Overview

Dataset statistics

Number of variables12
Number of observations91
Missing cells14
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory105.5 B

Variable types

Text1
Categorical3
Numeric8

Dataset

Description강좌명,교육장소,교육지역,신청시작날짜,신청마감날짜,교육시작날짜,교육종료날짜,정원,신청인원,수강료,선정발표일,문의처
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15598/S/1/datasetView.do

Alerts

신청시작날짜 is highly overall correlated with 신청마감날짜 and 6 other fieldsHigh correlation
신청마감날짜 is highly overall correlated with 신청시작날짜 and 5 other fieldsHigh correlation
교육시작날짜 is highly overall correlated with 신청시작날짜 and 5 other fieldsHigh correlation
교육종료날짜 is highly overall correlated with 신청시작날짜 and 5 other fieldsHigh correlation
정원 is highly overall correlated with 수강료High correlation
수강료 is highly overall correlated with 신청시작날짜 and 4 other fieldsHigh correlation
선정발표일 is highly overall correlated with 신청시작날짜 and 5 other fieldsHigh correlation
교육장소 is highly overall correlated with 수강료 and 2 other fieldsHigh correlation
교육지역 is highly overall correlated with 신청시작날짜 and 7 other fieldsHigh correlation
문의처 is highly overall correlated with 신청시작날짜 and 7 other fieldsHigh correlation
교육장소 is highly imbalanced (51.4%)Imbalance
신청시작날짜 has 2 (2.2%) missing valuesMissing
신청마감날짜 has 2 (2.2%) missing valuesMissing
교육시작날짜 has 2 (2.2%) missing valuesMissing
교육종료날짜 has 2 (2.2%) missing valuesMissing
정원 has 2 (2.2%) missing valuesMissing
수강료 has 2 (2.2%) missing valuesMissing
선정발표일 has 2 (2.2%) missing valuesMissing
정원 has 4 (4.4%) zerosZeros
신청인원 has 15 (16.5%) zerosZeros
수강료 has 12 (13.2%) zerosZeros

Reproduction

Analysis started2024-05-11 01:44:20.281256
Analysis finished2024-05-11 01:44:50.703878
Duration30.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

강좌명
Text

Distinct84
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size860.0 B
2024-05-11T01:44:51.022353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length37
Mean length28.054945
Min length3

Characters and Unicode

Total characters2553
Distinct characters104
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

Unique78 ?
Unique (%)85.7%

Sample

1st row(마감)2019년도 집수리 아카데미 현장실습 교육(기초과정 3회차)
2nd row2018년도 집수리아카데미 현장실습(1회차)
3rd row(마감)2019년도 집수리 아카데미 기초과정4회차(주말반) 교육
4th row2020년도 집수리 아카데미 기초과정 1회차(주말반) 교육
5th row2020년도 집수리 아카데미 기초과정 3회차(주말반) 교육
ValueCountFrequency (%)
교육신청 49
 
12.2%
아카데미 45
 
11.2%
집수리 45
 
11.2%
기초과정 43
 
10.7%
집수리아카데미 31
 
7.7%
2020년도 19
 
4.7%
2023년 14
 
3.5%
교육 9
 
2.2%
심화과정 8
 
2.0%
코로나백신 6
 
1.5%
Other values (88) 133
33.1%
2024-05-11T01:44:52.164879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
314
 
12.3%
2 101
 
4.0%
( 90
 
3.5%
) 89
 
3.5%
수 84
 
3.3%
집 80
 
3.1%
아 78
 
3.1%
카 78
 
3.1%
데 78
 
3.1%
미 78
 
3.1%
Other values (94) 1483
58.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1714
67.1%
Space Separator 314
 
12.3%
Decimal Number 286
 
11.2%
Open Punctuation 91
 
3.6%
Close Punctuation 90
 
3.5%
Other Punctuation 47
 
1.8%
Uppercase Letter 6
 
0.2%
Lowercase Letter 4
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 84
 
4.9%
집 80
 
4.7%
아 78
 
4.6%
카 78
 
4.6%
데 78
 
4.6%
미 78
 
4.6%
리 77
 
4.5%
정 70
 
4.1%
과 69
 
4.0%
차 68
 
4.0%
Other values (69) 954
55.7%
Decimal Number
ValueCountFrequency (%)
2 101
35.3%
0 72
25.2%
1 47
16.4%
3 24
 
8.4%
7 9
 
3.1%
8 8
 
2.8%
9 7
 
2.4%
6 7
 
2.4%
5 6
 
2.1%
4 5
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 39
83.0%
? 6
 
12.8%
# 2
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
D 2
33.3%
Y 2
33.3%
Lowercase Letter
ValueCountFrequency (%)
t 2
50.0%
e 1
25.0%
s 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 90
98.9%
[ 1
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 89
98.9%
] 1
 
1.1%
Space Separator
ValueCountFrequency (%)
314
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1714
67.1%
Common 829
32.5%
Latin 10
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 84
 
4.9%
집 80
 
4.7%
아 78
 
4.6%
카 78
 
4.6%
데 78
 
4.6%
미 78
 
4.6%
리 77
 
4.5%
정 70
 
4.1%
과 69
 
4.0%
차 68
 
4.0%
Other values (69) 954
55.7%
Common
ValueCountFrequency (%)
314
37.9%
2 101
 
12.2%
( 90
 
10.9%
) 89
 
10.7%
0 72
 
8.7%
1 47
 
5.7%
, 39
 
4.7%
3 24
 
2.9%
7 9
 
1.1%
8 8
 
1.0%
Other values (9) 36
 
4.3%
Latin
ValueCountFrequency (%)
A 2
20.0%
D 2
20.0%
t 2
20.0%
Y 2
20.0%
e 1
10.0%
s 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1714
67.1%
ASCII 839
32.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
314
37.4%
2 101
 
12.0%
( 90
 
10.7%
) 89
 
10.6%
0 72
 
8.6%
1 47
 
5.6%
, 39
 
4.6%
3 24
 
2.9%
7 9
 
1.1%
8 8
 
1.0%
Other values (15) 46
 
5.5%
Hangul
ValueCountFrequency (%)
수 84
 
4.9%
집 80
 
4.7%
아 78
 
4.6%
카 78
 
4.6%
데 78
 
4.6%
미 78
 
4.6%
리 77
 
4.5%
정 70
 
4.1%
과 69
 
4.0%
차 68
 
4.0%
Other values (69) 954
55.7%

교육장소
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size860.0 B
아카데미교육장9
64 
아카데미 교육장3
 
5
별도 공지
 
5
아카데미 교육장1
 
2
아카데미 교육장(0)
 
2
Other values (11)
13 

Length

Max length27
Median length8
Mean length8.7362637
Min length4

Unique

Unique9 ?
Unique (%)9.9%

Sample

1st row아카데미교육장9
2nd rowtest
3rd row아카데미교육장9
4th row아카데미교육장9
5th row아카데미교육장9

Common Values

ValueCountFrequency (%)
아카데미교육장9 64
70.3%
아카데미 교육장3 5
 
5.5%
별도 공지 5
 
5.5%
아카데미 교육장1 2
 
2.2%
아카데미 교육장(0) 2
 
2.2%
중구 집수리공구대여소<br>(아카데미교육장10) 2
 
2.2%
아카데미 교육장5 2
 
2.2%
test 1
 
1.1%
아카데미 교육장2 1
 
1.1%
은평구 신사동 1
 
1.1%
Other values (6) 6
 
6.6%

Length

2024-05-11T01:44:52.752299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아카데미교육장9 64
55.2%
아카데미 15
 
12.9%
교육장3 5
 
4.3%
별도 5
 
4.3%
공지 5
 
4.3%
집수리공구대여소<br>(아카데미교육장10 2
 
1.7%
교육장5 2
 
1.7%
test 2
 
1.7%
중구 2
 
1.7%
교육장(0 2
 
1.7%
Other values (11) 12
 
10.3%

교육지역
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Memory size860.0 B
은평구 불광동 서울혁신파크
32 
서울시 은평구 서울혁신파크 등
11 
서울혁신파크(지하철3호선 불광역 인근)
4 
서울혁신파크(은평구 불광동 소재) 등
4 
은평구 서울혁신센터 등
 
3
Other values (30)
37 

Length

Max length27
Median length25
Mean length13.483516
Min length1

Unique

Unique23 ?
Unique (%)25.3%

Sample

1st row서울혁신파크 실습장 등
2nd row서울시
3rd row서울혁신파크 실습장, 강북구 실습장 등
4th row은평구 서울혁신파크 등
5th row서울혁신파크(은평구 불광동 소재) 등

Common Values

ValueCountFrequency (%)
은평구 불광동 서울혁신파크 32
35.2%
서울시 은평구 서울혁신파크 등 11
 
12.1%
서울혁신파크(지하철3호선 불광역 인근) 4
 
4.4%
서울혁신파크(은평구 불광동 소재) 등 4
 
4.4%
은평구 서울혁신센터 등 3
 
3.3%
테스트 2
 
2.2%
청계광장 2
 
2.2%
<NA> 2
 
2.2%
청계 광장 2
 
2.2%
신청 테스트 입니다. 2
 
2.2%
Other values (25) 27
29.7%

Length

2024-05-11T01:44:53.308279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 52
18.6%
서울혁신파크 48
17.2%
불광동 37
13.3%
등 23
 
8.2%
서울시 12
 
4.3%
테스트 5
 
1.8%
서울혁신파크(은평구 4
 
1.4%
소재 4
 
1.4%
인근 4
 
1.4%
불광역 4
 
1.4%
Other values (53) 86
30.8%

신청시작날짜
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct68
Distinct (%)76.4%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.0200039 × 1011
Minimum2.0101001 × 1011
Maximum2.0230921 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2024-05-11T01:44:54.231570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0101001 × 1011
5-th percentile2.0160427 × 1011
Q12.018062 × 1011
median2.0200903 × 1011
Q32.0220719 × 1011
95-th percentile2.0230821 × 1011
Maximum2.0230921 × 1011
Range1.2991997 × 109
Interquartile range (IQR)4.0099 × 108

Descriptive statistics

Standard deviation2.4974578 × 108
Coefficient of variation (CV)0.0012363629
Kurtosis1.2950702
Mean2.0200039 × 1011
Median Absolute Deviation (MAD)1.99 × 108
Skewness-0.91968378
Sum1.7978034 × 1013
Variance6.2372952 × 1016
MonotonicityNot monotonic
2024-05-11T01:44:54.788907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202005250900 3
 
3.3%
202106290900 3
 
3.3%
201604270000 3
 
3.3%
202010300900 3
 
3.3%
202008190900 3
 
3.3%
201709190000 2
 
2.2%
201604250000 2
 
2.2%
202308230900 2
 
2.2%
202208030900 2
 
2.2%
202207190900 2
 
2.2%
Other values (58) 64
70.3%
ValueCountFrequency (%)
201010011205 1
 
1.1%
201604250000 2
2.2%
201604270000 3
3.3%
201605190000 1
 
1.1%
201608310000 2
2.2%
201611170000 1
 
1.1%
201705171700 1
 
1.1%
201705260000 1
 
1.1%
201708090000 2
2.2%
201709190000 2
2.2%
ValueCountFrequency (%)
202309210900 1
1.1%
202309150900 1
1.1%
202309120900 1
1.1%
202308230900 2
2.2%
202308170900 1
1.1%
202305160900 1
1.1%
202305110900 1
1.1%
202304140900 1
1.1%
202304110900 1
1.1%
202304040900 1
1.1%

신청마감날짜
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct76
Distinct (%)85.4%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.0200384 × 1011
Minimum2.0101001 × 1011
Maximum2.0230921 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2024-05-11T01:44:55.330141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0101001 × 1011
5-th percentile2.0160516 × 1011
Q12.018062 × 1011
median2.0201005 × 1011
Q32.0220719 × 1011
95-th percentile2.0230821 × 1011
Maximum2.0230921 × 1011
Range1.2991998 × 109
Interquartile range (IQR)4.0099017 × 108

Descriptive statistics

Standard deviation2.5174395 × 108
Coefficient of variation (CV)0.0012462335
Kurtosis1.2309984
Mean2.0200384 × 1011
Median Absolute Deviation (MAD)1.979798 × 108
Skewness-0.91700595
Sum1.7978342 × 1013
Variance6.3375018 × 1016
MonotonicityNot monotonic
2024-05-11T01:44:55.856798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202008191800 4
 
4.4%
202010301200 3
 
3.3%
201604292359 2
 
2.2%
202008171800 2
 
2.2%
202207191100 2
 
2.2%
202204271200 2
 
2.2%
202208031000 2
 
2.2%
202005261800 2
 
2.2%
202106291200 2
 
2.2%
201709212359 2
 
2.2%
Other values (66) 66
72.5%
(Missing) 2
 
2.2%
ValueCountFrequency (%)
201010011230 1
1.1%
201604292359 2
2.2%
201605092359 1
1.1%
201605152359 1
1.1%
201605182359 1
1.1%
201605202359 1
1.1%
201609212359 1
1.1%
201610212359 1
1.1%
201611212359 1
1.1%
201705171840 1
1.1%
ValueCountFrequency (%)
202309211000 1
1.1%
202309151000 1
1.1%
202309121000 1
1.1%
202308231800 1
1.1%
202308231000 1
1.1%
202308171000 1
1.1%
202307202359 1
1.1%
202305161000 1
1.1%
202305111000 1
1.1%
202304141000 1
1.1%

교육시작날짜
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)91.0%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean20200756
Minimum20130501
Maximum20231230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2024-05-11T01:44:56.450173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20130501
5-th percentile20160648
Q120180630
median20201010
Q320220724
95-th percentile20230828
Maximum20231230
Range100729
Interquartile range (IQR)40094

Descriptive statistics

Standard deviation24015.486
Coefficient of variation (CV)0.0011888409
Kurtosis-0.53367127
Mean20200756
Median Absolute Deviation (MAD)19801
Skewness-0.56124578
Sum1.7978673 × 109
Variance5.7674356 × 108
MonotonicityNot monotonic
2024-05-11T01:44:57.154227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200606 3
 
3.3%
20230828 2
 
2.2%
20180602 2
 
2.2%
20200818 2
 
2.2%
20170923 2
 
2.2%
20170922 2
 
2.2%
20170812 2
 
2.2%
20221008 1
 
1.1%
20220915 1
 
1.1%
20220912 1
 
1.1%
Other values (71) 71
78.0%
(Missing) 2
 
2.2%
ValueCountFrequency (%)
20130501 1
1.1%
20160502 1
1.1%
20160512 1
1.1%
20160528 1
1.1%
20160610 1
1.1%
20160705 1
1.1%
20160707 1
1.1%
20160922 1
1.1%
20161027 1
1.1%
20161126 1
1.1%
ValueCountFrequency (%)
20231230 1
1.1%
20231002 1
1.1%
20230923 1
1.1%
20230921 1
1.1%
20230828 2
2.2%
20230826 1
1.1%
20230527 1
1.1%
20230518 1
1.1%
20230422 1
1.1%
20230420 1
1.1%

교육종료날짜
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)91.0%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean20200814
Minimum20130503
Maximum20231230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2024-05-11T01:44:57.860493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20130503
5-th percentile20160711
Q120180722
median20201101
Q320220724
95-th percentile20230926
Maximum20231230
Range100727
Interquartile range (IQR)40002

Descriptive statistics

Standard deviation24019.105
Coefficient of variation (CV)0.0011890167
Kurtosis-0.5318555
Mean20200814
Median Absolute Deviation (MAD)19801
Skewness-0.56057748
Sum1.7978724 × 109
Variance5.7691739 × 108
MonotonicityNot monotonic
2024-05-11T01:44:58.381016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200628 3
 
