Overview

Dataset statistics

Number of variables20
Number of observations609
Missing cells665
Missing cells (%)5.5%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory97.1 KiB
Average record size in memory163.2 B

Variable types

Text12
Categorical3
Numeric3
DateTime2

Alerts

last_load_dttm has constant value ""Constant
Dataset has 1 (0.2%) duplicate rowsDuplicates
gugun is highly overall correlated with lat and 3 other fieldsHigh correlation
apr_at is highly overall correlated with pbl_pl and 4 other fieldsHigh correlation
instt_code is highly overall correlated with lat and 3 other fieldsHigh correlation
pbl_pl is highly overall correlated with apr_atHigh 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
apr_at is highly imbalanced (68.3%)Imbalance
addr_road has 8 (1.3%) missing valuesMissing
aprv_date has 7 (1.1%) missing valuesMissing
area has 32 (5.3%) missing valuesMissing
purpose has 33 (5.4%) missing valuesMissing
pbl_pl has 63 (10.3%) missing valuesMissing
pbl_loc has 173 (28.4%) missing valuesMissing
pbl_amn has 77 (12.6%) missing valuesMissing
data_day has 61 (10.0%) missing valuesMissing
lat has 61 (10.0%) missing valuesMissing
lng has 61 (10.0%) missing valuesMissing
last_load_dttm has 61 (10.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 09:34:02.444853
Analysis finished2023-12-10 09:34:08.000945
Duration5.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Text

Distinct584
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2023-12-10T18:34:08.622307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.9753695
Min length2

Characters and Unicode

Total characters2421
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

Unique581 ?
Unique (%)95.4%

Sample

1st row4845
2nd row4846
3rd row4847
4th row4848
5th row4849
ValueCountFrequency (%)
업무시설 18
 
3.0%
근생 8
 
1.3%
판매시설 2
 
0.3%
5076 1
 
0.2%
4845 1
 
0.2%
5125 1
 
0.2%
5132 1
 
0.2%
5126 1
 
0.2%
5127 1
 
0.2%
5128 1
 
0.2%
Other values (574) 574
94.3%
2023-12-10T18:34:09.746700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 575
23.8%
4 284
11.7%
1 236
9.7%
3 204
 
8.4%
2 203
 
8.4%
9 189
 
7.8%
0 177
 
7.3%
8 166
 
6.9%
6 138
 
5.7%
7 134
 
5.5%
Other values (16) 115
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2306
95.2%
Other Letter 115
 
4.8%

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 (%)
5 575
24.9%
4 284
12.3%
1 236
10.2%
3 204
 
8.8%
2 203
 
8.8%
9 189
 
8.2%
0 177
 
7.7%
8 166
 
7.2%
6 138
 
6.0%
7 134
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2306
95.2%
Hangul 115
 
4.8%

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 (%)
5 575
24.9%
4 284
12.3%
1 236
10.2%
3 204
 
8.8%
2 203
 
8.8%
9 189
 
8.2%
0 177
 
7.7%
8 166
 
7.2%
6 138
 
6.0%
7 134
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2306
95.2%
Hangul 115
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 575
24.9%
4 284
12.3%
1 236
10.2%
3 204
 
8.8%
2 203
 
8.8%
9 189
 
8.2%
0 177
 
7.7%
8 166
 
7.2%
6 138
 
6.0%
7 134
 
5.8%
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 

Distinct20
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
3330000
126 
3290000
70 
3370000
49 
3250000
36 
3300000
35 
Other values (15)
293 

Length

Max length7
Median length7
Mean length6.7060755
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
3330000 126
20.7%
3290000 70
11.5%
3370000 49
 
8.0%
3250000 36
 
5.9%
3300000 35
 
5.7%
3390000 35
 
5.7%
3380000 33
 
5.4%
3350000 30
 
4.9%
3270000 29
 
4.8%
3280000 28
 
4.6%
Other values (10) 138
22.7%

Length

2023-12-10T18:34:10.064810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3330000 126
20.7%
3290000 70
11.5%
3370000 49
 
8.0%
3250000 36
 
5.9%
3300000 35
 
5.7%
3390000 35
 
5.7%
3380000 33
 
5.4%
3350000 30
 
4.9%
3270000 29
 
4.8%
3280000 28
 
4.6%
Other values (10) 138
22.7%

bild_nm
Text

Distinct574
Distinct (%)95.0%
Missing5
Missing (%)0.8%
Memory size4.9 KiB
2023-12-10T18:34:10.611201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length17
Mean length7.0728477
Min length1

Characters and Unicode

Total characters4272
Distinct characters420
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

Unique564 ?
Unique (%)93.4%

Sample

1st row홈플러스
2nd row국민연금공단사옥
3rd row연제우체국
4th row킴스힐타워
5th row㈜부원 사옥
ValueCountFrequency (%)
전면 15
 
1.9%
오피스텔 13
 
1.6%
해운대 7
 
0.9%
봄여름가을겨울 6
 
0.7%
내 5
 
0.6%
이마트 5
 
0.6%
온천동 5
 
0.6%
대지 5
 
0.6%
아파트 4
 
0.5%
더 4
 
0.5%
Other values (679) 739
91.5%
2023-12-10T18:34:11.546038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
208
 
4.9%
스 172
 
4.0%
이 90
 
2.1%
리 87
 
2.0%
산 73
 
1.7%
아 71
 
1.7%
트 67
 
1.6%
부 65
 
1.5%
대 58
 
1.4%
텔 56
 
1.3%
Other values (410) 3325
77.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3810
89.2%
Space Separator 208
 
4.9%
Uppercase Letter 135
 
3.2%
Decimal Number 45
 
1.1%
Open Punctuation 19
 
0.4%
Close Punctuation 19
 
0.4%
Lowercase Letter 17
 
0.4%
Other Symbol 7
 
0.2%
Other Punctuation 6
 
0.1%
Dash Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 172
 
4.5%
이 90
 
2.4%
리 87
 
2.3%
산 73
 
1.9%
아 71
 
1.9%
트 67
 
1.8%
부 65
 
1.7%
대 58
 
1.5%
텔 56
 
1.5%
타 55
 
1.4%
Other values (362) 3016
79.2%
Uppercase Letter
ValueCountFrequency (%)
K 12
 
8.9%
S 12
 
8.9%
T 10
 
7.4%
W 10
 
7.4%
C 10
 
7.4%
L 9
 
6.7%
E 8
 
5.9%
A 8
 
5.9%
H 6
 
4.4%
N 6
 
4.4%
Other values (13) 44
32.6%
Decimal Number
ValueCountFrequency (%)
2 22
48.9%
3 9
20.0%
1 6
 
