Overview

Dataset statistics

Number of variables8
Number of observations7494
Missing cells7753
Missing cells (%)12.9%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory475.8 KiB
Average record size in memory65.0 B

Variable types

Text5
Categorical2
Numeric1

Dataset

Description한국자산관리공사 교육과정을 수강한 학습자 별로 수강한 과정명, 차수, 교육 참여방식에 대한 데이터를 제공합니다.
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15111489/fileData.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
성별 is highly imbalanced (63.0%)Imbalance
교육 참여 방식 is highly imbalanced (53.8%)Imbalance
사번 has 7320 (97.7%) missing valuesMissing
교육과정명 has 195 (2.6%) missing valuesMissing
차수 has 238 (3.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 11:24:17.961331
Analysis finished2023-12-12 11:24:19.562391
Duration1.6 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

학습자 번호
Text

Distinct7028
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
2023-12-12T20:24:20.011860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters37470
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6678 ?
Unique (%)89.1%

Sample

1st rowH0047
2nd rowH0382
3rd rowH0383
4th rowH0384
5th rowH0385
ValueCountFrequency (%)
h0030 8
 
0.1%
h0394 7
 
0.1%
h0375 7
 
0.1%
h0685 6
 
0.1%
h0031 6
 
0.1%
h0265 6
 
0.1%
h0237 5
 
0.1%
h1056 5
 
0.1%
h0599 5
 
0.1%
h0595 4
 
0.1%
Other values (7018) 7435
99.2%
2023-12-12T20:24:20.815317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
H 7494
20.0%
0 3679
9.8%
1 3384
9.0%
6 3217
8.6%
8 3204
8.6%
7 3197
8.5%
9 3194
8.5%
3 2914
 
7.8%
5 2573
 
6.9%
2 2354
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29976
80.0%
Uppercase Letter 7494
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3679
12.3%
1 3384
11.3%
6 3217
10.7%
8 3204
10.7%
7 3197
10.7%
9 3194
10.7%
3 2914
9.7%
5 2573
8.6%
2 2354
7.9%
4 2260
7.5%
Uppercase Letter
ValueCountFrequency (%)
H 7494
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29976
80.0%
Latin 7494
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3679
12.3%
1 3384
11.3%
6 3217
10.7%
8 3204
10.7%
7 3197
10.7%
9 3194
10.7%
3 2914
9.7%
5 2573
8.6%
2 2354
7.9%
4 2260
7.5%
Latin
ValueCountFrequency (%)
H 7494
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 7494
20.0%
0 3679
9.8%
1 3384
9.0%
6 3217
8.6%
8 3204
8.6%
7 3197
8.5%
9 3194
8.5%
3 2914
 
7.8%
5 2573
 
6.9%
2 2354
 
6.3%

사번
Text

MISSING 

Distinct89
Distinct (%)51.1%
Missing7320
Missing (%)97.7%
Memory size58.7 KiB
2023-12-12T20:24:21.352219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.683908
Min length5

Characters and Unicode

Total characters989
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

Unique39 ?
Unique (%)22.4%

Sample

1st row8***3
2nd row1***03
3rd row7***5
4th row8***3
5th row1***63
ValueCountFrequency (%)
9***5 5
 
2.9%
1***03 5
 
2.9%
1***40 4
 
2.3%
1***67 4
 
2.3%
9***4 4
 
2.3%
1***49 4
 
2.3%
1***12 4
 
2.3%
8***1 4
 
2.3%
1***07 4
 
2.3%
1***51 3
 
1.7%
Other values (79) 133
76.4%
2023-12-12T20:24:22.054948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 522
52.8%
1 147
 
14.9%
9 57
 
5.8%
8 41
 
4.1%
5 40
 
4.0%
6 38
 
3.8%
0 36
 
3.6%
4 36
 
3.6%
7 27
 
2.7%
3 24
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 522
52.8%
Decimal Number 467
47.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 147
31.5%
9 57
 
12.2%
8 41
 
8.8%
5 40
 
8.6%
6 38
 
8.1%
0 36
 
7.7%
4 36
 
7.7%
7 27
 
5.8%
3 24
 
5.1%
2 21
 
4.5%
Other Punctuation
ValueCountFrequency (%)
* 522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 522
52.8%
1 147
 
14.9%
9 57
 
5.8%
8 41
 
4.1%
5 40
 
4.0%
6 38
 
3.8%
0 36
 
3.6%
4 36
 
3.6%
7 27
 
2.7%
3 24
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 522
52.8%
1 147
 
14.9%
9 57
 
5.8%
8 41
 
4.1%
5 40
 
4.0%
6 38
 
3.8%
0 36
 
3.6%
4 36
 
3.6%
7 27
 
2.7%
3 24
 
2.4%

이름
Text

Distinct113
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
2023-12-12T20:24:22.430140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0032026
Min length3

Characters and Unicode

Total characters22506
Distinct characters101
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.4%

Sample

1st row천**
2nd row경**
3rd row김**
4th row최**
5th row김**
ValueCountFrequency (%)
김 1454
19.4%
이 1056
14.1%
박 603
 
8.0%
정 374
 
5.0%
최 359
 
4.8%
천 350
 
4.7%
조 206
 
2.7%
임 158
 
2.1%
장 157
 
2.1%
강 149
 
2.0%
Other values (92) 2628
35.1%
2023-12-12T20:24:23.050025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 14988
66.6%
김 1454
 
6.5%
이 1056
 
4.7%
박 606
 
2.7%
정 374
 
1.7%
최 359
 
1.6%
천 350
 
1.6%
조 206
 
0.9%
임 158
 
0.7%
장 157
 
0.7%
Other values (91) 2798
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 14988
66.6%
Other Letter 7500
33.3%
Space Separator 18
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 1454
19.4%
이 1056
14.1%
박 606
 
8.1%
정 374
 
5.0%
최 359
 
4.8%
천 350
 
4.7%
조 206
 
2.7%
임 158
 
2.1%
장 157
 
2.1%
강 150
 
2.0%
Other values (89) 2630
35.1%
Other Punctuation
ValueCountFrequency (%)
* 14988
100.0%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15006
66.7%
Hangul 7500
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 1454
19.4%
이 1056
14.1%
박 606
 
8.1%
정 374
 
5.0%
최 359
 
4.8%
천 350
 
4.7%
조 206
 
2.7%
임 158
 
2.1%
장 157
 
2.1%
강 150
 
2.0%
Other values (89) 2630
35.1%
Common
ValueCountFrequency (%)
* 14988
99.9%
18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15006
66.7%
Hangul 7500
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 14988
99.9%
18
 
0.1%
Hangul
ValueCountFrequency (%)
김 1454
19.4%
이 1056
14.1%
박 606
 
8.1%
정 374
 
5.0%
최 359
 
4.8%
천 350
 
4.7%
조 206
 
2.7%
임 158
 
2.1%
장 157
 
2.1%
강 150
 
2.0%
Other values (89) 2630
35.1%

성별
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
<NA>
6069 
남성
796 
여성
 
520
 
87
여성
 
21

Length

Max length4
Median length4
Mean length3.6138244
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6069
81.0%
남성 796
 
10.6%
여성 520
 
6.9%
87
 
1.2%
여성 21
 
0.3%
남성 1
 
< 0.1%

Length

2023-12-12T20:24:23.295121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:24:23.615353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6069
81.9%
남성 797
 
10.8%
여성 541
 
7.3%

과정번호
Text

Distinct244
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
2023-12-12T20:24:24.193430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters29976
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS001
2nd rowS001
3rd rowS001
4th rowS001
5th rowS001
ValueCountFrequency (%)
s219 224
 
