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    <title>topic Re: How to split the data for drawing number wise all features from mdb or gdb file? in Python Questions</title>
    <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642784#M50142</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;&lt;SPAN style="display: inline !important; float: none; background-color: transparent; color: #3d3d3d; font-family: arial,helvetica,'helvetica neue',verdana,sans-serif; font-size: 15px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;"&gt;Dear Randy Burton,&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="display: inline !important; float: none; background-color: transparent; color: #3d3d3d; font-family: arial,helvetica,'helvetica neue',verdana,sans-serif; font-size: 15px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;"&gt;I am getting error can you give me the solution for this error. I am using ArcGIS 10.1&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: arial,helvetica,'helvetica neue',verdana,sans-serif;"&gt;Attached below image for your reference.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG style=": ; text-decoration: underline; font-family: arial,helvetica,'helvetica neue',verdana,sans-serif; "&gt;Error Below Code&lt;/STRONG&gt;&lt;/P&gt;&lt;DIV&gt;Processing HVAC&lt;BR /&gt;&amp;nbsp;HVAC_Fan&lt;/DIV&gt;&lt;DIV&gt;Traceback (most recent call last):&lt;BR /&gt;&amp;nbsp; File "C:\path\to\folder\COBW_Firealarm\split drawing.py", line 15, in &amp;lt;module&amp;gt;&lt;BR /&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; arcpy.CreateFileGDB_management(out_folder, '{}.gdb'.format(fc))&lt;BR /&gt;&amp;nbsp; File "C:\Program Files\ArcGIS\Desktop10.1\arcpy\arcpy\management.py", line 14458, in CreateFileGDB&lt;BR /&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; raise e&lt;BR /&gt;ExecuteError: ERROR 000258: Output C:\path\to\folder\COBW_Firealarm\HVAC_Fan.gdb already exists&lt;BR /&gt;Failed to execute (CreateFileGDB).&lt;/DIV&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: arial,helvetica,'helvetica neue',verdana,sans-serif;"&gt;&lt;IMG 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" /&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: arial,helvetica,'helvetica neue',verdana,sans-serif;"&gt;Thanks&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: arial,helvetica,'helvetica neue',verdana,sans-serif;"&gt;Santhosh&lt;/SPAN&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Wed, 06 Feb 2019 11:45:51 GMT</pubDate>
    <dc:creator>santhoshp</dc:creator>
    <dc:date>2019-02-06T11:45:51Z</dc:date>
    <item>
      <title>How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642776#M50134</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hi Dear Friends,&lt;/P&gt;&lt;P&gt;How to split the data &lt;SPAN style="display: inline !important; float: none; background-color: transparent; color: #3d3d3d; font-family: Helvetica Neue,Helvetica,Arial,Lucida Grande,sans-serif; font-size: 15px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px; word-wrap: break-word;"&gt;drawing number wise&lt;/SPAN&gt; for all features from mdb or gdb file.&lt;/P&gt;&lt;P&gt;I have 8 types of drawing numbers available in data I want to split each drawing number separate data mdb format or gdb&amp;nbsp;format file.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thanks&lt;/P&gt;&lt;P&gt;Santhosh&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sat, 02 Feb 2019 16:00:59 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642776#M50134</guid>
      <dc:creator>santhoshp</dc:creator>
      <dc:date>2019-02-02T16:00:59Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642777#M50135</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;sounds like ...&lt;/P&gt;&lt;P&gt;&lt;A class="link-titled" href="http://pro.arcgis.com/en/pro-app/tool-reference/analysis/split-by-attributes.htm" title="http://pro.arcgis.com/en/pro-app/tool-reference/analysis/split-by-attributes.htm"&gt;Split By Attributes—Help | ArcGIS Desktop&lt;/A&gt;&amp;nbsp;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 04 Feb 2019 06:22:14 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642777#M50135</guid>
      <dc:creator>DanPatterson_Retired</dc:creator>
      <dc:date>2019-02-04T06:22:14Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642778#M50136</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Dear Dan Patterson,&lt;/P&gt;&lt;P&gt;yes It is working as a shape file but i nedd direct feature class it's possible or not if it's possible plz help me.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thanks&lt;/P&gt;&lt;P&gt;Santhosh&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 04 Feb 2019 15:38:55 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642778#M50136</guid>
      <dc:creator>santhoshp</dc:creator>
