Overview

Dataset statistics

Number of variables13
Number of observations698
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.7 KiB
Average record size in memory108.2 B

Variable types

Numeric4
Text1
Categorical8

Dataset

Description각 부처에서 자체적으로 수립하는 3년 이상의 과학기술관련 중장기계획(2021년 8월 30일 기준) 해당 데이터가 보유한 컬럼은 다음과 같습니다. 컬럼명: 중장기계획 일련번호, 계획명, 조사년도, 시작년도, 종료년도, 정책분야코드, 정책분야코드한글명, 기술분야코드, 기술분야코드한글명, 신부처코드, 신부처코드한글명, 구부처 코드, 구부처코드한글명
Author한국과학기술기획평가원(KISTEP)
URLhttps://www.data.go.kr/data/15065865/fileData.do

Alerts

기술분야코드 is highly overall correlated with 기술분야코드한글명High correlation
정책분야코드한글명 is highly overall correlated with 정책분야코드High correlation
신부처코드 is highly overall correlated with 신부처코드한글명High correlation
기술분야코드한글명 is highly overall correlated with 기술분야코드High correlation
정책분야코드 is highly overall correlated with 정책분야코드한글명High correlation
구부처코드한글명 is highly overall correlated with 조사년도 and 2 other fieldsHigh correlation
신부처코드한글명 is highly overall correlated with 신부처코드High correlation
구부처코드 is highly overall correlated with 조사년도 and 2 other fieldsHigh correlation
중장기계획일련번호 is highly overall correlated with 조사년도 and 2 other fieldsHigh correlation
조사년도 is highly overall correlated with 중장기계획일련번호 and 4 other fieldsHigh correlation
시작년도 is highly overall correlated with 중장기계획일련번호 and 2 other fieldsHigh correlation
종료년도 is highly overall correlated with 중장기계획일련번호 and 4 other fieldsHigh correlation
정책분야코드 is highly imbalanced (81.7%)Imbalance
정책분야코드한글명 is highly imbalanced (81.7%)Imbalance
기술분야코드 is highly imbalanced (54.6%)Imbalance
기술분야코드한글명 is highly imbalanced (54.6%)Imbalance
구부처코드 is highly imbalanced (73.0%)Imbalance
구부처코드한글명 is highly imbalanced (73.0%)Imbalance
중장기계획일련번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:33:57.954899
Analysis finished2023-12-12 18:34:00.963364
Duration3.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

중장기계획일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct698
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean486.54298
Minimum1
Maximum878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-12-13T03:34:01.025569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37.85
Q1268.25
median527.5
Q3703.75
95-th percentile843.15
Maximum878
Range877
Interquartile range (IQR)435.5

Descriptive statistics

Standard deviation258.90913
Coefficient of variation (CV)0.53214031
Kurtosis-1.0521551
Mean486.54298
Median Absolute Deviation (MAD)216.5
Skewness-0.36871916
Sum339607
Variance67033.939
MonotonicityStrictly increasing
2023-12-13T03:34:01.148252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
650 1
 
0.1%
642 1
 
0.1%
643 1
 
0.1%
644 1
 
0.1%
645 1
 
0.1%
646 1
 
0.1%
647 1
 
0.1%
648 1
 
0.1%
649 1
 
0.1%
Other values (688) 688
98.6%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
878 1
0.1%
877 1
0.1%
876 1
0.1%
875 1
0.1%
874 1
0.1%
873 1
0.1%
872 1
0.1%
871 1
0.1%
870 1
0.1%
869 1
0.1%

계획명
Text

Distinct334
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2023-12-13T03:34:01.370697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length28
Mean length14.404011
Min length7

Characters and Unicode

Total characters10054
Distinct characters277
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)24.4%

Sample

1st row선진일류국가를향한이명박정부의과학기술기본계획(577전략)
2nd row기초연구진흥종합계획
3rd row줄기세포연구종합추진계획
4th row제2차뇌연구촉진기본계획
5th row제2차생명공학육성기본계획(’07∼’16)
ValueCountFrequency (%)
기본계획 17
 
