Convert percent string to float in pandas read_csv
Is there a way to convert values like ‘34%’ directly to int or float when using read_csv in pandas? I want ‘34%’ to be directly read as 0.34
Is there a way to convert values like ‘34%’ directly to int or float when using read_csv in pandas? I want ‘34%’ to be directly read as 0.34
I have a distance matrix n*n M where M_ij is the distance between object_i and object_j. So as expected, it takes the following form:
I can’t figure out the difference between Pandas .aggregate and .apply functions.
Take the following as an example: I load a dataset, do a groupby, define a simple function,
and either user .agg or .apply.
Is it possible to do something like the following to modify the navigation toolbar in matplotlib?
As far as I can see, if an exception occurs in a slot under PyQt, the exception is printed to screen, but not bubbled. This creates a problem in my testing strategy, because if an exception occurs in a slot, I will not see the test fail.
I need to visualize some data. It’s basic 2D grid, where each cell have float value. I know how to e.g. assign color to value and paint grid in OpenCV. But the point here is that there are so many values so it’s nearly impossible to do that. I am looking for some method, where I could use gradient. For example value -5.0 will be represented by blue, 0 – black, and +5.0 as red. Is there any way to do that in Python?
My python script uses matplotlib to plot a 2D “heat map” of an x, y, z dataset. My x- and y-values represent amino acid residues in a protein and can therefore only be integers. When I zoom into the plot, it looks like this:
urllib.urlretrieve returns silently even if the file doesn’t exist on the remote http server, it just saves a html page to the named file. For example:
I’m having trouble getting Kivy to work with PyCharm on Windows 7. I’ve managed to add most of the external libraries through File > Settings > Python interpreters > Paths Tab.
Update: I’ve done a full write-up of the way I found to do this on my blog at http://blog.rtwilson.com/producing-polar-contour-plots-with-matplotlib/ – you may want to check there first.