Overview

Dataset statistics

Number of variables11
Number of observations3382
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory323.8 KiB
Average record size in memory98.0 B

Variable types

Text1
Numeric6
Categorical4

Dataset

Description국외인적자원관리시스템을 통해 관리중인 정부초청외국인장학생의 전공과정별 현황※ 시스템에 등록된 데이터 기준으로 실제 사업부서의 보유자료와 일부 차이가 있을 수 있음
Author교육부 국립국제교육원
URLhttps://www.data.go.kr/data/15067913/fileData.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
입국연기 is highly overall correlated with 어학연수 and 1 other fieldsHigh correlation
기타 is highly overall correlated with 석사 and 3 other fieldsHigh correlation
석사 is highly overall correlated with 기타High correlation
어학연수 is highly overall correlated with 기타 and 1 other fieldsHigh correlation
연구 is highly overall correlated with 연수 and 1 other fieldsHigh correlation
연수 is highly overall correlated with 연구High correlation
연수 is highly imbalanced (98.4%)Imbalance
석박사 is highly imbalanced (97.8%)Imbalance
기타 is highly imbalanced (99.1%)Imbalance
입국연기 is highly imbalanced (99.5%)Imbalance
어학연수 is highly skewed (γ1 = 46.33482542)Skewed
학사 is highly skewed (γ1 = 22.5416372)Skewed
석사 has 1168 (34.5%) zerosZeros
박사 has 2443 (72.2%) zerosZeros
어학연수 has 2687 (79.5%) zerosZeros
학사 has 2656 (78.5%) zerosZeros
전문학사 has 3284 (97.1%) zerosZeros
연구 has 3270 (96.7%) zerosZeros

Reproduction

Analysis started2023-12-12 00:12:16.976251
Analysis finished2023-12-12 00:12:22.713357
Duration5.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전공명
Text

Distinct3371
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
2023-12-12T09:12:22.935744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length89
Median length65
Mean length10.856002
Min length1

Characters and Unicode

Total characters36715
Distinct characters381
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3360 ?
Unique (%)99.3%

Sample

1st row(WCU)기계항공공학부 멀티스케일기계설계전공
2nd row(디지털경영학과) 인터넷 무역
3rd row(외국어로서의)한국어
4th row(외국어로서의)한국어교육학과
5th row(원주)국제관계학과
ValueCountFrequency (%)
and 112
 
2.2%
engineering 91
 
1.8%
및 57
 
1.1%
science 57
 
1.1%
of 42
 
0.8%
studies 41
 
0.8%
41
 
0.8%
international 40
 
0.8%
management 33
 
0.7%
경영학과 30
 
0.6%
Other values (3165) 4485
89.2%
2023-12-12T09:12:23.425742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 2886
 
7.9%
과 1740
 
4.7%
1653
 
4.5%
E 1421
 
3.9%
N 1352
 
3.7%
I 1301
 
3.5%
공 1247
 
3.4%
A 1140
 
3.1%
O 873
 
2.4%
T 866
 
2.4%
Other values (371) 22236
60.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21361
58.2%
Uppercase Letter 12430
33.9%
Space Separator 1653
 
4.5%
Close Punctuation 436
 
1.2%
Open Punctuation 436
 
1.2%
Other Punctuation 294
 
0.8%
Dash Punctuation 82
 
0.2%
Connector Punctuation 11
 
< 0.1%
Modifier Symbol 5
 
< 0.1%
Decimal Number 3
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 2886
 
13.5%
과 1740
 
8.1%
공 1247
 
5.8%
전 626
 
2.9%
국 505
 
2.4%
경 468
 
2.2%
영 417
 
2.0%
정 364
 
1.7%
부 360
 
1.7%
어 360
 
1.7%
Other values (326) 12388
58.0%
Uppercase Letter
ValueCountFrequency (%)
E 1421
11.4%
N 1352
10.9%
I 1301
10.5%
A 1140
9.2%
O 873
 
7.0%
T 866
 
7.0%
C 711
 
5.7%
R 697
 
5.6%
S 636
 
5.1%
L 612
 
4.9%
Other values (16) 2821
22.7%
Other Punctuation
ValueCountFrequency (%)
· 81
27.6%
& 70
23.8%
/ 47
16.0%
; 40
13.6%
: 34
11.6%
. 20
 
6.8%
* 1
 
0.3%
' 1
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
0 1
33.3%
Math Symbol
ValueCountFrequency (%)
> 2
66.7%
~ 1
33.3%
Space Separator
ValueCountFrequency (%)
1653
100.0%
Close Punctuation
ValueCountFrequency (%)
) 436
100.0%
Open Punctuation
ValueCountFrequency (%)
( 436
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 11
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 5
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21361
58.2%
Latin 12431
33.9%
Common 2923
 
