Overview

Dataset statistics

Number of variables15
Number of observations48
Missing cells8
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory130.7 B

Variable types

Categorical8
Text3
Numeric4

Dataset

Description교육청명,교육지원청명,유치원코드,유치원명,설립유형,독립편성학급수,오후재편성학급수,운영시간,독립편성참여원아수,오후재편성참여원아수,정규교사수,기간제교사수,강사수,공시차수,주소
Author송파구
URLhttps://data.seoul.go.kr/dataList/OA-22295/S/1/datasetView.do

Alerts

교육청명 has constant value ""Constant
교육지원청명 has constant value ""Constant
설립유형 is highly overall correlated with 강사수 and 2 other fieldsHigh correlation
독립편성참여원아수 is highly overall correlated with 설립유형 and 1 other fieldsHigh correlation
공시차수 is highly overall correlated with 독립편성참여원아수High correlation
오후재편성학급수 is highly overall correlated with 오후재편성참여원아수 and 1 other fieldsHigh correlation
오후재편성참여원아수 is highly overall correlated with 오후재편성학급수 and 1 other fieldsHigh correlation
정규교사수 is highly overall correlated with 오후재편성학급수 and 2 other fieldsHigh correlation
강사수 is highly overall correlated with 정규교사수 and 1 other fieldsHigh correlation
독립편성학급수 is highly overall correlated with 설립유형 and 1 other fieldsHigh correlation
기간제교사수 is highly overall correlated with 독립편성학급수High correlation
독립편성학급수 is highly imbalanced (75.1%)Imbalance
기간제교사수 is highly imbalanced (68.0%)Imbalance
공시차수 is highly imbalanced (75.0%)Imbalance
오후재편성학급수 has 2 (4.2%) missing valuesMissing
오후재편성참여원아수 has 2 (4.2%) missing valuesMissing
정규교사수 has 2 (4.2%) missing valuesMissing
강사수 has 2 (4.2%) missing valuesMissing
유치원코드 has unique valuesUnique
유치원명 has unique valuesUnique
정규교사수 has 19 (39.6%) zerosZeros
강사수 has 23 (47.9%) zerosZeros

Reproduction

Analysis started2024-04-14 11:13:05.873287
Analysis finished2024-04-14 11:13:14.670257
Duration8.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

교육청명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size512.0 B
서울특별시교육청
48 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시교육청 48
100.0%

Length

2024-04-14T20:13:14.882917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:13:15.184171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시교육청 48
100.0%

교육지원청명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size512.0 B
강동송파교육지원청
48 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강동송파교육지원청 48
100.0%

Length

2024-04-14T20:13:15.492130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:13:15.777901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강동송파교육지원청 48
100.0%

유치원코드
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size512.0 B
2024-04-14T20:13:16.437886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters1728
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row009c017d-43bd-4d9f-ac64-d217abdd570e
2nd row04a021da-cdd7-4e7e-a434-2b388c2f6b63
3rd row0f7d2827-8bb2-4a0c-9926-77fca1f60a54
4th row1ecec08c-ed94-b044-e053-0a32095ab044
5th row1ecec08c-eee8-b044-e053-0a32095ab044
ValueCountFrequency (%)
009c017d-43bd-4d9f-ac64-d217abdd570e 1
 
2.1%
04a021da-cdd7-4e7e-a434-2b388c2f6b63 1
 
2.1%
1ecec08d-0ba4-b044-e053-0a32095ab044 1
 
2.1%
1ecec08d-059a-b044-e053-0a32095ab044 1
 
2.1%
1ecec08d-06af-b044-e053-0a32095ab044 1
 
2.1%
1ecec08d-06b0-b044-e053-0a32095ab044 1
 
2.1%
1ecec08d-079a-b044-e053-0a32095ab044 1
 
2.1%
1ecec08d-08de-b044-e053-0a32095ab044 1
 
2.1%
1ecec08d-08e2-b044-e053-0a32095ab044 1
 
2.1%
1ecec08d-09f2-b044-e053-0a32095ab044 1
 
2.1%
Other values (38) 38
79.2%
2024-04-14T20:13:17.556383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 271
15.7%
- 192
11.1%
4 189
10.9%
e 138
 
