I'd like to plot line chart with error bar with the following style.
However, pandas plot draws error bars with only vertical line.
pd.DataFrame([1,2,3]).plot(yerr=[0.3,.3,.3])
How do I change style of error bar for pandas plot?
The versions are:
pandas '0.18.0'
matplotlib '1.5.1'
Update
One of the reason seems using the seaborn style. The following code give the nice style plot.
# plt.style.use('seaborn-paper')
pd.DataFrame([1,2,3]).plot(yerr=[0.3,.3,.3],capsize=4)
But, I have a reason to keep using seaborn style... Please help.
You can change the capsize inline when you call plot on your DataFrame, using the capsize kwarg (which gets passed on to plt.errorbar):
pd.DataFrame([1,2,3]).plot(yerr=[0.3,.3,.3],capsize=4)
Alternatively, you can change this setting using rcParams
You can find out what your default errorbar cap size is by printing plt.rcParams['errorbar.capsize']. If that is 0 (which is why I suspect you are currently getting no errorbar caps), you can set the default size of the errorbar caps to something nonzero, using:
plt.rcParams['errorbar.capsize']=4
Make sure to have that at the beginning of any plotting script.
Update:
It seems using the seaborn-paper style sets the cap thickness to 0. You can override this with the capthick kwarg:
plt.style.use('seaborn-paper')
pd.DataFrame([1,2,3]).plot(yerr=[0.3,.3,.3],capsize=4,capthick=1)
Related
i use jupyterlab together with matplotlib widgets. I have ipywidgets installed.
My goal is to choose which y-axis data is displayed in the bottom of the figure.
When i use the interactive tool to see the coordinates i get only the data of the right y-axis displayed. Both would be really nice^^ My minimal code example:
import matplotlib.pyplot as plt
import numpy as np
%matplotlib widgets
x=np.linspace(0,100)
y=x**2
y2=x**3
fig,ax=plt.subplots()
ax2=ax.twinx()
ax.plot(x,y)
ax2.plot(x,y2)
plt.show()
With this example you might ask why not to plot them to the same y-axis but thats why it is a minimal example. I would like to plot data of different units.
To choose which y-axis is used, you can set the zorder property of the axes containing this y-axis to a higher value than that of the other axes (0 is the default):
ax.zorder = 1
However, that will cause this Axes to obscure the other Axes. To counteract this, use
ax.set_facecolor((0, 0, 0, 0))
to make the background color of this Axes transparent.
Alternatively, use the grab_mouse function of the figure canvas:
fig.canvas.grab_mouse(ax)
See here for the (minimal) documentation for grab_mouse.
The reason this works is this:
The coordinate line shown below the figure is obtained by an event callback which ultimately calls matplotlib.Axes.format_coord() on the axes instance returned by the inaxes property of the matplotlib events that are being generated by your mouse movement. This Axes is the one returned by FigureCanvasBase.inaxes() which uses the Axes zorder, and in case of ties, chooses the last Axes created.
However, you can tell the figure canvas that one Axes should receive all mouse events, in which case this Axes is also set as the inaxes property of generated events (see the code).
I have not found a clean way to make the display show data from both Axes. The only solution I have found would be to monkey-patch NavigationToolbar2._mouse_event_to_message (also here) to do what you want.
I'm trying to change the outline of the symbol (marker) inside a scatter plot in Julia using the PyPlot backend. I've tried edgecolor, edgewidth, edgelinewidth, markercolor, markerwidth, markerlinewidth and a variety of other key/values from various sources, but Julia/PyPlot recognizes none of these. How do I change the outline of the symbol/marker in a scatter plot executed in Julia using the PyPlot backend?
In Julia using the PyPlot backend, the correct key to manipulate the symbol/marker outline is markerstroke.... For example, markerstrokewidth=1 sets the outline of the marker to 1px, while markerstrokecolor="red" sets the outline color of the marker to red. I hope this helps.
For anyone reading this in 2022, PyPlot in Julia has completely changed. To change the edge size, the keyword is linewidths. In general, the repl is the best way to get up to date info. Just type ? (to get the help prompt), then PyPlot.scatter.
Seaborn has a handy function pairplot to create a matrix of scatter plots. Unfortunately, some standard matplotlib commands don't work with it.
sns.pairplot(matrix[cols[:4]].head(100), plot_kws=dict(alpha=.5, mew=0))
The markers get some ugly white edges. I tried mew for markeredgewidth keyword to remove them as it would be used in matplotlib, but that is an unknown property for seaborn. How can I remove these edges?
A scatter does not have a mew keyword. It is edgecolor instead. Hence
sns.pairplot(data, plot_kws=dict(edgecolor="none"))
would remove the white edge around the scatterpoints.
ImportanceOfBeingErnest's answer is much more precise. Alternatively, you can also use a workaround: Set the color of choice for both the face and the edges of the markers as (example from the docs)
import seaborn as sns
sns.set(style="ticks", color_codes=True)
iris = sns.load_dataset("iris")
g = sns.pairplot(iris, plot_kws=dict(facecolor='b', edgecolor="b"))
EDIT based on comments below IOBE's answer: Just specifying the linewidth=0 also works the same way on markers as specifying edgecolor="none".
g = sns.pairplot(iris, plot_kws=dict(linewidth=0))
I am using Seaborn to make lineplots with a band indicating standard deviations. Something just like the second/third plot in the doc below:
https://seaborn.pydata.org/generated/seaborn.lineplot.html?highlight=lineplot#seaborn.lineplot
I am wondering is that possible to set the edgecolor for the error band separately? I can change linestyle of the band through err_kws. But, if I pass "edgecolor" through err_kws, it seems that nothing happens. Is there someway to allow me to get control with the edges?
Thanks!
As djakubosky notes, the color of the line and the error band are coupled together internally in seaborn's lineplot. I suggest that it is cleaner to modify the properties of the artists after the plot has been generated. This is a cleaner alternative than editing the library source code directly (maintenance headaches, etc).
For the example data shown on the sns.lineplot docs, we can update the error band properties as follows:
import seaborn as sns
fmri = sns.load_dataset("fmri")
ax = sns.lineplot(x="timepoint", y="signal", data=fmri)
# by inspection we see that the PolyCollection is the first artist
for child in ax.get_children():
print(type(child))
# and so we can update its properties
ax.get_children()[0].set_color('k')
ax.get_children()[0].set_hatch('//')
It may be more robust to select by property of the artist rather than selecting the first artist (especially if you have already rendered something on the same axes), e.g. along these lines:
from matplotlib.collections import PolyCollection
for child in ax.findobj(PolyCollection):
child.set_color('k')
child.set_hatch('//')
It appears that it isn't really possible to change this color under the current seaborn implementation. This is because they pass the color of the main line explicitly to the error band as ax.fillbetweenx(... color=original_color). After playing around in the past, I found that this color arg seems to supersede the other color arguments such as facecolor and edgecolor, thus it doesn't matter what you put in there in the err_kws. However you could fix it by editing line 810 in site-packages/seaborn/relational.py from:
ax.fill_between(x, low, high, color=line_color, **err_kws)
to
ax.fill_between(x, low, high, **err_kws)
and passing the colors explicitly through err_kws.
Plots is simple and powerful but sometimes I would like to have a little bit more control over individual elements of the plot to fine-tune its appearance.
Is it possible to update the plot object of the backend directly?
E.g., for the default pyplot backend, I tried
using Plots
p = plot(sin)
p.o[:axes][1][:xaxis][:set_ticks_position]("top")
but the plot does not change. Calling p.o[:show]() afterwards does not help, either.
In other words: Is there a way to use the PyPlot interface for a plot that was initially created with Plots?
Edit:
The changes to the PyPlot object become visible (also in the gui) when saving the figure:
using Plots
using PyPlot
p = Plots.plot(sin, top_margin=1cm)
gui() # not needed when using the REPL
gca()[:xaxis][:set_ticks_position]("top")
PyPlot.savefig("test.png")
Here, I used p.o[:axes][1] == gca(). One has to set top_margin=1cm because the plot area is not adjusted automatically (for my actual fine-tuning, this doesn't matter).
This also works for subsequent updates as long as only the PyPlot interface is used. E.g., after the following commands, the plot will have a red right border in addition to labels at the top:
gca()[:spines]["right"][:set_color]("red")
PyPlot.savefig("test.png")
However, when a Plots command like plot!(xlabel="foo") is used, all previous changes made with PyPlot are overwritten (which is not suprising).
The remaining question is how to update the gui interactively without having to call PyPlot.savefig explicitly.
No - the plot is a Plots object, not a PyPlot object. In your specific example you can do plot(sin, xmirror = true).
I'm trying to do the same but didn't find a solution to update an existing plot. But here is a partial answer: you can query information from the PyPlot axes object
julia> Plots.plot(sin, 1:4)
julia> Plots.PyPlot.plt[:xlim]()
(1.0,4.0)
julia> Plots.plot(sin, 20:24)
julia> ax = Plots.PyPlot.plt[:xlim]()
(20.0,24.0)
and it gets updated.