8.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 2886
 
13.5%
과 1740
 
8.1%
공 1247
 
5.8%
전 626
 
2.9%
국 505
 
2.4%
경 468
 
2.2%
영 417
 
2.0%
정 364
 
1.7%
부 360
 
1.7%
어 360
 
1.7%
Other values (326) 12388
58.0%
Latin
ValueCountFrequency (%)
E 1421
11.4%
N 1352
10.9%
I 1301
10.5%
A 1140
9.2%
O 873
 
7.0%
T 866
 
7.0%
C 711
 
5.7%
R 697
 
5.6%
S 636
 
5.1%
L 612
 
4.9%
Other values (17) 2822
22.7%
Common
ValueCountFrequency (%)
1653
56.6%
) 436
 
14.9%
( 436
 
14.9%
- 82
 
2.8%
· 81
 
2.8%
& 70
 
2.4%
/ 47
 
1.6%
; 40
 
1.4%
: 34
 
1.2%
. 20
 
0.7%
Other values (8) 24
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21358
58.2%
ASCII 15272
41.6%
None 81
 
0.2%
Compat Jamo 3
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 2886
 
13.5%
과 1740
 
8.1%
공 1247
 
5.8%
전 626
 
2.9%
국 505
 
2.4%
경 468
 
2.2%
영 417
 
2.0%
정 364
 
1.7%
부 360
 
1.7%
어 360
 
1.7%
Other values (325) 12385
58.0%
ASCII
ValueCountFrequency (%)
1653
 
10.8%
E 1421
 
9.3%
N 1352
 
8.9%
I 1301
 
8.5%
A 1140
 
7.5%
O 873
 
5.7%
T 866
 
5.7%
C 711
 
4.7%
R 697
 
4.6%
S 636
 
4.2%
Other values (33) 4622
30.3%
None
ValueCountFrequency (%)
· 81
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 3
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

석사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4861029
Minimum0
Maximum354
Zeros1168
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2023-12-12T09:12:23.605188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile8
Maximum354
Range354
Interquartile range (IQR)2

Descriptive statistics

Standard deviation11.416361
Coefficient of variation (CV)4.5920708
Kurtosis538.48786
Mean2.4861029
Median Absolute Deviation (MAD)1
Skewness19.9746
Sum8408
Variance130.33329
MonotonicityNot monotonic
2023-12-12T09:12:23.777070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1352
40.0%
0 1168
34.5%
2 347
 
10.3%
3 142
 
4.2%
4 73
 
2.2%
5 51
 
1.5%
7 34
 
1.0%
6 30
 
0.9%
8 20
 
0.6%
9 19
 
0.6%
Other values (48) 146
 
4.3%
ValueCountFrequency (%)
0 1168
34.5%
1 1352
40.0%
2 347
 
10.3%
3 142
 
4.2%
4 73
 
2.2%
5 51
 
1.5%
6 30
 
0.9%
7 34
 
1.0%
8 20
 
0.6%
9 19
 
0.6%
ValueCountFrequency (%)
354 1
< 0.1%
346 1
< 0.1%
155 1
< 0.1%
138 1
< 0.1%
128 1
< 0.1%
126 1
< 0.1%
109 1
< 0.1%
96 1
< 0.1%
93 1
< 0.1%
92 1
< 0.1%

박사
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65050266
Minimum0
Maximum73
Zeros2443
Zeros (%)72.2%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2023-12-12T09:12:23.923170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum73
Range73
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.6467513
Coefficient of variation (CV)4.0687786
Kurtosis324.07272
Mean0.65050266
Median Absolute Deviation (MAD)0
Skewness14.858222
Sum2200
Variance7.0052926
MonotonicityNot monotonic
2023-12-12T09:12:24.063400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 2443
72.2%
1 651
 
19.2%
2 120
 
3.5%
3 51
 
1.5%
4 28
 
0.8%
5 14
 
0.4%
6 14
 
0.4%
7 13
 
0.4%
9 7
 
0.2%
11 6
 
0.2%
Other values (16) 35
 
1.0%
ValueCountFrequency (%)
0 2443
72.2%
1 651
 
19.2%
2 120
 
3.5%
3 51
 
1.5%
4 28
 
0.8%
5 14
 
0.4%
6 14
 
0.4%
7 13
 
0.4%
8 4
 
0.1%
9 7
 
0.2%
ValueCountFrequency (%)
73 1
 
< 0.1%
66 1
 
< 0.1%
49 1
 
< 0.1%
31 1
 
< 0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 3
0.1%
20 1
 
< 0.1%
17 2
0.1%

어학연수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct23
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55529273
Minimum0
Maximum293
Zeros2687
Zeros (%)79.5%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2023-12-12T09:12:24.207778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum293
Range293
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.4709353
Coefficient of variation (CV)9.8523446
Kurtosis2429.3426
Mean0.55529273
Median Absolute Deviation (MAD)0
Skewness46.334825
Sum1878
Variance29.931133
MonotonicityNot monotonic
2023-12-12T09:12:24.327815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 2687
79.5%
1 459
 
