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As someone in the process of setting up a pipeline and currently using pandas.to_csv as my output, I'm curious what makes you recommend parquet in particular? H

As someone in the process of setting up a pipeline and currently using pandas.to_csv as my output, I'm curious what makes you recommend parquet in particular? How does it compare to HDF or Feather?

awild 5y ago
Just a heads up, pandas to Csv is laughably slow. You're literally better off converting the pandas df to arrow and then serialise to Csv with arrow, though the format is quite limited with arrow.
- NavinF 5y ago
  Well at least it’s better than numpy’s csv functions. For my use case (~4 million images, one row per image, 2048 fp32 columns for features extracted with resnet), parsing/encoding csv with pandas was 10x faster than with numpy. Of course serializing the arrays to/from npy was 10x faster than any csv library.
IanCal 5y ago
It depends on what you're trying to optimise, parquet is a very good all round option. HDF I've never really gotten into as it always felt like a good solution only if I move everything over. It's great if your use case fits. Feather is a layer on top of arrow and was a proof of concept (so I'm not sure how heavily it's used now), and arrow is fast becoming the interchange format. It's exactly laid out as things will be in memory - which means zero copy for shuttling it around from one place to another. I _think_ there is less support for feather but that is likely changing as everything converges. Parquet should be * Faster to write * Faster to read (even if you're reading the whole file, which actually isn't required, the format helps you read just sections of the columns you need) * Smaller * Better at handling actual floating points than CSV, while having actual standards alongside it. Be a little wary of pandas guessing the right column types for you if you're creating partitioned files btw. When you're working with pandas, etc (check out Dask) you can pretty much just swap out some reading and writing functions. You can also use pyarrow directly if you need to be very careful about column types. For your use case you may want to explicitly use a single column for the features that is a list, I'm not sure if that's better/worse than having so many columns. If a reader may want to find just some images where a small subset of features are > X, you might benefit from multiple columns so that the reader only processes the data it needs. Worth testing out, but I expect you should be able to try it out in an afternoon if you're already working with pandas/similar. Just install pyarrow and use a to_parquet. Things like dask (or straight pyarrow) give you partitioned files as output if you want too, if there's a useful column or columns to split on https://arrow.apache.org/docs/python/parquet.html#partitioned-datasets-multiple-files https://arrow.apache.org/docs/python/parquet.html#partitione...

As someone in the process of setting up a pipeline and currently using pandas.to_csv as my output, I'm curious what makes you recommend parquet in particular? How does it compare to HDF or Feather?

awild 5y ago

Just a heads up, pandas to Csv is laughably slow. You're literally better off converting the pandas df to arrow and then serialise to Csv with arrow, though the format is quite limited with arrow.

NavinF 5y ago
Well at least it’s better than numpy’s csv functions. For my use case (~4 million images, one row per image, 2048 fp32 columns for features extracted with resnet), parsing/encoding csv with pandas was 10x faster than with numpy. Of course serializing the arrays to/from npy was 10x faster than any csv library.

IanCal 5y ago

It depends on what you're trying to optimise, parquet is a very good all round option. HDF I've never really gotten into as it always felt like a good solution only if I move everything over. It's great if your use case fits. Feather is a layer on top of arrow and was a proof of concept (so I'm not sure how heavily it's used now), and arrow is fast becoming the interchange format. It's exactly laid out as things will be in memory - which means zero copy for shuttling it around from one place to another. I _think_ there is less support for feather but that is likely changing as everything converges. Parquet should be * Faster to write * Faster to read (even if you're reading the whole file, which actually isn't required, the format helps you read just sections of the columns you need) * Smaller * Better at handling actual floating points than CSV, while having actual standards alongside it. Be a little wary of pandas guessing the right column types for you if you're creating partitioned files btw. When you're working with pandas, etc (check out Dask) you can pretty much just swap out some reading and writing functions. You can also use pyarrow directly if you need to be very careful about column types. For your use case you may want to explicitly use a single column for the features that is a list, I'm not sure if that's better/worse than having so many columns. If a reader may want to find just some images where a small subset of features are > X, you might benefit from multiple columns so that the reader only processes the data it needs. Worth testing out, but I expect you should be able to try it out in an afternoon if you're already working with pandas/similar. Just install pyarrow and use a to_parquet. Things like dask (or straight pyarrow) give you partitioned files as output if you want too, if there's a useful column or columns to split on https://arrow.apache.org/docs/python/parquet.html#partitioned-datasets-multiple-files https://arrow.apache.org/docs/python/parquet.html#partitione...