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    <title>topic how to dynamically perform aggregation on all columns in a data frame even when some columns have different types like int , double string datetime or float in pyspark (i have 140-200 columns and need to perform aggregation/avg on each column) in Data Engineering</title>
    <link>https://community.databricks.com/t5/data-engineering/how-to-dynamically-perform-aggregation-on-all-columns-in-a-data/m-p/8465#M4109</link>
    <description>&lt;P&gt;need to aggregate all the numerical columns but need to this dynamically &lt;/P&gt;</description>
    <pubDate>Wed, 01 Mar 2023 15:02:44 GMT</pubDate>
    <dc:creator>STummala</dc:creator>
    <dc:date>2023-03-01T15:02:44Z</dc:date>
    <item>
      <title>how to dynamically perform aggregation on all columns in a data frame even when some columns have different types like int , double string datetime or float in pyspark (i have 140-200 columns and need to perform aggregation/avg on each column)</title>
      <link>https://community.databricks.com/t5/data-engineering/how-to-dynamically-perform-aggregation-on-all-columns-in-a-data/m-p/8465#M4109</link>
      <description>&lt;P&gt;need to aggregate all the numerical columns but need to this dynamically &lt;/P&gt;</description>
      <pubDate>Wed, 01 Mar 2023 15:02:44 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/how-to-dynamically-perform-aggregation-on-all-columns-in-a-data/m-p/8465#M4109</guid>
      <dc:creator>STummala</dc:creator>
      <dc:date>2023-03-01T15:02:44Z</dc:date>
    </item>
    <item>
      <title>Re: how to dynamically perform aggregation on all columns in a data frame even when some columns have different types like int , double string datetime or float in pyspark (i have 140-200 columns and need to perform aggregation/avg on each column)</title>
      <link>https://community.databricks.com/t5/data-engineering/how-to-dynamically-perform-aggregation-on-all-columns-in-a-data/m-p/8467#M4111</link>
      <description>&lt;P&gt;Hi ​@sandeep tummala​&amp;nbsp;, &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thank you for your question! To assist you better, please take a moment to review the answer and let me know if it best fits your needs.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Please help us select the best solution by clicking on "Select As Best" if it does.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Your feedback will help us ensure that we are providing the best possible service to you. &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thank you!&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;</description>
      <pubDate>Mon, 06 Mar 2023 07:16:13 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/how-to-dynamically-perform-aggregation-on-all-columns-in-a-data/m-p/8467#M4111</guid>
      <dc:creator>Anonymous</dc:creator>
      <dc:date>2023-03-06T07:16:13Z</dc:date>
    </item>
    <item>
      <title>Re: how to dynamically perform aggregation on all columns in a data frame even when some columns have different types like int , double string datetime or float in pyspark (i have 140-200 columns and need to perform aggregation/avg on each column)</title>
      <link>https://community.databricks.com/t5/data-engineering/how-to-dynamically-perform-aggregation-on-all-columns-in-a-data/m-p/8466#M4110</link>
      <description>&lt;P&gt;Hi, Have you tried using the aggregate function which may help in this case? &lt;/P&gt;&lt;P&gt;&lt;A href="https://docs.databricks.com/sql/language-manual/functions/aggregate.html" alt="https://docs.databricks.com/sql/language-manual/functions/aggregate.html" target="_blank"&gt;https://docs.databricks.com/sql/language-manual/functions/aggregate.html&lt;/A&gt;&lt;/P&gt;</description>
      <pubDate>Mon, 06 Mar 2023 04:57:08 GMT</pubDate>
      <guid>https://community.databricks.com/t5/data-engineering/how-to-dynamically-perform-aggregation-on-all-columns-in-a-data/m-p/8466#M4110</guid>
      <dc:creator>Debayan</dc:creator>
      <dc:date>2023-03-06T04:57:08Z</dc:date>
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