Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory43.3 B

Variable types

Numeric2
Text2
Categorical1

Alerts

seq_no is highly overall correlated with begin_de and 1 other fieldsHigh correlation
begin_de is highly overall correlated with seq_no and 1 other fieldsHigh correlation
trget_pd is highly overall correlated with seq_no and 1 other fieldsHigh correlation
seq_no has unique valuesUnique
trget_cd has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:01:31.732506
Analysis finished2023-12-10 10:01:33.341450
Duration1.61 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.69
Minimum1
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:01:33.458750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum588
Range587
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation96.020483
Coefficient of variation (CV)1.4185328
Kurtosis24.484825
Mean67.69
Median Absolute Deviation (MAD)25.5
Skewness4.8419703
Sum6769
Variance9219.9332
MonotonicityNot monotonic
2023-12-10T19:01:33.705974image/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%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
588 1
1.0%
587 1
1.0%
586 1
1.0%
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%

trget_cd
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:01:34.193850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters2000
Distinct characters15
Distinct categories2 ?
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 rowV00A004S0013FCS00803
2nd rowV00A004S0013FCS01234
3rd rowV00A004S0013FCS00805
4th rowV00A004S0013FCS00806
5th rowV00A004S0013FCS00807
ValueCountFrequency (%)
v00a004s0013fcs00803 1
 
1.0%
v00a004s0016fcs00307 1
 
1.0%
v00a004s0013fcs00862 1
 
1.0%
v00a004s0013fcs00861 1
 
1.0%
v00a004s0013fcs00860 1
 
1.0%
v00a004s0013fcs00859 1
 
1.0%
v00a004s0016fcs00314 1
 
1.0%
v00a004s0016fcs00313 1
 
1.0%
v00a004s0016fcs00312 1
 
1.0%
v00a004s0016fcs00311 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:01:35.179942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 820
41.0%
S 200
 
10.0%
1 126
 
6.3%
3 123
 
6.2%
4 121
 
6.0%
8 102
 
5.1%
V 100
 
5.0%
A 100
 
5.0%
F 100
 
5.0%
C 100
 
5.0%
Other values (5) 108
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
70.0%
Uppercase Letter 600
30.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 820
58.6%
1 126
 
9.0%
3 123
 
8.8%
4 121
 
8.6%
8 102
 
7.3%
6 35
 
2.5%
2 23
 
1.6%
5 21
 
1.5%
7 20
 
1.4%
9 9
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
S 200
33.3%
V 100
16.7%
A 100
16.7%
F 100
16.7%
C 100
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1400
70.0%
Latin 600
30.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 820
58.6%
1 126
 
9.0%
3 123
 
8.8%
4 121
 
8.6%
8 102
 
7.3%
6 35
 
2.5%
2 23
 
1.6%
5 21
 
1.5%
7 20
 
1.4%
9 9
 
0.6%
Latin
ValueCountFrequency (%)
S 200
33.3%
V 100
16.7%
A 100
16.7%
F 100
16.7%
C 100
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 820
41.0%
S 200
 
10.0%
1 126
 
6.3%
3 123
 
6.2%
4 121
 
6.0%
8 102
 
5.1%
V 100
 
5.0%
A 100
 
5.0%
F 100
 
5.0%
C 100
 
5.0%
Other values (5) 108
 
5.4%

trget_cd_nm
Text

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:01:35.958501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length16.73
Min length5

Characters and Unicode

Total characters1673
Distinct characters286
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)88.0%

Sample

1st row(測試用) 瓦城 200元好運金
2nd row鳳梨芭樂青茶L-冰 60元，特價55元
3rd row(測試用) 瓦城 388元平日好運金
4th row(測試用) 酥炸蝦醬雞米花1份 大心招待券
5th row(測試)泰國奶茶1杯(或70元以下冰飲)大心招待券
ValueCountFrequency (%)
限全家${xx店}兌換 6
 
