Overview

Dataset statistics

Number of variables5
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory45.7 B

Variable types

Text1
Numeric4

Dataset

DescriptionSample
Author제타럭스시스템
URLhttps://bigdata-geo.kr/user/dataset/view.do?data_sn=486

Alerts

PUL_GRAD is highly overall correlated with CMPTT_GRADHigh correlation
LIFE_INFRA is highly overall correlated with TOTL_GRADHigh correlation
CMPTT_GRAD is highly overall correlated with PUL_GRADHigh correlation
TOTL_GRAD is highly overall correlated with LIFE_INFRAHigh correlation
GRID_NO has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:20:38.440833
Analysis finished2023-12-10 13:20:42.508702
Duration4.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GRID_NO
Text

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
2023-12-10T22:20:42.844202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters790
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

Unique79 ?
Unique (%)100.0%

Sample

1st row마라45ab86aa
2nd row마라45ab86ab
3rd row마라45ba85ba
4th row마라45ba85bb
5th row마라45ba86aa
ValueCountFrequency (%)
마라45ab86aa 1
 
1.3%
마라47aa86aa 1
 
1.3%
마라47aa85ab 1
 
1.3%
마라47aa85aa 1
 
1.3%
마라47aa84bb 1
 
1.3%
마라46bb87ab 1
 
1.3%
마라46bb87aa 1
 
1.3%
마라46bb86bb 1
 
1.3%
마라46bb86ba 1
 
1.3%
마라46bb86aa 1
 
1.3%
Other values (69) 69
87.3%
2023-12-10T22:20:43.477010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 165
20.9%
b 151
19.1%
4 81
10.3%
마 79
10.0%
라 79
10.0%
8 79
10.0%
6 73
9.2%
5 49
 
6.2%
7 34
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 316
40.0%
Decimal Number 316
40.0%
Other Letter 158
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 81
25.6%
8 79
25.0%
6 73
23.1%
5 49
15.5%
7 34
10.8%
Lowercase Letter
ValueCountFrequency (%)
a 165
52.2%
b 151
47.8%
Other Letter
ValueCountFrequency (%)
마 79
50.0%
라 79
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316
40.0%
Common 316
40.0%
Hangul 158
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 81
25.6%
8 79
25.0%
6 73
23.1%
5 49
15.5%
7 34
10.8%
Latin
ValueCountFrequency (%)
a 165
52.2%
b 151
47.8%
Hangul
ValueCountFrequency (%)
마 79
50.0%
라 79
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 632
80.0%
Hangul 158
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 165
26.1%
b 151
23.9%
4 81
12.8%
8 79
12.5%
6 73
11.6%
5 49
 
7.8%
7 34
 
5.4%
Hangul
ValueCountFrequency (%)
마 79
50.0%
라 79
50.0%

PUL_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6455696
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-10T22:20:43.721136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile11.2
Maximum27
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.6740911
Coefficient of variation (CV)1.7667617
Kurtosis14.647182
Mean2.6455696
Median Absolute Deviation (MAD)0
Skewness3.6872494
Sum209
Variance21.847128
MonotonicityNot monotonic
2023-12-10T22:20:43.924581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 65
82.3%
5 5
 
6.3%
24 1
 
1.3%
6 1
 
1.3%
13 1
 
1.3%
27 1
 
1.3%
10 1
 
1.3%
4 1
 
1.3%
8 1
 
1.3%
16 1
 
1.3%
ValueCountFrequency (%)
1 65
82.3%
4 1
 
1.3%
5 5
 
6.3%
6 1
 
1.3%
8 1
 
1.3%
10 1
 
1.3%
11 1
 
1.3%
13 1
 
1.3%
16 1
 
1.3%
24 1
 
1.3%
ValueCountFrequency (%)
27 1
 
1.3%
24 1
 
1.3%
16 1
 
1.3%
13 1
 
1.3%
11 1
 
1.3%
10 1
 
1.3%
8 1
 
1.3%
6 1
 
1.3%
5 5
6.3%
4 1
 
1.3%

LIFE_INFRA
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.455696
Minimum7
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-10T22:20:44.101117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9
Q117
median17
Q317
95-th percentile33
Maximum41
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.976015
Coefficient of variation (CV)0.32380328
Kurtosis4.6090619
Mean18.455696
Median Absolute Deviation (MAD)0
Skewness1.8940994
Sum1458
Variance35.712756
MonotonicityNot monotonic
2023-12-10T22:20:44.358452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
17 58
73.4%
33 3
 