      <dc:date>2019-02-04T15:38:55Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642779#M50137</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;make the destination output a geodatabase, or easier still, use Copy Features to bring the shapefile into a gdb, then do the split&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 04 Feb 2019 17:26:02 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642779#M50137</guid>
      <dc:creator>DanPatterson_Retired</dc:creator>
      <dc:date>2019-02-04T17:26:02Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642780#M50138</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;When you say 'drawing number', are you referring to the DRAWING_REFERENCE field&amp;nbsp;in your data?&amp;nbsp; If so, I'm counting 10 different values.&lt;/P&gt;&lt;PRE class="lia-code-sample line-numbers language-none"&gt;&lt;CODE&gt;111-111-AP
C0500-GS-0117-01-EL-FA-A0-03-001-001
C0500-GS-0117-01-EL-FA-A0-B1-001-001
C0500-GS-0117-04-EL-FA-A0-01-001-001
C0500-GS-0117-04-EL-FA-A0-02-001-001
C0500-GS-0117-04-EL-FA-A0-04-001-001
C0500-GS-0117-04-EL-FA-A0-05-001-001
C0500-GS-0117-04-EL-FA-A0-06-001-001
C0500-GS-0117-04-EL-FA-A0-B2-001-001
C0500-GS-0117-04-EL-FA-A0-GF-001-001
‍‍‍‍‍‍‍‍‍‍&lt;SPAN class="line-numbers-rows"&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/CODE&gt;&lt;/PRE&gt;&lt;P&gt;If this is not what you mean, could you explain 'drawing number'.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sun, 12 Dec 2021 03:19:27 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642780#M50138</guid>
      <dc:creator>RandyBurton</dc:creator>
      <dc:date>2021-12-12T03:19:27Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642781#M50139</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Dear Randy Burton,&lt;/P&gt;&lt;P&gt;In this gdb have&amp;nbsp;&lt;SPAN style="display: inline !important; float: none; background-color: transparent; color: #3d3d3d; font-family: arial,helvetica,'helvetica neue',verdana,sans-serif; font-size: 15px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;"&gt;DRAWING_REFERENCE field&lt;/SPAN&gt; in that different drawing numbers available in gdb.&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt; for example:&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="display: inline !important; float: none; background-color: transparent; color: #000000; font-family: monospace; font-size: 15px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-shadow: 0px 1px #fff; text-transform: none; -webkit-text-stroke-width: 0px; white-space: pre; word-spacing: 0px;"&gt;&lt;STRONG&gt;C0500-GS-0117-01-EL-FA-A0-03-001-001&lt;/STRONG&gt; this is the drawing number. &lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="display: inline !important; float: none; background-color: transparent; color: #000000; font-family: monospace; font-size: 15px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-shadow: 0px 1px #fff; text-transform: none; -webkit-text-stroke-width: 0px; white-space: pre; word-spacing: 0px;"&gt;Thanks&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="display: inline !important; float: none; background-color: transparent; color: #000000; font-family: monospace; font-size: 15px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-shadow: 0px 1px #fff; text-transform: none; -webkit-text-stroke-width: 0px; white-space: pre; word-spacing: 0px;"&gt;Santhosh&lt;/SPAN&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 05 Feb 2019 16:13:36 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642781#M50139</guid>
      <dc:creator>santhoshp</dc:creator>
      <dc:date>2019-02-05T16:13:36Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642782#M50140</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;And your version of ArcGIS?&amp;nbsp; 10.5 or 10.6 or Pro ?&amp;nbsp; Looks like &lt;A href="http://desktop.arcgis.com/en/arcmap/10.5/tools/analysis-toolbox/split-by-attributes.htm"&gt;Split By Attributes&lt;/A&gt;&amp;nbsp;requires one of these versions.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Tue, 05 Feb 2019 17:34:55 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642782#M50140</guid>
      <dc:creator>RandyBurton</dc:creator>
      <dc:date>2019-02-05T17:34:55Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642783#M50141</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;The following code will split your features based on DRAWING_REFERENCE and place them in a geodatabase named with the feature class.&amp;nbsp; From here you should be able to copy them into the desired structure with&amp;nbsp;additional scripting.&amp;nbsp; It does appear that domains and subtypes are preserved.&amp;nbsp; I could not get SplitAttributes to create the features inside a dataset, but it&amp;nbsp;might&amp;nbsp;output to shape files.&lt;/P&gt;&lt;PRE class="lia-code-sample line-numbers language-none"&gt;&lt;CODE&gt;&lt;SPAN class="keyword token"&gt;import&lt;/SPAN&gt; arcpy
&lt;SPAN class="keyword token"&gt;import&lt;/SPAN&gt; os

gdb &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; r&lt;SPAN class="string token"&gt;'C:\path\to\folder\COBW_Firealarm\COBW_Firealarm.gdb'&lt;/SPAN&gt;
arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;env&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;workspace &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; gdb
out_folder &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt;  r&lt;SPAN class="string token"&gt;'C:\path\to\folder\COBW_Firealarm'&lt;/SPAN&gt; &lt;SPAN class="comment token"&gt;# geodatabases go here&lt;/SPAN&gt;

fields &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'DRAWING_REFERENCE'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;

&lt;SPAN class="keyword token"&gt;for&lt;/SPAN&gt; fds &lt;SPAN class="keyword token"&gt;in&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;ListDatasets&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;''&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'Feature'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;:&lt;/SPAN&gt;
    &lt;SPAN class="keyword token"&gt;print&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"Processing {}"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;format&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;fds&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
    sr &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;Describe&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;fds&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;SpatialReference
    &lt;SPAN class="keyword token"&gt;for&lt;/SPAN&gt; fc &lt;SPAN class="keyword token"&gt;in&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;ListFeatureClasses&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;''&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;''&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;fds&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;:&lt;/SPAN&gt;
        &lt;SPAN class="keyword token"&gt;print&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"\t{}"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;format&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;fc&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt; &lt;SPAN class="comment token"&gt;# use fc for name of target geodatabase&lt;/SPAN&gt;
        arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;CreateFileGDB_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;out_folder&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'{}.gdb'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;format&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;fc&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
        in_fc &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; os&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;path&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;join&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;gdb&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;fds&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;fc&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
        target &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; os&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;path&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;join&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;out_folder&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'{}.gdb'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;format&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;fc&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
        &lt;SPAN class="comment token"&gt;# print in_fc&lt;/SPAN&gt;
        &lt;SPAN class="comment token"&gt;# print target&lt;/SPAN&gt;
        &lt;SPAN class="comment token"&gt;# save split in file geodatabase with name of original feature class&lt;/SPAN&gt;
        arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;SplitByAttributes_analysis&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;in_fc&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; target&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; fields&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="line-numbers-rows"&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/CODE&gt;&lt;/PRE&gt;&lt;P&gt;Here's what the output looked like:&lt;/P&gt;&lt;P&gt;&lt;IMG alt="split output" class="image-1 jive-image j-img-original" src="https://community.esri.com/legacyfs/online/436637_split.png" /&gt;&lt;/P&gt;&lt;P&gt;Hope this helps.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sun, 12 Dec 2021 03:19:30 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642783#M50141</guid>
      <dc:creator>RandyBurton</dc:creator>
      <dc:date>2021-12-12T03:19:30Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642784#M50142</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;&lt;SPAN style="display: inline !important; float: none; background-color: transparent; color: #3d3d3d; font-family: arial,helvetica,'helvetica neue',verdana,sans-serif; font-size: 15px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;"&gt;Dear Randy Burton,&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="display: inline !important; float: none; background-color: transparent; color: #3d3d3d; font-family: arial,helvetica,'helvetica neue',verdana,sans-serif; font-size: 15px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;"&gt;I am getting error can you give me the solution for this error. I am using ArcGIS 10.1&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: arial,helvetica,'helvetica neue',verdana,sans-serif;"&gt;Attached below image for your reference.