Overview

Dataset statistics

Number of variables15
Number of observations433
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.3 KiB
Average record size in memory128.3 B

Variable types

Numeric6
Categorical6
Text2
DateTime1

Dataset

Description고유번호,구명,법정동명,산지여부,주지번,부지번,새주소명,건물명,조성년도,조성면적,구분,생성일,사진파일명,경도,위도
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1369/S/1/datasetView.do

Alerts

새주소명 has constant value ""Constant
구분 has constant value ""Constant
생성일 has constant value ""Constant
사진파일명 has constant value ""Constant
고유번호 is highly overall correlated with 조성년도High correlation
경도 is highly overall correlated with 구명High correlation
위도 is highly overall correlated with 구명High correlation
구명 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
조성년도 is highly overall correlated with 고유번호High correlation
산지여부 is highly imbalanced (86.7%)Imbalance
고유번호 has unique valuesUnique
부지번 has 93 (21.5%) zerosZeros

Reproduction

Analysis started2023-12-11 06:25:09.756986
Analysis finished2023-12-11 06:25:14.605514
Duration4.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct433
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean741.47575
Minimum1
Maximum975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T15:25:14.680224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile564.6
Q1651
median759
Q3867
95-th percentile953.4
Maximum975
Range974
Interquartile range (IQR)216

Descriptive statistics

Standard deviation179.53313
Coefficient of variation (CV)0.24212946
Kurtosis6.5753585
Mean741.47575
Median Absolute Deviation (MAD)108
Skewness-2.0411451
Sum321059
Variance32232.144
MonotonicityNot monotonic
2023-12-11T15:25:14.839770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
949 1
 
0.2%
885 1
 
0.2%
659 1
 
0.2%
660 1
 
0.2%
663 1
 
0.2%
879 1
 
0.2%
9 1
 
0.2%
688 1
 
0.2%
670 1
 
0.2%
8 1
 
0.2%
Other values (423) 423
97.7%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
975 1
0.2%
974 1
0.2%
973 1
0.2%
972 1
0.2%
971 1
0.2%
970 1
0.2%
969 1
0.2%
968 1
0.2%
967 1
0.2%
966 1
0.2%

구명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
중구
53 
강남구
35 
서초구
 
27
송파구
 
24
동대문구
 
22
Other values (21)
272 

Length

Max length4
Median length3
Mean length2.9630485
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row성북구
2nd row성동구
3rd row중구
4th row금천구
5th row관악구

Common Values

ValueCountFrequency (%)
중구 53
 
12.2%
강남구 35
 
8.1%
서초구 27
 
6.2%
송파구 24
 
5.5%
동대문구 22
 
5.1%
성북구 20
 
4.6%
용산구 20
 
4.6%
마포구 20
 
4.6%
양천구 17
 
3.9%
강동구 17
 
3.9%
Other values (16) 178
41.1%

Length

2023-12-11T15:25:15.003877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중구 53
 
12.2%
강남구 35
 
8.1%
서초구 27
 
6.2%
송파구 24
 
5.5%
동대문구 22
 
5.1%
성북구 20
 
4.6%
용산구 20
 
4.6%
마포구 20
 
4.6%
양천구 17
 
3.9%
강동구 17
 
3.9%
Other values (16) 178
41.1%

법정동명
Text

Distinct178
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-11T15:25:15.365050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2794457
Min length2

Characters and Unicode

Total characters1420
Distinct characters153
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)19.2%

Sample

1st row돈암동
2nd row금호동1가
3rd row정동
4th row시흥동
5th row봉천동
ValueCountFrequency (%)
장충동2가 15
 
3.5%
신당동 14
 
3.2%
서초동 12
 
2.8%
구로동 11
 
2.5%
전농동 10
 
2.3%
역삼동 10
 
2.3%
신정동 9
 
2.1%
상계동 8
 
1.8%
성내동 7
 
1.6%
시흥동 7
 
1.6%
Other values (168) 330
76.2%
2023-12-11T15:25:15.888488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 426
30.0%
가 60
 
4.2%
신 50
 
3.5%
2 32
 
2.3%
삼 24
 
1.7%
서 23
 
1.6%
장 23
 
1.6%
정 21
 
1.5%
상 21
 
1.5%
로 20
 
1.4%
Other values (143) 720
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1367
96.3%
Decimal Number 53
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 426
31.2%
가 60
 
4.4%
신 50
 
3.7%
삼 24
 
1.8%
서 23
 
1.7%
장 23
 
1.7%
정 21
 
1.5%
상 21
 
1.5%
로 20
 
1.5%
수 20
 
1.5%
Other values (137) 679
49.7%
Decimal Number
ValueCountFrequency (%)
2 32
60.4%
5 7
 
13.2%
1 6
 
11.3%
3 5
 
9.4%
6 2
 
3.8%
8 1
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1367
96.3%
Common 53
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 426
31.2%
가 60
 
4.4%
신 50
 
3.7%
삼 24
 
1.8%
서 23
 
1.7%
장 23
 
1.7%
정 21
 
1.5%
상 21
 
1.5%
로 20
 
1.5%
수 20
 
1.5%
Other values (137) 679
49.7%
Common
ValueCountFrequency (%)
2 32
60.4%
5 7
 
13.2%
1 6
 
11.3%
3 5
 
9.4%
6 2
 
3.8%
8 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1367
96.3%
ASCII 53
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 426
31.2%
가 60
 
4.4%
신 50
 
3.7%
삼 24
 
1.8%
서 23
 
1.7%
장 23
 
1.7%
정 21
 
1.5%
상 21
 
1.5%
로 20
 
1.5%
수 20
 
1.5%
Other values (137) 679
49.7%
ASCII
ValueCountFrequency (%)
2 32
60.4%
5 7
 
13.2%
1 6
 
11.3%
3 5
 
9.4%
6 2
 
3.8%
8 1
 
1.9%

산지여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
1
425 
2
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 425
98.2%
2 8
 
1.8%

Length

2023-12-11T15:25:16.058440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:25:16.152012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 425
98.2%
2 8
 
1.8%

주지번
Real number (ℝ)

Distinct297
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean387.14781
Minimum1
Maximum1735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T15:25:16.260173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.2
Q185
median229
Q3595
95-th percentile1263.6
Maximum1735
Range1734
Interquartile range (IQR)510

Descriptive statistics

Standard deviation394.42901
Coefficient of variation (CV)1.0188073
Kurtosis1.6765292
Mean387.14781
Median Absolute Deviation (MAD)197
Skewness1.4155027
Sum167635
Variance155574.24
MonotonicityNot monotonic
2023-12-11T15:25:16.419908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192 11
 
2.5%
1 10
 
2.3%
72 7
 
1.6%
90 6
 
1.4%
16 5
 
1.2%
139 5
 
1.2%
447 5
 
1.2%
19 5
 
1.2%
114 4
 
0.9%
432 4
 
0.9%
Other values (287) 371
85.7%
ValueCountFrequency (%)
1 10
2.3%
2 1
 
0.2%
3 3
 
0.7%
4 2
 
0.5%
5 1
 
0.2%
6 2
 
0.5%
7 3
 
0.7%
9 2
 
0.5%
10 2
 
0.5%
11 1
 
0.2%
ValueCountFrequency (%)
1735 1
0.2%
1724 1
0.2%
1712 1
0.2%
1706 2
0.5%
1670 1
0.2%
1605 1
0.2%
1572 1
0.2%
1570 1
0.2%
1566 1
0.2%
1552 1
0.2%

부지번
Real number (ℝ)

ZEROS 

Distinct81
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.944573
Minimum0
Maximum1305
Zeros93
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T15:25:16.590336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q313
95-th percentile116.8
Maximum1305
Range1305
Interquartile range (IQR)12

Descriptive statistics

Standard deviation123.77487
Coefficient of variation (CV)3.8746761
Kurtosis63.811233
Mean31.944573
Median Absolute Deviation (MAD)4
Skewness7.5244681
Sum13832
Variance15320.219
MonotonicityNot monotonic
2023-12-11T15:25:16.760175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93
21.5%
1 66
15.2%
5 27
 
