Overview

Dataset statistics

Number of variables9
Number of observations7426
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory544.0 KiB
Average record size in memory75.0 B

Variable types

Numeric3
Categorical2
Text3
DateTime1

Dataset

Description부산시 부산동중개업 현황에 대한 데이터로 연번, 시군구, 중개소구분, 중개업소명, 사무보주소, 위도, 경도, 데이터기준일자 항목정보를 제공합니다.
URLhttps://www.data.go.kr/data/15083344/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 시군구High correlation
위도 is highly overall correlated with 시군구High correlation
경도 is highly overall correlated with 시군구High correlation
시군구 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
중개업소 구분 is highly imbalanced (86.1%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:36:33.055676
Analysis finished2023-12-12 23:36:35.639455
Duration2.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct7426
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3921.164
Minimum1
Maximum7788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2023-12-13T08:36:35.724541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile385.25
Q12063.25
median3932.5
Q35821.75
95-th percentile7413.75
Maximum7788
Range7787
Interquartile range (IQR)3758.5

Descriptive statistics

Standard deviation2236.9554
Coefficient of variation (CV)0.57048249
Kurtosis-1.1601021
Mean3921.164
Median Absolute Deviation (MAD)1879.5
Skewness-0.023252861
Sum29118564
Variance5003969.5
MonotonicityStrictly increasing
2023-12-13T08:36:35.891962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5191 1
 
< 0.1%
5203 1
 
< 0.1%
5202 1
 
< 0.1%
5201 1
 
< 0.1%
5200 1
 
< 0.1%
5199 1
 
< 0.1%
5198 1
 
< 0.1%
5197 1
 
< 0.1%
5196 1
 
< 0.1%
Other values (7416) 7416
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
7788 1
< 0.1%
7787 1
< 0.1%
7786 1
< 0.1%
7785 1
< 0.1%
7784 1
< 0.1%
7783 1
< 0.1%
7782 1
< 0.1%
7781 1
< 0.1%
7780 1
< 0.1%
7779 1
< 0.1%

시군구
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
해운대구
938 
부산진구
928 
동래구
729 
연제구
622 
강서구
618 
Other values (11)
3591 

Length

Max length5
Median length4
Mean length4.0680043
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 938
12.6%
부산진구 928
12.5%
동래구 729
9.8%
연제구 622
8.4%
강서구 618
8.3%
수영구 557
7.5%
금정구 539
7.3%
남구 523
7.0%
사하구 500
6.7%
북구 423
5.7%
Other values (6) 1049
14.1%

Length

2023-12-13T08:36:36.050552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해운대구 938
12.6%
부산진구 928
12.5%
동래구 729
9.8%
연제구 622
8.4%
강서구 618
8.3%
수영구 557
7.5%
금정구 539
7.3%
남구 523
7.0%
사하구 500
6.7%
북구 423
5.7%
Other values (6) 1049
14.1%

중개업소 구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
공인중개사
7207 
법인
 
125
중개인
 
94

Length

Max length5
Median length5
Mean length4.9241853
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공인중개사 7207
97.1%
법인 125
 
1.7%
중개인 94
 
1.3%

Length

2023-12-13T08:36:36.196583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:36:36.301191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 7207
97.1%
법인 125
 
1.7%
중개인 94
 
1.3%

중개업자명
Text

Distinct6182
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
2023-12-13T08:36:36.602845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length3
Mean length3.0053865
Min length2

Characters and Unicode

Total characters22318
Distinct characters337
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5453 ?
Unique (%)73.4%

Sample

1st row이동준
2nd row김양순
3rd row백경애
4th row전현아
5th row김정현
ValueCountFrequency (%)
김정희 17
 
0.2%
김미숙 12
 
0.2%
김미경 12
 
0.2%
김영희 11
 
0.1%
김민정 11
 
0.1%
이영주 10
 
0.1%
김인숙 9
 
0.1%
이은주 9
 
0.1%
김미희 8
 
0.1%
김민주 8
 
0.1%
Other values (6180) 7327
98.6%
2023-12-13T08:36:37.096259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 1625
 