3.0%
s125 209
 
2.8%
s171 168
 
2.2%
s156 108
 
1.4%
s130 103
 
1.4%
s183 89
 
1.2%
s161 88
 
1.2%
s207 82
 
1.1%
s247 79
 
1.1%
s137 75
 
1.0%
Other values (234) 6269
83.7%
2023-12-12T20:24:25.028179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 7494
25.0%
1 5587
18.6%
0 3501
11.7%
2 3117
10.4%
5 1678
 
5.6%
3 1608
 
5.4%
7 1537
 
5.1%
6 1528
 
5.1%
4 1374
 
4.6%
9 1302
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22482
75.0%
Uppercase Letter 7494
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5587
24.9%
0 3501
15.6%
2 3117
13.9%
5 1678
 
7.5%
3 1608
 
7.2%
7 1537
 
6.8%
6 1528
 
6.8%
4 1374
 
6.1%
9 1302
 
5.8%
8 1250
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
S 7494
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22482
75.0%
Latin 7494
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5587
24.9%
0 3501
15.6%
2 3117
13.9%
5 1678
 
7.5%
3 1608
 
7.2%
7 1537
 
6.8%
6 1528
 
6.8%
4 1374
 
6.1%
9 1302
 
5.8%
8 1250
 
5.6%
Latin
ValueCountFrequency (%)
S 7494
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 7494
25.0%
1 5587
18.6%
0 3501
11.7%
2 3117
10.4%
5 1678
 
5.6%
3 1608
 
5.4%
7 1537
 
5.1%
6 1528
 
5.1%
4 1374
 
4.6%
9 1302
 
4.3%

교육과정명
Text

MISSING 

Distinct155
Distinct (%)2.1%
Missing195
Missing (%)2.6%
Memory size58.7 KiB
2023-12-12T20:24:25.419957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length29
Mean length17.266338
Min length5

Characters and Unicode

Total characters126027
Distinct characters214
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(교육청)공유재산 관리실무
2nd row(교육청)공유재산 관리실무
3rd row(교육청)공유재산 관리실무
4th row(교육청)공유재산 관리실무
5th row(교육청)공유재산 관리실무
ValueCountFrequency (%)
관리실무 1443
 
5.6%
국유재산 1432
 
5.6%
교육 1294
 
5.1%
공유재산 1153
 
4.5%
2018년 909
 
3.6%
담당자 904
 
3.5%
워크숍 714
 
2.8%
온비드 614
 
2.4%
맞춤형 510
 
2.0%
용도폐지 501
 
2.0%
Other values (181) 16124
63.0%
2023-12-12T20:24:26.066563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18802
 
14.9%
무 4568
 
3.6%
재 4260
 
3.4%
관 4213
 
3.3%
산 4157
 
3.3%
교 3653
 
2.9%
육 3598
 
2.9%
2 3338
 
2.6%
유 3292
 
2.6%
리 2842
 
2.3%
Other values (204) 73304
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 94580
75.0%
Space Separator 18802
 
14.9%
Decimal Number 9399
 
7.5%
Open Punctuation 1106
 
0.9%
Close Punctuation 1106
 
0.9%
Uppercase Letter 576
 
0.5%
Lowercase Letter 266
 
0.2%
Other Punctuation 168
 
0.1%
Math Symbol 24
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
무 4568
 
4.8%
재 4260
 
4.5%
관 4213
 
4.5%
산 4157
 
4.4%
교 3653
 
3.9%
육 3598
 
3.8%
유 3292
 
3.5%
리 2842
 
3.0%
공 2806
 
3.0%
실 2766
 
2.9%
Other values (159) 58425
61.8%
Lowercase Letter
ValueCountFrequency (%)
n 39
14.7%
t 34
12.8%
o 29
10.9%
y 26
9.8%
e 21
7.9%
a 21
7.9%
h 21
7.9%
s 20
7.5%
r 16
6.0%
g 8
 
3.0%
Other values (5) 31
11.7%
Uppercase Letter
ValueCountFrequency (%)
P 162
28.1%
M 84
14.6%
B 76
13.2%
A 76
13.2%
D 57
 
