Overview

Dataset statistics

Number of variables20
Number of observations739
Missing cells543
Missing cells (%)3.7%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory117.8 KiB
Average record size in memory163.2 B

Variable types

Text12
Categorical5
Numeric3

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
data_day is highly overall correlated with lng and 4 other fieldsHigh correlation
last_load_dttm is highly overall correlated with pbl_pl and 6 other fieldsHigh correlation
instt_code is highly overall correlated with lat and 5 other fieldsHigh correlation
gugun is highly overall correlated with lat and 5 other fieldsHigh correlation
apr_at is highly overall correlated with lat and 5 other fieldsHigh correlation
pbl_pl is highly overall correlated with last_load_dttmHigh correlation
lat is highly overall correlated with instt_code and 3 other fieldsHigh correlation
lng is highly overall correlated with instt_code and 4 other fieldsHigh correlation
last_load_dttm is highly imbalanced (58.9%)Imbalance
addr_road has 8 (1.1%) missing valuesMissing
area has 32 (4.3%) missing valuesMissing
purpose has 33 (4.5%) missing valuesMissing
pbl_pl has 63 (8.5%) missing valuesMissing
pbl_loc has 173 (23.4%) missing valuesMissing
pbl_amn has 77 (10.4%) missing valuesMissing
lat has 61 (8.3%) missing valuesMissing
lng has 61 (8.3%) missing valuesMissing

Reproduction

Analysis started2023-12-10 09:33:32.604273
Analysis finished2023-12-10 09:33:38.701140
Duration6.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Text

Distinct714
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2023-12-10T18:33:39.172780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.9797023
Min length2

Characters and Unicode

Total characters2941
Distinct characters26
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

Unique711 ?
Unique (%)96.2%

Sample

1st row4845
2nd row4846
3rd row4847
4th row4848
5th row4849
ValueCountFrequency (%)
업무시설 18
 
2.4%
근생 8
 
1.1%
판매시설 2
 
0.3%
5563 1
 
0.1%
5394 1
 
0.1%
5565 1
 
0.1%
5554 1
 
0.1%
4845 1
 
0.1%
5556 1
 
0.1%
5395 1
 
0.1%
Other values (704) 704
95.3%
2023-12-10T18:33:40.083327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 828
28.2%
4 330
 
11.2%
1 259
 
8.8%
3 227
 
7.7%
2 226
 
7.7%
9 222
 
7.5%
0 207
 
7.0%
8 199
 
6.8%
6 168
 
5.7%
7 160
 
5.4%
Other values (16) 115
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2826
96.1%
Other Letter 115
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 23
20.0%
설 23
20.0%
무 18
15.7%
업 18
15.7%
생 10
8.7%
근 10
8.7%
매 2
 
1.7%
판 2
 
1.7%
동 2
 
1.7%
의 1
 
0.9%
Other values (6) 6
 
5.2%
Decimal Number
ValueCountFrequency (%)
5 828
29.3%
4 330
 
11.7%
1 259
 
9.2%
3 227
 
8.0%
2 226
 
8.0%
9 222
 
7.9%
0 207
 
7.3%
8 199
 
7.0%
6 168
 
5.9%
7 160
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2826
96.1%
Hangul 115
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 23
20.0%
설 23
20.0%
무 18
15.7%
업 18
15.7%
생 10
8.7%
근 10
8.7%
매 2
 
1.7%
판 2
 
1.7%
동 2
 
1.7%
의 1
 
0.9%
Other values (6) 6
 
5.2%
Common
ValueCountFrequency (%)
5 828
29.3%
4 330
 
11.7%
1 259
 
9.2%
3 227
 
8.0%
2 226
 
8.0%
9 222
 
7.9%
0 207
 
7.3%
8 199
 
7.0%
6 168
 
5.9%
7 160
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2826
96.1%
Hangul 115
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 828
29.3%
4 330
 
11.7%
1 259
 
9.2%
3 227
 
8.0%
2 226
 
8.0%
9 222
 
7.9%
0 207
 
7.3%
8 199
 
7.0%
6 168
 
5.9%
7 160
 
5.7%
Hangul
ValueCountFrequency (%)
시 23
20.0%
설 23
20.0%
무 18
15.7%
업 18
15.7%
생 10
8.7%
근 10
8.7%
매 2
 
1.7%
판 2
 
1.7%
동 2
 
1.7%
의 1
 
0.9%
Other values (6) 6
 
5.2%

instt_code
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
3330000
130 
3330000
126 
3290000
70 
3370000
49 
3250000
36 
Other values (16)
328 

Length

Max length20
Median length7
Mean length9.0446549
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3370000
2nd row3370000
3rd row3370000
4th row3370000
5th row3370000

Common Values

ValueCountFrequency (%)
3330000 130
17.6%
3330000 126
17.1%
3290000 70
9.5%
3370000 49
 
6.6%
3250000 36
 
4.9%
3300000 35
 
4.7%
3390000 35
 
4.7%
3380000 33
 
4.5%
3350000 30
 
4.1%
3270000 29
 
3.9%
Other values (11) 166
22.5%

Length

2023-12-10T18:33:40.477348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3330000 256
34.6%
3290000 70
 
9.5%
3370000 49
 
6.6%
3250000 36
 
4.9%
3300000 35
 
4.7%
3390000 35
 
4.7%
3380000 33
 
4.5%
3350000 30
 
4.1%
3270000 29
 
3.9%
3280000 28
 
3.8%
Other values (10) 138
18.7%

bild_nm
Text

Distinct578
Distinct (%)78.7%
Missing5
Missing (%)0.7%
Memory size5.9 KiB
2023-12-10T18:33:41.097455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length17
Mean length7.1008174
Min length1

Characters and Unicode

Total characters5212
Distinct characters422
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

Unique446 ?
Unique (%)60.8%

Sample

1st row홈플러스
2nd row국민연금공단사옥
3rd row연제우체국
4th row킴스힐타워
5th row㈜부원 사옥
ValueCountFrequency (%)
전면 15
 
1.5%
오피스텔 15
 
1.5%
해운대 14
 
1.4%
이마트 6
 
0.6%
봄여름가을겨울 6
 
0.6%
대지 5
 
0.5%
내 5
 
0.5%
더 5
 
0.5%
더샵 5
 
0.5%
온천동 5
 
0.5%
Other values (683) 894
91.7%
2023-12-10T18:33:41.875884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
245
 
4.7%
스 213
 
4.1%
이 115
 
2.2%
리 107
 
2.1%
산 83
 
1.6%
대 82
 
1.6%
트 81
 
1.6%
아 80
 
1.5%
텔 78
 
1.5%
부 70
 
1.3%
Other values (412) 4058
77.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4644
89.1%
Space Separator 245
 
