Overview

Dataset statistics

Number of variables10
Number of observations71
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory88.9 B

Variable types

Numeric5
Categorical4
Text1

Alerts

promtn_ty_cd has constant value ""Constant
promtn_ty_nm has constant value ""Constant
coupon_use_rt has constant value ""Constant
seq_no is highly overall correlated with issu_co and 3 other fieldsHigh correlation
issu_co is highly overall correlated with seq_no and 4 other fieldsHigh correlation
partcptn_co is highly overall correlated with seq_no and 4 other fieldsHigh correlation
psnby_issu_co is highly overall correlated with seq_no and 3 other fieldsHigh correlation
coupon_crtfc_co is highly overall correlated with seq_no and 4 other fieldsHigh correlation
svc_nm is highly overall correlated with issu_co and 2 other fieldsHigh correlation
svc_nm is highly imbalanced (86.5%)Imbalance
seq_no has unique valuesUnique
brand_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:40:14.988528
Analysis finished2023-12-10 09:40:21.020073
Duration6.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36
Minimum1
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-10T18:40:21.166469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.5
Q118.5
median36
Q353.5
95-th percentile67.5
Maximum71
Range70
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.639767
Coefficient of variation (CV)0.57332687
Kurtosis-1.2
Mean36
Median Absolute Deviation (MAD)18
Skewness0
Sum2556
Variance426
MonotonicityStrictly increasing
2023-12-10T18:40:21.430142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
2 1
 
1.4%
53 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%
46 1
 
1.4%
Other values (61) 61
85.9%
ValueCountFrequency (%)
1 1
1.4%
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%
ValueCountFrequency (%)
71 1
1.4%
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%

promtn_ty_cd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
9
71 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
9 71
100.0%

Length

2023-12-10T18:40:21.682807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:21.852508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9 71
100.0%

promtn_ty_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
대만프로모션발급인증통계
71 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대만프로모션발급인증통계 71
100.0%

Length

2023-12-10T18:40:22.037831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:22.203680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대만프로모션발급인증통계 71
100.0%

brand_nm
Text

UNIQUE 

Distinct71
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
2023-12-10T18:40:22.642708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length18
Mean length9.8450704
Min length2

Characters and Unicode

Total characters699
Distinct characters244
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)100.0%

Sample

1st rowClarins
2nd rowGQ-B.R Junior
3rd rowGQ-BRUN不然早午餐
4th rowGQ-Bite 2 go 義式快餐店
5th rowGQ-Burger Ray
ValueCountFrequency (%)
2
 
1.9%
company 2
 
1.9%
gq-有你真好 2
 
1.9%
hennessy 1
 
1.0%
moet 1
 
1.0%
nail 1
 
1.0%
minou 1
 
1.0%
南區國稅局 1
 
1.0%
lsy林三益 1
 
1.0%
gq-餵我早餐 1
 
1.0%
Other values (92) 92
87.6%
2023-12-10T18:40:23.508604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 44
 
6.3%
- 44
 
6.3%
Q 43
 
6.2%
34
 
4.9%
a 28
 
4.0%
n 20
 
2.9%
e 19
 
2.7%
r 14
 
2.0%
s 14
 
2.0%
o 14
 
2.0%
Other values (234) 425
60.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
37.8%
Lowercase Letter 186
26.6%
Uppercase Letter 164
23.5%
Dash Punctuation 44
 
6.3%
Space Separator 34
 
4.9%
Other Punctuation 4
 
0.6%
Decimal Number 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
肉 9
 
3.4%
店 6
 
2.3%
餐 5
 
1.9%
牛 5
 
1.9%
燒 5
 
1.9%
樂 4
 
1.5%
酒 3
 
1.1%
館 3
 
1.1%
好 3
 
1.1%
有 3
 
1.1%
Other values (179) 218
82.6%
Lowercase Letter
ValueCountFrequency (%)
a 28
15.1%
n 20
10.8%
e 19
10.2%
r 14
 
7.5%
s 14
 
7.5%
o 14
 
7.5%
i 12
 
6.5%
u 11
 
5.9%
t 9
 
4.8%
y 8
 
4.3%
Other values (14) 37
19.9%
Uppercase Letter
ValueCountFrequency (%)
G 44
26.8%
Q 43
26.2%
S 8
 
4.9%
B 8
 
4.9%
N 7
 
4.3%
R 7
 
4.3%
T 7
 
4.3%
L 5
 
3.0%
M 5
 
3.0%
C 4
 
2.4%
Other values (13) 26
15.9%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
' 1
25.0%
. 1
25.0%
Decimal Number
ValueCountFrequency (%)
4 1
33.3%
6 1
33.3%
2 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 350
50.1%
Han 264
37.8%
Common 85
 
12.2%

Most frequent character per script

Han
ValueCountFrequency (%)
肉 9
 
3.4%
店 6
 
2.3%
餐 5
 
1.9%
牛 5
 
1.9%
燒 5
 
1.9%
樂 4
 
1.5%
酒 3
 
1.1%
館 3
 
1.1%
好 3
 
1.1%
有 3
 
1.1%
Other values (179) 218
82.6%
Latin
ValueCountFrequency (%)
G 44
 
12.6%
Q 43
 
12.3%
a 28
 
8.0%
n 20
 
5.7%
e 19
 
5.4%
r 14
 
4.0%
s 14
 
4.0%
o 14
 
4.0%
i 12
 
3.4%
u 11
 
3.1%
Other values (37) 131
37.4%
Common
ValueCountFrequency (%)
- 44
51.8%
34
40.0%
& 2
 
2.4%
' 1
 
1.2%
4 1
 
1.2%
6 1
 
1.2%
. 1
 
1.2%
2 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 434
62.1%
CJK 263
37.6%
CJK Ext A 1
 
0.1%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 44
 
10.1%
- 44
 
10.1%
Q 43
 
9.9%
34
 
7.8%
a 28
 
6.5%
n 20
 
4.6%
e 19
 
4.4%
r 14
 
3.2%
s 14
 
3.2%
o 14
 
3.2%
Other values (44) 160
36.9%
CJK
ValueCountFrequency (%)
肉 9
 
3.4%
店 6
 
2.3%
餐 5
 
1.9%
牛 5
 
1.9%
燒 5
 
1.9%
樂 4
 
1.5%
酒 3
 
1.1%
館 3
 
1.1%
好 3
 
1.1%
有 3
 
1.1%
Other values (178) 217
82.5%
CJK Ext A
ValueCountFrequency (%)
㕩 1
100.0%
None
ValueCountFrequency (%)
Ò 1
100.0%

svc_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size700.0 B
PF2.0 - event
69 
MissionLottery
 
1
Starbucks
 
1

Length

Max length14
Median length13
Mean length12.957746
Min length9

Unique

Unique2 ?
Unique (%)2.8%

Sample

1st rowPF2.0 - event
2nd rowPF2.0 - event
3rd rowPF2.0 - event
4th rowPF2.0 - event
5th rowPF2.0 - event

Common Values

ValueCountFrequency (%)
PF2.0 - event 69
97.2%
MissionLottery 1
 
1.4%
Starbucks 1
 
1.4%

Length

2023-12-10T18:40:23.778580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:23.979340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pf2.0 69
33.0%
69
33.0%
event 69
33.0%
missionlottery 1
 
0.5%
starbucks 1
 
0.5%

issu_co
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54604.254
Minimum1
Maximum3261717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-10T18:40:24.415627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17.5
median40
Q3750.5
95-th percentile46129.5
Maximum3261717
Range3261716
Interquartile range (IQR)743

Descriptive statistics

Standard deviation387351.76
Coefficient of variation (CV)7.093802
Kurtosis70.01024
Mean54604.254
Median Absolute Deviation (MAD)38
Skewness8.3419465
Sum3876902
Variance1.5004139 × 1011
MonotonicityNot monotonic
2023-12-10T18:40:24.883272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
8 8
 
11.3%
4 5
 
7.0%
7 4
 
5.6%
2 4
 
5.6%
9 3
 
4.2%
5 3
 
4.2%
1 2
 
2.8%
4011 2
 
2.8%
75 2
 
2.8%
47012 1
 
1.4%
Other values (37) 37
52.1%
ValueCountFrequency (%)
1 2
 
2.8%
2 4
5.6%
4 5
7.0%
5 3
 
4.2%
7 4
5.6%
8 8
11.3%
9 3
 
4.2%
10 1
 
1.4%
12 1
 
1.4%
17 1
 
1.4%
ValueCountFrequency (%)
3261717 1
1.4%
193600 1
1.4%
176208 1
1.4%
47012 1
1.4%
45247 1
1.4%
36170 1
1.4%
25004 1
1.4%
18521 1
1.4%
15402 1
1.4%
13420 1
1.4%

partcptn_co
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33791.859
Minimum1
Maximum1854000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-10T18:40:25.287723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17.5
median40
Q3540.5
95-th percentile41866.5
Maximum1854000
Range1853999
Interquartile range (IQR)533

Descriptive statistics

Standard deviation220877.11
Coefficient of variation (CV)6.5364001
Kurtosis68.653731
Mean33791.859
Median Absolute Deviation (MAD)36
Skewness8.2296965
Sum2399222
Variance4.8786698 × 1010
MonotonicityNot monotonic
2023-12-10T18:40:25.707948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
8 8
 
11.3%
4 5
 
7.0%
7 4
 
5.6%
2 4
 
5.6%
9 3
 
4.2%
5 3
 
4.2%
1 2
 
2.8%
75 2
 
2.8%
46212 1
 
1.4%
521 1
 
1.4%
Other values (38) 38
53.5%
ValueCountFrequency (%)
1 2
 
2.8%
2 4
5.6%
4 5
7.0%
5 3
 
4.2%
7 4
5.6%
8 8
11.3%
9 3
 
4.2%
10 1
 
1.4%
12 1
 
1.4%
17 1
 
1.4%
ValueCountFrequency (%)
1854000 1
1.4%
185222 1
1.4%
135121 1
1.4%
46212 1
1.4%
37521 1
1.4%
34214 1
1.4%
24587 1
1.4%
18227 1
1.4%
15227 1
1.4%
11251 1
1.4%

psnby_issu_co
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3316901
Minimum1
Maximum12.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-10T18:40:26.005028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.155
95-th percentile2.26
Maximum12.93
Range11.93
Interquartile range (IQR)0.155

Descriptive statistics

Standard deviation1.4434087
Coefficient of variation (CV)1.0838923
Kurtosis61.779775
Mean1.3316901
Median Absolute Deviation (MAD)0
Skewness7.6466512
Sum94.55
Variance2.0834285
MonotonicityNot monotonic
2023-12-10T18:40:26.284587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1.0 43
60.6%
1.02 4
 
5.6%
1.19 2
 
2.8%
2.59 1
 
1.4%
12.93 1
 
1.4%
1.27 1
 
1.4%
1.86 1
 
1.4%
1.15 1
 
1.4%
1.85 1
 
1.4%
1.16 1
 
1.4%
Other values (15) 15
 
21.1%
ValueCountFrequency (%)
1.0 43
60.6%
1.01 1
 
1.4%
1.02 4
 
5.6%
1.04 1
 
1.4%
1.05 1
 
1.4%
1.06 1
 
1.4%
1.09 1
 
1.4%
1.15 1
 
1.4%
1.16 1
 
1.4%
1.19 2
 
2.8%
ValueCountFrequency (%)
12.93 1
1.4%
2.59 1
1.4%
2.58 1
1.4%
2.32 1
1.4%
2.2 1
1.4%
1.86 1
1.4%
1.85 1
1.4%
1.76 1
1.4%
1.57 1
1.4%
1.46 1
1.4%

coupon_crtfc_co
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54604.254
Minimum1
Maximum3261717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-10T18:40:26.540339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17.5
median40
Q3750.5
95-th percentile46129.5
Maximum3261717
Range3261716
Interquartile range (IQR)743

Descriptive statistics

Standard deviation387351.76
Coefficient of variation (CV)7.093802
Kurtosis70.01024
Mean54604.254
Median Absolute Deviation (MAD)38
Skewness8.3419465
Sum3876902
Variance1.5004139 × 1011
MonotonicityNot monotonic
2023-12-10T18:40:27.237767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
8 8
 
11.3%
4 5
 
7.0%
7 4
 
5.6%
2 4
 
5.6%
9 3
 
4.2%
5 3
 
4.2%
1 2
 
2.8%
4011 2
 
2.8%
75 2
 
2.8%
47012 1
 
1.4%
Other values (37) 37
52.1%
ValueCountFrequency (%)
1 2
 
2.8%
2 4
5.6%
4 5
7.0%
5 3
 
4.2%
7 4
5.6%
8 8
11.3%
9 3
 
4.2%
10 1
 
1.4%
12 1
 
1.4%
17 1
 
1.4%
ValueCountFrequency (%)
3261717 1
1.4%
193600 1
1.4%
176208 1
1.4%
47012 1
1.4%
45247 1
1.4%
36170 1
1.4%
25004 1
1.4%
18521 1
1.4%
15402 1
1.4%
13420 1
1.4%

coupon_use_rt
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
1
71 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 71
100.0%

