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<h1>Source code for pyspark.sql.readwriter</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="kn">import</span> <span class="nn">sys</span>
<span class="kn">from</span> <span class="nn">py4j.java_gateway</span> <span class="kn">import</span> <span class="n">JavaClass</span>
<span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">RDD</span><span class="p">,</span> <span class="n">since</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.column</span> <span class="kn">import</span> <span class="n">_to_seq</span><span class="p">,</span> <span class="n">_to_java_column</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.types</span> <span class="kn">import</span> <span class="n">StructType</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">utils</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.utils</span> <span class="kn">import</span> <span class="n">to_str</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;DataFrameReader&quot;</span><span class="p">,</span> <span class="s2">&quot;DataFrameWriter&quot;</span><span class="p">]</span>
<span class="k">class</span> <span class="nc">OptionUtils</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_set_opts</span><span class="p">(</span><span class="bp">self</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="o">**</span><span class="n">options</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Set named options (filter out those the value is None)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">schema</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">schema</span><span class="p">(</span><span class="n">schema</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">options</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="n">v</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">option</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">DataFrameReader</span><span class="p">(</span><span class="n">OptionUtils</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Interface used to load a :class:`DataFrame` from external storage systems</span>
<span class="sd"> (e.g. file systems, key-value stores, etc). Use :attr:`SparkSession.read`</span>
<span class="sd"> to access this.</span>
<span class="sd"> .. versionadded:: 1.4</span>
<span class="sd"> &quot;&quot;&quot;</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">spark</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">_ssql_ctx</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_spark</span> <span class="o">=</span> <span class="n">spark</span>
<span class="k">def</span> <span class="nf">_df</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">jdf</span><span class="p">):</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.dataframe</span> <span class="kn">import</span> <span class="n">DataFrame</span>
<span class="k">return</span> <span class="n">DataFrame</span><span class="p">(</span><span class="n">jdf</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="p">)</span>
<div class="viewcode-block" id="DataFrameReader.format"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.format.html#pyspark.sql.DataFrameReader.format">[docs]</a> <span class="k">def</span> <span class="nf">format</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="sd">&quot;&quot;&quot;Specifies the input data source format.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> source : str</span>
<span class="sd"> string, name of the data source, e.g. &#39;json&#39;, &#39;parquet&#39;.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df = spark.read.format(&#39;json&#39;).load(&#39;python/test_support/sql/people.json&#39;)</span>
<span class="sd"> &gt;&gt;&gt; df.dtypes</span>
<span class="sd"> [(&#39;age&#39;, &#39;bigint&#39;), (&#39;name&#39;, &#39;string&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameReader.schema"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.schema.html#pyspark.sql.DataFrameReader.schema">[docs]</a> <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="n">schema</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Specifies the input schema.</span>
<span class="sd"> Some data sources (e.g. JSON) can infer the input schema automatically from data.</span>
<span class="sd"> By specifying the schema here, the underlying data source can skip the schema</span>
<span class="sd"> inference step, and thus speed up data loading.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> schema : :class:`pyspark.sql.types.StructType` or str</span>
<span class="sd"> a :class:`pyspark.sql.types.StructType` object or a DDL-formatted string</span>
<span class="sd"> (For example ``col0 INT, col1 DOUBLE``).</span>
<span class="sd"> &gt;&gt;&gt; s = spark.read.schema(&quot;col0 INT, col1 DOUBLE&quot;)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span>
<span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">schema</span><span class="p">,</span> <span class="n">StructType</span><span class="p">):</span>
<span class="n">jschema</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">_jsparkSession</span><span class="o">.</span><span class="n">parseDataType</span><span class="p">(</span><span class="n">schema</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">schema</span><span class="p">(</span><span class="n">jschema</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">schema</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">_jreader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">schema</span><span class="p">(</span><span class="n">schema</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="s2">&quot;schema should be StructType or string&quot;</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameReader.option"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.option.html#pyspark.sql.DataFrameReader.option">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">option</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="n">value</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Adds an input option for the underlying data source.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">to_str</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameReader.options"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.options.html#pyspark.sql.DataFrameReader.options">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">options</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Adds input options for the underlying data source.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">options</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">to_str</span><span class="p">(</span><span class="n">options</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameReader.load"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.load.html#pyspark.sql.DataFrameReader.load">[docs]</a> <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="nb">format</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="o">**</span><span class="n">options</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Loads data from a data source and returns it as a :class:`DataFrame`.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str or list, optional</span>
<span class="sd"> optional string or a list of string for file-system backed data sources.</span>
<span class="sd"> format : str, optional</span>
<span class="sd"> optional string for format of the data source. Default to &#39;parquet&#39;.</span>
<span class="sd"> schema : :class:`pyspark.sql.types.StructType` or str, optional</span>
<span class="sd"> optional :class:`pyspark.sql.types.StructType` for the input schema</span>
<span class="sd"> or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).</span>
<span class="sd"> **options : dict</span>
<span class="sd"> all other string options</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df = spark.read.format(&quot;parquet&quot;).load(&#39;python/test_support/sql/parquet_partitioned&#39;,</span>
<span class="sd"> ... opt1=True, opt2=1, opt3=&#39;str&#39;)</span>
<span class="sd"> &gt;&gt;&gt; df.dtypes</span>
<span class="sd"> [(&#39;name&#39;, &#39;string&#39;), (&#39;year&#39;, &#39;int&#39;), (&#39;month&#39;, &#39;int&#39;), (&#39;day&#39;, &#39;int&#39;)]</span>
<span class="sd"> &gt;&gt;&gt; df = spark.read.format(&#39;json&#39;).load([&#39;python/test_support/sql/people.json&#39;,</span>
<span class="sd"> ... &#39;python/test_support/sql/people1.json&#39;])</span>
<span class="sd"> &gt;&gt;&gt; df.dtypes</span>
<span class="sd"> [(&#39;age&#39;, &#39;bigint&#39;), (&#39;aka&#39;, &#39;string&#39;), (&#39;name&#39;, &#39;string&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">format</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">format</span><span class="p">(</span><span class="nb">format</span><span class="p">)</span>
<span class="k">if</span> <span class="n">schema</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">schema</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">options</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">isinstance</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
<span class="k">elif</span> <span class="n">path</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">type</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">list</span><span class="p">:</span>
<span class="n">path</span> <span class="o">=</span> <span class="p">[</span><span class="n">path</span><span class="p">]</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonUtils</span><span class="o">.</span><span class="n">toSeq</span><span class="p">(</span><span class="n">path</span><span class="p">)))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">load</span><span class="p">())</span></div>
