Overview

Dataset statistics

Number of variables14
Number of observations354
Missing cells723
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.9 KiB
Average record size in memory115.4 B

Variable types

Text6
Categorical5
Numeric1
Unsupported2

Alerts

gubun is highly overall correlated with data_day and 1 other fieldsHigh correlation
school_kind is highly overall correlated with data_day and 1 other fieldsHigh correlation
last_load_dttm is highly overall correlated with lng and 4 other fieldsHigh correlation
data_day is highly overall correlated with lng and 4 other fieldsHigh correlation
inst_center is highly overall correlated with data_day and 1 other fieldsHigh correlation
lng is highly overall correlated with data_day and 1 other fieldsHigh correlation
gubun is highly imbalanced (63.3%)Imbalance
school_kind is highly imbalanced (60.8%)Imbalance
data_day is highly imbalanced (89.3%)Imbalance
last_load_dttm is highly imbalanced (89.3%)Imbalance
lng has 5 (1.4%) missing valuesMissing
apr_at has 354 (100.0%) missing valuesMissing
instt_code has 354 (100.0%) missing valuesMissing
skey has unique valuesUnique
apr_at is an unsupported type, check if it needs cleaning or further analysisUnsupported
instt_code is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-20 19:16:12.973265
Analysis finished2024-04-20 19:16:15.564958
Duration2.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Text

UNIQUE 

Distinct354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2024-04-21T04:16:16.779418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length4
Mean length4.0706215
Min length4

Characters and Unicode

Total characters1441
Distinct characters41
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique354 ?
Unique (%)100.0%

Sample

1st row3892
2nd row3893
3rd row3894
4th row3895
5th row3896
ValueCountFrequency (%)
3892 1
 
0.3%
3980 1
 
0.3%
4000 1
 
0.3%
3999 1
 
0.3%
3998 1
 
0.3%
3997 1
 
0.3%
3996 1
 
0.3%
3995 1
 
0.3%
3994 1
 
0.3%
4002 1
 
0.3%
Other values (347) 347
97.2%
2024-04-21T04:16:18.562397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 311
21.6%
3 183
12.7%
1 175
12.1%
0 175
12.1%
9 173
12.0%
2 119
 
8.3%
8 73
 
5.1%
5 66
 
4.6%
7 65
 
4.5%
6 65
 
4.5%
Other values (31) 36
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1405
97.5%
Other Letter 28
 
1.9%
Space Separator 3
 
0.2%
Other Punctuation 2
 
0.1%
Uppercase Letter 1
 
0.1%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 2
 
7.1%
영 2
 
7.1%
동 2
 
7.1%
클 1
 
3.6%
중 1
 
3.6%
흥 1
 
3.6%
움 1
 
3.6%
래 1
 
3.6%
스 1
 
3.6%
프 1
 
3.6%
Other values (15) 15
53.6%
Decimal Number
ValueCountFrequency (%)
4 311
22.1%
3 183
13.0%
1 175
12.5%
0 175
12.5%
9 173
12.3%
2 119
 
8.5%
8 73
 
5.2%
5 66
 
4.7%
7 65
 
4.6%
6 65
 
4.6%
Other Punctuation
ValueCountFrequency (%)
@ 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1412
98.0%
Hangul 28
 
1.9%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 2
 
7.1%
영 2
 
7.1%
동 2
 
7.1%
클 1
 
3.6%
중 1
 
3.6%
흥 1
 
3.6%
움 1
 
3.6%
래 1
 
3.6%
스 1
 
3.6%
프 1
 
3.6%
Other values (15) 15
53.6%
Common
ValueCountFrequency (%)
4 311
22.0%
3 183
13.0%
1 175
12.4%
0 175
12.4%
9 173
12.3%
2 119
 
8.4%
8 73
 
5.2%
5 66
 
4.7%
7 65
 
4.6%
6 65
 
4.6%
Other values (5) 7
 
0.5%
Latin
ValueCountFrequency (%)
S 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1413
98.1%
Hangul 28
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 311
22.0%
3 183
13.0%
1 175
12.4%
0 175
12.4%
9 173
12.2%
2 119
 
