npdoc-to-md 0.2

Easy conversion of Python docstrings in numpy style to markdown! Includes a Markdown renderer.

npdoc_to_md

Turns numpy docstrings to pretty markdown.

Table of contents

Features

Caveats

Installation

Demos

Usage

Contributing

Testing

Features

• Very easy to use
• Flexible rendering for example outputs (render a markdown table, python code, raw code etc.) on a per-example basis or a per-docstring basis
• Provides file rendering with very simple placeholders

Caveats

• GitHub does not support colors for markdown 😿

Installation

pip install npdoc_to_md.

Demos

First demo: Demonstration with the docstring of pandas.Series.

Before

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

Data structure also contains labeled axes (rows and columns).
Arithmetic operations align on both row and column labels. Can be
thought of as a dict-like container for Series objects. The primary
pandas data structure.

Parameters
----------
data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame
    Dict can contain Series, arrays, constants, or list-like objects.

    .. versionchanged:: 0.23.0
       If data is a dict, column order follows insertion-order for
       Python 3.6 and later.

    .. versionchanged:: 0.25.0
       If data is a list of dicts, column order follows insertion-order
       for Python 3.6 and later.

index : Index or array-like
    Index to use for resulting frame. Will default to RangeIndex if
    no indexing information part of input data and no index provided.
columns : Index or array-like
    Column labels to use for resulting frame. Will default to
    RangeIndex (0, 1, 2, ..., n) if no column labels are provided.
dtype : dtype, default None
    Data type to force. Only a single dtype is allowed. If None, infer.
copy : bool, default False
    Copy data from inputs. Only affects DataFrame / 2d ndarray input.

See Also
--------
DataFrame.from_records : Constructor from tuples, also record arrays.
DataFrame.from_dict : From dicts of Series, arrays, or dicts.
read_csv
read_table
read_clipboard

Examples
--------
Constructing DataFrame from a dictionary.

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
   col1  col2
0     1     3
1     2     4

Notice that the inferred dtype is int64.

>>> df.dtypes
col1    int64
col2    int64
dtype: object

To enforce a single dtype:

>>> df = pd.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1    int8
col2    int8
dtype: object

Constructing DataFrame from numpy ndarray:

>>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
...                    columns=['a', 'b', 'c'])
>>> df2
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

After

pd.DataFrame(data=None, index: Union[Collection, NoneType] = None, columns: Union[Collection, NoneType] = None, dtype: Union[str, numpy.dtype, ForwardRef('ExtensionDtype'), NoneType] = None, copy: bool = False)

Parameters

• data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame Dict can contain Series, arrays, constants, or list-like objects.

  .. versionchanged:: 0.23.0 If data is a dict, column order follows insertion-order for Python 3.6 and later.

  .. versionchanged:: 0.25.0 If data is a list of dicts, column order follows insertion-order for Python 3.6 and later.

• index : Index or array-like Index to use for resulting frame. Will default to RangeIndex if no indexing information part of input data and no index provided.

• columns : Index or array-like Column labels to use for resulting frame. Will default to RangeIndex (0, 1, 2, ..., n) if no column labels are provided.

• dtype : dtype, default None Data type to force. Only a single dtype is allowed. If None, infer.

• copy : bool, default False Copy data from inputs. Only affects DataFrame / 2d ndarray input.

See Also

• DataFrame.from_records : Constructor from tuples, also record arrays.
• DataFrame.from_dict : From dicts of Series, arrays, or dicts.
• read_csv
• read_table
• read_clipboard

Examples

Constructing DataFrame from a dictionary.

d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)
df

col1  col2
0     1     3
1     2     4

Notice that the inferred dtype is int64.

df.dtypes

col1    int64
col2    int64
dtype: object

To enforce a single dtype:

df = pd.DataFrame(data=d, dtype=np.int8)
df.dtypes

col1    int8
col2    int8
dtype: object

Constructing DataFrame from numpy ndarray:

df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
                   columns=['a', 'b', 'c'])
df2

a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

Second demo: the library pangres (also from me) which uses markdown tables in examples!

See https://github.com/ThibTrip/pangres/wiki/Upsert#examples

Usage

Head over to npdoc_to_md's wiki!

Contributing

Pull requests/issues are welcome.

Testing

Clone npdoc_to_md then set your curent working directory to the root of the cloned repository folder. Then use the commands below:

# 1. Create and activate the build environment
conda env create -f environment.yml
conda activate npdoc_to_md-dev
# 2. Install npdoc_to_md in editable mode (changes are reflected upon reimporting)
pip install -e .
# 3. Run pytest
# -s prints stdout
# -v prints test parameters
# --cov=./npdoc_to_md shows coverage only for npdoc_to_md
pytest -s -v npdoc_to_md --cov=./npdoc_to_md --doctest-modules