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;STRONG style=": ; text-decoration: underline; font-family: arial,helvetica,'helvetica neue',verdana,sans-serif; "&gt;Error Below Code&lt;/STRONG&gt;&lt;/P&gt;&lt;DIV&gt;Processing HVAC&lt;BR /&gt;&amp;nbsp;HVAC_Fan&lt;/DIV&gt;&lt;DIV&gt;Traceback (most recent call last):&lt;BR /&gt;&amp;nbsp; File "C:\path\to\folder\COBW_Firealarm\split drawing.py", line 15, in &amp;lt;module&amp;gt;&lt;BR /&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; arcpy.CreateFileGDB_management(out_folder, '{}.gdb'.format(fc))&lt;BR /&gt;&amp;nbsp; File "C:\Program Files\ArcGIS\Desktop10.1\arcpy\arcpy\management.py", line 14458, in CreateFileGDB&lt;BR /&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp; raise e&lt;BR /&gt;ExecuteError: ERROR 000258: Output C:\path\to\folder\COBW_Firealarm\HVAC_Fan.gdb already exists&lt;BR /&gt;Failed to execute (CreateFileGDB).&lt;/DIV&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: arial,helvetica,'helvetica neue',verdana,sans-serif;"&gt;&lt;IMG 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" /&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: arial,helvetica,'helvetica neue',verdana,sans-serif;"&gt;Thanks&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="font-family: arial,helvetica,'helvetica neue',verdana,sans-serif;"&gt;Santhosh&lt;/SPAN&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 06 Feb 2019 11:45:51 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642784#M50142</guid>
      <dc:creator>santhoshp</dc:creator>
      <dc:date>2019-02-06T11:45:51Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642785#M50143</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;check the folder in the error message... the geodatabase already exists and you can't overwrite it.&amp;nbsp;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 06 Feb 2019 11:53:12 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642785#M50143</guid>
      <dc:creator>DanPatterson_Retired</dc:creator>
      <dc:date>2019-02-06T11:53:12Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642786#M50144</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I am assuming that the picture in my last post is generally what you are looking for.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;As &lt;A href="https://community.esri.com/migrated-users/3116" target="_blank"&gt;Dan Patterson&lt;/A&gt;‌ mentioned, since the database exists you can't overwrite it.&amp;nbsp;&amp;nbsp; You can delete it with code or just use it.&amp;nbsp; A line of code&amp;nbsp;could be added to check for the existence of the database.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;As seen in your attached jpeg, you are getting a second error:&lt;/P&gt;&lt;BLOCKQUOTE class="jive_macro_quote jive-quote jive_text_macro"&gt;&lt;P&gt;object has no attribute 'SplitByAttributes_analysis'&lt;/P&gt;&lt;/BLOCKQUOTE&gt;&lt;P&gt;&lt;A href="http://desktop.arcgis.com/en/arcmap/10.5/tools/analysis-toolbox/split-by-attributes.htm" rel="nofollow noopener noreferrer" target="_blank"&gt;Split By Attributes&lt;/A&gt;&amp;nbsp;was added at version 10.5 and Pro.&amp;nbsp; Since you are at 10.1, we will need a different solution.&amp;nbsp; I would suggest looping through your datasets and features and using &lt;A href="http://desktop.arcgis.com/en/arcmap/latest/tools/data-management-toolbox/select-layer-by-attribute.htm" rel="nofollow noopener noreferrer" target="_blank"&gt;Select Layer By Attribute&lt;/A&gt;&amp;nbsp;with a where clause, something&amp;nbsp;like:&lt;/P&gt;&lt;PRE class="lia-code-sample line-numbers language-none"&gt;&lt;CODE&gt;arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;SelectLayerByAttribute_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'FA_Box'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'NEW_SELECTION'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; 
     where_clause &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"DRAWING_REFERENCE = 'C0500-GS-0117-01-EL-FA-A0-03-001-001'"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;‍‍‍‍‍‍‍‍&lt;SPAN class="line-numbers-rows"&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/CODE&gt;&lt;/PRE&gt;&lt;P&gt;If records are selected, then copy them to the appropriate database.&amp;nbsp; Hope this helps.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sun, 12 Dec 2021 03:19:33 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642786#M50144</guid>
      <dc:creator>RandyBurton</dc:creator>
      <dc:date>2021-12-12T03:19:33Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642787#M50145</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;10.1?!&amp;nbsp; missed that&lt;/P&gt;&lt;P&gt;&lt;A class="link-titled" href="https://www.arcgis.com/home/item.html?id=15ca63aebb4647a4b07bc94f3d051da5" title="https://www.arcgis.com/home/item.html?id=15ca63aebb4647a4b07bc94f3d051da5"&gt;https://www.arcgis.com/home/item.html?id=15ca63aebb4647a4b07bc94f3d051da5&lt;/A&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;the original split by attribute... can't remember if it works on that old a version.&lt;/P&gt;&lt;P&gt;but it has the coolest page icon, if I may say so&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 06 Feb 2019 19:46:47 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642787#M50145</guid>
      <dc:creator>DanPatterson_Retired</dc:creator>
      <dc:date>2019-02-06T19:46:47Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642788#M50146</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Since you are using 10.1, I experimented with SelectLayerByAttributes.&amp;nbsp; I found it easiest to code to work with one geodatabase (the one in your zip file).&amp;nbsp;&amp;nbsp;I first ran a script to get a list of the unique attributes used in the DRAWING_REFERENCE fields of all the features in the gdb.&amp;nbsp; Since the drawing reference is rather long, I paired it with a shorter substring.&amp;nbsp; This is what the gdb looks like in catalog:&lt;/P&gt;&lt;P&gt;&lt;IMG alt="split attributes" class="image-1 jive-image j-img-original" src="https://community.esri.com/legacyfs/online/436939_split_attributes.png" /&gt;&lt;/P&gt;&lt;P&gt;Each feature is appended with the short substring; in this case, GF is highlighted.&amp;nbsp; Here's the code:&lt;/P&gt;&lt;PRE class="lia-code-sample line-numbers language-none"&gt;&lt;CODE&gt;&lt;SPAN class="keyword token"&gt;import&lt;/SPAN&gt; arcpy
&lt;SPAN class="keyword token"&gt;import&lt;/SPAN&gt; os