6.2%
2 25
 
5.8%
4 23
 
5.3%
3 20
 
4.6%
6 17
 
3.9%
8 10
 
2.3%
7 9
 
2.1%
10 9
 
2.1%
Other values (71) 134
30.9%
ValueCountFrequency (%)
0 93
21.5%
1 66
15.2%
2 25
 
5.8%
3 20
 
4.6%
4 23
 
5.3%
5 27
 
6.2%
6 17
 
3.9%
7 9
 
2.1%
8 10
 
2.3%
9 8
 
1.8%
ValueCountFrequency (%)
1305 1
0.2%
1260 1
0.2%
1010 1
0.2%
816 1
0.2%
720 1
0.2%
715 1
0.2%
380 1
0.2%
366 1
0.2%
348 1
0.2%
267 1
0.2%

새주소명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
433 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
433
100.0%

Length

2023-12-11T15:25:16.897246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:25:17.015715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

건물명
Text

Distinct430
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2023-12-11T15:25:17.276728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length6.7736721
Min length3

Characters and Unicode

Total characters2933
Distinct characters335
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

Unique427 ?
Unique (%)98.6%

Sample

1st row해오름어린이도서관
2nd row금호1가동 주민센터
3rd row캐나다대사관
4th row남부여성발전센터 창업보육센터
5th row청룡동주민센타
ValueCountFrequency (%)
동국대 11
 
2.2%
주민센터 10
 
2.0%
홍익대 6
 
1.2%
본관 4
 
0.8%
남부여성발전센터 4
 
0.8%
한성대학교 4
 
0.8%
광운대학교 3
 
0.6%
고려대학교 2
 
0.4%
숙명여대 2
 
0.4%
국립극장 2
 
0.4%
Other values (453) 460
90.6%
2023-12-11T15:25:17.800744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 100
 
3.4%
빌 93
 
3.2%
딩 88
 
3.0%
동 84
 
2.9%
79
 
2.7%
서 72
 
2.5%
원 65
 
2.2%
대 60
 
2.0%
터 57
 
1.9%
센 57
 
1.9%
Other values (325) 2178
74.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2745
93.6%
Space Separator 79
 
2.7%
Decimal Number 45
 
1.5%
Uppercase Letter 22
 
0.8%
Open Punctuation 11
 
0.4%
Close Punctuation 11
 
0.4%
Lowercase Letter 10
 
0.3%
Other Punctuation 7
 
0.2%
Dash Punctuation 2
 
0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 100
 
3.6%
빌 93
 
3.4%
딩 88
 
3.2%
동 84
 
3.1%
서 72
 
2.6%
원 65
 
2.4%
대 60
 
2.2%
터 57
 
2.1%
센 57
 
2.1%
소 47
 
1.7%
Other values (291) 2022
73.7%
Uppercase Letter
ValueCountFrequency (%)
M 4
18.2%
C 3
13.6%
D 3
13.6%
B 3
13.6%
N 2
9.1%
G 2
9.1%
S 2
9.1%
L 1
 
4.5%
A 1
 
4.5%
K 1
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 15
33.3%
2 11
24.4%
5 5
 
11.1%
3 5
 
11.1%
9 3
 
6.7%
4 3
 
6.7%
7 1
 
2.2%
6 1
 
2.2%
0 1
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
s 3
30.0%
b 2
20.0%
e 1
 
10.0%
i 1
 
10.0%
z 1
 
10.0%
p 1
 
10.0%
o 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
? 4
57.1%
. 2
28.6%
/ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2746
93.6%
Common 155
 
5.3%
Latin 32
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 100
 
3.6%
빌 93
 
3.4%
딩 88
 
3.2%
동 84
 
3.1%
서 72
 
2.6%
원 65
 
2.4%
대 60
 
2.2%
터 57
 
2.1%
센 57
 
2.1%
소 47
 
1.7%
Other values (292) 2023
73.7%
Latin
ValueCountFrequency (%)
M 4
12.5%
C 3
9.4%
s 3
9.4%
D 3
9.4%
B 3
9.4%
N 2
 
