lookforward | small library that helps you to create smooth transitions | Animation library

Do you have to use regexes? Whats wrong with doing it like this?

The following solution works:

How about this, simply either match the parent directory, or any sub-directory (asserting that the parent must be followed by a /):

You may use string.split(/}, ?(?={)/) (where (?<=}) positive lookbehind is converted to a consuming }) and then append a } to each item in the resulting array other than the last one.

This does what you asked, but the desired result you are showing doesn't quite match the results one gets by following the algorithm you described, as far as I can tell.

Anyway, the algorithm is simple to accomplish in a loop:

You could try something like the following code I wrote:

Explanation:

If behind/forward is 0 we set variable behind_counter/forward_counter to 1 in order for the following loops to atleast loop once.

Outer loop loops over the range of seq, the two inner loops over range of behind_counter(counted down) and forward_counter(counted up),respectively. Inside the most inner loop we set the respective lookahead/-behind indices and then check with help of the three if statements whether indices are out of bound in order to set the respective values to the out of bound value ('null') if necessary. The fourth if statement is chosen if the indices are not out of bound. Inside each if statement there are if-elif-elif-else statements which change the appended tuple depending on the lookahead/-behind values stored in behind and forward. If both are 0 only append seq[i], if behind is 0 only append a tuple consisting of seq[i] and current lookahead value, and so on.

After the work is done we print the value of res in order to visualize the result.

Source Code:

You may use

Edit 181017: this approach doesn't work due to forward rolling functions on sparse time series not currently being supported by pandas, see the comments.

Using for loops can be a performance killer when doing pandas operations.

The for loop around the rows plus their timedelta of 7 days can be replaced with a .rolling("7D"). To get a forward-rolling time delta (current date + 7 days), we reverse the df by date, as shown here.

Then no custom function is required anymore, and you can just take .quantity.sum() from the groupby.

Perhaps filter the unwanted words first then use grep against the filtered words: