Overview

Dataset statistics

Number of variables12
Number of observations92
Missing cells14
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory105.4 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 (51.8%)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 (4.3%) zerosZeros
신청인원 has 16 (17.4%) zerosZeros
수강료 has 12 (13.0%) zerosZeros

Reproduction

Analysis started2024-05-17 23:40:18.256314
Analysis finished2024-05-17 23:40:36.814426
Duration18.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

강좌명
Text

Distinct85
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size868.0 B
2024-05-18T08:40:37.013602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length37
Mean length28.141304
Min length3

Characters and Unicode

Total characters2589
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

Unique79 ?
Unique (%)85.9%

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 50
 
12.3%
아카데미 46
 
11.3%
집수리 46
 
11.3%
기초과정 44
 
10.8%
집수리아카데미 31
 
7.6%
2020년도 19
 
4.7%
2023년 14
 
3.4%
교육 9
 
2.2%
심화과정 8
 
2.0%
코로나백신 6
 
1.5%
Other values (89) 135
33.1%
2024-05-18T08:40:37.735334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
319
 
12.3%
2 103
 
4.0%
( 91
 
3.5%
) 90
 
3.5%
수 85
 
3.3%
집 81
 
3.1%
미 79
 
3.1%
카 79
 
3.1%
데 79
 
3.1%
아 79
 
3.1%
Other values (94) 1504
58.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1737
67.1%
Space Separator 319
 
12.3%
Decimal Number 291
 
11.2%
Open Punctuation 92
 
3.6%
Close Punctuation 91
 
3.5%
Other Punctuation 48
 
1.9%
Uppercase Letter 6
 
0.2%
Lowercase Letter 4
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 85
 
4.9%
집 81
 
4.7%
미 79
 
4.5%
카 79
 
4.5%
데 79
 
4.5%
아 79
 
4.5%
리 78
 
4.5%
정 71
 
4.1%
과 70
 
4.0%
차 69
 
4.0%
Other values (69) 967
55.7%
Decimal Number
ValueCountFrequency (%)
2 103
35.4%
0 73
25.1%
1 48
16.5%
3 24
 
8.2%
7 9
 
3.1%
8 8
 
2.7%
9 7
 
2.4%
6 7
 
2.4%
4 6
 
2.1%
5 6
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 40
83.3%
? 6
 
12.5%
# 2
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
D 2
33.3%
Y 2
33.3%
A 2
33.3%
Lowercase Letter
ValueCountFrequency (%)
t 2
50.0%
e 1
25.0%
s 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 91
98.9%
[ 1
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 90
98.9%
] 1
 
1.1%
Space Separator
ValueCountFrequency (%)
319
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1737
67.1%
Common 842
32.5%
Latin 10
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 85
 
4.9%
집 81
 
4.7%
미 79
 
4.5%
카 79
 
4.5%
데 79
 
4.5%
아 79
 
4.5%
리 78
 
4.5%
정 71
 
4.1%
과 70
 
4.0%
차 69
 
4.0%
Other values (69) 967
55.7%
Common
ValueCountFrequency (%)
319
37.9%
2 103
 
12.2%
( 91
 
10.8%
) 90
 
10.7%
0 73
 
8.7%
1 48
 
5.7%
, 40
 
4.8%
3 24
 
2.9%
7 9
 
1.1%
8 8
 
1.0%
Other values (9) 37
 
4.4%
Latin
ValueCountFrequency (%)
D 2
20.0%
Y 2
20.0%
t 2
20.0%
A 2
20.0%
e 1
10.0%
s 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1737
67.1%
ASCII 852
32.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319
37.4%
2 103
 
12.1%
( 91
 
10.7%
) 90
 
10.6%
0 73
 
8.6%
1 48
 
5.6%
, 40
 
4.7%
3 24
 
2.8%
7 9
 
1.1%
8 8
 
0.9%
Other values (15) 47
 
5.5%
Hangul
ValueCountFrequency (%)
수 85
 
4.9%
집 81
 
4.7%
미 79
 
4.5%
카 79
 
4.5%
데 79
 
4.5%
아 79
 
4.5%
리 78
 
4.5%
정 71
 
4.1%
과 70
 
4.0%
차 69
 
4.0%
Other values (69) 967
55.7%

교육장소
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size868.0 B
아카데미교육장9
65 
아카데미 교육장3
 
5
별도 공지
 
5
아카데미 교육장1
 
2
아카데미 교육장(0)
 
2
Other values (11)
13 

Length

Max length27
Median length8
Mean length8.7282609
Min length4

Unique

Unique9 ?
Unique (%)9.8%

Sample

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

Common Values

ValueCountFrequency (%)
아카데미교육장9 65
70.7%
아카데미 교육장3 5
 
5.4%
별도 공지 5
 
5.4%
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
 
6.5%

Length

2024-05-18T08:40:37.985631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아카데미교육장9 65
55.6%
아카데미 15
 
12.8%
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 

Distinct36
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
은평구 불광동 서울혁신파크
32 
서울시 은평구 서울혁신파크 등
11 
서울혁신파크(은평구 불광동 소재) 등
4 
서울혁신파크(지하철3호선 불광역 인근)
4 
은평구 서울혁신센터 등
 
3
Other values (31)
38 

Length

Max length34
Median length26
Mean length13.706522
Min length1

Unique

Unique24 ?
Unique (%)26.1%

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
34.8%
서울시 은평구 서울혁신파크 등 11
 
12.0%
서울혁신파크(은평구 불광동 소재) 등 4
 
4.3%
서울혁신파크(지하철3호선 불광역 인근) 4
 
4.3%
은평구 서울혁신센터 등 3
 
3.3%
청계 광장 2
 
2.2%
<NA> 2
 
2.2%
은평구 및 금천구 2
 
2.2%
테스트 2
 
2.2%
신청 테스트 입니다. 2
 
2.2%
Other values (26) 28
30.4%

Length

2024-05-18T08:40:38.421277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 52
18.2%
서울혁신파크 48
16.8%
불광동 37
13.0%
등 23
 
8.1%
서울시 13
 
4.6%
인근 5
 
1.8%
테스트 5
 
1.8%
서울혁신파크(지하철3호선 4
 
1.4%
소재 4
 
1.4%
서울혁신파크(은평구 4
 
1.4%
Other values (56) 90
31.6%

신청시작날짜
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct69
Distinct (%)76.7%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.0200488 × 1011
Minimum2.0101001 × 1011
Maximum2.024052 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-18T08:40:38.849803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0101001 × 1011
5-th percentile2.0160427 × 1011
Q12.0180648 × 1011
median2.0200954 × 1011
Q32.0220719 × 1011
95-th percentile2.0230823 × 1011
Maximum2.024052 × 1011
Range1.3951897 × 109
Interquartile range (IQR)4.00715 × 108

Descriptive statistics

Standard deviation2.5197811 × 108
Coefficient of variation (CV)0.0012473862
Kurtosis1.2361532
Mean2.0200488 × 1011
Median Absolute Deviation (MAD)1.9849 × 108
Skewness-0.89229505
Sum1.818044 × 1013
Variance6.3492968 × 1016
MonotonicityNot monotonic
2024-05-18T08:40:39.298129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202005250900 3
 
3.3%
202106290900 3
 
3.3%
202010300900 3
 
3.3%
201604270000 3
 
3.3%
202008190900 3
 
3.3%
201604250000 2
 
2.2%
202208030900 2
 
2.2%
201805230900 2
 
2.2%
201709190000 2
 
2.2%
201608310000 2
 
2.2%
Other values (59) 65
70.7%
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 (%)
202405200900 1
1.1%
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%

신청마감날짜
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct77
Distinct (%)85.6%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.020083 × 1011
Minimum2.0101001 × 1011
Maximum2.024052 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-18T08:40:39.860912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0101001 × 1011
5-th percentile2.0160517 × 1011
Q12.0180648 × 1011
median2.0201006 × 1011
Q32.0220782 × 1011
95-th percentile2.0230823 × 1011
Maximum2.024052 × 1011
Range1.3951898 × 109
Interquartile range (IQR)4.013451 × 108

