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I have the following document.

It has been sometime since i tried working in python

I have this data frame with many columns too many to name

I have created internal modules which follow the structure such as follows-

I am currently working on the following:

I have two dataframes. One dataframe contains a number of inventors per company and I would like to know how often their name appears in another dataframe in the same company.The company identifier (df_itemnumber_rounded) in both dataframes is called the same and present in both dataframes.

Example:

First dataframe includes:

I would like to plot estimated marginal means from a three-way factorial experiment with letters indicating significantly different means, adjusted for multiple comparisons. My current workflow is to fit the model with lmer(), calculate estimated marginal means with emmeans(), then implement the compact letter display algorithm with cld().

My problem is that the graph is too busy when you plot all three-way interactions on the same plot. So I would like to split up the plot and generate different sets of letters for each subplot, starting with "a". The problem is that when I use the by argument in cld to split it up, it does a separate correction for multiple comparisons within each by group. Because there are now fewer tests within each group, this results in a less conservative correction. But if I try to manually split up the output of cld() without a by group, I would have to manually re-implement the letter algorithm for each subplot. I guess I could do that but it seems cumbersome. I am trying to share this code with a client for him to modify later, so that solution would probably be too complex. Does anyone have an easy way to either:

1. Get the output of cld() to use one combined correction for all by groups.
2. Using a relatively simple method, reduce the compact letter display for each subgroup to the minimal necessary number of letters.

Load packages and data.

I need to call a webapi POST method using javascript.

The method accepts a model. I am able to invoke this method correctly from POSTMAN and also see the entries in the database.

I just started learning C#. I want to combine 2 lists and return the output. For example:

List 1 = Peter, Tony, Steve
List 2 = Parker, Stark, Rogers

Final List/Output:
Peter Parker
Tony Stark
Steve Rogers

Here is my codes:

I have loaded some JSON API data as a Pandas dataframe, as such, there are some columns that come out as lists. I also have some NaN values.

First and foremost I want to replace the NaN with a single word such as 'empty' but the rest of the data are already in list forms. I want to ultimately create a new column that operates on this list structure and essentially turns it into a string since I will be using the strings to perform mapping logic later on.

Here is some sample data and logic:

Best practice when analysing data from an RCT is to adjust for the baseline measure (ancova). However, researchers often still ask for change from baseline in each group and their relative difference (given by the treatment x time interaction). When one adjusts for the baseline measure, contrasts involving time can only be made from the first post-baseline measure.

Is there a way to perform a contrast where you subtract the model prediction at each post-treatment time point from the baseline measure (e.g. the overall pre-treatment mean of the combined groups)? I could do this manually but wouldn’t get C.I.'s.

Some dummy data are below: