Python library to extend functional operations in the spirit of functional programming in scala. Provides support for missing operations and chain style functional calls.
@benjaminsims Currently there isn't, but it doesn't seem to hard to implement. Mostly a matter of figuring out the right api. Currently
csv.reader is being used in the seq.csv so if its not possible to pass the right parameters to that then it might make sense to add another method like seq.csv_dict etc
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
map(lambda cell: [cell, cell_level, user_input.area_name]).to_csv('/tmp/test.csv' ,mode='wa').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').csv_writer.writerow([six.u(str(element)) for element in row])
TypeError: must be unicode, not str
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?
seems a lot of dependencies are outdated
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)
And what is train_df.values ?
Anyone has a good way of error handling? I found this issue: EntilZha/PyFunctional#65 nut its not really clear what the solution is.
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.
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.