2.0%
제3차 10
 
1.2%
제2차 10
 
1.2%
종합계획 10
 
1.2%
제4차 9
 
1.1%
이차전지경쟁력강화방안 7
 
0.8%
환경보건종합계획 7
 
0.8%
국방과학기술진흥정책서 7
 
0.8%
국제과학비즈니스벨트기본계획 7
 
0.8%
전파진흥기본계획 6
 
0.7%
Other values (385) 740
89.2%
2023-12-13T03:34:01.728230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 634
 
6.3%
계 606
 
6.0%
획 593
 
5.9%
제 355
 
3.5%
차 348
 
3.5%
본 265
 
2.6%
술 221
 
2.2%
전 209
 
2.1%
성 190
 
1.9%
발 187
 
1.9%
Other values (267) 6446
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8815
87.7%
Decimal Number 575
 
5.7%
Uppercase Letter 216
 
2.1%
Space Separator 132
 
1.3%
Other Punctuation 126
 
1.3%
Close Punctuation 55
 
0.5%
Open Punctuation 55
 
0.5%
Math Symbol 25
 
0.2%
Final Punctuation 24
 
0.2%
Lowercase Letter 20
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 634
 
7.2%
계 606
 
6.9%
획 593
 
6.7%
제 355
 
4.0%
차 348
 
3.9%
본 265
 
3.0%
술 221
 
2.5%
전 209
 
2.4%
성 190
 
2.2%
발 187
 
2.1%
Other values (218) 5207
59.1%
Uppercase Letter
ValueCountFrequency (%)
D 51
23.6%
R 50
23.1%
C 25
11.6%
T 21
9.7%
I 19
 
8.8%
S 15
 
6.9%
W 7
 
3.2%
A 6
 
2.8%
E 5
 
2.3%
M 5
 
2.3%
Other values (4) 12
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
a 4
20.0%
e 3
15.0%
r 3
15.0%
t 2
10.0%
y 1
 
5.0%
k 1
 
5.0%
u 1
 
5.0%
q 1
 
5.0%
h 1
 
5.0%
m 1
 
5.0%
Other values (2) 2
10.0%
Decimal Number
ValueCountFrequency (%)
2 156
27.1%
3 124
21.6%
1 91
15.8%
4 69
12.0%
5 52
 
9.0%
0 39
 
6.8%
7 17
 
3.0%
6 13
 
2.3%
8 13
 
2.3%
9 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
· 55
43.7%
& 46
36.5%
' 20
 
15.9%
. 3
 
2.4%
, 2
 
1.6%
Math Symbol
ValueCountFrequency (%)
~ 21
84.0%
∼ 2
 
8.0%
+ 2
 
8.0%
Space Separator
ValueCountFrequency (%)
132
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%
Open Punctuation
ValueCountFrequency (%)
( 55
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8815
87.7%
Common 1003
 
10.0%
Latin 236
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 634
 
7.2%
계 606
 
6.9%
획 593
 
6.7%
제 355
 
4.0%
차 348
 
3.9%
본 265
 
3.0%
술 221
 
2.5%
전 209
 
2.4%
성 190
 
2.2%
발 187
 
2.1%
Other values (218) 5207
59.1%
Latin
ValueCountFrequency (%)
D 51
21.6%
R 50
21.2%
C 25
10.6%
T 21
8.9%
I 19
 
8.1%
S 15
 
6.4%
W 7
 
3.0%
A 6
 
2.5%
E 5
 
2.1%
M 5
 
2.1%
Other values (16) 32
13.6%
Common
ValueCountFrequency (%)
2 156
15.6%
132
13.2%
3 124
12.4%
1 91
9.1%
4 69
6.9%
) 55
 
5.5%
· 55
 
5.5%
( 55
 
5.5%
5 52
 
5.2%
& 46
 
4.6%
Other values (13) 168
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8811
87.6%
ASCII 1158
 
11.5%
None 55
 
0.5%
Punctuation 24
 
0.2%
Compat Jamo 4
 
< 0.1%
Math Operators 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 634
 
7.2%
계 606
 
6.9%
획 593
 
6.7%
제 355
 
4.0%
차 348
 
3.9%
본 265
 
3.0%
술 221
 
2.5%
전 209
 
2.4%
성 190
 
2.2%
발 187
 
2.1%
Other values (217) 5203
59.1%
ASCII
ValueCountFrequency (%)
2 156
13.5%
132
11.4%
3 124
 
10.7%
1 91
 
7.9%
4 69
 
6.0%
) 55
 
4.7%
( 55
 
4.7%
5 52
 
4.5%
D 51
 
4.4%
R 50
 
4.3%
Other values (36) 323
27.9%
None
ValueCountFrequency (%)
· 55
100.0%
Punctuation
ValueCountFrequency (%)
’ 24
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 4
100.0%
Math Operators
ValueCountFrequency (%)
∼ 2
100.0%

조사년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.6074
Minimum2012
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-12-13T03:34:01.844541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2016
Q32017
95-th percentile2019
Maximum2019
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2371717
Coefficient of variation (CV)0.0011099243
Kurtosis-1.0195067
Mean2015.6074
Median Absolute Deviation (MAD)2
Skewness-0.12177178
Sum1406894
Variance5.0049372
MonotonicityIncreasing
2023-12-13T03:34:01.961305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2014 119
17.0%
2012 112
16.0%
2015 111
15.9%
2017 93
13.3%
2016 92
13.2%
2019 88
12.6%
2018 83
11.9%
ValueCountFrequency (%)
2012 112
16.0%
2014 119
17.0%
2015 111
15.9%
2016 92
13.2%
2017 93
13.3%
2018 83
11.9%
2019 88
12.6%
ValueCountFrequency (%)
2019 88
12.6%
2018 83
11.9%
2017 93
13.3%
2016 92
13.2%
2015 111
15.9%
2014 119
17.0%
2012 112
16.0%

시작년도
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.2751
Minimum2004
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-12-13T03:34:02.095417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2004
5-th percentile2008
Q12011
median2013
Q32016
95-th percentile2018
Maximum2019
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9605697
Coefficient of variation (CV)0.0014705242
Kurtosis-0.48330829
Mean2013.2751
Median Absolute Deviation (MAD)2
Skewness-0.078066592
Sum1405266
Variance8.7649731
MonotonicityNot monotonic
2023-12-13T03:34:02.200712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2013 122
17.5%
2014 97
13.9%
2011 78
11.2%
2012 72
10.3%
2016 66
9.5%
2018 60
8.6%
2010 48
 
6.9%
2017 46
 
6.6%
2008 34
 
4.9%
2015 25
 
3.6%
Other values (5) 50
7.2%
ValueCountFrequency (%)
2004 1
 
0.1%
2006 8
 
1.1%
2007 3
 
0.4%
2008 34
 
4.9%
2009 24
 
3.4%
2010 48
 
6.9%
2011 78
11.2%
2012 72
10.3%
2013 122
17.5%
2014 97
13.9%
ValueCountFrequency (%)
2019 14
 
2.0%
2018 60
8.6%
2017 46
 
6.6%
2016 66
9.5%
2015 25
 
3.6%
2014 97
13.9%
2013 122
17.5%
2012 72
10.3%
2011 78
11.2%
2010 48
 
6.9%

종료년도
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.947
Minimum2008
Maximum2040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-12-13T03:34:02.314947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2014
Q12016
median2018
Q32021
95-th percentile2027
Maximum2040
Range32
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.3483599
Coefficient of variation (CV)0.0021537761
Kurtosis6.6617899
Mean2018.947
Median Absolute Deviation (MAD)2
Skewness1.9244512
Sum1409225
Variance18.908233
MonotonicityNot monotonic
2023-12-13T03:34:02.426561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2017 135
19.3%
2020 119
17.0%
2015 73
10.5%
2022 64
9.2%
2018 59
8.5%
2016 58
8.3%
2021 42
 
6.0%
2023 26
 
3.7%
2019 19
 
2.7%
2014 19
 
2.7%
Other values (13) 84
12.0%
ValueCountFrequency (%)
2008 1
 
0.1%
2012 17
 
2.4%
2013 14
 
2.0%
2014 19
 
2.7%
2015 73
10.5%
2016 58
8.3%
2017 135
19.3%
2018 59
8.5%
2019 19
 
2.7%
2020 119
17.0%
ValueCountFrequency (%)
2040 9
1.3%
2035 4
 
0.6%
2033 1
 
0.1%
2030 5
0.7%
2029 1
 
0.1%
2028 6
0.9%
2027 12
1.7%
2026 3
 
0.4%
2025 10
1.4%
2024 1
 
0.1%

정책분야코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
645 
PLP03
 
13
PLP07
 
9
PLP01
 
7
PLP09
 
7
Other values (5)
 
17

Length

Max length5
Median length4
Mean length4.0759312
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowPLP01
2nd rowPLP04
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 645
92.4%
PLP03 13
 
1.9%
PLP07 9
 
1.3%
PLP01 7
 
1.0%
PLP09 7
 
1.0%
PLP06 6
 
0.9%
PLP04 5
 
0.7%
PLP08 3
 
0.4%
PLP02 2
 
0.3%
PLP05 1
 
0.1%

Length

2023-12-13T03:34:02.575701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:34:02.777953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 645
92.4%
plp03 13
 
1.9%
plp07 9
 
1.3%
plp01 7
 
1.0%
plp09 7
 
1.0%
plp06 6
 
0.9%
plp04 5
 
0.7%
plp08 3
 
0.4%
plp02 2
 
0.3%
plp05 1
 
0.1%

정책분야코드한글명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
645 
인력양성
 
13
과학기술하부구조
 
9
정책일반
 
7
중소벤처기술혁신
 
7
Other values (5)
 
17

Length

Max length10
Median length4
Mean length4.1475645
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 645
92.4%
인력양성 13
 
1.9%
과학기술하부구조 9
 
1.3%
정책일반 7
 
1.0%
중소벤처기술혁신 7
 
1.0%
지역기술혁신 6
 
0.9%
기초원천연구 5
 
0.7%
과학기술문화 3
 
0.4%
투자확대 및 효율화 2
 
0.3%
국제화 1
 
0.1%

Length

2023-12-13T03:34:02.938353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:34:03.101829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 645
91.9%
인력양성 13
 
1.9%
과학기술하부구조 9
 
1.3%
정책일반 7
 
1.0%
중소벤처기술혁신 7
 
1.0%
지역기술혁신 6
 
0.9%
기초원천연구 5
 
0.7%
과학기술문화 3
 
0.4%
투자확대 2
 
0.3%
및 2
 
0.3%
Other values (2) 3
 
0.4%

기술분야코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
529 
PLT02
53 
PLT06
 
38
PLT04
 
25
PLT08
 
17
Other values (4)
 
36

Length

Max length5
Median length4
Mean length4.2421203
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd rowPLT02
4th rowPLT02
5th rowPLT02

Common Values

ValueCountFrequency (%)
<NA> 529
75.8%
PLT02 53
 
7.6%
PLT06 38
 
5.4%
PLT04 25
 
3.6%
PLT08 17
 
2.4%
PLT07 11
 
1.6%
PLT01 10
 
1.4%
PLT03 9
 
1.3%
PLT05 6
 
0.9%

Length

2023-12-13T03:34:03.258118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:34:03.411819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 529
75.8%
plt02 53
 
