Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory390.6 KiB
Average record size in memory40.0 B

Variable types

Text2
Categorical1
DateTime1

Dataset

Description경상남도 기상종합정보시스템 레이더이미지데이터에 관한 데이터입니다.
Author경상남도
URLhttps://www.data.go.kr/data/15049558/fileData.do

Alerts

이미지경로(IMGPATH) has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:04:54.084976
Analysis finished2023-12-12 23:04:54.662338
Duration0.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

이미지경로(IMGPATH)
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:04:54.865267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length50
Mean length50.6742
Min length44

Characters and Unicode

Total characters506742
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row/GNWSIMG/RADARIMG_COMP/20190725/COMP201907252300.png
2nd row/GNWSIMG/RADARIMG_BSM/20190422/BSL201904220610.png
3rd row/GNWSIMG/RADARIMG_BSM/20190711/BSL201907112217.png
4th row/GNWSIMG/RADARIMG_GDM/20190824/GDM201908242025.jpg
5th row/GNWSIMG/RADARIMG_GDM/20190626/GDM201906260820.jpg
ValueCountFrequency (%)
gnwsimg/radarimg_comp/20190725/comp201907252300.png 1
 
< 0.1%
gnwsimg/radarimg_comp/20190507/comp201905072020.png 1
 
< 0.1%
gnwsimg/radarimg_bsm/20190725/bsl201907251030.png 1
 
< 0.1%
gnwsimg/radarimg_comp/20190421/comp201904210340.png 1
 
< 0.1%
gnwsimg/radarimg_comp/20190722/comp201907222239.png 1
 
< 0.1%
gnwsimg/radarimg_bsm/20190409/bsl201904092107.png 1
 
< 0.1%
gnwsimg/radarimg_bsm/20190209/bsl201902091740.png 1
 
< 0.1%
gnwsimg/radarimg_bsm/20190816/bsl201908162255.png 1
 
< 0.1%
gnwsimg/radarimg_bsm/20190507/bsl201905071720.png 1
 
< 0.1%
gnwsimg/radarimg_comp/20190118/comp201901182220.png 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-13T08:04:55.347049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 61576
 
12.2%
/ 40000
 
7.9%
1 37805
 
7.5%
M 36655
 
7.2%
G 36553
 
7.2%
2 34860
 
6.9%
9 24154
 
4.8%
I 20005
 
3.9%
R 20000
 
3.9%
A 20000
 
3.9%
Other values (22) 175134
34.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 216762
42.8%
Decimal Number 199980
39.5%
Other Punctuation 50000
 
9.9%
Lowercase Letter 30000
 
5.9%
Connector Punctuation 10000
 
2.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 36655
16.9%
G 36553
16.9%
I 20005
9.2%
R 20000
9.2%
A 20000
9.2%
S 16670
7.7%
D 16548
7.6%
N 10005
 
4.6%
W 10000
 
4.6%
P 6772
 
3.1%
Other values (5) 23554
10.9%
Decimal Number
ValueCountFrequency (%)
0 61576
30.8%
1 37805
18.9%
2 34860
17.4%
9 24154
 
12.1%
5 8521
 
4.3%
3 7855
 
3.9%
7 7408
 
3.7%
8 6765
 
3.4%
4 6425
 
3.2%
6 4611
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
g 10000
33.3%
p 10000
33.3%
n 6721
22.4%
j 3279
 
10.9%
Other Punctuation
ValueCountFrequency (%)
/ 40000
80.0%
. 10000
 
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 259980
51.3%
Latin 246762
48.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 36655
14.9%
G 36553
14.8%
I 20005
 
8.1%
R 20000
 
8.1%
A 20000
 
8.1%
S 16670
 
6.8%
D 16548
 
6.7%
N 10005
 
4.1%
g 10000
 
4.1%
p 10000
 
4.1%
Other values (9) 50326
20.4%
Common
ValueCountFrequency (%)
0 61576
23.7%
/ 40000
15.4%
1 37805
14.5%
2 34860
13.4%
9 24154
 
