Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells263
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.0 KiB
Average record size in memory153.3 B

Variable types

Numeric4
Categorical9
Text3
Unsupported2

Alerts

PROMTN_TY_CD has constant value ""Constant
PROMTN_TY_NM has constant value ""Constant
TRMNL_MODL_OPERSYSM_TY_CD has constant value ""Constant
COUPON_NM is highly overall correlated with COUPON_USE_DT and 5 other fieldsHigh correlation
COUPON_BNEF_CN is highly overall correlated with COUPON_NM and 3 other fieldsHigh correlation
BRAND_NM is highly overall correlated with COUPON_USE_DT and 4 other fieldsHigh correlation
COUPON_USGSTT_NM is highly overall correlated with COUPON_USE_DT and 5 other fieldsHigh correlation
CLUSTR_ID is highly overall correlated with COUPON_USE_DT and 5 other fieldsHigh correlation
ESNTL_ID is highly overall correlated with COUPON_USE_DT and 5 other fieldsHigh correlation
SEQ_NO is highly overall correlated with COUPON_ISSU_DT and 2 other fieldsHigh correlation
COUPON_ISSU_DT is highly overall correlated with SEQ_NO and 2 other fieldsHigh correlation
COUPON_USE_DT is highly overall correlated with SEQ_NO and 7 other fieldsHigh correlation
COUPON_ID is highly overall correlated with SEQ_NO and 2 other fieldsHigh correlation
TRMNL_MODL_NM has 100 (100.0%) missing valuesMissing
TRMNL_MODL_OPERSYSM_TY_NM has 100 (100.0%) missing valuesMissing
COUPON_USE_DT has 21 (21.0%) missing valuesMissing
CRTFC_STR_NM has 21 (21.0%) missing valuesMissing
CRTFC_STR_ADDR has 21 (21.0%) missing valuesMissing
SEQ_NO has unique valuesUnique
COUPON_ISSU_DT has unique valuesUnique
COUPON_ID has unique valuesUnique
TRMNL_MODL_NM is an unsupported type, check if it needs cleaning or further analysisUnsupported
TRMNL_MODL_OPERSYSM_TY_NM is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:06:23.760935
Analysis finished2023-12-10 10:06:29.385198
Duration5.62 seconds
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%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:29.598288image/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
2023-12-10T19:06:29.988696image/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%

PROMTN_TY_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 100
100.0%

Length

2023-12-10T19:06:30.238746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:30.394212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 100
100.0%

PROMTN_TY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
대만게임프로모션참여고객성향
100 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대만게임프로모션참여고객성향
2nd row대만게임프로모션참여고객성향
3rd row대만게임프로모션참여고객성향
4th row대만게임프로모션참여고객성향
5th row대만게임프로모션참여고객성향

Common Values

ValueCountFrequency (%)
대만게임프로모션참여고객성향 100
100.0%

Length

2023-12-10T19:06:30.623302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:30.773177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대만게임프로모션참여고객성향 100
100.0%

COUPON_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
酩悅軒尼詩集點抽獎趣
72 
全家單店活動驗證券
13 
孕媽咪保養體驗券
9 
三澧企業活動驗證
 
3
手部潤澤再生抗老保養課程60分鐘1堂
 
3

Length

Max length18
Median length10
Mean length9.87
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row酩悅軒尼詩集點抽獎趣
2nd row酩悅軒尼詩集點抽獎趣
3rd row酩悅軒尼詩集點抽獎趣
4th row全家單店活動驗證券
5th row酩悅軒尼詩集點抽獎趣

Common Values

ValueCountFrequency (%)
酩悅軒尼詩集點抽獎趣 72
72.0%
全家單店活動驗證券 13
 
13.0%
孕媽咪保養體驗券 9
 
9.0%
三澧企業活動驗證 3
 
3.0%
手部潤澤再生抗老保養課程60分鐘1堂 3
 
3.0%

Length

2023-12-10T19:06:30.931566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:31.123771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
酩悅軒尼詩集點抽獎趣 72
72.0%
全家單店活動驗證券 13
 
13.0%
孕媽咪保養體驗券 9
 
9.0%
三澧企業活動驗證 3
 
3.0%
手部潤澤再生抗老保養課程60分鐘1堂 3
 
3.0%

COUPON_BNEF_CN
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
75 
1 2 3
13 
<h4><b>注意事項</b></h4><ol><li>本券使用期限請以券上標示為準。</li><li>本券限本人使用，每人使用一次，不得與克蘭詩其他同期活動重複兌換。</li><li>本券恕不兌換現金、掛失、或轉發與轉賣。</li><li>本券限使用於克蘭詩全台灣百貨實體通路專櫃。</li><li>本券核發與使用辦法以現場公告為準。</li><li>克蘭詩保有提前結束兌換或更換兌換內容之權利，恕不另行通知。</li></ol>
9 
<h5>使用注意事項</h5><ol><li>本券請在有效期限前使用完畢，逾期恕無法使用</li><li>本券每人每次限抵用一張</li><li>本券恕不兌換現金、找零、或轉賣</li><li>本券限使用於實體通路</li><li>本券發放及使用辦法以現場標示為主</li><li>本券為無償取得之贈品，使用時恕不開立統一發票</li></ol>
 
