Overview

Dataset statistics

Number of variables5
Number of observations69
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory44.9 B

Variable types

DateTime1
Text1
Numeric3

Alerts

2019-07-18 has constant value ""Constant
54 is highly overall correlated with 1 and 1 other fieldsHigh correlation
1 is highly overall correlated with 54 and 1 other fieldsHigh correlation
3.765690 is highly overall correlated with 54 and 1 other fieldsHigh correlation
배추김치 has unique valuesUnique
1 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:36:36.608777
Analysis finished2023-12-10 06:36:38.894023
Duration2.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2019-07-18
Date

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
Minimum2019-07-18 00:00:00
Maximum2019-07-18 00:00:00
2023-12-10T15:36:38.978765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:39.133379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

배추김치
Text

UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size684.0 B
2023-12-10T15:36:39.524507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.4927536
Min length3

Characters and Unicode

Total characters379
Distinct characters144
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

Unique69 ?
Unique (%)100.0%

Sample

1st row 쌀밥
2nd row 아이스 아메리카노
3rd row 흑미밥
4th row 토마토
5th row 하리보
ValueCountFrequency (%)
아메리카노 2
 
2.8%
바나나 2
 
2.8%
쌀밥 1
 
1.4%
참외 1
 
1.4%
잡채밥 1
 
1.4%
계란찜 1
 
1.4%
콩나물밥 1
 
1.4%
오이 1
 
1.4%
오이피클 1
 
1.4%
콩나물무침 1
 
1.4%
Other values (60) 60
83.3%
2023-12-10T15:36:40.171005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72
 
19.0%
밥 10
 
2.6%
나 9
 
2.4%
장 7
 
1.8%
볶 7
 
1.8%
리 7
 
1.8%
음 7
 
1.8%
치 6
 
1.6%
아 6
 
1.6%
이 6
 
1.6%
Other values (134) 242
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 272
71.8%
Space Separator 72
 
19.0%
Lowercase Letter 19
 
5.0%
Open Punctuation 5
 
1.3%
Close Punctuation 5
 
1.3%
Uppercase Letter 3
 
0.8%
Decimal Number 2
 
0.5%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
밥 10
 
3.7%
나 9
 
3.3%
장 7
 
2.6%
볶 7
 
2.6%
리 7
 
2.6%
음 7
 
2.6%
치 6
 
2.2%
아 6
 
2.2%
이 6
 
2.2%
스 5
 
1.8%
Other values (112) 202
74.3%
Lowercase Letter
ValueCountFrequency (%)
o 3
15.8%
n 2
10.5%
i 2
10.5%
a 2
10.5%
m 2
10.5%
s 1
 
5.3%
g 1
 
5.3%
j 1
 
5.3%
e 1
 
5.3%
f 1
 
5.3%
Other values (3) 3
15.8%
Uppercase Letter
ValueCountFrequency (%)
S 1
33.3%
N 1
33.3%
K 1
33.3%
Decimal Number
ValueCountFrequency (%)
0 1
50.0%
5 1
50.0%
Space Separator
ValueCountFrequency (%)
72
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Other Punctuation
ValueCountFrequency (%)
% 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 272
71.8%
Common 85
 
22.4%
Latin 22
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
밥 10
 
3.7%
나 9
 
3.3%
장 7
 
2.6%
볶 7
 
2.6%
리 7
 
2.6%
음 7
 
2.6%
치 6
 
2.2%
아 6
 
2.2%
이 6
 
2.2%
스 5
 
1.8%
Other values (112) 202
74.3%
Latin
ValueCountFrequency (%)
o 3
13.6%
n 2
 
9.1%
i 2
 
9.1%
a 2
 
9.1%
m 2
 
9.1%
s 1
 
4.5%
g 1
 
4.5%
j 1
 
4.5%
S 1
 
4.5%
N 1
 
4.5%
Other values (6) 6
27.3%
Common
ValueCountFrequency (%)
72
84.7%
( 5
 
5.9%
) 5
 
5.9%
% 1
 
1.2%
0 1
 
1.2%
5 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 272
71.8%
ASCII 107
 
28.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
67.3%
( 5
 
4.7%
) 5
 
4.7%
o 3
 
2.8%
n 2
 
1.9%
i 2
 
1.9%
a 2
 
1.9%
m 2
 
1.9%
s 1
 
0.9%
g 1
 
0.9%
Other values (12) 12
 
11.2%
Hangul
ValueCountFrequency (%)
밥 10
 
3.7%
나 9
 
3.3%
장 7
 
2.6%
볶 7
 
2.6%
리 7
 
2.6%
음 7
 
2.6%
치 6
 
2.2%
아 6
 
2.2%
이 6
 
2.2%
스 5
 
1.8%
Other values (112) 202
74.3%

54
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1304348
Minimum4
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-10T15:36:40.356378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q15
median7
Q39
95-th percentile14
Maximum38
Range34
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.0114957
Coefficient of variation (CV)0.61638718
Kurtosis18.726275
Mean8.1304348
Median Absolute Deviation (MAD)2
Skewness3.7001251
Sum561
Variance25.11509
MonotonicityDecreasing
2023-12-10T15:36:40.524890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 16
23.2%
8 12
17.4%
6 8
11.6%
7 7
10.1%
9 6
 
8.7%
4 6
 
8.7%
10 4
 
5.8%
12 3
 
4.3%
21 2
 
2.9%
14 2
 
2.9%
Other values (2) 3
 
4.3%
ValueCountFrequency (%)
4 6
 
8.7%
5 16
23.2%
6 8
11.6%
7 7
10.1%
8 12
17.4%
9 6
 
8.7%
10 4
 
5.8%
12 3
 
4.3%
13 2
 
2.9%
14 2
 
2.9%
ValueCountFrequency (%)
38 1
 
1.4%
21 2
 
2.9%
14 2
 
2.9%
13 2
 
2.9%
12 3
 
4.3%
10 4
 
5.8%
9 6
8.7%
8 12
17.4%
7 7
10.1%
6 8
11.6%

1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36
Minimum2
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-10T15:36:40.779500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.4
Q119
median36
Q353
95-th percentile66.6
Maximum70
Range68
Interquartile range (IQR)34

Descriptive statistics

Standard deviation20.062403
Coefficient of variation (CV)0.55728896
Kurtosis-1.2
Mean36
Median Absolute Deviation (MAD)17
Skewness0
Sum2484
Variance402.5
MonotonicityStrictly increasing
2023-12-10T15:36:41.011651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
1.4%
46 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
45 1
 
1.4%
54 1
 
1.4%
Other values (59) 59
85.5%
ValueCountFrequency (%)
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
11 1
1.4%
ValueCountFrequency (%)
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%
63 1
1.4%
62 1
1.4%
61 1
1.4%

3.765690
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56697587
Minimum0.27894
Maximum2.64993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-10T15:36:41.222584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.27894
5-th percentile0.27894
Q10.348675
median0.488145
Q30.627615
95-th percentile0.97629
Maximum2.64993
Range2.37099
Interquartile range (IQR)0.27894

Descriptive statistics

Standard deviation0.34947666
Coefficient of variation (CV)0.61638718
Kurtosis18.726275
Mean0.56697587
Median Absolute Deviation (MAD)0.13947
Skewness3.7001251
Sum39.121335
Variance0.12213393
MonotonicityDecreasing
2023-12-10T15:36:41.438545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.348675 16
23.2%
0.55788 12
17.4%
0.41841 8
11.6%
0.488145 7
10.1%
0.627615 6
 
8.7%
0.27894 6
 
8.7%
0.69735 4
 
5.8%
0.83682 3
 
4.3%
1.464435 2
 
2.9%
0.97629 2
 
2.9%
Other values (2) 3
 
4.3%
ValueCountFrequency (%)
0.27894 6
 
8.7%
0.348675 16
23.2%
0.41841 8
11.6%
0.488145 7
10.1%
0.55788 12
17.4%
0.627615 6
 
8.7%
0.69735 4
 
5.8%
0.83682 3
 
4.3%
0.906555 2
 
2.9%
0.97629 2
 
2.9%
ValueCountFrequency (%)
2.64993 1
 
1.4%
1.464435 2
 
2.9%
0.97629 2
 
2.9%
0.906555 2
 
2.9%
0.83682 3
 
4.3%
0.69735 4
 
5.8%
0.627615 6
8.7%
0.55788 12
17.4%
0.488145 7
10.1%
0.41841 8
11.6%

Interactions

2023-12-10T15:36:37.852354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:36.860385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:37.311918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:38.380839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:37.018260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:37.461724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:38.527068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:37.179836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:37.617890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:36:41.575214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배추김치5413.765690
배추김치1.0001.0001.0001.000
541.0001.0000.9011.000
11.0000.9011.0000.901
3.7656901.0001.0000.9011.000
2023-12-10T15:36:41.789400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5413.765690
541.000-0.9891.000
1-0.9891.000-0.989
3.7656901.000-0.9891.000

Missing values

2023-12-10T15:36:38.693534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:36:38.834173image/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

2019-07-18배추김치5413.765690
02019-07-18쌀밥3822.64993
12019-07-18아이스 아메리카노2131.464435
22019-07-18흑미밥2141.464435
32019-07-18토마토1450.97629
42019-07-18하리보1460.97629
52019-07-18닭가슴살샐러드1370.906555
62019-07-18채소샐러드1380.906555
72019-07-18깍두기1290.83682
82019-07-18계란후라이12100.83682
92019-07-18샌드위치12110.83682
2019-07-18배추김치5413.765690
592019-07-18돈까스5610.348675
602019-07-18양파장아찌5620.348675
612019-07-18비빔밥5630.348675
622019-07-18멸치볶음5640.348675
632019-07-18돼지갈비(생것)4650.27894
642019-07-18상추4660.27894
652019-07-18브로콜리4670.27894
662019-07-18총각김치4680.27894
672019-07-18바나나 스플릿4690.27894
682019-07-18닭볶음탕4700.27894