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ViewsPlotting shaded uncertainty region in line plot in matplotlib when data has NaNs
I would like a plot which looks like this:
I am trying to do this with matplotlib:
fig, ax = plt.subplots()
with sns.axes_style("darkgrid"):
for i in range(5):
ax.plot(means.ix[i][list(range(3,104))], label=means.ix[i]["label"])
ax.fill_between(means.ix[i][list(range(3,104))]-stds.ix[i][list(range(3,104))], means.ix[i][list(range(3,104))]+stds.ix[i][list(range(3,104))])
ax.legend()
I want the shaded region to be the same colour as the line in the centre. But right now, my problem is that means has some NaNs and fill_between does not accept that. I get the error
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
Any ideas on how I could achieve what I want? The solution doesn't need to use matplotlib as long as it can plot my series of points with their uncertainties for multiple series.
2 answers
Answer question0
Ok. So one of the problem was that the dtype of my data was object and not float and this caused fill_between to fail when it looked to see if the numbers were finite. I finally managed to do it by (a) converting to float and then (b) to solve the problem of the matching colours for uncertainty and line, to use a colour palette. So I have:
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
fig, ax = plt.subplots()
clrs = sns.color_palette("husl", 5)
with sns.axes_style("darkgrid"):
epochs = list(range(101))
for i in range(5):
meanst = np.array(means.ix[i].values[3:-1], dtype=np.float64)
sdt = np.array(stds.ix[i].values[3:-1], dtype=np.float64)
ax.plot(epochs, meanst, label=means.ix[i]["label"], c=clrs[i])
ax.fill_between(epochs, meanst-sdt, meanst+sdt ,alpha=0.3, facecolor=clrs[i])
ax.legend()
ax.set_yscale('log')
which gave me the following result:
0
You could simply drop the NaNs from your means DataFrame and plot that resulting dataframe instead?
In the example below, I tried to get close to your structure, I have a means DataFrame with some NaN sprinkled around. I suppose the stds DataFrame probably has NaN at the same locations, but in this case it doesn't really matter, I drop the NaN from means to get temp_means and I use the indices left in temp_means to extract the std values from stds.
The plots show the results before (top) and after (bottom) dropping the NaNs
x = np.linspace(0, 30, 100)
y = np.sin(x/6*np.pi)
error = 0.2
means = pd.DataFrame(np.array([x,y]).T,columns=['time','mean'])
stds = pd.DataFrame(np.zeros(y.shape)+error)
#sprinkle some NaN in the mean
sprinkles = means.sample(10).index
means.loc[sprinkles] = np.NaN
fig, axs = plt.subplots(2,1)
axs[0].plot(means.ix[:,0], means.ix[:,1])
axs[0].fill_between(means.ix[:,0], means.ix[:,1]-stds.ix[:,0], means.ix[:,1]+stds.ix[:,0])
temp_means = means.dropna()
axs[1].plot(temp_means.ix[:,0], temp_means.ix[:,1])
axs[1].fill_between(temp_means.ix[:,0], temp_means.ix[:,1]-stds.loc[temp_means.index,0], temp_means.ix[:,1]+stds.loc[temp_means.index,0])
plt.show()