13.3%
5 2
 
4.4%
6 2
 
4.4%
4 2
 
4.4%
0 1
 
2.2%
7 1
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
e 6
35.3%
i 3
17.6%
b 2
 
11.8%
w 2
 
11.8%
k 1
 
5.9%
v 1
 
5.9%
s 1
 
5.9%
d 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 3
50.0%
& 2
33.3%
/ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
208
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
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 3817
89.3%
Common 302
 
7.1%
Latin 153
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 172
 
4.5%
이 90
 
2.4%
리 87
 
2.3%
산 73
 
1.9%
아 71
 
1.9%
트 67
 
1.8%
부 65
 
1.7%
대 58
 
1.5%
텔 56
 
1.5%
타 55
 
1.4%
Other values (363) 3023
79.2%
Latin
ValueCountFrequency (%)
K 12
 
7.8%
S 12
 
7.8%
T 10
 
6.5%
W 10
 
6.5%
C 10
 
6.5%
L 9
 
5.9%
E 8
 
5.2%
A 8
 
5.2%
H 6
 
3.9%
e 6
 
3.9%
Other values (22) 62
40.5%
Common
ValueCountFrequency (%)
208
68.9%
2 22
 
7.3%
( 19
 
6.3%
) 19
 
6.3%
3 9
 
3.0%
1 6
 
2.0%
- 5
 
1.7%
. 3
 
1.0%
& 2
 
0.7%
5 2
 
0.7%
Other values (5) 7
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3810
89.2%
ASCII 454
 
10.6%
None 7
 
0.2%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
208
45.8%
2 22
 
4.8%
( 19
 
4.2%
) 19
 
4.2%
K 12
 
2.6%
S 12
 
2.6%
T 10
 
2.2%
W 10
 
2.2%
C 10
 
2.2%
3 9
 
2.0%
Other values (36) 123
27.1%
Hangul
ValueCountFrequency (%)
스 172
 
4.5%
이 90
 
2.4%
리 87
 
2.3%
산 73
 
1.9%
아 71
 
1.9%
트 67
 
1.8%
부 65
 
1.7%
대 58
 
1.5%
텔 56
 
1.5%
타 55
 
1.4%
Other values (362) 3016
79.2%
None
ValueCountFrequency (%)
㈜ 7
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

pbl_area
Text

Distinct588
Distinct (%)97.5%
Missing6
Missing (%)1.0%
Memory size4.9 KiB
2023-12-10T18:34:12.280447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length6.0414594
Min length2

Characters and Unicode

Total characters3643
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

Unique577 ?
Unique (%)95.7%

Sample

1st row12,008.98
2nd row863.02
3rd row394.33
4th row56.84
5th row68.02
ValueCountFrequency (%)
1 21
 
3.1%
표지판 21
 
3.1%
의자 19
 
2.8%
4 4
 
0.6%
2 4
 
0.6%
6 3
 
0.4%
52.58 2
 
0.3%
5 2
 
0.3%
파고라 2
 
0.3%
표지판1 2
 
0.3%
Other values (585) 594
88.1%
2023-12-10T18:34:13.194230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 540
14.8%
1 440
12.1%
2 304
8.3%
6 266
7.3%
5 265
7.3%
4 261
7.2%
3 255
7.0%
0 255
7.0%
9 240
6.6%
7 235
6.5%
Other values (41) 582
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2753
75.6%
Other Punctuation 635
 
17.4%
Other Letter 166
 
4.6%
Space Separator 89
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
판 24
14.5%
지 24
14.5%
표 23
13.9%
자 22
13.3%
의 22
13.3%
라 4
 
2.4%
평 3
 
1.8%
치 3
 
1.8%
조 3
 
1.8%
벤 3
 
1.8%
Other values (28) 35
21.1%
Decimal Number
ValueCountFrequency (%)
1 440
16.0%
2 304
11.0%
6 266
9.7%
5 265
9.6%
4 261
9.5%
3 255
9.3%
0 255
9.3%
9 240
8.7%
7 235
8.5%
8 232
8.4%
Other Punctuation
ValueCountFrequency (%)
. 540
85.0%
, 95
 
15.0%
Space Separator
ValueCountFrequency (%)
89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3477
95.4%
Hangul 166
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
판 24
14.5%
지 24
14.5%
표 23
13.9%
자 22
13.3%
의 22
13.3%
라 4
 
2.4%
평 3
 
1.8%
치 3
 
1.8%
조 3
 
1.8%
벤 3
 
1.8%
Other values (28) 35
21.1%
Common
ValueCountFrequency (%)
. 540
15.5%
1 440
12.7%
2 304
8.7%
6 266
7.7%
5 265
7.6%
4 261
7.5%
3 255
7.3%
0 255
7.3%
9 240
6.9%
7 235
6.8%
Other values (3) 416
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3477
95.4%
Hangul 166
 
4.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 540
15.5%
1 440
12.7%
2 304
8.7%
6 266
7.7%
5 265
7.6%
4 261
7.5%
3 255
7.3%
0 255
7.3%
9 240
6.9%
7 235
6.8%
Other values (3) 416
12.0%
Hangul
ValueCountFrequency (%)
판 24
14.5%
지 24
14.5%
표 23
13.9%
자 22
13.3%
의 22
13.3%
라 4
 
2.4%
평 3
 
1.8%
치 3
 
1.8%
조 3
 
1.8%
벤 3
 
1.8%
Other values (28) 35
21.1%

addr_road
Text

MISSING 

Distinct575
Distinct (%)95.7%
Missing8
Missing (%)1.3%
Memory size4.9 KiB
2023-12-10T18:34:13.736330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length33
Mean length18.697171
Min length5

