Bokeh ValueError: Out-of-Range Float Values Not JSON Compliant: Causes, Fixes & Best Practices

Bokeh ValueError: Out-of-Range Float Values Not JSON Compliant: Causes, Fixes & Best Practices

When using Bokeh for data visualization, encountering the error “ValueError: Out of range float values are not JSON compliant” can be significant. This error occurs because JSON, the format Bokeh uses to communicate data, does not support special float values like NaN or infinity. This can disrupt the rendering of visualizations, leading to incomplete or incorrect displays. Understanding and addressing this error is crucial for ensuring accurate and reliable data visualizations with Bokeh.

Understanding the Error

The error message “ValueError: Out of range float values are not JSON compliant” in Bokeh occurs when the library attempts to serialize a float value that is outside the range allowed by the JSON specification. JSON (JavaScript Object Notation) is a widely-used data format that does not support special float values like NaN (Not a Number), Infinity, or -Infinity. When Bokeh tries to convert these values into JSON, it raises a ValueError because these values are not compliant with the JSON standard.

Technical Reasons Behind the Error

  1. JSON Specification: JSON does not support NaN, Infinity, or -Infinity values. These values are common in Python and other programming languages but are not part of the JSON standard. When Bokeh tries to serialize a document containing these values, it fails because the JSON encoder cannot handle them.

  2. Data Handling in Bokeh: Bokeh uses JSON to communicate between the Python backend and the JavaScript frontend. If the data contains out-of-range float values, the serialization process fails, causing the application to throw an error.

  3. Serialization Process: During the serialization process, Bokeh converts its internal data structures into JSON format. If any of the data points contain out-of-range float values, the json.dumps function in Python raises a ValueError because it cannot represent these values in JSON.

Impact on Bokeh Functionality

  • Visualization Failure: The primary impact is that the visualization will not be rendered. Since Bokeh relies on JSON to transfer data to the browser, any failure in this process means that the plot or chart will not be displayed.

  • Data Integrity: If out-of-range float values are present in the data, it indicates potential issues with data integrity. These values might be the result of errors in data collection or processing, which need to be addressed to ensure accurate visualizations.

  • Error Handling: Developers need to implement error handling to check for and manage out-of-range float values before passing data to Bokeh. This can involve cleaning the data, replacing out-of-range values with valid ones, or using alternative representations.

To resolve this issue, you can preprocess your data to remove or replace NaN, Infinity, and -Infinity values before passing it to Bokeh. This ensures that all float values are within the valid range for JSON serialization.

Common Causes

Here are common scenarios that lead to the ValueError: out of range float values are not JSON compliant error in Bokeh, along with examples of code snippets that might trigger this error:

  1. NaN (Not a Number) Values:

    • Scenario: Including NaN values in your data.
    • Example: 
      from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource
import numpy as np

data = {'x': [1, 2, 3], 'y': [4, np.nan, 6]}
source = ColumnDataSource(data)
p = figure()
p.line('x', 'y', source=source)
show(p)

  2. Infinity Values:

    • Scenario: Including positive or negative infinity values.
    • Example: 
      from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource
import numpy as np

data = {'x': [1, 2, 3], 'y': [4, np.inf, 6]}
source = ColumnDataSource(data)
p = figure()
p.line('x', 'y', source=source)
show(p)

  3. Out-of-Range Float Values:

    • Scenario: Using float values that are too large or too small to be represented in JSON.
    • Example: 
      from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource

data = {'x': [1, 2, 3], 'y': [4, 1e309, 6]}  # 1e309 is out of range
source = ColumnDataSource(data)
p = figure()
p.line('x', 'y', source=source)
show(p)

  4. Invalid Data Types:

    • Scenario: Using data types that cannot be serialized to JSON.
    • Example: 
      from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource

data = {'x': [1, 2, 3], 'y': [4, complex(1, 1), 6]}  # complex number is invalid
source = ColumnDataSource(data)
p = figure()
p.line('x', 'y', source=source)
show(p)

These scenarios often arise when the data being visualized contains values that are not JSON serializable, leading to the ValueError.

