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<div class="section" id="Quickstart:-DataFrame">
<h1>Quickstart: DataFrame<a class="headerlink" href="#Quickstart:-DataFrame" title="Permalink to this headline"></a></h1>
<p>This is a short introduction and quickstart for the PySpark DataFrame API. PySpark DataFrames are lazily evaluated. They are implemented on top of <a class="reference external" href="https://spark.apache.org/docs/latest/rdd-programming-guide.html#overview">RDD</a>s. When Spark <a class="reference external" href="https://spark.apache.org/docs/latest/rdd-programming-guide.html#transformations">transforms</a> data, it does not immediately compute the transformation but plans how to compute later. When
<a class="reference external" href="https://spark.apache.org/docs/latest/rdd-programming-guide.html#actions">actions</a> such as <code class="docutils literal notranslate"><span class="pre">collect()</span></code> are explicitly called, the computation starts. This notebook shows the basic usages of the DataFrame, geared mainly for new users. You can run the latest version of these examples by yourself in ‘Live Notebook: DataFrame’ at <a class="reference external" href="https://spark.apache.org/docs/latest/api/python/getting_started/index.html">the quickstart page</a>.</p>
<p>There is also other useful information in Apache Spark documentation site, see the latest version of <a class="reference external" href="https://spark.apache.org/docs/latest/sql-programming-guide.html">Spark SQL and DataFrames</a>, <a class="reference external" href="https://spark.apache.org/docs/latest/rdd-programming-guide.html">RDD Programming Guide</a>, <a class="reference external" href="https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html">Structured Streaming Programming Guide</a>, <a class="reference external" href="https://spark.apache.org/docs/latest/streaming-programming-guide.html">Spark Streaming Programming
Guide</a> and <a class="reference external" href="https://spark.apache.org/docs/latest/ml-guide.html">Machine Learning Library (MLlib) Guide</a>.</p>
<p>PySpark applications start with initializing <code class="docutils literal notranslate"><span class="pre">SparkSession</span></code> which is the entry point of PySpark as below. In case of running it in PySpark shell via pyspark executable, the shell automatically creates the session in the variable spark for users.</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql</span><span class="w"> </span><span class="kn">import</span> <span class="n">SparkSession</span>
<span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="DataFrame-Creation">
<h2>DataFrame Creation<a class="headerlink" href="#DataFrame-Creation" title="Permalink to this headline"></a></h2>
<p>A PySpark DataFrame can be created via <code class="docutils literal notranslate"><span class="pre">pyspark.sql.SparkSession.createDataFrame</span></code> typically by passing a list of lists, tuples, dictionaries and <code class="docutils literal notranslate"><span class="pre">pyspark.sql.Row</span></code>s, a <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">pandas DataFrame</a> and an RDD consisting of such a list. <code class="docutils literal notranslate"><span class="pre">pyspark.sql.SparkSession.createDataFrame</span></code> takes the <code class="docutils literal notranslate"><span class="pre">schema</span></code> argument to specify the schema of the DataFrame. When it is omitted, PySpark infers the corresponding schema by taking a sample from
the data.</p>
<p>Firstly, you can create a PySpark DataFrame from a list of rows</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
</pre></div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">datetime</span><span class="w"> </span><span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">date</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql</span><span class="w"> </span><span class="kn">import</span> <span class="n">Row</span>
<span class="n">df</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">Row</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mf">2.</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;string1&#39;</span><span class="p">,</span> <span class="n">d</span><span class="o">=</span><span class="n">date</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">e</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
<span class="n">Row</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mf">3.</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;string2&#39;</span><span class="p">,</span> <span class="n">d</span><span class="o">=</span><span class="n">date</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">e</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2000</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">12</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
<span class="n">Row</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mf">5.</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;string3&#39;</span><span class="p">,</span> <span class="n">d</span><span class="o">=</span><span class="n">date</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">e</span><span class="o">=</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
<span class="p">])</span>
<span class="n">df</span>
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DataFrame[a: bigint, b: double, c: string, d: date, e: timestamp]
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<p>Create a PySpark DataFrame with an explicit schema.</p>
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<span></span><span class="n">df</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="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="s1">&#39;string1&#39;</span><span class="p">,</span> <span class="n">date</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
<span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="s1">&#39;string2&#39;</span><span class="p">,</span> <span class="n">date</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2000</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">12</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span>
<span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="s1">&#39;string3&#39;</span><span class="p">,</span> <span class="n">date</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
<span class="p">],</span> <span class="n">schema</span><span class="o">=</span><span class="s1">&#39;a long, b double, c string, d date, e timestamp&#39;</span><span class="p">)</span>
<span class="n">df</span>
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DataFrame[a: bigint, b: double, c: string, d: date, e: timestamp]
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<p>Create a PySpark DataFrame from a pandas DataFrame</p>
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<span></span><span class="n">pandas_df</span> <span class="o">=</span> <span class="n">pd</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="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">],</span>
<span class="s1">&#39;c&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;string1&#39;</span><span class="p">,</span> <span class="s1">&#39;string2&#39;</span><span class="p">,</span> <span class="s1">&#39;string3&#39;</span><span class="p">],</span>
<span class="s1">&#39;d&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">date</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">date</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">date</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)],</span>
<span class="s1">&#39;e&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2000</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">12</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
<span class="p">})</span>
<span class="n">df</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">pandas_df</span><span class="p">)</span>
<span class="n">df</span>
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DataFrame[a: bigint, b: double, c: string, d: date, e: timestamp]
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<p>The DataFrames created above all have the same results and schema.</p>
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<span></span><span class="c1"># All DataFrames above result same.</span>
<span class="n">df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
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+---+---+-------+----------+-------------------+
| a| b| c| d| e|
+---+---+-------+----------+-------------------+
| 1|2.0|string1|2000-01-01|2000-01-01 12:00:00|
| 2|3.0|string2|2000-02-01|2000-01-02 12:00:00|
| 3|4.0|string3|2000-03-01|2000-01-03 12:00:00|
+---+---+-------+----------+-------------------+
root
|-- a: long (nullable = true)
|-- b: double (nullable = true)
|-- c: string (nullable = true)
|-- d: date (nullable = true)
|-- e: timestamp (nullable = true)
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<div class="section" id="Viewing-Data">
<h2>Viewing Data<a class="headerlink" href="#Viewing-Data" title="Permalink to this headline"></a></h2>
<p>The top rows of a DataFrame can be displayed using <code class="docutils literal notranslate"><span class="pre">DataFrame.show()</span></code>.</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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+---+---+-------+----------+-------------------+
| a| b| c| d| e|
+---+---+-------+----------+-------------------+
| 1|2.0|string1|2000-01-01|2000-01-01 12:00:00|
+---+---+-------+----------+-------------------+
only showing top 1 row
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<p>Alternatively, you can enable <code class="docutils literal notranslate"><span class="pre">spark.sql.repl.eagerEval.enabled</span></code> configuration for the eager evaluation of PySpark DataFrame in notebooks such as Jupyter. The number of rows to show can be controlled via <code class="docutils literal notranslate"><span class="pre">spark.sql.repl.eagerEval.maxNumRows</span></code> configuration.</p>
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<span></span><span class="n">spark</span><span class="o">.</span><span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s1">&#39;spark.sql.repl.eagerEval.enabled&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">df</span>
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<table border='1'>
<tr><th>a</th><th>b</th><th>c</th><th>d</th><th>e</th></tr>
<tr><td>1</td><td>2.0</td><td>string1</td><td>2000-01-01</td><td>2000-01-01 12:00:00</td></tr>
<tr><td>2</td><td>3.0</td><td>string2</td><td>2000-02-01</td><td>2000-01-02 12:00:00</td></tr>
<tr><td>3</td><td>4.0</td><td>string3</td><td>2000-03-01</td><td>2000-01-03 12:00:00</td></tr>
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<p>The rows can also be shown vertically. This is useful when rows are too long to show horizontally.</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">vertical</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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-RECORD 0------------------
a | 1
b | 2.0
c | string1
d | 2000-01-01
e | 2000-01-01 12:00:00
only showing top 1 row
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<p>You can see the DataFrame’s schema and column names as follows:</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">columns</span>
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[&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;]
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
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root
|-- a: long (nullable = true)
|-- b: double (nullable = true)
|-- c: string (nullable = true)
|-- d: date (nullable = true)
|-- e: timestamp (nullable = true)
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<p>Show the summary of the DataFrame</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+-------+---+---+-------+
|summary| a| b| c|
+-------+---+---+-------+
| count| 3| 3| 3|
| mean|2.0|3.0| null|
| stddev|1.0|1.0| null|
| min| 1|2.0|string1|
| max| 3|4.0|string3|
+-------+---+---+-------+
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<p><code class="docutils literal notranslate"><span class="pre">DataFrame.collect()</span></code> collects the distributed data to the driver side as the local data in Python. Note that this can throw an out-of-memory error when the dataset is too large to fit in the driver side because it collects all the data from executors to the driver side.</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
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[Row(a=1, b=2.0, c=&#39;string1&#39;, d=datetime.date(2000, 1, 1), e=datetime.datetime(2000, 1, 1, 12, 0)),
Row(a=2, b=3.0, c=&#39;string2&#39;, d=datetime.date(2000, 2, 1), e=datetime.datetime(2000, 1, 2, 12, 0)),
Row(a=3, b=4.0, c=&#39;string3&#39;, d=datetime.date(2000, 3, 1), e=datetime.datetime(2000, 1, 3, 12, 0))]
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<p>In order to avoid throwing an out-of-memory exception, use <code class="docutils literal notranslate"><span class="pre">DataFrame.take()</span></code> or <code class="docutils literal notranslate"><span class="pre">DataFrame.tail()</span></code>.</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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[Row(a=1, b=2.0, c=&#39;string1&#39;, d=datetime.date(2000, 1, 1), e=datetime.datetime(2000, 1, 1, 12, 0))]
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<p>PySpark DataFrame also provides the conversion back to a <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">pandas DataFrame</a> to leverage pandas API. Note that <code class="docutils literal notranslate"><span class="pre">toPandas</span></code> also collects all data into the driver side that can easily cause an out-of-memory-error when the data is too large to fit into the driver side.</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">toPandas</span><span class="p">()</span>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>a</th>
<th>b</th>
<th>c</th>
<th>d</th>
<th>e</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1</td>
<td>2.0</td>
<td>string1</td>
<td>2000-01-01</td>
<td>2000-01-01 12:00:00</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
<td>3.0</td>
<td>string2</td>
<td>2000-02-01</td>
<td>2000-01-02 12:00:00</td>
</tr>
<tr>
<th>2</th>
<td>3</td>
<td>4.0</td>
<td>string3</td>
<td>2000-03-01</td>
<td>2000-01-03 12:00:00</td>
</tr>
</tbody>
</table>
</div></div>
</div>
</div>
<div class="section" id="Selecting-and-Accessing-Data">
<h2>Selecting and Accessing Data<a class="headerlink" href="#Selecting-and-Accessing-Data" title="Permalink to this headline"></a></h2>
<p>PySpark DataFrame is lazily evaluated and simply selecting a column does not trigger the computation but it returns a <code class="docutils literal notranslate"><span class="pre">Column</span></code> instance.</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">a</span>
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Column&lt;b&#39;a&#39;&gt;
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<p>In fact, most of column-wise operations return <code class="docutils literal notranslate"><span class="pre">Column</span></code>s.</p>
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<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql</span><span class="w"> </span><span class="kn">import</span> <span class="n">Column</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql.functions</span><span class="w"> </span><span class="kn">import</span> <span class="n">upper</span>
<span class="nb">type</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">c</span><span class="p">)</span> <span class="o">==</span> <span class="nb">type</span><span class="p">(</span><span class="n">upper</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">c</span><span class="p">))</span> <span class="o">==</span> <span class="nb">type</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">c</span><span class="o">.</span><span class="n">isNull</span><span class="p">())</span>
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True
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<p>These <code class="docutils literal notranslate"><span class="pre">Column</span></code>s can be used to select the columns from a DataFrame. For example, <code class="docutils literal notranslate"><span class="pre">DataFrame.select()</span></code> takes the <code class="docutils literal notranslate"><span class="pre">Column</span></code> instances that returns another DataFrame.</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">c</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+-------+
| c|
+-------+
|string1|
|string2|
|string3|
+-------+
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<p>Assign new <code class="docutils literal notranslate"><span class="pre">Column</span></code> instance.</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s1">&#39;upper_c&#39;</span><span class="p">,</span> <span class="n">upper</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">c</span><span class="p">))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+---+---+-------+----------+-------------------+-------+
| a| b| c| d| e|upper_c|
+---+---+-------+----------+-------------------+-------+
| 1|2.0|string1|2000-01-01|2000-01-01 12:00:00|STRING1|
| 2|3.0|string2|2000-02-01|2000-01-02 12:00:00|STRING2|
| 3|4.0|string3|2000-03-01|2000-01-03 12:00:00|STRING3|
+---+---+-------+----------+-------------------+-------+
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<p>To select a subset of rows, use <code class="docutils literal notranslate"><span class="pre">DataFrame.filter()</span></code>.</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">a</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+---+---+-------+----------+-------------------+
| a| b| c| d| e|
+---+---+-------+----------+-------------------+
| 1|2.0|string1|2000-01-01|2000-01-01 12:00:00|
+---+---+-------+----------+-------------------+
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</div>
</div>
<div class="section" id="Applying-a-Function">
<h2>Applying a Function<a class="headerlink" href="#Applying-a-Function" title="Permalink to this headline"></a></h2>
<p>PySpark supports various UDFs and APIs to allow users to execute Python native functions. See also the latest <a class="reference external" href="https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html#pandas-udfs-aka-vectorized-udfs">Pandas UDFs</a> and <a class="reference external" href="https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html#pandas-function-apis">Pandas Function APIs</a>. For instance, the example below allows users to directly use the APIs in <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html">a pandas
Series</a> within Python native function.</p>
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<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql.functions</span><span class="w"> </span><span class="kn">import</span> <span class="n">pandas_udf</span>
<span class="nd">@pandas_udf</span><span class="p">(</span><span class="s1">&#39;long&#39;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">pandas_plus_one</span><span class="p">(</span><span class="n">series</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
<span class="c1"># Simply plus one by using pandas Series.</span>
<span class="k">return</span> <span class="n">series</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">pandas_plus_one</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">a</span><span class="p">))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+------------------+
|pandas_plus_one(a)|
+------------------+
| 2|
| 3|
| 4|
+------------------+
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<p>Another example is <code class="docutils literal notranslate"><span class="pre">DataFrame.mapInPandas</span></code> which allows users directly use the APIs in a <a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">pandas DataFrame</a> without any restrictions such as the result length.</p>
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<span></span><span class="k">def</span><span class="w"> </span><span class="nf">pandas_filter_func</span><span class="p">(</span><span class="n">iterator</span><span class="p">):</span>
<span class="k">for</span> <span class="n">pandas_df</span> <span class="ow">in</span> <span class="n">iterator</span><span class="p">:</span>
<span class="k">yield</span> <span class="n">pandas_df</span><span class="p">[</span><span class="n">pandas_df</span><span class="o">.</span><span class="n">a</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">df</span><span class="o">.</span><span class="n">mapInPandas</span><span class="p">(</span><span class="n">pandas_filter_func</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">schema</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+---+---+-------+----------+-------------------+
| a| b| c| d| e|
+---+---+-------+----------+-------------------+
| 1|2.0|string1|2000-01-01|2000-01-01 12:00:00|
+---+---+-------+----------+-------------------+
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</div>
<div class="section" id="Grouping-Data">
<h2>Grouping Data<a class="headerlink" href="#Grouping-Data" title="Permalink to this headline"></a></h2>
<p>PySpark DataFrame also provides a way of handling grouped data by using the common approach, split-apply-combine strategy. It groups the data by a certain condition applies a function to each group and then combines them back to the DataFrame.</p>
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<span></span><span class="n">df</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="p">[</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;banana&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="s1">&#39;banana&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">20</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;carrot&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">30</span><span class="p">],</span>