3.3%
20230926 2
 
2.2%
20180624 2
 
2.2%
20200818 2
 
2.2%
20170923 2
 
2.2%
20170922 2
 
2.2%
20170930 2
 
2.2%
20221030 1
 
1.1%
20221007 1
 
1.1%
20221004 1
 
1.1%
Other values (71) 71
78.0%
(Missing) 2
 
2.2%
ValueCountFrequency (%)
20130503 1
1.1%
20160506 1
1.1%
20160602 1
1.1%
20160612 1
1.1%
20160701 1
1.1%
20160726 1
1.1%
20160728 1
1.1%
20161019 1
1.1%
20161124 1
1.1%
20161211 1
1.1%
ValueCountFrequency (%)
20231230 1
1.1%
20231024 1
1.1%
20231022 1
1.1%
20231020 1
1.1%
20230926 2
2.2%
20230917 1
1.1%
20230618 1
1.1%
20230609 1
1.1%
20230514 1
1.1%
20230512 1
1.1%

정원
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)11.2%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean27.685393
Minimum0
Maximum60
Zeros4
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size951.0 B
2024-05-11T01:44:58.736088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.4
Q130
median30
Q330
95-th percentile40
Maximum60
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.580423
Coefficient of variation (CV)0.38216626
Kurtosis2.6853762
Mean27.685393
Median Absolute Deviation (MAD)0
Skewness-0.69868685
Sum2464
Variance111.94535
MonotonicityNot monotonic
2024-05-11T01:44:59.138151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
30 67
73.6%
40 6
 
6.6%
10 5
 
5.5%
0 4
 
4.4%
60 2
 
2.2%
11 1
 
1.1%
9 1
 
1.1%
4 1
 
1.1%
5 1
 
1.1%
15 1
 
1.1%
(Missing) 2
 
2.2%
ValueCountFrequency (%)
0 4
 
4.4%
4 1
 
1.1%
5 1
 
1.1%
9 1
 
1.1%
10 5
 
5.5%
11 1
 
1.1%
15 1
 
1.1%
30 67
73.6%
40 6
 
6.6%
60 2
 
2.2%
ValueCountFrequency (%)
60 2
 
2.2%
40 6
 
6.6%
30 67
73.6%
15 1
 
1.1%
11 1
 
1.1%
10 5
 
5.5%
9 1
 
1.1%
5 1
 
1.1%
4 1
 
1.1%
0 4
 
4.4%

신청인원
Real number (ℝ)

ZEROS 

Distinct61
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.21978
Minimum0
Maximum232
Zeros15
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size951.0 B
2024-05-11T01:44:59.607001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median52
Q381
95-th percentile126.5
Maximum232
Range232
Interquartile range (IQR)76

Descriptive statistics

Standard deviation45.67489
Coefficient of variation (CV)0.84240272
Kurtosis1.5498475
Mean54.21978
Median Absolute Deviation (MAD)33
Skewness0.93685428
Sum4934
Variance2086.1956
MonotonicityNot monotonic
2024-05-11T01:44:59.994506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
16.5%
31 4
 
4.4%
50 3
 
3.3%
5 3
 
3.3%
64 3
 
3.3%
1 2
 
2.2%
87 2
 
2.2%
56 2
 
2.2%
59 2
 
2.2%
58 2
 
2.2%
Other values (51) 53
58.2%
ValueCountFrequency (%)
0 15
16.5%
1 2
 
2.2%
2 1
 
1.1%
3 2
 
2.2%
4 1
 
1.1%
5 3
 
3.3%
18 1
 
1.1%
28 1
 
1.1%
30 1
 
1.1%
31 4
 
4.4%
ValueCountFrequency (%)
232 1
1.1%
172 1
1.1%
151 1
1.1%
148 1
1.1%
128 1
1.1%
125 1
1.1%
122 1
1.1%
118 1
1.1%
114 1
1.1%
110 1
1.1%

수강료
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)6.7%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean62247.315
Minimum0
Maximum80000
Zeros12
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size951.0 B
2024-05-11T01:45:00.307539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160000
median80000
Q380000
95-th percentile80000
Maximum80000
Range80000
Interquartile range (IQR)20000