<div class="viewcode-block" id="DataFrameReader.json"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.json.html#pyspark.sql.DataFrameReader.json">[docs]</a> <span class="k">def</span> <span class="nf">json</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">schema</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">primitivesAsString</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">prefersDecimal</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">allowComments</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allowUnquotedFieldNames</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allowSingleQuotes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">allowNumericLeadingZero</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allowBackslashEscapingAnyCharacter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">mode</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">columnNameOfCorruptRecord</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dateFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">timestampFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">multiLine</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allowUnquotedControlChars</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lineSep</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">samplingRatio</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">dropFieldIfAllNull</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">locale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pathGlobFilter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">recursiveFileLookup</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allowNonNumericNumbers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">modifiedBefore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">modifiedAfter</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads JSON files and returns the results as a :class:`DataFrame`.</span>
<span class="sd"> `JSON Lines &lt;http://jsonlines.org/&gt;`_ (newline-delimited JSON) is supported by default.</span>
<span class="sd"> For JSON (one record per file), set the ``multiLine`` parameter to ``true``.</span>
<span class="sd"> If the ``schema`` parameter is not specified, this function goes</span>
<span class="sd"> through the input once to determine the input schema.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str, list or :class:`RDD`</span>
<span class="sd"> string represents path to the JSON dataset, or a list of paths,</span>
<span class="sd"> or RDD of Strings storing JSON objects.</span>
<span class="sd"> schema : :class:`pyspark.sql.types.StructType` or str, optional</span>
<span class="sd"> an optional :class:`pyspark.sql.types.StructType` for the input schema or</span>
<span class="sd"> a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df1 = spark.read.json(&#39;python/test_support/sql/people.json&#39;)</span>
<span class="sd"> &gt;&gt;&gt; df1.dtypes</span>
<span class="sd"> [(&#39;age&#39;, &#39;bigint&#39;), (&#39;name&#39;, &#39;string&#39;)]</span>
<span class="sd"> &gt;&gt;&gt; rdd = sc.textFile(&#39;python/test_support/sql/people.json&#39;)</span>
<span class="sd"> &gt;&gt;&gt; df2 = spark.read.json(rdd)</span>
<span class="sd"> &gt;&gt;&gt; df2.dtypes</span>
<span class="sd"> [(&#39;age&#39;, &#39;bigint&#39;), (&#39;name&#39;, &#39;string&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_opts</span><span class="p">(</span>
<span class="n">schema</span><span class="o">=</span><span class="n">schema</span><span class="p">,</span> <span class="n">primitivesAsString</span><span class="o">=</span><span class="n">primitivesAsString</span><span class="p">,</span> <span class="n">prefersDecimal</span><span class="o">=</span><span class="n">prefersDecimal</span><span class="p">,</span>
<span class="n">allowComments</span><span class="o">=</span><span class="n">allowComments</span><span class="p">,</span> <span class="n">allowUnquotedFieldNames</span><span class="o">=</span><span class="n">allowUnquotedFieldNames</span><span class="p">,</span>
<span class="n">allowSingleQuotes</span><span class="o">=</span><span class="n">allowSingleQuotes</span><span class="p">,</span> <span class="n">allowNumericLeadingZero</span><span class="o">=</span><span class="n">allowNumericLeadingZero</span><span class="p">,</span>
<span class="n">allowBackslashEscapingAnyCharacter</span><span class="o">=</span><span class="n">allowBackslashEscapingAnyCharacter</span><span class="p">,</span>
<span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span> <span class="n">columnNameOfCorruptRecord</span><span class="o">=</span><span class="n">columnNameOfCorruptRecord</span><span class="p">,</span> <span class="n">dateFormat</span><span class="o">=</span><span class="n">dateFormat</span><span class="p">,</span>
<span class="n">timestampFormat</span><span class="o">=</span><span class="n">timestampFormat</span><span class="p">,</span> <span class="n">multiLine</span><span class="o">=</span><span class="n">multiLine</span><span class="p">,</span>
<span class="n">allowUnquotedControlChars</span><span class="o">=</span><span class="n">allowUnquotedControlChars</span><span class="p">,</span> <span class="n">lineSep</span><span class="o">=</span><span class="n">lineSep</span><span class="p">,</span>
<span class="n">samplingRatio</span><span class="o">=</span><span class="n">samplingRatio</span><span class="p">,</span> <span class="n">dropFieldIfAllNull</span><span class="o">=</span><span class="n">dropFieldIfAllNull</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">,</span>
<span class="n">locale</span><span class="o">=</span><span class="n">locale</span><span class="p">,</span> <span class="n">pathGlobFilter</span><span class="o">=</span><span class="n">pathGlobFilter</span><span class="p">,</span> <span class="n">recursiveFileLookup</span><span class="o">=</span><span class="n">recursiveFileLookup</span><span class="p">,</span>
<span class="n">modifiedBefore</span><span class="o">=</span><span class="n">modifiedBefore</span><span class="p">,</span> <span class="n">modifiedAfter</span><span class="o">=</span><span class="n">modifiedAfter</span><span class="p">,</span>
<span class="n">allowNonNumericNumbers</span><span class="o">=</span><span class="n">allowNonNumericNumbers</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">path</span> <span class="o">=</span> <span class="p">[</span><span class="n">path</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="o">==</span> <span class="nb">list</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">json</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonUtils</span><span class="o">.</span><span class="n">toSeq</span><span class="p">(</span><span class="n">path</span><span class="p">)))</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">RDD</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">iterator</span><span class="p">):</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">iterator</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">x</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
<span class="k">yield</span> <span class="n">x</span>
<span class="n">keyed</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
<span class="n">keyed</span><span class="o">.</span><span class="n">_bypass_serializer</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">jrdd</span> <span class="o">=</span> <span class="n">keyed</span><span class="o">.</span><span class="n">_jrdd</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">BytesToString</span><span class="p">())</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">json</span><span class="p">(</span><span class="n">jrdd</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="s2">&quot;path can be only string, list or RDD&quot;</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataFrameReader.table"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.table.html#pyspark.sql.DataFrameReader.table">[docs]</a> <span class="k">def</span> <span class="nf">table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tableName</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns the specified table as a :class:`DataFrame`.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> tableName : str</span>
<span class="sd"> string, name of the table.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df = spark.read.parquet(&#39;python/test_support/sql/parquet_partitioned&#39;)</span>
<span class="sd"> &gt;&gt;&gt; df.createOrReplaceTempView(&#39;tmpTable&#39;)</span>
<span class="sd"> &gt;&gt;&gt; spark.read.table(&#39;tmpTable&#39;).dtypes</span>
<span class="sd"> [(&#39;name&#39;, &#39;string&#39;), (&#39;year&#39;, &#39;int&#39;), (&#39;month&#39;, &#39;int&#39;), (&#39;day&#39;, &#39;int&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">tableName</span><span class="p">))</span></div>
<div class="viewcode-block" id="DataFrameReader.parquet"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.parquet.html#pyspark.sql.DataFrameReader.parquet">[docs]</a> <span class="k">def</span> <span class="nf">parquet</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">paths</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads Parquet files, returning the result as a :class:`DataFrame`.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> paths : str</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> **options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df = spark.read.parquet(&#39;python/test_support/sql/parquet_partitioned&#39;)</span>
<span class="sd"> &gt;&gt;&gt; df.dtypes</span>
<span class="sd"> [(&#39;name&#39;, &#39;string&#39;), (&#39;year&#39;, &#39;int&#39;), (&#39;month&#39;, &#39;int&#39;), (&#39;day&#39;, &#39;int&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">mergeSchema</span> <span class="o">=</span> <span class="n">options</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mergeSchema&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">pathGlobFilter</span> <span class="o">=</span> <span class="n">options</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pathGlobFilter&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">modifiedBefore</span> <span class="o">=</span> <span class="n">options</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;modifiedBefore&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">modifiedAfter</span> <span class="o">=</span> <span class="n">options</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;modifiedAfter&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">recursiveFileLookup</span> <span class="o">=</span> <span class="n">options</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;recursiveFileLookup&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">datetimeRebaseMode</span> <span class="o">=</span> <span class="n">options</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;datetimeRebaseMode&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">int96RebaseMode</span> <span class="o">=</span> <span class="n">options</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;int96RebaseMode&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_opts</span><span class="p">(</span><span class="n">mergeSchema</span><span class="o">=</span><span class="n">mergeSchema</span><span class="p">,</span> <span class="n">pathGlobFilter</span><span class="o">=</span><span class="n">pathGlobFilter</span><span class="p">,</span>
<span class="n">recursiveFileLookup</span><span class="o">=</span><span class="n">recursiveFileLookup</span><span class="p">,</span> <span class="n">modifiedBefore</span><span class="o">=</span><span class="n">modifiedBefore</span><span class="p">,</span>
<span class="n">modifiedAfter</span><span class="o">=</span><span class="n">modifiedAfter</span><span class="p">,</span> <span class="n">datetimeRebaseMode</span><span class="o">=</span><span class="n">datetimeRebaseMode</span><span class="p">,</span>
<span class="n">int96RebaseMode</span><span class="o">=</span><span class="n">int96RebaseMode</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">parquet</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">paths</span><span class="p">)))</span></div>
<span class="k">def</span> <span class="nf">text</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">paths</span><span class="p">,</span> <span class="n">wholetext</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lineSep</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pathGlobFilter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">recursiveFileLookup</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">modifiedBefore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">modifiedAfter</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads text files and returns a :class:`DataFrame` whose schema starts with a</span>
<span class="sd"> string column named &quot;value&quot;, and followed by partitioned columns if there</span>
<span class="sd"> are any.</span>
<span class="sd"> The text files must be encoded as UTF-8.</span>
<span class="sd"> By default, each line in the text file is a new row in the resulting DataFrame.</span>
<span class="sd"> .. versionadded:: 1.6.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> paths : str or list</span>
<span class="sd"> string, or list of strings, for input path(s).</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-text.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df = spark.read.text(&#39;python/test_support/sql/text-test.txt&#39;)</span>
<span class="sd"> &gt;&gt;&gt; df.collect()</span>
<span class="sd"> [Row(value=&#39;hello&#39;), Row(value=&#39;this&#39;)]</span>
<span class="sd"> &gt;&gt;&gt; df = spark.read.text(&#39;python/test_support/sql/text-test.txt&#39;, wholetext=True)</span>
<span class="sd"> &gt;&gt;&gt; df.collect()</span>
<span class="sd"> [Row(value=&#39;hello\\nthis&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_opts</span><span class="p">(</span>
<span class="n">wholetext</span><span class="o">=</span><span class="n">wholetext</span><span class="p">,</span> <span class="n">lineSep</span><span class="o">=</span><span class="n">lineSep</span><span class="p">,</span> <span class="n">pathGlobFilter</span><span class="o">=</span><span class="n">pathGlobFilter</span><span class="p">,</span>
<span class="n">recursiveFileLookup</span><span class="o">=</span><span class="n">recursiveFileLookup</span><span class="p">,</span> <span class="n">modifiedBefore</span><span class="o">=</span><span class="n">modifiedBefore</span><span class="p">,</span>
<span class="n">modifiedAfter</span><span class="o">=</span><span class="n">modifiedAfter</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">paths</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">paths</span><span class="p">]</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonUtils</span><span class="o">.</span><span class="n">toSeq</span><span class="p">(</span><span class="n">paths</span><span class="p">)))</span>
<div class="viewcode-block" id="DataFrameReader.csv"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.csv.html#pyspark.sql.DataFrameReader.csv">[docs]</a> <span class="k">def</span> <span class="nf">csv</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">schema</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">quote</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">escape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">comment</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">inferSchema</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignoreLeadingWhiteSpace</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">ignoreTrailingWhiteSpace</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nullValue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nanValue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">positiveInf</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">negativeInf</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dateFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">timestampFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">maxColumns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">maxCharsPerColumn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">maxMalformedLogPerPartition</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">columnNameOfCorruptRecord</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">multiLine</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">charToEscapeQuoteEscaping</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">samplingRatio</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">enforceSchema</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">emptyValue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">locale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lineSep</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">pathGlobFilter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">recursiveFileLookup</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">modifiedBefore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">modifiedAfter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">unescapedQuoteHandling</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Loads a CSV file and returns the result as a :class:`DataFrame`.</span>
<span class="sd"> This function will go through the input once to determine the input schema if</span>
<span class="sd"> ``inferSchema`` is enabled. To avoid going through the entire data once, disable</span>
<span class="sd"> ``inferSchema`` option or specify the schema explicitly using ``schema``.</span>
<span class="sd"> .. versionadded:: 2.0.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str or list</span>
<span class="sd"> string, or list of strings, for input path(s),</span>
<span class="sd"> or RDD of Strings storing CSV rows.</span>
<span class="sd"> schema : :class:`pyspark.sql.types.StructType` or str, optional</span>
<span class="sd"> an optional :class:`pyspark.sql.types.StructType` for the input schema</span>
<span class="sd"> or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df = spark.read.csv(&#39;python/test_support/sql/ages.csv&#39;)</span>
<span class="sd"> &gt;&gt;&gt; df.dtypes</span>
<span class="sd"> [(&#39;_c0&#39;, &#39;string&#39;), (&#39;_c1&#39;, &#39;string&#39;)]</span>
<span class="sd"> &gt;&gt;&gt; rdd = sc.textFile(&#39;python/test_support/sql/ages.csv&#39;)</span>
<span class="sd"> &gt;&gt;&gt; df2 = spark.read.csv(rdd)</span>
<span class="sd"> &gt;&gt;&gt; df2.dtypes</span>
<span class="sd"> [(&#39;_c0&#39;, &#39;string&#39;), (&#39;_c1&#39;, &#39;string&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_opts</span><span class="p">(</span>
<span class="n">schema</span><span class="o">=</span><span class="n">schema</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="n">sep</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">,</span> <span class="n">quote</span><span class="o">=</span><span class="n">quote</span><span class="p">,</span> <span class="n">escape</span><span class="o">=</span><span class="n">escape</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="n">comment</span><span class="p">,</span>
<span class="n">header</span><span class="o">=</span><span class="n">header</span><span class="p">,</span> <span class="n">inferSchema</span><span class="o">=</span><span class="n">inferSchema</span><span class="p">,</span> <span class="n">ignoreLeadingWhiteSpace</span><span class="o">=</span><span class="n">ignoreLeadingWhiteSpace</span><span class="p">,</span>
<span class="n">ignoreTrailingWhiteSpace</span><span class="o">=</span><span class="n">ignoreTrailingWhiteSpace</span><span class="p">,</span> <span class="n">nullValue</span><span class="o">=</span><span class="n">nullValue</span><span class="p">,</span>
<span class="n">nanValue</span><span class="o">=</span><span class="n">nanValue</span><span class="p">,</span> <span class="n">positiveInf</span><span class="o">=</span><span class="n">positiveInf</span><span class="p">,</span> <span class="n">negativeInf</span><span class="o">=</span><span class="n">negativeInf</span><span class="p">,</span>
<span class="n">dateFormat</span><span class="o">=</span><span class="n">dateFormat</span><span class="p">,</span> <span class="n">timestampFormat</span><span class="o">=</span><span class="n">timestampFormat</span><span class="p">,</span> <span class="n">maxColumns</span><span class="o">=</span><span class="n">maxColumns</span><span class="p">,</span>
<span class="n">maxCharsPerColumn</span><span class="o">=</span><span class="n">maxCharsPerColumn</span><span class="p">,</span>
<span class="n">maxMalformedLogPerPartition</span><span class="o">=</span><span class="n">maxMalformedLogPerPartition</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span>
<span class="n">columnNameOfCorruptRecord</span><span class="o">=</span><span class="n">columnNameOfCorruptRecord</span><span class="p">,</span> <span class="n">multiLine</span><span class="o">=</span><span class="n">multiLine</span><span class="p">,</span>
<span class="n">charToEscapeQuoteEscaping</span><span class="o">=</span><span class="n">charToEscapeQuoteEscaping</span><span class="p">,</span> <span class="n">samplingRatio</span><span class="o">=</span><span class="n">samplingRatio</span><span class="p">,</span>