8.0%
c 117
 
6.8%
a 106
 
6.1%
b 101
 
5.8%
5 97
 
5.6%
3 96
 
5.6%
2 73
 
4.2%
Other values (7) 348
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 977
56.5%
Lowercase Letter 559
32.3%
Dash Punctuation 192
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 271
27.7%
4 189
19.3%
5 97
 
9.9%
3 96
 
9.8%
2 73
 
7.5%
8 72
 
7.4%
1 61
 
6.2%
9 57
 
5.8%
7 34
 
3.5%
6 27
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
e 138
24.7%
c 117
20.9%
a 106
19.0%
b 101
18.1%
d 55
 
9.8%
f 42
 
7.5%
Dash Punctuation
ValueCountFrequency (%)
- 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1169
67.7%
Latin 559
32.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 271
23.2%
- 192
16.4%
4 189
16.2%
5 97
 
8.3%
3 96
 
8.2%
2 73
 
6.2%
8 72
 
6.2%
1 61
 
5.2%
9 57
 
4.9%
7 34
 
2.9%
Latin
ValueCountFrequency (%)
e 138
24.7%
c 117
20.9%
a 106
19.0%
b 101
18.1%
d 55
 
9.8%
f 42
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 271
15.7%
- 192
11.1%
4 189
10.9%
e 138
 
8.0%
c 117
 
6.8%
a 106
 
6.1%
b 101
 
5.8%
5 97
 
5.6%
3 96
 
5.6%
2 73
 
4.2%
Other values (7) 348
20.1%

유치원명
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size512.0 B
2024-04-14T20:13:18.408316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length8.5
Min length5

Characters and Unicode

Total characters408
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row서울솔방울유치원
2nd row란키즈유치원
3rd row서울송파위례유치원
4th row꿀벌유치원
5th row서울신천초등학교병설유치원
ValueCountFrequency (%)
서울솔방울유치원 1
 
2.1%
란키즈유치원 1
 
2.1%
서울평화초등학교병설유치원 1
 
2.1%
리틀바움유치원 1
 
2.1%
혜림유치원 1
 
2.1%
푸른꿈유치원 1
 
2.1%
동화나라유치원 1
 
2.1%
서울세륜초등학교병설유치원 1
 
2.1%
은선유치원 1
 
2.1%
삼성유치원 1
 
2.1%
Other values (38) 38
79.2%
2024-04-14T20:13:19.635184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 50
 
12.3%
유 49
 
12.0%
치 48
 
11.8%
울 21
 
5.1%
서 20
 
4.9%
학 15
 
3.7%
설 15
 
3.7%
병 15
 
3.7%
교 15
 
3.7%
초 15
 
3.7%
Other values (81) 145
35.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 408
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 50
 
12.3%
유 49
 
12.0%
치 48
 
11.8%
울 21
 
5.1%
서 20
 
4.9%
학 15
 
3.7%
설 15
 
3.7%
병 15
 
3.7%
교 15
 
3.7%
초 15
 
3.7%
Other values (81) 145
35.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 408
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 50
 
12.3%
유 49
 
12.0%
치 48
 
11.8%
울 21
 
5.1%
서 20
 
4.9%
학 15
 
3.7%
설 15
 
3.7%
병 15
 
3.7%
교 15
 
3.7%
초 15
 
3.7%
Other values (81) 145
35.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 408
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 50
 
12.3%
유 49
 
12.0%
치 48
 
11.8%
울 21
 
5.1%
서 20
 
4.9%
학 15
 
3.7%
설 15
 
3.7%
병 15
 
3.7%
교 15
 
3.7%
초 15
 
3.7%
Other values (81) 145
35.5%

설립유형
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size512.0 B
사립(사인)
25 
공립(병설)
15 
공립(단설)
5 
사립(법인)
3 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공립(단설)
2nd row사립(사인)
3rd row공립(단설)
4th row사립(사인)
5th row공립(병설)