4.7%
Uppercase Letter 175
 
3.4%
Decimal Number 53
 
1.0%
Open Punctuation 25
 
0.5%
Close Punctuation 25
 
0.5%
Lowercase Letter 24
 
0.5%
Other Punctuation 8
 
0.2%
Other Symbol 7
 
0.1%
Dash Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 213
 
4.6%
이 115
 
2.5%
리 107
 
2.3%
산 83
 
1.8%
대 82
 
1.8%
트 81
 
1.7%
아 80
 
1.7%
텔 78
 
1.7%
부 70
 
1.5%
센 68
 
1.5%
Other values (364) 3667
79.0%
Uppercase Letter
ValueCountFrequency (%)
K 16
 
9.1%
C 15
 
8.6%
S 15
 
8.6%
W 12
 
6.9%
T 12
 
6.9%
A 12
 
6.9%
E 11
 
6.3%
L 10
 
5.7%
N 9
 
5.1%
H 8
 
4.6%
Other values (13) 55
31.4%
Decimal Number
ValueCountFrequency (%)
2 26
49.1%
1 9
 
17.0%
3 9
 
17.0%
0 2
 
3.8%
5 2
 
3.8%
4 2
 
3.8%
6 2
 
3.8%
7 1
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
e 9
37.5%
i 5
20.8%
b 3
 
12.5%
s 2
 
8.3%
w 2
 
8.3%
k 1
 
4.2%
v 1
 
4.2%
d 1
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 5
62.5%
& 2
 
25.0%
/ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
245
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4651
89.2%
Common 361
 
6.9%
Latin 200
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 213
 
4.6%
이 115
 
2.5%
리 107
 
2.3%
산 83
 
1.8%
대 82
 
1.8%
트 81
 
1.7%
아 80
 
1.7%
텔 78
 
1.7%
부 70
 
1.5%
센 68
 
1.5%
Other values (365) 3674
79.0%
Latin
ValueCountFrequency (%)
K 16
 
8.0%
C 15
 
7.5%
S 15
 
7.5%
W 12
 
6.0%
T 12
 
6.0%
A 12
 
6.0%
E 11
 
5.5%
L 10
 
5.0%
N 9
 
4.5%
e 9
 
4.5%
Other values (22) 79
39.5%
Common
ValueCountFrequency (%)
245
67.9%
2 26
 
7.2%
( 25
 
6.9%
) 25
 
6.9%
1 9
 
2.5%
3 9
 
2.5%
- 5
 
1.4%
. 5
 
1.4%
0 2
 
0.6%
5 2
 
0.6%
Other values (5) 8
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4644
89.1%
ASCII 560
 
10.7%
None 7
 
0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
245
43.8%
2 26
 
4.6%
( 25
 
4.5%
) 25
 
4.5%
K 16
 
2.9%
C 15
 
2.7%
S 15
 
2.7%
W 12
 
2.1%
T 12
 
2.1%
A 12
 
2.1%
Other values (36) 157
28.0%
Hangul
ValueCountFrequency (%)
스 213
 
4.6%
이 115
 
2.5%
리 107
 
2.3%
산 83
 
1.8%
대 82
 
1.8%
트 81
 
1.7%
아 80
 
1.7%
텔 78
 
1.7%
부 70
 
1.5%
센 68
 
1.5%
Other values (364) 3667
79.0%
None
ValueCountFrequency (%)
㈜ 7
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

pbl_area
Text

Distinct608
Distinct (%)82.9%
Missing6
Missing (%)0.8%
Memory size5.9 KiB
2023-12-10T18:33:42.559025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length6.0381992
Min length2

Characters and Unicode

Total characters4426
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique491 ?
Unique (%)67.0%

Sample

1st row12,008.98
2nd row863.02
3rd row394.33
4th row56.84
5th row68.02
ValueCountFrequency (%)
1 21
 
2.6%
표지판 21
 
2.6%
의자 19
 
2.4%
666.56 4
 
0.5%
111.17 4
 
0.5%
2 4
 
0.5%
4 4
 
0.5%
137.79 3
 
0.4%
6 3
 
0.4%
330.27 2
 
0.2%
Other values (596) 719
89.4%
2023-12-10T18:33:43.483597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 656
14.8%
1 541
12.2%
2 384
8.7%
6 330
7.5%
5 321
7.3%
4 319
7.2%
0 310
7.0%
3 305
6.9%
9 305
6.9%
7 289
6.5%
Other values (41) 666
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3388
76.5%
Other Punctuation 783
 
17.7%
Other Letter 166
 
3.8%
Space Separator 89
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 24
14.5%
판 24
14.5%
표 23
13.9%
의 22
13.3%
자 22
13.3%
라 4
 
2.4%
평 3
 
1.8%
조 3
 
1.8%
치 3
 
1.8%
벤 3
 
1.8%
Other values (28) 35
21.1%
Decimal Number
ValueCountFrequency (%)
1 541
16.0%
2 384
11.3%
6 330
9.7%
5 321
9.5%
4 319
9.4%
0 310
9.1%
3 305
9.0%
9 305
9.0%
7 289
8.5%
8 284
8.4%
Other Punctuation
ValueCountFrequency (%)
. 656
83.8%
, 127
 
16.2%
Space Separator
ValueCountFrequency (%)
89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4260
96.2%
Hangul 166
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 24
14.5%
판 24
14.5%
표 23
13.9%
의 22
13.3%
자 22
13.3%
라 4
 
2.4%
평 3
 
1.8%
조 3
 
1.8%
치 3
 
1.8%
벤 3
 
1.8%
Other values (28) 35
21.1%
Common
ValueCountFrequency (%)
. 656
15.4%
1 541
12.7%
2 384
9.0%
6 330
7.7%
5 321
7.5%
4 319
7.5%
0 310
7.3%
3 305
7.2%
9 305
7.2%
7 289
6.8%
Other values (3) 500
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4260
96.2%
Hangul 166
 
3.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 656
15.4%
1 541
12.7%
2 384
9.0%
6 330
7.7%
5 321
7.5%
4 319
7.5%
0 310
7.3%
3 305
7.2%
9 305
7.2%
7 289
6.8%
Other values (3) 500
11.7%
Hangul
ValueCountFrequency (%)
지 24
14.5%
판 24
14.5%
표 23
13.9%
의 22
13.3%
자 22
13.3%
라 4
 
2.4%
평 3
 
1.8%
조 3
 
1.8%
치 3
 
1.8%
벤 3
 
1.8%
Other values (28) 35
21.1%

addr_road
Text

MISSING 

Distinct579
Distinct (%)79.2%
Missing8
Missing (%)1.1%
Memory size5.9 KiB
2023-12-10T18:33:44.099158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length33
Mean length18.912449
Min length5

Characters and Unicode

Total characters13825
Distinct characters209
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

Unique450 ?
Unique (%)61.6%

Sample

1st row부산 연제구 종합운동장로 7
2nd row부산 연제구 중앙대로 1000
3rd row부산 연제구 법원북로 33
4th row부산 연제구 중앙대로1054번길 27
5th row부산 연제구 중앙대로 1117
ValueCountFrequency (%)
부산광역시 686
23.8%
해운대구 259
 
9.0%
부산진구 70
 
2.4%
연제구 49
 
1.7%
부산 44
 
1.5%
중앙대로 42
 
1.5%
서구 39
 
1.4%
중구 36
 
1.2%
사상구 35
 
1.2%
동래구 35
 
1.2%
Other values (638) 1589
55.1%
2023-12-10T18:33:44.921592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2200
15.9%
부 806
 
5.8%
산 803
 
5.8%
구 733
 
5.3%
광 731
 
5.3%
시 730
 
5.3%
로 692
 
5.0%
역 687
 
5.0%
대 529
 
3.8%
1 435
 
3.1%
Other values (199) 5479
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9259
67.0%
Decimal Number 2217
 
16.0%
Space Separator 2200
 
15.9%
Open Punctuation 40
 
0.3%
Close Punctuation 40
 
0.3%
Dash Punctuation 37
 
0.3%
Uppercase Letter 17
 
0.1%
Other Punctuation 13
 
0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 806
 
8.7%
산 803
 
8.7%
구 733
 
7.9%
광 731
 
7.9%
시 730
 
7.9%
로 692
 
7.5%
역 687
 
7.4%
대 529
 
5.7%
해 415
 
4.5%
운 363
 
3.9%
Other values (178) 2770
29.9%
Decimal Number
ValueCountFrequency (%)
1 435
19.6%
2 304
13.7%
3 262
11.8%
7 203
9.2%
4 199
9.0%
5 192
8.7%
6 176
7.9%
9 171
 
7.7%
0 160
 
7.2%
8 115
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
P 4
23.5%
E 4
23.5%
A 4
23.5%
C 4
23.5%
V 1
 
5.9%
Space Separator
ValueCountFrequency (%)
2200
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9259
67.0%
Common 4547
32.9%
Latin 19
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 806
 
8.7%
산 803
 
8.7%
구 733
 
7.9%
광 731
 
7.9%
시 730
 
7.9%
로 692
 
7.5%
역 687
 
7.4%
대 529
 
5.7%
해 415
 
4.5%
운 363
 
3.9%
Other values (178) 2770
29.9%
Common
ValueCountFrequency (%)
2200
48.4%
1 435
 
9.6%
2 304
 
6.7%
3 262
 
5.8%
7 203
 
4.5%
4 199
 
4.4%
5 192
 
4.2%
6 176
 
3.9%
9 171
 
3.8%
0 160
 
3.5%
Other values (5) 245
 
5.4%
Latin
ValueCountFrequency (%)
P 4
21.1%
E 4
21.1%
A 4
21.1%
C 4
21.1%
Ⅱ 2
10.5%
V 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9259
67.0%
ASCII 4564
33.0%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2200
48.2%
1 435
 
9.5%
2 304
 
6.7%
3 262
 
5.7%
7 203
 
4.4%
4 199
 
4.4%
5 192
 
4.2%
6 176
 
3.9%
9 171
 
3.7%
0 160
 
3.5%
Other values (10) 262
 
5.7%
Hangul
ValueCountFrequency (%)
부 806
 
8.7%
산 803
 
8.7%
구 733
 
7.9%
광 731
 
7.9%
시 730
 
7.9%
로 692
 
7.5%
역 687
 
7.4%
대 529
 
5.7%
해 415
 
4.5%
운 363
 
3.9%
Other values (178) 2770
29.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%

addr_jibun
Text

Distinct582
Distinct (%)79.3%
Missing5
Missing (%)0.7%
Memory size5.9 KiB
2023-12-10T18:33:45.362786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length19.070845
Min length10

Characters and Unicode

Total characters13998
Distinct characters122
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique453 ?
Unique (%)61.7%

Sample

1st row부산 연제구 거제동 1208 외
2nd row부산 연제구 연산동 1422-8
3rd row부산 연제구 거제동 1473
4th row부산 연제구 연산동 1305-12
5th row부산 연제구 연산동 1124-7
ValueCountFrequency (%)
부산광역시 663
22.4%
해운대구 257
 
8.7%
우동 133
 
4.5%
부산진구 70
 
2.4%
외 66
 
2.2%
중동 56
 
1.9%
연제구 49
 
1.7%
부산 44
 
1.5%
좌동 38
 
1.3%
연산동 37
 
1.3%
Other values (699) 1546
52.2%
2023-12-10T18:33:46.090425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2242
16.0%
산 818
 
5.8%
부 813
 
5.8%
1 775
 
5.5%
동 759
 
5.4%
구 705
 
5.0%
광 683
 
4.9%
시 666
 
4.8%
역 663
 
4.7%
- 581
 
4.2%
Other values (112) 5293
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7884
56.3%
Decimal Number 3290
23.5%
Space Separator 2242
 
16.0%
Dash Punctuation 581
 
4.2%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 818
10.4%
부 813
10.3%
동 759
 
9.6%
구 705
 
8.9%
광 683
 
8.7%
시 666
 
8.4%
역 663
 
8.4%
대 297
 
3.8%
해 257
 
3.3%
운 257
 
3.3%
Other values (99) 1966
24.9%
Decimal Number
ValueCountFrequency (%)
1 775
23.6%
2 382
11.6%
4 372
11.3%
3 321
9.8%
5 300
 
9.1%
0 263
 
8.0%
7 257
 
7.8%
6 251
 
7.6%
8 205
 
6.2%
9 164
 
5.0%
Space Separator
ValueCountFrequency (%)
2242
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 581
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7884
56.3%
Common 6114
43.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 818
10.4%
부 813
10.3%
동 759
 
9.6%
구 705
 
8.9%
광 683
 
8.7%
시 666
 
8.4%
역 663
 
8.4%
대 297
 
3.8%
해 257
 
3.3%
운 257
 
3.3%
Other values (99) 1966
24.9%
Common
ValueCountFrequency (%)
2242
36.7%
1 775
 
12.7%
- 581
 
9.5%
2 382
 
6.2%
4 372
 
6.1%
3 321
 
5.3%
5 300
 
4.9%
0 263
 
4.3%
7 257
 
4.2%
6 251
 
4.1%
Other values (3) 370
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7884
56.3%
ASCII 6114
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2242
36.7%
1 775
 
12.7%
- 581
 
9.5%
2 382
 
6.2%
4 372
 
6.1%
3 321
 
5.3%
5 300
 
4.9%
0 263
 
4.3%
7 257
 
4.2%
6 251
 
4.1%
Other values (3) 370
 
6.1%
Hangul
ValueCountFrequency (%)
산 818
10.4%
부 813
10.3%
동 759
 
9.6%
구 705
 
8.9%
광 683
 
8.7%
시 666
 
8.4%
역 663
 
8.4%
대 297
 
3.8%
해 257
 
3.3%
운 257
 
3.3%
Other values (99) 1966
24.9%

prmt_date
Text

Distinct563
Distinct (%)76.8%
Missing6
Missing (%)0.8%
Memory size5.9 KiB
2023-12-10T18:33:46.598963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length10.00955
Min length5

Characters and Unicode

Total characters7337
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique416 ?
Unique (%)56.8%

Sample

1st row2001-05-18
2nd row2000-07-21
3rd row2002-06-26
4th row2013-10-18
5th row1993-06-30
ValueCountFrequency (%)
2003-01-07 8
 
1.1%
2009-01-14 4
 
0.5%
2002-04-17 4
 
0.5%
2003-03-20 4
 
0.5%
2017-12-07 3
 
0.4%
2002-12-31 3
 
0.4%
2007-02-16 3
 
0.4%
2017-11-15 3
 
0.4%
2002-03-04 3
 
0.4%
2002-05-31 3
 
0.4%
Other values (558) 703
94.9%
2023-12-10T18:33:47.347047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1775
24.2%
- 1392
19.0%
2 1180
16.1%
1 1123
15.3%
9 360
 
