Can anyone shed some light on the expected behavior of code using
array(..., copy=True) with pandas objects? We ran into this in statsmodels
and I think there are probably plenty of places where we explicitly call
array(..., copy=True) and think we should have a totally independent copy
of the data. One workaround is to use np.require(...,requirements="O") but
it would help to understand the expected behavior.
Here is a simple example:
import numpy as np
import pandas as pd
weeks = 2
now = pd.to_datetime('2024-01-01')
testdata = pd.DataFrame(columns=['dates', 'values'])
rg = np.random.default_rng(0)
testdata['dates'] = pd.date_range(start=now, periods=weeks * 7, freq='D')
testdata['values']=rg.integers(0, 100, size=(weeks * 7))
values = testdata['values']
print("*"*10, " Before ", "*"*10)
print(values.head())
arr = np.array(values, copy=True)
arr.sort()
print("*"*10, " After ", "*"*10)
print(values.head())
print("*"*10, " Flags ", "*"*10)
print(arr.flags)
This produces
********** Before **********
0 85
1 63
2 51
3 26
4 30
Name: values, dtype: int64
********** After **********
0 1
1 4
2 7
3 17
4 26
Name: values, dtype: int64
********** Flags **********
C_CONTIGUOUS : True
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
Thanks,
Kevin
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