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| <section id="pyspark-pandas-dataframe-assign"> |
| <h1>pyspark.pandas.DataFrame.assign<a class="headerlink" href="#pyspark-pandas-dataframe-assign" title="Permalink to this headline">¶</a></h1> |
| <dl class="py method"> |
| <dt id="pyspark.pandas.DataFrame.assign"> |
| <code class="sig-prename descclassname">DataFrame.</code><code class="sig-name descname">assign</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span></em><span class="sig-paren">)</span> → pyspark.pandas.frame.DataFrame<a class="reference internal" href="../../../_modules/pyspark/pandas/frame.html#DataFrame.assign"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.pandas.DataFrame.assign" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Assign new columns to a DataFrame.</p> |
| <p>Returns a new object with all original columns in addition to new ones. |
| Existing columns that are re-assigned will be overwritten.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>**kwargs</strong><span class="classifier">dict of {str: callable, Series or Index}</span></dt><dd><p>The column names are keywords. If the values are |
| callable, they are computed on the DataFrame and |
| assigned to the new columns. The callable must not |
| change input DataFrame (though pandas-on-Spark doesn’t check it). |
| If the values are not callable, (e.g. a Series or a literal), |
| they are simply assigned.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt>DataFrame</dt><dd><p>A new DataFrame with the new columns in addition to |
| all the existing columns.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Notes</p> |
| <p>Assigning multiple columns within the same <code class="docutils literal notranslate"><span class="pre">assign</span></code> is possible |
| but you cannot refer to newly created or modified columns. This |
| feature is supported in pandas for Python 3.6 and later but not in |
| pandas-on-Spark. In pandas-on-Spark, all items are computed first, |
| and then assigned.</p> |
| <p class="rubric">Examples</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">'temp_c'</span><span class="p">:</span> <span class="p">[</span><span class="mf">17.0</span><span class="p">,</span> <span class="mf">25.0</span><span class="p">]},</span> |
| <span class="gp">... </span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'Portland'</span><span class="p">,</span> <span class="s1">'Berkeley'</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">df</span> |
| <span class="go"> temp_c</span> |
| <span class="go">Portland 17.0</span> |
| <span class="go">Berkeley 25.0</span> |
| </pre></div> |
| </div> |
| <p>Where the value is a callable, evaluated on <cite>df</cite>:</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">assign</span><span class="p">(</span><span class="n">temp_f</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">temp_c</span> <span class="o">*</span> <span class="mi">9</span> <span class="o">/</span> <span class="mi">5</span> <span class="o">+</span> <span class="mi">32</span><span class="p">)</span> |
| <span class="go"> temp_c temp_f</span> |
| <span class="go">Portland 17.0 62.6</span> |
| <span class="go">Berkeley 25.0 77.0</span> |
| </pre></div> |
| </div> |
| <p>Alternatively, the same behavior can be achieved by directly |
| referencing an existing Series or sequence and you can also |
| create multiple columns within the same assign.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">assigned</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">temp_f</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'temp_c'</span><span class="p">]</span> <span class="o">*</span> <span class="mi">9</span> <span class="o">/</span> <span class="mi">5</span> <span class="o">+</span> <span class="mi">32</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="n">temp_k</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">'temp_c'</span><span class="p">]</span> <span class="o">+</span> <span class="mf">273.15</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="n">temp_idx</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">assigned</span><span class="p">[[</span><span class="s1">'temp_c'</span><span class="p">,</span> <span class="s1">'temp_f'</span><span class="p">,</span> <span class="s1">'temp_k'</span><span class="p">,</span> <span class="s1">'temp_idx'</span><span class="p">]]</span> |
| <span class="go"> temp_c temp_f temp_k temp_idx</span> |
| <span class="go">Portland 17.0 62.6 290.15 Portland</span> |
| <span class="go">Berkeley 25.0 77.0 298.15 Berkeley</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| </section> |
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