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<div class="section" id="pyspark-pandas-dataframe-nunique">
<h1>pyspark.pandas.DataFrame.nunique<a class="headerlink" href="#pyspark-pandas-dataframe-nunique" title="Permalink to this headline"></a></h1>
<dl class="py method">
<dt id="pyspark.pandas.DataFrame.nunique">
<code class="sig-prename descclassname">DataFrame.</code><code class="sig-name descname">nunique</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">axis</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>int<span class="p">, </span>str<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">0</span></em>, <em class="sig-param"><span class="n">dropna</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">True</span></em>, <em class="sig-param"><span class="n">approx</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">False</span></em>, <em class="sig-param"><span class="n">rsd</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.05</span></em><span class="sig-paren">)</span> &#x2192; Series<a class="reference internal" href="../../../_modules/pyspark/pandas/frame.html#DataFrame.nunique"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.pandas.DataFrame.nunique" title="Permalink to this definition"></a></dt>
<dd><p>Return number of unique elements in the object.</p>
<p>Excludes NA values by default.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>axis</strong><span class="classifier">int, default 0 or ‘index’</span></dt><dd><p>Can only be set to 0 at the moment.</p>
</dd>
<dt><strong>dropna</strong><span class="classifier">bool, default True</span></dt><dd><p>Don’t include NaN in the count.</p>
</dd>
<dt><strong>approx: bool, default False</strong></dt><dd><p>If False, will use the exact algorithm and return the exact number of unique.
If True, it uses the HyperLogLog approximate algorithm, which is significantly faster
for large amount of data.
Note: This parameter is specific to pandas-on-Spark and is not found in pandas.</p>
</dd>
<dt><strong>rsd: float, default 0.05</strong></dt><dd><p>Maximum estimation error allowed in the HyperLogLog algorithm.
Note: Just like <code class="docutils literal notranslate"><span class="pre">approx</span></code> this parameter is specific to pandas-on-Spark.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>The number of unique values per column as a pandas-on-Spark Series.</dt><dd></dd>
</dl>
</dd>
</dl>
<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">df</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="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]})</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
<span class="go">A 3</span>
<span class="go">B 1</span>
<span class="go">dtype: int64</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">df</span><span class="o">.</span><span class="n">nunique</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="go">A 3</span>
<span class="go">B 2</span>
<span class="go">dtype: int64</span>
</pre></div>
</div>
<p>On big data, we recommend using the approximate algorithm to speed up this function.
The result will be very close to the exact unique count.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">df</span><span class="o">.</span><span class="n">nunique</span><span class="p">(</span><span class="n">approx</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">A 3</span>
<span class="go">B 1</span>
<span class="go">dtype: int64</span>
</pre></div>
</div>
</dd></dl>
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