Overview

Dataset statistics

Number of variables20
Number of observations766
Missing cells146
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory122.1 KiB
Average record size in memory163.2 B

Variable types

Text12
Categorical6
Numeric2

Alerts

data_day is highly overall correlated with instt_code and 2 other fieldsHigh correlation
last_load_dttm is highly overall correlated with lat 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 4 other fieldsHigh correlation
apr_at is highly overall correlated with instt_code and 1 other fieldsHigh correlation
pbl_pl is highly overall correlated with last_load_dttmHigh correlation
lat is highly overall correlated with instt_code and 2 other fieldsHigh correlation
lng is highly overall correlated with instt_code and 2 other fieldsHigh correlation
pbl_pl is highly imbalanced (54.2%)Imbalance
data_day is highly imbalanced (56.8%)Imbalance
last_load_dttm is highly imbalanced (91.6%)Imbalance
pbl_loc has 96 (12.5%) missing valuesMissing
pbl_amn has 23 (3.0%) missing valuesMissing
lat has 8 (1.0%) missing valuesMissing
lng has 8 (1.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 09:32:07.199423
Analysis finished2023-12-10 09:32:11.667724
Duration4.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Text

Distinct764
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2023-12-10T18:32:12.198349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9895561
Min length2

Characters and Unicode

Total characters3056
Distinct characters13
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

Unique763 ?
Unique (%)99.6%

Sample

1st row5646
2nd row5647
3rd row5648
4th row5649
5th row5650
ValueCountFrequency (%)
근생 3
 
0.4%
6103 1
 
0.1%
6104 1
 
0.1%
5724 1
 
0.1%
5725 1
 
0.1%
5726 1
 
0.1%
5840 1
 
0.1%
5841 1
 
0.1%
5842 1
 
0.1%
5843 1
 
0.1%
Other values (754) 754
98.4%
2023-12-10T18:32:13.025061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 875
28.6%
6 370
12.1%
1 344
 
11.3%
7 251
 
8.2%
8 248
 
8.1%
9 243
 
8.0%
0 224
 
7.3%
4 178
 
5.8%
2 168
 
5.5%
3 148
 
4.8%
Other values (3) 7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3049
99.8%
Other Letter 7
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 875
28.7%
6 370
12.1%
1 344
 
11.3%
7 251
 
8.2%
8 248
 
8.1%
9 243
 
8.0%
0 224
 
7.3%
4 178
 
5.8%
2 168
 
5.5%
3 148
 
4.9%
Other Letter
ValueCountFrequency (%)
근 3
42.9%
생 3
42.9%
동 1
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3049
99.8%
Hangul 7
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 875
28.7%
6 370
12.1%
1 344
 
11.3%
7 251
 
8.2%
8 248
 
8.1%
9 243
 
8.0%
0 224
 
7.3%
4 178
 
5.8%
2 168
 
5.5%
3 148
 
4.9%
Hangul
ValueCountFrequency (%)
근 3
42.9%
생 3
42.9%
동 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3049
99.8%
Hangul 7
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 875
28.7%
6 370
12.1%
1 344
 
11.3%
7 251
 
8.2%
8 248
 
8.1%
9 243
 
8.0%
0 224
 
7.3%
4 178
 
5.8%
2 168
 
5.5%
3 148
 
4.9%
Hangul
ValueCountFrequency (%)
근 3
42.9%
생 3
42.9%
동 1
 
14.3%

instt_code
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
3330000
130 
3330000
126 
3270000
66 
3370000
50 
3290000
47 
Other values (16)
347 

Length

Max length20
Median length20
Mean length17.275457
Min length1

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row3270000
2nd row3270000
3rd row3270000
4th row3270000
5th row3270000

Common Values

ValueCountFrequency (%)
3330000 130
17.0%
3330000 126
16.4%
3270000 66
 
8.6%
3370000 50
 
6.5%
3290000 47
 
6.1%
3390000 40
 
5.2%
3300000 38
 
5.0%
3250000 37
 
4.8%
3380000 35
 
4.6%
3350000 31
 
4.0%
Other values (11) 166
21.7%

Length

2023-12-10T18:32:13.264752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3330000 256
33.4%
3270000 95
 
12.4%
3370000 50
 
6.5%
3290000 47
 
6.1%
3390000 40
 
5.2%
3300000 38
 
5.0%
3250000 37
 
4.8%
3380000 35
 
4.6%
3350000 31
 
4.0%
3280000 28
 
3.7%
Other values (9) 109
14.2%

bild_nm
Text

Distinct566
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2023-12-10T18:32:13.703225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length17
Mean length7.3616188
Min length1

Characters and Unicode

Total characters5639
Distinct characters415
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

Unique403 ?
Unique (%)52.6%

Sample

1st row부산은행별관
2nd row마제스타워
3rd row현대해상부산사옥
4th rowLIG손해보험부산사옥
5th row봄여름가을겨울아파트
ValueCountFrequency (%)
오피스텔 15
 
1.4%
해운대 14
 
1.3%
부산역 9
 
0.9%
이마트 6
 
0.6%
더 6
 
0.6%
봄여름가을겨울 6
 
0.6%
유림 6
 
0.6%
호텔 6
 
0.6%
더샵 5
 
0.5%
부산항 5
 
0.5%
Other values (671) 961
92.5%
2023-12-10T18:32:14.415880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
277
 
4.9%
스 219
 
3.9%
이 118
 
2.1%
리 114
 
2.0%
산 107
 
1.9%
부 93
 
1.6%
트 85
 
1.5%
아 84
 
1.5%
대 82
 
1.5%
텔 79
 
1.4%
Other values (405) 4381
77.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5000
88.7%
Space Separator 277
 
4.9%
Uppercase Letter 207
 
3.7%
Decimal Number 55
 
1.0%
Open Punctuation 25
 
0.4%
Close Punctuation 25
 
0.4%
Lowercase Letter 23
 
0.4%
Other Symbol 12
 
0.2%
Other Punctuation 8
 
0.1%
Dash Punctuation 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 219
 
4.4%
이 118
 
2.4%
리 114
 
2.3%
산 107
 
2.1%
부 93
 
1.9%
트 85
 
1.7%
아 84
 
1.7%
대 82
 
1.6%
텔 79
 
1.6%
타 72
 
1.4%
Other values (359) 3947
78.9%
Uppercase Letter
ValueCountFrequency (%)
T 18
 
8.7%
A 16
 
7.7%
K 15
 
7.2%
L 15
 
7.2%
C 15
 
7.2%
S 15
 
7.2%
E 15
 
7.2%
W 12
 
5.8%
O 11
 
5.3%
N 11
 
5.3%
Other values (13) 64
30.9%
Decimal Number
ValueCountFrequency (%)
2 29
52.7%
3 9
 