<span class="n">enforceSchema</span><span class="o">=</span><span class="n">enforceSchema</span><span class="p">,</span> <span class="n">emptyValue</span><span class="o">=</span><span class="n">emptyValue</span><span class="p">,</span> <span class="n">locale</span><span class="o">=</span><span class="n">locale</span><span class="p">,</span> <span class="n">lineSep</span><span class="o">=</span><span class="n">lineSep</span><span class="p">,</span>
<span class="n">pathGlobFilter</span><span class="o">=</span><span class="n">pathGlobFilter</span><span class="p">,</span> <span class="n">recursiveFileLookup</span><span class="o">=</span><span class="n">recursiveFileLookup</span><span class="p">,</span>
<span class="n">modifiedBefore</span><span class="o">=</span><span class="n">modifiedBefore</span><span class="p">,</span> <span class="n">modifiedAfter</span><span class="o">=</span><span class="n">modifiedAfter</span><span class="p">,</span>
<span class="n">unescapedQuoteHandling</span><span class="o">=</span><span class="n">unescapedQuoteHandling</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">path</span> <span class="o">=</span> <span class="p">[</span><span class="n">path</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="o">==</span> <span class="nb">list</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">PythonUtils</span><span class="o">.</span><span class="n">toSeq</span><span class="p">(</span><span class="n">path</span><span class="p">)))</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">RDD</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">func</span><span class="p">(</span><span class="n">iterator</span><span class="p">):</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">iterator</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">x</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
<span class="k">yield</span> <span class="n">x</span>
<span class="n">keyed</span> <span class="o">=</span> <span class="n">path</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
<span class="n">keyed</span><span class="o">.</span><span class="n">_bypass_serializer</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">jrdd</span> <span class="o">=</span> <span class="n">keyed</span><span class="o">.</span><span class="n">_jrdd</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">BytesToString</span><span class="p">())</span>
<span class="c1"># see SPARK-22112</span>
<span class="c1"># There aren&#39;t any jvm api for creating a dataframe from rdd storing csv.</span>
<span class="c1"># We can do it through creating a jvm dataset firstly and using the jvm api</span>
<span class="c1"># for creating a dataframe from dataset storing csv.</span>
<span class="n">jdataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_ssql_ctx</span><span class="o">.</span><span class="n">createDataset</span><span class="p">(</span>
<span class="n">jrdd</span><span class="o">.</span><span class="n">rdd</span><span class="p">(),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">Encoders</span><span class="o">.</span><span class="n">STRING</span><span class="p">())</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">csv</span><span class="p">(</span><span class="n">jdataset</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="s2">&quot;path can be only string, list or RDD&quot;</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataFrameReader.orc"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.orc.html#pyspark.sql.DataFrameReader.orc">[docs]</a> <span class="k">def</span> <span class="nf">orc</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">mergeSchema</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pathGlobFilter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">recursiveFileLookup</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">modifiedBefore</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">modifiedAfter</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Loads ORC files, returning the result as a :class:`DataFrame`.</span>
<span class="sd"> .. versionadded:: 1.5.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str or list</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-orc.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df = spark.read.orc(&#39;python/test_support/sql/orc_partitioned&#39;)</span>
<span class="sd"> &gt;&gt;&gt; df.dtypes</span>
<span class="sd"> [(&#39;a&#39;, &#39;bigint&#39;), (&#39;b&#39;, &#39;int&#39;), (&#39;c&#39;, &#39;int&#39;)]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_opts</span><span class="p">(</span><span class="n">mergeSchema</span><span class="o">=</span><span class="n">mergeSchema</span><span class="p">,</span> <span class="n">pathGlobFilter</span><span class="o">=</span><span class="n">pathGlobFilter</span><span class="p">,</span>
<span class="n">modifiedBefore</span><span class="o">=</span><span class="n">modifiedBefore</span><span class="p">,</span> <span class="n">modifiedAfter</span><span class="o">=</span><span class="n">modifiedAfter</span><span class="p">,</span>
<span class="n">recursiveFileLookup</span><span class="o">=</span><span class="n">recursiveFileLookup</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">path</span> <span class="o">=</span> <span class="p">[</span><span class="n">path</span><span class="p">]</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">orc</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">path</span><span class="p">)))</span></div>
<div class="viewcode-block" id="DataFrameReader.jdbc"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameReader.jdbc.html#pyspark.sql.DataFrameReader.jdbc">[docs]</a> <span class="k">def</span> <span class="nf">jdbc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">url</span><span class="p">,</span> <span class="n">table</span><span class="p">,</span> <span class="n">column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lowerBound</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">upperBound</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numPartitions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">predicates</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">properties</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Construct a :class:`DataFrame` representing the database table named ``table``</span>
<span class="sd"> accessible via JDBC URL ``url`` and connection ``properties``.</span>
<span class="sd"> Partitions of the table will be retrieved in parallel if either ``column`` or</span>
<span class="sd"> ``predicates`` is specified. ``lowerBound``, ``upperBound`` and ``numPartitions``</span>
<span class="sd"> is needed when ``column`` is specified.</span>
<span class="sd"> If both ``column`` and ``predicates`` are specified, ``column`` will be used.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> table : str</span>
<span class="sd"> the name of the table</span>
<span class="sd"> column : str, optional</span>
<span class="sd"> alias of ``partitionColumn`` option. Refer to ``partitionColumn`` in</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> predicates : list, optional</span>
<span class="sd"> a list of expressions suitable for inclusion in WHERE clauses;</span>
<span class="sd"> each one defines one partition of the :class:`DataFrame`</span>
<span class="sd"> properties : dict, optional</span>
<span class="sd"> a dictionary of JDBC database connection arguments. Normally at</span>
<span class="sd"> least properties &quot;user&quot; and &quot;password&quot; with their corresponding values.</span>
<span class="sd"> For example { &#39;user&#39; : &#39;SYSTEM&#39;, &#39;password&#39; : &#39;mypassword&#39; }</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Don&#39;t create too many partitions in parallel on a large cluster;</span>
<span class="sd"> otherwise Spark might crash your external database systems.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`DataFrame`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">properties</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">properties</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="n">jprop</span> <span class="o">=</span> <span class="n">JavaClass</span><span class="p">(</span><span class="s2">&quot;java.util.Properties&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_gateway</span><span class="o">.</span><span class="n">_gateway_client</span><span class="p">)()</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">properties</span><span class="p">:</span>
<span class="n">jprop</span><span class="o">.</span><span class="n">setProperty</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">properties</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
<span class="k">if</span> <span class="n">column</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">lowerBound</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;lowerBound can not be None when ``column`` is specified&quot;</span>
<span class="k">assert</span> <span class="n">upperBound</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;upperBound can not be None when ``column`` is specified&quot;</span>
<span class="k">assert</span> <span class="n">numPartitions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
<span class="s2">&quot;numPartitions can not be None when ``column`` is specified&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">jdbc</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">table</span><span class="p">,</span> <span class="n">column</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">lowerBound</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">upperBound</span><span class="p">),</span>
<span class="nb">int</span><span class="p">(</span><span class="n">numPartitions</span><span class="p">),</span> <span class="n">jprop</span><span class="p">))</span>
<span class="k">if</span> <span class="n">predicates</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">gateway</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_gateway</span>
<span class="n">jpredicates</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">toJArray</span><span class="p">(</span><span class="n">gateway</span><span class="p">,</span> <span class="n">gateway</span><span class="o">.</span><span class="n">jvm</span><span class="o">.</span><span class="n">java</span><span class="o">.</span><span class="n">lang</span><span class="o">.</span><span class="n">String</span><span class="p">,</span> <span class="n">predicates</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">jdbc</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">table</span><span class="p">,</span> <span class="n">jpredicates</span><span class="p">,</span> <span class="n">jprop</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_df</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_jreader</span><span class="o">.</span><span class="n">jdbc</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">table</span><span class="p">,</span> <span class="n">jprop</span><span class="p">))</span></div>
<span class="k">class</span> <span class="nc">DataFrameWriter</span><span class="p">(</span><span class="n">OptionUtils</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Interface used to write a :class:`DataFrame` to external storage systems</span>
<span class="sd"> (e.g. file systems, key-value stores, etc). Use :attr:`DataFrame.write`</span>
<span class="sd"> to access this.</span>
<span class="sd"> .. versionadded:: 1.4</span>
<span class="sd"> &quot;&quot;&quot;</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">df</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_df</span> <span class="o">=</span> <span class="n">df</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_spark</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">sql_ctx</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">_jdf</span><span class="o">.</span><span class="n">write</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_sq</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">jsq</span><span class="p">):</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.streaming</span> <span class="kn">import</span> <span class="n">StreamingQuery</span>
<span class="k">return</span> <span class="n">StreamingQuery</span><span class="p">(</span><span class="n">jsq</span><span class="p">)</span>
<div class="viewcode-block" id="DataFrameWriter.mode"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.mode.html#pyspark.sql.DataFrameWriter.mode">[docs]</a> <span class="k">def</span> <span class="nf">mode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">saveMode</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Specifies the behavior when data or table already exists.</span>
<span class="sd"> Options include:</span>
<span class="sd"> * `append`: Append contents of this :class:`DataFrame` to existing data.</span>
<span class="sd"> * `overwrite`: Overwrite existing data.</span>
<span class="sd"> * `error` or `errorifexists`: Throw an exception if data already exists.</span>
<span class="sd"> * `ignore`: Silently ignore this operation if data already exists.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.write.mode(&#39;append&#39;).parquet(os.path.join(tempfile.mkdtemp(), &#39;data&#39;))</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># At the JVM side, the default value of mode is already set to &quot;error&quot;.</span>
<span class="c1"># So, if the given saveMode is None, we will not call JVM-side&#39;s mode method.</span>
<span class="k">if</span> <span class="n">saveMode</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">_jwrite</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">saveMode</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameWriter.format"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.format.html#pyspark.sql.DataFrameWriter.format">[docs]</a> <span class="k">def</span> <span class="nf">format</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="sd">&quot;&quot;&quot;Specifies the underlying output data source.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> source : str</span>
<span class="sd"> string, name of the data source, e.g. &#39;json&#39;, &#39;parquet&#39;.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.write.format(&#39;json&#39;).save(os.path.join(tempfile.mkdtemp(), &#39;data&#39;))</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameWriter.option"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.option.html#pyspark.sql.DataFrameWriter.option">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">option</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="n">value</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Adds an output option for the underlying data source.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">to_str</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameWriter.options"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.options.html#pyspark.sql.DataFrameWriter.options">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">options</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Adds output options for the underlying data source.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">options</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">to_str</span><span class="p">(</span><span class="n">options</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameWriter.partitionBy"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.partitionBy.html#pyspark.sql.DataFrameWriter.partitionBy">[docs]</a> <span class="k">def</span> <span class="nf">partitionBy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Partitions the output by the given columns on the file system.</span>
<span class="sd"> If specified, the output is laid out on the file system similar</span>
<span class="sd"> to Hive&#39;s partitioning scheme.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> cols : str or list</span>
<span class="sd"> name of columns</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.write.partitionBy(&#39;year&#39;, &#39;month&#39;).parquet(os.path.join(tempfile.mkdtemp(), &#39;data&#39;))</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">cols</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
<span class="n">cols</span> <span class="o">=</span> <span class="n">cols</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">_jwrite</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">partitionBy</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameWriter.bucketBy"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.bucketBy.html#pyspark.sql.DataFrameWriter.bucketBy">[docs]</a> <span class="k">def</span> <span class="nf">bucketBy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">numBuckets</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Buckets the output by the given columns. If specified,</span>
<span class="sd"> the output is laid out on the file system similar to Hive&#39;s bucketing scheme,</span>
<span class="sd"> but with a different bucket hash function and is not compatible with Hive&#39;s bucketing.</span>
<span class="sd"> .. versionadded:: 2.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> numBuckets : int</span>
<span class="sd"> the number of buckets to save</span>
<span class="sd"> col : str, list or tuple</span>
<span class="sd"> a name of a column, or a list of names.</span>
<span class="sd"> cols : str</span>
<span class="sd"> additional names (optional). If `col` is a list it should be empty.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Applicable for file-based data sources in combination with</span>
<span class="sd"> :py:meth:`DataFrameWriter.saveAsTable`.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; (df.write.format(&#39;parquet&#39;) # doctest: +SKIP</span>
<span class="sd"> ... .bucketBy(100, &#39;year&#39;, &#39;month&#39;)</span>
<span class="sd"> ... .mode(&quot;overwrite&quot;)</span>
<span class="sd"> ... .saveAsTable(&#39;bucketed_table&#39;))</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">numBuckets</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;numBuckets should be an int, got </span><span class="si">{0}</span><span class="s2">.&quot;</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="n">numBuckets</span><span class="p">)))</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">cols</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;col is a </span><span class="si">{0}</span><span class="s2"> but cols are not empty&quot;</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="n">col</span><span class="p">)))</span>
<span class="n">col</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">col</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">col</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cols</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="nb">str</span><span class="p">)):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;all names should be `str`&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">bucketBy</span><span class="p">(</span><span class="n">numBuckets</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">_to_seq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameWriter.sortBy"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.sortBy.html#pyspark.sql.DataFrameWriter.sortBy">[docs]</a> <span class="k">def</span> <span class="nf">sortBy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Sorts the output in each bucket by the given columns on the file system.</span>
<span class="sd"> .. versionadded:: 2.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> col : str, tuple or list</span>
<span class="sd"> a name of a column, or a list of names.</span>
<span class="sd"> cols : str</span>
<span class="sd"> additional names (optional). If `col` is a list it should be empty.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; (df.write.format(&#39;parquet&#39;) # doctest: +SKIP</span>
<span class="sd"> ... .bucketBy(100, &#39;year&#39;, &#39;month&#39;)</span>
<span class="sd"> ... .sortBy(&#39;day&#39;)</span>
<span class="sd"> ... .mode(&quot;overwrite&quot;)</span>
<span class="sd"> ... .saveAsTable(&#39;sorted_bucketed_table&#39;))</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">cols</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;col is a </span><span class="si">{0}</span><span class="s2"> but cols are not empty&quot;</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="n">col</span><span class="p">)))</span>
<span class="n">col</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">col</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">col</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cols</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="nb">str</span><span class="p">)):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;all names should be `str`&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">sortBy</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">_to_seq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DataFrameWriter.save"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.save.html#pyspark.sql.DataFrameWriter.save">[docs]</a> <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partitionBy</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="sd">&quot;&quot;&quot;Saves the contents of the :class:`DataFrame` to a data source.</span>
<span class="sd"> The data source is specified by the ``format`` and a set of ``options``.</span>
<span class="sd"> If ``format`` is not specified, the default data source configured by</span>
<span class="sd"> ``spark.sql.sources.default`` will be used.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str, optional</span>
<span class="sd"> the path in a Hadoop supported file system</span>
<span class="sd"> format : str, optional</span>
<span class="sd"> the format used to save</span>
<span class="sd"> mode : str, optional</span>
<span class="sd"> specifies the behavior of the save operation when data already exists.</span>
<span class="sd"> * ``append``: Append contents of this :class:`DataFrame` to existing data.</span>
<span class="sd"> * ``overwrite``: Overwrite existing data.</span>
<span class="sd"> * ``ignore``: Silently ignore this operation if data already exists.</span>
<span class="sd"> * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \</span>
<span class="sd"> exists.</span>
<span class="sd"> partitionBy : list, optional</span>
<span class="sd"> names of partitioning columns</span>
<span class="sd"> **options : dict</span>
<span class="sd"> all other string options</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.write.mode(&quot;append&quot;).save(os.path.join(tempfile.mkdtemp(), &#39;data&#39;))</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span><span class="o">.</span><span class="n">options</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">partitionBy</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">partitionBy</span><span class="p">(</span><span class="n">partitionBy</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">format</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">format</span><span class="p">(</span><span class="nb">format</span><span class="p">)</span>
<span class="k">if</span> <span class="n">path</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">save</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">_jwrite</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataFrameWriter.insertInto"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.insertInto.html#pyspark.sql.DataFrameWriter.insertInto">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">insertInto</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tableName</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Inserts the content of the :class:`DataFrame` to the specified table.</span>
<span class="sd"> It requires that the schema of the :class:`DataFrame` is the same as the</span>
<span class="sd"> schema of the table.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> overwrite : bool, optional</span>
<span class="sd"> If true, overwrites existing data. Disabled by default</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Unlike :meth:`DataFrameWriter.saveAsTable`, :meth:`DataFrameWriter.insertInto` ignores</span>
<span class="sd"> the column names and just uses position-based resolution.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">overwrite</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">mode</span><span class="p">(</span><span class="s2">&quot;overwrite&quot;</span> <span class="k">if</span> <span class="n">overwrite</span> <span class="k">else</span> <span class="s2">&quot;append&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">insertInto</span><span class="p">(</span><span class="n">tableName</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataFrameWriter.saveAsTable"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.saveAsTable.html#pyspark.sql.DataFrameWriter.saveAsTable">[docs]</a> <span class="k">def</span> <span class="nf">saveAsTable</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="nb">format</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partitionBy</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="sd">&quot;&quot;&quot;Saves the content of the :class:`DataFrame` as the specified table.</span>
<span class="sd"> In the case the table already exists, behavior of this function depends on the</span>
<span class="sd"> save mode, specified by the `mode` function (default to throwing an exception).</span>
<span class="sd"> When `mode` is `Overwrite`, the schema of the :class:`DataFrame` does not need to be</span>
<span class="sd"> the same as that of the existing table.</span>
<span class="sd"> * `append`: Append contents of this :class:`DataFrame` to existing data.</span>
<span class="sd"> * `overwrite`: Overwrite existing data.</span>
<span class="sd"> * `error` or `errorifexists`: Throw an exception if data already exists.</span>
<span class="sd"> * `ignore`: Silently ignore this operation if data already exists.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> When `mode` is `Append`, if there is an existing table, we will use the format and</span>
<span class="sd"> options of the existing table. The column order in the schema of the :class:`DataFrame`</span>
<span class="sd"> doesn&#39;t need to be same as that of the existing table. Unlike</span>
<span class="sd"> :meth:`DataFrameWriter.insertInto`, :meth:`DataFrameWriter.saveAsTable` will use the</span>
<span class="sd"> column names to find the correct column positions.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> the table name</span>
<span class="sd"> format : str, optional</span>
<span class="sd"> the format used to save</span>
<span class="sd"> mode : str, optional</span>
<span class="sd"> one of `append`, `overwrite`, `error`, `errorifexists`, `ignore` \</span>
<span class="sd"> (default: error)</span>
<span class="sd"> partitionBy : str or list</span>
<span class="sd"> names of partitioning columns</span>
<span class="sd"> **options : dict</span>
<span class="sd"> all other string options</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span><span class="o">.</span><span class="n">options</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">partitionBy</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">partitionBy</span><span class="p">(</span><span class="n">partitionBy</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">format</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">format</span><span class="p">(</span><span class="nb">format</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">saveAsTable</span><span class="p">(</span><span class="n">name</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataFrameWriter.json"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.json.html#pyspark.sql.DataFrameWriter.json">[docs]</a> <span class="k">def</span> <span class="nf">json</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">mode</span><span class="o">=</span><span class="kc">None</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="n">dateFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">timestampFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">lineSep</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignoreNullFields</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Saves the content of the :class:`DataFrame` in JSON format</span>
<span class="sd"> (`JSON Lines text format or newline-delimited JSON &lt;http://jsonlines.org/&gt;`_) at the</span>
<span class="sd"> specified path.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> the path in any Hadoop supported file system</span>
<span class="sd"> mode : str, optional</span>
<span class="sd"> specifies the behavior of the save operation when data already exists.</span>
<span class="sd"> * ``append``: Append contents of this :class:`DataFrame` to existing data.</span>
<span class="sd"> * ``overwrite``: Overwrite existing data.</span>
<span class="sd"> * ``ignore``: Silently ignore this operation if data already exists.</span>
<span class="sd"> * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \</span>
<span class="sd"> exists.</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.write.json(os.path.join(tempfile.mkdtemp(), &#39;data&#39;))</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_opts</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">dateFormat</span><span class="o">=</span><span class="n">dateFormat</span><span class="p">,</span> <span class="n">timestampFormat</span><span class="o">=</span><span class="n">timestampFormat</span><span class="p">,</span>
<span class="n">lineSep</span><span class="o">=</span><span class="n">lineSep</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">,</span> <span class="n">ignoreNullFields</span><span class="o">=</span><span class="n">ignoreNullFields</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">json</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataFrameWriter.parquet"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.parquet.html#pyspark.sql.DataFrameWriter.parquet">[docs]</a> <span class="k">def</span> <span class="nf">parquet</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">mode</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partitionBy</span><span class="o">=</span><span class="kc">None</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="sd">&quot;&quot;&quot;Saves the content of the :class:`DataFrame` in Parquet format at the specified path.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> the path in any Hadoop supported file system</span>
<span class="sd"> mode : str, optional</span>
<span class="sd"> specifies the behavior of the save operation when data already exists.</span>
<span class="sd"> * ``append``: Append contents of this :class:`DataFrame` to existing data.</span>
<span class="sd"> * ``overwrite``: Overwrite existing data.</span>
<span class="sd"> * ``ignore``: Silently ignore this operation if data already exists.</span>
<span class="sd"> * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \</span>
<span class="sd"> exists.</span>
<span class="sd"> partitionBy : str or list, optional</span>
<span class="sd"> names of partitioning columns</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.write.parquet(os.path.join(tempfile.mkdtemp(), &#39;data&#39;))</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span>
<span class="k">if</span> <span class="n">partitionBy</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">partitionBy</span><span class="p">(</span><span class="n">partitionBy</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_opts</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="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">parquet</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataFrameWriter.text"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.text.html#pyspark.sql.DataFrameWriter.text">[docs]</a> <span class="k">def</span> <span class="nf">text</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">compression</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lineSep</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Saves the content of the DataFrame in a text file at the specified path.</span>
<span class="sd"> The text files will be encoded as UTF-8.</span>
<span class="sd"> .. versionadded:: 1.6.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> the path in any Hadoop supported file system</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-text.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> The DataFrame must have only one column that is of string type.</span>
<span class="sd"> Each row becomes a new line in the output file.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_opts</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">lineSep</span><span class="o">=</span><span class="n">lineSep</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataFrameWriter.csv"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.csv.html#pyspark.sql.DataFrameWriter.csv">[docs]</a> <span class="k">def</span> <span class="nf">csv</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">mode</span><span class="o">=</span><span class="kc">None</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="n">sep</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">quote</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">escape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nullValue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">escapeQuotes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">quoteAll</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dateFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">timestampFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignoreLeadingWhiteSpace</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignoreTrailingWhiteSpace</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">charToEscapeQuoteEscaping</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">emptyValue</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lineSep</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Saves the content of the :class:`DataFrame` in CSV format at the specified path.</span>
<span class="sd"> .. versionadded:: 2.0.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> the path in any Hadoop supported file system</span>
<span class="sd"> mode : str, optional</span>
<span class="sd"> specifies the behavior of the save operation when data already exists.</span>
<span class="sd"> * ``append``: Append contents of this :class:`DataFrame` to existing data.</span>
<span class="sd"> * ``overwrite``: Overwrite existing data.</span>
<span class="sd"> * ``ignore``: Silently ignore this operation if data already exists.</span>
<span class="sd"> * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \</span>
<span class="sd"> exists.</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; df.write.csv(os.path.join(tempfile.mkdtemp(), &#39;data&#39;))</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_opts</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">sep</span><span class="o">=</span><span class="n">sep</span><span class="p">,</span> <span class="n">quote</span><span class="o">=</span><span class="n">quote</span><span class="p">,</span> <span class="n">escape</span><span class="o">=</span><span class="n">escape</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="n">header</span><span class="p">,</span>
<span class="n">nullValue</span><span class="o">=</span><span class="n">nullValue</span><span class="p">,</span> <span class="n">escapeQuotes</span><span class="o">=</span><span class="n">escapeQuotes</span><span class="p">,</span> <span class="n">quoteAll</span><span class="o">=</span><span class="n">quoteAll</span><span class="p">,</span>
<span class="n">dateFormat</span><span class="o">=</span><span class="n">dateFormat</span><span class="p">,</span> <span class="n">timestampFormat</span><span class="o">=</span><span class="n">timestampFormat</span><span class="p">,</span>
<span class="n">ignoreLeadingWhiteSpace</span><span class="o">=</span><span class="n">ignoreLeadingWhiteSpace</span><span class="p">,</span>
<span class="n">ignoreTrailingWhiteSpace</span><span class="o">=</span><span class="n">ignoreTrailingWhiteSpace</span><span class="p">,</span>
<span class="n">charToEscapeQuoteEscaping</span><span class="o">=</span><span class="n">charToEscapeQuoteEscaping</span><span class="p">,</span>
<span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">,</span> <span class="n">emptyValue</span><span class="o">=</span><span class="n">emptyValue</span><span class="p">,</span> <span class="n">lineSep</span><span class="o">=</span><span class="n">lineSep</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">csv</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataFrameWriter.orc"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.orc.html#pyspark.sql.DataFrameWriter.orc">[docs]</a> <span class="k">def</span> <span class="nf">orc</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">mode</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partitionBy</span><span class="o">=</span><span class="kc">None</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="sd">&quot;&quot;&quot;Saves the content of the :class:`DataFrame` in ORC format at the specified path.</span>
<span class="sd"> .. versionadded:: 1.5.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> path : str</span>
<span class="sd"> the path in any Hadoop supported file system</span>
<span class="sd"> mode : str, optional</span>
<span class="sd"> specifies the behavior of the save operation when data already exists.</span>
<span class="sd"> * ``append``: Append contents of this :class:`DataFrame` to existing data.</span>
<span class="sd"> * ``overwrite``: Overwrite existing data.</span>
<span class="sd"> * ``ignore``: Silently ignore this operation if data already exists.</span>
<span class="sd"> * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \</span>
<span class="sd"> exists.</span>
<span class="sd"> partitionBy : str or list, optional</span>
<span class="sd"> names of partitioning columns</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-orc.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; orc_df = spark.read.orc(&#39;python/test_support/sql/orc_partitioned&#39;)</span>
<span class="sd"> &gt;&gt;&gt; orc_df.write.orc(os.path.join(tempfile.mkdtemp(), &#39;data&#39;))</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span>
<span class="k">if</span> <span class="n">partitionBy</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">partitionBy</span><span class="p">(</span><span class="n">partitionBy</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_set_opts</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="bp">self</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">orc</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
<div class="viewcode-block" id="DataFrameWriter.jdbc"><a class="viewcode-back" href="../../../reference/api/pyspark.sql.DataFrameWriter.jdbc.html#pyspark.sql.DataFrameWriter.jdbc">[docs]</a> <span class="k">def</span> <span class="nf">jdbc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">url</span><span class="p">,</span> <span class="n">table</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">properties</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Saves the content of the :class:`DataFrame` to an external database table via JDBC.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> table : str</span>
<span class="sd"> Name of the table in the external database.</span>
<span class="sd"> mode : str, optional</span>
<span class="sd"> specifies the behavior of the save operation when data already exists.</span>
<span class="sd"> * ``append``: Append contents of this :class:`DataFrame` to existing data.</span>
<span class="sd"> * ``overwrite``: Overwrite existing data.</span>
<span class="sd"> * ``ignore``: Silently ignore this operation if data already exists.</span>
<span class="sd"> * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \</span>
<span class="sd"> exists.</span>
<span class="sd"> properties : dict</span>
<span class="sd"> a dictionary of JDBC database connection arguments. Normally at</span>
<span class="sd"> least properties &quot;user&quot; and &quot;password&quot; with their corresponding values.</span>
<span class="sd"> For example { &#39;user&#39; : &#39;SYSTEM&#39;, &#39;password&#39; : &#39;mypassword&#39; }</span>
<span class="sd"> Other Parameters</span>
<span class="sd"> ----------------</span>
<span class="sd"> Extra options</span>
<span class="sd"> For the extra options, refer to</span>
<span class="sd"> `Data Source Option &lt;https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html#data-source-option&gt;`_</span>
<span class="sd"> in the version you use.</span>
<span class="sd"> .. # noqa</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> Don&#39;t create too many partitions in parallel on a large cluster;</span>
<span class="sd"> otherwise Spark might crash your external database systems.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">properties</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">properties</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="n">jprop</span> <span class="o">=</span> <span class="n">JavaClass</span><span class="p">(</span><span class="s2">&quot;java.util.Properties&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="o">.</span><span class="n">_gateway</span><span class="o">.</span><span class="n">_gateway_client</span><span class="p">)()</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">properties</span><span class="p">:</span>
<span class="n">jprop</span><span class="o">.</span><span class="n">setProperty</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">properties</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">mode</span><span class="p">)</span><span class="o">.</span><span class="n">_jwrite</span><span class="o">.</span><span class="n">jdbc</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">table</span><span class="p">,</span> <span class="n">jprop</span><span class="p">)</span></div>
<span class="k">class</span> <span class="nc">DataFrameWriterV2</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Interface used to write a class:`pyspark.sql.dataframe.DataFrame`</span>
<span class="sd"> to external storage using the v2 API.</span>
<span class="sd"> .. versionadded:: 3.1.0</span>
<span class="sd"> &quot;&quot;&quot;</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">df</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">_df</span> <span class="o">=</span> <span class="n">df</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_spark</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">sql_ctx</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">_jdf</span><span class="o">.</span><span class="n">writeTo</span><span class="p">(</span><span class="n">table</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">using</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">provider</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Specifies a provider for the underlying output data source.</span>
<span class="sd"> Spark&#39;s default catalog supports &quot;parquet&quot;, &quot;json&quot;, etc.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">using</span><span class="p">(</span><span class="n">provider</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">option</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="n">value</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Add a write option.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">to_str</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">options</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">options</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Add write options.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">options</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">to_str</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">options</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">options</span><span class="p">(</span><span class="n">options</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">tableProperty</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">property</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Add table property.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">tableProperty</span><span class="p">(</span><span class="nb">property</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">partitionedBy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Partition the output table created by `create`, `createOrReplace`, or `replace` using</span>
<span class="sd"> the given columns or transforms.</span>
<span class="sd"> When specified, the table data will be stored by these values for efficient reads.</span>
<span class="sd"> For example, when a table is partitioned by day, it may be stored</span>
<span class="sd"> in a directory layout like:</span>
<span class="sd"> * `table/day=2019-06-01/`</span>
<span class="sd"> * `table/day=2019-06-02/`</span>
<span class="sd"> Partitioning is one of the most widely used techniques to optimize physical data layout.</span>
<span class="sd"> It provides a coarse-grained index for skipping unnecessary data reads when queries have</span>
<span class="sd"> predicates on the partitioned columns. In order for partitioning to work well, the number</span>
<span class="sd"> of distinct values in each column should typically be less than tens of thousands.</span>
<span class="sd"> `col` and `cols` support only the following functions:</span>
<span class="sd"> * :py:func:`pyspark.sql.functions.years`</span>
<span class="sd"> * :py:func:`pyspark.sql.functions.months`</span>
<span class="sd"> * :py:func:`pyspark.sql.functions.days`</span>
<span class="sd"> * :py:func:`pyspark.sql.functions.hours`</span>
<span class="sd"> * :py:func:`pyspark.sql.functions.bucket`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">col</span> <span class="o">=</span> <span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
<span class="n">cols</span> <span class="o">=</span> <span class="n">_to_seq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spark</span><span class="o">.</span><span class="n">_sc</span><span class="p">,</span> <span class="p">[</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cols</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">partitionedBy</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">cols</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">create</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Create a new table from the contents of the data frame.</span>
<span class="sd"> The new table&#39;s schema, partition layout, properties, and other configuration will be</span>
<span class="sd"> based on the configuration set on this writer.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">create</span><span class="p">()</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">replace</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Replace an existing table with the contents of the data frame.</span>
<span class="sd"> The existing table&#39;s schema, partition layout, properties, and other configuration will be</span>
<span class="sd"> replaced with the contents of the data frame and the configuration set on this writer.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">replace</span><span class="p">()</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">createOrReplace</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Create a new table or replace an existing table with the contents of the data frame.</span>
<span class="sd"> The output table&#39;s schema, partition layout, properties,</span>
<span class="sd"> and other configuration will be based on the contents of the data frame</span>
<span class="sd"> and the configuration set on this writer.</span>
<span class="sd"> If the table exists, its configuration and data will be replaced.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">createOrReplace</span><span class="p">()</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">append</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Append the contents of the data frame to the output table.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">append</span><span class="p">()</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">overwrite</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">condition</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Overwrite rows matching the given filter condition with the contents of the data frame in</span>
<span class="sd"> the output table.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">overwrite</span><span class="p">(</span><span class="n">condition</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">3.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">overwritePartitions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Overwrite all partition for which the data frame contains at least one row with the contents</span>
<span class="sd"> of the data frame in the output table.</span>
<span class="sd"> This operation is equivalent to Hive&#39;s `INSERT OVERWRITE ... PARTITION`, which replaces</span>
<span class="sd"> partitions dynamically depending on the contents of the data frame.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_jwriter</span><span class="o">.</span><span class="n">overwritePartitions</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_test</span><span class="p">():</span>
<span class="kn">import</span> <span class="nn">doctest</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">tempfile</span>
<span class="kn">import</span> <span class="nn">py4j</span>
<span class="kn">from</span> <span class="nn">pyspark.context</span> <span class="kn">import</span> <span class="n">SparkContext</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span>
<span class="kn">import</span> <span class="nn">pyspark.sql.readwriter</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">sql</span><span class="o">.</span><span class="n">readwriter</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">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="s1">&#39;local[4]&#39;</span><span class="p">,</span> <span class="s1">&#39;PythonTest&#39;</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
<span class="k">except</span> <span class="n">py4j</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">Py4JError</span><span class="p">:</span>
<span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
<span class="n">globs</span><span class="p">[</span><span class="s1">&#39;tempfile&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tempfile</span>
<span class="n">globs</span><span class="p">[</span><span class="s1">&#39;os&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span>
<span class="n">globs</span><span class="p">[</span><span class="s1">&#39;sc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span>
<span class="n">globs</span><span class="p">[</span><span class="s1">&#39;spark&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</span>
<span class="n">globs</span><span class="p">[</span><span class="s1">&#39;df&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">parquet</span><span class="p">(</span><span class="s1">&#39;python/test_support/sql/parquet_partitioned&#39;</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">sql</span><span class="o">.</span><span class="n">readwriter</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="o">|</span> <span class="n">doctest</span><span class="o">.</span><span class="n">REPORT_NDIFF</span><span class="p">)</span>
<span class="n">sc</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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