7.3%
이 1115
 
5.0%
정 1112
 
5.0%
영 771
 
3.5%
박 687
 
3.1%
희 549
 
2.5%
경 499
 
2.2%
미 484
 
2.2%
현 426
 
1.9%
숙 426
 
1.9%
Other values (327) 14624
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22251
99.7%
Uppercase Letter 20
 
0.1%
Lowercase Letter 17
 
0.1%
Close Punctuation 11
 
< 0.1%
Open Punctuation 11
 
< 0.1%
Space Separator 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 1625
 
7.3%
이 1115
 
5.0%
정 1112
 
5.0%
영 771
 
3.5%
박 687
 
3.1%
희 549
 
2.5%
경 499
 
2.2%
미 484
 
2.2%
현 426
 
1.9%
숙 426
 
1.9%
Other values (298) 14557
65.4%
Uppercase Letter
ValueCountFrequency (%)
L 3
15.0%
K 2
 
10.0%
N 2
 
10.0%
A 2
 
10.0%
H 1
 
5.0%
O 1
 
5.0%
R 1
 
5.0%
I 1
 
5.0%
M 1
 
5.0%
Y 1
 
5.0%
Other values (5) 5
25.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
23.5%
a 3
17.6%
y 2
11.8%
w 1
 
5.9%
g 1
 
5.9%
n 1
 
5.9%
u 1
 
5.9%
s 1
 
5.9%
m 1
 
5.9%
k 1
 
5.9%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22219
99.6%
Latin 37
 
0.2%
Han 32
 
0.1%
Common 30
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 1625
 
7.3%
이 1115
 
5.0%
정 1112
 
5.0%
영 771
 
3.5%
박 687
 
3.1%
희 549
 
2.5%
경 499
 
2.2%
미 484
 
2.2%
현 426
 
1.9%
숙 426
 
1.9%
Other values (271) 14525
65.4%
Han
ValueCountFrequency (%)
金 3
 
9.4%
美 2
 
6.2%
李 2
 
6.2%
順 2
 
6.2%
萬 1
 
3.1%
秀 1
 
3.1%
曺 1
 
3.1%
喜 1
 
3.1%
熙 1
 
3.1%
洪 1
 
3.1%
Other values (17) 17
53.1%
Latin
ValueCountFrequency (%)
e 4
 
10.8%
L 3
 
8.1%
a 3
 
8.1%
K 2
 
5.4%
N 2
 
5.4%
y 2
 
5.4%
A 2
 
5.4%
w 1
 
2.7%
H 1
 
2.7%
g 1
 
2.7%
Other values (16) 16
43.2%
Common
ValueCountFrequency (%)
) 11
36.7%
( 11
36.7%
8
26.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22219
99.6%
ASCII 67
 
0.3%
CJK 30
 
0.1%
CJK Compat Ideographs 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 1625
 
7.3%
이 1115
 
5.0%
정 1112
 
5.0%
영 771
 
3.5%
박 687
 
3.1%
희 549
 
2.5%
경 499
 
2.2%
미 484
 
2.2%
현 426
 
1.9%
숙 426
 
1.9%
Other values (271) 14525
65.4%
ASCII
ValueCountFrequency (%)
) 11
16.4%
( 11
16.4%
8
11.9%
e 4
 
6.0%
L 3
 
4.5%
a 3
 
4.5%
K 2
 
3.0%
N 2
 
3.0%
y 2
 
3.0%
A 2
 
3.0%
Other values (19) 19
28.4%
CJK
ValueCountFrequency (%)
金 3
 
10.0%
美 2
 
6.7%
順 2
 
6.7%
萬 1
 
3.3%
秀 1
 
3.3%
曺 1
 
3.3%
喜 1
 
3.3%
熙 1
 
3.3%
洪 1
 
3.3%
烈 1
 
3.3%
Other values (16) 16
53.3%
CJK Compat Ideographs
ValueCountFrequency (%)
李 2
100.0%

중개업소명
Text

Distinct4918
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
2023-12-13T08:36:37.332777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length11.407487
Min length4

Characters and Unicode

Total characters84712
Distinct characters626
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4085 ?
Unique (%)55.0%

Sample

1st row명지JW부동산공인중개사사무소
2nd row창공수공인중개사사무소
3rd row뉴창공공인중개사사무소
4th row끌림부동산공인중개사사무소
5th row델타공인중개사사무소
ValueCountFrequency (%)
사무소 57
 
0.7%
공인중개사사무소 50
 
0.7%
현대공인중개사사무소 35
 
0.5%
주식회사 33
 
0.4%
미래공인중개사사무소 32
 
0.4%
삼성공인중개사사무소 31
 
0.4%
태양공인중개사사무소 31
 
0.4%
행운공인중개사사무소 30
 
0.4%
탑공인중개사사무소 27
 
0.4%
우리공인중개사사무소 24
 
0.3%
Other values (4930) 7279
95.4%
2023-12-13T08:36:37.720988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 13347
15.8%
중 7463
 
8.8%
개 7448
 
8.8%
소 7008
 
8.3%
무 6958
 
8.2%
인 6451
 
7.6%
공 6254
 
7.4%
동 2948
 
3.5%
부 2757
 
3.3%
산 2704
 
3.2%
Other values (616) 21374
25.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82963
97.9%
Uppercase Letter 823
 
1.0%
Decimal Number 324
 
0.4%
Space Separator 203
 
0.2%
Lowercase Letter 169
 
0.2%
Open Punctuation 98
 
0.1%
Close Punctuation 98
 
0.1%
Other Punctuation 20
 
< 0.1%
Dash Punctuation 10
 
< 0.1%
Letter Number 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 13347
16.1%
중 7463
 
9.0%
개 7448
 
9.0%
소 7008
 
8.4%
무 6958
 
8.4%
인 6451
 
7.8%
공 6254
 
7.5%
동 2948
 
3.6%
부 2757
 
3.3%
산 2704
 
3.3%
Other values (554) 19625
23.7%
Uppercase Letter
ValueCountFrequency (%)
K 155
18.8%
S 104
12.6%
T 75
9.1%
L 72
 
8.7%
C 61
 
7.4%
O 41
 
5.0%
W 40
 
4.9%
E 36
 
4.4%
B 30
 
3.6%
H 28
 
3.4%
Other values (14) 181
22.0%
Lowercase Letter
ValueCountFrequency (%)
e 76
45.0%
h 29
 
17.2%
w 14
 
8.3%
t 13
 
7.7%
c 10
 
5.9%
s 5
 
3.0%
n 5
 
3.0%
k 5
 
3.0%
o 3
 
1.8%
l 2
 
1.2%
Other values (6) 7
 
4.1%
Decimal Number
ValueCountFrequency (%)
1 132
40.7%
8 40
 
12.3%
4 34
 
10.5%
2 33
 
10.2%
3 24
 
7.4%
5 16
 
4.9%
9 14
 
4.3%
0 13
 
4.0%
6 9
 
2.8%
7 9
 
2.8%
Other Punctuation
ValueCountFrequency (%)
& 8
40.0%
. 7
35.0%
· 2
 
10.0%
# 2
 
10.0%
! 1
 
5.0%
Space Separator
ValueCountFrequency (%)
203
100.0%
Open Punctuation
ValueCountFrequency (%)
( 98
100.0%
Close Punctuation
ValueCountFrequency (%)
) 98
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%
Other Symbol
ValueCountFrequency (%)
ⓡ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82951
97.9%
Latin 994
 
1.2%
Common 755
 
0.9%
Han 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 13347
16.1%
중 7463
 
9.0%
개 7448
 
9.0%
소 7008
 
8.4%
무 6958
 
8.4%
인 6451
 
7.8%
공 6254
 
7.5%
동 2948
 
3.6%
부 2757
 
3.3%
산 2704
 
3.3%
Other values (545) 19613
23.6%
Latin
ValueCountFrequency (%)
K 155
15.6%
S 104
 
10.5%
e 76
 
7.6%
T 75
 
7.5%
L 72
 
7.2%
C 61
 
6.1%
O 41
 
4.1%
W 40
 
4.0%
E 36
 
3.6%
B 30
 
3.0%
Other values (31) 304
30.6%
Common
ValueCountFrequency (%)
203
26.9%
1 132
17.5%
( 98
13.0%
) 98
13.0%
8 40
 
5.3%
4 34
 
4.5%
2 33
 
4.4%
3 24
 
3.2%
5 16
 
2.1%
9 14
 
1.9%
Other values (11) 63
 
8.3%
Han
ValueCountFrequency (%)
福 2
16.7%
明 2
16.7%
堂 2
16.7%
利 1
8.3%
人 1
8.3%
太 1
8.3%
甲 1
8.3%
本 1
8.3%
炫 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82951
97.9%
ASCII 1744
 
2.1%
CJK 12
 
< 0.1%
Number Forms 2
 
< 0.1%
None 2
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 13347
16.1%
중 7463
 
9.0%
개 7448
 
9.0%
소 7008
 
8.4%
무 6958
 
8.4%
인 6451
 
7.8%
공 6254
 
7.5%
동 2948
 
3.6%
부 2757
 
3.3%
산 2704
 
3.3%
Other values (545) 19613
23.6%
ASCII
ValueCountFrequency (%)
203
 
11.6%
K 155
 
8.9%
1 132
 
7.6%
S 104
 
6.0%
( 98
 
5.6%
) 98
 
5.6%
e 76
 
4.4%
T 75
 
4.3%
L 72
 
4.1%
C 61
 
3.5%
Other values (49) 670
38.4%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%
None
ValueCountFrequency (%)
· 2
100.0%
CJK
ValueCountFrequency (%)
福 2
16.7%
明 2
16.7%
堂 2
16.7%
利 1
8.3%
人 1
8.3%
太 1
8.3%
甲 1
8.3%
本 1
8.3%
炫 1
8.3%
Enclosed Alphanum
ValueCountFrequency (%)
ⓡ 1
100.0%

사무소주소
Text

Distinct6887
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
2023-12-13T08:36:38.067351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length52
Mean length33.091974
Min length17

Characters and Unicode

Total characters245741
Distinct characters534
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6464 ?
Unique (%)87.0%

Sample

1st row부산광역시 강서구 명지국제8로10번길 38 123호(명지동)
2nd row부산광역시 강서구 대저로 259 A동(대저1동)
3rd row부산광역시 강서구 대저로 259 A동(대저1동)
4th row부산광역시 강서구 명지국제2로 41 119호(명지동, 더샵 명지퍼스트월드 3단지)
5th row부산광역시 강서구 명지국제1로 56-2 108호(명지동)
ValueCountFrequency (%)
부산광역시 7427
 
17.0%
해운대구 938
 
2.1%
부산진구 928
 
2.1%
동래구 729
 
1.7%
연제구 622
 
1.4%
강서구 619
 
1.4%
수영구 557
 
1.3%
금정구 539
 
1.2%
남구 523
 
1.2%
사하구 500
 
1.1%
Other values (7363) 30395
69.4%
2023-12-13T08:36:38.523320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36385
 
14.8%
1 12341
 
5.0%
동 10932
 
4.4%
산 9336
 
3.8%
부 9262
 
3.8%
광 8134
 
3.3%
시 8105
 
3.3%
역 7501
 
3.1%
구 7482
 
3.0%
로 7354
 
3.0%
Other values (524) 128909
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 147975
60.2%
Decimal Number 42224
 
17.2%
Space Separator 36385
 
14.8%
Open Punctuation 6902
 
2.8%
Close Punctuation 6900
 
2.8%
Other Punctuation 3219
 
1.3%
Dash Punctuation 1147
 
0.5%
Uppercase Letter 879
 
0.4%
Lowercase Letter 104
 
< 0.1%
Letter Number 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 10932
 
7.4%
산 9336
 
6.3%
부 9262
 
6.3%
광 8134
 
5.5%
시 8105
 
5.5%
역 7501
 
5.1%
구 7482
 
5.1%
로 7354
 
5.0%
호 4079
 
2.8%
대 3894
 
2.6%
Other values (468) 71896
48.6%
Uppercase Letter
ValueCountFrequency (%)
B 220
25.0%
A 128
14.6%
S 97
11.0%
K 79
 