<span class="p">[</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="s1">&#39;grape&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">40</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;carrot&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">50</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="s1">&#39;carrot&#39;</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">60</span><span class="p">],</span>
<span class="p">[</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;banana&#39;</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">70</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;grape&#39;</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">80</span><span class="p">]],</span> <span class="n">schema</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">,</span> <span class="s1">&#39;fruit&#39;</span><span class="p">,</span> <span class="s1">&#39;v1&#39;</span><span class="p">,</span> <span class="s1">&#39;v2&#39;</span><span class="p">])</span>
<span class="n">df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+-----+------+---+---+
|color| fruit| v1| v2|
+-----+------+---+---+
| red|banana| 1| 10|
| blue|banana| 2| 20|
| red|carrot| 3| 30|
| blue| grape| 4| 40|
| red|carrot| 5| 50|
|black|carrot| 6| 60|
| red|banana| 7| 70|
| red| grape| 8| 80|
+-----+------+---+---+
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<p>Grouping and then applying the <code class="docutils literal notranslate"><span class="pre">avg()</span></code> function to the resulting groups.</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;color&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">avg</span><span class="p">()</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+-----+-------+-------+
|color|avg(v1)|avg(v2)|
+-----+-------+-------+
| red| 4.8| 48.0|
|black| 6.0| 60.0|
| blue| 3.0| 30.0|
+-----+-------+-------+
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<p>You can also apply a Python native function against each group by using pandas API.</p>
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<span></span><span class="k">def</span><span class="w"> </span><span class="nf">plus_mean</span><span class="p">(</span><span class="n">pandas_df</span><span class="p">):</span>
<span class="k">return</span> <span class="n">pandas_df</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">v1</span><span class="o">=</span><span class="n">pandas_df</span><span class="o">.</span><span class="n">v1</span> <span class="o">-</span> <span class="n">pandas_df</span><span class="o">.</span><span class="n">v1</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;color&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">applyInPandas</span><span class="p">(</span><span class="n">plus_mean</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">schema</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+-----+------+---+---+
|color| fruit| v1| v2|
+-----+------+---+---+
| red|banana| -3| 10|
| red|carrot| -1| 30|
| red|carrot| 0| 50|
| red|banana| 2| 70|
| red| grape| 3| 80|
|black|carrot| 0| 60|
| blue|banana| -1| 20|
| blue| grape| 1| 40|
+-----+------+---+---+
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<p>Co-grouping and applying a function.</p>
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<span></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="p">[(</span><span class="mi">20000101</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">20000101</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">20000102</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">),</span> <span class="p">(</span><span class="mi">20000102</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">)],</span>
<span class="p">(</span><span class="s1">&#39;time&#39;</span><span class="p">,</span> <span class="s1">&#39;id&#39;</span><span class="p">,</span> <span class="s1">&#39;v1&#39;</span><span class="p">))</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="p">[(</span><span class="mi">20000101</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">),</span> <span class="p">(</span><span class="mi">20000101</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">)],</span>
<span class="p">(</span><span class="s1">&#39;time&#39;</span><span class="p">,</span> <span class="s1">&#39;id&#39;</span><span class="p">,</span> <span class="s1">&#39;v2&#39;</span><span class="p">))</span>
<span class="k">def</span><span class="w"> </span><span class="nf">merge_ordered</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
<span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">merge_ordered</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
<span class="n">df1</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;id&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">cogroup</span><span class="p">(</span><span class="n">df2</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;id&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">applyInPandas</span><span class="p">(</span>
<span class="n">merge_ordered</span><span class="p">,</span> <span class="n">schema</span><span class="o">=</span><span class="s1">&#39;time int, id int, v1 double, v2 string&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+--------+---+---+---+
| time| id| v1| v2|
+--------+---+---+---+
|20000101| 1|1.0| x|
|20000102| 1|3.0| x|
|20000101| 2|2.0| y|
|20000102| 2|4.0| y|
+--------+---+---+---+
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<div class="section" id="Getting-Data-In/Out">
<h2>Getting Data In/Out<a class="headerlink" href="#Getting-Data-In/Out" title="Permalink to this headline"></a></h2>
<p>CSV is straightforward and easy to use. Parquet and ORC are efficient and compact file formats to read and write faster.</p>
<p>There are many other data sources available in PySpark such as JDBC, text, binaryFile, Avro, etc. See also the latest <a class="reference external" href="https://spark.apache.org/docs/latest/sql-programming-guide.html">Spark SQL, DataFrames and Datasets Guide</a> in Apache Spark documentation.</p>
<div class="section" id="CSV">
<h3>CSV<a class="headerlink" href="#CSV" title="Permalink to this headline"></a></h3>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">write</span><span class="o">.</span><span class="n">csv</span><span class="p">(</span><span class="s1">&#39;foo.csv&#39;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">spark</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">csv</span><span class="p">(</span><span class="s1">&#39;foo.csv&#39;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+-----+------+---+---+
|color| fruit| v1| v2|
+-----+------+---+---+
| red|banana| 1| 10|
| blue|banana| 2| 20|
| red|carrot| 3| 30|
| blue| grape| 4| 40|
| red|carrot| 5| 50|
|black|carrot| 6| 60|
| red|banana| 7| 70|
| red| grape| 8| 80|
+-----+------+---+---+
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<div class="section" id="Parquet">
<h3>Parquet<a class="headerlink" href="#Parquet" title="Permalink to this headline"></a></h3>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">write</span><span class="o">.</span><span class="n">parquet</span><span class="p">(</span><span class="s1">&#39;bar.parquet&#39;</span><span class="p">)</span>
<span class="n">spark</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">parquet</span><span class="p">(</span><span class="s1">&#39;bar.parquet&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+-----+------+---+---+
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+-----+------+---+---+
| red|banana| 1| 10|
| blue|banana| 2| 20|
| red|carrot| 3| 30|
| blue| grape| 4| 40|
| red|carrot| 5| 50|
|black|carrot| 6| 60|
| red|banana| 7| 70|
| red| grape| 8| 80|
+-----+------+---+---+
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<div class="section" id="ORC">
<h3>ORC<a class="headerlink" href="#ORC" title="Permalink to this headline"></a></h3>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">write</span><span class="o">.</span><span class="n">orc</span><span class="p">(</span><span class="s1">&#39;zoo.orc&#39;</span><span class="p">)</span>
<span class="n">spark</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">orc</span><span class="p">(</span><span class="s1">&#39;zoo.orc&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+-----+------+---+---+
|color| fruit| v1| v2|
+-----+------+---+---+
| red|banana| 1| 10|
| blue|banana| 2| 20|
| red|carrot| 3| 30|
| blue| grape| 4| 40|
| red|carrot| 5| 50|
|black|carrot| 6| 60|
| red|banana| 7| 70|
| red| grape| 8| 80|
+-----+------+---+---+
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<div class="section" id="Working-with-SQL">
<h2>Working with SQL<a class="headerlink" href="#Working-with-SQL" title="Permalink to this headline"></a></h2>
<p>DataFrame and Spark SQL share the same execution engine so they can be interchangeably used seamlessly. For example, you can register the DataFrame as a table and run a SQL easily as below:</p>
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<span></span><span class="n">df</span><span class="o">.</span><span class="n">createOrReplaceTempView</span><span class="p">(</span><span class="s2">&quot;tableA&quot;</span><span class="p">)</span>
<span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s2">&quot;SELECT count(*) from tableA&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>In addition, UDFs can be registered and invoked in SQL out of the box:</p>
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<span></span><span class="nd">@pandas_udf</span><span class="p">(</span><span class="s2">&quot;integer&quot;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">add_one</span><span class="p">(</span><span class="n">s</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
<span class="k">return</span> <span class="n">s</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">spark</span><span class="o">.</span><span class="n">udf</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s2">&quot;add_one&quot;</span><span class="p">,</span> <span class="n">add_one</span><span class="p">)</span>
<span class="n">spark</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s2">&quot;SELECT add_one(v1) FROM tableA&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>These SQL expressions can directly be mixed and used as PySpark columns.</p>
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<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql.functions</span><span class="w"> </span><span class="kn">import</span> <span class="n">expr</span>
<span class="n">df</span><span class="o">.</span><span class="n">selectExpr</span><span class="p">(</span><span class="s1">&#39;add_one(v1)&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">df</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">expr</span><span class="p">(</span><span class="s1">&#39;count(*)&#39;</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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+-----------+
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| 6|
| 7|
| 8|
| 9|
+-----------+
+--------------+
|(count(1) &gt; 0)|
+--------------+
| true|
+--------------+
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