Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations690
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.6 KiB
Average record size in memory112.2 B

Variable types

Categorical7
Text1
Numeric4
Boolean2

Alerts

11 is highly overall correlated with 3 and 3 other fieldsHigh correlation
13 is highly overall correlated with 8High correlation
3 is highly overall correlated with 11 and 2 other fieldsHigh correlation
4 is highly overall correlated with 11 and 2 other fieldsHigh correlation
5 is highly overall correlated with 11 and 1 other fieldsHigh correlation
6 is highly overall correlated with 11 and 3 other fieldsHigh correlation
8 is highly overall correlated with 13High correlation
3 is highly imbalanced (55.8%) Imbalance
4 is highly imbalanced (55.8%) Imbalance
11 is highly imbalanced (68.4%) Imbalance
2 has 19 (2.8%) zeros Zeros
7 has 70 (10.1%) zeros Zeros
10 has 395 (57.2%) zeros Zeros
12 has 295 (42.8%) zeros Zeros

Reproduction

Analysis started2025-01-30 09:31:34.826819
Analysis finished2025-01-30 09:31:37.268308
Duration2.44 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

0
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
b
468 
a
210 
?
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb
2nd rowa
3rd rowa
4th rowb
5th rowb

Common Values

ValueCountFrequency (%)
b 468
67.8%
a 210
30.4%
? 12
 
1.7%

Length

2025-01-30T16:31:37.350051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T16:31:37.457162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
b 468
67.8%
a 210
30.4%
12
 
1.7%

Most occurring characters

ValueCountFrequency (%)
b 468
67.8%
a 210
30.4%
? 12
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 678
98.3%
Other Punctuation 12
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 468
69.0%
a 210
31.0%
Other Punctuation
ValueCountFrequency (%)
? 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 678
98.3%
Common 12
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 468
69.0%
a 210
31.0%
Common
ValueCountFrequency (%)
? 12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 468
67.8%
a 210
30.4%
? 12
 
1.7%

1
Text

Distinct350
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2025-01-30T16:31:37.759982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9304348
Min length1

Characters and Unicode

Total characters3402
Distinct characters12
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

Unique170 ?
Unique (%)24.6%

Sample

1st row30.83
2nd row58.67
3rd row24.50
4th row27.83
5th row20.17
ValueCountFrequency (%)
12
 
1.7%
22.67 9
 
1.3%
20.42 7
 
1.0%
18.83 6
 
0.9%
24.50 6
 
0.9%
25.00 6
 
0.9%
19.17 6
 
0.9%
22.50 6
 
0.9%
20.67 6
 
0.9%
23.58 6
 
0.9%
Other values (340) 620
89.9%
2025-01-30T16:31:38.209732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 678
19.9%
2 518
15.2%
3 381
11.2%
5 333
9.8%
0 287
8.4%
7 271
 
8.0%
8 250
 
7.3%
1 227
 
6.7%
4 197
 
5.8%
6 136
 
4.0%
Other values (2) 124
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2712
79.7%
Other Punctuation 690
 
20.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 518
19.1%
3 381
14.0%
5 333
12.3%
0 287
10.6%
7 271
10.0%
8 250
9.2%
1 227
8.4%
4 197
 
7.3%
6 136
 
5.0%
9 112
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 678
98.3%
? 12
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3402
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 678
19.9%
2 518
15.2%
3 381
11.2%
5 333
9.8%
0 287
8.4%
7 271
 
8.0%
8 250
 
7.3%
1 227
 
6.7%
4 197
 
5.8%
6 136
 
4.0%
Other values (2) 124
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3402
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 678
19.9%
2 518
15.2%
3 381
11.2%
5 333
9.8%
0 287
8.4%
7 271
 
8.0%
8 250
 
7.3%
1 227
 
6.7%
4 197
 
5.8%
6 136
 
4.0%
Other values (2) 124
 
3.6%

2
Real number (ℝ)

Zeros 

Distinct215
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7587246
Minimum0
Maximum28
Zeros19
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-30T16:31:38.334231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.165
Q11
median2.75
Q37.2075
95-th percentile14
Maximum28
Range28
Interquartile range (IQR)6.2075

