Rotate label text in seaborn factorplot - python

I have a simple factorplot
import seaborn as sns
g = sns.factorplot("name", "miss_ratio", "policy", dodge=.2,
linestyles=["none", "none", "none", "none"], data=df[df["level"] == 2])
The problem is that the x labels all run together, making them unreadable. How do you rotate the text so that the labels are readable?


I had a problem with the answer by #mwaskorn, namely that
g.set_xticklabels(rotation=30)
fails, because this also requires the labels. A bit easier than the answer by #Aman is to just add
plt.xticks(rotation=45)


You can rotate tick labels with the tick_params method on matplotlib Axes objects. To provide a specific example:
ax.tick_params(axis='x', rotation=90)


This is still a matplotlib object. Try this:
# <your code here>
locs, labels = plt.xticks()
plt.setp(labels, rotation=45)


Any seaborn plots suported by facetgrid won't work with (e.g. catplot)
g.set_xticklabels(rotation=30)
however barplot, countplot, etc. will work as they are not supported by facetgrid. Below will work for them.
g.set_xticklabels(g.get_xticklabels(), rotation=30)
Also, in case you have 2 graphs overlayed on top of each other, try set_xticklabels on graph which supports it.


If anyone wonders how to this for clustermap CorrGrids (part of a given seaborn example):
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(context="paper", font="monospace")
# Load the datset of correlations between cortical brain networks
df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)
corrmat = df.corr()
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(12, 9))
# Draw the heatmap using seaborn
g=sns.clustermap(corrmat, vmax=.8, square=True)
rotation = 90
for i, ax in enumerate(g.fig.axes): ## getting all axes of the fig object
ax.set_xticklabels(ax.get_xticklabels(), rotation = rotation)
g.fig.show()


You can also use plt.setp as follows:
import matplotlib.pyplot as plt
import seaborn as sns
plot=sns.barplot(data=df, x=" ", y=" ")
plt.setp(plot.get_xticklabels(), rotation=90)
to rotate the labels 90 degrees.


For a seaborn.heatmap, you can rotate these using (based on #Aman's answer)
pandas_frame = pd.DataFrame(data, index=names, columns=names)
heatmap = seaborn.heatmap(pandas_frame)
loc, labels = plt.xticks()
heatmap.set_xticklabels(labels, rotation=45)
heatmap.set_yticklabels(labels[::-1], rotation=45) # reversed order for y


One can do this with matplotlib.pyplot.xticks
import matplotlib.pyplot as plt
plt.xticks(rotation = 'vertical')
# Or use degrees explicitly
degrees = 70 # Adjust according to one's preferences/needs
plt.xticks(rotation=degrees)
Here one can see an example of how it works.


Use ax.tick_params(labelrotation=45). You can apply this to the axes figure from the plot without having to provide labels. This is an alternative to using the FacetGrid if that's not the path you want to take.


If the labels have long names it may be hard to get it right. A solution that worked well for me using catplot was:
import matplotlib.pyplot as plt
fig = plt.gcf()
fig.autofmt_xdate()

Related

How to increase the figure size of a Seaborn Plot? [duplicate]

How do I change the size of my image so it's suitable for printing?
For example, I'd like to use to A4 paper, whose dimensions are 11.7 inches by 8.27 inches in landscape orientation.

You can also set figure size by passing dictionary to rc parameter with key 'figure.figsize' in seaborn set method:
import seaborn as sns
sns.set(rc={'figure.figsize':(11.7,8.27)})
Other alternative may be to use figure.figsize of rcParams to set figure size as below:
from matplotlib import rcParams
# figure size in inches
rcParams['figure.figsize'] = 11.7,8.27
More details can be found in matplotlib documentation

You need to create the matplotlib Figure and Axes objects ahead of time, specifying how big the figure is:
from matplotlib import pyplot
import seaborn
import mylib
a4_dims = (11.7, 8.27)
df = mylib.load_data()
fig, ax = pyplot.subplots(figsize=a4_dims)
seaborn.violinplot(ax=ax, data=df, **violin_options)

Note that if you are trying to pass to a "figure level" method in seaborn (for example lmplot, catplot / factorplot, jointplot) you can and should specify this within the arguments using height and aspect.
sns.catplot(data=df, x='xvar', y='yvar',
hue='hue_bar', height=8.27, aspect=11.7/8.27)
See https://github.com/mwaskom/seaborn/issues/488 and Plotting with seaborn using the matplotlib object-oriented interface for more details on the fact that figure level methods do not obey axes specifications.

first import matplotlib and use it to set the size of the figure
from matplotlib import pyplot as plt
import seaborn as sns
plt.figure(figsize=(15,8))
ax = sns.barplot(x="Word", y="Frequency", data=boxdata)

You can set the context to be poster or manually set fig_size.
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
np.random.seed(0)
n, p = 40, 8
d = np.random.normal(0, 2, (n, p))
d += np.log(np.arange(1, p + 1)) * -5 + 10
# plot
sns.set_style('ticks')
fig, ax = plt.subplots()
# the size of A4 paper
fig.set_size_inches(11.7, 8.27)
sns.violinplot(data=d, inner="points", ax=ax)
sns.despine()
fig.savefig('example.png')

This can be done using:
plt.figure(figsize=(15,8))
sns.kdeplot(data,shade=True)

In addition to elz answer regarding "figure level" methods that return multi-plot grid objects it is possible to set the figure height and width explicitly (that is without using aspect ratio) using the following approach:
import seaborn as sns
g = sns.catplot(data=df, x='xvar', y='yvar', hue='hue_bar')
g.fig.set_figwidth(8.27)
g.fig.set_figheight(11.7)

This shall also work.
from matplotlib import pyplot as plt
import seaborn as sns
plt.figure(figsize=(15,16))
sns.countplot(data=yourdata, ...)

For my plot (a sns factorplot) the proposed answer didn't works fine.
Thus I use
plt.gcf().set_size_inches(11.7, 8.27)
Just after the plot with seaborn (so no need to pass an ax to seaborn or to change the rc settings).

See How to change the image size for seaborn.objects for a solution with the new seaborn.objects interface from seaborn v0.12, which is not the same as seaborn axes-level or figure-level plots.
Adjusting the size of the plot depends if the plot is a figure-level plot like seaborn.displot, or an axes-level plot like seaborn.histplot. This answer applies to any figure or axes level plots.
See the the seaborn API reference
seaborn is a high-level API for matplotlib, so seaborn works with matplotlib methods
Tested in python 3.8.12, matplotlib 3.4.3, seaborn 0.11.2
Imports and Data
import seaborn as sns
import matplotlib.pyplot as plt
# load data
df = sns.load_dataset('penguins')
sns.displot
The size of a figure-level plot can be adjusted with the height and/or aspect parameters
Additionally, the dpi of the figure can be set by accessing the fig object and using .set_dpi()
p = sns.displot(data=df, x='flipper_length_mm', stat='density', height=4, aspect=1.5)
p.fig.set_dpi(100)
Without p.fig.set_dpi(100)
With p.fig.set_dpi(100)
sns.histplot
The size of an axes-level plot can be adjusted with figsize and/or dpi
# create figure and axes
fig, ax = plt.subplots(figsize=(6, 5), dpi=100)
# plot to the existing fig, by using ax=ax
p = sns.histplot(data=df, x='flipper_length_mm', stat='density', ax=ax)
Without dpi=100
With dpi=100

# Sets the figure size temporarily but has to be set again the next plot
plt.figure(figsize=(18,18))
sns.barplot(x=housing.ocean_proximity, y=housing.median_house_value)
plt.show()

Some tried out ways:
import seaborn as sns
import matplotlib.pyplot as plt
ax, fig = plt.subplots(figsize=[15,7])
sns.boxplot(x="feature1", y="feature2",data=df) # where df would be your dataframe
or
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=[15,7])
sns.boxplot(x="feature1", y="feature2",data=df) # where df would be your dataframe

The top answers by Paul H and J. Li do not work for all types of seaborn figures. For the FacetGrid type (for instance sns.lmplot()), use the size and aspect parameter.
Size changes both the height and width, maintaining the aspect ratio.
Aspect only changes the width, keeping the height constant.
You can always get your desired size by playing with these two parameters.
Credit: https://stackoverflow.com/a/28765059/3901029

Change color of seaborn lineplot

I have a seaborn lineplot:
plt.figure(figsize=(22,14))
sns.lineplot(x="Datum", y="Value", ci=None, hue='Type', data=df)
plt.show()
Which leads to the following output:
How can i change the linecolors? For me the difference is hard to see.

You can change colors using palettes. Referring to https://seaborn.pydata.org/tutorial/color_palettes.html, try:
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
fmri = sns.load_dataset("fmri")
# Try playing with one set or another:
#sns.set_palette("husl")
sns.set_palette("PuBuGn_d")
ax = sns.lineplot(x="timepoint", y="signal", hue="event", data=fmri)
you'll get different line colors, like this
or this

You can use colour inside lineplot() method, however this, far as I know, works only with Series.
You can transform your data to Series with this this:
data = pd.Series(another_data)
Then plot Your data
sns.lineplot(..., data=data, color='red')
Another way is to use pallets
palette = sns.color_palette("mako_r", 6)
sns.lineplot(..., palette=palette, data=data)
More you can find in Seaborn lineplot reference: https://seaborn.pydata.org/generated/seaborn.lineplot.html
Or here:
https://stackoverflow.com/a/58432483/12366487

At least in version 0.11.2 of seaborn, the lineplot function (http://seaborn.pydata.org/generated/seaborn.lineplot.html) has a parameter called palette that allows changing the color map used for the hue.
To check the available color maps you can refer to https://matplotlib.org/stable/tutorials/colors/colormaps.html
import seaborn as sns
import matplotlib.pyplot as plt
fmri = sns.load_dataset("fmri")
#sns.lineplot(x="timepoint", y="signal", hue="event", data=fmri, palette="tab10")
sns.lineplot(x="timepoint", y="signal", hue="event", data=fmri, palette="Accent")

Change y-axis scale - FacetGrid

I cannot work out how to change the scale of the y-axis. My code is:
grid = sns.catplot(x='Nationality', y='count',
row='Age', col='Gender',
hue='Type',
data=dfNorthumbria2, kind='bar', ci='No')
I wanted to just go up in full numbers rather than in .5

Update
I just now found this tutorial the probably easiest solution will be the following:
grid.set(yticks=list(range(5)))
From the help of grid.set
Help on method set in module seaborn.axisgrid:
set(**kwargs) method of seaborn.axisgrid.FacetGrid instance
Set attributes on each subplot Axes.
Since seaborn is build on top of matplotlib you can use yticks from plt
import matplotlib.pyplot as plt
plt.yticks(range(5))
However this changed only the yticks of the upper row in my mockup example.
For this reason you probably want to change the y ticks based on the axis with ax.set_yticks(). To get the axis from your grid object you can implemented a list comprehension as follows:
[ax[0].set_yticks(range(0,150,5) )for ax in grid.axes]
A full replicable example would look like this (adapted from here)
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="ticks")
exercise = sns.load_dataset("exercise")
grid = sns.catplot(x="time", y="pulse", hue="kind",
row="diet", data=exercise)
# plt.yticks(range(0,150,5)) # Changed only one y-axis
# Changed y-ticks to steps of 20
[ax[0].set_yticks(range(0,150,20) )for ax in grid.axes]

Plot another point on top of swarmplot

I want to plot a "highlighted" point on top of swarmplot like this
The swarmplot don't have the y-axis, so I have no idea how to plot that point.
import seaborn as sns
sns.set(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.swarmplot(x=tips["total_bill"])

This approach is predicated on knowing the index of the data point you wish to highlight, but it should work - although if you have multiple swarmplots on a single Axes instance it will become slightly more complex.
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
sns.set(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.swarmplot(x=tips["total_bill"])
artists = ax.get_children()
offsets = []
for a in artists:
if type(a) is matplotlib.collections.PathCollection:
offsets = a.get_offsets()
break
plt.scatter(offsets[50,0], offsets[50,1], marker='o', color='orange', zorder=10)

You can highlight a point/s using the hue attribute if you add a grouping variable for the y axis (so that they appear as a single group), and then use another variable to highlight the point that you're interested in.
Then you can remove the y labels and styling and legend.
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="whitegrid")
# Get data and mark point you want to highlight
tips = sns.load_dataset("tips")
tips['highlighted_point'] = 0
tips.loc[tips[tips.total_bill > 50].index, 'highlighted_point'] = 1
# Add holding 'group' variable so they appear as one
tips['y_variable'] = 'testing'
# Use 'hue' to differentiate the highlighted point
ax = sns.swarmplot(x=tips["total_bill"], y=tips['y_variable'], hue=tips['highlighted_point'])
# Remove legend
ax.get_legend().remove()
# Hide y axis formatting
ax.set_ylabel('')
ax.get_yaxis().set_ticks([])
plt.show()

Python Seaborn Facetgrid change xlabels

I just can't figure out how to change the xlabels in a Seaborn Facetgrid. It offers a method for changing the x labels with set_xlabels() but unfortunately not individually for each subplot.
I have two subplots which share the y-axis but have a different x-axes and i want to label them with different texts.
Can anybody give me a hint. Thank you in advance.

You can access the individual axes of the FacetGrid using the axes property, and then use set_xlabel() on each of them. For example:
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="ticks", color_codes=True)
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col="time", hue="smoker")
g = g.map(plt.scatter, "total_bill", "tip", edgecolor="w")
g.axes[0,0].set_xlabel('axes label 1')
g.axes[0,1].set_xlabel('axes label 2')
plt.show()
Note in this example, g.axes has a shape of (1,2) (one row, two columns).

for all axis to set them once use this
g.set_axis_labels("Total bill ($)")

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