3.5%
好友分享 6
 
3.5%
200元好運金 6
 
3.5%
測試用 6
 
3.5%
平日好運金 5
 
2.9%
388元平日好運金 4
 
2.3%
全家購物金10元 4
 
2.3%
288元 4
 
2.3%
line 4
 
2.3%
kitchen 3
 
1.7%
Other values (102) 125
72.3%
2023-12-10T19:01:37.099737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
73
 
4.4%
元 71
 
4.2%
0 69
 
4.1%
， 40
 
2.4%
5 38
 
2.3%
- 31
 
1.9%
1 31
 
1.9%
試 29
 
1.7%
測 29
 
1.7%
券 27
 
1.6%
Other values (276) 1235
73.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1018
60.8%
Decimal Number 229
 
13.7%
Uppercase Letter 130
 
7.8%
Space Separator 73
 
4.4%
Other Punctuation 49
 
2.9%
Open Punctuation 45
 
2.7%
Close Punctuation 45
 
2.7%
Lowercase Letter 40
 
2.4%
Dash Punctuation 31
 
1.9%
Currency Symbol 10
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
元 71
 
7.0%
試 29
 
2.8%
測 29
 
2.8%
券 27
 
2.7%
好 27
 
2.7%
用 26
 
2.6%
金 26
 
2.6%
特 24
 
2.4%
價 24
 
2.4%
冰 23
 
2.3%
Other values (219) 712
69.9%
Uppercase Letter
ValueCountFrequency (%)
L 26
20.0%
X 15
11.5%
I 13
10.0%
M 12
9.2%
B 10
 
7.7%
N 8
 
6.2%
E 8
 
6.2%
A 6
 
4.6%
T 5
 
3.8%
P 4
 
3.1%
Other values (9) 23
17.7%
Lowercase Letter
ValueCountFrequency (%)
o 8
20.0%
i 6
15.0%
n 6
15.0%
s 5
12.5%
t 3
 
7.5%
a 3
 
7.5%
e 2
 
5.0%
v 1
 
2.5%
m 1
 
2.5%
l 1
 
2.5%
Other values (4) 4
10.0%
Decimal Number
ValueCountFrequency (%)
0 69
30.1%
5 38
16.6%
1 31
13.5%
6 27
 
11.8%
8 22
 
9.6%
2 19
 
8.3%
4 10
 
4.4%
3 9
 
3.9%
7 3
 
1.3%
9 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 26
57.8%
「 12
26.7%
{ 6
 
13.3%
【 1
 
2.2%
Close Punctuation
ValueCountFrequency (%)
) 26
57.8%
」 12
26.7%
} 6
 
13.3%
】 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
， 40
81.6%
、 9
 
18.4%
Space Separator
ValueCountFrequency (%)
73
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 10
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 1018
60.8%
Common 485
29.0%
Latin 170
 
10.2%

Most frequent character per script

Han
ValueCountFrequency (%)
元 71
 
7.0%
試 29
 
2.8%
測 29
 
2.8%
券 27
 
2.7%
好 27
 
2.7%
用 26
 
2.6%
金 26
 
2.6%
特 24
 
2.4%
價 24
 
2.4%
冰 23
 
2.3%
Other values (219) 712
69.9%
Latin
ValueCountFrequency (%)
L 26
15.3%
X 15
 
8.8%
I 13
 
7.6%
M 12
 
7.1%
B 10
 
5.9%
N 8
 
4.7%
E 8
 
4.7%
o 8
 
4.7%
i 6
 
3.5%
n 6
 
3.5%
Other values (23) 58
34.1%
Common
ValueCountFrequency (%)
73
15.1%
0 69
14.2%
， 40
 
8.2%
5 38
 
7.8%
- 31
 
6.4%
1 31
 
6.4%
6 27
 
5.6%
( 26
 
5.4%
) 26
 
5.4%
8 22
 
4.5%
Other values (14) 102
21.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 1018
60.8%
ASCII 580
34.7%
None 75
 
