Overview

Dataset statistics

Number of variables16
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory141.3 B

Variable types

Categorical5
Text5
Numeric6

Dataset

Description이 데이터는 서울특별시 동작구 소재의 초등학교현황입니다. 이 데이터에는 소재지,학급수,전화번호 등이 포함되어 있습니다.
Author서울특별시 동작구
URLhttps://www.data.go.kr/data/15063913/fileData.do

Alerts

교육청 has constant value ""Constant
소재구 has constant value ""Constant
기준일자 has constant value ""Constant
1학년 학급수 is highly overall correlated with 2학년 학급수 and 4 other fieldsHigh correlation
2학년 학급수 is highly overall correlated with 1학년 학급수 and 4 other fieldsHigh correlation
3학년 학급수 is highly overall correlated with 1학년 학급수 and 4 other fieldsHigh correlation
4학년 학급수 is highly overall correlated with 1학년 학급수 and 4 other fieldsHigh correlation
5학년 학급수 is highly overall correlated with 1학년 학급수 and 4 other fieldsHigh correlation
6학년 학급수 is highly overall correlated with 1학년 학급수 and 4 other fieldsHigh correlation
설립 is highly imbalanced (72.4%)Imbalance
학교명 has unique valuesUnique
홈페이지 주소 has unique valuesUnique
주소 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:48:48.981835
Analysis finished2023-12-12 17:48:53.058872
Duration4.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

교육청
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
동작관악
21 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동작관악 21
100.0%

Length

2023-12-13T02:48:53.121251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:53.219255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동작관악 21
100.0%

소재구
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
동작구
21 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동작구 21
100.0%

Length

2023-12-13T02:48:53.316334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:53.416013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동작구 21
100.0%

학교명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T02:48:53.598624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length8
Mean length8.5238095
Min length8

Characters and Unicode

Total characters179
Distinct characters42
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

Unique21 ?
Unique (%)100.0%

Sample

1st row서울강남초등학교
2nd row서울남사초등학교
3rd row서울남성초등학교
4th row서울노량진초등학교
5th row서울대림초등학교
ValueCountFrequency (%)
서울강남초등학교 1
 
4.8%
서울상현초등학교 1
 
4.8%
서울흑석초등학교 1
 
4.8%
서울행림초등학교 1
 
4.8%
서울은로초등학교 1
 
4.8%
서울영화초등학교 1
 
4.8%
서울영본초등학교 1
 
4.8%
서울신상도초등학교 1
 
4.8%
서울신남성초등학교 1
 
4.8%
서울신길초등학교 1
 
4.8%
Other values (11) 11
52.4%
2023-12-13T02:48:54.042509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 23
12.8%
교 22
12.3%
초 21
11.7%
등 21
11.7%
서 20
11.2%
울 20
11.2%
남 4
 
2.2%
신 3
 
1.7%
상 3
 
1.7%
대 3
 
1.7%
Other values (32) 39
21.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 179
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 23
12.8%
교 22
12.3%
초 21
11.7%
등 21
11.7%
서 20
11.2%
울 20
11.2%
남 4
 
2.2%
신 3
 
1.7%
상 3
 
1.7%
대 3
 
1.7%
Other values (32) 39
21.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 179
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 23
12.8%
교 22
12.3%
초 21
11.7%
등 21
11.7%
서 20
11.2%
울 20
11.2%
남 4
 
2.2%
신 3
 
1.7%
상 3
 
1.7%
대 3
 
1.7%
Other values (32) 39
21.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 179
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 23
12.8%
교 22
12.3%
초 21
11.7%
등 21
11.7%
서 20
11.2%
울 20
11.2%
남 4
 
2.2%
신 3
 
1.7%
상 3
 
1.7%
대 3
 
1.7%
Other values (32) 39
21.8%

홈페이지 주소
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T02:48:54.330408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length23.428571
Min length17

Characters and Unicode

Total characters492
Distinct characters25
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

Unique21 ?
Unique (%)100.0%

Sample

1st rowhttp://www.kangnam-se.es.kr
2nd rowhttp://www.namsa.es.kr/
3rd rowhttp://www.namsung.es.kr
4th rowhttp://www.noryangjin.es.kr
5th rowhttp://www.seouldaelim.es.kr
ValueCountFrequency (%)
http://www.kangnam-se.es.kr 1
 
4.8%
http://www.ssh.es.kr 1
 
4.8%
http://heukseok.es.kr 1
 
4.8%
http://www.hrim.es.kr 1
 
4.8%
http://eunlo.es.kr 1
 
4.8%
www.youngwha.es.kr 1
 
4.8%
http://www.yeongbon.es.kr 1
 
4.8%
http://www.shinsangdo.es.kr 1
 
4.8%
http://www.shinnamsung.es.kr 1
 
4.8%
http://www.singil.es.kr 1
 
4.8%
Other values (11) 11
52.4%
2023-12-13T02:48:54.792371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 60
12.2%
w 55
11.2%
/ 43
 
8.7%
t 38
 
7.7%
s 36
 
7.3%
e 31
 
6.3%
h 26
 
5.3%
k 25
 
5.1%
n 24
 
4.9%
r 24
 
4.9%
Other values (15) 130
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 369
75.0%
Other Punctuation 122
 
24.8%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 55
14.9%
t 38
10.3%
s 36
9.8%
e 31
8.4%
h 26
 
7.0%
k 25
 
6.8%
n 24
 
6.5%
r 24
 
6.5%
p 19
 
5.1%
a 17
 
4.6%
Other values (11) 74
20.1%
Other Punctuation
ValueCountFrequency (%)
. 60
49.2%
/ 43
35.2%
: 19
 
15.6%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 369
75.0%
Common 123
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 55
14.9%
t 38
10.3%
s 36
9.8%
e 31
8.4%
h 26
 
7.0%
k 25
 
6.8%
n 24
 
6.5%
r 24
 
6.5%
p 19
 
5.1%
a 17
 
4.6%
Other values (11) 74
20.1%
Common
ValueCountFrequency (%)
. 60
48.8%
/ 43
35.0%
: 19
 
15.4%
- 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 60
12.2%
w 55
11.2%
/ 43
 
8.7%
t 38
 
7.7%
s 36
 
7.3%
e 31
 
6.3%
h 26
 
5.3%
k 25
 
5.1%
n 24
 
4.9%
r 24
 
4.9%
Other values (15) 130
26.4%

설립
Categorical

IMBALANCE 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
공립
20 
사립
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row 공립
2nd row 공립
3rd row 공립
4th row 공립
5th row 공립

Common Values

ValueCountFrequency (%)
공립 20
95.2%
사립 1
 
4.8%

Length

2023-12-13T02:48:54.973824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:55.117013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공립 20
95.2%
사립 1
 
4.8%

우편번호
Text

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T02:48:55.322822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters147
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)90.5%

Sample

1st row156-881
2nd row156-824
3rd row156-820
4th row156-800
5th row156-810
ValueCountFrequency (%)
156-060 2
 
9.5%
156-881 1
 
4.8%
156-030 1
 
4.8%
156-877 1
 
4.8%
156-829 1
 
4.8%
156-862 1
 
4.8%
156-807 1
 
4.8%
156-837 1
 
4.8%
156-818 1
 
4.8%
156-808 1
 
4.8%
Other values (10) 10
47.6%
2023-12-13T02:48:55.778660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 26
17.7%
6 25
17.0%
5 24
16.3%
- 21
14.3%
8 20
13.6%
0 16
10.9%
2 4
 
2.7%
7 4
 
2.7%
3 3
 
2.0%
9 2
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126
85.7%
Dash Punctuation 21
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26
20.6%
6 25
19.8%
5 24
19.0%
8 20
15.9%
0 16
12.7%
2 4
 
3.2%
7 4
 
3.2%
3 3
 
2.4%
9 2
 
1.6%
4 2
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 26
17.7%
6 25
17.0%
5 24
16.3%
- 21
14.3%
8 20
13.6%
0 16
10.9%
2 4
 
2.7%
7 4
 
2.7%
3 3
 
2.0%
9 2
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26
17.7%
6 25
17.0%
5 24
16.3%
- 21
14.3%
8 20
13.6%
0 16
10.9%
2 4
 
2.7%
7 4
 
2.7%
3 3
 
2.0%
9 2
 
1.4%

주소
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T02:48:56.174310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length31.333333
Min length19

Characters and Unicode

Total characters658
Distinct characters78
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row서울특별시 동작구 강남초등길 15 (상도1동,강남초등학교)
2nd row서울특별시 동작구 동작대로13길 22 (사당동,서울남사초등학교)
3rd row서울특별시 동작구 사당로23길 57-14 (사당동,남성초등학교)
4th row서울특별시 동작구 장승배기로 160 (노량진동,노량진초등학교)
5th row서울특별시 동작구 대방동1길 22 (대방동,서울대림초등학교)
ValueCountFrequency (%)
서울특별시 21
19.8%
동작구 21
19.8%
장승배기로 2
 
1.9%
14 2
 
1.9%
22 2
 
1.9%
서달로 2
 
1.9%
사당동 2
 
1.9%
만양로12가길 1
 
0.9%
상도동,서울신상도초등학교 1
 
0.9%
146 1
 
0.9%
Other values (51) 51
48.1%
2023-12-13T02:48:56.693350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
12.9%
동 45
 
6.8%
서 32
 
4.9%
울 30
 
4.6%
작 23
 
3.5%
특 21
 
3.2%
별 21
 
3.2%
시 21
 
3.2%
구 21
 
3.2%
( 20
 
3.0%
Other values (68) 339
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 437
66.4%
Space Separator 85
 
12.9%
Decimal Number 77
 
11.7%
Open Punctuation 20
 
3.0%
Close Punctuation 20
 
3.0%
Other Punctuation 16
 
2.4%
Dash Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 45
 
10.3%
서 32
 
7.3%
울 30
 
6.9%
작 23
 
5.3%
특 21
 
4.8%
별 21
 
4.8%
시 21
 
4.8%
구 21
 
4.8%
로 18
 
4.1%
등 18
 
4.1%
Other values (53) 187
42.8%
Decimal Number
ValueCountFrequency (%)
1 19
24.7%
2 17
22.1%
6 8
10.4%
4 8
10.4%
3 7
 
9.1%
5 5
 
6.5%
7 4
 
5.2%
8 4
 
5.2%
0 3
 
3.9%
9 2
 
2.6%
Space Separator
ValueCountFrequency (%)
85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 437
66.4%
Common 221
33.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 45
 
10.3%
서 32
 
7.3%
울 30
 
6.9%
작 23
 
5.3%
특 21
 
4.8%
별 21
 
4.8%
시 21
 
4.8%
구 21
 
4.8%
로 18
 
4.1%
등 18
 
4.1%
Other values (53) 187
42.8%
Common
ValueCountFrequency (%)
85
38.5%
( 20
 
9.0%
) 20
 
9.0%
1 19
 
8.6%
2 17
 
7.7%
, 16
 
7.2%
6 8
 
3.6%
4 8
 
3.6%
3 7
 
3.2%
5 5
 
2.3%
Other values (5) 16
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 437
66.4%
ASCII 221
33.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
38.5%
( 20
 
9.0%
) 20
 
9.0%
1 19
 
8.6%
2 17
 
7.7%
, 16
 
7.2%
6 8
 
3.6%
4 8
 
3.6%
3 7
 
3.2%
5 5
 
2.3%
Other values (5) 16
 
7.2%
Hangul
ValueCountFrequency (%)
동 45
 
10.3%
서 32
 
7.3%
울 30
 
6.9%
작 23
 
5.3%
특 21
 
4.8%
별 21
 
4.8%
시 21
 
4.8%
구 21
 
4.8%
로 18
 
4.1%
등 18
 
4.1%
Other values (53) 187
42.8%

전화번호
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T02:48:56.935457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.047619
Min length11

