Overview

Dataset statistics

Number of variables6
Number of observations149
Missing cells109
Missing cells (%)12.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory53.9 B

Variable types

Text1
Numeric5

Dataset

DescriptionSample
Author제타럭스시스템
URLhttps://bigdata-geo.kr/user/dataset/view.do?data_sn=134

Alerts

pop_grade is highly overall correlated with comp_gradeHigh correlation
life_grade is highly overall correlated with total_gradHigh correlation
comp_grade is highly overall correlated with pop_gradeHigh correlation
total_grad is highly overall correlated with life_gradeHigh correlation
sales_grad has 109 (73.2%) missing valuesMissing
gid has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:25:20.544893
Analysis finished2023-12-10 13:25:28.239521
Duration7.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

gid
Text

UNIQUE 

Distinct149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-10T22:25:28.572464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1490
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

Unique149 ?
Unique (%)100.0%

Sample

1st row다사67ab47aa
2nd row다사68bb42ba
3rd row다사67aa43ab
4th row다사66aa46ba
5th row다사64bb48bb
ValueCountFrequency (%)
다사67ab47aa 1
 
0.7%
다사67bb45bb 1
 
0.7%
다사68ab45bb 1
 
0.7%
다사66ba44bb 1
 
0.7%
다사69aa42bb 1
 
0.7%
다사66aa46bb 1
 
0.7%
다사64ab48ab 1
 
0.7%
다사65aa48ab 1
 
0.7%
다사68ab46ab 1
 
0.7%
다사65aa48ba 1
 
0.7%
Other values (139) 139
93.3%
2023-12-10T22:25:29.407016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 309
20.7%
a 287
19.3%
6 203
13.6%
4 198
13.3%
다 149
10.0%
사 149
10.0%
5 45
 
3.0%
7 37
 
2.5%
3 34
 
2.3%
8 34
 
2.3%
Other values (4) 45
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 596
40.0%
Decimal Number 596
40.0%
Other Letter 298
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 203
34.1%
4 198
33.2%
5 45
 
7.6%
7 37
 
6.2%
3 34
 
5.7%
8 34
 
5.7%
2 20
 
3.4%
9 13
 
2.2%
1 11
 
1.8%
0 1
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
b 309
51.8%
a 287
48.2%
Other Letter
ValueCountFrequency (%)
다 149
50.0%
사 149
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 596
40.0%
Common 596
40.0%
Hangul 298
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 203
34.1%
4 198
33.2%
5 45
 
7.6%
7 37
 
6.2%
3 34
 
5.7%
8 34
 
5.7%
2 20
 
3.4%
9 13
 
2.2%
1 11
 
1.8%
0 1
 
0.2%
Latin
ValueCountFrequency (%)
b 309
51.8%
a 287
48.2%
Hangul
ValueCountFrequency (%)
다 149
50.0%
사 149
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1192
80.0%
Hangul 298
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 309
25.9%
a 287
24.1%
6 203
17.0%
4 198
16.6%
5 45
 
3.8%
7 37
 
3.1%
3 34
 
2.9%
8 34
 
2.9%
2 20
 
1.7%
9 13
 
1.1%
Other values (2) 12
 
1.0%
Hangul
ValueCountFrequency (%)
다 149
50.0%
사 149
50.0%

pop_grade
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3959732
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:25:29.613865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile8
Maximum39
Range38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.7290379
Coefficient of variation (CV)2.3911111
Kurtosis27.631369
Mean2.3959732
Median Absolute Deviation (MAD)0
Skewness5.1738443
Sum357
Variance32.821876
MonotonicityNot monotonic
2023-12-10T22:25:29.815321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 127
85.2%
2 10
 