4.9%
3 319
 
4.3%
5 266
 
3.6%
4 255
 
3.5%
6 225
 
3.1%
7 203
 
2.8%
Other values (3) 239
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5902
80.4%
Dash Punctuation 1392
 
19.0%
Other Punctuation 35
 
0.5%
Space Separator 8
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1775
30.1%
2 1180
20.0%
1 1123
19.0%
9 360
 
6.1%
3 319
 
5.4%
5 266
 
4.5%
4 255
 
4.3%
6 225
 
3.8%
7 203
 
3.4%
8 196
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 1392
100.0%
Other Punctuation
ValueCountFrequency (%)
. 35
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7337
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1775
24.2%
- 1392
19.0%
2 1180
16.1%
1 1123
15.3%
9 360
 
4.9%
3 319
 
4.3%
5 266
 
3.6%
4 255
 
3.5%
6 225
 
3.1%
7 203
 
2.8%
Other values (3) 239
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7337
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1775
24.2%
- 1392
19.0%
2 1180
16.1%
1 1123
15.3%
9 360
 
4.9%
3 319
 
4.3%
5 266
 
3.6%
4 255
 
3.5%
6 225
 
3.1%
7 203
 
2.8%
Other values (3) 239
 
3.3%

aprv_date
Text

Distinct572
Distinct (%)78.1%
Missing7
Missing (%)0.9%
Memory size5.9 KiB
2023-12-10T18:33:47.899207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length10.04235
Min length5

Characters and Unicode

Total characters7351
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique423 ?
Unique (%)57.8%

Sample

1st row2003-03-24
2nd row2004-12-29
3rd row2005-03-18
4th row2015-10-15
5th row1997-07-07
ValueCountFrequency (%)
2006-09-22 6
 
0.8%
2018-09-19 3
 
0.4%
2014-11-24 3
 
0.4%
2007-04-26 3
 
0.4%
2002-09-18 3
 
0.4%
2016-01-11 3
 
0.4%
2007-04-27 3
 
0.4%
2006-09-15 3
 
0.4%
2002-07-29 2
 
0.3%
2015-05-28 2
 
0.3%
Other values (562) 701
95.8%
2023-12-10T18:33:48.732371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1765
24.0%
- 1394
19.0%
2 1246
17.0%
1 1147
15.6%
9 327
 
4.4%
3 282
 
3.8%
6 248
 
3.4%
4 243
 
3.3%
8 233
 
3.2%
7 221
 
3.0%
Other values (2) 245
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5929
80.7%
Dash Punctuation 1394
 
19.0%
Other Punctuation 28
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1765
29.8%
2 1246
21.0%
1 1147
19.3%
9 327
 
5.5%
3 282
 
4.8%
6 248
 
4.2%
4 243
 
4.1%
8 233
 
3.9%
7 221
 
3.7%
5 217
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 1394
100.0%
Other Punctuation
ValueCountFrequency (%)
. 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7351
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1765
24.0%
- 1394
19.0%
2 1246
17.0%
1 1147
15.6%
9 327
 
4.4%
3 282
 
3.8%
6 248
 
3.4%
4 243
 
3.3%
8 233
 
3.2%
7 221
 
3.0%
Other values (2) 245
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1765
24.0%
- 1394
19.0%
2 1246
17.0%
1 1147
15.6%
9 327
 
4.4%
3 282
 
3.8%
6 248
 
3.4%
4 243
 
3.3%
8 233
 
3.2%
7 221
 
3.0%
Other values (2) 245
 
3.3%

area
Text

MISSING 

Distinct587
Distinct (%)83.0%
Missing32
Missing (%)4.3%
Memory size5.9 KiB
2023-12-10T18:33:49.277310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.2673267
Min length3

Characters and Unicode

Total characters5845
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique469 ?
Unique (%)66.3%

Sample

1st row64,516.70
2nd row43,431.70
3rd row16,519.20
4th row4,983.30
5th row10,530.00
ValueCountFrequency (%)
121,051.40 4
 
0.6%
129,394.00 2
 
0.3%
46,410.40 2
 
0.3%
30,996.60 2
 
0.3%
63,596.90 2
 
0.3%
19,922.10 2
 
0.3%
25,177.10 2
 
0.3%
91,274.70 2
 
0.3%
85,889.80 2
 
0.3%
35,636.70 2
 
0.3%
Other values (569) 685
96.9%
2023-12-10T18:33:50.088350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 719
12.3%
. 684
11.7%
1 589
10.1%
, 469
8.0%
2 448
7.7%
5 436
7.5%
9 432
7.4%
6 428
7.3%
3 424
7.3%
8 424
7.3%
Other values (3) 792
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4684
80.1%
Other Punctuation 1153
 
19.7%
Space Separator 8
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 719
15.4%
1 589
12.6%
2 448
9.6%
5 436
9.3%
9 432
9.2%
6 428
9.1%
3 424
9.1%
8 424
9.1%
4 417
8.9%
7 367
7.8%
Other Punctuation
ValueCountFrequency (%)
. 684
59.3%
, 469
40.7%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5845
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 719
12.3%
. 684
11.7%
1 589
10.1%
, 469
8.0%
2 448
7.7%
5 436
7.5%
9 432
7.4%
6 428
7.3%
3 424
7.3%
8 424
7.3%
Other values (3) 792
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 719
12.3%
. 684
11.7%
1 589
10.1%
, 469
8.0%
2 448
7.7%
5 436
7.5%
9 432
7.4%
6 428
7.3%
3 424
7.3%
8 424
7.3%
Other values (3) 792
13.6%

nmb_floors
Text

Distinct293
Distinct (%)40.0%
Missing6
Missing (%)0.8%
Memory size5.9 KiB
2023-12-10T18:33:50.535963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length8
Mean length8.4092769
Min length3

Characters and Unicode

Total characters6164
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)19.6%

Sample

1st row지하2/지상1
2nd row지하4/지상22
3rd row지하1/지상9
4th row지하1/지상23
5th row지하2/지상13
ValueCountFrequency (%)
지하1/지상15 34
 
4.4%
지하1/지상20 28
 
3.6%
16:57:38 27
 
3.5%
2021-01-05 27
 
3.5%
지하2/지상15 18
 
2.3%
지하1/지상5 17
 
2.2%
지하2/지상20 17
 
2.2%
지하4/지상15 11
 
1.4%
지하3/지상15 11
 
1.4%
지하3/지상14 10
 
1.3%
Other values (244) 571
74.1%
2023-12-10T18:33:51.295728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 1318
21.4%
/ 684
11.1%
상 671
10.9%
하 647
10.5%
1 603
9.8%
2 480
 
7.8%
5 287
 
4.7%
3 260
 
4.2%
0 207
 
3.4%
190
 
3.1%
Other values (13) 817
13.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2776
45.0%
Decimal Number 2367
38.4%
Other Punctuation 741
 
12.0%
Space Separator 190
 
3.1%
Dash Punctuation 88
 
1.4%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 603
25.5%
2 480
20.3%
5 287
12.1%
3 260
11.0%
0 207
 
8.7%
4 185
 
7.8%
6 106
 
4.5%
7 96
 
4.1%
8 91
 
3.8%
9 52
 
2.2%
Other Letter
ValueCountFrequency (%)
지 1318
47.5%
상 671
24.2%
하 647
23.3%
층 114
 