9.0%
E 56
 
6.4%
W 49
 
5.6%
C 47
 
5.3%
I 44
 
5.0%
V 35
 
4.0%
D 18
 
2.0%
Other values (12) 106
12.1%
Lowercase Letter
ValueCountFrequency (%)
e 57
54.8%
c 15
 
14.4%
k 10
 
9.6%
s 6
 
5.8%
l 4
 
3.8%
a 3
 
2.9%
i 3
 
2.9%
w 2
 
1.9%
t 2
 
1.9%
p 1
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 12341
29.2%
2 5750
13.6%
0 5612
13.3%
3 4213
 
10.0%
4 3007
 
7.1%
5 2837
 
6.7%
6 2475
 
5.9%
7 2270
 
5.4%
9 1862
 
4.4%
8 1857
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 3192
99.2%
@ 9
 
0.3%
. 6
 
0.2%
/ 5
 
0.2%
· 4
 
0.1%
& 3
 
0.1%
Letter Number
ValueCountFrequency (%)
Ⅱ 4
80.0%
Ⅴ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
36385
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6902
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6900
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1147
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 147975
60.2%
Common 96778
39.4%
Latin 988
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 10932
 
7.4%
산 9336
 
6.3%
부 9262
 
6.3%
광 8134
 
5.5%
시 8105
 
5.5%
역 7501
 
5.1%
구 7482
 
5.1%
로 7354
 
5.0%
호 4079
 
2.8%
대 3894
 
2.6%
Other values (468) 71896
48.6%
Latin
ValueCountFrequency (%)
B 220
22.3%
A 128
13.0%
S 97
9.8%
K 79
 
8.0%
e 57
 
5.8%
E 56
 
5.7%
W 49
 
5.0%
C 47
 
4.8%
I 44
 
4.5%
V 35
 
3.5%
Other values (25) 176
17.8%
Common
ValueCountFrequency (%)
36385
37.6%
1 12341
 
12.8%
( 6902
 
7.1%
) 6900
 
7.1%
2 5750
 
5.9%
0 5612
 
5.8%
3 4213
 
4.4%
, 3192
 
3.3%
4 3007
 
3.1%
5 2837
 
2.9%
Other values (11) 9639
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 147975
60.2%
ASCII 97757
39.8%
Number Forms 5
 
< 0.1%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36385
37.2%
1 12341
 
12.6%
( 6902
 
7.1%
) 6900
 
7.1%
2 5750
 
5.9%
0 5612
 
5.7%
3 4213
 
4.3%
, 3192
 
3.3%
4 3007
 
3.1%
5 2837
 
2.9%
Other values (43) 10618
 
10.9%
Hangul
ValueCountFrequency (%)
동 10932
 
7.4%
산 9336
 
6.3%
부 9262
 
6.3%
광 8134
 
5.5%
시 8105
 
5.5%
역 7501
 
5.1%
구 7482
 
5.1%
로 7354
 
5.0%
호 4079
 
2.8%
대 3894
 
2.6%
Other values (468) 71896
48.6%
None
ValueCountFrequency (%)
· 4
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 4
80.0%
Ⅴ 1
 
20.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct4797
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.169761
Minimum35.012142
Maximum35.369222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2023-12-13T08:36:38.675548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.012142
5-th percentile35.093683
Q135.13873
median35.168461
Q335.20065
95-th percentile35.251419
Maximum35.369222
Range0.35708006
Interquartile range (IQR)0.061919785

Descriptive statistics

Standard deviation0.049119683
Coefficient of variation (CV)0.0013966454
Kurtosis0.41800042
Mean35.169761
Median Absolute Deviation (MAD)0.030956965
Skewness0.25548156
Sum261170.64
Variance0.0024127433
MonotonicityNot monotonic
2023-12-13T08:36:38.798526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.21075332 34
 
0.5%
35.18431236 31
 
0.4%
35.16132822 28
 
0.4%
35.22636131 25
 
0.3%
35.09754964 23
 
0.3%
35.10752754 19
 
0.3%
35.22758916 19
 
0.3%
35.14192583 18
 
0.2%
35.1650205 17
 
0.2%
35.13998175 17
 
0.2%
Other values (4787) 7195
96.9%
ValueCountFrequency (%)
35.01214235 1
< 0.1%
35.01228527 1
< 0.1%
35.01264734 1
< 0.1%
35.0129303 1
< 0.1%
35.03191452 1
< 0.1%
35.03228254 1
< 0.1%
35.04827535 1
< 0.1%
35.04848711 2
< 0.1%
35.04876808 1
< 0.1%
35.04930565 1
< 0.1%
ValueCountFrequency (%)
35.36922241 1
< 0.1%
35.36848123 1
< 0.1%
35.34093551 1
< 0.1%
35.33953009 2
< 0.1%
35.33739004 1
< 0.1%
35.33624444 1
< 0.1%
35.33602392 1
< 0.1%
35.33580536 2
< 0.1%
35.33498268 1
< 0.1%
35.33428036 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct4795
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.06526
Minimum128.81175
Maximum129.28328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.4 KiB
2023-12-13T08:36:39.228012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.81175
5-th percentile128.91868
Q1129.02027
median129.07649
Q3129.10906
95-th percentile129.17777
Maximum129.28328
Range0.4715326
Interquartile range (IQR)0.0887875

Descriptive statistics

Standard deviation0.072890863
Coefficient of variation (CV)0.00056475973
Kurtosis0.3524891
Mean129.06526
Median Absolute Deviation (MAD)0.0384617
Skewness-0.37024453
Sum958438.65
Variance0.005313078
MonotonicityNot monotonic
2023-12-13T08:36:39.378056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0713686 34
 
0.5%
129.0798485 31
 
0.4%
129.1680126 28
 
0.4%
129.0122107 25
 
0.3%
128.9054867 23
 
0.3%
128.9211187 19
 
0.3%
129.0867388 19
 
0.3%
129.1110673 18
 
0.2%
129.0627876 17
 
0.2%
129.1038533 17
 
0.2%
Other values (4785) 7195
96.9%
ValueCountFrequency (%)
128.8117504 1
 
< 0.1%
128.8119842 1
 
< 0.1%
128.828473 1
 
< 0.1%
128.8296001 1
 
< 0.1%
128.8303751 1
 
< 0.1%
128.8311832 1
 
< 0.1%
128.8318103 1
 
< 0.1%
128.83201 3
< 0.1%
128.832202 1
 
< 0.1%
128.8322119 1
 
< 0.1%
ValueCountFrequency (%)
129.283283 1
< 0.1%
129.2832079 1
< 0.1%
129.2790285 1
< 0.1%
129.2780721 1
< 0.1%
129.2638016 1
< 0.1%
129.2612822 1
< 0.1%
129.2601403 1
< 0.1%
129.2590115 1
< 0.1%
129.2588888 1
< 0.1%
129.2569703 1
< 0.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
Minimum2023-01-30 00:00:00
Maximum2023-01-30 00:00:00
2023-12-13T08:36:39.490032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:39.631358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T08:36:35.033082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:34.403168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:34.724697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:35.144805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:34.517827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:34.844507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:35.245111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:34.619411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:34.936179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:36:39.728514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군구중개업소 구분위도경도
연번1.0000.9780.0750.8820.908
시군구0.9781.0000.1140.8590.910
중개업소 구분0.0750.1141.0000.0000.049
위도0.8820.8590.0001.0000.800
경도0.9080.9100.0490.8001.000
2023-12-13T08:36:39.842578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중개업소 구분시군구
중개업소 구분1.0000.061
시군구0.0611.000
2023-12-13T08:36:39.931121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도시군구중개업소 구분
연번1.000-0.1230.3140.8950.044
위도-0.1231.0000.3840.5640.000
경도0.3140.3841.0000.6690.029
시군구0.8950.5640.6691.0000.061
중개업소 구분0.0440.0000.0290.0611.000