Descriptive statistics

Standard deviation4.9781632
Coefficient of variation (CV)1.0461129
Kurtosis2.2740219
Mean4.7587246
Median Absolute Deviation (MAD)2.21
Skewness1.4888131
Sum3283.52
Variance24.782109
MonotonicityNot monotonic
2025-01-30T16:31:38.451866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 21
 
3.0%
0 19
 
2.8%
3 19
 
2.8%
2.5 19
 
2.8%
0.75 16
 
2.3%
1.25 16
 
2.3%
0.5 15
 
2.2%
5 14
 
2.0%
6.5 12
 
1.7%
1.75 12
 
1.7%
Other values (205) 527
76.4%
ValueCountFrequency (%)
0 19
2.8%
0.04 5
 
0.7%
0.08 1
 
0.1%
0.085 1
 
0.1%
0.125 5
 
0.7%
0.165 8
1.2%
0.17 1
 
0.1%
0.205 3
 
0.4%
0.21 3
 
0.4%
0.25 6
 
0.9%
ValueCountFrequency (%)
28 1
0.1%
26.335 1
0.1%
25.21 1
0.1%
25.125 1
0.1%
25.085 1
0.1%
22.29 1
0.1%
22 1
0.1%
21.5 1
0.1%
21 1
0.1%
20 1
0.1%

3
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
u
519 
y
163 
?
 
6
l
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
u 519
75.2%
y 163
 
23.6%
? 6
 
0.9%
l 2
 
0.3%

Length

2025-01-30T16:31:38.573304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T16:31:38.665751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
u 519
75.2%
y 163
 
23.6%
6
 
0.9%
l 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
u 519
75.2%
y 163
 
23.6%
? 6
 
0.9%
l 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 684
99.1%
Other Punctuation 6
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 519
75.9%
y 163
 
23.8%
l 2
 
0.3%
Other Punctuation
ValueCountFrequency (%)
? 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 684
99.1%
Common 6
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 519
75.9%
y 163
 
23.8%
l 2
 
0.3%
Common
ValueCountFrequency (%)
? 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 519
75.2%
y 163
 
23.6%
? 6
 
0.9%
l 2
 
0.3%

4
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
g
519 
p
163 
?
 
6
gg
 
2

Length

Max length2
Median length1
Mean length1.0028986
Min length1

Characters and Unicode

Total characters692
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
g 519
75.2%
p 163
 
23.6%
? 6
 
0.9%
gg 2
 
0.3%

Length

2025-01-30T16:31:38.770308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T16:31:38.866163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
g 519
75.2%
p 163
 
23.6%
6
 
0.9%
gg 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
g 523
75.6%
p 163
 
23.6%
? 6
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 686
99.1%
Other Punctuation 6
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 523
76.2%
p 163
 
23.8%
Other Punctuation
ValueCountFrequency (%)
? 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 686
99.1%
Common 6
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 523
76.2%
p 163
 
23.8%
Common
ValueCountFrequency (%)
? 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 523
75.6%
p 163
 
23.6%
? 6
 
0.9%

5
Categorical

High correlation 

Distinct15
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
c
137 
q
78 
w
64 
i
59 
aa
54 
Other values (10)
298 

Length

Max length2
Median length1
Mean length1.2144928
Min length1

Characters and Unicode

Total characters838
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roww
2nd rowq
3rd rowq
4th roww
5th roww

Common Values

ValueCountFrequency (%)
c 137
19.9%
q 78
11.3%
w 64
9.3%
i 59
8.6%
aa 54
 
7.8%
ff 53
 
7.7%
k 51
 
7.4%
cc 41
 
5.9%
m 38
 
5.5%
x 38
 
5.5%
Other values (5) 77
11.2%

Length

2025-01-30T16:31:38.968463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c 137
19.9%
q 78
11.3%
w 64
9.3%
i 59
8.6%
aa 54
 