16.4%
1 8
 
14.5%
0 2
 
3.6%
4 2
 
3.6%
6 2
 
3.6%
5 2
 
3.6%
7 1
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
e 11
47.8%
i 5
21.7%
b 2
 
8.7%
s 2
 
8.7%
w 2
 
8.7%
v 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 5
62.5%
& 2
 
25.0%
/ 1
 
12.5%
Space Separator
ValueCountFrequency (%)
277
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5012
88.9%
Common 396
 
7.0%
Latin 231
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 219
 
4.4%
이 118
 
2.4%
리 114
 
2.3%
산 107
 
2.1%
부 93
 
1.9%
트 85
 
1.7%
아 84
 
1.7%
대 82
 
1.6%
텔 79
 
1.6%
타 72
 
1.4%
Other values (360) 3959
79.0%
Latin
ValueCountFrequency (%)
T 18
 
7.8%
A 16
 
6.9%
K 15
 
6.5%
L 15
 
6.5%
C 15
 
6.5%
S 15
 
6.5%
E 15
 
6.5%
W 12
 
5.2%
O 11
 
4.8%
N 11
 
4.8%
Other values (20) 88
38.1%
Common
ValueCountFrequency (%)
277
69.9%
2 29
 
7.3%
( 25
 
6.3%
) 25
 
6.3%
3 9
 
2.3%
1 8
 
2.0%
- 6
 
1.5%
. 5
 
1.3%
0 2
 
0.5%
4 2
 
0.5%
Other values (5) 8
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5000
88.7%
ASCII 626
 
11.1%
None 12
 
0.2%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
277
44.2%
2 29
 
4.6%
( 25
 
4.0%
) 25
 
4.0%
T 18
 
2.9%
A 16
 
2.6%
K 15
 
2.4%
L 15
 
2.4%
C 15
 
2.4%
S 15
 
2.4%
Other values (34) 176
28.1%
Hangul
ValueCountFrequency (%)
스 219
 
4.4%
이 118
 
2.4%
리 114
 
2.3%
산 107
 
2.1%
부 93
 
1.9%
트 85
 
1.7%
아 84
 
1.7%
대 82
 
1.6%
텔 79
 
1.6%
타 72
 
1.4%
Other values (359) 3947
78.9%
None
ValueCountFrequency (%)
㈜ 12
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

pbl_area
Text

Distinct585
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2023-12-10T18:32:15.075107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.7937337
Min length2

Characters and Unicode

Total characters4438
Distinct characters22
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

Unique437 ?
Unique (%)57.0%

Sample

1st row67.17
2nd row495.3
3rd row226.35
4th row678.84
5th row172.38
ValueCountFrequency (%)
666.56 4
 
0.5%
111.17 4
 
0.5%
46.5 3
 
0.4%
134.85 3
 
0.4%
137.79 3
 
0.4%
80.2 3
 
0.4%
135.42 3
 
0.4%
721.47 3
 
0.4%
495.3 3
 
0.4%
160.86 3
 
0.4%
Other values (567) 740
95.9%
2023-12-10T18:32:16.016303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 709
16.0%
1 561
12.6%
2 402
9.1%
6 349
7.9%
4 345
7.8%
5 339
7.6%
3 337
7.6%
9 329
7.4%
7 317
7.1%
0 312
7.0%
Other values (12) 438
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3590
80.9%
Other Punctuation 807
 
18.2%
Space Separator 23
 
0.5%
Other Letter 18
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 561
15.6%
2 402
11.2%
6 349
9.7%
4 345
9.6%
5 339
9.4%
3 337
9.4%
9 329
9.2%
7 317
8.8%
0 312
8.7%
8 299
8.3%
Other Letter
ValueCountFrequency (%)
지 4
22.2%
표 3
16.7%
판 3
16.7%
의 2
11.1%
자 2
11.1%
상 1
 
5.6%
조 1
 
5.6%
형 1
 
5.6%
물 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 709
87.9%
, 98
 
12.1%
Space Separator
ValueCountFrequency (%)
23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4420
99.6%
Hangul 18
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
. 709
16.0%
1 561
12.7%
2 402
9.1%
6 349
7.9%
4 345
7.8%
5 339
7.7%
3 337
7.6%
9 329
7.4%
7 317
7.2%
0 312
7.1%
Other values (3) 420
9.5%
Hangul
ValueCountFrequency (%)
지 4
22.2%
표 3
16.7%
판 3
16.7%
의 2
11.1%
자 2
11.1%
상 1
 
5.6%
조 1
 
5.6%
형 1
 
5.6%
물 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4420
99.6%
Hangul 18
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 709
16.0%
1 561
12.7%
2 402
9.1%
6 349
7.9%
4 345
7.8%
5 339
7.7%
3 337
7.6%
9 329
7.4%
7 317
7.2%
0 312
7.1%
Other values (3) 420
9.5%
Hangul
ValueCountFrequency (%)
지 4
22.2%
표 3
16.7%
판 3
16.7%
의 2
11.1%
자 2
11.1%
상 1
 
5.6%
조 1
 
5.6%
형 1
 
5.6%
물 1
 
5.6%

addr_road
Text

Distinct572
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2023-12-10T18:32:16.694664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length33
Mean length19.169713
Min length5

Characters and Unicode

Total characters14684
Distinct characters211
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

Unique412 ?
Unique (%)53.8%

Sample

1st row부산광역시 동구 범일로 85
2nd row부산광역시 동구 자성로133번길 6
3rd row부산광역시 동구 중앙대로 240
4th row부산광역시 동구 자성로133번길 15
5th row부산광역시 동구 자성로133번길 46
ValueCountFrequency (%)
부산광역시 720
23.5%
해운대구 259
 
8.4%
동구 92
 
3.0%
중앙대로 53
 
1.7%
연제구 50
 
1.6%
부산진구 47
 
1.5%
부산 45
 
1.5%
사상구 40
 
1.3%
동래구 38
 
1.2%
중구 37
 
1.2%
Other values (636) 1689
55.0%
2023-12-10T18:32:17.894496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2349
 
16.0%
부 819
 
5.6%
산 816
 
5.6%
광 770
 
5.2%
시 766
 
5.2%
구 766
 
5.2%
로 751
 
5.1%
역 721
 
4.9%
대 552
 
3.8%
1 477
 
3.2%
Other values (201) 5897
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9734
66.3%
Decimal Number 2431
 
16.6%
Space Separator 2349
 
16.0%
Dash Punctuation 54
 
0.4%
Close Punctuation 42
 
0.3%
Open Punctuation 42
 
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 819
 
8.4%
산 816
 
8.4%
광 770
 
7.9%
시 766
 
7.9%
구 766
 
7.9%
로 751
 
7.7%
역 721
 
7.4%
대 552
 
5.7%
해 416
 
4.3%
운 363
 
3.7%
Other values (180) 2994
30.8%
Decimal Number
ValueCountFrequency (%)
1 477
19.6%
2 335
13.8%
3 286
11.8%
7 224
9.2%
4 223
9.2%
5 215
8.8%
6 193
7.9%
9 180
 
7.4%
0 174
 
7.2%
8 124
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
C 4
23.5%
E 4
23.5%
P 4
23.5%
A 4
23.5%
V 1
 
5.9%
Space Separator
ValueCountFrequency (%)
2349
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9734
66.3%
Common 4931
33.6%
Latin 19
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 819
 
8.4%
산 816
 
8.4%
광 770
 
7.9%
시 766
 
7.9%
구 766
 
7.9%
로 751
 
7.7%
역 721
 
7.4%
대 552
 
5.7%
해 416
 
4.3%
운 363
 
3.7%
Other values (180) 2994
30.8%
Common
ValueCountFrequency (%)
2349
47.6%
1 477
 
9.7%
2 335
 
6.8%
3 286
 
5.8%
7 224
 
4.5%
4 223
 
4.5%
5 215
 
4.4%
6 193
 
3.9%
9 180
 
3.7%
0 174
 
3.5%
Other values (5) 275
 
5.6%
Latin
ValueCountFrequency (%)
C 4
21.1%
E 4
21.1%
P 4
21.1%
A 4
21.1%
Ⅱ 2
10.5%
V 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9734
66.3%
ASCII 4948
33.7%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2349
47.5%
1 477
 
9.6%
2 335
 
6.8%
3 286
 
5.8%
7 224
 
4.5%
4 223
 
4.5%
5 215
 
4.3%
6 193
 
3.9%
9 180
 
3.6%
0 174
 
3.5%
Other values (10) 292
 
5.9%
Hangul
ValueCountFrequency (%)
부 819
 
8.4%
산 816
 
8.4%
광 770
 
7.9%
시 766
 
7.9%
구 766
 
7.9%
로 751
 
7.7%
역 721
 
7.4%
대 552
 
5.7%
해 416
 
4.3%
운 363
 
3.7%
Other values (180) 2994
30.8%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%

addr_jibun
Text

Distinct573
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2023-12-10T18:32:18.381346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length19.381201
Min length10

Characters and Unicode

Total characters14846
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

Unique414 ?
Unique (%)54.0%

Sample

1st row부산광역시 동구 범일동 937-3 외
2nd row부산광역시 동구 범일동 830-296
3rd row부산광역시 동구 초량동 1193-5
4th row부산광역시 동구 범일동 830-30
5th row부산광역시 동구 범일동 830-77
ValueCountFrequency (%)
부산광역시 719
22.7%
해운대구 257
 
8.1%
우동 133
 
4.2%
동구 95
 
3.0%
외 70
 
2.2%
초량동 57
 
1.8%
중동 56
 
1.8%
연제구 50
 
1.6%
부산진구 47
 
1.5%
부산 44
 
1.4%
Other values (693) 1645
51.8%
2023-12-10T18:32:19.230179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2425
16.3%
동 885
 
6.0%
1 863
 
5.8%
산 852
 
5.7%
부 840
 
5.7%
구 762
 
5.1%
광 741
 
5.0%
시 722
 
4.9%
역 719
 
4.8%
- 582
 
3.9%
Other values (112) 5455
36.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8446
56.9%
Decimal Number 3392
22.8%
Space Separator 2425
 
16.3%
Dash Punctuation 582
 
3.9%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 885
10.5%
산 852
10.1%
부 840
 
9.9%
구 762
 
9.0%
광 741
 
8.8%
시 722
 
8.5%
역 719
 
8.5%
대 297
 
3.5%
해 257
 
3.0%
운 257
 
3.0%
Other values (99) 2114
25.0%
Decimal Number
ValueCountFrequency (%)
1 863
25.4%
4 394
11.6%
2 356
10.5%
5 327
 
9.6%
3 323
 
9.5%
6 259
 
7.6%
7 249
 
7.3%
8 224
 
6.6%
0 215
 
6.3%
9 182
 
5.4%
Space Separator
ValueCountFrequency (%)
2425
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 582
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8446
56.9%
Common 6400
43.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 885
10.5%
산 852
10.1%
부 840
 
9.9%
구 762
 
9.0%
광 741
 
8.8%
시 722
 
8.5%
역 719
 
8.5%
대 297
 
3.5%
해 257
 
3.0%
운 257
 
3.0%
Other values (99) 2114
25.0%
Common
ValueCountFrequency (%)
2425
37.9%
1 863
 
13.5%
- 582
 
9.1%
4 394
 
6.2%
2 356
 
5.6%
5 327
 
5.1%
3 323
 
5.0%
6 259
 
4.0%
7 249
 
3.9%
8 224
 
3.5%
Other values (3) 398
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8446
56.9%
ASCII 6400
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2425
37.9%
1 863
 
13.5%
- 582
 
9.1%
4 394
 
6.2%
2 356
 
5.6%
5 327
 
5.1%
3 323
 
5.0%
6 259
 
4.0%
7 249
 
3.9%
8 224
 
3.5%
Other values (3) 398
 
6.2%
Hangul
ValueCountFrequency (%)
동 885
10.5%
산 852
10.1%
부 840
 
9.9%
구 762
 
9.0%
광 741
 
8.8%
시 722
 
8.5%
역 719
 
8.5%
대 297
 
3.5%
해 257
 
3.0%
운 257
 
3.0%
Other values (99) 2114
25.0%

prmt_date
Text

Distinct535
Distinct (%)69.9%
Missing1
Missing (%)0.1%
Memory size6.1 KiB
2023-12-10T18:32:19.727155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.9986928
Min length9

Characters and Unicode

Total characters7649
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

Unique360 ?
Unique (%)47.1%

Sample

1st row2003-01-27
2nd row1995-04-25
3rd row2010-12-28
4th row2008-12-22
5th row2011-05-30
ValueCountFrequency (%)
2003-01-07 8
 
1.0%
2017-11-15 5
 
0.7%
2009-01-14 4
 
0.5%
2012-03-08 4
 
0.5%
2003-03-20 4
 
0.5%
2011-10-20 4
 
0.5%
2002-04-17 4
 
0.5%
2014-04-02 4
 
0.5%
2005-10-21 3
 
0.4%
2012-08-24 3
 
0.4%
Other values (523) 722
94.4%
2023-12-10T18:32:20.529579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1899
24.8%
- 1526
20.0%
2 1258
16.4%
1 1206
15.8%
9 349
 
4.6%
3 288
 
3.8%
4 250
 
3.3%
5 242
 
3.2%
6 227
 
3.0%
8 201
 
2.6%
Other values (2) 203
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6120
80.0%
Dash Punctuation 1526
 
20.0%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1899
31.0%
2 1258
20.6%
1 1206
19.7%
9 349
 
5.7%
3 288
 
4.7%
4 250
 
4.1%
5 242
 
4.0%
6 227
 
3.7%
8 201
 
3.3%
7 200
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 1526
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1899
24.8%
- 1526
20.0%
2 1258
16.4%
1 1206
15.8%
9 349
 
4.6%
3 288
 
3.8%
4 250
 
3.3%
5 242
 
3.2%
6 227
 
3.0%
8 201
 
2.6%
Other values (2) 203
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1899
24.8%
- 1526
20.0%
2 1258
16.4%
1 1206
15.8%
9 349
 
4.6%
3 288
 
3.8%
4 250
 
3.3%
5 242
 
3.2%
6 227
 
3.0%
8 201
 
2.6%
Other values (2) 203
 
2.7%

aprv_date
Text

Distinct539
Distinct (%)70.5%
Missing2
Missing (%)0.3%
Memory size6.1 KiB
2023-12-10T18:32:21.068916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9986911
Min length9

Characters and Unicode

Total characters7639
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

Unique360 ?
Unique (%)47.1%

Sample

1st row2009-07-07
2nd row2010-11-09
3rd row2013-05-24
4th row2011-01-28
5th row2013-11-18
ValueCountFrequency (%)
2006-09-22 6
 
0.8%
2014-05-27 6
 
0.8%
2013-05-24 4
 
0.5%
2019-04-12 4
 
0.5%
2019-03-15 4
 
0.5%
2020-07-20 4
 
0.5%
2016-11-11 3
 
0.4%
2009-06-01 3
 
0.4%
2009-07-07 3
 
0.4%
2005-02-24 3
 
0.4%
Other values (529) 724
94.8%
2023-12-10T18:32:21.862830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1872
24.5%
- 1520
19.9%
2 1309
17.1%
1 1185
15.5%
9 327
 
4.3%
3 268
 
3.5%
6 237
 
3.1%
7 232
 
3.0%
8 232
 
3.0%
5 230
 
3.0%
Other values (2) 227
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6115
80.0%
Dash Punctuation 1520
 
19.9%
Other Punctuation 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1872
30.6%
2 1309
21.4%
1 1185
19.4%
9 327
 
5.3%
3 268
 
4.4%
6 237
 
3.9%
7 232
 
3.8%
8 232
 
3.8%
5 230
 
3.8%
4 223
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 1520
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7639
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1872
24.5%
- 1520
19.9%
2 1309
17.1%
1 1185
15.5%
9 327
 
4.3%
3 268
 
3.5%
6 237
 
3.1%
7 232
 
3.0%
8 232
 
3.0%
5 230
 
3.0%
Other values (2) 227
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1872
24.5%
- 1520
19.9%
2 1309
17.1%
1 1185
15.5%
9 327
 
4.3%
3 268
 
3.5%
6 237
 
3.1%
7 232
 
3.0%
8 232
 
3.0%
5 230
 
3.0%
Other values (2) 227
 
3.0%

area
Text

Distinct581
Distinct (%)76.1%
Missing3
Missing (%)0.4%
Memory size6.1 KiB
2023-12-10T18:32:22.448956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.1769332
Min length3

Characters and Unicode

Total characters6239
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

Unique432 ?
Unique (%)56.6%

Sample

1st row6862.4
2nd row84860.5
3rd row26341.5
4th row24205.6
5th row13690.1
ValueCountFrequency (%)
21315.8 6
 
0.8%
121,051.40 4
 
0.5%
1623.5 3
 
0.4%
6862.4 3
 
0.4%
21934.997 3
 
0.4%
8143.9 3
 
0.4%
7788.6 3
 
0.4%
6140 3
 
0.4%
12488.6 3
 
0.4%
14582.2 3
 
0.4%
Other values (563) 729
95.5%
2023-12-10T18:32:23.540364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 733
11.7%
. 731
11.7%
1 639
10.2%
2 485
7.8%
, 480
7.7%
6 471
7.5%
8 466
7.5%
9 461
7.4%
3 459
7.4%
5 453
7.3%
Other values (3) 861
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5020
80.5%
Other Punctuation 1211
 
19.4%
Space Separator 8
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 733
14.6%
1 639
12.7%
2 485
9.7%
6 471
9.4%
8 466
9.3%
9 461
9.2%
3 459
9.1%
5 453
9.0%
4 451
9.0%
7 402
8.0%
Other Punctuation
ValueCountFrequency (%)
. 731
60.4%
, 480
39.6%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 733
11.7%
. 731
11.7%
1 639
10.2%
2 485
7.8%
, 480
7.7%
6 471
7.5%
8 466
7.5%
9 461
7.4%
3 459
7.4%
5 453
7.3%
Other values (3) 861
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 733
11.7%
. 731
11.7%
1 639
10.2%
2 485
7.8%
, 480
7.7%
6 471
7.5%
8 466
7.5%
9 461
7.4%
3 459
7.4%
5 453
7.3%
Other values (3) 861
13.8%

nmb_floors
Text

Distinct280
Distinct (%)36.6%
Missing1
Missing (%)0.1%
Memory size6.1 KiB
2023-12-10T18:32:24.184275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length8
Mean length8.1816993
Min length3

Characters and Unicode

Total characters6259
Distinct characters25
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

Unique128 ?
Unique (%)16.7%

Sample

1st row지하2/지상10
2nd row지하5/지상40 3동
3rd row지하5/지상15
4th row지하3/지상15
5th row지하3/지상23
ValueCountFrequency (%)
지하1/지상15 35
 
4.5%
지하1/지상20 33
 
4.2%
지하1/지상5 19
 
2.4%
지하2/지상20 19
 
2.4%
지하2/지상15 17
 
2.2%
지하3/지상15 13
 
1.7%
지하3/지상14 12
 
1.5%
지하4/지상15 11
 
1.4%
지하3/지상20 10
 
1.3%
지하3/지상23 10
 
1.3%
Other values (244) 601
77.1%
2023-12-10T18:32:24.939520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 1419
22.7%
상 722
11.5%
/ 701
11.2%
하 698
11.2%
1 556
 
8.9%
2 473
 
7.6%
3 281
 
4.5%
5 278
 
4.4%
4 220
 
3.5%
0 212
 
3.4%
Other values (15) 699
11.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2989
47.8%
Decimal Number 2353
37.6%
Other Punctuation 749
 
12.0%
Space Separator 121
 
1.9%
Dash Punctuation 45
 
0.7%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 556
23.6%
2 473
20.1%
3 281
11.9%
5 278
11.8%
4 220
 
9.3%
0 212
 
9.0%
6 119
 
5.1%
7 89
 
3.8%
8 71
 
3.0%
9 54
 
2.3%
Other Letter
ValueCountFrequency (%)
지 1419
47.5%
상 722
24.2%
하 698
23.4%
층 122
 
4.1%
동 25
 
0.8%
자 1
 
< 0.1%
시 1
 
< 0.1%
장 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 701
93.6%
. 39
 
5.2%
: 6
 
0.8%
, 3
 
0.4%
Space Separator
ValueCountFrequency (%)
121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3270
52.2%
Hangul 2989
47.8%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 701
21.4%
1 556
17.0%
2 473
14.5%
3 281
8.6%
5 278
 
8.5%
4 220
 
6.7%
0 212
 
6.5%
121
 
3.7%
6 119
 
3.6%
7 89
 
2.7%
Other values (7) 220
 
6.7%
Hangul
ValueCountFrequency (%)
지 1419
47.5%
상 722
24.2%
하 698
23.4%
층 122
 
4.1%
동 25
 
0.8%
자 1
 
< 0.1%
시 1
 
< 0.1%
장 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3270
52.2%
Hangul 2989
47.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 1419
47.5%
상 722
24.2%
하 698
23.4%
층 122
 
4.1%
동 25
 
0.8%
자 1
 
< 0.1%
시 1
 
< 0.1%
장 1
 
< 0.1%
ASCII
ValueCountFrequency (%)
/ 701
21.4%
1 556
17.0%
2 473
14.5%
3 281
8.6%
5 278
 
8.5%
4 220
 
6.7%
0 212
 
6.5%
121
 
3.7%
6 119
 
3.6%
7 89
 
2.7%
Other values (7) 220
 
6.7%

purpose
Text

Distinct160
Distinct (%)21.0%
Missing4
Missing (%)0.5%
Memory size6.1 KiB
2023-12-10T18:32:25.320848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length7.6062992
Min length2

Characters and Unicode

Total characters5796
Distinct characters78
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

Unique75 ?
Unique (%)9.8%

Sample

1st row판매시설
2nd row아파트업무시설
3rd row업무시설
4th row업무시설
5th row공동주택, 업무시설, 제1종근린생활시설
ValueCountFrequency (%)
업무시설 296
28.5%
공동주택 153
14.7%
숙박시설 74
 
7.1%
근생 59
 
5.7%
판매시설 58
 
5.6%
근린생활시설 46
 
4.4%
의료시설 35
 
3.4%
공동주택(아파트 20
 
1.9%
문화및집회 19
 
1.8%
업무시설(오피스텔 18
 
1.7%
Other values (86) 260
25.0%
2023-12-10T18:32:25.923291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 740
 
12.8%
설 740
 
12.8%
업 437
 
7.5%
414
 
7.1%
무 414
 
7.1%
, 263
 
4.5%
공 258
 
4.5%
동 229
 
4.0%
주 220
 
3.8%
택 220
 
3.8%
Other values (68) 1861
32.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4939
85.2%
Space Separator 414
 
7.1%
Other Punctuation 302
 
5.2%
Decimal Number 47
 
0.8%
Open Punctuation 46
 
0.8%
Close Punctuation 46
 
0.8%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 740
15.0%
설 740
15.0%
업 437
 
8.8%
무 414
 
8.4%
공 258
 
5.2%
동 229
 
4.6%
주 220
 
4.5%
택 220
 
4.5%
근 197
 
4.0%
생 197
 
4.0%
Other values (54) 1287
26.1%
Decimal Number
ValueCountFrequency (%)
1 21
44.7%
2 16
34.0%
0 5
 
10.6%
5 2
 
4.3%
3 1
 
2.1%
6 1
 
2.1%
4 1
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 263
87.1%
/ 37
 
12.3%
: 2
 
0.7%
Space Separator
ValueCountFrequency (%)
414
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4939
85.2%
Common 857
 
14.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 740
15.0%
설 740
15.0%
업 437
 
8.8%
무 414
 
8.4%
공 258
 
5.2%
동 229
 
4.6%
주 220
 
4.5%
택 220
 
4.5%
근 197
 
4.0%
생 197
 
4.0%
Other values (54) 1287
26.1%
Common
ValueCountFrequency (%)
414
48.3%
, 263
30.7%
( 46
 
5.4%
) 46
 
5.4%
/ 37
 
4.3%
1 21
 
2.5%
2 16
 
1.9%
0 5
 
0.6%
- 2
 
0.2%
: 2
 
0.2%
Other values (4) 5
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4939
85.2%
ASCII 857
 
14.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 740
15.0%
설 740
15.0%
업 437
 
8.8%
무 414
 
8.4%
공 258
 
5.2%
동 229
 
4.6%
주 220
 
4.5%
택 220
 
4.5%
근 197
 
4.0%
생 197
 
4.0%
Other values (54) 1287
26.1%
ASCII
ValueCountFrequency (%)
414
48.3%
, 263
30.7%
( 46
 
5.4%
) 46
 
5.4%
/ 37
 
4.3%
1 21
 
2.5%
2 16
 
1.9%
0 5
 
0.6%
- 2
 
0.2%
: 2
 
0.2%
Other values (4) 5
 
0.6%

pbl_pl
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
1
513 
2
217 
3
 
20
<NA>
 
9
4
 
6

Length

Max length4
Median length1
Mean length1.035248
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 513
67.0%
2 217
28.3%
3 20
 
2.6%
<NA> 9
 
1.2%
4 6
 
0.8%
5 1
 
0.1%

Length

2023-12-10T18:32:26.184643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:32:26.391590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 513
67.0%
2 217
28.3%
3 20
 
2.6%
na 9
 
1.2%
4 6
 
0.8%
5 1
 
0.1%

pbl_loc
Text

MISSING 

Distinct93
Distinct (%)13.9%
Missing96
Missing (%)12.5%
Memory size6.1 KiB
2023-12-10T18:32:26.856849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length2
Mean length5.0223881
Min length2

Characters and Unicode

Total characters3365
Distinct characters70
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

Unique77 ?
Unique (%)11.5%

Sample

1st row대지 내 위치
2nd row대지 내 위치
3rd row대지 내 위치
4th row대지 내 위치
5th row대지 내 위치
ValueCountFrequency (%)
지상 248
21.5%
전면 131
11.4%
대지 126
10.9%
내 126
10.9%
위치 95
 
8.2%
부산광역시 68
 
5.9%
사상구 40
 
3.5%
건물 36
 
3.1%
1층 35
 
3.0%
영도구 28
 
2.4%
Other values (108) 221
19.2%
2023-12-10T18:32:27.624947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
485
 