Characters and Unicode

Total characters232
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row02-824-4471
2nd row02-522-0333
3rd row02-595-5471
4th row02-815-1274
5th row02-822-1984
ValueCountFrequency (%)
02-824-4471 1
 
4.8%
02-826-9740 1
 
4.8%
02-815-3276 1
 
4.8%
02-523-1352 1
 
4.8%
02-824-0309 1
 
4.8%
02-824-6051 1
 
4.8%
02-815-4371 1
 
4.8%
02-823-5461 1
 
4.8%
02-521-6268 1
 
4.8%
02-815-3741 1
 
4.8%
Other values (11) 11
52.4%
2023-12-13T02:48:57.322500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 42
18.1%
2 39
16.8%
0 31
13.4%
1 22
9.5%
3 19
8.2%
8 18
7.8%
4 17
7.3%
5 17
7.3%
7 13
 
5.6%
6 9
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190
81.9%
Dash Punctuation 42
 
18.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 39
20.5%
0 31
16.3%
1 22
11.6%
3 19
10.0%
8 18
9.5%
4 17
8.9%
5 17
8.9%
7 13
 
6.8%
6 9
 
4.7%
9 5
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 232
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 42
18.1%
2 39
16.8%
0 31
13.4%
1 22
9.5%
3 19
8.2%
8 18
7.8%
4 17
7.3%
5 17
7.3%
7 13
 
5.6%
6 9
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 42
18.1%
2 39
16.8%
0 31
13.4%
1 22
9.5%
3 19
8.2%
8 18
7.8%
4 17
7.3%
5 17
7.3%
7 13
 
5.6%
6 9
 
3.9%

1학년 학급수
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7619048
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:48:57.478030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median5
Q38
95-th percentile9
Maximum10
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1190744
Coefficient of variation (CV)0.36777324
Kurtosis-0.58264244
Mean5.7619048
Median Absolute Deviation (MAD)1
Skewness0.27547277
Sum121
Variance4.4904762
MonotonicityNot monotonic
2023-12-13T02:48:57.657546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 7
33.3%
8 4
19.0%
3 2
 
9.5%
4 2
 
9.5%
6 2
 
9.5%
9 1
 
4.8%
2 1
 
4.8%
7 1
 
4.8%
10 1
 
4.8%
ValueCountFrequency (%)
2 1
 
4.8%
3 2
 
9.5%
4 2
 
9.5%
5 7
33.3%
6 2
 
9.5%
7 1
 
4.8%
8 4
19.0%
9 1
 
4.8%
10 1
 
4.8%
ValueCountFrequency (%)
10 1
 
4.8%
9 1
 
4.8%
8 4
19.0%
7 1
 
4.8%
6 2
 
9.5%
5 7
33.3%
4 2
 
9.5%
3 2
 
9.5%
2 1
 
4.8%

2학년 학급수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3333333
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:48:57.803401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q37
95-th percentile8
Maximum8
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9061305
Coefficient of variation (CV)0.35739946
Kurtosis-1.2017861
Mean5.3333333
Median Absolute Deviation (MAD)2
Skewness0.044330775
Sum112
Variance3.6333333
MonotonicityNot monotonic
2023-12-13T02:48:57.937090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 4
19.0%
4 4
19.0%
8 4
19.0%
3 3
14.3%
7 3
14.3%
6 2
9.5%
2 1
 
4.8%
ValueCountFrequency (%)
2 1
 
4.8%
3 3
14.3%
4 4
19.0%
5 4
19.0%
6 2
9.5%
7 3
14.3%
8 4
19.0%
ValueCountFrequency (%)
8 4
19.0%
7 3
14.3%
6 2
9.5%
5 4
19.0%
4 4
19.0%
3 3
14.3%
2 1
 
