TableToNumPyArray (arcpy.da)

Récapitulatif

Converts a table to NumPy structured array.

Discussion

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.

To convert feature classes to a NumPy array, use the FeatureClassToNumPyArray function instead.

Syntaxe

TableToNumPyArray (in_table, field_names, {where_clause}, {skip_nulls}, {null_value})

Paramètre	Explication	Type de données
in_table	The feature class, layer, table, or table view.	String
field_names [field_names,...]	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: OID@ —Returns the value of the ObjectID field. (La valeur par défaut est *)	String
where_clause	An optional expression that limits the records returned. For more information on WHERE clauses and SQL statements, see Building a query expression and Specifying a query in Python. (La valeur par défaut est "")	String
skip_nulls	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. In a NumPy array, a null is represented as a nan (not a number) for floating-point numeric values but not for integers. Below is an example of skipping all records that include a null. import arcpy array = arcpy.da.TableToNumPyArray(table, fields, skip_nulls=True) 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. import arcpy def getnull(oid): nullRows.append(oid) return True nullRows = list() array = arcpy.da.TableToNumPyArray(table, fields, skip_nulls=getnull) print(nullRows) Use a lambda expression to capture all records that are skipped because of nulls. import arcpy nullRows = list() array = arcpy.da.TableToNumPyArray( table, fields, skip_nulls=lambda oid: nullRows.append(oid)) print(nullRows) Remarque : In NumPy arrays, nulls are represented in float types such as nan and in text types such as None. Integer types do not support the concept of null values. (La valeur par défaut est False)	Variant
null_value	Replaces null values from the input with a new value. null_value is replaced before skip_nulls is evaluated. Mask None values in integer fields with a -9999. import arcpy fields = ['field1', 'field2'] arcpy.da.TableToNumPyArray(table, fields, null_value=-9999) Mask None values in integer fields with different values using a dictionary. import arcpy fields = ['field1', 'field2'] nullDict = {'field1':-999999, 'field2':-9999} arcpy.da.TableToNumPyArray(table, fields, null_value=nullDict) Attention : 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. (La valeur par défaut est None)	Integer

Valeur renvoyée

Type de données	Explication
NumPyArray	A NumPy structured array.

Exemple de code

TableToNumPyArray example 1

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())

TableToNumPyArray example 2

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])))