Overview

Dataset statistics

Number of variables17
Number of observations516
Missing cells0
Missing cells (%)0.0%
Total size in memory68.7 KiB
Average record size in memory136.2 B

Variable types

Text9
Numeric8

Alerts

gross margin percentage has constant value "4.761904762"Constant
Invoice ID has unique valuesUnique

Reproduction

Analysis started2024-12-05 17:23:59.578332
Analysis finished2024-12-05 17:23:59.742829
Duration0.16 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Invoice ID
Text

UNIQUE 

Distinct516
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:00.265213image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters5676
Distinct characters11
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

Unique516 ?
Unique (%)100.0%

Sample

1st row750-67-8428
2nd row226-31-3081
3rd row631-41-3108
4th row123-19-1176
5th row373-73-7910
ValueCountFrequency (%)
750-67-8428 1
 
0.2%
853-23-2453 1
 
0.2%
631-41-3108 1
 
0.2%
123-19-1176 1
 
0.2%
373-73-7910 1
 
0.2%
699-14-3026 1
 
0.2%
355-53-5943 1
 
0.2%
315-22-5665 1
 
0.2%
665-32-9167 1
 
0.2%
692-92-5582 1
 
0.2%
Other values (506) 506
98.1%
2024-12-05T10:24:00.966569image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1032
18.2%
1 511
9.0%
2 497
8.8%
6 484
8.5%
5 482
8.5%
8 470
8.3%
3 465
8.2%
7 458
8.1%
4 454
8.0%
0 425
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1032
18.2%
1 511
9.0%
2 497
8.8%
6 484
8.5%
5 482
8.5%
8 470
8.3%
3 465
8.2%
7 458
8.1%
4 454
8.0%
0 425
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1032
18.2%
1 511
9.0%
2 497
8.8%
6 484
8.5%
5 482
8.5%
8 470
8.3%
3 465
8.2%
7 458
8.1%
4 454
8.0%
0 425
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1032
18.2%
1 511
9.0%
2 497
8.8%
6 484
8.5%
5 482
8.5%
8 470
8.3%
3 465
8.2%
7 458
8.1%
4 454
8.0%
0 425
7.5%

Branch
Text

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:01.175935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters516
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 rowA
2nd rowC
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
c 180
34.9%
a 171
33.1%
b 165
32.0%
2024-12-05T10:24:01.623332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 180
34.9%
A 171
33.1%
B 165
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 180
34.9%
A 171
33.1%
B 165
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 180
34.9%
A 171
33.1%
B 165
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 180
34.9%
A 171
33.1%
B 165
32.0%

City
Text

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:01.920929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.686046512
Min length6

Characters and Unicode

Total characters3966
Distinct characters14
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 rowYangon
2nd rowNaypyitaw
3rd rowYangon
4th rowYangon
5th rowYangon
ValueCountFrequency (%)
naypyitaw 180
34.9%
yangon 171
33.1%
mandalay 165
32.0%
2024-12-05T10:24:02.654858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1026
25.9%
y 525
13.2%
n 507
12.8%
N 180
 
4.5%
p 180
 
4.5%
i 180
 
4.5%
t 180
 
4.5%
w 180
 
4.5%
Y 171
 
4.3%
g 171
 
4.3%
Other values (4) 666
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1026
25.9%
y 525
13.2%
n 507
12.8%
N 180
 
4.5%
p 180
 
4.5%
i 180
 
4.5%
t 180
 
4.5%
w 180
 
4.5%
Y 171
 
4.3%
g 171
 
4.3%
Other values (4) 666
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1026
25.9%
y 525
13.2%
n 507
12.8%
N 180
 
4.5%
p 180
 
4.5%
i 180
 
4.5%
t 180
 
4.5%
w 180
 
4.5%
Y 171
 
4.3%
g 171
 
4.3%
Other values (4) 666
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1026
25.9%
y 525
13.2%
n 507
12.8%
N 180
 
4.5%
p 180
 
4.5%
i 180
 
4.5%
t 180
 
4.5%
w 180
 
4.5%
Y 171
 
4.3%
g 171
 
4.3%
Other values (4) 666
16.8%

Customer_type
Text

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:02.905293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3096
Distinct characters9
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 rowMember
2nd rowNormal
3rd rowNormal
4th rowMember
5th rowNormal
ValueCountFrequency (%)
normal 266
51.6%
member 250
48.4%
2024-12-05T10:24:03.367225image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 516
16.7%
m 516
16.7%
e 500
16.1%
N 266
8.6%
o 266
8.6%
a 266
8.6%
l 266
8.6%
M 250
8.1%
b 250
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 516
16.7%
m 516
16.7%
e 500
16.1%
N 266
8.6%
o 266
8.6%
a 266
8.6%
l 266
8.6%
M 250
8.1%
b 250
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 516
16.7%
m 516
16.7%
e 500
16.1%
N 266
8.6%
o 266
8.6%
a 266
8.6%
l 266
8.6%
M 250
8.1%
b 250
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 516
16.7%
m 516
16.7%
e 500
16.1%
N 266
8.6%
o 266
8.6%
a 266
8.6%
l 266
8.6%
M 250
8.1%
b 250
8.1%

Gender
Text

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:03.651439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.988372093
Min length4

Characters and Unicode

Total characters2574
Distinct characters6
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 rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
male 261
50.6%
female 255
49.4%
2024-12-05T10:24:04.337306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 771
30.0%
a 516
20.0%
l 516
20.0%
M 261
 
10.1%
F 255
 
9.9%
m 255
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2574
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 771
30.0%
a 516
20.0%
l 516
20.0%
M 261
 
10.1%
F 255
 
9.9%
m 255
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2574
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 771
30.0%
a 516
20.0%
l 516
20.0%
M 261
 
10.1%
F 255
 
9.9%
m 255
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2574
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 771
30.0%
a 516
20.0%
l 516
20.0%
M 261
 
10.1%
F 255
 
9.9%
m 255
 
9.9%

Product line
Text

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:04.789720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length22
Median length19
Mean length18.5251938
Min length17

Characters and Unicode

Total characters9559
Distinct characters25
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 rowHealth and beauty
2nd rowElectronic accessories
3rd rowHome and lifestyle
4th rowHealth and beauty
5th rowSports and travel
ValueCountFrequency (%)
and 340
24.8%
accessories 176
12.8%
sports 91
 
6.6%
travel 91
 
6.6%
electronic 88
 
6.4%
fashion 88
 
6.4%
food 87
 
6.3%
beverages 87
 
6.3%
home 84
 
6.1%
lifestyle 84
 
6.1%
Other values (2) 156
11.4%
2024-12-05T10:24:05.876466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1200
12.6%
a 938
 
9.8%
s 878
 
9.2%
856
 
9.0%
o 701
 
7.3%
r 533
 
5.6%
c 528
 
5.5%
n 516
 
5.4%
t 510
 
5.3%
i 436
 
4.6%
Other values (15) 2463
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1200
12.6%
a 938
 
9.8%
s 878
 
9.2%
856
 
9.0%
o 701
 
7.3%
r 533
 
5.6%
c 528
 
5.5%
n 516
 
5.4%
t 510
 
5.3%
i 436
 
4.6%
Other values (15) 2463
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1200
12.6%
a 938
 
9.8%
s 878
 
9.2%
856
 
9.0%
o 701
 
7.3%
r 533
 
5.6%
c 528
 
5.5%
n 516
 
5.4%
t 510
 
5.3%
i 436
 
4.6%
Other values (15) 2463
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1200
12.6%
a 938
 
9.8%
s 878
 
9.2%
856
 
9.0%
o 701
 
7.3%
r 533
 
5.6%
c 528
 
5.5%
n 516
 
5.4%
t 510
 
5.3%
i 436
 
4.6%
Other values (15) 2463
25.8%

Unit price
Real number (ℝ)

Distinct504
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.06474806
Minimum10.59
Maximum99.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:06.432891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10.59
5-th percentile15.5375
Q131.8575
median52.82
Q377.935
95-th percentile97.2125
Maximum99.96
Range89.37
Interquartile range (IQR)46.0775

Descriptive statistics

Standard deviation26.81219128
Coefficient of variation (CV)0.4869211651
Kurtosis-1.283202236
Mean55.06474806
Median Absolute Deviation (MAD)23.64
Skewness0.08060751712
Sum28413.41
Variance718.8936013
MonotonicityNot monotonic
2024-12-05T10:24:07.152883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.4 2
 
0.4%
20.01 2
 
0.4%
36.36 2
 
0.4%
38.6 2
 
0.4%
23.75 2
 
0.4%
65.94 2
 
0.4%
84.05 2
 
0.4%
32.25 2
 
0.4%
40.3 2
 
0.4%
34.56 2
 
0.4%
Other values (494) 496
96.1%
ValueCountFrequency (%)
10.59 1
0.2%
10.96 1
0.2%
11.81 1
0.2%
12.03 1
0.2%
12.12 1
0.2%
ValueCountFrequency (%)
99.96 1
0.2%
99.89 1
0.2%
99.83 1
0.2%
99.82 1
0.2%
99.79 1
0.2%

