Hi Wes Thanks for your quick answer. I took a look to pyarrow csv reader : https://github.com/apache/arrow/blob/master/cpp/src/arrow/csv/reader.cc and https://github.com/apache/arrow/blob/master/python/pyarrow/_csv.pyx
I have a lot of code to undertand and write in order to expose a *.bed reader in python. I will try to do my best Thanks Have a nice day Le lundi 23 mars 2020 à 18:24 -0500, Wes McKinney a écrit : > hi Jonathan -- generally my approach would be to write some Cython or > C/C++ code to create the file loader. Any time you are writing a file > loader that deals with individual table cells in pure Python it's > going to suffer from some performance problems. > > We've talked about exposing the Arrow C++ incremental builder classes > in Python or Cython -- I didn't find a JIRA issue about this but I > created > > https://issues.apache.org/jira/browse/ARROW-8189 > > Hope this helps > Wes > > On Mon, Mar 23, 2020 at 3:10 PM jonathan mercier > <[email protected]> wrote: > > Dear, > > > > I would like to parse *.bed file to pyarrow > > > > A Bed file look like this: > > #This is a comment > > chr1 10000 69091 > > chr1 80608 106842 > > chr3 70008 207666 > > chr14 257666 297968 > > > > > > So we can see it is a tabulated text file with 3 columns. Some line > > can > > be a comment if starts with a # > > > > > > My way to hadle such file is not efficient and I would like your > > insight to load such data > > > > My way, I read file lini by line with bython builtin open, if line > > do > > not starts with a # ; I split the line each column is converted to > > expected column type (i.e str, int …) and append each data to their > > columns. And finally I create a pyarrow table and write it to > > parquet. > > > > > > > > import pyarrow as pa > > from pyarrow.parquet import ParquetWriter > > bed3_schema = pa.schema([('chr', pa.string()), > > ('start', pa.int64()), > > ('end', pa.float64())]) > > bed3_column_type = [str, int, int] > > > > > > def bed_to_parquet(bed_path: str, parquet_path: str, dataset=None): > > columns = [[], [], []] > > with open(bed_path) as stream: > > for row in stream: > > if not row.startswith('#'): > > cols = row.split('\t') > > for i, item in enumerate(cols): > > casted_value = bed3_column_type[i](item) > > columns[i].append(casted_value) > > arrays = [pa.array(column) for column in columns] > > table = pa.Table.from_arrays(arrays, schema=bed3_schema) > > with ParquetWriter(parquet_path, table.schema, > > use_dictionary=True, version='2.0') as > > writer: > > if dataset: > > writer.write_to_dataset(table, dataset) > > else: > > writer.write_table(table) > >
