time-series | High-performance .NET time series types | Time Series Database library

I have a dataframe like that:

I am working on a project where I am using a shape file to make a choropleth map of the United States. To do this, I downloaded the standard shape file here from the US Census Bureau. After a little bit of cleaning up (there were some extraneous island territories which I removed by changing the plot's axis limits), I was able to get the contiguous states to fit neatly within the bounds of the matplotlib figure. For reference, please see Edit 4 below.

Edit 1: I am using the cb_2018_us_state_500k.zip [3.2 MB] shape file.

The only problem now is that by setting axis limits I now am no longer able to view Alaska and Hawaii (as these are obviously cut out by restricting the axis limits). I would now like to add both of these polygons back in my map but now towards the lower part of the plot figure (the treatment that is given by most other maps of this type) despite its geographical inaccuracy.

To put this more concretely, I am interested in selecting the polygon shapes representing Alaska and Hawaii and moving them to the lower left hand side of my figure. Is this something that would be possible?

I can create a Boolean mask using:

Hi I am using newly Timeseries mongodb collection. My mongodb version is 5.0.6. I am following this tutorial. I create a collection like this.

I have been dealing with time-series with one entry per hour over a year. In order to better analyse the data, I have been resampling by month with pandas and summing the results with df = df.resample('M').sum()

As the last hour of the last day runs from 23:00 31/12 to 00:00 01/01 of the following year, the final hour is resampled into January of the following year (e.g. my time-series is for 2020, the last hour of 31/12/2020 is resampled into January 2021). This means I lose data for December.

I have considered adding the data back in to December, but is there a better way to achieve this?

I want to filter some columns in dataframe where there is little to no change in the data throughout, an example plot of one of the columns is shown below:

What I'm doing currently is quite simple and is probably very inefficient.

I have a time-series panel dataset that is structured in the following way: There are 2 funds that each own different stocks at each time period.

I am attempting to use Spark for a very simple use case: given a large set of files (90k) with device time-series data for millions of devices group all of the time-series reads for a given device into a single set of files (partition). For now let’s say we are targeting 100 partitions, and it is not critical that a given devices data shows up in the same output file, just the same partition.

Given this problem we’ve come up with two ways to do this - repartition then write or write with partitionBy applied to the Writer. The code for either of these is very simple:

repartition (hash column is added to ensure that comparison to partitionBy code below is one-to-one):

Say I have two pandas time-series dataframes:

I have a time series data set of multiple IDs and multiple variables, each variable has 3 time series entries - "baseline", "3 month", "6 month". The dataframe is structured like this, df =