7.8%
ff 53
 
7.7%
k 51
 
7.4%
cc 41
 
5.9%
m 38
 
5.5%
x 38
 
5.5%
Other values (5) 77
11.2%

Most occurring characters

ValueCountFrequency (%)
c 219
26.1%
a 108
12.9%
f 106
12.6%
q 78
 
9.3%
w 64
 
7.6%
i 59
 
7.0%
k 51
 
6.1%
m 38
 
4.5%
x 38
 
4.5%
d 30
 
3.6%
Other values (4) 47
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 829
98.9%
Other Punctuation 9
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 219
26.4%
a 108
13.0%
f 106
12.8%
q 78
 
9.4%
w 64
 
7.7%
i 59
 
7.1%
k 51
 
6.2%
m 38
 
4.6%
x 38
 
4.6%
d 30
 
3.6%
Other values (3) 38
 
4.6%
Other Punctuation
ValueCountFrequency (%)
? 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 829
98.9%
Common 9
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 219
26.4%
a 108
13.0%
f 106
12.8%
q 78
 
9.4%
w 64
 
7.7%
i 59
 
7.1%
k 51
 
6.2%
m 38
 
4.6%
x 38
 
4.6%
d 30
 
3.6%
Other values (3) 38
 
4.6%
Common
ValueCountFrequency (%)
? 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 219
26.1%
a 108
12.9%
f 106
12.6%
q 78
 
9.3%
w 64
 
7.6%
i 59
 
7.0%
k 51
 
6.1%
m 38
 
4.5%
x 38
 
4.5%
d 30
 
3.6%
Other values (4) 47
 
5.6%

6
Categorical

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
v
399 
h
138 
bb
59 
ff
57 
?
 
9
Other values (5)
 
28

Length

Max length2
Median length1
Mean length1.1768116
Min length1

Characters and Unicode

Total characters812
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowv
2nd rowh
3rd rowh
4th rowv
5th rowv

Common Values

ValueCountFrequency (%)
v 399
57.8%
h 138
 
20.0%
bb 59
 
8.6%
ff 57
 
8.3%
? 9
 
1.3%
j 8
 
1.2%
z 8
 
1.2%
dd 6
 
0.9%
n 4
 
0.6%
o 2
 
0.3%

Length

2025-01-30T16:31:39.075888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T16:31:39.186750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
v 399
57.8%
h 138
 
20.0%
bb 59
 
8.6%
ff 57
 
8.3%
9
 
1.3%
j 8
 
1.2%
z 8
 
1.2%
dd 6
 
0.9%
n 4
 
0.6%
o 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
v 399
49.1%
h 138
 
17.0%
b 118
 
14.5%
f 114
 
14.0%
d 12
 
1.5%
? 9
 
1.1%
j 8
 
1.0%
z 8
 
1.0%
n 4
 
0.5%
o 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 803
98.9%
Other Punctuation 9
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 399
49.7%
h 138
 
17.2%
b 118
 
14.7%
f 114
 
14.2%
d 12
 
1.5%
j 8
 
1.0%
z 8
 
1.0%
n 4
 
0.5%
o 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
? 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 803
98.9%
Common 9
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 399
49.7%
h 138
 
17.2%
b 118
 
14.7%
f 114
 
14.2%
d 12
 
1.5%
j 8
 
1.0%
z 8
 
1.0%
n 4
 
0.5%
o 2
 
0.2%
Common
ValueCountFrequency (%)
? 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 399
49.1%
h 138
 
17.0%
b 118
 
14.5%
f 114
 
14.0%
d 12
 
1.5%
? 9
 
1.1%
j 8
 
1.0%
z 8
 
1.0%
n 4
 
0.5%
o 2
 
0.2%

7
Real number (ℝ)

Zeros 

Distinct132
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2234058
Minimum0
Maximum28.5
Zeros70
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-30T16:31:39.313589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.165
median1
Q32.625
95-th percentile8.56875
Maximum28.5
Range28.5
Interquartile range (IQR)2.46

Descriptive statistics

Standard deviation3.3465134
Coefficient of variation (CV)1.5051294
Kurtosis11.200192
Mean2.2234058
Median Absolute Deviation (MAD)0.915
Skewness2.8913304
Sum1534.15
Variance11.199152
MonotonicityNot monotonic
2025-01-30T16:31:39.436297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
10.1%
0.25 35
 
