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<h1>Source code for pyspark.pandas.config</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c1"># contributor license agreements. See the NOTICE file distributed with</span>
<span class="c1"># this work for additional information regarding copyright ownership.</span>
<span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c1"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c1"># 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, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Infrastructure of options for pandas-on-Spark.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">contextlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">contextmanager</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">json</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Iterator</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark._globals</span><span class="w"> </span><span class="kn">import</span> <span class="n">_NoValue</span><span class="p">,</span> <span class="n">_NoValueType</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.pandas.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">default_session</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;get_option&quot;</span><span class="p">,</span> <span class="s2">&quot;set_option&quot;</span><span class="p">,</span> <span class="s2">&quot;reset_option&quot;</span><span class="p">,</span> <span class="s2">&quot;options&quot;</span><span class="p">,</span> <span class="s2">&quot;option_context&quot;</span><span class="p">]</span>
<span class="k">class</span><span class="w"> </span><span class="nc">Option</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Option class that defines an option with related properties.</span>
<span class="sd"> This class holds all information relevant to the one option. Also,</span>
<span class="sd"> Its instance can validate if the given value is acceptable or not.</span>
<span class="sd"> It is currently for internal usage only.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> key: str, keyword-only argument</span>
<span class="sd"> the option name to use.</span>
<span class="sd"> doc: str, keyword-only argument</span>
<span class="sd"> the documentation for the current option.</span>
<span class="sd"> default: Any, keyword-only argument</span>
<span class="sd"> default value for this option.</span>
<span class="sd"> types: Union[Tuple[type, ...], type], keyword-only argument</span>
<span class="sd"> default is str. It defines the expected types for this option. It is</span>
<span class="sd"> used with `isinstance` to validate the given value to this option.</span>
<span class="sd"> check_func: Tuple[Callable[[Any], bool], str], keyword-only argument</span>
<span class="sd"> default is a function that always returns `True` with an empty string.</span>
<span class="sd"> It defines:</span>
<span class="sd"> - a function to check the given value to this option</span>
<span class="sd"> - the error message to show when this check is failed</span>
<span class="sd"> When new value is set to this option, this function is called to check</span>
<span class="sd"> if the given value is valid.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; option = Option(</span>
<span class="sd"> ... key=&#39;option.name&#39;,</span>
<span class="sd"> ... doc=&quot;this is a test option&quot;,</span>
<span class="sd"> ... default=&quot;default&quot;,</span>
<span class="sd"> ... types=(float, int),</span>
<span class="sd"> ... check_func=(lambda v: v &gt; 0, &quot;should be a positive float&quot;))</span>
<span class="sd"> &gt;&gt;&gt; option.validate(&#39;abc&#39;) # doctest: +NORMALIZE_WHITESPACE</span>
<span class="sd"> Traceback (most recent call last):</span>
<span class="sd"> ...</span>
<span class="sd"> TypeError: The value for option &#39;option.name&#39; was &lt;class &#39;str&#39;&gt;;</span>
<span class="sd"> however, expected types are [(&lt;class &#39;float&#39;&gt;, &lt;class &#39;int&#39;&gt;)].</span>
<span class="sd"> &gt;&gt;&gt; option.validate(-1.1)</span>
<span class="sd"> Traceback (most recent call last):</span>
<span class="sd"> ...</span>
<span class="sd"> ValueError: should be a positive float</span>
<span class="sd"> &gt;&gt;&gt; option.validate(1.1)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">doc</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">default</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
<span class="n">types</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">type</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">type</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">check_func</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="n">Any</span><span class="p">],</span> <span class="nb">bool</span><span class="p">],</span> <span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">),</span>
<span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">key</span> <span class="o">=</span> <span class="n">key</span>
<span class="bp">self</span><span class="o">.</span><span class="n">doc</span> <span class="o">=</span> <span class="n">doc</span>
<span class="bp">self</span><span class="o">.</span><span class="n">default</span> <span class="o">=</span> <span class="n">default</span>
<span class="bp">self</span><span class="o">.</span><span class="n">types</span> <span class="o">=</span> <span class="n">types</span>
<span class="bp">self</span><span class="o">.</span><span class="n">check_func</span> <span class="o">=</span> <span class="n">check_func</span>
