Overview

Dataset statistics

Number of variables14
Number of observations270
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.7 KiB
Average record size in memory116.5 B

Variable types

Categorical4
Text5
DateTime1
Numeric4

Dataset

Description파일 다운로드
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-22047/F/1/datasetView.do

Alerts

형태 is highly overall correlated with 최소층수 and 2 other fieldsHigh correlation
난방 is highly overall correlated with 호수 and 1 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 (68.2%)Imbalance
최소층수 has 13 (4.8%) zerosZeros
최대층수 has 13 (4.8%) zerosZeros

Reproduction

Analysis started2024-04-17 16:40:41.674115
Analysis finished2024-04-17 16:40:43.494136
Duration1.82 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

형태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
아파트
247 
연립
 
13
주상복합
 
10

Length

Max length4
Median length3
Mean length2.9888889
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 247
91.5%
연립 13
 
4.8%
주상복합 10
 
3.7%

Length

2024-04-18T01:40:43.551743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:40:43.630796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 247
91.5%
연립 13
 
4.8%
주상복합 10
 
3.7%

관리구분
Categorical

Distinct5
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
임의관리
135 
의무관리
97 
혼합
15 
<NA>
 
13
임대아파트
 
10

Length

Max length5
Median length4
Mean length3.9259259
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
임의관리 135
50.0%
의무관리 97
35.9%
혼합 15
 
5.6%
<NA> 13
 
4.8%
임대아파트 10
 
3.7%

Length

2024-04-18T01:40:43.724150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:40:43.819446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임의관리 135
50.0%
의무관리 97
35.9%
혼합 15
 
5.6%
na 13
 
4.8%
임대아파트 10
 
3.7%

건물명
Text

Distinct261
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-04-18T01:40:43.968200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length5.9592593
Min length2

Characters and Unicode

Total characters1609
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

Unique252 ?
Unique (%)93.3%

Sample

1st row목동1단지
2nd row목동2단지
3rd row목동3단지
4th row목동4단지
5th row목동5단지
ValueCountFrequency (%)
정은스카이빌 3
 
1.1%
목동삼성 2
 
0.7%
신정대성유니드 2
 
0.7%
현대 2
 
0.7%
2단지 2
 
0.7%
신정뉴타운 2
 
0.7%
명지해드는터 2
 
0.7%
목동성원 2
 
0.7%
건영 2
 
0.7%
동일하이빌 2
 
0.7%
Other values (258) 260
92.5%
2024-04-18T01:40:44.231029image/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
 
3.0%
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) 1122
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1452
90.2%
Decimal Number 101
 
6.3%
Close Punctuation 15
 
0.9%
Open 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) 965
66.5%
Uppercase Letter
ValueCountFrequency (%)
S 2
16.7%
C 1
8.3%
B 1
8.3%
A 1
8.3%
M 1
8.3%
G 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%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open 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 1452
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) 965
66.5%
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 (%)
e 2
14.3%
S 2
14.3%
C 1
7.1%
B 1
7.1%
A 1
7.1%
M 1
7.1%
G 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 1452
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) 965
66.5%
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

Distinct18
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
신정3동
34 
신월2동
27 
신월4동
26 
목4동
24 
목1동
19 
Other values (13)
140 

Length

Max length4
Median length4
Mean length3.7259259
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신정3동 34
12.6%
신월2동 27
10.0%
신월4동 26
9.6%
목4동 24
8.9%
목1동 19
 
7.0%
신정4동 18
 
6.7%
목2동 17
 
6.3%
신정2동 17
 
6.3%
신월1동 15
 
5.6%
신월5동 14
 
5.2%
Other values (8) 59
21.9%

Length

2024-04-18T01:40:44.339327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신정3동 34
12.6%
신월2동 27
10.0%
신월4동 26
9.6%
목4동 24
8.9%
목1동 19
 
7.0%
신정4동 18
 
6.7%
목2동 17
 
6.3%
신정2동 17
 
6.3%
신월1동 15
 
5.6%
신월5동 14
 
5.2%
Other values (8) 59
21.9%

지번 주소
Text

Distinct269
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-04-18T01:40:44.624318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length17.422222
Min length12

Characters and Unicode

Total characters4704
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

Unique268 ?
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 270
25.0%
서울시 247
22.9%
신정3동 32
 
3.0%
신월2동 27
 
2.5%
신월4동 26
 
2.4%
목4동 23
 
2.1%
서울특별시 23
 
2.1%
목1동 19
 
1.8%
신정4동 18
 
1.7%
신정2동 17
 
1.6%
Other values (283) 376
34.9%
2024-04-18T01:40:45.029143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
809
17.2%
1 280
 
6.0%
서 270
 
5.7%
시 270
 
5.7%
양 270
 
5.7%
천 270
 
5.7%
구 270
 
5.7%
울 270
 
5.7%
동 269
 
5.7%
신 195
 
4.1%
Other values (20) 1531
32.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2411
51.3%
Decimal Number 1349
28.7%
Space Separator 809
 
17.2%
Dash Punctuation 127
 
2.7%
Other Punctuation 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 270
11.2%
시 270
11.2%
양 270
11.2%
천 270
11.2%
구 270
11.2%
울 270
11.2%
동 269
11.2%
신 195
8.1%
월 104
 
4.3%
정 91
 
3.8%
Other values (6) 132
5.5%
Decimal Number
ValueCountFrequency (%)
1 280
20.8%
2 181
13.4%
3 162
12.0%
4 162
12.0%
5 115
8.5%
0 113
8.4%
7 112
 
8.3%
9 103
 
7.6%
8 61
 
4.5%
6 60
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 7
87.5%
. 1
 
12.5%
Space Separator
ValueCountFrequency (%)
809
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2411
51.3%
Common 2293
48.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 270
11.2%
시 270
11.2%
양 270
11.2%
천 270
11.2%
구 270
11.2%
울 270
11.2%
동 269
11.2%
신 195
8.1%
월 104
 
4.3%
정 91
 
3.8%
Other values (6) 132
5.5%
Common
ValueCountFrequency (%)
809
35.3%
1 280
 
12.2%
2 181
 
7.9%
3 162
 
7.1%
4 162
 
7.1%
- 127
 
5.5%
5 115
 
5.0%
0 113
 
4.9%
7 112
 
4.9%
9 103
 
4.5%
Other values (4) 129
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2411
51.3%
ASCII 2293
48.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
809
35.3%
1 280
 
12.2%
2 181
 
7.9%
3 162
 
7.1%
4 162
 
7.1%
- 127
 
5.5%
5 115
 
5.0%
0 113
 
4.9%
7 112
 
4.9%
9 103
 
4.5%
Other values (4) 129
 
5.6%
Hangul
ValueCountFrequency (%)
서 270
11.2%
시 270
11.2%
양 270
11.2%
천 270
11.2%
구 270
11.2%
울 270
11.2%
동 269
11.2%
신 195
8.1%
월 104
 
4.3%
정 91
 
3.8%
Other values (6) 132
5.5%

도로명주소
Text

Distinct267
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-04-18T01:40:45.248757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length175
Median length23
Mean length17.585185
Min length13

Characters and Unicode

Total characters4748
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

Unique264 ?
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 270
25.0%
서울시 247
22.9%
서울특별시 23
 
2.1%
목동동로 19
 
1.8%
오목로 13
 
1.2%
목동서로 10
 
0.9%
16 9
 
0.8%
11 9
 
0.8%
월정로 8
 
0.7%
10 8
 
0.7%
Other values (258) 464
43.0%
2024-04-18T01:40:45.569529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
966
20.3%
서 285
 
6.0%
양 277
 
5.8%
로 274
 
5.8%
시 270
 
5.7%
천 270
 
5.7%
구 270
 
5.7%
울 270
 
5.7%
1 195
 
4.1%
길 164
 
3.5%
Other values (41) 1507
31.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2878
60.6%
Space Separator 966
 
20.3%
Decimal Number 876
 
18.4%
Dash Punctuation 28
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 285
9.9%
양 277
9.6%
로 274
9.5%
시 270
9.4%
천 270
9.4%
구 270
9.4%
울 270
9.4%
길 164
 
