Overview

Dataset statistics

Number of variables11
Number of observations1494
Missing cells176
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory128.5 KiB
Average record size in memory88.1 B

Variable types

Text6
Categorical4
DateTime1

Alerts

last_load_dttm has constant value ""Constant
gugun is highly overall correlated with mng_agency_nm and 2 other fieldsHigh correlation
instt_code is highly overall correlated with mng_agency_nm and 2 other fieldsHigh correlation
mng_agency_nm is highly overall correlated with gugun and 2 other fieldsHigh correlation
reference_date is highly overall correlated with mng_agency_nm and 2 other fieldsHigh correlation
reference_date is highly imbalanced (55.7%)Imbalance
addr has 32 (2.1%) missing valuesMissing
lat has 33 (2.2%) missing valuesMissing
lng has 35 (2.3%) missing valuesMissing
last_load_dttm has 65 (4.4%) missing valuesMissing

Reproduction

Analysis started2024-04-18 02:02:15.128932
Analysis finished2024-04-18 02:02:17.373464
Duration2.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Text

Distinct1484
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-04-18T11:02:17.580056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length4
Mean length4.2376171
Min length4

Characters and Unicode

Total characters6331
Distinct characters103
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1482 ?
Unique (%)99.2%

Sample

1st row9812
2nd row9813
3rd row9814
4th row9815
5th row9816
ValueCountFrequency (%)
pc방 11
 
0.7%
온요양병원 2
 
0.1%
9232 1
 
0.1%
9584 1
 
0.1%
9770 1
 
0.1%
9812 1
 
0.1%
9582 1
 
0.1%
9590 1
 
0.1%
9589 1
 
0.1%
9588 1
 
0.1%
Other values (1484) 1484
98.6%
2024-04-18T11:02:18.007325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 1430
22.6%
1 717
11.3%
0 716
11.3%
8 626
9.9%
6 489
 
7.7%
4 459
 
7.3%
5 439
 
6.9%
2 416
 
6.6%
3 397
 
6.3%
7 388
 
6.1%
Other values (93) 254
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6077
96.0%
Other Letter 167
 
2.6%
Uppercase Letter 26
 
0.4%
Close Punctuation 20
 
0.3%
Open Punctuation 20
 
0.3%
Space Separator 12
 
0.2%
Other Punctuation 8
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 14
 
8.4%
병 14
 
8.4%
방 13
 
7.8%
양 9
 
5.4%
요 9
 
5.4%
층 6
 
3.6%
부 4
 
2.4%
지 4
 
2.4%
온 4
 
2.4%
일 3
 
1.8%
Other values (69) 87
52.1%
Decimal Number
ValueCountFrequency (%)
9 1430
23.5%
1 717
11.8%
0 716
11.8%
8 626
10.3%
6 489
 
8.0%
4 459
 
7.6%
5 439
 
7.2%
2 416
 
6.8%
3 397
 
6.5%
7 388
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
P 11
42.3%
C 11
42.3%
D 1
 
3.8%
R 1
 
3.8%
T 1
 
3.8%
V 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 4
50.0%
. 2
25.0%
＆ 1
 
12.5%
: 1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6138
97.0%
Hangul 167
 
2.6%
Latin 26
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 14
 
8.4%
병 14
 
8.4%
방 13
 
7.8%
양 9
 
5.4%
요 9
 
5.4%
층 6
 
3.6%
부 4
 
2.4%
지 4
 
2.4%
온 4
 
2.4%
일 3
 
1.8%
Other values (69) 87
52.1%
Common
ValueCountFrequency (%)
9 1430
23.3%
1 717
11.7%
0 716
11.7%
8 626
10.2%
6 489
 
8.0%
4 459
 
7.5%
5 439
 
7.2%
2 416
 
6.8%
3 397
 
6.5%
7 388
 
6.3%
Other values (8) 61
 
1.0%
Latin
ValueCountFrequency (%)
P 11
42.3%
C 11
42.3%
D 1
 
3.8%
R 1
 
3.8%
T 1
 
3.8%
V 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6163
97.3%
Hangul 167
 
2.6%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 1430
23.2%
1 717
11.6%
0 716
11.6%
8 626
10.2%
6 489
 
7.9%
4 459
 
7.4%
5 439
 
7.1%
2 416
 
6.7%
3 397
 
6.4%
7 388
 
6.3%
Other values (13) 86
 
1.4%
Hangul
ValueCountFrequency (%)
원 14
 
8.4%
병 14
 
8.4%
방 13
 
7.8%
양 9
 
5.4%
요 9
 
5.4%
층 6
 
3.6%
부 4
 
2.4%
지 4
 
2.4%
온 4
 
2.4%
일 3
 
1.8%
Other values (69) 87
52.1%
None
ValueCountFrequency (%)
＆ 1
100.0%

facility_gubun
Text

Distinct90
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-04-18T11:02:18.193141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length40
Mean length5.3714859
Min length2

Characters and Unicode

Total characters8025
Distinct characters160
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

Unique41 ?
Unique (%)2.7%

Sample

1st row대규모점포
2nd row대규모점포
3rd row대규모점포
4th row도서관
5th row목욕장
ValueCountFrequency (%)
의료기관 287
17.9%
실내주차장 222
13.8%
어린이집 172
 
10.7%
지하역사 81
 
5.0%
대규모점포 74
 
4.6%
보육시설 69
 
4.3%
노인요양시설 50
 
3.1%
pc방 47
 
2.9%
17.실내주차장 44
 
2.7%
pc영업시설 35
 
2.2%
Other values (116) 524
32.6%
2024-04-18T11:02:18.481991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 375
 
4.7%
장 354
 
4.4%
기 323
 
4.0%
의 321
 
4.0%
료 321
 
4.0%
시 284
 
3.5%
실 269
 
3.4%
내 269
 
3.4%
차 268
 
3.3%
주 266
 
3.3%
Other values (150) 4975
62.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6931
86.4%
Decimal Number 462
 
5.8%
Uppercase Letter 212
 
2.6%
Other Punctuation 210
 
2.6%
Space Separator 164
 
2.0%
Lowercase Letter 26
 
0.3%
Close Punctuation 10
 
0.1%
Open Punctuation 10
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 375
 
5.4%
장 354
 
5.1%
기 323
 
4.7%
의 321
 
4.6%
료 321
 
4.6%
시 284
 
4.1%
실 269
 
3.9%
내 269
 
3.9%
차 268
 
3.9%
주 266
 
3.8%
Other values (123) 3881
56.0%
Decimal Number
ValueCountFrequency (%)
1 216
46.8%
7 61
 
13.2%
2 55
 
11.9%
8 40
 
8.7%
6 28
 
6.1%
9 24
 
5.2%
0 13
 
2.8%
4 10
 
2.2%
3 10
 
2.2%
5 5
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
P 100
47.2%
C 99
46.7%
O 5
 
2.4%
B 2
 
0.9%
K 1
 
0.5%
X 1
 
0.5%
R 1
 
0.5%
E 1
 
0.5%
N 1
 
0.5%
Z 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 199
94.8%
, 11
 
5.2%
Lowercase Letter
ValueCountFrequency (%)
c 13
50.0%
p 13
50.0%
Space Separator
ValueCountFrequency (%)
164
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6931
86.4%
Common 856
 
10.7%
Latin 238
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 375
 
5.4%
장 354
 
5.1%
기 323
 
4.7%
의 321
 
4.6%
료 321
 
4.6%
시 284
 
4.1%
실 269
 
3.9%
내 269
 
3.9%
차 268
 
3.9%
주 266
 
3.8%
Other values (123) 3881
56.0%
Common
ValueCountFrequency (%)
1 216
25.2%
. 199
23.2%
164
19.2%
7 61
 
