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<section id="pyspark-testing-assertdataframeequal">
<h1>pyspark.testing.assertDataFrameEqual<a class="headerlink" href="#pyspark-testing-assertdataframeequal" title="Permalink to this headline">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="pyspark.testing.assertDataFrameEqual">
<span class="sig-prename descclassname"><span class="pre">pyspark.testing.</span></span><span class="sig-name descname"><span class="pre">assertDataFrameEqual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">actual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkRowOrder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignoreNullable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignoreColumnOrder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignoreColumnName</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignoreColumnType</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxErrors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">showOnlyDiff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">includeDiffRows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/testing/utils.html#assertDataFrameEqual"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyspark.testing.assertDataFrameEqual" title="Permalink to this definition">#</a></dt>
<dd><p>A util function to assert equality between <cite>actual</cite> and <cite>expected</cite>
(DataFrames or lists of Rows), with optional parameters <cite>checkRowOrder</cite>, <cite>rtol</cite>, and <cite>atol</cite>.</p>
<p>Supports Spark, Spark Connect, pandas, and pandas-on-Spark DataFrames.
For more information about pandas-on-Spark DataFrame equality, see the docs for
<cite>assertPandasOnSparkEqual</cite>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 3.5.0.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>actual</strong><span class="classifier">DataFrame (Spark, Spark Connect, pandas, or pandas-on-Spark) or list of Rows</span></dt><dd><p>The DataFrame that is being compared or tested.</p>
</dd>
<dt><strong>expected</strong><span class="classifier">DataFrame (Spark, Spark Connect, pandas, or pandas-on-Spark) or list of Rows</span></dt><dd><p>The expected result of the operation, for comparison with the actual result.</p>
</dd>
<dt><strong>checkRowOrder</strong><span class="classifier">bool, optional</span></dt><dd><p>A flag indicating whether the order of rows should be considered in the comparison.
If set to <cite>False</cite> (default), the row order is not taken into account.
If set to <cite>True</cite>, the order of rows is important and will be checked during comparison.
(See Notes)</p>
</dd>
<dt><strong>rtol</strong><span class="classifier">float, optional</span></dt><dd><p>The relative tolerance, used in asserting approximate equality for float values in actual
and expected. Set to 1e-5 by default. (See Notes)</p>
</dd>
<dt><strong>atol</strong><span class="classifier">float, optional</span></dt><dd><p>The absolute tolerance, used in asserting approximate equality for float values in actual
and expected. Set to 1e-8 by default. (See Notes)</p>
</dd>
<dt><strong>ignoreNullable</strong><span class="classifier">bool, default True</span></dt><dd><p>Specifies whether a column’s nullable property is included when checking for
schema equality.
When set to <cite>True</cite> (default), the nullable property of the columns being compared
is not taken into account and the columns will be considered equal even if they have
different nullable settings.
When set to <cite>False</cite>, columns are considered equal only if they have the same nullable
setting.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 4.0.0.</span></p>
</div>
</dd>
<dt><strong>ignoreColumnOrder</strong><span class="classifier">bool, default False</span></dt><dd><p>Specifies whether to compare columns in the order they appear in the DataFrame or by
column name.
If set to <cite>False</cite> (default), columns are compared in the order they appear in the
DataFrames.
When set to <cite>True</cite>, a column in the expected DataFrame is compared to the column with the
same name in the actual DataFrame.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 4.0.0.</span></p>
</div>
</dd>
<dt><strong>ignoreColumnName</strong><span class="classifier">bool, default False</span></dt><dd><p>Specifies whether to fail the initial schema equality check if the column names in the two
DataFrames are different.
When set to <cite>False</cite> (default), column names are checked and the function fails if they are
different.
When set to <cite>True</cite>, the function will succeed even if column names are different.
Column data types are compared for columns in the order they appear in the DataFrames.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 4.0.0.</span></p>
</div>
</dd>
<dt><strong>ignoreColumnType</strong><span class="classifier">bool, default False</span></dt><dd><p>Specifies whether to ignore the data type of the columns when comparing.
When set to <cite>False</cite> (default), column data types are checked and the function fails if they
are different.
When set to <cite>True</cite>, the schema equality check will succeed even if column data types are
different and the function will attempt to compare rows.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 4.0.0.</span></p>
</div>
</dd>
<dt><strong>maxErrors</strong><span class="classifier">bool, optional</span></dt><dd><p>The maximum number of row comparison failures to encounter before returning.
When this number of row comparisons have failed, the function returns independent of how
many rows have been compared.
Set to None by default which means compare all rows independent of number of failures.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 4.0.0.</span></p>
</div>
</dd>
<dt><strong>showOnlyDiff</strong><span class="classifier">bool, default False</span></dt><dd><p>If set to <cite>True</cite>, the error message will only include rows that are different.
If set to <cite>False</cite> (default), the error message will include all rows
(when there is at least one row that is different).</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 4.0.0.</span></p>
</div>
</dd>
<dt><strong>includeDiffRows: bool, False</strong></dt><dd><p>If set to <cite>True</cite>, the unequal rows are included in PySparkAssertionError for further
debugging. If set to <cite>False</cite> (default), the unequal rows are not returned as a data set.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 4.0.0.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>When <cite>assertDataFrameEqual</cite> fails, the error message uses the Python <cite>difflib</cite> library to
display a diff log of each row that differs in <cite>actual</cite> and <cite>expected</cite>.</p>
<p>For <cite>checkRowOrder</cite>, note that PySpark DataFrame ordering is non-deterministic, unless
explicitly sorted.</p>
<p>Note that schema equality is checked only when <cite>expected</cite> is a DataFrame (not a list of Rows).</p>
<p>For DataFrames with float/decimal values, assertDataFrame asserts approximate equality.
Two float/decimal values a and b are approximately equal if the following equation is True:</p>
<p><code class="docutils literal notranslate"><span class="pre">absolute(a</span> <span class="pre">-</span> <span class="pre">b)</span> <span class="pre">&lt;=</span> <span class="pre">(atol</span> <span class="pre">+</span> <span class="pre">rtol</span> <span class="pre">*</span> <span class="pre">absolute(b))</span></code>.</p>
<p><cite>ignoreColumnOrder</cite> cannot be set to <cite>True</cite> if <cite>ignoreColumnNames</cite> is also set to <cite>True</cite>.
<cite>ignoreColumnNames</cite> cannot be set to <cite>True</cite> if <cite>ignoreColumnOrder</cite> is also set to <cite>True</cite>.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df1</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="mi">3000</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;amount&quot;</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="mi">3000</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;amount&quot;</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">)</span> <span class="c1"># pass, DataFrames are identical</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df1</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="mf">3.23</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;amount&quot;</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="mf">0.109</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="mf">3.23</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;amount&quot;</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">)</span> <span class="c1"># pass, DataFrames are approx equal by rtol</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df1</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3000</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;amount&quot;</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">list_of_rows</span> <span class="o">=</span> <span class="p">[</span><span class="n">Row</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="n">Row</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3000</span><span class="p">)]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">list_of_rows</span><span class="p">)</span> <span class="c1"># pass, actual and expected data are equal</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pyspark.pandas</span> <span class="k">as</span> <span class="nn">ps</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df1</span> <span class="o">=</span> <span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="s1">&#39;c&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]})</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2</span> <span class="o">=</span> <span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="s1">&#39;c&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]})</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># pass, pandas-on-Spark DataFrames are equal</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df1</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="mf">1000.00</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="mf">3000.00</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="mf">2000.00</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;amount&quot;</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="mf">1001.00</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="mf">3000.00</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="mf">2003.00</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;amount&quot;</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="c">...</span>
<span class="gr">PySparkAssertionError</span>: <span class="n">[DIFFERENT_ROWS] Results do not match: ( 66.66667 % )</span>
<span class="x">*** actual ***</span>
<span class="x">! Row(id=&#39;1&#39;, amount=1000.0)</span>
<span class="x"> Row(id=&#39;2&#39;, amount=3000.0)</span>
<span class="x">! Row(id=&#39;3&#39;, amount=2000.0)</span>
<span class="x">*** expected ***</span>
<span class="x">! Row(id=&#39;1&#39;, amount=1001.0)</span>
<span class="x"> Row(id=&#39;2&#39;, amount=3000.0)</span>
<span class="x">! Row(id=&#39;3&#39;, amount=2003.0)</span>
</pre></div>
</div>
<p>Example for ignoreNullable</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </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="p">,</span> <span class="n">StructField</span><span class="p">,</span> <span class="n">StringType</span><span class="p">,</span> <span class="n">LongType</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df1_nullable</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="mi">1000</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">5000</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">)],</span>
<span class="gp">... </span> <span class="n">schema</span><span class="o">=</span><span class="n">StructType</span><span class="p">(</span>
<span class="gp">... </span> <span class="p">[</span><span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;amount&quot;</span><span class="p">,</span> <span class="n">LongType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">),</span> <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="n">StringType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">)]</span>
<span class="gp">... </span> <span class="p">)</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2_nullable</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="mi">1000</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">5000</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">)],</span>
<span class="gp">... </span> <span class="n">schema</span><span class="o">=</span><span class="n">StructType</span><span class="p">(</span>
<span class="gp">... </span> <span class="p">[</span><span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;amount&quot;</span><span class="p">,</span> <span class="n">LongType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">),</span> <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="n">StringType</span><span class="p">(),</span> <span class="kc">False</span><span class="p">)]</span>
<span class="gp">... </span> <span class="p">)</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1_nullable</span><span class="p">,</span> <span class="n">df2_nullable</span><span class="p">,</span> <span class="n">ignoreNullable</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># pass</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">df1_nullable</span><span class="p">,</span> <span class="n">df2_nullable</span><span class="p">,</span> <span class="n">ignoreNullable</span><span class="o">=</span><span class="kc">False</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="c">...</span>
<span class="gr">PySparkAssertionError</span>: <span class="n">[DIFFERENT_SCHEMA] Schemas do not match.</span>
<span class="x">--- actual</span>
<span class="x">+++ expected</span>
<span class="x">- StructType([StructField(&#39;amount&#39;, LongType(), True), StructField(&#39;id&#39;, StringType(), True)])</span>
<span class="x">? ^^^</span>
<span class="x">+ StructType([StructField(&#39;amount&#39;, LongType(), True), StructField(&#39;id&#39;, StringType(), False)])</span>
<span class="x">? ^^^^</span>
</pre></div>
</div>
<p>Example for ignoreColumnOrder</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df1_col_order</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="mi">1000</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">5000</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;amount&quot;</span><span class="p">,</span> <span class="s2">&quot;id&quot;</span><span class="p">]</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2_col_order</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="mi">1000</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="mi">5000</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;amount&quot;</span><span class="p">]</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1_col_order</span><span class="p">,</span> <span class="n">df2_col_order</span><span class="p">,</span> <span class="n">ignoreColumnOrder</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>Example for ignoreColumnName</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df1_col_names</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="mi">1000</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">5000</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;amount&quot;</span><span class="p">,</span> <span class="s2">&quot;identity&quot;</span><span class="p">]</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2_col_names</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="mi">1000</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">5000</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;amount&quot;</span><span class="p">,</span> <span class="s2">&quot;id&quot;</span><span class="p">]</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1_col_names</span><span class="p">,</span> <span class="n">df2_col_names</span><span class="p">,</span> <span class="n">ignoreColumnName</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>Example for ignoreColumnType</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df1_col_types</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="mi">1000</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">5000</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;amount&quot;</span><span class="p">,</span> <span class="s2">&quot;id&quot;</span><span class="p">]</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2_col_types</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="mf">1000.0</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mf">5000.0</span><span class="p">,</span> <span class="s2">&quot;2&quot;</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;amount&quot;</span><span class="p">,</span> <span class="s2">&quot;id&quot;</span><span class="p">]</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1_col_types</span><span class="p">,</span> <span class="n">df2_col_types</span><span class="p">,</span> <span class="n">ignoreColumnType</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>Example for maxErrors (will only report the first mismatching row)</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df1</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">([(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;A&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">)])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">([(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;A&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">)])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">maxErrors</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="c">...</span>
<span class="gr">PySparkAssertionError</span>: <span class="n">[DIFFERENT_ROWS] Results do not match: ( 33.33333 % )</span>
<span class="x">*** actual ***</span>
<span class="x"> Row(_1=1, _2=&#39;A&#39;)</span>
<span class="x">! Row(_1=2, _2=&#39;B&#39;)</span>
<span class="x">*** expected ***</span>
<span class="x"> Row(_1=1, _2=&#39;A&#39;)</span>
<span class="x">! Row(_1=2, _2=&#39;X&#39;)</span>
</pre></div>
</div>
<p>Example for showOnlyDiff (will only report the mismatching rows)</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df1</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">([(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;A&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">)])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">([(</span><span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;A&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;X&quot;</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;Y&quot;</span><span class="p">)])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">showOnlyDiff</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gt">Traceback (most recent call last):</span>
<span class="c">...</span>
<span class="gr">PySparkAssertionError</span>: <span class="n">[DIFFERENT_ROWS] Results do not match: ( 66.66667 % )</span>
<span class="x">*** actual ***</span>
<span class="x">! Row(_1=2, _2=&#39;B&#39;)</span>
<span class="x">! Row(_1=3, _2=&#39;C&#39;)</span>
<span class="x">*** expected ***</span>
<span class="x">! Row(_1=2, _2=&#39;X&#39;)</span>
<span class="x">! Row(_1=3, _2=&#39;Y&#39;)</span>
</pre></div>
</div>
<p>The <cite>includeDiffRows</cite> parameter can be used to include the rows that did not match
in the PySparkAssertionError. This can be useful for debugging or further analysis.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df1</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="mf">1000.00</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="mf">3000.00</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="mf">2000.00</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;amount&quot;</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df2</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">data</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="mf">1001.00</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;2&quot;</span><span class="p">,</span> <span class="mf">3000.00</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;3&quot;</span><span class="p">,</span> <span class="mf">2003.00</span><span class="p">)],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="s2">&quot;amount&quot;</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="k">try</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">assertDataFrameEqual</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">includeDiffRows</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">... </span><span class="k">except</span> <span class="n">PySparkAssertionError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">spark</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="go">+-----------+-----------+</span>
<span class="go">| _1| _2|</span>
<span class="go">+-----------+-----------+</span>
<span class="go">|{1, 1000.0}|{1, 1001.0}|</span>
<span class="go">|{3, 2000.0}|{3, 2003.0}|</span>
<span class="go">+-----------+-----------+</span>
</pre></div>
</div>
</dd></dl>
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