Overview

Dataset statistics

Number of variables5
Number of observations91
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory45.5 B

Variable types

Text1
Numeric4

Dataset

DescriptionSample
Author제타럭스시스템
URLhttps://bigdata-geo.kr/user/dataset/view.do?data_sn=487

Alerts

PUL_GRAD is highly overall correlated with CMPTT_GRADHigh correlation
LIFE_INFRA is highly overall correlated with CMPTT_GRADHigh correlation
CMPTT_GRAD is highly overall correlated with PUL_GRAD and 1 other fieldsHigh correlation
GRID_NO has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:20:47.447175
Analysis finished2023-12-10 13:20:50.494368
Duration3.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GRID_NO
Text

UNIQUE 

Distinct91
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size860.0 B
2023-12-10T22:20:50.785493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters910
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

Unique91 ?
Unique (%)100.0%

Sample

1st row다바91aa16bb
2nd row다바91ab16bb
3rd row다바91aa17aa
4th row다바91ab17aa
5th row다바90ab18ba
ValueCountFrequency (%)
다바91aa16bb 1
 
1.1%
다바89ab17ab 1
 
1.1%
다바89bb18ab 1
 
1.1%
다바89bb18aa 1
 
1.1%
다바89bb17bb 1
 
1.1%
다바89bb17ba 1
 
1.1%
다바89bb17ab 1
 
1.1%
다바89bb17aa 1
 
1.1%
다바89bb16bb 1
 
1.1%
다바89ba18ba 1
 
1.1%
Other values (81) 81
89.0%
2023-12-10T22:20:51.455496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 182
20.0%
b 182
20.0%
1 103
11.3%
다 91
10.0%
바 91
10.0%
8 84
9.2%
9 80
8.8%
7 48
 
5.3%
0 32
 
3.5%
6 17
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 364
40.0%
Decimal Number 364
40.0%
Other Letter 182
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 103
28.3%
8 84
23.1%
9 80
22.0%
7 48
13.2%
0 32
 
8.8%
6 17
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
a 182
50.0%
b 182
50.0%
Other Letter
ValueCountFrequency (%)
다 91
50.0%
바 91
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 364
40.0%
Common 364
40.0%
Hangul 182
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 103
28.3%
8 84
23.1%
9 80
22.0%
7 48
13.2%
0 32
 
8.8%
6 17
 
4.7%
Latin
ValueCountFrequency (%)
a 182
50.0%
b 182
50.0%
Hangul
ValueCountFrequency (%)
다 91
50.0%
바 91
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 728
80.0%
Hangul 182
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 182
25.0%
b 182
25.0%
1 103
14.1%
8 84
11.5%
9 80
11.0%
7 48
 
6.6%
0 32
 
4.4%
6 17
 
2.3%
Hangul
ValueCountFrequency (%)
다 91
50.0%
바 91
50.0%

PUL_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8901099
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-10T22:20:51.767918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5.5
Maximum30
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.7251415
Coefficient of variation (CV)1.9708597
Kurtosis40.032796
Mean1.8901099
Median Absolute Deviation (MAD)0
Skewness5.9945806
Sum172
Variance13.876679
MonotonicityNot monotonic
2023-12-10T22:20:51.935406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 80
87.9%
3 4
 
4.4%
6 2
 
2.2%
11 1
 
1.1%
18 1
 
1.1%
5 1
 
1.1%
4 1
 
1.1%
30 1
 
1.1%
ValueCountFrequency (%)
1 80
87.9%
3 4
 
4.4%
4 1
 
1.1%
5 1
 
1.1%
6 2
 
2.2%
11 1
 
1.1%
18 1
 
1.1%
30 1
 
1.1%
ValueCountFrequency (%)
30 1
 
1.1%
18 1
 
1.1%
11 1
 
1.1%
6 2
 
2.2%
5 1
 
1.1%
4 1
 
1.1%
3 4
 
4.4%
1 80
87.9%

LIFE_INFRA
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.175824
Minimum12
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-10T22:20:52.136056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile15
Q117
median17
Q320.5
95-th percentile37
Maximum41
Range29
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation6.7783862
Coefficient of variation (CV)0.33596577
Kurtosis2.2068197
Mean20.175824
Median Absolute Deviation (MAD)0
Skewness1.8078564
Sum1836
Variance45.94652
MonotonicityNot monotonic
2023-12-10T22:20:52.338959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
17 57
62.6%
33 3
 
