Overview

Dataset statistics

Number of variables5
Number of observations144
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory44.9 B

Variable types

Text1
Numeric4

Dataset

DescriptionSample
Author제타럭스시스템
URLhttps://bigdata-geo.kr/user/dataset/view.do?data_sn=498

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 13:21:24.964374
Analysis finished2023-12-10 13:21:27.979263
Duration3.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GRID_NO
Text

UNIQUE 

Distinct144
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-10T22:21:28.236856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1440
Distinct characters14
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

Unique144 ?
Unique (%)100.0%

Sample

1st row다사66aa34ab
2nd row다사66aa34ba
3rd row다사66aa34bb
4th row다사66aa35aa
5th row다사66ab34aa
ValueCountFrequency (%)
다사66aa34ab 1
 
0.7%
다사66aa34ba 1
 
0.7%
다사69ab33ba 1
 
0.7%
다사69aa33bb 1
 
0.7%
다사69aa34aa 1
 
0.7%
다사69aa34ab 1
 
0.7%
다사69aa34ba 1
 
0.7%
다사69ab33aa 1
 
0.7%
다사69ab33ab 1
 
0.7%
다사69ab33bb 1
 
0.7%
Other values (134) 134
93.1%
2023-12-10T22:21:28.954859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 303
21.0%
b 273
19.0%
3 201
14.0%
다 144
10.0%
사 144
10.0%
6 139
9.7%
4 66
 
4.6%
7 60
 
4.2%
8 34
 
2.4%
9 28
 
1.9%
Other values (4) 48
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 576
40.0%
Decimal Number 576
40.0%
Other Letter 288
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 201
34.9%
6 139
24.1%
4 66
 
11.5%
7 60
 
10.4%
8 34
 
5.9%
9 28
 
4.9%
0 24
 
4.2%
5 19
 
3.3%
1 3
 
0.5%
2 2
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
a 303
52.6%
b 273
47.4%
Other Letter
ValueCountFrequency (%)
다 144
50.0%
사 144
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 576
40.0%
Common 576
40.0%
Hangul 288
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 201
34.9%
6 139
24.1%
4 66
 
11.5%
7 60
 
10.4%
8 34
 
5.9%
9 28
 
4.9%
0 24
 
4.2%
5 19
 
3.3%
1 3
 
0.5%
2 2
 
0.3%
Latin
ValueCountFrequency (%)
a 303
52.6%
b 273
47.4%
Hangul
ValueCountFrequency (%)
다 144
50.0%
사 144
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1152
80.0%
Hangul 288
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 303
26.3%
b 273
23.7%
3 201
17.4%
6 139
12.1%
4 66
 
5.7%
7 60
 
5.2%
8 34
 
3.0%
9 28
 
2.4%
0 24
 
2.1%
5 19
 
1.6%
Other values (2) 5
 
0.4%
Hangul
ValueCountFrequency (%)
다 144
50.0%
사 144
50.0%

PUL_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3819444
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:21:29.152693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum30
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6098161
Coefficient of variation (CV)1.8885102
Kurtosis103.26934
Mean1.3819444
Median Absolute Deviation (MAD)0
Skewness9.6842634
Sum199
Variance6.8111402
MonotonicityNot monotonic
2023-12-10T22:21:29.339815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 138
95.8%
5 2
 
1.4%
4 1
 
0.7%
8 1
 
0.7%
9 1
 
0.7%
30 1
 
0.7%
ValueCountFrequency (%)
1 138
95.8%
4 1
 
0.7%
5 2
 
1.4%
8 1
 
0.7%
9 1
 
0.7%
30 1
 
0.7%
ValueCountFrequency (%)
30 1
 
0.7%
9 1
 
0.7%
8 1
 
0.7%
5 2
 
1.4%
4 1
 
0.7%
1 138
95.8%

LIFE_INFRA
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.444444
Minimum12
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:21:29.540688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile17
Q117
median17
Q317
95-th percentile32.85
Maximum53
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.0427125
Coefficient of variation (CV)0.32761694
Kurtosis15.700702
Mean18.444444
Median Absolute Deviation (MAD)0
Skewness3.9258458
Sum2656
Variance36.514375
MonotonicityNot monotonic
2023-12-10T22:21:29.789744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17 125
86.8%
15 2
 
