Overview

Dataset statistics

Number of variables2
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory18.3 B

Variable types

Numeric1
Text1

Dataset

DescriptionSample
Author경기대학교 빅데이터센터
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KGUDLIVERYLCIMG00001

Alerts

순번 has unique valuesUnique
파일명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:57:17.433812
Analysis finished2023-12-10 06:57:17.766272
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:57:17.871989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T15:57:18.033053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

파일명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T15:57:18.218484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters2400
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row2019_11_08_21_A13934.png
2nd row2019_11_08_21_A13944.png
3rd row2019_11_08_21_A13947.png
4th row2019_11_08_21_A13949.png
5th row2019_11_08_21_A13953.png
ValueCountFrequency (%)
2019_11_08_21_a13934.png 1
 
1.0%
2019_11_08_21_a14377.png 1
 
1.0%
2019_11_08_21_a14442.png 1
 
1.0%
2019_11_08_21_a14431.png 1
 
1.0%
2019_11_08_21_a14419.png 1
 
1.0%
2019_11_08_21_a14412.png 1
 
1.0%
2019_11_08_21_a14405.png 1
 
1.0%
2019_11_08_21_a14401.png 1
 
1.0%
2019_11_08_21_a14399.png 1
 
1.0%
2019_11_08_21_a14396.png 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T15:57:18.540479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 529
22.0%
_ 400
16.7%
2 239
10.0%
0 236
9.8%
9 137
 
5.7%
4 126
 
5.2%
8 117
 
4.9%
A 100
 
4.2%
. 100
 
4.2%
p 100
 
4.2%
Other values (6) 316
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1500
62.5%
Connector Punctuation 400
 
16.7%
Lowercase Letter 300
 
12.5%
Uppercase Letter 100
 
4.2%
Other Punctuation 100
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 529
35.3%
2 239
15.9%
0 236
15.7%
9 137
 
9.1%
4 126
 
8.4%
8 117
 
7.8%
3 44
 
2.9%
5 38
 
2.5%
6 21
 
1.4%
7 13
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
p 100
33.3%
n 100
33.3%
g 100
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 400
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 100
100.0%
Other Punctuation
ValueCountFrequency (%)
. 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
83.3%
Latin 400
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 529
26.5%
_ 400
20.0%
2 239
11.9%
0 236
11.8%
9 137
 
6.9%
4 126
 
6.3%
8 117
 
5.9%
. 100
 
5.0%
3 44
 
2.2%
5 38
 
1.9%
Other values (2) 34
 
1.7%
Latin
ValueCountFrequency (%)
A 100
25.0%
p 100
25.0%
n 100
25.0%
g 100
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 529
22.0%
_ 400
16.7%
2 239
10.0%
0 236
9.8%
9 137
 
5.7%
4 126
 
5.2%
8 117
 
4.9%
A 100
 
4.2%
. 100
 
4.2%
p 100
 
4.2%
Other values (6) 316
13.2%

Interactions

2023-12-10T15:57:17.499031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:57:18.638651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번파일명
순번1.0001.000
파일명1.0001.000

Missing values

2023-12-10T15:57:17.640226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:57:17.732048image/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

순번파일명
012019_11_08_21_A13934.png
122019_11_08_21_A13944.png
232019_11_08_21_A13947.png
342019_11_08_21_A13949.png
452019_11_08_21_A13953.png
562019_11_08_21_A13954.png
672019_11_08_21_A13955.png
782019_11_08_21_A13960.png
892019_11_08_21_A13978.png
9102019_11_08_21_A13979.png
순번파일명
90912019_11_08_21_A14546.png
91922019_11_08_21_A14559.png
92932019_11_08_21_A14573.png
93942019_11_08_21_A14580.png
94952019_11_08_21_A14600.png
95962019_11_08_21_A14605.png
96972019_11_08_21_A14614.png
97982019_11_08_21_A14615.png
98992019_11_08_21_A14650.png
991002019_11_08_21_A14655.png