EntilZha/PyFunctional

Python library to extend functional operations in the spirit of functional programming in scala. Provides support for missing operations and chain style functional calls.

Pedro Rodriguez

@EntilZha

If you have an example in mind that would be useful. Looks like csv.DictReader makes each row a named tuple according to a list of field names or if thats not given then the first row of the csv file. Seems like having another method is good here, question is what to name it.

This would play well with this issue as well as it turns out EntilZha/PyFunctional#91

EntilZha/PyFunctional#92

For implementing that

Pedro Rodriguez

@EntilZha

@benjaminsims that has been added via EntilZha/PyFunctional#92. I am planning on doing the 1.0 release sometime in the next week since I would like to have the starmap stuff on pip

Benny Elgazar

@Bennyelg

to_csv but specify append mode?

Pedro Rodriguez

@EntilZha

@Bennyelg Did you try passing mode='wa'. Those are passed to open which if I recall correctly opens the file in append mode

Benny Elgazar

@Bennyelg

map(lambda cell: [cell, cell_level, user_input.area_name]).to_csv('/tmp/test.csv' ,mode='wa').

File "/usr/local/lib/python2.7/dist-packages/functional/pipeline.py", line 1498, in to_csv
with universal_write_open(path, mode=mode, compression=compression) as output:
File "/usr/local/lib/python2.7/dist-packages/functional/io.py", line 221, in universal_write_open
newline=newline)
ValueError: must have exactly one of read/write/append mode

map(lambda cell: [cell, cell_level, user_input.area_name]).to_csv('/tmp/test.csv' ,mode=u'a').

File "/usr/local/lib/python2.7/dist-packages/functional/pipeline.py", line 1501, in to_csv
csv_writer.writerow([six.u(str(element)) for element in row])
TypeError: must be unicode, not str

Pedro Rodriguez

@EntilZha

Sorry, try just 'a'

Ram Janovski

@RamJanovski_twitter

hey Pedro, great package :)

I'm running into what looks like caching issues (second call to a function use the first call results although the parameters are different)

is there a way to disable the caching/lazy nature?

Pedro Rodriguez

@EntilZha

Can you give an example? Hard to say what to do without some more context.

Ram Janovski

@RamJanovski_twitter

The consistency issues came from my code, sorry. but I still need to add .cache() manually when accessing a seq several times, otherwise the second access ends up empty. So, no way to disable lazy/cache optimization?

Pedro Rodriguez

@EntilZha

It would still be useful to have an example. Disabling lazy computation would be hard, but caching is normally only done for a select few cases like calls to array access and repr

Florian Kromer

@fkromer

I want to write a little database wrapper. Is it possible to wrap files = seq.sqlite3(db_path, 'SELECT * FROM file').map(lambda x: FileTableRow(*x)) (FileTableRow is a namedtuple) to something like files(db_path)?

Florian Kromer

@fkromer

Figured out that wrapping a sequence generation into a function is simple:

def files(db_path):
    return seq.sqlite3(db_path, 'SELECT * FROM file').map(lambda x: FileTableRow(*x))

York

@ysyyork

is this lib still actively maintained?

seems a lot of dependencies are outdated

Ⓜ️R. Developer 🇵🇰

@OyyNomi_twitter

Anyone here to help me to convert a small piece of python code into scala?

from sklearn.preprocessing import LabelEncoder

y_train = train_['country_destination']
train_user.drop(['country_destination', 'id'], axis=1, inplace=True)
x_train = train_df.values

label_encoder = LabelEncoder()
encoded_y_train = label_encoder.fit_transform(y_train)

Ⓜ️R. Developer 🇵🇰

@OyyNomi_twitter

Can anyone explain what is train_ ?

And what is train_df.values ?

Sjoerd Arendsen

@sarendsen

Anyone has a good way of error handling? I found this issue: EntilZha/PyFunctional#65 nut its not really clear what the solution is.

bmsan

@bmsan

Hi all and Happy New Year.
I saw that multiprocessing is supported as a way of parallelizing, I was wondering if you have taken/are taking into consideration multithreading(which should have lower overhead in transferring data between threads than processes).
A neat way to achieve both worlds under the hood could be the python concurrent module.

Where communities thrive

People

Repo info

Activity