Troubleshooting Steps

  1. Identify the Source of the Error:

    • Check the data being passed to Bokeh for any NaN, inf, or -inf values.
    • Use print statements or debugging tools to inspect the data.

  2. Replace Out-of-Range Values:

    • Replace NaN, inf, or -inf values with None or a valid float.

    import numpy as np
data = np.nan_to_num(data, nan=None, posinf=None, neginf=None)

  3. Validate Data Before Passing to Bokeh:

    • Ensure all data values are within the valid float range.

    def validate_data(data):
    if not np.isfinite(data).all():
        raise ValueError("Data contains out-of-range float values.")

  4. Convert Data to JSON-Compatible Format:

    • Convert problematic float values to strings.

    import json
data = json.dumps(data, allow_nan=False)

  5. Update Bokeh Version:

    • Ensure you are using the latest version of Bokeh, as bugs are often fixed in newer releases.

    pip install --upgrade bokeh

  6. Use Safe Data Types:

    • Use ColumnDataSource with numpy arrays instead of lists.

    from bokeh.models import ColumnDataSource
import numpy as np
source = ColumnDataSource({'x': np.array([]), 'y': np.array([])})

  7. Handle Data in Custom JSON Encoder:

    • Implement a custom JSON encoder to handle out-of-range float values.

    import json

class CustomJSONEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, float) and (np.isnan(obj) or np.isinf(obj)):
            return str(obj)
        return super().default(obj)

json_data = json.dumps(data, cls=CustomJSONEncoder)

  8. Check for Known Issues:

    • Review Bokeh’s GitHub issues for similar problems and solutions.

: GitHub Issue #5439
: GitHub Issue #6222

Best Practices

To avoid the “ValueError: out of range float values are not JSON compliant” error in Bokeh, follow these best practices:

  1. Data Validation:

    • Check for NaN and Infinite Values: Use numpy or pandas to identify and handle NaN or infinite values before passing data to Bokeh. 
      import numpy as np
import pandas as pd

df = pd.DataFrame({'a': [1, 2, np.nan, np.inf]})
df = df.replace([np.inf, -np.inf], np.nan).dropna()

  2. Handling Float Values:

    • Limit Float Precision: Ensure float values are within the JSON-compliant range. You can use the round() function to limit precision. 
      df['a'] = df['a'].round(2)

  3. Custom JSON Encoder:

    • Use a Custom JSON Encoder: Implement a custom JSON encoder to handle out-of-range float values. 
      import json

class CustomJSONEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, float) and (obj != obj or obj == float('inf') or obj == float('-inf')):
            return None
        return super(CustomJSONEncoder, self).default(obj)

json.dumps(data, cls=CustomJSONEncoder)

  4. Bokeh Configuration:

    • Use Bokeh’s Built-in Tools: Utilize Bokeh’s ColumnDataSource to manage data and ensure it is JSON-compliant. 
      from bokeh.models import ColumnDataSource

source = ColumnDataSource(data=dict(x=[1, 2, 3], y=[4, 5, 6]))

By implementing these practices, you can minimize the risk of

To Avoid the ‘ValueError: out-of-range float values are not JSON compliant’ Error in Bokeh

It’s essential to understand and address this issue for effective data visualization. The key points discussed include:

  • Data validation is crucial, especially checking for NaN and infinite values using libraries like numpy or pandas.
  • Limits float precision can help ensure that float values are within the JSON-compliant range by rounding them to a specific number of decimal places.
  • Implementing a custom JSON encoder can handle out-of-range float values by replacing them with a suitable alternative, such as None.
  • Utilizing Bokeh’s built-in tools, like ColumnDataSource, can help manage data and ensure it is JSON-compliant.

By following these best practices, developers can minimize the risk of encountering this error and create effective data visualizations with Bokeh.

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