Descriptive statistics

Standard deviation2.538756 × 108
Coefficient of variation (CV)0.0012567583
Kurtosis1.1768084
Mean2.020083 × 1011
Median Absolute Deviation (MAD)1.980048 × 108
Skewness-0.89232431
Sum1.8180747 × 1013
Variance6.4452822 × 1016
MonotonicityNot monotonic
2024-05-18T08:40:40.333157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202008191800 4
 
4.3%
202010301200 3
 
3.3%
201604292359 2
 
2.2%
202106291200 2
 
2.2%
202204271200 2
 
2.2%
202207191100 2
 
2.2%
202208031000 2
 
2.2%
202008171800 2
 
2.2%
201709212359 2
 
2.2%
202005261800 2
 
2.2%
Other values (67) 67
72.8%
(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 (%)
202405201000 1
1.1%
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%

교육시작날짜
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct82
Distinct (%)91.1%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean20201198
Minimum20130501
Maximum20240527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-18T08:40:40.783261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20130501
5-th percentile20160653
Q120180675
median20201012
Q320220787
95-th percentile20230879
Maximum20240527
Range110026
Interquartile range (IQR)40111.75

Descriptive statistics

Standard deviation24245.369
Coefficient of variation (CV)0.0012001946
Kurtosis-0.53188759
Mean20201198
Median Absolute Deviation (MAD)19800.5
Skewness-0.54437362
Sum1.8181078 × 109
Variance5.8783791 × 108
MonotonicityNot monotonic
2024-05-18T08:40:41.224709image/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%
20230420 1
 
1.1%
20221008 1
 
1.1%
20221017 1
 
1.1%
Other values (72) 72
78.3%
(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 (%)
20240527 1
1.1%
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%

교육종료날짜
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct82
Distinct (%)91.1%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean20201256
Minimum20130503
Maximum20240618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-18T08:40:41.696666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20130503
5-th percentile20160712
Q120180793
median20201102
Q320220804
95-th percentile20230978
Maximum20240618
Range110115
Interquartile range (IQR)40010.5

Descriptive statistics

Standard deviation24249.524
Coefficient of variation (CV)0.0012003968
Kurtosis-0.53016084
Mean20201256
Median Absolute Deviation (MAD)19800.5
Skewness-0.54363357
Sum1.818113 × 109
Variance5.8803941 × 108
MonotonicityNot monotonic
2024-05-18T08:40:42.165660image/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%
20230512 1
 
1.1%
20221030 1
 
1.1%
20221018 1
 
1.1%
Other values (72) 72
78.3%
(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 (%)
20240618 1
1.1%
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%

정원
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)11.1%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean27.822222
Minimum0
Maximum60
Zeros4
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-18T08:40:42.546888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.45
Q130
median30
Q330
95-th percentile40
Maximum60
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.600591
Coefficient of variation (CV)0.38101167
Kurtosis2.6328006
Mean27.822222
Median Absolute Deviation (MAD)0
Skewness-0.7083155
Sum2504
Variance112.37253
MonotonicityNot monotonic
2024-05-18T08:40:42.902056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
30 67
72.8%
40 7
 
7.6%
10 5
 
5.4%
0 4
 
4.3%
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
 
4.3%
4 1
 
1.1%
5 1
 
1.1%
9 1
 
1.1%
10 5
 
5.4%
11 1
 
1.1%
15 1
 
1.1%
30 67
72.8%
40 7
 
7.6%
60 2
 
2.2%
ValueCountFrequency (%)
60 2
 
2.2%
40 7
 
7.6%
30 67
72.8%
15 1
 
1.1%
11 1
 
1.1%
10 5
 
5.4%
9 1
 
1.1%
5 1
 
1.1%
4 1
 
1.1%
0 4
 
4.3%

신청인원
Real number (ℝ)

ZEROS 

Distinct61
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.630435
Minimum0
Maximum232
Zeros16
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-18T08:40:43.314823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median51.5
Q380.5
95-th percentile126.35
Maximum232
Range232
Interquartile range (IQR)75.5

Descriptive statistics

Standard deviation45.773623
Coefficient of variation (CV)0.85350087
Kurtosis1.52966
Mean53.630435
Median Absolute Deviation (MAD)33.5
Skewness0.94116837
Sum4934
Variance2095.2246
MonotonicityNot monotonic
2024-05-18T08:40:43.748583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
17.4%
31 4
 
4.3%
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
57.6%
ValueCountFrequency (%)
0 16
17.4%
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
 
4.3%
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%
Mean62444.567
Minimum0
Maximum80000
Zeros12
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-18T08:40:44.147994image/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 deviation30216.605
Coefficient of variation (CV)0.48389487
Kurtosis0.11365692
Mean62444.567
Median Absolute Deviation (MAD)0
Skewness-1.3691494
Sum5620011
Variance9.1304324 × 108
MonotonicityNot monotonic
2024-05-18T08:40:44.472614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
80000 63
68.5%
0 12
 
13.0%
20000 6
 
6.5%
60000 6
 
6.5%
50000 2
 
2.2%
11 1
 
1.1%
(Missing) 2
 
2.2%
ValueCountFrequency (%)
0 12
 
13.0%
11 1
 
1.1%
20000 6
 
6.5%
50000 2
 
2.2%
60000 6
 
6.5%
80000 63
68.5%
ValueCountFrequency (%)
80000 63
68.5%
60000 6
 
6.5%
50000 2
 
2.2%
20000 6
 
6.5%
11 1
 
1.1%
0 12
 
13.0%

선정발표일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)74.4%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean20201284
Minimum20140515
Maximum20240523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-18T08:40:44.914380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20140515
5-th percentile20160502
Q120180669
median20201006
Q320220783
95-th percentile20230877
Maximum20240523
Range100008
Interquartile range (IQR)40114

Descriptive statistics

Standard deviation23948.741
Coefficient of variation (CV)0.0011855059
Kurtosis-0.80663457
Mean20201284
Median Absolute Deviation (MAD)19800.5
Skewness-0.4706892
Sum1.8181155 × 109
Variance5.7354221 × 108
MonotonicityNot monotonic
2024-05-18T08:40:45.383035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20160502 4
 
4.3%
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 (57) 63
68.5%
ValueCountFrequency (%)
20140515 1
 
1.1%
20160429 1
 
1.1%
20160502 4
4.3%
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 (%)
20240523 1
1.1%
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%

문의처
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size868.0 B
02-2133-7265
28 
02-2133-7260
18 
02-2133-7262
15 
02-2133-1216
8 
02-2133-7258
6 
Other values (9)
17 

Length

Max length12
Median length12
Mean length11.532609
Min length2

Unique

Unique4 ?
Unique (%)4.3%

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
30.4%
02-2133-7260 18
19.6%
02-2133-7262 15
16.3%
02-2133-1216 8
 
8.7%
02-2133-7258 6
 
6.5%
02-2133-7255 4
 
4.3%
02-1234-5678 3
 
3.3%
02-2133-7264 2
 
2.2%
<NA> 2
 
2.2%
-- 2
 
2.2%
Other values (4) 4
 
4.3%

Length

2024-05-18T08:40:45.831859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02-2133-7265 28
30.4%
02-2133-7260 18
19.6%
02-2133-7262 15
16.3%
02-2133-1216 8
 