8.4%
8 73
 
5.2%
5 66
 
4.7%
7 65
 
4.6%
6 65
 
4.6%
Other values (6) 8
 
0.6%
Hangul
ValueCountFrequency (%)
부 2
 
7.1%
영 2
 
7.1%
동 2
 
7.1%
클 1
 
3.6%
중 1
 
3.6%
흥 1
 
3.6%
움 1
 
3.6%
래 1
 
3.6%
스 1
 
3.6%
프 1
 
3.6%
Other values (15) 15
53.6%

gubun
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
어린이집
273 
유치원
52 
초등학교
 
22
기존
 
2
고등학교
 
2
Other values (3)
 
3

Length

Max length4
Median length4
Mean length3.8333333
Min length2

Unique

Unique3 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
어린이집 273
77.1%
유치원 52
 
14.7%
초등학교 22
 
6.2%
기존 2
 
0.6%
고등학교 2
 
0.6%
40 1
 
0.3%
특수학교 1
 
0.3%
중학교 1
 
0.3%

Length

2024-04-21T04:16:19.013306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T04:16:19.388993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
어린이집 273
77.1%
유치원 52
 
14.7%
초등학교 22
 
6.2%
기존 2
 
0.6%
고등학교 2
 
0.6%
40 1
 
0.3%
특수학교 1
 
0.3%
중학교 1
 
0.3%

school_name
Text

Distinct343
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2024-04-21T04:16:20.134118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length7.240113
Min length5

Characters and Unicode

Total characters2563
Distinct characters296
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique332 ?
Unique (%)93.8%

Sample

1st row노틀담어린이집
2nd row보수어린이집
3rd row보현어린이집
4th row부산세관어린이집
5th row숲속어린이집
ValueCountFrequency (%)
어린이집 7
 
1.9%
미래어린이집 2
 
0.5%
늘푸른어린이집 2
 
0.5%
한솔어린이집 2
 
0.5%
병설유치원 2
 
0.5%
동심어린이집 2
 
0.5%
한마음어린이집 2
 
0.5%
꿈동산어린이집 2
 
0.5%
유치원 2
 
0.5%
우신어린이집 2
 
0.5%
Other values (338) 343
93.2%
2024-04-21T04:16:21.327588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 285
 
11.1%
린 275
 
10.7%
어 272
 
10.6%
집 272
 
10.6%
원 66
 
2.6%
유 53
 
2.1%
치 53
 
2.1%
동 40
 
1.6%
교 38
 
1.5%
초 38
 
1.5%
Other values (286) 1171
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2483
96.9%
Decimal Number 30
 
1.2%
Uppercase Letter 23
 
0.9%
Space Separator 22
 
0.9%
Dash Punctuation 2
 
0.1%
Lowercase Letter 2
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 285
 
11.5%
린 275
 
11.1%
어 272
 
11.0%
집 272
 
11.0%
원 66
 
2.7%
유 53
 
2.1%
치 53
 
2.1%
동 40
 
1.6%
교 38
 
1.5%
초 38
 
1.5%
Other values (263) 1091
43.9%
Decimal Number
ValueCountFrequency (%)
2 8
26.7%
1 7
23.3%
4 4
13.3%
5 3
 
10.0%
0 2
 
6.7%
3 2
 
6.7%
6 2
 
6.7%
8 1
 
3.3%
7 1
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
L 5
21.7%
G 5
21.7%
K 3
13.0%
B 3
13.0%
I 2
 
8.7%
C 2
 
8.7%
F 1
 
4.3%
R 1
 
4.3%
A 1
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
s 1
50.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
! 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2483
96.9%
Common 55
 
