I am trying to decompose a Time Series, however my data does not have Dates, it is composed of entries taken at regular (and unknown) time intervals.
This solution is great and exactly what I want, however it assumed that my series has a datetime index, which it does not.
I can estimate the frequency parameter in this specific case, however this will need to be automated for different data, and as such I can not use the freq parameter of the seasonal_decompose function (unless there is some way to automatically calculate this) to make do for the fact that my series lacks a datetime index.
I have managed to estimate season lenght by utilizing the seasonal python package.
Using fit_seasons function and then seeing the lenght of the returned seasons.
Related
I have a pandas dataframe with an index and jut one column. Index has Dates. Column has Values. I would like to find the NewValue of a NewDate that is not in the index. To do that i suppose i may use interpolation function as: NewValue=InterpolationFunction(NewDate,Index,Column,method ext..). So what is the InterpolationFunction? It seems that most of interpolation functions are used for padding, finding the missing values ext. This is not what i want. I just want the NewValue. Not built a new Dataframe ext..
Thank you very very much for taking the time to read this.
I am afraid that you cannot find the missing values without constructing a base for your data. here is the answer to your question if you make a base dataframe:
You need to construct a panel in order to set up your data for proper interpolation. For instance, in the case of the date, let's say that you have yearly data and you want to add information for a missing year in between or generate data for quarterly intervals.
You need to construct a base for the time series i.e.:
dates = pd.date_range(start="1987-01-01",end="2021-01-01", freq="Q").values
panel = pd.DataFrame({'date_Q' :dates})
Now you can join your data to this structure and it will have dates without information as missing. Now you need to use a proper interpolation algorithm to fill the missing values. Pandas .interpolate() method has some basic interpolation methods such as polynomial and linear which you can find here.
However, much more ways of interpolation are offered by Scipy which you can find in the tutorials here.
I have an experimental bench where I retrieve data of the power of a compressor.
I import the csv using python and pandas. So it's a pandas dataframe with index datetime and a float column with P_comp.
And I would like to define and calculate the area under the curve for each period like this :
For the moment, I do it manually which is really annoying, I'm plotting all the data and manually selecting a range where there is a periodic steady state and then I'm integrating P_comp using np.trapz on this range.
I tried scipy.signal but I’m not sure it’s a good tool to do this job. Do you have any ideas ?
Looks like the intervals are fairly regular and the low-values are almost equal, too, so you might get away with taking the first value below a defined threshold, and then after a period of time the next etc.
Thank you, I found the solution using scipy.signal.find_peaks and numpy diff
I am trying to plot this time series in a chart, but the canvas is empty.
As you can see in the image above, my time series is quite simple. I want to plot DATE in x-axis and PAYEMS in the y-axis.
At first, I was getting an error because my dates were strings, so I converted it in cell 11.
You do not want to use a tsplot to plot a time series. The name is a bit confusing, but as the documentation puts it, tsplot is "intended to be used with data where observations are nested within sampling units that were measured at multiple timepoints". As a rule of thumb: If you understand this sentence, you will know when to use it, if you don't understand this sentence, don't use it. Apart, tsplot will even be removed or significantly altered in the future, so its use is deprecated.
But that doesn't matter, because you can directly use pandas to plot the time series.
df.plot(x="Date", y="Payems")
tl;dr: Is it possible to .set_index() method on several Dask Dataframes in parallel concurrently? Alternatively, is it possible to .set_index() lazily on several Dask Dataframes which, consequently, would lead to the indexes being set in parallel concurrently?
Here is the scenario:
I have several time series
Each time series is stored is several .csv files. Each file contains data related to a specific day. Also, files are scattered amongst different folders (each folder is contains data for one month)
Each time series has different sampling rates
All time series have the same columns. All have a column which contains DateTime, amongst others.
Data is too large to be processed in memory. That's why I am using Dask.
I want to merge all the time series into a single DataFrame, aligned by DateTime. For this, I need to first resample() each and all time series to a common sampling rate. And then .join() all time series.
.resample() can only be applied on index. Hence, before resampling I need to .set_index() on the DateTime column on each time series.
When I ask .set_index() method on one time series, computation starts immediately. Which leads to my code being blocked and waiting. At this moment, if I check my machine resources usage, I can see that many cores are being used but the usage does not go above ~15%. Which makes me think that, ideally, I could have the .set_index() method being applied to more than one time series at the same time.
After reaching the above situation, I've tried some not elegant solutions to parallelize application of .set_index() method on several time series (e.g. create a multiprocessing.Pool ), which were not successful. Before giving more details on those, is there a clean way on how to solve the situation above? Was the above scenario thought at some point when implementing Dask?
Alternatively, is it possible to .set_index() lazily? If .set_index() method could be applied lazily, I would create a full computation graph with the steps described above and in the end ,everything would be computed in parallel concurrently (I think).
Dask.dataframe needs to know the min and max values of all of the partitions of the dataframe in order to sensibly do datetime operations in parallel. By default it will read the data once in order to find good partitions. If the data is not sorted it will then do a shuffle (perhaps very expensive) to sort
In your case it sounds like your data is already sorted and that you might be able to provide these explicitly. You should look at the last example of the dd.DataFrame.set_index docstring
A common case is when we have a datetime column that we know to be
sorted and is cleanly divided by day. We can set this index for free
by specifying both that the column is pre-sorted and the particular
divisions along which is is separated
>>> import pandas as pd
>>> divisions = pd.date_range('2000', '2010', freq='1D')
>>> df2 = df.set_index('timestamp', sorted=True, divisions=divisions) # doctest: +SKIP
I'm looking for a neat way to detect particular events in time series data.
In my case, an event might consist of a value changing by more than a certain amount from one sample to the next, or it might consist of a sample being (for example) greater than a threshold while another parameter is less than another threshold.
e.g. imagine a time series list in which I've got three parameters; a timestamp, some temperature data and some humidity data:
time_series = []
# time, temp, humidity
time_series.append([0.0, 12.5, 87.5])
time_series.append([0.1, 12.8, 92.5])
time_series.append([0.2, 12.9, 95.5])
Obviously a useful time series would be much longer than this.
I can obviously loop through this data checking each row (and potentially the previous row) to see if it meets my criteria, but I'm wondering if there's a neat library or technique that I can use to search time series data for particular events - especially where an event might be defined as a function of a number of contiguous samples, or a function of samples in more than one column.
Does anyone know of such a library or technique?
You might like to investigate pandas, which includes time series tools see this pandas doc.
I think that what you are trying to do is take "slices" through the data. [This link on earthpy.org] (http://earthpy.org/pandas-basics.html) has a nice introduction to using time series data with pandas, and if you follow down through the examples it shows how to take out slices, which I think would correspond to pulling out parameters that exceed thresholds, etc. in your data.