Overview

Dataset statistics

Number of variables5
Number of observations399
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.9 KiB
Average record size in memory43.3 B

Variable types

DateTime1
Numeric3
Text1

Alerts

1 is highly overall correlated with 4High correlation
4 is highly overall correlated with 1High correlation

Reproduction

Analysis started2023-12-10 06:41:43.507966
Analysis finished2023-12-10 06:41:45.630166
Duration2.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2019-07-23
Date

Distinct20
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Minimum2019-07-23 00:00:00
Maximum2019-08-17 00:00:00
2023-12-10T15:41:45.722882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:45.903414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

1
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.52381
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:41:46.065803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q315.5
95-th percentile19.1
Maximum20
Range19
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation5.7610553
Coefficient of variation (CV)0.5474306
Kurtosis-1.2046542
Mean10.52381
Median Absolute Deviation (MAD)5
Skewness-0.0011379893
Sum4199
Variance33.189758
MonotonicityNot monotonic
2023-12-10T15:41:46.241237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 20
 
5.0%
3 20
 
5.0%
20 20
 
5.0%
19 20
 
5.0%
18 20
 
5.0%
17 20
 
5.0%
16 20
 
5.0%
15 20
 
5.0%
14 20
 
5.0%
13 20
 
5.0%
Other values (10) 199
49.9%
ValueCountFrequency (%)
1 19
4.8%
2 20
5.0%
3 20
5.0%
4 20
5.0%
5 20
5.0%
6 20
5.0%
7 20
5.0%
8 20
5.0%
9 20
5.0%
10 20
5.0%
ValueCountFrequency (%)
20 20
5.0%
19 20
5.0%
18 20
5.0%
17 20
5.0%
16 20
5.0%
15 20
5.0%
14 20
5.0%
13 20
5.0%
12 20
5.0%
11 20
5.0%

원피스
Text

Distinct147
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2023-12-10T15:41:46.684039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length16
Mean length4.122807
Min length2

Characters and Unicode

Total characters1645
Distinct characters268
Distinct categories8 ?
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 (%)22.8%

Sample

1st row선풍기
2nd row에어팟
3rd row시서스
4th row쿨매트
5th row플레이도우
ValueCountFrequency (%)
원피스 19
 
4.5%
나이키 19
 
4.5%
선풍기 18
 
4.3%
에어프라이어 18
 
4.3%
블라우스 17
 
4.0%
에어팟 17
 
4.0%
데싱디바 14
 
3.3%
가방 13
 
3.1%
x1 13
 
3.1%
여성샌들 9
 
2.1%
Other values (149) 264
62.7%
2023-12-10T15:41:47.321170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 103
 
6.3%
어 73
 
4.4%
이 69
 
4.2%
에 56
 
3.4%
라 52
 
3.2%
원 50
 
3.0%
기 41
 
2.5%
피 41
 
2.5%
가 31
 
1.9%
프 26
 
1.6%
Other values (258) 1103
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1500
91.2%
Lowercase Letter 35
 
2.1%
Decimal Number 34
 
2.1%
Other Punctuation 26
 
1.6%
Space Separator 24
 
1.5%
Uppercase Letter 24
 
1.5%
Dash Punctuation 1
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 103
 
6.9%
어 73
 
4.9%
이 69
 
4.6%
에 56
 
3.7%
라 52
 
3.5%
원 50
 
3.3%
기 41
 
2.7%
피 41
 
2.7%
가 31
 
2.1%
프 26
 
1.7%
Other values (226) 958
63.9%
Lowercase Letter
ValueCountFrequency (%)
v 6
17.1%
i 4
11.4%
t 4
11.4%
e 4
11.4%
p 3
8.6%
c 2
 
5.7%
f 2
 
5.7%
a 2
 
5.7%
h 2
 
5.7%
g 2
 
5.7%
Other values (4) 4
11.4%
Uppercase Letter
ValueCountFrequency (%)
X 13
54.2%
M 2
 
8.3%
G 2
 
8.3%
A 2
 
8.3%
S 1
 
4.2%
T 1
 
4.2%
C 1
 
4.2%
H 1
 
4.2%
D 1
 
4.2%
Decimal Number
ValueCountFrequency (%)
1 21
61.8%
0 6
 
17.6%
2 5
 
14.7%
9 1
 
2.9%
4 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
" 26
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1500
91.2%
Common 86
 
5.2%
Latin 59
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 103
 
6.9%
어 73
 
4.9%
이 69
 
4.6%
에 56
 
3.7%
라 52
 
3.5%
원 50
 
3.3%
기 41
 
2.7%
피 41
 
2.7%
가 31
 
2.1%
프 26
 
1.7%
Other values (226) 958
63.9%
Latin
ValueCountFrequency (%)
X 13
22.0%
v 6
 
10.2%
i 4
 
6.8%
t 4
 
6.8%
e 4
 
6.8%
p 3
 
5.1%
c 2
 
3.4%
f 2
 
3.4%
a 2
 
3.4%
h 2
 
3.4%
Other values (13) 17
28.8%
Common
ValueCountFrequency (%)
" 26
30.2%
24
27.9%
1 21
24.4%
0 6
 