6.7%
8 2
 
1.3%
7 1
 
0.7%
27 1
 
0.7%
38 1
 
0.7%
10 1
 
0.7%
13 1
 
0.7%
5 1
 
0.7%
33 1
 
0.7%
Other values (3) 3
 
2.0%
ValueCountFrequency (%)
1 127
85.2%
2 10
 
6.7%
5 1
 
0.7%
6 1
 
0.7%
7 1
 
0.7%
8 2
 
1.3%
10 1
 
0.7%
13 1
 
0.7%
16 1
 
0.7%
27 1
 
0.7%
ValueCountFrequency (%)
39 1
0.7%
38 1
0.7%
33 1
0.7%
27 1
0.7%
16 1
0.7%
13 1
0.7%
10 1
0.7%
8 2
1.3%
7 1
0.7%
6 1
0.7%

life_grade
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.348993
Minimum4
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:25:30.035398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile13
Q115
median15
Q315
95-th percentile41.6
Maximum70
Range66
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.190699
Coefficient of variation (CV)0.55538193
Kurtosis10.184438
Mean18.348993
Median Absolute Deviation (MAD)0
Skewness3.115943
Sum2734
Variance103.85035
MonotonicityNot monotonic
2023-12-10T22:25:30.278437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
15 112
75.2%
12 4
 
2.7%
64 2
 
1.3%
13 2
 
1.3%
34 2
 
1.3%
19 2
 
1.3%
18 2
 
1.3%
23 2
 
1.3%
31 2
 
1.3%
14 2
 
1.3%
Other values (17) 17
 
11.4%
ValueCountFrequency (%)
4 1
 
0.7%
8 1
 
0.7%
10 1
 
0.7%
12 4
 
2.7%
13 2
 
1.3%
14 2
 
1.3%
15 112
75.2%
18 2
 
1.3%
19 2
 
1.3%
20 1
 
0.7%
ValueCountFrequency (%)
70 1
0.7%
64 2
1.3%
54 1
0.7%
47 1
0.7%
46 1
0.7%
44 1
0.7%
42 1
0.7%
41 1
0.7%
40 1
0.7%
35 1
0.7%

comp_grade
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.939597
Minimum41
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:25:30.459073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile66
Q176
median76
Q376
95-th percentile76
Maximum76
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.8452097
Coefficient of variation (CV)0.065529295
Kurtosis20.953212
Mean73.939597
Median Absolute Deviation (MAD)0
Skewness-3.982741
Sum11017
Variance23.476057
MonotonicityNot monotonic
2023-12-10T22:25:30.721224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
76 112
75.2%
71 22
 
14.8%
66 3
 
2.0%
64 3
 
2.0%
68 2
 
1.3%
67 2
 
1.3%
41 1
 
0.7%
46 1
 
0.7%
72 1
 
0.7%
63 1
 
0.7%
ValueCountFrequency (%)
41 1
 
0.7%
46 1
 
0.7%
61 1
 
0.7%
63 1
 
0.7%
64 3
 
2.0%
66 3
 
2.0%
67 2
 
1.3%
68 2
 
1.3%
71 22
14.8%
72 1
 
0.7%
ValueCountFrequency (%)
76 112
75.2%
72 1
 
0.7%
71 22
 
14.8%
68 2
 
1.3%
67 2
 
1.3%
66 3
 
2.0%
64 3
 
2.0%
63 1
 
0.7%
61 1
 
0.7%
46 1
 
0.7%

sales_grad
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)40.0%
Missing109
Missing (%)73.2%
Infinite0
Infinite (%)0.0%
Mean10.2
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:25:30.947059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q15
median7.5
Q310
95-th percentile24.05
Maximum100
Range99
Interquartile range (IQR)5

Descriptive statistics

Standard deviation15.46576
Coefficient of variation (CV)1.516251
Kurtosis30.816629
Mean10.2
Median Absolute Deviation (MAD)2.5
Skewness5.2895895
Sum408
Variance239.18974
MonotonicityNot monotonic
2023-12-10T22:25:31.284555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 5
 
3.4%
6 4
 
2.7%
7 4
 
2.7%
8 4
 
2.7%
10 4
 
2.7%
2 3
 
2.0%
5 3
 
2.0%
3 3
 
2.0%
11 2
 
1.3%
1 2
 
1.3%
Other values (6) 6
 
4.0%
(Missing) 109
73.2%
ValueCountFrequency (%)
1 2
 
1.3%
2 3
2.0%
3 3
2.0%
4 1
 
0.7%
5 3
2.0%
6 4
2.7%
7 4
2.7%
8 4
2.7%
9 5
3.4%
10 4
2.7%
ValueCountFrequency (%)
100 1
 