Quantity
Real number (ℝ)

Distinct10
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.649224806
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:07.667890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.899450798
Coefficient of variation (CV)0.5132475513
Kurtosis-1.169510941
Mean5.649224806
Median Absolute Deviation (MAD)2
Skewness-0.05859272778
Sum2915
Variance8.406814932
MonotonicityNot monotonic
2024-12-05T10:24:08.067543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 63
12.2%
5 60
11.6%
4 59
11.4%
1 56
10.9%
6 53
10.3%
9 52
10.1%
7 50
9.7%
8 46
8.9%
2 42
8.1%
3 35
6.8%
ValueCountFrequency (%)
1 56
10.9%
2 42
8.1%
3 35
6.8%
4 59
11.4%
5 60
11.6%
ValueCountFrequency (%)
10 63
12.2%
9 52
10.1%
8 46
8.9%
7 50
9.7%
6 53
10.3%

Tax 5%
Real number (ℝ)

Distinct513
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.62913566
Minimum0.627
Maximum49.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:08.536721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.627
5-th percentile1.836125
Q16.36325
median12.61575
Q322.63425
95-th percentile39.0875
Maximum49.65
Range49.023
Interquartile range (IQR)16.271

Descriptive statistics

Standard deviation11.67366774
Coefficient of variation (CV)0.7469170395
Kurtosis-0.07478672087
Mean15.62913566
Median Absolute Deviation (MAD)7.757
Skewness0.8663046917
Sum8064.634
Variance136.2745184
MonotonicityNot monotonic
2024-12-05T10:24:09.124107image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.188 2
 
0.4%
10.3635 2
 
0.4%
9.0045 2
 
0.4%
19.269 1
 
0.2%
18.0915 1
 
0.2%
49.65 1
 
0.2%
6.75 1
 
0.2%
9.107 1
 
0.2%
35.7 1
 
0.2%
3.5975 1
 
0.2%
Other values (503) 503
97.5%
ValueCountFrequency (%)
0.627 1
0.2%
0.639 1
0.2%
0.699 1
0.2%
0.767 1
0.2%
0.7715 1
0.2%
ValueCountFrequency (%)
49.65 1
0.2%
49.49 1
0.2%
48.605 1
0.2%
47.79 1
0.2%
47.72 1
0.2%

Total
Real number (ℝ)

Distinct513
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean328.2118488
Minimum13.167
Maximum1042.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:09.469374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum13.167
5-th percentile38.558625
Q1133.62825
median264.93075
Q3475.31925
95-th percentile820.8375
Maximum1042.65
Range1029.483
Interquartile range (IQR)341.691

Descriptive statistics

Standard deviation245.1470225
Coefficient of variation (CV)0.7469170395
Kurtosis-0.07478672087
Mean328.2118488
Median Absolute Deviation (MAD)162.897
Skewness0.8663046917
Sum169357.314
Variance60097.06262
MonotonicityNot monotonic
2024-12-05T10:24:09.804354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
276.948 2
 
0.4%
217.6335 2
 
0.4%
189.0945 2
 
0.4%
404.649 1
 
0.2%
379.9215 1
 
0.2%
1042.65 1
 
0.2%
141.75 1
 
0.2%
191.247 1
 
0.2%
749.7 1
 
0.2%
75.5475 1
 
0.2%
Other values (503) 503
97.5%
ValueCountFrequency (%)
13.167 1
0.2%
13.419 1
0.2%
14.679 1
0.2%
16.107 1
0.2%
16.2015 1
0.2%
ValueCountFrequency (%)
1042.65 1
0.2%
1039.29 1
0.2%
1020.705 1
0.2%
1003.59 1
0.2%
1002.12 1
0.2%

Date
Text

Distinct89
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:10.372853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.651162791
Min length8

Characters and Unicode

Total characters4464
Distinct characters11
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

Unique1 ?
Unique (%)0.2%

Sample

1st row1/5/2021
2nd row3/8/2021
3rd row3/3/2021
4th row1/27/2021
5th row2/8/2021
ValueCountFrequency (%)
1/25/2021 13
 