9.3%
. 10000
 
3.8%
_ 10000
 
3.8%
5 8521
 
3.3%
3 7855
 
3.0%
7 7408
 
2.8%
Other values (3) 17801
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 506742
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61576
 
12.2%
/ 40000
 
7.9%
1 37805
 
7.5%
M 36655
 
7.2%
G 36553
 
7.2%
2 34860
 
6.9%
9 24154
 
4.8%
I 20005
 
3.9%
R 20000
 
3.9%
A 20000
 
3.9%
Other values (22) 175134
34.6%

레이더구분코드(RDIMGCODE)
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
COMP
3386 
BSL
3335 
GDM
3274 
CMI
 
5

Length

Max length4
Median length3
Mean length3.3386
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMP
2nd rowBSL
3rd rowBSL
4th rowGDM
5th rowGDM

Common Values

ValueCountFrequency (%)
COMP 3386
33.9%
BSL 3335
33.4%
GDM 3274
32.7%
CMI 5
 
0.1%

Length

2023-12-13T08:04:55.534435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:04:55.631755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
comp 3386
33.9%
bsl 3335
33.4%
gdm 3274
32.7%
cmi 5
 
< 0.1%

계측시간(COLTM)
Date

Distinct9623
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-01-01 02:17:00
Maximum2019-09-20 11:50:00
2023-12-13T08:04:55.759244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:55.903983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

파일유효성검사(VALIDCHKNAME)
Text

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:04:56.124098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length19.3351
Min length12

Characters and Unicode

Total characters193351
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9998 ?
Unique (%)> 99.9%

Sample

1st rowCOMP201907252300.png
2nd rowBSL201904220610.png
3rd rowBSL201907112217.png
4th rowGDM201908242025.jpg
5th rowGDM201906260820.jpg
ValueCountFrequency (%)
gdm201907182355.jpg 2
 
< 0.1%
comp201905072020.png 1
 
< 0.1%
bsl201907251030.png 1
 
< 0.1%
bsl201904070050.png 1
 
< 0.1%
comp201907222239.png 1
 
< 0.1%
bsl201904092107.png 1
 
< 0.1%
bsl201902091740.png 1
 
< 0.1%
bsl201908162255.png 1
 
< 0.1%
bsl201905071720.png 1
 
< 0.1%
comp201901182220.png 1
 
< 0.1%
Other values (9989) 9989
99.9%
2023-12-13T08:04:56.484614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37381
19.3%
1 22368
11.6%
2 19906
 
10.3%
9 12561
 
6.5%
p 10000
 
5.2%
g 10000
 
5.2%
. 10000
 
5.2%
n 6721
 
3.5%
M 6660
 
3.4%
5 6653
 
3.4%
Other values (14) 51101
26.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119980
62.1%
Uppercase Letter 33371
 
17.3%
Lowercase Letter 30000
 
15.5%
Other Punctuation 10000
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37381
31.2%
1 22368
18.6%
2 19906
16.6%
9 12561
 
10.5%
5 6653
 
5.5%
3 5291
 
4.4%
7 4728
 
3.9%
4 4466
 
3.7%
8 3859
 
3.2%
6 2767
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
M 6660
20.0%
P 3386
10.1%
O 3386
10.1%
C 3386
10.1%
B 3335
10.0%
S 3335
10.0%
L 3335
10.0%
D 3274
9.8%
G 3274
9.8%
Lowercase Letter
ValueCountFrequency (%)
p 10000
33.3%
g 10000
33.3%
n 6721
22.4%
j 3279
 
10.9%
Other Punctuation
ValueCountFrequency (%)
. 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129980
67.2%
Latin 63371
32.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 10000
15.8%
g 10000
15.8%
n 6721
10.6%
M 6660
10.5%
P 3386
 
5.3%
O 3386
 
5.3%
C 3386
 
5.3%
B 3335
 
5.3%
S 3335
 
5.3%
L 3335
 
5.3%
Other values (3) 9827
15.5%
Common
ValueCountFrequency (%)
0 37381
28.8%
1 22368
17.2%
2 19906
15.3%
9 12561
 