9.9%
R 24
 
4.2%
T 20
 
3.5%
I 20
 
3.5%
L 19
 
3.3%
Q 19
 
3.3%
Decimal Number
ValueCountFrequency (%)
2 3338
35.5%
0 2504
26.6%
1 1843
19.6%
8 1116
 
11.9%
9 526
 
5.6%
5 24
 
0.3%
3 24
 
0.3%
6 24
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 1046
94.6%
「 47
 
4.2%
[ 13
 
1.2%
Close Punctuation
ValueCountFrequency (%)
) 1046
94.6%
」 47
 
4.2%
] 13
 
1.2%
Other Punctuation
ValueCountFrequency (%)
· 142
84.5%
, 13
 
7.7%
: 13
 
7.7%
Space Separator
ValueCountFrequency (%)
18802
100.0%
Math Symbol
ValueCountFrequency (%)
~ 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 94580
75.0%
Common 30605
 
24.3%
Latin 842
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
무 4568
 
4.8%
재 4260
 
4.5%
관 4213
 
4.5%
산 4157
 
4.4%
교 3653
 
3.9%
육 3598
 
3.8%
유 3292
 
3.5%
리 2842
 
3.0%
공 2806
 
3.0%
실 2766
 
2.9%
Other values (159) 58425
61.8%
Latin
ValueCountFrequency (%)
P 162
19.2%
M 84
 
10.0%
B 76
 
9.0%
A 76
 
9.0%
D 57
 
6.8%
n 39
 
4.6%
t 34
 
4.0%
o 29
 
3.4%
y 26
 
3.1%
R 24
 
2.9%
Other values (16) 235
27.9%
Common
ValueCountFrequency (%)
18802
61.4%
2 3338
 
10.9%
0 2504
 
8.2%
1 1843
 
6.0%
8 1116
 
3.6%
( 1046
 
3.4%
) 1046
 
3.4%
9 526
 
1.7%
· 142
 
0.5%
「 47
 
0.2%
Other values (9) 195
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 94529
75.0%
ASCII 31211
 
24.8%
None 236
 
0.2%
Compat Jamo 51
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18802
60.2%
2 3338
 
10.7%
0 2504
 
8.0%
1 1843
 
5.9%
8 1116
 
3.6%
( 1046
 
3.4%
) 1046
 
3.4%
9 526
 
1.7%
P 162
 
0.5%
M 84
 
0.3%
Other values (32) 744
 
2.4%
Hangul
ValueCountFrequency (%)
무 4568
 
4.8%
재 4260
 
4.5%
관 4213
 
4.5%
산 4157
 
4.4%
교 3653
 
3.9%
육 3598
 
3.8%
유 3292
 
3.5%
리 2842
 
3.0%
공 2806
 
3.0%
실 2766
 
2.9%
Other values (158) 58374
61.8%
None
ValueCountFrequency (%)
· 142
60.2%
「 47
 
19.9%
」 47
 
19.9%
Compat Jamo
ValueCountFrequency (%)
ㆍ 51
100.0%

차수
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)0.1%
Missing238
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean1.8980154
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size66.0 KiB
2023-12-12T20:24:26.388546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3883701
Coefficient of variation (CV)0.73148515
Kurtosis3.0826509
Mean1.8980154
Median Absolute Deviation (MAD)0
Skewness1.8191485
Sum13772
Variance1.9275715
MonotonicityNot monotonic
2023-12-12T20:24:26.568727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 4189
55.9%
2 1457
 
19.4%
3 632
 
8.4%
4 465
 
6.2%
5 291
 
3.9%
6 133
 
1.8%
7 52
 
0.7%
8 37
 
0.5%
(Missing) 238
 
3.2%
ValueCountFrequency (%)
1 4189
55.9%
2 1457
 
19.4%
3 632
 
8.4%
4 465
 
6.2%
5 291
 
3.9%
6 133
 
1.8%
7 52
 
0.7%
8 37
 
0.5%
ValueCountFrequency (%)
8 37
 
0.5%
7 52
 
0.7%
6 133
 
1.8%
5 291
 
3.9%
4 465
 
6.2%
3 632
 
8.4%
2 1457
 
19.4%
1 4189
55.9%

교육 참여 방식
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
오프라인
5189 
온라인
2031 
<NA>
 