7.1%
2 55
 
6.4%
8 40
 
4.7%
6 28
 
3.3%
9 24
 
2.8%
0 13
 
1.5%
, 11
 
1.3%
Other values (5) 45
 
5.3%
Latin
ValueCountFrequency (%)
P 100
42.0%
C 99
41.6%
c 13
 
5.5%
p 13
 
5.5%
O 5
 
2.1%
B 2
 
0.8%
K 1
 
0.4%
X 1
 
0.4%
R 1
 
0.4%
E 1
 
0.4%
Other values (2) 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6931
86.4%
ASCII 1094
 
13.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 375
 
5.4%
장 354
 
5.1%
기 323
 
4.7%
의 321
 
4.6%
료 321
 
4.6%
시 284
 
4.1%
실 269
 
3.9%
내 269
 
3.9%
차 268
 
3.9%
주 266
 
3.8%
Other values (123) 3881
56.0%
ASCII
ValueCountFrequency (%)
1 216
19.7%
. 199
18.2%
164
15.0%
P 100
9.1%
C 99
9.0%
7 61
 
5.6%
2 55
 
5.0%
8 40
 
3.7%
6 28
 
2.6%
9 24
 
2.2%
Other values (17) 108
9.9%

facility_nm
Text

Distinct1396
Distinct (%)94.1%
Missing11
Missing (%)0.7%
Memory size11.8 KiB
2024-04-18T11:02:18.743340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length33
Mean length8.6979096
Min length3

Characters and Unicode

Total characters12899
Distinct characters572
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

Unique1329 ?
Unique (%)89.6%

Sample

1st row홈플러스 센텀시티점
2nd row홈플러스 해운대점
3rd row화목데파트 상가
4th row해운대도서관
5th row(주)해운대레저
ValueCountFrequency (%)
부산광역시 33
 
1.6%
의료법인 33
 
1.6%
pc 31
 
1.5%
pc방 23
 
1.1%
어린이집 23
 
1.1%
부산진구청 17
 
0.8%
실내주차장 14
 
0.7%
지하철 14
 
0.7%
부산도시철도 12
 
0.6%
해운대 12
 
0.6%
Other values (1597) 1891
89.9%
2024-04-18T11:02:19.123253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
685
 
5.3%
원 440
 
3.4%
이 379
 
2.9%
산 327
 
2.5%
병 324
 
2.5%
어 290
 
2.2%
린 280
 
2.2%
집 270
 
2.1%
부 267
 
2.1%
양 209
 
1.6%
Other values (562) 9428
73.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11074
85.9%
Space Separator 685
 
5.3%
Uppercase Letter 566
 
4.4%
Open Punctuation 152
 
1.2%
Close Punctuation 152
 
1.2%
Decimal Number 118
 
0.9%
Lowercase Letter 71
 
0.6%
Other Symbol 44
 
0.3%
Other Punctuation 29
 
0.2%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 440
 
4.0%
이 379
 
3.4%
산 327
 
3.0%
병 324
 
2.9%
어 290
 
2.6%
린 280
 
2.5%
집 270
 
2.4%
부 267
 
2.4%
양 209
 
1.9%
요 180
 
1.6%
Other values (497) 8108
73.2%
Uppercase Letter
ValueCountFrequency (%)
C 153
27.0%
P 136
24.0%
O 30
 
5.3%
A 25
 
4.4%
S 22
 
3.9%
K 19
 
3.4%
G 19
 
3.4%
N 17
 
3.0%
E 15
 
2.7%
V 14
 
2.5%
Other values (15) 116
20.5%
Lowercase Letter
ValueCountFrequency (%)
p 11
15.5%
c 11
15.5%
o 9
12.7%
e 8
11.3%
a 6
8.5%
n 5
7.0%
y 4
 
5.6%
t 3
 
4.2%
s 2
 
2.8%
k 2
 
2.8%
Other values (8) 10
14.1%
Decimal Number
ValueCountFrequency (%)
2 30
25.4%
1 28
23.7%
3 25
21.2%
4 13
11.0%
8 6
 
5.1%
7 5
 
4.2%
5 5
 
4.2%
9 3
 
2.5%
0 2
 
1.7%
6 1
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 149
98.0%
[ 3
 
2.0%
Close Punctuation
ValueCountFrequency (%)
) 149
98.0%
] 3
 
2.0%
Other Symbol
ValueCountFrequency (%)
㈜ 42
95.5%
� 2
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 15
51.7%
, 14
48.3%
Math Symbol
ValueCountFrequency (%)
~ 2
66.7%
～ 1
33.3%
Space Separator
ValueCountFrequency (%)
685
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11116
86.2%
Common 1146
 
8.9%
Latin 637
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 440
 
4.0%
이 379
 
3.4%
산 327
 
2.9%
병 324
 
2.9%
어 290
 
2.6%
린 280
 
2.5%
집 270
 
2.4%
부 267
 
2.4%
양 209
 
1.9%
요 180
 
1.6%
Other values (498) 8150
73.3%
Latin
ValueCountFrequency (%)
C 153
24.0%
P 136
21.4%
O 30
 
4.7%
A 25
 
3.9%
S 22
 
3.5%
K 19
 
3.0%
G 19
 
3.0%
N 17
 
2.7%
E 15
 
2.4%
V 14
 
2.2%
Other values (33) 187
29.4%
Common
ValueCountFrequency (%)
685
59.8%
( 149
 
13.0%
) 149
 
13.0%
2 30
 
2.6%
1 28
 
2.4%
3 25
 
2.2%
. 15
 
1.3%
, 14
 
1.2%
4 13
 
1.1%
8 6
 
0.5%
Other values (11) 32
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11074
85.9%
ASCII 1779
 
13.8%
None 44
 
0.3%
Specials 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
685
38.5%
C 153
 
8.6%
( 149
 
8.4%
) 149
 
8.4%
P 136
 
7.6%
2 30
 
1.7%
O 30
 
1.7%
1 28
 
1.6%
A 25
 
1.4%
3 25
 
1.4%
Other values (51) 369
20.7%
Hangul
ValueCountFrequency (%)
원 440
 
4.0%
이 379
 
3.4%
산 327
 
3.0%
병 324
 
2.9%
어 290
 
2.6%
린 280
 
2.5%
집 270
 
2.4%
부 267
 
2.4%
양 209
 
1.9%
요 180
 
1.6%
Other values (497) 8108
73.2%
None
ValueCountFrequency (%)
㈜ 42
95.5%
é 1
 
2.3%
～ 1
 
2.3%
Specials
ValueCountFrequency (%)
� 2
100.0%

addr
Text

MISSING 

Distinct1288
Distinct (%)88.1%
Missing32
Missing (%)2.1%
Memory size11.8 KiB
2024-04-18T11:02:19.411750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length43
Mean length24.154583
Min length8

Characters and Unicode

Total characters35314
Distinct characters338
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1171 ?
Unique (%)80.1%

Sample

1st row부산광역시 해운대구 센텀동로 6 (우동)
2nd row부산광역시 해운대구 해운대해변로 140 (우동)
3rd row부산광역시 해운대구 세실로 64 (좌동, 화목데파트)
4th row부산광역시 해운대구 양운로 183 (좌동)
5th row부산광역시 해운대구 해운대로 814 (좌동)
ValueCountFrequency (%)
부산광역시 1301
 
19.5%
해운대구 213
 
3.2%
부산진구 196
 
2.9%
사하구 143
 
2.1%
중앙대로 127
 
1.9%
북구 94
 
1.4%
금정구 94
 
1.4%
사상구 93
 
1.4%
동래구 87
 
1.3%
연제구 84
 
1.3%
Other values (1708) 4233
63.5%
2024-04-18T11:02:19.817736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6246
 
17.7%
산 1615
 
4.6%
부 1568
 
4.4%
동 1453
 
4.1%
로 1386
 
3.9%
광 1378
 
3.9%
시 1355
 
3.8%
구 1309
 
3.7%
역 1302
 
3.7%
) 1116
 
3.2%
Other values (328) 16586
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21240
60.1%
Space Separator 6248
 
17.7%
Decimal Number 5092
 
14.4%
Close Punctuation 1134
 
3.2%
Open Punctuation 1134
 
3.2%
Other Punctuation 222
 
0.6%
Dash Punctuation 140
 
0.4%
Uppercase Letter 61
 
0.2%
Math Symbol 41
 
0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 1615
 
7.6%
부 1568
 
7.4%
동 1453
 
6.8%
로 1386
 
6.5%
광 1378
 
6.5%
시 1355
 
6.4%
구 1309
 
6.2%
역 1302
 
6.1%
대 868
 
4.1%
길 423
 
2.0%
Other values (290) 8583
40.4%
Uppercase Letter
ValueCountFrequency (%)
C 12
19.7%
E 12
19.7%
A 11
18.0%
P 10
16.4%
S 3
 
4.9%
K 3
 
4.9%
W 2
 
3.3%
J 1
 
1.6%
B 1
 
1.6%
V 1
 
1.6%
Other values (5) 5
8.2%
Decimal Number
ValueCountFrequency (%)
1 971
19.1%
2 700
13.7%
3 611
12.0%
4 484
9.5%
5 467
9.2%
7 437
8.6%
6 428
8.4%
0 378
 
7.4%
9 313
 
6.1%
8 303
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 219
98.6%
. 2
 
0.9%
· 1
 
0.5%
Space Separator
ValueCountFrequency (%)
6246
> 99.9%
  2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1116
98.4%
] 18
 
1.6%
Open Punctuation
ValueCountFrequency (%)
( 1116
98.4%
[ 18
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
s 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 140
100.0%
Math Symbol
ValueCountFrequency (%)
~ 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21240
60.1%
Common 14011
39.7%
Latin 63
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 1615
 
7.6%
부 1568
 
7.4%
동 1453
 
6.8%
로 1386
 
6.5%
광 1378
 
6.5%
시 1355
 
6.4%
구 1309
 
6.2%
역 1302
 
6.1%
대 868
 
4.1%
길 423
 
2.0%
Other values (290) 8583
40.4%
Common
ValueCountFrequency (%)
6246
44.6%
) 1116
 
8.0%
( 1116
 
8.0%
1 971
 
6.9%
2 700
 
5.0%
3 611
 
4.4%
4 484
 
3.5%
5 467
 
3.3%
7 437
 
3.1%
6 428
 
3.1%
Other values (11) 1435
 
10.2%
Latin
ValueCountFrequency (%)
C 12
19.0%
E 12
19.0%
A 11
17.5%
P 10
15.9%
S 3
 
4.8%
K 3
 
4.8%
W 2
 
3.2%
J 1
 
1.6%
B 1
 
1.6%
k 1
 
1.6%
Other values (7) 7
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21240
60.1%
ASCII 14071
39.8%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6246
44.4%
) 1116
 
7.9%
( 1116
 
7.9%
1 971
 
6.9%
2 700
 
5.0%
3 611
 
4.3%
4 484
 
3.4%
5 467
 
3.3%
7 437
 
3.1%
6 428
 
3.0%
Other values (26) 1495
 
10.6%
Hangul
ValueCountFrequency (%)
산 1615
 
7.6%
부 1568
 
7.4%
동 1453
 
6.8%
로 1386
 
6.5%
광 1378
 
6.5%
시 1355
 
6.4%
구 1309
 
6.2%
역 1302
 
6.1%
대 868
 
4.1%
길 423
 
2.0%
Other values (290) 8583
40.4%
None
ValueCountFrequency (%)
  2
66.7%
· 1
33.3%

mng_agency_nm
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
부산광역시 해운대구청
213 
부산광역시 부산진구청
181 
부산광역시 사하구청
143 
부산광역시 남구청
102 
부산광역시 북구청
94 
Other values (17)
761 

