I am generating bar plots using matplotlib and it looks like there is a bug with the stacked bar plot. The sum for each vertical stack should be 100. However, for X-AXIS ticks 65, 70, 75 and 80 we get completely arbitrary results which do not make any sense. I do not understand what the problem is. Please find the MWE below.
In pyplot, you can change the order of different graphs using the zorder option or by changing the order of the plot() commands. However, when you add an alternative axis via ax2 = twinx(), the new axis will always overlay the old axis (as described in the documentation).
I have a 3-dimensional numpy array. I’d like to display (in matplotlib) a nice 3D plot of an isosurface of this array (or more strictly, display an isosurface of the 3D scalar field defined by interpolating between the sample points).
I’m using matplotlib to make a histogram.
I am using the following code to plot a bar-chart:
I want to plot data, in two different subplots. After plotting, I want to go back to the first subplot and plot an additional dataset in it. However, when I do so I get this warning:
I’ve taken my Series and coerced it to a datetime column of dtype=datetime64[ns] (though only need day resolution…not sure how to change).
In Matplotlib, I make dashed grid lines as follows:
I want to to create a figure using matplotlib where I can explicitly specify the size of the axes, i.e. I want to set the width and height of the axes bbox.
Visualizing scipy.stats distributions A histogram can be made of the scipy.stats normal random variable to see what the distribution looks like. % matplotlib inline import pandas as pd import scipy.stats as stats d = stats.norm() rv = d.rvs(100000) pd.Series(rv).hist(bins=32, normed=True) What do the other distributions look like? Answers: Thank you for visiting the Q&A section … Read more