Overview

Dataset statistics

Number of variables8
Number of observations172
Missing cells8
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.5 KiB
Average record size in memory68.7 B

Variable types

Numeric4
Categorical2
Text2

Dataset

Description이 데이터는 서울특별시 동작구 관내 일부 주택 유형별 위치정보 및 세대수 현황에 대한 데이터입니다. 이 데이터에는 분류, 건물명, 행정동, 주소, 세대수, 위도, 경도 등이 포함되어 있습니다.
Author서울특별시 동작구
URLhttps://www.data.go.kr/data/15066101/fileData.do

Alerts

연번 is highly overall correlated with 분류 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 행정동High correlation
경도 is highly overall correlated with 행정동High correlation
분류 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
행정동 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
위도 has 4 (2.3%) missing valuesMissing
경도 has 4 (2.3%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-13 12:01:25.298709
Analysis finished2024-04-13 12:01:32.953880
Duration7.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.5
Minimum1
Maximum172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-13T21:01:33.247038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.55
Q143.75
median86.5
Q3129.25
95-th percentile163.45
Maximum172
Range171
Interquartile range (IQR)85.5

Descriptive statistics

Standard deviation49.796252
Coefficient of variation (CV)0.57567921
Kurtosis-1.2
Mean86.5
Median Absolute Deviation (MAD)43
Skewness0
Sum14878
Variance2479.6667
MonotonicityStrictly increasing
2024-04-13T21:01:33.496799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
120 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
Other values (162) 162
94.2%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%
164 1
0.6%
163 1
0.6%

분류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
아파트
139 
연립주택
21 
주상복합
 
12

Length

Max length4
Median length3
Mean length3.1918605
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 139
80.8%
연립주택 21
 
12.2%
주상복합 12
 
7.0%

Length

2024-04-13T21:01:33.737244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:01:33.932111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 139
80.8%
연립주택 21
 
12.2%
주상복합 12
 
7.0%

건물명
Text

Distinct171
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-13T21:01:34.567828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length7.9767442
Min length2

Characters and Unicode

Total characters1372
Distinct characters228
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)98.8%

Sample

1st row노량진우성
2nd row노량진삼익
3rd row신동아리버파크(분양 1,696,임대 925)
4th row노량진쌍용예가
5th row형인한강
ValueCountFrequency (%)
임대 4
 
1.9%
삼성 2
 
1.0%
e편한세상 2
 
1.0%
상도 2
 
1.0%
래미안 2
 
1.0%
이수역 2
 
1.0%
성락 1
 
0.5%
신대방한성 1
 
0.5%
대방성원 1
 
0.5%
대방현대1차 1
 
0.5%
Other values (192) 192
91.4%
2024-04-13T21:01:35.451377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 68
 
5.0%
43
 
3.1%
1 36
 
2.6%
상 35
 
2.6%
) 31
 
2.3%
동 31
 
2.3%
( 31
 
2.3%
도 31
 
2.3%
임 25
 
1.8%
2 25
 
1.8%
Other values (218) 1016
74.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1077
78.5%
Decimal Number 143
 
10.4%
Space Separator 43
 
3.1%
Close Punctuation 31
 
2.3%
Open Punctuation 31
 
2.3%
Other Punctuation 22
 
1.6%
Uppercase Letter 18
 
1.3%
Lowercase Letter 4
 
0.3%
Dash Punctuation 2
 
0.1%
Letter Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 68
 
6.3%
상 35
 
3.2%
동 31
 
2.9%
도 31
 
2.9%
임 25
 
2.3%
성 23
 
2.1%
양 22
 
2.0%
이 21
 
1.9%
분 20
 
1.9%
방 20
 
1.9%
Other values (192) 781
72.5%
Decimal Number
ValueCountFrequency (%)
1 36
25.2%
2 25
17.5%
4 16
11.2%
5 13
 
9.1%
6 12
 
8.4%
8 11
 
7.7%
7 9
 
6.3%
3 7
 
4.9%
9 7
 
4.9%
0 7
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
C 6
33.3%
K 4
22.2%
H 2
 
11.1%
S 2
 
11.1%
I 1
 
5.6%
P 1
 
5.6%
A 1
 
5.6%
R 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 21
95.5%
' 1
 
4.5%
Space Separator
ValueCountFrequency (%)
43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1077
78.5%
Common 272
 
19.8%
Latin 23
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 68
 
6.3%
상 35
 
3.2%
동 31
 
2.9%
도 31
 
2.9%
임 25
 
2.3%
성 23
 
2.1%
양 22
 
2.0%
이 21
 
1.9%
분 20
 
1.9%
방 20
 
1.9%
Other values (192) 781
72.5%
Common
ValueCountFrequency (%)
43
15.8%
1 36
13.2%
) 31
11.4%
( 31
11.4%
2 25
9.2%
, 21
7.7%
4 16
 