4.1%
동 24
 
0.9%
시 1
 
< 0.1%
장 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 684
92.3%
: 54
 
7.3%
, 3
 
0.4%
Space Separator
ValueCountFrequency (%)
190
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3388
55.0%
Hangul 2776
45.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 684
20.2%
1 603
17.8%
2 480
14.2%
5 287
8.5%
3 260
 
7.7%
0 207
 
6.1%
190
 
5.6%
4 185
 
5.5%
6 106
 
3.1%
7 96
 
2.8%
Other values (6) 290
8.6%
Hangul
ValueCountFrequency (%)
지 1318
47.5%
상 671
24.2%
하 647
23.3%
층 114
 
4.1%
동 24
 
0.9%
시 1
 
< 0.1%
장 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3388
55.0%
Hangul 2776
45.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 1318
47.5%
상 671
24.2%
하 647
23.3%
층 114
 
4.1%
동 24
 
0.9%
시 1
 
< 0.1%
장 1
 
< 0.1%
ASCII
ValueCountFrequency (%)
/ 684
20.2%
1 603
17.8%
2 480
14.2%
5 287
8.5%
3 260
 
7.7%
0 207
 
6.1%
190
 
5.6%
4 185
 
5.5%
6 106
 
3.1%
7 96
 
2.8%
Other values (6) 290
8.6%

purpose
Text

MISSING 

Distinct154
Distinct (%)21.8%
Missing33
Missing (%)4.5%
Memory size5.9 KiB
2023-12-10T18:33:51.621940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length7.0892351
Min length2

Characters and Unicode

Total characters5005
Distinct characters77
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)11.0%

Sample

1st row판매시설 운동시설 문화및집회
2nd row업무시설
3rd row공공업무
4th row공동주택
5th row업무시설
ValueCountFrequency (%)
업무시설 256
27.7%
공동주택 144
15.6%
숙박시설 68
 
7.4%
판매시설 56
 
6.1%
근생 55
 
6.0%
근린생활시설 33
 
3.6%
의료시설 32
 
3.5%
문화및집회 21
 
2.3%
판매및영업 17
 
1.8%
업무시설(오피스텔 15
 
1.6%
Other values (81) 227
24.6%
2023-12-10T18:33:52.198011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 625
12.5%
설 625
12.5%
382
 
7.6%
업 375
 
7.5%
무 352
 
7.0%
공 240
 
4.8%
, 225
 
4.5%
동 214
 
4.3%
주 204
 
4.1%
택 204
 
4.1%
Other values (67) 1559
31.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4256
85.0%
Space Separator 382
 
7.6%
Other Punctuation 261
 
5.2%
Open Punctuation 36
 
0.7%
Close Punctuation 36
 
0.7%
Decimal Number 32
 
0.6%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 625
14.7%
설 625
14.7%
업 375
 
8.8%
무 352
 
8.3%
공 240
 
5.6%
동 214
 
5.0%
주 204
 
4.8%
택 204
 
4.8%
근 157
 
3.7%
생 157
 
3.7%
Other values (52) 1103
25.9%
Decimal Number
ValueCountFrequency (%)
1 13
40.6%
2 10
31.2%
0 3
 
9.4%
5 2
 
6.2%
8 1
 
3.1%
3 1
 
3.1%
7 1
 
3.1%
6 1
 
3.1%
Other Punctuation
ValueCountFrequency (%)
, 225
86.2%
/ 34
 
13.0%
: 2
 
0.8%
Space Separator
ValueCountFrequency (%)
382
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4256
85.0%
Common 749
 
15.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 625
14.7%
설 625
14.7%
업 375
 
8.8%
무 352
 
8.3%
공 240
 
5.6%
동 214
 
5.0%
주 204
 
4.8%
택 204
 
4.8%
근 157
 
3.7%
생 157
 
3.7%
Other values (52) 1103
25.9%
Common
ValueCountFrequency (%)
382
51.0%
, 225
30.0%
( 36
 
4.8%
) 36
 
4.8%
/ 34
 
4.5%
1 13
 
1.7%
2 10
 
1.3%
0 3
 
0.4%
: 2
 
0.3%
- 2
 
0.3%
Other values (5) 6
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4256
85.0%
ASCII 749
 
15.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 625
14.7%
설 625
14.7%
업 375
 
8.8%
무 352
 
8.3%
공 240
 
5.6%
동 214
 
5.0%
주 204
 
4.8%
택 204
 
4.8%
근 157
 
3.7%
생 157
 
3.7%
Other values (52) 1103
25.9%
ASCII
ValueCountFrequency (%)
382
51.0%
, 225
30.0%
( 36
 
4.8%
) 36
 
4.8%
/ 34
 
4.5%
1 13
 
1.7%
2 10
 
1.3%
0 3
 
0.4%
: 2
 
0.3%
- 2
 
0.3%
Other values (5) 6
 
0.8%

pbl_pl
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.9%
Missing63
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean1.3890533
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-10T18:33:52.408892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.65396681
Coefficient of variation (CV)0.47080038
Kurtosis17.617798
Mean1.3890533
Median Absolute Deviation (MAD)0
Skewness2.8702284
Sum939
Variance0.42767258
MonotonicityNot monotonic
2023-12-10T18:33:52.593076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 453
61.3%
2 196
26.5%
3 19
 
2.6%
4 6
 
0.8%
5 1
 
0.1%
8 1
 
0.1%
(Missing) 63
 
8.5%
ValueCountFrequency (%)
1 453
61.3%
2 196
26.5%
3 19
 
2.6%
4 6
 
0.8%
5 1
 
0.1%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
5 1
 
0.1%
4 6
 
0.8%
3 19
 
2.6%
2 196
26.5%
1 453
61.3%

pbl_loc
Text

MISSING 

Distinct86
Distinct (%)15.2%
Missing173
Missing (%)23.4%
Memory size5.9 KiB
2023-12-10T18:33:53.135758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length2
Mean length4.7738516
Min length2

Characters and Unicode

Total characters2702
Distinct characters69
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)12.4%

Sample

1st row전면
2nd row전면
3rd row측면
4th row전면
5th row전면
ValueCountFrequency (%)
지상 248
27.8%
전면 113
12.7%
부산광역시 63
 
7.1%
대지 54
 
6.0%
내 54
 
6.0%
사상구 35
 
3.9%
건물 34
 
3.8%
1층 31
 
3.5%
위치 29
 
3.2%
영도구 28
 
3.1%
Other values (101) 204
22.8%
2023-12-10T18:33:53.844579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 347
 
12.8%
328
 
12.1%
상 283
 
10.5%
면 188
 
7.0%
전 136
 
5.0%
1 98
 
3.6%
동 69
 
2.6%
대 63
 
2.3%
부 63
 
2.3%
구 63
 
2.3%
Other values (59) 1064
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2025
74.9%
Space Separator 328
 
12.1%
Decimal Number 283
 
10.5%
Dash Punctuation 42
 
1.6%
Other Punctuation 24
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 347
17.1%
상 283
14.0%
면 188
 
9.3%
전 136
 
6.7%
동 69
 
3.4%
대 63
 
3.1%
부 63
 
3.1%
구 63
 
3.1%
시 63
 
3.1%
역 63
 
3.1%
Other values (46) 687
33.9%
Decimal Number
ValueCountFrequency (%)
1 98
34.6%
5 35
 
