Overview

Dataset statistics

Number of variables20
Number of observations5630
Missing cells1856
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory879.8 KiB
Average record size in memory160.0 B

Variable types

Numeric11
Categorical9

Alerts

CustomerID is highly overall correlated with HourSpendOnAppHigh correlation
HourSpendOnApp is highly overall correlated with CustomerIDHigh correlation
CouponUsed is highly overall correlated with OrderCountHigh correlation
OrderCount is highly overall correlated with CouponUsedHigh correlation
CashbackAmount is highly overall correlated with PreferedOrderCatHigh correlation
PreferedOrderCat is highly overall correlated with CashbackAmountHigh correlation
Tenure has 264 (4.7%) missing valuesMissing
WarehouseToHome has 251 (4.5%) missing valuesMissing
HourSpendOnApp has 255 (4.5%) missing valuesMissing
OrderAmountHikeFromlastYear has 265 (4.7%) missing valuesMissing
CouponUsed has 256 (4.5%) missing valuesMissing
OrderCount has 258 (4.6%) missing valuesMissing
DaySinceLastOrder has 307 (5.5%) missing valuesMissing
CustomerID is uniformly distributedUniform
CustomerID has unique valuesUnique
Tenure has 508 (9.0%) zerosZeros
CouponUsed has 1030 (18.3%) zerosZeros
DaySinceLastOrder has 496 (8.8%) zerosZeros

Reproduction

Analysis started2023-08-16 12:29:33.471151
Analysis finished2023-08-16 12:30:05.352309
Duration31.88 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

CustomerID
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct5630
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52815.5
Minimum50001
Maximum55630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:05.524753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50001
5-th percentile50282.45
Q151408.25
median52815.5
Q354222.75
95-th percentile55348.55
Maximum55630
Range5629
Interquartile range (IQR)2814.5

Descriptive statistics

Standard deviation1625.3853
Coefficient of variation (CV)0.030774779
Kurtosis-1.2
Mean52815.5
Median Absolute Deviation (MAD)1407.5
Skewness0
Sum2.9735126 × 108
Variance2641877.5
MonotonicityStrictly increasing
2023-08-16T12:30:05.915526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50001 1
 
< 0.1%
53751 1
 
< 0.1%
53759 1
 
< 0.1%
53758 1
 
< 0.1%
53757 1
 
< 0.1%
53756 1
 
< 0.1%
53755 1
 
< 0.1%
53754 1
 
< 0.1%
53753 1
 
< 0.1%
53752 1
 
< 0.1%
Other values (5620) 5620
99.8%
ValueCountFrequency (%)
50001 1
< 0.1%
50002 1
< 0.1%
50003 1
< 0.1%
50004 1
< 0.1%
50005 1
< 0.1%
50006 1
< 0.1%
50007 1
< 0.1%
50008 1
< 0.1%
50009 1
< 0.1%
50010 1
< 0.1%
ValueCountFrequency (%)
55630 1
< 0.1%
55629 1
< 0.1%
55628 1
< 0.1%
55627 1
< 0.1%
55626 1
< 0.1%
55625 1
< 0.1%
55624 1
< 0.1%
55623 1
< 0.1%
55622 1
< 0.1%
55621 1
< 0.1%

Churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
0
4682 
1
948 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Length

2023-08-16T12:30:06.235999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T12:30:06.510270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Most occurring characters

ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5630
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5630
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4682
83.2%
1 948
 
16.8%

Tenure
Real number (ℝ)

MISSING  ZEROS 

Distinct36
Distinct (%)0.7%
Missing264
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean10.189899
Minimum0
Maximum61
Zeros508
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:06.841501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q316
95-th percentile27
Maximum61
Range61
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.557241
Coefficient of variation (CV)0.83977679
Kurtosis-0.0073694695
Mean10.189899
Median Absolute Deviation (MAD)7
Skewness0.73651338
Sum54679
Variance73.226373
MonotonicityNot monotonic
2023-08-16T12:30:07.229947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 690
 