2.1%
Latin 25
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 285
 
11.5%
린 275
 
11.1%
어 272
 
11.0%
집 272
 
11.0%
원 66
 
2.7%
유 53
 
2.1%
치 53
 
2.1%
동 40
 
1.6%
교 38
 
1.5%
초 38
 
1.5%
Other values (263) 1091
43.9%
Common
ValueCountFrequency (%)
22
40.0%
2 8
 
14.5%
1 7
 
12.7%
4 4
 
7.3%
5 3
 
5.5%
0 2
 
3.6%
- 2
 
3.6%
3 2
 
3.6%
6 2
 
3.6%
8 1
 
1.8%
Other values (2) 2
 
3.6%
Latin
ValueCountFrequency (%)
L 5
20.0%
G 5
20.0%
K 3
12.0%
B 3
12.0%
I 2
 
8.0%
C 2
 
8.0%
F 1
 
4.0%
R 1
 
4.0%
A 1
 
4.0%
k 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2483
96.9%
ASCII 80
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 285
 
11.5%
린 275
 
11.1%
어 272
 
11.0%
집 272
 
11.0%
원 66
 
2.7%
유 53
 
2.1%
치 53
 
2.1%
동 40
 
1.6%
교 38
 
1.5%
초 38
 
1.5%
Other values (263) 1091
43.9%
ASCII
ValueCountFrequency (%)
22
27.5%
2 8
 
10.0%
1 7
 
8.8%
L 5
 
6.2%
G 5
 
6.2%
4 4
 
5.0%
K 3
 
3.8%
B 3
 
3.8%
5 3
 
3.8%
I 2
 
2.5%
Other values (13) 18
22.5%

student_num
Text

Distinct140
Distinct (%)39.9%
Missing3
Missing (%)0.8%
Memory size2.9 KiB
2024-04-21T04:16:22.505923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length2
Mean length2.3048433
Min length1

Characters and Unicode

Total characters809
Distinct characters29
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

Unique63 ?
Unique (%)17.9%

Sample

1st row73
2nd row45
3rd row40
4th row58
5th row60
ValueCountFrequency (%)
20 17
 
4.8%
19 9
 
2.5%
65 8
 
2.3%
46 8
 
2.3%
45 8
 
2.3%
60 7
 
2.0%
16 7
 
2.0%
49 7
 
2.0%
40 6
 
1.7%
13 6
 
1.7%
Other values (134) 272
76.6%
2024-04-21T04:16:24.310786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 121
15.0%
2 86
10.6%
3 86
10.6%
6 81
10.0%
4 80
9.9%
0 79
9.8%
5 74
9.1%
7 70
8.7%
8 57
7.0%
9 51
6.3%
Other values (19) 24
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 785
97.0%
Other Letter 16
 
2.0%
Space Separator 4
 
0.5%
Other Punctuation 2
 
0.2%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2
 
12.5%
리 1
 
6.2%
로 1
 
6.2%
관 1
 
6.2%
동 1
 
6.2%
호 1
 
6.2%
단 1
 
6.2%
시 1
 
6.2%
신 1
 
6.2%
구 1
 
6.2%
Other values (5) 5
31.2%
Decimal Number
ValueCountFrequency (%)
1 121
15.4%
2 86
11.0%
3 86
11.0%
6 81
10.3%
4 80
10.2%
0 79
10.1%
5 74
9.4%
7 70
8.9%
8 57
7.3%
9 51
6.5%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 793
98.0%
Hangul 16
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 2
 
12.5%
리 1
 
6.2%
로 1
 
6.2%
관 1
 
6.2%
동 1
 
6.2%
호 1
 
6.2%
단 1
 
6.2%
시 1
 
6.2%
신 1
 
6.2%
구 1
 
6.2%
Other values (5) 5
31.2%
Common
ValueCountFrequency (%)
1 121
15.3%
2 86
10.8%
3 86
10.8%
6 81
10.2%
4 80
10.1%
0 79
10.0%
5 74
9.3%
7 70
8.8%
8 57
7.2%
9 51
6.4%
Other values (4) 8
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 793
98.0%
Hangul 16
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 121
15.3%
2 86
10.8%
3 86
10.8%
6 81
10.2%
4 80
10.1%
0 79
10.0%
5 74
9.3%
7 70
8.8%
8 57
7.2%
9 51
6.4%
Other values (4) 8
 
1.0%
Hangul
ValueCountFrequency (%)
산 2
 
12.5%
리 1
 
6.2%
로 1
 
6.2%
관 1
 
6.2%
동 1
 
6.2%
호 1
 
6.2%
단 1
 
6.2%
시 1
 
6.2%
신 1
 
6.2%
구 1
 
6.2%
Other values (5) 5
31.2%

tel
Text

Distinct351
Distinct (%)99.7%
Missing2
Missing (%)0.6%
Memory size2.9 KiB
2024-04-21T04:16:25.244222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.039773
Min length10

Characters and Unicode

Total characters4238
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

Unique350 ?
Unique (%)99.4%

Sample

1st row051-464-0570
2nd row051-242-3583
3rd row051-244-3362
4th row051-620-6088
5th row051-256-1843
ValueCountFrequency (%)
051-325-7650 2
 
0.6%
051-315-3580 2
 
0.6%
051-532-3211 1
 
0.3%
051-894-5486 1
 
0.3%
051-464-0570 1
 
0.3%
051-935-4258 1
 
0.3%
051-524-0014 1
 
0.3%
051-507-2005 1
 
0.3%
051-524-2340 1
 
0.3%
051-501-4321 1
 
0.3%
Other values (340) 340
96.6%
2024-04-21T04:16:26.583091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 700
16.5%
5 625
14.7%
0 622
14.7%
1 603
14.2%
2 317
7.5%
3 282
6.7%
7 248
 
5.9%
4 247
 
5.8%
6 234
 
5.5%
8 194
 
4.6%
Other values (3) 166
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3524
83.2%
Dash Punctuation 700
 
16.5%
Space Separator 12
 
0.3%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 625
17.7%
0 622
17.7%
1 603
17.1%
2 317
9.0%
3 282
8.0%
7 248
 
7.0%
4 247
 
7.0%
6 234
 
6.6%
8 194
 
5.5%
9 152
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 700
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4238
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 700
16.5%
5 625
14.7%
0 622
14.7%
1 603
14.2%
2 317
7.5%
3 282
6.7%
7 248
 
5.9%
4 247
 
5.8%
6 234
 
5.5%
8 194
 
4.6%
Other values (3) 166
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 700
16.5%
5 625
14.7%
0 622
14.7%
1 603
14.2%
2 317
7.5%
3 282
6.7%
7 248
 
5.9%
4 247
 
5.8%
6 234
 
5.5%
8 194
 
4.6%
Other values (3) 166
 
3.9%

school_addr
Text

Distinct344
Distinct (%)97.7%
Missing2
Missing (%)0.6%
Memory size2.9 KiB
2024-04-21T04:16:28.004581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length39
Mean length23.360795
Min length10

Characters and Unicode

Total characters8223
Distinct characters281
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique336 ?
Unique (%)95.5%

Sample

1st row부산광역시 중구 망양로 309
2nd row부산광역시 중구 망양로 319번길
3rd row부산광역시 중구 흑교로 31번길 34-1
4th row부산광역시 중구 충장대로 20, 별관 2층
5th row부산광역시 중구 고가길 40
ValueCountFrequency (%)
부산광역시 347
 
19.7%
북구 35
 
2.0%
해운대구 32
 
1.8%
영도구 29
 
1.6%
금정구 29
 
1.6%
사상구 29
 
1.6%
기장군 28
 
1.6%
사하구 23
 
1.3%
남구 22
 
1.2%
동구 21
 
1.2%
Other values (693) 1167
66.2%
2024-04-21T04:16:29.594217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1429
 