13.6%
2 104
 
3.1%
3 43
 
1.3%
4 21
 
0.6%
5 16
 
0.5%
6 13
 
0.4%
7 8
 
0.2%
8 7
 
0.2%
10 4
 
0.1%
Other values (13) 20
 
0.6%
ValueCountFrequency (%)
0 2687
79.5%
1 459
 
13.6%
2 104
 
3.1%
3 43
 
1.3%
4 21
 
0.6%
5 16
 
0.5%
6 13
 
0.4%
7 8
 
0.2%
8 7
 
0.2%
9 2
 
0.1%
ValueCountFrequency (%)
293 1
 
< 0.1%
78 1
 
< 0.1%
35 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
20 1
 
< 0.1%
17 3
0.1%
15 2
0.1%
14 2
0.1%

학사
Real number (ℝ)

SKEWED  ZEROS 

Distinct30
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65878179
Minimum0
Maximum149
Zeros2656
Zeros (%)78.5%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2023-12-12T09:12:24.753217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum149
Range149
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.7694672
Coefficient of variation (CV)5.7218753
Kurtosis757.80219
Mean0.65878179
Median Absolute Deviation (MAD)0
Skewness22.541637
Sum2228
Variance14.208883
MonotonicityNot monotonic
2023-12-12T09:12:24.899541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 2656
78.5%
1 463
 
13.7%
2 104
 
3.1%
3 42
 
1.2%
4 23
 
0.7%
7 14
 
0.4%
6 11
 
0.3%
5 10
 
0.3%
9 9
 
0.3%
11 7
 
0.2%
Other values (20) 43
 
1.3%
ValueCountFrequency (%)
0 2656
78.5%
1 463
 
13.7%
2 104
 
3.1%
3 42
 
1.2%
4 23
 
0.7%
5 10
 
0.3%
6 11
 
0.3%
7 14
 
0.4%
8 4
 
0.1%
9 9
 
0.3%
ValueCountFrequency (%)
149 1
< 0.1%
56 1
< 0.1%
51 1
< 0.1%
47 1
< 0.1%
46 1
< 0.1%
42 1
< 0.1%
39 1
< 0.1%
37 1
< 0.1%
26 2
0.1%
24 2
0.1%

전문학사
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.058840923
Minimum0
Maximum13
Zeros3284
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2023-12-12T09:12:25.030952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.50001202
Coefficient of variation (CV)8.4976918
Kurtosis346.73981
Mean0.058840923
Median Absolute Deviation (MAD)0
Skewness16.238182
Sum199
Variance0.25001202
MonotonicityNot monotonic
2023-12-12T09:12:25.193377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 3284
97.1%
1 57
 
1.7%
2 24
 
0.7%
3 6
 
0.2%
5 4
 
0.1%
4 3
 
0.1%
13 2
 
0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 3284
97.1%
1 57
 
1.7%
2 24
 
0.7%
3 6
 
0.2%
4 3
 
0.1%
5 4
 
0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
13 2
 
0.1%
ValueCountFrequency (%)
13 2
 
0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
5 4
 
0.1%
4 3
 
0.1%
3 6
 
0.2%
2 24
 
0.7%
1 57
 
1.7%
0 3284
97.1%

연구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.047309284
Minimum0
Maximum12
Zeros3270
Zeros (%)96.7%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2023-12-12T09:12:25.366962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35763135
Coefficient of variation (CV)7.5594326
Kurtosis453.42559
Mean0.047309284
Median Absolute Deviation (MAD)0
Skewness17.328422
Sum160
Variance0.12790018
MonotonicityNot monotonic
2023-12-12T09:12:25.483093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3270
96.7%
1 92
 
2.7%
2 11
 
0.3%
4 3
 
0.1%
3 3
 
0.1%
12 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 3270
96.7%
1 92
 
2.7%
2 11
 
0.3%
3 3
 
0.1%
4 3
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
4 3
 
0.1%
3 3
 
0.1%
2 11
 
0.3%
1 92
 
2.7%
0 3270
96.7%

연수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
0
3372 
1
 
7
2
 
2
37
 
1

Length

Max length2
Median length1
Mean length1.0002957
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 3372
99.7%
1 7
 
0.2%
2 2
 
0.1%
37 1
 
< 0.1%

Length

2023-12-12T09:12:25.644528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:12:25.772134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3372
99.7%
1 7
 
0.2%
2 2
 
0.1%
37 1
 
< 0.1%

석박사
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
0
3367 
1
 
13
2
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 3367
99.6%
1 13
 
0.4%
2 1
 
< 0.1%
3 1
 
< 0.1%

Length

2023-12-12T09:12:25.904865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:12:26.034427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3367
99.6%
1 13
 
