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What is a word boundary in regex?
...ic output, among other purposes) and cannot find a precise definition of \b ("word boundary"). I had assumed that -12 would be an "integer word" (matched by \b\-?\d+\b ) but it appears that this does not work. I'd be grateful to know of ways of matching space-separated numbers.
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Get statistics for each group (such as count, mean, etc) using pandas GroupBy?
I have a data frame df and I use several columns from it to groupby :
7 Answers
7
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How to define an enumerated type (enum) in C?
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Declaring an enum variable is done like this:
enum strategy {RANDOM, IMMEDIATE, SEARCH};
enum strategy my_strategy = IMMEDIATE;
However, you can use a typedef to shorten the variable declarations, like so:
typedef enum {RANDOM, IMMEDIATE, SEARC...
Append a NumPy array to a NumPy array
I have a numpy_array. Something like [ a b c ] .
9 Answers
9
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Java Generics (Wildcards)
I have a couple of questions about generic wildcards in Java:
6 Answers
6
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Convert list of dictionaries to a pandas DataFrame
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cs95
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answered Dec 17 '13 at 15:35
jorisjoris
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Converting bool to text in C++
Maybe this is a dumb question, but is there any way to convert a boolean value to a string such that 1 turns to "true" and 0 turns to "false"? I could just use an if statement, but it would be nice to know if there is a way to do that with the language or standard libraries. Plus, I'm a pedant. :...
How does this code generate the map of India?
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The long string is simply a binary sequence converted to ASCII. The first for statement makes b start out at 10, and the [b+++21] after the string yields 31. Treating the string as an array, offset 31 is the start of the "real" data in the string (the...
Naming returned columns in Pandas aggregate function? [duplicate]
I'm having trouble with Pandas' groupby functionality. I've read the documentation , but I can't see to figure out how to apply aggregate functions to multiple columns and have custom names for those columns.
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Pandas: drop a level from a multi-level column index?
...ltiIndex.droplevel:
>>> cols = pd.MultiIndex.from_tuples([("a", "b"), ("a", "c")])
>>> df = pd.DataFrame([[1,2], [3,4]], columns=cols)
>>> df
a
b c
0 1 2
1 3 4
[2 rows x 2 columns]
>>> df.columns = df.columns.droplevel()
>>> df
b c
0 ...