Characters and Unicode

Total characters11237
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

Unique568 ?
Unique (%)94.5%

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 556
23.5%
해운대구 129
 
5.5%
부산진구 70
 
3.0%
연제구 49
 
2.1%
부산 44
 
1.9%
중앙대로 42
 
1.8%
서구 39
 
1.6%
중구 36
 
1.5%
금정구 35
 
1.5%
사상구 35
 
1.5%
Other values (635) 1329
56.2%
2023-12-10T18:34:14.645515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1810
16.1%
부 676
 
6.0%
산 673
 
6.0%
광 601
 
5.3%
구 599
 
5.3%
시 585
 
5.2%
로 565
 
5.0%
역 557
 
5.0%
1 366
 
3.3%
대 352
 
3.1%
Other values (199) 4453
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7467
66.5%
Decimal Number 1825
 
16.2%
Space Separator 1810
 
16.1%
Close Punctuation 40
 
0.4%
Open Punctuation 40
 
0.4%
Dash Punctuation 31
 
0.3%
Other Punctuation 13
 
0.1%
Uppercase Letter 9
 
0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 676
 
9.1%
산 673
 
9.0%
광 601
 
8.0%
구 599
 
8.0%
시 585
 
7.8%
로 565
 
7.6%
역 557
 
7.5%
대 352
 
4.7%
해 215
 
2.9%
운 181
 
2.4%
Other values (178) 2463
33.0%
Decimal Number
ValueCountFrequency (%)
1 366
20.1%
2 253
13.9%
3 210
11.5%
4 166
9.1%
7 159
8.7%
6 152
8.3%
5 147
8.1%
9 140
 
7.7%
0 136
 
7.5%
8 96
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
22.2%
P 2
22.2%
C 2
22.2%
E 2
22.2%
V 1
11.1%
Space Separator
ValueCountFrequency (%)
1810
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7467
66.5%
Common 3759
33.5%
Latin 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 676
 
9.1%
산 673
 
9.0%
광 601
 
8.0%
구 599
 
8.0%
시 585
 
7.8%
로 565
 
7.6%
역 557
 
7.5%
대 352
 
4.7%
해 215
 
2.9%
운 181
 
2.4%
Other values (178) 2463
33.0%
Common
ValueCountFrequency (%)
1810
48.2%
1 366
 
9.7%
2 253
 
6.7%
3 210
 
5.6%
4 166
 
4.4%
7 159
 
4.2%
6 152
 
4.0%
5 147
 
3.9%
9 140
 
3.7%
0 136
 
3.6%
Other values (5) 220
 
5.9%
Latin
ValueCountFrequency (%)
A 2
18.2%
P 2
18.2%
C 2
18.2%
E 2
18.2%
Ⅱ 2
18.2%
V 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7467
66.5%
ASCII 3768
33.5%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1810
48.0%
1 366
 
9.7%
2 253
 
6.7%
3 210
 
5.6%
4 166
 
4.4%
7 159
 
4.2%
6 152
 
4.0%
5 147
 
3.9%
9 140
 
3.7%
0 136
 
3.6%
Other values (10) 229
 
6.1%
Hangul
ValueCountFrequency (%)
부 676
 
9.1%
산 673
 
9.0%
광 601
 
8.0%
구 599
 
8.0%
시 585
 
7.8%
로 565
 
7.6%
역 557
 
7.5%
대 352
 
4.7%
해 215
 
2.9%
운 181
 
2.4%
Other values (178) 2463
33.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%

addr_jibun
Text

Distinct578
Distinct (%)95.7%
Missing5
Missing (%)0.8%
Memory size4.9 KiB
2023-12-10T18:34:15.113929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length18.93543
Min length10

Characters and Unicode

Total characters11437
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

Unique571 ?
Unique (%)94.5%

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 533
 
22.0%
해운대구 127
 
5.2%
부산진구 70
 
2.9%
우동 66
 
2.7%
외 52
 
2.1%
연제구 49
 
2.0%
부산 44
 
1.8%
연산동 37
 
1.5%
중구 35
 
1.4%
동래구 35
 
1.4%
Other values (695) 1372
56.7%
2023-12-10T18:34:15.838372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1830
16.0%
산 688
 
6.0%
부 683
 
6.0%
동 632
 
5.5%
1 606
 
5.3%
구 575
 
5.0%
광 553
 
4.8%
시 536
 
4.7%
역 533
 
4.7%
- 502
 
4.4%
Other values (112) 4299
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6416
56.1%
Decimal Number 2688
23.5%
Space Separator 1830
 
16.0%
Dash Punctuation 502
 
4.4%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 688
 
10.7%
부 683
 
10.6%
동 632
 
9.9%
구 575
 
9.0%
광 553
 
8.6%
시 536
 
8.4%
역 533
 
8.3%
대 166
 
2.6%
운 127
 
2.0%
해 127
 
2.0%
Other values (99) 1796
28.0%
Decimal Number
ValueCountFrequency (%)
1 606
22.5%
2 336
12.5%
4 278
10.3%
3 272
10.1%
5 250
9.3%
0 231
 
8.6%
7 207
 
7.7%
6 197
 
7.3%
8 177
 
6.6%
9 134
 
5.0%
Space Separator
ValueCountFrequency (%)
1830
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 502
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6416
56.1%
Common 5021
43.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 688
 
10.7%
부 683
 
10.6%
동 632
 
9.9%
구 575
 
9.0%
광 553
 
8.6%
시 536
 
8.4%
역 533
 
8.3%
대 166
 
2.6%
운 127
 
2.0%
해 127
 
2.0%
Other values (99) 1796
28.0%
Common
ValueCountFrequency (%)
1830
36.4%
1 606
 
12.1%
- 502
 
10.0%
2 336
 
6.7%
4 278
 
5.5%
3 272
 
5.4%
5 250
 
5.0%
0 231
 
4.6%
7 207
 
4.1%
6 197
 
3.9%
Other values (3) 312
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6416
56.1%
ASCII 5021
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1830
36.4%
1 606
 