9.7%
. 10000
 
7.7%
5 6653
 
5.1%
3 5291
 
4.1%
7 4728
 
3.6%
4 4466
 
3.4%
8 3859
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37381
19.3%
1 22368
11.6%
2 19906
 
10.3%
9 12561
 
6.5%
p 10000
 
5.2%
g 10000
 
5.2%
. 10000
 
5.2%
n 6721
 
3.5%
M 6660
 
3.4%
5 6653
 
3.4%
Other values (14) 51101
26.4%

Missing values

2023-12-13T08:04:54.494295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:04:54.601592image/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

이미지경로(IMGPATH)레이더구분코드(RDIMGCODE)계측시간(COLTM)파일유효성검사(VALIDCHKNAME)
64432/GNWSIMG/RADARIMG_COMP/20190725/COMP201907252300.pngCOMP2019-07-25 23:00COMP201907252300.png
34187/GNWSIMG/RADARIMG_BSM/20190422/BSL201904220610.pngBSL2019-04-22 06:10BSL201904220610.png
56663/GNWSIMG/RADARIMG_BSM/20190711/BSL201907112217.pngBSL2019-07-11 22:17BSL201907112217.png
84197/GNWSIMG/RADARIMG_GDM/20190824/GDM201908242025.jpgGDM2019-08-24 20:29GDM201908242025.jpg
52153/GNWSIMG/RADARIMG_GDM/20190626/GDM201906260820.jpgGDM2019-06-26 08:25GDM201906260820.jpg
53031/GNWSIMG/RADARIMG_BSM/20190629/BSL201906290930.pngBSL2019-06-29 09:30BSL201906290930.png
78657/GNWSIMG/RADARIMG_COMP/20190811/COMP201908112302.pngCOMP2019-08-11 23:02COMP201908112302.png
57853/GNWSIMG/RADARIMG_BSM/20190716/BSL201907162130.pngBSL2019-07-16 21:30BSL201907162130.png
81752/GNWSIMG/RADARIMG_BSM/20190817/BSL201908171050.pngBSL2019-08-17 10:50BSL201908171050.png
52105/GNWSIMG/RADARIMG_BSM/20190626/BSL201906260420.pngBSL2019-06-26 04:20BSL201906260420.png
이미지경로(IMGPATH)레이더구분코드(RDIMGCODE)계측시간(COLTM)파일유효성검사(VALIDCHKNAME)
19932/GNWSIMG/RADARIMG_BSM/20190310/BSL201903102032.pngBSL2019-03-10 20:32BSL201903102032.png
71912/GNWSIMG/RADARIMG_GDM/20190804/GDM201908032355.jpgGDM2019-08-04 00:01GDM201908032355.jpg
54485/GNWSIMG/RADARIMG_COMP/20190704/COMP201907040750.pngCOMP2019-07-04 07:50COMP201907040750.png
71490/GNWSIMG/RADARIMG_GDM/20190803/GDM201908030950.jpgGDM2019-08-03 09:55GDM201908030950.jpg
42050/GNWSIMG/RADARIMG_COMP/20190521/COMP201905210020.pngCOMP2019-05-21 00:20COMP201905210020.png
49234/GNWSIMG/RADARIMG_COMP/20190615/COMP201906151400.pngCOMP2019-06-15 14:00COMP201906151400.png
17620/GNWSIMG/RADARIMG_BSM/20190302/BSL201903021640.pngBSL2019-03-02 16:40BSL201903021640.png
13653/GNWSIMG/RADARIMG_GDM/20190216/GDM201902162140.jpgGDM2019-02-16 21:55GDM201902162140.jpg
61240/GNWSIMG/RADARIMG_GDM/20190721/GDM201907211130.jpgGDM2019-07-21 11:33GDM201907211130.jpg
61257/GNWSIMG/RADARIMG_COMP/20190721/COMP201907211200.pngCOMP2019-07-21 12:00COMP201907211200.png