How to use the pyarrow.parquet.ParquetFile function in pyarrow

def test_write_include_by_name():
    csv2parquet.main_with_args(csv2parquet.convert, ['csvs/simple.csv', '--include', 'a'])
    pqf = pq.ParquetFile('csvs/simple.parquet')
    schema = pqf.schema
    assert schema.names == ['a']
    row_group = pqf.read_row_group(0)
    assert row_group.num_rows == 3
    col_a = row_group.column(0).to_pylist()
    assert col_a == ['1', '2', '3']

def test_opt_invalid_ints():
    csv2parquet.main_with_args(csv2parquet.convert,
                               ['csvs/ints.csv', '--type',
                                'int8=int8?', 'int16=int16?', 'int32=int32?'])
    pqf = pq.ParquetFile('csvs/ints.parquet')
    schema = pqf.schema
    assert schema.names == ['int8', 'int16', 'int32']
    row_group = pqf.read_row_group(0)
    assert row_group.num_rows == 2
    int8 = row_group.column(0).to_pylist()
    assert int8 == [1, None]
    int16 = row_group.column(1).to_pylist()
    assert int16 == [2, None]
    int32 = row_group.column(2).to_pylist()
    assert int32 == [3, None]

def test_write_from_tsv():
    csv2parquet.main_with_args(csv2parquet.convert, ['csvs/simple2.tsv'])
    pqf = pq.ParquetFile('csvs/simple2.parquet')
    assert pqf.num_row_groups == 1
    schema = pqf.schema
    assert schema.names == ['a', 'b']
    assert schema.column(0).logical_type.type == 'STRING'
    assert schema.column(1).logical_type.type == 'STRING'
    row_group = pqf.read_row_group(0)
    assert row_group.num_rows == 1
    col_a = row_group.column(0).to_pylist()
    assert col_a == ['1']
    col_b = row_group.column(1).to_pylist()
    assert col_b == ['b']

if path == '':
                msg = 'path to store data must be given on first use'
                raise FileNotFoundError(msg)

            conf = {'icd9': {'data_path': path}}

            with open(Path.home() / '.medicare_utils.json', 'w') as f:
                json.dump(conf, f)

        self.conf = conf

        Path(conf['icd9']['data_path']).mkdir(parents=True, exist_ok=True)
        icd9_sg_path = Path(conf['icd9']['data_path']) / 'icd9_sg.parquet'
        icd9_dx_path = Path(conf['icd9']['data_path']) / 'icd9_dx.parquet'
        try:
            pq.ParquetFile(icd9_sg_path)
            pq.ParquetFile(icd9_dx_path)
        except:
            self._download(icd9_sg_path=icd9_sg_path, icd9_dx_path=icd9_dx_path)

        sg_cols = ['icd_prcdr_cd', 'year']
        dx_cols = ['icd_dgns_cd', 'year']
        if long:
            sg_cols.append('desc_long')
            dx_cols.append('desc_long')
        else:
            sg_cols.append('desc_short')
            dx_cols.append('desc_short')

        sg = pd.read_parquet(icd9_sg_path, engine='pyarrow', columns=sg_cols)
        dx = pd.read_parquet(icd9_dx_path, engine='pyarrow', columns=dx_cols)

Args:
        file_ - file-like object opened in binary mode (+b)

    Returns:
        dict
            body - summary of main contents (if applicable)
            info - metdata for user consumption
    """
    # TODO: generalize to datasets, multipart files
    # As written, only works for single files, and metadata
    # is slanted towards the first row_group
    # local import reduces amortized latency, saves memory
    import pyarrow.parquet as pq

    pf = pq.ParquetFile(file_)

    meta = pf.metadata

    info = {}
    info['created_by'] = meta.created_by
    info['format_version'] = meta.format_version
    info['metadata'] = {
        k.decode(): v.decode()
        for k, v in meta.metadata.items()
    } if meta.metadata is not None else {}
    info['num_row_groups'] = meta.num_row_groups

    info['schema'] = {
        name: {
            'logical_type': meta.schema.column(i).logical_type.type,
            'max_definition_level': meta.schema.column(i).max_definition_level,

file (str):
            File directory of all the parquet file to encode.
            Parquet file must have more than one row group.

        fs (feast.feature_set.FeatureSet):
            FeatureSet describing parquet files.

        row_group_idx(int):
            Row group index to read and encode into byte like FeatureRow
            protobuf objects.

