Overview

Dataset statistics

Number of variables8
Number of observations257
Missing cells68
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.4 KiB
Average record size in memory65.5 B

Variable types

Numeric1
Text5
Categorical2

Dataset

Description서울특별시 양천구의 공동주택관리업현황(아파트관리사무소, 행정동, 주소(지번, 도로명), 연락처, 팩스번호 등)의 정보를 제공합니다.
Author서울특별시 양천구
URLhttps://www.data.go.kr/data/15049417/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
관리사무소 전화번호 has 15 (5.8%) missing valuesMissing
관리사무소 팩스번호 has 53 (20.6%) missing valuesMissing
번호 has unique valuesUnique
소재지(지번주소) has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:18:36.564964
Analysis finished2023-12-12 09:18:37.327978
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct257
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129
Minimum1
Maximum257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-12T18:18:37.423126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.8
Q165
median129
Q3193
95-th percentile244.2
Maximum257
Range256
Interquartile range (IQR)128

Descriptive statistics

Standard deviation74.333707
Coefficient of variation (CV)0.57623029
Kurtosis-1.2
Mean129
Median Absolute Deviation (MAD)64
Skewness0
Sum33153
Variance5525.5
MonotonicityStrictly increasing
2023-12-12T18:18:37.921556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
194 1
 
0.4%
164 1
 
0.4%
165 1
 
0.4%
166 1
 
0.4%
167 1
 
0.4%
168 1
 
0.4%
169 1
 
0.4%
170 1
 
0.4%
171 1
 
0.4%
Other values (247) 247
96.1%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
257 1
0.4%
256 1
0.4%
255 1
0.4%
254 1
0.4%
253 1
0.4%
252 1
0.4%
251 1
0.4%
250 1
0.4%
249 1
0.4%
248 1
0.4%

아파트 관리사무소
Text

Distinct248
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T18:18:38.223955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.0466926
Min length2

Characters and Unicode

Total characters1554
Distinct characters229
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

Unique239 ?
Unique (%)93.0%

Sample

1st row목동1단지
2nd row목동2단지
3rd row목동3단지
4th row목동4단지
5th row목동5단지
ValueCountFrequency (%)
정은스카이빌 3
 
1.1%
목동삼성 2
 
0.7%
신정뉴타운 2
 
0.7%
건영 2
 
0.7%
목동성원 2
 
0.7%
탑건위너빌 2
 
0.7%
명지해드는터 2
 
0.7%
현대 2
 
0.7%
동일하이빌 2
 
0.7%
신월동코아루 2
 
0.7%
Other values (245) 246
92.1%
2023-12-12T18:18:38.684577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 96
 
6.2%
목 65
 
4.2%
신 53
 
3.4%
지 49
 
3.2%
단 42
 
2.7%
아 38
 
2.4%
트 36
 
2.3%
이 35
 
2.3%
스 34
 
2.2%
1 32
 
2.1%
Other values (219) 1074
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1398
90.0%
Decimal Number 101
 
6.5%
Open Punctuation 15
 
1.0%
Close Punctuation 15
 
1.0%
Uppercase Letter 12
 
0.8%
Space Separator 10
 
0.6%
Lowercase Letter 2
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 96
 
6.9%
목 65
 
4.6%
신 53
 
3.8%
지 49
 
3.5%
단 42
 
3.0%
아 38
 
2.7%
트 36
 
2.6%
이 35
 
2.5%
스 34
 
2.4%
빌 31
 
2.2%
Other values (193) 919
65.7%
Uppercase Letter
ValueCountFrequency (%)
S 2
16.7%
A 1
8.3%
G 1
8.3%
C 1
8.3%
B 1
8.3%
M 1
8.3%
K 1
8.3%
W 1
8.3%
E 1
8.3%
I 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%
6 3
 
3.0%
5 3
 
3.0%
7 1
 
1.0%
8 1
 
1.0%
9 1
 
1.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1398
90.0%
Common 142
 
9.1%
Latin 14
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 96
 
6.9%
목 65
 
4.6%
신 53
 
3.8%
지 49
 
3.5%
단 42
 
3.0%
아 38
 
2.7%
트 36
 
2.6%
이 35
 
2.5%
스 34
 
2.4%
빌 31
 
2.2%
Other values (193) 919
65.7%
Common
ValueCountFrequency (%)
1 32
22.5%
2 31
21.8%
( 15
10.6%
) 15
10.6%
3 14
9.9%
10
 