238
온오프라인 병행
 
35
오프라인(온라인 참가 확인)
 
1

Length

Max length15
Median length4
Mean length3.7491326
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row온라인
2nd row오프라인
3rd row오프라인
4th row오프라인
5th row온라인

Common Values

ValueCountFrequency (%)
오프라인 5189
69.2%
온라인 2031
 
27.1%
<NA> 238
 
3.2%
온오프라인 병행 35
 
0.5%
오프라인(온라인 참가 확인) 1
 
< 0.1%

Length

2023-12-12T20:24:26.795502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:24:27.461049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오프라인 5189
68.9%
온라인 2031
 
27.0%
na 238
 
3.2%
온오프라인 35
 
0.5%
병행 35
 
0.5%
오프라인(온라인 1
 
< 0.1%
참가 1
 
< 0.1%
확인 1
 
< 0.1%

Interactions

2023-12-12T20:24:18.751944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:24:27.595748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사번성별차수교육 참여 방식
사번1.0000.630NaN0.000
성별0.6301.0000.3410.157
차수NaN0.3411.0000.359
교육 참여 방식0.0000.1570.3591.000
2023-12-12T20:24:27.783647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교육 참여 방식성별
교육 참여 방식1.0000.192
성별0.1921.000
2023-12-12T20:24:27.947472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차수성별교육 참여 방식
차수1.0000.2260.167
성별0.2261.0000.192
교육 참여 방식0.1670.1921.000

Missing values

2023-12-12T20:24:19.008298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:24:19.221307image/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.
2023-12-12T20:24:19.420963image/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

학습자 번호사번이름성별과정번호교육과정명차수교육 참여 방식
0H0047<NA>천**<NA>S001(교육청)공유재산 관리실무1온라인
1H0382<NA>경**<NA>S001(교육청)공유재산 관리실무1오프라인
2H0383<NA>김**<NA>S001(교육청)공유재산 관리실무1오프라인
3H0384<NA>최**<NA>S001(교육청)공유재산 관리실무1오프라인
4H0385<NA>김**<NA>S001(교육청)공유재산 관리실무1온라인
5H0386<NA>이**<NA>S001(교육청)공유재산 관리실무1온라인
6H0387<NA>구**<NA>S001(교육청)공유재산 관리실무1온라인
7H0388<NA>김**<NA>S001(교육청)공유재산 관리실무1온라인
8H0389<NA>홍**<NA>S001(교육청)공유재산 관리실무1온라인
9H0390<NA>김**<NA>S001(교육청)공유재산 관리실무1온라인
학습자 번호사번이름성별과정번호교육과정명차수교육 참여 방식
7484H4043<NA>이**<NA>S258용도폐지 담당자 교육6온라인
7485H4044<NA>김**<NA>S258용도폐지 담당자 교육6온라인
7486H4045<NA>박**<NA>S258용도폐지 담당자 교육6온라인
7487H4046<NA>정**<NA>S258용도폐지 담당자 교육6온라인
7488H4047<NA>이**<NA>S258용도폐지 담당자 교육6온라인
7489H4048<NA>김**<NA>S258용도폐지 담당자 교육6온라인
7490H4049<NA>류**<NA>S258용도폐지 담당자 교육6온라인
7491H4050<NA>윤**<NA>S258용도폐지 담당자 교육6온라인
7492H4051<NA>박**<NA>S258용도폐지 담당자 교육6온라인
7493H4052<NA>성**<NA>S258용도폐지 담당자 교육6온라인

Duplicate rows

Most frequently occurring

학습자 번호사번이름성별과정번호교육과정명차수교육 참여 방식# duplicates
0H3382<NA>권**여성S252(교육청) 공유재산 관리실무5온라인2