12.1%
- 502
 
10.0%
2 336
 
6.7%
4 278
 
5.5%
3 272
 
5.4%
5 250
 
5.0%
0 231
 
4.6%
7 207
 
4.1%
6 197
 
3.9%
Other values (3) 312
 
6.2%
Hangul
ValueCountFrequency (%)
산 688
 
10.7%
부 683
 
10.6%
동 632
 
9.9%
구 575
 
9.0%
광 553
 
8.6%
시 536
 
8.4%
역 533
 
8.3%
대 166
 
2.6%
운 127
 
2.0%
해 127
 
2.0%
Other values (99) 1796
28.0%

prmt_date
Text

Distinct560
Distinct (%)92.9%
Missing6
Missing (%)1.0%
Memory size4.9 KiB
2023-12-10T18:34:16.332705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length10.011609
Min length5

Characters and Unicode

Total characters6037
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

Unique520 ?
Unique (%)86.2%

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 4
 
0.7%
2017-11-15 3
 
0.5%
2012-04-17 2
 
0.3%
2018-03-22 2
 
0.3%
2011-10-20 2
 
0.3%
2012-03-08 2
 
0.3%
2004-05-13 2
 
0.3%
2002-12-31 2
 
0.3%
2011-06-15 2
 
0.3%
2002-05-31 2
 
0.3%
Other values (555) 588
96.2%
2023-12-10T18:34:17.214324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1419
23.5%
- 1132
18.8%
2 961
15.9%
1 946
15.7%
9 304
 
5.0%
3 265
 
4.4%
5 225
 
3.7%
4 217
 
3.6%
6 188
 
3.1%
7 169
 
2.8%
Other values (3) 211
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
80.5%
Dash Punctuation 1132
 
18.8%
Other Punctuation 35
 
0.6%
Space Separator 8
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1419
29.2%
2 961
19.8%
1 946
19.5%
9 304
 
6.3%
3 265
 
5.5%
5 225
 
4.6%
4 217
 
4.5%
6 188
 
3.9%
7 169
 
3.5%
8 168
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 1132
100.0%
Other Punctuation
ValueCountFrequency (%)
. 35
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6037
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1419
23.5%
- 1132
18.8%
2 961
15.9%
1 946
15.7%
9 304
 
5.0%
3 265
 
4.4%
5 225
 
3.7%
4 217
 
3.6%
6 188
 
3.1%
7 169
 
2.8%
Other values (3) 211
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1419
23.5%
- 1132
18.8%
2 961
15.9%
1 946
15.7%
9 304
 
5.0%
3 265
 
4.4%
5 225
 
3.7%
4 217
 
3.6%
6 188
 
3.1%
7 169
 
2.8%
Other values (3) 211
 
3.5%

aprv_date
Text

MISSING 

Distinct569
Distinct (%)94.5%
Missing7
Missing (%)1.1%
Memory size4.9 KiB
2023-12-10T18:34:17.705733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length10.051495
Min length5

Characters and Unicode

Total characters6051
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

Unique537 ?
Unique (%)89.2%

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 3
 
0.5%
2018-09-19 2
 
0.3%
2006-09-15 2
 
0.3%
2019-08-28 2
 
0.3%
2020-04-24 2
 
0.3%
2019-10-29 2
 
0.3%
2012-05-29 2
 
0.3%
2019-03-15 2
 
0.3%
2017-05-15 2
 
0.3%
2018-04-30 2
 
0.3%
Other values (559) 581
96.5%
2023-12-10T18:34:18.430710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1421
23.5%
- 1134
18.7%
2 1011
16.7%
1 948
15.7%
9 292
 
4.8%
3 239
 
3.9%
4 210
 
3.5%
6 199
 
3.3%
8 197
 
3.3%
7 188
 
3.1%
Other values (2) 212
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4889
80.8%
Dash Punctuation 1134
 
18.7%
Other Punctuation 28
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1421
29.1%
2 1011
20.7%
1 948
19.4%
9 292
 
6.0%
3 239
 
4.9%
4 210
 
4.3%
6 199
 
4.1%
8 197
 
4.0%
7 188
 
3.8%
5 184
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 1134
100.0%
Other Punctuation
ValueCountFrequency (%)
. 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6051
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1421
23.5%
- 1134
18.7%
2 1011
16.7%
1 948
15.7%
9 292
 
4.8%
3 239
 
3.9%
4 210
 
3.5%
6 199
 
3.3%
8 197
 
3.3%
7 188
 
3.1%
Other values (2) 212
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1421
23.5%
- 1134
18.7%
2 1011
16.7%
1 948
15.7%
9 292
 
4.8%
3 239
 
3.9%
4 210
 
3.5%
6 199
 
3.3%
8 197
 
3.3%
7 188
 
3.1%
Other values (2) 212
 
3.5%

area
Text

MISSING 

Distinct574
Distinct (%)99.5%
Missing32
Missing (%)5.3%
Memory size4.9 KiB
2023-12-10T18:34:19.056056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.1195841
Min length3

Characters and Unicode

Total characters4685
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

Unique571 ?
Unique (%)99.0%

Sample

1st row64,516.70
2nd row43,431.70
3rd row16,519.20
4th row4,983.30
5th row10,530.00
ValueCountFrequency (%)
21315.8 2
 
0.3%
121,051.40 2
 
0.3%
19,010.80 2
 
0.3%
12460.73 1
 
0.2%
8,546.80 1
 
0.2%
15,005.28 1
 
0.2%
9,362.90 1
 
0.2%
63,596.90 1
 
0.2%
125,513.60 1
 
0.2%
8484.3 1
 
0.2%
Other values (564) 564
97.7%
2023-12-10T18:34:20.370943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 554
11.8%
0 539
11.5%
1 479
10.2%
2 373
8.0%
5 361
7.7%
8 355
7.6%
9 355
7.6%
3 346
7.4%
, 343
7.3%
6 341
7.3%
Other values (3) 639
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3780
80.7%
Other Punctuation 897
 
19.1%
Space Separator 8
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 539
14.3%
1 479
12.7%
2 373
9.9%
5 361
9.6%
8 355
9.4%
9 355
9.4%
3 346
9.2%
6 341
9.0%
4 327
8.7%
7 304
8.0%
Other Punctuation
ValueCountFrequency (%)
. 554
61.8%
, 343
38.2%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4685
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 554
11.8%
0 539
11.5%
1 479
10.2%
2 373
8.0%
5 361
7.7%
8 355
7.6%
9 355
7.6%
3 346
7.4%
, 343
7.3%
6 341
7.3%
Other values (3) 639
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 554
11.8%
0 539
11.5%
1 479
10.2%
2 373
8.0%
5 361
7.7%
8 355
7.6%
9 355
7.6%
3 346
7.4%
, 343
7.3%
6 341
7.3%
Other values (3) 639
13.6%

nmb_floors
Text

Distinct289
Distinct (%)47.9%
Missing6
Missing (%)1.0%
Memory size4.9 KiB
2023-12-10T18:34:20.883476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length8
Mean length8.4859038
Min length3

