How to Fix AttributeError: module ‘numpy’ has no attribute ‘typeDict’ in Python

When working with NumPy in Python, you may encounter the error AttributeError: module 'numpy' has no attribute 'typeDict'. This error occurs when your code tries to access the typeDict attribute or method on the numpy module, which does not exist.

In this article, we’ll understand why this error happens and how to properly fix it.

Overview of the AttributeError

The full error looks like:

AttributeError: module 'numpy' has no attribute 'typeDict'

This tells us that Python is unable to find the attribute typeDict as part of the numpy module. The numpy module (also commonly imported as np) contains lots of useful functions and attributes, but typeDict is not one of them.

Some key points about this error:

  • It arises when trying to access numpy.typeDict or np.typeDict
  • The numpy module does not contain a typeDict attribute
  • It likely indicates a typo or incorrect usage in your code

To fix it, we need to examine where in the code the non-existent typeDict is being accessed, and correct it.

Common Causes of the AttributeError

Some typical reasons why you might get the “has no attribute ‘typeDict'” error include:


It’s easy to mistype typeDict when trying to use a different numpy attribute. For example:

# Typo in attribute name

# Should be:

Always double check for typos in attribute names when seeing this error.

Confusion with Python’s typing Module

The typing module contains a TypedDict class. The error can arise if you mistakenly try to access it via numpy:

from typing import TypedDict 

# Incorrect module 
MyTypeDict = numpy.TypedDict('MyTypeDict', ...)

# Should be:
MyTypeDict = TypedDict('MyTypeDict', ...)

Be aware TypedDict comes from typing, not numpy.

Outdated Examples or Documentation

Older code samples that use the erroneous typeDict attribute can lead to this error if copied. Always double check documentation and examples are up-to-date when copying code.

Incorrect Library Imports

If you import another library with a name that overlaps numpy, it can cause issues accessing the right module. For example:

# Overrides numpy import
import other_library as numpy

numpy.typeDict # Error!

Be careful when importing libraries to avoid name conflicts.

Fixing the AttributeError in Code

Once you know why the typeDict error is occurring, you can take steps to fix it:

1. Check for Typos

Double check all usage of numpy attributes and methods to spot any typos. Common typos like typedict or typeDIct can cause the error.

Here is an example fixing a typo:

# Typo causing error

# Fix typo  

Carefully proofread and use a code linter to catch typos.

2. Import TypedDict Correctly

If trying to use TypedDict from the typing module, import it properly:

# Wrong module
from numpy import TypedDict

# Correct module
from typing import TypedDict

Never try to import TypedDict from numpy, which does not contain it.

3. Update Outdated Code

If following old code samples using numpy.typeDict, update it to modern Python conventions. Omit the erroneous typeDict reference.

4. Check Library Import Names

Examine your import statements to ensure no other modules get imported with a name that overlaps numpy. Refactor imports if needed to avoid namespace collisions.

5. Reinstall/Update NumPy

In rare cases, an outdated or corrupted NumPy install can cause unusual issues. Try reinstalling NumPy:

pip uninstall numpy
pip install numpy

Use the latest stable NumPy version compatible with your Python environment.

6. Search for Attribute Usage

Scan your full codebase using a tool like grep to find all usages of numpy.typeDict and fix each one. This ensures you catch every instance causing problems.

With typos and import issues fixed, you can access real numpy attributes without any typeDict errors.

Why NumPy Doesn’t Have a typeDict Attribute

The numpy module centers around providing support for multi-dimensional numeric arrays and matrices in Python. It contains attributes like:

  • ndarray – NumPy array class
  • dtype – Data types for arrays
  • arange – Generate numeric ranges
  • linspace – Create spaced arrays
  • pi – Math constants

But why doesn’t numpy have a typeDict attribute then?

The main reasons are:

  • typeDict doesn’t fit numpy‘s purpose and domain. It sounds more dictionary-related.
  • Python’s built-in dict type already handles generic dictionaries well.
  • The TypedDict class from the typing module provides typed dict functionality.
  • numpy aims to provide optimized numeric array operations rather than general data structures.

So in summary, a typeDict attribute does not align with numpy‘s goals and domain focus. The error arises from incorrect assumptions that it exists.

Example Fixing typeDict Error in Real Code

Let’s look at a real code example that triggers this error, and walk through resolving it:

import numpy as np

# Define a type dict schema
MetricSchema = np.typeDict({
    'metric_name': str,
    'value': float,
    'timestamp': int

metrics = [

# Use metrics...

# Error raised:
AttributeError: module 'numpy' has no attribute 'typeDict'

This example tries to use numpy.typeDict to define a typed dictionary schema, but ends up generating the attribute error.

To fix it:

  1. Import TypedDict from typing instead of numpy:
from typing import TypedDict
  1. Define schema with TypedDict:
MetricSchema = TypedDict('MetricSchema', {
   # ... 
  1. Remove numpy.typeDict references entirely.

Now the code uses the proper TypedDict typing functionality without any numpy attribute errors.

Carefully examining attribute errors like this provides a great way to learn more about Python imports, modules, and attributes. The solution ends up being simple once you know where to look.


The AttributeError: module 'numpy' has no attribute 'typeDict' arises when trying to access a non-existent typeDict attribute on NumPy. It typically indicates a typo, incorrect import, or outdated example was used in code.

To resolve it, double check for typos, ensure TypedDict comes from the typing module, and modernize any deprecated NumPy attribute usage. With the improper references corrected, the issue can be fixed and NumPy used as intended.

Thoroughly understanding errors like this helps identify gaps in Python knowledge. The solution traces back to properly utilizing the vast modules and tools Python makes available – like NumPy for numeric arrays and typing for type annotations. Mastering these powerful capabilities unlocks new levels of programming and productivity.

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