qpyson 0.2.0

JQ-ish tool to query/munge JSON files using Python

qpyson (WIP)

The JSON querying tool, jq, is a really powerful tool. However, it’s sometimes a bit involved and has a learning curve that requires digging into the jq manual and familiarizing yourself with a custom language.

qpyson is a thin tool to explore, transform, or munge JSON using Python as the processing language.

Goals

• Process JSON file using Python
• Thin layer to process or apply transforms written in Python
• Provide the Python func as a string to the commandline or reference an external file where the function is defined
• Custom functions can be paramaterized and configured from the commandline
• Output results are emitted as JSON or in tabular form (using tabulate for quick viewing from the commandline

Non-Goals

• A replacement for jq
• No custom DSL for filtering or querying (use Python directly)
• Does not support streaming (JSON files are loaded into memory)

Installation

Recommended to install using a virtualenv or conda env to install.

pip install qpyson

Quick Tour

Example data from the Iris dataset.

head examples/iris.json

[
  {"sepalLength": 5.1, "sepalWidth": 3.5, "petalLength": 1.4, "petalWidth": 0.2, "species": "setosa"},
  {"sepalLength": 4.9, "sepalWidth": 3.0, "petalLength": 1.4, "petalWidth": 0.2, "species": "setosa"},
  {"sepalLength": 4.7, "sepalWidth": 3.2, "petalLength": 1.3, "petalWidth": 0.2, "species": "setosa"},
  {"sepalLength": 4.6, "sepalWidth": 3.1, "petalLength": 1.5, "petalWidth": 0.2, "species": "setosa"},
  {"sepalLength": 5.0, "sepalWidth": 3.6, "petalLength": 1.4, "petalWidth": 0.2, "species": "setosa"},
  {"sepalLength": 5.4, "sepalWidth": 3.9, "petalLength": 1.7, "petalWidth": 0.4, "species": "setosa"},
  {"sepalLength": 4.6, "sepalWidth": 3.4, "petalLength": 1.4, "petalWidth": 0.3, "species": "setosa"},
  {"sepalLength": 5.0, "sepalWidth": 3.4, "petalLength": 1.5, "petalWidth": 0.2, "species": "setosa"},
  {"sepalLength": 4.4, "sepalWidth": 2.9, "petalLength": 1.4, "petalWidth": 0.2, "species": "setosa"},

We can define a custom function to process the JSON dataset. By default the function is named f and can be customized by -f or --function-name commandline argument.

qpyson "def f(d): return d[0]" examples/iris.json

{
  "sepalLength": 5.1,
  "sepalWidth": 3.5,
  "petalLength": 1.4,
  "petalWidth": 0.2,
  "species": "setosa"
}

We can also write custom functions in a Python file.

cat examples/iris_explore.py

def f(d):
    return d[0]


def f2(d, max_items: int = 10):
    return d[:max_items]


def f3(d, max_items: int = 5):
    return [x for x in d if x["species"] == "setosa"][:max_items]


def f4(d, sort_field: str, sort_direction: str = "asc", max_items: int = 5):
    reverse = not (sort_direction == "asc")
    d.sort(key=lambda x: x[sort_field], reverse=reverse)
    return d[:max_items]


def f0(d):
    # Identity operator
    return d


def first(d):
    return d[0]


def p(d, field: str = "sepalLength"):
    import pandas as pd

    df = pd.DataFrame.from_dict(d)
    sdf = df.describe()
    return sdf.to_dict()[field]

Executing --help will show the output options.

qpyson examples/iris_explore.py examples/iris.json --help

usage: qpyson [-f FUNCTION_NAME] [-n] [--indent INDENT] [-t]
              [--table-style TABLE_STYLE]
              [--log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG,NOTSET}]
              [--help]
              path_or_cmd json_file

Util to use Python to process (e.g., filter, map) JSON files

positional arguments:
  path_or_cmd           Path to python file, or python cmd
  json_file             Path to JSON file

optional arguments:
  -f FUNCTION_NAME, --function-name FUNCTION_NAME
                        Function name (default: f)
  -n, --no-pretty       Non-table Pretty print the output of dicts and list of
                        dicts (default: False)
  --indent INDENT       Non-table Pretty print indent spacing (default: 2)
  -t, --print-table     Pretty print results (default: False)
  --table-style TABLE_STYLE
                        Table fmt style using Tabulate. See
                        https://github.com/astanin/python-tabulate#table-
                        format for available options (default: simple)
  --log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG,NOTSET}
                        Log level (default: NOTSET)
  --help                Show this help message and exit (default: False)

Executing function f, yields:

qpyson examples/iris_explore.py examples/iris.json 

{
  "sepalLength": 5.1,
  "sepalWidth": 3.5,
  "petalLength": 1.4,
  "petalWidth": 0.2,
  "species": "setosa"
}

The output view can be changed to a table view using --print-table or -t.

qpyson examples/iris_explore.py examples/iris.json --print-table --table-style github

|   sepalLength |   sepalWidth |   petalLength |   petalWidth | species   |
|---------------|--------------|---------------|--------------|-----------|
|           5.1 |          3.5 |           1.4 |          0.2 | setosa    |

A better example using function f2 defined in iris_explore.py

qpyson examples/iris_explore.py examples/iris.json  --function-name f2 --print-table

  sepalLength    sepalWidth    petalLength    petalWidth  species
-------------  ------------  -------------  ------------  ---------
          5.1           3.5            1.4           0.2  setosa
          4.9           3              1.4           0.2  setosa
          4.7           3.2            1.3           0.2  setosa
          4.6           3.1            1.5           0.2  setosa
          5             3.6            1.4           0.2  setosa
          5.4           3.9            1.7           0.4  setosa
          4.6           3.4            1.4           0.3  setosa
          5             3.4            1.5           0.2  setosa
          4.4           2.9            1.4           0.2  setosa
          4.9           3.1            1.5           0.1  setosa

Custom functions can be defined with required or optional values (with defaults) combined with Python 3 type annotations to generate

cat examples/iris.py

# More examples that demonstrate generating commandline arguments


def f(d, sort_field: str, sort_direction: str = "asc", max_items: int = 5):
    reverse = not (sort_direction == "asc")
    d.sort(key=lambda x: x[sort_field], reverse=reverse)
    return d[:max_items]


def g(d, field: str = "sepalLength"):
    import pandas as pd

    df = pd.DataFrame.from_dict(d)
    sdf = df.describe()
    return sdf.to_dict()[field]

And calling --help will show the custom function specific arguments (e.g., --max_items and --sort_direction)

qpyson examples/iris.py examples/iris.json --help

usage: qpyson [-f FUNCTION_NAME] [-n] [--indent INDENT] [-t]
              [--table-style TABLE_STYLE]
              [--log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG,NOTSET}]
              [--help] [--sort_field SORT_FIELD]
              [--sort_direction SORT_DIRECTION] [--max_items MAX_ITEMS]
              path_or_cmd json_file

Util to use Python to process (e.g., filter, map) JSON files

positional arguments:
  path_or_cmd           Path to python file, or python cmd
  json_file             Path to JSON file

optional arguments:
  -f FUNCTION_NAME, --function-name FUNCTION_NAME
                        Function name (default: f)
  -n, --no-pretty       Non-table Pretty print the output of dicts and list of
                        dicts (default: False)
  --indent INDENT       Non-table Pretty print indent spacing (default: 2)
  -t, --print-table     Pretty print results (default: False)
  --table-style TABLE_STYLE
                        Table fmt style using Tabulate. See
                        https://github.com/astanin/python-tabulate#table-
                        format for available options (default: simple)
  --log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG,NOTSET}
                        Log level (default: NOTSET)
  --help                Show this help message and exit (default: False)
  --sort_field SORT_FIELD
                        sort_field type:<class 'str'> from custom func `f`
                        (default: _empty)
  --sort_direction SORT_DIRECTION
                        sort_direction type:<class 'str'> from custom func `f`
                        (default: asc)
  --max_items MAX_ITEMS
                        max_items type:<class 'int'> from custom func `f`
                        (default: 5)

And calling with custom options yields:

qpyson examples/iris.py examples/iris.json -t --max_items=3 --sort_direction=desc --sort_field sepalLength

  sepalLength    sepalWidth    petalLength    petalWidth  species
-------------  ------------  -------------  ------------  ---------
          7.9           3.8            6.4           2    virginica
          7.7           3.8            6.7           2.2  virginica
          7.7           2.6            6.9           2.3  virginica

Another Example calling pandas underneath the hood to get a quick summary of the data.

qpyson examples/iris.py examples/iris.json -t -f g --field=sepalLength

  count     mean       std    min    25%    50%    75%    max
-------  -------  --------  -----  -----  -----  -----  -----
    150  5.84333  0.828066    4.3    5.1    5.8    6.4    7.9

It’s also possible to create thin JSON munging tools for configuration of systems or tools that take JSON as input.

For example a JSON configuration template with defaults.

cat examples/config_template.json

{
  "alpha":  1234,
  "beta": null,
  "gamma": 90.1234
}

And a processing function, f.

cat examples/config_processor.py

def f(dx, alpha: float, beta: float, gamma: float):
    """Simple example of config munging"""

    def _set(k, v):
        if v:
            dx[k] = v

    items = [("alpha", alpha), ("beta", beta), ("gamma", gamma)]

    for name, value in items:
        _set(name, value)

    dx["_comment"] = "Configured with qpyson"
    return dx

Running --help will show the supported configuration options.

qpyson examples/config_processor.py examples/config_template.json --help

usage: qpyson [-f FUNCTION_NAME] [-n] [--indent INDENT] [-t]
              [--table-style TABLE_STYLE]
              [--log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG,NOTSET}]
              [--help] [--alpha ALPHA] [--beta BETA] [--gamma GAMMA]
              path_or_cmd json_file

Util to use Python to process (e.g., filter, map) JSON files

positional arguments:
  path_or_cmd           Path to python file, or python cmd
  json_file             Path to JSON file

optional arguments:
  -f FUNCTION_NAME, --function-name FUNCTION_NAME
                        Function name (default: f)
  -n, --no-pretty       Non-table Pretty print the output of dicts and list of
                        dicts (default: False)
  --indent INDENT       Non-table Pretty print indent spacing (default: 2)
  -t, --print-table     Pretty print results (default: False)
  --table-style TABLE_STYLE
                        Table fmt style using Tabulate. See
                        https://github.com/astanin/python-tabulate#table-
                        format for available options (default: simple)
  --log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG,NOTSET}
                        Log level (default: NOTSET)
  --help                Show this help message and exit (default: False)
  --alpha ALPHA         alpha type:<class 'float'> from custom func `f`
                        (default: _empty)
  --beta BETA           beta type:<class 'float'> from custom func `f`
                        (default: _empty)
  --gamma GAMMA         gamma type:<class 'float'> from custom func `f`
                        (default: _empty)

Now configuring alpha, beta and gamma.

qpyson examples/config_processor.py examples/config_template.json --alpha 1.23 --beta 2.34 --gamma 3.45

{
  "alpha": 1.23,
  "beta": 2.34,
  "gamma": 3.45,
  "_comment": "Configured with qpyson"
}

Testing

Testing is currently done using RMarkdown using the make target doc.

This should probably be ported to non-R based approach. However, this current approach does keep the docs (e.g., README.md) up to date.

Related JQ-ish tools

https://github.com/dbohdan/structured-text-tools#json