Converts a table to NumPy structured array.
The FeatureClassToNumPyArray function is similar to the TableToNumPyArray function. The FeatureClassToNumPyArray function also provides additional arguments for dealing with feature data.
NumPy is a fundamental package for scientific computing in Python, including support for a powerful N-dimensional array object. For more information, see Working with NumPy in ArcGIS.
The feature class, layer, table, or table view.
A list (or tuple) of field names. For a single field, you can use a string instead of a list of strings.
Use an asterisk (*) instead of a list of fields if you want to access all fields from the input table (raster and BLOB fields are excluded). However, for faster performance and reliable field order, it is recommended that the list of fields be narrowed to only those that are actually needed.
Date, geometry, raster, and BLOB fields are not supported.
Additional information can be accessed using tokens (such as OID@) in place of field names:
(The default value is *)
(The default value is "")
Control whether records using nulls are skipped. It can be either a Boolean true or false or a Python function or lambda expression.
When set to True, if any of the record's attributes is null (including geometry), the record is skipped. With a False setting, skip_nulls attempts to use all records regardless of nulls (NumPy supports nan for floating-point numeric values, but not for integers).
Below is an example of skipping all records that include a null.
A Python function or lambda expression can be used to allow finer control, including logging the OID values of all records that include a null value. In both examples below, the lambda expression or function is used to identify OIDs that include null records.
Use a function to capture all records that are skipped because of nulls.
Use a lambda expression to capture all records that are skipped because of nulls.
In NumPy arrays, nulls are represented in float types as nan and in text types such as None. Integer types do not support the concept of null values.
(The default value is False)
Replaces null values from the input with a new value.
null_value is replaced before skip_null is evaluated.
Mask all None's in integer fields with a -9999.
Mask None's in integer fields with different values using a dictionary.
Providing a mask such as -9999 allows integer fields containing nulls to be exported to a NumPy array, but be cautious when using these values in any analysis. The results may be inadvertently skewed by the introduced value.
(The default value is None)
A NumPy structured array.
Convert a table to a NumPy array and perform some basic statistics with NumPy.
import arcpy import numpy input = "c:/data/usa.gdb/USA/counties" arr = arcpy.da.TableToNumPyArray(input, ('STATE_NAME', 'POP1990', 'POP2000')) # Sum the total population for 1990 and 2000 # print(arr["POP1990"].sum()) print(arr["POP2000"].sum()) # Sum the population for the state of Minnesota # print(arr[arr['STATE_NAME'] == "Minnesota"]['POP2000'].sum())
Use TableToNumPyArray to determine correlation coefficients for two fields.
import arcpy import numpy input = arcpy.GetParameterAsText(0) field1 = arcpy.GetParameterAsText(1) field2 = arcpy.GetParameterAsText(2) arr = arcpy.da.TableToNumPyArray(input, (field1, field2)) # Print correlation coefficients for comparison of 2 field values # print(numpy.corrcoef((arr[field1], arr[field2])))