Overview

Dataset statistics

Number of variables15
Number of observations232
Missing cells51
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.9 KiB
Average record size in memory127.6 B

Variable types

Numeric7
Text3
Categorical4
DateTime1

Dataset

Description서울특별시 구로구 공동주택 현황으로 공동주택명, 법정동명, 도로명주소, 세대수, 준공일자, 동수, 최고층수 등의 정보를 제공합니다.이전 현황은 구로구 주택과 부서자료실(https://www.guro.go.kr/www/selectBbsNttList.do?bbsNo=844&&pageUnit=10&searchCnd=SJ&searchKrwd=%EA%B3%B5%EB%8F%99%EC%A3%BC%ED%83%9D&key=1865&pageIndex=1) 참고 바랍니다.
Author서울특별시 구로구
URLhttps://www.data.go.kr/data/15122612/fileData.do

Alerts

데이터 기준일자 has constant value ""Constant
연번 is highly overall correlated with 법정동명High correlation
우편번호 is highly overall correlated with 법정동명High correlation
건축면적 is highly overall correlated with 동수 and 3 other fieldsHigh correlation
동수 is highly overall correlated with 건축면적 and 1 other fieldsHigh correlation
최고층수 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
주차대수 is highly overall correlated with 건축면적 and 3 other fieldsHigh correlation
승강기 수량 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
법정동명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
난방방법 is highly imbalanced (61.2%)Imbalance
우편번호 has 3 (1.3%) missing valuesMissing
건축면적 has 18 (7.8%) missing valuesMissing
최고층수 has 5 (2.2%) missing valuesMissing
주차대수 has 13 (5.6%) missing valuesMissing
승강기 수량 has 12 (5.2%) missing valuesMissing
연번 has unique valuesUnique
도로명 주소 has unique valuesUnique
건축면적 has 24 (10.3%) zerosZeros
승강기 수량 has 55 (23.7%) zerosZeros

Reproduction

Analysis started2023-12-12 08:00:19.684326
Analysis finished2023-12-12 08:00:25.897106
Duration6.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct232
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.5
Minimum1
Maximum232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:00:25.979056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.55
Q158.75
median116.5
Q3174.25
95-th percentile220.45
Maximum232
Range231
Interquartile range (IQR)115.5

Descriptive statistics

Standard deviation67.116814
Coefficient of variation (CV)0.57610999
Kurtosis-1.2
Mean116.5
Median Absolute Deviation (MAD)58
Skewness0
Sum27028
Variance4504.6667
MonotonicityStrictly increasing
2023-12-12T17:00:26.136208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
161 1
 
0.4%
149 1
 
0.4%
150 1
 
0.4%
151 1
 
0.4%
152 1
 
0.4%
153 1
 
0.4%
154 1
 
0.4%
155 1
 
0.4%
156 1
 
0.4%
Other values (222) 222
95.7%
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 (%)
232 1
0.4%
231 1
0.4%
230 1
0.4%
229 1
0.4%
228 1
0.4%
227 1
0.4%
226 1
0.4%
225 1
0.4%
224 1
0.4%
223 1
0.4%

공동주택명
Text

Distinct227
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T17:00:26.370167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length16
Mean length7.6077586
Min length2

Characters and Unicode

Total characters1765
Distinct characters230
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique222 ?
Unique (%)95.7%

Sample

1st row신도림미성아파트
2nd row신도림우성1차아파트
3rd row신도림우성2차아파트
4th row신도림우성3차아파트
5th row신도림우성5차아파트
ValueCountFrequency (%)
이펜하우스 6
 
2.3%
고척 5
 
1.9%
1단지 3
 
1.2%
2단지 3
 
1.2%
신도림 3
 
1.2%
연지타운 2
 
0.8%
우석빌라 2
 
0.8%
신도림현대아파트 2
 
0.8%
lig리가 2
 
0.8%
대흥연립 2
 
0.8%
Other values (226) 229
88.4%
2023-12-12T17:00:26.711686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 151
 
