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<h1>Source code for pyspark.sql.functions</h1><div class="highlight"><pre>
<span class="c">#</span>
<span class="c"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c"># contributor license agreements. See the NOTICE file distributed with</span>
<span class="c"># this work for additional information regarding copyright ownership.</span>
<span class="c"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c"># (the "License"); you may not use this file except in compliance with</span>
<span class="c"># the License. You may obtain a copy of the License at</span>
<span class="c">#</span>
<span class="c"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c">#</span>
<span class="c"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c"># distributed under the License is distributed on an "AS IS" BASIS,</span>
<span class="c"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c"># See the License for the specific language governing permissions and</span>
<span class="c"># limitations under the License.</span>
<span class="c">#</span>
<span class="sd">"""</span>
<span class="sd">A collections of builtin functions</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version</span> <span class="o"><</span> <span class="s">"3"</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">imap</span> <span class="k">as</span> <span class="nb">map</span>
<span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">SparkContext</span>
<span class="kn">from</span> <span class="nn">pyspark.rdd</span> <span class="kn">import</span> <span class="n">_prepare_for_python_RDD</span><span class="p">,</span> <span class="n">ignore_unicode_prefix</span>
<span class="kn">from</span> <span class="nn">pyspark.serializers</span> <span class="kn">import</span> <span class="n">PickleSerializer</span><span class="p">,</span> <span class="n">AutoBatchedSerializer</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">since</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.types</span> <span class="kn">import</span> <span class="n">StringType</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.column</span> <span class="kn">import</span> <span class="n">Column</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">,</span> <span class="n">_to_seq</span>
<span class="k">def</span> <span class="nf">_create_function</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">doc</span><span class="o">=</span><span class="s">""</span><span class="p">):</span>
<span class="sd">""" Create a function for aggregator by name"""</span>
<span class="k">def</span> <span class="nf">_</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="p">,</span> <span class="n">name</span><span class="p">)(</span><span class="n">col</span><span class="o">.</span><span class="n">_jc</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">Column</span><span class="p">)</span> <span class="k">else</span> <span class="n">col</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
<span class="n">_</span><span class="o">.</span><span class="n">__name__</span> <span class="o">=</span> <span class="n">name</span>
<span class="n">_</span><span class="o">.</span><span class="n">__doc__</span> <span class="o">=</span> <span class="n">doc</span>
<span class="k">return</span> <span class="n">_</span>
<span class="k">def</span> <span class="nf">_create_binary_mathfunction</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">doc</span><span class="o">=</span><span class="s">""</span><span class="p">):</span>
<span class="sd">""" Create a binary mathfunction by name"""</span>
<span class="k">def</span> <span class="nf">_</span><span class="p">(</span><span class="n">col1</span><span class="p">,</span> <span class="n">col2</span><span class="p">):</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="c"># users might write ints for simplicity. This would throw an error on the JVM side.</span>
<span class="n">jc</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="p">,</span> <span class="n">name</span><span class="p">)(</span><span class="n">col1</span><span class="o">.</span><span class="n">_jc</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">col1</span><span class="p">,</span> <span class="n">Column</span><span class="p">)</span> <span class="k">else</span> <span class="nb">float</span><span class="p">(</span><span class="n">col1</span><span class="p">),</span>
<span class="n">col2</span><span class="o">.</span><span class="n">_jc</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">col2</span><span class="p">,</span> <span class="n">Column</span><span class="p">)</span> <span class="k">else</span> <span class="nb">float</span><span class="p">(</span><span class="n">col2</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
<span class="n">_</span><span class="o">.</span><span class="n">__name__</span> <span class="o">=</span> <span class="n">name</span>
<span class="n">_</span><span class="o">.</span><span class="n">__doc__</span> <span class="o">=</span> <span class="n">doc</span>
<span class="k">return</span> <span class="n">_</span>
<span class="k">def</span> <span class="nf">_create_window_function</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">doc</span><span class="o">=</span><span class="s">''</span><span class="p">):</span>
<span class="sd">""" Create a window function by name """</span>
<span class="k">def</span> <span class="nf">_</span><span class="p">():</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="p">,</span> <span class="n">name</span><span class="p">)()</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
<span class="n">_</span><span class="o">.</span><span class="n">__name__</span> <span class="o">=</span> <span class="n">name</span>
<span class="n">_</span><span class="o">.</span><span class="n">__doc__</span> <span class="o">=</span> <span class="s">'Window function: '</span> <span class="o">+</span> <span class="n">doc</span>
<span class="k">return</span> <span class="n">_</span>
<span class="n">_functions</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">'lit'</span><span class="p">:</span> <span class="s">'Creates a :class:`Column` of literal value.'</span><span class="p">,</span>
<span class="s">'col'</span><span class="p">:</span> <span class="s">'Returns a :class:`Column` based on the given column name.'</span><span class="p">,</span>
<span class="s">'column'</span><span class="p">:</span> <span class="s">'Returns a :class:`Column` based on the given column name.'</span><span class="p">,</span>
<span class="s">'asc'</span><span class="p">:</span> <span class="s">'Returns a sort expression based on the ascending order of the given column name.'</span><span class="p">,</span>
<span class="s">'desc'</span><span class="p">:</span> <span class="s">'Returns a sort expression based on the descending order of the given column name.'</span><span class="p">,</span>
<span class="s">'upper'</span><span class="p">:</span> <span class="s">'Converts a string expression to upper case.'</span><span class="p">,</span>
<span class="s">'lower'</span><span class="p">:</span> <span class="s">'Converts a string expression to upper case.'</span><span class="p">,</span>
<span class="s">'sqrt'</span><span class="p">:</span> <span class="s">'Computes the square root of the specified float value.'</span><span class="p">,</span>
<span class="s">'abs'</span><span class="p">:</span> <span class="s">'Computes the absolute value.'</span><span class="p">,</span>
<span class="s">'max'</span><span class="p">:</span> <span class="s">'Aggregate function: returns the maximum value of the expression in a group.'</span><span class="p">,</span>
<span class="s">'min'</span><span class="p">:</span> <span class="s">'Aggregate function: returns the minimum value of the expression in a group.'</span><span class="p">,</span>
<span class="s">'first'</span><span class="p">:</span> <span class="s">'Aggregate function: returns the first value in a group.'</span><span class="p">,</span>
<span class="s">'last'</span><span class="p">:</span> <span class="s">'Aggregate function: returns the last value in a group.'</span><span class="p">,</span>
<span class="s">'count'</span><span class="p">:</span> <span class="s">'Aggregate function: returns the number of items in a group.'</span><span class="p">,</span>
<span class="s">'sum'</span><span class="p">:</span> <span class="s">'Aggregate function: returns the sum of all values in the expression.'</span><span class="p">,</span>
<span class="s">'avg'</span><span class="p">:</span> <span class="s">'Aggregate function: returns the average of the values in a group.'</span><span class="p">,</span>
<span class="s">'mean'</span><span class="p">:</span> <span class="s">'Aggregate function: returns the average of the values in a group.'</span><span class="p">,</span>
<span class="s">'sumDistinct'</span><span class="p">:</span> <span class="s">'Aggregate function: returns the sum of distinct values in the expression.'</span><span class="p">,</span>
<span class="p">}</span>
<span class="n">_functions_1_4</span> <span class="o">=</span> <span class="p">{</span>
<span class="c"># unary math functions</span>
<span class="s">'acos'</span><span class="p">:</span> <span class="s">'Computes the cosine inverse of the given value; the returned angle is in the range'</span> <span class="o">+</span>
<span class="s">'0.0 through pi.'</span><span class="p">,</span>
<span class="s">'asin'</span><span class="p">:</span> <span class="s">'Computes the sine inverse of the given value; the returned angle is in the range'</span> <span class="o">+</span>
<span class="s">'-pi/2 through pi/2.'</span><span class="p">,</span>
<span class="s">'atan'</span><span class="p">:</span> <span class="s">'Computes the tangent inverse of the given value.'</span><span class="p">,</span>
<span class="s">'cbrt'</span><span class="p">:</span> <span class="s">'Computes the cube-root of the given value.'</span><span class="p">,</span>
<span class="s">'ceil'</span><span class="p">:</span> <span class="s">'Computes the ceiling of the given value.'</span><span class="p">,</span>
<span class="s">'cos'</span><span class="p">:</span> <span class="s">'Computes the cosine of the given value.'</span><span class="p">,</span>
<span class="s">'cosh'</span><span class="p">:</span> <span class="s">'Computes the hyperbolic cosine of the given value.'</span><span class="p">,</span>
<span class="s">'exp'</span><span class="p">:</span> <span class="s">'Computes the exponential of the given value.'</span><span class="p">,</span>
<span class="s">'expm1'</span><span class="p">:</span> <span class="s">'Computes the exponential of the given value minus one.'</span><span class="p">,</span>
<span class="s">'floor'</span><span class="p">:</span> <span class="s">'Computes the floor of the given value.'</span><span class="p">,</span>
<span class="s">'log'</span><span class="p">:</span> <span class="s">'Computes the natural logarithm of the given value.'</span><span class="p">,</span>
<span class="s">'log10'</span><span class="p">:</span> <span class="s">'Computes the logarithm of the given value in Base 10.'</span><span class="p">,</span>
<span class="s">'log1p'</span><span class="p">:</span> <span class="s">'Computes the natural logarithm of the given value plus one.'</span><span class="p">,</span>
<span class="s">'rint'</span><span class="p">:</span> <span class="s">'Returns the double value that is closest in value to the argument and'</span> <span class="o">+</span>
<span class="s">' is equal to a mathematical integer.'</span><span class="p">,</span>
