Python matplotlib in the wrong order

Question

Basically, I have two arrays, one of which contains the values ​​of the x axis, and the second contains the values ​​of the y axis. The problem is that when I do

plt.semilogy(out_samp,error_mc) 

I get it

It makes no sense. This is because the plot functions build everything as it occurs in the x array, without worrying about whether it is sorted in ascending order or not. How can I sort these two arrays so that the x array is sorted in ascending order of magnitude, and the y axis is sorted the same way so that the points are the same, but the graph is connected so that it does not make this mess?

Thank you in advance!

Answer 1

Sort by x-axis value before plotting. Here is the MWE.

 import itertools x = [3, 5, 6, 1, 2] y = [6, 7, 8, 9, 10] lists = sorted(itertools.izip(*[x, y])) new_x, new_y = list(itertools.izip(*lists)) # import operator # new_x = map(operator.itemgetter(0), lists) # [1, 2, 3, 5, 6] # new_y = map(operator.itemgetter(1), lists) # [9, 10, 6, 7, 8] # Plot import matplotlib.pylab as plt plt.plot(new_x, new_y) plt.show() 

For small data, zip enough (as mentioned by other responders).

 new_x, new_y = zip(*sorted(zip(x, y))) 

---

Result,

Answer 2

It is easier to zip , sort and un zip two data lists.

Example:

 xs = [...] ys = [...] xs, ys = zip(*sorted(zip(xs, ys))) plot(xs, ys) 

See the zip documentation here: https://docs.python.org/3.5/library/functions.html#zip

Answer 3

An alternative to sorting lists would be to use NumPy arrays and use np.sort() to sort. The advantage of using arrays would be a vectorized operation when computing a function such as y = f (x). The following is an example of constructing a normal distribution:

Without using sorted data

 mu, sigma = 0, 0.1 x = np.random.normal(mu, sigma, 200) f = 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (x - mu)**2 / (2 * sigma**2) ) plt.plot(x,f, '-bo', ms = 2) 

Output 1

Using np.sort () This allows you to directly use the sorted array x in calculating the normal distribution.

 mu, sigma = 0, 0.1 x = np.sort(np.random.normal(mu, sigma, 200)) # or use x = np.random.normal(mu, sigma, 200).sort() f = 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (x - mu)**2 / (2 * sigma**2) ) plt.plot(x,f, '-bo', ms = 2) 

Alternatively, if you already have x and y numpy.argsort , you can use numpy.argsort to sort them posteriori

 mu, sigma = 0, 0.1 x = np.random.normal(mu, sigma, 200) f = 1/(sigma * np.sqrt(2 * np.pi)) *np.exp( - (x - mu)**2 / (2 * sigma**2) ) plt.plot(np.sort(x), f[np.argsort(x)], '-bo', ms = 2) 

In both cases, exit

Output 2

Answer 4

You can convert arrays to numpy arrays, then use argsort for the first array, take an array and sort both arrays with argsort array.

Answer 5

just do it

 list=zip(*sorted(zip(*(x,y)))) plt.plot(*list) 

the sorted function sorts according to the 1st argument ie the value of x

Answer 6

I think you need to sort one array, and the other array should also be sorted based on the first array. I got this solution from another stack overflow question. Most likely, this should be your decision.

 out_samp,error_mc=zip(*sorted(zip(out_samp,error_mc))) 

Now sketch these two values, you will get the correct graph.