Hi, I want to know what is the best way to keep the databases I use in different projects? I use a lot of CSVs that I need to prepare every time I'm working with them (I just copy paste the code from other projects) but would like to make some module that I can import and it have all the processes of the databases for example for this database I usually do columns = [(configuration of, my columns)], names = [names], dates = [list of columns dates], dtypes ={column: type},

then database_1 = pd.read_fwf(**kwargs), database_2 = pd.read_fwf(**kwargs), database_3 = pd.read_fwf(**kwargs)...

Then database = pd.concat([database_1...])

But I would like to have a module that I could import and have all my databases and configuration of ETL in it so I could just do something like 'database = my_module.dabase' to import the database, without all that process everytime.

Thanks for any help.

Hi, I'm currently using this to log my python process

logging.basicConfig(filename='filename.log', level=logging.DEBUG)

logger = logging.getLogger()

sys.stderr.write = logger.error

sys.stdout.write = logger.info

And then using print(f'{datetime.now()} log message') where I want to log.

It's working OK, buy I would like to live it to ic, but can't find any info on how to send the ic output to the logger.

Thanks for any help.

Guardando está notícia pra mandar ao pessoal quando falar que na favela não pagam energia.

Hi, When im working with some big dataframes and I need to create some columns based on functions. So i have some code like this

Def function(row): function

And then I run the function on the df as

df['new column'] = df.apply(function, axis=1)

But I do this with 10 or more columns/functions at time. I don't think this is efficient because each time a column is created it had to parce the entire data frame. There's a way to create all the columns at the same time while parsing the rows only once?

Thanks for any help.