12.3%
0 508
 
9.0%
8 263
 
4.7%
9 247
 
4.4%
7 221
 
3.9%
10 213
 
3.8%
5 204
 
3.6%
4 203
 
3.6%
3 195
 
3.5%
11 194
 
3.4%
Other values (26) 2428
43.1%
(Missing) 264
 
4.7%
ValueCountFrequency (%)
0 508
9.0%
1 690
12.3%
2 167
 
3.0%
3 195
 
3.5%
4 203
 
3.6%
5 204
 
3.6%
6 183
 
3.3%
7 221
 
3.9%
8 263
 
4.7%
9 247
 
4.4%
ValueCountFrequency (%)
61 1
 
< 0.1%
60 1
 
< 0.1%
51 1
 
< 0.1%
50 1
 
< 0.1%
31 49
0.9%
30 66
1.2%
29 55
1.0%
28 70
1.2%
27 66
1.2%
26 60
1.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Mobile Phone
2765 
Computer
1634 
Phone
1231 

Length

Max length12
Median length8
Mean length9.3085258
Min length5

Characters and Unicode

Total characters52407
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile Phone
2nd rowPhone
3rd rowPhone
4th rowPhone
5th rowPhone

Common Values

ValueCountFrequency (%)
Mobile Phone 2765
49.1%
Computer 1634
29.0%
Phone 1231
21.9%

Length

2023-08-16T12:30:07.450568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T12:30:07.668635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
phone 3996
47.6%
mobile 2765
32.9%
computer 1634
19.5%

Most occurring characters

ValueCountFrequency (%)
o 8395
16.0%
e 8395
16.0%
P 3996
7.6%
h 3996
7.6%
n 3996
7.6%
M 2765
 
5.3%
b 2765
 
5.3%
i 2765
 
5.3%
l 2765
 
5.3%
2765
 
5.3%
Other values (6) 9804
18.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41247
78.7%
Uppercase Letter 8395
 
16.0%
Space Separator 2765
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8395
20.4%
e 8395
20.4%
h 3996
9.7%
n 3996
9.7%
b 2765
 
6.7%
i 2765
 
6.7%
l 2765
 
6.7%
m 1634
 
4.0%
p 1634
 
4.0%
u 1634
 
4.0%
Other values (2) 3268
 
7.9%
Uppercase Letter
ValueCountFrequency (%)
P 3996
47.6%
M 2765
32.9%
C 1634
19.5%
Space Separator
ValueCountFrequency (%)
2765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49642
94.7%
Common 2765
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 8395
16.9%
e 8395
16.9%
P 3996
8.0%
h 3996
8.0%
n 3996
8.0%
M 2765
 
5.6%
b 2765
 
5.6%
i 2765
 
5.6%
l 2765
 
5.6%
C 1634
 
3.3%
Other values (5) 8170
16.5%
Common
ValueCountFrequency (%)
2765
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 8395
16.0%
e 8395
16.0%
P 3996
7.6%
h 3996
7.6%
n 3996
7.6%
M 2765
 
5.3%
b 2765
 
5.3%
i 2765
 
5.3%
l 2765
 
5.3%
2765
 
5.3%
Other values (6) 9804
18.7%

CityTier
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
1
3666 
3
1722 
2
 
242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

Length

2023-08-16T12:30:07.844742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T12:30:08.032572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

Most occurring characters

ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5630
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5630
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3666
65.1%
3 1722
30.6%
2 242
 
4.3%

WarehouseToHome
Real number (ℝ)

Distinct34
Distinct (%)0.6%
Missing251
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean15.639896
Minimum5
Maximum127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:08.209970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median14
Q320
95-th percentile33
Maximum127
Range122
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.5314752
Coefficient of variation (CV)0.54549437
Kurtosis9.9869304
Mean15.639896
Median Absolute Deviation (MAD)5
Skewness1.6191537
Sum84127
Variance72.786069
MonotonicityNot monotonic
2023-08-16T12:30:08.434314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
9 559
 
9.9%
8 444
 
7.9%
7 389
 
6.9%
16 322
 
5.7%
14 299
 
5.3%
6 295
 
5.2%
15 288
 
5.1%
10 274
 
4.9%
13 249
 
4.4%
11 233
 
4.1%
Other values (24) 2027
36.0%
(Missing) 251
 
4.5%
ValueCountFrequency (%)
5 8
 
0.1%
6 295
5.2%
7 389
6.9%
8 444
7.9%
9 559
9.9%
10 274
4.9%
11 233
4.1%
12 221
 
3.9%
13 249
4.4%
14 299
5.3%
ValueCountFrequency (%)
127 1
 
< 0.1%
126 1
 
< 0.1%
36 51
0.9%
35 93
1.7%
34 63
1.1%
33 67
1.2%
32 94
1.7%
31 101
1.8%
30 94
1.7%
29 81
1.4%
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Debit Card
2314 
Credit Card
1501 
E wallet
614 
UPI
414 
COD
365 
Other values (2)
422 