3.8%
9 3
 
3.8%
20 3
 
3.8%
19 2
 
2.5%
27 2
 
2.5%
37 2
 
2.5%
7 1
 
1.3%
8 1
 
1.3%
41 1
 
1.3%
Other values (3) 3
 
3.8%
ValueCountFrequency (%)
7 1
 
1.3%
8 1
 
1.3%
9 3
 
3.8%
13 1
 
1.3%
17 58
73.4%
19 2
 
2.5%
20 3
 
3.8%
21 1
 
1.3%
27 2
 
2.5%
30 1
 
1.3%
ValueCountFrequency (%)
41 1
 
1.3%
37 2
 
2.5%
33 3
 
3.8%
30 1
 
1.3%
27 2
 
2.5%
21 1
 
1.3%
20 3
 
3.8%
19 2
 
2.5%
17 58
73.4%
13 1
 
1.3%

CMPTT_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.886076
Minimum41
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-10T22:20:44.603778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile61.1
Q171
median72
Q372
95-th percentile72
Maximum72
Range31
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.6021684
Coefficient of variation (CV)0.080161439
Kurtosis13.282876
Mean69.886076
Median Absolute Deviation (MAD)0
Skewness-3.5726063
Sum5521
Variance31.384291
MonotonicityNot monotonic
2023-12-10T22:20:44.881097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
72 56
70.9%
71 8
 
10.1%
67 4
 
5.1%
65 2
 
2.5%
49 2
 
2.5%
69 2
 
2.5%
63 1
 
1.3%
53 1
 
1.3%
41 1
 
1.3%
62 1
 
1.3%
ValueCountFrequency (%)
41 1
 
1.3%
49 2
 
2.5%
53 1
 
1.3%
62 1
 
1.3%
63 1
 
1.3%
65 2
 
2.5%
67 4
5.1%
68 1
 
1.3%
69 2
 
2.5%
71 8
10.1%
ValueCountFrequency (%)
72 56
70.9%
71 8
 
10.1%
69 2
 
2.5%
68 1
 
1.3%
67 4
 
5.1%
65 2
 
2.5%
63 1
 
1.3%
62 1
 
1.3%
53 1
 
1.3%
49 2
 
2.5%

TOTL_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.493671
Minimum17
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-10T22:20:45.197348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26.9
Q134
median34
Q334
95-th percentile43.2
Maximum60
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.1810142
Coefficient of variation (CV)0.15020188
Kurtosis9.4734605
Mean34.493671
Median Absolute Deviation (MAD)0
Skewness1.5702699
Sum2725
Variance26.842908
MonotonicityNot monotonic
2023-12-10T22:20:45.401258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
34 57
72.2%
37 3
 
3.8%
26 2
 
2.5%
29 2
 
2.5%
40 2
 
2.5%
28 1
 
1.3%
51 1
 
1.3%
25 1
 
1.3%
43 1
 
1.3%
27 1
 
1.3%
Other values (8) 8
 
10.1%
ValueCountFrequency (%)
17 1
 
1.3%
25 1
 
1.3%
26 2
 
2.5%
27 1
 
1.3%
28 1
 
1.3%
29 2
 
2.5%
32 1
 
1.3%
33 1
 
1.3%
34 57
72.2%
37 3
 
3.8%
ValueCountFrequency (%)
60 1
 
1.3%
51 1
 
1.3%
46 1
 
1.3%
45 1
 
1.3%
43 1
 
1.3%
41 1
 
1.3%
40 2
 
2.5%
38 1
 
1.3%
37 3
 
3.8%
34 57
72.2%

Interactions

2023-12-10T22:20:41.625068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:39.393777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.057709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.979184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.757836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:39.566516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.219480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.130742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.910462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:39.704336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.376513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.307942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:42.069113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:39.878129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.528778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.473008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:20:45.533682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GRID_NOPUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
GRID_NO1.0001.0001.0001.0001.000
PUL_GRAD1.0001.0000.8410.8410.893
LIFE_INFRA1.0000.8411.0000.7280.887
CMPTT_GRAD1.0000.8410.7281.0000.835
TOTL_GRAD1.0000.8930.8870.8351.000
2023-12-10T22:20:45.838632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
PUL_GRAD1.0000.456-0.7440.365
LIFE_INFRA0.4561.000-0.4190.770
CMPTT_GRAD-0.744-0.4191.000-0.065
TOTL_GRAD0.3650.770-0.0651.000

Missing values

2023-12-10T22:20:42.270431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:20:42.447140image/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
0마라45ab86aa1177234
1마라45ab86ab1177234
2마라45ba85ba1177234
3마라45ba85bb1177234
4마라45ba86aa1177234
5마라45ab85bb1177234
6마라45ba86ab1177234
7마라45ba85ab1177234
8마라45bb85aa1177234
9마라45bb85ab1177234
GRID_NOPUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
69마라47ab86aa1177234
70마라47ab86ab1177234
71마라47ab86ba5197137
72마라47ab86bb1136929
73마라47ab87aa1177234
74마라47ba85ba1206934
75마라47ba85bb1206833
76마라47ba86aa1177234
77마라47ba86ab1177234
78마라47ba87aa1177234