4.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
73
 
12.6%
0 69
 
11.9%
5 38
 
6.6%
- 31
 
5.3%
1 31
 
5.3%
6 27
 
4.7%
( 26
 
4.5%
) 26
 
4.5%
L 26
 
4.5%
8 22
 
3.8%
Other values (41) 211
36.4%
CJK
ValueCountFrequency (%)
元 71
 
7.0%
試 29
 
2.8%
測 29
 
2.8%
券 27
 
2.7%
好 27
 
2.7%
用 26
 
2.6%
金 26
 
2.6%
特 24
 
2.4%
價 24
 
2.4%
冰 23
 
2.3%
Other values (219) 712
69.9%
None
ValueCountFrequency (%)
， 40
53.3%
」 12
 
16.0%
「 12
 
16.0%
、 9
 
12.0%
】 1
 
1.3%
【 1
 
1.3%

trget_pd
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021-01-05 ~ 2021-01-31
18 
2021-01-01 ~ 2021-12-31
11 
2021-02-01 ~ 2021-02-28
10 
2021-02-08 ~ 2021-03-12
10 
2021-03-01 ~ 2021-03-31
9 
Other values (22)
42 

Length

Max length23
Median length23
Mean length23
Min length23

Unique

Unique10 ?
Unique (%)10.0%

Sample

1st row2021-01-05 ~ 2021-01-31
2nd row2021-10-20 ~ 2021-10-31
3rd row2021-01-05 ~ 2021-01-31
4th row2021-01-05 ~ 2021-01-31
5th row2021-01-05 ~ 2021-01-31

Common Values

ValueCountFrequency (%)
2021-01-05 ~ 2021-01-31 18
18.0%
2021-01-01 ~ 2021-12-31 11
11.0%
2021-02-01 ~ 2021-02-28 10
 
10.0%
2021-02-08 ~ 2021-03-12 10
 
10.0%
2021-03-01 ~ 2021-03-31 9
 
9.0%
2021-03-02 ~ 2021-04-30 4
 
4.0%
2021-03-08 ~ 2021-04-18 4
 
4.0%
2021-02-01 ~ 2021-02-25 3
 
3.0%
2021-03-01 ~ 2021-03-25 3
 
3.0%
2021-10-20 ~ 2021-10-31 3
 
3.0%
Other values (17) 25
25.0%

Length

2023-12-10T19:01:37.400778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100
33.3%
2021-01-05 18
 
6.0%
2021-01-31 18
 
6.0%
2021-03-31 13
 
4.3%
2021-12-31 13
 
4.3%
2021-02-01 13
 
4.3%
2021-03-01 12
 
4.0%
2021-02-28 12
 
4.0%
2021-02-08 11
 
3.7%
2021-01-01 11
 
3.7%
Other values (28) 79
26.3%

begin_de
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210192
Minimum20210105
Maximum20211019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:01:37.651153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210105
5-th percentile20210105
Q120210112
median20210126
Q320210222
95-th percentile20210308
Maximum20211019
Range914
Interquartile range (IQR)110

Descriptive statistics

Standard deviation158.18359
Coefficient of variation (CV)7.8269213 × 10-6
Kurtosis20.963873
Mean20210192
Median Absolute Deviation (MAD)21.5
Skewness4.3687481
Sum2.0210192 × 109
Variance25022.047
MonotonicityNot monotonic
2023-12-10T19:01:37.936723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20210105 18
18.0%
20210126 12
12.0%
20210112 11
11.0%
20210208 11
11.0%
20210222 9
9.0%
20210226 7
 
7.0%
20210308 4
 
4.0%
20210118 4
 
4.0%
20210218 4
 
4.0%
20211019 3
 
3.0%
Other values (9) 17
17.0%
ValueCountFrequency (%)
20210105 18
18.0%
20210108 1
 