8.6%
트 144
 
8.2%
파 140
 
7.9%
신 41
 
2.3%
우 38
 
2.2%
림 37
 
2.1%
36
 
2.0%
빌 36
 
2.0%
성 35
 
2.0%
라 34
 
1.9%
Other values (220) 1073
60.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1644
93.1%
Decimal Number 46
 
2.6%
Space Separator 36
 
2.0%
Uppercase Letter 15
 
0.8%
Open Punctuation 6
 
0.3%
Close Punctuation 6
 
0.3%
Lowercase Letter 5
 
0.3%
Dash Punctuation 5
 
0.3%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 151
 
9.2%
트 144
 
8.8%
파 140
 
8.5%
신 41
 
2.5%
우 38
 
2.3%
림 37
 
2.3%
빌 36
 
2.2%
성 35
 
2.1%
라 34
 
2.1%
동 34
 
2.1%
Other values (198) 954
58.0%
Uppercase Letter
ValueCountFrequency (%)
G 3
20.0%
L 3
20.0%
I 3
20.0%
S 2
13.3%
E 1
 
6.7%
K 1
 
6.7%
W 1
 
6.7%
V 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
2 16
34.8%
1 13
28.3%
3 6
 
13.0%
5 4
 
8.7%
4 3
 
6.5%
6 2
 
4.3%
7 2
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1644
93.1%
Common 101
 
5.7%
Latin 20
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 151
 
9.2%
트 144
 
8.8%
파 140
 
8.5%
신 41
 
2.5%
우 38
 
2.3%
림 37
 
2.3%
빌 36
 
2.2%
성 35
 
2.1%
라 34
 
2.1%
동 34
 
2.1%
Other values (198) 954
58.0%
Common
ValueCountFrequency (%)
36
35.6%
2 16
15.8%
1 13
 
12.9%
( 6
 
5.9%
) 6
 
5.9%
3 6
 
5.9%
- 5
 
5.0%
5 4
 
4.0%
4 3
 
3.0%
6 2
 
2.0%
Other values (3) 4
 
4.0%
Latin
ValueCountFrequency (%)
e 5
25.0%
G 3
15.0%
L 3
15.0%
I 3
15.0%
S 2
 
10.0%
E 1
 
5.0%
K 1
 
5.0%
W 1
 
5.0%
V 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1644
93.1%
ASCII 121
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 151
 
9.2%
트 144
 
8.8%
파 140
 
8.5%
신 41
 
2.5%
우 38
 
2.3%
림 37
 
2.3%
빌 36
 
2.2%
성 35
 
2.1%
라 34
 
2.1%
동 34
 
2.1%
Other values (198) 954
58.0%
ASCII
ValueCountFrequency (%)
36
29.8%
2 16
13.2%
1 13
 
10.7%
( 6
 
5.0%
) 6
 
5.0%
3 6
 
5.0%
e 5
 
4.1%
- 5
 
4.1%
5 4
 
3.3%
G 3
 
2.5%
Other values (12) 21
17.4%

법정동명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
구로동
66 
개봉동
35 
오류동
35 
고척동
33 
신도림동
23 
Other values (7)
40 

Length

Max length4
Median length3
Mean length3.0474138
Min length2

Unique

Unique2 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
구로동 66
28.4%
개봉동 35
15.1%
오류동 35
15.1%
고척동 33
14.2%
신도림동 23
 
9.9%
항동 11
 
4.7%
천왕동 8
 
3.4%
궁동 7
 
3.0%
온수동 7
 
3.0%
<NA> 5
 
2.2%
Other values (2) 2
 
0.9%

Length

2023-12-12T17:00:26.860349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구로동 66
28.4%
개봉동 35
15.1%
오류동 35
15.1%
고척동 33
14.2%
신도림동 23
 