7.0%
0 8
 
5.6%
4 7
 
4.9%
6 3
 
2.1%
5 3
 
2.1%
Other values (4) 4
 
2.8%
Latin
ValueCountFrequency (%)
e 2
14.3%
S 2
14.3%
A 1
7.1%
G 1
7.1%
C 1
7.1%
B 1
7.1%
M 1
7.1%
K 1
7.1%
W 1
7.1%
E 1
7.1%
Other values (2) 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1398
90.0%
ASCII 156
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 96
 
6.9%
목 65
 
4.6%
신 53
 
3.8%
지 49
 
3.5%
단 42
 
3.0%
아 38
 
2.7%
트 36
 
2.6%
이 35
 
2.5%
스 34
 
2.4%
빌 31
 
2.2%
Other values (193) 919
65.7%
ASCII
ValueCountFrequency (%)
1 32
20.5%
2 31
19.9%
( 15
9.6%
) 15
9.6%
3 14
9.0%
10
 
6.4%
0 8
 
5.1%
4 7
 
4.5%
6 3
 
1.9%
5 3
 
1.9%
Other values (16) 18
11.5%

행정동
Categorical

Distinct19
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
신정3동
34 
신월4동
26 
신월2동
24 
목4동
24 
신정2동
16 
Other values (14)
133 

Length

Max length5
Median length4
Mean length3.7431907
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신정3동 34
13.2%
신월4동 26
10.1%
신월2동 24
 
9.3%
목4동 24
 
9.3%
신정2동 16
 
6.2%
신정4동 16
 
6.2%
목1동 16
 
6.2%
목2동 16
 
6.2%
신월5동 13
 
5.1%
신월1동 12
 
4.7%
Other values (9) 60
23.3%

Length

2023-12-12T18:18:38.887250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신정3동 34
13.2%
신월4동 26
10.1%
신월2동 24
9.3%
목4동 24
9.3%
목1동 20
 
7.8%
신정2동 16
 
6.2%
신정4동 16
 
6.2%
목2동 16
 
6.2%
신월5동 13
 
5.1%
신월1동 12
 
4.7%
Other values (8) 56
21.8%

소재지(지번주소)
Text

UNIQUE 

Distinct257
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T18:18:39.312149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length19.22179
Min length16

Characters and Unicode

Total characters4940
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

Unique257 ?
Unique (%)100.0%

Sample

1st row서울특별시 양천구 목5동 901
2nd row서울특별시 양천구 목5동 902
3rd row서울특별시 양천구 목5동 903
4th row서울특별시 양천구 목5동 904
5th row서울특별시 양천구 목5동 912
ValueCountFrequency (%)
서울특별시 257
25.0%
양천구 257
25.0%
신정3동 32
 
3.1%
신월4동 26
 
2.5%
신월2동 24
 
2.3%
목4동 23
 
2.2%
목1동 20
 
1.9%
신정4동 16
 
1.6%
신정2동 16
 
1.6%
목2동 15
 
1.5%
Other values (270) 341
33.2%
2023-12-12T18:18:39.908547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
771
15.6%
1 267
 
5.4%
서 257
 
5.2%
특 257
 
5.2%
별 257
 
5.2%
시 257
 
5.2%
양 257
 
5.2%
천 257
 
5.2%
구 257
 
5.2%
울 257
 
5.2%
Other values (20) 1846
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2765
56.0%
Decimal Number 1283
26.0%
Space Separator 771
 
15.6%
Dash Punctuation 113
 
2.3%
Other Punctuation 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 257
9.3%
특 257
9.3%
별 257
9.3%
시 257
9.3%
양 257
9.3%
천 257
9.3%
구 257
9.3%
울 257
9.3%
동 257
9.3%
신 183
6.6%
Other values (6) 269
9.7%
Decimal Number
ValueCountFrequency (%)
1 267
20.8%
2 170
13.3%
3 161
12.5%
4 149
11.6%
5 110
8.6%
0 109
8.5%
9 101
 
