Overview

Dataset statistics

Number of variables10
Number of observations274
Missing cells130
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.4 KiB
Average record size in memory87.5 B

Variable types

Numeric6
Categorical1
Text3

Dataset

Description년도,차수,일차,교육번호,교육명,일자,최대수강인원,신청인원,위치,정렬순서
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15705/S/1/datasetView.do

Alerts

년도 is highly overall correlated with 최대수강인원High correlation
최대수강인원 is highly overall correlated with 년도 and 1 other fieldsHigh correlation
정렬순서 is highly overall correlated with 최대수강인원High correlation
정렬순서 has 130 (47.4%) missing valuesMissing
신청인원 has 101 (36.9%) zerosZeros
정렬순서 has 117 (42.7%) zerosZeros

Reproduction

Analysis started2024-05-11 09:24:46.185977
Analysis finished2024-05-11 09:24:58.626096
Duration12.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.7409
Minimum2017
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T09:24:58.855667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2023
Maximum2024
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9710377
Coefficient of variation (CV)0.00097588644
Kurtosis-0.51431198
Mean2019.7409
Median Absolute Deviation (MAD)1
Skewness0.59089898
Sum553409
Variance3.8849897
MonotonicityDecreasing
2024-05-11T09:24:59.341132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2019 68
24.8%
2018 48
17.5%
2021 44
16.1%
2020 38
13.9%
2017 32
11.7%
2023 31
11.3%
2024 13
 
4.7%
ValueCountFrequency (%)
2017 32
11.7%
2018 48
17.5%
2019 68
24.8%
2020 38
13.9%
2021 44
16.1%
2023 31
11.3%
2024 13
 
4.7%
ValueCountFrequency (%)
2024 13
 
4.7%
2023 31
11.3%
2021 44
16.1%
2020 38
13.9%
2019 68
24.8%
2018 48
17.5%
2017 32
11.7%

차수
Real number (ℝ)

Distinct13
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5328467
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T09:24:59.638552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q310
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.0156308
Coefficient of variation (CV)0.72578024
Kurtosis-1.1815933
Mean5.5328467
Median Absolute Deviation (MAD)3
Skewness0.50211695
Sum1516
Variance16.125291
MonotonicityNot monotonic
2024-05-11T09:24:59.973138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 51
18.6%
1 44
16.1%
3 32
11.7%
7 24
8.8%
11 24
8.8%
10 22
8.0%
13 20
 
7.3%
6 18
 
6.6%
4 17
 
6.2%
8 8
 
2.9%
Other values (3) 14
 
5.1%
ValueCountFrequency (%)
1 44
16.1%
2 51
18.6%
3 32
11.7%
4 17
 
6.2%
5 4
 
1.5%
6 18
 
6.6%
7 24
8.8%
8 8
 
2.9%
9 6
 
2.2%
10 22
8.0%
ValueCountFrequency (%)
13 20
7.3%
12 4
 
1.5%
11 24
8.8%
10 22
8.0%
9 6
 
2.2%
8 8
 
2.9%
7 24
8.8%
6 18
6.6%
5 4
 
1.5%
4 17
6.2%

일차
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
1
141 
2
133 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 141
51.5%
2 133
48.5%

Length

2024-05-11T09:25:00.364238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:25:01.031082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 141
51.5%
2 133
48.5%

교육번호
Real number (ℝ)

Distinct20
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.379562
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T09:25:01.298638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.9763613
Coefficient of variation (CV)0.73916078
Kurtosis1.2652639
Mean5.379562
Median Absolute Deviation (MAD)2
Skewness1.1996076
Sum1474
Variance15.811449
MonotonicityNot monotonic
2024-05-11T09:25:01.890095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 39
14.2%
2 38
13.9%
3 32
11.7%
4 32
11.7%
5 24
8.8%
6 23
8.4%
8 18
6.6%
7 18
6.6%
9 9
 
