Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells7169
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory839.8 KiB
Average record size in memory86.0 B

Variable types

Numeric5
Text3
Categorical1

Dataset

Description한국연구재단이 보유하고있는 기초학문자료센터 시스템에 있는 검색엔진 매핑 테이블 데이터 입니다. 대표 데이터로는 KEY_SEQ , 연구과제ID 등이 있습니다.
Author한국연구재단
URLhttps://www.data.go.kr/data/15092376/fileData.do

Alerts

순서 is highly overall correlated with 키(SEQ) and 2 other fieldsHigh correlation
키(SEQ) is highly overall correlated with 순서 and 2 other fieldsHigh correlation
연구과제(ID) is highly overall correlated with 순서 and 3 other fieldsHigh correlation
사업년도 is highly overall correlated with 순서 and 3 other fieldsHigh correlation
저자(SEQ_NUM) is highly overall correlated with 연구과제(ID) and 1 other fieldsHigh correlation
사업차수 is highly imbalanced (65.1%)Imbalance
저자(SEQ_NUM) has 7045 (70.5%) missing valuesMissing
전공코드1 has 124 (1.2%) missing valuesMissing
순서 has unique valuesUnique
키(SEQ) has unique valuesUnique

Reproduction

Analysis started2024-04-17 11:52:13.220657
Analysis finished2024-04-17 11:52:16.508779
Duration3.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순서
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50213.387
Minimum1
Maximum99994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T20:52:16.574799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4707.95
Q125145.75
median50382
Q375798.75
95-th percentile95052.55
Maximum99994
Range99993
Interquartile range (IQR)50653

Descriptive statistics

Standard deviation29055.415
Coefficient of variation (CV)0.57863883
Kurtosis-1.2086734
Mean50213.387
Median Absolute Deviation (MAD)25344.5
Skewness-0.01295384
Sum5.0213387 × 108
Variance8.4421715 × 108
MonotonicityNot monotonic
2024-04-17T20:52:16.687454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72095 1
 
< 0.1%
83544 1
 
< 0.1%
2566 1
 
< 0.1%
59044 1
 
< 0.1%
35751 1
 
< 0.1%
13465 1
 
< 0.1%
5229 1
 
< 0.1%
40212 1
 
< 0.1%
40841 1
 
< 0.1%
36422 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
11 1
< 0.1%
28 1
< 0.1%
36 1
< 0.1%
43 1
< 0.1%
57 1
< 0.1%
66 1
< 0.1%
69 1
< 0.1%
75 1
< 0.1%
ValueCountFrequency (%)
99994 1
< 0.1%
99990 1
< 0.1%
99989 1
< 0.1%
99982 1
< 0.1%
99978 1
< 0.1%
99973 1
< 0.1%
99972 1
< 0.1%
99965 1
< 0.1%
99940 1
< 0.1%
99938 1
< 0.1%

키(SEQ)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130198.08
Minimum79170
Maximum197697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T20:52:16.801520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79170
5-th percentile83890.95
Q1104344.75
median129582
Q3155056.75
95-th percentile178770.55
Maximum197697
Range118527
Interquartile range (IQR)50712

Descriptive statistics

Standard deviation30340.366
Coefficient of variation (CV)0.23303236
Kurtosis-1.0336762
Mean130198.08
Median Absolute Deviation (MAD)25381.5
Skewness0.11822242
Sum1.3019808 × 109
Variance9.2053781 × 108
MonotonicityNot monotonic
2024-04-17T20:52:16.925704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151353 1
 
< 0.1%
164170 1
 
< 0.1%
81746 1
 
< 0.1%
138244 1
 
< 0.1%
114951 1
 
< 0.1%
92656 1
 
< 0.1%
84412 1
 
< 0.1%
119412 1
 
< 0.1%
120041 1
 
< 0.1%
115622 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
79170 1
< 0.1%
79172 1
< 0.1%
79180 1
< 0.1%
79197 1
< 0.1%
79205 1
< 0.1%
79212 1
< 0.1%
79226 1
< 0.1%
79235 1
< 0.1%
79238 1
< 0.1%
79244 1
< 0.1%
ValueCountFrequency (%)
197697 1
< 0.1%
197693 1
< 0.1%
197692 1
< 0.1%
197685 1
< 0.1%
197681 1
< 0.1%
197676 1
< 0.1%
197675 1
< 0.1%
197668 1
< 0.1%
197643 1
< 0.1%
197641 1
< 0.1%

연구과제(ID)
Real number (ℝ)

HIGH CORRELATION 

Distinct8968
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10020314
Minimum10000008
Maximum10060790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T20:52:17.052228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000008
5-th percentile10001780
Q110008173
median10017778
Q310027582
95-th percentile10055907
Maximum10060790
Range60782
Interquartile range (IQR)19409.5

Descriptive statistics

Standard deviation14890.703
Coefficient of variation (CV)0.0014860515
Kurtosis0.21371932
Mean10020314
Median Absolute Deviation (MAD)9745
Skewness0.87012427
Sum1.0020314 × 1011
Variance2.2173303 × 108
MonotonicityNot monotonic
2024-04-17T20:52:17.176771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10002648 8
 
0.1%
10011786 7
 
0.1%
10002692 7
 
0.1%
10010966 6
 
0.1%
10020901 6
 
0.1%
10002580 6
 
0.1%
10015983 5
 
0.1%
10006184 5
 
0.1%
10011826 5
 
0.1%
10010956 5
 
0.1%
Other values (8958) 9940
99.4%
ValueCountFrequency (%)
10000008 1
< 0.1%
10000015 1
< 0.1%
10000019 1
< 0.1%
10000024 1
< 0.1%
10000037 1
< 0.1%
10000043 2
< 0.1%
10000048 1
< 0.1%
10000050 1
< 0.1%
10000052 1
< 0.1%
10000057 1
< 0.1%
ValueCountFrequency (%)
10060790 1
< 0.1%
10060763 1
< 0.1%
10060752 1
< 0.1%
10060749 1
< 0.1%
10060737 1
< 0.1%
10060729 1
< 0.1%
10060711 1
< 0.1%
10060708 1
< 0.1%
10060705 1
< 0.1%
10060702 1
< 0.1%

사업년도
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.7085
Minimum1997
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T20:52:17.285854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2000
Q12003
median2007
Q32011
95-th percentile2014
Maximum2014
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.361494
Coefficient of variation (CV)0.0021734567
Kurtosis-1.096761
Mean2006.7085
Median Absolute Deviation (MAD)4
Skewness-0.07122435
Sum20067085
Variance19.02263
MonotonicityNot monotonic
2024-04-17T20:52:17.612034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2011 850
 
8.5%
2002 796
 
8.0%
2008 734
 
7.3%
2012 728
 
7.3%
2009 716
 
7.2%
2005 671
 
6.7%
2007 671
 
6.7%
2004 657
 
6.6%
2000 642
 
6.4%
2006 612
 
6.1%
Other values (8) 2923
29.2%
ValueCountFrequency (%)
1997 42
 
0.4%
1998 93
 
0.9%
1999 125
 
1.2%
2000 642
6.4%
2001 529
5.3%
2002 796
8.0%
2003 589
5.9%
2004 657
6.6%
2005 671
6.7%
2006 612
6.1%
ValueCountFrequency (%)
2014 529
5.3%
2013 438
4.4%
2012 728
7.3%
2011 850
8.5%
2010 578
5.8%
2009 716
7.2%
2008 734
7.3%
2007 671
6.7%
2006 612
6.1%
2005 671
6.7%

사업코드
Text

Distinct115
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T20:52:17.817461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length3.4053
Min length1

Characters and Unicode

Total characters34053
Distinct characters14
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

Unique18 ?
Unique (%)0.2%

Sample

1st row908
2nd row41
3rd row332
4th row327
5th row41
ValueCountFrequency (%)
327 1093
 
10.9%
41 874
 
8.7%
332 793
 
7.9%
s1a5b5a07 624
 
6.2%
3 441
 
4.4%
321 395
 
4.0%
13 372
 
3.7%
35c 370
 
3.7%
37 351
 
3.5%
s1a5a2a01 307
 
3.1%
Other values (105) 4380
43.8%
2024-04-17T20:52:18.150573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 6025
17.7%
1 4883
14.3%
2 4601
13.5%
A 4006
11.8%
5 3777
11.1%
7 2857
8.4%
4 1864
 
5.5%
S 1695
 
5.0%
0 1545
 
4.5%
8 895
 
2.6%
Other values (4) 1905
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27256
80.0%
Uppercase Letter 6797
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 6025
22.1%
1 4883
17.9%
2 4601
16.9%
5 3777
13.9%
7 2857
10.5%
4 1864
 
6.8%
0 1545
 
5.7%
8 895
 
3.3%
6 461
 
1.7%
9 348
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
A 4006
58.9%
S 1695
24.9%
B 726
 
10.7%
C 370
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 27256
80.0%
Latin 6797
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 6025
22.1%
1 4883
17.9%
2 4601
16.9%
5 3777
13.9%
7 2857
10.5%
4 1864
 
6.8%
0 1545
 
5.7%
8 895
 
3.3%
6 461
 
1.7%
9 348
 
1.3%
Latin
ValueCountFrequency (%)
A 4006
58.9%
S 1695
24.9%
B 726
 
10.7%
C 370
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 6025
17.7%
1 4883
14.3%
2 4601
13.5%
A 4006
11.8%
5 3777
11.1%
7 2857
8.4%
4 1864
 
5.5%
S 1695
 
5.0%
0 1545
 
4.5%
8 895
 
2.6%
Other values (4) 1905
 
5.6%

사업차수
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8465 
2
1397 
3
 
94
4
 
44

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 8465
84.7%
2 1397
 
14.0%
3 94
 
0.9%
4 44
 
0.4%

Length

2024-04-17T20:52:18.262637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:52:18.344967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8465
84.7%
2 1397
 
14.0%
3 94
 
0.9%
4 44
 
0.4%

과제번호
Text

Distinct4806
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T20:52:18.632873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length6
Mean length8.0609
Min length2

Characters and Unicode

Total characters80609
Distinct characters28
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

Unique3230 ?
Unique (%)32.3%

Sample

1st rowIN0003
2nd rowC00447
3rd rowH00036
4th rowB00764
5th rowB00499
ValueCountFrequency (%)
a00004 27
 
0.3%
b00009 24
 
0.2%
b00007 23
 
0.2%
a00003 21
 
0.2%
b00011 20
 
0.2%
b00022 19
 
0.2%
b00005 18
 
0.2%
a00024 18
 
0.2%
h00003 18
 
0.2%
a00025 18
 
0.2%
Other values (4796) 9794
97.9%
2024-04-17T20:52:19.043361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26655
33.1%
1 8838
 
11.0%
A 7139
 
8.9%
2 6996
 
8.7%
5 4827
 
6.0%
3 3936
 
4.9%
4 3735
 
4.6%
B 3686
 
4.6%
7 3032
 
3.8%
6 2611
 
3.2%
Other values (18) 9154
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65222
80.9%
Uppercase Letter 15387
 
19.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 7139
46.4%
B 3686
24.0%
S 2004
 
13.0%
C 641
 
4.2%
G 586
 
3.8%
H 509
 
3.3%
M 229
 
1.5%
D 175
 
1.1%
J 121
 
0.8%
E 97
 
0.6%
Other values (8) 200
 
1.3%
Decimal Number
ValueCountFrequency (%)
0 26655
40.9%
1 8838
 
13.6%
2 6996
 
10.7%
5 4827
 
7.4%
3 3936
 
6.0%
4 3735
 
5.7%
7 3032
 
4.6%
6 2611
 
4.0%
8 2516
 
3.9%
9 2076
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 65222
80.9%
Latin 15387
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 7139
46.4%
B 3686
24.0%
S 2004
 