7.6%
plt06 38
 
5.4%
plt04 25
 
3.6%
plt08 17
 
2.4%
plt07 11
 
1.6%
plt01 10
 
1.4%
plt03 9
 
1.3%
plt05 6
 
0.9%

기술분야코드한글명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
529 
생명
53 
에너지/환경
 
38
전자/정보통신
 
25
건설/교통/안전
 
17
Other values (4)
 
36

Length

Max length8
Median length4
Mean length4.234957
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 529
75.8%
생명 53
 
7.6%
에너지/환경 38
 
5.4%
전자/정보통신 25
 
3.6%
건설/교통/안전 17
 
2.4%
우주/항공/해양 11
 
1.6%
융합 10
 
1.4%
나노/소재 9
 
1.3%
기계/제조공정 6
 
0.9%

Length

2023-12-13T03:34:03.641547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:34:03.860983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 529
75.8%
생명 53
 
7.6%
에너지/환경 38
 
5.4%
전자/정보통신 25
 
3.6%
건설/교통/안전 17
 
2.4%
우주/항공/해양 11
 
1.6%
융합 10
 
1.4%
나노/소재 9
 
1.3%
기계/제조공정 6
 
0.9%

신부처코드
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
M26
126 
P22
116 
<NA>
112 
P29
58 
P18
39 
Other values (32)
247 

Length

Max length4
Median length3
Mean length3.1604585
Min length3

Unique

Unique4 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
M26 126
18.1%
P22 116
16.6%
<NA> 112
16.0%
P29 58
8.3%
P18 39
 
5.6%
M33 33
 
4.7%
M22 27
 
3.9%
P39 22
 
3.2%
P15 16
 
2.3%
P27 14
 
2.0%
Other values (27) 135
19.3%

Length

2023-12-13T03:34:04.080298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m26 126
18.1%
p22 116
16.6%
na 112
16.0%
p29 58
8.3%
p18 39
 
5.6%
m33 33
 
4.7%
m22 27
 
3.9%
p39 22
 
3.2%
p15 16
 
2.3%
p27 14
 
2.0%
Other values (27) 135
19.3%

신부처코드한글명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
과학기술정보통신부
126 
미래창조과학부
116 
<NA>
112 
산업통상자원부
91 
농림축산식품부
66 
Other values (19)
187 

Length

Max length9
Median length8
Mean length6.2893983
Min length3

Unique

Unique3 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
과학기술정보통신부 126
18.1%
미래창조과학부 116
16.6%
<NA> 112
16.0%
산업통상자원부 91
13.0%
농림축산식품부 66
9.5%
해양수산부 30
 
4.3%
국토교통부 27
 
3.9%
보건복지부 23
 
3.3%
기상청 21
 
3.0%
환경부 17
 
2.4%
Other values (14) 69
9.9%

Length

2023-12-13T03:34:04.303099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
과학기술정보통신부 126
18.1%
미래창조과학부 116
16.6%
na 112
16.0%
산업통상자원부 91
13.0%
농림축산식품부 66
9.5%
해양수산부 30
 
4.3%
국토교통부 27
 
3.9%
보건복지부 23
 
3.3%
기상청 21
 
3.0%
환경부 17
 
2.4%
Other values (14) 69
9.9%

구부처코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
586 
N38
 
32
N15
 
25
N16
 
11
N17
 
11
Other values (14)
 
33

Length

Max length4
Median length4
Mean length3.8395415
Min length3

Unique

Unique7 ?
Unique (%)1.0%

Sample

1st rowN70
2nd rowN15
3rd rowN15
4th rowN15
5th rowN15

Common Values

ValueCountFrequency (%)
<NA> 586
84.0%
N38 32
 
4.6%
N15 25
 
3.6%
N16 11
 
1.6%
N17 11
 
1.6%
N18 6
 
0.9%
N36 6
 
0.9%
N70 4
 
0.6%
N69 3
 
0.4%
N34 3
 
0.4%
Other values (9) 11
 
1.6%

Length

2023-12-13T03:34:04.497225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 586
84.0%
n38 32
 