3.3%
21 3
 
3.3%
19 3
 
3.3%
13 3
 
3.3%
23 3
 
3.3%
39 2
 
2.2%
28 2
 
2.2%
22 1
 
1.1%
14 1
 
1.1%
Other values (13) 13
 
14.3%
ValueCountFrequency (%)
12 1
 
1.1%
13 3
 
3.3%
14 1
 
1.1%
16 1
 
1.1%
17 57
62.6%
18 1
 
1.1%
19 3
 
3.3%
20 1
 
1.1%
21 3
 
3.3%
22 1
 
1.1%
ValueCountFrequency (%)
41 1
 
1.1%
40 1
 
1.1%
39 2
2.2%
38 1
 
1.1%
36 1
 
1.1%
35 1
 
1.1%
33 3
3.3%
31 1
 
1.1%
30 1
 
1.1%
29 1
 
1.1%

CMPTT_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.758242
Minimum32
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-10T22:20:52.536671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile56.5
Q169
median72
Q372
95-th percentile72
Maximum72
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation7.1217049
Coefficient of variation (CV)0.10357602
Kurtosis12.809829
Mean68.758242
Median Absolute Deviation (MAD)0
Skewness-3.3383212
Sum6257
Variance50.718681
MonotonicityNot monotonic
2023-12-10T22:20:53.065965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
72 54
59.3%
71 11
 
12.1%
69 5
 
5.5%
64 4
 
4.4%
67 3
 
3.3%
59 2
 
2.2%
57 1
 
1.1%
32 1
 
1.1%
35 1
 
1.1%
48 1
 
1.1%
Other values (8) 8
 
8.8%
ValueCountFrequency (%)
32 1
1.1%
35 1
1.1%
48 1
1.1%
54 1
1.1%
56 1
1.1%
57 1
1.1%
58 1
1.1%
59 2
2.2%
62 1
1.1%
63 1
1.1%
ValueCountFrequency (%)
72 54
59.3%
71 11
 
12.1%
70 1
 
1.1%
69 5
 
5.5%
68 1
 
1.1%
67 3
 
3.3%
65 1
 
1.1%
64 4
 
4.4%
63 1
 
1.1%
62 1
 
1.1%

TOTL_GRAD
Real number (ℝ)

Distinct22
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.285714
Minimum24
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-10T22:20:53.299565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile28
Q134
median34
Q334
95-th percentile42
Maximum56
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.4052638
Coefficient of variation (CV)0.12848686
Kurtosis6.9716574
Mean34.285714
Median Absolute Deviation (MAD)0
Skewness1.636519
Sum3120
Variance19.406349
MonotonicityNot monotonic
2023-12-10T22:20:53.548893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
34 56
61.5%
30 5
 
5.5%
29 3
 
3.3%
37 3
 
3.3%
33 2
 
2.2%
40 2
 
2.2%
41 2
 
2.2%
28 2
 
2.2%
35 2
 
2.2%
46 2
 
2.2%
Other values (12) 12
 
13.2%
ValueCountFrequency (%)
24 1
 
1.1%
25 1
 
1.1%
26 1
 
1.1%
27 1
 
1.1%
28 2
 
2.2%
29 3
3.3%
30 5
5.5%
31 1
 
1.1%
32 1
 
1.1%
33 2
 
2.2%
ValueCountFrequency (%)
56 1
 
1.1%
46 2
2.2%
45 1
 
1.1%
43 1
 
1.1%
41 2
2.2%
40 2
2.2%
39 1
 
1.1%
38 1
 
1.1%
37 3
3.3%
36 1
 
1.1%

Interactions

2023-12-10T22:20:49.613514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:47.752169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:48.432755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:49.010748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:49.743217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:47.898622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:48.622496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:49.165782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:49.901602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:48.121394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:48.756034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:49.303080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:50.069431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:48.305368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:48.889385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:49.468260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:20:53.814934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GRID_NOPUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
GRID_NO1.0001.0001.0001.0001.000
PUL_GRAD1.0001.0000.7440.7170.779
LIFE_INFRA1.0000.7441.0000.7380.819
CMPTT_GRAD1.0000.7170.7381.0000.682
TOTL_GRAD1.0000.7790.8190.6821.000
2023-12-10T22:20:54.062976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
PUL_GRAD1.0000.369-0.5480.087
LIFE_INFRA0.3691.000-0.5880.449
CMPTT_GRAD-0.548-0.5881.0000.164
TOTL_GRAD0.0870.4490.1641.000

Missing values

2023-12-10T22:20:50.260378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:20:50.437587image/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
0다바91aa16bb1177234
1다바91ab16bb1177234
2다바91aa17aa1177234
3다바91ab17aa1177234
4다바90ab18ba1177234
5다바90bb18ba1177234
6다바90ba18ba1177234
7다바90ba18aa1177234
8다바90ba18ab1177234
9다바90bb18aa1177234
GRID_NOPUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
81다바90ab18aa1177234
82다바90ab18ab1177234
83다바90ba16bb1176529
84다바90ba17aa1387146
85다바90ba17ab1177234
86다바90ba17ba1146930
87다바90ba17bb1177234
88다바90bb16bb1196832
89다바90bb17aa1177234
90다바90bb17ab1136929