Here is a code snippet
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col = 'time')
g = g.map(plt.hist, "tip")
with the following output
I want to introduce despine offset to these plots while keeping the rest unchanged. Therefore, I inserted the despine function in the existing code:
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col = 'time')
g.despine(offset=10)
g = g.map(plt.hist, "tip")
which results in the following plots
As a result, the offset is applied to the axes. However, the ytick labels on the right plot are back, which I don't want.
Could anyone help me on this?
To remove the yaxis tick labels, you can use the code below:
The libs:
import seaborn as sns
sns.set_style('ticks')
The adjusted code:
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col = 'time')
g.despine(offset=10)
g = g.map(plt.hist, "tip")
# IMPORTANT: I assume that you use colwrap=None in FacetGrid constructor
# loop over the non-left axes:
for ax in g.axes[:, 1:].flat:
# get the yticklabels from the axis and set visibility to False
for label in ax.get_yticklabels():
label.set_visible(False)
ax.yaxis.offsetText.set_visible(False)
A bit more general, image you now have a 2x2 FacetGrid, you want to despine with an offset, but the x- and yticklabels return:
Remove them all using this code:
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col = 'time', row='sex')
g.despine(offset=10)
g = g.map(plt.hist, "tip")
# IMPORTANT: I assume that you use colwrap=None in FacetGrid constructor
# loop over the non-left axes:
for ax in g.axes[:, 1:].flat:
# get the yticklabels from the axis and set visibility to False
for label in ax.get_yticklabels():
label.set_visible(False)
ax.yaxis.offsetText.set_visible(False)
# loop over the top axes:
for ax in g.axes[:-1, :].flat:
# get the xticklabels from the axis and set visibility to False
for label in ax.get_xticklabels():
label.set_visible(False)
ax.xaxis.offsetText.set_visible(False)
UPDATE:
for completeness, mwaskom (ref to github issue) gave an explanation why this issue is occuring:
So this happens because matplotlib calls axis.reset_ticks() internally when moving the spine. Otherwise, the spine gets moved but the ticks stay in the same place. It's not configurable in matplotlib and, even if it were, I don't know if there is a public API for moving individual ticks. Unfortunately I think you'll have to remove the tick labels yourself after offsetting the spines.
Related
My dataframe has a column 'rideable_type' which has 3 unique values:
1.classic_bike
2.docked_bike
3.electric_bike
While plotting a barplot using the following code:
g = sns.FacetGrid(electric_casual_type_week, col='member_casual', hue='rideable_type', height=7, aspect=0.65)
g.map(sns.barplot, 'day_of_week', 'number_of_rides').add_legend()
I only get a plot showing 2 unique 'rideable_type' values.
Here is the plot:
As you can see only 'electric_bike' and 'classic_bike' are seen and not 'docked_bike'.
The main problem is that all the bars are drawn on top of each other. Seaborn's barplots don't easily support stacked bars. Also, this way of creating the barplot doesn't support the default "dodging" (barplot is called separately for each hue value, while it would be needed to call it in one go for dodging to work).
Therefore, the recommended way is to use catplot, a special version of FacetGrid for categorical plots.
g = sns.catplot(kind='bar', data=electric_casual_type_week, x='day_of_week', y='number_of_rides',
col='member_casual', hue='rideable_type', height=7, aspect=0.65)
Here is an example using Seaborn's 'tips' dataset:
import seaborn as sns
tips = sns.load_dataset('tips')
g = sns.FacetGrid(data=tips, col='time', hue='sex', height=7, aspect=0.65)
g.map_dataframe(sns.barplot, x='day', y='total_bill')
g.add_legend()
When comparing with sns.catplot, the coinciding bars are clear:
g = sns.catplot(kind='bar', data=tips, x='day', y='total_bill', col='time', hue='sex', height=7, aspect=0.65)
I have a dataframe 'Spreads' where one of the columns is 'HY_OAS'. My goal is to draw a horizontal line (basically representing a range of values for 'HY_OAS') and plot the column mean on that line. In addition, I wanted the x axis min/max to be the min/max for that column and I'd like to include text boxes annotating the min/max. The problem is I'm not sure how to proceed because all I have is the below. Thanks for any and all help. The goal is the second image and the current code is the first image.
fig8 = px.scatter(x=[Spreads['HY_OAS'].mean()], y=[0])
fig8.update_xaxes(visible=True,showticklabels=False,range=[Spreads['HY_OAS'].min(),Spreads['HY_OAS'].max()])
fig8.update_yaxes(visible=True,showticklabels=False, range=[0,0])
Following what you describe and what you have coded
generate some sample data in a dataframe
scatter values along x-axis and use constant for y-axis
add mean marker
format figure
add required annotations
import numpy as np
import plotly.express as px
import pandas as pd
# simulate some data
Spreads = pd.DataFrame({"HY_OAS": np.sin(np.random.uniform(0, np.pi * 2, 50))})
# scatter values along x-axis and and larger point for mean
fig = px.scatter(Spreads, x="HY_OAS", y=np.full(len(Spreads), 0)).add_traces(
px.scatter(x=[Spreads.mean()], y=[0])
.update_traces(marker={"color": "red", "size": 20})
.data
)
# fix up figure config
fig.update_layout(
xaxis_visible=False,
yaxis_visible=False,
showlegend=False,
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
)
# finally required annootations
fig.add_annotation(x=Spreads["HY_OAS"].mean(), y=0, text=Spreads["HY_OAS"].mean().round(4))
fig.add_annotation(x=Spreads["HY_OAS"].min(), y=0, text=Spreads["HY_OAS"].min().round(2), showarrow=False, xshift=-20)
fig.add_annotation(x=Spreads["HY_OAS"].max(), y=0, text=Spreads["HY_OAS"].max().round(2), showarrow=False, xshift=20)
straight line
build base figure as follows
then same code to add annotations and configure layout
fig = px.line(x=[Spreads["HY_OAS"].min(), Spreads["HY_OAS"].max()], y=[0,0]).add_traces(
px.scatter(x=[Spreads.mean()], y=[0])
.update_traces(marker={"color": "red", "size": 20})
.data
)
I plotted a scatterplot with seaborn library and I want to change the legend text but dont know how to do that.
example:
The following is iris dataset with species columns encoded in 0/1/2 as per species.
plt.figure(figsize=(8,8))
pl = sns.scatterplot(x='petal_length', y ='petal_width', hue='Species', data=data, s=40,
palette='Set1', legend='full')
I want to change the legends text from [0, 1, 2] to ['setosa', 'versicolor', 'virginica'].
can anybody help.
First, Seaborn (and Matplotlib) usually picks up the labels to put into the legend for hue from the unique values of the array you provide as hue. So as a first step, check that the column Species in your dataframe actually contains the values "setosa", "versicolor", "virginica". If not, one solution is to temporarily map them to other values, for the purpose of plotting:
legend_map = {0: 'setosa',
1: 'versicolor',
2: 'virginica'}
plt.figure(figsize=(8,8))
ax = sns.scatterplot(x=data['petal_length'], y =data['petal_width'], hue=data['species'].map(legend_map),
s=40, palette='Set1', legend='full')
plt.show()
Alternatively, if you want to directly manipulate the plot information and not the underlying data, you can do by accessing the legend names directly:
plt.figure(figsize=(8,8))
ax = sns.scatterplot(x='petal_length', y ='petal_width', hue='species', data=data, s=40,
palette='Set1', legend='full')
l = ax.legend()
l.get_texts()[0].set_text('Species') # You can also change the legend title
l.get_texts()[1].set_text('Setosa')
l.get_texts()[2].set_text('Versicolor')
l.get_texts()[3].set_text('Virginica')
plt.show()
This methodology allows you to also change the legend title, if need be.
I use code like this to make a plot in MATLAB 2015:
plot(rand(1,40))
box off
ax=gca;
ax.TickDir='out';
ax.TickLength=3*ax.TickLength;
]
I want to put a bit of distance between the axes and the plot like in the figure below, which I made using Photoshop:
How can I do this?
I can think of a way to do it, but:
I don't know if it's the "right" way.
It's only good for static images (i.e. if you zoom/pan the plot, the bounds won't change accordingly).