17.4%
산 390
 
4.7%
1 382
 
4.6%
부 374
 
4.5%
시 362
 
4.4%
광 355
 
4.3%
역 347
 
4.2%
로 333
 
4.0%
구 332
 
4.0%
2 186
 
2.3%
Other values (271) 3733
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5092
61.9%
Decimal Number 1549
 
18.8%
Space Separator 1429
 
17.4%
Dash Punctuation 63
 
0.8%
Close Punctuation 31
 
0.4%
Open Punctuation 31
 
0.4%
Other Punctuation 22
 
0.3%
Uppercase Letter 3
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 390
 
7.7%
부 374
 
7.3%
시 362
 
7.1%
광 355
 
7.0%
역 347
 
6.8%
로 333
 
6.5%
구 332
 
6.5%
길 178
 
3.5%
번 159
 
3.1%
동 135
 
2.7%
Other values (249) 2127
41.8%
Decimal Number
ValueCountFrequency (%)
1 382
24.7%
2 186
12.0%
3 181
11.7%
0 168
10.8%
4 138
 
8.9%
6 123
 
7.9%
5 111
 
7.2%
7 96
 
6.2%
9 82
 
5.3%
8 82
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
A 1
33.3%
L 1
33.3%
G 1
33.3%
Lowercase Letter
ValueCountFrequency (%)
s 1
33.3%
k 1
33.3%
e 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 19
86.4%
@ 3
 
13.6%
Space Separator
ValueCountFrequency (%)
1429
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5092
61.9%
Common 3125
38.0%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 390
 
7.7%
부 374
 
7.3%
시 362
 
7.1%
광 355
 
7.0%
역 347
 
6.8%
로 333
 
6.5%
구 332
 
6.5%
길 178
 
3.5%
번 159
 
3.1%
동 135
 
2.7%
Other values (249) 2127
41.8%
Common
ValueCountFrequency (%)
1429
45.7%
1 382
 
12.2%
2 186
 
6.0%
3 181
 
5.8%
0 168
 
5.4%
4 138
 
4.4%
6 123
 
3.9%
5 111
 
3.6%
7 96
 
3.1%
9 82
 
2.6%
Other values (6) 229
 
7.3%
Latin
ValueCountFrequency (%)
A 1
16.7%
L 1
16.7%
G 1
16.7%
s 1
16.7%
k 1
16.7%
e 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5092
61.9%
ASCII 3131
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1429
45.6%
1 382
 
12.2%
2 186
 
5.9%
3 181
 
5.8%
0 168
 
5.4%
4 138
 
4.4%
6 123
 
3.9%
5 111
 
3.5%
7 96
 
3.1%
9 82
 
2.6%
Other values (12) 235
 
7.5%
Hangul
ValueCountFrequency (%)
산 390
 
7.7%
부 374
 
7.3%
시 362
 
7.1%
광 355
 
7.0%
역 347
 
6.8%
로 333
 
6.5%
구 332
 
6.5%
길 178
 
3.5%
번 159
 
3.1%
동 135
 
2.7%
Other values (249) 2127
41.8%

school_kind
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
기존
240 
신규
104 
<NA>
 
5
기존)
 
2
가존
 
1
Other values (2)
 
2

Length

Max length4
Median length2
Mean length2.039548
Min length2

Unique

Unique3 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
기존 240
67.8%
신규 104
29.4%
<NA> 5
 
1.4%
기존) 2
 
0.6%
가존 1
 
0.3%
기존 1
 
0.3%
신규 1
 
0.3%

Length

2024-04-21T04:16:29.860341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T04:16:30.113633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기존 243
68.6%
신규 105
29.7%
na 5
 
1.4%
가존 1
 
0.3%

inst_center
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
부산북구보건소
35 
해운대구보건소
32 
금정구보건소
29 
영도구보건소
29 
사상구보건소
29 
Other values (12)
200 

Length

Max length8
Median length6
Mean length6.4519774
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산북구보건소 35
9.9%
해운대구보건소 32
 