Length

Max length16
Median length11
Mean length8.8507993
Min length2

Characters and Unicode

Total characters49830
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDebit Card
2nd rowUPI
3rd rowDebit Card
4th rowDebit Card
5th rowCC

Common Values

ValueCountFrequency (%)
Debit Card 2314
41.1%
Credit Card 1501
26.7%
E wallet 614
 
10.9%
UPI 414
 
7.4%
COD 365
 
6.5%
CC 273
 
4.8%
Cash on Delivery 149
 
2.6%

Length

2023-08-16T12:30:08.653351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T12:30:08.890538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
card 3815
36.8%
debit 2314
22.3%
credit 1501
 
14.5%
e 614
 
5.9%
wallet 614
 
5.9%
upi 414
 
4.0%
cod 365
 
3.5%
cc 273
 
2.6%
cash 149
 
1.4%
on 149
 
1.4%

Most occurring characters

ValueCountFrequency (%)
C 6376
12.8%
r 5465
11.0%
d 5316
10.7%
e 4727
9.5%
4727
9.5%
a 4578
9.2%
t 4429
8.9%
i 3964
8.0%
D 2828
5.7%
b 2314
 
4.6%
Other values (13) 5106
10.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33678
67.6%
Uppercase Letter 11425
 
22.9%
Space Separator 4727
 
9.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 5465
16.2%
d 5316
15.8%
e 4727
14.0%
a 4578
13.6%
t 4429
13.2%
i 3964
11.8%
b 2314
6.9%
l 1377
 
4.1%
w 614
 
1.8%
s 149
 
0.4%
Other values (5) 745
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
C 6376
55.8%
D 2828
24.8%
E 614
 
5.4%
U 414
 
3.6%
P 414
 
3.6%
I 414
 
3.6%
O 365
 
3.2%
Space Separator
ValueCountFrequency (%)
4727
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45103
90.5%
Common 4727
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 6376
14.1%
r 5465
12.1%
d 5316
11.8%
e 4727
10.5%
a 4578
10.2%
t 4429
9.8%
i 3964
8.8%
D 2828
6.3%
b 2314
 
5.1%
l 1377
 
3.1%
Other values (12) 3729
8.3%
Common
ValueCountFrequency (%)
4727
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 6376
12.8%
r 5465
11.0%
d 5316
10.7%
e 4727
9.5%
4727
9.5%
a 4578
9.2%
t 4429
8.9%
i 3964
8.0%
D 2828
5.7%
b 2314
 
4.6%
Other values (13) 5106
10.2%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Male
3384 
Female
2246 

Length

Max length6
Median length4
Mean length4.7978686
Min length4

Characters and Unicode

Total characters27012
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 3384
60.1%
Female 2246
39.9%

Length

2023-08-16T12:30:09.123950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T12:30:09.323287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 3384
60.1%
female 2246
39.9%

Most occurring characters

ValueCountFrequency (%)
e 7876
29.2%
a 5630
20.8%
l 5630
20.8%
M 3384
12.5%
F 2246
 
8.3%
m 2246
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21382
79.2%
Uppercase Letter 5630
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7876
36.8%
a 5630
26.3%
l 5630
26.3%
m 2246
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
M 3384
60.1%
F 2246
39.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 27012
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7876
29.2%
a 5630
20.8%
l 5630
20.8%
M 3384
12.5%
F 2246
 
8.3%
m 2246
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7876
29.2%
a 5630
20.8%
l 5630
20.8%
M 3384
12.5%
F 2246
 
8.3%
m 2246
 
8.3%

HourSpendOnApp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.1%
Missing255
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2.9315349
Minimum0
Maximum5
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:09.474462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72192585
Coefficient of variation (CV)0.24626207
Kurtosis-0.66707614
Mean2.9315349
Median Absolute Deviation (MAD)1
Skewness-0.027212622
Sum15757
Variance0.52117693
MonotonicityNot monotonic
2023-08-16T12:30:09.642917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2687
47.7%
2 1471
26.1%
4 1176
20.9%
1 35
 
0.6%
0 3
 
0.1%
5 3
 
0.1%
(Missing) 255
 
4.5%
ValueCountFrequency (%)
0 3
 
0.1%
1 35
 
0.6%
2 1471
26.1%
3 2687
47.7%
4 1176
20.9%
5 3
 
0.1%
ValueCountFrequency (%)
5 3
 
0.1%
4 1176
20.9%
3 2687
47.7%
2 1471
26.1%
1 35
 
0.6%
0 3
 
0.1%

NumberOfDeviceRegistered
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6889876
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:09.812791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0239985
Coefficient of variation (CV)0.27758253
Kurtosis0.58284873
Mean3.6889876
Median Absolute Deviation (MAD)1
Skewness-0.39696864
Sum20769
Variance1.048573
MonotonicityNot monotonic
2023-08-16T12:30:09.981723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 2377
42.2%
3 1699
30.2%
5 881
 
15.6%
2 276
 
4.9%
1 235
 
4.2%
6 162
 
2.9%
ValueCountFrequency (%)
1 235
 
4.2%
2 276
 
4.9%
3 1699
30.2%
4 2377
42.2%
5 881
 
15.6%
6 162
 
2.9%
ValueCountFrequency (%)
6 162
 
2.9%
5 881
 
15.6%
4 2377
42.2%
3 1699
30.2%
2 276
 
4.9%
1 235
 
4.2%

PreferedOrderCat
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Laptop & Accessory
2050 
Mobile Phone
1271 
Fashion
826 
Mobile
809 
Grocery
410 

Length

Max length18
Median length12
Mean length11.943517
Min length6

Characters and Unicode

Total characters67242
Distinct characters23
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaptop & Accessory
2nd rowMobile
3rd rowMobile
4th rowLaptop & Accessory
5th rowMobile

Common Values

ValueCountFrequency (%)
Laptop & Accessory 2050
36.4%
Mobile Phone 1271
22.6%
Fashion 826
14.7%
Mobile 809
 
14.4%
Grocery 410
 
7.3%
Others 264
 
4.7%

Length

2023-08-16T12:30:10.173811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T12:30:10.391445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mobile 2080
18.9%
laptop 2050
18.6%
2050
18.6%
accessory 2050
18.6%
phone 1271
11.6%
fashion 826
 
7.5%
grocery 410
 
3.7%
others 264
 
2.4%

Most occurring characters

ValueCountFrequency (%)
o 8687
 
12.9%
e 6075
 
9.0%
5371
 
8.0%
s 5190
 
7.7%
c 4510
 
6.7%
p 4100
 
6.1%
r 3134
 
4.7%
i 2906
 
4.3%
a 2876
 
4.3%
y 2460
 
3.7%
Other values (13) 21933
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50870
75.7%
Uppercase Letter 8951
 
13.3%
Space Separator 5371
 
8.0%
Other Punctuation 2050
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8687
17.1%
e 6075
11.9%
s 5190
10.2%
c 4510
8.9%
p 4100
8.1%
r 3134
 
6.2%
i 2906
 
5.7%
a 2876
 
5.7%
y 2460
 
4.8%
h 2361
 
4.6%
Other values (4) 8571
16.8%
Uppercase Letter
ValueCountFrequency (%)
M 2080
23.2%
L 2050
22.9%
A 2050
22.9%
P 1271
14.2%
F 826
 
9.2%
G 410
 
4.6%
O 264
 
2.9%
Space Separator
ValueCountFrequency (%)
5371
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 59821
89.0%
Common 7421
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 8687
14.5%
e 6075
 
10.2%
s 5190
 
8.7%
c 4510
 
7.5%
p 4100
 
6.9%
r 3134
 
5.2%
i 2906
 
4.9%
a 2876
 
4.8%
y 2460
 
4.1%
h 2361
 
3.9%
Other values (11) 17522
29.3%
Common
ValueCountFrequency (%)
5371
72.4%
& 2050
 
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67242
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 8687
 
12.9%
e 6075
 
9.0%
5371
 
8.0%
s 5190
 
7.7%
c 4510
 
6.7%
p 4100
 
6.1%
r 3134
 
4.7%
i 2906
 
4.3%
a 2876
 
4.3%
y 2460
 
3.7%
Other values (13) 21933
32.6%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
3
1698 
1
1164 
5
1108 
4
1074 
2
586 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

Length

2023-08-16T12:30:10.594235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T12:30:10.832459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

Most occurring characters

ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5630
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5630
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1698
30.2%
1 1164
20.7%
5 1108
19.7%
4 1074
19.1%
2 586
 
10.4%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Married
2986 
Single
1796 
Divorced
848 

Length

Max length8
Median length7
Mean length6.8316163
Min length6

Characters and Unicode

Total characters38462
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Married 2986
53.0%
Single 1796
31.9%
Divorced 848
 
