Table To NumPyArray consistent error

BrandonMcAlister

I have a script that calculates several fields in a table, then publishes that table to a sql server.

 

I use pandas to publish to the sql server, I create the pandas data frame using a numpy array.

I use arcpy.da.TableToNumpyArray() to create the dataframe.

Whenever line 6 is run I receive a type error int() argument must be a string, a bytes-like object or a number, not 'NoneType'.

See code below:

 

Childcare = mp.listTables("Childcare_Coordinates")[0] #create table variable
Fields = [f.name for f in arcpy.ListFields(Childcare)] #gets field names
Fields.pop(0) #removes objectid

#create NumpyArray
NumpyArray = arcpy.da.TableToNumPyArray(Childcare, Fields, skip_nulls = False)

#Covert NumpyArray to Pandas Dataframe
df = pd.DataFrame(NumpyArray)
#rename objectid to objectid_1
df = df.rename(columns = {"objectid_1": "objectid"})

 

 

Every other piece functions as it should with these two variable except line 6. Why

This has worked in the past, then one day it woke up and said I don't want to be friends with you any more. It is not isolated to this table, it is all tables.

I have tried to reference the table from a workspace instead of a map as well. Same error

Tonyalmeida

I think Dan is right about nulls , null_value=0 replaces None values with 0. Adjust this value based on your data requirements.

 

 

arcpy.ListTables("Childcare_Coordinates")[0]  # Create table variable
Fields = [f.name for f in arcpy.ListFields(Childcare)]  # Get field names
Fields.pop(0)  # Remove objectid

# Create NumpyArray with null_value parameter
NumpyArray = arcpy.da.TableToNumPyArray(Childcare, Fields, skip_nulls=False, null_value=0)

# Convert NumpyArray to Pandas DataFrame
df = pd.DataFrame(NumpyArray)
# Rename objectid to objectid_1
df = df.rename(columns={"objectid_1": "objectid"})