Plot smoothing matplotlib and seaborn

I am trying to display my data in a nice way such as seen on the seaborn documentation:

I am not too sure how to proceed. I managed to get the values of points and their respective standard deviation but it looks scattered while I just want to show a tendency:

I look into here, and there trying to apply the proposed solution but I couldn't make it work.

Here is what I play with:

Final_array = Mean Std 0 0.739269 0.157892 1 0.807382 0.160464 2 0.800024 0.137239 3 0.825854 0.132472 4 0.864854 0.070544 .. ... ... 95 0.797202 0.101961 96 0.747578 0.143394 97 0.751472 0.158651 98 0.587009 0.198987 99 0.728447 0.104601 sns.set(style="darkgrid", palette="muted", color_codes=True) fig, ax = plt.subplots(figsize=(7,5)) y_pos = np.arange(Final_array.shape[0]) ax.errorbar(y_pos, Final_array[:,0], yerr=Final_array[:,1], elinewidth=0.5) plt.show() 

Does anyone have an idea? I am very beginner in using plots. Would it be possible to smooth? and get the nice overlay as in the seaborn image instead of the error bars?

These might be silly questions.

Kind regards,

2 Answers

You can use fillbetween for smoothed upper and lower curves. Choosing a higher sigma would give more smoothness.

Here is some example code:

import matplotlib.pyplot as plt import numpy as np from scipy.ndimage.filters import gaussian_filter1d x = np.linspace(0, 100, 100) y = 0.95 - ((50 - x) / 200) ** 2 err = (1 - y) / 2 y += np.random.normal(0, err / 10, y.size) upper = gaussian_filter1d(y + err, sigma=3) lower = gaussian_filter1d(y - err, sigma=3) fig, ax = plt.subplots(ncols=2) ax[0].errorbar(x, y, err, color='dodgerblue') ax[1].plot(x, y, color='dodgerblue') ax[1].fill_between(x, upper, lower, color='crimson', alpha=0.2) plt.show() 

Thank you for your help! I managed to generate the graph that I wanted!

First, the spline wouldn't work because my data is not sorted. Hence, I used gaussian_filter1d proposed by @JohanC and found here as well. However, apparently it can alter the data (read comment on here) so I decided to plot both of the graph together:

Using this final version:

import numpy as np import matplotlib.pyplot as plt import seaborn as sns from scipy.ndimage.filters import gaussian_filter1d Final_array = Mean Std 0 0.739269 0.157892 1 0.807382 0.160464 2 0.800024 0.137239 3 0.825854 0.132472 4 0.864854 0.070544 .. ... ... 95 0.797202 0.101961 96 0.747578 0.143394 97 0.751472 0.158651 98 0.587009 0.198987 99 0.728447 0.104601 sns.set(style="darkgrid", palette="muted", color_codes=True) fig, ax = plt.subplots(figsize=(7,5)) y_pos = np.arange(Final_array.shape[0]) # Smoothing Final_array_smooth = gaussian_filter1d(Final_array[:,0], sigma=2) # Error formating upper_err = gaussian_filter1d(Final_array[:,0] + (Final_array[:,1]/2), sigma=5) lower_err = gaussian_filter1d(Final_array[:,0] - (Final_array[:,1]/2), sigma=5) ax.plot(y_pos, Final_array[:,0], '--', linewidth=0.7, color='k', alpha=0.45) ax.plot(y_pos, Final_array_smooth) ax.fill_between(y_pos, upper_err, lower_err, color='crimson', alpha=0.2) ax.set_ylim(np.min(Final_array[:,0])-(np.min((Final_array[:,0])*20)/100), np.max(Final_array[:,0])+(np.max((Final_array[:,0])*10)/100)) plt.show() 

Thank you very much !