7.9%
7 99
 
7.7%
6 60
 
4.7%
8 57
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 7
87.5%
. 1
 
12.5%
Space Separator
ValueCountFrequency (%)
771
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2765
56.0%
Common 2175
44.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 257
9.3%
특 257
9.3%
별 257
9.3%
시 257
9.3%
양 257
9.3%
천 257
9.3%
구 257
9.3%
울 257
9.3%
동 257
9.3%
신 183
6.6%
Other values (6) 269
9.7%
Common
ValueCountFrequency (%)
771
35.4%
1 267
 
12.3%
2 170
 
7.8%
3 161
 
7.4%
4 149
 
6.9%
- 113
 
5.2%
5 110
 
5.1%
0 109
 
5.0%
9 101
 
4.6%
7 99
 
4.6%
Other values (4) 125
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2765
56.0%
ASCII 2175
44.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
771
35.4%
1 267
 
12.3%
2 170
 
7.8%
3 161
 
7.4%
4 149
 
6.9%
- 113
 
5.2%
5 110
 
5.1%
0 109
 
5.0%
9 101
 
4.6%
7 99
 
4.6%
Other values (4) 125
 
5.7%
Hangul
ValueCountFrequency (%)
서 257
9.3%
특 257
9.3%
별 257
9.3%
시 257
9.3%
양 257
9.3%
천 257
9.3%
구 257
9.3%
울 257
9.3%
동 257
9.3%
신 183
6.6%
Other values (6) 269
9.7%

도로명 주소
Text

Distinct255
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T18:18:40.239655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length18.770428
Min length15

Characters and Unicode

Total characters4824
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

Unique253 ?
Unique (%)98.4%

Sample

1st row서울특별시 양천구 목동서로 38
2nd row서울특별시 양천구 목동서로 70
3rd row서울특별시 양천구 목동서로 100
4th row서울특별시 양천구 목동서로 130
5th row서울특별시 양천구 목동동로 350
ValueCountFrequency (%)
서울특별시 257
25.0%
양천구 257
25.0%
목동동로 19
 
1.8%
오목로 13
 
1.3%
목동서로 10
 
1.0%
11 9
 
0.9%
중앙로29길 9
 
0.9%
월정로 8
 
0.8%
10 8
 
0.8%
신정로 8
 
0.8%
Other values (243) 430
41.8%
2023-12-12T18:18:40.867949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
771
16.0%
서 272
 
5.6%
양 262
 
5.4%
로 259
 
5.4%
특 257
 
5.3%
별 257
 
5.3%
시 257
 
5.3%
천 257
 
5.3%
구 257
 
5.3%
울 257
 
5.3%
Other values (41) 1718
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3203
66.4%
Decimal Number 826
 
17.1%
Space Separator 771
 
16.0%
Dash Punctuation 24
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 272
 
8.5%
양 262
 
8.2%
로 259
 
8.1%
특 257
 
8.0%
별 257
 
8.0%
시 257
 
8.0%
천 257
 
8.0%
구 257
 
8.0%
울 257
 
8.0%
길 151
 
4.7%
Other values (29) 717
22.4%
Decimal Number
ValueCountFrequency (%)
1 184
22.3%
2 121
14.6%
3 95
11.5%
5 85
10.3%
0 82
9.9%
7 67
 
8.1%
6 54
 
6.5%
4 51
 
6.2%
9 50
 
6.1%
8 37
 
4.5%
Space Separator
ValueCountFrequency (%)
771
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3203
66.4%
Common 1621
33.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 272
 
8.5%
양 262
 
8.2%
로 259
 
8.1%
특 257
 
8.0%
별 257
 
8.0%
시 257
 
8.0%
천 257
 
8.0%
구 257
 
8.0%
울 257
 
8.0%
길 151
 
4.7%
Other values (29) 717
22.4%
Common
ValueCountFrequency (%)
771
47.6%
1 184
 
11.4%
2 121
 
7.5%
3 95
 
5.9%
5 85
 
5.2%
0 82
 
5.1%
7 67
 
4.1%
6 54
 
3.3%
4 51
 
3.1%
9 50
 
3.1%
Other values (2) 61
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3203
66.4%
ASCII 1621
33.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
771
47.6%
1 184
 
11.4%
2 121
 
7.5%
3 95
 
5.9%
5 85
 
5.2%
0 82
 
5.1%
7 67
 
4.1%
6 54
 
3.3%
4 51
 
3.1%
9 50
 
3.1%
Other values (2) 61
 
3.8%
Hangul
ValueCountFrequency (%)
서 272
 
8.5%
양 262
 
8.2%
로 259
 
8.1%
특 257
 
8.0%
별 257
 
8.0%
시 257
 
8.0%
천 257
 
8.0%
구 257
 
8.0%
울 257
 
8.0%
길 151
 
4.7%
Other values (29) 717
22.4%

관리사무소 전화번호
Text

MISSING 

Distinct232
Distinct (%)95.9%
Missing15
Missing (%)5.8%
Memory size2.1 KiB
2023-12-12T18:18:41.178502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.012397
Min length11

Characters and Unicode

Total characters2907
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

Unique225 ?
Unique (%)93.0%

Sample

1st row02-2648-3110
2nd row02-2647-0539
3rd row02-2647-0337
4th row02-2647-0898
5th row02-2647-0049
ValueCountFrequency (%)
02-2699-8010 4
 
1.7%
02-2603-3149 3
 
1.2%
02-2696-4581 2
 
0.8%
02-2648-8164 2
 
0.8%
02-2651-0211 2
 
0.8%
02-2625-0273 2
 
0.8%
02-2061-1584 2
 
0.8%
02-2605-6767 1
 
0.4%
02-2643-9598 1
 
0.4%
02-2696-5583 1
 
0.4%
Other values (222) 222
91.7%
2023-12-12T18:18:41.658922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 597
20.5%
- 484
16.6%
0 448
15.4%
6 342
11.8%
4 200
 
6.9%
9 183
 
6.3%
5 146
 
5.0%
1 132
 
4.5%
3 132
 
4.5%
8 125
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2423
83.4%
Dash Punctuation 484
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 597
24.6%
0 448
18.5%
6 342
14.1%
4 200
 
8.3%
9 183
 
7.6%
5 146
 
6.0%
1 132
 
5.4%
3 132
 
5.4%
8 125
 
5.2%
7 118
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2907
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 597
20.5%
- 484
16.6%
0 448
15.4%
6 342
11.8%
4 200
 
6.9%
9 183
 
6.3%
5 146
 
5.0%
1 132
 
4.5%
3 132
 
4.5%
8 125
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2907
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 597
20.5%
- 484
16.6%
0 448
15.4%
6 342
11.8%
4 200
 
6.9%
9 183
 
6.3%
5 146
 
5.0%
1 132
 
4.5%
3 132
 
4.5%
8 125
 
4.3%

관리사무소 팩스번호
Text

MISSING 

Distinct194
Distinct (%)95.1%
Missing53
Missing (%)20.6%
Memory size2.1 KiB
2023-12-12T18:18:41.975955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.014706
Min length11

Characters and Unicode

Total characters2451
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

Unique187 ?
Unique (%)91.7%

Sample

1st row02-6739-3110
2nd row02-2647-0540
3rd row02-2648-3377
4th row02-2642-9810
5th row02-2647-1449
ValueCountFrequency (%)
02-2699-8019 4
 
2.0%
02-2690-2948 3
 
1.5%
02-2645-2333 2
 
1.0%
02-2061-1586 2
 
1.0%
02-2625-0274 2
 
1.0%
02-2651-4591 2
 
1.0%
02-6737-4581 2
 
1.0%
02-2065-6094 1
 
0.5%
02-2605-0955 1
 
0.5%
02-2643-7095 1
 
0.5%
Other values (184) 184
90.2%
2023-12-12T18:18:42.436026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 480
19.6%
- 408
16.6%
0 365
14.9%
6 300
12.2%
4 170
 
6.9%
9 166
 
6.8%
5 125
 
5.1%
3 120
 
4.9%
1 117
 
4.8%
7 105
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2043
83.4%
Dash Punctuation 408
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 480
23.5%
0 365
17.9%
6 300
14.7%
4 170
 
8.3%
9 166
 
8.1%
5 125
 
6.1%
3 120
 
5.9%
1 117
 
5.7%
7 105
 
5.1%
8 95
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 408
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2451
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 480
19.6%
- 408
16.6%
0 365
14.9%
6 300
12.2%
4 170
 
6.9%
9 166
 
6.8%
5 125
 
5.1%
3 120
 
4.9%
1 117
 
4.8%
7 105
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 480
19.6%
- 408
16.6%
0 365
14.9%
6 300
12.2%
4 170
 
