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Posted to commits@arrow.apache.org by gi...@apache.org on 2022/08/10 00:23:33 UTC
[arrow-site] branch asf-site updated: Updating dev docs (build nightly-tests-2022-08-09-0)
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The following commit(s) were added to refs/heads/asf-site by this push:
new 6db51aec43 Updating dev docs (build nightly-tests-2022-08-09-0)
6db51aec43 is described below
commit 6db51aec4387f538eda4bf598c673161a48aec6f
Author: github-actions[bot] <gi...@users.noreply.github.com>
AuthorDate: Wed Aug 10 00:23:19 2022 +0000
Updating dev docs (build nightly-tests-2022-08-09-0)
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docs/dev/python/integration/python_r.html | 4 +-
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docs/dev/searchindex.js | 2 +-
docs/dev/status.html | 8 +-
3299 files changed, 15917 insertions(+), 7428 deletions(-)
diff --git a/docs/dev/.buildinfo b/docs/dev/.buildinfo
index dab8a486e7..04f813f938 100644
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<li><a href="pyarrow/orc.html">pyarrow.orc</a></li>
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index 0fc484a415..37593195e7 100644
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index 2bfe35e8fc..efb73c2ac1 100644
--- a/docs/dev/_modules/pyarrow/orc.html
+++ b/docs/dev/_modules/pyarrow/orc.html
@@ -5,7 +5,7 @@
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>pyarrow.orc — Apache Arrow v9.0.0.dev545</title>
+ <title>pyarrow.orc — Apache Arrow v9.0.0.dev554</title>
<link href="../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
<link href="../../_static/styles/pydata-sphinx-theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
@@ -91,7 +91,7 @@
<div id="version-button" class="dropdown">
<button type="button" class="btn btn-secondary btn-sm navbar-btn dropdown-toggle" id="version_switcher_button" data-toggle="dropdown">
- 9.0.0.dev545
+ 9.0.0.dev554
<span class="caret"></span>
</button>
<div id="version_switcher" class="dropdown-menu list-group-flush py-0" aria-labelledby="version_switcher_button">
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new file mode 100644
index 0000000000..b59651c907
--- /dev/null
+++ b/docs/dev/_modules/pyarrow/parquet/core.html
@@ -0,0 +1,8163 @@
+
+<!DOCTYPE html>
+
+<html lang="en">
+ <head>
+ <meta charset="utf-8" />
+ <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+ <title>pyarrow.parquet.core — Apache Arrow v9.0.0.dev554</title>
+
+ <link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
+<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=1999514e3f237ded88cf" rel="stylesheet">
+
+
+ <link rel="stylesheet"
+ href="../../../_static/vendor/fontawesome/5.13.0/css/all.min.css">
+ <link rel="preload" as="font" type="font/woff2" crossorigin
+ href="../../../_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.woff2">
+ <link rel="preload" as="font" type="font/woff2" crossorigin
+ href="../../../_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.woff2">
+
+
+
+
+
+ <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
+ <link rel="stylesheet" type="text/css" href="../../../_static/styles/pydata-sphinx-theme.css" />
+ <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css" />
+ <link rel="stylesheet" type="text/css" href="../../../_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css" />
+ <link rel="stylesheet" type="text/css" href="../../../_static/theme_overrides.css" />
+
+ <link rel="preload" as="script" href="../../../_static/scripts/pydata-sphinx-theme.js?digest=1999514e3f237ded88cf">
+
+ <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+ <script src="../../../_static/jquery.js"></script>
+ <script src="../../../_static/underscore.js"></script>
+ <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
+ <script src="../../../_static/doctools.js"></script>
+ <script src="../../../_static/clipboard.min.js"></script>
+ <script src="../../../_static/copybutton.js"></script>
+ <script src="../../../_static/design-tabs.js"></script>
+ <link rel="canonical" href="https://arrow.apache.org/docs/_modules/pyarrow/parquet/core.html" />
+ <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
+ <link rel="index" title="Index" href="../../../genindex.html" />
+ <link rel="search" title="Search" href="../../../search.html" />
+
+ <meta name="viewport" content="width=device-width, initial-scale=1" />
+ <meta name="docsearch:language" content="en">
+
+
+ <!-- Google Analytics -->
+
+
+ <!-- Matomo -->
+ <script>
+ var _paq = window._paq = window._paq || [];
+ /* tracker methods like "setCustomDimension" should be called before "trackPageView" */
+ /* We explicitly disable cookie tracking to avoid privacy issues */
+ _paq.push(['disableCookies']);
+ _paq.push(['trackPageView']);
+ _paq.push(['enableLinkTracking']);
+ (function() {
+ var u="https://analytics.apache.org/";
+ _paq.push(['setTrackerUrl', u+'matomo.php']);
+ _paq.push(['setSiteId', '20']);
+ var d=document, g=d.createElement('script'), s=d.getElementsByTagName('script')[0];
+ g.async=true; g.src=u+'matomo.js'; s.parentNode.insertBefore(g,s);
+ })();
+ </script>
+ <!-- End Matomo Code -->
+
+ </head>
+ <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
+
+ <div class="container-fluid" id="banner"></div>
+
+
+
+
+ <div class="container-xl">
+ <div class="row">
+
+
+ <!-- Only show if we have sidebars configured, else just a small margin -->
+ <div class="col-12 col-md-3 bd-sidebar">
+ <div class="sidebar-start-items">
+<a class="navbar-brand" href="../../../index.html">
+ <img src="../../../_static/arrow.png" class="logo" alt="logo">
+</a>
+
+<div id="version-search-wrapper">
+
+<div id="version-button" class="dropdown">
+ <button type="button" class="btn btn-secondary btn-sm navbar-btn dropdown-toggle" id="version_switcher_button" data-toggle="dropdown">
+ 9.0.0.dev554
+ <span class="caret"></span>
+ </button>
+ <div id="version_switcher" class="dropdown-menu list-group-flush py-0" aria-labelledby="version_switcher_button">
+ <!-- dropdown will be populated by javascript on page load -->
+ </div>
+</div>
+
+<script type="text/javascript">
+// Function to construct the target URL from the JSON components
+function buildURL(entry) {
+ var template = "https://arrow.apache.org/docs/{version}"; // supplied by jinja
+ template = template.replace("{version}", entry.version);
+ return template;
+}
+
+// Function to check if corresponding page path exists in other version of docs
+// and, if so, go there instead of the homepage of the other docs version
+function checkPageExistsAndRedirect(event) {
+ const currentFilePath = "_modules/pyarrow/parquet/core.html",
+ otherDocsHomepage = event.target.getAttribute("href");
+ let tryUrl = `${otherDocsHomepage}${currentFilePath}`;
+ $.ajax({
+ type: 'HEAD',
+ url: tryUrl,
+ // if the page exists, go there
+ success: function() {
+ location.href = tryUrl;
+ }
+ }).fail(function() {
+ location.href = otherDocsHomepage;
+ });
+ return false;
+}
+
+// Function to populate the version switcher
+(function () {
+ // get JSON config
+ $.getJSON("/docs/_static/versions.json", function(data, textStatus, jqXHR) {
+ // create the nodes first (before AJAX calls) to ensure the order is
+ // correct (for now, links will go to doc version homepage)
+ $.each(data, function(index, entry) {
+ // if no custom name specified (e.g., "latest"), use version string
+ if (!("name" in entry)) {
+ entry.name = entry.version;
+ }
+ // construct the appropriate URL, and add it to the dropdown
+ entry.url = buildURL(entry);
+ const node = document.createElement("a");
+ node.setAttribute("class", "list-group-item list-group-item-action py-1");
+ node.setAttribute("href", `${entry.url}`);
+ node.textContent = `${entry.name}`;
+ node.onclick = checkPageExistsAndRedirect;
+ $("#version_switcher").append(node);
+ });
+ });
+})();
+</script>
+
+<form id="search-box" class="bd-search d-flex align-items-center" action="../../../search.html" method="get">
+ <i class="icon fas fa-search"></i>
+ <input type="search" class="form-control" name="q" id="search-input" placeholder="Search the docs ..." aria-label="Search the docs ..." autocomplete="off" >
+</form>
+
+</div>
+
+<nav class="bd-links" id="bd-docs-nav" aria-label="Main navigation">
+ <div class="bd-toc-item active">
+
+ <p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+ Supported Environments
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+ <a class="reference internal" href="../../../c_glib/index.html">
+ C/GLib
+ </a>
+ </li>
+ <li class="toctree-l1 has-children">
+ <a class="reference internal" href="../../../cpp/index.html">
+ C++
+ </a>
+ <input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/>
+ <label for="toctree-checkbox-1">
+ <i class="fas fa-chevron-down">
+ </i>
+ </label>
+ <ul>
+ <li class="toctree-l2 has-children">
+ <a class="reference internal" href="../../../cpp/getting_started.html">
+ User Guide
+ </a>
+ <input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/>
+ <label for="toctree-checkbox-2">
+ <i class="fas fa-chevron-down">
+ </i>
+ </label>
+ <ul>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/overview.html">
+ High-Level Overview
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/conventions.html">
+ Conventions
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/build_system.html">
+ Using Arrow C++ in your own project
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/memory.html">
+ Memory Management
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/arrays.html">
+ Arrays
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/datatypes.html">
+ Data Types
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/tables.html">
+ Tabular Data
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/compute.html">
+ Compute Functions
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/streaming_execution.html">
+ Acero: A C++ streaming execution engine
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/io.html">
+ Input / output and filesystems
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/ipc.html">
+ Reading and writing the Arrow IPC format
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/orc.html">
+ Reading and Writing ORC files
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/parquet.html">
+ Reading and writing Parquet files
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/csv.html">
+ Reading and Writing CSV files
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/json.html">
+ Reading JSON files
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/dataset.html">
+ Tabular Datasets
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/flight.html">
+ Arrow Flight RPC
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/gdb.html">
+ Debugging code using Arrow
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/threading.html">
+ Thread Management
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/env_vars.html">
+ Environment Variables
+ </a>
+ </li>
+ </ul>
+ </li>
+ <li class="toctree-l2 has-children">
+ <a class="reference internal" href="../../../cpp/examples/index.html">
+ Examples
+ </a>
+ <input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/>
+ <label for="toctree-checkbox-3">
+ <i class="fas fa-chevron-down">
+ </i>
+ </label>
+ <ul>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/examples/cmake_minimal_build.html">
+ Minimal build using CMake
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/examples/compute_and_write_example.html">
+ Compute and Write CSV Example
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/examples/dataset_documentation_example.html">
+ Arrow Datasets example
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/examples/row_columnar_conversion.html">
+ Row to columnar conversion
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/examples/tuple_range_conversion.html">
+ std::tuple-like ranges to Arrow
+ </a>
+ </li>
+ </ul>
+ </li>
+ <li class="toctree-l2 has-children">
+ <a class="reference internal" href="../../../cpp/api.html">
+ API Reference
+ </a>
+ <input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/>
+ <label for="toctree-checkbox-4">
+ <i class="fas fa-chevron-down">
+ </i>
+ </label>
+ <ul>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/support.html">
+ Programming Support
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/memory.html">
+ Memory (management)
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/thread.html">
+ Thread (management)
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/datatype.html">
+ Data Types
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/array.html">
+ Arrays
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/scalar.html">
+ Scalars
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/builder.html">
+ Array Builders
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/table.html">
+ Two-dimensional Datasets
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/c_abi.html">
+ C Interfaces
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/compute.html">
+ Compute Functions
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/tensor.html">
+ Tensors
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/utilities.html">
+ Utilities
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/async.html">
+ Asynchronous programming
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/io.html">
+ Input / output
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/ipc.html">
+ Arrow IPC
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/formats.html">
+ File Formats
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/cuda.html">
+ CUDA support
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/flight.html">
+ Arrow Flight RPC
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/flightsql.html">
+ Arrow Flight SQL
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/filesystem.html">
+ Filesystems
+ </a>
+ </li>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../cpp/api/dataset.html">
+ Dataset
+ </a>
+ </li>
+ </ul>
+ </li>
+ </ul>
+ </li>
+ <li class="toctree-l1">
+ <a class="reference external" href="https://github.com/apache/arrow/blob/master/csharp/README.md">
+ C#
+ </a>
+ </li>
+ <li class="toctree-l1">
+ <a class="reference external" href="https://pkg.go.dev/github.com/apache/arrow/go">
+ Go
+ </a>
+ </li>
+ <li class="toctree-l1 has-children">
+ <a class="reference internal" href="../../../java/index.html">
+ Java
+ </a>
+ <input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/>
+ <label for="toctree-checkbox-5">
+ <i class="fas fa-chevron-down">
+ </i>
+ </label>
+ <ul>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/quickstartguide.html">
+ Quick Start Guide
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/overview.html">
+ High-Level Overview
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/install.html">
+ Installing Java Modules
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/memory.html">
+ Memory Management
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/vector.html">
+ ValueVector
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/vector_schema_root.html">
+ Tabular Data
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/ipc.html">
+ Reading/Writing IPC formats
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/algorithm.html">
+ Java Algorithms
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/flight.html">
+ Arrow Flight RPC
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/dataset.html">
+ Dataset
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/cdata.html">
+ C Data Interface
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/jdbc.html">
+ Arrow JDBC Adapter
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../java/reference/index.html">
+ Reference (javadoc)
+ </a>
+ </li>
+ </ul>
+ </li>
+ <li class="toctree-l1">
+ <a class="reference internal" href="../../../js/index.html">
+ JavaScript
+ </a>
+ </li>
+ <li class="toctree-l1">
+ <a class="reference external" href="https://github.com/apache/arrow-julia/blob/main/README.md">
+ Julia
+ </a>
+ </li>
+ <li class="toctree-l1">
+ <a class="reference external" href="https://github.com/apache/arrow/blob/master/matlab/README.md">
+ MATLAB
+ </a>
+ </li>
+ <li class="toctree-l1 has-children">
+ <a class="reference internal" href="../../../python/index.html">
+ Python
+ </a>
+ <input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" type="checkbox"/>
+ <label for="toctree-checkbox-6">
+ <i class="fas fa-chevron-down">
+ </i>
+ </label>
+ <ul>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../python/install.html">
+ Installing PyArrow
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../python/getstarted.html">
+ Getting Started
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../python/data.html">
+ Data Types and In-Memory Data Model
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../python/compute.html">
+ Compute Functions
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../python/memory.html">
+ Memory and IO Interfaces
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../python/ipc.html">
+ Streaming, Serialization, and IPC
+ </a>
+ </li>
+ <li class="toctree-l2">
+ <a class="reference internal" href="../../../python/filesystems.html">
+ Filesystem Interface
+ </a>
+ </li>
+ <li class="toctree-l2 has-children">
+ <a class="reference internal" href="../../../python/filesystems_deprecated.html">
+ Filesystem Interface (legacy)
+ </a>
+ <input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" type="checkbox"/>
+ <label for="toctree-checkbox-7">
+ <i class="fas fa-chevron-down">
+ </i>
+ </label>
+ <ul>
+ <li class="toctree-l3">
+ <a class="reference internal" href="../../../python/generated/pyarrow.hdfs.connect.html">
+ pyarrow.hdfs.connect
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+ </li>
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+ pyarrow.HadoopFileSystem.cat
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+ <a class="reference internal" href="../../../python/generated/pyarrow.HadoopFileSystem.delete.html">
+ pyarrow.HadoopFileSystem.delete
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+ <a class="reference internal" href="../../../python/generated/pyarrow.HadoopFileSystem.df.html">
+ pyarrow.HadoopFileSystem.df
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+ Cookbooks
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+ Java
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+ <li class="toctree-l1">
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+ Python
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+ </li>
+ <li class="toctree-l1">
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+ R
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+ Specifications and Protocols
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+ </li>
+ <li class="toctree-l1">
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+ Arrow Flight RPC
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+ Arrow Flight SQL
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+ <li class="toctree-l1">
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+ Integration Testing
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+ </li>
+ <li class="toctree-l1">
+ <a class="reference internal" href="../../../format/CDataInterface.html">
+ The Arrow C data interface
+ </a>
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+ <li class="toctree-l1">
+ <a class="reference internal" href="../../../format/CStreamInterface.html">
+ The Arrow C stream interface
+ </a>
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+ Other Data Structures
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+ Contributing to Apache Arrow
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+ Report bugs and propose features
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+ <div>
+
+ <h1>Source code for pyarrow.parquet.core</h1><div class="highlight"><pre>
+<span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span>
+<span class="c1"># or more contributor license agreements. See the NOTICE file</span>
+<span class="c1"># distributed with this work for additional information</span>
+<span class="c1"># regarding copyright ownership. The ASF licenses this file</span>
+<span class="c1"># to you under the Apache License, Version 2.0 (the</span>
+<span class="c1"># "License"); you may not use this file except in compliance</span>
+<span class="c1"># with the License. You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing,</span>
+<span class="c1"># software distributed under the License is distributed on an</span>
+<span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span>
+<span class="c1"># KIND, either express or implied. See the License for the</span>
+<span class="c1"># specific language governing permissions and limitations</span>
+<span class="c1"># under the License.</span>
+
+
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+<span class="kn">from</span> <span class="nn">concurrent</span> <span class="kn">import</span> <span class="n">futures</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span><span class="p">,</span> <span class="n">reduce</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">json</span>
+<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Collection</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">re</span>
+<span class="kn">import</span> <span class="nn">operator</span>
+<span class="kn">import</span> <span class="nn">urllib.parse</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">pyarrow</span> <span class="k">as</span> <span class="nn">pa</span>
+<span class="kn">import</span> <span class="nn">pyarrow.lib</span> <span class="k">as</span> <span class="nn">lib</span>
+<span class="kn">import</span> <span class="nn">pyarrow._parquet</span> <span class="k">as</span> <span class="nn">_parquet</span>
+
+<span class="kn">from</span> <span class="nn">pyarrow._parquet</span> <span class="kn">import</span> <span class="p">(</span><span class="n">ParquetReader</span><span class="p">,</span> <span class="n">Statistics</span><span class="p">,</span> <span class="c1"># noqa</span>
+ <span class="n">FileMetaData</span><span class="p">,</span> <span class="n">RowGroupMetaData</span><span class="p">,</span>
+ <span class="n">ColumnChunkMetaData</span><span class="p">,</span>
+ <span class="n">ParquetSchema</span><span class="p">,</span> <span class="n">ColumnSchema</span><span class="p">,</span>
+ <span class="n">ParquetLogicalType</span><span class="p">,</span>
+ <span class="n">FileEncryptionProperties</span><span class="p">,</span>
+ <span class="n">FileDecryptionProperties</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">pyarrow.fs</span> <span class="kn">import</span> <span class="p">(</span><span class="n">LocalFileSystem</span><span class="p">,</span> <span class="n">FileSystem</span><span class="p">,</span>
+ <span class="n">_resolve_filesystem_and_path</span><span class="p">,</span> <span class="n">_ensure_filesystem</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">pyarrow</span> <span class="kn">import</span> <span class="n">filesystem</span> <span class="k">as</span> <span class="n">legacyfs</span>
+<span class="kn">from</span> <span class="nn">pyarrow.util</span> <span class="kn">import</span> <span class="n">guid</span><span class="p">,</span> <span class="n">_is_path_like</span><span class="p">,</span> <span class="n">_stringify_path</span>
+
+<span class="n">_URI_STRIP_SCHEMES</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'hdfs'</span><span class="p">,)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_uri</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
+ <span class="n">path</span> <span class="o">=</span> <span class="n">_stringify_path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+ <span class="n">parsed_uri</span> <span class="o">=</span> <span class="n">urllib</span><span class="o">.</span><span class="n">parse</span><span class="o">.</span><span class="n">urlparse</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+ <span class="k">if</span> <span class="n">parsed_uri</span><span class="o">.</span><span class="n">scheme</span> <span class="ow">in</span> <span class="n">_URI_STRIP_SCHEMES</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">parsed_uri</span><span class="o">.</span><span class="n">path</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="c1"># ARROW-4073: On Windows returning the path with the scheme</span>
+ <span class="c1"># stripped removes the drive letter, if any</span>
+ <span class="k">return</span> <span class="n">path</span>
+
+
+<span class="k">def</span> <span class="nf">_get_filesystem_and_path</span><span class="p">(</span><span class="n">passed_filesystem</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
+ <span class="k">if</span> <span class="n">passed_filesystem</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">legacyfs</span><span class="o">.</span><span class="n">resolve_filesystem_and_path</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">passed_filesystem</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">passed_filesystem</span> <span class="o">=</span> <span class="n">legacyfs</span><span class="o">.</span><span class="n">_ensure_filesystem</span><span class="p">(</span><span class="n">passed_filesystem</span><span class="p">)</span>
+ <span class="n">parsed_path</span> <span class="o">=</span> <span class="n">_parse_uri</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">passed_filesystem</span><span class="p">,</span> <span class="n">parsed_path</span>
+
+
+<span class="k">def</span> <span class="nf">_check_contains_null</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
+ <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">):</span>
+ <span class="k">for</span> <span class="n">byte</span> <span class="ow">in</span> <span class="n">val</span><span class="p">:</span>
+ <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">byte</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">):</span>
+ <span class="n">compare_to</span> <span class="o">=</span> <span class="nb">chr</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">compare_to</span> <span class="o">=</span> <span class="mi">0</span>
+ <span class="k">if</span> <span class="n">byte</span> <span class="o">==</span> <span class="n">compare_to</span><span class="p">:</span>
+ <span class="k">return</span> <span class="kc">True</span>
+ <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+ <span class="k">return</span> <span class="s1">'</span><span class="se">\x00</span><span class="s1">'</span> <span class="ow">in</span> <span class="n">val</span>
+ <span class="k">return</span> <span class="kc">False</span>
+
+
+<span class="k">def</span> <span class="nf">_check_filters</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="n">check_null_strings</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Check if filters are well-formed.</span>
+<span class="sd"> """</span>
+ <span class="k">if</span> <span class="n">filters</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">filters</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">any</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">i [...]
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Malformed filters"</span><span class="p">)</span>
+ <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">filters</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
+ <span class="c1"># We have encountered the situation where we have one nesting level</span>
+ <span class="c1"># too few:</span>
+ <span class="c1"># We have [(,,), ..] instead of [[(,,), ..]]</span>
+ <span class="n">filters</span> <span class="o">=</span> <span class="p">[</span><span class="n">filters</span><span class="p">]</span>
+ <span class="k">if</span> <span class="n">check_null_strings</span><span class="p">:</span>
+ <span class="k">for</span> <span class="n">conjunction</span> <span class="ow">in</span> <span class="n">filters</span><span class="p">:</span>
+ <span class="k">for</span> <span class="n">col</span><span class="p">,</span> <span class="n">op</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">conjunction</span><span class="p">:</span>
+ <span class="k">if</span> <span class="p">(</span>
+ <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span>
+ <span class="nb">all</span><span class="p">(</span><span class="n">_check_contains_null</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">val</span><span class="p">)</span> <span class="ow">or</span>
+ <span class="n">_check_contains_null</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+ <span class="p">):</span>
+ <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
+ <span class="s2">"Null-terminated binary strings are not supported "</span>
+ <span class="s2">"as filter values."</span>
+ <span class="p">)</span>
+ <span class="k">return</span> <span class="n">filters</span>
+
+
+<span class="n">_DNF_filter_doc</span> <span class="o">=</span> <span class="s2">"""Predicates are expressed in disjunctive normal form (DNF),</span>
+<span class="s2"> like ``[[('x', '=', 0), ...], ...]``. DNF allows arbitrary boolean logical</span>
+<span class="s2"> combinations of single column predicates. The innermost tuples each</span>
+<span class="s2"> describe a single column predicate. The list of inner predicates is</span>
+<span class="s2"> interpreted as a conjunction (AND), forming a more selective and multiple</span>
+<span class="s2"> column predicate. Finally, the most outer list combines these filters as a</span>
+<span class="s2"> disjunction (OR).</span>
+
+<span class="s2"> Predicates may also be passed as List[Tuple]. This form is interpreted</span>
+<span class="s2"> as a single conjunction. To express OR in predicates, one must</span>
+<span class="s2"> use the (preferred) List[List[Tuple]] notation.</span>
+
+<span class="s2"> Each tuple has format: (``key``, ``op``, ``value``) and compares the</span>
+<span class="s2"> ``key`` with the ``value``.</span>
+<span class="s2"> The supported ``op`` are: ``=`` or ``==``, ``!=``, ``<``, ``>``, ``<=``,</span>
+<span class="s2"> ``>=``, ``in`` and ``not in``. If the ``op`` is ``in`` or ``not in``, the</span>
+<span class="s2"> ``value`` must be a collection such as a ``list``, a ``set`` or a</span>
+<span class="s2"> ``tuple``.</span>
+
+<span class="s2"> Examples:</span>
+
+<span class="s2"> .. code-block:: python</span>
+
+<span class="s2"> ('x', '=', 0)</span>
+<span class="s2"> ('y', 'in', ['a', 'b', 'c'])</span>
+<span class="s2"> ('z', 'not in', {'a','b'})</span>
+
+<span class="s2"> """</span>
+
+
+<span class="k">def</span> <span class="nf">_filters_to_expression</span><span class="p">(</span><span class="n">filters</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Check if filters are well-formed.</span>
+
+<span class="sd"> See _DNF_filter_doc above for more details.</span>
+<span class="sd"> """</span>
+ <span class="kn">import</span> <span class="nn">pyarrow.dataset</span> <span class="k">as</span> <span class="nn">ds</span>
+
+ <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">Expression</span><span class="p">):</span>
+ <span class="k">return</span> <span class="n">filters</span>
+
+ <span class="n">filters</span> <span class="o">=</span> <span class="n">_check_filters</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="n">check_null_strings</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="nf">convert_single_predicate</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">op</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
+ <span class="n">field</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="n">op</span> <span class="o">==</span> <span class="s2">"="</span> <span class="ow">or</span> <span class="n">op</span> <span class="o">==</span> <span class="s2">"=="</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">field</span> <span class="o">==</span> <span class="n">val</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s2">"!="</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">field</span> <span class="o">!=</span> <span class="n">val</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'<'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">field</span> <span class="o"><</span> <span class="n">val</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'>'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">field</span> <span class="o">></span> <span class="n">val</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'<='</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">field</span> <span class="o"><=</span> <span class="n">val</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'>='</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">field</span> <span class="o">>=</span> <span class="n">val</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'in'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">field</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'not in'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="o">~</span><span class="n">field</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+ <span class="s1">'"</span><span class="si">{0}</span><span class="s1">" is not a valid operator in predicates.'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+ <span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">op</span><span class="p">,</span> <span class="n">val</span><span class="p">)))</span>
+
+ <span class="n">disjunction_members</span> <span class="o">=</span> <span class="p">[]</span>
+
+ <span class="k">for</span> <span class="n">conjunction</span> <span class="ow">in</span> <span class="n">filters</span><span class="p">:</span>
+ <span class="n">conjunction_members</span> <span class="o">=</span> <span class="p">[</span>
+ <span class="n">convert_single_predicate</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">op</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
+ <span class="k">for</span> <span class="n">col</span><span class="p">,</span> <span class="n">op</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">conjunction</span>
+ <span class="p">]</span>
+
+ <span class="n">disjunction_members</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">reduce</span><span class="p">(</span><span class="n">operator</span><span class="o">.</span><span class="n">and_</span><span class="p">,</span> <span class="n">conjunction_members</span><span class="p">))</span>
+
+ <span class="k">return</span> <span class="n">reduce</span><span class="p">(</span><span class="n">operator</span><span class="o">.</span><span class="n">or_</span><span class="p">,</span> <span class="n">disjunction_members</span><span class="p">)</span>
+
+
+<span class="c1"># ----------------------------------------------------------------------</span>
+<span class="c1"># Reading a single Parquet file</span>
+
+
+<div class="viewcode-block" id="ParquetFile"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetFile.html#pyarrow.parquet.ParquetFile">[docs]</a><span class="k">class</span> <span class="nc">ParquetFile</span><span class="p">:</span>
+ <span class="sd">"""</span>
+<span class="sd"> Reader interface for a single Parquet file.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> source : str, pathlib.Path, pyarrow.NativeFile, or file-like object</span>
+<span class="sd"> Readable source. For passing bytes or buffer-like file containing a</span>
+<span class="sd"> Parquet file, use pyarrow.BufferReader.</span>
+<span class="sd"> metadata : FileMetaData, default None</span>
+<span class="sd"> Use existing metadata object, rather than reading from file.</span>
+<span class="sd"> common_metadata : FileMetaData, default None</span>
+<span class="sd"> Will be used in reads for pandas schema metadata if not found in the</span>
+<span class="sd"> main file's metadata, no other uses at the moment.</span>
+<span class="sd"> memory_map : bool, default False</span>
+<span class="sd"> If the source is a file path, use a memory map to read file, which can</span>
+<span class="sd"> improve performance in some environments.</span>
+<span class="sd"> buffer_size : int, default 0</span>
+<span class="sd"> If positive, perform read buffering when deserializing individual</span>
+<span class="sd"> column chunks. Otherwise IO calls are unbuffered.</span>
+<span class="sd"> pre_buffer : bool, default False</span>
+<span class="sd"> Coalesce and issue file reads in parallel to improve performance on</span>
+<span class="sd"> high-latency filesystems (e.g. S3). If True, Arrow will use a</span>
+<span class="sd"> background I/O thread pool.</span>
+<span class="sd"> read_dictionary : list</span>
+<span class="sd"> List of column names to read directly as DictionaryArray.</span>
+<span class="sd"> coerce_int96_timestamp_unit : str, default None.</span>
+<span class="sd"> Cast timestamps that are stored in INT96 format to a particular</span>
+<span class="sd"> resolution (e.g. 'ms'). Setting to None is equivalent to 'ns'</span>
+<span class="sd"> and therefore INT96 timestamps will be inferred as timestamps</span>
+<span class="sd"> in nanoseconds.</span>
+<span class="sd"> decryption_properties : FileDecryptionProperties, default None</span>
+<span class="sd"> File decryption properties for Parquet Modular Encryption.</span>
+<span class="sd"> thrift_string_size_limit : int, default None</span>
+<span class="sd"> If not None, override the maximum total string size allocated</span>
+<span class="sd"> when decoding Thrift structures. The default limit should be</span>
+<span class="sd"> sufficient for most Parquet files.</span>
+<span class="sd"> thrift_container_size_limit : int, default None</span>
+<span class="sd"> If not None, override the maximum total size of containers allocated</span>
+<span class="sd"> when decoding Thrift structures. The default limit should be</span>
+<span class="sd"> sufficient for most Parquet files.</span>
+
+<span class="sd"> Examples</span>
+<span class="sd"> --------</span>
+
+<span class="sd"> Generate an example PyArrow Table and write it to Parquet file:</span>
+
+<span class="sd"> >>> import pyarrow as pa</span>
+<span class="sd"> >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100],</span>
+<span class="sd"> ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="sd"> ... "Brittle stars", "Centipede"]})</span>
+
+<span class="sd"> >>> import pyarrow.parquet as pq</span>
+<span class="sd"> >>> pq.write_table(table, 'example.parquet')</span>
+
+<span class="sd"> Create a ``ParquetFile`` object from the Parquet file:</span>
+
+<span class="sd"> >>> parquet_file = pq.ParquetFile('example.parquet')</span>
+
+<span class="sd"> Read the data:</span>
+
+<span class="sd"> >>> parquet_file.read()</span>
+<span class="sd"> pyarrow.Table</span>
+<span class="sd"> n_legs: int64</span>
+<span class="sd"> animal: string</span>
+<span class="sd"> ----</span>
+<span class="sd"> n_legs: [[2,2,4,4,5,100]]</span>
+<span class="sd"> animal: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]]</span>
+
+<span class="sd"> Create a ParquetFile object with "animal" column as DictionaryArray:</span>
+
+<span class="sd"> >>> parquet_file = pq.ParquetFile('example.parquet',</span>
+<span class="sd"> ... read_dictionary=["animal"])</span>
+<span class="sd"> >>> parquet_file.read()</span>
+<span class="sd"> pyarrow.Table</span>
+<span class="sd"> n_legs: int64</span>
+<span class="sd"> animal: dictionary<values=string, indices=int32, ordered=0></span>
+<span class="sd"> ----</span>
+<span class="sd"> n_legs: [[2,2,4,4,5,100]]</span>
+<span class="sd"> animal: [ -- dictionary:</span>
+<span class="sd"> ["Flamingo","Parrot",...,"Brittle stars","Centipede"] -- indices:</span>
+<span class="sd"> [0,1,2,3,4,5]]</span>
+<span class="sd"> """</span>
+
+<div class="viewcode-block" id="ParquetFile.__init__"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetFile.html#pyarrow.parquet.ParquetFile.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><s [...]