Missing values

2023-12-13T08:36:35.407882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:36:35.563325image/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

연번시군구중개업소 구분중개업자명중개업소명사무소주소위도경도데이터기준일자
01강서구공인중개사이동준명지JW부동산공인중개사사무소부산광역시 강서구 명지국제8로10번길 38 123호(명지동)35.094401128.9034562023-01-30
12강서구공인중개사김양순창공수공인중개사사무소부산광역시 강서구 대저로 259 A동(대저1동)35.213666128.9809042023-01-30
23강서구공인중개사백경애뉴창공공인중개사사무소부산광역시 강서구 대저로 259 A동(대저1동)35.213666128.9809042023-01-30
34강서구공인중개사전현아끌림부동산공인중개사사무소부산광역시 강서구 명지국제2로 41 119호(명지동, 더샵 명지퍼스트월드 3단지)35.094665128.9066612023-01-30
45강서구공인중개사김정현델타공인중개사사무소부산광역시 강서구 명지국제1로 56-2 108호(명지동)35.09358128.9058812023-01-30
56강서구공인중개사이수정정음공인중개사사무소부산광역시 강서구 명지국제7로 120 상가1동 107호(명지동, 더 힐 시그니처)35.095964128.9149122023-01-30
67강서구공인중개사박윤미대저삼덕공인중개사사무소부산광역시 강서구 유통단지1로 41 본관동 105호(대저2동)35.167293128.9557022023-01-30
78강서구공인중개사손성민대저공인중개사사무소부산광역시 강서구 대저로155번길 21 (대저1동)35.215776128.969662023-01-30
89강서구법인김광수희망공인중개사사무소(주)부산광역시 강서구 녹산화전로 275 204호(녹산동)35.111324128.8722692023-01-30
910강서구공인중개사송유종위드공인중개사사무소부산광역시 강서구 범방3로68번길 7 101호(범방동)35.149159128.8818152023-01-30
연번시군구중개업소 구분중개업자명중개업소명사무소주소위도경도데이터기준일자
74167779해운대구공인중개사윤종호코아부동산공인중개사사무소부산광역시 해운대구 세실로 48 상가 133호(좌동, 해운대삼정코아주상복합)35.169681129.178552023-01-30
74177780해운대구공인중개사조순자대림공인중개사사무소부산광역시 해운대구 선수촌로 95 상가12동 102호(반여동, 센텀대림)35.200648129.1172262023-01-30
74187781해운대구공인중개사임필성집으로공인중개사사무소부산광역시 해운대구 양운로 59 102호(좌동, 경동윈츠타워)35.168516129.1759642023-01-30
74197782해운대구공인중개사김경림신도공인중개사사무소부산광역시 해운대구 센텀중앙로 145 상가6동 105호(재송동, 더샵센텀파크1차아파트)35.176757129.1235962023-01-30
74207783해운대구공인중개사박봉옥송천부동산중개부산광역시 해운대구 반여로 131 상가 124호(반여동, 아시아선수촌프레스센터상가)35.203458129.1233492023-01-30
74217784해운대구공인중개사박춘옥로얄부동산중개부산광역시 해운대구 반여로 131 상가 111호(반여동, 아시아선수촌)35.20387129.1216852023-01-30
74227785해운대구공인중개사문순용합동도우미공인중개사사무소부산광역시 해운대구 양운로 92 10층(좌동, 나하나빌딩)35.171257129.1750162023-01-30
74237786해운대구공인중개사고미순아델리스공인중개사사무소부산광역시 해운대구 마린시티3로 46 109호(우동, 해운대우신골든메르시아)35.155036129.1454012023-01-30
74247787해운대구공인중개사주은숙장산타운부동산중개부산광역시 해운대구 선수촌로 104-21 1층(반여동)35.202735129.1206262023-01-30
74257788해운대구공인중개사지화경제니스마린부동산중개부산광역시 해운대구 마린시티2로 33 104동 143호(우동, 두산위브더제니스)35.157205129.1448172023-01-30