6.9%
9 166
 
6.8%
5 125
 
5.1%
3 120
 
4.9%
1 117
 
4.8%
7 105
 
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-09-20
257 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-20
2nd row2023-09-20
3rd row2023-09-20
4th row2023-09-20
5th row2023-09-20

Common Values

ValueCountFrequency (%)
2023-09-20 257
100.0%

Length

2023-12-12T18:18:42.599953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:18:42.702855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-20 257
100.0%

Interactions

2023-12-12T18:18:36.890958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:18:42.767300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정동
번호1.0000.636
행정동0.6361.000
2023-12-12T18:18:42.887852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호행정동
번호1.0000.292
행정동0.2921.000

Missing values

2023-12-12T18:18:37.013668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:18:37.160819image/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.
2023-12-12T18:18:37.269224image/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

번호아파트 관리사무소행정동소재지(지번주소)도로명 주소관리사무소 전화번호관리사무소 팩스번호데이터기준일자
01목동1단지목5동서울특별시 양천구 목5동 901서울특별시 양천구 목동서로 3802-2648-311002-6739-31102023-09-20
12목동2단지목5동서울특별시 양천구 목5동 902서울특별시 양천구 목동서로 7002-2647-053902-2647-05402023-09-20
23목동3단지목5동서울특별시 양천구 목5동 903서울특별시 양천구 목동서로 10002-2647-033702-2648-33772023-09-20
34목동4단지목5동서울특별시 양천구 목5동 904서울특별시 양천구 목동서로 13002-2647-089802-2642-98102023-09-20
45목동5단지목5동서울특별시 양천구 목5동 912서울특별시 양천구 목동동로 35002-2647-004902-2647-14492023-09-20
56목동6단지목5동서울특별시 양천구 목5동 911서울특별시 양천구 목동동로 43002-2647-091602-2647-09132023-09-20
67목동7단지목1동서울특별시 양천구 목1동 925서울특별시 양천구 목동로 21202-2646-236702-2654-55982023-09-20
78목동8단지신정6동서울특별시 양천구 신정6동 314서울특별시 양천구 목동서로 28002-2648-722502-2647-97992023-09-20
89목동9단지신정1동서울특별시 양천구 신정1동 312서울특별시 양천구 목동서로 34002-2648-2250070-8134-07552023-09-20
910목동10단지신정1동서울특별시 양천구 신정1동 311서울특별시 양천구 목동서로 40002-2648-279802-2642-29562023-09-20
번호아파트 관리사무소행정동소재지(지번주소)도로명 주소관리사무소 전화번호관리사무소 팩스번호데이터기준일자
247248목동현대하이페리온목1동서울특별시 양천구 목1동 916서울특별시 양천구 목동동로 25702-2652-121302-2652-12142023-09-20
248249부영그린타운2차목5동서울특별시 양천구 목5동 908-28서울특별시 양천구 목동동로 40102-2062-208602-2062-20872023-09-20
249250부영그린타운3차목5동서울특별시 양천구 목5동 908-34서울특별시 양천구 목동동로 41102-2649-041402-2649-04152023-09-20
250251삼성쉐르빌1신정6동서울특별시 양천구 신정6동 318-10서울특별시 양천구 목동동로 18902-2651-333102-2651-34142023-09-20
251252삼성쉐르빌2신정6동서울특별시 양천구 신정6동 318-12서울특별시 양천구 목동동로 17702-2653-440702-2653-44082023-09-20
252253목동트윈빌목5동서울특별시 양천구 목5동 905-22서울특별시 양천구 목동동로 33902-2062-281102-2062-28122023-09-20
253254목동현대하이페리온2목1동서울특별시 양천구 목1동 961서울특별시 양천구 오목로 30002-2640-197002-2640-19742023-09-20
254255목동트라팰리스웨스턴에비뉴목1동서울특별시 양천구 목1동 962서울특별시 양천구 오목로 29902-2061-158402-2061-15862023-09-20
255256목동트라팰리스이스턴에비뉴목1동서울특별시 양천구 목1동 962-1서울특별시 양천구 오목로 29902-2061-158402-2061-15862023-09-20
256257목동센트럴푸르지오목1동서울특별시 양천구 목1동 404-13서울특별시 양천구 오목로 35402-2061-408702-2061-40862023-09-20