+ <span class="n">read_dictionary</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">memory_map</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">buffer_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+ <span class="n">pre_buffer</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">coerce_int96_timestamp_unit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">decryption_properties</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">thrift_string_size_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">thrift_container_size_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">reader</span> <span class="o">=</span> <span class="n">ParquetReader</span><span class="p">()</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">open</span><span class="p">(</span>
+ <span class="n">source</span><span class="p">,</span> <span class="n">use_memory_map</span><span class="o">=</span><span class="n">memory_map</span><span class="p">,</span>
+ <span class="n">buffer_size</span><span class="o">=</span><span class="n">buffer_size</span><span class="p">,</span> <span class="n">pre_buffer</span><span class="o">=</span><span class="n">pre_buffer</span><span class="p">,</span>
+ <span class="n">read_dictionary</span><span class="o">=</span><span class="n">read_dictionary</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="n">metadata</span><span class="p">,</span>
+ <span class="n">coerce_int96_timestamp_unit</span><span class="o">=</span><span class="n">coerce_int96_timestamp_unit</span><span class="p">,</span>
+ <span class="n">decryption_properties</span><span class="o">=</span><span class="n">decryption_properties</span><span class="p">,</span>
+ <span class="n">thrift_string_size_limit</span><span class="o">=</span><span class="n">thrift_string_size_limit</span><span class="p">,</span>
+ <span class="n">thrift_container_size_limit</span><span class="o">=</span><span class="n">thrift_container_size_limit</span><span class="p">,</span>
+ <span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">common_metadata</span> <span class="o">=</span> <span class="n">common_metadata</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_nested_paths_by_prefix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_build_nested_paths</span><span class="p">()</span></div>
+
+ <span class="k">def</span> <span class="nf">_build_nested_paths</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="n">paths</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">column_paths</span>
+
+ <span class="n">result</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+
+ <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">path</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">paths</span><span class="p">):</span>
+ <span class="n">key</span> <span class="o">=</span> <span class="n">path</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+ <span class="n">rest</span> <span class="o">=</span> <span class="n">path</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+ <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
+ <span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="ow">not</span> <span class="n">rest</span><span class="p">:</span>
+ <span class="k">break</span>
+
+ <span class="n">key</span> <span class="o">=</span> <span class="s1">'.'</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">key</span><span class="p">,</span> <span class="n">rest</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+ <span class="n">rest</span> <span class="o">=</span> <span class="n">rest</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+ <span class="k">return</span> <span class="n">result</span>
+
+ <span class="nd">@property</span>
+ <span class="k">def</span> <span class="nf">metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Return the Parquet metadata.</span>
+<span class="sd"> """</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">metadata</span>
+
+ <span class="nd">@property</span>
+ <span class="k">def</span> <span class="nf">schema</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Return the Parquet schema, unconverted to Arrow types</span>
+<span class="sd"> """</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">schema</span>
+
+ <span class="nd">@property</span>
+ <span class="k">def</span> <span class="nf">schema_arrow</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Return the inferred Arrow schema, converted from the whole Parquet</span>
+<span class="sd"> file's schema</span>
+
+<span class="sd"> Examples</span>
+<span class="sd"> --------</span>
+<span class="sd"> Generate an example Parquet file:</span>
+
+<span class="sd"> >>> import pyarrow as pa</span>
+<span class="sd"> >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100],</span>
+<span class="sd"> ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="sd"> ... "Brittle stars", "Centipede"]})</span>
+<span class="sd"> >>> import pyarrow.parquet as pq</span>
+<span class="sd"> >>> pq.write_table(table, 'example.parquet')</span>
+<span class="sd"> >>> parquet_file = pq.ParquetFile('example.parquet')</span>
+
+<span class="sd"> Read the Arrow schema:</span>
+
+<span class="sd"> >>> parquet_file.schema_arrow</span>
+<span class="sd"> n_legs: int64</span>
+<span class="sd"> animal: string</span>
+<span class="sd"> """</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">schema_arrow</span>
+
+ <span class="nd">@property</span>
+ <span class="k">def</span> <span class="nf">num_row_groups</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Return the number of row groups of the Parquet file.</span>
+
+<span class="sd"> Examples</span>
+<span class="sd"> --------</span>
+<span class="sd"> >>> import pyarrow as pa</span>
+<span class="sd"> >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100],</span>
+<span class="sd"> ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="sd"> ... "Brittle stars", "Centipede"]})</span>
+<span class="sd"> >>> import pyarrow.parquet as pq</span>
+<span class="sd"> >>> pq.write_table(table, 'example.parquet')</span>
+<span class="sd"> >>> parquet_file = pq.ParquetFile('example.parquet')</span>
+
+<span class="sd"> >>> parquet_file.num_row_groups</span>
+<span class="sd"> 1</span>
+<span class="sd"> """</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">num_row_groups</span>
+
+<div class="viewcode-block" id="ParquetFile.read_row_group"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetFile.html#pyarrow.parquet.ParquetFile.read_row_group">[docs]</a> <span class="k">def</span> <span class="nf">read_row_group</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="kc">None</span><span class [...]
+ <span class="n">use_pandas_metadata</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Read a single row group from a Parquet file.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> i : int</span>
+<span class="sd"> Index of the individual row group that we want to read.</span>
+<span class="sd"> columns : list</span>
+<span class="sd"> If not None, only these columns will be read from the row group. A</span>
+<span class="sd"> column name may be a prefix of a nested field, e.g. 'a' will select</span>
+<span class="sd"> 'a.b', 'a.c', and 'a.d.e'.</span>
+<span class="sd"> use_threads : bool, default True</span>
+<span class="sd"> Perform multi-threaded column reads.</span>
+<span class="sd"> use_pandas_metadata : bool, default False</span>
+<span class="sd"> If True and file has custom pandas schema metadata, ensure that</span>
+<span class="sd"> index columns are also loaded.</span>
+
+<span class="sd"> Returns</span>
+<span class="sd"> -------</span>
+<span class="sd"> pyarrow.table.Table</span>
+<span class="sd"> Content of the row group as a table (of columns)</span>
+
+<span class="sd"> Examples</span>
+<span class="sd"> --------</span>
+<span class="sd"> >>> import pyarrow as pa</span>
+<span class="sd"> >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100],</span>
+<span class="sd"> ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="sd"> ... "Brittle stars", "Centipede"]})</span>
+<span class="sd"> >>> import pyarrow.parquet as pq</span>
+<span class="sd"> >>> pq.write_table(table, 'example.parquet')</span>
+<span class="sd"> >>> parquet_file = pq.ParquetFile('example.parquet')</span>
+
+<span class="sd"> >>> parquet_file.read_row_group(0)</span>
+<span class="sd"> pyarrow.Table</span>
+<span class="sd"> n_legs: int64</span>
+<span class="sd"> animal: string</span>
+<span class="sd"> ----</span>
+<span class="sd"> n_legs: [[2,2,4,4,5,100]]</span>
+<span class="sd"> animal: [["Flamingo","Parrot",...,"Brittle stars","Centipede"]]</span>
+<span class="sd"> """</span>
+ <span class="n">column_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_column_indices</span><span class="p">(</span>
+ <span class="n">columns</span><span class="p">,</span> <span class="n">use_pandas_metadata</span><span class="o">=</span><span class="n">use_pandas_metadata</span><span class="p">)</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">read_row_group</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">column_indices</span><span class="o">=</span><span class="n">column_indices</span><span class="p">,</span>
+ <span class="n">use_threads</span><span class="o">=</span><span class="n">use_threads</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ParquetFile.read_row_groups"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetFile.html#pyarrow.parquet.ParquetFile.read_row_groups">[docs]</a> <span class="k">def</span> <span class="nf">read_row_groups</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row_groups</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="kc">None</span [...]
+ <span class="n">use_pandas_metadata</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Read a multiple row groups from a Parquet file.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> row_groups : list</span>
+<span class="sd"> Only these row groups will be read from the file.</span>
+<span class="sd"> columns : list</span>
+<span class="sd"> If not None, only these columns will be read from the row group. A</span>
+<span class="sd"> column name may be a prefix of a nested field, e.g. 'a' will select</span>
+<span class="sd"> 'a.b', 'a.c', and 'a.d.e'.</span>
+<span class="sd"> use_threads : bool, default True</span>
+<span class="sd"> Perform multi-threaded column reads.</span>
+<span class="sd"> use_pandas_metadata : bool, default False</span>
+<span class="sd"> If True and file has custom pandas schema metadata, ensure that</span>
+<span class="sd"> index columns are also loaded.</span>
+
+<span class="sd"> Returns</span>
+<span class="sd"> -------</span>
+<span class="sd"> pyarrow.table.Table</span>
+<span class="sd"> Content of the row groups as a table (of columns).</span>
+
+<span class="sd"> Examples</span>
+<span class="sd"> --------</span>
+<span class="sd"> >>> import pyarrow as pa</span>
+<span class="sd"> >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100],</span>
+<span class="sd"> ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="sd"> ... "Brittle stars", "Centipede"]})</span>
+<span class="sd"> >>> import pyarrow.parquet as pq</span>
+<span class="sd"> >>> pq.write_table(table, 'example.parquet')</span>
+<span class="sd"> >>> parquet_file = pq.ParquetFile('example.parquet')</span>
+
+<span class="sd"> >>> parquet_file.read_row_groups([0,0])</span>
+<span class="sd"> pyarrow.Table</span>
+<span class="sd"> n_legs: int64</span>
+<span class="sd"> animal: string</span>
+<span class="sd"> ----</span>
+<span class="sd"> n_legs: [[2,2,4,4,5,...,2,4,4,5,100]]</span>
+<span class="sd"> animal: [["Flamingo","Parrot","Dog",...,"Brittle stars","Centipede"]]</span>
+<span class="sd"> """</span>
+ <span class="n">column_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_column_indices</span><span class="p">(</span>
+ <span class="n">columns</span><span class="p">,</span> <span class="n">use_pandas_metadata</span><span class="o">=</span><span class="n">use_pandas_metadata</span><span class="p">)</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">read_row_groups</span><span class="p">(</span><span class="n">row_groups</span><span class="p">,</span>
+ <span class="n">column_indices</span><span class="o">=</span><span class="n">column_indices</span><span class="p">,</span>
+ <span class="n">use_threads</span><span class="o">=</span><span class="n">use_threads</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ParquetFile.iter_batches"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetFile.html#pyarrow.parquet.ParquetFile.iter_batches">[docs]</a> <span class="k">def</span> <span class="nf">iter_batches</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">65536</span><span class="p">,</span> <span class="n">row_groups</span><spa [...]
+ <span class="n">use_threads</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_pandas_metadata</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Read streaming batches from a Parquet file.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> batch_size : int, default 64K</span>
+<span class="sd"> Maximum number of records to yield per batch. Batches may be</span>
+<span class="sd"> smaller if there aren't enough rows in the file.</span>
+<span class="sd"> row_groups : list</span>
+<span class="sd"> Only these row groups will be read from the file.</span>
+<span class="sd"> columns : list</span>
+<span class="sd"> If not None, only these columns will be read from the file. A</span>
+<span class="sd"> column name may be a prefix of a nested field, e.g. 'a' will select</span>
+<span class="sd"> 'a.b', 'a.c', and 'a.d.e'.</span>
+<span class="sd"> use_threads : boolean, default True</span>
+<span class="sd"> Perform multi-threaded column reads.</span>
+<span class="sd"> use_pandas_metadata : boolean, default False</span>
+<span class="sd"> If True and file has custom pandas schema metadata, ensure that</span>
+<span class="sd"> index columns are also loaded.</span>
+
+<span class="sd"> Returns</span>
+<span class="sd"> -------</span>
+<span class="sd"> iterator of pyarrow.RecordBatch</span>
+<span class="sd"> Contents of each batch as a record batch</span>
+
+<span class="sd"> Examples</span>
+<span class="sd"> --------</span>
+<span class="sd"> Generate an example Parquet file:</span>
+
+<span class="sd"> >>> import pyarrow as pa</span>
+<span class="sd"> >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100],</span>
+<span class="sd"> ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="sd"> ... "Brittle stars", "Centipede"]})</span>
+<span class="sd"> >>> import pyarrow.parquet as pq</span>
+<span class="sd"> >>> pq.write_table(table, 'example.parquet')</span>
+<span class="sd"> >>> parquet_file = pq.ParquetFile('example.parquet')</span>
+<span class="sd"> >>> for i in parquet_file.iter_batches():</span>
+<span class="sd"> ... print("RecordBatch")</span>
+<span class="sd"> ... print(i.to_pandas())</span>
+<span class="sd"> ...</span>
+<span class="sd"> RecordBatch</span>
+<span class="sd"> n_legs animal</span>
+<span class="sd"> 0 2 Flamingo</span>
+<span class="sd"> 1 2 Parrot</span>
+<span class="sd"> 2 4 Dog</span>
+<span class="sd"> 3 4 Horse</span>
+<span class="sd"> 4 5 Brittle stars</span>
+<span class="sd"> 5 100 Centipede</span>
+<span class="sd"> """</span>
+ <span class="k">if</span> <span class="n">row_groups</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="n">row_groups</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">num_row_groups</span><span class="p">)</span>
+ <span class="n">column_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_column_indices</span><span class="p">(</span>
+ <span class="n">columns</span><span class="p">,</span> <span class="n">use_pandas_metadata</span><span class="o">=</span><span class="n">use_pandas_metadata</span><span class="p">)</span>
+
+ <span class="n">batches</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">iter_batches</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span>
+ <span class="n">row_groups</span><span class="o">=</span><span class="n">row_groups</span><span class="p">,</span>
+ <span class="n">column_indices</span><span class="o">=</span><span class="n">column_indices</span><span class="p">,</span>
+ <span class="n">use_threads</span><span class="o">=</span><span class="n">use_threads</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">batches</span></div>
+
+<div class="viewcode-block" id="ParquetFile.read"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetFile.html#pyarrow.parquet.ParquetFile.read">[docs]</a> <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_threads</span><span class="o">=</span><span c [...]
+ <span class="sd">"""</span>
+<span class="sd"> Read a Table from Parquet format.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> columns : list</span>
+<span class="sd"> If not None, only these columns will be read from the file. A</span>
+<span class="sd"> column name may be a prefix of a nested field, e.g. 'a' will select</span>
+<span class="sd"> 'a.b', 'a.c', and 'a.d.e'.</span>
+<span class="sd"> use_threads : bool, default True</span>
+<span class="sd"> Perform multi-threaded column reads.</span>
+<span class="sd"> use_pandas_metadata : bool, default False</span>
+<span class="sd"> If True and file has custom pandas schema metadata, ensure that</span>
+<span class="sd"> index columns are also loaded.</span>
+
+<span class="sd"> Returns</span>
+<span class="sd"> -------</span>
+<span class="sd"> pyarrow.table.Table</span>
+<span class="sd"> Content of the file as a table (of columns).</span>
+
+<span class="sd"> Examples</span>
+<span class="sd"> --------</span>
+<span class="sd"> Generate an example Parquet file:</span>
+
+<span class="sd"> >>> import pyarrow as pa</span>
+<span class="sd"> >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100],</span>
+<span class="sd"> ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="sd"> ... "Brittle stars", "Centipede"]})</span>
+<span class="sd"> >>> import pyarrow.parquet as pq</span>
+<span class="sd"> >>> pq.write_table(table, 'example.parquet')</span>
+<span class="sd"> >>> parquet_file = pq.ParquetFile('example.parquet')</span>
+
+<span class="sd"> Read a Table:</span>
+
+<span class="sd"> >>> parquet_file.read(columns=["animal"])</span>
+<span class="sd"> pyarrow.Table</span>
+<span class="sd"> animal: string</span>
+<span class="sd"> ----</span>
+<span class="sd"> animal: [["Flamingo","Parrot",...,"Brittle stars","Centipede"]]</span>
+<span class="sd"> """</span>
+ <span class="n">column_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_column_indices</span><span class="p">(</span>
+ <span class="n">columns</span><span class="p">,</span> <span class="n">use_pandas_metadata</span><span class="o">=</span><span class="n">use_pandas_metadata</span><span class="p">)</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">read_all</span><span class="p">(</span><span class="n">column_indices</span><span class="o">=</span><span class="n">column_indices</span><span class="p">,</span>
+ <span class="n">use_threads</span><span class="o">=</span><span class="n">use_threads</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ParquetFile.scan_contents"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetFile.html#pyarrow.parquet.ParquetFile.scan_contents">[docs]</a> <span class="k">def</span> <span class="nf">scan_contents</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_size</span><span [...]
+ <span class="sd">"""</span>
+<span class="sd"> Read contents of file for the given columns and batch size.</span>
+
+<span class="sd"> Notes</span>
+<span class="sd"> -----</span>
+<span class="sd"> This function's primary purpose is benchmarking.</span>
+<span class="sd"> The scan is executed on a single thread.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> columns : list of integers, default None</span>
+<span class="sd"> Select columns to read, if None scan all columns.</span>
+<span class="sd"> batch_size : int, default 64K</span>
+<span class="sd"> Number of rows to read at a time internally.</span>
+
+<span class="sd"> Returns</span>
+<span class="sd"> -------</span>
+<span class="sd"> num_rows : number of rows in file</span>
+
+<span class="sd"> Examples</span>
+<span class="sd"> --------</span>
+<span class="sd"> >>> import pyarrow as pa</span>
+<span class="sd"> >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100],</span>
+<span class="sd"> ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="sd"> ... "Brittle stars", "Centipede"]})</span>
+<span class="sd"> >>> import pyarrow.parquet as pq</span>
+<span class="sd"> >>> pq.write_table(table, 'example.parquet')</span>
+<span class="sd"> >>> parquet_file = pq.ParquetFile('example.parquet')</span>
+
+<span class="sd"> >>> parquet_file.scan_contents()</span>
+<span class="sd"> 6</span>
+<span class="sd"> """</span>
+ <span class="n">column_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_column_indices</span><span class="p">(</span><span class="n">columns</span><span class="p">)</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">scan_contents</span><span class="p">(</span><span class="n">column_indices</span><span class="p">,</span>
+ <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span></div>
+
+ <span class="k">def</span> <span class="nf">_get_column_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">column_names</span><span class="p">,</span> <span class="n">use_pandas_metadata</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+ <span class="k">if</span> <span class="n">column_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="k">return</span> <span class="kc">None</span>
+
+ <span class="n">indices</span> <span class="o">=</span> <span class="p">[]</span>
+
+ <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">column_names</span><span class="p">:</span>
+ <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nested_paths_by_prefix</span><span class="p">:</span>
+ <span class="n">indices</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_nested_paths_by_prefix</span><span class="p">[</span><span class="n">name</span><span class="p">])</span>
+
+ <span class="k">if</span> <span class="n">use_pandas_metadata</span><span class="p">:</span>
+ <span class="n">file_keyvalues</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">metadata</span>
+ <span class="n">common_keyvalues</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">common_metadata</span><span class="o">.</span><span class="n">metadata</span>
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">common_metadata</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+ <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="n">file_keyvalues</span> <span class="ow">and</span> <span class="sa">b</span><span class="s1">'pandas'</span> <span class="ow">in</span> <span class="n">file_keyvalues</span><span class="p">:</span>
+ <span class="n">index_columns</span> <span class="o">=</span> <span class="n">_get_pandas_index_columns</span><span class="p">(</span><span class="n">file_keyvalues</span><span class="p">)</span>
+ <span class="k">elif</span> <span class="n">common_keyvalues</span> <span class="ow">and</span> <span class="sa">b</span><span class="s1">'pandas'</span> <span class="ow">in</span> <span class="n">common_keyvalues</span><span class="p">:</span>
+ <span class="n">index_columns</span> <span class="o">=</span> <span class="n">_get_pandas_index_columns</span><span class="p">(</span><span class="n">common_keyvalues</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">index_columns</span> <span class="o">=</span> <span class="p">[]</span>
+
+ <span class="k">if</span> <span class="n">indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">index_columns</span><span class="p">:</span>
+ <span class="n">indices</span> <span class="o">+=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">column_name_idx</span><span class="p">(</span><span class="n">descr</span><span class="p">)</span>
+ <span class="k">for</span> <span class="n">descr</span> <span class="ow">in</span> <span class="n">index_columns</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">descr</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)]</span>
+
+ <span class="k">return</span> <span class="n">indices</span></div>
+
+
+<span class="n">_SPARK_DISALLOWED_CHARS</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="s1">'[ ,;</span><span class="si">{}</span><span class="s1">()</span><span class="se">\n\t</span><span class="s1">=]'</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_sanitized_spark_field_name</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
+ <span class="k">return</span> <span class="n">_SPARK_DISALLOWED_CHARS</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s1">'_'</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_sanitize_schema</span><span class="p">(</span><span class="n">schema</span><span class="p">,</span> <span class="n">flavor</span><span class="p">):</span>
+ <span class="k">if</span> <span class="s1">'spark'</span> <span class="ow">in</span> <span class="n">flavor</span><span class="p">:</span>
+ <span class="n">sanitized_fields</span> <span class="o">=</span> <span class="p">[]</span>
+
+ <span class="n">schema_changed</span> <span class="o">=</span> <span class="kc">False</span>
+
+ <span class="k">for</span> <span class="n">field</span> <span class="ow">in</span> <span class="n">schema</span><span class="p">:</span>
+ <span class="n">name</span> <span class="o">=</span> <span class="n">field</span><span class="o">.</span><span class="n">name</span>
+ <span class="n">sanitized_name</span> <span class="o">=</span> <span class="n">_sanitized_spark_field_name</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="n">sanitized_name</span> <span class="o">!=</span> <span class="n">name</span><span class="p">:</span>
+ <span class="n">schema_changed</span> <span class="o">=</span> <span class="kc">True</span>
+ <span class="n">sanitized_field</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">field</span><span class="p">(</span><span class="n">sanitized_name</span><span class="p">,</span> <span class="n">field</span><span class="o">.</span><span class="n">type</span><span class="p">,</span>
+ <span class="n">field</span><span class="o">.</span><span class="n">nullable</span><span class="p">,</span> <span class="n">field</span><span class="o">.</span><span class="n">metadata</span><span class="p">)</span>
+ <span class="n">sanitized_fields</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sanitized_field</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">sanitized_fields</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">field</span><span class="p">)</span>
+
+ <span class="n">new_schema</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">schema</span><span class="p">(</span><span class="n">sanitized_fields</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="n">schema</span><span class="o">.</span><span class="n">metadata</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">new_schema</span><span class="p">,</span> <span class="n">schema_changed</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">schema</span><span class="p">,</span> <span class="kc">False</span>
+
+
+<span class="k">def</span> <span class="nf">_sanitize_table</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">new_schema</span><span class="p">,</span> <span class="n">flavor</span><span class="p">):</span>
+ <span class="c1"># TODO: This will not handle prohibited characters in nested field names</span>
+ <span class="k">if</span> <span class="s1">'spark'</span> <span class="ow">in</span> <span class="n">flavor</span><span class="p">:</span>
+ <span class="n">column_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">table</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">table</span><span class="o">.</span><span class="n">num_columns</span><span class="p">)]</span>
+ <span class="k">return</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="n">column_data</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="n">new_schema</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">table</span>
+
+
+<span class="n">_parquet_writer_arg_docs</span> <span class="o">=</span> <span class="s2">"""version : {"1.0", "2.4", "2.6"}, default "2.4"</span>
+<span class="s2"> Determine which Parquet logical types are available for use, whether the</span>
+<span class="s2"> reduced set from the Parquet 1.x.x format or the expanded logical types</span>
+<span class="s2"> added in later format versions.</span>
+<span class="s2"> Files written with version='2.4' or '2.6' may not be readable in all</span>
+<span class="s2"> Parquet implementations, so version='1.0' is likely the choice that</span>
+<span class="s2"> maximizes file compatibility.</span>
+<span class="s2"> UINT32 and some logical types are only available with version '2.4'.</span>
+<span class="s2"> Nanosecond timestamps are only available with version '2.6'.</span>
+<span class="s2"> Other features such as compression algorithms or the new serialized</span>
+<span class="s2"> data page format must be enabled separately (see 'compression' and</span>
+<span class="s2"> 'data_page_version').</span>
+<span class="s2">use_dictionary : bool or list</span>
+<span class="s2"> Specify if we should use dictionary encoding in general or only for</span>
+<span class="s2"> some columns.</span>
+<span class="s2">use_deprecated_int96_timestamps : bool, default None</span>
+<span class="s2"> Write timestamps to INT96 Parquet format. Defaults to False unless enabled</span>
+<span class="s2"> by flavor argument. This take priority over the coerce_timestamps option.</span>
+<span class="s2">coerce_timestamps : str, default None</span>
+<span class="s2"> Cast timestamps to a particular resolution. If omitted, defaults are chosen</span>
+<span class="s2"> depending on `version`. By default, for ``version='1.0'`` (the default)</span>
+<span class="s2"> and ``version='2.4'``, nanoseconds are cast to microseconds ('us'), while</span>
+<span class="s2"> for other `version` values, they are written natively without loss</span>
+<span class="s2"> of resolution. Seconds are always cast to milliseconds ('ms') by default,</span>
+<span class="s2"> as Parquet does not have any temporal type with seconds resolution.</span>
+<span class="s2"> If the casting results in loss of data, it will raise an exception</span>
+<span class="s2"> unless ``allow_truncated_timestamps=True`` is given.</span>
+<span class="s2"> Valid values: {None, 'ms', 'us'}</span>
+<span class="s2">data_page_size : int, default None</span>
+<span class="s2"> Set a target threshold for the approximate encoded size of data</span>
+<span class="s2"> pages within a column chunk (in bytes). If None, use the default data page</span>
+<span class="s2"> size of 1MByte.</span>
+<span class="s2">allow_truncated_timestamps : bool, default False</span>
+<span class="s2"> Allow loss of data when coercing timestamps to a particular</span>
+<span class="s2"> resolution. E.g. if microsecond or nanosecond data is lost when coercing to</span>
+<span class="s2"> 'ms', do not raise an exception. Passing ``allow_truncated_timestamp=True``</span>
+<span class="s2"> will NOT result in the truncation exception being ignored unless</span>
+<span class="s2"> ``coerce_timestamps`` is not None.</span>
+<span class="s2">compression : str or dict</span>
+<span class="s2"> Specify the compression codec, either on a general basis or per-column.</span>
+<span class="s2"> Valid values: {'NONE', 'SNAPPY', 'GZIP', 'BROTLI', 'LZ4', 'ZSTD'}.</span>
+<span class="s2">write_statistics : bool or list</span>
+<span class="s2"> Specify if we should write statistics in general (default is True) or only</span>
+<span class="s2"> for some columns.</span>
+<span class="s2">flavor : {'spark'}, default None</span>
+<span class="s2"> Sanitize schema or set other compatibility options to work with</span>
+<span class="s2"> various target systems.</span>
+<span class="s2">filesystem : FileSystem, default None</span>
+<span class="s2"> If nothing passed, will be inferred from `where` if path-like, else</span>
+<span class="s2"> `where` is already a file-like object so no filesystem is needed.</span>
+<span class="s2">compression_level : int or dict, default None</span>
+<span class="s2"> Specify the compression level for a codec, either on a general basis or</span>
+<span class="s2"> per-column. If None is passed, arrow selects the compression level for</span>
+<span class="s2"> the compression codec in use. The compression level has a different</span>
+<span class="s2"> meaning for each codec, so you have to read the documentation of the</span>
+<span class="s2"> codec you are using.</span>
+<span class="s2"> An exception is thrown if the compression codec does not allow specifying</span>
+<span class="s2"> a compression level.</span>
+<span class="s2">use_byte_stream_split : bool or list, default False</span>
+<span class="s2"> Specify if the byte_stream_split encoding should be used in general or</span>
+<span class="s2"> only for some columns. If both dictionary and byte_stream_stream are</span>
+<span class="s2"> enabled, then dictionary is preferred.</span>
+<span class="s2"> The byte_stream_split encoding is valid only for floating-point data types</span>
+<span class="s2"> and should be combined with a compression codec.</span>
+<span class="s2">column_encoding : string or dict, default None</span>
+<span class="s2"> Specify the encoding scheme on a per column basis.</span>
+<span class="s2"> Currently supported values: {'PLAIN', 'BYTE_STREAM_SPLIT'}.</span>