gdb &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; r&lt;SPAN class="string token"&gt;'C:\Path\to\COBW_Firealarm.gdb'&lt;/SPAN&gt;
arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;env&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;workspace &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; gdb &lt;SPAN class="comment token"&gt;# needed for ListDatasets&lt;/SPAN&gt;

fields &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'DRAWING_REFERENCE'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;

draw_ref &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="comment token"&gt;# unique values in DRAWING_REFERENCE fields, name append value&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'111-111-AP'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'AP'&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'C0500-GS-0117-04-EL-FA-A0-01-001-001'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'01'&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'C0500-GS-0117-04-EL-FA-A0-02-001-001'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'02'&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'C0500-GS-0117-01-EL-FA-A0-03-001-001'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'03'&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'C0500-GS-0117-04-EL-FA-A0-04-001-001'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'04'&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'C0500-GS-0117-04-EL-FA-A0-05-001-001'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'05'&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'C0500-GS-0117-04-EL-FA-A0-06-001-001'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'06'&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'C0500-GS-0117-01-EL-FA-A0-B1-001-001'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'B1'&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'C0500-GS-0117-04-EL-FA-A0-B2-001-001'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'B2'&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'C0500-GS-0117-04-EL-FA-A0-GF-001-001'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;'GF'&lt;/SPAN&gt; &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;
    &lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt; &lt;SPAN class="comment token"&gt;# name append keeps feature name from becoming too long&lt;/SPAN&gt;


&lt;SPAN class="keyword token"&gt;for&lt;/SPAN&gt; fds &lt;SPAN class="keyword token"&gt;in&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;ListDatasets&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;''&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'Feature'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;:&lt;/SPAN&gt;

    &lt;SPAN class="keyword token"&gt;print&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"\nProcessing {}"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;format&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;fds&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;

    &lt;SPAN class="keyword token"&gt;for&lt;/SPAN&gt; fc &lt;SPAN class="keyword token"&gt;in&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;ListFeatureClasses&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;''&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;''&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;fds&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;:&lt;/SPAN&gt;
        &lt;SPAN class="keyword token"&gt;print&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"\t{}"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;format&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;fc&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt; &lt;SPAN class="comment token"&gt;# use fc for name of target geodatabase&lt;/SPAN&gt;
        in_fc &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; os&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;path&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;join&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;gdb&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;fds&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;fc&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
        &lt;SPAN class="keyword token"&gt;if&lt;/SPAN&gt; arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;Exists&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'temp_layer'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;:&lt;/SPAN&gt;
            arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;Delete_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'temp_layer'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
        arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;MakeFeatureLayer_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;in_fc&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'temp_layer'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
        ref_num &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="number token"&gt;0&lt;/SPAN&gt;
        
        &lt;SPAN class="keyword token"&gt;for&lt;/SPAN&gt; dr &lt;SPAN class="keyword token"&gt;in&lt;/SPAN&gt; draw_ref&lt;SPAN class="punctuation token"&gt;:&lt;/SPAN&gt; &lt;SPAN class="comment token"&gt;# loop through drawing reference list&lt;/SPAN&gt;
            &lt;SPAN class="comment token"&gt;# dr[0] = DRAWING_REFERENCE field value&lt;/SPAN&gt;
            &lt;SPAN class="comment token"&gt;# dr[1] = a substring of DRAWING_REFERENCE used in naming new feature class&lt;/SPAN&gt;
            arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;SelectLayerByAttribute_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'temp_layer'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
                                                    &lt;SPAN class="string token"&gt;'NEW_SELECTION'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;
                                                    where_clause &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"DRAWING_REFERENCE = '{}'"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;format&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;dr&lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt;&lt;SPAN class="number token"&gt;0&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
            count &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; int&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;GetCount_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'temp_layer'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;getOutput&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="number token"&gt;0&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;