<span class="k">def</span><span class="w"> </span><span class="nf">validate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Validate the given value and throw an exception with related information such as key.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">types</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
<span class="s2">&quot;The value for option &#39;</span><span class="si">%s</span><span class="s2">&#39; was </span><span class="si">%s</span><span class="s2">; however, expected types are &quot;</span>
<span class="s2">&quot;[</span><span class="si">%s</span><span class="s2">].&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">key</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">v</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">types</span><span class="p">))</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_func</span><span class="p">[</span><span class="mi">0</span><span class="p">](</span><span class="n">v</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">check_func</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="c1"># Available options.</span>
<span class="c1">#</span>
<span class="c1"># NOTE: if you are fixing or adding an option here, make sure you execute `show_options()` and</span>
<span class="c1"># copy &amp; paste the results into show_options</span>
<span class="c1"># &#39;python/docs/source/tutorial/pandas_on_spark/options.rst&#39; as well.</span>
<span class="c1"># See the examples below:</span>
<span class="c1"># &gt;&gt;&gt; from pyspark.pandas.config import show_options</span>
<span class="c1"># &gt;&gt;&gt; show_options()</span>
<span class="n">_options</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Option</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;display.max_rows&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;This sets the maximum number of rows pandas-on-Spark should output when printing out &quot;</span>
<span class="s2">&quot;various output. For example, this value determines the number of rows to be &quot;</span>
<span class="s2">&quot;shown at the repr() in a dataframe. Set `None` to unlimit the input length. &quot;</span>
<span class="s2">&quot;Default is 1000.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)),</span>
<span class="n">check_func</span><span class="o">=</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">v</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">v</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">,</span>
<span class="s2">&quot;&#39;display.max_rows&#39; should be greater than or equal to 0.&quot;</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;compute.max_rows&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;&#39;compute.max_rows&#39; sets the limit of the current pandas-on-Spark DataFrame. &quot;</span>
<span class="s2">&quot;Set `None` to unlimit the input length. When the limit is set, it is executed &quot;</span>
<span class="s2">&quot;by the shortcut by collecting the data into the driver, and then using the pandas &quot;</span>
<span class="s2">&quot;API. If the limit is unset, the operation is executed by PySpark. Default is 1000.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)),</span>
<span class="n">check_func</span><span class="o">=</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">v</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">v</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">,</span>
<span class="s2">&quot;&#39;compute.max_rows&#39; should be greater than or equal to 0.&quot;</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;compute.shortcut_limit&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;&#39;compute.shortcut_limit&#39; sets the limit for a shortcut. &quot;</span>
<span class="s2">&quot;It computes the specified number of rows and uses its schema. When the dataframe &quot;</span>
<span class="s2">&quot;length is larger than this limit, pandas-on-Spark uses PySpark to compute.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
<span class="n">check_func</span><span class="o">=</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">v</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">,</span>
<span class="s2">&quot;&#39;compute.shortcut_limit&#39; should be greater than or equal to 0.&quot;</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;compute.ops_on_diff_frames&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;This determines whether or not to operate between two different dataframes. &quot;</span>
<span class="s2">&quot;For example, &#39;combine_frames&#39; function internally performs a join operation which &quot;</span>
<span class="s2">&quot;can be expensive in general. So, if `compute.ops_on_diff_frames` variable is not &quot;</span>
<span class="s2">&quot;True, that method throws an exception.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">bool</span><span class="p">,</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;compute.default_index_type&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;This sets the default index type: sequence, distributed and distributed-sequence.&quot;</span><span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="s2">&quot;distributed-sequence&quot;</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
<span class="n">check_func</span><span class="o">=</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">v</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;sequence&quot;</span><span class="p">,</span> <span class="s2">&quot;distributed&quot;</span><span class="p">,</span> <span class="s2">&quot;distributed-sequence&quot;</span><span class="p">),</span>
<span class="s2">&quot;Index type should be one of &#39;sequence&#39;, &#39;distributed&#39;, &#39;distributed-sequence&#39;.&quot;</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;compute.default_index_cache&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;This sets the default storage level for temporary RDDs cached in &quot;</span>
<span class="s2">&quot;distributed-sequence indexing: &#39;NONE&#39;, &#39;DISK_ONLY&#39;, &#39;DISK_ONLY_2&#39;, &quot;</span>
<span class="s2">&quot;&#39;DISK_ONLY_3&#39;, &#39;MEMORY_ONLY&#39;, &#39;MEMORY_ONLY_2&#39;, &#39;MEMORY_ONLY_SER&#39;, &quot;</span>
<span class="s2">&quot;&#39;MEMORY_ONLY_SER_2&#39;, &#39;MEMORY_AND_DISK&#39;, &#39;MEMORY_AND_DISK_2&#39;, &quot;</span>
<span class="s2">&quot;&#39;MEMORY_AND_DISK_SER&#39;, &#39;MEMORY_AND_DISK_SER_2&#39;, &#39;OFF_HEAP&#39;, &quot;</span>
<span class="s2">&quot;&#39;LOCAL_CHECKPOINT&#39;.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="s2">&quot;MEMORY_AND_DISK_SER&quot;</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
<span class="n">check_func</span><span class="o">=</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">v</span>
<span class="ow">in</span> <span class="p">(</span>
<span class="s2">&quot;NONE&quot;</span><span class="p">,</span>
<span class="s2">&quot;DISK_ONLY&quot;</span><span class="p">,</span>
<span class="s2">&quot;DISK_ONLY_2&quot;</span><span class="p">,</span>
<span class="s2">&quot;DISK_ONLY_3&quot;</span><span class="p">,</span>
<span class="s2">&quot;MEMORY_ONLY&quot;</span><span class="p">,</span>
<span class="s2">&quot;MEMORY_ONLY_2&quot;</span><span class="p">,</span>
<span class="s2">&quot;MEMORY_ONLY_SER&quot;</span><span class="p">,</span>
<span class="s2">&quot;MEMORY_ONLY_SER_2&quot;</span><span class="p">,</span>
<span class="s2">&quot;MEMORY_AND_DISK&quot;</span><span class="p">,</span>
<span class="s2">&quot;MEMORY_AND_DISK_2&quot;</span><span class="p">,</span>
<span class="s2">&quot;MEMORY_AND_DISK_SER&quot;</span><span class="p">,</span>
<span class="s2">&quot;MEMORY_AND_DISK_SER_2&quot;</span><span class="p">,</span>
<span class="s2">&quot;OFF_HEAP&quot;</span><span class="p">,</span>
<span class="s2">&quot;LOCAL_CHECKPOINT&quot;</span><span class="p">,</span>
<span class="p">),</span>
<span class="s2">&quot;Index type should be one of &#39;NONE&#39;, &#39;DISK_ONLY&#39;, &#39;DISK_ONLY_2&#39;, &quot;</span>
<span class="s2">&quot;&#39;DISK_ONLY_3&#39;, &#39;MEMORY_ONLY&#39;, &#39;MEMORY_ONLY_2&#39;, &#39;MEMORY_ONLY_SER&#39;, &quot;</span>
<span class="s2">&quot;&#39;MEMORY_ONLY_SER_2&#39;, &#39;MEMORY_AND_DISK&#39;, &#39;MEMORY_AND_DISK_2&#39;, &quot;</span>
<span class="s2">&quot;&#39;MEMORY_AND_DISK_SER&#39;, &#39;MEMORY_AND_DISK_SER_2&#39;, &#39;OFF_HEAP&#39;, &quot;</span>
<span class="s2">&quot;&#39;LOCAL_CHECKPOINT&#39;.&quot;</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;compute.ordered_head&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;&#39;compute.ordered_head&#39; sets whether or not to operate head with natural ordering. &quot;</span>
<span class="s2">&quot;pandas-on-Spark does not guarantee the row ordering so `head` could return some &quot;</span>
<span class="s2">&quot;rows from distributed partitions. If &#39;compute.ordered_head&#39; is set to True, &quot;</span>
<span class="s2">&quot;pandas-on-Spark performs natural ordering beforehand, but it will cause a &quot;</span>
<span class="s2">&quot;performance overhead.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">bool</span><span class="p">,</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;compute.eager_check&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;&#39;compute.eager_check&#39; sets whether or not to launch some Spark jobs just for the sake &quot;</span>
<span class="s2">&quot;of validation. If &#39;compute.eager_check&#39; is set to True, pandas-on-Spark performs the &quot;</span>
<span class="s2">&quot;validation beforehand, but it will cause a performance overhead. Otherwise, &quot;</span>
<span class="s2">&quot;pandas-on-Spark skip the validation and will be slightly different from pandas. &quot;</span>
<span class="s2">&quot;Affected APIs: `Series.dot`, `Series.asof`, `Series.compare`, &quot;</span>
<span class="s2">&quot;`FractionalExtensionOps.astype`, `IntegralExtensionOps.astype`, &quot;</span>
<span class="s2">&quot;`FractionalOps.astype`, `DecimalOps.astype`, `skipna of statistical functions`.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">bool</span><span class="p">,</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;compute.isin_limit&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;&#39;compute.isin_limit&#39; sets the limit for filtering by &#39;Column.isin(list)&#39;. &quot;</span>
<span class="s2">&quot;If the length of the ‘list’ is above the limit, broadcast join is used instead &quot;</span>
<span class="s2">&quot;for better performance.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
<span class="n">check_func</span><span class="o">=</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">v</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">,</span>
<span class="s2">&quot;&#39;compute.isin_limit&#39; should be greater than or equal to 0.&quot;</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;compute.pandas_fallback&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;&#39;compute.pandas_fallback&#39; sets whether or not to fallback automatically &quot;</span>
<span class="s2">&quot;to Pandas&#39; implementation.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">bool</span><span class="p">,</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;compute.fail_on_ansi_mode&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;&#39;compute.fail_on_ansi_mode&#39; sets whether or not work with ANSI mode. &quot;</span>
<span class="s2">&quot;If True, pandas API on Spark raises an exception if the underlying Spark is &quot;</span>
<span class="s2">&quot;working with ANSI mode enabled; otherwise, it forces to work although it can &quot;</span>
<span class="s2">&quot;cause unexpected behavior.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">bool</span><span class="p">,</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;plotting.max_rows&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;&#39;plotting.max_rows&#39; sets the visual limit on top-n-based plots such as `plot.bar` &quot;</span>
<span class="s2">&quot;and `plot.pie`. If it is set to 1000, the first 1000 data points will be used &quot;</span>
<span class="s2">&quot;for plotting. Default is 1000.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
<span class="n">check_func</span><span class="o">=</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">v</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">,</span>
<span class="s2">&quot;&#39;plotting.max_rows&#39; should be greater than or equal to 0.&quot;</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;plotting.sample_ratio&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;&#39;plotting.sample_ratio&#39; sets the proportion of data that will be plotted for sample-&quot;</span>
<span class="s2">&quot;based plots such as `plot.line` and `plot.area`. &quot;</span>
<span class="s2">&quot;If not set, it is derived from &#39;plotting.max_rows&#39;, by calculating the ratio of &quot;</span>
<span class="s2">&quot;&#39;plotting.max_rows&#39; to the total data size.&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)),</span>
<span class="n">check_func</span><span class="o">=</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">v</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="mi">1</span> <span class="o">&gt;=</span> <span class="n">v</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">,</span>
<span class="s2">&quot;&#39;plotting.sample_ratio&#39; should be 1.0 &gt;= value &gt;= 0.0.&quot;</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">),</span>
<span class="n">Option</span><span class="p">(</span>
<span class="n">key</span><span class="o">=</span><span class="s2">&quot;plotting.backend&quot;</span><span class="p">,</span>
<span class="n">doc</span><span class="o">=</span><span class="p">(</span>
<span class="s2">&quot;Backend to use for plotting. Default is plotly. &quot;</span>
<span class="s2">&quot;Supports any package that has a top-level `.plot` method. &quot;</span>
<span class="s2">&quot;Known options are: [matplotlib, plotly].&quot;</span>
<span class="p">),</span>
<span class="n">default</span><span class="o">=</span><span class="s2">&quot;plotly&quot;</span><span class="p">,</span>
<span class="n">types</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">]</span>
<span class="n">_options_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Option</span><span class="p">]</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">((</span><span class="n">option</span><span class="o">.</span><span class="n">key</span> <span class="k">for</span> <span class="n">option</span> <span class="ow">in</span> <span class="n">_options</span><span class="p">),</span> <span class="n">_options</span><span class="p">))</span>
<span class="n">_key_format</span> <span class="o">=</span> <span class="s2">&quot;pandas_on_Spark.</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span>
<span class="k">class</span><span class="w"> </span><span class="nc">OptionError</span><span class="p">(</span><span class="ne">AttributeError</span><span class="p">,</span> <span class="ne">KeyError</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span><span class="w"> </span><span class="nf">show_options</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Make a pretty table that can be copied and pasted into public documentation.</span>
<span class="sd"> This is currently for an internal purpose.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; show_options() # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE</span>
<span class="sd"> ================... =======... =====================...</span>
<span class="sd"> Option Default Description</span>
<span class="sd"> ================... =======... =====================...</span>
<span class="sd"> display.max_rows 1000 This sets the maximum...</span>
<span class="sd"> ...</span>
<span class="sd"> ================... =======... =====================...</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">textwrap</span>
<span class="n">header</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Option&quot;</span><span class="p">,</span> <span class="s2">&quot;Default&quot;</span><span class="p">,</span> <span class="s2">&quot;Description&quot;</span><span class="p">]</span>
<span class="n">row_format</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{:&lt;31}</span><span class="s2"> </span><span class="si">{:&lt;23}</span><span class="s2"> </span><span class="si">{:&lt;53}</span><span class="s2">&quot;</span>
<span class="nb">print</span><span class="p">(</span><span class="n">row_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="mi">31</span><span class="p">,</span> <span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="mi">23</span><span class="p">,</span> <span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="mi">53</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">row_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">header</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">row_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="mi">31</span><span class="p">,</span> <span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="mi">23</span><span class="p">,</span> <span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="mi">53</span><span class="p">))</span>
<span class="k">for</span> <span class="n">option</span> <span class="ow">in</span> <span class="n">_options</span><span class="p">:</span>
<span class="n">doc</span> <span class="o">=</span> <span class="n">textwrap</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">option</span><span class="o">.</span><span class="n">doc</span><span class="p">,</span> <span class="mi">53</span><span class="p">)</span>
<span class="n">formatted</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">line</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="p">(</span><span class="s2">&quot; &quot;</span> <span class="o">*</span> <span class="mi">56</span><span class="p">)</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">doc</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)])</span><span class="o">.</span><span class="n">rstrip</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">row_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">option</span><span class="o">.</span><span class="n">key</span><span class="p">,</span> <span class="nb">repr</span><span class="p">(</span><span class="n">option</span><span class="o">.</span><span class="n">default</span><span class="p">),</span> <span class="n">formatted</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">row_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="mi">31</span><span class="p">,</span> <span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="mi">23</span><span class="p">,</span> <span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="mi">53</span><span class="p">))</span>
<div class="viewcode-block" id="get_option"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.get_option.html#pyspark.pandas.get_option">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">get_option</span><span class="p">(</span><span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">default</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">_NoValueType</span><span class="p">]</span> <span class="o">=</span> <span class="n">_NoValue</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Retrieves the value of the specified option.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> key : str</span>
<span class="sd"> The key which should match a single option.</span>
<span class="sd"> default : object</span>
<span class="sd"> The default value if the option is not set yet. The value should be JSON serializable.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> result : the value of the option</span>
<span class="sd"> Raises</span>
<span class="sd"> ------</span>
<span class="sd"> OptionError : if no such option exists and the default is not provided</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">_check_option</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
<span class="k">if</span> <span class="n">default</span> <span class="ow">is</span> <span class="n">_NoValue</span><span class="p">:</span>
<span class="n">default</span> <span class="o">=</span> <span class="n">_options_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">default</span>
<span class="n">_options_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">validate</span><span class="p">(</span><span class="n">default</span><span class="p">)</span>
<span class="n">spark_session</span> <span class="o">=</span> <span class="n">default_session</span><span class="p">(</span><span class="n">check_ansi_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">return</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">spark_session</span><span class="o">.</span><span class="n">conf</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">_key_format</span><span class="p">(</span><span class="n">key</span><span class="p">),</span> <span class="n">default</span><span class="o">=</span><span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">default</span><span class="p">)))</span></div>
<div class="viewcode-block" id="set_option"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.set_option.html#pyspark.pandas.set_option">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">set_option</span><span class="p">(</span><span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of the specified option.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> key : str</span>
<span class="sd"> The key which should match a single option.</span>
<span class="sd"> value : object</span>
<span class="sd"> New value of option. The value should be JSON serializable.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> None</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">_check_option</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
<span class="n">_options_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">validate</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="n">spark_session</span> <span class="o">=</span> <span class="n">default_session</span><span class="p">(</span><span class="n">check_ansi_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">spark_session</span><span class="o">.</span><span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">_key_format</span><span class="p">(</span><span class="n">key</span><span class="p">),</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">value</span><span class="p">))</span></div>
<div class="viewcode-block" id="reset_option"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.reset_option.html#pyspark.pandas.reset_option">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">reset_option</span><span class="p">(</span><span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reset one option to their default value.</span>
<span class="sd"> Pass &quot;all&quot; as an argument to reset all options.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> key : str</span>
<span class="sd"> If specified only option will be reset.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> None</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">_check_option</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
<span class="n">default_session</span><span class="p">(</span><span class="n">check_ansi_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">conf</span><span class="o">.</span><span class="n">unset</span><span class="p">(</span><span class="n">_key_format</span><span class="p">(</span><span class="n">key</span><span class="p">))</span></div>
<div class="viewcode-block" id="option_context"><a class="viewcode-back" href="../../../reference/pyspark.pandas/api/pyspark.pandas.option_context.html#pyspark.pandas.option_context">[docs]</a><span class="nd">@contextmanager</span>
<span class="k">def</span><span class="w"> </span><span class="nf">option_context</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterator</span><span class="p">[</span><span class="kc">None</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Context manager to temporarily set options in the `with` statement context.</span>
<span class="sd"> You need to invoke ``option_context(pat, val, [(pat, val), ...])``.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; with option_context(&#39;display.max_rows&#39;, 10, &#39;compute.max_rows&#39;, 5):</span>
<span class="sd"> ... print(get_option(&#39;display.max_rows&#39;), get_option(&#39;compute.max_rows&#39;))</span>
<span class="sd"> 10 5</span>
<span class="sd"> &gt;&gt;&gt; print(get_option(&#39;display.max_rows&#39;), get_option(&#39;compute.max_rows&#39;))</span>
<span class="sd"> 1000 1000</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">%</span> <span class="mi">2</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="s2">&quot;Need to invoke as option_context(pat, val, [(pat, val), ...]).&quot;</span><span class="p">)</span>
<span class="n">opts</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">args</span><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">]))</span>
<span class="n">orig_opts</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">get_option</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">opts</span><span class="p">}</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">opts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">set_option</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="k">yield</span>
<span class="k">finally</span><span class="p">:</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">orig_opts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">set_option</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span></div>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_option</span><span class="p">(</span><span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">_options_dict</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">OptionError</span><span class="p">(</span>
<span class="s2">&quot;No such option: &#39;</span><span class="si">{}</span><span class="s2">&#39;. Available options are [</span><span class="si">{}</span><span class="s2">]&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">key</span><span class="p">,</span> <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">_options_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">class</span><span class="w"> </span><span class="nc">DictWrapper</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;provide attribute-style access to a nested dict&quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Option</span><span class="p">],</span> <span class="n">prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="p">):</span>
<span class="nb">object</span><span class="o">.</span><span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
<span class="nb">object</span><span class="o">.</span><span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;prefix&quot;</span><span class="p">,</span> <span class="n">prefix</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__setattr__</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="nb">str</span><span class="p">,</span> <span class="n">val</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">prefix</span> <span class="o">=</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__getattribute__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;prefix&quot;</span><span class="p">)</span>
<span class="n">d</span> <span class="o">=</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__getattribute__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">prefix</span><span class="p">:</span>
<span class="n">prefix</span> <span class="o">+=</span> <span class="s2">&quot;.&quot;</span>
<span class="n">canonical_key</span> <span class="o">=</span> <span class="n">prefix</span> <span class="o">+</span> <span class="n">key</span>
<span class="n">candidates</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span> <span class="ow">in</span> <span class="n">k</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">canonical_key</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">))</span>
<span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">candidates</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">canonical_key</span><span class="p">:</span>
<span class="n">set_option</span><span class="p">(</span><span class="n">canonical_key</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="n">OptionError</span><span class="p">(</span>
<span class="s2">&quot;No such option: &#39;</span><span class="si">{}</span><span class="s2">&#39;. Available options are [</span><span class="si">{}</span><span class="s2">]&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">key</span><span class="p">,</span> <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">_options_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__getattr__</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="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="s2">&quot;DictWrapper&quot;</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
<span class="n">prefix</span> <span class="o">=</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__getattribute__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;prefix&quot;</span><span class="p">)</span>
<span class="n">d</span> <span class="o">=</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__getattribute__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">prefix</span><span class="p">:</span>
<span class="n">prefix</span> <span class="o">+=</span> <span class="s2">&quot;.&quot;</span>
<span class="n">canonical_key</span> <span class="o">=</span> <span class="n">prefix</span> <span class="o">+</span> <span class="n">key</span>
<span class="n">candidates</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span> <span class="ow">in</span> <span class="n">k</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">canonical_key</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">))</span>
<span class="p">]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">candidates</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">canonical_key</span><span class="p">:</span>
<span class="k">return</span> <span class="n">get_option</span><span class="p">(</span><span class="n">canonical_key</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">candidates</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="n">OptionError</span><span class="p">(</span>
<span class="s2">&quot;No such option: &#39;</span><span class="si">{}</span><span class="s2">&#39;. Available options are [</span><span class="si">{}</span><span class="s2">]&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">key</span><span class="p">,</span> <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">_options_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">DictWrapper</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">canonical_key</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__dir__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
<span class="n">prefix</span> <span class="o">=</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__getattribute__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;prefix&quot;</span><span class="p">)</span>
<span class="n">d</span> <span class="o">=</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__getattribute__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">prefix</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
<span class="n">candidates</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
<span class="n">offset</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">candidates</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span> <span class="ow">in</span> <span class="n">k</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">prefix</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">))]</span>
<span class="n">offset</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">prefix</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> <span class="c1"># prefix (e.g. &quot;compute.&quot;) to trim.</span>
<span class="k">return</span> <span class="p">[</span><span class="n">c</span><span class="p">[</span><span class="n">offset</span><span class="p">:]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">candidates</span><span class="p">]</span>
<span class="n">options</span> <span class="o">=</span> <span class="n">DictWrapper</span><span class="p">(</span><span class="n">_options_dict</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_test</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">doctest</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">sys</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql</span><span class="w"> </span><span class="kn">import</span> <span class="n">SparkSession</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pyspark.pandas.config</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;SPARK_HOME&quot;</span><span class="p">])</span>
<span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">pandas</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;ps&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">pandas</span>
<span class="n">spark</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">&quot;local[4]&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">&quot;pyspark.pandas.config tests&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
<span class="p">)</span>
<span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span>
<span class="n">pyspark</span><span class="o">.</span><span class="n">pandas</span><span class="o">.</span><span class="n">config</span><span class="p">,</span>
<span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span>
<span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span> <span class="o">|</span> <span class="n">doctest</span><span class="o">.</span><span class="n">NORMALIZE_WHITESPACE</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">spark</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
<span class="n">_test</span><span class="p">()</span>
</pre></div>
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