Overview

Dataset statistics

Number of variables14
Number of observations705
Missing cells1776
Missing cells (%)18.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.6 KiB
Average record size in memory114.2 B

Variable types

Text6
Categorical5
Numeric1
Boolean1
Unsupported1

Alerts

apr_at has constant value ""Constant
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 (51.0%)Imbalance
school_kind is highly imbalanced (62.4%)Imbalance
last_load_dttm is highly imbalanced (93.9%)Imbalance
tel has 353 (50.1%) missing valuesMissing
school_addr has 353 (50.1%) missing valuesMissing
apr_at has 354 (50.2%) missing valuesMissing
instt_code has 705 (100.0%) missing valuesMissing
skey has unique valuesUnique
instt_code is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-20 19:17:46.321594
Analysis finished2024-04-20 19:17:49.067247
Duration2.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Text

UNIQUE 

Distinct705
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-21T04:17:50.312502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length4
Mean length4.035461
Min length4

Characters and Unicode

Total characters2845
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

Unique705 ?
Unique (%)100.0%

Sample

1st row4542
2nd row4543
3rd row4544
4th row4545
5th row4546
ValueCountFrequency (%)
4542 1
 
0.1%
4143 1
 
0.1%
4077 1
 
0.1%
4066 1
 
0.1%
4058 1
 
0.1%
4059 1
 
0.1%
4060 1
 
0.1%
4061 1
 
0.1%
4062 1
 
0.1%
4063 1
 
0.1%
Other values (698) 698
98.6%
2024-04-21T04:17:52.168618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 834
29.3%
3 349
12.3%
9 242
 
8.5%
2 241
 
8.5%
0 240
 
8.4%
1 240
 
8.4%
5 235
 
8.3%
8 148
 
5.2%
7 140
 
4.9%
6 140
 
4.9%
Other values (31) 36
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2809
98.7%
Other Letter 28
 
1.0%
Space Separator 3
 
0.1%
Other Punctuation 2
 
0.1%
Open Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 2
 
7.1%
부 2
 
7.1%
동 2
 
7.1%
이 1
 
3.6%
로 1
 
3.6%
어 1
 
3.6%
린 1
 
3.6%
집 1
 
3.6%
신 1
 
3.6%
호 1
 
3.6%
Other values (15) 15
53.6%
Decimal Number
ValueCountFrequency (%)
4 834
29.7%
3 349
12.4%
9 242
 
8.6%
2 241
 
8.6%
0 240
 
8.5%
1 240
 
8.5%
5 235
 
8.4%
8 148
 
5.3%
7 140
 
5.0%
6 140
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
@ 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2816
99.0%
Hangul 28
 
1.0%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 2
 
7.1%
부 2
 
7.1%
동 2
 
7.1%
이 1
 
3.6%
로 1
 
3.6%
어 1
 
3.6%
린 1
 
3.6%
집 1
 
3.6%
신 1
 
3.6%
호 1
 
3.6%
Other values (15) 15
53.6%
Common
ValueCountFrequency (%)
4 834
29.6%
3 349
12.4%
9 242
 
8.6%
2 241
 
8.6%
0 240
 
8.5%
1 240
 
8.5%
5 235
 
8.3%
8 148
 
5.3%
7 140
 
5.0%
6 140
 
5.0%
Other values (5) 7
 
0.2%
Latin
ValueCountFrequency (%)
S 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2817
99.0%
Hangul 28
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 834
29.6%
3 349
12.4%
9 242
 
8.6%
2 241
 
8.6%
0 240
 
8.5%
1 240
 
8.5%
5 235
 
8.3%
8 148
 
5.3%
7 140
 
5.0%
6 140
 
5.0%
Other values (6) 8
 
0.3%
Hangul
ValueCountFrequency (%)
영 2
 
7.1%
부 2
 
7.1%
동 2
 
7.1%
이 1
 
3.6%
로 1
 
3.6%
어 1
 
3.6%
린 1
 
3.6%
집 1
 
3.6%
신 1
 
3.6%
호 1
 
3.6%
Other values (15) 15
53.6%

gubun
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
351 
어린이집
273 
유치원
52 
초등학교
 
