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stash@{1} is ambiguous?
...et info about my stash, but git is telling me that stash@{0} and stash@{1} are ambiguous. git stash list works fine, and .git/logs/refs/stash seems to have the appropriate content (not that I'm an expert on git internals).
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How to check whether a pandas DataFrame is empty?
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edited Dec 15 '17 at 17:37
Dave Thomas
1,38922 gold badges1010 silver badges1616 bronze badges
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adding noise to a signal in python
I want to add some random noise to some 100 bin signal that I am simulating in Python - to make it more realistic.
7 Answer...
Which is the correct shorthand - “regex” or “regexp” [closed]
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13 Answers
13
Active
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How to get row from R data.frame
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130
x[r,]
where r is the row you're interested in. Try this, for example:
#Add your data
x <...
Pandas DataFrame column to list [duplicate]
..._list method.
For example:
import pandas as pd
df = pd.DataFrame({'a': [1, 3, 5, 7, 4, 5, 6, 4, 7, 8, 9],
'b': [3, 5, 6, 2, 4, 6, 7, 8, 7, 8, 9]})
print(df['a'].to_list())
Output:
[1, 3, 5, 7, 4, 5, 6, 4, 7, 8, 9]
To drop duplicates you can do one of the following:
>&...
How to redirect output to a file and stdout
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10 Answers
10
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Printf width specifier to maintain precision of floating-point value
... in:
#include <float.h>
int Digs = DECIMAL_DIG;
double OneSeventh = 1.0/7.0;
printf("%.*e\n", Digs, OneSeventh);
// 1.428571428571428492127e-01
But let's dig deeper ...
Mathematically, the answer is "0.142857 142857 142857 ...", but we are using finite precision floating point numbers.
L...
Regular expression search replace in Sublime Text 2
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Usually a back-reference is either $1 or \1 (backslash one) for the first capture group (the first match of a pattern in parentheses), and indeed Sublime supports both syntaxes. So try:
my name used to be \1
or
my name used to be $1
Also note that your or...
Replacing Pandas or Numpy Nan with a None to use with MysqlDB
...n use where, it's worth noting that you can do this natively in pandas:
df1 = df.where(pd.notnull(df), None)
Note: this changes the dtype of all columns to object.
Example:
In [1]: df = pd.DataFrame([1, np.nan])
In [2]: df
Out[2]:
0
0 1
1 NaN
In [3]: df1 = df.where(pd.notnull(df), None...