Common Values

ValueCountFrequency (%)
사립(사인) 25
52.1%
공립(병설) 15
31.2%
공립(단설) 5
 
10.4%
사립(법인) 3
 
6.2%

Length

2024-04-14T20:13:20.037989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:13:20.351160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사립(사인 25
52.1%
공립(병설 15
31.2%
공립(단설 5
 
10.4%
사립(법인 3
 
6.2%

독립편성학급수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size512.0 B
0
45 
<NA>
 
2
6
 
1

Length

Max length4
Median length1
Mean length1.125
Min length1

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45
93.8%
<NA> 2
 
4.2%
6 1
 
2.1%

Length

2024-04-14T20:13:20.719447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:13:21.037468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 45
93.8%
na 2
 
4.2%
6 1
 
2.1%

오후재편성학급수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)23.9%
Missing2
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.9565217
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-04-14T20:13:21.330114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile10.75
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1052395
Coefficient of variation (CV)0.78484074
Kurtosis2.3924697
Mean3.9565217
Median Absolute Deviation (MAD)1
Skewness1.6577949
Sum182
Variance9.6425121
MonotonicityNot monotonic
2024-04-14T20:13:21.708800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 12
25.0%
3 8
16.7%
1 7
14.6%
5 6
12.5%
4 5
10.4%
10 2
 
4.2%
6 2
 
4.2%
11 1
 
2.1%
8 1
 
2.1%
14 1
 
2.1%
(Missing) 2
 
4.2%
ValueCountFrequency (%)
1 7
14.6%
2 12
25.0%
3 8
16.7%
4 5
10.4%
5 6
12.5%
6 2
 
4.2%
8 1
 
2.1%
10 2
 
4.2%
11 1
 
2.1%
12 1
 
2.1%
ValueCountFrequency (%)
14 1
 
2.1%
12 1
 
2.1%
11 1
 
2.1%
10 2
 
4.2%
8 1
 
2.1%
6 2
 
4.2%
5 6
12.5%
4 5
10.4%
3 8
16.7%
2 12
25.0%

운영시간
Categorical

Distinct18
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Memory size512.0 B
07시30분~19시30분
12 
07시00분~20시00분
7 
08시00분~18시00분
5 
09시00분~18시00분
4 
09시00분~17시00분
3 
Other values (13)
17 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique9 ?
Unique (%)18.8%

Sample

1st row07시00분~20시00분
2nd row08시00분~19시00분
3rd row07시00분~20시00분
4th row08시00분~18시00분
5th row07시00분~20시00분

Common Values

ValueCountFrequency (%)
07시30분~19시30분 12
25.0%
07시00분~20시00분 7
14.6%
08시00분~18시00분 5
10.4%
09시00분~18시00분 4
 
8.3%
09시00분~17시00분 3
 
6.2%
08시00분~19시00분 2
 
4.2%
07시00분~19시00분 2
 
4.2%
09시00분~14시00분 2
 
4.2%
09시00분~17시30분 2
 
4.2%
07시40분~19시00분 1
 
2.1%
Other values (8) 8
16.7%

Length

2024-04-14T20:13:22.103593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07시30분~19시30분 12
25.0%
07시00분~20시00분 7
14.6%
08시00분~18시00분 5
10.4%
09시00분~18시00분 4
 
8.3%
09시00분~17시00분 3
 
6.2%
08시00분~19시00분 2
 
4.2%
07시00분~19시00분 2
 
4.2%
09시00분~14시00분 2
 
4.2%
09시00분~17시30분 2
 
4.2%
08시20분~18시00분 1
 
2.1%
Other values (8) 8
16.7%

독립편성참여원아수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size512.0 B
0
28 
<NA>
19 
131
 
1

Length

Max length4
Median length1
Mean length2.2291667
Min length1

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row<NA>
2nd row0
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 28
58.3%
<NA> 19
39.6%
131 1
 
2.1%

Length

2024-04-14T20:13:22.495806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:13:22.768053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
58.3%
na 19
39.6%
131 1
 
2.1%

오후재편성참여원아수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)82.6%
Missing2
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean81.021739
Minimum2
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-04-14T20:13:22.954855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10.25
Q125.5
median54
Q3100
95-th percentile243.75
Maximum366
Range364
Interquartile range (IQR)74.5

