| (pandas) ➜ pandas git:(rohanp) ✗ pytest pandas/tests/test_nanops.py -k test_nanmedian --pdb | |
| ============================================= test session starts ============================================== | |
| platform darwin -- Python 3.5.2, pytest-3.2.0, py-1.4.34, pluggy-0.4.0 | |
| rootdir: /Users/rohanp/pandas, inifile: setup.cfg | |
| collected 55 items | |
| pandas/tests/test_nanops.py | |
| >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PDB set_trace (IO-capturing turned off) >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | |
| > /Users/rohanp/pandas/pandas/tests/test_nanops.py(196)check_fun_data() | |
| -> self.check_results(targ, res, axis, | |
| (Pdb) ll | |
| 183 def check_fun_data(self, testfunc, targfunc, testarval, targarval, | |
| 184 targarnanval, check_dtype=True, **kwargs): | |
| 185 for axis in list(range(targarval.ndim)) + [None]: | |
| 186 for skipna in [False, True]: | |
| 187 targartempval = targarval if skipna else targarnanval | |
| 188 try: | |
| 189 targ = targfunc(targartempval, axis=axis, **kwargs) | |
| 190 res = testfunc(testarval, axis=axis, skipna=skipna, | |
| 191 **kwargs) | |
| 192 | |
| 193 if not np.allclose(targ, res): | |
| 194 import pdb; pdb.set_trace() | |
| 195 | |
| 196 -> self.check_results(targ, res, axis, | |
| 197 check_dtype=check_dtype) | |
| 198 if skipna: | |
| 199 res = testfunc(testarval, axis=axis, **kwargs) | |
| 200 self.check_results(targ, res, axis, | |
| 201 check_dtype=check_dtype) | |
| 202 if axis is None: | |
| 203 res = testfunc(testarval, skipna=skipna, **kwargs) | |
| 204 self.check_results(targ, res, axis, | |
| 205 check_dtype=check_dtype) | |
| 206 if skipna and axis is None: | |
| 207 res = testfunc(testarval, **kwargs) | |
| 208 self.check_results(targ, res, axis, | |
| 209 check_dtype=check_dtype) | |
| 210 except BaseException as exc: | |
| 211 exc.args += ('axis: %s of %s' % (axis, testarval.ndim - 1), | |
| 212 'skipna: %s' % skipna, 'kwargs: %s' % kwargs) | |
| 213 | |
| 214 raise | |
| 215 | |
| 216 if testarval.ndim <= 1: | |
| 217 return | |
| 218 | |
| 219 try: | |
| 220 testarval2 = np.take(testarval, 0, axis=-1) | |
| 221 targarval2 = np.take(targarval, 0, axis=-1) | |
| 222 targarnanval2 = np.take(targarnanval, 0, axis=-1) | |
| 223 except ValueError: | |
| 224 return | |
| 225 self.check_fun_data(testfunc, targfunc, testarval2, targarval2, | |
| 226 targarnanval2, check_dtype=check_dtype, **kwargs) | |
| (Pdb) targ | |
| array([[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]]) | |
| (Pdb) res | |
| array([[ 0.05118711, -0.45872435, -0.02085726, 0.00471621, -0.48745034], | |
| [ 0.25417631, -0.03097218, -0.0738097 , -0.26466025, 0.0678109 ], | |
| [ 0.16513667, -0.92211551, -0.33636526, 0.63226656, -0.27500906], | |
| [ 0.00760543, -0.0346826 , -0.08828393, 0.2677941 , -0.03389868], | |
| [ 0.1601678 , -0.18806439, -0.88011583, -0.14156705, 0.75968485], | |
| [-0.10196248, 0.13446948, -0.41102928, -0.27228986, 0.17492847], | |
| [-0.43148189, 0.24527084, 0.14005599, -0.0727601 , 0.23868236]]) | |
| (Pdb) import inspect | |
| (Pdb) print(inspect.getsource(targfunc)) | |
| def median(a, axis=None, out=None, overwrite_input=False, keepdims=False): | |
| """ | |
| Compute the median along the specified axis. | |
| Returns the median of the array elements. | |
| Parameters | |
| ---------- | |
| a : array_like | |
| Input array or object that can be converted to an array. | |
| axis : {int, sequence of int, None}, optional | |
| Axis or axes along which the medians are computed. The default | |
| is to compute the median along a flattened version of the array. | |
| A sequence of axes is supported since version 1.9.0. | |
| out : ndarray, optional | |
| Alternative output array in which to place the result. It must | |
| have the same shape and buffer length as the expected output, | |
| but the type (of the output) will be cast if necessary. | |
| overwrite_input : bool, optional | |
| If True, then allow use of memory of input array `a` for | |
| calculations. The input array will be modified by the call to | |
| `median`. This will save memory when you do not need to preserve | |
| the contents of the input array. Treat the input as undefined, | |
| but it will probably be fully or partially sorted. Default is | |
| False. If `overwrite_input` is ``True`` and `a` is not already an | |
| `ndarray`, an error will be raised. | |
| keepdims : bool, optional | |
| If this is set to True, the axes which are reduced are left | |
| in the result as dimensions with size one. With this option, | |
| the result will broadcast correctly against the original `arr`. | |
| .. versionadded:: 1.9.0 | |
| Returns | |
| ------- | |
| median : ndarray | |
| A new array holding the result. If the input contains integers | |
| or floats smaller than ``float64``, then the output data-type is | |
| ``np.float64``. Otherwise, the data-type of the output is the | |
| same as that of the input. If `out` is specified, that array is | |
| returned instead. | |
| See Also | |
| -------- | |
| mean, percentile | |
| Notes | |
| ----- | |
| Given a vector ``V`` of length ``N``, the median of ``V`` is the | |
| middle value of a sorted copy of ``V``, ``V_sorted`` - i | |
| e., ``V_sorted[(N-1)/2]``, when ``N`` is odd, and the average of the | |
| two middle values of ``V_sorted`` when ``N`` is even. | |
| Examples | |
| -------- | |
| >>> a = np.array([[10, 7, 4], [3, 2, 1]]) | |
| >>> a | |
| array([[10, 7, 4], | |
| [ 3, 2, 1]]) | |
| >>> np.median(a) | |
| 3.5 | |
| >>> np.median(a, axis=0) | |
| array([ 6.5, 4.5, 2.5]) | |
| >>> np.median(a, axis=1) | |
| array([ 7., 2.]) | |
| >>> m = np.median(a, axis=0) | |
| >>> out = np.zeros_like(m) | |
| >>> np.median(a, axis=0, out=m) | |
| array([ 6.5, 4.5, 2.5]) | |
| >>> m | |
| array([ 6.5, 4.5, 2.5]) | |
| >>> b = a.copy() | |
| >>> np.median(b, axis=1, overwrite_input=True) | |
| array([ 7., 2.]) | |
| >>> assert not np.all(a==b) | |
| >>> b = a.copy() | |
| >>> np.median(b, axis=None, overwrite_input=True) | |
| 3.5 | |
| >>> assert not np.all(a==b) | |
| """ | |
| r, k = _ureduce(a, func=_median, axis=axis, out=out, | |
| overwrite_input=overwrite_input) | |
| if keepdims: | |
| return r.reshape(k) | |
| else: | |
| return r | |
| (Pdb) print(inspect.getsource(testfunc)) | |
| @disallow('M8') | |
| @bottleneck_switch() | |
| def nanmedian(values, axis=None, skipna=True): | |
| #import pdb; pdb.set_trace() | |
| values, mask, dtype, dtype_max = _get_values(values, skipna) | |
| np_nanmedian_available = StrictVersion(np.version.version) >= StrictVersion("1.9") | |
| #print("values!") | |
| #print(values) | |
| def get_median(x): | |
| mask = notnull(x) | |
| if not skipna and not mask.all(): | |
| return np.nan | |
| return algos.median(_values_from_object(x[mask])) | |
| if not is_float_dtype(values): | |
| values = values.astype('f8') | |
| values[mask] = np.nan | |
| if axis is None: | |
| values = values.ravel() | |
| notempty = values.size | |
| # an array from a frame | |
| if values.ndim > 1: | |
| # there's a non-empty array to apply over otherwise numpy raises | |
| if notempty: | |
| if np_nanmedian_available: | |
| return np.nanmedian(values, axis) | |
| else: | |
| #print("NOT HERE") | |
| return _wrap_results( | |
| np.apply_along_axis(get_median, axis, values), dtype) | |
| # must return the correct shape, but median is not defined for the | |
| # empty set so return nans of shape "everything but the passed axis" | |
| # since "axis" is where the reduction would occur if we had a nonempty | |
| # array | |
| shp = np.array(values.shape) | |
| dims = np.arange(values.ndim) | |
| ret = np.empty(shp[dims != axis]) | |
| ret.fill(np.nan) | |
| return _wrap_results(ret, dtype) | |
| # otherwise return a scalar value | |
| if np_nanmedian_available: | |
| return _wrap_results(np.nanmedian(values) if notempty else np.nan, dtype) | |
| else: | |
| return _wrap_results(get_median(values) if notempty else np.nan, dtype) | |
| (Pdb) targartempval | |
| array([[[-0.38825224, -0.45872435, 1.05486525, 0.31956083, 1.14809371], | |
| [-0.00578802, -0.14351561, -1.40698905, -0.34134135, -0.14323561], | |
| [ 0.16513667, -0.48747916, 0.63898755, 1.56494777, -0.7443864 ], | |
| [ 0.66294031, -0.8317759 , 1.31468696, 0.95759531, -0.03389868], | |
| [ 0.73086499, 0.70585301, 1.91718117, 2.27577977, 1.89121214], | |
| [-1.07458971, -1.08979394, -1.76423455, 0.80586292, 0.30584617], | |
| [ 0.92739786, -2.69452768, 2.04372204, 0.48859691, -1.57612352]], | |
| [[ 2.25028687, 0.63779974, 0.68342731, 0.00471621, -0.65399029], | |
| [-0.19476041, 1.81552545, 0.51889123, 0.87562612, -0.3892774 ], | |
| [ 0.01635034, -1.24305638, -0.36368474, -0.36445098, 0.42753209], | |
| [-0.4729695 , -0.01745559, 0.89452566, -1.4390385 , -1.26780395], | |
| [ 1.18201306, -1.1622051 , -2.71979787, 0.41992428, 0.88180422], | |
| [ 0.27834578, 0.13446948, 0.55212485, -0.98854351, 0.99006069], | |
| [-0.80094851, -0.08638208, -0.34687719, -0.0727601 , 1.3633947 ]], | |
| [[ 0.97792431, -0.50685917, -0.49734799, 0.30445786, 0.62872697], | |
| [ 0.67838492, 0.26102178, 1.61255621, -0.85968821, -2.05477625], | |
| [-0.89228212, 0.7917057 , -0.30265329, 0.35312641, -0.34641609], | |
| [-1.22003801, -0.84863456, 0.98217849, -1.53655233, -0.80637798], | |
| [-0.33071735, -1.89204786, -1.93527651, 0.19684946, -1.14932361], | |
| [ 0.43355292, 0.96918435, -0.41102928, 0.24262618, -1.13047781], | |
| [-0.84918425, 0.33501427, -1.69681832, -0.33944707, -0.55422857]], | |
| [[ 0.05118711, -0.57524952, -1.11626042, -0.97951706, 0.8940154 ], | |
| [ 0.31769978, 0.71076409, 1.7592069 , -1.09807486, 0.0678109 ], | |
| [-0.49852146, -0.22318572, -0.33636526, -0.91411015, -1.12652678], | |
| [ 0.64227766, 0.13517118, 0.10704381, 1.28589488, 0.69870227], | |
| [-0.87857642, 0.15461119, -1.02891304, 0.35705758, 0.12209429], | |
| [-0.32618092, -1.1629626 , 0.98892129, -0.27018407, 0.06803624], | |
| [-1.03207802, -0.69316651, 0.14005599, -0.11922739, -0.07816359]], | |
| [[-0.38908183, -0.81040345, 0.68370322, 0.90188594, -1.80321481], | |
| [ 0.12745242, 1.46988529, 1.15539722, 2.36676976, -0.23269785], | |
| [ 2.08116059, 3.36348586, -0.95406058, 0.33430095, 1.28731917], | |
| [-0.22204977, -0.0346826 , -0.84104312, 0.95147879, -0.52867337], | |
| [ 0.67371056, 0.21330956, -0.66596554, -1.08538032, 2.57846358], | |
| [-0.43980808, -1.02388464, -1.72123434, 0.6026453 , 1.34109299], | |
| [ 1.41656069, 1.7624381 , 0.20258937, -0.74805922, 0.72920807]], | |
| [[-1.25383019, -0.86054481, 1.25054603, 1.14458702, -0.26085491], | |
| [ 1.55586896, 0.02332803, -0.0738097 , 0.93617682, 0.48228872], | |
| [ 2.4965577 , -0.94128041, -1.22715694, 0.7998703 , 0.3431659 ], | |
| [ 0.57162177, -0.62947045, -1.32218205, 1.15827038, 0.58420174], | |
| [ 1.1200749 , -1.2067002 , -0.73592999, -0.71823314, 0.75968485], | |
| [-0.29155435, 1.34940108, 0.25466987, -1.57469254, 0.5947282 ], | |
| [ 1.28683932, 1.65044268, 0.20319533, 1.64323562, 0.42682942]], | |
| [[-0.97858595, 0.08474185, -1.62431349, 0.57411943, -1.02951143], | |
| [ 1.09678942, -0.87360806, -0.50266793, -0.79779213, 0.19842459], | |
| [ 0.31413511, -1.18696502, -1.16598118, -0.5097998 , 0.85985869], | |
| [-1.32148743, -0.32531782, -0.90164102, -0.26826046, 1.35848258], | |
| [ 0.33896427, -1.27730814, -1.78411941, -0.14156705, 1.67153113], | |
| [-0.13255231, -0.02392706, -1.15117929, -0.48422198, -1.13779698], | |
| [-0.43148189, -0.84148705, 0.8029512 , -1.02114507, 1.54490774]], | |
| [[ 0.50348946, 0.99861864, -0.22948088, -0.33243308, -1.19982988], | |
| [ 0.12821233, -0.77641173, -0.68633081, 0.542418 , -0.71827667], | |
| [-0.01899879, -0.94336419, -0.22560816, 0.63226656, 0.28848488], | |
| [ 0.00760543, 0.32084445, -0.08828393, -1.28400062, -2.24612778], | |
| [ 0.1601678 , -1.34652894, -0.88011583, -0.90701249, -0.67519159], | |
| [ 2.23324348, 0.19140225, -0.05528151, -0.27228986, 1.07504245], | |
| [-1.37579119, 0.85146862, -0.38325804, 1.9058229 , -0.02567673]], | |
| [[ 0.91971294, 0.14128946, -0.92976 , -1.27721231, 0.89395778], | |
| [ 0.25417631, -0.03097218, 0.66371714, 1.29501118, 0.90261116], | |
| [ 0.69055382, -0.92211551, -0.52063755, 0.67925763, -0.29163266], | |
| [-0.45724346, -0.39291401, 0.36451466, -1.63039786, 0.89032433], | |
| [-0.28970788, -0.13039404, 0.62613009, -1.25539236, 0.08363484], | |
| [ 0.36769137, -0.29022357, 0.41889391, -0.74074718, -0.40273736], | |
| [ 0.49654374, 0.24527084, -1.01815164, -0.51220951, 0.3033394 ]], | |
| [[ 0.18107761, -0.58778822, -0.02085726, -0.04116001, -0.88875231], | |
| [-0.38750674, -0.40634409, -0.62407662, -0.26466025, 0.30117429], | |
| [ 0.10072977, -2.04403817, 0.562087 , 1.04582238, -1.40481056], | |
| [ 1.75650654, 0.27738833, -0.86271809, 0.2677941 , 1.15538839], | |
| [-0.01359106, -0.18806439, -1.67248932, 0.41075649, 0.11251433], | |
| [-0.10196248, 2.57257769, -0.7059846 , -0.06504558, 0.17492847], | |
| [-1.58738364, -0.58217846, -1.30481354, 0.54497637, 0.23868236]], | |
| [[-0.97499552, 1.1338151 , 0.3757687 , -0.6285383 , -0.48745034], | |
| [ 1.17234195, -0.9577289 , -1.0635969 , -0.9060959 , 0.82134947], | |
| [ 1.10549235, 1.19218029, 0.78660926, 1.81719581, -0.27500906], | |
| [ 0.13658901, 1.63647576, -0.93375975, 1.21100674, -0.51331421], | |
| [ 0.03228562, 0.7437696 , -0.80044138, -0.29710109, 1.13082706], | |
| [ 1.02375849, 1.47633042, -0.49172581, -0.82026711, -0.13627042], | |
| [ 0.42524962, 0.58666376, 1.17249809, 1.10854989, -0.67451349]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]], | |
| [[ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan], | |
| [ nan, nan, nan, nan, nan]]]) | |
| (Pdb) testarval | |
| array([[[-0.38825224, -0.45872435, 1.05486525, 0.31956083, 1.14809371], | |
| [-0.00578802, -0.14351561, -1.40698905, -0.34134135, -0.14323561], | |
| [ 0.16513667, -0.48747916, 0.63898755, 1.56494777, -0.7443864 ], | |
| [ 0.66294031, -0.8317759 , 1.31468696, 0.95759531, -0.03389868], | |
| [ 0.73086499, 0.70585301, 1.91718117, 2.27577977, 1.89121214], | |
| [-1.07458971, -1.08979394, -1.76423455, 0.80586292, 0.30584617], | |
| [ 0.92739786, -2.69452768, 2.04372204, 0.48859691, -1.57612352]], | |
| [[ 2.25028687, 0.63779974, 0.68342731, 0.00471621, -0.65399029], | |
| [-0.19476041, 1.81552545, 0.51889123, 0.87562612, -0.3892774 ], | |
| [ 0.01635034, -1.24305638, -0.36368474, -0.36445098, 0.42753209], | |
| [-0.4729695 , -0.01745559, 0.89452566, -1.4390385 , -1.26780395], | |
| [ 1.18201306, -1.1622051 , -2.71979787, 0.41992428, 0.88180422], | |
| [ 0.27834578, 0.13446948, 0.55212485, -0.98854351, 0.99006069], | |
| [-0.80094851, -0.08638208, -0.34687719, -0.0727601 , 1.3633947 ]], | |
| [[ 0.97792431, -0.50685917, -0.49734799, 0.30445786, 0.62872697], | |
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| [ 1.41656069, 1.7624381 , 0.20258937, -0.74805922, 0.72920807]], | |
| [[-1.25383019, -0.86054481, 1.25054603, 1.14458702, -0.26085491], | |
| [ 1.55586896, 0.02332803, -0.0738097 , 0.93617682, 0.48228872], | |
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| [ 1.28683932, 1.65044268, 0.20319533, 1.64323562, 0.42682942]], | |
| [[-0.97858595, 0.08474185, -1.62431349, 0.57411943, -1.02951143], | |
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| [-1.37579119, 0.85146862, -0.38325804, 1.9058229 , -0.02567673]], | |
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| [ 0.25417631, -0.03097218, 0.66371714, 1.29501118, 0.90261116], | |
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| [ 0.49654374, 0.24527084, -1.01815164, -0.51220951, 0.3033394 ]], | |
| [[ 0.18107761, -0.58778822, -0.02085726, -0.04116001, -0.88875231], | |
| [-0.38750674, -0.40634409, -0.62407662, -0.26466025, 0.30117429], | |
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| [-0.01359106, -0.18806439, -1.67248932, 0.41075649, 0.11251433], | |
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| (Pdb) skipna | |
| False | |
| (Pdb) |
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