9.9%
항동 11
 
4.7%
천왕동 8
 
3.4%
궁동 7
 
3.0%
온수동 7
 
3.0%
na 5
 
2.2%
Other values (2) 2
 
0.9%

도로명 주소
Text

UNIQUE 

Distinct232
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T17:00:27.121081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length28.5
Mean length22.650862
Min length5

Characters and Unicode

Total characters5255
Distinct characters224
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique232 ?
Unique (%)100.0%

Sample

1st row신도림로11가길 36 (신도림동, 미성아파트)
2nd row신도림로 110 (신도림동, 신도림우성1차아파트)
3rd row신도림로 105 (신도림동, 신도림우성2차아파트)
4th row신도림로21길 25 (신도림동, 신도림우성3차아파트)
5th row신도림로21길 21 (신도림동, 신도림우성5차아파트)
ValueCountFrequency (%)
구로동 59
 
6.6%
오류동 34
 
3.8%
고척동 31
 
3.5%
개봉동 31
 
3.5%
신도림동 19
 
2.1%
경인로 16
 
1.8%
신도림로 9
 
1.0%
궁동 9
 
1.0%
온수동 7
 
0.8%
천왕동 7
 
0.8%
Other values (489) 666
75.0%
2023-12-12T17:00:27.631424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
693
 
13.2%
로 327
 
6.2%
동 255
 
4.9%
, 217
 
4.1%
( 215
 
4.1%
) 212
 
4.0%
1 185
 
3.5%
길 149
 
2.8%
아 145
 
2.8%
트 133
 
2.5%
Other values (214) 2724
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2996
57.0%
Decimal Number 865
 
16.5%
Space Separator 694
 
13.2%
Other Punctuation 217
 
4.1%
Open Punctuation 215
 
4.1%
Close Punctuation 212
 
4.0%
Dash Punctuation 43
 
0.8%
Uppercase Letter 8
 
0.2%
Lowercase Letter 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 327
 
10.9%
동 255
 
8.5%
길 149
 
5.0%
아 145
 
4.8%
트 133
 
4.4%
파 133
 
4.4%
구 110
 
3.7%
림 72
 
2.4%
신 71
 
2.4%
도 61
 
2.0%
Other values (194) 1540
51.4%
Decimal Number
ValueCountFrequency (%)
1 185
21.4%
2 129
14.9%
3 94
10.9%
8 76
8.8%
4 72
 
8.3%
5 69
 
8.0%
6 67
 
7.7%
7 62
 
7.2%
9 56
 
6.5%
0 55
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
L 3
37.5%
G 3
37.5%
I 2
25.0%
Space Separator
ValueCountFrequency (%)
693
99.9%
  1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 217
100.0%
Open Punctuation
ValueCountFrequency (%)
( 215
100.0%
Close Punctuation
ValueCountFrequency (%)
) 212
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2996
57.0%
Common 2246
42.7%
Latin 13
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 327
 
10.9%
동 255
 
8.5%
길 149
 
5.0%
아 145
 
4.8%
트 133
 
4.4%
파 133
 
4.4%
구 110
 
3.7%
림 72
 
2.4%
신 71
 
2.4%
도 61
 
2.0%
Other values (194) 1540
51.4%
Common
ValueCountFrequency (%)
693
30.9%
, 217
 
9.7%
( 215
 
9.6%
) 212
 
9.4%
1 185
 
8.2%
2 129
 
5.7%
3 94
 
4.2%
8 76
 
3.4%
4 72
 
3.2%
5 69
 
3.1%
Other values (6) 284
12.6%
Latin
ValueCountFrequency (%)
e 5
38.5%
L 3
23.1%
G 3
23.1%
I 2
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2996
57.0%
ASCII 2258
43.0%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
693
30.7%
, 217
 
