ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable#5018

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Merged
wesm merged 1 commit into from
Aug 6, 2019
Merged

ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable #5018

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112 changes: 56 additions & 56 deletions python/pyarrow/parquet.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -890,7 +890,63 @@ def _open_dataset_file(dataset, path, meta=None):
common_metadata=dataset.common_metadata)


_read_docstring_common = """\
read_dictionary : list, default None
List of names or column paths (for nested types) to read directly
as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
a flat column as dictionary-encoded pass the column name. For
nested types, you must pass the full column "path", which could be
something like level1.level2.list.item. Refer to the Parquet
file's schema to obtain the paths.
memory_map : boolean, default True
If the source is a file path, use a memory map to read file, which can
improve performance in some environments"""


class ParquetDataset(object):

__doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
A directory name, single file name, or list of file names
filesystem : FileSystem, default None
If nothing passed, paths assumed to be found in the local on-disk
filesystem
metadata : pyarrow.parquet.FileMetaData
Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
Use schema obtained elsewhere to validate file schemas. Alternative to
metadata parameter
split_row_groups : boolean, default False
Divide files into pieces for each row group in the file
validate_schema : boolean, default True
Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
implements partition-level (hive) filtering only, i.e., to prevent the
loading of some files of the dataset.

Predicates are expressed in disjunctive normal form (DNF). This means
that the innermost tuple describe a single column predicate. These
inner predicate make are all combined with a conjunction (AND) into a
larger predicate. The most outer list then combines all filters
with a disjunction (OR). By this, we should be able to express all
kinds of filters that are possible using boolean logic.

This function also supports passing in as List[Tuple]. These predicates
are evaluated as a conjunction. To express OR in predictates, one must
use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
How many threads to allow the thread pool which is used to read the
dataset metadata. Increasing this is helpful to read partitioned
datasets.
{0}
""".format(_read_docstring_common)

def __init__(self, path_or_paths, filesystem=None, schema=None,
metadata=None, split_row_groups=False, validate_schema=True,
filters=None, metadata_nthreads=1,
Expand DownExpand Up@@ -1105,62 +1161,6 @@ def _make_manifest(path_or_paths, fs, pathsep='/', metadata_nthreads=1,
return pieces, partitions, common_metadata_path, metadata_path


_read_docstring_common = """\
read_dictionary : list, default None
List of names or column paths (for nested types) to read directly
as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
a flat column as dictionary-encoded pass the column name. For
nested types, you must pass the full column "path", which could be
something like level1.level2.list.item. Refer to the Parquet
file's schema to obtain the paths.
memory_map : boolean, default True
If the source is a file path, use a memory map to read file, which can
improve performance in some environments"""


ParquetDataset.__doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
A directory name, single file name, or list of file names
filesystem : FileSystem, default None
If nothing passed, paths assumed to be found in the local on-disk
filesystem
metadata : pyarrow.parquet.FileMetaData
Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
Use schema obtained elsewhere to validate file schemas. Alternative to
metadata parameter
split_row_groups : boolean, default False
Divide files into pieces for each row group in the file
validate_schema : boolean, default True
Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
implements partition-level (hive) filtering only, i.e., to prevent the
loading of some files of the dataset.

Predicates are expressed in disjunctive normal form (DNF). This means
that the innermost tuple describe a single column predicate. These
inner predicate make are all combined with a conjunction (AND) into a
larger predicate. The most outer list then combines all filters
with a disjunction (OR). By this, we should be able to express all
kinds of filters that are possible using boolean logic.