            &lt;SPAN class="keyword token"&gt;if&lt;/SPAN&gt; count &lt;SPAN class="operator token"&gt;&amp;gt;&lt;/SPAN&gt; &lt;SPAN class="number token"&gt;0&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;:&lt;/SPAN&gt;
                ref_num &lt;SPAN class="operator token"&gt;+=&lt;/SPAN&gt; &lt;SPAN class="number token"&gt;1&lt;/SPAN&gt;
                new_fc &lt;SPAN class="operator token"&gt;=&lt;/SPAN&gt; os&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;path&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;join&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;fds&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"{}_{}"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;format&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;fc&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;dr&lt;SPAN class="punctuation token"&gt;[&lt;/SPAN&gt;&lt;SPAN class="number token"&gt;1&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;]&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
                &lt;SPAN class="keyword token"&gt;print&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"\t\t{}  -- count: {}"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;format&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;new_fc&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt;count&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
                &lt;SPAN class="comment token"&gt;# write the selected features to a new featureclass&lt;/SPAN&gt;
                arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;CopyFeatures_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'temp_layer'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;,&lt;/SPAN&gt; new_fc&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;

        &lt;SPAN class="keyword token"&gt;print&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"\t\t{} total new features"&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;format&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;ref_num&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;

arcpy&lt;SPAN class="punctuation token"&gt;.&lt;/SPAN&gt;Delete_management&lt;SPAN class="punctuation token"&gt;(&lt;/SPAN&gt;&lt;SPAN class="string token"&gt;'temp_layer'&lt;/SPAN&gt;&lt;SPAN class="punctuation token"&gt;)&lt;/SPAN&gt;
&lt;SPAN class="keyword token"&gt;print&lt;/SPAN&gt; &lt;SPAN class="string token"&gt;"\nDone."&lt;/SPAN&gt;‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍&lt;SPAN class="line-numbers-rows"&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;SPAN&gt;‍&lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/CODE&gt;&lt;/PRE&gt;&lt;P&gt;For the next steps, you can use a script to&amp;nbsp;loop through the&amp;nbsp;draw_ref list,&amp;nbsp;making a&amp;nbsp;copy the first gdb, deleting the features that don't match the current value in the draw_ref list, and repeat until each drawing reference has its own gdb.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Hope this helps.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sun, 12 Dec 2021 03:19:36 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642788#M50146</guid>
      <dc:creator>RandyBurton</dc:creator>
      <dc:date>2021-12-12T03:19:36Z</dc:date>
    </item>
    <item>
      <title>Re: How to split the data for drawing number wise all features from mdb or gdb file?</title>
      <link>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642789#M50147</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;&lt;SPAN style="display: inline !important; float: none; background-color: transparent; color: #3d3d3d; font-family: arial,helvetica,'helvetica neue',verdana,sans-serif; font-size: 15px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;"&gt;Dear Randy Burton&lt;SPAN style="font-family: Helvetica Neue,Helvetica,Arial,Lucida Grande,sans-serif;"&gt;,&lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I&lt;SPAN style="-webkit-text-stroke-width: 0px; color: #3d3d3d; white-space: normal; font-weight: 400; display: inline !important; letter-spacing: normal; text-decoration: none; font-size: 15px; font-style: normal; float: none; background-color: transparent; text-transform: none; word-spacing: 0px; font-variant: normal; text-indent: 0px; font-family: Helvetica Neue,Helvetica,Arial,Lucida Grande,sans-serif; orphans: 2; text-align: left; "&gt; really appreciate you for helpling thank you somuch.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="-webkit-text-stroke-width: 0px; color: #3d3d3d; white-space: normal; font-weight: 400; display: inline !important; letter-spacing: normal; text-decoration: none; font-size: 15px; font-style: normal; float: none; background-color: transparent; text-transform: none; word-spacing: 0px; font-variant: normal; text-indent: 0px; font-family: Helvetica Neue,Helvetica,Arial,Lucida Grande,sans-serif; orphans: 2; text-align: left; "&gt;Thanks&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;santhosh&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Sun, 10 Feb 2019 11:55:40 GMT</pubDate>
      <guid>https://community.esri.com/t5/python-questions/how-to-split-the-data-for-drawing-number-wise-all/m-p/642789#M50147</guid>
      <dc:creator>santhoshp</dc:creator>
      <dc:date>2019-02-10T11:55:40Z</dc:date>
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