14.4%
지 423
 
12.6%
상 288
 
8.6%
면 212
 
6.3%
전 156
 
4.6%
대 135
 
4.0%
내 126
 
3.7%
1 107
 
3.2%
위 96
 
2.9%
치 95
 
2.8%
Other values (60) 1242
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2497
74.2%
Space Separator 485
 
14.4%
Decimal Number 311
 
9.2%
Dash Punctuation 47
 
1.4%
Other Punctuation 25
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 423
16.9%
상 288
 
11.5%
면 212
 
8.5%
전 156
 
6.2%
대 135
 
5.4%
내 126
 
5.0%
위 96
 
3.8%
치 95
 
3.8%
동 74
 
3.0%
부 68
 
2.7%
Other values (47) 824
33.0%
Decimal Number
ValueCountFrequency (%)
1 107
34.4%
5 40
 
12.9%
2 32
 
10.3%
4 30
 
9.6%
3 22
 
7.1%
7 20
 
6.4%
6 17
 
5.5%
0 16
 
5.1%
9 14
 
4.5%
8 13
 
4.2%
Space Separator
ValueCountFrequency (%)
485
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2497
74.2%
Common 868
 
25.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 423
16.9%
상 288
 
11.5%
면 212
 
8.5%
전 156
 
6.2%
대 135
 
5.4%
내 126
 
5.0%
위 96
 
3.8%
치 95
 
3.8%
동 74
 
3.0%
부 68
 
2.7%
Other values (47) 824
33.0%
Common
ValueCountFrequency (%)
485
55.9%
1 107
 
12.3%
- 47
 
5.4%
5 40
 
4.6%
2 32
 
3.7%
4 30
 
3.5%
, 25
 
2.9%
3 22
 
2.5%
7 20
 
2.3%
6 17
 
2.0%
Other values (3) 43
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2497
74.2%
ASCII 868
 
25.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
485
55.9%
1 107
 
12.3%
- 47
 
5.4%
5 40
 
4.6%
2 32
 
3.7%
4 30
 
3.5%
, 25
 
2.9%
3 22
 
2.5%
7 20
 
2.3%
6 17
 
2.0%
Other values (3) 43
 
5.0%
Hangul
ValueCountFrequency (%)
지 423
16.9%
상 288
 
11.5%
면 212
 
8.5%
전 156
 
6.2%
대 135
 
5.4%
내 126
 
5.0%
위 96
 
3.8%
치 95
 
3.8%
동 74
 
3.0%
부 68
 
2.7%
Other values (47) 824
33.0%

pbl_amn
Text

MISSING 

Distinct222
Distinct (%)29.9%
Missing23
Missing (%)3.0%
Memory size6.1 KiB
2023-12-10T18:32:28.086813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length27
Mean length9.6204576
Min length2

Characters and Unicode

Total characters7148
Distinct characters107
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

Unique143 ?
Unique (%)19.2%

Sample

1st row표지판 1
2nd row분수, 표지판 1
3rd row의자, 조명, 표지판 1
4th row의자, 표지판 1
5th row표지판 1
ValueCountFrequency (%)
표지판 517
24.6%
1 408
19.4%
의자 399
19.0%
파고라 102
 
4.9%
2 67
 
3.2%
벤치 65
 
3.1%
표지판1 60
 
2.9%
조형물 51
 
2.4%
조명 33
 
1.6%
3 24
 
1.1%
Other values (134) 377
17.9%
2023-12-10T18:32:28.732703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1364
19.1%
, 808
11.3%
판 613
8.6%
표 611
8.5%
지 610
8.5%
자 514
 
7.2%
의 509
 
7.1%
1 509
 
7.1%
라 125
 
1.7%
2 124
 
1.7%
Other values (97) 1361
19.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4131
57.8%
Space Separator 1364
 
19.1%
Other Punctuation 824
 
11.5%
Decimal Number 802
 
11.2%
Lowercase Letter 14
 
0.2%
Open Punctuation 6
 
0.1%
Close Punctuation 6
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
판 613
14.8%
표 611
14.8%
지 610
14.8%
자 514
12.4%
의 509
12.3%
라 125
 
3.0%
파 124
 
3.0%
고 123
 
3.0%
조 116
 
2.8%
치 94
 
2.3%
Other values (80) 692
16.8%
Decimal Number
ValueCountFrequency (%)
1 509
63.5%
2 124
 
15.5%
3 50
 
6.2%
4 43
 
5.4%
5 20
 
2.5%
6 18
 
2.2%
7 17
 
2.1%
8 10
 
1.2%
9 7
 
0.9%
0 4
 
0.5%
Other Punctuation
ValueCountFrequency (%)
, 808
98.1%
. 16
 
1.9%
Space Separator
ValueCountFrequency (%)
1364
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4131
57.8%
Common 3003
42.0%
Latin 14
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
판 613
14.8%
표 611
14.8%
지 610
14.8%
자 514
12.4%
의 509
12.3%
라 125
 
3.0%
파 124
 
3.0%
고 123
 
3.0%
조 116
 
2.8%
치 94
 
2.3%
Other values (80) 692
16.8%
Common
ValueCountFrequency (%)
1364
45.4%
, 808
26.9%
1 509
 
16.9%
2 124
 
4.1%
3 50
 
1.7%
4 43
 
1.4%
5 20
 
0.7%
6 18
 
0.6%
7 17
 
0.6%
. 16
 
0.5%
Other values (6) 34
 
1.1%
Latin
ValueCountFrequency (%)
m 14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4131
57.8%
ASCII 3017
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1364
45.2%
, 808
26.8%
1 509
 
16.9%
2 124
 
4.1%
3 50
 
1.7%
4 43
 
1.4%
5 20
 
0.7%
6 18
 
0.6%
7 17
 
0.6%
. 16
 
0.5%
Other values (7) 48
 
1.6%
Hangul
ValueCountFrequency (%)
판 613
14.8%
표 611
14.8%
지 610
14.8%
자 514
12.4%
의 509
12.3%
라 125
 
3.0%
파 124
 
3.0%
고 123
 
3.0%
조 116
 
2.8%
치 94
 
2.3%
Other values (80) 692
16.8%

gugun
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
부산광역시 해운대구
252 
부산광역시 동구
95 
부산광역시 연제구
50 
부산광역시 부산진구
47 
부산광역시 사상구
40 
Other values (12)
282 

Length

Max length10
Median length9
Mean length9.0822454
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 252
32.9%
부산광역시 동구 95
 
12.4%
부산광역시 연제구 50
 
6.5%
부산광역시 부산진구 47
 
6.1%
부산광역시 사상구 40
 
5.2%
부산광역시 동래구 38
 
5.0%
부산광역시 중구 37
 
4.8%
부산광역시 수영구 35
 
4.6%
부산광역시 금정구 31
 
4.0%
부산광역시 영도구 28
 
3.7%
Other values (7) 113
14.8%

Length

2023-12-10T18:32:28.967207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 758
49.7%
해운대구 252
 