9.0%
금정구보건소 29
 
8.2%
영도구보건소 29
 
8.2%
사상구보건소 29
 
8.2%
기장군보건소 28
 
7.9%
사하구보건소 23
 
6.5%
부산남구보건소 22
 
6.2%
부산동구보건소 21
 
5.9%
부산진구보건소 20
 
5.6%
Other values (7) 86
24.3%

Length

2024-04-21T04:16:30.373405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산북구보건소 35
9.9%
해운대구보건소 32
 
9.0%
금정구보건소 29
 
8.2%
영도구보건소 29
 
8.2%
사상구보건소 29
 
8.2%
기장군보건소 28
 
7.9%
사하구보건소 23
 
6.5%
부산남구보건소 22
 
6.2%
부산동구보건소 21
 
5.9%
부산진구보건소 20
 
5.6%
Other values (7) 86
24.3%

lat
Text

Distinct316
Distinct (%)90.0%
Missing3
Missing (%)0.8%
Memory size2.9 KiB
2024-04-21T04:16:31.280909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length11
Mean length9.4900285
Min length4

Characters and Unicode

Total characters3331
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

Unique289 ?
Unique (%)82.3%

Sample

1st row35.10578162
2nd row35.10662134
3rd row35.1025574
4th row35.10450764
5th row35.10201442
ValueCountFrequency (%)
35.148 6
 
1.7%
35.162 3
 
0.8%
35.198 3
 
0.8%
35.127 3
 
0.8%
35.268 3
 
0.8%
35.129 2
 
0.6%
35.2 2
 
0.6%
35.202 2
 
0.6%
35.131 2
 
0.6%
35.083 2
 
0.6%
Other values (307) 325
92.1%
2024-04-21T04:16:32.421678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 572
17.2%
5 530
15.9%
1 363
10.9%
. 349
10.5%
2 301
9.0%
7 220
 
6.6%
0 216
 
6.5%
4 209
 
6.3%
6 198
 
5.9%
9 182
 
5.5%
Other values (4) 191
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2972
89.2%
Other Punctuation 353
 
10.6%
Dash Punctuation 4
 
0.1%
Space Separator 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 572
19.2%
5 530
17.8%
1 363
12.2%
2 301
10.1%
7 220
 
7.4%
0 216
 
7.3%
4 209
 
7.0%
6 198
 
6.7%
9 182
 
6.1%
8 181
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 349
98.9%
: 4
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 572
17.2%
5 530
15.9%
1 363
10.9%
. 349
10.5%
2 301
9.0%
7 220
 
6.6%
0 216
 
6.5%
4 209
 
6.3%
6 198
 
5.9%
9 182
 
5.5%
Other values (4) 191
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 572
17.2%
5 530
15.9%
1 363
10.9%
. 349
10.5%
2 301
9.0%
7 220
 
6.6%
0 216
 
6.5%
4 209
 
6.3%
6 198
 
5.9%
9 182
 
5.5%
Other values (4) 191
 
5.7%

lng
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct320
Distinct (%)91.7%
Missing5
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean129.02334
Minimum123.423
Maximum129.25804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-04-21T04:16:32.665633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123.423
5-th percentile128.9604
Q1129.012
median129.0584
Q3129.09497
95-th percentile129.17758
Maximum129.25804
Range5.835043
Interquartile range (IQR)0.0829665

Descriptive statistics

Standard deviation0.37403519
Coefficient of variation (CV)0.0028989731
Kurtosis163.01325
Mean129.02334
Median Absolute Deviation (MAD)0.0414914
Skewness-12.052053
Sum45029.146
Variance0.13990233
MonotonicityNot monotonic
2024-04-21T04:16:32.905892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.108 4
 
