Understanding Python Lambda Functions and the TypeError Argument of Type ‘float’ is Not Iterable

Python lambda functions are small, anonymous functions that can be defined inline within a larger expression. They are often used in combination with higher-order functions like map(), filter(), and reduce().

In this article, we will delve into Python lambda functions, specifically the TypeError: argument of type 'float' is not iterable exception that may occur when attempting to apply a lambda function to a non-iterable data structure.

A Look at the apply() Function

The apply() function in pandas DataFrames applies a given function element-wise along sequence axis. When used with a lambda function, it can transform the elements of a series (a one-dimensional labeled array) or DataFrame column into another Series or DataFrame column.

Here’s an example:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'matrix': [
        {'status': 'ZERO_RESULTS'},
        {'distance': {'text': '3,899 km', 'value': 3898595}, 'duration': {'text': '1 day 13 hours', 'value': 133445}, 'status': 'OK'},
        {'distance': {'text': '2,065 km', 'value': 2065157}, 'duration': {'text': '20 hours 7 mins', 'value': 72393}, 'status': 'OK'}
    ]
})

# Apply a lambda function to the 'matrix' column
df['dist_value'] = df['matrix'].apply(lambda x: round((x['distance']['value']) / 1000) if "status" not in str(x) else None)

print(df)

The TypeError Exception

When attempting to apply a lambda function to the ‘matrix’ column, you may encounter the TypeError: argument of type 'float' is not iterable exception. This occurs when the x value in the lambda function is not an iterable (e.g., list or tuple) but rather a scalar value.

The Solution

To resolve this issue, we need to ensure that the x value is properly converted to a string before checking if it contains the key 'status'.

Here’s the corrected code:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'matrix': [
        {'status': 'ZERO_RESULTS'},
        {'distance': {'text': '3,899 km', 'value': 3898595}, 'duration': {'text': '1 day 13 hours', 'value': 133445}, 'status': 'OK'},
        {'distance': {'text': '2,065 km', 'value': 2065157}, 'duration': {'text': '20 hours 7 mins', 'value': 72393}, 'status': 'OK'}
    ]
})

# Apply a lambda function to the 'matrix' column
df['dist_value'] = df['matrix'].apply(lambda x: round((x.get('distance', {}).get('value', None)) / 1000) if isinstance(x, dict) else None)

print(df)

Additional Considerations

When working with nested dictionaries and lambda functions in pandas, it’s essential to consider the following:

• Use str() to convert values to strings.
• Utilize the .get() method to safely access dictionary keys. If a key is missing, this method returns a default value (None by default).
• Employ isinstance(x, dict) checks to verify that x is a dictionary.

By addressing these considerations and properly applying lambda functions to non-iterable data structures, you can resolve the TypeError: argument of type 'float' is not iterable exception in Python.

Last modified on 2024-01-14