5.1%
0.04 33
 
4.8%
1 31
 
4.5%
0.125 30
 
4.3%
0.5 28
 
4.1%
0.085 26
 
3.8%
1.5 25
 
3.6%
0.165 22
 
3.2%
2.5 17
 
2.5%
Other values (122) 373
54.1%
ValueCountFrequency (%)
0 70
10.1%
0.04 33
4.8%
0.085 26
 
3.8%
0.125 30
4.3%
0.165 22
 
3.2%
0.21 6
 
0.9%
0.25 35
5.1%
0.29 12
 
1.7%
0.335 5
 
0.7%
0.375 7
 
1.0%
ValueCountFrequency (%)
28.5 1
 
0.1%
20 2
0.3%
18 1
 
0.1%
17.5 1
 
0.1%
16 1
 
0.1%
15.5 1
 
0.1%
15 3
0.4%
14.415 1
 
0.1%
14 3
0.4%
13.875 2
0.3%

8
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size822.0 B
True
361 
False
329 
ValueCountFrequency (%)
True 361
52.3%
False 329
47.7%
2025-01-30T16:31:39.536988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

9
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size822.0 B
False
395 
True
295 
ValueCountFrequency (%)
False 395
57.2%
True 295
42.8%
2025-01-30T16:31:39.618731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

10
Real number (ℝ)

Zeros 

Distinct23
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4
Minimum0
Maximum67
Zeros395
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-30T16:31:39.702866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile11
Maximum67
Range67
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.86294
Coefficient of variation (CV)2.026225
Kurtosis50.829431
Mean2.4
Median Absolute Deviation (MAD)0
Skewness5.1525199
Sum1656
Variance23.648186
MonotonicityNot monotonic
2025-01-30T16:31:39.810382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 395
57.2%
1 71
 
10.3%
2 45
 
6.5%
3 28
 
4.1%
6 23
 
3.3%
11 19
 
2.8%
5 18
 
2.6%
7 16
 
2.3%
4 15
 
2.2%
9 10
 
1.4%
Other values (13) 50
 
7.2%
ValueCountFrequency (%)
0 395
57.2%
1 71
 
10.3%
2 45
 
6.5%
3 28
 
4.1%
4 15
 
2.2%
5 18
 
2.6%
6 23
 
3.3%
7 16
 
2.3%
8 10
 
1.4%
9 10
 
1.4%
ValueCountFrequency (%)
67 1
 
0.1%
40 1
 
0.1%
23 1
 
0.1%
20 2
 
0.3%
19 1
 
0.1%
17 2
 
0.3%
16 3
 
0.4%
15 4
0.6%
14 8
1.2%
13 1
 
0.1%

11
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
g
625 
s
 
57
p
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
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 rowg
2nd rowg
3rd rowg
4th rowg
5th rows

Common Values

ValueCountFrequency (%)
g 625
90.6%
s 57
 
8.3%
p 8
 
1.2%

Length

2025-01-30T16:31:39.928989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T16:31:40.031142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
g 625
90.6%
s 57
 
8.3%
p 8
 
1.2%

Most occurring characters

ValueCountFrequency (%)
g 625
90.6%
s 57
 
8.3%
p 8
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 690
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 625
90.6%
s 57
 
8.3%
p 8
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 690
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 625
90.6%
s 57
 
8.3%
p 8
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 625
90.6%
s 57
 
8.3%
p 8
 
1.2%

12
Real number (ℝ)

Zeros 

Distinct240
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1017.3855
Minimum0
Maximum100000
Zeros295
Zeros (%)42.8%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-01-30T16:31:40.138104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q3395.5
95-th percentile4119.4
Maximum100000
Range100000
Interquartile range (IQR)395.5

Descriptive statistics

Standard deviation5210.1026
Coefficient of variation (CV)5.1210702
Kurtosis214.66997
Mean1017.3855
Median Absolute Deviation (MAD)5
Skewness13.140655
Sum701996
Variance27145169
MonotonicityNot monotonic
2025-01-30T16:31:40.269567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 295
42.8%
1 29
 
