Overview

Dataset statistics

Number of variables5
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory46.1 B

Variable types

Text1
Numeric4

Dataset

DescriptionSample
Author제타럭스시스템
URLhttps://bigdata-geo.kr/user/dataset/view.do?data_sn=485

Alerts

PUL_GRAD is highly overall correlated with LIFE_INFRA and 2 other fieldsHigh correlation
LIFE_INFRA is highly overall correlated with PUL_GRAD and 2 other fieldsHigh correlation
CMPTT_GRAD is highly overall correlated with PUL_GRAD and 2 other fieldsHigh correlation
TOTL_GRAD is highly overall correlated with PUL_GRAD and 2 other fieldsHigh correlation
GRID_NO has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:20:27.282353
Analysis finished2023-12-10 13:20:33.414153
Duration6.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GRID_NO
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-10T22:20:33.782418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters640
Distinct characters12
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

Unique64 ?
Unique (%)100.0%

Sample

1st row다사65ab43ab
2nd row다사65ba42bb
3rd row다사65ba43aa
4th row다사65ba43ab
5th row다사65ba43ba
ValueCountFrequency (%)
다사65ab43ab 1
 
1.6%
다사65ba42bb 1
 
1.6%
다사67aa43ba 1
 
1.6%
다사66bb41bb 1
 
1.6%
다사66bb42aa 1
 
1.6%
다사66bb42ab 1
 
1.6%
다사66bb42ba 1
 
1.6%
다사66bb42bb 1
 
1.6%
다사66bb43aa 1
 
1.6%
다사66bb43ab 1
 
1.6%
Other values (54) 54
84.4%
2023-12-10T22:20:34.530747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 131
20.5%
a 125
19.5%
6 93
14.5%
다 64
10.0%
사 64
10.0%
4 64
10.0%
3 35
 
5.5%
2 28
 
4.4%
7 21
 
3.3%
5 13
 
2.0%
Other values (2) 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 256
40.0%
Decimal Number 256
40.0%
Other Letter 128
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 93
36.3%
4 64
25.0%
3 35
 
13.7%
2 28
 
10.9%
7 21
 
8.2%
5 13
 
5.1%
1 1
 
0.4%
8 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
b 131
51.2%
a 125
48.8%
Other Letter
ValueCountFrequency (%)
다 64
50.0%
사 64
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 256
40.0%
Common 256
40.0%
Hangul 128
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 93
36.3%
4 64
25.0%
3 35
 
13.7%
2 28
 
10.9%
7 21
 
8.2%
5 13
 
5.1%
1 1
 
0.4%
8 1
 
0.4%
Latin
ValueCountFrequency (%)
b 131
51.2%
a 125
48.8%
Hangul
ValueCountFrequency (%)
다 64
50.0%
사 64
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 512
80.0%
Hangul 128
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 131
25.6%
a 125
24.4%
6 93
18.2%
4 64
12.5%
3 35
 
6.8%
2 28
 
5.5%
7 21
 
4.1%
5 13
 
2.5%
1 1
 
0.2%
8 1
 
0.2%
Hangul
ValueCountFrequency (%)
다 64
50.0%
사 64
50.0%

PUL_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.796875
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-10T22:20:34.738161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile21.65
Maximum34
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.0938822
Coefficient of variation (CV)1.8683476
Kurtosis10.181621
Mean3.796875
Median Absolute Deviation (MAD)0
Skewness3.2605852
Sum243
Variance50.323165
MonotonicityNot monotonic
2023-12-10T22:20:34.946562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 44
68.8%
4 4
 
6.2%
7 3
 
4.7%
2 3
 
4.7%
3 2
 
3.1%
14 1
 
1.6%
31 1
 
1.6%
8 1
 
1.6%
34 1
 
1.6%
6 1
 
1.6%
Other values (3) 3
 
4.7%
ValueCountFrequency (%)
1 44
68.8%
2 3
 
4.7%
3 2
 
3.1%
4 4
 
6.2%
5 1
 
1.6%
6 1
 
1.6%
7 3
 
4.7%
8 1
 
1.6%
14 1
 
1.6%
23 1
 
1.6%
ValueCountFrequency (%)
34 1
 
1.6%
31 1
 
1.6%
29 1
 
1.6%
23 1
 
1.6%
14 1
 
1.6%
8 1
 
1.6%
7 3
4.7%
6 1
 
1.6%
5 1
 
1.6%
4 4
6.2%

LIFE_INFRA
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.59375
Minimum8
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-10T22:20:35.165300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile17
Q117
median17
Q331.25
95-th percentile43.85
Maximum60
Range52
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation10.781699
Coefficient of variation (CV)0.43839184
Kurtosis0.73039137
Mean24.59375
Median Absolute Deviation (MAD)0.5
Skewness1.1790275
Sum1574
Variance116.24504
MonotonicityNot monotonic
2023-12-10T22:20:35.389176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
17 32
50.0%
30 5
 