4.8%

3학년 학급수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0952381
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:48:58.063507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median5
Q36
95-th percentile8
Maximum8
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6402671
Coefficient of variation (CV)0.32192158
Kurtosis0.01727704
Mean5.0952381
Median Absolute Deviation (MAD)1
Skewness-0.092570152
Sum107
Variance2.6904762
MonotonicityNot monotonic
2023-12-13T02:48:58.196326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 8
38.1%
4 3
 
14.3%
6 3
 
14.3%
8 2
 
9.5%
2 2
 
9.5%
7 2
 
9.5%
3 1
 
4.8%
ValueCountFrequency (%)
2 2
 
9.5%
3 1
 
4.8%
4 3
 
14.3%
5 8
38.1%
6 3
 
14.3%
7 2
 
9.5%
8 2
 
9.5%
ValueCountFrequency (%)
8 2
 
9.5%
7 2
 
9.5%
6 3
 
14.3%
5 8
38.1%
4 3
 
14.3%
3 1
 
4.8%
2 2
 
9.5%

4학년 학급수
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3333333
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:48:58.330987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q37
95-th percentile8
Maximum9
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9061305
Coefficient of variation (CV)0.35739946
Kurtosis-0.77199775
Mean5.3333333
Median Absolute Deviation (MAD)2
Skewness0.18796249
Sum112
Variance3.6333333
MonotonicityNot monotonic
2023-12-13T02:48:58.453423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 6
28.6%
7 4
19.0%
4 3
14.3%
3 3
14.3%
8 2
 
9.5%
2 1
 
4.8%
6 1
 
4.8%
9 1
 
4.8%
ValueCountFrequency (%)
2 1
 
4.8%
3 3
14.3%
4 3
14.3%
5 6
28.6%
6 1
 
4.8%
7 4
19.0%
8 2
 
9.5%
9 1
 
4.8%
ValueCountFrequency (%)
9 1
 
4.8%
8 2
 
9.5%
7 4
19.0%
6 1
 
4.8%
5 6
28.6%
4 3
14.3%
3 3
14.3%
2 1
 
4.8%

5학년 학급수
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3809524
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:48:58.590039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q37
95-th percentile9
Maximum9
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0609753
Coefficient of variation (CV)0.3830131
Kurtosis-0.77817825
Mean5.3809524
Median Absolute Deviation (MAD)1
Skewness0.41506571
Sum113
Variance4.247619
MonotonicityNot monotonic
2023-12-13T02:48:58.725336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 7
33.3%
8 3
14.3%
4 3
14.3%
3 3
14.3%
9 2
 
9.5%
2 1
 
4.8%
6 1
 
4.8%
7 1
 
4.8%
ValueCountFrequency (%)
2 1
 
4.8%
3 3
14.3%
4 3
14.3%
5 7
33.3%
6 1
 
4.8%
7 1
 
4.8%
8 3
14.3%
9 2
 
9.5%
ValueCountFrequency (%)
9 2
 
9.5%
8 3
14.3%
7 1
 
4.8%
6 1
 
4.8%
5 7
33.3%
4 3
14.3%
3 3
14.3%
2 1
 
4.8%

6학년 학급수
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5238095
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T02:48:58.837110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median6
Q37
95-th percentile9
Maximum9
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0885174
Coefficient of variation (CV)0.37809366
Kurtosis-0.99727865
Mean5.5238095
Median Absolute Deviation (MAD)2
Skewness0.16314266
Sum116
Variance4.3619048
MonotonicityNot monotonic
2023-12-13T02:48:58.959347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
6 5
23.8%
4 4
19.0%
8 3
14.3%
3 3
14.3%
9 2
 
