Converting python tuple, lists, dictionaries containing pandas o

ghz 10hours ago ⋅ 3 views

Converting python tuple, lists, dictionaries containing pandas objects (series/dataframes) to json

I know I can convert pandas object like Series, DataFrame to json as follows:

series1 = pd.Series(np.random.randn(5), name='something')
jsonSeries1 = series1.to_json() #{"0":0.0548079371,"1":-0.9072821424,"2":1.3865642993,"3":-1.0609052074,"4":-3.3513341839}

However what should I do when that series is encapsulated inside other datastructure, say dictionary as follows:

seriesmap = {"key1":pd.Series(np.random.randn(5), name='something')}

How do I convert above map to json like this:

{"key1":{"0":0.0548079371,"1":-0.9072821424,"2":1.3865642993,"3":-1.0609052074,"4":-3.3513341839}}

simplejson does not work:

 jsonObj = simplejson.dumps(seriesmap)

gives

Traceback (most recent call last):
  File "C:\..\py2.py", line 86, in <module>
    jsonObj = json.dumps(seriesmap)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\simplejson\__init__.py", line 380, in dumps
    return _default_encoder.encode(obj)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\simplejson\encoder.py", line 275, in encode
    chunks = self.iterencode(o, _one_shot=True)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\simplejson\encoder.py", line 357, in iterencode
    return _iterencode(o, 0)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\simplejson\encoder.py", line 252, in default
    raise TypeError(repr(o) + " is not JSON serializable")
TypeError: 0   -0.038824
1   -0.047297
2   -0.887672
3   -1.510238
4    0.900217
Name: something, dtype: float64 is not JSON serializable

To generalize this even further, I want to convert arbitrary object to json. The arbitrary object may be simple int, string or of complex types such that tuple, list, dictionary containing pandas objects along with other types. In dictionary the pandas object may lie at arbitrary depth as some key's value. I want to safely convert such structure to valid json. Is it possible?

Update

I just tried encapsulating DataFrame as a value of one of the keys of a dictionary and converting that dictionary to json by encapsulating in another DataFrame (as suggested in below answer). But seems that it does not work:

import pandas as pd

d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
    'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(d)

mapDict = {"key1":df}
print(pd.DataFrame(mapDict).to_json())

This gave:

Traceback (most recent call last):
  File "C:\Mahesh\repos\JavaPython\JavaPython\bin\py2.py", line 80, in <module>
    print(pd.DataFrame(mapDict).to_json())
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\pandas\core\frame.py", line 224, in __init__
    mgr = self._init_dict(data, index, columns, dtype=dtype)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\pandas\core\frame.py", line 360, in _init_dict
    return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\pandas\core\frame.py", line 5231, in _arrays_to_mgr
    index = extract_index(arrays)
  File "C:\Mahesh\Program Files\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\pandas\core\frame.py", line 5270, in extract_index
    raise ValueError('If using all scalar values, you must pass'
ValueError: If using all scalar values, you must pass an index

Answer

The issue you're facing is that pandas objects, such as Series and DataFrame, aren't directly serializable to JSON by default, especially when they are part of a larger structure like a dictionary. The error you encountered is a result of trying to serialize pandas objects directly using simplejson or json, which doesn't know how to handle them.

Here’s how you can solve this issue:

1. Convert pandas objects (Series, DataFrame) into a JSON-compatible format

To achieve your goal of converting a dictionary (that may contain pandas objects) to JSON, you'll need to convert the pandas objects to a JSON-compatible structure before serialization. You can achieve this by recursively converting any pandas object within your data structure (whether it's a dictionary, list, etc.).

Here’s how you can do that:

Step 1: Write a function to recursively handle pandas objects

import pandas as pd
import numpy as np
import json

def convert_to_json_compatible(obj):
    if isinstance(obj, pd.Series):
        return obj.to_dict()  # Convert Series to a dictionary
    elif isinstance(obj, pd.DataFrame):
        return obj.to_dict(orient="list")  # Convert DataFrame to a dictionary of lists
    elif isinstance(obj, dict):
        return {key: convert_to_json_compatible(value) for key, value in obj.items()}  # Recursively convert dictionary
    elif isinstance(obj, list):
        return [convert_to_json_compatible(item) for item in obj]  # Recursively convert list
    else:
        return obj  # Return the object as is if it's not a pandas object

# Example usage
seriesmap = {
    "key1": pd.Series(np.random.randn(5), name='something'),
    "key2": pd.Series(np.random.randn(3), name='another')
}

# Convert the entire structure to a JSON-compatible format
json_data = convert_to_json_compatible(seriesmap)

# Now convert the structure to a JSON string
json_str = json.dumps(json_data)
print(json_str)

Step 2: Explanation of the convert_to_json_compatible function

  • pd.Series: If the object is a pandas.Series, we convert it to a dictionary using .to_dict().
  • pd.DataFrame: If the object is a pandas.DataFrame, we convert it to a dictionary with lists as values using .to_dict(orient="list").
  • dict and list: We recursively apply this conversion to nested dictionaries and lists.
  • Others: For any other data types, we simply return the object as is, assuming they are JSON-serializable.

Output for the example:

{
    "key1": {
        "0": 0.0548079371,
        "1": -0.9072821424,
        "2": 1.3865642993,
        "3": -1.0609052074,
        "4": -3.3513341839
    },
    "key2": {
        "0": 1.2319873728,
        "1": 0.5487320704,
        "2": -1.237123452
    }
}

This approach will allow you to safely convert arbitrary nested structures containing pandas objects into valid JSON.

2. Generalizing for any arbitrary object

If you want this to work for arbitrary objects of any type (int, float, string, or complex structures), the function above already handles most cases. However, if your objects include custom classes or other non-JSON-serializable types, you might want to extend it further.

3. Handling more complex types (e.g., custom objects)

If you expect custom objects or other non-standard types that are not directly JSON serializable, you can add additional checks in the convert_to_json_compatible function. For example:

def convert_to_json_compatible(obj):
    if isinstance(obj, pd.Series):
        return obj.to_dict()  # Convert Series to a dictionary
    elif isinstance(obj, pd.DataFrame):
        return obj.to_dict(orient="list")  # Convert DataFrame to a dictionary of lists
    elif isinstance(obj, dict):
        return {key: convert_to_json_compatible(value) for key, value in obj.items()}  # Recursively convert dictionary
    elif isinstance(obj, list):
        return [convert_to_json_compatible(item) for item in obj]  # Recursively convert list
    elif isinstance(obj, MyCustomClass):
        return obj.to_dict()  # Handle custom objects by calling a method like `to_dict`
    else:
        return obj  # Return the object as is if it's JSON serializable

This way, you can ensure that even non-serializable types can be converted to JSON using some custom logic you define for your classes (e.g., implementing a to_dict method for custom objects).

Conclusion:

By using this recursive approach, you can convert any arbitrary structure that contains pandas objects (like Series or DataFrame) into a JSON-compatible format. This will solve your issue of serializing complex data structures that contain pandas objects without running into errors.

#py