16.5%
동구 95
 
6.2%
연제구 50
 
3.3%
부산진구 47
 
3.1%
사상구 40
 
2.6%
동래구 38
 
2.5%
중구 37
 
2.4%
수영구 35
 
2.3%
금정구 31
 
2.0%
Other values (8) 141
 
9.3%

data_day
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2020-12-31
598 
2020-04-28
120 
2020-12-30
 
38
<NA>
 
8
2020-08-29
 
2

Length

Max length10
Median length10
Mean length9.9373368
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 598
78.1%
2020-04-28 120
 
15.7%
2020-12-30 38
 
5.0%
<NA> 8
 
1.0%
2020-08-29 2
 
0.3%

Length

2023-12-10T18:32:29.189507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:32:29.423476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 598
78.1%
2020-04-28 120
 
15.7%
2020-12-30 38
 
5.0%
na 8
 
1.0%
2020-08-29 2
 
0.3%

lat
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct662
Distinct (%)87.3%
Missing8
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean35.157499
Minimum35.055987
Maximum35.323051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2023-12-10T18:32:29.676873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.055987
5-th percentile35.092505
Q135.133827
median35.160631
Q335.173978
95-th percentile35.224067
Maximum35.323051
Range0.267064
Interquartile range (IQR)0.040150778

Descriptive statistics

Standard deviation0.040003007
Coefficient of variation (CV)0.0011378229
Kurtosis1.2166716
Mean35.157499
Median Absolute Deviation (MAD)0.021044
Skewness0.45187405
Sum26649.384
Variance0.0016002406
MonotonicityNot monotonic
2023-12-10T18:32:29.957477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.140787 3
 
0.4%
35.124341 3
 
0.4%
35.111728 3
 
0.4%
35.112944 3
 
0.4%
35.121263 3
 
0.4%
35.1917142826 3
 
0.4%
35.121115 3
 
0.4%
35.123596 3
 
0.4%
35.112981 3
 
0.4%
35.127488 3
 
0.4%
Other values (652) 728
95.0%
(Missing) 8
 
1.0%
ValueCountFrequency (%)
35.055987 1
0.1%
35.068894 1
0.1%
35.07002 1
0.1%
35.075785 1
0.1%
35.07695271 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 

Distinct656
Distinct (%)86.5%
Missing8
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean129.08814
Minimum128.84025
Maximum129.99811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2023-12-10T18:32:30.236564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.84025
5-th percentile128.98466
Q1129.04266
median129.08204
Q3129.13602
95-th percentile129.17603
Maximum129.99811
Range1.157859
Interquartile range (IQR)0.0933565

Descriptive statistics

Standard deviation0.06987509
Coefficient of variation (CV)0.0005412975
Kurtosis37.046325
Mean129.08814
Median Absolute Deviation (MAD)0.045477
Skewness2.7014534
Sum97848.812
Variance0.0048825281
MonotonicityNot monotonic
2023-12-10T18:32:30.496809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.039082 4
 
0.5%
129.04512 3
 
0.4%
129.038173 3
 
0.4%
129.043501 3
 
0.4%
129.115 3
 
0.4%
129.113 3
 
0.4%
129.1538660862 3
 
0.4%
129.043894 3
 
0.4%
129.059048 3
 
0.4%
129.04363 3
 
0.4%
Other values (646) 727
94.9%
(Missing) 8
 
1.0%
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%
129.21388 1
0.1%
129.21331 1
0.1%
129.21278 1
0.1%
129.21234 1
0.1%

apr_at
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
N
607 
<NA>
130 
 
29

Length

Max length4
Median length1
Mean length1.5091384
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 607
79.2%
<NA> 130
 
17.0%
29
 
3.8%

Length

2023-12-10T18:32:30.733903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:32:30.988383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 607
82.4%
na 130
 
17.6%

last_load_dttm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2021-04-01 05:56:03
758 
<NA>
 
8

Length

Max length19
Median length19
Mean length18.843342
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-01 05:56:03
2nd row2021-04-01 05:56:03
3rd row2021-04-01 05:56:03
4th row2021-04-01 05:56:03
5th row2021-04-01 05:56:03

Common Values

ValueCountFrequency (%)
2021-04-01 05:56:03 758
99.0%
<NA> 8
 
1.0%

Length

2023-12-10T18:32:31.193296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:32:31.402929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 758
49.7%
05:56:03 758
49.7%
na 8
 