9.5%
5 2
 
9.5%
2 1
 
4.8%
7 1
 
4.8%
ValueCountFrequency (%)
2 1
 
4.8%
3 3
14.3%
4 4
19.0%
5 2
 
9.5%
6 5
23.8%
7 1
 
4.8%
8 3
14.3%
9 2
 
9.5%
ValueCountFrequency (%)
9 2
 
9.5%
8 3
14.3%
7 1
 
4.8%
6 5
23.8%
5 2
 
9.5%
4 4
19.0%
3 3
14.3%
2 1
 
4.8%

특수 학급 수
Categorical

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
1
10 
2
7 
0
4 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 10
47.6%
2 7
33.3%
0 4
 
19.0%

Length

2023-12-13T02:48:59.100951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:59.218614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10
47.6%
2 7
33.3%
0 4
 
19.0%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
2020-08-28
21 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-08-28
2nd row2020-08-28
3rd row2020-08-28
4th row2020-08-28
5th row2020-08-28

Common Values

ValueCountFrequency (%)
2020-08-28 21
100.0%

Length

2023-12-13T02:48:59.319930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:59.457624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-08-28 21
100.0%

Interactions

2023-12-13T02:48:52.173630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:49.447430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.012113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.526551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:51.019032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:51.512423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:52.256123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:49.540246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.103570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.615833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:51.101022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:51.587182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:52.337085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:49.628890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.201781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.691767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:51.175129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:51.655851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:52.409039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:49.721794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.276121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.768761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:51.247029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:51.953690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:52.485530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:49.826472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.361190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.849861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:51.342006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:52.024766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:52.563303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:49.916476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.440059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:50.941429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:51.427702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:52.096541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:48:59.527114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
학교명홈페이지 주소설립우편번호주소전화번호1학년 학급수2학년 학급수3학년 학급수4학년 학급수5학년 학급수6학년 학급수특수 학급 수
학교명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
홈페이지 주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
설립1.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.6090.220
우편번호1.0001.0001.0001.0001.0001.0000.3040.0000.8110.5430.7120.6391.000
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1학년 학급수1.0001.0000.0000.3041.0001.0001.0000.7940.7830.8260.8220.8060.000
2학년 학급수1.0001.0000.0000.0001.0001.0000.7941.0000.8780.8280.7840.6430.000
3학년 학급수1.0001.0000.0000.8111.0001.0000.7830.8781.0000.8360.7920.5430.000
4학년 학급수1.0001.0000.0000.5431.0001.0000.8260.8280.8361.0000.9460.9620.426
5학년 학급수1.0001.0000.0000.7121.0001.0000.8220.7840.7920.9461.0000.8600.350
6학년 학급수1.0001.0000.6090.6391.0001.0000.8060.6430.5430.9620.8601.0000.000
특수 학급 수1.0001.0000.2201.0001.0001.0000.0000.0000.0000.4260.3500.0001.000
2023-12-13T02:48:59.666738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수 학급 수설립
특수 학급 수1.0000.344
설립0.3441.000
2023-12-13T02:48:59.756361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1학년 학급수2학년 학급수3학년 학급수4학년 학급수5학년 학급수6학년 학급수설립특수 학급 수
1학년 학급수1.0000.9530.8570.7920.7660.7330.0000.000
2학년 학급수0.9531.0000.9370.8450.7940.7720.0000.000
3학년 학급수0.8570.9371.0000.9140.8780.8300.0000.000
4학년 학급수0.7920.8450.9141.0000.9450.9330.0000.220
5학년 학급수0.7660.7940.8780.9451.0000.9410.0000.152
6학년 학급수0.7330.7720.8300.9330.9411.0000.3630.000
설립0.0000.0000.0000.0000.0000.3631.0000.344
특수 학급 수0.0000.0000.0000.2200.1520.0000.3441.000

Missing values

2023-12-13T02:48:52.724733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:48:52.922616image/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.

Sample

교육청소재구학교명홈페이지 주소설립우편번호주소전화번호1학년 학급수2학년 학급수3학년 학급수4학년 학급수5학년 학급수6학년 학급수특수 학급 수기준일자
0동작관악동작구서울강남초등학교http://www.kangnam-se.es.kr공립156-881서울특별시 동작구 강남초등길 15 (상도1동,강남초등학교)02-824-447186578922020-08-28
1동작관악동작구서울남사초등학교http://www.namsa.es.kr/공립156-824서울특별시 동작구 동작대로13길 22 (사당동,서울남사초등학교)02-522-033355555612020-08-28
2동작관악동작구서울남성초등학교http://www.namsung.es.kr공립156-820서울특별시 동작구 사당로23길 57-14 (사당동,남성초등학교)02-595-547154444422020-08-28
3동작관악동작구서울노량진초등학교http://www.noryangjin.es.kr공립156-800서울특별시 동작구 장승배기로 160 (노량진동,노량진초등학교)02-815-127433343412020-08-28
4동작관악동작구서울대림초등학교http://www.seouldaelim.es.kr공립156-810서울특별시 동작구 대방동1길 22 (대방동,서울대림초등학교)02-822-198488888802020-08-28
5동작관악동작구서울동작초등학교http://www.dongjak.es.kr/공립156-090서울특별시 동작구 동작대로29길 214 (사당동)02-537-177343434312020-08-28
6동작관악동작구서울문창초등학교http://www.munchang.es.kr공립156-853서울특별시 동작구 신대방 2길 1402-836-203155555622020-08-28
7동작관악동작구서울보라매초등학교www.boramae.es.kr공립156-850서울특별시 동작구 여의대방로16길 30 (신대방동,서울보라매초등학교)02-836-300198889912020-08-28
8동작관악동작구서울본동초등학교http://bondong.es.kr공립156-060서울특별시 동작구 노량진로26길 16-40 (본동,서울본동초등학교)02-815-104422222212020-08-28
9동작관악동작구서울삼일초등학교http://www.samil.es.kr/공립156-815서울특별시 동작구 사당로23나길 27 (사당동,서울삼일초등학교)02-3477-947366666602020-08-28
교육청소재구학교명홈페이지 주소설립우편번호주소전화번호1학년 학급수2학년 학급수3학년 학급수4학년 학급수5학년 학급수6학년 학급수특수 학급 수기준일자
11동작관악동작구서울상현초등학교http://www.ssh.es.kr공립156-030서울특별시 동작구 상도로58길 21 (상도동)02-826-974087533312020-08-28
12동작관악동작구서울신길초등학교http://www.singil.es.kr/공립156-808서울특별시 동작구 알마타길 16 (대방동,서울신길초등학교)02-815-374177679822020-08-28
13동작관악동작구서울신남성초등학교http://www.shinnamsung.es.kr공립156-818서울특별시 동작구 사당로 146 (사당동)02-521-626854555512020-08-28
14동작관악동작구서울신상도초등학교http://www.shinsangdo.es.kr공립156-837서울특별시 동작구 장승배기로 14 (상도동,서울신상도초등학교)02-823-5461108798722020-08-28
15동작관악동작구서울영본초등학교http://www.yeongbon.es.kr공립156-060서울특별시 동작구 만양로12가길 69 (본동,서울영본초등학교)02-815-437167675612020-08-28
16동작관악동작구서울영화초등학교www.youngwha.es.kr공립156-807서울특별시 동작구 등용로8길 3 (대방동,영화초등학교)02-824-605144555612020-08-28
17동작관악동작구서울은로초등학교http://eunlo.es.kr공립156-862서울특별시 동작구 서달로 115 (흑석동,은로초등학교)02-824-030954444422020-08-28
18동작관악동작구서울행림초등학교http://www.hrim.es.kr공립156-829서울특별시 동작구 솔밭로 47 (사당동,행림초등학교)02-523-135233233302020-08-28
19동작관악동작구서울흑석초등학교http://heukseok.es.kr/공립156-877서울특별시 동작구 현충로 87 (흑석동,서울흑석초등학교)02-815-327655555412020-08-28
20동작관악동작구중앙대학교사범대학부속초등학교http://www.caude.es.kr사립156-861서울특별시 동작구 서달로 135 (흑석동,중앙대부속초등학교)02-815-014555555502020-08-28