football | Check out the new game server | Machine Learning library

You want to sum the TotalPledge values for each intTeamandClubID. For that you would need to create a different group for each intTeamandClubID. In your case, since you are selecting multiple columns, you will create a group for each unique combination of those columns, which is done by a GROUP BY statement.

You already have that in your query, but you are also grouping by TotalPledge, which you don't want. You want to SUM that value, so you should remove it from the GROUP BY:

Probably not the shortest solution. But I would do

To create a list with all the values of the src attribute you can use list comprehension and you can use either of the following locator strategies:

• Using CSS_SELECTOR:

Here's a way to achieve your result using functions from the tidyverse. There are a lot of different ways to get the same results, this is just one way. The code is structured in three main parts: first, building a big dataframe by binding the rows of the multiple lists, second removing the useless rows that were in the original dataframe, and third create all the variables.

The tab dataframe is also slightly different from your page original input, see the code in the data and functions part. I basically changed the column names so that they are not the same and rename them col1 and col2.

Only a few different functions are actually used. I created extract_digit, which extracts the nth occurrence of a number from a string. str_extract and str_match extract the specified pattern from a string, while str_detects only detects (and returns a logical, TRUE or FALSE). word gets the nth word from a string.

you can try this code

Yes, it's achievable using PEG. Here's an example using pegjs:

I see the problem is within your fromJson method:

When creating named constructors, you have 2 possible syntaxes to it:

In this situation, imagine you have a subclass called PickUpGameDetails.noReview where you'd create a fixed userReview object. In this case, you wouldn't process any extra params, only simplify the constructor using initialiser for some params. For this to work, you would add the initialiser before the {, using the :, being something like this (in this case you wouldn't even need to create the context with { }, unless you want to do extra operations besides the initialisers):

You can use Series.str.findall:

You will have to explicitly set the shapes of the tensors coming from tf.py_functions. Using None will allow variable input lengths. The Bert output dimension (384,) is, however, necessary: