Pandas: get first datetime-in and last datetime-out in one row - python-3.x

Question

First of all thanks in advance, there are always answers here so we learn a lot from the experts. I'm a noob using "pandas" (it's super handie for what i tried and achieved so far).
I have these data, handed to me like this (don't have access to the origin), 20k rows or more sometimes. The 'in' and 'out' columns may have one or more data per date, so when i get a 'in' the next data could be a 'out' or a 'in', depending, leaving me a blank cell, that's the problem (see first image).
I want to filter the first datetime-in, to left it in one column and the last datetime-out in another but the two in one row (see second image); the data comes in a csv file. I am doing this particular work manually with LibreOffice Calc (yeap).
So far, I have tried locating and relocating, tried merging, grouping... nothing works for me so i feel frustrated, ¿would you please lend me a hand? here is a minimal sample of the file
By the way english is not my language. ¡Thanks so much!

Answer 1

First:
out_column = df["out"].tolist()
This gives you all the out dates as a list, we will need that later.
in_column = df["in"].tolist() # in is used by python so I suggest renaming that row
I treat NaT as NaN (Null) in this Case.
Now we have to find what rows to keep, which we do by going through the in column and only keeping the rows after a NaN (and the first one):
filtered_df = []
tracker = False
for index, element in enumerate(in):
if index == 0 or tracker is True:
filtered_df.append(True)
tracker = False
continue
if element is None:
tracker = True
filtered_df.append(False)
Then you filter your df by this Boolean List:
df = df[filtered_df]
Now you fix up your out column by removing the null values:
while null in out_column:
out_column.remove(null)
Last but not least you overwrite your old out column with the new one:
df["out"] = out_column