15.1%

Length

2023-08-16T12:30:11.251587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T12:30:11.682998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
married 2986
53.0%
single 1796
31.9%
divorced 848
 
15.1%

Most occurring characters

ValueCountFrequency (%)
r 6820
17.7%
i 5630
14.6%
e 5630
14.6%
d 3834
10.0%
M 2986
7.8%
a 2986
7.8%
S 1796
 
4.7%
n 1796
 
4.7%
g 1796
 
4.7%
l 1796
 
4.7%
Other values (4) 3392
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32832
85.4%
Uppercase Letter 5630
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 6820
20.8%
i 5630
17.1%
e 5630
17.1%
d 3834
11.7%
a 2986
9.1%
n 1796
 
5.5%
g 1796
 
5.5%
l 1796
 
5.5%
v 848
 
2.6%
o 848
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
M 2986
53.0%
S 1796
31.9%
D 848
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 38462
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 6820
17.7%
i 5630
14.6%
e 5630
14.6%
d 3834
10.0%
M 2986
7.8%
a 2986
7.8%
S 1796
 
4.7%
n 1796
 
4.7%
g 1796
 
4.7%
l 1796
 
4.7%
Other values (4) 3392
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 6820
17.7%
i 5630
14.6%
e 5630
14.6%
d 3834
10.0%
M 2986
7.8%
a 2986
7.8%
S 1796
 
4.7%
n 1796
 
4.7%
g 1796
 
4.7%
l 1796
 
4.7%
Other values (4) 3392
8.8%

NumberOfAddress
Real number (ℝ)

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.214032
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:12.026496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile10
Maximum22
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5835855
Coefficient of variation (CV)0.6130911
Kurtosis0.95922927
Mean4.214032
Median Absolute Deviation (MAD)1
Skewness1.0886394
Sum23725
Variance6.6749141
MonotonicityNot monotonic
2023-08-16T12:30:12.641393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 1369
24.3%
3 1278
22.7%
4 588
10.4%
5 571
10.1%
6 382
 
6.8%
1 371
 
6.6%
8 280
 
5.0%
7 256
 
4.5%
9 239
 
4.2%
10 194
 
3.4%
Other values (5) 102
 
1.8%
ValueCountFrequency (%)
1 371
 
6.6%
2 1369
24.3%
3 1278
22.7%
4 588
10.4%
5 571
10.1%
6 382
 
6.8%
7 256
 
4.5%
8 280
 
5.0%
9 239
 
4.2%
10 194
 
3.4%
ValueCountFrequency (%)
22 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
11 98
 
1.7%
10 194
3.4%
9 239
4.2%
8 280
5.0%
7 256
4.5%
6 382
6.8%

Complain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
0
4026 
1
1604 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

Length

2023-08-16T12:30:13.030237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-16T12:30:13.632434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

Most occurring characters

ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5630
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5630
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4026
71.5%
1 1604
 
28.5%
Distinct16
Distinct (%)0.3%
Missing265
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean15.707922
Minimum11
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:13.936224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q113
median15
Q318
95-th percentile23
Maximum26
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6754855
Coefficient of variation (CV)0.23398929
Kurtosis-0.28038119
Mean15.707922
Median Absolute Deviation (MAD)3
Skewness0.79078536
Sum84273
Variance13.509193
MonotonicityNot monotonic
2023-08-16T12:30:14.241822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
14 750
13.3%
13 741
13.2%
12 728
12.9%
15 542
9.6%
11 391
6.9%
16 333
5.9%
18 321
5.7%
19 311
5.5%
17 297
 
5.3%
20 243
 
4.3%
Other values (6) 708
12.6%
(Missing) 265
 
4.7%
ValueCountFrequency (%)
11 391
6.9%
12 728
12.9%
13 741
13.2%
14 750
13.3%
15 542
9.6%
16 333
5.9%
17 297
 
5.3%
18 321
5.7%
19 311
5.5%
20 243
 
4.3%
ValueCountFrequency (%)
26 33
 
0.6%
25 73
 
1.3%
24 84
 
1.5%
23 144
2.6%
22 184
3.3%
21 190
3.4%
20 243
4.3%
19 311
5.5%
18 321
5.7%
17 297
5.3%

CouponUsed
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)0.3%
Missing256
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean1.7510234
Minimum0
Maximum16
Zeros1030
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:14.474147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile6
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8946214
Coefficient of variation (CV)1.082008
Kurtosis9.1322812
Mean1.7510234
Median Absolute Deviation (MAD)1
Skewness2.5456526
Sum9410
Variance3.5895904
MonotonicityNot monotonic
2023-08-16T12:30:14.684560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 2105
37.4%
2 1283
22.8%
0 1030
18.3%
3 327
 
5.8%
4 197
 
3.5%
5 129
 
2.3%
6 108
 
1.9%
7 89
 
1.6%
8 42
 
0.7%
10 14
 
0.2%
Other values (7) 50
 
0.9%
(Missing) 256
 
4.5%
ValueCountFrequency (%)
0 1030
18.3%
1 2105
37.4%
2 1283
22.8%
3 327
 
5.8%
4 197
 
3.5%
5 129
 
2.3%
6 108
 
1.9%
7 89
 
1.6%
8 42
 
0.7%
9 13
 
0.2%
ValueCountFrequency (%)
16 2
 
< 0.1%
15 1
 
< 0.1%
14 5
 
0.1%
13 8
 
0.1%
12 9
 
0.2%
11 12
 
0.2%
10 14
 
0.2%
9 13
 
0.2%
8 42
0.7%
7 89
1.6%

OrderCount
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)0.3%
Missing258
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean3.0080045
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:14.879359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9396795
Coefficient of variation (CV)0.97728563
Kurtosis4.7184661
Mean3.0080045
Median Absolute Deviation (MAD)1
Skewness2.1964141
Sum16159
Variance8.6417158
MonotonicityNot monotonic
2023-08-16T12:30:15.059412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 2025
36.0%
1 1751
31.1%
3 371
 
6.6%
7 206
 
3.7%
4 204
 
3.6%
5 181
 
3.2%
8 172
 
3.1%
6 137
 
2.4%
9 62
 
1.1%
12 54
 
1.0%
Other values (6) 209
 
3.7%
(Missing) 258
 
4.6%
ValueCountFrequency (%)
1 1751
31.1%
2 2025
36.0%
3 371
 
6.6%
4 204
 
3.6%
5 181
 
3.2%
6 137
 
2.4%
7 206
 
3.7%
8 172
 
3.1%
9 62
 
1.1%
10 36
 
0.6%
ValueCountFrequency (%)
16 23
 
0.4%
15 33
 
0.6%
14 36
 
0.6%
13 30
 
0.5%
12 54
 
1.0%
11 51
 
0.9%
10 36
 
0.6%
9 62
 
1.1%
8 172
3.1%
7 206
3.7%

DaySinceLastOrder
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)0.4%
Missing307
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean4.5434905
Minimum0
Maximum46
Zeros496
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:15.259117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum46
Range46
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6544332
Coefficient of variation (CV)0.80432284
Kurtosis4.0239643
Mean4.5434905
Median Absolute Deviation (MAD)2
Skewness1.1909995
Sum24185
Variance13.354882
MonotonicityNot monotonic
2023-08-16T12:30:15.780388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 900
16.0%
2 792
14.1%
1 614
10.9%
8 538
9.6%
0 496
8.8%
7 447
7.9%
4 431
7.7%
9 299
 
5.3%
5 228
 
4.0%
10 157
 
2.8%
Other values (12) 421
7.5%
(Missing) 307
 
5.5%
ValueCountFrequency (%)
0 496
8.8%
1 614
10.9%
2 792
14.1%
3 900
16.0%
4 431
7.7%
5 228
 
4.0%
6 113
 
2.0%
7 447
7.9%
8 538
9.6%
9 299
 
5.3%
ValueCountFrequency (%)
46 1
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
18 10
 
0.2%
17 17
 
0.3%
16 13
 
0.2%
15 19
 
0.3%
14 35
0.6%
13 51
0.9%
12 69
1.2%

CashbackAmount
Real number (ℝ)

Distinct220
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.22149
Minimum0
Maximum325
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-08-16T12:30:16.017243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile123
Q1146
median163
Q3196
95-th percentile292
Maximum325
Range325
Interquartile range (IQR)50

Descriptive statistics

Standard deviation49.193869
Coefficient of variation (CV)0.2775841
Kurtosis0.97354617
Mean177.22149
Median Absolute Deviation (MAD)23
Skewness1.149595
Sum997757
Variance2420.0367
MonotonicityNot monotonic
2023-08-16T12:30:16.255768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148 134
 