22
기존
 
2
Other values (4)
 
5

Length

Max length4
Median length4
Mean length3.9163121
Min length2

Unique

Unique3 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 351
49.8%
어린이집 273
38.7%
유치원 52
 
7.4%
초등학교 22
 
3.1%
기존 2
 
0.3%
고등학교 2
 
0.3%
40 1
 
0.1%
특수학교 1
 
0.1%
중학교 1
 
0.1%

Length

2024-04-21T04:17:52.616108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T04:17:52.999922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 351
49.8%
어린이집 273
38.7%
유치원 52
 
7.4%
초등학교 22
 
3.1%
기존 2
 
0.3%
고등학교 2
 
0.3%
40 1
 
0.1%
특수학교 1
 
0.1%
중학교 1
 
0.1%

school_name
Text

Distinct349
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-21T04:17:53.816641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length7.2382979
Min length5

Characters and Unicode

Total characters5103
Distinct characters301
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)1.7%

Sample

1st row영선어린이집
2nd row영지어린이집
3rd row와치어린이집
4th row원광어린이집
5th row은혜어린이집
ValueCountFrequency (%)
어린이집 17
 
2.3%
동심어린이집 4
 
0.5%
늘푸른어린이집 4
 
0.5%
유치원 4
 
0.5%
병설유치원 4
 
0.5%
한솔어린이집 4
 
0.5%
한마음어린이집 4
 
0.5%
꿈나무유치원 4
 
0.5%
꿈동산어린이집 4
 
0.5%
큰나무어린이집 4
 
0.5%
Other values (346) 685
92.8%
2024-04-21T04:17:55.028796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 570
 
11.2%
린 550
 
10.8%
어 544
 
10.7%
집 544
 
10.7%
원 132
 
2.6%
유 106
 
2.1%
치 106
 
2.1%
동 79
 
1.5%
초 76
 
1.5%
교 76
 
1.5%
Other values (291) 2320
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4949
97.0%
Decimal Number 50
 
1.0%
Space Separator 48
 
0.9%
Uppercase Letter 46
 
0.9%
Lowercase Letter 4
 
0.1%
Other Punctuation 4
 
0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 570
 
11.5%
린 550
 
11.1%
어 544
 
11.0%
집 544
 
11.0%
원 132
 
2.7%
유 106
 
2.1%
치 106
 
2.1%
동 79
 
1.6%
초 76
 
1.5%
교 76
 
1.5%
Other values (267) 2166
43.8%
Decimal Number
ValueCountFrequency (%)
2 14
28.0%
1 12
24.0%
4 8
16.0%
5 5
 
10.0%
6 3
 
6.0%
3 3
 
6.0%
7 2
 
4.0%
0 2
 
4.0%
8 1
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
L 10
21.7%
G 10
21.7%
K 6
13.0%
B 6
13.0%
I 4
 
8.7%
C 4
 
8.7%
F 2
 
4.3%
R 2
 
4.3%
A 2
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
k 2
50.0%
s 2
50.0%
Other Punctuation
ValueCountFrequency (%)
? 2
50.0%
! 2
50.0%
Space Separator
ValueCountFrequency (%)
48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4946
96.9%
Common 104
 
2.0%
Latin 50
 
1.0%
Han 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 570
 
11.5%
린 550
 
11.1%
어 544
 
11.0%
집 544
 
11.0%
원 132
 
2.7%
유 106
 
2.1%
치 106
 
2.1%
동 79
 
1.6%
초 76
 
1.5%
교 76
 
1.5%
Other values (264) 2163
43.7%
Common
ValueCountFrequency (%)
48
46.2%
2 14
 
13.5%
1 12
 
11.5%
4 8
 
7.7%
5 5
 
4.8%
6 3
 
2.9%
3 3
 
2.9%
7 2
 
1.9%
- 2
 
1.9%
0 2
 
1.9%
Other values (3) 5
 
4.8%
Latin
ValueCountFrequency (%)
L 10
20.0%
G 10
20.0%
K 6
12.0%
B 6
12.0%
I 4
 
8.0%
C 4
 
8.0%
k 2
 
4.0%
F 2
 
4.0%
s 2
 
4.0%
R 2
 
4.0%
Han
ValueCountFrequency (%)
恃 1
33.3%
訣 1
33.3%
低 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4946
96.9%
ASCII 154
 