This function also supports passing in as List[Tuple]. These predicates
are evaluated as a conjunction. To express OR in predictates, one must
use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
How many threads to allow the thread pool which is used to read the
dataset metadata. Increasing this is helpful to read partitioned
datasets.
{0}
""".format(_read_docstring_common)


_read_table_docstring = """
{0}

Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable#5018

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 Merged

wesm
merged 1 commit into

from

Aug 6, 2019
 Merged


ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable
#5018
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112 changes: 56 additions & 56 deletions
python/pyarrow/parquet.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -890,7 +890,63 @@ def _open_dataset_file(dataset, path, meta=None):
 common_metadata=dataset.common_metadata)


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


class ParquetDataset(object):

 __doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)

 def __init__(self, path_or_paths, filesystem=None, schema=None,
 metadata=None, split_row_groups=False, validate_schema=True,
 filters=None, metadata_nthreads=1,
Expand DownExpand Up@@ -1105,62 +1161,6 @@ def _make_manifest(path_or_paths, fs, pathsep='/', metadata_nthreads=1,
 return pieces, partitions, common_metadata_path, metadata_path


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


ParquetDataset.__doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)


_read_table_docstring = """
{0}

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable#5018

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privacy statement. We’ll occasionally send you account related emails.

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Sign in
to your account

 Merged

wesm
merged 1 commit into

from

Aug 6, 2019
 Merged


ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable
#5018
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112 changes: 56 additions & 56 deletions
python/pyarrow/parquet.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -890,7 +890,63 @@ def _open_dataset_file(dataset, path, meta=None):
 common_metadata=dataset.common_metadata)


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


class ParquetDataset(object):

 __doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)

 def __init__(self, path_or_paths, filesystem=None, schema=None,
 metadata=None, split_row_groups=False, validate_schema=True,
 filters=None, metadata_nthreads=1,
Expand DownExpand Up@@ -1105,62 +1161,6 @@ def _make_manifest(path_or_paths, fs, pathsep='/', metadata_nthreads=1,
 return pieces, partitions, common_metadata_path, metadata_path


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


ParquetDataset.__doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)


_read_table_docstring = """
{0}

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable#5018

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
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to your account

 Merged

wesm
merged 1 commit into

from

Aug 6, 2019
 Merged


ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable
#5018
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112 changes: 56 additions & 56 deletions
python/pyarrow/parquet.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -890,7 +890,63 @@ def _open_dataset_file(dataset, path, meta=None):
 common_metadata=dataset.common_metadata)


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


class ParquetDataset(object):

 __doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)

 def __init__(self, path_or_paths, filesystem=None, schema=None,
 metadata=None, split_row_groups=False, validate_schema=True,
 filters=None, metadata_nthreads=1,
Expand DownExpand Up@@ -1105,62 +1161,6 @@ def _make_manifest(path_or_paths, fs, pathsep='/', metadata_nthreads=1,
 return pieces, partitions, common_metadata_path, metadata_path


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


ParquetDataset.__doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)


_read_table_docstring = """
{0}

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable#5018

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 Merged

wesm
merged 1 commit into

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Aug 6, 2019
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ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable
#5018
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112 changes: 56 additions & 56 deletions
python/pyarrow/parquet.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -890,7 +890,63 @@ def _open_dataset_file(dataset, path, meta=None):
 common_metadata=dataset.common_metadata)


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


class ParquetDataset(object):

 __doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)

 def __init__(self, path_or_paths, filesystem=None, schema=None,
 metadata=None, split_row_groups=False, validate_schema=True,
 filters=None, metadata_nthreads=1,
Expand DownExpand Up@@ -1105,62 +1161,6 @@ def _make_manifest(path_or_paths, fs, pathsep='/', metadata_nthreads=1,
 return pieces, partitions, common_metadata_path, metadata_path


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


ParquetDataset.__doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)


_read_table_docstring = """
{0}

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable#5018

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 Merged

wesm
merged 1 commit into

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Aug 6, 2019
 Merged


ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable
#5018
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112 changes: 56 additions & 56 deletions
python/pyarrow/parquet.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -890,7 +890,63 @@ def _open_dataset_file(dataset, path, meta=None):
 common_metadata=dataset.common_metadata)


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


class ParquetDataset(object):

 __doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)

 def __init__(self, path_or_paths, filesystem=None, schema=None,
 metadata=None, split_row_groups=False, validate_schema=True,
 filters=None, metadata_nthreads=1,
Expand DownExpand Up@@ -1105,62 +1161,6 @@ def _make_manifest(path_or_paths, fs, pathsep='/', metadata_nthreads=1,
 return pieces, partitions, common_metadata_path, metadata_path