13.0%
C 641
 
4.2%
G 586
 
3.8%
H 509
 
3.3%
M 229
 
1.5%
D 175
 
1.1%
J 121
 
0.8%
E 97
 
0.6%
Other values (8) 200
 
1.3%
Common
ValueCountFrequency (%)
0 26655
40.9%
1 8838
 
13.6%
2 6996
 
10.7%
5 4827
 
7.4%
3 3936
 
6.0%
4 3735
 
5.7%
7 3032
 
4.6%
6 2611
 
4.0%
8 2516
 
3.9%
9 2076
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26655
33.1%
1 8838
 
11.0%
A 7139
 
8.9%
2 6996
 
8.7%
5 4827
 
6.0%
3 3936
 
4.9%
4 3735
 
4.6%
B 3686
 
4.6%
7 3032
 
3.8%
6 2611
 
3.2%
Other values (18) 9154
 
11.4%

저자(SEQ_NUM)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2955
Distinct (%)100.0%
Missing7045
Missing (%)70.5%
Infinite0
Infinite (%)0.0%
Mean168072.09
Minimum749
Maximum316904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T20:52:19.176057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum749
5-th percentile25758.1
Q162160.5
median100516
Q3289434
95-th percentile311201.9
Maximum316904
Range316155
Interquartile range (IQR)227273.5

Descriptive statistics

Standard deviation115297.53
Coefficient of variation (CV)0.68600042
Kurtosis-1.8231555
Mean168072.09
Median Absolute Deviation (MAD)77434
Skewness0.078312811
Sum4.9665303 × 108
Variance1.3293519 × 1010
MonotonicityNot monotonic
2024-04-17T20:52:19.299346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311772 1
 
< 0.1%
312390 1
 
< 0.1%
230903 1
 
< 0.1%
28705 1
 
< 0.1%
301373 1
 
< 0.1%
61241 1
 
< 0.1%
230457 1
 
< 0.1%
310094 1
 
< 0.1%
92735 1
 
< 0.1%
29488 1
 
< 0.1%
Other values (2945) 2945
29.4%
(Missing) 7045
70.5%
ValueCountFrequency (%)
749 1
< 0.1%
922 1
< 0.1%
20081 1
< 0.1%
20218 1
< 0.1%
20247 1
< 0.1%
20256 1
< 0.1%
20270 1
< 0.1%
20294 1
< 0.1%
20301 1
< 0.1%
20460 1
< 0.1%
ValueCountFrequency (%)
316904 1
< 0.1%
316879 1
< 0.1%
316878 1
< 0.1%
316859 1
< 0.1%
316857 1
< 0.1%
316854 1
< 0.1%
315868 1
< 0.1%
314811 1
< 0.1%
314790 1
< 0.1%
314783 1
< 0.1%

전공코드1
Text

MISSING 

Distinct1354
Distinct (%)13.7%
Missing124
Missing (%)1.2%
Memory size156.2 KiB
2024-04-17T20:52:19.545080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters69132
Distinct characters18
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

Unique418 ?
Unique (%)4.2%

Sample

1st rowG100104
2nd rowB080400
3rd rowH041600
4th rowB130330
5th rowB121208
ValueCountFrequency (%)
h019900 262
 
2.7%
a110206 117
 
1.2%
g100800 81
 
0.8%
b121202 72
 
0.7%
a020205 71
 
0.7%
b050202 70
 
0.7%
a150208 68
 
0.7%
a110202 64
 
0.6%
b121109 64
 
0.6%
g030105 62
 
0.6%
Other values (1344) 8945
90.6%
2024-04-17T20:52:19.900766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26761
38.7%
1 11164
16.1%
2 6615
 
9.6%
A 4069
 
5.9%
B 3955
 
5.7%
3 3736
 
5.4%
9 2656
 
3.8%
5 2521
 
3.6%
4 2220
 
3.2%
6 1385
 
2.0%
Other values (8) 4050
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59256
85.7%
Uppercase Letter 9876
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26761
45.2%
1 11164
18.8%
2 6615
 
11.2%
3 3736
 
6.3%
9 2656
 
4.5%
5 2521
 
4.3%
4 2220
 
3.7%
6 1385
 
2.3%
8 1168
 
2.0%
7 1030
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
A 4069
41.2%
B 3955
40.0%
G 856
 
