Overview

Dataset statistics

Number of variables5
Number of observations51
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory46.6 B

Variable types

Text1
Categorical1
Numeric3

Dataset

DescriptionSample
Author제타럭스시스템
URLhttps://bigdata-geo.kr/user/dataset/view.do?data_sn=500

Alerts

PUL_GRAD has constant value ""Constant
LIFE_INFRA is highly overall correlated with TOTL_GRADHigh correlation
TOTL_GRAD is highly overall correlated with LIFE_INFRAHigh correlation
GRID_NO has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:21:40.156769
Analysis finished2023-12-10 13:21:42.503605
Duration2.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GRID_NO
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-10T22:21:42.805083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters510
Distinct characters9
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

Unique51 ?
Unique (%)100.0%

Sample

1st row다바68bb66bb
2nd row다바68bb67ba
3rd row다바68bb67bb
4th row다바69aa66ba
5th row다바69aa66bb
ValueCountFrequency (%)
다바68bb66bb 1
 
2.0%
다바69bb67aa 1
 
2.0%
다바69bb67ba 1
 
2.0%
다바69bb67bb 1
 
2.0%
다바69bb68aa 1
 
2.0%
다바69bb68ab 1
 
2.0%
다바69bb68ba 1
 
2.0%
다바70aa66bb 1
 
2.0%
다바70aa67aa 1
 
2.0%
다바70aa67ab 1
 
2.0%
Other values (41) 41
80.4%
2023-12-10T22:21:43.607636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 103
20.2%
b 101
19.8%
6 91
17.8%
다 51
10.0%
바 51
10.0%
7 43
8.4%
9 30
 
5.9%
8 22
 
4.3%
0 18
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 204
40.0%
Decimal Number 204
40.0%
Other Letter 102
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 91
44.6%
7 43
21.1%
9 30
 
14.7%
8 22
 
10.8%
0 18
 
8.8%
Lowercase Letter
ValueCountFrequency (%)
a 103
50.5%
b 101
49.5%
Other Letter
ValueCountFrequency (%)
다 51
50.0%
바 51
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 204
40.0%
Common 204
40.0%
Hangul 102
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 91
44.6%
7 43
21.1%
9 30
 
14.7%
8 22
 
10.8%
0 18
 
8.8%
Latin
ValueCountFrequency (%)
a 103
50.5%
b 101
49.5%
Hangul
ValueCountFrequency (%)
다 51
50.0%
바 51
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 408
80.0%
Hangul 102
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 103
25.2%
b 101
24.8%
6 91
22.3%
7 43
10.5%
9 30
 
7.4%
8 22
 
5.4%
0 18
 
4.4%
Hangul
ValueCountFrequency (%)
다 51
50.0%
바 51
50.0%

PUL_GRAD
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
1
51 

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 51
100.0%

Length

2023-12-10T22:21:43.873207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:44.075873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 51
100.0%

LIFE_INFRA
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.078431
Minimum13
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-10T22:21:44.198366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile13.5
Q117
median17
Q317
95-th percentile36.5
Maximum68
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.6483366
Coefficient of variation (CV)0.45330438
Kurtosis21.203267
Mean19.078431
Median Absolute Deviation (MAD)0
Skewness4.3051145
Sum973
Variance74.793725
MonotonicityNot monotonic
2023-12-10T22:21:44.360208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
17 42
82.4%
13 3
 
5.9%
68 1
 
2.0%
38 1
 
2.0%
27 1
 
2.0%
36 1
 
2.0%
37 1
 
2.0%
14 1
 
2.0%
ValueCountFrequency (%)
13 3
 
5.9%
14 1
 
2.0%
17 42
82.4%
27 1
 
2.0%
36 1
 
2.0%
37 1
 
2.0%
38 1
 
2.0%
68 1
 
2.0%
ValueCountFrequency (%)
68 1
 
2.0%
38 1
 
2.0%
37 1
 
2.0%
36 1
 
2.0%
27 1
 
2.0%
17 42
82.4%
14 1
 
2.0%
13 3
 
5.9%

CMPTT_GRAD
Real number (ℝ)

Distinct6
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.176471
Minimum61
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-10T22:21:44.551796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile68
Q172
median72
Q372
95-th percentile72
Maximum72
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1138201
Coefficient of variation (CV)0.029698299
Kurtosis12.412241
Mean71.176471
Median Absolute Deviation (MAD)0
Skewness-3.3622384
Sum3630
Variance4.4682353
MonotonicityNot monotonic
2023-12-10T22:21:44.721413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
72 40
78.4%
69 5
 
9.8%
71 3
 
5.9%
67 1
 
2.0%
61 1
 
2.0%
64 1
 
2.0%
ValueCountFrequency (%)
61 1
 
2.0%
64 1
 
2.0%
67 1
 
2.0%
69 5
 
9.8%
71 3
 
5.9%
72 40
78.4%
ValueCountFrequency (%)
72 40
78.4%
71 3
 
5.9%
69 5
 
9.8%
67 1
 
2.0%
64 1
 
2.0%
61 1
 
2.0%

TOTL_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.666667
Minimum27
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-10T22:21:44.902055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile30
Q134
median34
Q334
95-th percentile42
Maximum63
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.9342342
Coefficient of variation (CV)0.14233368
Kurtosis22.313766
Mean34.666667
Median Absolute Deviation (MAD)0
Skewness4.138277
Sum1768
Variance24.346667
MonotonicityNot monotonic
2023-12-10T22:21:45.085880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
34 40
78.4%
30 3
 
5.9%
63 1
 
2.0%
32 1
 
2.0%
40 1
 
2.0%
38 1
 
2.0%
45 1
 
2.0%
44 1
 
2.0%
27 1
 
2.0%
29 1
 
2.0%
ValueCountFrequency (%)
27 1
 
2.0%
29 1
 
2.0%
30 3
 
5.9%
32 1
 
2.0%
34 40
78.4%
38 1
 
2.0%
40 1
 
2.0%
44 1
 
2.0%
45 1
 
2.0%
63 1
 
2.0%
ValueCountFrequency (%)
63 1
 
2.0%
45 1
 
2.0%
44 1
 
2.0%
40 1
 
2.0%
38 1
 
2.0%
34 40
78.4%
32 1
 
2.0%
30 3
 
5.9%
29 1
 
2.0%
27 1
 
2.0%

Interactions

2023-12-10T22:21:41.710857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:40.494121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:41.031721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:41.900638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:40.685195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:41.157784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:42.089318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:40.836786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:41.374709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:21:45.226571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GRID_NOLIFE_INFRACMPTT_GRADTOTL_GRAD
GRID_NO1.0001.0001.0001.000
LIFE_INFRA1.0001.0000.5080.879
CMPTT_GRAD1.0000.5081.0000.868
TOTL_GRAD1.0000.8790.8681.000
2023-12-10T22:21:45.367275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LIFE_INFRACMPTT_GRADTOTL_GRAD
LIFE_INFRA1.000-0.1220.917
CMPTT_GRAD-0.1221.0000.085
TOTL_GRAD0.9170.0851.000

Missing values

2023-12-10T22:21:42.268689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:21:42.436916image/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
0다바68bb66bb1686963
1다바68bb67ba1176730
2다바68bb67bb1137130
3다바69aa66ba1177234
4다바69aa66bb1177234
5다바69aa67aa1177234
6다바69aa67ab1177234
7다바69aa67ba1177234
8다바69aa67bb1177234
9다바69aa68aa1177234
GRID_NOPUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
41다바70ab67ba1177234
42다바70ab67bb1177234
43다바70ab68aa1136929
44다바70ab68ab1177234
45다바70ab68ba1177234
46다바70ab68bb1177234
47다바70ba67bb1177234
48다바70ba68aa1177234
49다바70ba68ab1177234
50다바70ba68ba1177234