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- update to 2024.11.0:
  * feat: support for writing to buffers
  * fix(_dtypes): np.float_ was deprecated
  * update for py3.13

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- Update to 2024.5.0
 * Allow zoneinfo objects (#916)
 * Use np.int64 type for day to nanosecond conversion (NEP50)
   (#922)

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- Update to 2024.2.0
  * allow loading categoricals even if not so in the pandas
    metadata, when a column is dict-encoded and we only have one
    row-group (#863)
  * apply dtype to the columns names series, even when selecting no
    columns (#861, 859)
  * don’t make strings while estimating bye column size (#858)
  * handle upstream depr (#857, 856)

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- Do not run tests in s390x, bsc#1218603

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- update to 2023.10.0:
  * Datetime units in empty() with tz (#893)
  * Fewer inplace decompressions for V2 pages (#890
  * Allow writing categorical column with no categories (#888)
  * Fixes for new numpy (#886)
  * RLE bools and DELTA for v1 pages (#885, 883)

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- update to 2023.8.0:
  * More general timestamp units (#874)
  * ReadTheDocs V2 (#871)
  * Better roundtrip dtypes (#861, 859)
  * No convert when computing bytes-per-item for str (#858)
- Add patch to fox the test test_delta_from_def_2 on
  * row-level filtering of the data. Whereas previously, only full
    row-groups could be excluded on the basis of their parquet
    metadata statistics (if present), filtering can now be done
    within row-groups too. The syntax is the same as before,
    allowing for multiple column expressions to be combined with
    AND|OR, depending on the list structure. This mechanism
    requires two passes: one to load the columns needed to create
    the boolean mask, and another to load the columns actually
    needed in the output. This will not be faster, and may be
    slower, but in some cases can save significant memory
    footprint, if a small fraction of rows are considered good and
    the columns for the filter expression are not in the output.
  * DELTA integer encoding (read-only): experimentally working,
    but we only have one test file to verify against, since it is
    not trivial to persuade Spark to produce files encoded this
    way. DELTA can be extremely compact a representation for
  * nanosecond resolution times: the new extended "logical" types
    system supports nanoseconds alongside the previous millis and
    micros. We now emit these for the default pandas time type,
    and produce full parquet schema including both "converted" and
    "logical" type information. Note that all output has
    isAdjustedToUTC=True, i.e., these are timestamps rather than
    local time. The time-zone is stored in the metadata, as
    before, and will be successfully recreated only in fastparquet

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update to latest version

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- update to 2023.4.0:
  * allow loading categoricals even if not so in the pandas metadata,
    when a column is dict-encodedand we only have one row-group (#863)
 * apply dtype to the columns names series, even when selecting no
    columns (#861, 859)
 * don't make strings while estimating bye column size (#858)
 * handle upstream depr (#857, 856)

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baserev update by copy to link target

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update to latest version

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baserev update by copy to link target

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