1.1%
128.971 3
 
0.8%
129.077 3
 
0.8%
129.213 3
 
0.8%
129.028 2
 
0.6%
129.023 2
 
0.6%
129.017 2
 
0.6%
129.012 2
 
0.6%
129.006 2
 
0.6%
129.16 2
 
0.6%
Other values (310) 324
91.5%
(Missing) 5
 
1.4%
ValueCountFrequency (%)
123.423 1
0.3%
125.7758087 1
0.3%
127.008 1
0.3%
128.503 1
0.3%
128.531 1
0.3%
128.661 1
0.3%
128.7402002 1
0.3%
128.816 1
0.3%
128.8513166 1
0.3%
128.8735538 1
0.3%
ValueCountFrequency (%)
129.258043 1
 
0.3%
129.243607 1
 
0.3%
129.223 1
 
0.3%
129.2163239 1
 
0.3%
129.213 3
0.8%
129.2127674 1
 
0.3%
129.2104953 1
 
0.3%
129.208 1
 
0.3%
129.2048929 1
 
0.3%
129.2045203 1
 
0.3%

data_day
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2020-06-30
349 
<NA>
 
5

Length

Max length10
Median length10
Mean length9.9152542
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-06-30
2nd row2020-06-30
3rd row2020-06-30
4th row2020-06-30
5th row2020-06-30

Common Values

ValueCountFrequency (%)
2020-06-30 349
98.6%
<NA> 5
 
1.4%

Length

2024-04-21T04:16:33.142332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T04:16:33.322718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-06-30 349
98.6%
na 5
 
1.4%

apr_at
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing354
Missing (%)100.0%
Memory size3.2 KiB

instt_code
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing354
Missing (%)100.0%
Memory size3.2 KiB

last_load_dttm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2020-12-22 14:32:51
349 
<NA>
 
5

Length

Max length19
Median length19
Mean length18.788136
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-22 14:32:51
2nd row2020-12-22 14:32:51
3rd row2020-12-22 14:32:51
4th row2020-12-22 14:32:51
5th row2020-12-22 14:32:51

Common Values

ValueCountFrequency (%)
2020-12-22 14:32:51 349
98.6%
<NA> 5
 
1.4%

Length

2024-04-21T04:16:33.518064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T04:16:33.703882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-22 349
49.6%
14:32:51 349
49.6%
na 5
 
0.7%

Interactions

2024-04-21T04:16:13.954488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T04:16:33.817375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gubunschool_kindinst_centerlng
gubun1.0000.0000.2060.000
school_kind0.0001.0000.3260.000
inst_center0.2060.3261.0000.000
lng0.0000.0000.0001.000
2024-04-21T04:16:33.978185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gubunschool_kindlast_load_dttmdata_dayinst_center
gubun1.0000.0001.0001.0000.098
school_kind0.0001.0001.0001.0000.160
last_load_dttm1.0001.0001.0001.0001.000
data_day1.0001.0001.0001.0001.000
inst_center0.0980.1601.0001.0001.000
2024-04-21T04:16:34.148626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
lnggubunschool_kindinst_centerdata_daylast_load_dttm
lng1.0000.0000.0000.0001.0001.000
gubun0.0001.0000.0000.0981.0001.000
school_kind0.0000.0001.0000.1601.0001.000
inst_center0.0000.0980.1601.0001.0001.000
data_day1.0001.0001.0001.0001.0001.000
last_load_dttm1.0001.0001.0001.0001.0001.000

Missing values

2024-04-21T04:16:14.319007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T04:16:14.891883image/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.
2024-04-21T04:16:15.308138image/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