4.2%
500 10
 
1.4%
1000 10
 
1.4%
2 9
 
1.3%
6 8
 
1.2%
5 8
 
1.2%
300 8
 
1.2%
200 6
 
0.9%
100 6
 
0.9%
Other values (230) 301
43.6%
ValueCountFrequency (%)
0 295
42.8%
1 29
 
4.2%
2 9
 
1.3%
3 6
 
0.9%
4 5
 
0.7%
5 8
 
1.2%
6 8
 
1.2%
7 4
 
0.6%
8 2
 
0.3%
9 1
 
0.1%
ValueCountFrequency (%)
100000 1
0.1%
51100 1
0.1%
50000 1
0.1%
31285 1
0.1%
26726 1
0.1%
18027 1
0.1%
15108 1
0.1%
15000 1
0.1%
13212 1
0.1%
11202 1
0.1%

13
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
-
383 
+
307 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
Distinct characters2
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 row+
2nd row+
3rd row+
4th row+
5th row+

Common Values

ValueCountFrequency (%)
- 383
55.5%
+ 307
44.5%

Length

2025-01-30T16:31:40.395957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-30T16:31:40.486887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
690
100.0%

Most occurring characters

ValueCountFrequency (%)
- 383
55.5%
+ 307
44.5%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 383
55.5%
Math Symbol 307
44.5%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 383
100.0%
Math Symbol
ValueCountFrequency (%)
+ 307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 690
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 383
55.5%
+ 307
44.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 383
55.5%
+ 307
44.5%

Interactions

2025-01-30T16:31:36.625130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:35.327232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:35.914216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:36.265267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:36.711452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:35.426876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:36.005733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:36.355903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:36.798351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:35.510575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:36.089388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:36.444059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:36.888591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:35.597513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:36.175647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-30T16:31:36.538073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-30T16:31:40.558760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
01011121323456789
01.0000.0000.0380.0000.0200.0000.0000.0000.2490.1680.0000.0800.070
100.0001.0000.0000.4270.3700.2040.0400.0400.0000.0000.3150.3380.455
110.0380.0001.0000.2330.1020.1000.6370.6370.5160.5230.0000.1390.243
120.0000.4270.2331.0000.1160.1060.3990.3990.2290.2790.0870.0870.013
130.0200.3700.1020.1161.0000.2220.1880.1880.3500.2290.2790.7170.454
20.0000.2040.1000.1060.2221.0000.1770.1770.0820.1630.2660.2370.168
30.0000.0400.6370.3990.1880.1771.0001.0000.4680.5470.0920.1730.188
40.0000.0400.6370.3990.1880.1771.0001.0000.4680.5470.0920.1730.188
50.2490.0000.5160.2290.3500.0820.4680.4681.0000.6370.0640.3070.262
60.1680.0000.5230.2790.2290.1630.5470.5470.6371.0000.1920.2610.113
70.0000.3150.0000.0870.2790.2660.0920.0920.0640.1921.0000.2940.171
80.0800.3380.1390.0870.7170.2370.1730.1730.3070.2610.2941.0000.428
90.0700.4550.2430.0130.4540.1680.1880.1880.2620.1130.1710.4281.000

Missing values

2025-01-30T16:31:37.019898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-30T16:31:37.200179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

012345678910111213
0b30.830.000ugwv1.250tt1g0+
1a58.674.460ugqh3.040tt6g560+
2a24.500.500ugqh1.500tf0g824+
3b27.831.540ugwv3.750tt5g3+
4b20.175.625ugwv1.710tf0s0+
5b32.084.000ugmv2.500tf0g0+
6b33.171.040ugrh6.500tf0g31285+
7a22.9211.585ugccv0.040tf0g1349+
8b54.420.500ypkh3.960tf0g314+
9b42.504.915ypwv3.165tf0g1442+
012345678910111213
680b19.500.290ugkv0.290ff0g364-
681b27.831.000ypdh3.000ff0g537-
682b17.083.290ugiv0.335ff0g2-
683b36.420.750ypdv0.585ff0g3-
684b40.583.290ugmv3.500ff0s0-
685b21.0810.085ypeh1.250ff0g0-
686a22.670.750ugcv2.000ft2g394-
687a25.2513.500ypffff2.000ft1g1-
688b17.920.205ugaav0.040ff0g750-
689b35.003.375ugch8.290ff0g0-