6.2%
b 2
 
6.2%
G 2
 
6.2%
S 2
 
6.2%
e 1
 
3.1%
Other values (7) 7
21.9%
Common
ValueCountFrequency (%)
79
51.0%
1 15
 
9.7%
2 11
 
7.1%
( 11
 
7.1%
) 11
 
7.1%
5 5
 
3.2%
3 5
 
3.2%
? 4
 
2.6%
9 3
 
1.9%
4 3
 
1.9%
Other values (6) 8
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2745
93.6%
ASCII 187
 
6.4%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 100
 
3.6%
빌 93
 
3.4%
딩 88
 
3.2%
동 84
 
3.1%
서 72
 
2.6%
원 65
 
2.4%
대 60
 
2.2%
터 57
 
2.1%
센 57
 
2.1%
소 47
 
1.7%
Other values (291) 2022
73.7%
ASCII
ValueCountFrequency (%)
79
42.2%
1 15
 
8.0%
2 11
 
5.9%
( 11
 
5.9%
) 11
 
5.9%
5 5
 
2.7%
3 5
 
2.7%
? 4
 
2.1%
M 4
 
2.1%
C 3
 
1.6%
Other values (23) 39
20.9%
None
ValueCountFrequency (%)
㈜ 1
100.0%

조성년도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2009
120 
2010
108 
2008
104 
2011
101 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2011
4th row2011
5th row2011

Common Values

ValueCountFrequency (%)
2009 120
27.7%
2010 108
24.9%
2008 104
24.0%
2011 101
23.3%

Length

2023-12-11T15:25:17.989525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:25:18.106560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2009 120
27.7%
2010 108
24.9%
2008 104
24.0%
2011 101
23.3%

조성면적
Real number (ℝ)

Distinct311
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.85219
Minimum57
Maximum3280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T15:25:18.264818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile114.6
Q1177
median300
Q3558
95-th percentile1226.6
Maximum3280
Range3223
Interquartile range (IQR)381

Descriptive statistics

Standard deviation452.78175
Coefficient of variation (CV)0.99984445
Kurtosis10.72875
Mean452.85219
Median Absolute Deviation (MAD)146
Skewness2.8841998
Sum196085
Variance205011.32
MonotonicityNot monotonic
2023-12-11T15:25:18.405008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 5
 
1.2%
120 5
 
1.2%
114 5
 
1.2%
230 4
 
0.9%
155 4
 
0.9%
138 3
 
0.7%
150 3
 
0.7%
122 3
 
0.7%
203 3
 
0.7%
260 3
 
0.7%
Other values (301) 395
91.2%
ValueCountFrequency (%)
57 1
 
0.2%
100 2
0.5%
101 3
0.7%
102 1
 
0.2%
103 1
 
0.2%
105 1
 
0.2%
106 3
0.7%
107 1
 
0.2%
110 1
 
0.2%
111 1
 
0.2%
ValueCountFrequency (%)
3280 1
0.2%
2940 1
0.2%
2763 1
0.2%
2738 1
0.2%
2499 1
0.2%
2393 1
0.2%
2331 1
0.2%
2100 1
0.2%
2087 1
0.2%
1994 1
0.2%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
433 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
433
100.0%

Length

2023-12-11T15:25:18.589247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:25:18.708757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

생성일
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Minimum2012-08-02 00:00:00
Maximum2012-08-02 00:00:00
2023-12-11T15:25:18.798963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:18.887296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

사진파일명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
433 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
433
100.0%

Length

2023-12-11T15:25:19.013213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:25:19.120578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct393
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.99831
Minimum126.80769
Maximum127.15429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T15:25:19.244882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80769
5-th percentile126.86419
Q1126.93737
median127.00692
Q3127.05699
95-th percentile127.12518
Maximum127.15429
Range0.3466023
Interquartile range (IQR)0.1196258

Descriptive statistics

Standard deviation0.077820285
Coefficient of variation (CV)0.0006127663
Kurtosis-0.62067838
Mean126.99831
Median Absolute Deviation (MAD)0.0522159
Skewness-0.27340737
Sum54990.268
Variance0.0060559967
MonotonicityNot monotonic
2023-12-11T15:25:19.408208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0005946 11
 
2.5%
126.9256563 7
 
1.6%
127.0573028 6
 
1.4%
126.9060567 4
 
0.9%
126.9744695 3
 
0.7%
127.056677 3
 
0.7%
127.0102729 3
 
0.7%
127.036764 2
 
0.5%
126.9037407 2
 
0.5%
126.8641938 2
 
0.5%
Other values (383) 390
90.1%
ValueCountFrequency (%)
126.8076881 1
0.2%
126.812231 1
0.2%
126.8234297 1
0.2%
126.8274692 1
0.2%
126.8292127 1
0.2%
126.8299911 1
0.2%
126.8360276 1
0.2%
126.8384234 1
0.2%
126.8399846 1
0.2%
126.8411456 1
0.2%
ValueCountFrequency (%)
127.1542904 1
0.2%
127.1483689 1
0.2%
127.1471726 1
0.2%
127.1455404 1
0.2%
127.142619 1
0.2%
127.1405665 1
0.2%
127.1379122 1
0.2%
127.1369084 1
0.2%
127.1338228 1
0.2%
127.1326818 1
0.2%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct393
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.545276
Minimum37.445691
Maximum37.690136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-11T15:25:19.584380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.445691
5-th percentile37.477987
Q137.505541
median37.550727
Q337.572174
95-th percentile37.634123
Maximum37.690136
Range0.2444447
Interquartile range (IQR)0.0666329

Descriptive statistics

Standard deviation0.047068562
Coefficient of variation (CV)0.0012536481
Kurtosis-0.2045898
Mean37.545276
Median Absolute Deviation (MAD)0.0333509
Skewness0.39266271
Sum16257.104
Variance0.0022154495
MonotonicityNot monotonic
2023-12-11T15:25:19.749283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5583883 11
 
2.5%
37.5507272 7
 
1.6%
37.5833824 6
 
1.4%
37.4631486 4
 
0.9%
37.5643226 3
 
0.7%
37.6541718 3
 
0.7%
37.582764 3
 
0.7%
37.5632196 2
 
0.5%
37.6043132 2
 
0.5%
37.5216226 2
 
0.5%
Other values (383) 390
90.1%
ValueCountFrequency (%)
37.4456913 1
 
0.2%
37.4533634 1
 
0.2%
37.4548884 1
 
0.2%
37.4561957 1
 
0.2%
37.4615392 1
 
0.2%
37.4631486 4
0.9%
37.4659605 1
 
0.2%
37.4678926 1
 
0.2%
37.4706239 1
 
0.2%
37.4717082 1
 
0.2%
ValueCountFrequency (%)
37.690136 1
 
0.2%
37.6743465 1
 
0.2%
37.6687158 1
 
0.2%
37.6629467 1
 
0.2%
37.6612679 1
 
0.2%
37.6597845 1
 
0.2%
37.6589424 1
 
0.2%
37.657992 1
 
0.2%
37.6541718 3
0.7%
37.6532547 1
 
0.2%

Interactions

2023-12-11T15:25:13.318894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:10.357430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:10.946385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:11.586120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.124016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.681257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:13.432668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:10.435328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:11.050111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:11.671308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.208446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.771072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:13.529361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:10.521457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:11.167464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:11.764146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.305625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.888859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:13.637874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:10.603738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:11.272866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:11.847058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.402574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.996703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:13.748263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:10.726634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:11.381448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:11.934432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.496779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:13.108354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:13.864375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:10.840405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:11.496107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.033609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:12.597161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:25:13.216201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:25:19.852187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호구명산지여부주지번부지번조성년도조성면적경도위도
고유번호1.0000.5210.1490.0000.0000.7120.1520.2730.329
구명0.5211.0000.4830.6800.1590.2880.0000.9440.929
산지여부0.1490.4831.0000.1140.0000.0000.0000.1550.248
주지번0.0000.6800.1141.0000.0000.1080.1960.4430.510
부지번0.0000.1590.0000.0001.0000.0700.0000.0000.000
조성년도0.7120.2880.0000.1080.0701.0000.1700.1850.186
조성면적0.1520.0000.0000.1960.0000.1701.0000.1840.000
경도0.2730.9440.1550.4430.0000.1850.1841.0000.642
위도0.3290.9290.2480.5100.0000.1860.0000.6421.000
2023-12-11T15:25:19.975383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구명산지여부조성년도
구명1.0000.3740.150
산지여부0.3741.0000.000
조성년도0.1500.0001.000
2023-12-11T15:25:20.063529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호주지번부지번조성면적경도위도구명산지여부조성년도
고유번호1.0000.045-0.1090.055-0.008-0.0400.2580.1070.543
주지번0.0451.000-0.041-0.067-0.035-0.2090.3160.0860.064
부지번-0.109-0.0411.000-0.246-0.060-0.0970.0660.0000.047
조성면적0.055-0.067-0.2461.000-0.0980.0560.0000.0000.101
경도-0.008-0.035-0.060-0.0981.0000.2090.7160.1170.111
위도-0.040-0.209-0.0970.0560.2091.0000.6710.1880.111
구명0.2580.3160.0660.0000.7160.6711.0000.3740.150
산지여부0.1070.0860.0000.0000.1170.1880.3741.0000.000
조성년도0.5430.0640.0470.1010.1110.1110.1500.0001.000

Missing values

2023-12-11T15:25:13.999393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:25:14.534949image/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

고유번호구명법정동명산지여부주지번부지번새주소명건물명조성년도조성면적구분생성일사진파일명경도위도
0949성북구돈암동16091해오름어린이도서관20112402012-08-02 00:00:00.0127.01033737.59481
1943성동구금호동1가11272금호1가동 주민센터20111742012-08-02 00:00:00.0127.02165537.554851
2939중구정동1161캐나다대사관20111832012-08-02 00:00:00.0126.97089137.566936
312금천구시흥동11392남부여성발전센터 창업보육센터20116312012-08-02 00:00:00.0126.90605737.463149
4970관악구봉천동115701청룡동주민센타20112712012-08-02 00:00:00.0126.95153737.478275
5971서초구서초동113602서초영유아플라자20111492012-08-02 00:00:00.0127.03134437.486726
6760성동구용답동12498예스코신관20115012012-08-02 00:00:00.0127.05743137.560306
7968동작구상도동12548?상도2동 주민센터20111632012-08-02 00:00:00.0126.94232937.505485
8800강동구천호동13570친구병원20114662012-08-02 00:00:00.0127.12532337.542272
9773양천구목동140621대학빌딩20114392012-08-02 00:00:00.0126.87373537.52516
고유번호구명법정동명산지여부주지번부지번새주소명건물명조성년도조성면적구분생성일사진파일명경도위도
423576광진구중곡동16491능동빌딩20081032012-08-02 00:00:00.0127.07865437.55821
424570용산구서빙고동1452비비안빌딩20086192012-08-02 00:00:00.0126.99115537.52257
425569용산구후암동1383후암천주교회20083832012-08-02 00:00:00.0126.97825737.551944
426578동대문구제기동1892118삼화빌딩20081572012-08-02 00:00:00.0127.03645137.582101
427586노원구공릉동16563다운복지관20082242012-08-02 00:00:00.0127.08029237.618988
428593마포구상수동1721홍익대 홍문관200811412012-08-02 00:00:00.0126.92565637.550727
429565중구장충동2가11925동국대 학술문화관200820872012-08-02 00:00:00.0127.00059537.558388
430596마포구성산동120041한사랑교회20081662012-08-02 00:00:00.0126.90912537.567371
431599강서구등촌동16802호서직업학교20082502012-08-02 00:00:00.0126.83998537.562696
432560중구장충동2가11925동국대 학림관200812782012-08-02 00:00:00.0127.00059537.558388