3

Length

Max length216
Median length4
Mean length28.28
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row1 2 3
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 75
75.0%
1 2 3 13
 
13.0%
<h4><b>注意事項</b></h4><ol><li>本券使用期限請以券上標示為準。</li><li>本券限本人使用，每人使用一次，不得與克蘭詩其他同期活動重複兌換。</li><li>本券恕不兌換現金、掛失、或轉發與轉賣。</li><li>本券限使用於克蘭詩全台灣百貨實體通路專櫃。</li><li>本券核發與使用辦法以現場公告為準。</li><li>克蘭詩保有提前結束兌換或更換兌換內容之權利，恕不另行通知。</li></ol> 9
 
9.0%
<h5>使用注意事項</h5><ol><li>本券請在有效期限前使用完畢，逾期恕無法使用</li><li>本券每人每次限抵用一張</li><li>本券恕不兌換現金、找零、或轉賣</li><li>本券限使用於實體通路</li><li>本券發放及使用辦法以現場標示為主</li><li>本券為無償取得之贈品，使用時恕不開立統一發票</li></ol> 3
 
3.0%

Length

2023-12-10T19:06:31.353861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:31.620587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 75
59.5%
1 13
 
10.3%
2 13
 
10.3%
3 13
 
10.3%
h4><b>注意事項</b></h4><ol><li>本券使用期限請以券上標示為準。</li><li>本券限本人使用，每人使用一次，不得與克蘭詩其他同期活動重複兌換。</li><li>本券恕不兌換現金、掛失、或轉發與轉賣。</li><li>本券限使用於克蘭詩全台灣百貨實體通路專櫃。</li><li>本券核發與使用辦法以現場公告為準。</li><li>克蘭詩保有提前結束兌換或更換兌換內容之權利，恕不另行通知。</li></ol 9
 
7.1%
h5>使用注意事項</h5><ol><li>本券請在有效期限前使用完畢，逾期恕無法使用</li><li>本券每人每次限抵用一張</li><li>本券恕不兌換現金、找零、或轉賣</li><li>本券限使用於實體通路</li><li>本券發放及使用辦法以現場標示為主</li><li>本券為無償取得之贈品，使用時恕不開立統一發票</li></ol 3
 
2.4%

BRAND_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Moet Hennessy Taiwan
71 
<NA>
21 
三澧企業
 
3
Minou Nail
 
3
Clarins
 
2

Length

Max length20
Median length20
Mean length15.6
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoet Hennessy Taiwan
2nd rowMoet Hennessy Taiwan
3rd rowMoet Hennessy Taiwan
4th row<NA>
5th rowMoet Hennessy Taiwan

Common Values

ValueCountFrequency (%)
Moet Hennessy Taiwan 71
71.0%
<NA> 21
 
21.0%
三澧企業 3
 
3.0%
Minou Nail 3
 
3.0%
Clarins 2
 
2.0%

Length

2023-12-10T19:06:31.999359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:32.234360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
moet 71
29.0%
hennessy 71
29.0%
taiwan 71
29.0%
na 21
 
8.6%
三澧企業 3
 
1.2%
minou 3
 
1.2%
nail 3
 
1.2%
clarins 2
 
0.8%

CSTMR_ID
Text

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:06:32.659010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length35.19
Min length27

Characters and Unicode

Total characters3519
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)81.0%

Sample

1st rowf80a9f04-d427-4689-aa4a-ea41c550dda5
2nd row4b28ba56-165e-482b-8fa9-5c2ae81f01b1
3rd row50f60339-1c32-49cc-a54e-3756f752983c
4th row075e4afe-51a6-48e8-b680-e3f4bd11df3a
5th rowfeabbc38-6123-4a34-991b-d4b8d3d8069d
ValueCountFrequency (%)
762e9ccf-0487-4aa9-b2b1-ba6e051fcb1c 5
 
5.0%
8f88380a-1838-4eb7-9f9e-c6f11e51e69e 3
 
3.0%
4a02928e-9b1b-4d8c-aed5-9410fd655130 3
 
3.0%
a16fd392-dcb3-4c58-a405-68043d9f830a 2
 
2.0%
cc47b8bb-141c-4f77-9986-088811f0ef85 2
 
2.0%
511b61cd-8fd3-4a3b-8433-2cb159b0f633 2
 
2.0%
c05194e0-e35e-4171-a30b-ae23f3e98226 2
 
2.0%
47845538-0b52-43eb-9720-238e779dd609 1
 
1.0%
d8334bd1-0106-4f20-b53b-9b4bc3ca9571 1
 
1.0%
threedom_clarins_c851555582 1
 
1.0%
Other values (78) 78
78.0%
2023-12-10T19:06:33.262332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 364
 
10.3%
4 252
 
7.2%
8 235
 
6.7%
b 205
 
5.8%
e 202
 
5.7%
1 200
 
5.7%
a 196
 
5.6%
9 195
 
5.5%
3 193
 
5.5%
5 192
 
5.5%
Other values (17) 1285
36.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1923
54.6%
Lowercase Letter 1214
34.5%
Dash Punctuation 364
 
10.3%
Connector Punctuation 18
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 205
16.9%
e 202
16.6%
a 196
16.1%
f 179
14.7%
c 173
14.3%
d 169
13.9%
r 18
 
1.5%
t 9
 
0.7%
h 9
 
0.7%
o 9
 
0.7%
Other values (5) 45
 
3.7%
Decimal Number
ValueCountFrequency (%)
4 252
13.1%
8 235
12.2%
1 200
10.4%
9 195
10.1%
3 193
10.0%
5 192
10.0%
0 174
9.0%
6 171
8.9%
7 157
8.2%
2 154
8.0%
Dash Punctuation
ValueCountFrequency (%)
- 364
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2305
65.5%
Latin 1214
34.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 205
16.9%
e 202
16.6%
a 196
16.1%
f 179
14.7%
c 173
14.3%
d 169
13.9%
r 18
 
1.5%
t 9
 
0.7%
h 9
 
0.7%
o 9
 
0.7%
Other values (5) 45
 
3.7%
Common
ValueCountFrequency (%)
- 364
15.8%
4 252
10.9%
8 235
10.2%
1 200
8.7%
9 195
8.5%
3 193
8.4%
5 192
8.3%
0 174
7.5%
6 171
7.4%
7 157
6.8%
Other values (2) 172
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 364
 
10.3%
4 252
 
7.2%
8 235
 
6.7%
b 205
 
5.8%
e 202
 
5.7%
1 200
 
5.7%
a 196
 
5.6%
9 195
 
5.5%
3 193
 
5.5%
5 192
 
5.5%
Other values (17) 1285
36.5%

TRMNL_MODL_NM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

TRMNL_MODL_OPERSYSM_TY_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
단말모델미분류
100 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row단말모델미분류
2nd row단말모델미분류
3rd row단말모델미분류
4th row단말모델미분류
5th row단말모델미분류

Common Values

ValueCountFrequency (%)
단말모델미분류 100
100.0%

Length

2023-12-10T19:06:33.470664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:33.642955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단말모델미분류 100
100.0%

TRMNL_MODL_OPERSYSM_TY_NM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

COUPON_ISSU_DT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.020093 × 1013
Minimum2.020093 × 1013
Maximum2.020093 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:33.922136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.020093 × 1013
5-th percentile2.020093 × 1013
Q12.020093 × 1013
median2.020093 × 1013
Q32.020093 × 1013
95-th percentile2.020093 × 1013
Maximum2.020093 × 1013
Range61776
Interquartile range (IQR)36209.75

Descriptive statistics

Standard deviation19336.929
Coefficient of variation (CV)9.5722964 × 10-10
Kurtosis-1.2523376
Mean2.020093 × 1013
Median Absolute Deviation (MAD)17846
Skewness0.090546605
Sum2.020093 × 1015
Variance3.7391682 × 108
MonotonicityStrictly decreasing
2023-12-10T19:06:34.144111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200930115719 1
 
1.0%
20200930072349 1
 
1.0%
20200930064656 1
 
1.0%
20200930065834 1
 
1.0%
20200930070124 1
 
1.0%
20200930070313 1
 
1.0%
20200930070440 1
 
1.0%
20200930070731 1
 
1.0%
20200930071607 1
 
1.0%
20200930071714 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
20200930053943 1
1.0%
20200930054042 1
1.0%
20200930054157 1
1.0%
20200930054223 1
1.0%
20200930054821 1
1.0%
20200930054924 1
1.0%
20200930054940 1
1.0%
20200930054954 1
1.0%
20200930055035 1
1.0%
20200930060020 1
1.0%
ValueCountFrequency (%)
20200930115719 1
1.0%
20200930115155 1
1.0%
20200930114920 1
1.0%
20200930114407 1
1.0%
20200930114240 1
1.0%
20200930113534 1
1.0%
20200930113529 1
1.0%
20200930113421 1
1.0%
20200930113357 1
1.0%
20200930112858 1
1.0%

COUPON_USGSTT_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
사용
79 
발급
21 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사용
2nd row사용
3rd row사용
4th row발급
5th row사용

Common Values

ValueCountFrequency (%)
사용 79
79.0%
발급 21
 
21.0%

Length

2023-12-10T19:06:34.753337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:34.953617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사용 79
79.0%
발급 21
 
21.0%

COUPON_USE_DT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct79
Distinct (%)100.0%
Missing21
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean2.0200934 × 1013
Minimum2.020093 × 1013
Maximum2.0201007 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:35.429085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.020093 × 1013
5-th percentile2.020093 × 1013
Q12.020093 × 1013
median2.020093 × 1013
Q32.020093 × 1013
95-th percentile2.0200937 × 1013
Maximum2.0201007 × 1013
Range77030653
Interquartile range (IQR)35957.5

Descriptive statistics

Standard deviation16391121
Coefficient of variation (CV)8.1140413 × 10-7
Kurtosis15.936804
Mean2.0200934 × 1013
Median Absolute Deviation (MAD)18115
Skewness4.1850962
Sum1.5958738 × 1015
Variance2.6866885 × 1014
MonotonicityNot monotonic
2023-12-10T19:06:35.738026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200930115200 1
 
1.0%
20200930064707 1
 
1.0%
20200930065845 1
 
1.0%
20200930070320 1
 
1.0%
20200930070447 1
 
1.0%
20200930070741 1
 
1.0%
20200930071648 1
 
1.0%
20200930071810 1
 
1.0%
20200930071737 1
 
1.0%
20200930071812 1
 
1.0%
Other values (69) 69
69.0%
(Missing) 21
 
21.0%
ValueCountFrequency (%)
20200930053952 1
1.0%
20200930054239 1
1.0%
20200930054833 1
1.0%
20200930054935 1
1.0%
20200930054947 1
1.0%
20200930055000 1
1.0%
20200930055041 1
1.0%
20200930060026 1
1.0%
20200930060844 1
1.0%
20200930061001 1
1.0%
ValueCountFrequency (%)
20201007084605 1
1.0%
20201005122344 1
1.0%
20201004013600 1
1.0%
20201001124106 1
1.0%
20200930115724 1
1.0%
20200930115200 1
1.0%
20200930114927 1
1.0%
20200930114248 1
1.0%
20200930113536 1
1.0%
20200930113425 1
1.0%

CRTFC_STR_NM
Text

MISSING 

Distinct42
Distinct (%)53.2%
Missing21
Missing (%)21.0%
Memory size932.0 B
2023-12-10T19:06:36.191749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length4.7468354
Min length2

Characters and Unicode

Total characters375
Distinct characters147
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

Unique26 ?
Unique (%)32.9%

Sample

1st row吉昌菸酒
2nd row吉昌菸酒
3rd row瀧德
4th row吉昌菸酒
5th row吉昌菸酒
ValueCountFrequency (%)
吉昌菸酒 9
 
11.0%
收藏家 7
 
8.5%
上醇 4
 
4.9%
海珊 4
 
4.9%
力盛門市 3
 
3.7%
合歡東門 3
 
3.7%
名家 3
 
3.7%
典藏洋酒 3
 
3.7%
建成(隆興 3
 
3.7%
mo-mo-paradise 3
 
3.7%
Other values (33) 40
48.8%
2023-12-10T19:06:36.929484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
酒 17
 
4.5%
- 13
 
3.5%
) 12
 
3.2%
( 12
 
3.2%
家 11
 
2.9%
菸 10
 
2.7%
藏 10
 
2.7%
昌 9
 
2.4%
吉 9
 
2.4%
收 7
 
1.9%
Other values (137) 265
70.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 294
78.4%
Lowercase Letter 29
 
7.7%
Dash Punctuation 13
 
3.5%
Close Punctuation 12
 
3.2%
Open Punctuation 12
 
3.2%
Uppercase Letter 10
 
2.7%
Space Separator 3
 
0.8%
Decimal Number 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
酒 17
 
5.8%
家 11
 
3.7%
菸 10
 
3.4%
藏 10
 
3.4%
昌 9
 
3.1%
吉 9
 
3.1%
收 7
 
2.4%
門 6
 
2.0%
上 6
 
2.0%
力 5
 
1.7%
Other values (121) 204
69.4%
Lowercase Letter
ValueCountFrequency (%)
o 7
24.1%
a 6
20.7%
e 4
13.8%
d 3
10.3%
i 3
10.3%
s 3
10.3%
r 3
10.3%
Uppercase Letter
ValueCountFrequency (%)
M 6
60.0%
P 3
30.0%
N 1
 
10.0%
Decimal Number
ValueCountFrequency (%)
9 1
50.0%
1 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 294
78.4%
Common 42
 
11.2%
Latin 39
 
10.4%

Most frequent character per script

Han
ValueCountFrequency (%)
酒 17
 
5.8%
家 11
 
3.7%
菸 10
 
3.4%
藏 10
 
3.4%
昌 9
 
3.1%
吉 9
 
3.1%
收 7
 
2.4%
門 6
 
2.0%
上 6
 
2.0%
力 5
 
1.7%
Other values (121) 204
69.4%
Latin
ValueCountFrequency (%)
o 7
17.9%
a 6
15.4%
M 6
15.4%
e 4
10.3%
P 3
7.7%
d 3
7.7%
i 3
7.7%
s 3
7.7%
r 3
7.7%
N 1
 
2.6%
Common
ValueCountFrequency (%)
- 13
31.0%
) 12
28.6%
( 12
28.6%
3
 
7.1%
9 1
 
2.4%
1 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
CJK 294
78.4%
ASCII 81
 
21.6%

Most frequent character per block

CJK
ValueCountFrequency (%)
酒 17
 
5.8%
家 11
 
3.7%
菸 10
 
3.4%
藏 10
 
3.4%
昌 9
 
3.1%
吉 9
 
3.1%
收 7
 
2.4%
門 6
 
2.0%
上 6
 
2.0%
力 5
 
1.7%
Other values (121) 204
69.4%
ASCII
ValueCountFrequency (%)
- 13
16.0%
) 12
14.8%
( 12
14.8%
o 7
8.6%
a 6
7.4%
M 6
7.4%
e 4
 
4.9%
P 3
 
3.7%
d 3
 
3.7%
i 3
 
3.7%
Other values (6) 12
14.8%

CRTFC_STR_ADDR
Text

MISSING 

Distinct42
Distinct (%)53.2%
Missing21
Missing (%)21.0%
Memory size932.0 B
2023-12-10T19:06:37.391161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length14.405063
Min length9

Characters and Unicode

Total characters1138
Distinct characters133
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)32.9%

Sample

1st row屏東縣枋寮鄉中山路75號1樓
2nd row屏東縣枋寮鄉中山路75號1樓
3rd row桃園市龜山區忠義路2段395號
4th row屏東縣枋寮鄉中山路75號1樓
5th row屏東縣枋寮鄉中山路75號1樓
ValueCountFrequency (%)
屏東縣枋寮鄉中山路75號1樓 9
 
11.4%
台中市南屯區永春東路198號 7
 
8.9%
花蓮縣吉安鄉中華路二段159號 4
 
5.1%
桃園市大園區新興路50號 4
 
5.1%
新北市中和區中正路1196號 3
 
3.8%
台南市東區東門路一段188號 3
 
3.8%
台南市新營區新進路二段183號 3
 
3.8%
雲林縣斗六市建成路5號 3
 
3.8%
嘉義市西區建成街62樓 3
 
3.8%
新竹市香山區牛埔南路532號 2
 
2.5%
Other values (32) 38
48.1%
2023-12-10T19:06:38.084945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
號 77
 
6.8%
路 75
 
6.6%
市 62
 
5.4%
1 61
 
5.4%
區 54
 
4.7%
中 40
 
3.5%
5 33
 
2.9%
2 31
 
2.7%
8 29
 
2.5%
段 28
 
2.5%
Other values (123) 648
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 876
77.0%
Decimal Number 252
 
22.1%
Uppercase Letter 4
 
0.4%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
號 77
 
8.8%
路 75
 
8.6%
市 62
 
7.1%
區 54
 
6.2%
中 40
 
4.6%
段 28
 
3.2%
台 27
 
3.1%
東 26
 
3.0%
新 24
 
2.7%
縣 23
 
2.6%
Other values (106) 440
50.2%
Decimal Number
ValueCountFrequency (%)
1 61
24.2%
5 33
13.1%
2 31
12.3%
8 29
11.5%
9 25
9.9%
3 22
 
8.7%
7 16
 
6.3%
6 14
 
5.6%
0 12
 
4.8%
4 9
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
F 1
25.0%
N 1
25.0%
E 1
25.0%
O 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 876
77.0%
Common 258
 
22.7%
Latin 4
 
0.4%

Most frequent character per script

Han
ValueCountFrequency (%)
號 77
 
8.8%
路 75
 
8.6%
市 62
 
7.1%
區 54
 
6.2%
中 40
 
4.6%
段 28
 
3.2%
台 27
 
3.1%
東 26
 
3.0%
新 24
 
2.7%
縣 23
 
2.6%
Other values (106) 440
50.2%
Common
ValueCountFrequency (%)
1 61
23.6%
5 33
12.8%
2 31
12.0%
8 29
11.2%
9 25
9.7%
3 22
 
8.5%
7 16
 
6.2%
6 14
 
5.4%
0 12
 
4.7%
4 9
 
3.5%
Other values (3) 6
 
2.3%
Latin
ValueCountFrequency (%)
F 1
25.0%
N 1
25.0%
E 1
25.0%
O 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 876
77.0%
ASCII 262
 
23.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
號 77
 
8.8%
路 75
 
8.6%
市 62
 
7.1%
區 54
 
6.2%
中 40
 
4.6%
段 28
 
3.2%
台 27
 
3.1%
東 26
 
3.0%
新 24
 
2.7%
縣 23
 
2.6%
Other values (106) 440
50.2%
ASCII
ValueCountFrequency (%)
1 61
23.3%
5 33
12.6%
2 31
11.8%
8 29
11.1%
9 25
9.5%
3 22
 
8.4%
7 16
 
6.1%
6 14
 
5.3%
0 12
 
4.6%
4 9
 
3.4%
Other values (7) 10
 
3.8%

COUPON_ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1464012 × 1011
Minimum2.1463823 × 1011
Maximum2.1464161 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:06:38.388201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1463823 × 1011
5-th percentile2.1463834 × 1011
Q12.1463891 × 1011
median2.1464045 × 1011
Q32.1464124 × 1011
95-th percentile2.1464155 × 1011
Maximum2.1464161 × 1011
Range3371519
Interquartile range (IQR)2321952

Descriptive statistics

Standard deviation1170784.6
Coefficient of variation (CV)5.4546399 × 10-6
Kurtosis-1.4687156
Mean2.1464012 × 1011
Median Absolute Deviation (MAD)1021184
Skewness-0.26395878
Sum2.1464012 × 1013
Variance1.3707365 × 1012
MonotonicityStrictly decreasing
2023-12-10T19:06:38.695973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214641606215 1
 
1.0%
214639379371 1
 
1.0%
214638915810 1
 
1.0%
214639031415 1
 
1.0%
214639070140 1
 
1.0%
214639097138 1
 
1.0%
214639116070 1
 
1.0%
214639160747 1
 
1.0%
214639259554 1
 
1.0%
214639271282 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
214638234696 1
1.0%
214638245333 1
1.0%
214638257400 1
1.0%
214638262166 1
1.0%
214638323671 1
1.0%
214638338795 1
1.0%
214638339124 1
1.0%
214638342190 1
1.0%
214638346252 1
1.0%
214638427353 1
1.0%
ValueCountFrequency (%)
214641606215 1
1.0%
214641590262 1
1.0%
214641581108 1
1.0%
214641563555 1
1.0%
214641560817 1
1.0%
214641545878 1
1.0%
214641544522 1
1.0%
214641542339 1
1.0%
214641540391 1
1.0%
214641529686 1
1.0%

CLUSTR_ID
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
6
71 
3
14 
0
8 
2
 
5
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row0
5th row6

Common Values

ValueCountFrequency (%)
6 71
71.0%
3 14
 
14.0%
0 8
 
8.0%
2 5
 
5.0%
4 2
 
2.0%

Length

2023-12-10T19:06:39.016626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:39.251608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6 71
71.0%
3 14
 
14.0%
0 8
 
8.0%
2 5
 
5.0%
4 2
 
2.0%

ESNTL_ID
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
V00A004S0013FCS00434
72 
V00A004S0013FCS00470
13 
V00A004S0013FCS00516
9 
V00A004S0013FCS00597
 
3
V00A004S0013FCS00430
 
3

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV00A004S0013FCS00434
2nd rowV00A004S0013FCS00434
3rd rowV00A004S0013FCS00434
4th rowV00A004S0013FCS00470
5th rowV00A004S0013FCS00434

Common Values

ValueCountFrequency (%)
V00A004S0013FCS00434 72
72.0%
V00A004S0013FCS00470 13
 
13.0%
V00A004S0013FCS00516 9
 
9.0%
V00A004S0013FCS00597 3
 
3.0%
V00A004S0013FCS00430 3
 
3.0%

Length

2023-12-10T19:06:39.452724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:06:39.630435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
v00a004s0013fcs00434 72
72.0%
v00a004s0013fcs00470 13
 
13.0%
v00a004s0013fcs00516 9
 
9.0%
v00a004s0013fcs00597 3
 
3.0%
v00a004s0013fcs00430 3
 
3.0%

Interactions

2023-12-10T19:06:27.448214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:25.087866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:26.023329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:26.713013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:27.594115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:25.222352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:26.212209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:26.893018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:27.746224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:25.442681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:26.385314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:27.087939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:27.941515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:25.789704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:26.560226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:27.278888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:06:39.792334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOCOUPON_NMCOUPON_BNEF_CNBRAND_NMCSTMR_IDCOUPON_ISSU_DTCOUPON_USGSTT_NMCOUPON_USE_DTCRTFC_STR_NMCRTFC_STR_ADDRCOUPON_IDCLUSTR_IDESNTL_ID
SEQ_NO1.0000.0000.0000.1740.9060.9880.0000.0000.9060.9060.9650.0000.000
COUPON_NM0.0001.0001.0001.0001.0000.0000.7850.8441.0001.0000.0000.9581.000
COUPON_BNEF_CN0.0001.0001.0000.6111.0000.0000.4990.0001.0001.0000.0000.6601.000
BRAND_NM0.1741.0000.6111.0001.0000.000NaN0.8441.0001.0000.0001.0001.000
CSTMR_ID0.9061.0001.0001.0001.0000.8731.0000.0001.0001.0000.8881.0001.000
COUPON_ISSU_DT0.9880.0000.0000.0000.8731.0000.0000.0000.8810.8810.9720.0000.000
COUPON_USGSTT_NM0.0000.7850.499NaN1.0000.0001.000NaNNaNNaN0.0000.7920.785
COUPON_USE_DT0.0000.8440.0000.8440.0000.000NaN1.0000.8470.8470.0000.3650.844
CRTFC_STR_NM0.9061.0001.0001.0001.0000.881NaN0.8471.0001.0000.8821.0001.000
CRTFC_STR_ADDR0.9061.0001.0001.0001.0000.881NaN0.8471.0001.0000.8821.0001.000
COUPON_ID0.9650.0000.0000.0000.8880.9720.0000.0000.8820.8821.0000.3210.000
CLUSTR_ID0.0000.9580.6601.0001.0000.0000.7920.3651.0001.0000.3211.0000.958
ESNTL_ID0.0001.0001.0001.0001.0000.0000.7850.8441.0001.0000.0000.9581.000
2023-12-10T19:06:40.081479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
COUPON_NMCOUPON_BNEF_CNBRAND_NMCOUPON_USGSTT_NMCLUSTR_IDESNTL_ID
COUPON_NM1.0001.0001.0000.9030.7101.000
COUPON_BNEF_CN1.0001.0000.3470.7420.6701.000
BRAND_NM1.0000.3471.0001.0000.9931.000
COUPON_USGSTT_NM0.9030.7421.0001.0000.9090.903
CLUSTR_ID0.7100.6700.9930.9091.0000.710
ESNTL_ID1.0001.0001.0000.9030.7101.000
2023-12-10T19:06:40.293280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOCOUPON_ISSU_DTCOUPON_USE_DTCOUPON_IDCOUPON_NMCOUPON_BNEF_CNBRAND_NMCOUPON_USGSTT_NMCLUSTR_IDESNTL_ID
SEQ_NO1.000-1.000-0.919-1.0000.0000.0000.0930.0000.0000.000
COUPON_ISSU_DT-1.0001.0000.9191.0000.0000.0000.0000.0000.0000.000
COUPON_USE_DT-0.9190.9191.0000.9190.6320.0000.6321.0000.5770.632
COUPON_ID-1.0001.0000.9191.0000.0000.0000.0000.0000.1310.000
COUPON_NM0.0000.0000.6320.0001.0001.0001.0000.9030.7101.000
COUPON_BNEF_CN0.0000.0000.0000.0001.0001.0000.3470.7420.6701.000
BRAND_NM0.0930.0000.6320.0001.0000.3471.0001.0000.9931.000
COUPON_USGSTT_NM0.0000.0001.0000.0000.9030.7421.0001.0000.9090.903
CLUSTR_ID0.0000.0000.5770.1310.7100.6700.9930.9091.0000.710
ESNTL_ID0.0000.0000.6320.0001.0001.0001.0000.9030.7101.000