Descriptive statistics

Standard deviation30329.477
Coefficient of variation (CV)0.48724154
Kurtosis0.064851281
Mean62247.315
Median Absolute Deviation (MAD)0
Skewness-1.3519501
Sum5540011
Variance9.1987719 × 108
MonotonicityNot monotonic
2024-05-11T01:45:00.652274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
80000 62
68.1%
0 12
 
13.2%
20000 6
 
6.6%
60000 6
 
6.6%
50000 2
 
2.2%
11 1
 
1.1%
(Missing) 2
 
2.2%
ValueCountFrequency (%)
0 12
 
13.2%
11 1
 
1.1%
20000 6
 
6.6%
50000 2
 
2.2%
60000 6
 
6.6%
80000 62
68.1%
ValueCountFrequency (%)
80000 62
68.1%
60000 6
 
6.6%
50000 2
 
2.2%
20000 6
 
6.6%
11 1
 
1.1%
0 12
 
13.2%

선정발표일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct66
Distinct (%)74.2%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean20200843
Minimum20140515
Maximum20231201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2024-05-11T01:45:01.142053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20140515
5-th percentile20160502
Q120180625
median20201006
Q320220720
95-th percentile20230825
Maximum20231201
Range90686
Interquartile range (IQR)40095

Descriptive statistics

Standard deviation23714.262
Coefficient of variation (CV)0.0011739244
Kurtosis-0.81787192
Mean20200843
Median Absolute Deviation (MAD)19798
Skewness-0.48602497
Sum1.797875 × 109
Variance5.6236624 × 108
MonotonicityNot monotonic
2024-05-11T01:45:01.571112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20160502 4
 
4.4%
20200529 3
 
3.3%
20220526 3
 
3.3%
20210629 3
 
3.3%
20200827 3
 
3.3%
20201103 3
 
3.3%
20190531 2
 
2.2%
20170811 2
 
2.2%
20220622 2
 
2.2%
20230825 2
 
2.2%
Other values (56) 62
68.1%
ValueCountFrequency (%)
20140515 1
 
1.1%
20160429 1
 
1.1%
20160502 4
4.4%
20160523 1
 
1.1%
20160921 1
 
1.1%
20161021 1
 
1.1%
20161122 1
 
1.1%
20170523 1
 
1.1%
20170530 1
 
1.1%
20170811 2
2.2%
ValueCountFrequency (%)
20231201 1
1.1%
20230926 1
1.1%
20230921 1
1.1%
20230919 1
1.1%
20230825 2
2.2%
20230824 1
1.1%
20230524 1
1.1%
20230516 1
1.1%
20230420 1
1.1%
20230414 1
1.1%

문의처
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size860.0 B
02-2133-7265
28 
02-2133-7260
17 
02-2133-7262
15 
02-2133-1216
8 
02-2133-7258
6 
Other values (9)
17 

Length

Max length12
Median length12
Mean length11.527473
Min length2

Unique

Unique4 ?
Unique (%)4.4%

Sample

1st row02-2133-7264
2nd row02-2133-7255
3rd row02-2133-7265
4th row02-2133-7265
5th row02-2133-7265

Common Values

ValueCountFrequency (%)
02-2133-7265 28
30.8%
02-2133-7260 17
18.7%
02-2133-7262 15
16.5%
02-2133-1216 8
 
8.8%
02-2133-7258 6
 
6.6%
02-2133-7255 4
 
4.4%
02-1234-5678 3
 
3.3%
02-2133-7264 2
 
2.2%
<NA> 2
 
2.2%
-- 2
 
2.2%
Other values (4) 4
 
4.4%

Length

2024-05-11T01:45:01.984869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02-2133-7265 28
30.8%
02-2133-7260 17
18.7%
02-2133-7262 15
16.5%
02-2133-1216 8
 