Characters and Unicode

Total characters5117
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

Unique188 ?
Unique (%)31.2%

Sample

1st row지하2/지상1
2nd row지하4/지상22
3rd row지하1/지상9
4th row지하1/지상23
5th row지하2/지상13
ValueCountFrequency (%)
지하1/지상15 34
 
5.3%
16:16:27 27
 
4.2%
2020-12-21 27
 
4.2%
지하1/지상20 26
 
4.1%
지하2/지상15 17
 
2.7%
지하2/지상20 16
 
2.5%
지하1/지상5 13
 
2.0%
지하2/지상7 9
 
1.4%
지하3/지상15 8
 
1.3%
지하1/지상19 8
 
1.3%
Other values (240) 453
71.0%
2023-12-10T18:34:21.682681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 1060
20.7%
1 570
11.1%
/ 556
10.9%
상 541
10.6%
하 519
10.1%
2 484
9.5%
187
 
3.7%
5 180
 
3.5%
3 170
 
3.3%
0 166
 
3.2%
Other values (13) 684
13.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2235
43.7%
Decimal Number 1993
38.9%
Other Punctuation 612
 
12.0%
Space Separator 187
 
3.7%
Dash Punctuation 88
 
1.7%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 570
28.6%
2 484
24.3%
5 180
 
9.0%
3 170
 
8.5%
0 166
 
8.3%
4 138
 
6.9%
6 111
 
5.6%
7 79
 
4.0%
8 48
 
2.4%
9 47
 
2.4%
Other Letter
ValueCountFrequency (%)
지 1060
47.4%
상 541
24.2%
하 519
23.2%
층 98
 
4.4%
동 15
 
0.7%
시 1
 
< 0.1%
장 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 556
90.8%
: 54
 
8.8%
, 2
 
0.3%
Space Separator
ValueCountFrequency (%)
187
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2882
56.3%
Hangul 2235
43.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 570
19.8%
/ 556
19.3%
2 484
16.8%
187
 
6.5%
5 180
 
6.2%
3 170
 
5.9%
0 166
 
5.8%
4 138
 
4.8%
6 111
 
3.9%
- 88
 
3.1%
Other values (6) 232
8.0%
Hangul
ValueCountFrequency (%)
지 1060
47.4%
상 541
24.2%
하 519
23.2%
층 98
 
4.4%
동 15
 
0.7%
시 1
 
< 0.1%
장 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2882
56.3%
Hangul 2235
43.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 1060
47.4%
상 541
24.2%
하 519
23.2%
층 98
 
4.4%
동 15
 
0.7%
시 1
 
< 0.1%
장 1
 
< 0.1%
ASCII
ValueCountFrequency (%)
1 570
19.8%
/ 556
19.3%
2 484
16.8%
187
 
6.5%
5 180
 
6.2%
3 170
 
5.9%
0 166
 
5.8%
4 138
 
4.8%
6 111
 
3.9%
- 88
 
3.1%
Other values (6) 232
8.0%

purpose
Text

MISSING 

Distinct154
Distinct (%)26.7%
Missing33
Missing (%)5.4%
Memory size4.9 KiB
2023-12-10T18:34:22.052889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length7.3472222
Min length2

Characters and Unicode

Total characters4232
Distinct characters74
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

Unique99 ?
Unique (%)17.2%

Sample

1st row판매시설 운동시설 문화및집회
2nd row업무시설
3rd row공공업무
4th row공동주택
5th row업무시설
ValueCountFrequency (%)
업무시설 207
27.4%
공동주택 131
17.3%
판매시설 45
 
6.0%
숙박시설 43
 
5.7%
근생 37
 
4.9%
의료시설 29
 
3.8%
근린생활시설 29
 
3.8%
업무시설(오피스텔 15
 
2.0%
공동주택(아파트 15
 
2.0%
문화및집회 14
 
1.9%
Other values (81) 191
25.3%
2023-12-10T18:34:22.681470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 516
12.2%
설 516
12.2%
344
 
8.1%
업 314
 
7.4%
무 296
 
7.0%
공 213
 
5.0%
동 200
 
4.7%
주 191
 
4.5%
택 191
 
4.5%
, 183
 
4.3%
Other values (64) 1268
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3570
84.4%
Space Separator 344
 
8.1%
Other Punctuation 219
 
5.2%
Open Punctuation 33
 
0.8%
Close Punctuation 33
 
0.8%
Decimal Number 31
 
0.7%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 516
14.5%
설 516
14.5%
업 314
 
8.8%
무 296
 
8.3%
공 213
 
6.0%
동 200
 
5.6%
주 191
 
5.4%
택 191
 
5.4%
근 126
 
3.5%
생 126
 
3.5%
Other values (52) 881
24.7%
Decimal Number
ValueCountFrequency (%)
2 13
41.9%
1 13
41.9%
6 2
 
6.5%
0 2
 
6.5%
7 1
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 183
83.6%
/ 34
 
15.5%
: 2
 
0.9%
Space Separator
ValueCountFrequency (%)
344
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3570
84.4%
Common 662
 
15.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 516
14.5%
설 516
14.5%
업 314
 
8.8%
무 296
 
8.3%
공 213
 
6.0%
동 200
 
5.6%
주 191
 
5.4%
택 191
 
5.4%
근 126
 
3.5%
생 126
 
3.5%
Other values (52) 881
24.7%
Common
ValueCountFrequency (%)
344
52.0%
, 183
27.6%
/ 34
 
5.1%
( 33
 
5.0%
) 33
 
5.0%
2 13
 
2.0%
1 13
 
2.0%
: 2
 
0.3%
6 2
 
0.3%
- 2
 
0.3%
Other values (2) 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3570
84.4%
ASCII 662
 
15.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 516
14.5%
설 516
14.5%
업 314
 
8.8%
무 296
 
8.3%
공 213
 
6.0%
동 200
 
5.6%
주 191
 
5.4%
택 191
 
5.4%
근 126
 
3.5%
생 126
 
3.5%
Other values (52) 881
24.7%
ASCII
ValueCountFrequency (%)
344
52.0%
, 183
27.6%
/ 34
 
5.1%
( 33
 
5.0%
) 33
 
5.0%
2 13
 
2.0%
1 13
 
2.0%
: 2
 
0.3%
6 2
 
0.3%
- 2
 
0.3%
Other values (2) 3
 
0.5%

pbl_pl
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)1.1%
Missing63
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean1.3699634
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-10T18:34:22.884319image/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 deviation0.66535139
Coefficient of variation (CV)0.48567094
Kurtosis20.470289
Mean1.3699634
Median Absolute Deviation (MAD)0
Skewness3.2047812
Sum748
Variance0.44269248
MonotonicityNot monotonic
2023-12-10T18:34:23.071748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 378
62.1%
2 146
 
24.0%
3 15
 
2.5%
4 5
 
0.8%
5 1
 
0.2%
8 1
 
0.2%
(Missing) 63
 
10.3%
ValueCountFrequency (%)
1 378
62.1%
2 146
 
24.0%
3 15
 
2.5%
4 5
 
0.8%
5 1
 
0.2%
8 1
 
0.2%
ValueCountFrequency (%)
8 1
 
0.2%
5 1
 
0.2%
4 5
 
0.8%
3 15
 
2.5%
2 146
 
24.0%
1 378
62.1%

pbl_loc
Text

MISSING 

Distinct86
Distinct (%)19.7%
Missing173
Missing (%)28.4%
Memory size4.9 KiB
2023-12-10T18:34:23.504639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length2
Mean length5.6009174
Min length2

Characters and Unicode

Total characters2442
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 (%)16.1%

Sample

1st row전면
2nd row전면
3rd row측면
4th row전면
5th row전면
ValueCountFrequency (%)
지상 120
15.7%
전면 113
14.8%
부산광역시 63
 
8.3%
대지 54
 
7.1%
내 54
 
7.1%
사상구 35
 
4.6%
건물 34
 
4.5%
1층 31
 
4.1%
위치 29
 
3.8%
영도구 28
 
3.7%
Other values (101) 202
26.5%
2023-12-10T18:34:24.198873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
328
 
13.4%
지 217
 
8.9%
면 188
 
7.7%
상 155
 
6.3%
전 136
 
5.6%
1 98
 
4.0%
동 69
 
2.8%
대 63
 
2.6%
부 63
 
2.6%
구 63
 
2.6%
Other values (59) 1062
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1765
72.3%
Space Separator 328
 
13.4%
Decimal Number 283
 
11.6%
Dash Punctuation 42
 
1.7%
Other Punctuation 24
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 217
 
12.3%
면 188
 
10.7%
상 155
 
8.8%
전 136
 
7.7%
동 69
 
3.9%
대 63
 
3.6%
부 63
 
3.6%
구 63
 
3.6%
시 63
 
3.6%
역 63
 
3.6%
Other values (46) 685
38.8%
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 1765
72.3%
Common 677
 
27.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 217
 
12.3%
면 188
 
10.7%
상 155
 
8.8%
전 136
 
7.7%
동 69
 
3.9%
대 63
 
3.6%
부 63
 
3.6%
구 63
 
3.6%
시 63
 
3.6%
역 63
 
3.6%
Other values (46) 685
38.8%
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 1765
72.3%
ASCII 677
 
27.7%

Most frequent character per block

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%
Hangul
ValueCountFrequency (%)
지 217
 
12.3%
면 188
 
10.7%
상 155
 
8.8%
전 136
 
7.7%
동 69
 
3.9%
대 63
 
3.6%
부 63
 
3.6%
구 63
 
3.6%
시 63
 
3.6%
역 63
 
3.6%
Other values (46) 685
38.8%

pbl_amn
Text

MISSING 

Distinct203
Distinct (%)38.2%
Missing77
Missing (%)12.6%
Memory size4.9 KiB
2023-12-10T18:34:24.589874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length24
Mean length9.843985
Min length2

Characters and Unicode

Total characters5237
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

Unique150 ?
Unique (%)28.2%

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 365
23.9%
의자 299
19.6%
1 295
19.3%
파고라 85
 
5.6%
표지판1 49
 
3.2%
2 47
 
3.1%
벤치 45
 
2.9%
조형물 41
 
2.7%
조명 33
 
2.2%
3 15
 
1.0%
Other values (114) 252
16.5%
2023-12-10T18:34:25.249219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1003
19.2%
, 617
11.8%
판 442
8.4%
표 441
8.4%
지 440
8.4%
자 381
 
7.3%
의 377
 
7.2%
1 374
 
7.1%
조 102
 
1.9%
라 101
 
1.9%
Other values (92) 959
18.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3038
58.0%
Space Separator 1003
 
19.2%
Other Punctuation 625
 
11.9%
Decimal Number 556
 
10.6%
Lowercase Letter 6
 
0.1%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
판 442
14.5%
표 441
14.5%
지 440
14.5%
자 381
12.5%
의 377
12.4%
조 102
 
3.4%
라 101
 
3.3%
고 100
 
3.3%
파 100
 
3.3%
벤 64
 
2.1%
Other values (75) 490
16.1%
Decimal Number
ValueCountFrequency (%)
1 374
67.3%
2 75
 
13.5%
3 34
 
6.1%
4 31
 
5.6%
5 13
 
2.3%
7 9
 
1.6%
6 9
 
1.6%
8 6
 
1.1%
9 3
 
0.5%
0 2
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 617
98.7%
. 8
 
1.3%
Space Separator
ValueCountFrequency (%)
1003
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3038
58.0%
Common 2193
41.9%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
판 442
14.5%
표 441
14.5%
지 440
14.5%
자 381
12.5%
의 377
12.4%
조 102
 
3.4%
라 101
 
3.3%
고 100
 
3.3%
파 100
 
3.3%
벤 64
 
2.1%
Other values (75) 490
16.1%
Common
ValueCountFrequency (%)
1003
45.7%
, 617
28.1%
1 374
 
17.1%
2 75
 
3.4%
3 34
 
1.6%
4 31
 
1.4%
5 13
 
0.6%
7 9
 
0.4%
6 9
 
0.4%
. 8
 
0.4%
Other values (6) 20
 
0.9%
Latin
ValueCountFrequency (%)
m 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3038
58.0%
ASCII 2199
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1003
45.6%
, 617
28.1%
1 374
 
17.0%
2 75
 
3.4%
3 34
 
1.5%
4 31
 
1.4%
5 13
 
0.6%
7 9
 
0.4%
6 9
 
0.4%
. 8
 
0.4%
Other values (7) 26
 
1.2%
Hangul
ValueCountFrequency (%)
판 442
14.5%
표 441
14.5%
지 440
14.5%
자 381
12.5%
의 377
12.4%
조 102
 
3.4%
라 101
 
3.3%
고 100
 
3.3%
파 100
 
3.3%
벤 64
 
2.1%
Other values (75) 490
16.1%

gugun
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
부산광역시 해운대구
122 
부산광역시 부산진구
70 
<NA>
61 
부산광역시 연제구
49 
부산광역시 중구
36 
Other values (12)
271 

Length

Max length10
Median length9
Mean length8.6305419
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 122
20.0%
부산광역시 부산진구 70
11.5%
<NA> 61
10.0%
부산광역시 연제구 49
8.0%
부산광역시 중구 36
 
5.9%
부산광역시 동래구 35
 
5.7%
부산광역시 사상구 35
 
5.7%
부산광역시 수영구 31
 
5.1%
부산광역시 동구 29
 
4.8%
부산광역시 영도구 28
 
4.6%
Other values (7) 113
18.6%

Length

2023-12-10T18:34:25.531383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 548
47.4%
해운대구 122
 
10.5%
부산진구 70
 
6.1%
na 61
 
5.3%
연제구 49
 
4.2%
중구 36
 
3.1%
동래구 35
 
3.0%
사상구 35
 
3.0%
수영구 31
 
2.7%
동구 29
 
2.5%
Other values (8) 141
 
12.2%

data_day
Date

MISSING 

Distinct8
Distinct (%)1.5%
Missing61
Missing (%)10.0%
Memory size4.9 KiB
Minimum2020-04-28 00:00:00
Maximum2020-09-09 00:00:00
2023-12-10T18:34:25.802162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:34:26.005440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

lat
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct542
Distinct (%)98.9%
Missing61
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean35.160072
Minimum35.055987
Maximum35.323051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-10T18:34:26.268870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.055987
5-th percentile35.090462
Q135.137348
median35.16074
Q335.182267
95-th percentile35.235422
Maximum35.323051
Range0.267064
Interquartile range (IQR)0.044919

Descriptive statistics

Standard deviation0.042985826
Coefficient of variation (CV)0.001222575
Kurtosis0.94088093
Mean35.160072
Median Absolute Deviation (MAD)0.0226272
Skewness0.38768476
Sum19267.719
Variance0.0018477812
MonotonicityNot monotonic
2023-12-10T18:34:26.555874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.089173 3
 
0.5%
35.1917142826 3
 
0.5%
35.160218875 2
 
0.3%
35.151 2
 
0.3%
35.15666 1
 
0.2%
35.171018 1
 
0.2%
35.169078 1
 
0.2%
35.158478 1
 
0.2%
35.167019 1
 
0.2%
35.172628 1
 
0.2%
Other values (532) 532
87.4%
(Missing) 61
 
10.0%
ValueCountFrequency (%)
35.055987 1
0.2%
35.068894 1
0.2%
35.07002 1
0.2%
35.075785 1
0.2%
35.0769527145 1
0.2%
35.077174 1
0.2%
35.077645 1
0.2%
35.077728 1
0.2%
35.078 1
0.2%
35.078406 1
0.2%
ValueCountFrequency (%)
35.323051 1
0.2%
35.322046 1
0.2%
35.320689 1
0.2%
35.319731 1
0.2%
35.291968 1
0.2%
35.274223 1
0.2%
35.272887 1
0.2%
35.271774 1
0.2%
35.260467 1
0.2%
35.260092 1
0.2%

lng
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct535
Distinct (%)97.6%
Missing61
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean129.07995
Minimum128.84025
Maximum129.99811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-10T18:34:26.807300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.84025
5-th percentile128.98116
Q1129.03991
median129.07715
Q3129.12173
95-th percentile129.17569
Maximum129.99811
Range1.157859
Interquartile range (IQR)0.081822079

Descriptive statistics

Standard deviation0.072336546
Coefficient of variation (CV)0.00056040108
Kurtosis46.671675
Mean129.07995
Median Absolute Deviation (MAD)0.038553697
Skewness3.5987484
Sum70735.815
Variance0.0052325758
MonotonicityNot monotonic
2023-12-10T18:34:27.135637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.044506 3
 
0.5%
129.113 3
 
0.5%
129.115 3
 
0.5%
129.1538660862 3
 
0.5%
129.090554 2
 
0.3%
129.037373 2
 
0.3%
129.109 2
 
0.3%
129.039082 2
 
0.3%
129.1652746732 2
 
0.3%
129.0242191557 1
 
0.2%
Other values (525) 525
86.2%
(Missing) 61
 
10.0%
ValueCountFrequency (%)
128.840253 1
0.2%
128.842489 1
0.2%
128.900708 1
0.2%
128.902286 1
0.2%
128.904052 1
0.2%
128.907186 1
0.2%
128.948028 1
0.2%
128.960393 1
0.2%
128.961516 1
0.2%
128.963118 1
0.2%
ValueCountFrequency (%)
129.998112 1
0.2%
129.2289 1
0.2%
129.22858 1
0.2%
129.21979 1
0.2%
129.21905 1
0.2%
129.21802 1
0.2%
129.21584 1
0.2%
129.21388 1
0.2%
129.21331 1
0.2%
129.21278 1
0.2%

apr_at
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
<NA>
574 
 
35

Length

Max length4
Median length4
Mean length3.8275862
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
94.3%
35
 
5.7%

Length

2023-12-10T18:34:27.453257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:34:27.647991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 574
100.0%

last_load_dttm
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing61
Missing (%)10.0%
Memory size4.9 KiB
Minimum2020-12-21 16:16:27
Maximum2020-12-21 16:16:27
2023-12-10T18:34:27.886595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:34:28.061396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T18:34:05.580893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:34:04.318272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:34:04.980237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:34:05.798370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:34:04.508223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:34:05.249994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:34:05.994654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:34:04.703423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:34:05.424501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:34:28.184532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
instt_codepbl_plpbl_locgugundata_daylatlng
instt_code1.0000.1220.9741.0000.9980.8790.923
pbl_pl0.1221.0000.7800.1220.0000.0000.000
pbl_loc0.9740.7801.0000.9740.9790.8610.730
gugun1.0000.1220.9741.0000.9980.8790.923
data_day0.9980.0000.9790.9981.0000.7300.743
lat0.8790.0000.8610.8790.7301.0000.654
lng0.9230.0000.7300.9230.7430.6541.000
2023-12-10T18:34:28.371553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gugunapr_atinstt_code
gugun1.0001.0001.000
apr_at1.0001.0001.000
instt_code1.0001.0001.000
2023-12-10T18:34:28.603043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
pbl_pllatlnginstt_codegugunapr_at
pbl_pl1.000-0.069-0.0240.0570.0571.000
lat-0.0691.0000.4630.5980.5981.000
lng-0.0240.4631.0000.7780.7781.000
instt_code0.0570.5980.7781.0001.0001.000
gugun0.0570.5980.7781.0001.0001.000
apr_at1.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T18:34:06.344297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:34:06.993056image/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.
2023-12-10T18:34:07.458038image/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>2020-12-21 16:16:27
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>2020-12-21 16:16:27
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>2020-12-21 16:16:27
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>2020-12-21 16:16:27
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>2020-12-21 16:16:27
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>2020-12-21 16:16:27
648513370000지성캐슬51.98부산광역시 연제구 월드컵대로 153번길 64부산광역시 연제구 연산동 1299-22018-03-142019-09-222753.44지하1/지상15공동주택, 업무시설1전면의자2, 표지판1부산광역시 연제구2020-07-3135.184302129.07546<NA>2020-12-21 16:16:27
749533280000더킹페로스아파트192.94부산광역시 영도구 절영로 48부산광역시 영도구 남항동1가 862017-01-262019-05-1713247.06지하1/지상20층공동주택4부산광역시 영도구 남항동1가 86의자, 표지판1부산광역시 영도구2020-07-3135.09061129.038416<NA>2020-12-21 16:16:27
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>2020-12-21 16:16:27
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>2020-12-21 16:16:27
skeyinstt_codebild_nmpbl_areaaddr_roadaddr_jibunprmt_dateaprv_dateareanmb_floorspurposepbl_plpbl_locpbl_amngugundata_daylatlngapr_atlast_load_dttm
59952763290000한진스카이뷰204.8부산광역시 부산진구 거제대로48번길 46부산광역시 부산진구 양정동 399-12012-03-302015-03-0315900.71지하3/지상26공동주택2<NA>의자, 파고라, 조명, 표지판 1부산광역시 부산진구2020-07-3135.175078129.053867<NA>2020-12-21 16:16:27
60052773290000몽뜨레152.9부산광역시 부산진구 전포대로189번길 30부산광역시 부산진구 전포동 693-22011-12-232013-12-2612460.73지하1/지상14공동주택2<NA>파고라 1, 의자 2부산광역시 부산진구2020-07-3135.153663129.061317<NA>2020-12-21 16:16:27
60152783290000더블루 2222.75부산광역시 부산진구 서전로38번길 65부산광역시 부산진구 전포동 686-12011-09-202013-12-2722101.61지하4/지상22업무시설1<NA>의자 6, 상징조형물 1, 표지판 1부산광역시 부산진구2020-07-3135.155549129.061775<NA>2020-12-21 16:16:27
60252793290000미래여성병원143.44부산광역시 부산진구 가야대로 460부산광역시 부산진구 개금동 204-62011-12-202014-01-239952.52지하2/지상10의료시설1<NA>의자, 파고라, 조명, 표지판 1부산광역시 부산진구2020-07-3135.152856129.02012<NA>2020-12-21 16:16:27
60352803290000천일메트로빌255.98부산광역시 부산진구 서전로46번길 9부산광역시 부산진구 전포동 675-32012-06-082014-01-285794.87지하1/지상22업무시설1<NA>의자, 파고라, 조명, 표지판 1부산광역시 부산진구2020-07-3135.155448129.062863<NA>2020-12-21 16:16:27
60452813290000KH마이우스58부산광역시 부산진구 신천대로62번길 2부산광역시 부산진구 부전동 535-38 외 12015-09-022018-02-069312.78지하2/지상19업무시설, 근린생활시설1<NA>벤치, 조경시설부산광역시 부산진구2020-07-3135.154074129.056489<NA>2020-12-21 16:16:27
60552823290000사랑모아빌딩60.01부산광역시 부산진구 중앙대로 632부산광역시 부산진구 범천동 857-62016-05-122018-01-118045.95지하2/지상11업무시설, 근린생활시설1<NA>플랜터, 앉음벽, 식재부산광역시 부산진구2020-07-3135.149155129.057372<NA>2020-12-21 16:16:27
60652833290000전포동 스윗팰리스88.73부산광역시 부산진구 전포대로 204부산광역시 부산진구 전포동 312-31 외 32016-12-262018-05-217050.81지하1/지상20공동주택, 업무시설1<NA>평의자5, 볼라드2, 표지판1, 플랜터2부산광역시 부산진구2020-07-3135.154722129.063464<NA>2020-12-21 16:16:27
60752843290000도시개발공사339.99부산광역시 부산진구 신천대로 156부산광역시 부산진구 부전동 384-71993-01-081995-11-101156.09지하3/지상13업무시설1<NA>의자, 표지판부산광역시 부산진구2020-07-3135.155966129.049228<NA>2020-12-21 16:16:27
60852853290000㈜kdb생명보험198.87부산광역시 부산진구 중앙대로 766부산광역시 부산진구 부전동 5-911992-11-111997-01-1124977.39지하6/지상20업무시설 근생1<NA>의자, 조형물, 표지판 1부산광역시 부산진구2020-07-3135.160486129.059631<NA>2020-12-21 16:16:27

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