5.9%
5 13
 
4.8%
6 12
 
4.4%
8 11
 
4.0%
Other values (6) 33
12.1%
Latin
ValueCountFrequency (%)
C 6
26.1%
K 4
17.4%
e 4
17.4%
H 2
 
8.7%
S 2
 
8.7%
I 1
 
4.3%
P 1
 
4.3%
A 1
 
4.3%
R 1
 
4.3%
Ⅱ 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1077
78.5%
ASCII 294
 
21.4%
Number Forms 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 68
 
6.3%
상 35
 
3.2%
동 31
 
2.9%
도 31
 
2.9%
임 25
 
2.3%
성 23
 
2.1%
양 22
 
2.0%
이 21
 
1.9%
분 20
 
1.9%
방 20
 
1.9%
Other values (192) 781
72.5%
ASCII
ValueCountFrequency (%)
43
14.6%
1 36
12.2%
) 31
10.5%
( 31
10.5%
2 25
8.5%
, 21
 
7.1%
4 16
 
5.4%
5 13
 
4.4%
6 12
 
4.1%
8 11
 
3.7%
Other values (15) 55
18.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

행정동
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
신대방2동
17 
상도1동
15 
상도2동
14 
흑석동
14 
대방동
14 
Other values (22)
98 

Length

Max length8
Median length5
Mean length4.0232558
Min length2

Unique

Unique8 ?
Unique (%)4.7%

Sample

1st row노량진1동
2nd row노량진1동
3rd row노량진1동
4th row노량진1동
5th row노량진1동

Common Values

ValueCountFrequency (%)
신대방2동 17
 
9.9%
상도1동 15
 
8.7%
상도2동 14
 
8.1%
흑석동 14
 
8.1%
대방동 14
 
8.1%
사당5동 13
 
7.6%
사당3동 13
 
7.6%
신대방1동 10
 
5.8%
본동 9
 
5.2%
사당2동 7
 
4.1%
Other values (17) 46
26.7%

Length

2024-04-13T21:01:35.678150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신대방2동 17
9.9%
상도2동 15
 
8.7%
대방동 15
 
8.7%
상도1동 15
 
8.7%
흑석동 14
 
8.1%
사당5동 14
 
8.1%
사당3동 14
 
8.1%
상도4동 10
 
5.8%
신대방1동 10
 
5.8%
본동 10
 
5.8%
Other values (8) 38
22.1%

주소
Text

Distinct169
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-13T21:01:36.715570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length18.953488
Min length15

Characters and Unicode

Total characters3260
Distinct characters74
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

Unique166 ?
Unique (%)96.5%

Sample

1st row서울특별시 동작구 만양로8길 50
2nd row서울특별시 동작구 만양로 84
3rd row서울특별시 동작구 만양로 19
4th row서울특별시 동작구 장승배기로16길 134
5th row서울특별시 동작구 만양로 36
ValueCountFrequency (%)
동작구 172
25.0%
서울특별시 171
24.8%
상도로 8
 
1.2%
동작대로29길 7
 
1.0%
양녕로 6
 
0.9%
서달로 6
 
0.9%
22 6
 
0.9%
보라매로5길 5
 
0.7%
여의대방로10길 5
 
0.7%
40 5
 
0.7%
Other values (194) 298
43.3%
2024-04-13T21:01:37.980329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
517
15.9%
동 189
 
5.8%
작 188
 
5.8%
서 180
 
5.5%
시 173
 
5.3%
별 172
 
5.3%
구 172
 
5.3%
울 172
 
5.3%
특 171
 
5.2%
로 164
 
5.0%
Other values (64) 1162
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2138
65.6%
Decimal Number 598
 
18.3%
Space Separator 517
 
15.9%
Dash Punctuation 7
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 189
8.8%
작 188
8.8%
서 180
8.4%
시 173
 
8.1%
별 172
 
8.0%
구 172
 
8.0%
울 172
 
8.0%
특 171
 
8.0%
로 164
 
7.7%
길 121
 
5.7%
Other values (52) 436
20.4%
Decimal Number
ValueCountFrequency (%)
1 114
19.1%
2 110
18.4%
3 69
11.5%
4 54
9.0%
5 53
8.9%
0 50
8.4%
7 42
 
7.0%
9 41
 
6.9%
6 37
 
6.2%
8 28
 
4.7%
Space Separator
ValueCountFrequency (%)
517
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2138
65.6%
Common 1122
34.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 189
8.8%
작 188
8.8%
서 180
8.4%
시 173
 
8.1%
별 172
 
8.0%
구 172
 
8.0%
울 172
 
8.0%
특 171
 
8.0%
로 164
 
7.7%
길 121
 
5.7%
Other values (52) 436
20.4%
Common
ValueCountFrequency (%)
517
46.1%
1 114
 
10.2%
2 110
 
9.8%
3 69
 
6.1%
4 54
 
4.8%
5 53
 
4.7%
0 50
 
4.5%
7 42
 
3.7%
9 41
 
3.7%
6 37
 
3.3%
Other values (2) 35
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2138
65.6%
ASCII 1122
34.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
517
46.1%
1 114
 
10.2%
2 110
 
9.8%
3 69
 
6.1%
4 54
 
4.8%
5 53
 
4.7%
0 50
 
4.5%
7 42
 
3.7%
9 41
 
3.7%
6 37
 
3.3%
Other values (2) 35
 
3.1%
Hangul
ValueCountFrequency (%)
동 189
8.8%
작 188
8.8%
서 180
8.4%
시 173
 
8.1%
별 172
 
8.0%
구 172
 
8.0%
울 172
 
8.0%
특 171
 
8.0%
로 164
 
7.7%
길 121
 
5.7%
Other values (52) 436
20.4%

세대수
Real number (ℝ)

Distinct148
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean378.5
Minimum21
Maximum2621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-13T21:01:38.224984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile30
Q188
median201
Q3516.25
95-th percentile1135.5
Maximum2621
Range2600
Interquartile range (IQR)428.25

Descriptive statistics

Standard deviation420.7285
Coefficient of variation (CV)1.111568
Kurtosis5.272406
Mean378.5
Median Absolute Deviation (MAD)155
Skewness2.0052151
Sum65102
Variance177012.47
MonotonicityNot monotonic
2024-04-13T21:01:38.485361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 4
 
2.3%
24 3
 
1.7%
42 3
 
1.7%
140 3
 
1.7%
30 3
 
1.7%
423 2
 
1.2%
959 2
 
1.2%
545 2
 
1.2%
32 2
 
1.2%
514 2
 
1.2%
Other values (138) 146
84.9%
ValueCountFrequency (%)
21 2
1.2%
22 1
 
0.6%
23 1
 
0.6%
24 3
1.7%
29 1
 
0.6%
30 3
1.7%
32 2
1.2%
35 1
 
0.6%
36 1
 
0.6%
37 1
 
0.6%
ValueCountFrequency (%)
2621 1
0.6%
1772 1
0.6%
1656 1
0.6%
1628 1
0.6%
1559 1
0.6%
1550 1
0.6%
1376 1
0.6%
1335 1
0.6%
1152 1
0.6%
1122 1
0.6%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct166
Distinct (%)98.8%
Missing4
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean37.497359
Minimum37.477376
Maximum37.51428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-13T21:01:38.732433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.477376
5-th percentile37.481833
Q137.490663
median37.496865
Q337.505195
95-th percentile37.511949
Maximum37.51428
Range0.03690419
Interquartile range (IQR)0.0145323

Descriptive statistics

Standard deviation0.009443482
Coefficient of variation (CV)0.00025184392
Kurtosis-0.85570461
Mean37.497359
Median Absolute Deviation (MAD)0.00730109
Skewness-0.04613587
Sum6299.5564
Variance8.9179352 × 10-5
MonotonicityNot monotonic
2024-04-13T21:01:38.990079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.50992631 2
 
1.2%
37.48772967 2
 
1.2%
37.49511473 1
 
0.6%
37.49121134 1
 
0.6%
37.51155229 1
 
0.6%
37.49106174 1
 
0.6%
37.49310363 1
 
0.6%
37.49527344 1
 
0.6%
37.49632776 1
 
0.6%
37.49005342 1
 
0.6%
Other values (156) 156
90.7%
(Missing) 4
 
2.3%
ValueCountFrequency (%)
37.47737607 1
0.6%
37.47763298 1
0.6%
37.4780357 1
0.6%
37.4784341 1
0.6%
37.47866901 1
0.6%
37.47869239 1
0.6%
37.47949611 1
0.6%
37.48056197 1
0.6%
37.48159582 1
0.6%
37.48227428 1
0.6%
ValueCountFrequency (%)
37.51428026 1
0.6%
37.51426247 1
0.6%
37.51417479 1
0.6%
37.51330347 1
0.6%
37.51301522 1
0.6%
37.51265989 1
0.6%
37.51238829 1
0.6%
37.51221007 1
0.6%
37.51203051 1
0.6%
37.51179644 1
0.6%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct166
Distinct (%)98.8%
Missing4
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean126.94984
Minimum126.90519
Maximum126.98197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-13T21:01:39.252887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.90519
5-th percentile126.91416
Q1126.93312
median126.95032
Q3126.96738
95-th percentile126.97875
Maximum126.98197
Range0.076779
Interquartile range (IQR)0.0342581

Descriptive statistics

Standard deviation0.020243332
Coefficient of variation (CV)0.00015945929
Kurtosis-0.93852559
Mean126.94984
Median Absolute Deviation (MAD)0.01721245
Skewness-0.28922854
Sum21327.573
Variance0.00040979249
MonotonicityNot monotonic
2024-04-13T21:01:39.515339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9304253 2
 
1.2%
126.9707248 2
 
1.2%
126.9150796 1
 
0.6%
126.9249903 1
 
0.6%
126.9253078 1
 
0.6%
126.9116424 1
 
0.6%
126.9106015 1
 
0.6%
126.9116905 1
 
0.6%
126.9139635 1
 
0.6%
126.9083379 1
 
0.6%
Other values (156) 156
90.7%
(Missing) 4
 
2.3%
ValueCountFrequency (%)
126.905187 1
0.6%
126.9069396 1
0.6%
126.9070485 1
0.6%
126.9083379 1
0.6%
126.9106015 1
0.6%
126.9116424 1
0.6%
126.9116905 1
0.6%
126.9125665 1
0.6%
126.9139635 1
0.6%
126.9145347 1
0.6%
ValueCountFrequency (%)
126.981966 1
0.6%
126.9815355 1
0.6%
126.9813339 1
0.6%
126.9809603 1
0.6%
126.9802816 1
0.6%
126.980183 1
0.6%
126.980134 1
0.6%
126.9799582 1
0.6%
126.9788425 1
0.6%
126.9785783 1
0.6%

Interactions

2024-04-13T21:01:31.053073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:27.945390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:28.992325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:30.003428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:31.316110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:28.211932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:29.246911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:30.268805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:31.566980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:28.461089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:29.485612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:30.518647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:31.835085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:28.725701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:29.745547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:01:30.782506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T21:01:39.692422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번분류행정동세대수위도경도
연번1.0000.9160.9390.1770.7480.871
분류0.9161.0000.8360.2460.3500.467
행정동0.9390.8361.0000.0000.8790.946
세대수0.1770.2460.0001.0000.2060.000
위도0.7480.3500.8790.2061.0000.723
경도0.8710.4670.9460.0000.7231.000
2024-04-13T21:01:39.860007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동분류
행정동1.0000.545
분류0.5451.000
2024-04-13T21:01:40.002800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세대수위도경도분류행정동
연번1.000-0.350-0.320-0.1780.8620.675
세대수-0.3501.0000.183-0.0200.1570.000
위도-0.3200.1831.000-0.3830.2190.536
경도-0.178-0.020-0.3831.0000.3110.697
분류0.8620.1570.2190.3111.0000.545
행정동0.6750.0000.5360.6970.5451.000

Missing values

2024-04-13T21:01:32.199833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T21:01:32.614396image/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.
2024-04-13T21:01:32.875950image/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

연번분류건물명행정동주소세대수위도경도
01아파트노량진우성노량진1동서울특별시 동작구 만양로8길 5090137.510304126.946866
12아파트노량진삼익노량진1동서울특별시 동작구 만양로 8417537.511367126.945226
23아파트신동아리버파크(분양 1,696,임대 925)노량진1동서울특별시 동작구 만양로 19262137.507073126.945718
34아파트노량진쌍용예가노량진1동서울특별시 동작구 장승배기로16길 13429937.510265126.943676
45아파트형인한강노량진1동서울특별시 동작구 만양로 367337.507664126.948235
56아파트노량진노량진2동서울특별시 동작구 장승배기로18길 274937.507709126.941717
67아파트극동강변본동서울특별시 동작구 매봉로 15812337.511445126.951574
78아파트본동신동아본동서울특별시 동작구 매봉로 13476537.510147126.951491
89아파트한강쌍용본동서울특별시 동작구 노량진로24길 216137.512388126.95305
910아파트유원강변본동서울특별시 동작구 노량진로23가길 1630637.514262126.951715
연번분류건물명행정동주소세대수위도경도
162163연립주택칠성연립사당1동서울특별시 동작구 사당로 20길 1093037.478692126.974677
163164연립주택태평빌라주택사당1동서울특별시 동작구 사당동 282-621<NA><NA>
164165연립주택동작연립동작동서울특별시 동작구 동작대로43길 223237.497001126.981334
165166연립주택성락사당3동서울특별시 동작구 사당로27길 2474237.492499126.968981
166167연립주택신아사당4동서울특별시 동작구 사당로16길 137-13037.477633126.970412
167168연립주택유성사당5동서울특별시 동작구 사당로10길 12437.484815126.970192
168169연립주택신남성연립사당5동서울특별시 동작구 사당로2가길 1313637.489332126.964193
169170연립주택연우연립사당5동서울특별시 동작구 사당로8길 682137.48279126.967112
170171연립주택능내연립사당5동서울특별시 동작구 사당로8길 392237.483599126.968672
171172연립주택천록대방동서울특별시 동작구 등용로 432937.505475126.933434