1.4%
13 2
 
1.4%
21 2
 
1.4%
33 2
 
1.4%
12 2
 
1.4%
38 1
 
0.7%
46 1
 
0.7%
43 1
 
0.7%
47 1
 
0.7%
Other values (5) 5
 
3.5%
ValueCountFrequency (%)
12 2
 
1.4%
13 2
 
1.4%
15 2
 
1.4%
17 125
86.8%
20 1
 
0.7%
21 2
 
1.4%
27 1
 
0.7%
32 1
 
0.7%
33 2
 
1.4%
37 1
 
0.7%
ValueCountFrequency (%)
53 1
0.7%
47 1
0.7%
46 1
0.7%
43 1
0.7%
38 1
0.7%
37 1
0.7%
33 2
1.4%
32 1
0.7%
27 1
0.7%
21 2
1.4%

CMPTT_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.125
Minimum35
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:21:29.960582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile67
Q172
median72
Q372
95-th percentile72
Maximum72
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9416638
Coefficient of variation (CV)0.055418823
Kurtosis57.407203
Mean71.125
Median Absolute Deviation (MAD)0
Skewness-7.0950825
Sum10242
Variance15.536713
MonotonicityNot monotonic
2023-12-10T22:21:30.244960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
72 124
86.1%
71 9
 
6.2%
67 3
 
2.1%
64 2
 
1.4%
65 1
 
0.7%
66 1
 
0.7%
35 1
 
0.7%
48 1
 
0.7%
69 1
 
0.7%
63 1
 
0.7%
ValueCountFrequency (%)
35 1
 
0.7%
48 1
 
0.7%
63 1
 
0.7%
64 2
 
1.4%
65 1
 
0.7%
66 1
 
0.7%
67 3
 
2.1%
69 1
 
0.7%
71 9
 
6.2%
72 124
86.1%
ValueCountFrequency (%)
72 124
86.1%
71 9
 
6.2%
69 1
 
0.7%
67 3
 
2.1%
66 1
 
0.7%
65 1
 
0.7%
64 2
 
1.4%
63 1
 
0.7%
48 1
 
0.7%
35 1
 
0.7%

TOTL_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.506944
Minimum28
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:21:30.432548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile34
Q134
median34
Q334
95-th percentile38.85
Maximum60
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.2127293
Coefficient of variation (CV)0.093103849
Kurtosis32.109157
Mean34.506944
Median Absolute Deviation (MAD)0
Skewness4.936052
Sum4969
Variance10.32163
MonotonicityNot monotonic
2023-12-10T22:21:30.723499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
34 125
86.8%
30 3
 
2.1%
35 3
 
2.1%
46 2
 
1.4%
32 1
 
0.7%
49 1
 
0.7%
33 1
 
0.7%
43 1
 
0.7%
60 1
 
0.7%
39 1
 
0.7%
Other values (5) 5
 
3.5%
ValueCountFrequency (%)
28 1
 
0.7%
29 1
 
0.7%
30 3
 
2.1%
32 1
 
0.7%
33 1
 
0.7%
34 125
86.8%
35 3
 
2.1%
38 1
 
0.7%
39 1
 
0.7%
40 1
 
0.7%
ValueCountFrequency (%)
60 1
 
0.7%
49 1
 
0.7%
46 2
 
1.4%
43 1
 
0.7%
41 1
 
0.7%
40 1
 
0.7%
39 1
 
0.7%
38 1
 
0.7%
35 3
 
2.1%
34 125
86.8%

Interactions

2023-12-10T22:21:27.134999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.231194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.816692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.498932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:27.272147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.377561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.040154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.643356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:27.410771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.530858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.165523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.806858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:27.573496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.667271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.336176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.961133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:21:30.868074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
PUL_GRAD1.0000.9010.6980.953
LIFE_INFRA0.9011.0000.9670.950
CMPTT_GRAD0.6980.9671.0000.737
TOTL_GRAD0.9530.9500.7371.000
2023-12-10T22:21:31.142632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
PUL_GRAD1.0000.472-0.5510.285
LIFE_INFRA0.4721.000-0.3820.945
CMPTT_GRAD-0.551-0.3821.000-0.267
TOTL_GRAD0.2850.945-0.2671.000

Missing values

2023-12-10T22:21:27.776916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:21:27.922087image/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
0다사66aa34ab1177234
1다사66aa34ba1177234
2다사66aa34bb1177234
3다사66aa35aa1177234
4다사66ab34aa1177234
5다사66ab34ab1177234
6다사66ab34ba1177234
7다사66ab34bb1157132
8다사66ab35aa1177234
9다사66ab35ab1177234
GRID_NOPUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
134다사70ba33bb1177234
135다사70ba34aa1177234
136다사70bb33aa1177234
137다사70bb33ab1177234
138다사70bb33ba1177234
139다사70bb33bb1177234
140다사70bb34aa1177234
141다사71aa33ba1177234
142다사71aa33bb1177234
143다사71aa34aa1177234