9.6%
( 215
 
9.5%
) 212
 
9.4%
1 185
 
8.2%
2 129
 
5.7%
3 94
 
4.2%
8 76
 
3.4%
4 72
 
3.2%
5 69
 
3.1%
Other values (9) 296
13.1%
Hangul
ValueCountFrequency (%)
로 327
 
10.9%
동 255
 
8.5%
길 149
 
5.0%
아 145
 
4.8%
트 133
 
4.4%
파 133
 
4.4%
구 110
 
3.7%
림 72
 
2.4%
신 71
 
2.4%
도 61
 
2.0%
Other values (194) 1540
51.4%
None
ValueCountFrequency (%)
  1
100.0%

우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct110
Distinct (%)48.0%
Missing3
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean8293.9083
Minimum8201
Maximum8395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:00:27.778628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8201
5-th percentile8208
Q18242
median8296
Q38343
95-th percentile8370
Maximum8395
Range194
Interquartile range (IQR)101

Descriptive statistics

Standard deviation56.039424
Coefficient of variation (CV)0.0067566968
Kurtosis-1.3157421
Mean8293.9083
Median Absolute Deviation (MAD)48
Skewness-0.10192613
Sum1899305
Variance3140.417
MonotonicityNot monotonic
2023-12-12T17:00:27.918115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8323 8
 
3.4%
8228 5
 
2.2%
8368 5
 
2.2%
8342 5
 
2.2%
8218 5
 
2.2%
8358 4
 
1.7%
8371 4
 
1.7%
8359 4
 
1.7%
8201 4
 
1.7%
8255 4
 
1.7%
Other values (100) 181
78.0%
ValueCountFrequency (%)
8201 4
1.7%
8202 1
 
0.4%
8203 1
 
0.4%
8205 2
0.9%
8206 1
 
0.4%
8207 2
0.9%
8208 2
0.9%
8209 3
1.3%
8210 1
 
0.4%
8211 4
1.7%
ValueCountFrequency (%)
8395 1
 
0.4%
8393 2
 
0.9%
8392 1
 
0.4%
8383 1
 
0.4%
8378 1
 
0.4%
8375 1
 
0.4%
8371 4
1.7%
8370 2
 
0.9%
8369 2
 
0.9%
8368 5
2.2%

구분
Categorical

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
의무
122 
임의
109 
임의(조심)
 
1

Length

Max length6
Median length2
Mean length2.0172414
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
의무 122
52.6%
임의 109
47.0%
임의(조심) 1
 
0.4%

Length

2023-12-12T17:00:28.066357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:00:28.183864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의무 122
52.6%
임의 109
47.0%
임의(조심 1
 
0.4%

세대수
Text

Distinct186
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T17:00:28.513946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8318966
Min length2

Characters and Unicode

Total characters657
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

Unique154 ?
Unique (%)66.4%

Sample

1st row824
2nd row169
3rd row239
4th row284
5th row154
ValueCountFrequency (%)
36 5
 
2.2%
72 4
 
1.7%
90 4
 
1.7%
24 3
 
1.3%
205 3
 
1.3%
245 3
 
1.3%
299 3
 
1.3%
118 3
 
1.3%
54 3
 
1.3%
84 3
 
1.3%
Other values (176) 198
85.3%
2023-12-12T17:00:29.085924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 96
14.6%
1 93
14.2%
4 76
11.6%
6 60
9.1%
3 59
9.0%
9 59
9.0%
0 55
8.4%
5 55
8.4%
8 51
7.8%
7 39
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 643
97.9%
Other Punctuation 14
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 96
14.9%
1 93
14.5%
4 76
11.8%
6 60
9.3%
3 59
9.2%
9 59
9.2%
0 55
8.6%
5 55
8.6%
8 51
7.9%
7 39
6.1%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 657
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 96
14.6%
1 93
14.2%
4 76
11.6%
6 60
9.1%
3 59
9.0%
9 59
9.0%
0 55
8.4%
5 55
8.4%
8 51
7.8%
7 39
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 96
14.6%
1 93
14.2%
4 76
11.6%
6 60
9.1%
3 59
9.0%
9 59
9.0%
0 55
8.4%
5 55
8.4%
8 51
7.8%
7 39
5.9%