4.6%
n15 25
 
3.6%
n16 11
 
1.6%
n17 11
 
1.6%
n18 6
 
0.9%
n36 6
 
0.9%
n70 4
 
0.6%
n34 3
 
0.4%
n69 3
 
0.4%
Other values (9) 11
 
1.6%

구부처코드한글명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
<NA>
586 
지식경제부
 
32
교육과학기술부
 
25
농림수산식품부
 
11
국토해양부
 
11
Other values (14)
 
33

Length

Max length9
Median length4
Mean length4.269341
Min length3

Unique

Unique7 ?
Unique (%)1.0%

Sample

1st row국가과학기술위원회
2nd row교육과학기술부
3rd row교육과학기술부
4th row교육과학기술부
5th row교육과학기술부

Common Values

ValueCountFrequency (%)
<NA> 586
84.0%
지식경제부 32
 
4.6%
교육과학기술부 25
 
3.6%
농림수산식품부 11
 
1.6%
국토해양부 11
 
1.6%
보건복지부 6
 
0.9%
기상청 6
 
0.9%
국가과학기술위원회 4
 
0.6%
방송통신위원회 3
 
0.4%
환경부 3
 
0.4%
Other values (9) 11
 
1.6%

Length

2023-12-13T03:34:04.686756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 586
84.0%
지식경제부 32
 
4.6%
교육과학기술부 25
 
3.6%
농림수산식품부 11
 
1.6%
국토해양부 11
 
1.6%
보건복지부 6
 
0.9%
기상청 6
 
0.9%
국가과학기술위원회 4
 
0.6%
환경부 3
 
0.4%
방송통신위원회 3
 
0.4%
Other values (9) 11
 
1.6%

Interactions

2023-12-13T03:34:00.361088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:59.077626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:59.509544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:59.946956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:34:00.450109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:59.164625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:59.603616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:34:00.047209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:34:00.541338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:59.281178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:59.712873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:34:00.156645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:34:00.627665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:59.407410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:59.819061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:34:00.256468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:34:04.848633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중장기계획일련번호조사년도시작년도종료년도정책분야코드정책분야코드한글명기술분야코드기술분야코드한글명신부처코드신부처코드한글명구부처코드구부처코드한글명
중장기계획일련번호1.0000.9910.6280.5620.3510.3510.0000.0000.7680.6210.2700.270
조사년도0.9911.0000.6300.531NaNNaNNaNNaN0.7370.506NaNNaN
시작년도0.6280.6301.0000.8750.3380.3380.2250.2250.7470.6110.0000.000
종료년도0.5620.5310.8751.0000.3200.3200.2100.2100.7780.6600.8010.801
정책분야코드0.351NaN0.3380.3201.0001.000NaNNaN0.1620.1620.3250.325
정책분야코드한글명0.351NaN0.3380.3201.0001.000NaNNaN0.1620.1620.3250.325
기술분야코드0.000NaN0.2250.210NaNNaN1.0001.0000.8370.8370.7930.793
기술분야코드한글명0.000NaN0.2250.210NaNNaN1.0001.0000.8370.8370.7930.793
신부처코드0.7680.7370.7470.7780.1620.1620.8370.8371.0001.000NaNNaN
신부처코드한글명0.6210.5060.6110.6600.1620.1620.8370.8371.0001.000NaNNaN
구부처코드0.270NaN0.0000.8010.3250.3250.7930.793NaNNaN1.0001.000
구부처코드한글명0.270NaN0.0000.8010.3250.3250.7930.793NaNNaN1.0001.000
2023-12-13T03:34:05.052532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기술분야코드정책분야코드한글명신부처코드기술분야코드한글명정책분야코드구부처코드한글명신부처코드한글명구부처코드
기술분야코드1.000NaN0.4341.000NaN0.3830.4340.383
정책분야코드한글명NaN1.0000.000NaN1.0000.1240.0000.124
신부처코드0.4340.0001.0000.4340.000NaN0.988NaN
기술분야코드한글명1.000NaN0.4341.000NaN0.3830.4340.383
정책분야코드NaN1.0000.000NaN1.0000.1240.0000.124
구부처코드한글명0.3830.124NaN0.3830.1241.000NaN1.000
신부처코드한글명0.4340.0000.9880.4340.000NaN1.000NaN
구부처코드0.3830.124NaN0.3830.1241.000NaN1.000
2023-12-13T03:34:05.588082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중장기계획일련번호조사년도시작년도종료년도정책분야코드정책분야코드한글명기술분야코드기술분야코드한글명신부처코드신부처코드한글명구부처코드구부처코드한글명
중장기계획일련번호1.0000.9890.7660.6690.2120.2120.0000.0000.4080.3010.1930.193
조사년도0.9891.0000.7640.6640.0000.0000.0000.0000.3880.2531.0001.000
시작년도0.7660.7641.0000.7370.1860.1860.1430.1430.3460.2840.0000.000
종료년도0.6690.6640.7371.0000.1910.1910.1140.1140.3760.3210.6010.601
정책분야코드0.2120.0000.1860.1911.0001.0000.0000.0000.0000.0000.1240.124
정책분야코드한글명0.2120.0000.1860.1911.0001.0000.0000.0000.0000.0000.1240.124
기술분야코드0.0000.0000.1430.1140.0000.0001.0001.0000.4340.4340.3830.383
기술분야코드한글명0.0000.0000.1430.1140.0000.0001.0001.0000.4340.4340.3830.383
신부처코드0.4080.3880.3460.3760.0000.0000.4340.4341.0000.9880.0000.000
신부처코드한글명0.3010.2530.2840.3210.0000.0000.4340.4340.9881.0000.0000.000
구부처코드0.1931.0000.0000.6010.1240.1240.3830.3830.0000.0001.0001.000
구부처코드한글명0.1931.0000.0000.6010.1240.1240.3830.3830.0000.0001.0001.000