7.8%
19 3
 
4.7%
41 2
 
3.1%
32 2
 
3.1%
34 2
 
3.1%
21 2
 
3.1%
43 2
 
3.1%
29 1
 
1.6%
31 1
 
1.6%
Other values (12) 12
 
18.8%
ValueCountFrequency (%)
8 1
 
1.6%
17 32
50.0%
18 1
 
1.6%
19 3
 
4.7%
21 2
 
3.1%
22 1
 
1.6%
26 1
 
1.6%
29 1
 
1.6%
30 5
 
7.8%
31 1
 
1.6%
ValueCountFrequency (%)
60 1
1.6%
50 1
1.6%
45 1
1.6%
44 1
1.6%
43 2
3.1%
41 2
3.1%
40 1
1.6%
37 1
1.6%
36 1
1.6%
35 1
1.6%

CMPTT_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.09375
Minimum53
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-10T22:20:35.667758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile61.15
Q168.75
median71
Q372
95-th percentile72
Maximum72
Range19
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation4.6962286
Coefficient of variation (CV)0.067968936
Kurtosis2.899181
Mean69.09375
Median Absolute Deviation (MAD)1
Skewness-1.8481966
Sum4422
Variance22.054563
MonotonicityNot monotonic
2023-12-10T22:20:35.930328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
72 31
48.4%
71 13
20.3%
64 4
 
6.2%
69 4
 
6.2%
62 3
 
4.7%
63 2
 
3.1%
61 1
 
1.6%
56 1
 
1.6%
53 1
 
1.6%
54 1
 
1.6%
Other values (3) 3
 
4.7%
ValueCountFrequency (%)
53 1
 
1.6%
54 1
 
1.6%
56 1
 
1.6%
61 1
 
1.6%
62 3
4.7%
63 2
3.1%
64 4
6.2%
65 1
 
1.6%
66 1
 
1.6%
68 1
 
1.6%
ValueCountFrequency (%)
72 31
48.4%
71 13
20.3%
69 4
 
6.2%
68 1
 
1.6%
66 1
 
1.6%
65 1
 
1.6%
64 4
 
6.2%
63 2
 
3.1%
62 3
 
4.7%
61 1
 
1.6%

TOTL_GRAD
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.375
Minimum23
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-10T22:20:36.237135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile33.15
Q134
median34
Q342.25
95-th percentile52.85
Maximum65
Range42
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation7.6313888
Coefficient of variation (CV)0.19886355
Kurtosis3.3006539
Mean38.375
Median Absolute Deviation (MAD)0.5
Skewness1.6540349
Sum2456
Variance58.238095
MonotonicityNot monotonic
2023-12-10T22:20:36.404331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
34 32
50.0%
37 5
 
7.8%
44 3
 
4.7%
43 3
 
4.7%
65 2
 
3.1%
48 2
 
3.1%
42 2
 
3.1%
53 2
 
3.1%
33 2
 
3.1%
49 2
 
3.1%
Other values (9) 9
 
14.1%
ValueCountFrequency (%)
23 1
 
1.6%
31 1
 
1.6%
33 2
 
3.1%
34 32
50.0%
35 1
 
1.6%
36 1
 
1.6%
37 5
 
7.8%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
ValueCountFrequency (%)
65 2
3.1%
53 2
3.1%
52 1
 
1.6%
49 2
3.1%
48 2
3.1%
45 1
 
1.6%
44 3
4.7%
43 3
4.7%
42 2
3.1%
41 1
 
1.6%

Interactions

2023-12-10T22:20:32.480639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:30.050634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:30.809601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:31.744155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:32.624218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:30.378675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:31.015021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:31.956764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:32.799736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:30.506315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:31.231134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:32.171856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:32.992589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:30.676527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:31.577104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:32.349156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:20:36.526356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GRID_NOPUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
GRID_NO1.0001.0001.0001.0001.000
PUL_GRAD1.0001.0000.0000.7220.598
LIFE_INFRA1.0000.0001.0000.7900.874
CMPTT_GRAD1.0000.7220.7901.0000.587
TOTL_GRAD1.0000.5980.8740.5871.000
2023-12-10T22:20:36.770579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
PUL_GRAD1.0000.561-0.6140.544
LIFE_INFRA0.5611.000-0.7960.872
CMPTT_GRAD-0.614-0.7961.000-0.557
TOTL_GRAD0.5440.872-0.5571.000

Missing values

2023-12-10T22:20:33.143644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:20:33.299829image/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
0다사65ab43ab1177234
1다사65ba42bb1177234
2다사65ba43aa1177234
3다사65ba43ab1177234
4다사65ba43ba1606153
5다사65ba42ba1177234
6다사65bb42ab1177234
7다사65bb42ba1177234
8다사65bb42bb1177234
9다사65bb43aa186323
GRID_NOPUL_GRADLIFE_INFRACMPTT_GRADTOTL_GRAD
54다사67ab43bb4306237
55다사67ba42bb1177234
56다사67ba43aa1177133
57다사67ba43ab2317142
58다사67ba43ba1297140
59다사67bb43aa1177234
60다사67bb43ab1177234
61다사67bb43ba1177234
62다사67bb43bb3216231
63다사68aa43bb1177234