gistfile1.py

import warnings
import operator
import itertools as IT
import numpy as np
from numpy import nan
import pandas as pd
pd.options.display.width = 1000
pd.options.display.max_rows = 1000

def comparisons():
    ops = [
        operator.eq,
        np.equal,
        ]
    
    items = [1,
             np.float64(1),
             np.int32(1),
             list(),
             [1,2],
             [nan, 2],
             np.array([], dtype='float'),
             np.array([], dtype='int'),             
             np.array([], dtype='O'),
             np.array([nan], dtype='float'),
             np.array([nan], dtype='O'),
             np.array([nan, 2], dtype='O'),
             np.array(['foo'], dtype='O'),
             np.array(['foo', 'bar'], dtype='O'),
             np.array([1, 2], dtype='O'),
             np.array([1, 2], dtype='int'),
             np.array([1, 2], dtype='float'), 
             np.array(['foo', 'bar', 'baz'], dtype='O'),
             np.array([1, 2], dtype='float'),
             np.array([1, 2], dtype='int'),
             np.array(['a', 'b', nan, 'd'], dtype=object),
             np.array(['2000-01-02T19:00'], dtype='datetime64[ns]'),
             # pd.Series([], dtype='O', index=[]), # index dtype object
             # pd.Series([], dtype='O'), # index dtype int64
             # pd.Series([], dtype='float'),
             # pd.Series([1], dtype='O'),
             # pd.Series([1], dtype='float'),
             # pd.Series([1, nan], dtype='O'),
             # pd.Series([1, nan], dtype='float'),
             # pd.Index([1, 2, nan], dtype='O'),
             # pd.Index([1, 2, nan], dtype='int64'),
             ]


    result = list()
    for (left, right), op in IT.product(IT.combinations_with_replacement(items, 2), ops):
        try:
            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                val = op(left, right)
        except Exception as err:
            val = type(err).__name__
        with warnings.catch_warnings():
            warnings.filterwarnings("error", category=DeprecationWarning)
            warnings.filterwarnings("error", category=FutureWarning)
            try:
                op(left, right)
                error = None
                msg = None
            except Exception as err:
                error = type(err).__name__
                msg = str(err)
        try:
            broadcastable = bool(np.broadcast(left, right))
        except ValueError:
            broadcastable = False

        try:
            same_shape = left.shape == right.shape
        except AttributeError:
            same_shape = False
            
        # try:
        #     same_dtype = left.dtype == right.dtype
        # except AttributeError:
        #     same_dtype = np.asarray(left).dtype == np.asarray(right).dtype
        #     # same_dtype = False

        left_array = isinstance(left, np.ndarray)
        right_array = isinstance(right, np.ndarray)

        # Use reprs because the objects cause hashing problems when using isin
        result.append((repr(left), repr(right),
                       op.__name__, 
                       # left_array, right_array,
                       repr(val), str(error), str(msg),
                       broadcastable,
                       same_shape,
                       ))
    columns = ['left', 'right', 'op', 
               # 'left_array', 'right_array',
               'val', 'error', 'msg',
               'broadcastable', 
               'same_shape',]
    df = pd.DataFrame(result, columns=columns)
    return df

df = comparisons()
print(df.loc[(df['error']=='DeprecationWarning')][['left','right','op','val', 'error']])
print(df.loc[(df['error']=='FutureWarning')][['left','right','op','val', 'error']])