<span class="s">'signum'</span><span class="p">:</span> <span class="s">'Computes the signum of the given value.'</span><span class="p">,</span>
<span class="s">'sin'</span><span class="p">:</span> <span class="s">'Computes the sine of the given value.'</span><span class="p">,</span>
<span class="s">'sinh'</span><span class="p">:</span> <span class="s">'Computes the hyperbolic sine of the given value.'</span><span class="p">,</span>
<span class="s">'tan'</span><span class="p">:</span> <span class="s">'Computes the tangent of the given value.'</span><span class="p">,</span>
<span class="s">'tanh'</span><span class="p">:</span> <span class="s">'Computes the hyperbolic tangent of the given value.'</span><span class="p">,</span>
<span class="s">'toDegrees'</span><span class="p">:</span> <span class="s">'Converts an angle measured in radians to an approximately equivalent angle '</span> <span class="o">+</span>
<span class="s">'measured in degrees.'</span><span class="p">,</span>
<span class="s">'toRadians'</span><span class="p">:</span> <span class="s">'Converts an angle measured in degrees to an approximately equivalent angle '</span> <span class="o">+</span>
<span class="s">'measured in radians.'</span><span class="p">,</span>
<span class="s">'bitwiseNOT'</span><span class="p">:</span> <span class="s">'Computes bitwise not.'</span><span class="p">,</span>
<span class="p">}</span>
<span class="c"># math functions that take two arguments as input</span>
<span class="n">_binary_mathfunctions</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">'atan2'</span><span class="p">:</span> <span class="s">'Returns the angle theta from the conversion of rectangular coordinates (x, y) to'</span> <span class="o">+</span>
<span class="s">'polar coordinates (r, theta).'</span><span class="p">,</span>
<span class="s">'hypot'</span><span class="p">:</span> <span class="s">'Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.'</span><span class="p">,</span>
<span class="s">'pow'</span><span class="p">:</span> <span class="s">'Returns the value of the first argument raised to the power of the second argument.'</span><span class="p">,</span>
<span class="p">}</span>
<span class="n">_window_functions</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">'rowNumber'</span><span class="p">:</span>
<span class="sd">"""returns a sequential number starting at 1 within a window partition.</span>
<span class="sd"> This is equivalent to the ROW_NUMBER function in SQL."""</span><span class="p">,</span>
<span class="s">'denseRank'</span><span class="p">:</span>
<span class="sd">"""returns the rank of rows within a window partition, without any gaps.</span>
<span class="sd"> The difference between rank and denseRank is that denseRank leaves no gaps in ranking</span>
<span class="sd"> sequence when there are ties. That is, if you were ranking a competition using denseRank</span>
<span class="sd"> and had three people tie for second place, you would say that all three were in second</span>
<span class="sd"> place and that the next person came in third.</span>
<span class="sd"> This is equivalent to the DENSE_RANK function in SQL."""</span><span class="p">,</span>
<span class="s">'rank'</span><span class="p">:</span>
<span class="sd">"""returns the rank of rows within a window partition.</span>
<span class="sd"> The difference between rank and denseRank is that denseRank leaves no gaps in ranking</span>
<span class="sd"> sequence when there are ties. That is, if you were ranking a competition using denseRank</span>
<span class="sd"> and had three people tie for second place, you would say that all three were in second</span>
<span class="sd"> place and that the next person came in third.</span>
<span class="sd"> This is equivalent to the RANK function in SQL."""</span><span class="p">,</span>
<span class="s">'cumeDist'</span><span class="p">:</span>
<span class="sd">"""returns the cumulative distribution of values within a window partition,</span>
<span class="sd"> i.e. the fraction of rows that are below the current row.</span>
<span class="sd"> This is equivalent to the CUME_DIST function in SQL."""</span><span class="p">,</span>
<span class="s">'percentRank'</span><span class="p">:</span>
<span class="sd">"""returns the relative rank (i.e. percentile) of rows within a window partition.</span>
<span class="sd"> This is equivalent to the PERCENT_RANK function in SQL."""</span><span class="p">,</span>
<span class="p">}</span>
<span class="k">for</span> <span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span> <span class="ow">in</span> <span class="n">_functions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">globals</span><span class="p">()[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">since</span><span class="p">(</span><span class="mf">1.3</span><span class="p">)(</span><span class="n">_create_function</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span><span class="p">))</span>
<span class="k">for</span> <span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span> <span class="ow">in</span> <span class="n">_functions_1_4</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">globals</span><span class="p">()[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)(</span><span class="n">_create_function</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span><span class="p">))</span>
<span class="k">for</span> <span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span> <span class="ow">in</span> <span class="n">_binary_mathfunctions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">globals</span><span class="p">()[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)(</span><span class="n">_create_binary_mathfunction</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span><span class="p">))</span>
<span class="k">for</span> <span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span> <span class="ow">in</span> <span class="n">_window_functions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">globals</span><span class="p">()[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)(</span><span class="n">_create_window_function</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span><span class="p">))</span>
<span class="k">del</span> <span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.3</span><span class="p">)</span>
<div class="viewcode-block" id="approxCountDistinct"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.approxCountDistinct">[docs]</a><span class="k">def</span> <span class="nf">approxCountDistinct</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">rsd</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Returns a new :class:`Column` for approximate distinct count of ``col``.</span>
<span class="sd"> >>> df.agg(approxCountDistinct(df.age).alias('c')).collect()</span>
<span class="sd"> [Row(c=2)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">if</span> <span class="n">rsd</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">approxCountDistinct</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">approxCountDistinct</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">rsd</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="coalesce"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.coalesce">[docs]</a><span class="k">def</span> <span class="nf">coalesce</span><span class="p">(</span><span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">"""Returns the first column that is not null.</span>
<span class="sd"> >>> cDf = sqlContext.createDataFrame([(None, None), (1, None), (None, 2)], ("a", "b"))</span>
<span class="sd"> >>> cDf.show()</span>
<span class="sd"> +----+----+</span>
<span class="sd"> | a| b|</span>
<span class="sd"> +----+----+</span>
<span class="sd"> |null|null|</span>
<span class="sd"> | 1|null|</span>
<span class="sd"> |null| 2|</span>
<span class="sd"> +----+----+</span>
<span class="sd"> >>> cDf.select(coalesce(cDf["a"], cDf["b"])).show()</span>
<span class="sd"> +-------------+</span>
<span class="sd"> |coalesce(a,b)|</span>
<span class="sd"> +-------------+</span>
<span class="sd"> | null|</span>
<span class="sd"> | 1|</span>
<span class="sd"> | 2|</span>
<span class="sd"> +-------------+</span>
<span class="sd"> >>> cDf.select('*', coalesce(cDf["a"], lit(0.0))).show()</span>
<span class="sd"> +----+----+---------------+</span>
<span class="sd"> | a| b|coalesce(a,0.0)|</span>
<span class="sd"> +----+----+---------------+</span>
<span class="sd"> |null|null| 0.0|</span>
<span class="sd"> | 1|null| 1.0|</span>
<span class="sd"> |null| 2| 0.0|</span>
<span class="sd"> +----+----+---------------+</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">coalesce</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.3</span><span class="p">)</span>
<div class="viewcode-block" id="countDistinct"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.countDistinct">[docs]</a><span class="k">def</span> <span class="nf">countDistinct</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">"""Returns a new :class:`Column` for distinct count of ``col`` or ``cols``.</span>
<span class="sd"> >>> df.agg(countDistinct(df.age, df.name).alias('c')).collect()</span>
<span class="sd"> [Row(c=2)]</span>
<span class="sd"> >>> df.agg(countDistinct("age", "name").alias('c')).collect()</span>
<span class="sd"> [Row(c=2)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">countDistinct</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="monotonicallyIncreasingId"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.monotonicallyIncreasingId">[docs]</a><span class="k">def</span> <span class="nf">monotonicallyIncreasingId</span><span class="p">():</span>
<span class="sd">"""A column that generates monotonically increasing 64-bit integers.</span>
<span class="sd"> The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive.</span>
<span class="sd"> The current implementation puts the partition ID in the upper 31 bits, and the record number</span>
<span class="sd"> within each partition in the lower 33 bits. The assumption is that the data frame has</span>
<span class="sd"> less than 1 billion partitions, and each partition has less than 8 billion records.</span>
<span class="sd"> As an example, consider a :class:`DataFrame` with two partitions, each with 3 records.</span>
<span class="sd"> This expression would return the following IDs:</span>
<span class="sd"> 0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594.</span>
<span class="sd"> >>> df0 = sc.parallelize(range(2), 2).mapPartitions(lambda x: [(1,), (2,), (3,)]).toDF(['col1'])</span>
<span class="sd"> >>> df0.select(monotonicallyIncreasingId().alias('id')).collect()</span>
<span class="sd"> [Row(id=0), Row(id=1), Row(id=2), Row(id=8589934592), Row(id=8589934593), Row(id=8589934594)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">monotonicallyIncreasingId</span><span class="p">())</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="rand"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.rand">[docs]</a><span class="k">def</span> <span class="nf">rand</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Generates a random column with i.i.d. samples from U[0.0, 1.0].</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">rand</span><span class="p">()</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="randn"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.randn">[docs]</a><span class="k">def</span> <span class="nf">randn</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Generates a column with i.i.d. samples from the standard normal distribution.</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">randn</span><span class="p">()</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="round"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.round">[docs]</a><span class="k">def</span> <span class="nf">round</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Round the value of `e` to `scale` decimal places if `scale` >= 0</span>
<span class="sd"> or at integral part when `scale` < 0.</span>
<span class="sd"> >>> sqlContext.createDataFrame([(2.546,)], ['a']).select(round('a', 1).alias('r')).collect()</span>
<span class="sd"> [Row(r=2.5)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">scale</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="shiftLeft"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.shiftLeft">[docs]</a><span class="k">def</span> <span class="nf">shiftLeft</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">numBits</span><span class="p">):</span>
<span class="sd">"""Shift the the given value numBits left.</span>
<span class="sd"> >>> sqlContext.createDataFrame([(21,)], ['a']).select(shiftLeft('a', 1).alias('r')).collect()</span>
<span class="sd"> [Row(r=42)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">shiftLeft</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">numBits</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="shiftRight"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.shiftRight">[docs]</a><span class="k">def</span> <span class="nf">shiftRight</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">numBits</span><span class="p">):</span>
<span class="sd">"""Shift the the given value numBits right.</span>
<span class="sd"> >>> sqlContext.createDataFrame([(42,)], ['a']).select(shiftRight('a', 1).alias('r')).collect()</span>
<span class="sd"> [Row(r=21)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">shiftRight</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">numBits</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="shiftRightUnsigned"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.shiftRightUnsigned">[docs]</a><span class="k">def</span> <span class="nf">shiftRightUnsigned</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">numBits</span><span class="p">):</span>
<span class="sd">"""Unsigned shift the the given value numBits right.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([(-42,)], ['a'])</span>
<span class="sd"> >>> df.select(shiftRightUnsigned('a', 1).alias('r')).collect()</span>
<span class="sd"> [Row(r=9223372036854775787)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">shiftRightUnsigned</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">numBits</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="sparkPartitionId"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.sparkPartitionId">[docs]</a><span class="k">def</span> <span class="nf">sparkPartitionId</span><span class="p">():</span>
<span class="sd">"""A column for partition ID of the Spark task.</span>
<span class="sd"> Note that this is indeterministic because it depends on data partitioning and task scheduling.</span>
<span class="sd"> >>> df.repartition(1).select(sparkPartitionId().alias("pid")).collect()</span>
<span class="sd"> [Row(pid=0), Row(pid=0)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">sparkPartitionId</span><span class="p">())</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="expr"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.expr">[docs]</a><span class="k">def</span> <span class="nf">expr</span><span class="p">(</span><span class="nb">str</span><span class="p">):</span>
<span class="sd">"""Parses the expression string into the column that it represents</span>
<span class="sd"> >>> df.select(expr("length(name)")).collect()</span>
<span class="sd"> [Row('length(name)=5), Row('length(name)=3)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">expr</span><span class="p">(</span><span class="nb">str</span><span class="p">))</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="struct"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.struct">[docs]</a><span class="k">def</span> <span class="nf">struct</span><span class="p">(</span><span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">"""Creates a new struct column.</span>
<span class="sd"> :param cols: list of column names (string) or list of :class:`Column` expressions</span>
<span class="sd"> >>> df.select(struct('age', 'name').alias("struct")).collect()</span>
<span class="sd"> [Row(struct=Row(age=2, name=u'Alice')), Row(struct=Row(age=5, name=u'Bob'))]</span>
<span class="sd"> >>> df.select(struct([df.age, df.name]).alias("struct")).collect()</span>
<span class="sd"> [Row(struct=Row(age=2, name=u'Alice')), Row(struct=Row(age=5, name=u'Bob'))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cols</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">set</span><span class="p">)):</span>
<span class="n">cols</span> <span class="o">=</span> <span class="n">cols</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">struct</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="greatest"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.greatest">[docs]</a><span class="k">def</span> <span class="nf">greatest</span><span class="p">(</span><span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the greatest value of the list of column names, skipping null values.</span>
<span class="sd"> This function takes at least 2 parameters. It will return null iff all parameters are null.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([(1, 4, 3)], ['a', 'b', 'c'])</span>
<span class="sd"> >>> df.select(greatest(df.a, df.b, df.c).alias("greatest")).collect()</span>
<span class="sd"> [Row(greatest=4)]</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cols</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s">"greatest should take at least two columns"</span><span class="p">)</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">greatest</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="least"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.least">[docs]</a><span class="k">def</span> <span class="nf">least</span><span class="p">(</span><span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the least value of the list of column names, skipping null values.</span>
<span class="sd"> This function takes at least 2 parameters. It will return null iff all parameters are null.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([(1, 4, 3)], ['a', 'b', 'c'])</span>
<span class="sd"> >>> df.select(least(df.a, df.b, df.c).alias("least")).collect()</span>
<span class="sd"> [Row(least=1)]</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cols</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s">"least should take at least two columns"</span><span class="p">)</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">least</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="when"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.when">[docs]</a><span class="k">def</span> <span class="nf">when</span><span class="p">(</span><span class="n">condition</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="sd">"""Evaluates a list of conditions and returns one of multiple possible result expressions.</span>
<span class="sd"> If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions.</span>
<span class="sd"> :param condition: a boolean :class:`Column` expression.</span>
<span class="sd"> :param value: a literal value, or a :class:`Column` expression.</span>
<span class="sd"> >>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect()</span>
<span class="sd"> [Row(age=3), Row(age=4)]</span>
<span class="sd"> >>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect()</span>
<span class="sd"> [Row(age=3), Row(age=None)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">condition</span><span class="p">,</span> <span class="n">Column</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s">"condition should be a Column"</span><span class="p">)</span>
<span class="n">v</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">_jc</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Column</span><span class="p">)</span> <span class="k">else</span> <span class="n">value</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">when</span><span class="p">(</span><span class="n">condition</span><span class="o">.</span><span class="n">_jc</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="log"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.log">[docs]</a><span class="k">def</span> <span class="nf">log</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">arg2</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""Returns the first argument-based logarithm of the second argument.</span>
<span class="sd"> If there is only one argument, then this takes the natural logarithm of the argument.</span>
<span class="sd"> >>> df.select(log(10.0, df.age).alias('ten')).map(lambda l: str(l.ten)[:7]).collect()</span>
<span class="sd"> ['0.30102', '0.69897']</span>
<span class="sd"> >>> df.select(log(df.age).alias('e')).map(lambda l: str(l.e)[:7]).collect()</span>
<span class="sd"> ['0.69314', '1.60943']</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">if</span> <span class="n">arg2</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">arg1</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">arg1</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">(</span><span class="n">arg2</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="log2"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.log2">[docs]</a><span class="k">def</span> <span class="nf">log2</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""Returns the base-2 logarithm of the argument.</span>
<span class="sd"> >>> sqlContext.createDataFrame([(4,)], ['a']).select(log2('a').alias('log2')).collect()</span>
<span class="sd"> [Row(log2=2.0)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="conv"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.conv">[docs]</a><span class="k">def</span> <span class="nf">conv</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">fromBase</span><span class="p">,</span> <span class="n">toBase</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Convert a number in a string column from one base to another.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([("010101",)], ['n'])</span>
<span class="sd"> >>> df.select(conv(df.n, 2, 16).alias('hex')).collect()</span>
<span class="sd"> [Row(hex=u'15')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">fromBase</span><span class="p">,</span> <span class="n">toBase</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="factorial"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.factorial">[docs]</a><span class="k">def</span> <span class="nf">factorial</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Computes the factorial of the given value.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([(5,)], ['n'])</span>
<span class="sd"> >>> df.select(factorial(df.n).alias('f')).collect()</span>
<span class="sd"> [Row(f=120)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">factorial</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
<span class="c"># --------------- Window functions ------------------------</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="lag"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.lag">[docs]</a><span class="k">def</span> <span class="nf">lag</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">count</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Window function: returns the value that is `offset` rows before the current row, and</span>