2.5%
3/5/2021 13
 
2.5%
2/15/2021 11
 
2.1%
3/9/2021 11
 
2.1%
3/8/2021 10
 
1.9%
3/12/2021 10
 
1.9%
2/2/2021 10
 
1.9%
3/3/2021 9
 
1.7%
1/27/2021 9
 
1.7%
3/19/2021 9
 
1.7%
Other values (79) 411
79.7%
2024-12-05T10:24:11.739770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1396
31.3%
/ 1032
23.1%
1 896
20.1%
0 560
12.5%
3 264
 
5.9%
5 75
 
1.7%
8 53
 
1.2%
7 50
 
1.1%
6 49
 
1.1%
9 46
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1396
31.3%
/ 1032
23.1%
1 896
20.1%
0 560
12.5%
3 264
 
5.9%
5 75
 
1.7%
8 53
 
1.2%
7 50
 
1.1%
6 49
 
1.1%
9 46
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1396
31.3%
/ 1032
23.1%
1 896
20.1%
0 560
12.5%
3 264
 
5.9%
5 75
 
1.7%
8 53
 
1.2%
7 50
 
1.1%
6 49
 
1.1%
9 46
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1396
31.3%
/ 1032
23.1%
1 896
20.1%
0 560
12.5%
3 264
 
5.9%
5 75
 
1.7%
8 53
 
1.2%
7 50
 
1.1%
6 49
 
1.1%
9 46
 
1.0%

Time
Text

Distinct351
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:12.393602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2580
Distinct characters11
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

Unique228 ?
Unique (%)44.2%

Sample

1st row13:08
2nd row10:29
3rd row13:23
4th row20:33
5th row10:37
ValueCountFrequency (%)
19:48 6
 
1.2%
16:28 4
 
0.8%
13:48 4
 
0.8%
10:25 4
 
0.8%
19:39 4
 
0.8%
19:17 3
 
0.6%
13:21 3
 
0.6%
13:05 3
 
0.6%
13:58 3
 
0.6%
19:42 3
 
0.6%
Other values (341) 479
92.8%
2024-12-05T10:24:13.278080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 637
24.7%
: 516
20.0%
2 237
 
9.2%
0 230
 
8.9%
4 189
 
7.3%
3 180
 
7.0%
5 177
 
6.9%
9 118
 
4.6%
8 107
 
4.1%
7 96
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 637
24.7%
: 516
20.0%
2 237
 
9.2%
0 230
 
8.9%
4 189
 
7.3%
3 180
 
7.0%
5 177
 
6.9%
9 118
 
4.6%
8 107
 
4.1%
7 96
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 637
24.7%
: 516
20.0%
2 237
 
9.2%
0 230
 
8.9%
4 189
 
7.3%
3 180
 
7.0%
5 177
 
6.9%
9 118
 
4.6%
8 107
 
4.1%
7 96
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 637
24.7%
: 516
20.0%
2 237
 
9.2%
0 230
 
8.9%
4 189
 
7.3%
3 180
 
7.0%
5 177
 
6.9%
9 118
 
4.6%
8 107
 
4.1%
7 96
 
3.7%

Payment
Text

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:13.595177image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length11
Median length7
Mean length7.122093023
Min length4

Characters and Unicode

Total characters3675
Distinct characters14
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 rowEwallet
2nd rowCash
3rd rowCredit card
4th rowEwallet
5th rowEwallet
ValueCountFrequency (%)
cash 191
28.3%
ewallet 166
24.6%
credit 159
23.6%
card 159
23.6%
2024-12-05T10:24:14.114057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 516
14.0%
C 350
9.5%
l 332
9.0%
e 325
8.8%
t 325
8.8%
r 318
8.7%
d 318
8.7%
s 191
 
5.2%
h 191
 
5.2%
E 166
 
4.5%
Other values (4) 643
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 516
14.0%
C 350
9.5%
l 332
9.0%
e 325
8.8%
t 325
8.8%
r 318
8.7%
d 318
8.7%
s 191
 
5.2%
h 191
 
5.2%
E 166
 
4.5%
Other values (4) 643
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 516
14.0%
C 350
9.5%
l 332
9.0%
e 325
8.8%
t 325
8.8%
r 318
8.7%
d 318
8.7%
s 191
 
5.2%
h 191
 
5.2%
E 166
 
4.5%
Other values (4) 643
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 516
14.0%
C 350
9.5%
l 332
9.0%
e 325
8.8%
t 325
8.8%
r 318
8.7%
d 318
8.7%
s 191
 
5.2%
h 191
 
5.2%
E 166
 
4.5%
Other values (4) 643
17.5%

cogs
Real number (ℝ)

Distinct513
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.5827132
Minimum12.54
Maximum993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:14.412763image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum12.54
5-th percentile36.7225
Q1127.265
median252.315
Q3452.685
95-th percentile781.75
Maximum993
Range980.46
Interquartile range (IQR)325.42

Descriptive statistics

Standard deviation233.4733547
Coefficient of variation (CV)0.7469170395
Kurtosis-0.07478672087
Mean312.5827132
Median Absolute Deviation (MAD)155.14
Skewness0.8663046917
Sum161292.68
Variance54509.80737
MonotonicityNot monotonic
2024-12-05T10:24:14.699106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
263.76 2
 
0.4%
207.27 2
 
0.4%
180.09 2
 
0.4%
385.38 1
 
0.2%
361.83 1
 
0.2%
993 1
 
0.2%
135 1
 
0.2%
182.14 1
 
0.2%
714 1
 
0.2%
71.95 1
 
0.2%
Other values (503) 503
97.5%
ValueCountFrequency (%)
12.54 1
0.2%
12.78 1
0.2%
13.98 1
0.2%
15.34 1
0.2%
15.43 1
0.2%
ValueCountFrequency (%)
993 1
0.2%
989.8 1
0.2%
972.1 1
0.2%
955.8 1
0.2%
954.4 1
0.2%

gross margin percentage
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.761904762
Minimum4.761904762
Maximum4.761904762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:14.914917image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4.761904762
5-th percentile4.761904762
Q14.761904762
median4.761904762
Q34.761904762
95-th percentile4.761904762
Maximum4.761904762
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean4.761904762
Median Absolute Deviation (MAD)0
Skewness0
Sum2457.142857
Variance0
MonotonicityIncreasing
2024-12-05T10:24:15.311643image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
4.761904762 516
100.0%
ValueCountFrequency (%)
4.761904762 516
100.0%
ValueCountFrequency (%)
4.761904762 516
100.0%

gross income
Real number (ℝ)

Distinct513
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.62913566
Minimum0.627
Maximum49.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:15.566797image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.627
5-th percentile1.836125
Q16.36325
median12.61575
Q322.63425
95-th percentile39.0875
Maximum49.65
Range49.023
Interquartile range (IQR)16.271

Descriptive statistics

Standard deviation11.67366774
Coefficient of variation (CV)0.7469170395
Kurtosis-0.07478672087
Mean15.62913566
Median Absolute Deviation (MAD)7.757
Skewness0.8663046917
Sum8064.634
Variance136.2745184
MonotonicityNot monotonic
2024-12-05T10:24:15.898980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.188 2
 
0.4%
10.3635 2
 
0.4%
9.0045 2
 
0.4%
19.269 1
 
0.2%
18.0915 1
 
0.2%
49.65 1
 
0.2%
6.75 1
 
0.2%
9.107 1
 
0.2%
35.7 1
 
0.2%
3.5975 1
 
0.2%
Other values (503) 503
97.5%
ValueCountFrequency (%)
0.627 1
0.2%
0.639 1
0.2%
0.699 1
0.2%
0.767 1
0.2%
0.7715 1
0.2%
ValueCountFrequency (%)
49.65 1
0.2%
49.49 1
0.2%
48.605 1
0.2%
47.79 1
0.2%
47.72 1
0.2%

Rating
Real number (ℝ)

Distinct61
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.036434109
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-12-05T10:24:16.217806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.3
Q15.6
median7
Q38.5
95-th percentile9.7
Maximum10
Range6
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation1.714911778
Coefficient of variation (CV)0.2437188711
Kurtosis-1.132330969
Mean7.036434109
Median Absolute Deviation (MAD)1.45
Skewness-0.005287193188
Sum3630.8
Variance2.940922405
MonotonicityNot monotonic
2024-12-05T10:24:16.585451image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 15
 
2.9%
8.7 13
 
2.5%
6.2 12
 
2.3%
5.5 12
 
2.3%
9.6 12
 
2.3%
6.6 11
 
2.1%
6.7 11
 
2.1%
7 11
 
2.1%
9.5 11
 
2.1%
9.9 11
 
2.1%
Other values (51) 397
76.9%
ValueCountFrequency (%)
4 5
1.0%
4.1 8
1.6%
4.2 9
1.7%
4.3 7
1.4%
4.4 9
1.7%
ValueCountFrequency (%)
10 4
 
0.8%
9.9 11
2.1%
9.8 8
1.6%
9.7 10
1.9%
9.6 12
2.3%