    Returns:
        List[bytes]:
            List of byte encoded FeatureRows from the parquet file.
    """
    pq_file = pq.ParquetFile(file)
    # Read parquet file as a PyArrow table
    table = pq_file.read_row_group(row_group_idx)

    # Add datetime column
    datetime_col = pa_column_to_timestamp_proto_column(table.column(DATETIME_COLUMN))

    # Preprocess the columns by converting all its values to Proto values
    proto_columns = {
        field_name: pa_column_to_proto_column(field.dtype, table.column(field_name))
        for field_name, field in fs.fields.items()
    }

    feature_set = f"{fs.project}/{fs.name}:{fs.version}"

    # List to store result
    feature_rows = []

def get_schema(parquet_file):
    r = pq.ParquetFile(parquet_file)
    return r.schema

None
        """

        if isinstance(feature_set, FeatureSet):
            name = feature_set.name
            if version is None:
                version = feature_set.version
        elif isinstance(feature_set, str):
            name = feature_set
        else:
            raise Exception(f"Feature set name must be provided")

        # Read table and get row count
        dir_path, dest_path = _read_table_from_source(source, chunk_size, max_workers)

        pq_file = pq.ParquetFile(dest_path)

        row_count = pq_file.metadata.num_rows

        # Update the feature set based on PyArrow table of first row group
        if force_update:
            feature_set.infer_fields_from_pa(
                table=pq_file.read_row_group(0),
                discard_unused_fields=True,
                replace_existing_features=True,
            )
            self.apply(feature_set)
        current_time = time.time()

        print("Waiting for feature set to be ready for ingestion...")
        while True:
            if timeout is not None and time.time() - current_time >= timeout:

def split_points_unclaimed(stop_position):
      if next_block_start >= stop_position:
        # Next block starts at or after the suggested stop position. Hence
        # there will not be split points to be claimed for the range ending at
        # suggested stop position.
        return 0
      return RangeTracker.SPLIT_POINTS_UNKNOWN

    range_tracker.set_split_points_unclaimed_callback(split_points_unclaimed)

    start_offset = range_tracker.start_position()
    if start_offset is None:
      start_offset = 0

    with self.open_file(file_name) as f:
      pf = pq.ParquetFile(f)

      # find the first dictionary page (or data page if there's no dictionary
      # page available) offset after the given start_offset. This offset is also
      # the starting offset of any row group since the Parquet specification
      # describes that the data pages always come first before the meta data in
      # each row group.
      index = _ParquetUtils.find_first_row_group_index(pf, start_offset)
      if index != -1:
        next_block_start = _ParquetUtils.get_offset(pf, index)
      else:
        next_block_start = range_tracker.stop_position()
      number_of_row_groups = _ParquetUtils.get_number_of_row_groups(pf)

      while range_tracker.try_claim(next_block_start):
        table = pf.read_row_group(index, self._columns)

toload_regex.append(r'^(buyin\d{2})$')
        if hmo_val is not None:
            toload_regex.append(r'^(hmoind\d{2})$')
        if self.year_type == 'age':
            toload_regex.append(r'^(bene_dob)$')
        for keep_var in keep_vars:
            if isinstance(keep_var, str):
                toload_regex.append(r'^({})$'.format(keep_var))

        toload_regex = re.compile('|'.join(toload_regex)).search

        toload_vars: Dict[int, List[str]] = {}
        for year in self.years:
            if self.parquet_engine == 'pyarrow':
                try:
                    pf = pq.ParquetFile(self._fpath(self.percent, year, 'bsfab'))
                except pa.ArrowIOError:
                    pf = pq.ParquetDataset(self._fpath(self.percent, year, 'bsfab'))
                cols = pf.schema.names
            elif self.parquet_engine == 'fastparquet':
                pf = fp.ParquetFile(self._fpath(self.percent, year, 'bsfab'))
                cols = pf.columns

            toload_vars[year] = [x for x in cols if toload_regex(x)]
            for keep_var in keep_vars:
                if isinstance(keep_var, re._pattern_type):
                    toload_vars[year].extend([
                        x for x in cols if keep_var.search(x)])

            # Deduplicate while preserving order
            toload_vars[year] = list(dict.fromkeys(toload_vars[year]))