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| <div class="section" id="pyspark-pandas-dataframe-values"> |
| <h1>pyspark.pandas.DataFrame.values<a class="headerlink" href="#pyspark-pandas-dataframe-values" title="Permalink to this headline">¶</a></h1> |
| <dl class="py method"> |
| <dt id="pyspark.pandas.DataFrame.values"> |
| <em class="property">property </em><code class="sig-prename descclassname">DataFrame.</code><code class="sig-name descname">values</code><a class="headerlink" href="#pyspark.pandas.DataFrame.values" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return a Numpy representation of the DataFrame or the Series.</p> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>We recommend using <cite>DataFrame.to_numpy()</cite> or <cite>Series.to_numpy()</cite> instead.</p> |
| </div> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This method should only be used if the resulting NumPy ndarray is expected |
| to be small, as all the data is loaded into the driver’s memory.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt>numpy.ndarray</dt><dd></dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <p>A DataFrame where all columns are the same type (e.g., int64) results in an array of |
| the same type.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </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">'age'</span><span class="p">:</span> <span class="p">[</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">29</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'height'</span><span class="p">:</span> <span class="p">[</span><span class="mi">94</span><span class="p">,</span> <span class="mi">170</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'weight'</span><span class="p">:</span> <span class="p">[</span><span class="mi">31</span><span class="p">,</span> <span class="mi">115</span><span class="p">]})</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> age height weight</span> |
| <span class="go">0 3 94 31</span> |
| <span class="go">1 29 170 115</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">age int64</span> |
| <span class="go">height int64</span> |
| <span class="go">weight int64</span> |
| <span class="go">dtype: object</span> |
| <span class="gp">>>> </span><span class="n">df</span><span class="o">.</span><span class="n">values</span> |
| <span class="go">array([[ 3, 94, 31],</span> |
| <span class="go"> [ 29, 170, 115]])</span> |
| </pre></div> |
| </div> |
| <p>A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray |
| of the broadest type that accommodates these mixed types (e.g., object).</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </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">'parrot'</span><span class="p">,</span> <span class="mf">24.0</span><span class="p">,</span> <span class="s1">'second'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s1">'lion'</span><span class="p">,</span> <span class="mf">80.5</span><span class="p">,</span> <span class="s1">'first'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="p">(</span><span class="s1">'monkey'</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="kc">None</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="n">columns</span><span class="o">=</span><span class="p">(</span><span class="s1">'name'</span><span class="p">,</span> <span class="s1">'max_speed'</span><span class="p">,</span> <span class="s1">'rank'</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">df2</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">name object</span> |
| <span class="go">max_speed float64</span> |
| <span class="go">rank object</span> |
| <span class="go">dtype: object</span> |
| <span class="gp">>>> </span><span class="n">df2</span><span class="o">.</span><span class="n">values</span> |
| <span class="go">array([['parrot', 24.0, 'second'],</span> |
| <span class="go"> ['lion', 80.5, 'first'],</span> |
| <span class="go"> ['monkey', nan, None]], dtype=object)</span> |
| </pre></div> |
| </div> |
| <p>For Series,</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ps</span><span class="o">.</span><span class="n">Series</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="o">.</span><span class="n">values</span> |
| <span class="go">array([1, 2, 3])</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ps</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="s1">'aabc'</span><span class="p">))</span><span class="o">.</span><span class="n">values</span> |
| <span class="go">array(['a', 'a', 'b', 'c'], dtype=object)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
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