<span class="sd"> `defaultValue` if there is less than `offset` rows before the current row. For example,</span>
<span class="sd"> an `offset` of one will return the previous row at any given point in the window partition.</span>
<span class="sd"> This is equivalent to the LAG function in SQL.</span>
<span class="sd"> :param col: name of column or expression</span>
<span class="sd"> :param count: number of row to extend</span>
<span class="sd"> :param default: default value</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">lag</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">count</span><span class="p">,</span> <span class="n">default</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="lead"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.lead">[docs]</a><span class="k">def</span> <span class="nf">lead</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">count</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Window function: returns the value that is `offset` rows after the current row, and</span>
<span class="sd"> `defaultValue` if there is less than `offset` rows after the current row. For example,</span>
<span class="sd"> an `offset` of one will return the next row at any given point in the window partition.</span>
<span class="sd"> This is equivalent to the LEAD function in SQL.</span>
<span class="sd"> :param col: name of column or expression</span>
<span class="sd"> :param count: number of row to extend</span>
<span class="sd"> :param default: default value</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">lead</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">count</span><span class="p">,</span> <span class="n">default</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="ntile"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.ntile">[docs]</a><span class="k">def</span> <span class="nf">ntile</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Window function: returns the ntile group id (from 1 to `n` inclusive)</span>
<span class="sd"> in an ordered window partition. For example, if `n` is 4, the first</span>
<span class="sd"> quarter of the rows will get value 1, the second quarter will get 2,</span>
<span class="sd"> the third quarter will get 3, and the last quarter will get 4.</span>
<span class="sd"> This is equivalent to the NTILE function in SQL.</span>
<span class="sd"> :param n: an integer</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">ntile</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">n</span><span class="p">)))</span>
<span class="c"># ---------------------- Date/Timestamp functions ------------------------------</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="current_date"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.current_date">[docs]</a><span class="k">def</span> <span class="nf">current_date</span><span class="p">():</span>
<span class="sd">"""</span>
<span class="sd"> Returns the current date as a date column.</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">current_date</span><span class="p">())</span>
</div>
<div class="viewcode-block" id="current_timestamp"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.current_timestamp">[docs]</a><span class="k">def</span> <span class="nf">current_timestamp</span><span class="p">():</span>
<span class="sd">"""</span>
<span class="sd"> Returns the current timestamp as a timestamp column.</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">current_timestamp</span><span class="p">())</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="date_format"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.date_format">[docs]</a><span class="k">def</span> <span class="nf">date_format</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">format</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Converts a date/timestamp/string to a value of string in the format specified by the date</span>
<span class="sd"> format given by the second argument.</span>
<span class="sd"> A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All</span>
<span class="sd"> pattern letters of the Java class `java.text.SimpleDateFormat` can be used.</span>
<span class="sd"> NOTE: Use when ever possible specialized functions like `year`. These benefit from a</span>
<span class="sd"> specialized implementation.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])</span>
<span class="sd"> >>> df.select(date_format('a', 'MM/dd/yyy').alias('date')).collect()</span>
<span class="sd"> [Row(date=u'04/08/2015')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">date_format</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">date</span><span class="p">),</span> <span class="n">format</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="year"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.year">[docs]</a><span class="k">def</span> <span class="nf">year</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Extract the year of a given date as integer.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])</span>
<span class="sd"> >>> df.select(year('a').alias('year')).collect()</span>
<span class="sd"> [Row(year=2015)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">year</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="quarter"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.quarter">[docs]</a><span class="k">def</span> <span class="nf">quarter</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Extract the quarter of a given date as integer.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])</span>
<span class="sd"> >>> df.select(quarter('a').alias('quarter')).collect()</span>
<span class="sd"> [Row(quarter=2)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">quarter</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="month"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.month">[docs]</a><span class="k">def</span> <span class="nf">month</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Extract the month of a given date as integer.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])</span>
<span class="sd"> >>> df.select(month('a').alias('month')).collect()</span>
<span class="sd"> [Row(month=4)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">month</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="dayofmonth"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.dayofmonth">[docs]</a><span class="k">def</span> <span class="nf">dayofmonth</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Extract the day of the month of a given date as integer.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])</span>
<span class="sd"> >>> df.select(dayofmonth('a').alias('day')).collect()</span>
<span class="sd"> [Row(day=8)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">dayofmonth</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="dayofyear"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.dayofyear">[docs]</a><span class="k">def</span> <span class="nf">dayofyear</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Extract the day of the year of a given date as integer.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])</span>
<span class="sd"> >>> df.select(dayofyear('a').alias('day')).collect()</span>
<span class="sd"> [Row(day=98)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">dayofyear</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="hour"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.hour">[docs]</a><span class="k">def</span> <span class="nf">hour</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Extract the hours of a given date as integer.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08 13:08:15',)], ['a'])</span>
<span class="sd"> >>> df.select(hour('a').alias('hour')).collect()</span>
<span class="sd"> [Row(hour=13)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">hour</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="minute"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.minute">[docs]</a><span class="k">def</span> <span class="nf">minute</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Extract the minutes of a given date as integer.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08 13:08:15',)], ['a'])</span>
<span class="sd"> >>> df.select(minute('a').alias('minute')).collect()</span>
<span class="sd"> [Row(minute=8)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">minute</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="second"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.second">[docs]</a><span class="k">def</span> <span class="nf">second</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Extract the seconds of a given date as integer.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08 13:08:15',)], ['a'])</span>
<span class="sd"> >>> df.select(second('a').alias('second')).collect()</span>
<span class="sd"> [Row(second=15)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">second</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="weekofyear"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.weekofyear">[docs]</a><span class="k">def</span> <span class="nf">weekofyear</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Extract the week number of a given date as integer.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['a'])</span>
<span class="sd"> >>> df.select(weekofyear(df.a).alias('week')).collect()</span>
<span class="sd"> [Row(week=15)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">weekofyear</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="date_add"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.date_add">[docs]</a><span class="k">def</span> <span class="nf">date_add</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">days</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the date that is `days` days after `start`</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['d'])</span>
<span class="sd"> >>> df.select(date_add(df.d, 1).alias('d')).collect()</span>
<span class="sd"> [Row(d=datetime.date(2015, 4, 9))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">date_add</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="n">days</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="date_sub"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.date_sub">[docs]</a><span class="k">def</span> <span class="nf">date_sub</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">days</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the date that is `days` days before `start`</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['d'])</span>
<span class="sd"> >>> df.select(date_sub(df.d, 1).alias('d')).collect()</span>
<span class="sd"> [Row(d=datetime.date(2015, 4, 7))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">date_sub</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="n">days</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="datediff"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.datediff">[docs]</a><span class="k">def</span> <span class="nf">datediff</span><span class="p">(</span><span class="n">end</span><span class="p">,</span> <span class="n">start</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the number of days from `start` to `end`.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08','2015-05-10')], ['d1', 'd2'])</span>
<span class="sd"> >>> df.select(datediff(df.d2, df.d1).alias('diff')).collect()</span>