1.0%
20210111 3
 
3.0%
20210112 11
11.0%
20210118 4
 
4.0%
20210119 1
 
1.0%
20210126 12
12.0%
20210127 2
 
2.0%
20210203 2
 
2.0%
20210205 3
 
3.0%
ValueCountFrequency (%)
20211019 3
 
3.0%
20210308 4
 
4.0%
20210304 2
 
2.0%
20210226 7
7.0%
20210223 2
 
2.0%
20210222 9
9.0%
20210220 1
 
1.0%
20210218 4
 
4.0%
20210208 11
11.0%
20210205 3
 
3.0%

Interactions

2023-12-10T19:01:32.712190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:32.287909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:32.905003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:32.476247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:01:38.115527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_notrget_cdtrget_cd_nmtrget_pdbegin_de
seq_no1.0001.0000.8271.0000.878
trget_cd1.0001.0001.0001.0001.000
trget_cd_nm0.8271.0001.0000.0000.924
trget_pd1.0001.0000.0001.0001.000
begin_de0.8781.0000.9241.0001.000
2023-12-10T19:01:38.343919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nobegin_detrget_pd
seq_no1.0000.9940.859
begin_de0.9941.0000.872
trget_pd0.8590.8721.000

Missing values

2023-12-10T19:01:33.112377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:01:33.283153image/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

seq_notrget_cdtrget_cd_nmtrget_pdbegin_de
01V00A004S0013FCS00803(測試用) 瓦城 200元好運金2021-01-05 ~ 2021-01-3120210105
1586V00A004S0013FCS01234鳳梨芭樂青茶L-冰 60元，特價55元2021-10-20 ~ 2021-10-3120211019
23V00A004S0013FCS00805(測試用) 瓦城 388元平日好運金2021-01-05 ~ 2021-01-3120210105
34V00A004S0013FCS00806(測試用) 酥炸蝦醬雞米花1份 大心招待券2021-01-05 ~ 2021-01-3120210105
45V00A004S0013FCS00807(測試)泰國奶茶1杯(或70元以下冰飲)大心招待券2021-01-05 ~ 2021-01-3120210105
56V00A004S0013FCS00808(測試用) 非常泰 200元好運金2021-01-05 ~ 2021-01-3120210105
67V00A004S0013FCS00809(測試用) 非常泰 288元 平日好運金2021-01-05 ~ 2021-01-3120210105
7587V00A004S0013FCS01235草莓厚奶茶L-冰 60元，特價55元2021-10-20 ~ 2021-10-3120211019
89V00A004S0013FCS00811(測試用)YABI KITCHEN 200元好運金2021-01-05 ~ 2021-01-3120210105
910V00A004S0013FCS00812測試YABI KITCHEN 288元 平日好運金2021-01-05 ~ 2021-01-3120210105
seq_notrget_cdtrget_cd_nmtrget_pdbegin_de
9091V00A004S0013FCS00879(測試)泰國奶茶1杯 大心招待券2021-03-02 ~ 2021-04-3020210226
9192V00A004S0013FCS00880(測試)200元 瓦城集團品牌抵用券2021-03-02 ~ 2021-04-3020210226
9293V00A004S0013FCS00881(測試)LINE TAXI 乘車金兌換券2021-02-26 ~ 2021-03-3120210226
9394V00A004S0013FCS00882克蘭詩氧氣亮白精華精巧瓶兌換券2021-02-26 ~ 2021-03-0720210226
9495V00A004S0013FCS00883小小公爵甜蜜禮2021-03-04 ~ 2021-05-0920210304
9596V00A004S0013FCS00884甜蜜購物金2021-03-04 ~ 2021-05-0920210304
9697V00A004S0013FCS00885D+AF消費滿$1500贈50點2021-03-08 ~ 2021-04-1820210308
9798V00A004S0013FCS00886OB嚴選消費滿$1000贈60點2021-03-08 ~ 2021-04-1820210308
9899V00A004S0013FCS00887plain-me消費滿$1200贈105點2021-03-08 ~ 2021-04-1820210308
99100V00A004S0013FCS00888露比午茶消費滿$1000贈60點2021-03-08 ~ 2021-04-1820210308