The idea is you create two more axes and specify their positions such that they're far enough from your data, then hide the original axes (either fully or partially), which gives the desired result:
%% // Create axes and plot something:
figure();
hA(1) = axes;
hA(2) = copyobj(hA(1),gcf);
hA(3) = copyobj(hA(1),gcf);
plot(hA(1),rand(1,40));
%% // Add the new axes:
%// Move them around
FRACTION_OF_POSITION = 0.6;
hA(2).Position(1) = FRACTION_OF_POSITION*hA(2).Position(1);
hA(3).Position(2) = FRACTION_OF_POSITION*hA(3).Position(2);
%// Change their style
hA(2).Color = 'none'; hA(3).Color = 'none';
hA(2).XTick = []; hA(3).YTick = [];
hA(2).XRuler.Axle.Visible = 'off';
hA(3).YRuler.Axle.Visible = 'off';
%% // Remove the box/ticks/labels of the original axes:
if true
%// Alternative 1 (Remove everything at once, including background):
set(hA(1),'Visible','off')
else
%// Alternative 2 (Remove only certain elements):
hA(1).Box = 'off';
hA(1).XRuler.Axle.Visible = 'off';
hA(1).YRuler.Axle.Visible = 'off';
end
Which results in:
Additional considerations:
If you copyobj the axes before plotting, the axis tick values would be default - you probably don't want that. You will have to set the ticks and labels manually, OR, copyobj after plotting but then delete the line child-objects of hA(2:3).
If you want to support zoom/pan behavior, this might be acheivable using linkaxes.
Credits: the idea to use .XRuler.Axle was taken from Dan here, who in turn took it from UndocumentedMatlab.
I have a 3D array, of which the first two dimensions are spatial, so say (x,y). The third dimension contains point-specific information.
print H.shape # --> (200, 480, 640) spatial extents (200,480)
Now, by selecting a certain plane in the third dimension, I can display an image with
imdat = H[:,:,100] # shape (200, 480)
img = ax.imshow(imdat, cmap='jet',vmin=imdat.min(),vmax=imdat.max(), animated=True, aspect='equal')
I want to now rotate the cube, so that I switch from (x,y) to (y,x).
H = np.rot90(H) # could also use H.swapaxes(0,1) or H.transpose((1,0,2))
print H.shape # --> (480, 200, 640)
Now, when I call:
imdat = H[:,:,100] # shape (480,200)
img.set_data(imdat)
ax.relim()
ax.autoscale_view(tight=True)
I get weird behavior. The image along the rows displays the data till 200th row, and then it is black until the end of the y-axis (480). The x-axis extends from 0 to 200 and shows the rotated data. Now on, another rotation by 90-degrees, the image displays correctly (just rotated 180 degrees of course)
It seems to me like after rotating the data, the axis limits, (or image extents?) or something is not refreshing correctly. Can somebody help?
PS: to indulge in bad hacking, I also tried to regenerate a new image (by calling ax.imshow) after each rotation, but I still get the same behavior.
Below I include a solution to your problem. The method resetExtent uses the data and the image to explicitly set the extent to the desired values. Hopefully I correctly emulated the intended outcome.
import matplotlib.pyplot as plt
import numpy as np
def resetExtent(data,im):
"""
Using the data and axes from an AxesImage, im, force the extent and
axis values to match shape of data.
"""
ax = im.get_axes()
dataShape = data.shape
if im.origin == 'upper':
im.set_extent((-0.5,dataShape[0]-.5,dataShape[1]-.5,-.5))
ax.set_xlim((-0.5,dataShape[0]-.5))
ax.set_ylim((dataShape[1]-.5,-.5))
else:
im.set_extent((-0.5,dataShape[0]-.5,-.5,dataShape[1]-.5))
ax.set_xlim((-0.5,dataShape[0]-.5))
ax.set_ylim((-.5,dataShape[1]-.5))
def main():
fig = plt.gcf()
ax = fig.gca()
H = np.zeros((200,480,10))
# make distinguishing corner of data
H[100:,...] = 1
H[100:,240:,:] = 2
imdat = H[:,:,5]
datShape = imdat.shape
im = ax.imshow(imdat,cmap='jet',vmin=imdat.min(),
vmax=imdat.max(),animated=True,
aspect='equal',
# origin='lower'
)
resetExtent(imdat,im)
fig.savefig("img1.png")
H = np.rot90(H)
imdat = H[:,:,0]
im.set_data(imdat)
resetExtent(imdat,im)
fig.savefig("img2.png")
if __name__ == '__main__':
main()
This script produces two images:
First un-rotated:
Then rotated:
I thought just explicitly calling set_extent would do everything resetExtent does, because it should adjust the axes limits if 'autoscle' is True. But for some unknown reason, calling set_extent alone does not do the job.