0.4%
2 1
 
< 0.1%
3 1
 
< 0.1%

기타
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
0
3377 
1
 
3
2
 
1
10
 
1

Length

Max length2
Median length1
Mean length1.0002957
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 3377
99.9%
1 3
 
0.1%
2 1
 
< 0.1%
10 1
 
< 0.1%

Length

2023-12-12T09:12:26.181875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:12:26.338987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3377
99.9%
1 3
 
0.1%
2 1
 
< 0.1%
10 1
 
< 0.1%

입국연기
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
0
3380 
2
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.000887
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 3380
99.9%
2 1
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2023-12-12T09:12:26.495144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:12:26.606060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3380
99.9%
2 1
 
< 0.1%
na 1
 
< 0.1%

Interactions

2023-12-12T09:12:21.788260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:18.041172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:18.818451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:19.570824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:20.331548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:21.098557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:21.886408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:18.182284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:18.932394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:19.700603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:20.474291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:21.251148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:22.001706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:18.306487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:19.045516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:19.832691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:20.609022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:21.381865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:22.112844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:18.423280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:19.184487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:19.951211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:20.728233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:21.494145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:22.210631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:18.550293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:19.307240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:20.071675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:20.855256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:21.604621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:22.299459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:18.684620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:19.427634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:20.200758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:20.976443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:12:21.693305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:12:26.691280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
석사박사어학연수학사전문학사연구연수석박사기타입국연기
석사1.0000.7820.6790.5680.0000.8650.6040.3370.7540.323
박사0.7821.0000.8440.7090.0520.5780.4760.3280.5980.279
어학연수0.6790.8441.0000.6880.0000.2590.5050.2700.8951.000
학사0.5680.7090.6881.0000.0000.2470.1260.1190.0000.000
전문학사0.0000.0520.0000.0001.0000.0000.0000.0000.0000.000
연구0.8650.5780.2590.2470.0001.0000.7430.1910.7980.415
연수0.6040.4760.5050.1260.0000.7431.0000.1280.8180.551
석박사0.3370.3280.2700.1190.0000.1910.1281.0000.0000.000
기타0.7540.5980.8950.0000.0000.7980.8180.0001.0001.000
입국연기0.3230.2791.0000.0000.0000.4150.5510.0001.0001.000
2023-12-12T09:12:26.861188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연수석박사입국연기기타
연수1.0000.0510.3770.462
석박사0.0511.0000.0000.000
입국연기0.3770.0001.0001.000
기타0.4620.0001.0001.000
2023-12-12T09:12:27.001505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
석사박사어학연수학사전문학사연구연수석박사기타입국연기
석사1.0000.1120.118-0.109-0.1450.1120.4350.2230.5920.232
박사0.1121.0000.1480.023-0.0630.1670.3470.2300.4570.298
어학연수0.1180.1481.0000.0810.0050.0770.2160.1090.5771.000
학사-0.1090.0230.0811.000-0.0490.0480.1030.0970.0000.000
전문학사-0.145-0.0630.005-0.0491.000-0.0220.0000.0000.0000.000
연구0.1120.1670.0770.048-0.0221.0000.6000.1230.6880.298
연수0.4350.3470.2160.1030.0000.6001.0000.0510.4620.377
석박사0.2230.2300.1090.0970.0000.1230.0511.0000.0000.000
기타0.5920.4570.5770.0000.0000.6880.4620.0001.0001.000
입국연기0.2320.2981.0000.0000.0000.2980.3770.0001.0001.000

Missing values

2023-12-12T09:12:22.451831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:12:22.652646image/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

전공명석사박사어학연수학사전문학사연구연수석박사기타입국연기
0(WCU)기계항공공학부 멀티스케일기계설계전공1000000000
1(디지털경영학과) 인터넷 무역1000000000
2(외국어로서의)한국어1000000000
3(외국어로서의)한국어교육학과0100000000
4(원주)국제관계학과2100000000
5ACCOUNTING3000000000
6ACOOUNTING1000000000
7ACTING0100000000
8ADMINISTRATION1000000000
9ADVANCED INTERPRETATION AND TRANSLATION PROGRAM2000000000
전공명석사박사어학연수학사전문학사연구연수석박사기타입국연기
3372환경조경학과2000000000
3373환경학과0100000000
3374환경학과(환경학)0010000000
3375환공공학과0010000000
3376회계1100000000
3377회계 세무학과1000000000
3378회계학3200010000
3379회계학과6431000000
3380회화1100000000
3381휴먼컴퓨터인터랙션학과0020000000

Duplicate rows

Most frequently occurring

전공명석사박사어학연수학사전문학사연구연수석박사기타입국연기# duplicates
0국제학과(한국학전공)20000000002