Overview

Dataset statistics

Number of variables3
Number of observations23
Missing cells4
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory684.0 B
Average record size in memory29.7 B

Variable types

Text3

Dataset

DescriptionAPEC기후센터는 15여개의 기관에서 계절예측모델을 제공받아 MME 자료를 생산합니다.APEC기후센터에 계절예측자료를 제공하는 기관별 예측모델의 목록을 제공합니다.자세한 내용은 다음의 url을 참고하십시오.https://apcc21.org/ser/global/modelDescription.do?lang=ko
AuthorAPEC기후센터
URLhttps://www.data.go.kr/data/15122801/fileData.do

Alerts

상세모델명 has 4 (17.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 13:22:47.389917
Analysis finished2023-12-12 13:22:47.688790
Duration0.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

제공기관명
Text

Distinct16
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T22:22:47.791195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length3.7391304
Min length3

Characters and Unicode

Total characters86
Distinct characters19
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

Unique10 ?
Unique (%)43.5%

Sample

1st rowAPCC
2nd rowBCC
3rd rowBOM
4th rowBOM
5th rowCMCC
ValueCountFrequency (%)
cmcc 3
13.0%
bom 2
 
8.7%
kma 2
 
8.7%
metfr 2
 
8.7%
pnu 2
 
8.7%
ukmo 2
 
8.7%
apcc 1
 
4.3%
bcc 1
 
4.3%
cwb 1
 
4.3%
eccc 1
 
4.3%
Other values (6) 6
26.1%
2023-12-12T22:22:48.085090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 20
23.3%
M 14
16.3%
A 6
 
7.0%
P 5
 
5.8%
O 5
 
5.8%
U 5
 
5.8%
N 5
 
5.8%
B 4
 
4.7%
K 4
 
4.7%
E 4
 
4.7%
Other values (9) 14
16.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 85
98.8%
Dash Punctuation 1
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 20
23.5%
M 14
16.5%
A 6
 
7.1%
P 5
 
5.9%
O 5
 
5.9%
U 5
 
5.9%
N 5
 
5.9%
B 4
 
4.7%
K 4
 
4.7%
E 4
 
4.7%
Other values (8) 13
15.3%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 85
98.8%
Common 1
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 20
23.5%
M 14
16.5%
A 6
 
7.1%
P 5
 
5.9%
O 5
 
5.9%
U 5
 
5.9%
N 5
 
5.9%
B 4
 
4.7%
K 4
 
4.7%
E 4
 
4.7%
Other values (8) 13
15.3%
Common
ValueCountFrequency (%)
- 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 20
23.3%
M 14
16.3%
A 6
 
7.0%
P 5
 
5.8%
O 5
 
5.8%
U 5
 
5.8%
N 5
 
5.8%
B 4
 
4.7%
K 4
 
4.7%
E 4
 
4.7%
Other values (9) 14
16.3%

모델명
Text

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T22:22:48.299990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length10
Mean length9.3478261
Min length4

Characters and Unicode

Total characters215
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)91.3%

Sample

1st rowSCOPS
2nd rowCSM1.1M
3rd rowACCESS-S1
4th rowACCESS-S2
5th rowSPS2
ValueCountFrequency (%)
cgcmv2.0 2
 
7.1%
mgoam-2 1
 
3.6%
version 1
 
3.6%
system 1
 
3.6%
forecasting 1
 
3.6%
seasonal 1
 
3.6%
glosea6global 1
 
3.6%
csm1.1m 1
 
3.6%
glosea5 1
 
3.6%
cgcmv1.1 1
 
3.6%
Other values (17) 17
60.7%
2023-12-12T22:22:48.638251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 34
 
15.8%
C 18
 
8.4%
G 12
 
5.6%
2 12
 
5.6%
A 10
 
4.7%
1 10
 
4.7%
. 9
 
4.2%
v 8
 
3.7%
M 7
 
3.3%
O 7
 
3.3%
Other values (33) 88
40.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 119
55.3%
Lowercase Letter 41
 
19.1%
Decimal Number 35
 
16.3%
Other Punctuation 9
 
4.2%
Dash Punctuation 6
 
2.8%
Space Separator 5
 
2.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 34
28.6%
C 18
15.1%
G 12
 
10.1%
A 10
 
8.4%
M 7
 
5.9%
O 7
 
5.9%
E 7
 
5.9%
P 6
 
5.0%
L 5
 
4.2%
I 2
 
1.7%
Other values (7) 11
 
9.2%
Lowercase Letter
ValueCountFrequency (%)
v 8
19.5%
a 4
9.8%
s 4
9.8%
e 4
9.8%
o 4
9.8%
n 3
 
7.3%
l 3
 
7.3%
i 2
 
4.9%
t 2
 
4.9%
r 2
 
4.9%
Other values (5) 5
12.2%
Decimal Number
ValueCountFrequency (%)
2 12
34.3%
1 10
28.6%
5 4
 
11.4%
3 3
 
8.6%
0 2
 
5.7%
6 2
 
5.7%
8 1
 
2.9%
7 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 160
74.4%
Common 55
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 34
21.2%
C 18
 
11.2%
G 12
 
7.5%
A 10
 
6.2%
v 8
 
5.0%
M 7
 
4.4%
O 7
 
4.4%
E 7
 
4.4%
P 6
 
3.8%
L 5
 
3.1%
Other values (22) 46
28.7%
Common
ValueCountFrequency (%)
2 12
21.8%
1 10
18.2%
. 9
16.4%
- 6
10.9%
5
9.1%
5 4
 