3.3%
10 9
 
3.3%
Other values (10) 32
11.7%
ValueCountFrequency (%)
1 39
14.2%
2 38
13.9%
3 32
11.7%
4 32
11.7%
5 24
8.8%
6 23
8.4%
7 18
6.6%
8 18
6.6%
9 9
 
3.3%
10 9
 
3.3%
ValueCountFrequency (%)
20 1
 
0.4%
19 1
 
0.4%
18 2
 
0.7%
17 2
 
0.7%
16 2
 
0.7%
15 2
 
0.7%
14 3
 
1.1%
13 3
 
1.1%
12 8
2.9%
11 8
2.9%

교육명
Text

Distinct109
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-05-11T09:25:02.703829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length7.879562
Min length2

Characters and Unicode

Total characters2159
Distinct characters98
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)4.0%

Sample

1st row심화과정 5
2nd row심화과정 1
3rd row심화과정 2
4th row심화과정 3
5th row심화과정 4
ValueCountFrequency (%)
오후반 26
 
5.8%
야간반 24
 
5.4%
1기 22
 
4.9%
a 20
 
4.5%
b 20
 
4.5%
평일주간반 18
 
4.0%
평일야간반 18
 
4.0%
2기 16
 
3.6%
3기 12
 
2.7%
주말반 12
 
2.7%
Other values (92) 260
58.0%
2024-05-11T09:25:03.841991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
반 194
 
9.0%
기 175
 
8.1%
174
 
8.1%
( 171
 
7.9%
) 171
 
7.9%
간 98
 
4.5%
오 84
 
3.9%
야 74
 
3.4%
1 73
 
3.4%
후 72
 
3.3%
Other values (88) 873
40.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1302
60.3%
Decimal Number 225
 
10.4%
Space Separator 174
 
8.1%
Open Punctuation 171
 
7.9%
Close Punctuation 171
 
7.9%
Other Punctuation 66
 
3.1%
Uppercase Letter 46
 
2.1%
Connector Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
반 194
14.9%
기 175
13.4%
간 98
 
7.5%
오 84
 
6.5%
야 74
 
5.7%
후 72
 
5.5%
평 50
 
3.8%
일 48
 
3.7%
권 38
 
2.9%
주 36
 
2.8%
Other values (68) 433
33.3%
Decimal Number
ValueCountFrequency (%)
1 73
32.4%
2 39
17.3%
3 27
 
12.0%
4 25
 
11.1%
5 24
 
10.7%
6 12
 
5.3%
9 7
 
3.1%
8 6
 
2.7%
7 6
 
2.7%
0 6
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
B 20
43.5%
A 20
43.5%
C 4
 
8.7%
D 2
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 64
97.0%
* 2
 
3.0%
Space Separator
ValueCountFrequency (%)
174
100.0%
Open Punctuation
ValueCountFrequency (%)
( 171
100.0%
Close Punctuation
ValueCountFrequency (%)
) 171
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1302
60.3%
Common 811
37.6%
Latin 46
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
반 194
14.9%
기 175
13.4%
간 98
 
7.5%
오 84
 
6.5%
야 74
 
5.7%
후 72
 
5.5%
평 50
 
3.8%
일 48
 
3.7%
권 38
 
2.9%
주 36
 
2.8%
Other values (68) 433
33.3%
Common
ValueCountFrequency (%)
174
21.5%
( 171
21.1%
) 171
21.1%
1 73
9.0%
, 64
 
7.9%
2 39
 
4.8%
3 27
 
3.3%
4 25
 
3.1%
5 24
 
3.0%
6 12
 
1.5%
Other values (6) 31
 
3.8%
Latin
ValueCountFrequency (%)
B 20
43.5%
A 20
43.5%
C 4
 
8.7%
D 2
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1302
60.3%
ASCII 857
39.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
반 194
14.9%
기 175
13.4%
간 98
 
7.5%
오 84
 
6.5%
야 74
 
5.7%
후 72
 
5.5%
평 50
 
3.8%
일 48
 
3.7%
권 38
 
2.9%
주 36
 
2.8%
Other values (68) 433
33.3%
ASCII
ValueCountFrequency (%)
174
20.3%
( 171
20.0%
) 171
20.0%
1 73
8.5%
, 64
 