5.7%
목 140
 
4.9%
동 123
 
4.3%
Other values (29) 535
18.6%
Decimal Number
ValueCountFrequency (%)
1 195
22.3%
2 129
14.7%
3 96
11.0%
5 89
10.2%
0 83
9.5%
7 72
 
8.2%
6 65
 
7.4%
4 56
 
6.4%
9 53
 
6.1%
8 38
 
4.3%
Space Separator
ValueCountFrequency (%)
966
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2878
60.6%
Common 1870
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 285
9.9%
양 277
9.6%
로 274
9.5%
시 270
9.4%
천 270
9.4%
구 270
9.4%
울 270
9.4%
길 164
 
5.7%
목 140
 
4.9%
동 123
 
4.3%
Other values (29) 535
18.6%
Common
ValueCountFrequency (%)
966
51.7%
1 195
 
10.4%
2 129
 
6.9%
3 96
 
5.1%
5 89
 
4.8%
0 83
 
4.4%
7 72
 
3.9%
6 65
 
3.5%
4 56
 
3.0%
9 53
 
2.8%
Other values (2) 66
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2878
60.6%
ASCII 1870
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
966
51.7%
1 195
 
10.4%
2 129
 
6.9%
3 96
 
5.1%
5 89
 
4.8%
0 83
 
4.4%
7 72
 
3.9%
6 65
 
3.5%
4 56
 
3.0%
9 53
 
2.8%
Other values (2) 66
 
3.5%
Hangul
ValueCountFrequency (%)
서 285
9.9%
양 277
9.6%
로 274
9.5%
시 270
9.4%
천 270
9.4%
구 270
9.4%
울 270
9.4%
길 164
 
5.7%
목 140
 
4.9%
동 123
 
4.3%
Other values (29) 535
18.6%

준공일자
Date

Distinct241
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Minimum1981-09-17 00:00:00
Maximum2022-12-13 00:00:00
2024-04-18T01:40:45.674320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:45.777108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3407407
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-18T01:40:45.868399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile19.55
Maximum37
Range36
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.8457926
Coefficient of variation (CV)1.5771024
Kurtosis10.216401
Mean4.3407407
Median Absolute Deviation (MAD)1
Skewness3.1621557
Sum1172
Variance46.864877
MonotonicityNot monotonic
2024-04-18T01:40:45.957595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 127
47.0%
2 43
 
15.9%
3 20
 
7.4%
4 15
 
5.6%
5 12
 
4.4%
6 10
 
3.7%
7 7
 
2.6%
8 6
 
2.2%
34 4
 
1.5%
12 4
 
1.5%
Other values (16) 22
 
8.1%
ValueCountFrequency (%)
1 127
47.0%
2 43
 
15.9%
3 20
 
7.4%
4 15
 
5.6%
5 12
 
4.4%
6 10
 
3.7%
7 7
 
2.6%
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 

Distinct187
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.67037
Minimum24
Maximum3100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-18T01:40:46.057891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile32.45
Q149
median119.5
Q3347.75
95-th percentile1505.25
Maximum3100
Range3076
Interquartile range (IQR)298.75

Descriptive statistics

Standard deviation510.86938
Coefficient of variation (CV)1.5638681
Kurtosis9.2145846
Mean326.67037
Median Absolute Deviation (MAD)80.5
Skewness2.9037478
Sum88201
Variance260987.52
MonotonicityNot monotonic
2024-04-18T01:40:46.158442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 13
 
4.8%
39 7
 
2.6%
49 6
 
2.2%
99 4
 
1.5%
45 4
 
1.5%
36 4
 
1.5%
30 3
 
1.1%
60 3
 
1.1%
41 3
 
1.1%
55 3
 
1.1%
Other values (177) 220
81.5%
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  ZEROS 

Distinct29
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.544444
Minimum0
Maximum69
Zeros13
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-18T01:40:46.253939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.9
Q18
median12
Q315
95-th percentile22
Maximum69
Range69
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.5014166
Coefficient of variation (CV)0.59798715
Kurtosis15.598043
Mean12.544444
Median Absolute Deviation (MAD)3
Skewness2.7109772
Sum3387
Variance56.271252
MonotonicityNot monotonic
2024-04-18T01:40:46.344963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
15 41
15.2%
12 28
10.4%
7 22
 