Descriptive statistics

Standard deviation79.200306
Coefficient of variation (CV)0.9775192
Kurtosis3.1212963
Mean81.021739
Median Absolute Deviation (MAD)32.5
Skewness1.7615687
Sum3727
Variance6272.6884
MonotonicityNot monotonic
2024-04-14T20:13:23.193879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
54 3
 
6.2%
23 2
 
4.2%
24 2
 
4.2%
100 2
 
4.2%
31 2
 
4.2%
15 2
 
4.2%
45 2
 
4.2%
11 1
 
2.1%
115 1
 
2.1%
16 1
 
2.1%
Other values (28) 28
58.3%
(Missing) 2
 
4.2%
ValueCountFrequency (%)
2 1
2.1%
4 1
2.1%
10 1
2.1%
11 1
2.1%
15 2
4.2%
16 1
2.1%
20 1
2.1%
23 2
4.2%
24 2
4.2%
30 1
2.1%
ValueCountFrequency (%)
366 1
2.1%
258 1
2.1%
245 1
2.1%
240 1
2.1%
221 1
2.1%
197 1
2.1%
148 1
2.1%
131 1
2.1%
129 1
2.1%
115 1
2.1%

정규교사수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)26.1%
Missing2
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.0869565
Minimum0
Maximum14
Zeros19
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-04-14T20:13:23.397695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile10.75
Maximum14
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.7108016
Coefficient of variation (CV)1.2020907
Kurtosis1.0215213
Mean3.0869565
Median Absolute Deviation (MAD)2
Skewness1.281386
Sum142
Variance13.770048
MonotonicityNot monotonic
2024-04-14T20:13:23.585224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 19
39.6%
2 6
 
12.5%
5 5
 
10.4%
3 3
 
6.2%
4 3
 
6.2%
6 3
 
6.2%
10 2
 
4.2%
11 1
 
2.1%
8 1
 
2.1%
14 1
 
2.1%
Other values (2) 2
 
4.2%
(Missing) 2
 
4.2%
ValueCountFrequency (%)
0 19
39.6%
1 1
 
2.1%
2 6
 
12.5%
3 3
 
6.2%
4 3
 
6.2%
5 5
 
10.4%
6 3
 
6.2%
8 1
 
2.1%
10 2
 
4.2%
11 1
 
2.1%
ValueCountFrequency (%)
14 1
 
2.1%
12 1
 
2.1%
11 1
 
2.1%
10 2
 
4.2%
8 1
 
2.1%
6 3
6.2%
5 5
10.4%
4 3
6.2%
3 3
6.2%
2 6
12.5%

기간제교사수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size512.0 B
0
43 
1
 
2
<NA>
 
2
2
 
1

Length

Max length4
Median length1
Mean length1.125
Min length1

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43
89.6%
1 2
 
4.2%
<NA> 2
 
4.2%
2 1
 
2.1%

Length

2024-04-14T20:13:23.805428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:13:23.986287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 43
89.6%
1 2
 
4.2%
na 2
 
4.2%
2 1
 
2.1%

강사수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)13.0%
Missing2
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean1.1956522
Minimum0
Maximum5
Zeros23
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-04-14T20:13:24.142950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4239158
Coefficient of variation (CV)1.1909114
Kurtosis-0.19011527
Mean1.1956522
Median Absolute Deviation (MAD)0.5
Skewness0.89405018
Sum55
Variance2.0275362
MonotonicityNot monotonic
2024-04-14T20:13:24.327208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 23
47.9%
2 11
22.9%
3 4
 
8.3%
1 4
 
8.3%
4 3
 
6.2%
5 1
 
2.1%
(Missing) 2
 
4.2%
ValueCountFrequency (%)
0 23
47.9%
1 4
 
8.3%
2 11
22.9%
3 4
 
8.3%
4 3
 
6.2%
5 1
 
2.1%
ValueCountFrequency (%)
5 1
 
2.1%
4 3
 
6.2%
3 4
 
8.3%
2 11
22.9%
1 4
 
8.3%
0 23
47.9%

공시차수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size512.0 B
20231
46 
20211
 
2

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20231
2nd row20231
3rd row20231
4th row20231
5th row20231

Common Values

ValueCountFrequency (%)
20231 46
95.8%
20211 2
 
4.2%

Length

2024-04-14T20:13:24.526766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:13:24.689143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20231 46
95.8%
20211 2
 
4.2%

주소
Text

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size512.0 B
2024-04-14T20:13:25.438492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length18.854167
Min length16

Characters and Unicode

Total characters905
Distinct characters66
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

Unique46 ?
Unique (%)95.8%

Sample

1st row서울특별시 송파구 오금로24길 25
2nd row서울특별시 송파구 새말로8길 22-5
3rd row서울특별시 송파구 위례송파로 121
4th row서울특별시 송파구 양산로8길 8
5th row서울특별시 송파구 올림픽로 215
ValueCountFrequency (%)
서울특별시 48
25.0%
송파구 48
25.0%
올림픽로 5
 
2.6%
양재대로 3
 
1.6%
위례순환로 3
 
1.6%
17 3
 
1.6%
32 2
 
1.0%
올림픽로4길 2
 
1.0%
13 2
 
1.0%
20 2
 
1.0%
Other values (71) 74
38.5%
2024-04-14T20:13:26.458923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
144
15.9%
송 53
 
5.9%
파 53
 
5.9%
서 48
 
5.3%
로 48
 
5.3%
특 48
 
5.3%
별 48
 
5.3%
시 48
 
5.3%
구 48
 
5.3%
울 48
 
5.3%
Other values (56) 319
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 596
65.9%
Decimal Number 161
 
17.8%
Space Separator 144
 
15.9%
Dash Punctuation 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
송 53
8.9%
파 53
8.9%
서 48
 
8.1%
로 48
 
8.1%
특 48
 
8.1%
별 48
 
8.1%
시 48
 
8.1%
구 48
 
8.1%
울 48
 
8.1%
길 27
 
4.5%
Other values (44) 127
21.3%
Decimal Number
ValueCountFrequency (%)
1 32
19.9%
2 29
18.0%
3 20
12.4%
4 16
9.9%
5 15
9.3%
7 13
8.1%
8 13
8.1%
6 10
 
6.2%
0 7
 
4.3%
9 6
 
3.7%
Space Separator
ValueCountFrequency (%)
144
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 596
65.9%
Common 309
34.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
송 53
8.9%
파 53
8.9%
서 48
 
8.1%
로 48
 
8.1%
특 48
 
8.1%
별 48
 
8.1%
시 48
 
8.1%
구 48
 
8.1%
울 48
 
8.1%
길 27
 
4.5%
Other values (44) 127
21.3%
Common
ValueCountFrequency (%)
144
46.6%
1 32
 
10.4%
2 29
 
9.4%
3 20
 
6.5%
4 16
 
5.2%
5 15
 
4.9%
7 13
 
4.2%
8 13
 
4.2%
6 10
 
3.2%
0 7
 
2.3%
Other values (2) 10
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 596
65.9%
ASCII 309
34.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
144
46.6%
1 32
 