8.8%
02-2133-7258 6
 
6.6%
02-2133-7255 4
 
4.4%
02-1234-5678 3
 
3.3%
02-2133-7264 2
 
2.2%
na 2
 
2.2%
2
 
2.2%
Other values (4) 4
 
4.4%

Interactions

2024-05-11T01:44:45.428773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:25.622662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:28.397951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:31.311473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:34.417192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:36.778270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:39.765309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:42.437456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:45.940550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:25.957268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:28.823504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:31.646819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:34.808070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:37.144386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:40.134226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:42.753476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:46.326651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:26.368062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:29.173954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:32.051172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:35.078291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:37.586029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:40.464227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:43.045977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:46.759890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:26.683801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:29.485606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:32.405156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:35.389673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:37.998015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:40.806509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:43.357714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:47.155650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:26.999675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:29.814008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:32.759413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:35.670923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:38.410431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:41.139127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:43.671213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:47.756030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:27.376038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:30.296609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:33.053580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:35.917334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:38.772151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:41.426103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:44.048658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:48.131769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:27.683545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:30.672522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:33.734380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:36.163854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:39.131124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:41.743902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:44.425413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:48.577028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:28.079919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:31.010469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:34.148936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:36.493006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:39.464206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:42.093161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:44:44.961048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T01:45:02.241767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강좌명교육장소교육지역신청시작날짜신청마감날짜교육시작날짜교육종료날짜정원신청인원수강료선정발표일문의처
강좌명1.0000.8460.0001.0001.0001.0001.0000.9580.9541.0001.0000.986
교육장소0.8461.0000.9980.8100.8090.8400.8770.0000.0000.9470.8680.895
교육지역0.0000.9981.0000.9900.9900.9610.9650.6440.0000.9830.9710.991
신청시작날짜1.0000.8100.9901.0001.0000.9010.8910.6770.4060.6961.0000.935
신청마감날짜1.0000.8090.9901.0001.0000.9060.8960.6470.4160.6941.0000.938
교육시작날짜1.0000.8400.9610.9010.9061.0000.9970.4820.2660.6280.9880.897
교육종료날짜1.0000.8770.9650.8910.8960.9971.0000.4870.2560.6220.9800.900
정원0.9580.0000.6440.6770.6470.4820.4871.0000.3130.5510.5340.755
신청인원0.9540.0000.0000.4060.4160.2660.2560.3131.0000.1430.3090.000
수강료1.0000.9470.9830.6960.6940.6280.6220.5510.1431.0000.7140.831
선정발표일1.0000.8680.9711.0001.0000.9880.9800.5340.3090.7141.0000.943
문의처0.9860.8950.9910.9350.9380.8970.9000.7550.0000.8310.9431.000
2024-05-11T01:45:02.598566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교육장소문의처교육지역
교육장소1.0000.5960.844
문의처0.5961.0000.780
교육지역0.8440.7801.000
2024-05-11T01:45:02.865115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신청시작날짜신청마감날짜교육시작날짜교육종료날짜정원신청인원수강료선정발표일교육장소교육지역문의처
신청시작날짜1.0000.9790.9730.9730.4680.4290.5330.9740.4500.7110.827
신청마감날짜0.9791.0000.9990.9980.4180.4060.4850.9990.4490.7110.833
교육시작날짜0.9730.9991.0000.9990.4100.4000.4791.0000.3870.6240.739
교육종료날짜0.9730.9980.9991.0000.4270.4080.4910.9990.4380.6340.745
정원0.4680.4180.4100.4271.0000.3110.5720.4090.0000.2750.382
신청인원0.4290.4060.4000.4080.3111.0000.3120.4020.0000.0000.000
수강료0.5330.4850.4790.4910.5720.3121.0000.4800.7820.7360.625
선정발표일0.9740.9991.0000.9990.4090.4020.4801.0000.4240.6520.837
교육장소0.4500.4490.3870.4380.0000.0000.7820.4241.0000.8440.596
교육지역0.7110.7110.6240.6340.2750.0000.7360.6520.8441.0000.780
문의처0.8270.8330.7390.7450.3820.0000.6250.8370.5960.7801.000

Missing values

2024-05-11T01:44:49.126138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T01:44:49.787175image/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.
2024-05-11T01:44:50.366879image/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