8.1%
10 22
 
8.1%
5 19
 
7.0%
14 19
 
7.0%
8 14
 
5.2%
0 13
 
4.8%
11 13
 
4.8%
13 12
 
4.4%
Other values (19) 67
24.8%
ValueCountFrequency (%)
0 13
4.8%
3 1
 
0.4%
5 19
7.0%
6 5
 
1.9%
7 22
8.1%
8 14
5.2%
9 7
 
2.6%
10 22
8.1%
11 13
4.8%
12 28
10.4%
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  ZEROS 

Distinct31
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.148148
Minimum0
Maximum69
Zeros13
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-18T01:40:46.441386image/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 deviation7.5358065
Coefficient of variation (CV)0.53263554
Kurtosis13.195989
Mean14.148148
Median Absolute Deviation (MAD)3
Skewness2.250822
Sum3820
Variance56.788379
MonotonicityNot monotonic
2024-04-18T01:40:46.760654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15 64
23.7%
12 25
 
9.3%
14 21
 
7.8%
7 18
 
6.7%
10 14
 
5.2%
0 13
 
4.8%
18 12
 
4.4%
20 12
 
4.4%
19 10
 
3.7%
8 10
 
3.7%
Other values (21) 71
26.3%
ValueCountFrequency (%)
0 13
4.8%
5 8
 
3.0%
6 2
 
0.7%
7 18
6.7%
8 10
 
3.7%
9 6
 
2.2%
10 14
5.2%
11 7
 
2.6%
12 25
9.3%
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 (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
개별
168 
지역
76 
<NA>
23 
중앙→개별
 
2
중앙→지역
 
1

Length

Max length5
Median length2
Mean length2.2037037
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
개별 168
62.2%
지역 76
28.1%
<NA> 23
 
8.5%
중앙→개별 2
 
0.7%
중앙→지역 1
 
0.4%

Length

2024-04-18T01:40:46.889512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:40:46.982873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개별 168
62.2%
지역 76
28.1%
na 23
 
8.5%
중앙→개별 2
 
0.7%
중앙→지역 1
 
0.4%

관리사무소연락처
Text

Distinct235
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-04-18T01:40:47.197716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.022222
Min length12

Characters and Unicode

Total characters3246
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 (%)84.4%

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 27
 
10.0%
02-2699-8010 4
 
1.5%
02-2603-3149 3
 
1.1%
02-2696-4581 2
 
0.7%
02-2061-1584 2
 
0.7%
02-2651-0211 2
 
0.7%
02-2625-0273 2
 
0.7%
02-2636-2382 1
 
0.4%
02-2643-9598 1
 
0.4%
02-2690-8925 1
 
0.4%
Other values (225) 225
83.3%
2024-04-18T01:40:47.529442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 694
21.4%
2 627
19.3%
- 540
16.6%
6 335
10.3%
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 2706
83.4%
Dash Punctuation 540
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 694
25.6%
2 627
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
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 540
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3246
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 694
21.4%
2 627
19.3%
- 540
16.6%
6 335
10.3%
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 3246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 694
21.4%
2 627
19.3%
- 540
16.6%
6 335
10.3%
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

Distinct200
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-04-18T01:40:47.795953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.014815
Min length11

Characters and Unicode

Total characters3244
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 (%)71.5%

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 62
 
23.0%
02-2699-8019 4
 
1.5%
02-2690-2948 3
 
1.1%
02-2061-1586 2
 
0.7%
02-2651-4591 2
 
0.7%
02-2625-0274 2
 
0.7%
02-6737-4581 2
 
0.7%
02-834-3406 1
 
0.4%
02-2690-4324 1
 
0.4%
02-2693-9014 1
 
0.4%
Other values (190) 190
70.4%
2024-04-18T01:40:48.193020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 931
28.7%
2 544
16.8%
- 540
16.6%
6 304
 
9.4%
4 170
 
5.2%
9 165
 
5.1%
5 134
 
4.1%
3 128
 
3.9%
1 125
 
3.9%
7 105
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2704
83.4%
Dash Punctuation 540
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 931
34.4%
2 544
20.1%
6 304
 