Length

Max length11
Median length10
Mean length9.896921
Min length4

Unique

Unique3 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 해운대구청 213
14.3%
부산광역시 부산진구청 181
12.1%
부산광역시 사하구청 143
9.6%
부산광역시 남구청 102
 
6.8%
부산광역시 북구청 94
 
6.3%
부산광역시 사상구청 93
 
6.2%
부산광역시 금정구청 93
 
6.2%
부산광역시 동래구청 87
 
5.8%
부산광역시 연제구청 84
 
5.6%
부산광역시 수영구청 76
 
5.1%
Other values (12) 328
22.0%

Length

2024-04-18T11:02:19.932884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 1438
49.0%
해운대구청 213
 
7.3%
부산진구청 181
 
6.2%
사하구청 143
 
4.9%
남구청 102
 
3.5%
북구청 94
 
3.2%
사상구청 93
 
3.2%
금정구청 93
 
3.2%
동래구청 87
 
3.0%
연제구청 84
 
2.9%
Other values (13) 405
 
13.8%

gugun
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
부산광역시 해운대구
213 
부산광역시 부산진구
181 
부산광역시 사하구
143 
부산광역시 남구
102 
부산광역시 북구
94 
Other values (32)
761 

Length

Max length16
Median length11
Mean length8.9665328
Min length4

Unique

Unique17 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 213
14.3%
부산광역시 부산진구 181
12.1%
부산광역시 사하구 143
9.6%
부산광역시 남구 102
 
6.8%
부산광역시 북구 94
 
6.3%
부산광역시 사상구 93
 
6.2%
부산광역시 금정구 93
 
6.2%
부산광역시 동래구 87
 
5.8%
부산광역시 연제구 84
 
5.6%
부산광역시 수영구 76
 
5.1%
Other values (27) 328
22.0%

Length

2024-04-18T11:02:20.031867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 1429
48.9%
해운대구 213
 
7.3%
부산진구 181
 
6.2%
사하구 143
 
4.9%
남구 102
 
3.5%
북구 94
 
3.2%
사상구 93
 
3.2%
금정구 93
 
3.2%
동래구 87
 
3.0%
연제구 84
 
2.9%
Other values (28) 404
 
13.8%

reference_date
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct35
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2020-07-31
798 
2020-09-01
213 
2020-08-27
169 
2020-03-31
102 
2020-08-31
84 
Other values (30)
128 

Length

Max length16
Median length10
Mean length9.8882195
Min length4

Unique

Unique25 ?
Unique (%)1.7%

Sample

1st row2020-09-01
2nd row2020-09-01
3rd row2020-09-01
4th row2020-09-01
5th row2020-09-01

Common Values

ValueCountFrequency (%)
2020-07-31 798
53.4%
2020-09-01 213
 
14.3%
2020-08-27 169
 
11.3%
2020-03-31 102
 
6.8%
2020-08-31 84
 
5.6%
2020-09-06 63
 
4.2%
<NA> 33
 
2.2%
129.0500288 3
 
0.2%
3290000 2
 
0.1%
129.0596019 2
 
0.1%
Other values (25) 25
 
1.7%

Length

2024-04-18T11:02:20.137940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-07-31 798
53.4%
2020-09-01 213
 
14.3%
2020-08-27 169
 
11.3%
2020-03-31 102
 
6.8%
2020-08-31 84
 
5.6%
2020-09-06 63
 
4.2%
na 33
 
2.2%
129.0500288 3
 
0.2%
3290000 2
 
0.1%
129.0596019 2
 
0.1%
Other values (25) 25
 
1.7%

lat
Text

MISSING 

Distinct1253
Distinct (%)85.8%
Missing33
Missing (%)2.2%
Memory size11.8 KiB
2024-04-18T11:02:20.325123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length10.273785
Min length6