12.4%
2 28
 
9.9%
4 27
 
9.5%
3 21
 
7.4%
7 19
 
6.7%
6 16
 
5.7%
0 14
 
4.9%
9 13
 
4.6%
8 12
 
4.2%
Space Separator
ValueCountFrequency (%)
328
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%
Other Punctuation
ValueCountFrequency (%)
, 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2025
74.9%
Common 677
 
25.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 347
17.1%
상 283
14.0%
면 188
 
9.3%
전 136
 
6.7%
동 69
 
3.4%
대 63
 
3.1%
부 63
 
3.1%
구 63
 
3.1%
시 63
 
3.1%
역 63
 
3.1%
Other values (46) 687
33.9%
Common
ValueCountFrequency (%)
328
48.4%
1 98
 
14.5%
- 42
 
6.2%
5 35
 
5.2%
2 28
 
4.1%
4 27
 
4.0%
, 24
 
3.5%
3 21
 
3.1%
7 19
 
2.8%
6 16
 
2.4%
Other values (3) 39
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2025
74.9%
ASCII 677
 
25.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 347
17.1%
상 283
14.0%
면 188
 
9.3%
전 136
 
6.7%
동 69
 
3.4%
대 63
 
3.1%
부 63
 
3.1%
구 63
 
3.1%
시 63
 
3.1%
역 63
 
3.1%
Other values (46) 687
33.9%
ASCII
ValueCountFrequency (%)
328
48.4%
1 98
 
14.5%
- 42
 
6.2%
5 35
 
5.2%
2 28
 
4.1%
4 27
 
4.0%
, 24
 
3.5%
3 21
 
3.1%
7 19
 
2.8%
6 16
 
2.4%
Other values (3) 39
 
5.8%

pbl_amn
Text

MISSING 

Distinct207
Distinct (%)31.3%
Missing77
Missing (%)10.4%
Memory size5.9 KiB
2023-12-10T18:33:54.212300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length24
Mean length9.5377644
Min length2

Characters and Unicode

Total characters6314
Distinct characters102
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)21.1%

Sample

1st row의자, 파고라, 표지판 1
2nd row의자, 조명, 표지판 1
3rd row의자 8, 조명, 분수 1, 표지판 1
4th row의자 2, 표지판 1
5th row의자 4, 표지판 1
ValueCountFrequency (%)
표지판 468
25.1%
의자 380
20.4%
1 371
19.9%
파고라 105
 
5.6%
2 62
 
3.3%
조형물 51
 
2.7%
표지판1 51
 
2.7%
벤치 48
 
2.6%
조명 34
 
1.8%
3 18
 
1.0%
Other values (115) 274
14.7%
2023-12-10T18:33:54.822487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1209
19.1%
, 731
11.6%
판 550
8.7%
표 549
8.7%
지 548
8.7%
자 473
 
7.5%
의 469
 
7.4%
1 453
 
7.2%
라 124
 
2.0%
파 123
 
1.9%
Other values (92) 1085
17.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3683
58.3%
Space Separator 1209
 
19.1%
Other Punctuation 739
 
11.7%
Decimal Number 664
 
10.5%
Lowercase Letter 6
 
0.1%
Close Punctuation 6
 
0.1%
Open Punctuation 6
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
판 550
14.9%
표 549
14.9%
지 548
14.9%
자 473
12.8%
의 469
12.7%
라 124
 
3.4%
파 123
 
3.3%
고 123
 
3.3%
조 114
 
3.1%
벤 70
 
1.9%
Other values (75) 540
14.7%
Decimal Number
ValueCountFrequency (%)
1 453
68.2%
2 96
 
14.5%
3 40
 
6.0%
4 31
 
4.7%
5 13
 
2.0%
6 10
 
1.5%
7 10
 
1.5%
8 6
 
0.9%
9 3
 
0.5%
0 2
 
0.3%
Other Punctuation
ValueCountFrequency (%)
, 731
98.9%
. 8
 
1.1%
Space Separator
ValueCountFrequency (%)
1209
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3683
58.3%
Common 2625
41.6%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
판 550
14.9%
표 549
14.9%
지 548
14.9%
자 473
12.8%
의 469
12.7%
라 124
 
3.4%
파 123
 
3.3%
고 123
 
3.3%
조 114
 
3.1%
벤 70
 
1.9%
Other values (75) 540
14.7%
Common
ValueCountFrequency (%)
1209
46.1%
, 731
27.8%
1 453
 
17.3%
2 96
 
3.7%
3 40
 
1.5%
4 31
 
1.2%
5 13
 
0.5%
6 10
 
0.4%
7 10
 
0.4%
. 8
 
0.3%
Other values (6) 24
 
0.9%
Latin
ValueCountFrequency (%)
m 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3683
58.3%
ASCII 2631
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1209
46.0%
, 731
27.8%
1 453
 
17.2%
2 96
 
3.6%
3 40
 
1.5%
4 31
 
1.2%
5 13
 
0.5%
6 10
 
0.4%
7 10
 
0.4%
. 8
 
0.3%
Other values (7) 30
 
1.1%
Hangul
ValueCountFrequency (%)
판 550
14.9%
표 549
14.9%
지 548
14.9%
자 473
12.8%
의 469
12.7%
라 124
 
3.4%
파 123
 
3.3%
고 123
 
3.3%
조 114
 
3.1%
벤 70
 
1.9%
Other values (75) 540
14.7%

gugun
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
부산광역시 해운대구
252 
부산광역시 부산진구
70 
<NA>
61 
부산광역시 연제구
49 
부산광역시 중구
36 
Other values (12)
271 

Length

Max length10
Median length9
Mean length8.8714479
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 연제구
2nd row부산광역시 연제구
3rd row부산광역시 연제구
4th row부산광역시 연제구
5th row부산광역시 연제구

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 252
34.1%
부산광역시 부산진구 70
 
9.5%
<NA> 61
 
8.3%
부산광역시 연제구 49
 
6.6%
부산광역시 중구 36
 
4.9%
부산광역시 동래구 35
 
4.7%
부산광역시 사상구 35
 
4.7%
부산광역시 수영구 31
 
4.2%
부산광역시 동구 29
 
3.9%
부산광역시 영도구 28
 
3.8%
Other values (7) 113
15.3%

Length

2023-12-10T18:33:55.084492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 678
47.8%
해운대구 252
 
17.8%
부산진구 70
 
4.9%
na 61
 
4.3%
연제구 49
 
3.5%
중구 36
 
2.5%
동래구 35
 
2.5%
사상구 35
 
2.5%
수영구 31
 
2.2%
동구 29
 
2.0%
Other values (8) 141
 
10.0%

data_day
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2020-07-31
300 
2020-12-31
130 
2020-04-28
120 
<NA>
61 
2020-09-02
35 
Other values (5)
93 

Length

Max length10
Median length10
Mean length9.5047361
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-07-31
2nd row2020-07-31
3rd row2020-07-31
4th row2020-07-31
5th row2020-07-31

Common Values

ValueCountFrequency (%)
2020-07-31 300
40.6%
2020-12-31 130
17.6%
2020-04-28 120
 
16.2%
<NA> 61
 
8.3%
2020-09-02 35
 
4.7%
2020-08-30 31
 
4.2%
2020-09-09 29
 
3.9%
2020-08-26 25
 
3.4%
2020-09-01 6
 
0.8%
2020-08-29 2
 
0.3%

Length

2023-12-10T18:33:55.333635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:33:55.577765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-07-31 300
40.6%
2020-12-31 130
17.6%
2020-04-28 120
 
16.2%
na 61
 
8.3%
2020-09-02 35
 
4.7%
2020-08-30 31
 
4.2%
2020-09-09 29
 
3.9%
2020-08-26 25
 
3.4%
2020-09-01 6
 
0.8%
2020-08-29 2
 
0.3%

lat
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct644
Distinct (%)95.0%
Missing61
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean35.16139
Minimum35.055987
Maximum35.323051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-10T18:33:55.846126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.055987
5-th percentile35.091955
Q135.148994
median35.162251
Q335.175409
95-th percentile35.225679
Maximum35.323051
Range0.267064
Interquartile range (IQR)0.026414067

Descriptive statistics

Standard deviation0.038963939
Coefficient of variation (CV)0.0011081456
Kurtosis1.6756064
Mean35.16139
Median Absolute Deviation (MAD)0.013227793
Skewness0.33209716
Sum23839.423
Variance0.0015181885
MonotonicityNot monotonic
2023-12-10T18:33:56.175472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.089173 3
 
0.4%
35.19171428 3
 
0.4%
35.1917142826 3
 
0.4%
35.164138 2
 
0.3%
35.161746 2
 
0.3%
35.160376 2
 
0.3%
35.160308 2
 
0.3%
35.160983 2
 
0.3%
35.16425677 2
 
0.3%
35.160218875 2
 
0.3%
Other values (634) 655
88.6%
(Missing) 61
 
8.3%
ValueCountFrequency (%)
35.055987 1
0.1%
35.068894 1
0.1%
35.07002 1
0.1%
35.075785 1
0.1%
35.0769527145 1
0.1%
35.077174 1
0.1%
35.077645 1
0.1%
35.077728 1
0.1%
35.078 1
0.1%
35.078406 1
0.1%
ValueCountFrequency (%)
35.323051 1
0.1%
35.322046 1
0.1%
35.320689 1
0.1%
35.319731 1
0.1%
35.291968 1
0.1%
35.274223 1
0.1%
35.272887 1
0.1%
35.271774 1
0.1%
35.260467 1
0.1%
35.260092 1
0.1%

lng
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct638
Distinct (%)94.1%
Missing61
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean129.09379
Minimum128.84025
Maximum129.99811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-12-10T18:33:56.490757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.84025
5-th percentile128.98426
Q1129.05225
median129.09055
Q3129.14649
95-th percentile129.17642
Maximum129.99811
Range1.157859
Interquartile range (IQR)0.09424107

Descriptive statistics

Standard deviation0.071423173
Coefficient of variation (CV)0.00055326574
Kurtosis37.112708
Mean129.09379
Median Absolute Deviation (MAD)0.046845
Skewness2.6654982
Sum87525.592
Variance0.0051012696
MonotonicityNot monotonic
2023-12-10T18:33:56.804181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.044506 3
 
0.4%
129.113 3
 
0.4%
129.1538661 3
 
0.4%
129.1538660862 3
 
0.4%
129.115 3
 
0.4%
129.176884 2
 
0.3%
129.1652746732 2
 
0.3%
129.162337 2
 
0.3%
129.169664 2
 
0.3%
129.176327 2
 
0.3%
Other values (628) 653
88.4%
(Missing) 61
 
8.3%
ValueCountFrequency (%)
128.840253 1
0.1%
128.842489 1
0.1%
128.900708 1
0.1%
128.902286 1
0.1%
128.904052 1
0.1%
128.907186 1
0.1%
128.948028 1
0.1%
128.960393 1
0.1%
128.961516 1
0.1%
128.963118 1
0.1%
ValueCountFrequency (%)
129.998112 1
0.1%
129.2289 1
0.1%
129.22858 1
0.1%
129.21979 1
0.1%
129.21905 1
0.1%
129.21802 1
0.1%
129.21584 1
0.1%
129.21388 1
0.1%
129.21331 1
0.1%
129.21278 1
0.1%

apr_at
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
<NA>
574 
N
130 
 
35

Length

Max length4
Median length4
Mean length3.3301759
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 574
77.7%
N 130
 
17.6%
35
 
4.7%

Length

2023-12-10T18:33:57.135561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:33:57.338182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 574
81.5%
n 130
 
18.5%

last_load_dttm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
2021-01-05 16:57:38
678 
<NA>
 
61

Length

Max length19
Median length19
Mean length17.76184
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-05 16:57:38
2nd row2021-01-05 16:57:38
3rd row2021-01-05 16:57:38
4th row2021-01-05 16:57:38
5th row2021-01-05 16:57:38

Common Values

ValueCountFrequency (%)
2021-01-05 16:57:38 678
91.7%
<NA> 61
 
8.3%

Length

2023-12-10T18:33:57.548803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:33:57.757995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-05 678
47.8%
16:57:38 678
47.8%
na 61
 
4.3%

Interactions

2023-12-10T18:33:36.219025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:33:34.947034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:33:35.638641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:33:36.409243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:33:35.208383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:33:35.850666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:33:36.584977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:33:35.436894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:33:36.044162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:33:57.891356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
instt_codepbl_plpbl_locgugundata_daylatlngapr_at
instt_code1.0000.1350.9721.0000.9870.8770.9171.000
pbl_pl0.1351.0000.7270.1550.0000.0000.0000.000
pbl_loc0.9720.7271.0000.9770.9670.8740.6911.000
gugun1.0000.1550.9771.0000.9670.8760.9211.000
data_day0.9870.0000.9670.9671.0000.7240.7781.000
lat0.8770.0000.8740.8760.7241.0000.6660.779
lng0.9170.0000.6910.9210.7780.6661.0001.000
apr_at1.0000.0001.0001.0001.0000.7791.0001.000
2023-12-10T18:33:58.501786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
data_daylast_load_dttminstt_codegugunapr_at
data_day1.0001.0000.9280.8580.997
last_load_dttm1.0001.0001.0001.0001.000
instt_code0.9281.0001.0000.9990.997
gugun0.8581.0000.9991.0000.997
apr_at0.9971.0000.9970.9971.000
2023-12-10T18:33:58.683413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
pbl_pllatlnginstt_codegugundata_dayapr_atlast_load_dttm
pbl_pl1.000-0.0330.0300.0630.0740.0000.0001.000
lat-0.0331.0000.3950.5920.5940.4370.8311.000
lng0.0300.3951.0000.7730.7740.5980.9941.000
instt_code0.0630.5920.7731.0000.9990.9280.9971.000
gugun0.0740.5940.7740.9991.0000.8580.9971.000
data_day0.0000.4370.5980.9280.8581.0000.9971.000
apr_at0.0000.8310.9940.9970.9970.9971.0001.000
last_load_dttm1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T18:33:36.888265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:33:37.385669image/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.
2023-12-10T18:33:38.240761image/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

