Pandas can handle Parquet files using two different engines: one based on a module similar to pyarrow and another resembling fastparquet. I am using an Intel Conda build that lets me export a DataFrame to a Parquet file, yet I do not have the module analogous to pyarrow installed. Thus, it appears the alternative engine is in use, though I cannot confirm this.
Below is an example snippet demonstrating a custom export function:
import pandas as pd
record = {'num': [10, 20, 30]}
data_frame = pd.DataFrame(record)
# Hypothetical method to save as Parquet, using the detected engine
data_frame.save_as_parq('output_file.parq')
Is there a built-in way to verify which engine is actually in operation?
The current implementation of Pandas does not expose an explicit setting or flag that indicates which Parquet engine is being used at runtime. From my experience, Pandas detects the available engine by checking the environment during function execution. This process is handled internally, and as a result, it does not provide a documented method to query the engine in use directly. I usually verify engine behavior by inspecting library installations or setting up a controlled environment where only one engine is available, thus deducing the active module indirectly.
i haven’t found a built in method. you kinda have to check your installed modules to see which one is active. i usually simply remove one to force an error. it works fine but isnt exactly a straightforward funciton call.
hey, i was thinkin if tryin to inject a little debug into pandas internals might expsoure which parquet engine is getting used. anyone got experimantal tricks or neat hacks to reveal the active module? curious about other approaches!
Pandas dynamically selects the Parquet engine based on the available modules, and there is no built-in function to directly query which engine is in use. In my experience, confirming the active engine typically requires environment manipulation. For instance, isolating installed packages so that only one potential engine is present can clarify which one is used. Alternatively, enabling verbose logging within pandas, or even inspecting the source code, may shed light on the internal engine selection mechanisms.