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BUG: Don't over-optimize memory with jagged CSV (pandas-dev#23527)
With jagged CSV's, we risk being too quick
to dump memory that we need to allocate
because previous chunks would have
indicated much larger rows than we can
anticipate in subsequent chunks.
Closespandas-devgh-23509.
Copy file name to clipboardExpand all lines: doc/source/whatsnew/v0.24.0.txt
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@@ -1299,6 +1299,7 @@ Notice how we now instead output ``np.nan`` itself instead of a stringified form
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- :func:`read_excel()` will correctly show the deprecation warning for previously deprecated ``sheetname`` (:issue:`17994`)
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- :func:`read_csv()` and func:`read_table()` will throw ``UnicodeError`` and not coredump on badly encoded strings (:issue:`22748`)
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- :func:`read_csv()` will correctly parse timezone-aware datetimes (:issue:`22256`)
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- Bug in :func:`read_csv()` in which memory management was prematurely optimized for the C engine when the data was being read in chunks (:issue:`23509`)
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- :func:`read_sas()` will parse numbers in sas7bdat-files that have width less than 8 bytes correctly. (:issue:`21616`)
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- :func:`read_sas()` will correctly parse sas7bdat files with many columns (:issue:`22628`)
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- :func:`read_sas()` will correctly parse sas7bdat files with data page types having also bit 7 set (so page type is 128 + 256 = 384) (:issue:`16615`)
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