 381985-12-19341882515지역02-2648-311002-6739-3110
1아파트의무관리목동2단지목5동서울시 양천구 목5동 902서울시 양천구 목동서로 701986-10-12371640515지역02-2647-053902-2647-0540
2아파트의무관리목동3단지목5동서울시 양천구 목5동 903서울시 양천구 목동서로 1001986-10-12301588515지역02-2647-033702-2648-3377
3아파트의무관리목동4단지목5동서울시 양천구 목5동 904서울시 양천구 목동서로 1301986-10-17161382520지역02-2647-089802-2642-9810
4아파트의무관리목동5단지목5동서울시 양천구 목5동 912서울시 양천구 목동동로 3501986-10-17361848515지역02-2647-004902-2647-1449
5아파트의무관리목동6단지목5동서울시 양천구 목5동 911서울시 양천구 목동동로 4301986-10-311513621220지역02-2647-091602-2647-0913
6아파트의무관리목동7단지목1동서울시 양천구 목1동 925서울시 양천구 목동로 2121986-11-01342550515지역02-2646-236702-2654-5598
7아파트의무관리목동8단지신정6동서울시 양천구 신정6동 314서울시 양천구 목동서로 2801987-09-301213521220지역02-2648-722502-2647-9799
8아파트의무관리목동9단지신정1동서울시 양천구 신정1동 312서울시 양천구 목동서로 3401987-07-25322030515지역02-2648-2250070-8134-0755
9아파트의무관리목동10단지신정1동서울시 양천구 신정1동 311서울시 양천구 목동서로 4001987-07-29342160515지역02-2648-279802-2642-2956
형태관리구분건물명행정동지번 주소도로명주소준공일자동수호수최소층수최대층수난방관리사무소연락처관리사무소 팩스
265연립<NA>세흥주택신월7동서울특별시 양천구 신월7동 945-1서울특별시 양천구 지양로11길 241986-08-0824800<NA>02-0000-000002-0000-0000
266연립<NA>대영연립신월7동서울특별시 양천구 신월7동 947-1서울특별시 양천구 지양로7길 28-21986-12-2913000<NA>02-0000-000002-0000-0000
267연립<NA>아신빌라신정4동서울특별시 양천구 신정4동 876-2서울특별시 양천구 은행정로19길 161988-04-1923300<NA>02-0000-000002-0000-0000
268연립<NA>코끼리연립신정4동서울특별시 양천구 신정4동 880-1서울특별시 양천구 은행정로19가길 81981-12-1724200<NA>02-0000-000002-0000-0000
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Most frequently occurring

형태관리구분건물명행정동지번 주소도로명주소준공일자동수호수최소층수최대층수난방관리사무소연락처관리사무소 팩스# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>6