Feature Request: Data Frames
opened this issue · comments
Hi,
In my use case, I have data sets in parquet/CSV format which I then read into a pandas dataframe for processing.
Before starting to use ArrayFire Python, I would like to know if the following operations are at all supported.
- Reading a dataframe, with its headers into an ArrayFire equivalent data structure
- Reading a CSV, with its headers into an ArrayFire equivalent data structure
- Writing an ArrayFire equivalent data structure into a dataframe, with its headers
- Filtering as follows:
X_df['Rx_10G_1G'] = X_df.apply(lambda x: findGE(x['NE_OBJECT']), axis=1)
def findGE (str_ne):
if str_ne.find('10GE-') !=-1:
return 10000
if str_ne.find('GE-') !=-1:
return 1000
else:
return 1
-
Filtering as follows:
X_df=X_df[X_df['Rx_Octets']> 0.0]
x_neg_df=X_df[X_df['RxUtilization_pct']< 0]
-
Sorting by a time stamp based index:
X_df = X_df.sort_index(by='ReportTime')
Many thanks,
@QuantScientist ArrayFire does not support data frames as of now.
@QuantScientist you need to use af.to_array(array_foo)
@QuantScientist Where did you find that documentation in the screenshot?
From "Matrix computations on the GPU with ArrayFire
for Python and C/C++"
Where did you take the example you quoted?
@QuantScientist That's from a few years ago and outdated. Please use the documentation from here:
http://arrayfire.org/arrayfire-python
Where did you take the example you quoted?
From memory :) I wrote the function.