Length

2023-12-10T18:40:27.495601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:27.656570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 71
100.0%

Interactions

2023-12-10T18:40:19.513176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:15.706649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.845348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.726819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.597480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:19.688637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.259435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.006219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.922461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.748621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:19.844838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.394531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.158408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.106387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.923531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:20.070284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.544142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.359276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.277250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:19.156321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:20.223916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.667780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:17.513383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:18.441242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:19.328018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:40:27.781944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nobrand_nmsvc_nmissu_copartcptn_copsnby_issu_cocoupon_crtfc_co
seq_no1.0001.0000.1090.0870.0870.3980.087
brand_nm1.0001.0001.0001.0001.0001.0001.000
svc_nm0.1091.0001.0001.0001.0000.0001.000
issu_co0.0871.0001.0001.0000.6870.0000.687
partcptn_co0.0871.0001.0000.6871.0000.0000.687
psnby_issu_co0.3981.0000.0000.0000.0001.0000.000
coupon_crtfc_co0.0871.0001.0000.6870.6870.0001.000
2023-12-10T18:40:27.968259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_noissu_copartcptn_copsnby_issu_cocoupon_crtfc_cosvc_nm
seq_no1.0000.6510.6340.6340.6510.044
issu_co0.6511.0000.9950.7301.0000.993
partcptn_co0.6340.9951.0000.7050.9950.993
psnby_issu_co0.6340.7300.7051.0000.7300.000
coupon_crtfc_co0.6511.0000.9950.7301.0000.993
svc_nm0.0440.9930.9930.0000.9931.000

Missing values

2023-12-10T18:40:20.509675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:40:20.898651image/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_nopromtn_ty_cdpromtn_ty_nmbrand_nmsvc_nmissu_copartcptn_copsnby_issu_cocoupon_crtfc_cocoupon_use_rt
019대만프로모션발급인증통계ClarinsPF2.0 - event47012462121.02470121
129대만프로모션발급인증통계GQ-B.R JuniorPF2.0 - event441.041
239대만프로모션발급인증통계GQ-BRUN不然早午餐PF2.0 - event881.081
349대만프로모션발급인증통계GQ-Bite 2 go 義式快餐店PF2.0 - event881.081
459대만프로모션발급인증통계GQ-Burger RayPF2.0 - event771.071
569대만프로모션발급인증통계GQ-Capstone SteakhousePF2.0 - event441.041
679대만프로모션발급인증통계GQ-Danny & CompanyPF2.0 - event441.041
789대만프로모션발급인증통계GQ-Isaac 愛時刻PF2.0 - event771.071
899대만프로모션발급인증통계GQ-Liquid Bread CompanyPF2.0 - event10101.0101
9109대만프로모션발급인증통계GQ-Muzeo Gastronomy & DraftPF2.0 - event881.081
seq_nopromtn_ty_cdpromtn_ty_nmbrand_nmsvc_nmissu_copartcptn_copsnby_issu_cocoupon_crtfc_cocoupon_use_rt
61629대만프로모션발급인증통계美麗殿集團PF2.0 - event36170342141.06361701
62639대만프로모션발급인증통계藏酒酒莊有限公司PF2.0 - event9327821.199321
63649대만프로모션발급인증통계蘭芝PF2.0 - event3871672.323871
64659대만프로모션발급인증통계覺旅咖啡PF2.0 - event756365421.1675631
65669대만프로모션발급인증통계諾貝兒PF2.0 - event152821.851521
66679대만프로모션발급인증통계起士公爵PF2.0 - event975484521.1597541
67689대만프로모션발급인증통계軒尼詩百鬼夜行PF2.0 - event401121541.8640111
68699대만프로모션발급인증통계雪花秀PF2.0 - event5045041.05041
69709대만프로모션발급인증통계震旦通訊PF2.0 - event153612111.2715361
70719대만프로모션발급인증통계頂呱呱PF2.0 - event5434212.935431