0.7%
25 1
 
0.7%
24 1
 
0.7%
17 1
 
0.7%
15 1
 
0.7%
11 2
 
1.3%
10 4
2.7%
9 5
3.4%
8 4
2.7%
7 4
2.7%

total_grad
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.342282
Minimum20
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T22:25:31.635664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile38
Q141
median41
Q341
95-th percentile53
Maximum73
Range53
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.662551
Coefficient of variation (CV)0.13373278
Kurtosis11.659739
Mean42.342282
Median Absolute Deviation (MAD)0
Skewness2.4859726
Sum6309
Variance32.064484
MonotonicityNot monotonic
2023-12-10T22:25:32.065459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
41 113
75.8%
51 5
 
3.4%
38 4
 
2.7%
37 3
 
2.0%
40 2
 
1.3%
44 2
 
1.3%
45 2
 
1.3%
53 2
 
1.3%
55 2
 
1.3%
47 2
 
1.3%
Other values (12) 12
 
8.1%
ValueCountFrequency (%)
20 1
 
0.7%
35 1
 
0.7%
36 1
 
0.7%
37 3
 
2.0%
38 4
 
2.7%
39 1
 
0.7%
40 2
 
1.3%
41 113
75.8%
42 1
 
0.7%
43 1
 
0.7%
ValueCountFrequency (%)
73 1
 
0.7%
67 1
 
0.7%
66 1
 
0.7%
63 1
 
0.7%
56 1
 
0.7%
55 2
 
1.3%
53 2
 
1.3%
51 5
3.4%
50 1
 
0.7%
47 2
 
1.3%

Interactions

2023-12-10T22:25:27.081954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:23.360487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:24.568828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:25.414230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:26.278979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:27.249351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:23.665282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:24.730428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:25.625472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:26.438587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:27.432636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:23.940921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:24.879245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:25.820946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:26.632478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:27.576475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:24.184207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:25.077136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:25.974293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:26.767581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:27.726783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:24.396042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:25.218512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:26.136127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:25:26.912066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:25:32.316427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
pop_gradelife_gradecomp_gradesales_gradtotal_grad
pop_grade1.0000.6410.6560.2770.740
life_grade0.6411.0000.7590.0000.809
comp_grade0.6560.7591.0000.0000.769
sales_grad0.2770.0000.0001.0000.000
total_grad0.7400.8090.7690.0001.000
2023-12-10T22:25:32.514373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
pop_gradelife_gradecomp_gradesales_gradtotal_grad
pop_grade1.0000.416-0.723-0.0230.496
life_grade0.4161.000-0.4220.3300.702
comp_grade-0.723-0.4221.000-0.158-0.266
sales_grad-0.0230.330-0.1581.0000.090
total_grad0.4960.702-0.2660.0901.000

Missing values

2023-12-10T22:25:27.947236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:25:28.136992image/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

gidpop_gradelife_gradecomp_gradesales_gradtotal_grad
0다사67ab47aa11576<NA>41
1다사68bb42ba11576<NA>41
2다사67aa43ab11576341
3다사66aa46ba11576<NA>41
4다사64bb48bb11576<NA>41
5다사64bb46bb11576<NA>41
6다사64bb45bb11576741
7다사67ab44ba11471<NA>38
8다사63ba44bb11576<NA>41
9다사63ab45bb76441751
gidpop_gradelife_gradecomp_gradesales_gradtotal_grad
139다사66ba40bb11576<NA>41
140다사63aa45bb63464847
141다사68ab44ab11576<NA>41
142다사67ab41ab11576<NA>41
143다사62bb45ba81971244
144다사64bb44aa11576<NA>41
145다사62ba47aa11576<NA>41
146다사66ba44ba11576<NA>41
147다사63ba45ba11576641
148다사69ba44ba11576<NA>41