7.5%
2 39
 
4.6%
3 27
 
3.2%
4 25
 
2.9%
5 24
 
2.8%
B 20
 
2.3%
Other values (10) 69
 
8.1%

일자
Text

Distinct273
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-05-11T09:25:05.008243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length19.20438
Min length1

Characters and Unicode

Total characters5262
Distinct characters26
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique272 ?
Unique (%)99.3%

Sample

1st row4. 15.(월) 14:00?17:00
2nd row4. 4.(목) 14:00?17:00
3rd row4. 7.(일) 14:00?17:00
4th row4. 9.(화) 19:00?22:00
5th row4. 12.(금) 10:00?13:00
ValueCountFrequency (%)
14:00~17:00 95
 
14.8%
19:00~22:00 68
 
10.6%
10:00~13:00 39
 
6.1%
8 28
 
4.4%
7 18
 
2.8%
13:30~17:30 18
 
2.8%
4 12
 
1.9%
15:00~18:00 11
 
1.7%
12 10
 
1.6%
9 9
 
1.4%
Other values (204) 333
52.0%
2024-05-11T09:25:06.548357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1128
21.4%
1 680
12.9%
: 530
10.1%
368
 
7.0%
2 293
 
5.6%
. 290
 
5.5%
( 273
 
5.2%
) 273
 
5.2%
~ 255
 
4.8%
7 192
 
3.6%
Other values (16) 980
18.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2955
56.2%
Other Punctuation 848
 
16.1%
Space Separator 368
 
7.0%
Other Letter 289
 
5.5%
Open Punctuation 273
 
5.2%
Close Punctuation 273
 
5.2%
Math Symbol 255
 
4.8%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1128
38.2%
1 680
23.0%
2 293
 
9.9%
7 192
 
6.5%
3 189
 
6.4%
4 141
 
4.8%
9 134
 
4.5%
8 98
 
3.3%
6 57
 
1.9%
5 43
 
1.5%
Other Letter
ValueCountFrequency (%)
화 73
25.3%
목 65
22.5%
월 47
16.3%
금 37
12.8%
수 32
11.1%
토 21
 
7.3%
일 14
 
4.8%
Other Punctuation
ValueCountFrequency (%)
: 530
62.5%
. 290
34.2%
/ 18
 
2.1%
? 10
 
1.2%
Space Separator
ValueCountFrequency (%)
368
100.0%
Open Punctuation
ValueCountFrequency (%)
( 273
100.0%
Close Punctuation
ValueCountFrequency (%)
) 273
100.0%
Math Symbol
ValueCountFrequency (%)
~ 255
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4973
94.5%
Hangul 289
 
5.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1128
22.7%
1 680
13.7%
: 530
10.7%
368
 
7.4%
2 293
 
5.9%
. 290
 
5.8%
( 273
 
5.5%
) 273
 
5.5%
~ 255
 
5.1%
7 192
 
3.9%
Other values (9) 691
13.9%
Hangul
ValueCountFrequency (%)
화 73
25.3%
목 65
22.5%
월 47
16.3%
금 37
12.8%
수 32
11.1%
토 21
 
7.3%
일 14
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4973
94.5%
Hangul 289
 
5.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1128
22.7%
1 680
13.7%
: 530
10.7%
368
 
7.4%
2 293
 
5.9%
. 290
 
5.8%
( 273
 
5.5%
) 273
 
5.5%
~ 255
 
5.1%
7 192
 
3.9%
Other values (9) 691
13.9%
Hangul
ValueCountFrequency (%)
화 73
25.3%
목 65
22.5%
월 47
16.3%
금 37
12.8%
수 32
11.1%
토 21
 
7.3%
일 14
 
4.8%

최대수강인원
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.456204
Minimum20
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T09:25:07.027550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q125
median50
Q360
95-th percentile100
Maximum200
Range180
Interquartile range (IQR)35

Descriptive statistics

Standard deviation28.387815
Coefficient of variation (CV)0.57399906
Kurtosis7.4734085
Mean49.456204
Median Absolute Deviation (MAD)20
Skewness2.0969201
Sum13551
Variance805.86804
MonotonicityNot monotonic
2024-05-11T09:25:07.560289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
50 50
18.2%
20 40
14.6%
60 40
14.6%
25 33
12.0%
40 30
10.9%
80 28
10.2%
30 20
 
7.3%
55 12
 
4.4%
100 10
 
3.6%
180 4
 
1.5%
Other values (3) 7
 
2.6%
ValueCountFrequency (%)
20 40
14.6%
23 2
 
0.7%
25 33
12.0%
30 20
 
7.3%
40 30
10.9%
50 50
18.2%
55 12
 
4.4%
60 40
14.6%
80 28
10.2%
90 4
 
1.5%
ValueCountFrequency (%)
200 1
 
0.4%
180 4
 
1.5%
100 10
 
3.6%
90 4
 
1.5%
80 28
10.2%
60 40
14.6%
55 12
 
4.4%
50 50
18.2%
40 30
10.9%
30 20
 
7.3%

신청인원
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2481752
Minimum-2
Maximum99
Zeros101
Zeros (%)36.9%
Negative5
Negative (%)1.8%
Memory size2.5 KiB
2024-05-11T09:25:08.037757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q10
median2
Q39
95-th percentile50.85
Maximum99
Range101
Interquartile range (IQR)9

Descriptive statistics

Standard deviation17.574658
Coefficient of variation (CV)1.9003379
Kurtosis8.8147223
Mean9.2481752
Median Absolute Deviation (MAD)2
Skewness2.9093471
Sum2534
Variance308.86859
MonotonicityNot monotonic
2024-05-11T09:25:08.568857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 101
36.9%
2 23
 
8.4%
1 18
 
6.6%
7 13
 
4.7%
6 12
 
4.4%
3 12
 
4.4%
5 9
 
3.3%
9 7
 
2.6%
11 7
 
2.6%
4 6
 
2.2%
Other values (34) 66
24.1%
ValueCountFrequency (%)
-2 3
 
1.1%
-1 2
 
0.7%
0 101
36.9%
1 18
 
6.6%
2 23
 
8.4%
3 12
 
4.4%
4 6
 
2.2%
5 9
 
3.3%
6 12
 
4.4%
7 13
 
4.7%
ValueCountFrequency (%)
99 1
 
0.4%
89 1
 
0.4%
88 1
 
0.4%
85 1
 
0.4%
84 1
 
0.4%
72 1
 
0.4%
70 1
 
0.4%
65 1
 
0.4%
64 3
1.1%
60 2
0.7%

위치
Text

Distinct58
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-05-11T09:25:09.256004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length37
Mean length13.605839
Min length1

Characters and Unicode

Total characters3728
Distinct characters162
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.5%

Sample

1st row서울시청 서소문별관 후생동 4층 강당
2nd row서울시청 지하2층 시민청 태평홀
3rd row서울시청 본관 3층 대회의실
4th row서울시청 본관 3층 대회의실
5th row서울시청 본관 8층 다목적홀
ValueCountFrequency (%)
희망제작소 52
 
7.5%
다목적실(2층 50
 
7.2%
화상교육 38
 
5.5%
서소문청사 22
 
3.2%
13층 22
 
3.2%
대회의실 21
 
3.0%
서울특별시 19
 
2.8%
1동 16
 
2.3%
누구나학교 14
 
2.0%
3층 13
 
1.9%
Other values (126) 423
61.3%
2024-05-11T09:25:10.928571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
416
 
11.2%
( 155
 
4.2%
) 155
 
4.2%
층 155
 
4.2%
소 135
 
3.6%
2 119
 
3.2%
실 110
 
3.0%
서 99
 
2.7%
교 94
 
2.5%
1 91
 
2.4%
Other values (152) 2199
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2623
70.4%
Space Separator 416
 
11.2%
Decimal Number 346
 
9.3%
Open Punctuation 155
 
4.2%
Close Punctuation 155
 
4.2%
Lowercase Letter 24
 
0.6%
Uppercase Letter 8
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
층 155
 
5.9%
소 135
 
5.1%
실 110
 
4.2%
서 99
 
3.8%
교 94
 
3.6%
청 81
 
3.1%
대 78
 
3.0%
망 72
 
2.7%
육 72
 
2.7%
구 69
 
2.6%
Other values (131) 1658
63.2%
Decimal Number
ValueCountFrequency (%)
2 119
34.4%
1 91
26.3%
3 53
15.3%
4 33
 
9.5%
6 16
 
4.6%
0 11
 
3.2%
5 9
 
2.6%
8 8
 
2.3%
7 4
 
1.2%
9 2
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
n 8
33.3%
u 8
33.3%
a 4
16.7%
g 4
16.7%
Uppercase Letter
ValueCountFrequency (%)
C 4
50.0%
J 2
25.0%
U 2
25.0%
Space Separator
ValueCountFrequency (%)
416
100.0%
Open Punctuation
ValueCountFrequency (%)
( 155
100.0%
Close Punctuation
ValueCountFrequency (%)
) 155
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2623
70.4%
Common 1073
28.8%
Latin 32
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
층 155
 
5.9%
소 135
 
5.1%
실 110
 
4.2%
서 99
 
3.8%
교 94
 
3.6%
청 81
 
3.1%
대 78
 
3.0%
망 72
 
2.7%
육 72
 
2.7%
구 69
 
2.6%
Other values (131) 1658
63.2%
Common
ValueCountFrequency (%)
416
38.8%
( 155
 
14.4%
) 155
 
14.4%
2 119
 
11.1%
1 91
 
8.5%
3 53
 
4.9%
4 33
 
3.1%
6 16
 
1.5%
0 11
 
1.0%
5 9
 
0.8%
Other values (4) 15
 
1.4%
Latin
ValueCountFrequency (%)
n 8
25.0%
u 8
25.0%
a 4
12.5%
C 4
12.5%
g 4
12.5%
J 2
 
6.2%
U 2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2623
70.4%
ASCII 1105
29.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
416
37.6%
( 155
 
14.0%
) 155
 
14.0%
2 119
 
10.8%
1 91
 
8.2%
3 53
 
4.8%
4 33
 
3.0%
6 16
 
1.4%
0 11
 
1.0%
5 9
 
0.8%
Other values (11) 47
 
4.3%
Hangul
ValueCountFrequency (%)
층 155
 
5.9%
소 135
 
5.1%
실 110
 
4.2%
서 99
 
3.8%
교 94
 
3.6%
청 81
 
3.1%
대 78
 
3.0%
망 72
 
2.7%
육 72
 
2.7%
구 69
 
2.6%
Other values (131) 1658
63.2%

정렬순서
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)6.9%
Missing130
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean0.80555556
Minimum0
Maximum9
Zeros117
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T09:25:11.433162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.000971
Coefficient of variation (CV)2.483964
Kurtosis6.3484018
Mean0.80555556
Median Absolute Deviation (MAD)0
Skewness2.6616827
Sum116
Variance4.003885
MonotonicityNot monotonic
2024-05-11T09:25:11.773896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 117
42.7%
1 4
 
