Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory45.3 B

Variable types

Text1
Categorical4

Dataset

DescriptionSample
Author제타럭스시스템
URLhttps://bigdata-geo.kr/user/dataset/view.do?data_sn=504

Alerts

PUL_GRAD has constant value ""Constant
LIFE_INFRA has constant value ""Constant
CMPTT_GRAD has constant value ""Constant
TOTL_GRAD has constant value ""Constant
GRID_NO has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:21:58.061107
Analysis finished2023-12-10 13:21:59.064819
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GRID_NO
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:21:59.347452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1000
Distinct characters10
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row마라04ba87ab
2nd row마라04ba87ba
3rd row마라04bb86bb
4th row마라04bb87aa
5th row마라04bb87ab
ValueCountFrequency (%)
마라04ba87ab 1
 
1.0%
마라06ba85aa 1
 
1.0%
마라06ba87bb 1
 
1.0%
마라06ba87ba 1
 
1.0%
마라06ba87ab 1
 
1.0%
마라06ba87aa 1
 
1.0%
마라06ba86bb 1
 
1.0%
마라06ba86ba 1
 
1.0%
마라06ba86ab 1
 
1.0%
마라06ba86aa 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T22:21:59.987106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 205
20.5%
a 195
19.5%
마 100
10.0%
라 100
10.0%
0 100
10.0%
8 100
10.0%
6 82
 
8.2%
7 54
 
5.4%
5 54
 
5.4%
4 10
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 400
40.0%
Decimal Number 400
40.0%
Other Letter 200
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100
25.0%
8 100
25.0%
6 82
20.5%
7 54
13.5%
5 54
13.5%
4 10
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
b 205
51.2%
a 195
48.8%
Other Letter
ValueCountFrequency (%)
마 100
50.0%
라 100
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 400
40.0%
Common 400
40.0%
Hangul 200
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 100
25.0%
8 100
25.0%
6 82
20.5%
7 54
13.5%
5 54
13.5%
4 10
 
2.5%
Latin
ValueCountFrequency (%)
b 205
51.2%
a 195
48.8%
Hangul
ValueCountFrequency (%)
마 100
50.0%
라 100
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
80.0%
Hangul 200
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 205
25.6%
a 195
24.4%
0 100
12.5%
8 100
12.5%
6 82
 
10.2%
7 54
 
6.8%
5 54
 
6.8%
4 10
 
1.2%
Hangul
ValueCountFrequency (%)
마 100
50.0%
라 100
50.0%

PUL_GRAD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

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 (%)
1 100
100.0%

Length

2023-12-10T22:22:00.303092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:00.669851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

LIFE_INFRA
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
17
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17
2nd row17
3rd row17
4th row17
5th row17

Common Values

ValueCountFrequency (%)
17 100
100.0%

Length

2023-12-10T22:22:00.914117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:01.128996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
17 100
100.0%

CMPTT_GRAD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
72
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row72
2nd row72
3rd row72
4th row72
5th row72

Common Values

ValueCountFrequency (%)
72 100
100.0%

Length

2023-12-10T22:22:01.357151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:01.660833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
72 100
100.0%

TOTL_GRAD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
34
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row34
2nd row34
3rd row34
4th row34
5th row34

Common Values

ValueCountFrequency (%)
34 100
100.0%

Length

2023-12-10T22:22:01.931734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:02.125674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
34 100
100.0%

Missing values

2023-12-10T22:21:58.860141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:21:59.012066image/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

GRID_NOPUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
0마라04ba87ab1177234
1마라04ba87ba1177234
2마라04bb86bb1177234
3마라04bb87aa1177234
4마라04bb87ab1177234
5마라04bb87ba1177234
6마라04bb87bb1177234
7마라05aa86ba1177234
8마라05aa86bb1177234
9마라05aa87aa1177234
GRID_NOPUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
90마라07aa85bb1177234
91마라07aa86aa1177234
92마라07aa86bb1177234
93마라07aa87aa1177234
94마라07aa87ab1177234
95마라07ab85ab1177234
96마라07ab85ba1177234
97마라07ab85bb1177234
98마라07ab86bb1177234
99마라07ab87aa1177234