<span class="sd"> [Row(diff=32)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">datediff</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">end</span><span class="p">),</span> <span class="n">_to_java_column</span><span class="p">(</span><span class="n">start</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="add_months"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.add_months">[docs]</a><span class="k">def</span> <span class="nf">add_months</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">months</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the date that is `months` months after `start`</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-04-08',)], ['d'])</span>
<span class="sd"> >>> df.select(add_months(df.d, 1).alias('d')).collect()</span>
<span class="sd"> [Row(d=datetime.date(2015, 5, 8))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">add_months</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="n">months</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="months_between"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.months_between">[docs]</a><span class="k">def</span> <span class="nf">months_between</span><span class="p">(</span><span class="n">date1</span><span class="p">,</span> <span class="n">date2</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the number of months between date1 and date2.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('1997-02-28 10:30:00', '1996-10-30')], ['t', 'd'])</span>
<span class="sd"> >>> df.select(months_between(df.t, df.d).alias('months')).collect()</span>
<span class="sd"> [Row(months=3.9495967...)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">months_between</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">date1</span><span class="p">),</span> <span class="n">_to_java_column</span><span class="p">(</span><span class="n">date2</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="to_date"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.to_date">[docs]</a><span class="k">def</span> <span class="nf">to_date</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Converts the column of StringType or TimestampType into DateType.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('1997-02-28 10:30:00',)], ['t'])</span>
<span class="sd"> >>> df.select(to_date(df.t).alias('date')).collect()</span>
<span class="sd"> [Row(date=datetime.date(1997, 2, 28))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">to_date</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="trunc"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.trunc">[docs]</a><span class="k">def</span> <span class="nf">trunc</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">format</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns date truncated to the unit specified by the format.</span>
<span class="sd"> :param format: 'year', 'YYYY', 'yy' or 'month', 'mon', 'mm'</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('1997-02-28',)], ['d'])</span>
<span class="sd"> >>> df.select(trunc(df.d, 'year').alias('year')).collect()</span>
<span class="sd"> [Row(year=datetime.date(1997, 1, 1))]</span>
<span class="sd"> >>> df.select(trunc(df.d, 'mon').alias('month')).collect()</span>
<span class="sd"> [Row(month=datetime.date(1997, 2, 1))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">trunc</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">date</span><span class="p">),</span> <span class="n">format</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="next_day"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.next_day">[docs]</a><span class="k">def</span> <span class="nf">next_day</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">dayOfWeek</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the first date which is later than the value of the date column.</span>
<span class="sd"> Day of the week parameter is case insensitive, and accepts:</span>
<span class="sd"> "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun".</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('2015-07-27',)], ['d'])</span>
<span class="sd"> >>> df.select(next_day(df.d, 'Sun').alias('date')).collect()</span>
<span class="sd"> [Row(date=datetime.date(2015, 8, 2))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">next_day</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">date</span><span class="p">),</span> <span class="n">dayOfWeek</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="last_day"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.last_day">[docs]</a><span class="k">def</span> <span class="nf">last_day</span><span class="p">(</span><span class="n">date</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the last day of the month which the given date belongs to.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('1997-02-10',)], ['d'])</span>
<span class="sd"> >>> df.select(last_day(df.d).alias('date')).collect()</span>
<span class="sd"> [Row(date=datetime.date(1997, 2, 28))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">last_day</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">date</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="from_unixtime"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.from_unixtime">[docs]</a><span class="k">def</span> <span class="nf">from_unixtime</span><span class="p">(</span><span class="n">timestamp</span><span class="p">,</span> <span class="n">format</span><span class="o">=</span><span class="s">"yyyy-MM-dd HH:mm:ss"</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string</span>
<span class="sd"> representing the timestamp of that moment in the current system time zone in the given</span>
<span class="sd"> format.</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">from_unixtime</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">timestamp</span><span class="p">),</span> <span class="n">format</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="unix_timestamp"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.unix_timestamp">[docs]</a><span class="k">def</span> <span class="nf">unix_timestamp</span><span class="p">(</span><span class="n">timestamp</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">format</span><span class="o">=</span><span class="s">'yyyy-MM-dd HH:mm:ss'</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Convert time string with given pattern ('yyyy-MM-dd HH:mm:ss', by default)</span>
<span class="sd"> to Unix time stamp (in seconds), using the default timezone and the default</span>
<span class="sd"> locale, return null if fail.</span>
<span class="sd"> if `timestamp` is None, then it returns current timestamp.</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">if</span> <span class="n">timestamp</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">unix_timestamp</span><span class="p">())</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">unix_timestamp</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">timestamp</span><span class="p">),</span> <span class="n">format</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="from_utc_timestamp"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.from_utc_timestamp">[docs]</a><span class="k">def</span> <span class="nf">from_utc_timestamp</span><span class="p">(</span><span class="n">timestamp</span><span class="p">,</span> <span class="n">tz</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Assumes given timestamp is UTC and converts to given timezone.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('1997-02-28 10:30:00',)], ['t'])</span>
<span class="sd"> >>> df.select(from_utc_timestamp(df.t, "PST").alias('t')).collect()</span>
<span class="sd"> [Row(t=datetime.datetime(1997, 2, 28, 2, 30))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">from_utc_timestamp</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">timestamp</span><span class="p">),</span> <span class="n">tz</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="to_utc_timestamp"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.to_utc_timestamp">[docs]</a><span class="k">def</span> <span class="nf">to_utc_timestamp</span><span class="p">(</span><span class="n">timestamp</span><span class="p">,</span> <span class="n">tz</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Assumes given timestamp is in given timezone and converts to UTC.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('1997-02-28 10:30:00',)], ['t'])</span>
<span class="sd"> >>> df.select(to_utc_timestamp(df.t, "PST").alias('t')).collect()</span>
<span class="sd"> [Row(t=datetime.datetime(1997, 2, 28, 18, 30))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">to_utc_timestamp</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">timestamp</span><span class="p">),</span> <span class="n">tz</span><span class="p">))</span>
<span class="c"># ---------------------------- misc functions ----------------------------------</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="crc32"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.crc32">[docs]</a><span class="k">def</span> <span class="nf">crc32</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Calculates the cyclic redundancy check value (CRC32) of a binary column and</span>
<span class="sd"> returns the value as a bigint.</span>
<span class="sd"> >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(crc32('a').alias('crc32')).collect()</span>
<span class="sd"> [Row(crc32=2743272264)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">crc32</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="md5"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.md5">[docs]</a><span class="k">def</span> <span class="nf">md5</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""Calculates the MD5 digest and returns the value as a 32 character hex string.</span>
<span class="sd"> >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(md5('a').alias('hash')).collect()</span>
<span class="sd"> [Row(hash=u'902fbdd2b1df0c4f70b4a5d23525e932')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">md5</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="sha1"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.sha1">[docs]</a><span class="k">def</span> <span class="nf">sha1</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""Returns the hex string result of SHA-1.</span>
<span class="sd"> >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(sha1('a').alias('hash')).collect()</span>
<span class="sd"> [Row(hash=u'3c01bdbb26f358bab27f267924aa2c9a03fcfdb8')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">sha1</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="sha2"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.sha2">[docs]</a><span class="k">def</span> <span class="nf">sha2</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">numBits</span><span class="p">):</span>
<span class="sd">"""Returns the hex string result of SHA-2 family of hash functions (SHA-224, SHA-256, SHA-384,</span>
<span class="sd"> and SHA-512). The numBits indicates the desired bit length of the result, which must have a</span>
<span class="sd"> value of 224, 256, 384, 512, or 0 (which is equivalent to 256).</span>
<span class="sd"> >>> digests = df.select(sha2(df.name, 256).alias('s')).collect()</span>
<span class="sd"> >>> digests[0]</span>
<span class="sd"> Row(s=u'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043')</span>
<span class="sd"> >>> digests[1]</span>
<span class="sd"> Row(s=u'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961')</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">sha2</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">numBits</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
<span class="c"># ---------------------- String/Binary functions ------------------------------</span>
</div>
<span class="n">_string_functions</span> <span class="o">=</span> <span class="p">{</span>
<span class="s">'ascii'</span><span class="p">:</span> <span class="s">'Computes the numeric value of the first character of the string column.'</span><span class="p">,</span>
<span class="s">'base64'</span><span class="p">:</span> <span class="s">'Computes the BASE64 encoding of a binary column and returns it as a string column.'</span><span class="p">,</span>
<span class="s">'unbase64'</span><span class="p">:</span> <span class="s">'Decodes a BASE64 encoded string column and returns it as a binary column.'</span><span class="p">,</span>
<span class="s">'initcap'</span><span class="p">:</span> <span class="s">'Returns a new string column by converting the first letter of each word to '</span> <span class="o">+</span>
<span class="s">'uppercase. Words are delimited by whitespace.'</span><span class="p">,</span>
<span class="s">'lower'</span><span class="p">:</span> <span class="s">'Converts a string column to lower case.'</span><span class="p">,</span>
<span class="s">'upper'</span><span class="p">:</span> <span class="s">'Converts a string column to upper case.'</span><span class="p">,</span>
<span class="s">'reverse'</span><span class="p">:</span> <span class="s">'Reverses the string column and returns it as a new string column.'</span><span class="p">,</span>
<span class="s">'ltrim'</span><span class="p">:</span> <span class="s">'Trim the spaces from right end for the specified string value.'</span><span class="p">,</span>
<span class="s">'rtrim'</span><span class="p">:</span> <span class="s">'Trim the spaces from right end for the specified string value.'</span><span class="p">,</span>
<span class="s">'trim'</span><span class="p">:</span> <span class="s">'Trim the spaces from both ends for the specified string column.'</span><span class="p">,</span>
<span class="p">}</span>
<span class="k">for</span> <span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span> <span class="ow">in</span> <span class="n">_string_functions</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">globals</span><span class="p">()[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)(</span><span class="n">_create_function</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span><span class="p">))</span>
<span class="k">del</span> <span class="n">_name</span><span class="p">,</span> <span class="n">_doc</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="concat"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.concat">[docs]</a><span class="k">def</span> <span class="nf">concat</span><span class="p">(</span><span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Concatenates multiple input string columns together into a single string column.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('abcd','123')], ['s', 'd'])</span>
<span class="sd"> >>> df.select(concat(df.s, df.d).alias('s')).collect()</span>
<span class="sd"> [Row(s=u'abcd123')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="concat_ws"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.concat_ws">[docs]</a><span class="k">def</span> <span class="nf">concat_ws</span><span class="p">(</span><span class="n">sep</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Concatenates multiple input string columns together into a single string column,</span>
<span class="sd"> using the given separator.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('abcd','123')], ['s', 'd'])</span>
<span class="sd"> >>> df.select(concat_ws('-', df.s, df.d).alias('s')).collect()</span>
<span class="sd"> [Row(s=u'abcd-123')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">concat_ws</span><span class="p">(</span><span class="n">sep</span><span class="p">,</span> <span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="decode"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.decode">[docs]</a><span class="k">def</span> <span class="nf">decode</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">charset</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Computes the first argument into a string from a binary using the provided character set</span>
<span class="sd"> (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">charset</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="encode"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.encode">[docs]</a><span class="k">def</span> <span class="nf">encode</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">charset</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Computes the first argument into a binary from a string using the provided character set</span>
<span class="sd"> (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">charset</span><span class="p">))</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="format_number"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.format_number">[docs]</a><span class="k">def</span> <span class="nf">format_number</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Formats the number X to a format like '#,--#,--#.--', rounded to d decimal places,</span>
<span class="sd"> and returns the result as a string.</span>
<span class="sd"> :param col: the column name of the numeric value to be formatted</span>
<span class="sd"> :param d: the N decimal places</span>
<span class="sd"> >>> sqlContext.createDataFrame([(5,)], ['a']).select(format_number('a', 4).alias('v')).collect()</span>
<span class="sd"> [Row(v=u'5.0000')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">format_number</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">d</span><span class="p">))</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="format_string"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.format_string">[docs]</a><span class="k">def</span> <span class="nf">format_string</span><span class="p">(</span><span class="n">format</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Formats the arguments in printf-style and returns the result as a string column.</span>
<span class="sd"> :param col: the column name of the numeric value to be formatted</span>
<span class="sd"> :param d: the N decimal places</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([(5, "hello")], ['a', 'b'])</span>
<span class="sd"> >>> df.select(format_string('%d %s', df.a, df.b).alias('v')).collect()</span>
<span class="sd"> [Row(v=u'5 hello')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">format_string</span><span class="p">(</span><span class="n">format</span><span class="p">,</span> <span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="instr"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.instr">[docs]</a><span class="k">def</span> <span class="nf">instr</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">substr</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Locate the position of the first occurrence of substr column in the given string.</span>
<span class="sd"> Returns null if either of the arguments are null.</span>
<span class="sd"> NOTE: The position is not zero based, but 1 based index, returns 0 if substr</span>
<span class="sd"> could not be found in str.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])</span>
<span class="sd"> >>> df.select(instr(df.s, 'b').alias('s')).collect()</span>
<span class="sd"> [Row(s=2)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">instr</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span> <span class="n">substr</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="substring"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.substring">[docs]</a><span class="k">def</span> <span class="nf">substring</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="nb">len</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Substring starts at `pos` and is of length `len` when str is String type or</span>
<span class="sd"> returns the slice of byte array that starts at `pos` in byte and is of length `len`</span>
<span class="sd"> when str is Binary type</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])</span>
<span class="sd"> >>> df.select(substring(df.s, 1, 2).alias('s')).collect()</span>
<span class="sd"> [Row(s=u'ab')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">substring</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span> <span class="n">pos</span><span class="p">,</span> <span class="nb">len</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="substring_index"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.substring_index">[docs]</a><span class="k">def</span> <span class="nf">substring_index</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">delim</span><span class="p">,</span> <span class="n">count</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the substring from string str before count occurrences of the delimiter delim.</span>
<span class="sd"> If count is positive, everything the left of the final delimiter (counting from left) is</span>
<span class="sd"> returned. If count is negative, every to the right of the final delimiter (counting from the</span>
<span class="sd"> right) is returned. substring_index performs a case-sensitive match when searching for delim.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('a.b.c.d',)], ['s'])</span>
<span class="sd"> >>> df.select(substring_index(df.s, '.', 2).alias('s')).collect()</span>
<span class="sd"> [Row(s=u'a.b')]</span>
<span class="sd"> >>> df.select(substring_index(df.s, '.', -3).alias('s')).collect()</span>
<span class="sd"> [Row(s=u'b.c.d')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">substring_index</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span> <span class="n">delim</span><span class="p">,</span> <span class="n">count</span><span class="p">))</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="levenshtein"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.levenshtein">[docs]</a><span class="k">def</span> <span class="nf">levenshtein</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">right</span><span class="p">):</span>
<span class="sd">"""Computes the Levenshtein distance of the two given strings.</span>
<span class="sd"> >>> df0 = sqlContext.createDataFrame([('kitten', 'sitting',)], ['l', 'r'])</span>
<span class="sd"> >>> df0.select(levenshtein('l', 'r').alias('d')).collect()</span>
<span class="sd"> [Row(d=3)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">levenshtein</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">left</span><span class="p">),</span> <span class="n">_to_java_column</span><span class="p">(</span><span class="n">right</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="locate"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.locate">[docs]</a><span class="k">def</span> <span class="nf">locate</span><span class="p">(</span><span class="n">substr</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Locate the position of the first occurrence of substr in a string column, after position pos.</span>
<span class="sd"> NOTE: The position is not zero based, but 1 based index. returns 0 if substr</span>
<span class="sd"> could not be found in str.</span>
<span class="sd"> :param substr: a string</span>
<span class="sd"> :param str: a Column of StringType</span>
<span class="sd"> :param pos: start position (zero based)</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])</span>
<span class="sd"> >>> df.select(locate('b', df.s, 1).alias('s')).collect()</span>
<span class="sd"> [Row(s=2)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">locate</span><span class="p">(</span><span class="n">substr</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span> <span class="n">pos</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="lpad"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.lpad">[docs]</a><span class="k">def</span> <span class="nf">lpad</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="nb">len</span><span class="p">,</span> <span class="n">pad</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Left-pad the string column to width `len` with `pad`.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])</span>
<span class="sd"> >>> df.select(lpad(df.s, 6, '#').alias('s')).collect()</span>
<span class="sd"> [Row(s=u'##abcd')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">lpad</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="nb">len</span><span class="p">,</span> <span class="n">pad</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="rpad"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.rpad">[docs]</a><span class="k">def</span> <span class="nf">rpad</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="nb">len</span><span class="p">,</span> <span class="n">pad</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Right-pad the string column to width `len` with `pad`.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])</span>
<span class="sd"> >>> df.select(rpad(df.s, 6, '#').alias('s')).collect()</span>
<span class="sd"> [Row(s=u'abcd##')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">rpad</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="nb">len</span><span class="p">,</span> <span class="n">pad</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="repeat"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.repeat">[docs]</a><span class="k">def</span> <span class="nf">repeat</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Repeats a string column n times, and returns it as a new string column.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('ab',)], ['s',])</span>
<span class="sd"> >>> df.select(repeat(df.s, 3).alias('s')).collect()</span>
<span class="sd"> [Row(s=u'ababab')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">n</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="split"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.split">[docs]</a><span class="k">def</span> <span class="nf">split</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">pattern</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Splits str around pattern (pattern is a regular expression).</span>
<span class="sd"> NOTE: pattern is a string represent the regular expression.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('ab12cd',)], ['s',])</span>
<span class="sd"> >>> df.select(split(df.s, '[0-9]+').alias('s')).collect()</span>
<span class="sd"> [Row(s=[u'ab', u'cd'])]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span> <span class="n">pattern</span><span class="p">))</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="regexp_extract"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.regexp_extract">[docs]</a><span class="k">def</span> <span class="nf">regexp_extract</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">pattern</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
<span class="sd">"""Extract a specific(idx) group identified by a java regex, from the specified string column.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])</span>
<span class="sd"> >>> df.select(regexp_extract('str', '(\d+)-(\d+)', 1).alias('d')).collect()</span>
<span class="sd"> [Row(d=u'100')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">regexp_extract</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span> <span class="n">pattern</span><span class="p">,</span> <span class="n">idx</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="regexp_replace"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.regexp_replace">[docs]</a><span class="k">def</span> <span class="nf">regexp_replace</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">pattern</span><span class="p">,</span> <span class="n">replacement</span><span class="p">):</span>
<span class="sd">"""Replace all substrings of the specified string value that match regexp with rep.</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])</span>
<span class="sd"> >>> df.select(regexp_replace('str', '(\\d+)', '--').alias('d')).collect()</span>
<span class="sd"> [Row(d=u'-----')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">regexp_replace</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="nb">str</span><span class="p">),</span> <span class="n">pattern</span><span class="p">,</span> <span class="n">replacement</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="initcap"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.initcap">[docs]</a><span class="k">def</span> <span class="nf">initcap</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""Translate the first letter of each word to upper case in the sentence.</span>
<span class="sd"> >>> sqlContext.createDataFrame([('ab cd',)], ['a']).select(initcap("a").alias('v')).collect()</span>
<span class="sd"> [Row(v=u'Ab Cd')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">initcap</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<span class="nd">@ignore_unicode_prefix</span>
<div class="viewcode-block" id="soundex"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.soundex">[docs]</a><span class="k">def</span> <span class="nf">soundex</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Returns the SoundEx encoding for a string</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([("Peters",),("Uhrbach",)], ['name'])</span>
<span class="sd"> >>> df.select(soundex(df.name).alias("soundex")).collect()</span>
<span class="sd"> [Row(soundex=u'P362'), Row(soundex=u'U612')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">soundex</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="bin"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.bin">[docs]</a><span class="k">def</span> <span class="nf">bin</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""Returns the string representation of the binary value of the given column.</span>
<span class="sd"> >>> df.select(bin(df.age).alias('c')).collect()</span>
<span class="sd"> [Row(c=u'10'), Row(c=u'101')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">bin</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="hex"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.hex">[docs]</a><span class="k">def</span> <span class="nf">hex</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""Computes hex value of the given column, which could be StringType,</span>
<span class="sd"> BinaryType, IntegerType or LongType.</span>
<span class="sd"> >>> sqlContext.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect()</span>
<span class="sd"> [Row(hex(a)=u'414243', hex(b)=u'3')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">hex</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="unhex"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.unhex">[docs]</a><span class="k">def</span> <span class="nf">unhex</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""Inverse of hex. Interprets each pair of characters as a hexadecimal number</span>
<span class="sd"> and converts to the byte representation of number.</span>
<span class="sd"> >>> sqlContext.createDataFrame([('414243',)], ['a']).select(unhex('a')).collect()</span>
<span class="sd"> [Row(unhex(a)=bytearray(b'ABC'))]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">unhex</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="length"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.length">[docs]</a><span class="k">def</span> <span class="nf">length</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""Calculates the length of a string or binary expression.</span>
<span class="sd"> >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(length('a').alias('length')).collect()</span>
<span class="sd"> [Row(length=3)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">length</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@ignore_unicode_prefix</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="translate"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.translate">[docs]</a><span class="k">def</span> <span class="nf">translate</span><span class="p">(</span><span class="n">srcCol</span><span class="p">,</span> <span class="n">matching</span><span class="p">,</span> <span class="n">replace</span><span class="p">):</span>
<span class="sd">"""A function translate any character in the `srcCol` by a character in `matching`.</span>
<span class="sd"> The characters in `replace` is corresponding to the characters in `matching`.</span>
<span class="sd"> The translate will happen when any character in the string matching with the character</span>
<span class="sd"> in the `matching`.</span>
<span class="sd"> >>> sqlContext.createDataFrame([('translate',)], ['a']).select(translate('a', "rnlt", "123")\</span>
<span class="sd"> .alias('r')).collect()</span>
<span class="sd"> [Row(r=u'1a2s3ae')]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">translate</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">srcCol</span><span class="p">),</span> <span class="n">matching</span><span class="p">,</span> <span class="n">replace</span><span class="p">))</span>
<span class="c"># ---------------------- Collection functions ------------------------------</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="array"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.array">[docs]</a><span class="k">def</span> <span class="nf">array</span><span class="p">(</span><span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="sd">"""Creates a new array column.</span>
<span class="sd"> :param cols: list of column names (string) or list of :class:`Column` expressions that have</span>
<span class="sd"> the same data type.</span>
<span class="sd"> >>> df.select(array('age', 'age').alias("arr")).collect()</span>
<span class="sd"> [Row(arr=[2, 2]), Row(arr=[5, 5])]</span>
<span class="sd"> >>> df.select(array([df.age, df.age]).alias("arr")).collect()</span>
<span class="sd"> [Row(arr=[2, 2]), Row(arr=[5, 5])]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cols</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cols</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">set</span><span class="p">)):</span>
<span class="n">cols</span> <span class="o">=</span> <span class="n">cols</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="array_contains"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.array_contains">[docs]</a><span class="k">def</span> <span class="nf">array_contains</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Collection function: returns True if the array contains the given value. The collection</span>
<span class="sd"> elements and value must be of the same type.</span>
<span class="sd"> :param col: name of column containing array</span>
<span class="sd"> :param value: value to check for in array</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])</span>
<span class="sd"> >>> df.select(array_contains(df.data, "a")).collect()</span>
<span class="sd"> [Row(array_contains(data,a)=True), Row(array_contains(data,a)=False)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">array_contains</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">value</span><span class="p">))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.4</span><span class="p">)</span>
<div class="viewcode-block" id="explode"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.explode">[docs]</a><span class="k">def</span> <span class="nf">explode</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""Returns a new row for each element in the given array or map.</span>
<span class="sd"> >>> from pyspark.sql import Row</span>