Missing values

2023-12-10T19:06:28.190803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:06:28.647008image/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.
2023-12-10T19:06:29.157144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SEQ_NOPROMTN_TY_CDPROMTN_TY_NMCOUPON_NMCOUPON_BNEF_CNBRAND_NMCSTMR_IDTRMNL_MODL_NMTRMNL_MODL_OPERSYSM_TY_CDTRMNL_MODL_OPERSYSM_TY_NMCOUPON_ISSU_DTCOUPON_USGSTT_NMCOUPON_USE_DTCRTFC_STR_NMCRTFC_STR_ADDRCOUPON_IDCLUSTR_IDESNTL_ID
013대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwanf80a9f04-d427-4689-aa4a-ea41c550dda5<NA>단말모델미분류<NA>20200930115719사용20200930115724吉昌菸酒屏東縣枋寮鄉中山路75號1樓2146416062156V00A004S0013FCS00434
123대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwan4b28ba56-165e-482b-8fa9-5c2ae81f01b1<NA>단말모델미분류<NA>20200930115155사용20200930115200吉昌菸酒屏東縣枋寮鄉中山路75號1樓2146415902626V00A004S0013FCS00434
233대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwan50f60339-1c32-49cc-a54e-3756f752983c<NA>단말모델미분류<NA>20200930114920사용20200930114927瀧德桃園市龜山區忠義路2段395號2146415811086V00A004S0013FCS00434
343대만게임프로모션참여고객성향全家單店活動驗證券1 2 3<NA>075e4afe-51a6-48e8-b680-e3f4bd11df3a<NA>단말모델미분류<NA>20200930114407발급<NA><NA><NA>2146415635550V00A004S0013FCS00470
453대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwanfeabbc38-6123-4a34-991b-d4b8d3d8069d<NA>단말모델미분류<NA>20200930114240사용20200930114248吉昌菸酒屏東縣枋寮鄉中山路75號1樓2146415608176V00A004S0013FCS00434
563대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwan762e9ccf-0487-4aa9-b2b1-ba6e051fcb1c<NA>단말모델미분류<NA>20200930113534사용20201007084605吉昌菸酒屏東縣枋寮鄉中山路75號1樓2146415458786V00A004S0013FCS00434
673대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwane8bf2536-0e67-4d59-ae55-47f36a31e31e<NA>단말모델미분류<NA>20200930113529사용20200930113536吉昌菸酒屏東縣枋寮鄉中山路75號1樓2146415445226V00A004S0013FCS00434
783대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwan762e9ccf-0487-4aa9-b2b1-ba6e051fcb1c<NA>단말모델미분류<NA>20200930113421사용20200930113425吉昌菸酒屏東縣枋寮鄉中山路75號1樓2146415423396V00A004S0013FCS00434
893대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwan762e9ccf-0487-4aa9-b2b1-ba6e051fcb1c<NA>단말모델미분류<NA>20200930113357사용20200930113402吉昌菸酒屏東縣枋寮鄉中山路75號1樓2146415403916V00A004S0013FCS00434
9103대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA><NA>a7c1260e-ddb9-4bfe-b335-367b98104a11<NA>단말모델미분류<NA>20200930112858발급<NA><NA><NA>2146415296863V00A004S0013FCS00434
SEQ_NOPROMTN_TY_CDPROMTN_TY_NMCOUPON_NMCOUPON_BNEF_CNBRAND_NMCSTMR_IDTRMNL_MODL_NMTRMNL_MODL_OPERSYSM_TY_CDTRMNL_MODL_OPERSYSM_TY_NMCOUPON_ISSU_DTCOUPON_USGSTT_NMCOUPON_USE_DTCRTFC_STR_NMCRTFC_STR_ADDRCOUPON_IDCLUSTR_IDESNTL_ID
90913대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwan4bd22bf6-f9fc-4d3b-aed4-fb04bd4cc3b9<NA>단말모델미분류<NA>20200930060020사용20200930060026名家台南市新營區新進路二段183號2146384273536V00A004S0013FCS00434
91923대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwancc47b8bb-141c-4f77-9986-088811f0ef85<NA>단말모델미분류<NA>20200930055035사용20200930055041合歡東門台南市東區東門路一段188號2146383462526V00A004S0013FCS00434
92933대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwan733f5a5b-074e-49e8-80c9-a8d039603dd4<NA>단말모델미분류<NA>20200930054954사용20200930055000開普鳳山高雄市鳳山市青年路一段360號2146383421906V00A004S0013FCS00434
93943대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwan3a5c5bb6-b847-4cd3-a663-f6f1cbe7b9a9<NA>단말모델미분류<NA>20200930054940사용20200930054947新生活宜蘭縣礁溪鄉礁溪路一段149號2146383391246V00A004S0013FCS00434
94953대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwanddc0bebd-04dc-4349-a659-777c21a58f86<NA>단말모델미분류<NA>20200930054924사용20200930054935名家台南市新營區新進路二段183號2146383387956V00A004S0013FCS00434
95963대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwancc47b8bb-141c-4f77-9986-088811f0ef85<NA>단말모델미분류<NA>20200930054821사용20200930054833合歡東門台南市東區東門路一段188號2146383236716V00A004S0013FCS00434
96973대만게임프로모션참여고객성향孕媽咪保養體驗券<h4><b>注意事項</b></h4><ol><li>本券使用期限請以券上標示為準。</li><li>本券限本人使用，每人使用一次，不得與克蘭詩其他同期活動重複兌換。</li><li>本券恕不兌換現金、掛失、或轉發與轉賣。</li><li>本券限使用於克蘭詩全台灣百貨實體通路專櫃。</li><li>本券核發與使用辦法以現場公告為準。</li><li>克蘭詩保有提前結束兌換或更換兌換內容之權利，恕不另行通知。</li></ol>Clarinsthreedom_clarins_c305414535<NA>단말모델미분류<NA>20200930054223사용20200930054239新光三越嘉義垂楊店嘉義市西區垂楊路726號2146382621662V00A004S0013FCS00516
97983대만게임프로모션참여고객성향孕媽咪保養體驗券<h4><b>注意事項</b></h4><ol><li>本券使用期限請以券上標示為準。</li><li>本券限本人使用，每人使用一次，不得與克蘭詩其他同期活動重複兌換。</li><li>本券恕不兌換現金、掛失、或轉發與轉賣。</li><li>本券限使用於克蘭詩全台灣百貨實體通路專櫃。</li><li>本券核發與使用辦法以現場公告為準。</li><li>克蘭詩保有提前結束兌換或更換兌換內容之權利，恕不另行通知。</li></ol><NA>threedom_clarins_c727318579<NA>단말모델미분류<NA>20200930054157발급<NA><NA><NA>2146382574003V00A004S0013FCS00516
98993대만게임프로모션참여고객성향全家單店活動驗證券1 2 3<NA>ff6f9b77-ba96-47a7-91f9-040598cef6f4<NA>단말모델미분류<NA>20200930054042발급<NA><NA><NA>2146382453330V00A004S0013FCS00470
991003대만게임프로모션참여고객성향酩悅軒尼詩集點抽獎趣<NA>Moet Hennessy Taiwane81d56f3-4b43-4c63-9b02-f67ffd7d1211<NA>단말모델미분류<NA>20200930053943사용20200930053952收藏家台中市南屯區永春東路198號2146382346966V00A004S0013FCS00434