강좌명교육장소교육지역신청시작날짜신청마감날짜교육시작날짜교육종료날짜정원신청인원수강료선정발표일문의처
0(마감)2019년도 집수리 아카데미 현장실습 교육(기초과정 3회차)아카데미교육장9서울혁신파크 실습장 등20190529090020190529105920190608201906303052800002019053102-2133-7264
12018년도 집수리아카데미 현장실습(1회차)test서울시2018052309002018052718002018060220180624300800002018052902-2133-7255
2(마감)2019년도 집수리 아카데미 기초과정4회차(주말반) 교육아카데미교육장9서울혁신파크 실습장, 강북구 실습장 등20190801090020190801101020190817201909083045800002019080202-2133-7265
32020년도 집수리 아카데미 기초과정 1회차(주말반) 교육아카데미교육장9은평구 서울혁신파크 등2020052509002020052618002020060620200628300800002020052902-2133-7265
42020년도 집수리 아카데미 기초과정 3회차(주말반) 교육아카데미교육장9서울혁신파크(은평구 불광동 소재) 등20200622090020200622090320200704202007263068800002020062502-2133-7265
52020년도 집수리 아카데미 기초과정 4회차(목,금요일반) 교육신청아카데미교육장9은평구 서울혁신센터 등202007230900202007230910202008062020082830148800002020072802-2133-7265
6개강안내 받기(집수리아카데미 기초반(5,6,7차)아카데미교육장9개강안내 받기(집수리아카데미 기초반(5,6,7차)2020081810002020081918002020081920200819015102020081902-2133-7265
72020년도 집수리 아카데미 기초과정 5회차(화,수요일반) 교육신청아카데미교육장9은평구 서울혁신센터 등20200902090020200902180020200908202010073064800002020090402-2133-7265
82020년도 집수리 아카데미 기초과정 6회차(목,금요일반) 교육신청아카데미교육장9서울시 은평구 서울혁신파크 등20200903090020200903180020200910202010093070800002020090802-2133-7265
9집수리 아카데미 기초과정(6월)아카데미 교육장2관악구 구암길 106 관악드림타운 관리동 3층20160427000020160515235920160610201607013031200002016050202-2133-1216
강좌명교육장소교육지역신청시작날짜신청마감날짜교육시작날짜교육종료날짜정원신청인원수강료선정발표일문의처
81집수리아카데미 심화과정 2회차(목,금요일반) 교육신청아카데미교육장9은평구 불광동 서울혁신파크20220621110020220621130020220630202206223036800002022062202-2133-7262
82집수리아카데미 기초과정 8회차(월,화요일반) 교육신청아카데미교육장9은평구 불광동 서울혁신파크20220622090020220622110020220627202207193067800002022062302-2133-7262
83집수리아카데미 심화과정 3회차(목,금요일반) 교육신청아카데미교육장9은평구 불광동 서울혁신파크20220913110020220913140020220915202210073056800002022091402-2133-7262
84집수리 아카데미 심화과정(4회차) 교육생 모집아카데미교육장9은평구 불광동 서울혁신파크20220929110020220929140020221008202210303050800002022100402-2133-7260
852023년 집수리아카데미 기초과정 4회차(목?금요일반) 교육신청아카데미교육장9은평구 불광동 서울혁신파크20230404090020230404100020230420202305123091800002023041202-2133-7260
862023년 집수리아카데미 기초과정 5회차(토,일요일반) 교육신청아카데미교육장9은평구 불광동 서울혁신파크202304140900202304141000202304222023051430172800002023042002-2133-7260
872023년 집수리 아카데미 기초과정 6회차(목,금요일반) 교육신청아카데미교육장9은평구 불광동 서울혁신파크20230511090020230511100020230518202306093098800002023051602-2133-7260
882023년 집수리아카데미 심화과정 2회차(토,일요일반) 교육신청아카데미교육장9은평구 불광동 서울혁신파크20230516090020230516100020230527202306183077800002023052402-2133-7260
892023년 집수리 아카데미 기초과정 7회차(토?일요일반) 교육신청아카데미교육장9은평구 불광동 서울혁신파크20230817090020230817100020230826202309174061800002023082402-2133-7260
902023년 집수리 아카데미 기초과정 8회차(월?화요일반) 교육신청아카데미교육장9은평구 불광동 서울혁신파크2023082309002023082310002023082820230926403800002023082502-2133-7260