7.0%
2 5
 
5.8%
9 1
 
1.2%
- 1
 
1.2%
+ 1
 
1.2%
4 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1500
91.2%
ASCII 145
 
8.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 103
 
6.9%
어 73
 
4.9%
이 69
 
4.6%
에 56
 
3.7%
라 52
 
3.5%
원 50
 
3.3%
기 41
 
2.7%
피 41
 
2.7%
가 31
 
2.1%
프 26
 
1.7%
Other values (226) 958
63.9%
ASCII
ValueCountFrequency (%)
" 26
17.9%
24
16.6%
1 21
14.5%
X 13
9.0%
0 6
 
4.1%
v 6
 
4.1%
2 5
 
3.4%
i 4
 
2.8%
t 4
 
2.8%
e 4
 
2.8%
Other values (22) 32
22.1%

4
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0902256
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:41:47.513752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98321578
Coefficient of variation (CV)0.4703874
Kurtosis3.113008
Mean2.0902256
Median Absolute Deviation (MAD)0
Skewness1.539763
Sum834
Variance0.96671327
MonotonicityNot monotonic
2023-12-10T15:41:47.675551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 223
55.9%
1 97
24.3%
3 46
 
11.5%
4 16
 
4.0%
5 13
 
3.3%
6 4
 
1.0%
ValueCountFrequency (%)
1 97
24.3%
2 223
55.9%
3 46
 
11.5%
4 16
 
4.0%
5 13
 
3.3%
6 4
 
1.0%
ValueCountFrequency (%)
6 4
 
1.0%
5 13
 
3.3%
4 16
 
4.0%
3 46
 
11.5%
2 223
55.9%
1 97
24.3%

3.75
Real number (ℝ)

Distinct134
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.53162
Minimum1
Maximum62.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:41:47.846618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median10
Q328
95-th percentile46
Maximum62.5
Range61.5
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.965298
Coefficient of variation (CV)0.96353744
Kurtosis-0.47559235
Mean15.53162
Median Absolute Deviation (MAD)9
Skewness0.80908282
Sum6197.1165
Variance223.96014
MonotonicityNot monotonic
2023-12-10T15:41:48.050642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 92
 
23.1%
4.5 10
 
2.5%
5.5 10
 
2.5%
2.0 10
 
2.5%
4.0 10
 
2.5%
8.0 7
 
1.8%
6.5 6
 
1.5%
30.0 6
 
1.5%
8.5 5
 
1.3%
18.0 5
 
1.3%
Other values (124) 238
59.6%
ValueCountFrequency (%)
1.0 92
23.1%
1.2 1
 
0.3%
1.25 1
 
0.3%
1.6 1
 
0.3%
1.6667 1
 
0.3%
1.75 2
 
0.5%
1.8 1
 
0.3%
2.0 10
 
2.5%
2.2 1
 
0.3%
2.3333 2
 
0.5%
ValueCountFrequency (%)
62.5 1
 
0.3%
53.5 2
 
0.5%
50.5 1
 
0.3%
50.0 3
0.8%
49.5 1
 
0.3%
49.0 2
 
0.5%
48.5 1
 
0.3%
47.5 3
0.8%
46.5 5
1.3%
46.0 3
0.8%

Interactions

2023-12-10T15:41:44.964206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:43.834892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:44.550254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:45.120504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:44.272682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:44.680067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:45.254043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:44.407107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:44.821109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:41:48.208311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2019-07-23143.75
2019-07-231.0000.0000.2380.000
10.0001.0000.7590.574
40.2380.7591.0000.514
3.750.0000.5740.5141.000
2023-12-10T15:41:48.349702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
143.75
11.000-0.806-0.104
4-0.8061.0000.496
3.75-0.1040.4961.000

Missing values

2023-12-10T15:41:45.431804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:41:45.574246image/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

2019-07-231원피스43.75
02019-07-232선풍기48.5
12019-07-233에어팟417.5
22019-07-234시서스33.6667
32019-07-235쿨매트331.0
42019-07-236플레이도우24.5
52019-07-237나이키25.5
62019-07-238블라우스212.5
72019-07-239크록스217.0
82019-07-2310아쿠아슈즈228.5
92019-07-2311에어프라이어241.0
2019-07-231원피스43.75
3892019-08-1711vip데이11.0
3902019-08-1712X111.0
3912019-08-1713노트10케이스11.0
3922019-08-1714모유 유산균11.0
3932019-08-1715셀럽샵11.0
3942019-08-1716슬릿팬츠11.0
3952019-08-1717아이즈원11.0
3962019-08-1718침대11.0
3972019-08-1719"TS샴푸"12.0
3982019-08-1720A+G 엣지12.0