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


ParquetDataset.__doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)


_read_table_docstring = """
{0}

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable#5018

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 Merged

wesm
merged 1 commit into

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Aug 6, 2019
 Merged


ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable
#5018
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112 changes: 56 additions & 56 deletions
python/pyarrow/parquet.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -890,7 +890,63 @@ def _open_dataset_file(dataset, path, meta=None):
 common_metadata=dataset.common_metadata)


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


class ParquetDataset(object):

 __doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)

 def __init__(self, path_or_paths, filesystem=None, schema=None,
 metadata=None, split_row_groups=False, validate_schema=True,
 filters=None, metadata_nthreads=1,
Expand DownExpand Up@@ -1105,62 +1161,6 @@ def _make_manifest(path_or_paths, fs, pathsep='/', metadata_nthreads=1,
 return pieces, partitions, common_metadata_path, metadata_path


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


ParquetDataset.__doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)


_read_table_docstring = """
{0}

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable#5018

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 Merged

wesm
merged 1 commit into

from

Aug 6, 2019
 Merged


ARROW-3325: [Python][FOLLOWUP] In Python 2.7, a class's __doc__ member is not writable
#5018
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112 changes: 56 additions & 56 deletions
python/pyarrow/parquet.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -890,7 +890,63 @@ def _open_dataset_file(dataset, path, meta=None):
 common_metadata=dataset.common_metadata)


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


class ParquetDataset(object):

 __doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)

 def __init__(self, path_or_paths, filesystem=None, schema=None,
 metadata=None, split_row_groups=False, validate_schema=True,
 filters=None, metadata_nthreads=1,
Expand DownExpand Up@@ -1105,62 +1161,6 @@ def _make_manifest(path_or_paths, fs, pathsep='/', metadata_nthreads=1,
 return pieces, partitions, common_metadata_path, metadata_path


_read_docstring_common = """\
read_dictionary : list, default None
 List of names or column paths (for nested types) to read directly
 as DictionaryArray. Only supported for BYTE_ARRAY storage. To read
 a flat column as dictionary-encoded pass the column name. For
 nested types, you must pass the full column "path", which could be
 something like level1.level2.list.item. Refer to the Parquet
 file's schema to obtain the paths.
memory_map : boolean, default True
 If the source is a file path, use a memory map to read file, which can
 improve performance in some environments"""


ParquetDataset.__doc__ = """
Encapsulates details of reading a complete Parquet dataset possibly
consisting of multiple files and partitions in subdirectories

Parameters
----------
path_or_paths : str or List[str]
 A directory name, single file name, or list of file names
filesystem : FileSystem, default None
 If nothing passed, paths assumed to be found in the local on-disk
 filesystem
metadata : pyarrow.parquet.FileMetaData
 Use metadata obtained elsewhere to validate file schemas
schema : pyarrow.parquet.Schema
 Use schema obtained elsewhere to validate file schemas. Alternative to
 metadata parameter
split_row_groups : boolean, default False
 Divide files into pieces for each row group in the file
validate_schema : boolean, default True
 Check that individual file schemas are all the same / compatible
filters : List[Tuple] or List[List[Tuple]] or None (default)
 List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This
 implements partition-level (hive) filtering only, i.e., to prevent the
 loading of some files of the dataset.

 Predicates are expressed in disjunctive normal form (DNF). This means
 that the innermost tuple describe a single column predicate. These
 inner predicate make are all combined with a conjunction (AND) into a
 larger predicate. The most outer list then combines all filters
 with a disjunction (OR). By this, we should be able to express all
 kinds of filters that are possible using boolean logic.

 This function also supports passing in as List[Tuple]. These predicates
 are evaluated as a conjunction. To express OR in predictates, one must
 use the (preferred) List[List[Tuple]] notation.
metadata_nthreads: int, default 1
 How many threads to allow the thread pool which is used to read the
 dataset metadata. Increasing this is helpful to read partitioned
 datasets.
{0}
""".format(_read_docstring_common)


_read_table_docstring = """
{0}

Expand Down