Overview

Brought to you by YData

Dataset statistics

 TrainTest
Number of variables22
Number of observations36669
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory8.6 KiB1.6 KiB
Average record size in memory24.0 B24.0 B

Variable types

 TrainTest
DateTime11
TimeSeries11

Timeseries statistics

 TrainTest
Number of series11
Time series length36669
Starting point24882854
Ending point28532922
Period11
2025-05-06T10:01:59.506275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T10:01:59.569328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

TrainTest
close is non stationary close is non stationary Non stationary
date has unique values date has unique values Unique
close has unique values close has unique values Unique

Reproduction

 TrainTest
Analysis started2025-05-06 07:01:58.9881712025-05-06 07:01:59.239995
Analysis finished2025-05-06 07:01:59.2363892025-05-06 07:01:59.386733
Duration0.25 seconds0.15 seconds
Software versionydata-profiling vv4.16.1ydata-profiling vv4.16.1
Download configurationconfig.jsonconfig.json

Variables

date
Date

 TrainTest
Distinct36669
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Memory size5.7 KiB1.1 KiB
 TrainTest
Minimum2024-01-01 00:00:002025-01-01 00:00:00
Maximum2024-12-31 00:00:002025-03-10 00:00:00
Invalid dates00
Invalid dates (%)0.0%0.0%
2025-05-06T10:01:59.672059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T10:01:59.798327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

close
Numeric time series

 TrainTest
Distinct36669
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
 TrainTest
Mean65912.46496194.986
Minimum3916278906
Maximum106735106183
Zeros00
Zeros (%)0.0%0.0%
Memory size5.7 KiB1.1 KiB
2025-05-06T10:01:59.899995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

 TrainTest
Minimum3916278906
5-th percentile42904.2584148.8
Q15906794143
median6423396589
Q369152.25101200
95-th percentile97370.25104912.6
Maximum106735106183
Range6757327277
Interquartile range (IQR)10085.257057

Descriptive statistics

 TrainTest
Standard deviation14675.7296351.0781
Coefficient of variation (CV)0.222654830.066022965
Kurtosis0.640272680.035918658
Mean65912.46496194.986
Median Absolute Deviation (MAD)50093027
Skewness0.82865406-0.6412335
Sum241239626637454
Variance2.1537701 × 10840336194
MonotonicityNot monotonicNot monotonic
Augmented Dickey-Fuller test p-value0.76700464330.8355438409
2025-05-06T10:02:00.006387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-06T10:02:00.966969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

 TrainTest
number of gaps00
min00
max00
mean00
std00
2025-05-06T10:02:01.015098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
43683 1
 
0.3%
54343 1
 
0.3%
53499 1
 
0.3%
56020 1
 
0.3%
58064 1
 
0.3%
58004 1
 
0.3%
58437 1
 
0.3%
58489 1
 
0.3%
58859 1
 
0.3%
58724 1
 
0.3%
Other values (356) 356
97.3%
ValueCountFrequency (%)
94588 1
 
1.4%
97216 1
 
1.4%
98498 1
 
1.4%
96136 1
 
1.4%
94143 1
 
1.4%
95994 1
 
1.4%
96923 1
 
1.4%
97531 1
 
1.4%
96284 1
 
1.4%
97326 1
 
1.4%
Other values (59) 59
85.5%
ValueCountFrequency (%)
39162 1
0.3%
39563 1
0.3%
39766 1
0.3%
40123 1
0.3%
40786 1
0.3%
41513 1
0.3%
41626 1
0.3%
41714 1
0.3%
41943 1
0.3%
41975 1
0.3%
ValueCountFrequency (%)
78906 1
1.4%
82573 1
1.4%
83397 1
1.4%
84108 1
1.4%
84210 1
1.4%
85475 1
1.4%
85937 1
1.4%
86257 1
1.4%
86813 1
1.4%
86965 1
1.4%
ValueCountFrequency (%)
78906 1
0.3%
82573 1
0.3%
83397 1
0.3%
84108 1
0.3%
84210 1
0.3%
85475 1
0.3%
85937 1
0.3%
86257 1
0.3%
86813 1
0.3%
86965 1
0.3%
ValueCountFrequency (%)
39162 1
1.4%
39563 1
1.4%
39766 1
1.4%
40123 1
1.4%
40786 1
1.4%
41513 1
1.4%
41626 1
1.4%
41714 1
1.4%
41943 1
1.4%
41975 1
1.4%
2025-05-06T10:02:00.765587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Interactions

Train

2025-05-06T10:01:59.022559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Test

2025-05-06T10:01:59.258011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Missing values

Train

2025-05-06T10:01:59.184040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.

Test

2025-05-06T10:01:59.328124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.

Train

2025-05-06T10:01:59.215677image/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.

Test

2025-05-06T10:01:59.353926image/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

Train

dateclose
24882024-01-0143683
24892024-01-0244818
24902024-01-0342740
24912024-01-0444180
24922024-01-0543997
24932024-01-0643856
24942024-01-0744158
24952024-01-0846799
24962024-01-0946651
24972024-01-1046159

Test

dateclose
28542025-01-0194588
28552025-01-0297228
28562025-01-0398309
28572025-01-0498590
28582025-01-0598252
28592025-01-06102259
28602025-01-0796462
28612025-01-0893929
28622025-01-0991860
28632025-01-1094674

Train

dateclose
28442024-12-2295492
28452024-12-2393033
28462024-12-2497242
28472024-12-2599074
28482024-12-2695454
28492024-12-2794448
28502024-12-2894907
28512024-12-2993668
28522024-12-3094202
28532024-12-3193414

Test

dateclose
29132025-03-0185475
29142025-03-0294357
29152025-03-0385937
29162025-03-0486813
29172025-03-0590491
29182025-03-0689025
29192025-03-0786965
29202025-03-0886257
29212025-03-0982573
29222025-03-1078906

Duplicate rows

Train

dateclose# duplicates
Dataset does not contain duplicate rows.

Test

dateclose# duplicates
Dataset does not contain duplicate rows.