7.3%
3 3
 
5.5%
0 2
 
3.6%
6 2
 
3.6%
8 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 34
 
15.8%
C 18
 
8.4%
G 12
 
5.6%
2 12
 
5.6%
A 10
 
4.7%
1 10
 
4.7%
. 9
 
4.2%
v 8
 
3.7%
M 7
 
3.3%
O 7
 
3.3%
Other values (33) 88
40.9%

상세모델명
Text

MISSING 

Distinct18
Distinct (%)94.7%
Missing4
Missing (%)17.4%
Memory size316.0 B
2023-12-12T22:22:48.853489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length104
Median length45
Mean length47.631579
Min length20

Characters and Unicode

Total characters905
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)89.5%

Sample

1st rowSeamless Coupled Prediction System
2nd rowClimate System Model
3rd rowAustralian Community Climate Earth-System Simulator-Seasonal prediction system version 1
4th rowAustralian Community Climate Earth-System Simulator-Seasonal prediction system version 2
5th rowSeasonal Prediction System version 2
ValueCountFrequency (%)
system 17
 
14.5%
version 12
 
10.3%
seasonal 8
 
6.8%
prediction 8
 
6.8%
model 5
 
4.3%
circulation 4
 
3.4%
2 4
 
3.4%
climate 4
 
3.4%
general 4
 
3.4%
coupled 4
 
3.4%
Other values (30) 47
40.2%
2023-12-12T22:22:49.212335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
98
 
10.8%
e 93
 
10.3%
a 65
 
7.2%
n 61
 
6.7%
o 60
 
6.6%
t 56
 
6.2%
s 55
 
6.1%
i 55
 
6.1%
r 47
 
5.2%
l 45
 
5.0%
Other values (33) 270
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 680
75.1%
Space Separator 98
 
10.8%
Uppercase Letter 88
 
9.7%
Decimal Number 24
 
2.7%
Dash Punctuation 8
 
0.9%
Other Punctuation 7
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 93
13.7%
a 65
9.6%
n 61
9.0%
o 60
8.8%
t 56
8.2%
s 55
8.1%
i 55
8.1%
r 47
6.9%
l 45
6.6%
m 32
 
4.7%
Other values (9) 111
16.3%
Uppercase Letter
ValueCountFrequency (%)
S 32
36.4%
C 16
18.2%
M 7
 
8.0%
G 7
 
8.0%
P 6
 
6.8%
F 6
 
6.8%
A 4
 
4.5%
E 2
 
2.3%
T 2
 
2.3%
I 2
 
2.3%
Other values (3) 4
 
4.5%
Decimal Number
ValueCountFrequency (%)
2 9
37.5%
1 5
20.8%
5 2
 
8.3%
0 2
 
8.3%
3 2
 
8.3%
6 2
 
8.3%
7 1
 
4.2%
8 1
 
4.2%
Space Separator
ValueCountFrequency (%)
98
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 768
84.9%
Common 137
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 93
12.1%
a 65
 
8.5%
n 61
 
7.9%
o 60
 
7.8%
t 56
 
7.3%
s 55
 
7.2%
i 55
 
7.2%
r 47
 
6.1%
l 45
 
5.9%
m 32
 
4.2%
Other values (22) 199
25.9%
Common
ValueCountFrequency (%)
98
71.5%
2 9
 
6.6%
- 8
 
5.8%
. 7
 
5.1%
1 5
 
3.6%
5 2
 
1.5%
0 2
 
1.5%
3 2
 
1.5%
6 2
 
1.5%
7 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
98
 
10.8%
e 93
 
10.3%
a 65
 
7.2%
n 61
 
6.7%
o 60
 
6.6%
t 56
 
6.2%
s 55
 
6.1%
i 55
 
6.1%
r 47
 
5.2%
l 45
 
5.0%
Other values (33) 270
29.8%

Correlations

2023-12-12T22:22:49.302565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제공기관명모델명상세모델명
제공기관명1.0000.9400.917
모델명0.9401.0001.000
상세모델명0.9171.0001.000

Missing values

2023-12-12T22:22:47.564406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:22:47.652231image/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

제공기관명모델명상세모델명
0APCCSCOPSSeamless Coupled Prediction System
1BCCCSM1.1MClimate System Model
2BOMACCESS-S1Australian Community Climate Earth-System Simulator-Seasonal prediction system version 1
3BOMACCESS-S2Australian Community Climate Earth-System Simulator-Seasonal prediction system version 2
4CMCCSPS2Seasonal Prediction System version 2
5CMCCSPS3Seasonal Prediction System version 3
6CMCCSPS3.5Seasonal Prediction System version 3.5
7CWBTCWB1Tv1.1<NA>
8ECCCCANSIPSv2.1The Canadian Seasonal to Interannual Prediction System ver. 2.1
9HMCSL-AV<NA>
제공기관명모델명상세모델명
13METFRSYS8Meteo-France System 8
14MGOMGOAM-2<NA>
15MSCCANSIPSv2The Canadian Seasonal to Interannual Prediction System version 2
16NASAGEOS-S2S-2.1Observing System Atmosphere-Ocean General Circulation Model and Data Assimilation System Version S2S-2.1
17NCEPCFSv2Climate Forecast System version 2
18PNUCGCMv1.1Coupled General Circulation Model version 1.1
19PNUCGCMv2.0Coupled General Circulation Model version 2.0
20PNU-RDACGCMv2.0Coupled General Circulation Model version 2.0
21UKMOGLOSEA5Global Seasonal Forecasting System version 6
22UKMOGLOSEA6Global Seasonal Forecasting System version 5<NA>