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CLN: unreachable code in algos.diff #40406

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Mar 12, 2021
Merged

CLN: unreachable code in algos.diff #40406

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40 changes: 10 additions & 30 deletions pandas/core/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1634,10 +1634,10 @@ def diff(arr, n: int, axis: int = 0, stacklevel=3):

Parameters
----------
arr : ndarray
arr : ndarray or ExtensionArray
n : int
number of periods
axis : int
axis : {0, 1}
axis to shift on
stacklevel : int
The stacklevel for the lost dtype warning.
Expand All @@ -1651,7 +1651,8 @@ def diff(arr, n: int, axis: int = 0, stacklevel=3):
na = np.nan
dtype = arr.dtype

if dtype.kind == "b":
is_bool = is_bool_dtype(dtype)
if is_bool:
op = operator.xor
else:
op = operator.sub
Expand All @@ -1677,17 +1678,15 @@ def diff(arr, n: int, axis: int = 0, stacklevel=3):
dtype = arr.dtype

is_timedelta = False
is_bool = False
if needs_i8_conversion(arr.dtype):
dtype = np.int64
arr = arr.view("i8")
na = iNaT
is_timedelta = True

elif is_bool_dtype(dtype):
elif is_bool:
# We have to cast in order to be able to hold np.nan
dtype = np.object_
is_bool = True

elif is_integer_dtype(dtype):
# We have to cast in order to be able to hold np.nan
Expand All @@ -1708,45 +1707,26 @@ def diff(arr, n: int, axis: int = 0, stacklevel=3):
dtype = np.dtype(dtype)
out_arr = np.empty(arr.shape, dtype=dtype)

na_indexer = [slice(None)] * arr.ndim
na_indexer = [slice(None)] * 2
na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None)
out_arr[tuple(na_indexer)] = na

if arr.ndim == 2 and arr.dtype.name in _diff_special:
if arr.dtype.name in _diff_special:
# TODO: can diff_2d dtype specialization troubles be fixed by defining
# out_arr inside diff_2d?
algos.diff_2d(arr, out_arr, n, axis, datetimelike=is_timedelta)
else:
# To keep mypy happy, _res_indexer is a list while res_indexer is
# a tuple, ditto for lag_indexer.
_res_indexer = [slice(None)] * arr.ndim
_res_indexer = [slice(None)] * 2
_res_indexer[axis] = slice(n, None) if n >= 0 else slice(None, n)
res_indexer = tuple(_res_indexer)

_lag_indexer = [slice(None)] * arr.ndim
_lag_indexer = [slice(None)] * 2
_lag_indexer[axis] = slice(None, -n) if n > 0 else slice(-n, None)
lag_indexer = tuple(_lag_indexer)

# need to make sure that we account for na for datelike/timedelta
# we don't actually want to subtract these i8 numbers
if is_timedelta:
res = arr[res_indexer]
lag = arr[lag_indexer]

mask = (arr[res_indexer] == na) | (arr[lag_indexer] == na)
if mask.any():
res = res.copy()
res[mask] = 0
lag = lag.copy()
lag[mask] = 0

result = res - lag
result[mask] = na
out_arr[res_indexer] = result
elif is_bool:
out_arr[res_indexer] = arr[res_indexer] ^ arr[lag_indexer]
else:
out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer]
out_arr[res_indexer] = op(arr[res_indexer], arr[lag_indexer])

if is_timedelta:
out_arr = out_arr.view("timedelta64[ns]")
Expand Down