skeygubunschool_namestudent_numtelschool_addrschool_kindinst_centerlatlngdata_dayapr_atinstt_codelast_load_dttm
03892어린이집노틀담어린이집73051-464-0570부산광역시 중구 망양로 309기존부산중구보건소35.10578162129.0282442020-06-30<NA><NA>2020-12-22 14:32:51
13893어린이집보수어린이집45051-242-3583부산광역시 중구 망양로 319번길기존부산중구보건소35.10662134129.0270152020-06-30<NA><NA>2020-12-22 14:32:51
23894어린이집보현어린이집40051-244-3362부산광역시 중구 흑교로 31번길 34-1기존부산중구보건소35.1025574129.0215242020-06-30<NA><NA>2020-12-22 14:32:51
33895어린이집부산세관어린이집58051-620-6088부산광역시 중구 충장대로 20, 별관 2층기존부산중구보건소35.10450764129.0391532020-06-30<NA><NA>2020-12-22 14:32:51
43896어린이집숲속어린이집60051-256-1843부산광역시 중구 고가길 40기존부산중구보건소35.10201442129.0313442020-06-30<NA><NA>2020-12-22 14:32:51
53897어린이집성모희보어린이집65051-466-0363부산광역시 중구 중구로 97번길 7신규부산중구보건소35.105868129.0292020-06-30<NA><NA>2020-12-22 14:32:51
63898어린이집영주어린이집74051-462-2855부산광역시 중구 망양로 396기존부산중구보건소35.11186392129.029562020-06-30<NA><NA>2020-12-22 14:32:51
74201어린이집금강어린이집16051-317-8880부산광역시 사상구 대동로64번길 25 금강아파트 103동 110호가존사상구보건소35.13612818128.9786772020-06-30<NA><NA>2020-12-22 14:32:51
84202어린이집꼬마나라어린이집18051-315-4566부산광역시 사상구 백양대로 372-15 주례한일유엔아이아파트 109동 103호기존사상구보건소35.15394769129.010622020-06-30<NA><NA>2020-12-22 14:32:51
94203유치원감전초등학교병설유치원47051-310-8393부산광역시 사상구 괘감로 132 감전초등학교병설유치원기존사상구보건소35.15574257128.9886692020-06-30<NA><NA>2020-12-22 14:32:51
skeygubunschool_namestudent_numtelschool_addrschool_kindinst_centerlatlngdata_dayapr_atinstt_codelast_load_dttm
3443910유치원영락유치원73051-254-2509부산광역시 서구 대청로 8기존부산서구보건소35.103129.0192020-06-30<NA><NA>2020-12-22 14:32:51
3453911유치원토성초등학교 병설유치원46051-250-0847부산광역시 서구 구덕로 134번길 45기존부산서구보건소35.09949563129.0211672020-06-30<NA><NA>2020-12-22 14:32:51
3463912어린이집꼬망쎼어린이집80051-255-9222부산광역시 서구 대영로 73번길 76신규부산서구보건소35.114129.0152020-06-30<NA><NA>2020-12-22 14:32:51
3473913유치원동신초등학교 병설유치원65051-240-0794부산광역시 서구 대영로 85번길 81-19기존부산서구보건소35.11385804129.0175952020-06-30<NA><NA>2020-12-22 14:32:51
3483914어린이집한마음어린이집49051-253-1968부산광역시 서구 망양로 193번길 104기존부산서구보건소35.11117899129.0263592020-06-30<NA><NA>2020-12-22 14:32:51
3493915유치원노틀담유치원103051-253-1843부산광역시 서구 임시수도기념로 61-32기존부산서구보건소35.10275915129.0168312020-06-30<NA><NA>2020-12-22 14:32:51
3503916초등학교부민초등학교590051-603-2119부산광역시 서구 고운들로 12신규부산서구보건소35.107129.0172020-06-30<NA><NA>2020-12-22 14:32:51
3513917고등학교경남고등학교574051-250-5090부산광역시 서구 망양로 111번길 65기존부산서구보건소35.12020133129.0200442020-06-30<NA><NA>2020-12-22 14:32:51
3523918어린이집GKL행복어린이집27051-647-9551부산광역시 동구 자성로 134(눌원빌딩 1층)기존부산동구보건소35.13663383129.0650412020-06-30<NA><NA>2020-12-22 14:32:51
3533919어린이집IBK참!좋은어린이집25051-633-3800부산광역시 동구 중앙대로 489 (2층)기존부산동구보건소35.13694596129.0561672020-06-30<NA><NA>2020-12-22 14:32:51