+<span class="s2"> Certain encodings are only compatible with certain data types.</span>
+<span class="s2"> Please refer to the encodings section of `Reading and writing Parquet</span>
+<span class="s2"> files <https://arrow.apache.org/docs/cpp/parquet.html#encodings>`_.</span>
+<span class="s2">data_page_version : {"1.0", "2.0"}, default "1.0"</span>
+<span class="s2"> The serialized Parquet data page format version to write, defaults to</span>
+<span class="s2"> 1.0. This does not impact the file schema logical types and Arrow to</span>
+<span class="s2"> Parquet type casting behavior; for that use the "version" option.</span>
+<span class="s2">use_compliant_nested_type : bool, default False</span>
+<span class="s2"> Whether to write compliant Parquet nested type (lists) as defined</span>
+<span class="s2"> `here <https://github.com/apache/parquet-format/blob/master/</span>
+<span class="s2"> LogicalTypes.md#nested-types>`_, defaults to ``False``.</span>
+<span class="s2"> For ``use_compliant_nested_type=True``, this will write into a list</span>
+<span class="s2"> with 3-level structure where the middle level, named ``list``,</span>
+<span class="s2"> is a repeated group with a single field named ``element``::</span>
+
+<span class="s2"> <list-repetition> group <name> (LIST) {</span>
+<span class="s2"> repeated group list {</span>
+<span class="s2"> <element-repetition> <element-type> element;</span>
+<span class="s2"> }</span>
+<span class="s2"> }</span>
+
+<span class="s2"> For ``use_compliant_nested_type=False``, this will also write into a list</span>
+<span class="s2"> with 3-level structure, where the name of the single field of the middle</span>
+<span class="s2"> level ``list`` is taken from the element name for nested columns in Arrow,</span>
+<span class="s2"> which defaults to ``item``::</span>
+
+<span class="s2"> <list-repetition> group <name> (LIST) {</span>
+<span class="s2"> repeated group list {</span>
+<span class="s2"> <element-repetition> <element-type> item;</span>
+<span class="s2"> }</span>
+<span class="s2"> }</span>
+<span class="s2">encryption_properties : FileEncryptionProperties, default None</span>
+<span class="s2"> File encryption properties for Parquet Modular Encryption.</span>
+<span class="s2"> If None, no encryption will be done.</span>
+<span class="s2"> The encryption properties can be created using:</span>
+<span class="s2"> ``CryptoFactory.file_encryption_properties()``.</span>
+<span class="s2">write_batch_size : int, default None</span>
+<span class="s2"> Number of values to write to a page at a time. If None, use the default of</span>
+<span class="s2"> 1024. ``write_batch_size`` is complementary to ``data_page_size``. If pages</span>
+<span class="s2"> are exceeding the ``data_page_size`` due to large column values, lowering</span>
+<span class="s2"> the batch size can help keep page sizes closer to the intended size.</span>
+<span class="s2">dictionary_pagesize_limit : int, default None</span>
+<span class="s2"> Specify the dictionary page size limit per row group. If None, use the</span>
+<span class="s2"> default 1MB.</span>
+<span class="s2">"""</span>
+
+<span class="n">_parquet_writer_example_doc</span> <span class="o">=</span> <span class="s2">"""</span><span class="se">\</span>
+<span class="s2">Generate an example PyArrow Table and RecordBatch:</span>
+
+<span class="s2">>>> import pyarrow as pa</span>
+<span class="s2">>>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100],</span>
+<span class="s2">... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="s2">... "Brittle stars", "Centipede"]})</span>
+<span class="s2">>>> batch = pa.record_batch([[2, 2, 4, 4, 5, 100],</span>
+<span class="s2">... ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="s2">... "Brittle stars", "Centipede"]],</span>
+<span class="s2">... names=['n_legs', 'animal'])</span>
+
+<span class="s2">create a ParquetWriter object:</span>
+
+<span class="s2">>>> import pyarrow.parquet as pq</span>
+<span class="s2">>>> writer = pq.ParquetWriter('example.parquet', table.schema)</span>
+
+<span class="s2">and write the Table into the Parquet file:</span>
+
+<span class="s2">>>> writer.write_table(table)</span>
+<span class="s2">>>> writer.close()</span>
+
+<span class="s2">>>> pq.read_table('example.parquet').to_pandas()</span>
+<span class="s2"> n_legs animal</span>
+<span class="s2">0 2 Flamingo</span>
+<span class="s2">1 2 Parrot</span>
+<span class="s2">2 4 Dog</span>
+<span class="s2">3 4 Horse</span>
+<span class="s2">4 5 Brittle stars</span>
+<span class="s2">5 100 Centipede</span>
+
+<span class="s2">create a ParquetWriter object for the RecordBatch:</span>
+
+<span class="s2">>>> writer2 = pq.ParquetWriter('example2.parquet', batch.schema)</span>
+
+<span class="s2">and write the RecordBatch into the Parquet file:</span>
+
+<span class="s2">>>> writer2.write_batch(batch)</span>
+<span class="s2">>>> writer2.close()</span>
+
+<span class="s2">>>> pq.read_table('example2.parquet').to_pandas()</span>
+<span class="s2"> n_legs animal</span>
+<span class="s2">0 2 Flamingo</span>
+<span class="s2">1 2 Parrot</span>
+<span class="s2">2 4 Dog</span>
+<span class="s2">3 4 Horse</span>
+<span class="s2">4 5 Brittle stars</span>
+<span class="s2">5 100 Centipede</span>
+<span class="s2">"""</span>
+
+
+<div class="viewcode-block" id="ParquetWriter"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetWriter.html#pyarrow.parquet.ParquetWriter">[docs]</a><span class="k">class</span> <span class="nc">ParquetWriter</span><span class="p">:</span>
+
+ <span class="vm">__doc__</span> <span class="o">=</span> <span class="s2">"""</span>
+<span class="s2">Class for incrementally building a Parquet file for Arrow tables.</span>
+
+<span class="s2">Parameters</span>
+<span class="s2">----------</span>
+<span class="s2">where : path or file-like object</span>
+<span class="s2">schema : pyarrow.Schema</span>
+<span class="si">{}</span><span class="s2"></span>
+<span class="s2">writer_engine_version : unused</span>
+<span class="s2">**options : dict</span>
+<span class="s2"> If options contains a key `metadata_collector` then the</span>
+<span class="s2"> corresponding value is assumed to be a list (or any object with</span>
+<span class="s2"> `.append` method) that will be filled with the file metadata instance</span>
+<span class="s2"> of the written file.</span>
+
+<span class="s2">Examples</span>
+<span class="s2">--------</span>
+<span class="si">{}</span><span class="s2"></span>
+<span class="s2">"""</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">_parquet_writer_arg_docs</span><span class="p">,</span> <span class="n">_parquet_writer_example_doc</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ParquetWriter.__init__"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetWriter.html#pyarrow.parquet.ParquetWriter.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">where</span><span class="p">,</span> <span class="n">schema</span><span class="p">,</span> <span class="n">filesystem</span><span class="o [...]
+ <span class="n">flavor</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">version</span><span class="o">=</span><span class="s1">'2.4'</span><span class="p">,</span>
+ <span class="n">use_dictionary</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+ <span class="n">compression</span><span class="o">=</span><span class="s1">'snappy'</span><span class="p">,</span>
+ <span class="n">write_statistics</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+ <span class="n">use_deprecated_int96_timestamps</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">compression_level</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">use_byte_stream_split</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+ <span class="n">column_encoding</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">writer_engine_version</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">data_page_version</span><span class="o">=</span><span class="s1">'1.0'</span><span class="p">,</span>
+ <span class="n">use_compliant_nested_type</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+ <span class="n">encryption_properties</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">write_batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">dictionary_pagesize_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="o">**</span><span class="n">options</span><span class="p">):</span>
+ <span class="k">if</span> <span class="n">use_deprecated_int96_timestamps</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="c1"># Use int96 timestamps for Spark</span>
+ <span class="k">if</span> <span class="n">flavor</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="s1">'spark'</span> <span class="ow">in</span> <span class="n">flavor</span><span class="p">:</span>
+ <span class="n">use_deprecated_int96_timestamps</span> <span class="o">=</span> <span class="kc">True</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">use_deprecated_int96_timestamps</span> <span class="o">=</span> <span class="kc">False</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">flavor</span> <span class="o">=</span> <span class="n">flavor</span>
+ <span class="k">if</span> <span class="n">flavor</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="n">schema</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">schema_changed</span> <span class="o">=</span> <span class="n">_sanitize_schema</span><span class="p">(</span><span class="n">schema</span><span class="p">,</span> <span class="n">flavor</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">schema_changed</span> <span class="o">=</span> <span class="kc">False</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">schema</span> <span class="o">=</span> <span class="n">schema</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">where</span> <span class="o">=</span> <span class="n">where</span>
+
+ <span class="c1"># If we open a file using a filesystem, store file handle so we can be</span>
+ <span class="c1"># sure to close it when `self.close` is called.</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">file_handle</span> <span class="o">=</span> <span class="kc">None</span>
+
+ <span class="n">filesystem</span><span class="p">,</span> <span class="n">path</span> <span class="o">=</span> <span class="n">_resolve_filesystem_and_path</span><span class="p">(</span>
+ <span class="n">where</span><span class="p">,</span> <span class="n">filesystem</span><span class="p">,</span> <span class="n">allow_legacy_filesystem</span><span class="o">=</span><span class="kc">True</span>
+ <span class="p">)</span>
+ <span class="k">if</span> <span class="n">filesystem</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">filesystem</span><span class="p">,</span> <span class="n">legacyfs</span><span class="o">.</span><span class="n">FileSystem</span><span class="p">):</span>
+ <span class="c1"># legacy filesystem (eg custom subclass)</span>
+ <span class="c1"># TODO deprecate</span>
+ <span class="n">sink</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_handle</span> <span class="o">=</span> <span class="n">filesystem</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="c1"># ARROW-10480: do not auto-detect compression. While</span>
+ <span class="c1"># a filename like foo.parquet.gz is nonconforming, it</span>
+ <span class="c1"># shouldn't implicitly apply compression.</span>
+ <span class="n">sink</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_handle</span> <span class="o">=</span> <span class="n">filesystem</span><span class="o">.</span><span class="n">open_output_stream</span><span class="p">(</span>
+ <span class="n">path</span><span class="p">,</span> <span class="n">compression</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">sink</span> <span class="o">=</span> <span class="n">where</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_metadata_collector</span> <span class="o">=</span> <span class="n">options</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'metadata_collector'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+ <span class="n">engine_version</span> <span class="o">=</span> <span class="s1">'V2'</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">writer</span> <span class="o">=</span> <span class="n">_parquet</span><span class="o">.</span><span class="n">ParquetWriter</span><span class="p">(</span>
+ <span class="n">sink</span><span class="p">,</span> <span class="n">schema</span><span class="p">,</span>
+ <span class="n">version</span><span class="o">=</span><span class="n">version</span><span class="p">,</span>
+ <span class="n">compression</span><span class="o">=</span><span class="n">compression</span><span class="p">,</span>
+ <span class="n">use_dictionary</span><span class="o">=</span><span class="n">use_dictionary</span><span class="p">,</span>
+ <span class="n">write_statistics</span><span class="o">=</span><span class="n">write_statistics</span><span class="p">,</span>
+ <span class="n">use_deprecated_int96_timestamps</span><span class="o">=</span><span class="n">use_deprecated_int96_timestamps</span><span class="p">,</span>
+ <span class="n">compression_level</span><span class="o">=</span><span class="n">compression_level</span><span class="p">,</span>
+ <span class="n">use_byte_stream_split</span><span class="o">=</span><span class="n">use_byte_stream_split</span><span class="p">,</span>
+ <span class="n">column_encoding</span><span class="o">=</span><span class="n">column_encoding</span><span class="p">,</span>
+ <span class="n">writer_engine_version</span><span class="o">=</span><span class="n">engine_version</span><span class="p">,</span>
+ <span class="n">data_page_version</span><span class="o">=</span><span class="n">data_page_version</span><span class="p">,</span>
+ <span class="n">use_compliant_nested_type</span><span class="o">=</span><span class="n">use_compliant_nested_type</span><span class="p">,</span>
+ <span class="n">encryption_properties</span><span class="o">=</span><span class="n">encryption_properties</span><span class="p">,</span>
+ <span class="n">write_batch_size</span><span class="o">=</span><span class="n">write_batch_size</span><span class="p">,</span>
+ <span class="n">dictionary_pagesize_limit</span><span class="o">=</span><span class="n">dictionary_pagesize_limit</span><span class="p">,</span>
+ <span class="o">**</span><span class="n">options</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">is_open</span> <span class="o">=</span> <span class="kc">True</span></div>
+
+ <span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="k">if</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'is_open'</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+ <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="k">return</span> <span class="bp">self</span>
+
+ <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+ <span class="c1"># return false since we want to propagate exceptions</span>
+ <span class="k">return</span> <span class="kc">False</span>
+
+<div class="viewcode-block" id="ParquetWriter.write"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetWriter.html#pyarrow.parquet.ParquetWriter.write">[docs]</a> <span class="k">def</span> <span class="nf">write</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">table_or_batch</span><span class="p">,</span> <span class="n">row_group_size</span><span class="o">=</span><span class="kc">None</span><span class= [...]
+ <span class="sd">"""</span>
+<span class="sd"> Write RecordBatch or Table to the Parquet file.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> table_or_batch : {RecordBatch, Table}</span>
+<span class="sd"> row_group_size : int, default None</span>
+<span class="sd"> Maximum size of each written row group. If None, the</span>
+<span class="sd"> row group size will be the minimum of the input</span>
+<span class="sd"> table or batch length and 64 * 1024 * 1024.</span>
+<span class="sd"> """</span>
+ <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">table_or_batch</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">RecordBatch</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">write_batch</span><span class="p">(</span><span class="n">table_or_batch</span><span class="p">,</span> <span class="n">row_group_size</span><span class="p">)</span>
+ <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">table_or_batch</span><span class="p">,</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">write_table</span><span class="p">(</span><span class="n">table_or_batch</span><span class="p">,</span> <span class="n">row_group_size</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">table_or_batch</span><span class="p">))</span></div>
+
+<div class="viewcode-block" id="ParquetWriter.write_batch"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetWriter.html#pyarrow.parquet.ParquetWriter.write_batch">[docs]</a> <span class="k">def</span> <span class="nf">write_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">row_group_size</span><span class="o">=</span><span class="kc">None</span><sp [...]
+ <span class="sd">"""</span>
+<span class="sd"> Write RecordBatch to the Parquet file.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> batch : RecordBatch</span>
+<span class="sd"> row_group_size : int, default None</span>
+<span class="sd"> Maximum size of each written row group. If None, the</span>
+<span class="sd"> row group size will be the minimum of the RecordBatch</span>
+<span class="sd"> size and 64 * 1024 * 1024.</span>
+<span class="sd"> """</span>
+ <span class="n">table</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">Table</span><span class="o">.</span><span class="n">from_batches</span><span class="p">([</span><span class="n">batch</span><span class="p">],</span> <span class="n">batch</span><span class="o">.</span><span class="n">schema</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">write_table</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">row_group_size</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ParquetWriter.write_table"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetWriter.html#pyarrow.parquet.ParquetWriter.write_table">[docs]</a> <span class="k">def</span> <span class="nf">write_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">table</span><span class="p">,</span> <span class="n">row_group_size</span><span class="o">=</span><span class="kc">None</span><sp [...]
+ <span class="sd">"""</span>
+<span class="sd"> Write Table to the Parquet file.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> table : Table</span>
+<span class="sd"> row_group_size : int, default None</span>
+<span class="sd"> Maximum size of each written row group. If None, the</span>
+<span class="sd"> row group size will be the minimum of the Table size</span>
+<span class="sd"> and 64 * 1024 * 1024.</span>
+
+<span class="sd"> """</span>
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">schema_changed</span><span class="p">:</span>
+ <span class="n">table</span> <span class="o">=</span> <span class="n">_sanitize_table</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">schema</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">flavor</span><span class="p">)</span>
+ <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_open</span>
+
+ <span class="k">if</span> <span class="ow">not</span> <span class="n">table</span><span class="o">.</span><span class="n">schema</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">schema</span><span class="p">,</span> <span class="n">check_metadata</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+ <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'Table schema does not match schema used to create file: '</span>
+ <span class="s1">'</span><span class="se">\n</span><span class="s1">table:</span><span class="se">\n</span><span class="si">{!s}</span><span class="s1"> vs. </span><span class="se">\n</span><span class="s1">file:</span><span class="se">\n</span><span class="si">{!s}</span><span class="s1">'</span>
+ <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">table</span><span class="o">.</span><span class="n">schema</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">schema</span><span class="p">))</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">writer</span><span class="o">.</span><span class="n">write_table</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">row_group_size</span><span class="o">=</span><span class="n">row_group_size</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ParquetWriter.close"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetWriter.html#pyarrow.parquet.ParquetWriter.close">[docs]</a> <span class="k">def</span> <span class="nf">close</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Close the connection to the Parquet file.</span>
+<span class="sd"> """</span>
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_open</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">is_open</span> <span class="o">=</span> <span class="kc">False</span>
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata_collector</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_metadata_collector</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">writer</span><span class="o">.</span><span class="n">metadata</span><span class="p">)</span>
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_handle</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">file_handle</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div></div>
+
+
+<span class="k">def</span> <span class="nf">_get_pandas_index_columns</span><span class="p">(</span><span class="n">keyvalues</span><span class="p">):</span>
+ <span class="k">return</span> <span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">keyvalues</span><span class="p">[</span><span class="sa">b</span><span class="s1">'pandas'</span><span class="p">]</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">'utf8'</span><span class="p">))</span>
+ <span class="p">[</span><span class="s1">'index_columns'</span><span class="p">])</span>
+
+
+<span class="c1"># ----------------------------------------------------------------------</span>
+<span class="c1"># Metadata container providing instructions about reading a single Parquet</span>
+<span class="c1"># file, possibly part of a partitioned dataset</span>
+
+
+<span class="k">class</span> <span class="nc">ParquetDatasetPiece</span><span class="p">:</span>
+ <span class="sd">"""</span>
+<span class="sd"> DEPRECATED: A single chunk of a potentially larger Parquet dataset to read.</span>
+
+<span class="sd"> The arguments will indicate to read either a single row group or all row</span>
+<span class="sd"> groups, and whether to add partition keys to the resulting pyarrow.Table.</span>
+
+<span class="sd"> .. deprecated:: 5.0</span>
+<span class="sd"> Directly constructing a ``ParquetDatasetPiece`` is deprecated, as well</span>
+<span class="sd"> as accessing the pieces of a ``ParquetDataset`` object. Specify</span>
+<span class="sd"> ``use_legacy_dataset=False`` when constructing the ``ParquetDataset``</span>
+<span class="sd"> and use the ``ParquetDataset.fragments`` attribute instead.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> path : str or pathlib.Path</span>
+<span class="sd"> Path to file in the file system where this piece is located.</span>
+<span class="sd"> open_file_func : callable</span>
+<span class="sd"> Function to use for obtaining file handle to dataset piece.</span>
+<span class="sd"> partition_keys : list of tuples</span>
+<span class="sd"> Two-element tuples of ``(column name, ordinal index)``.</span>
+<span class="sd"> row_group : int, default None</span>
+<span class="sd"> Row group to load. By default, reads all row groups.</span>
+<span class="sd"> file_options : dict</span>
+<span class="sd"> Options</span>
+<span class="sd"> """</span>
+
+ <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">open_file_func</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span><span class="nb">open</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'rb'</span><span class="p">),</span>
+ <span class="n">file_options</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">row_group</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partition_keys</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+ <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+ <span class="s2">"ParquetDatasetPiece is deprecated as of pyarrow 5.0.0 and will "</span>
+ <span class="s2">"be removed in a future version."</span><span class="p">,</span>
+ <span class="ne">FutureWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_init</span><span class="p">(</span>
+ <span class="n">path</span><span class="p">,</span> <span class="n">open_file_func</span><span class="p">,</span> <span class="n">file_options</span><span class="p">,</span> <span class="n">row_group</span><span class="p">,</span> <span class="n">partition_keys</span><span class="p">)</span>
+
+ <span class="nd">@staticmethod</span>
+ <span class="k">def</span> <span class="nf">_create</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">open_file_func</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span><span class="nb">open</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'rb'</span><span class="p">),</span>
+ <span class="n">file_options</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">row_group</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partition_keys</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+ <span class="bp">self</span> <span class="o">=</span> <span class="n">ParquetDatasetPiece</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">ParquetDatasetPiece</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_init</span><span class="p">(</span>
+ <span class="n">path</span><span class="p">,</span> <span class="n">open_file_func</span><span class="p">,</span> <span class="n">file_options</span><span class="p">,</span> <span class="n">row_group</span><span class="p">,</span> <span class="n">partition_keys</span><span class="p">)</span>
+ <span class="k">return</span> <span class="bp">self</span>
+
+ <span class="k">def</span> <span class="nf">_init</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">open_file_func</span><span class="p">,</span> <span class="n">file_options</span><span class="p">,</span> <span class="n">row_group</span><span class="p">,</span>
+ <span class="n">partition_keys</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">path</span> <span class="o">=</span> <span class="n">_stringify_path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">open_file_func</span> <span class="o">=</span> <span class="n">open_file_func</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">row_group</span> <span class="o">=</span> <span class="n">row_group</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">partition_keys</span> <span class="o">=</span> <span class="n">partition_keys</span> <span class="ow">or</span> <span class="p">[]</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">file_options</span> <span class="o">=</span> <span class="n">file_options</span> <span class="ow">or</span> <span class="p">{}</span>
+
+ <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">ParquetDatasetPiece</span><span class="p">):</span>
+ <span class="k">return</span> <span class="kc">False</span>
+ <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">path</span> <span class="ow">and</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">row_group</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">row_group</span> <span class="ow">and</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">partition_keys</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">partition_keys</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="k">return</span> <span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1">(</span><span class="si">{!r}</span><span class="s1">, row_group=</span><span class="si">{!r}</span><span class="s1">, partition_keys=</span><span class="si">{!r}</span><span class="s1">)'</span>
+ <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">path</span><span class="p">,</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">row_group</span><span class="p">,</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">partition_keys</span><span class="p">))</span>
+
+ <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="n">result</span> <span class="o">=</span> <span class="s1">''</span>
+
+ <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partition_keys</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
+ <span class="n">partition_str</span> <span class="o">=</span> <span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1">=</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">index</span><span class="p" [...]
+ <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partition_keys</span><span class="p">)</span>
+ <span class="n">result</span> <span class="o">+=</span> <span class="s1">'partition[</span><span class="si">{}</span><span class="s1">] '</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">partition_str</span><span class="p">)</span>
+
+ <span class="n">result</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">path</span>
+
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">row_group</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="n">result</span> <span class="o">+=</span> <span class="s1">' | row_group=</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_group</span><span class="p">)</span>
+
+ <span class="k">return</span> <span class="n">result</span>
+
+ <span class="k">def</span> <span class="nf">get_metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Return the file's metadata.</span>
+
+<span class="sd"> Returns</span>
+<span class="sd"> -------</span>
+<span class="sd"> metadata : FileMetaData</span>
+<span class="sd"> """</span>
+ <span class="n">f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">open</span><span class="p">()</span>
+ <span class="k">return</span> <span class="n">f</span><span class="o">.</span><span class="n">metadata</span>
+
+ <span class="k">def</span> <span class="nf">open</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Return instance of ParquetFile.</span>
+<span class="sd"> """</span>
+ <span class="n">reader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">open_file_func</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path</span><span class="p">)</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reader</span><span class="p">,</span> <span class="n">ParquetFile</span><span class="p">):</span>
+ <span class="n">reader</span> <span class="o">=</span> <span class="n">ParquetFile</span><span class="p">(</span><span class="n">reader</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">file_options</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">reader</span>
+
+ <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_threads</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">partitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_pandas_metadata</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Read this piece as a pyarrow.Table.</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> columns : list of column names, default None</span>
+<span class="sd"> use_threads : bool, default True</span>
+<span class="sd"> Perform multi-threaded column reads.</span>
+<span class="sd"> partitions : ParquetPartitions, default None</span>
+<span class="sd"> file : file-like object</span>
+<span class="sd"> Passed to ParquetFile.</span>
+<span class="sd"> use_pandas_metadata : bool</span>
+<span class="sd"> If pandas metadata should be used or not.</span>
+
+<span class="sd"> Returns</span>
+<span class="sd"> -------</span>
+<span class="sd"> table : pyarrow.Table</span>
+<span class="sd"> """</span>
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">open_file_func</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="n">reader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">open</span><span class="p">()</span>
+ <span class="k">elif</span> <span class="n">file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="n">reader</span> <span class="o">=</span> <span class="n">ParquetFile</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">file_options</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="c1"># try to read the local path</span>
+ <span class="n">reader</span> <span class="o">=</span> <span class="n">ParquetFile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">file_options</span><span class="p">)</span>
+
+ <span class="n">options</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span>
+ <span class="n">use_threads</span><span class="o">=</span><span class="n">use_threads</span><span class="p">,</span>
+ <span class="n">use_pandas_metadata</span><span class="o">=</span><span class="n">use_pandas_metadata</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">row_group</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="n">table</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">read_row_group</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_group</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">table</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="o">**</span><span class="n">options</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partition_keys</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
+ <span class="k">if</span> <span class="n">partitions</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Must pass partition sets'</span><span class="p">)</span>
+
+ <span class="c1"># Here, the index is the categorical code of the partition where</span>
+ <span class="c1"># this piece is located. Suppose we had</span>
+ <span class="c1">#</span>
+ <span class="c1"># /foo=a/0.parq</span>
+ <span class="c1"># /foo=b/0.parq</span>
+ <span class="c1"># /foo=c/0.parq</span>
+ <span class="c1">#</span>
+ <span class="c1"># Then we assign a=0, b=1, c=2. And the resulting Table pieces will</span>
+ <span class="c1"># have a DictionaryArray column named foo having the constant index</span>
+ <span class="c1"># value as indicated. The distinct categories of the partition have</span>
+ <span class="c1"># been computed in the ParquetManifest</span>
+ <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">index</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partition_keys</span><span class="p">):</span>
+ <span class="c1"># The partition code is the same for all values in this piece</span>
+ <span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">table</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'i4'</span><span class="p">)</span>
+
+ <span class="c1"># This is set of all partition values, computed as part of the</span>
+ <span class="c1"># manifest, so ['a', 'b', 'c'] as in our example above.</span>
+ <span class="n">dictionary</span> <span class="o">=</span> <span class="n">partitions</span><span class="o">.</span><span class="n">levels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">dictionary</span>
+
+ <span class="n">arr</span> <span class="o">=</span> <span class="n">pa</span><span class="o">.</span><span class="n">DictionaryArray</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">dictionary</span><span class="p">)</span>
+ <span class="n">table</span> <span class="o">=</span> <span class="n">table</span><span class="o">.</span><span class="n">append_column</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">arr</span><span class="p">)</span>
+
+ <span class="k">return</span> <span class="n">table</span>
+
+
+<span class="k">class</span> <span class="nc">PartitionSet</span><span class="p">:</span>
+ <span class="sd">"""</span>
+<span class="sd"> A data structure for cataloguing the observed Parquet partitions at a</span>
+<span class="sd"> particular level. So if we have</span>
+
+<span class="sd"> /foo=a/bar=0</span>
+<span class="sd"> /foo=a/bar=1</span>
+<span class="sd"> /foo=a/bar=2</span>
+<span class="sd"> /foo=b/bar=0</span>
+<span class="sd"> /foo=b/bar=1</span>
+<span class="sd"> /foo=b/bar=2</span>
+
+<span class="sd"> Then we have two partition sets, one for foo, another for bar. As we visit</span>
+<span class="sd"> levels of the partition hierarchy, a PartitionSet tracks the distinct</span>
+<span class="sd"> values and assigns categorical codes to use when reading the pieces</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> name : str</span>
+<span class="sd"> Name of the partition set. Under which key to collect all values.</span>
+<span class="sd"> keys : list</span>
+<span class="sd"> All possible values that have been collected for that partition set.</span>
+<span class="sd"> """</span>
+
+ <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">keys</span> <span class="o">=</span> <span class="n">keys</span> <span class="ow">or</span> <span class="p">[]</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">key_indices</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span clas [...]
+ <span class="bp">self</span><span class="o">.</span><span class="n">_dictionary</span> <span class="o">=</span> <span class="kc">None</span>
+
+ <span class="k">def</span> <span class="nf">get_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Get the index of the partition value if it is known, otherwise assign</span>
+<span class="sd"> one</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> key : The value for which we want to known the index.</span>
+<span class="sd"> """</span>
+ <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">key_indices</span><span class="p">:</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">key_indices</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">index</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">key_indices</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">key_indices</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">index</span>
+ <span class="k">return</span> <span class="n">index</span>
+
+ <span class="nd">@property</span>
+ <span class="k">def</span> <span class="nf">dictionary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dictionary</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dictionary</span>
+
+ <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'No known partition keys'</span><span class="p">)</span>
+
+ <span class="c1"># Only integer and string partition types are supported right now</span>
+ <span class="k">try</span><span class="p">:</span>
+ <span class="n">integer_keys</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">]</span>
+ <span class="n">dictionary</span> <span class="o">=</span> <span class="n">lib</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">integer_keys</span><span class="p">)</span>
+ <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+ <span class="n">dictionary</span> <span class="o">=</span> <span class="n">lib</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">)</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">_dictionary</span> <span class="o">=</span> <span class="n">dictionary</span>
+ <span class="k">return</span> <span class="n">dictionary</span>
+
+ <span class="nd">@property</span>
+ <span class="k">def</span> <span class="nf">is_sorted</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">)</span> <span class="o">==</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">keys</span><span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">ParquetPartitions</span><span class="p">:</span>
+
+ <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">levels</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">partition_names</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+
+ <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+ <span class="k">def</span> <span class="nf">equals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">ParquetPartitions</span><span class="p">):</span>
+ <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'`other` must be an instance of ParquetPartitions'</span><span class="p">)</span>
+
+ <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">levels</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">levels</span> <span class="ow">and</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">partition_names</span> <span class="o">==</span> <span class="n">other</span><span class="o">.</span><span class="n">partition_names</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+ <span class="k">try</span><span class="p">:</span>
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">equals</span><span class="p">(</span><span class="n">other</span><span class="p">)</span>
+ <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+ <span class="k">return</span> <span class="bp">NotImplemented</span>
+
+ <span class="k">def</span> <span class="nf">get_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
+ <span class="sd">"""</span>
+<span class="sd"> Record a partition value at a particular level, returning the distinct</span>
+<span class="sd"> code for that value at that level.</span>
+
+<span class="sd"> Examples</span>
+<span class="sd"> --------</span>
+
+<span class="sd"> partitions.get_index(1, 'foo', 'a') returns 0</span>
+<span class="sd"> partitions.get_index(1, 'foo', 'b') returns 1</span>
+<span class="sd"> partitions.get_index(1, 'foo', 'c') returns 2</span>
+<span class="sd"> partitions.get_index(1, 'foo', 'a') returns 0</span>
+
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> level : int</span>
+<span class="sd"> The nesting level of the partition we are observing</span>
+<span class="sd"> name : str</span>
+<span class="sd"> The partition name</span>
+<span class="sd"> key : str or int</span>
+<span class="sd"> The partition value</span>
+<span class="sd"> """</span>
+ <span class="k">if</span> <span class="n">level</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="p">):</span>
+ <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partition_names</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1"> was the name of the partition in '</span>
+ <span class="s1">'another level'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">))</span>
+
+ <span class="n">part_set</span> <span class="o">=</span> <span class="n">PartitionSet</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">part_set</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">partition_names</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+
+ <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="p">[</span><span class="n">level</span><span class="p">]</span><span class="o">.</span><span class="n">get_index</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="nf">filter_accepts_partition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">part_key</span><span class="p">,</span> <span class="nb">filter</span><span class="p">,</span> <span class="n">level</span><span class="p">):</span>
+ <span class="n">p_column</span><span class="p">,</span> <span class="n">p_value_index</span> <span class="o">=</span> <span class="n">part_key</span>
+ <span class="n">f_column</span><span class="p">,</span> <span class="n">op</span><span class="p">,</span> <span class="n">f_value</span> <span class="o">=</span> <span class="nb">filter</span>
+ <span class="k">if</span> <span class="n">p_column</span> <span class="o">!=</span> <span class="n">f_column</span><span class="p">:</span>
+ <span class="k">return</span> <span class="kc">True</span>
+
+ <span class="n">f_type</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">f_value</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="n">op</span> <span class="ow">in</span> <span class="p">{</span><span class="s1">'in'</span><span class="p">,</span> <span class="s1">'not in'</span><span class="p">}:</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f_value</span><span class="p">,</span> <span class="n">Collection</span><span class="p">):</span>
+ <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+ <span class="s2">"'</span><span class="si">%s</span><span class="s2">' object is not a collection"</span><span class="p">,</span> <span class="n">f_type</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="n">f_value</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Cannot use empty collection as filter value"</span><span class="p">)</span>
+ <span class="k">if</span> <span class="nb">len</span><span class="p">({</span><span class="nb">type</span><span class="p">(</span><span class="n">item</span><span class="p">)</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">f_value</span><span class="p">})</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"All elements of the collection '</span><span class="si">%s</span><span class="s2">' must be"</span>
+ <span class="s2">" of same type"</span><span class="p">,</span> <span class="n">f_value</span><span class="p">)</span>
+ <span class="n">f_type</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">f_value</span><span class="p">)))</span>
+
+ <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f_value</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f_value</span><span class="p">,</span> <span class="n">Collection</span><span class="p">):</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+ <span class="s2">"Op '</span><span class="si">%s</span><span class="s2">' not supported with a collection value"</span><span class="p">,</span> <span class="n">op</span><span class="p">)</span>
+
+ <span class="n">p_value</span> <span class="o">=</span> <span class="n">f_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">levels</span><span class="p">[</span><span class="n">level</span><span class="p">]</span>
+ <span class="o">.</span><span class="n">dictionary</span><span class="p">[</span><span class="n">p_value_index</span><span class="p">]</span><span class="o">.</span><span class="n">as_py</span><span class="p">())</span>
+
+ <span class="k">if</span> <span class="n">op</span> <span class="o">==</span> <span class="s2">"="</span> <span class="ow">or</span> <span class="n">op</span> <span class="o">==</span> <span class="s2">"=="</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">p_value</span> <span class="o">==</span> <span class="n">f_value</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s2">"!="</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">p_value</span> <span class="o">!=</span> <span class="n">f_value</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'<'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">p_value</span> <span class="o"><</span> <span class="n">f_value</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'>'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">p_value</span> <span class="o">></span> <span class="n">f_value</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'<='</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">p_value</span> <span class="o"><=</span> <span class="n">f_value</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'>='</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">p_value</span> <span class="o">>=</span> <span class="n">f_value</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'in'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">p_value</span> <span class="ow">in</span> <span class="n">f_value</span>
+ <span class="k">elif</span> <span class="n">op</span> <span class="o">==</span> <span class="s1">'not in'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">p_value</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">f_value</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"'</span><span class="si">%s</span><span class="s2">' is not a valid operator in predicates."</span><span class="p">,</span>
+ <span class="nb">filter</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+
+<span class="k">class</span> <span class="nc">ParquetManifest</span><span class="p">:</span>
+
+ <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dirpath</span><span class="p">,</span> <span class="n">open_file_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">filesystem</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">pathsep</span><span class="o">=</span><span class="s1">'/'</span><span class="p">,</span> <span class="n">partition_scheme</span><span class="o">=</span><span class="s1">'hive'</span><span class="p">,</span> <span class="n">metadata_nthreads</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+ <span class="n">filesystem</span><span class="p">,</span> <span class="n">dirpath</span> <span class="o">=</span> <span class="n">_get_filesystem_and_path</span><span class="p">(</span><span class="n">filesystem</span><span class="p">,</span> <span class="n">dirpath</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">filesystem</span> <span class="o">=</span> <span class="n">filesystem</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">open_file_func</span> <span class="o">=</span> <span class="n">open_file_func</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">pathsep</span> <span class="o">=</span> <span class="n">pathsep</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">dirpath</span> <span class="o">=</span> <span class="n">_stringify_path</span><span class="p">(</span><span class="n">dirpath</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">partition_scheme</span> <span class="o">=</span> <span class="n">partition_scheme</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span> <span class="o">=</span> <span class="n">ParquetPartitions</span><span class="p">()</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">pieces</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_metadata_nthreads</span> <span class="o">=</span> <span class="n">metadata_nthreads</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_thread_pool</span> <span class="o">=</span> <span class="n">futures</span><span class="o">.</span><span class="n">ThreadPoolExecutor</span><span class="p">(</span>
+ <span class="n">max_workers</span><span class="o">=</span><span class="n">metadata_nthreads</span><span class="p">)</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">common_metadata_path</span> <span class="o">=</span> <span class="kc">None</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">metadata_path</span> <span class="o">=</span> <span class="kc">None</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">_visit_level</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dirpath</span><span class="p">,</span> <span class="p">[])</span>
+
+ <span class="c1"># Due to concurrency, pieces will potentially by out of order if the</span>
+ <span class="c1"># dataset is partitioned so we sort them to yield stable results</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">pieces</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">piece</span><span class="p">:</span> <span class="n">piece</span><span class="o">.</span><span class="n">path</span><span class="p">)</span>
+
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">common_metadata_path</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="c1"># _common_metadata is a subset of _metadata</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">common_metadata_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_path</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">_thread_pool</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
+
+ <span class="k">def</span> <span class="nf">_visit_level</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="n">base_path</span><span class="p">,</span> <span class="n">part_keys</span><span class="p">):</span>
+ <span class="n">fs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">filesystem</span>
+
+ <span class="n">_</span><span class="p">,</span> <span class="n">directories</span><span class="p">,</span> <span class="n">files</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">fs</span><span class="o">.</span><span class="n">walk</span><span class="p">(</span><span class="n">base_path</span><span class="p">))</span>
+
+ <span class="n">filtered_files</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span>
+ <span class="n">full_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pathsep</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">base_path</span><span class="p">,</span> <span class="n">path</span><span class="p">))</span>
+ <span class="k">if</span> <span class="n">path</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'_common_metadata'</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">common_metadata_path</span> <span class="o">=</span> <span class="n">full_path</span>
+ <span class="k">elif</span> <span class="n">path</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'_metadata'</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">metadata_path</span> <span class="o">=</span> <span class="n">full_path</span>
+ <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_should_silently_exclude</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
+ <span class="k">continue</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">filtered_files</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">full_path</span><span class="p">)</span>
+
+ <span class="c1"># ARROW-1079: Filter out "private" directories starting with underscore</span>
+ <span class="n">filtered_directories</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pathsep</span><span class="o">.</span><span class="n">join</span><span class="p">((</span><span class="n">base_path</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
+ <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">directories</span>
+ <span class="k">if</span> <span class="ow">not</span> <span class="n">_is_private_directory</span><span class="p">(</span><span class="n">x</span><span class="p">)]</span>
+
+ <span class="n">filtered_files</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
+ <span class="n">filtered_directories</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
+
+ <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">filtered_files</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">filtered_directories</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Found files in an intermediate '</span>
+ <span class="s1">'directory: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">base_path</span><span class="p">))</span>
+ <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">filtered_directories</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_visit_directories</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">filtered_directories</span><span class="p">,</span> <span class="n">part_keys</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_push_pieces</span><span class="p">(</span><span class="n">filtered_files</span><span class="p">,</span> <span class="n">part_keys</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="nf">_should_silently_exclude</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_name</span><span class="p">):</span>
+ <span class="k">return</span> <span class="p">(</span><span class="n">file_name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'.crc'</span><span class="p">)</span> <span class="ow">or</span> <span class="c1"># Checksums</span>
+ <span class="n">file_name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'_$folder$'</span><span class="p">)</span> <span class="ow">or</span> <span class="c1"># HDFS directories in S3</span>
+ <span class="n">file_name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'.'</span><span class="p">)</span> <span class="ow">or</span> <span class="c1"># Hidden files starting with .</span>
+ <span class="n">file_name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'_'</span><span class="p">)</span> <span class="ow">or</span> <span class="c1"># Hidden files starting with _</span>
+ <span class="n">file_name</span> <span class="ow">in</span> <span class="n">EXCLUDED_PARQUET_PATHS</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="nf">_visit_directories</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="n">directories</span><span class="p">,</span> <span class="n">part_keys</span><span class="p">):</span>
+ <span class="n">futures_list</span> <span class="o">=</span> <span class="p">[]</span>
+ <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">directories</span><span class="p">:</span>
+ <span class="n">head</span><span class="p">,</span> <span class="n">tail</span> <span class="o">=</span> <span class="n">_path_split</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pathsep</span><span class="p">)</span>
+ <span class="n">name</span><span class="p">,</span> <span class="n">key</span> <span class="o">=</span> <span class="n">_parse_hive_partition</span><span class="p">(</span><span class="n">tail</span><span class="p">)</span>
+
+ <span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span><span class="o">.</span><span class="n">get_index</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">key</span><span class="p">)</span>
+ <span class="n">dir_part_keys</span> <span class="o">=</span> <span class="n">part_keys</span> <span class="o">+</span> <span class="p">[(</span><span class="n">name</span><span class="p">,</span> <span class="n">index</span><span class="p">)]</span>
+ <span class="c1"># If you have less threads than levels, the wait call will block</span>
+ <span class="c1"># indefinitely due to multiple waits within a thread.</span>
+ <span class="k">if</span> <span class="n">level</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metadata_nthreads</span><span class="p">:</span>
+ <span class="n">future</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_thread_pool</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_visit_level</span><span class="p">,</span>
+ <span class="n">level</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
+ <span class="n">path</span><span class="p">,</span>
+ <span class="n">dir_part_keys</span><span class="p">)</span>
+ <span class="n">futures_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">future</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">_visit_level</span><span class="p">(</span><span class="n">level</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">dir_part_keys</span><span class="p">)</span>
+ <span class="k">if</span> <span class="n">futures_list</span><span class="p">:</span>
+ <span class="n">futures</span><span class="o">.</span><span class="n">wait</span><span class="p">(</span><span class="n">futures_list</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="nf">_parse_partition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dirname</span><span class="p">):</span>
+ <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partition_scheme</span> <span class="o">==</span> <span class="s1">'hive'</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">_parse_hive_partition</span><span class="p">(</span><span class="n">dirname</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'partition schema: </span><span class="si">{}</span><span class="s1">'</span>
+ <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partition_scheme</span><span class="p">))</span>
+
+ <span class="k">def</span> <span class="nf">_push_pieces</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">files</span><span class="p">,</span> <span class="n">part_keys</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">pieces</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span>
+ <span class="n">ParquetDatasetPiece</span><span class="o">.</span><span class="n">_create</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">partition_keys</span><span class="o">=</span><span class="n">part_keys</span><span class="p">,</span>
+ <span class="n">open_file_func</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">open_file_func</span><span class="p">)</span>
+ <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">files</span>
+ <span class="p">])</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_hive_partition</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
+ <span class="k">if</span> <span class="s1">'='</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">value</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Directory name did not appear to be a '</span>
+ <span class="s1">'partition: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
+ <span class="k">return</span> <span class="n">value</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'='</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_is_private_directory</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+ <span class="n">_</span><span class="p">,</span> <span class="n">tail</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+ <span class="k">return</span> <span class="p">(</span><span class="n">tail</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'_'</span><span class="p">)</span> <span class="ow">or</span> <span class="n">tail</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'.'</span><span class="p">))</span> <span class="ow">and</span> <span class="s1">'='</span> <span [...]
+
+
+<span class="k">def</span> <span class="nf">_path_split</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">sep</span><span class="p">):</span>
+ <span class="n">i</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="n">sep</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+ <span class="n">head</span><span class="p">,</span> <span class="n">tail</span> <span class="o">=</span> <span class="n">path</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span> <span class="n">path</span><span class="p">[</span><span class="n">i</span><span class="p">:]</span>
+ <span class="n">head</span> <span class="o">=</span> <span class="n">head</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="n">sep</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">head</span><span class="p">,</span> <span class="n">tail</span>
+
+
+<span class="n">EXCLUDED_PARQUET_PATHS</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'_SUCCESS'</span><span class="p">}</span>
+
+
+<span class="k">class</span> <span class="nc">_ParquetDatasetMetadata</span><span class="p">:</span>
+ <span class="vm">__slots__</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'fs'</span><span class="p">,</span> <span class="s1">'memory_map'</span><span class="p">,</span> <span class="s1">'read_dictionary'</span><span class="p">,</span> <span class="s1">'common_metadata'</span><span class="p">,</span>
+ <span class="s1">'buffer_size'</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_open_dataset_file</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">meta</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+ <span class="k">if</span> <span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">fs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span>
+ <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">fs</span><span class="p">,</span> <span class="n">legacyfs</span><span class="o">.</span><span class="n">LocalFileSystem</span><span class="p">)):</span>
+ <span class="n">path</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">fs</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'rb'</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">ParquetFile</span><span class="p">(</span>
+ <span class="n">path</span><span class="p">,</span>
+ <span class="n">metadata</span><span class="o">=</span><span class="n">meta</span><span class="p">,</span>
+ <span class="n">memory_map</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">memory_map</span><span class="p">,</span>
+ <span class="n">read_dictionary</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">read_dictionary</span><span class="p">,</span>
+ <span class="n">common_metadata</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">common_metadata</span><span class="p">,</span>
+ <span class="n">buffer_size</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">buffer_size</span>
+ <span class="p">)</span>
+
+
+<span class="n">_DEPR_MSG</span> <span class="o">=</span> <span class="p">(</span>
+ <span class="s2">"'</span><span class="si">{}</span><span class="s2">' attribute is deprecated as of pyarrow 5.0.0 and will be removed "</span>
+ <span class="s2">"in a future version.</span><span class="si">{}</span><span class="s2">"</span>
+<span class="p">)</span>
+
+
+<span class="n">_read_docstring_common</span> <span class="o">=</span> <span class="s2">"""</span><span class="se">\</span>
+<span class="s2">read_dictionary : list, default None</span>
+<span class="s2"> List of names or column paths (for nested types) to read directly</span>
+<span class="s2"> as DictionaryArray. Only supported for BYTE_ARRAY storage. To read</span>
+<span class="s2"> a flat column as dictionary-encoded pass the column name. For</span>
+<span class="s2"> nested types, you must pass the full column "path", which could be</span>
+<span class="s2"> something like level1.level2.list.item. Refer to the Parquet</span>
+<span class="s2"> file's schema to obtain the paths.</span>
+<span class="s2">memory_map : bool, default False</span>
+<span class="s2"> If the source is a file path, use a memory map to read file, which can</span>
+<span class="s2"> improve performance in some environments.</span>
+<span class="s2">buffer_size : int, default 0</span>
+<span class="s2"> If positive, perform read buffering when deserializing individual</span>
+<span class="s2"> column chunks. Otherwise IO calls are unbuffered.</span>
+<span class="s2">partitioning : pyarrow.dataset.Partitioning or str or list of str, </span><span class="se">\</span>
+<span class="s2">default "hive"</span>
+<span class="s2"> The partitioning scheme for a partitioned dataset. The default of "hive"</span>
+<span class="s2"> assumes directory names with key=value pairs like "/year=2009/month=11".</span>
+<span class="s2"> In addition, a scheme like "/2009/11" is also supported, in which case</span>
+<span class="s2"> you need to specify the field names or a full schema. See the</span>
+<span class="s2"> ``pyarrow.dataset.partitioning()`` function for more details."""</span>
+
+<span class="n">_parquet_dataset_example</span> <span class="o">=</span> <span class="s2">"""</span><span class="se">\</span>
+<span class="s2">Generate an example PyArrow Table and write it to a partitioned dataset:</span>
+
+<span class="s2">>>> import pyarrow as pa</span>
+<span class="s2">>>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],</span>
+<span class="s2">... 'n_legs': [2, 2, 4, 4, 5, 100],</span>
+<span class="s2">... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",</span>
+<span class="s2">... "Brittle stars", "Centipede"]})</span>
+
+<span class="s2">>>> import pyarrow.parquet as pq</span>
+<span class="s2">>>> pq.write_to_dataset(table, root_path='dataset_name',</span>
+<span class="s2">... partition_cols=['year'],</span>
+<span class="s2">... use_legacy_dataset=False)</span>
+
+<span class="s2">create a ParquetDataset object from the dataset source:</span>
+
+<span class="s2">>>> dataset = pq.ParquetDataset('dataset_name/', use_legacy_dataset=False)</span>
+
+<span class="s2">and read the data:</span>
+
+<span class="s2">>>> dataset.read().to_pandas()</span>
+<span class="s2"> n_legs animal year</span>
+<span class="s2">0 5 Brittle stars 2019</span>
+<span class="s2">1 2 Flamingo 2020</span>
+<span class="s2">2 4 Dog 2021</span>
+<span class="s2">3 100 Centipede 2021</span>
+<span class="s2">4 2 Parrot 2022</span>
+<span class="s2">5 4 Horse 2022</span>
+
+<span class="s2">create a ParquetDataset object with filter:</span>
+
+<span class="s2">>>> dataset = pq.ParquetDataset('dataset_name/', use_legacy_dataset=False,</span>
+<span class="s2">... filters=[('n_legs','=',4)])</span>
+<span class="s2">>>> dataset.read().to_pandas()</span>
+<span class="s2"> n_legs animal year</span>
+<span class="s2">0 4 Dog 2021</span>
+<span class="s2">1 4 Horse 2022</span>
+<span class="s2">"""</span>
+
+
+<div class="viewcode-block" id="ParquetDataset"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetDataset.html#pyarrow.parquet.ParquetDataset">[docs]</a><span class="k">class</span> <span class="nc">ParquetDataset</span><span class="p">:</span>
+
+ <span class="vm">__doc__</span> <span class="o">=</span> <span class="s2">"""</span>
+<span class="s2">Encapsulates details of reading a complete Parquet dataset possibly</span>
+<span class="s2">consisting of multiple files and partitions in subdirectories.</span>
+
+<span class="s2">Parameters</span>
+<span class="s2">----------</span>
+<span class="s2">path_or_paths : str or List[str]</span>
+<span class="s2"> A directory name, single file name, or list of file names.</span>
+<span class="s2">filesystem : FileSystem, default None</span>
+<span class="s2"> If nothing passed, will be inferred based on path.</span>
+<span class="s2"> Path will try to be found in the local on-disk filesystem otherwise</span>
+<span class="s2"> it will be parsed as an URI to determine the filesystem.</span>
+<span class="s2">metadata : pyarrow.parquet.FileMetaData</span>
+<span class="s2"> Use metadata obtained elsewhere to validate file schemas.</span>
+<span class="s2">schema : pyarrow.parquet.Schema</span>
+<span class="s2"> Use schema obtained elsewhere to validate file schemas. Alternative to</span>
+<span class="s2"> metadata parameter.</span>
+<span class="s2">split_row_groups : bool, default False</span>
+<span class="s2"> Divide files into pieces for each row group in the file.</span>
+<span class="s2">validate_schema : bool, default True</span>
+<span class="s2"> Check that individual file schemas are all the same / compatible.</span>
+<span class="s2">filters : List[Tuple] or List[List[Tuple]] or None (default)</span>
+<span class="s2"> Rows which do not match the filter predicate will be removed from scanned</span>
+<span class="s2"> data. Partition keys embedded in a nested directory structure will be</span>
+<span class="s2"> exploited to avoid loading files at all if they contain no matching rows.</span>
+<span class="s2"> If `use_legacy_dataset` is True, filters can only reference partition</span>
+<span class="s2"> keys and only a hive-style directory structure is supported. When</span>
+<span class="s2"> setting `use_legacy_dataset` to False, also within-file level filtering</span>
+<span class="s2"> and different partitioning schemes are supported.</span>
+
+<span class="s2"> </span><span class="si">{1}</span><span class="s2"></span>
+<span class="s2">metadata_nthreads : int, default 1</span>
+<span class="s2"> How many threads to allow the thread pool which is used to read the</span>
+<span class="s2"> dataset metadata. Increasing this is helpful to read partitioned</span>
+<span class="s2"> datasets.</span>
+<span class="si">{0}</span><span class="s2"></span>
+<span class="s2">use_legacy_dataset : bool, default True</span>
+<span class="s2"> Set to False to enable the new code path (using the</span>
+<span class="s2"> new Arrow Dataset API). Among other things, this allows to pass</span>
+<span class="s2"> `filters` for all columns and not only the partition keys, enables</span>
+<span class="s2"> different partitioning schemes, etc.</span>
+<span class="s2">pre_buffer : bool, default True</span>
+<span class="s2"> Coalesce and issue file reads in parallel to improve performance on</span>
+<span class="s2"> high-latency filesystems (e.g. S3). If True, Arrow will use a</span>
+<span class="s2"> background I/O thread pool. This option is only supported for</span>
+<span class="s2"> use_legacy_dataset=False. If using a filesystem layer that itself</span>
+<span class="s2"> performs readahead (e.g. fsspec's S3FS), disable readahead for best</span>
+<span class="s2"> results.</span>
+<span class="s2">coerce_int96_timestamp_unit : str, default None.</span>
+<span class="s2"> Cast timestamps that are stored in INT96 format to a particular resolution</span>
+<span class="s2"> (e.g. 'ms'). Setting to None is equivalent to 'ns' and therefore INT96</span>
+<span class="s2"> timestamps will be inferred as timestamps in nanoseconds.</span>
+<span class="s2">thrift_string_size_limit : int, default None</span>
+<span class="s2"> If not None, override the maximum total string size allocated</span>
+<span class="s2"> when decoding Thrift structures. The default limit should be</span>
+<span class="s2"> sufficient for most Parquet files.</span>
+<span class="s2">thrift_container_size_limit : int, default None</span>
+<span class="s2"> If not None, override the maximum total size of containers allocated</span>
+<span class="s2"> when decoding Thrift structures. The default limit should be</span>
+<span class="s2"> sufficient for most Parquet files.</span>
+
+<span class="s2">Examples</span>
+<span class="s2">--------</span>
+<span class="si">{2}</span><span class="s2"></span>
+<span class="s2">"""</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">_read_docstring_common</span><span class="p">,</span> <span class="n">_DNF_filter_doc</span><span class="p">,</span> <span class="n">_parquet_dataset_example</span><span class="p">)</span>
+
+ <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path_or_paths</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">filesystem</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">metadata</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">split_row_groups</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">validate_schema</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+ <span class="n">filters</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metadata_nthreads</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">read_dictionary</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">memory_map</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">buffer_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">partitioning</span><span class="o">=</span><span class="s2">"hive"</span><span class="p">,</span>
+ <span class="n">use_legacy_dataset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pre_buffer</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+ <span class="n">coerce_int96_timestamp_unit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">thrift_string_size_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">thrift_container_size_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+ <span class="k">if</span> <span class="n">use_legacy_dataset</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="c1"># if a new filesystem is passed -> default to new implementation</span>
+ <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">filesystem</span><span class="p">,</span> <span class="n">FileSystem</span><span class="p">):</span>
+ <span class="n">use_legacy_dataset</span> <span class="o">=</span> <span class="kc">False</span>
+ <span class="c1"># otherwise the default is still True</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">use_legacy_dataset</span> <span class="o">=</span> <span class="kc">True</span>
+
+ <span class="k">if</span> <span class="ow">not</span> <span class="n">use_legacy_dataset</span><span class="p">:</span>
+ <span class="k">return</span> <span class="n">_ParquetDatasetV2</span><span class="p">(</span>
+ <span class="n">path_or_paths</span><span class="p">,</span> <span class="n">filesystem</span><span class="o">=</span><span class="n">filesystem</span><span class="p">,</span>
+ <span class="n">filters</span><span class="o">=</span><span class="n">filters</span><span class="p">,</span>
+ <span class="n">partitioning</span><span class="o">=</span><span class="n">partitioning</span><span class="p">,</span>
+ <span class="n">read_dictionary</span><span class="o">=</span><span class="n">read_dictionary</span><span class="p">,</span>
+ <span class="n">memory_map</span><span class="o">=</span><span class="n">memory_map</span><span class="p">,</span>
+ <span class="n">buffer_size</span><span class="o">=</span><span class="n">buffer_size</span><span class="p">,</span>
+ <span class="n">pre_buffer</span><span class="o">=</span><span class="n">pre_buffer</span><span class="p">,</span>
+ <span class="n">coerce_int96_timestamp_unit</span><span class="o">=</span><span class="n">coerce_int96_timestamp_unit</span><span class="p">,</span>
+ <span class="c1"># unsupported keywords</span>
+ <span class="n">schema</span><span class="o">=</span><span class="n">schema</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="n">metadata</span><span class="p">,</span>
+ <span class="n">split_row_groups</span><span class="o">=</span><span class="n">split_row_groups</span><span class="p">,</span>
+ <span class="n">validate_schema</span><span class="o">=</span><span class="n">validate_schema</span><span class="p">,</span>
+ <span class="n">metadata_nthreads</span><span class="o">=</span><span class="n">metadata_nthreads</span><span class="p">,</span>
+ <span class="n">thrift_string_size_limit</span><span class="o">=</span><span class="n">thrift_string_size_limit</span><span class="p">,</span>
+ <span class="n">thrift_container_size_limit</span><span class="o">=</span><span class="n">thrift_container_size_limit</span><span class="p">,</span>
+ <span class="p">)</span>
+ <span class="bp">self</span> <span class="o">=</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
+ <span class="k">return</span> <span class="bp">self</span>
+
+<div class="viewcode-block" id="ParquetDataset.__init__"><a class="viewcode-back" href="../../../python/generated/pyarrow.parquet.ParquetDataset.html#pyarrow.parquet.ParquetDataset.__init__">[docs]</a> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path_or_paths</span><span class="p">,</span> <span class="n">filesystem</span><span class="o">=</span><span class="kc">None</span><span [...]
+ <span class="n">metadata</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">split_row_groups</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">validate_schema</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+ <span class="n">filters</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metadata_nthreads</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">read_dictionary</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">memory_map</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">buffer_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">partitioning</span><span class="o">=</span><span class="s2">"hive"</span><span class="p">,</span>
+ <span class="n">use_legacy_dataset</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">pre_buffer</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+ <span class="n">coerce_int96_timestamp_unit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">thrift_string_size_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+ <span class="n">thrift_container_size_limit</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+ <span class="k">if</span> <span class="n">partitioning</span> <span class="o">!=</span> <span class="s2">"hive"</span><span class="p">:</span>
+ <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+ <span class="s1">'Only "hive" for hive-like partitioning is supported when '</span>
+ <span class="s1">'using use_legacy_dataset=True'</span><span class="p">)</span>
+ <span class="k">if</span> <span class="n">metadata_nthreads</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+ <span class="s2">"Specifying the 'metadata_nthreads' argument is deprecated as "</span>
+ <span class="s2">"of pyarrow 8.0.0, and the argument will be removed in a "</span>
+ <span class="s2">"future version"</span><span class="p">,</span>
+ <span class="ne">FutureWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+ <span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="n">metadata_nthreads</span> <span class="o">=</span> <span class="mi">1</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">_ds_metadata</span> <span class="o">=</span> <span class="n">_ParquetDatasetMetadata</span><span class="p">()</span>
+ <span class="n">a_path</span> <span class="o">=</span> <span class="n">path_or_paths</span>
+ <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a_path</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+ <span class="n">a_path</span> <span class="o">=</span> <span class="n">a_path</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+ <span class="bp">self</span><span class="o">.</span><span class="n">_ds_metadata</span><span class="o">.</span><span class="n">fs</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_get_filesystem_and_path</span><span class="p">(</span><span class="n">filesystem</span><span class="p">,</span> <span class="n">a_path</span><span class="p">)</span>
+ <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">path_or_paths</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">_parse_uri</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">path_or_paths</span><span class="p">]</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">paths</span> <span class="o">=</span> <span class="n">_parse_uri</span><span class="p">(</span><span class="n">path_or_paths</span><span class="p">)</span>
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