준공일자
Date

Distinct221
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Minimum1976-05-17 00:00:00
Maximum2020-01-28 00:00:00
2023-12-12T17:00:29.227400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:29.376707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

건축면적
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct181
Distinct (%)84.6%
Missing18
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean2961.9486
Minimum0
Maximum57914
Zeros24
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:00:29.529406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1752.75
median1505.5
Q33197.25
95-th percentile9395.5
Maximum57914
Range57914
Interquartile range (IQR)2444.5

Descriptive statistics

Standard deviation5142.6999
Coefficient of variation (CV)1.7362556
Kurtosis63.175784
Mean2961.9486
Median Absolute Deviation (MAD)1010
Skewness6.6914837
Sum633857
Variance26447362
MonotonicityNot monotonic
2023-12-12T17:00:29.688646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
10.3%
2319 2
 
0.9%
1102 2
 
0.9%
2515 2
 
0.9%
743 2
 
0.9%
908 2
 
0.9%
428 2
 
0.9%
1317 2
 
0.9%
784 2
 
0.9%
750 2
 
0.9%
Other values (171) 172
74.1%
(Missing) 18
 
7.8%
ValueCountFrequency (%)
0 24
10.3%
152 1
 
0.4%
182 1
 
0.4%
247 1
 
0.4%
405 1
 
0.4%
418 1
 
0.4%
428 2
 
0.9%
451 1
 
0.4%
459 1
 
0.4%
462 1
 
0.4%
ValueCountFrequency (%)
57914 1
0.4%
25498 1
0.4%
21225 1
0.4%
15351 1
0.4%
14904 1
0.4%
14274 2
0.9%
12416 1
0.4%
10553 1
0.4%
10040 1
0.4%
10039 1
0.4%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8836207
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:00:29.893202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile14.45
Maximum42
Range41
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.5094793
Coefficient of variation (CV)1.1281546
Kurtosis12.138251
Mean4.8836207
Median Absolute Deviation (MAD)2
Skewness2.8897483
Sum1133
Variance30.354363
MonotonicityNot monotonic
2023-12-12T17:00:30.018917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 65
28.0%
2 38
16.4%
3 30
12.9%
4 18
 
7.8%
5 18
 
7.8%
11 11
 
4.7%
6 10
 
4.3%
7 7
 
3.0%
10 6
 
2.6%
9 6
 
2.6%
Other values (13) 23
 
9.9%
ValueCountFrequency (%)
1 65
28.0%
2 38
16.4%
3 30
12.9%
4 18
 
7.8%
5 18
 
7.8%
6 10
 
4.3%
7 7
 
3.0%
8 2
 
0.9%
9 6
 
2.6%
10 6
 
2.6%
ValueCountFrequency (%)
42 1
 
0.4%
31 1
 
0.4%
28 1
 
0.4%
26 1
 
0.4%
22 1
 
0.4%
19 1
 
0.4%
18 1
 
0.4%
16 3
1.3%
15 2
0.9%
14 2
0.9%

최고층수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)13.7%
Missing5
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean14.704846
Minimum2
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:00:30.157937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median15
Q321
95-th percentile27
Maximum51
Range49
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.5220237
Coefficient of variation (CV)0.57953846
Kurtosis0.16009241
Mean14.704846
Median Absolute Deviation (MAD)7
Skewness0.26234661
Sum3338
Variance72.624888
MonotonicityNot monotonic
2023-12-12T17:00:30.278310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3 36
15.5%
15 34
14.7%
25 14
 
6.0%
5 13
 
5.6%
19 12
 
5.2%
18 12
 
5.2%
24 9
 
3.9%
20 8
 
3.4%
11 8
 
3.4%
7 8
 
3.4%
Other values (21) 73
31.5%
ValueCountFrequency (%)
2 4
 
1.7%
3 36
15.5%
4 5
 
2.2%
5 13
 
5.6%
7 8
 
3.4%
8 1
 
0.4%
10 4
 
1.7%
11 8
 
3.4%
12 3
 
1.3%
13 6
 
2.6%
ValueCountFrequency (%)
51 1
 
0.4%
36 1
 
0.4%
32 1
 
0.4%
31 1
 
0.4%
30 1
 
0.4%
29 1
 
0.4%
28 2
 
0.9%
27 6
2.6%
26 1
 
0.4%
25 14
6.0%

주차대수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct180
Distinct (%)82.2%
Missing13
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean353.36986
Minimum11
Maximum3068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:00:30.438929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile23.8
Q180.5
median180
Q3451.5
95-th percentile1301.4
Maximum3068
Range3057
Interquartile range (IQR)371

Descriptive statistics

Standard deviation458.29411
Coefficient of variation (CV)1.2969247
Kurtosis9.50805
Mean353.36986
Median Absolute Deviation (MAD)125
Skewness2.7165897
Sum77388
Variance210033.49
MonotonicityNot monotonic
2023-12-12T17:00:30.937377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 5
 
2.2%
123 4
 
1.7%
18 3
 
1.3%
27 3
 
1.3%
12 3
 
1.3%
45 3
 
1.3%
62 3
 
1.3%
60 3
 
1.3%
103 3
 
1.3%
24 2
 
0.9%
Other values (170) 187
80.6%
(Missing) 13
 
5.6%
ValueCountFrequency (%)
11 1
 
0.4%
12 3
1.3%
15 1
 
0.4%
17 1
 
0.4%
18 3
1.3%
20 1
 
0.4%
22 1
 
0.4%
24 2
0.9%
25 1
 
0.4%
26 2
0.9%
ValueCountFrequency (%)
3068 1
0.4%
2504 1
0.4%
2445 1
0.4%
1990 1
0.4%
1533 1
0.4%
1499 1
0.4%
1484 1
0.4%
1459 1
0.4%
1449 1
0.4%
1364 1
0.4%

승강기 수량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct38
Distinct (%)17.3%
Missing12
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean8.1090909
Minimum0
Maximum62
Zeros55
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:00:31.075340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median5
Q311.25
95-th percentile26.05
Maximum62
Range62
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation9.9920932
Coefficient of variation (CV)1.2322088
Kurtosis6.9683248
Mean8.1090909
Median Absolute Deviation (MAD)5
Skewness2.2598875
Sum1784
Variance99.841926
MonotonicityNot monotonic
2023-12-12T17:00:31.210343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 55
23.7%
3 16
 
6.9%
2 16
 
6.9%
4 14
 
6.0%
10 10
 
4.3%
5 10
 
4.3%
6 10
 
4.3%
8 9
 
3.9%
14 7
 
3.0%
11 7
 
3.0%
Other values (28) 66
28.4%
(Missing) 12
 
5.2%
ValueCountFrequency (%)
0 55
23.7%
1 6
 
2.6%
2 16
 
6.9%
3 16
 
6.9%
4 14
 
6.0%
5 10
 
4.3%
6 10
 
4.3%
7 7
 
3.0%
8 9
 
3.9%
9 5
 
2.2%
ValueCountFrequency (%)
62 1
0.4%
55 1
0.4%
50 1
0.4%
42 1
0.4%
38 1
0.4%
35 1
0.4%
34 1
0.4%
31 1
0.4%
29 1
0.4%
28 1
0.4%

난방방법
Categorical

IMBALANCE 

Distinct14
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
개별,도시가스
177 
개별, 도시가스
 
17
<NA>
 
12
중앙,도시가스
 
7
지역난방
 
7
Other values (9)
 
12

Length

Max length22
Median length7
Mean length6.9913793
Min length2

Unique

Unique7 ?
Unique (%)3.0%

Sample

1st row중앙, 도시가스
2nd row개별, 도시가스
3rd row개별, 도시가스
4th row개별, 도시가스
5th row개별, 도시가스

Common Values

ValueCountFrequency (%)
개별,도시가스 177
76.3%
개별, 도시가스 17
 
7.3%
<NA> 12
 
5.2%
중앙,도시가스 7
 
3.0%
지역난방 7
 
3.0%
지역,열병합 3
 
1.3%
개별,연탄석유 2
 
0.9%
중앙, 도시가스 1
 
0.4%
중앙,소형 열병합발전(2007),도시가스 1
 
0.4%
중앙,소형열병합발전, 도시가스 1
 
0.4%
Other values (4) 4
 
1.7%

Length

2023-12-12T17:00:31.345951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
개별,도시가스 177
70.0%
도시가스 20
 
7.9%
개별 18
 
7.1%
na 12
 
4.7%
중앙,도시가스 7
 
2.8%
지역난방 7
 
2.8%
지역,열병합 3
 
1.2%
개별,연탄석유 2
 
0.8%
중앙 1
 
0.4%
중앙,소형 1
 
0.4%
Other values (5) 5
 
2.0%

데이터 기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-06-01
232 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-01
2nd row2023-06-01
3rd row2023-06-01
4th row2023-06-01
5th row2023-06-01

Common Values

ValueCountFrequency (%)
2023-06-01 232
100.0%

Length

2023-12-12T17:00:31.469219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:00:31.547466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-01 232
100.0%

Interactions

2023-12-12T17:00:24.726472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:20.372793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:21.019420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:21.858863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:22.599693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.272508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.855510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:24.815508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:20.463931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:21.107074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:21.936182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:22.689954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.349616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.921989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:24.929728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:20.558675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:21.259545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:22.042030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:22.807741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.447577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:24.236362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:25.019519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:20.639476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:21.408802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:22.155442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:22.908533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.543302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:24.337158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:25.120110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:20.736208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:21.539523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:22.277147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:22.997172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.620514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:24.436799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:25.237159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:20.833376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:21.635835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:22.389006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.086045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.701476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:24.534829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:25.328752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:20.918069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:21.749326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:22.496437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.179544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:23.785017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:00:24.629143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:00:31.609784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정동명우편번호구분건축면적동수최고층수주차대수승강기 수량난방방법
연번1.0000.8440.8450.3980.1420.0250.4520.2320.1980.693
법정동명0.8441.0000.8370.2110.1740.3370.4140.1800.4170.690
우편번호0.8450.8371.0000.2150.0000.0000.3560.1220.0740.541
구분0.3980.2110.2151.0000.4160.4210.5820.6430.7300.228
건축면적0.1420.1740.0000.4161.0000.8430.1970.6520.8020.716
동수0.0250.3370.0000.4210.8431.0000.0640.9180.9420.000
최고층수0.4520.4140.3560.5820.1970.0641.0000.7000.3630.689
주차대수0.2320.1800.1220.6430.6520.9180.7001.0000.9130.794
승강기 수량0.1980.4170.0740.7300.8020.9420.3630.9131.0000.345
난방방법0.6930.6900.5410.2280.7160.0000.6890.7940.3451.000
2023-12-12T17:00:31.753719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명난방방법구분
법정동명1.0000.3590.122
난방방법0.3591.0000.127
구분0.1220.1271.000
2023-12-12T17:00:31.854471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번우편번호건축면적동수최고층수주차대수승강기 수량법정동명구분난방방법
연번1.0000.271-0.1710.032-0.269-0.071-0.1530.5680.2570.372
우편번호0.2711.0000.0250.075-0.099-0.050-0.0100.5610.1420.271
건축면적-0.1710.0251.0000.5330.5680.8050.7700.0870.1880.465
동수0.0320.0750.5331.0000.1100.6290.4390.1580.2020.000
최고층수-0.269-0.0990.5680.1101.0000.7290.7050.2070.4440.395
주차대수-0.071-0.0500.8050.6290.7291.0000.8700.0800.3540.495
승강기 수량-0.153-0.0100.7700.4390.7050.8701.0000.1960.4290.151
법정동명0.5680.5610.0870.1580.2070.0800.1961.0000.1220.359
구분0.2570.1420.1880.2020.4440.3540.4290.1221.0000.127
난방방법0.3720.2710.4650.0000.3950.4950.1510.3590.1271.000