10.4%
2 29
 
9.4%
3 20
 
6.5%
4 16
 
5.2%
5 15
 
4.9%
7 13
 
4.2%
8 13
 
4.2%
6 10
 
3.2%
0 7
 
2.3%
Other values (2) 10
 
3.2%
Hangul
ValueCountFrequency (%)
송 53
8.9%
파 53
8.9%
서 48
 
8.1%
로 48
 
8.1%
특 48
 
8.1%
별 48
 
8.1%
시 48
 
8.1%
구 48
 
8.1%
울 48
 
8.1%
길 27
 
4.5%
Other values (44) 127
21.3%

Interactions

2024-04-14T20:13:12.139376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:09.088170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:10.101305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:11.129263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:12.385466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:09.346884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:10.361372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:11.380221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:12.635781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:09.604081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:10.622522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:11.639867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:12.887576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:09.859011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:10.881569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:13:11.896292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-14T20:13:26.633353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유치원코드유치원명설립유형독립편성학급수오후재편성학급수운영시간독립편성참여원아수오후재편성참여원아수정규교사수기간제교사수강사수공시차수주소
유치원코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
유치원명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
설립유형1.0001.0001.0000.8700.2810.4891.0000.2730.5800.4100.7510.0001.000
독립편성학급수1.0001.0000.8701.0000.0000.0000.6530.0000.4180.4380.0000.0001.000
오후재편성학급수1.0001.0000.2810.0001.0000.7960.0000.9830.9780.0000.1150.0000.991
운영시간1.0001.0000.4890.0000.7961.0000.0000.8460.7720.0000.0000.0000.956
독립편성참여원아수1.0001.0001.0000.6530.0000.0001.0000.0000.2160.6530.000NaN1.000
오후재편성참여원아수1.0001.0000.2730.0000.9830.8460.0001.0000.9790.0000.0000.0000.986
정규교사수1.0001.0000.5800.4180.9780.7720.2160.9791.0000.0000.1950.0000.960
기간제교사수1.0001.0000.4100.4380.0000.0000.6530.0000.0001.0000.5760.0001.000
강사수1.0001.0000.7510.0000.1150.0000.0000.0000.1950.5761.0000.0000.855
공시차수1.0001.0000.0000.0000.0000.000NaN0.0000.0000.0000.0001.0001.000
주소1.0001.0001.0001.0000.9910.9561.0000.9860.9601.0000.8551.0001.000
2024-04-14T20:13:26.886650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운영시간설립유형독립편성참여원아수공시차수기간제교사수독립편성학급수
운영시간1.0000.2220.0000.0000.0000.000
설립유형0.2221.0000.9620.0000.3950.657
독립편성참여원아수0.0000.9621.0001.0000.4520.452
공시차수0.0000.0001.0001.0000.0000.000
기간제교사수0.0000.3950.4520.0001.0000.674
독립편성학급수0.0000.6570.4520.0000.6741.000
2024-04-14T20:13:27.077144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
오후재편성학급수오후재편성참여원아수정규교사수강사수설립유형독립편성학급수운영시간독립편성참여원아수기간제교사수공시차수
오후재편성학급수1.0000.9020.691-0.3380.1600.0000.4100.0000.0000.000
오후재편성참여원아수0.9021.0000.720-0.3700.1540.0000.4770.0000.0000.000
정규교사수0.6910.7201.000-0.7160.3820.3790.3830.1570.0000.000
강사수-0.338-0.370-0.7161.0000.5730.0000.0000.0000.2730.000
설립유형0.1600.1540.3820.5731.0000.6570.2220.9620.3950.000
독립편성학급수0.0000.0000.3790.0000.6571.0000.0000.4520.6740.000
운영시간0.4100.4770.3830.0000.2220.0001.0000.0000.0000.000
독립편성참여원아수0.0000.0000.1570.0000.9620.4520.0001.0000.4521.000
기간제교사수0.0000.0000.0000.2730.3950.6740.0000.4521.0000.000
공시차수0.0000.0000.0000.0000.0000.0000.0001.0000.0001.000

Missing values

2024-04-14T20:13:13.250984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T20:13:13.866664image/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.
2024-04-14T20:13:14.439988image/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

교육청명교육지원청명유치원코드유치원명설립유형독립편성학급수오후재편성학급수운영시간독립편성참여원아수오후재편성참여원아수정규교사수기간제교사수강사수공시차수주소
0서울특별시교육청강동송파교육지원청009c017d-43bd-4d9f-ac64-d217abdd570e서울솔방울유치원공립(단설)0507시00분~20시00분<NA>8902320231서울특별시 송파구 오금로24길 25
1서울특별시교육청강동송파교육지원청04a021da-cdd7-4e7e-a434-2b388c2f6b63란키즈유치원사립(사인)0308시00분~19시00분07330020231서울특별시 송파구 새말로8길 22-5
2서울특별시교육청강동송파교육지원청0f7d2827-8bb2-4a0c-9926-77fca1f60a54서울송파위례유치원공립(단설)0407시00분~20시00분08000420231서울특별시 송파구 위례송파로 121
3서울특별시교육청강동송파교육지원청1ecec08c-ed94-b044-e053-0a32095ab044꿀벌유치원사립(사인)0408시00분~18시00분<NA>8340020231서울특별시 송파구 양산로8길 8
4서울특별시교육청강동송파교육지원청1ecec08c-eee8-b044-e053-0a32095ab044서울신천초등학교병설유치원공립(병설)0307시00분~20시00분05400320231서울특별시 송파구 올림픽로 215
5서울특별시교육청강동송파교육지원청1ecec08c-eeec-b044-e053-0a32095ab044장미유치원사립(법인)0208시00분~19시00분<NA>4420020231서울특별시 송파구 올림픽로35길 118
6서울특별시교육청강동송파교육지원청1ecec08c-f209-b044-e053-0a32095ab044우일유치원사립(사인)01107시40분~19시00분<NA>245110020231서울특별시 송파구 토성로 38-6
7서울특별시교육청강동송파교육지원청1ecec08c-f20a-b044-e053-0a32095ab044석촌유치원사립(사인)01008시40분~16시40분<NA>240100020231서울특별시 송파구 백제고분로40길 20
8서울특별시교육청강동송파교육지원청1ecec08c-f366-b044-e053-0a32095ab044생명나무유치원사립(사인)0508시00분~18시00분<NA>13150220231서울특별시 송파구 송파대로8길 17
9서울특별시교육청강동송파교육지원청1ecec08c-f56d-b044-e053-0a32095ab044잠실럭키유치원사립(사인)0509시00분~17시00분<NA>10050020231서울특별시 송파구 올림픽로 135
교육청명교육지원청명유치원코드유치원명설립유형독립편성학급수오후재편성학급수운영시간독립편성참여원아수오후재편성참여원아수정규교사수기간제교사수강사수공시차수주소
38서울특별시교육청강동송파교육지원청1ecec08d-0f6c-b044-e053-0a32095ab044수정유치원사립(사인)0309시00분~17시00분06120120231서울특별시 송파구 위례성대로 28
39서울특별시교육청강동송파교육지원청1fd6777e-14fd-e0b0-e053-0a32095ae0b0위례우일유치원사립(사인)01208시00분~18시30분0258120020231서울특별시 송파구 성내천로47길 38-1
40서울특별시교육청강동송파교육지원청2ab4825b-780b-4443-b195-77d1c3851b7d서울아주초등학교병설유치원공립(병설)0107시00분~20시00분<NA>1500120231서울특별시 송파구 올림픽로4길 59
41서울특별시교육청강동송파교육지원청4954e1f2-8d93-4db0-a8de-fcabfa4d6a0c서울거원초등학교병설유치원공립(병설)0207시30분~19시30분03320220231서울특별시 송파구 양산로2길 26
42서울특별시교육청강동송파교육지원청7f130073-a125-4e1f-ac7a-0d743c58822b서울영풍초등학교병설유치원공립(병설)0207시30분~19시30분<NA>3100220231서울특별시 송파구 오금로57길 15
43서울특별시교육청강동송파교육지원청7f2f03dd-4d3c-4cbc-bfd3-f88b47041426서울위례솔초등학교병설유치원공립(병설)0307시30분~19시30분04600320231서울특별시 송파구 위례북로2길 12
44서울특별시교육청강동송파교육지원청98918a25-18ce-48c6-8188-620954527bc8서울솔가람유치원분원공립(단설)0207시30분~19시30분03600220231서울특별시 송파구 위례순환로 477
45서울특별시교육청강동송파교육지원청d3a54882-16bb-4eae-9dc2-7a63f3a43215홍익가족유치원사립(사인)0509시30분~17시30분012950020231서울특별시 송파구 삼학사로19길 20
46서울특별시교육청강동송파교육지원청e1def744-a9f2-4422-8301-d3548bd6074c서울위례별유치원공립(단설)0407시30분~19시30분07200420231서울특별시 송파구 위례순환로 371
47서울특별시교육청강동송파교육지원청fd8c9e02-86d2-48a2-a655-ca442ef5cde8서울송파초등학교병설유치원공립(병설)0207시30분~19시30분<NA>2300220231서울특별시 송파구 백제고분로 400