<span class="sd"> >>> eDF = sqlContext.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})])</span>
<span class="sd"> >>> eDF.select(explode(eDF.intlist).alias("anInt")).collect()</span>
<span class="sd"> [Row(anInt=1), Row(anInt=2), Row(anInt=3)]</span>
<span class="sd"> >>> eDF.select(explode(eDF.mapfield).alias("key", "value")).show()</span>
<span class="sd"> +---+-----+</span>
<span class="sd"> |key|value|</span>
<span class="sd"> +---+-----+</span>
<span class="sd"> | a| b|</span>
<span class="sd"> +---+-----+</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">explode</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="size"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.size">[docs]</a><span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Collection function: returns the length of the array or map stored in the column.</span>
<span class="sd"> :param col: name of column or expression</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([([1, 2, 3],),([1],),([],)], ['data'])</span>
<span class="sd"> >>> df.select(size(df.data)).collect()</span>
<span class="sd"> [Row(size(data)=3), Row(size(data)=1), Row(size(data)=0)]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">)))</span>
</div>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.5</span><span class="p">)</span>
<div class="viewcode-block" id="sort_array"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.sort_array">[docs]</a><span class="k">def</span> <span class="nf">sort_array</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">asc</span><span class="o">=</span><span class="bp">True</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Collection function: sorts the input array for the given column in ascending order.</span>
<span class="sd"> :param col: name of column or expression</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([([2, 1, 3],),([1],),([],)], ['data'])</span>
<span class="sd"> >>> df.select(sort_array(df.data).alias('r')).collect()</span>
<span class="sd"> [Row(r=[1, 2, 3]), Row(r=[1]), Row(r=[])]</span>
<span class="sd"> >>> df.select(sort_array(df.data, asc=False).alias('r')).collect()</span>
<span class="sd"> [Row(r=[3, 2, 1]), Row(r=[1]), Row(r=[])]</span>
<span class="sd"> """</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">sort_array</span><span class="p">(</span><span class="n">_to_java_column</span><span class="p">(</span><span class="n">col</span><span class="p">),</span> <span class="n">asc</span><span class="p">))</span>
<span class="c"># ---------------------------- User Defined Function ----------------------------------</span>
</div>
<span class="k">class</span> <span class="nc">UserDefinedFunction</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> User defined function in Python</span>
<span class="sd"> .. versionadded:: 1.3</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">returnType</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="n">func</span>
<span class="bp">self</span><span class="o">.</span><span class="n">returnType</span> <span class="o">=</span> <span class="n">returnType</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_broadcast</span> <span class="o">=</span> <span class="bp">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_judf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_judf</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_create_judf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="n">f</span><span class="p">,</span> <span class="n">returnType</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">returnType</span> <span class="c"># put them in closure `func`</span>
<span class="n">func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">_</span><span class="p">,</span> <span class="n">it</span><span class="p">:</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">returnType</span><span class="o">.</span><span class="n">toInternal</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="o">*</span><span class="n">x</span><span class="p">)),</span> <span class="n">it</span><span class="p">)</span>
<span class="n">ser</span> <span class="o">=</span> <span class="n">AutoBatchedSerializer</span><span class="p">(</span><span class="n">PickleSerializer</span><span class="p">())</span>
<span class="n">command</span> <span class="o">=</span> <span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="bp">None</span><span class="p">,</span> <span class="n">ser</span><span class="p">,</span> <span class="n">ser</span><span class="p">)</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">pickled_command</span><span class="p">,</span> <span class="n">broadcast_vars</span><span class="p">,</span> <span class="n">env</span><span class="p">,</span> <span class="n">includes</span> <span class="o">=</span> <span class="n">_prepare_for_python_RDD</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">command</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span>
<span class="n">ssql_ctx</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">_jsc</span><span class="o">.</span><span class="n">sc</span><span class="p">())</span>
<span class="n">jdt</span> <span class="o">=</span> <span class="n">ssql_ctx</span><span class="o">.</span><span class="n">parseDataType</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">returnType</span><span class="o">.</span><span class="n">json</span><span class="p">())</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">__name__</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="s">'__name__'</span><span class="p">)</span> <span class="k">else</span> <span class="n">f</span><span class="o">.</span><span class="n">__class__</span><span class="o">.</span><span class="n">__name__</span>
<span class="n">judf</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">UserDefinedPythonFunction</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="nb">bytearray</span><span class="p">(</span><span class="n">pickled_command</span><span class="p">),</span> <span class="n">env</span><span class="p">,</span> <span class="n">includes</span><span class="p">,</span>
<span class="n">sc</span><span class="o">.</span><span class="n">pythonExec</span><span class="p">,</span> <span class="n">sc</span><span class="o">.</span><span class="n">pythonVer</span><span class="p">,</span> <span class="n">broadcast_vars</span><span class="p">,</span>
<span class="n">sc</span><span class="o">.</span><span class="n">_javaAccumulator</span><span class="p">,</span> <span class="n">jdt</span><span class="p">)</span>
<span class="k">return</span> <span class="n">judf</span>
<span class="k">def</span> <span class="nf">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_broadcast</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_broadcast</span><span class="o">.</span><span class="n">unpersist</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_broadcast</span> <span class="o">=</span> <span class="bp">None</span>
<span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">cols</span><span class="p">):</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="o">.</span><span class="n">_active_spark_context</span>
<span class="n">jc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_judf</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_to_seq</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="n">_to_java_column</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Column</span><span class="p">(</span><span class="n">jc</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="mf">1.3</span><span class="p">)</span>
<div class="viewcode-block" id="udf"><a class="viewcode-back" href="../../../pyspark.sql.html#pyspark.sql.functions.udf">[docs]</a><span class="k">def</span> <span class="nf">udf</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">returnType</span><span class="o">=</span><span class="n">StringType</span><span class="p">()):</span>
<span class="sd">"""Creates a :class:`Column` expression representing a user defined function (UDF).</span>
<span class="sd"> >>> from pyspark.sql.types import IntegerType</span>
<span class="sd"> >>> slen = udf(lambda s: len(s), IntegerType())</span>
<span class="sd"> >>> df.select(slen(df.name).alias('slen')).collect()</span>
<span class="sd"> [Row(slen=5), Row(slen=3)]</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">UserDefinedFunction</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">returnType</span><span class="p">)</span>
</div>
<span class="n">blacklist</span> <span class="o">=</span> <span class="p">[</span><span class="s">'map'</span><span class="p">,</span> <span class="s">'since'</span><span class="p">,</span> <span class="s">'ignore_unicode_prefix'</span><span class="p">]</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">k</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s">'_'</span><span class="p">)</span> <span class="ow">and</span> <span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">islower</span><span class="p">()</span> <span class="ow">and</span> <span class="nb">callable</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="ow">and</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">blacklist</span><span class="p">]</span>
<span class="n">__all__</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_test</span><span class="p">():</span>
<span class="kn">import</span> <span class="nn">doctest</span>
<span class="kn">from</span> <span class="nn">pyspark.context</span> <span class="kn">import</span> <span class="n">SparkContext</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">Row</span><span class="p">,</span> <span class="n">SQLContext</span>
<span class="kn">import</span> <span class="nn">pyspark.sql.functions</span>
<span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="s">'local[4]'</span><span class="p">,</span> <span class="s">'PythonTest'</span><span class="p">)</span>
<span class="n">globs</span><span class="p">[</span><span class="s">'sc'</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span>
<span class="n">globs</span><span class="p">[</span><span class="s">'sqlContext'</span><span class="p">]</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
<span class="n">globs</span><span class="p">[</span><span class="s">'df'</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([</span><span class="n">Row</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s">'Alice'</span><span class="p">,</span> <span class="n">age</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <span class="n">Row</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s">'Bob'</span><span class="p">,</span> <span class="n">age</span><span class="o">=</span><span class="mi">5</span><span class="p">)])</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span>
<span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span>
<span class="n">pyspark</span><span class="o">.</span><span class="n">sql</span><span class="o">.</span><span class="n">functions</span><span class="p">,</span> <span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span>
<span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span> <span class="o">|</span> <span class="n">doctest</span><span class="o">.</span><span class="n">NORMALIZE_WHITESPACE</span><span class="p">)</span>
<span class="n">globs</span><span class="p">[</span><span class="s">'sc'</span><span class="p">]</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
<span class="nb">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">"__main__"</span><span class="p">:</span>
<span class="n">_test</span><span class="p">()</span>
</pre></div>
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