Missing values

2023-12-12T17:00:25.464775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:00:25.669288image/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.
2023-12-12T17:00:25.809851image/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

연번공동주택명법정동명도로명 주소우편번호구분세대수준공일자건축면적동수최고층수주차대수승강기 수량난방방법데이터 기준일자
01신도림미성아파트신도림동신도림로11가길 36 (신도림동, 미성아파트)8205의무8241987-12-1152786134557중앙, 도시가스2023-06-01
12신도림우성1차아파트신도림동신도림로 110 (신도림동, 신도림우성1차아파트)8207의무1691992-12-04120721510214개별, 도시가스2023-06-01
23신도림우성2차아파트신도림동신도림로 105 (신도림동, 신도림우성2차아파트)8201의무2391996-11-28183531519014개별, 도시가스2023-06-01
34신도림우성3차아파트신도림동신도림로21길 25 (신도림동, 신도림우성3차아파트)8201의무2841993-12-13237231518110개별, 도시가스2023-06-01
45신도림우성5차아파트신도림동신도림로21길 21 (신도림동, 신도림우성5차아파트)8201의무1541994-12-1515531111362개별, 도시가스2023-06-01
56신도림현대아파트신도림동신도림로19길 16 (신도림동, 현대아파트)8201의무2601999-04-1323475112125개별, 도시가스2023-06-01
67신도림1차동아아파트신도림동신도림로 87 (신도림동, 신도림1차동아아파트)8202의무1,0951999-11-3070281127153322개별, 도시가스2023-06-01
78신도림2차동아아파트신도림동경인로 643 (신도림동, 신도림2차동아아파트)8208의무6552000-05-17565852677913개별, 도시가스2023-06-01
89신도림3차동아아파트신도림동신도림로 78 (신도림동, 신도림3차동아아파트)8207의무8132000-11-145103927106416개별, 도시가스2023-06-01
910신도림 대림1차아파트신도림동신도림로 16 (신도림동, 신도림 대림1차아파트)8211의무1,0562001-11-30142741127132324개별, 도시가스2023-06-01
연번공동주택명법정동명도로명 주소우편번호구분세대수준공일자건축면적동수최고층수주차대수승강기 수량난방방법데이터 기준일자
222223신도림3차푸르지오신도림동경인로 619-3<NA>임의1182007-01-20<NA>1<NA><NA>6<NA>2023-06-01
223224구로에스케이뷰구로동새말로 258291임의922006-08-11<NA>1<NA><NA>4<NA>2023-06-01
224225항동하버라인3단지항동항동로 438368의무1,1702019-02-25105531218129938지역난방2023-06-01
225226항동하버라인2단지항동연동로 2348362의무6462019-01-15624171874318지역난방2023-06-01
226227항동하버라인4단지항동항동로 728368의무2972019-01-31308041531013지역난방2023-06-01
227228항동한양수자인에듀힐즈아파트(하버라인5단지)부개동항동로 428368의무6342019-01-27744091882326지역난방2023-06-01
228229항동 중흥S클래스 베르데카운티 아파트(하버라인1단지)항동연동로 2338362의무4192019-01-06526462066114지역난방2023-06-01
229230항동제일풍경채 포레스트(하버라인7단지)항동부광로 96-168362의무3452020-01-27407341745313지역난방2023-06-01
230231우남퍼스트빌더센트럴항동항동로 88368의무3372020-01-0331675205009지역난방2023-06-01
231232노블리안아파트구로동구일로2길 298325임의982020-01-287941151203개별, 도시가스2023-06-01