11.2%
4 170
 
6.3%
9 165
 
6.1%
5 134
 
5.0%
3 128
 
4.7%
1 125
 
4.6%
7 105
 
3.9%
8 98
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 540
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 931
28.7%
2 544
16.8%
- 540
16.6%
6 304
 
9.4%
4 170
 
5.2%
9 165
 
5.1%
5 134
 
4.1%
3 128
 
3.9%
1 125
 
3.9%
7 105
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 931
28.7%
2 544
16.8%
- 540
16.6%
6 304
 
9.4%
4 170
 
5.2%
9 165
 
5.1%
5 134
 
4.1%
3 128
 
3.9%
1 125
 
3.9%
7 105
 
3.2%

Interactions

2024-04-18T01:40:42.980654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.155673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.439545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.704577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:43.051621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.227143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.506334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.773590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:43.120479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.299163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.570662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.840814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:43.191025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.369668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.636747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:40:42.909311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T01:40:48.283990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
형태관리구분행정동동수호수최소층수최대층수난방
형태1.0000.3530.6300.0000.0000.9490.970NaN
관리구분0.3531.0000.6710.6120.6170.2710.4720.735
행정동0.6300.6711.0000.6390.6620.5040.6320.680
동수0.0000.6120.6391.0000.8110.1320.0000.549
호수0.0000.6170.6620.8111.0000.3840.3240.714
최소층수0.9490.2710.5040.1320.3841.0000.9970.264
최대층수0.9700.4720.6320.0000.3240.9971.0000.568
난방NaN0.7350.6800.5490.7140.2640.5681.000
2024-04-18T01:40:48.371372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
형태관리구분난방행정동
형태1.0000.2351.0000.357
관리구분0.2351.0000.3730.423
난방1.0000.3731.0000.432
행정동0.3570.4230.4321.000
2024-04-18T01:40:48.446104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동수호수최소층수최대층수형태관리구분행정동난방
동수1.0000.8160.0270.3970.0000.4410.2750.416
호수0.8161.0000.3840.7080.0000.4140.3190.511
최소층수0.0270.3841.0000.7950.7240.1740.1860.106
최대층수0.3970.7080.7951.0000.7820.3180.2570.251
형태0.0000.0000.7240.7821.0000.2350.3571.000
관리구분0.4410.4140.1740.3180.2351.0000.4230.373
행정동0.2750.3190.1860.2570.3570.4231.0000.432
난방0.4160.5110.1060.2511.0000.3730.4321.000

Missing values

2024-04-18T01:40:43.292396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T01:40:43.441340image/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

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
260연립<NA>세화빌라신월2동서울특별시 양천구 신월2동 457-4서울특별시 양천구 중앙로57길 171981-10-2323900<NA>02-0000-000002-0000-0000
261연립<NA>대광주택신월2동서울특별시 양천구 신월2동 477-3서울특별시 양천구 중앙로51길 26-101987-09-2424800<NA>02-0000-000002-0000-0000
262연립<NA>영곡연립신월2동서울특별시 양천구 신월2동 477-12서울특별시 양천구 중앙로51길 26-21987-11-0224200<NA>02-0000-000002-0000-0000
263연립<NA>세원주택신월3동서울특별시 양천구 신월3동 201-11서울특별시 양천구 남부순환로62길 241981-10-1512400<NA>02-0000-000002-0000-0000
264연립<NA>신반도연립신월5동서울특별시 양천구 신월5동 74-4서울특별시 양천구 가로공원로67길 161981-09-1713600<NA>02-0000-000002-0000-0000
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>대경연립신정2동서울특별시 양천구 신정2동 127-4서울특별시 양천구 신목로4길 241983-10-2224500<NA>02-0000-000002-0000-0000
268연립<NA>아신빌라신정4동서울특별시 양천구 신정4동 876-2서울특별시 양천구 은행정로19길 161988-04-1923300<NA>02-0000-000002-0000-0000
269연립<NA>코끼리연립신정4동서울특별시 양천구 신정4동 880-1서울특별시 양천구 은행정로19가길 81981-12-1724200<NA>02-0000-000002-0000-0000