Missing values

2023-12-13T03:34:00.747808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:34:00.905449image/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

중장기계획일련번호계획명조사년도시작년도종료년도정책분야코드정책분야코드한글명기술분야코드기술분야코드한글명신부처코드신부처코드한글명구부처코드구부처코드한글명
01선진일류국가를향한이명박정부의과학기술기본계획(577전략)201220082012PLP01정책일반<NA><NA><NA><NA>N70국가과학기술위원회
12기초연구진흥종합계획201220082012PLP04기초원천연구<NA><NA><NA><NA>N15교육과학기술부
23줄기세포연구종합추진계획201220062015<NA><NA>PLT02생명<NA><NA>N15교육과학기술부
34제2차뇌연구촉진기본계획201220082017<NA><NA>PLT02생명<NA><NA>N15교육과학기술부
45제2차생명공학육성기본계획(’07∼’16)201220122016<NA><NA>PLT02생명<NA><NA>N15교육과학기술부
56국가융합기술발전기본계획201220092013<NA><NA>PLT01융합<NA><NA>N15교육과학기술부
67제4차원자력진흥종합계획201220122016<NA><NA>PLT06에너지/환경<NA><NA>N15교육과학기술부
78제2차 핵융합에너지개발진흥기본계획201220122016<NA><NA>PLT06에너지/환경<NA><NA>N15교육과학기술부
89연구성과관리·활용기본계획201220112015PLP02투자확대 및 효율화<NA><NA><NA><NA>N70국가과학기술위원회
910제4차원자력연구개발5개년계획201220122016<NA><NA>PLT06에너지/환경<NA><NA>N15교육과학기술부
중장기계획일련번호계획명조사년도시작년도종료년도정책분야코드정책분야코드한글명기술분야코드기술분야코드한글명신부처코드신부처코드한글명구부처코드구부처코드한글명
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