2.4%
149 120
 
2.1%
146 118
 
2.1%
152 116
 
2.1%
153 108
 
1.9%
123 97
 
1.7%
151 95
 
1.7%
154 89
 
1.6%
147 87
 
1.5%
150 84
 
1.5%
Other values (210) 4582
81.4%
ValueCountFrequency (%)
0 4
 
0.1%
12 1
 
< 0.1%
25 4
 
0.1%
37 1
 
< 0.1%
56 1
 
< 0.1%
81 1
 
< 0.1%
110 2
 
< 0.1%
111 14
0.2%
112 5
 
0.1%
113 6
0.1%
ValueCountFrequency (%)
325 4
 
0.1%
324 6
 
0.1%
323 6
 
0.1%
322 10
0.2%
321 12
0.2%
320 9
0.2%
319 10
0.2%
318 6
 
0.1%
317 18
0.3%
316 9
0.2%

Interactions

2023-08-16T12:30:01.046388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:36.136794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:38.813241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:41.783632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:44.160964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:46.356131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:48.591824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:50.694717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:54.339772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:56.701813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:58.927395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:01.252654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:36.319168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:39.143219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:41.985678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:44.342458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:46.561463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:48.777240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:50.987557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:54.540059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:56.899074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:59.111889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:01.475578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:36.514262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:39.475963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:42.191239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:44.547556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:46.768012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:48.976927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:51.608207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:54.753604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:57.099390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:59.301136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:01.959033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:36.734788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:39.819663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:42.385438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:44.764627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:46.969635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:49.177432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:51.951294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:54.959275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:57.307797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:59.503538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:02.157791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:36.962516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:40.158231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:42.597876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:44.957905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:47.180057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:49.365478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:52.242610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:55.177570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:57.513729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:59.687546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:02.354204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:37.156798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:40.504481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:42.796405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:45.161702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:47.370682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:49.547175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:52.585703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:55.411030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:57.704166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:59.875812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:02.561397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:37.333339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:40.768869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:42.986314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:45.350637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:47.555495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:49.717152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:52.908353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:55.629657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:57.886834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:00.055703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:02.776805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:37.536896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:40.994864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:43.191899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:45.549375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:47.768079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:49.914648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:53.265984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:55.861964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:58.094186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:00.256400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:02.999841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:37.878808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:41.207724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:43.557097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:45.755328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:47.987391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:50.110898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:53.630948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:56.083308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:58.303796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:00.473917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:03.201914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:38.220388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:41.409389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:43.761048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:45.950124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:48.207817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:50.318797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:53.925359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:56.287949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:58.524083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:00.672557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:03.387616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:38.493222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:41.587621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:43.942775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:46.149878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:48.396766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:50.498385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:54.116606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:56.488106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:29:58.714208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-16T12:30:00.850718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-16T12:30:16.481537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
CustomerIDTenureWarehouseToHomeHourSpendOnAppNumberOfDeviceRegisteredNumberOfAddressOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmountChurnPreferredLoginDeviceCityTierPreferredPaymentModeGenderPreferedOrderCatSatisfactionScoreMaritalStatusComplain
CustomerID1.0000.0530.0890.6040.4680.2230.1390.4040.4030.1520.3100.0000.2120.0000.0950.0000.2140.2550.3930.000
Tenure0.0531.000-0.046-0.004-0.0140.2950.0140.1240.1740.2050.4470.3620.1500.0530.0690.0550.2490.0360.0850.052
WarehouseToHome0.089-0.0461.0000.0810.0230.0210.0420.0170.0230.0250.0210.0850.0340.0030.0310.0100.0630.0000.0320.047
HourSpendOnApp0.604-0.0040.0811.0000.3620.1830.1350.3230.3040.1070.1950.0290.1300.0000.0550.0100.1360.0000.0220.000
NumberOfDeviceRegistered0.468-0.0140.0230.3621.0000.1290.0960.2630.2630.0420.2110.1160.1690.0100.0760.0070.1590.0000.0350.000
NumberOfAddress0.2230.2950.0210.1830.1291.0000.0490.1100.075-0.0760.2650.0840.0970.0270.0510.0280.0950.0370.0520.038
OrderAmountHikeFromlastYear0.1390.0140.0420.1350.0960.0491.0000.0710.0580.0010.0360.0620.0300.0550.0280.0380.0490.0320.0000.049
CouponUsed0.4040.1240.0170.3230.2630.1100.0711.0000.7270.3190.3370.0200.1420.0210.0620.0460.1650.0130.0280.000
OrderCount0.4030.1740.0230.3040.2630.0750.0580.7271.0000.4690.4020.0550.1260.0330.0670.0430.1880.0250.0400.000
DaySinceLastOrder0.1520.2050.0250.1070.042-0.0760.0010.3190.4691.0000.3830.1310.1540.0320.0680.0300.1950.0310.0310.032
CashbackAmount0.3100.4470.0210.1950.2110.2650.0360.3370.4020.3831.0000.1890.4380.1580.2370.0620.7060.0080.0660.040
Churn0.0000.3620.0850.0290.1160.0840.0620.0200.0550.1310.1891.0000.1130.0830.1130.0260.2240.1080.1820.249
PreferredLoginDevice0.2120.1500.0340.1300.1690.0970.0300.1420.1260.1540.4380.1131.0000.1050.2280.0360.4170.0420.0350.000
CityTier0.0000.0530.0030.0000.0100.0270.0550.0210.0330.0320.1580.0830.1051.0000.4380.0460.2160.0570.0420.000
PreferredPaymentMode0.0950.0690.0310.0550.0760.0510.0280.0620.0670.0680.2370.1130.2280.4381.0000.0690.2740.0480.0270.000
Gender0.0000.0550.0100.0100.0070.0280.0380.0460.0430.0300.0620.0260.0360.0460.0691.0000.0690.0330.0330.038
PreferedOrderCat0.2140.2490.0630.1360.1590.0950.0490.1650.1880.1950.7060.2240.4170.2160.2740.0691.0000.0130.0680.000
SatisfactionScore0.2550.0360.0000.0000.0000.0370.0320.0130.0250.0310.0080.1080.0420.0570.0480.0330.0131.0000.2380.042
MaritalStatus0.3930.0850.0320.0220.0350.0520.0000.0280.0400.0310.0660.1820.0350.0420.0270.0330.0680.2381.0000.000
Complain0.0000.0520.0470.0000.0000.0380.0490.0000.0000.0320.0400.2490.0000.0000.0000.0380.0000.0420.0001.000

