Feature Request: Data Frames
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
Research direction
No files or tests are named. Start by reviewing the ArrayFire Python bindings and their existing data-structure support, then compare it with the requested pandas operations: loading and writing headers, filtering, row-wise functions, and timestamp sorting. Done should be a concrete scope for which operations are supported and an agreed implementation plan.
Written by the indexing model from the issue text.
Description
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,
- Dominant language
- Python
- Stars
- 422
- Forks
- 63
- PR merge metrics
- No merged PRs in 30d
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