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| <div class="section" id="pyspark-pandas-dataframe-transpose"> |
| <h1>pyspark.pandas.DataFrame.transpose<a class="headerlink" href="#pyspark-pandas-dataframe-transpose" title="Permalink to this headline">¶</a></h1> |
| <dl class="py method"> |
| <dt id="pyspark.pandas.DataFrame.transpose"> |
| <code class="sig-prename descclassname">DataFrame.</code><code class="sig-name descname">transpose</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → pyspark.pandas.frame.DataFrame<a class="reference internal" href="../../../_modules/pyspark/pandas/frame.html#DataFrame.transpose"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.pandas.DataFrame.transpose" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Transpose index and columns.</p> |
| <p>Reflect the DataFrame over its main diagonal by writing rows as columns |
| and vice-versa. The property <a class="reference internal" href="pyspark.pandas.DataFrame.T.html#pyspark.pandas.DataFrame.T" title="pyspark.pandas.DataFrame.T"><code class="xref py py-attr docutils literal notranslate"><span class="pre">T</span></code></a> is an accessor to the method |
| <a class="reference internal" href="#pyspark.pandas.DataFrame.transpose" title="pyspark.pandas.DataFrame.transpose"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transpose()</span></code></a>.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This method is based on an expensive operation due to the nature |
| of big data. Internally it needs to generate each row for each value, and |
| then group twice - it is a huge operation. To prevent misusage, this method |
| has the ‘compute.max_rows’ default limit of input length, and raises a ValueError.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">pyspark.pandas.config</span> <span class="kn">import</span> <span class="n">option_context</span> |
| <span class="gp">>>> </span><span class="k">with</span> <span class="n">option_context</span><span class="p">(</span><span class="s1">'compute.max_rows'</span><span class="p">,</span> <span class="mi">1000</span><span class="p">):</span> |
| <span class="gp">... </span> <span class="n">ps</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'a'</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1001</span><span class="p">)})</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> |
| <span class="gt">Traceback (most recent call last):</span> |
| <span class="c">...</span> |
| <span class="gr">ValueError</span>: <span class="n">Current DataFrame has more then the given limit 1000 rows.</span> |
| <span class="go">Please set 'compute.max_rows' by using 'pyspark.pandas.config.set_option'</span> |
| <span class="go">to retrieve to retrieve more than 1000 rows. Note that, before changing the</span> |
| <span class="go">'compute.max_rows', this operation is considerably expensive.</span> |
| </pre></div> |
| </div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt>DataFrame</dt><dd><p>The transposed DataFrame.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Notes</p> |
| <p>Transposing a DataFrame with mixed dtypes will result in a homogeneous |
| DataFrame with the coerced dtype. For instance, if int and float have |
| to be placed in same column, it becomes float. If type coercion is not |
| possible, it fails.</p> |
| <p>Also, note that the values in index should be unique because they become |
| unique column names.</p> |
| <p>In addition, if Spark 2.3 is used, the types should always be exactly same.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>Square DataFrame with homogeneous dtype</strong></p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">d1</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'col1'</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="s1">'col2'</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]}</span> |
| <span class="gp">>>> </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="n">data</span><span class="o">=</span><span class="n">d1</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'col1'</span><span class="p">,</span> <span class="s1">'col2'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df1</span> |
| <span class="go"> col1 col2</span> |
| <span class="go">0 1 3</span> |
| <span class="go">1 2 4</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df1_transposed</span> <span class="o">=</span> <span class="n">df1</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">df1_transposed</span> |
| <span class="go"> 0 1</span> |
| <span class="go">col1 1 2</span> |
| <span class="go">col2 3 4</span> |
| </pre></div> |
| </div> |
| <p>When the dtype is homogeneous in the original DataFrame, we get a |
| transposed DataFrame with the same dtype:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df1</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">col1 int64</span> |
| <span class="go">col2 int64</span> |
| <span class="go">dtype: object</span> |
| <span class="gp">>>> </span><span class="n">df1_transposed</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">0 int64</span> |
| <span class="go">1 int64</span> |
| <span class="go">dtype: object</span> |
| </pre></div> |
| </div> |
| <p><strong>Non-square DataFrame with mixed dtypes</strong></p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">d2</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'score'</span><span class="p">:</span> <span class="p">[</span><span class="mf">9.5</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'kids'</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="s1">'age'</span><span class="p">:</span> <span class="p">[</span><span class="mi">12</span><span class="p">,</span> <span class="mi">22</span><span class="p">]}</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="n">data</span><span class="o">=</span><span class="n">d2</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'score'</span><span class="p">,</span> <span class="s1">'kids'</span><span class="p">,</span> <span class="s1">'age'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df2</span> |
| <span class="go"> score kids age</span> |
| <span class="go">0 9.5 0 12</span> |
| <span class="go">1 8.0 0 22</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df2_transposed</span> <span class="o">=</span> <span class="n">df2</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">df2_transposed</span> |
| <span class="go"> 0 1</span> |
| <span class="go">age 12.0 22.0</span> |
| <span class="go">kids 0.0 0.0</span> |
| <span class="go">score 9.5 8.0</span> |
| </pre></div> |
| </div> |
| <p>When the DataFrame has mixed dtypes, we get a transposed DataFrame with |
| the coerced dtype:</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">dtypes</span> |
| <span class="go">score float64</span> |
| <span class="go">kids int64</span> |
| <span class="go">age int64</span> |
| <span class="go">dtype: object</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">df2_transposed</span><span class="o">.</span><span class="n">dtypes</span> |
| <span class="go">0 float64</span> |
| <span class="go">1 float64</span> |
| <span class="go">dtype: object</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
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