Missing values

2023-08-16T12:30:03.716329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-16T12:30:04.400262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-16T12:30:05.041884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CustomerIDChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferedOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
05000114.0Mobile Phone36.0Debit CardFemale3.03Laptop & Accessory2Single9111.01.01.05.0160
1500021NaNPhone18.0UPIMale3.04Mobile3Single7115.00.01.00.0121
2500031NaNPhone130.0Debit CardMale2.04Mobile3Single6114.00.01.03.0120
35000410.0Phone315.0Debit CardMale2.04Laptop & Accessory5Single8023.00.01.03.0134
45000510.0Phone112.0CCMaleNaN3Mobile5Single3011.01.01.03.0130
55000610.0Computer122.0Debit CardFemale3.05Mobile Phone5Single2122.04.06.07.0139
6500071NaNPhone311.0Cash on DeliveryMale2.03Laptop & Accessory2Divorced4014.00.01.00.0121
7500081NaNPhone16.0CCMale3.03Mobile2Divorced3116.02.02.00.0123
850009113.0Phone39.0E walletMaleNaN4Mobile3Divorced2114.00.01.02.0127
9500101NaNPhone131.0Debit CardMale2.05Mobile3Single2012.01.01.01.0123
CustomerIDChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferedOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
56205562103.0Mobile Phone135.0Credit CardFemale4.05Mobile Phone5Single3015.01.02.05.0163
562155622114.0Mobile Phone335.0E walletMale3.05Fashion5Married6114.03.0NaN1.0234
562255623013.0Mobile Phone331.0E walletFemale3.05Grocery1Married2012.04.0NaN7.0245
56235562405.0Computer112.0Credit CardMale4.04Laptop & Accessory5Single2020.02.02.0NaN224
56245562501.0Mobile Phone312.0UPIFemale2.05Mobile Phone3Single2019.02.02.01.0155
562555626010.0Computer130.0Credit CardMale3.02Laptop & Accessory1Married6018.01.02.04.0151
562655627013.0Mobile Phone113.0Credit CardMale3.05Fashion5Married6016.01.02.0NaN225
56275562801.0Mobile Phone111.0Debit CardMale3.02Laptop & Accessory4Married3121.01.02.04.0186
562855629023.0Computer39.0Credit CardMale4.05Laptop & Accessory4Married4015.02.02.09.0179
56295563008.0Mobile Phone115.0Credit CardMale3.02Laptop & Accessory3Married4013.02.02.03.0169