3.0%
CJK 3
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 570
 
11.5%
린 550
 
11.1%
어 544
 
11.0%
집 544
 
11.0%
원 132
 
2.7%
유 106
 
2.1%
치 106
 
2.1%
동 79
 
1.6%
초 76
 
1.5%
교 76
 
1.5%
Other values (264) 2163
43.7%
ASCII
ValueCountFrequency (%)
48
31.2%
2 14
 
9.1%
1 12
 
7.8%
L 10
 
6.5%
G 10
 
6.5%
4 8
 
5.2%
K 6
 
3.9%
B 6
 
3.9%
5 5
 
3.2%
I 4
 
2.6%
Other values (14) 31
20.1%
CJK
ValueCountFrequency (%)
恃 1
33.3%
訣 1
33.3%
低 1
33.3%

student_num
Text

Distinct143
Distinct (%)20.4%
Missing3
Missing (%)0.4%
Memory size5.6 KiB
2024-04-21T04:17:56.389760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length2
Mean length2.2535613
Min length1

Characters and Unicode

Total characters1582
Distinct characters30
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

Unique8 ?
Unique (%)1.1%

Sample

1st row60
2nd row75
3rd row62
4th row46
5th row33
ValueCountFrequency (%)
20 34
 
4.8%
19 18
 
2.5%
45 16
 
2.3%
46 16
 
2.3%
65 16
 
2.3%
60 14
 
2.0%
49 14
 
2.0%
16 14
 
2.0%
40 13
 
1.8%
33 12
 
1.7%
Other values (137) 539
76.3%
2024-04-21T04:17:57.984111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 236
14.9%
2 171
10.8%
3 171
10.8%
6 161
10.2%
4 159
10.1%
0 156
9.9%
5 147
9.3%
7 140
8.8%
8 113
7.1%
9 100
6.3%
Other values (20) 28
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1554
98.2%
Other Letter 16
 
1.0%
Space Separator 4
 
0.3%
Other Punctuation 4
 
0.3%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2
 
12.5%
부 1
 
6.2%
호 1
 
6.2%
동 1
 
6.2%
리 1
 
6.2%
관 1
 
6.2%
로 1
 
6.2%
단 1
 
6.2%
구 1
 
6.2%
신 1
 
6.2%
Other values (5) 5
31.2%
Decimal Number
ValueCountFrequency (%)
1 236
15.2%
2 171
11.0%
3 171
11.0%
6 161
10.4%
4 159
10.2%
0 156
10.0%
5 147
9.5%
7 140
9.0%
8 113
7.3%
9 100
6.4%
Other Punctuation
ValueCountFrequency (%)
, 2
50.0%
. 2
50.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1566
99.0%
Hangul 16
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 236
15.1%
2 171
10.9%
3 171
10.9%
6 161
10.3%
4 159
10.2%
0 156
10.0%
5 147
9.4%
7 140
8.9%
8 113
7.2%
9 100
6.4%
Other values (5) 12
 
0.8%
Hangul
ValueCountFrequency (%)
산 2
 
12.5%
부 1
 
6.2%
호 1
 
6.2%
동 1
 
6.2%
리 1
 
6.2%
관 1
 
6.2%
로 1
 
6.2%
단 1
 
6.2%
구 1
 
6.2%
신 1
 
6.2%
Other values (5) 5
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1566
99.0%
Hangul 16
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 236
15.1%
2 171
10.9%
3 171
10.9%
6 161
10.3%
4 159
10.2%
0 156
10.0%
5 147
9.4%
7 140
8.9%
8 113
7.2%
9 100
6.4%
Other values (5) 12
 
0.8%
Hangul
ValueCountFrequency (%)
산 2
 
12.5%
부 1
 
6.2%
호 1
 
6.2%
동 1
 
6.2%
리 1
 
6.2%
관 1
 
6.2%
로 1
 
6.2%
단 1
 
6.2%
구 1
 
6.2%
신 1
 
6.2%
Other values (5) 5
31.2%

tel
Text

MISSING 

Distinct351
Distinct (%)99.7%
Missing353
Missing (%)50.1%
Memory size5.6 KiB
2024-04-21T04:17:58.922893image/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%
051-646-2218 1
 
0.3%
051-893-6723 1
 
0.3%
051-894-5486 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%
2024-04-21T04:18:00.296858image/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

MISSING 

Distinct344
Distinct (%)97.7%
Missing353
Missing (%)50.1%
Memory size5.6 KiB
2024-04-21T04:18:01.718702image/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%
금정구 29
 
1.6%
영도구 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%
2024-04-21T04:18:03.620049image/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%
Open Punctuation 31
 
0.4%
Close 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%
G 1
33.3%
L 1
33.3%
Lowercase Letter
ValueCountFrequency (%)
e 1
33.3%
k 1
33.3%
s 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 19
86.4%
@ 3
 
13.6%
Space Separator
ValueCountFrequency (%)
1429
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close 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%
e 1
16.7%
k 1
16.7%
s 1
16.7%
G 1
16.7%
L 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 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
기존
472 
신규
203 
기존
 
14
신규
 
6
<NA>
 
5
Other values (4)
 
5

Length

Max length4
Median length2
Mean length2.0765957
Min length2

Unique

Unique3 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
기존 472
67.0%
신규 203
28.8%
기존 14
 
2.0%
신규 6
 
0.9%
<NA> 5
 
0.7%
기존) 2
 
0.3%
가존 1
 
0.1%
기존 1
 
0.1%
신규 1
 
0.1%

Length

2024-04-21T04:18:04.069292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T04:18:04.449311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기존 489
69.4%
신규 210
29.8%
na 5
 
0.7%
가존 1
 
0.1%

inst_center
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
356 
부산북구보건소
 
35
해운대구보건소
 
32
금정구보건소
 
29
사상구보건소
 
29
Other values (12)
224 

Length

Max length8
Median length4
Mean length5.2312057
Min length4

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> 356
50.5%
부산북구보건소 35
 
5.0%
해운대구보건소 32
 
4.5%
금정구보건소 29
 
4.1%
사상구보건소 29
 
4.1%
영도구보건소 29
 
4.1%
기장군보건소 28
 
4.0%
사하구보건소 23
 
3.3%
부산남구보건소 22
 
3.1%
부산동구보건소 21
 
3.0%
Other values (7) 101
 
14.3%

Length

2024-04-21T04:18:04.890166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 356
50.5%
부산북구보건소 35
 
5.0%
해운대구보건소 32
 
4.5%
금정구보건소 29
 
4.1%
사상구보건소 29
 
4.1%
영도구보건소 29
 
4.1%
기장군보건소 28
 
4.0%
사하구보건소 23
 
3.3%
부산남구보건소 22
 
3.1%
부산동구보건소 21
 
3.0%
Other values (7) 101
 
14.3%

lat
Text

Distinct462
Distinct (%)65.8%
Missing3
Missing (%)0.4%
Memory size5.6 KiB
2024-04-21T04:18:05.966861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length11
Mean length10.193732
Min length4

Characters and Unicode

Total characters7156
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

Unique243 ?
Unique (%)34.6%

Sample

1st row35.07961019
2nd row35.08991597
3rd row35.09202146
4th row35.0909773
5th row35.09400744
ValueCountFrequency (%)
35.148 6
 
