Grouper | A PowerShell script for helping to find vulnerable settings in AD Group Policy. (deprecated, use Gro | Command Line Interface library

I'm trying to create a multi-page pdf using FacetGrid from this (https://seaborn.pydata.org/examples/many_facets.html). There are 20 grids images and I want to save the first 10 grids in the first page of pdf and the second 10 grids to the second page of pdf file. I got the idea of create mutipage pdf file from this (Export huge seaborn chart into pdf with multiple pages). This example works on sns.catplot() but in my case (sns.FacetGrid) the output pdf file has two pages and each page has all of the 20 grids instead of dividing 10 grids in each page.

I've got a dataframe with power profiles. The dataframe shows start and endtime and consumed power during a transaction. It looks something like this:

My Goal is to assign a new Start- and EndTime which are set at 15 min values. Like so:

The old Timestamps can be deleted afterwards. However I don't want to aggregate them. So I guess

df.groupby(pd.Grouper(key="StartTime", freq="15min")).sum()

or

df.groupby(pd.Grouper(key="StartEndtime", freq="15min")).mean()

etc. is not an option. Another idea I had was creating a dataframe with values between 2018.01.01 00:00:00 and 2018.01.01 23:45:00. However I am not sure how to iterate true the two dataframes, to achieve my goal and if iteration true dataframes is a good idea in the first place.

I'd like to have a running year to date pct change column in my pandas dataframe:

Here is the dataframe:

Lets say I have the following Time Series with an item:

I have a timeseries dataframe of rain values for every given hour.

This is the current dataframe:

print(assomption_rain_df.head(25))

I am trying to aggregate a bunch of dictionaries, with string keys and lists of binary numbers as values, stored in a pandas dataframe. Like this:

Example dataframe that this problem occurs with:

I have a dataframe that is made up of hourly electricity price data. What I am trying to do is find a way to calculate the average of the n lowest price hourly periods in day. The data spans many years and aiming to get the average of the n lowest price periods for each day. Synthetic data can be created using the following:

I have some hierarchical data from 2003 to 2011 which bottoms out into time series data which looks something like this:

I have a pandas dataframe the looks like the following: