Overview

Dataset statistics

Number of variables5
Number of observations466
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.8 KiB
Average record size in memory41.3 B

Variable types

Numeric1
Text2
Categorical2

Dataset

Description해양수산과 관련하여 특성평가에 대한 경남특성평가격자 정보를 담고 있으며 공간정보일련번호, 격자아이디, 레이어명등과같은 데이터를 제공한다
URLhttps://www.data.go.kr/data/15113914/fileData.do

Alerts

레이어명(lyr_nm) has constant value ""Constant
레이어분류내용(lyr_cl_cn) has constant value ""Constant
공간정보일련번호(gid) has unique valuesUnique
특성평가격자아이디(msp_id) has unique valuesUnique
격자아이디(og_id) has unique valuesUnique

Reproduction

Analysis started2023-12-13 00:14:03.549782
Analysis finished2023-12-13 00:14:03.884307
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공간정보일련번호(gid)
Real number (ℝ)

UNIQUE 

Distinct466
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400.92918
Minimum10
Maximum666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-13T09:14:03.937848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile97.25
Q1265
median418.5
Q3546.75
95-th percentile642.75
Maximum666
Range656
Interquartile range (IQR)281.75

Descriptive statistics

Standard deviation170.86447
Coefficient of variation (CV)0.4261712
Kurtosis-0.91668522
Mean400.92918
Median Absolute Deviation (MAD)138
Skewness-0.31698224
Sum186833
Variance29194.668
MonotonicityStrictly increasing
2023-12-13T09:14:04.039530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1
 
0.2%
502 1
 
0.2%
514 1
 
0.2%
513 1
 
0.2%
512 1
 
0.2%
511 1
 
0.2%
510 1
 
0.2%
509 1
 
0.2%
508 1
 
0.2%
507 1
 
0.2%
Other values (456) 456
97.9%
ValueCountFrequency (%)
10 1
0.2%
11 1
0.2%
12 1
0.2%
13 1
0.2%
14 1
0.2%
36 1
0.2%
37 1
0.2%
49 1
0.2%
50 1
0.2%
64 1
0.2%
ValueCountFrequency (%)
666 1
0.2%
665 1
0.2%
664 1
0.2%
663 1
0.2%
662 1
0.2%
661 1
0.2%
660 1
0.2%
659 1
0.2%
658 1
0.2%
657 1
0.2%

특성평가격자아이디(msp_id)
Text

UNIQUE 

Distinct466
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-13T09:14:04.230518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.978541
Min length13

Characters and Unicode

Total characters6980
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique466 ?
Unique (%)100.0%

Sample

1st rowMSP_GR2_G3E43
2nd rowMSP_GR2_G3E44
3rd rowMSP_GR2_G3I11
4th rowMSP_GR2_G3I12
5th rowMSP_GR2_G3I21
ValueCountFrequency (%)
msp_gr2_g3e43 1
 
0.2%
msp_gr3_g3e42_q 1
 
0.2%
msp_gr3_g3e32_n 1
 
0.2%
msp_gr3_g3e32_m 1
 
0.2%
msp_gr3_g3e32_l 1
 
0.2%
msp_gr3_g3e32_k 1
 
0.2%
msp_gr3_g3e31_o 1
 
0.2%
msp_gr3_g3e31_n 1
 
0.2%
msp_gr3_g3e31_m 1
 
0.2%
msp_gr3_g3e31_l 1
 
0.2%
Other values (456) 456
97.9%
2023-12-13T09:14:04.519711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 1393
20.0%
3 1003
14.4%
G 794
11.4%
S 487
 
7.0%
P 485
 
6.9%
M 484
 
6.9%
R 482
 
6.9%
4 398
 
5.7%
E 295
 
4.2%
2 248
 
3.6%
Other values (20) 911
13.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3723
53.3%
Decimal Number 1864
26.7%
Connector Punctuation 1393
 
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 794
21.3%
S 487
13.1%
P 485
13.0%
M 484
13.0%
R 482
12.9%
E 295
 
7.9%
F 172
 
4.6%
H 165
 
4.4%
A 46
 
1.2%
L 25
 
0.7%
Other values (15) 288
 
7.7%
Decimal Number
ValueCountFrequency (%)
3 1003
53.8%
4 398
 
21.4%
2 248
 
13.3%
1 215
 
11.5%
Connector Punctuation
ValueCountFrequency (%)
_ 1393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3723
53.3%
Common 3257
46.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 794
21.3%
S 487
13.1%
P 485
13.0%
M 484
13.0%
R 482
12.9%
E 295
 
7.9%
F 172
 
4.6%
H 165
 
4.4%
A 46
 
1.2%
L 25
 
0.7%
Other values (15) 288
 
7.7%
Common
ValueCountFrequency (%)
_ 1393
42.8%
3 1003
30.8%
4 398
 
12.2%
2 248
 
7.6%
1 215
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1393
20.0%
3 1003
14.4%
G 794
11.4%
S 487
 
7.0%
P 485
 
6.9%
M 484
 
6.9%
R 482
 
6.9%
4 398
 
5.7%
E 295
 
4.2%
2 248
 
3.6%
Other values (20) 911
13.1%

격자아이디(og_id)
Text

UNIQUE 

Distinct466
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-13T09:14:04.753169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.978541
Min length9

Characters and Unicode

Total characters5116
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique466 ?
Unique (%)100.0%

Sample

1st rowGR2_G3E43
2nd rowGR2_G3E44
3rd rowGR2_G3I11
4th rowGR2_G3I12
5th rowGR2_G3I21
ValueCountFrequency (%)
gr2_g3e43 1
 
0.2%
gr3_g3e42_q 1
 
0.2%
gr3_g3e32_n 1
 
0.2%
gr3_g3e32_m 1
 
0.2%
gr3_g3e32_l 1
 
0.2%
gr3_g3e32_k 1
 
0.2%
gr3_g3e31_o 1
 
0.2%
gr3_g3e31_n 1
 
0.2%
gr3_g3e31_m 1
 
0.2%
gr3_g3e31_l 1
 
0.2%
Other values (456) 456
97.9%
2023-12-13T09:14:05.103345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1003
19.6%
_ 927
18.1%
G 794
15.5%
R 482
9.4%
4 398
 
7.8%
E 295
 
5.8%
2 248
 
4.8%
1 215
 
4.2%
F 172
 
3.4%
H 165
 
3.2%
Other values (20) 417
8.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2325
45.4%
Decimal Number 1864
36.4%
Connector Punctuation 927
 
18.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 794
34.2%
R 482
20.7%
E 295
 
12.7%
F 172
 
7.4%
H 165
 
7.1%
A 46
 
2.0%
L 25
 
1.1%
X 22
 
0.9%
I 21
 
0.9%
N 21
 
0.9%
Other values (15) 282
 
12.1%
Decimal Number
ValueCountFrequency (%)
3 1003
53.8%
4 398
 
21.4%
2 248
 
13.3%
1 215
 
11.5%
Connector Punctuation
ValueCountFrequency (%)
_ 927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2791
54.6%
Latin 2325
45.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 794
34.2%
R 482
20.7%
E 295
 
12.7%
F 172
 
7.4%
H 165
 
7.1%
A 46
 
2.0%
L 25
 
1.1%
X 22
 
0.9%
I 21
 
0.9%
N 21
 
0.9%
Other values (15) 282
 
12.1%
Common
ValueCountFrequency (%)
3 1003
35.9%
_ 927
33.2%
4 398
 
14.3%
2 248
 
8.9%
1 215
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1003
19.6%
_ 927
18.1%
G 794
15.5%
R 482
9.4%
4 398
 
7.8%
E 295
 
5.8%
2 248
 
4.8%
1 215
 
4.2%
F 172
 
3.4%
H 165
 
3.2%
Other values (20) 417
8.2%

레이어명(lyr_nm)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
경남격자
466 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경남격자 466
100.0%

Length

2023-12-13T09:14:05.203456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:14:05.265513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경남격자 466
100.0%

레이어분류내용(lyr_cl_cn)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
특성평가격자
466 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
특성평가격자 466
100.0%

Length

2023-12-13T09:14:05.331779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:14:05.394039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
특성평가격자 466
100.0%

Interactions

2023-12-13T09:14:03.653200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-13T09:14:03.770286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:14:03.853178image/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

공간정보일련번호(gid)특성평가격자아이디(msp_id)격자아이디(og_id)레이어명(lyr_nm)레이어분류내용(lyr_cl_cn)
010MSP_GR2_G3E43GR2_G3E43경남격자특성평가격자
111MSP_GR2_G3E44GR2_G3E44경남격자특성평가격자
212MSP_GR2_G3I11GR2_G3I11경남격자특성평가격자
313MSP_GR2_G3I12GR2_G3I12경남격자특성평가격자
414MSP_GR2_G3I21GR2_G3I21경남격자특성평가격자
536MSP_GR3_G3A43_BGR3_G3A43_B경남격자특성평가격자
637MSP_GR3_G3A43_CGR3_G3A43_C경남격자특성평가격자
749MSP_GR3_G3A43_GGR3_G3A43_G경남격자특성평가격자
850MSP_GR3_G3A43_HGR3_G3A43_H경남격자특성평가격자
964MSP_GR3_G3A34_NGR3_G3A34_N경남격자특성평가격자
공간정보일련번호(gid)특성평가격자아이디(msp_id)격자아이디(og_id)레이어명(lyr_nm)레이어분류내용(lyr_cl_cn)
456657MSP_GR3_G3E34_WGR3_G3E34_W경남격자특성평가격자
457658MSP_GR3_G3E34_XGR3_G3E34_X경남격자특성평가격자
458659MSP_GR3_G3E34_YGR3_G3E34_Y경남격자특성평가격자
459660MSP_GR3_F4L21_DGR3_F4L21_D경남격자특성평가격자
460661MSP_GR3_F4L21_EGR3_F4L21_E경남격자특성평가격자
461662MSP_GR3_F4L22_AGR3_F4L22_A경남격자특성평가격자
462663MSP_GR3_F4L22_BGR3_F4L22_B경남격자특성평가격자
463664MSP_GR3_F4L22_CGR3_F4L22_C경남격자특성평가격자
464665MSP_GR3_F4L22_DGR3_F4L22_D경남격자특성평가격자
465666MSP_GR3_F4L22_EGR3_F4L22_E경남격자특성평가격자