139 行
3.3 KiB
Cython
139 行
3.3 KiB
Cython
cimport cython
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from cython cimport Py_ssize_t
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from numpy cimport (
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int64_t,
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ndarray,
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uint8_t,
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)
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import numpy as np
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cimport numpy as cnp
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cnp.import_array()
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from pandas._libs.dtypes cimport numeric_object_t
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from pandas._libs.lib cimport c_is_list_like
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def unstack(numeric_object_t[:, :] values, const uint8_t[:] mask,
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Py_ssize_t stride, Py_ssize_t length, Py_ssize_t width,
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numeric_object_t[:, :] new_values, uint8_t[:, :] new_mask) -> None:
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"""
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Transform long values to wide new_values.
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Parameters
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----------
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values : typed ndarray
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mask : np.ndarray[bool]
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stride : int
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length : int
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width : int
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new_values : np.ndarray[bool]
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result array
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new_mask : np.ndarray[bool]
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result mask
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"""
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cdef:
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Py_ssize_t i, j, w, nulls, s, offset
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if numeric_object_t is not object:
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# evaluated at compile-time
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with nogil:
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for i in range(stride):
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nulls = 0
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for j in range(length):
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for w in range(width):
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offset = j * width + w
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if mask[offset]:
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s = i * width + w
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new_values[j, s] = values[offset - nulls, i]
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new_mask[j, s] = 1
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else:
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nulls += 1
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else:
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# object-dtype, identical to above but we cannot use nogil
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for i in range(stride):
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nulls = 0
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for j in range(length):
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for w in range(width):
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offset = j * width + w
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if mask[offset]:
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s = i * width + w
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new_values[j, s] = values[offset - nulls, i]
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new_mask[j, s] = 1
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else:
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nulls += 1
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def explode(ndarray[object] values):
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"""
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transform array list-likes to long form
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preserve non-list entries
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Parameters
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----------
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values : ndarray[object]
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Returns
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-------
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ndarray[object]
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result
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ndarray[int64_t]
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counts
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"""
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cdef:
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Py_ssize_t i, j, count, n
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object v
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ndarray[object] result
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ndarray[int64_t] counts
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# find the resulting len
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n = len(values)
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counts = np.zeros(n, dtype='int64')
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for i in range(n):
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v = values[i]
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if c_is_list_like(v, True):
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if len(v):
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counts[i] += len(v)
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else:
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# empty list-like, use a nan marker
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counts[i] += 1
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else:
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counts[i] += 1
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result = np.empty(counts.sum(), dtype='object')
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count = 0
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for i in range(n):
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v = values[i]
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if c_is_list_like(v, True):
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if len(v):
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v = list(v)
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for j in range(len(v)):
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result[count] = v[j]
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count += 1
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else:
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# empty list-like, use a nan marker
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result[count] = np.nan
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count += 1
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else:
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# replace with the existing scalar
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result[count] = v
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count += 1
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return result, counts
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