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How to deal with SettingWithCopyWarning in Pandas?
I just upgraded my Pandas from 0.11 to 0.13.0rc1. Now, the application is popping out many new warnings. One of them like this:
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NameError: global name 'unicode' is not defined - in Python 3
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Python 3 renamed the unicode type to str, the old str type has been replaced by bytes.
if isinstance(unicode_or_str, str):
text = unicode_or_str
decoded = False
else:
text = unicode_or_str.decode(encoding)
decoded =...
map function for objects (instead of arrays)
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38 Answers
38
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Reorder levels of a factor without changing order of values
...f <- data.frame(f = 1:4, g = letters[1:4])
df
# f g
# 1 1 a
# 2 2 b
# 3 3 c
# 4 4 d
levels(df$g)
# [1] "a" "b" "c" "d"
df$g <- factor(df$g, levels = letters[4:1])
# levels(df$g)
# [1] "d" "c" "b" "a"
df
# f g
# 1 1 a
# 2 2 b
# 3 3 c
# 4 4 d
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Modulo operator with negative values [duplicate]
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3 Answers
3
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How to do multiple arguments to map function where one remains the same in python?
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One option is a list comprehension:
[add(x, 2) for x in [1, 2, 3]]
More options:
a = [1, 2, 3]
import functools
map(functools.partial(add, y=2), a)
import itertools
map(add, a, itertools.repeat(2, len(a)))
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Get a random boolean in python?
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349
Adam's answer is quite fast, but I found that random.getrandbits(1) to be quite a lot faster. ...
How to check if variable is string with python 2 and 3 compatibility
I'm aware that I can use: isinstance(x, str) in python-3.x but I need to check if something is a string in python-2.x as well. Will isinstance(x, str) work as expected in python-2.x? Or will I need to check the version and use isinstance(x, basestr) ?
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How do I detect the Python version at runtime? [duplicate]
I have a Python file which might have to support Python versions < 3.x and >= 3.x. Is there a way to introspect the Python runtime to know the version which it is running (for example, 2.6 or 3.2.x )?
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Detect and exclude outliers in Pandas data frame
...ression would do that in one shot.
df = pd.DataFrame(np.random.randn(100, 3))
from scipy import stats
df[(np.abs(stats.zscore(df)) < 3).all(axis=1)]
description:
For each column, first it computes the Z-score of each value in the
column, relative to the column mean and standard deviation.
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