Overcoming Typeerrors When Summing Gis Attributes In Python

Troubleshooting TypeErrors in Attribute Summations

When working with Geographic Information Systems (GIS) data in Python, a common task is summarizing attributes across spatial features. This often involves totalling or averaging numeric fields attached to GIS vectors or rasters. However, these calculations can easily fail with cryptic TypeErrors if attributes have inconsistent data types or invalid values.

Tracking down and resolving these type errors requires strategic troubleshooting to handle different data types gracefully. This article outlines common causes of summation TypeErrors in GIS analysis and demonstrates methods to standardize, validate, and convert field values to enable robust numeric operations.

Common Causes of TypeErrors

Attempting mathematical operations on GIS attributes with incompatible data types is the primary trigger for TypeErrors. The two main sources of these incompatibilities are:

Mixing Data Types

GIS features may have a mixture of textual and numeric attributes. Calculations like .sum() or .mean() often assume fields contain only integers or floats. Trying to sum text strings and numbers leads to errors.

Incorrect Variable Assignments

Another frequent culprit is assigning attribute values to variables with a data type that doesn’t match. For example, treating text from a .csv as integers without explicit conversion. This also manifests as type errors in subsequent math on those variables.

Handling TypeErrors Gracefully

When attribute summation fails due to data types, there are structured ways to trap, inspect, and handle the exceptions:

Checking Data Types

Explicitly checking field types with functions like .dtype lets you validate attributes before calculation:

import pandas as pd
data = pd.DataFrame(csv_data) 

if data['MyField'].dtype == 'object':
    # Field contains strings, handle differently

This prevents blind assumptions about data types when working with new datasets.

Standardizing Attributes

Converting all relevant fields to consistent types (e.g. integer, float) avoids mixed data surprises:

std_data = data.astype({'MyField':'float64'}) 

Standardization also facilitates matched type comparisons and calculations.

Using Exception Handling

Wrap math operations in try/except blocks to respond appropriately if attributes are incompatible:

try:
   pop_sum = data['Population'].sum() 
except TypeError:
   # Handle TypeError specifically
   print('Incompatible data type in Population field')

This controls failures gracefully compared to crashing on unhandled exceptions.

Working Example: Summing Population Attributes

To demonstrate resolving type errors, we’ll walk through a complete workflow for summing population values across a set of counties. The techniques from the previous section will help make this process robust to real-world data issues.

Importing GIS Data

First, we import a counties shapefile as a GeoDataFrame and access the population field:

import geopandas as gpd
counties = gpd.read_file('counties.shp') 
pop = counties['Population']

Defining Population Field

Before calculations, inspect the field dtype (often loaded as object):

dtype = pop.dtype # Could be 'object'
print(dtype)

And handle any conversions upfront through .astype():

if dtype == 'object':
   pop = pop.astype('int64') 
   print('Converted Population to integers')

Catching Type Errors

Now summation logic can be wrapped to respond to potential TypeErrors:

try:
    total_pop = pop.sum()
except TypeError: 
    print('Population field has incompatible data')
    total_pop = None

Converting Data Types

If a type error does occur, individual values would need standardized prior to re-attempting summation. This handles cases where the field has mixed types:

 
pop = pop.apply(lambda x: float(x)) 
print('Converted Population to floats')

Calculating Total Population

After data types are unified, the total can be reliably calculated:

total_pop = pop.sum() 
print('Total population:', total_pop) 

Additional Tips for Avoiding Errors

Along with the exception handling workflow outlined above, following Pythonic best practices helps minimize type issues when working with GIS data:

Use Consistent Data Types

Standardize attribute types after loading, and convert to numbers before mathematical operations.

Validate Assumptions About Data

Don’t assume loaded fields contain certain data types. Manually check with .dtype.

Print/Examine Intermediate Values

Spot type issues early by printing and validating variable types within workflows.

Conclusion: Robust Code for Reliable Analysis

Handling unexpected data types is a critical skill for GIS analysts. Python offers many tools for wrangling geospatial data, but math operations will still fail if applied blindly. By proactively checking types, standardizing fields, gracefully catching exceptions, and converting values, analysts can write robust code for accurate attribute summations.

The techniques covered here transfer beyond summation to other mathematical operations. Overall, these principles of defensive data handling lay the foundation for building reliable geoprocessing scripts that are resilient to real-world datasets.