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