Feature Request: Data Frames

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.

1. Reading a dataframe, with its headers into an ArrayFire equivalent data structure
2. Reading a CSV, with its headers into an ArrayFire equivalent data structure
3. Writing an ArrayFire equivalent data structure into a dataframe, with its headers
4. 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
```

5. Filtering as follows:
`X_df=X_df[X_df['Rx_Octets']> 0.0]`
`x_neg_df=X_df[X_df['RxUtilization_pct']< 0]`

6. Sorting by a time stamp based index:
`X_df = X_df.sort_index(by='ReportTime') `

Many thanks,

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.