skeyinstt_codebild_nmpbl_areaaddr_roadaddr_jibunprmt_dateaprv_dateareanmb_floorspurposepbl_plpbl_locpbl_amngugundata_daylatlngapr_atlast_load_dttm
048453370000홈플러스12,008.98부산 연제구 종합운동장로 7부산 연제구 거제동 1208 외2001-05-182003-03-2464,516.70지하2/지상1판매시설 운동시설 문화및집회2전면의자, 파고라, 표지판 1부산광역시 연제구2020-07-3135.191345129.064287<NA>2021-01-05 16:57:38
148463370000국민연금공단사옥863.02부산 연제구 중앙대로 1000부산 연제구 연산동 1422-82000-07-212004-12-2943,431.70지하4/지상22업무시설1전면의자, 조명, 표지판 1부산광역시 연제구2020-07-3135.177789129.075644<NA>2021-01-05 16:57:38
248473370000연제우체국394.33부산 연제구 법원북로 33부산 연제구 거제동 14732002-06-262005-03-1816,519.20지하1/지상9공공업무1측면의자 8, 조명, 분수 1, 표지판 1부산광역시 연제구2020-07-3135.19412129.070602<NA>2021-01-05 16:57:38
348483370000킴스힐타워56.84부산 연제구 중앙대로1054번길 27부산 연제구 연산동 1305-122013-10-182015-10-154,983.30지하1/지상23공동주택1전면의자 2, 표지판 1부산광역시 연제구2020-07-3135.182188129.080505<NA>2021-01-05 16:57:38
448493370000㈜부원 사옥68.02부산 연제구 중앙대로 1117부산 연제구 연산동 1124-71993-06-301997-07-0710,530.00지하2/지상13업무시설2전면의자 4, 표지판 1부산광역시 연제구2020-07-3135.187252129.080905<NA>2021-01-05 16:57:38
548503370000연산동 더라임시티뷰75.84부산광역시 연제구 중앙대로1066번길 10부산광역시 연제구 연산동 704-9번지 외2필지2017-11-142019-08-304173.44지하1/지상20공동주택, 업무시설2전면의자4, 표지판1부산광역시 연제구2020-07-3135.182813129.077695<NA>2021-01-05 16:57:38
648513370000지성캐슬51.98부산광역시 연제구 월드컵대로 153번길 64부산광역시 연제구 연산동 1299-22018-03-142019-09-222753.44지하1/지상15공동주택, 업무시설1전면의자2, 표지판1부산광역시 연제구2020-07-3135.184302129.07546<NA>2021-01-05 16:57:38
749533280000더킹페로스아파트192.94부산광역시 영도구 절영로 48부산광역시 영도구 남항동1가 862017-01-262019-05-1713247.06지하1/지상20층공동주택4부산광역시 영도구 남항동1가 86의자, 표지판1부산광역시 영도구2020-07-3135.09061129.038416<NA>2021-01-05 16:57:38
849543280000로웰타워57.64부산광역시 영도구 봉래나루로 48부산광역시 영도구 대교동1가 64-12017-06-212019-07-035762.067지하1층/지상20층공동주택1부산광역시 영도구 대교동1가 64-1의자, 표지판1부산광역시 영도구2020-07-3135.09337129.038162<NA>2021-01-05 16:57:38
949553280000신화더하니엘 더마린288.79부산광역시 여도구 절영로94번길 33부산광역시 영도구 영선동4가 1-22017-10-312019-11-268453.566지하1/지상15층업무시설1부산광역시 영도구 영선동4가 1-2의자, 표지판1부산광역시 영도구2020-07-3135.084928129.039328<NA>2021-01-05 16:57:38
skeyinstt_codebild_nmpbl_areaaddr_roadaddr_jibunprmt_dateaprv_dateareanmb_floorspurposepbl_plpbl_locpbl_amngugundata_daylatlngapr_atlast_load_dttm
72955033330000해운대롯데캐슬비치 101동774.84부산광역시 해운대구 달맞이길 41부산광역시 해운대구 중동1775-12000-02-082003-01-2247,663.53지하2/지상32업무시설2지상의자, 표지판 1부산광역시 해운대구2020-12-3135.162752129.169543N2021-01-05 16:57:38
73055043330000웰비치249.28부산광역시 해운대구 좌동순환로 503부산광역시 해운대구 중동 1768-42002-03-042003-09-3013,984.42지하3/지상15업무시설근생2지상의자, 표지판 1부산광역시 해운대구2020-12-3135.16537129.168382N2021-01-05 16:57:38
73155053330000경동윈츠타워 오피스텔123부산광역시 해운대구 양운로 59부산광역시 해운대구 좌동 1473-62002-03-052003-10-2418,323.91지하2/지상25업무시설근생1지상의자, 표지판 1부산광역시 해운대구2020-12-3135.168493129.176017N2021-01-05 16:57:38
73255063330000디베르비타168.24부산광역시 해운대구 좌동로 98부산광역시 해운대구 좌동 1473-62002-02-022003-12-1614,308.47지하2/지상24업무시설 근생1지상조형물, 표지판 1부산광역시 해운대구2020-12-3135.172419129.175911N2021-01-05 16:57:38
73355073330000두산위브센티움152.05부산광역시 해운대구 양운로 55부산광역시 해운대구 좌동 1475-12002-04-172004-04-3015,005.28지하4/지상15업무시설2지상의자, 표지판 1부산광역시 해운대구2020-12-3135.16822129.176327N2021-01-05 16:57:38
73455083330000대림아크로텔380부산광역시 해운대구 해운대로 790부산광역시 해운대구 좌동 1473-1 외2002-04-032004-12-2744,365.34지하3/지상27업무시설1지상의자, 파고라, 표지판 1부산광역시 해운대구2020-12-3135.168538129.175327N2021-01-05 16:57:38
73555093330000좌동SK허브올리브297부산광역시 해운대구 양운로 56부산광역시 해운대구 좌동 1478-12002-06-192005-02-0218,436.25지하4/지상18업무시설2지상표지판 1부산광역시 해운대구2020-12-3135.16849129.176884N2021-01-05 16:57:38
73655103330000쌍용플래티넘트윈263부산광역시 해운대구 양운로 88부산광역시 해운대구 좌동 1478-22002-05-182005-03-1739,182.81지하6/지상22업무시설1지상의자, 표지판 1부산광역시 해운대구2020-12-3135.171018129.175245N2021-01-05 16:57:38
73755113330000벡스코12,500.00부산광역시 해운대구 APEC로 55부산광역시 해운대구 우동 15001998-06-022006-08-2192,786.19지하1/지상7문화및집회1지상의자, 표지판 1부산광역시 해운대구2020-12-3135.169078129.136021N2021-01-05 16:57:38
73855123330000현대베네시티2,849.94부산광역시 해운대구 해운대해변로 163부산광역시 해운대구 우동 14321997-10-232005-06-28143,479.19지상36, 4동공동주택1지상표지판 1부산광역시 해운대구2020-12-3135.158478129.150443N2021-01-05 16:57:38

Duplicate rows

Most frequently occurring

skeyinstt_codebild_nmpbl_areaaddr_roadaddr_jibunprmt_dateaprv_dateareanmb_floorspurposepbl_plpbl_locpbl_amngugundata_daylatlngapr_atlast_load_dttm# duplicates
0판매시설<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2