Characters and Unicode

Total characters15010
Distinct characters14
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

Unique1121 ?
Unique (%)76.7%

Sample

1st row35.171152
2nd row35.158465
3rd row35.170995
4th row35.178173
5th row35.169686
ValueCountFrequency (%)
3290000 16
 
1.1%
35.20367126 8
 
0.5%
35.2246883892 7
 
0.5%
35.22128992 7
 
0.5%
35.15978 7
 
0.5%
35.16263953 6
 
0.4%
35.15679253 5
 
0.3%
35.21170859 5
 
0.3%
35.0983089384273 5
 
0.3%
35.1662582302 4
 
0.3%
Other values (1244) 1393
95.2%
2024-04-18T11:02:20.625640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 2397
16.0%
5 2373
15.8%
1 1838
12.2%
. 1439
9.6%
2 1218
8.1%
0 1099
7.3%
6 1072
7.1%
9 970
6.5%
7 886
 
5.9%
8 871
 
5.8%
Other values (4) 847
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13539
90.2%
Other Punctuation 1443
 
9.6%
Space Separator 24
 
0.2%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2397
17.7%
5 2373
17.5%
1 1838
13.6%
2 1218
9.0%
0 1099
8.1%
6 1072
7.9%
9 970
7.2%
7 886
 
6.5%
8 871
 
6.4%
4 815
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 1439
99.7%
: 4
 
0.3%
Space Separator
ValueCountFrequency (%)
24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15010
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2397
16.0%
5 2373
15.8%
1 1838
12.2%
. 1439
9.6%
2 1218
8.1%
0 1099
7.3%
6 1072
7.1%
9 970
6.5%
7 886
 
5.9%
8 871
 
5.8%
Other values (4) 847
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15010
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2397
16.0%
5 2373
15.8%
1 1838
12.2%
. 1439
9.6%
2 1218
8.1%
0 1099
7.3%
6 1072
7.1%
9 970
6.5%
7 886
 
5.9%
8 871
 
5.8%
Other values (4) 847
 
5.6%

lng
Text

MISSING 

Distinct1234
Distinct (%)84.6%
Missing35
Missing (%)2.3%
Memory size11.8 KiB
2024-04-18T11:02:20.852159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length10.995888
Min length7

Characters and Unicode

Total characters16043
Distinct characters14
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

Unique1101 ?
Unique (%)75.5%

Sample

1st row129.133661
2nd row129.146441
3rd row129.177675
4th row129.168929
5th row129.177688
ValueCountFrequency (%)
2020-12-22 20
 
1.4%
14:10:05 20
 
1.4%
3300000 10
 
0.7%
129.0872104 8
 
0.5%
129.0171440336 7
 
0.5%
128.9858 7
 
0.5%
129.0855783 6
 
0.4%
129.129747 6
 
0.4%
129.0505507 6
 
0.4%
129.036704785495 5
 
0.3%
Other values (1225) 1384
93.6%
2024-04-18T11:02:21.183838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2728
17.0%
2 2383
14.9%
9 2284
14.2%
0 1630
10.2%
. 1429
8.9%
8 1125
7.0%
7 989
 
6.2%
5 880
 
5.5%
6 860
 
5.4%
3 813
 
5.1%
Other values (4) 922
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14491
90.3%
Other Punctuation 1469
 
9.2%
Space Separator 43
 
0.3%
Dash Punctuation 40
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2728
18.8%
2 2383
16.4%
9 2284
15.8%
0 1630
11.2%
8 1125
7.8%
7 989
 
6.8%
5 880
 
6.1%
6 860
 
5.9%
3 813
 
5.6%
4 799
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 1429
97.3%
: 40
 
2.7%
Space Separator
ValueCountFrequency (%)
43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16043
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2728
17.0%
2 2383
14.9%
9 2284
14.2%
0 1630
10.2%
. 1429
8.9%
8 1125
7.0%
7 989
 
6.2%
5 880
 
5.5%
6 860
 
5.4%
3 813
 
5.1%
Other values (4) 922
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2728
17.0%
2 2383
14.9%
9 2284
14.2%
0 1630
10.2%
. 1429
8.9%
8 1125
7.0%
7 989
 
6.2%
5 880
 
5.5%
6 860
 
5.4%
3 813
 
5.1%
Other values (4) 922
 
5.7%

instt_code
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
3330000
213 
3290000
181 
3340000
143 
3310000
102 
3320000
94 
Other values (13)
761 

Length

Max length19
Median length7
Mean length6.9698795
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3330000
2nd row3330000
3rd row3330000
4th row3330000
5th row3330000

Common Values

ValueCountFrequency (%)
3330000 213
14.3%
3290000 181
12.1%
3340000 143
9.6%
3310000 102
 
6.8%
3320000 94
 
6.3%
3350000 93
 
6.2%
3390000 93
 
6.2%
3300000 87
 
5.8%
3370000 84
 
5.6%
3380000 76
 
5.1%
Other values (8) 328
22.0%

Length

2024-04-18T11:02:21.303520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3330000 213
14.2%
3290000 181
12.0%
3340000 143
9.5%
3310000 102
 
6.8%
3320000 94
 
6.2%
3350000 93
 
6.2%
3390000 93
 
6.2%
3300000 87
 
5.8%
3370000 84
 
5.6%
3380000 76
 
5.1%
Other values (9) 338
22.5%

last_load_dttm
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing65
Missing (%)4.4%
Memory size11.8 KiB
Minimum2020-12-22 14:10:05
Maximum2020-12-22 14:10:05
2024-04-18T11:02:21.405344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T11:02:21.479031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2024-04-18T11:02:21.540805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
facility_gubunmng_agency_nmgugunreference_dateinstt_code
facility_gubun1.0000.9300.9940.9990.887
mng_agency_nm0.9301.0000.9990.9541.000
gugun0.9940.9991.0000.9941.000
reference_date0.9990.9540.9941.0000.925
instt_code0.8871.0001.0000.9251.000
2024-04-18T11:02:21.624560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
guguninstt_codemng_agency_nmreference_date
gugun1.0001.0000.9690.848
instt_code1.0001.0001.0000.627
mng_agency_nm0.9691.0001.0000.619
reference_date0.8480.6270.6191.000
2024-04-18T11:02:21.699771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
mng_agency_nmgugunreference_dateinstt_code
mng_agency_nm1.0000.9690.6191.000
gugun0.9691.0000.8481.000
reference_date0.6190.8481.0000.627
instt_code1.0001.0000.6271.000

Missing values

2024-04-18T11:02:17.132915image/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.
2024-04-18T11:02:17.277831image/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

skeyfacility_gubunfacility_nmaddrmng_agency_nmgugunreference_datelatlnginstt_codelast_load_dttm
09812대규모점포홈플러스 센텀시티점부산광역시 해운대구 센텀동로 6 (우동)부산광역시 해운대구청부산광역시 해운대구2020-09-0135.171152129.13366133300002020-12-22 14:10:05
19813대규모점포홈플러스 해운대점부산광역시 해운대구 해운대해변로 140 (우동)부산광역시 해운대구청부산광역시 해운대구2020-09-0135.158465129.14644133300002020-12-22 14:10:05
29814대규모점포화목데파트 상가부산광역시 해운대구 세실로 64 (좌동, 화목데파트)부산광역시 해운대구청부산광역시 해운대구2020-09-0135.170995129.17767533300002020-12-22 14:10:05
39815도서관해운대도서관부산광역시 해운대구 양운로 183 (좌동)부산광역시 해운대구청부산광역시 해운대구2020-09-0135.178173129.16892933300002020-12-22 14:10:05
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