0.9%
35.18408371 4
 
0.6%
35.15574257 4
 
0.6%
35.13663383 4
 
0.6%
35.14389408 4
 
0.6%
35.127 3
 
0.4%
35.268 3
 
0.4%
35.24953561 3
 
0.4%
35.18959425 3
 
0.4%
35.20578417 3
 
0.4%
Other values (453) 667
94.7%
2024-04-21T04:18:07.483632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1185
16.6%
5 1129
15.8%
1 801
11.2%
. 700
9.8%
2 640
8.9%
0 478
6.7%
7 476
6.7%
4 448
 
6.3%
9 436
 
6.1%
6 430
 
6.0%
Other values (4) 433
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6446
90.1%
Other Punctuation 704
 
9.8%
Dash Punctuation 4
 
0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1185
18.4%
5 1129
17.5%
1 801
12.4%
2 640
9.9%
0 478
7.4%
7 476
7.4%
4 448
 
7.0%
9 436
 
6.8%
6 430
 
6.7%
8 423
 
6.6%
Other Punctuation
ValueCountFrequency (%)
. 700
99.4%
: 4
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7156
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1185
16.6%
5 1129
15.8%
1 801
11.2%
. 700
9.8%
2 640
8.9%
0 478
6.7%
7 476
6.7%
4 448
 
6.3%
9 436
 
6.1%
6 430
 
6.0%
Other values (4) 433
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1185
16.6%
5 1129
15.8%
1 801
11.2%
. 700
9.8%
2 640
8.9%
0 478
6.7%
7 476
6.7%
4 448
 
6.3%
9 436
 
6.1%
6 430
 
6.0%
Other values (4) 433
 
6.1%

lng
Real number (ℝ)

HIGH CORRELATION 

Distinct466
Distinct (%)66.6%
Missing5
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean129.04377
Minimum123.423
Maximum129.28265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-04-21T04:18:07.885375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123.423
5-th percentile128.96774
Q1129.017
median129.06175
Q3129.09871
95-th percentile129.1805
Maximum129.28265
Range5.8596453
Interquartile range (IQR)0.0817144

Descriptive statistics

Standard deviation0.26897566
Coefficient of variation (CV)0.0020843754
Kurtosis308.38948
Mean129.04377
Median Absolute Deviation (MAD)0.0426522
Skewness-16.299949
Sum90330.641
Variance0.072347908
MonotonicityNot monotonic
2024-04-21T04:18:08.313353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.108 4
 
0.6%
129.0650406 4
 
0.6%
128.9886692 4
 
0.6%
129.0802187 4
 
0.6%
128.9902706 4
 
0.6%
129.2048929 3
 
0.4%
129.1275334 3
 
0.4%
129.213 3
 
0.4%
129.0333375 3
 
0.4%
129.0144 3
 
0.4%
Other values (456) 665
94.3%
(Missing) 5
 
0.7%
ValueCountFrequency (%)
123.423 1
0.1%
125.7758087 1
0.1%
127.008 1
0.1%
128.503 1
0.1%
128.531 1
0.1%
128.661 1
0.1%
128.7402002 1
0.1%
128.816 1
0.1%
128.83574 1
0.1%
128.8513166 1
0.1%
ValueCountFrequency (%)
129.2826453 1
 
0.1%
129.258043 1
 
0.1%
129.243607 2
0.3%
129.2238676 1
 
0.1%
129.223 1
 
0.1%
129.2163239 2
0.3%
129.2154125 2
0.3%
129.2152822 1
 
0.1%
129.213 3
0.4%
129.2127674 2
0.3%

data_day
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
356 
2020-06-30
349 

Length

Max length10
Median length4
Mean length6.9702128
Min length4

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> 356
50.5%
2020-06-30 349
49.5%

Length

2024-04-21T04:18:08.667655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T04:18:08.879972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 356
50.5%
2020-06-30 349
49.5%

apr_at
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing354
Missing (%)50.2%
Memory size1.5 KiB
False
351 
(Missing)
354 
ValueCountFrequency (%)
False 351
49.8%
(Missing) 354
50.2%
2024-04-21T04:18:09.059126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

instt_code
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing705
Missing (%)100.0%
Memory size6.3 KiB

last_load_dttm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2021-01-05 14:02:45
700 
<NA>
 
