You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Currently the PandasParser only allows for the following file_formats:
csv
feather
json
pickle
A trivial improvement would be to add other functions that are already supported by Pandas including:
html
excel
fwf
table
sql
clipboard
parquet
orc
xml
stata
sas
spss
It might not be worthwhile to include all of them, but some might be more useful than others (excel, parquet, xml?).
Additionally, I think we could write a custom function that reads numpy files that would look something like the following:
def read_numpy(path, **kwargs):
data = np.load(path)
return pd.DataFrame(data, **kwargs)
The current structure of the PandasParser should allow any simple function that returns a pd.DataFrame to be used with only minor modifications to the parser module. This saves the work of re-writing an entire parser module that outputs a Pandas DataFrame for a new file format.
The text was updated successfully, but these errors were encountered:
Currently the PandasParser only allows for the following file_formats:
A trivial improvement would be to add other functions that are already supported by Pandas including:
It might not be worthwhile to include all of them, but some might be more useful than others (excel, parquet, xml?).
Additionally, I think we could write a custom function that reads numpy files that would look something like the following:
The current structure of the PandasParser should allow any simple function that returns a
pd.DataFrame
to be used with only minor modifications to the parser module. This saves the work of re-writing an entire parser module that outputs a Pandas DataFrame for a new file format.The text was updated successfully, but these errors were encountered: