Overview

Dataset statistics

Number of variables3
Number of observations1846
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory144.1 KiB
Average record size in memory79.9 B

Variable types

Categorical1
Numeric2

Alerts

dataset is uniformly distributedUniform

Reproduction

Analysis started2026-02-22 12:00:16.893775
Analysis finished2026-02-22 12:00:17.395988
Duration0.5 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

dataset
Categorical

Uniform 

Distinct13
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size115.2 KiB
dino
142 
away
142 
h_lines
142 
v_lines
142 
x_shape
142 
Other values (8)
1136 

Length

Max length10
Median length8
Mean length6.8461538
Min length4

Characters and Unicode

Total characters12638
Distinct characters21
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 rowdino
2nd rowdino
3rd rowdino
4th rowdino
5th rowdino

Common Values

ValueCountFrequency (%)
dino142
 
7.7%
away142
 
7.7%
h_lines142
 
7.7%
v_lines142
 
7.7%
x_shape142
 
7.7%
star142
 
7.7%
high_lines142
 
7.7%
dots142
 
7.7%
circle142
 
7.7%
bullseye142
 
7.7%
Other values (3)426
23.1%

Length

2026-02-22T12:00:17.446536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dino142
 
7.7%
away142
 
7.7%
h_lines142
 
7.7%
v_lines142
 
7.7%
x_shape142
 
7.7%
star142
 
7.7%
high_lines142
 
7.7%
dots142
 
7.7%
circle142
 
7.7%
bullseye142
 
7.7%
Other values (3)426
23.1%

Most occurring characters

ValueCountFrequency (%)
s1420
11.2%
e1278
10.1%
l1278
10.1%
n1136
 
9.0%
i1136
 
9.0%
_994
 
7.9%
a852
 
6.7%
t568
 
4.5%
d568
 
4.5%
h568
 
4.5%
Other values (11)2840
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)12638
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s1420
11.2%
e1278
10.1%
l1278
10.1%
n1136
 
9.0%
i1136
 
9.0%
_994
 
7.9%
a852
 
6.7%
t568
 
4.5%
d568
 
4.5%
h568
 
4.5%
Other values (11)2840
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12638
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s1420
11.2%
e1278
10.1%
l1278
10.1%
n1136
 
9.0%
i1136
 
9.0%
_994
 
7.9%
a852
 
6.7%
t568
 
4.5%
d568
 
4.5%
h568
 
4.5%
Other values (11)2840
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12638
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s1420
11.2%
e1278
10.1%
l1278
10.1%
n1136
 
9.0%
i1136
 
9.0%
_994
 
7.9%
a852
 
6.7%
t568
 
4.5%
d568
 
4.5%
h568
 
4.5%
Other values (11)2840
22.5%

x
Real number (ℝ)

Distinct1804
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.265695
Minimum15.56075
Maximum98.288123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2026-02-22T12:00:17.525589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.56075
5-th percentile27.892787
Q141.073403
median52.591269
Q367.277845
95-th percentile81.143638
Maximum98.288123
Range82.727374
Interquartile range (IQR)26.204442

Descriptive statistics

Standard deviation16.713001
Coefficient of variation (CV)0.30798466
Kurtosis-0.69556623
Mean54.265695
Median Absolute Deviation (MAD)12.997931
Skewness0.13530469
Sum100174.47
Variance279.32442
MonotonicityNot monotonic
2026-02-22T12:00:17.612158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.74364
 
0.2%
56.66674
 
0.2%
504
 
0.2%
67.94873
 
0.2%
59.23083
 
0.2%
44.10263
 
0.2%
71.53852
 
0.1%
48.20512
 
0.1%
61.28212
 
0.1%
61.79492
 
0.1%
Other values (1794)1817
98.4%
ValueCountFrequency (%)
15.560749521
0.1%
17.893498711
0.1%
18.109472291
0.1%
19.288204741
0.1%
20.024500571
0.1%
20.209778161
0.1%
20.408947891
0.1%
20.689149051
0.1%
20.931999681
0.1%
20.959464811
0.1%
ValueCountFrequency (%)
98.288123271
0.1%
98.20511
0.1%
96.080519371
0.1%
95.59341641
0.1%
95.443487811
0.1%
95.38461
0.1%
95.260527841
0.1%
95.249233961
0.1%
95.065274841
0.1%
94.997488051
0.1%

y
Real number (ℝ)

Distinct1807
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.835099
Minimum0.015119325
Maximum99.69468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2026-02-22T12:00:17.698710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.015119325
5-th percentile10.550425
Q122.561073
median47.59445
Q371.810778
95-th percentile90.121254
Maximum99.69468
Range99.679561
Interquartile range (IQR)49.249705

Descriptive statistics

Standard deviation26.847766
Coefficient of variation (CV)0.56125663
Kurtosis-1.2804472
Mean47.835099
Median Absolute Deviation (MAD)24.773552
Skewness0.15962518
Sum88303.593
Variance720.80256
MonotonicityNot monotonic
2026-02-22T12:00:17.793295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.6416
 
0.3%
18.33333
 
0.2%
55.25643
 
0.2%
46.02563
 
0.2%
51.41033
 
0.2%
51.02563
 
0.2%
25.25643
 
0.2%
14.87183
 
0.2%
31.41032
 
0.1%
42.17952
 
0.1%
Other values (1797)1815
98.3%
ValueCountFrequency (%)
0.015119325161
0.1%
0.217006271
0.1%
0.30387242061
0.1%
0.50910673521
0.1%
0.6014909421
0.1%
1.1338803661
0.1%
1.2105516631
0.1%
1.4881323331
0.1%
1.5044181751
0.1%
1.7414617131
0.1%
ValueCountFrequency (%)
99.694680141
0.1%
99.644179171
0.1%
99.613471681
0.1%
99.579591131
0.1%
99.48721
0.1%
99.283763951
0.1%
99.256867291
0.1%
99.10261
0.1%
98.931027041
0.1%
98.628369441
0.1%

Interactions

2026-02-22T12:00:17.093817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T12:00:16.967128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T12:00:17.243903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-22T12:00:17.033701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-22T12:00:17.849879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
datasetxy
dataset1.0000.2050.198
x0.2051.000-0.069
y0.198-0.0691.000

Missing values

2026-02-22T12:00:17.326959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-22T12:00:17.366474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datasetxy
0dino55.384697.1795
1dino51.538596.0256
2dino46.153894.4872
3dino42.820591.4103
4dino40.769288.3333
5dino38.717984.8718
6dino35.641079.8718
7dino33.076977.5641
8dino28.974474.4872
9dino26.153871.4103
datasetxy
1836wide_lines64.90035816.245258
1837wide_lines68.7634348.700573
1838wide_lines66.81691412.273294
1839wide_lines67.3093470.217006
1840wide_lines34.73182919.601795
1841wide_lines33.67444226.090490
1842wide_lines75.62725537.128752
1843wide_lines40.61012589.136240
1844wide_lines39.11436696.481751
1845wide_lines34.58382989.588902