1.5%
2 4
 
1.5%
3 4
 
1.5%
4 4
 
1.5%
6 3
 
1.1%
7 2
 
0.7%
8 2
 
0.7%
9 2
 
0.7%
5 2
 
0.7%
(Missing) 130
47.4%
ValueCountFrequency (%)
0 117
42.7%
1 4
 
1.5%
2 4
 
1.5%
3 4
 
1.5%
4 4
 
1.5%
5 2
 
0.7%
6 3
 
1.1%
7 2
 
0.7%
8 2
 
0.7%
9 2
 
0.7%
ValueCountFrequency (%)
9 2
 
0.7%
8 2
 
0.7%
7 2
 
0.7%
6 3
 
1.1%
5 2
 
0.7%
4 4
 
1.5%
3 4
 
1.5%
2 4
 
1.5%
1 4
 
1.5%
0 117
42.7%

Interactions

2024-05-11T09:24:55.781270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:47.279806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:49.345865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:50.947238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:52.549620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:54.119909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:56.049125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:47.583172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:49.606731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:51.220489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:52.824078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:54.379860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:56.355430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:47.911132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:49.821465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:51.501310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:53.080617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:54.629678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:56.637687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:48.290755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:50.088064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:51.772619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:53.350543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:54.894161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:57.044415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:48.676540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:50.425590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:52.040532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:53.622092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:55.242500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:57.303512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:49.090873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:50.713506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:52.294087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:53.873013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:24:55.511330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:25:12.119719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도차수일차교육번호최대수강인원신청인원위치정렬순서
년도1.0000.7160.0000.2170.9400.6400.9920.606
차수0.7161.0000.0000.2260.5880.5470.9610.000
일차0.0000.0001.0000.0000.0000.0000.0000.000
교육번호0.2170.2260.0001.0000.1810.0000.6820.784
최대수강인원0.9400.5880.0000.1811.0000.8430.9950.867
신청인원0.6400.5470.0000.0000.8431.0000.9440.557
위치0.9920.9610.0000.6820.9950.9441.0000.866
정렬순서0.6060.0000.0000.7840.8670.5570.8661.000
2024-05-11T09:25:12.472163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도차수교육번호최대수강인원신청인원정렬순서일차
년도1.0000.241-0.170-0.6550.035-0.2680.000
차수0.2411.0000.077-0.3730.061-0.4970.000
교육번호-0.1700.0771.0000.140-0.0690.2500.000
최대수강인원-0.655-0.3730.1401.0000.2780.6630.000
신청인원0.0350.061-0.0690.2781.0000.0830.000
정렬순서-0.268-0.4970.2500.6630.0831.0000.000
일차0.0000.0000.0000.0000.0000.0001.000

Missing values

2024-05-11T09:24:57.702194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:24:58.374244image/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

년도차수일차교육번호교육명일자최대수강인원신청인원위치정렬순서
02024316심화과정 54. 15.(월) 14:00?17:0010064서울시청 서소문별관 후생동 4층 강당6
12024311심화과정 14. 4.(목) 14:00?17:0010042서울시청 지하2층 시민청 태평홀0
22024312심화과정 24. 7.(일) 14:00?17:0010036서울시청 본관 3층 대회의실1
32024313심화과정 34. 9.(화) 19:00?22:0010047서울시청 본관 3층 대회의실2
42024314심화과정 44. 12.(금) 10:00?13:0010065서울시청 본관 8층 다목적홀3
52024315심화과정 5(삭제)4. 14.(일) 14:00?17:0010099서울시청 본관 8층 다목적홀4
62024224기본과정 2일차_24. 8.(월) 14:00?17:0010088서울시청 지하2층 시민청 태평홀4
72024211기본과정 1일차_14. 3.(수) 19:00?22:0010085서울시청 본관 3층 대회의실0
82024212기본과정 1일차_24. 4.(목) 14:00?17:0010089서울시청 본관 3층 대회의실1
92024215온라인-20058-2
년도차수일차교육번호교육명일자최대수강인원신청인원위치정렬순서
2642017113주간반 B3/14(화) 14:00~17:00800교육장소24
2652017124주간반 B3/16(목) 14:00~17:00800교육장소24
2662017115야간반 A3/7(화) 19:00~22:0080-1교육장소33
2672017117야간반 B3/14(화) 19:00~22:00801교육장소45
2682017126야간반 A3/9(목) 19:00~22:00800교육장소32
2692017128야간반 B3/16(목) 19:00~22:00801교육장소45
2702017119야간반 C3/21(화) 19:00~22:00801교육장소47
27120171210야간반 C3/23(목) 19:00~22:00801교육장소46
27220171111주말반 A3/4(토) 10:00~13:00800교육장소51
27320171212주말반 A3/11(토) 10:00~13:00800교육장소53