I am importing csv files into a gdb, and need to change a couple field types during the import. I am trying to use the field mapping in TableToTable_conversion to change the field type as the table gets imported.

I am confused about how to identify the field that needs to be updated. When setting up the field mapping I am using the fm.addInputField line, which requires a field name. But because the gdb table does not exist yet as it has not yet been imported, I can't define the field to input.

Should the approach be to use the csv rows to define which field I want to change, or am I missing something completely? Is there an easier way to change a field type upon import rather than using Field Mappings?


I don't know how sophisticated are your QA/QC operations you run on the input .csv files (what if user has added a row with a string in the column that you expect to be an integer?). If you target ArcGIS 10.4+, I may recommend using pandas Python package to read the .csv file and cast the columns into the proper types so you don't have deal with the cast errors yourself. When you are done, you can always export the produced data frame into an output .csv into a user temp folder using the tempfile module.

If you are only interested in getting your columns right (without actually checking whether all rows would qualify), I suggest converting the .csv file into an in_memory layer first.

Say you have a .csv file with the rows:

   ID FieldInt FieldStr   FieldDate
   1       10   Value1  2018-02-12
   2       20   Value2  2018-02-14
  3a      20a   Value3  2018-02-16

You would like all the fields to be of string types. If you would convert this .csv into a table using the arcpy.TableToTable_conversion(, you would get:

enter image description here

As you can see, ArcGIS decided to cast the ID and FieldInt fields into Integer fields and values that could not have been casted are now just null.

enter image description here

You will not be able to restore the null values, but you can still move the data left into the columns of right type. You create a new table with the fields found in the .csv file using the data types you need:

  1. Create an empty geodatabase table.
  2. Add fields with the necessary types (using arcpy.AddField_management.
  3. Convert source .csv into a temp table in_memory\data.
  4. Append with the arcpy.Append_management(src, target) moving the data from the temp table into a production one.

Even if you would have a field map in place, the situation I'm describing above would make it impossible to import all the data right. Try yourself to run the TableToTable tool in ArcMap UI.

arcpy.TableToTable_conversion(in_rows="C:/GIS/Temp/data.csv", out_path="C:/GIS/Temp/ArcGISHomeFolder/sample.gdb", out_name="trick1", where_clause="", field_mapping='ID "ID" true true false 4 Text 0 0 ,First,#,C:\GIS\Temp\data.csv,ID,-1,-1;FieldInt "FieldInt" true true false 4 Text 0 0 ,First,#,C:\GIS\Temp\data.csv,FieldInt,-1,-1;FieldStr "FieldStr" true true false 8000 Text 0 0 ,First,#,C:\GIS\Temp\data.csv,FieldStr,-1,-1;FieldDate "FieldDate" true true false 20 Text 0 0 ,First,#,C:\GIS\Temp\data.csv,FieldDate,-1,-1', config_keyword="")

Even after you've specified all the fields to be of Text type, the last row is not loaded (only null present`).

PS. A dirty workaround I've seen in someone's code was to put a top row in the .csv file with values of the type one wanted to have and then delete the row after the data import was done. This could be done using Python's csv module and then using arcpy.da.UpdateCursor to delete the first row.

  • Thanks for the suggestions - in this case the data doesn't need a QA/QC, I just can't loose any data, even if it is invalid. This is why in the case of an ID for example, I need it to import as a string so I don't loose something like 3a, which is what was happening if course when TabletoTable_conversion changes a string field to a integer field. I did manage to get the Field Mappings to work so I will post that as an answer now. My new problem is the leading 0s being lost on the import into the string field, for example 01008 becomes 1008, which the field mapping doesn't solve. – JS24 Mar 27 '18 at 10:10

In response to the issue

This doesn't prevent leading zeros from being lost in the transition from int to text field, for example 01008 becomes 1008, so I am still working on that.

To keep leading 0s, add letter to the front of the value prior to import then remove it once imported


Not sure if that fit your requirement but by creating a "schema.ini" text file in the same folder as your csv you can specify the field data type.

Have a look at this question for more detail How to auto-create a schema.ini file for a .csv?

  • I came across that as a fix but it won't work in this case because the script will be used by a number of different users who obtain the csvs themselves and store them locally. – JS24 Feb 12 '18 at 15:10

Just to follow up, I got the field mappings to work based on the csv fields, using this below:

input = 'mydata.csv'
fms = arcpy.FieldMappings()
with open(input, 'rb') as f:
    d_reader = csv.DictReader(f)
    headers = d_reader.fieldnames
    for header in headers:
        fm = arcpy.FieldMap()
        fm.addInputField(output, header)
        newField = fm.outputField
        newField.type = "Text"
        newField.length = 8000
        fm.outputField = newField
arcpy.TableToTable_conversion(output, outputgdb, fnametable, field_mapping=fms)

This doesn't prevent leading zeros from being lost in the transition from int to text field, for example 01008 becomes 1008, so I am still working on that.

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