Overview

Brought to you by YData

Dataset statistics

Number of variables2
Number of observations2923
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.8 KiB
Average record size in memory16.0 B

Variable types

DateTime1
TimeSeries1

Timeseries statistics

Number of series1
Time series length2923
Starting point0
Ending point2922
Period1
2025-05-06T10:01:57.342555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T10:01:57.397945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

close is non stationary Non stationary
close is seasonal Seasonal
date has unique values Unique

Reproduction

Analysis started2025-05-06 07:01:57.068153
Analysis finished2025-05-06 07:01:57.334688
Duration0.27 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

date
Date

Unique 

Distinct2923
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
Minimum2017-03-10 00:00:00
Maximum2025-03-10 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-06T10:01:57.467993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-06T10:01:57.547097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

close
Numeric time series

Non stationary  Seasonal 

Distinct2803
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27286.762
Minimum943
Maximum106735
Zeros0
Zeros (%)0.0%
Memory size23.0 KiB
2025-05-06T10:01:57.628326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum943
5-th percentile2879
Q17841.5
median19247
Q342292.5
95-th percentile69578.6
Maximum106735
Range105792
Interquartile range (IQR)34451

Descriptive statistics

Standard deviation24417.989
Coefficient of variation (CV)0.89486577
Kurtosis0.62905506
Mean27286.762
Median Absolute Deviation (MAD)12857
Skewness1.134126
Sum79759206
Variance5.9623821 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.8306128181
2025-05-06T10:01:57.700257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-06T10:01:58.527955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-06T10:01:58.568744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
6419 4
 
0.1%
2568 3
 
0.1%
3433 3
 
0.1%
10494 3
 
0.1%
6619 3
 
0.1%
8243 3
 
0.1%
9501 3
 
0.1%
6542 3
 
0.1%
4006 3
 
0.1%
7311 3
 
0.1%
Other values (2793) 2892
98.9%
ValueCountFrequency (%)
943 1
< 0.1%
978 1
< 0.1%
982 1
< 0.1%
983 1
< 0.1%
1014 2
0.1%
1024 1
< 0.1%
1030 1
< 0.1%
1034 1
< 0.1%
1039 2
0.1%
1053 1
< 0.1%
ValueCountFrequency (%)
106735 1
< 0.1%
106183 1
< 0.1%
106126 1
< 0.1%
105883 1
< 0.1%
104950 1
< 0.1%
104923 1
< 0.1%
104897 1
< 0.1%
104812 1
< 0.1%
104768 1
< 0.1%
104270 1
< 0.1%
2025-05-06T10:01:58.357886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Interactions

2025-05-06T10:01:57.201928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Missing values

2025-05-06T10:01:57.277919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-06T10:01:57.308021image/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

dateclose
02017-03-101286
12017-03-111172
22017-03-121231
32017-03-131239
42017-03-141250
52017-03-151249
62017-03-161186
72017-03-171125
82017-03-18982
92017-03-191053
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