5

Length

Max length19
Median length19
Mean length18.893617
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-05 14:02:45
2nd row2021-01-05 14:02:45
3rd row2021-01-05 14:02:45
4th row2021-01-05 14:02:45
5th row2021-01-05 14:02:45

Common Values

ValueCountFrequency (%)
2021-01-05 14:02:45 700
99.3%
<NA> 5
 
0.7%

Length

2024-04-21T04:18:09.249931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T04:18:09.437787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-05 700
49.8%
14:02:45 700
49.8%
na 5
 
0.4%

Interactions

2024-04-21T04:17:47.396280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T04:18:09.550816image/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
2024-04-21T04:18:09.710939image/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
2024-04-21T04:18:09.886370image/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

2024-04-21T04:17:47.768824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T04:17:48.342689image/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.
2024-04-21T04:17:48.777508image/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
04542<NA>영선어린이집60<NA><NA>기존<NA>35.07961019129.046556<NA>N<NA>2021-01-05 14:02:45
14543<NA>영지어린이집75<NA><NA>기존<NA>35.08991597129.057655<NA>N<NA>2021-01-05 14:02:45
24544<NA>와치어린이집62<NA><NA>기존<NA>35.09202146129.057057<NA>N<NA>2021-01-05 14:02:45
34545<NA>원광어린이집46<NA><NA>기존<NA>35.0909773129.067166<NA>N<NA>2021-01-05 14:02:45
44546<NA>은혜어린이집33<NA><NA>기존<NA>35.09400744129.052586<NA>N<NA>2021-01-05 14:02:45
54547<NA>자비유치원69<NA><NA>기존<NA>35.08639435129.064529<NA>N<NA>2021-01-05 14:02:45
64548<NA>절영어린이집84<NA><NA>기존<NA>35.07221265129.061746<NA>N<NA>2021-01-05 14:02:45
74549<NA>지성어린이집90<NA><NA>기존<NA>35.06688592129.079021<NA>N<NA>2021-01-05 14:02:45
84550<NA>큰나무어린이집35<NA><NA>기존<NA>35.09401683129.046302<NA>N<NA>2021-01-05 14:02:45
94551<NA>해돋이어린이집85<NA><NA>기존<NA>35.09335357129.040634<NA>N<NA>2021-01-05 14:02:45
skeygubunschool_namestudent_numtelschool_addrschool_kindinst_centerlatlngdata_dayapr_atinstt_codelast_load_dttm
6954307<NA>해누리유치원119<NA><NA>기존<NA>35.32925993129.171977<NA>N<NA>2021-01-05 14:02:45
6964308<NA>행복엔젤유치원46<NA><NA>기존<NA>35.23712301129.210353<NA>N<NA>2021-01-05 14:02:45
6974309<NA>유니어린이집30<NA><NA>신규<NA>35.11638703129.10936<NA>N<NA>2021-01-05 14:02:45
6984310<NA>21세기영아전담어린이집36<NA><NA>신규<NA>35.12232511129.081706<NA>N<NA>2021-01-05 14:02:45
6994311<NA>BIFC어린이집96<NA><NA>기존<NA>35.14648787129.065861<NA>N<NA>2021-01-05 14:02:45
7004312<NA>LG메트로시티어린이집60<NA><NA>신규<NA>35.12692281129.109525<NA>N<NA>2021-01-05 14:02:45
7014313<NA>LG삐아제어린이집14<NA><NA>신규<NA>35.12883325129.108596<NA>N<NA>2021-01-05 14:02:45
7024314<NA>경성어린이집71<NA><NA>기존<NA>35.12717204129.074912<NA>N<NA>2021-01-05 14:02:45
7034315<NA>공립감만어린이집78<NA><NA>기존<NA>35.11651132129.08571<NA>N<NA>2021-01-05 14:02:45
7044316<NA>대천유치원136<NA><NA>신규<NA>35.13088489129.0991<NA>N<NA>2021-01-05 14:02:45