8.7%
H 565
 
5.7%
C 169
 
1.7%
D 167
 
1.7%
E 69
 
0.7%
F 26
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 59256
85.7%
Latin 9876
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26761
45.2%
1 11164
18.8%
2 6615
 
11.2%
3 3736
 
6.3%
9 2656
 
4.5%
5 2521
 
4.3%
4 2220
 
3.7%
6 1385
 
2.3%
8 1168
 
2.0%
7 1030
 
1.7%
Latin
ValueCountFrequency (%)
A 4069
41.2%
B 3955
40.0%
G 856
 
8.7%
H 565
 
5.7%
C 169
 
1.7%
D 167
 
1.7%
E 69
 
0.7%
F 26
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26761
38.7%
1 11164
16.1%
2 6615
 
9.6%
A 4069
 
5.9%
B 3955
 
5.7%
3 3736
 
5.4%
9 2656
 
3.8%
5 2521
 
3.6%
4 2220
 
3.2%
6 1385
 
2.0%
Other values (8) 4050
 
5.9%

Interactions

2024-04-17T20:52:15.733115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:13.889049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.358470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.810095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:15.265226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:15.810753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:13.966512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.448278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.904534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:15.362231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:15.895404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.058307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.534777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.994306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:15.462801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:15.979406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.150078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.625510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:15.085176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:15.552096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:16.086417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.241837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:14.722072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:15.173050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:52:15.639666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T20:52:19.996711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순서키(SEQ)연구과제(ID)사업년도사업차수저자(SEQ_NUM)
순서1.0000.9860.8290.7540.2810.623
키(SEQ)0.9861.0000.8520.7560.2690.643
연구과제(ID)0.8290.8521.0000.9490.4560.646
사업년도0.7540.7560.9491.0000.5230.703
사업차수0.2810.2690.4560.5231.0000.349
저자(SEQ_NUM)0.6230.6430.6460.7030.3491.000
2024-04-17T20:52:20.090976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순서키(SEQ)연구과제(ID)사업년도저자(SEQ_NUM)사업차수
순서1.0001.0000.5860.5130.4860.171
키(SEQ)1.0001.0000.5860.5130.4860.164
연구과제(ID)0.5860.5861.0000.7670.6340.289
사업년도0.5130.5130.7671.0000.8700.340
저자(SEQ_NUM)0.4860.4860.6340.8701.0000.223
사업차수0.1710.1640.2890.3400.2231.000

Missing values

2024-04-17T20:52:16.214943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T20:52:16.340035image/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.
2024-04-17T20:52:16.459157image/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

순서키(SEQ)연구과제(ID)사업년도사업코드사업차수과제번호저자(SEQ_NUM)전공코드1
72094720951513531000452620019081IN0003<NA>G100104
3307933080112280100002112000411C00447<NA>B080400
85272852731661491001407720073321H00036<NA>H041600
46221462221254221002165520093271B00764<NA>B130330
2189621897101096100075832004411B00499<NA>B121208
4415944160123360100163262008131C00013<NA>C020802
1608016081952721002099520093511A00058291279A090700
9572495725180001100583372014S1A5B5A0712014S1A5B5A07042317<NA>A110206
40512405131197131000990620002161H00172<NA>H019900
3439434395113595100023452002741AS1099<NA>A150210
순서키(SEQ)연구과제(ID)사업년도사업코드사업차수과제번호저자(SEQ_NUM)전공코드1
27206272071064061001818720093512A00239<NA>A080200
28165281661073661002078320093711D00009<NA>D131402
54304543051335051000994120002164H01461<NA>H019900
3200232003111203100035442002421B00168<NA>B121214
8901889019169897100378542012S1A5B5A0112012S1A5B5A01025284<NA>A110207
124821248391674100059392004721AM2007142393A020299
9647496475182445100590352014S1A5B5A0712014S1A5B5A07041666<NA>A090500
9124091241172119100209142009131B00088298653B130310
9602696027180338100575782014S1A6A412014S1A6A4027418<NA>A130206
6236162362141562100363832012S1A5A812012S1A5A8024945<NA>B130202