0.5%

Interactions

2023-12-10T18:32:09.886463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:32:09.529818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:32:10.079800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:32:09.688658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:32:31.538743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
instt_codepbl_plpbl_locgugundata_daylatlngapr_at
instt_code1.0000.1680.9671.0000.9340.8780.9191.000
pbl_pl0.1681.0000.7250.1950.0320.0000.0000.000
pbl_loc0.9670.7251.0000.9760.8570.8780.7190.421
gugun1.0000.1950.9761.0000.9200.8740.9210.634
data_day0.9340.0320.8570.9201.0000.5770.2760.000
lat0.8780.0000.8780.8740.5771.0000.6960.448
lng0.9190.0000.7190.9210.2760.6961.0000.166
apr_at1.0000.0000.4210.6340.0000.4480.1661.000
2023-12-10T18:32:31.776342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
data_daylast_load_dttminstt_codegugunapr_atpbl_pl
data_day1.0001.0000.8040.6650.0000.026
last_load_dttm1.0001.0001.0001.0001.0001.000
instt_code0.8041.0001.0000.9990.9880.085
gugun0.6651.0000.9991.0000.4990.099
apr_at0.0001.0000.9880.4991.0000.000
pbl_pl0.0261.0000.0850.0990.0001.000
2023-12-10T18:32:32.033292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
latlnginstt_codepbl_plgugundata_dayapr_atlast_load_dttm
lat1.0000.4880.5870.0000.5890.3830.3421.000
lng0.4881.0000.7730.0000.7740.2280.2021.000
instt_code0.5870.7731.0000.0850.9990.8040.9881.000
pbl_pl0.0000.0000.0851.0000.0990.0260.0001.000
gugun0.5890.7740.9990.0991.0000.6650.4991.000
data_day0.3830.2280.8040.0260.6651.0000.0001.000
apr_at0.3420.2020.9880.0000.4990.0001.0001.000
last_load_dttm1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T18:32:10.540711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:32:11.016029image/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.
2023-12-10T18:32:11.364250image/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
056463270000부산은행별관67.17부산광역시 동구 범일로 85부산광역시 동구 범일동 937-3 외2003-01-272009-07-076862.4지하2/지상10판매시설3대지 내 위치표지판 1부산광역시 동구2020-12-3135.137769129.058899N2021-04-01 05:56:03
156473270000마제스타워495.3부산광역시 동구 자성로133번길 6부산광역시 동구 범일동 830-2961995-04-252010-11-0984860.5지하5/지상40 3동아파트업무시설2대지 내 위치분수, 표지판 1부산광역시 동구2020-12-3135.137377129.065566N2021-04-01 05:56:03
256483270000현대해상부산사옥226.35부산광역시 동구 중앙대로 240부산광역시 동구 초량동 1193-52010-12-282013-05-2426341.5지하5/지상15업무시설1대지 내 위치의자, 조명, 표지판 1부산광역시 동구2020-12-3135.118221129.041861N2021-04-01 05:56:03
356493270000LIG손해보험부산사옥678.84부산광역시 동구 자성로133번길 15부산광역시 동구 범일동 830-302008-12-222011-01-2824205.6지하3/지상15업무시설2대지 내 위치의자, 표지판 1부산광역시 동구2020-12-3135.138273129.064074N2021-04-01 05:56:03
456503270000봄여름가을겨울아파트172.38부산광역시 동구 자성로133번길 46부산광역시 동구 범일동 830-772011-05-302013-11-1813690.1지하3/지상23공동주택, 업무시설, 제1종근린생활시설1대지 내 위치표지판 1부산광역시 동구2020-12-3135.140879129.062978N2021-04-01 05:56:03
556513270000동일타워830.49부산광역시 동구 조방로 14부산광역시 동구 범일동 830-1401995-05-302014-10-017752.2지하6/지상30업무시설판매시설2대지 내 위치파고라 1, 의자 17, 플랜트 9부산광역시 동구2020-12-3135.13873129.063454N2021-04-01 05:56:03
656523270000부산항 국제여객터미널4550.32부산광역시 동구 충장대로 206부산광역시 동구 초량동 45-392012-10-162015-03-1393931.9지하1/지상5운수시설1대지 내 위치표지판 1, 등의자 3, 평의자 3, 앉음벽 16.2m, 벤치 7, 파고라 4부산광역시 동구2020-12-3135.117666129.046703N2021-04-01 05:56:03
756533270000부산역 유림 로미오175.74부산광역시 동구 중앙대로274번길 7-7부산광역시 동구 초량동 1165-12011-10-202014-05-2714341.1지하4/지상17공동주택 업무시설 근린생활시설1대지 내 위치표지판 1, 등의자 3, 연식의자 8.5m, 연식플랜터 24.5m부산광역시 동구2020-12-3135.121492129.044097N2021-04-01 05:56:03
856543270000좌천동 아이유파크71.38부산광역시 동구 자성로8번길 4-5부산광역시 동구 좌천동 165-5 외12017-11-152019-04-125407.77지하0/지상19업무시설,제1종근린생활시설,제2종근린생활시설1대지 내 위치표지판1, 벤치3부산광역시 동구2020-12-3135.132776129.053941N2021-04-01 05:56:03
956553270000오름 레지던스69.13부산광역시 동구 중앙대로180번길 16-2부산광역시 동구 초량동 1213-5 외12017-08-102019-03-158217.09지하1/지상20숙박시설1대지 내 위치표지판1, 벤치4부산광역시 동구2020-12-3135.112476129.040193N2021-04-01 05:56:03
skeyinstt_codebild_nmpbl_areaaddr_roadaddr_jibunprmt_dateaprv_dateareanmb_floorspurposepbl_plpbl_locpbl_amngugundata_daylatlngapr_atlast_load_dttm
75655033330000해운대롯데캐슬비치 101동774.84부산광역시 해운대구 달맞이길 41부산광역시 해운대구 중동1775-12000-02-082003-01-2247,663.53지하2/지상32업무시설2지상의자, 표지판 1부산광역시 해운대구2020-12-3135.162752129.169543N2021-04-01 05:56:03
75755043330000웰비치249.28부산광역시 해운대구 좌동순환로 503부산광역시 해운대구 중동 1768-42002-03-042003-09-3013,984.42지하3/지상15업무시설근생2지상의자, 표지판 1부산광역시 해운대구2020-12-3135.16537129.168382N2021-04-01 05:56:03
75855053330000경동윈츠타워 오피스텔123부산광역시 해운대구 양운로 59부산광역시 해운대구 좌동 1473-62002-03-052003-10-2418,323.91지하2/지상25업무시설근생1지상의자, 표지판 1부산광역시 해운대구2020-12-3135.168493129.176017N2021-04-01 05:56:03
75955063330000디베르비타168.24부산광역시 해운대구 좌동로 98부산광역시 해운대구 좌동 1473-62002-02-022003-12-1614,308.47지하2/지상24업무시설 근생1지상조형물, 표지판 1부산광역시 해운대구2020-12-3135.172419129.175911N2021-04-01 05:56:03
76055073330000두산위브센티움152.05부산광역시 해운대구 양운로 55부산광역시 해운대구 좌동 1475-12002-04-172004-04-3015,005.28지하4/지상15업무시설2지상의자, 표지판 1부산광역시 해운대구2020-12-3135.16822129.176327N2021-04-01 05:56:03
76155083330000대림아크로텔380부산광역시 해운대구 해운대로 790부산광역시 해운대구 좌동 1473-1 외2002-04-032004-12-2744,365.34지하3/지상27업무시설1지상의자, 파고라, 표지판 1부산광역시 해운대구2020-12-3135.168538129.175327N2021-04-01 05:56:03
76255093330000좌동SK허브올리브297부산광역시 해운대구 양운로 56부산광역시 해운대구 좌동 1478-12002-06-192005-02-0218,436.25지하4/지상18업무시설2지상표지판 1부산광역시 해운대구2020-12-3135.16849129.176884N2021-04-01 05:56:03
76355103330000쌍용플래티넘트윈263부산광역시 해운대구 양운로 88부산광역시 해운대구 좌동 1478-22002-05-182005-03-1739,182.81지하6/지상22업무시설1지상의자, 표지판 1부산광역시 해운대구2020-12-3135.171018129.175245N2021-04-01 05:56:03
76455113330000벡스코12,500.00부산광역시 해운대구 APEC로 55부산광역시 해운대구 우동 15001998-06-022006-08-2192,786.19지하1/지상7문화및집회1지상의자, 표지판 1부산광역시 해운대구2020-12-3135.169078129.136021N2021-04-01 05:56:03
76555123330000현대베네시티2,849.94부산광역시 해운대구 해운대해변로 163부산광역시 해운대구 우동 14321997-10-232005-06-28143,479.19지상36, 4동공동주택1지상표지판 1부산광역시 해운대구2020-12-3135.158478129.150443N2021-04-01 05:56:03