8.7%
02-2133-7258 6
 
6.5%
02-2133-7255 4
 
4.3%
02-1234-5678 3
 
3.3%
02-2133-7264 2
 
2.2%
na 2
 
2.2%
2
 
2.2%
Other values (4) 4
 
4.3%

Interactions

2024-05-18T08:40:33.456873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:19.741269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:21.148817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:23.060235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:25.060914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:27.060953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:29.223905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:31.257046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:33.740310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:19.922853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:21.330987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:23.321314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:25.235075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:27.337228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:29.570272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:31.533881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:34.010654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:20.108232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:21.509787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:23.570614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:25.488868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:27.604792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:29.869502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:31.807911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:34.268242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:20.274271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:21.730821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:23.819463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:25.742462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:27.867524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:30.104032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:32.065354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:34.520609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:20.439134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:21.919375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:24.072827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:25.988057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:28.121764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:30.365300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:32.369647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:34.808931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:20.642043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:22.085221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:24.322629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:26.248333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:28.402077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:30.608442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:32.661230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:35.034945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:20.801930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:22.346409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:24.563541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:26.532901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:28.679407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:30.832259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:32.913635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:35.289617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:20.982239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:22.804700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:24.824349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:26.805336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:28.954872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:31.053194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T08:40:33.189781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T08:40:46.119163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강좌명교육장소교육지역신청시작날짜신청마감날짜교육시작날짜교육종료날짜정원신청인원수강료선정발표일문의처
강좌명1.0000.8360.0001.0001.0001.0001.0000.9640.9531.0001.0000.986
교육장소0.8361.0000.9980.7390.7420.8690.8690.0000.0000.9470.8230.896
교육지역0.0000.9981.0000.9590.9610.9710.9710.6910.0000.9950.9710.991
신청시작날짜1.0000.7390.9591.0001.0000.9900.9900.6960.2450.5960.9640.953
신청마감날짜1.0000.7420.9611.0001.0000.9950.9950.6700.2210.5920.9690.957
교육시작날짜1.0000.8690.9710.9900.9951.0001.0000.5490.2870.7150.9590.944
교육종료날짜1.0000.8690.9710.9900.9951.0001.0000.5490.2870.7150.9590.944
정원0.9640.0000.6910.6960.6700.5490.5491.0000.2910.5530.7180.766
신청인원0.9530.0000.0000.2450.2210.2870.2870.2911.0000.1130.3140.000
수강료1.0000.9470.9950.5960.5920.7150.7150.5530.1131.0000.7460.831
선정발표일1.0000.8230.9710.9640.9690.9590.9590.7180.3140.7461.0000.890
문의처0.9860.8960.9910.9530.9570.9440.9440.7660.0000.8310.8901.000
2024-05-18T08:40:46.522243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교육장소문의처교육지역
교육장소1.0000.5970.838
문의처0.5971.0000.776
교육지역0.8380.7761.000
2024-05-18T08:40:46.800457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신청시작날짜신청마감날짜교육시작날짜교육종료날짜정원신청인원수강료선정발표일교육장소교육지역문의처
신청시작날짜1.0000.9800.9740.9730.4880.3890.5360.9750.3670.6050.865
신청마감날짜0.9801.0000.9990.9980.4410.3660.4891.0000.3710.6090.874
교육시작날짜0.9740.9991.0000.9990.4330.3610.4841.0000.4260.6490.838
교육종료날짜0.9730.9980.9991.0000.4490.3680.4950.9990.4260.6490.838
정원0.4880.4410.4330.4491.0000.2660.5700.4310.0000.3050.392
신청인원0.3890.3660.3610.3680.2661.0000.2960.3630.0000.0000.000
수강료0.5360.4890.4840.4950.5700.2961.0000.4840.7830.7310.626
선정발표일0.9751.0001.0000.9990.4310.3630.4841.0000.4540.6420.715
교육장소0.3670.3710.4260.4260.0000.0000.7830.4541.0000.8380.597
교육지역0.6050.6090.6490.6490.3050.0000.7310.6420.8381.0000.776
문의처0.8650.8740.8380.8380.3920.0000.6260.7150.5970.7761.000

Missing values

2024-05-18T08:40:35.666276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T08:40:36.092104image/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.
2024-05-18T08:40:36.566861image/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
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
912024년 집수리 아카데미 기초과정 1회차(월,화요일반) 교육신청아카데미교육장9서울시 관악구 신림동 1513[신림선 서울대벤처타운역 인근]2024052009002024052010002024052720240618400800002024052302-2133-7260