I'm trying to define a colorbar for the following type of plot.
import matplotlib.pyplot as plt
import numpy as np
for i in np.arange(0,10,0.1):
plt.plot(range(10),np.ones(10)*i,c=[i/10.,0.5,0.25])
plt.show()
This is just a simplified version of my actual data, but basically, I'd like a series of lines plotted and colored by another variable with a colorbar key. This is easy to do in scatter, but I can't get scatter to plot connected lines. Points are too clunky. I know this sounds like basic stuff, but I'm having a helluva time finding a simple solution ... what obvious solution am I missing?
You can build a custom color map and a mappable from it, then pass to colorbar:
from matplotlib.cm import ScalarMappable
from matplotlib.colors import Normalize
import matplotlib.colors as mcolors
color_list = [(i/10, 0.5,0.25) for i in np.arange(0,10,0.1)]
cmap = mcolors.LinearSegmentedColormap.from_list("my_colormap", color_list)
cmappable = ScalarMappable(norm=Normalize(0,10), cmap=cmap)
plt.figure(figsize=(10,10))
for j,i in enumerate(np.arange(0,10,0.1)):
plt.plot(range(10),np.ones(10)*i,c=color_list[j])
plt.colorbar(cmappable)
plt.show()
Output:
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
I am trying to plot clusters from K-means alogrith on an image. All I can reach is plotting them on a graph. How can I plot them on an image as a background?
This image is of fixed size and I cant alter its size.
Sorry for silly question, but, am pretty new to python and looks exciting!
I have used K-means alogrithum based on few examples provided, but only reached upto plotting it on a graph.
What I would like to see is those clusters on a custom image of fixed size. How can I achieve it.
Thanking in advance for your replies!
First plot the image and then plot the points.
>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> # Image
>>> img = np.random.randint(0,255,size=(50,50))
>>> x = np.random.randint(0,50,size=100)
>>> y = np.random.randint(0,50,size=100)
>>> plt.imshow(img, cmap='gray')
>>> plt.scatter(x,y)
>>> plt.show()
da is my dataframe. I want to make this figure into one subplot out of 2 that I will have. When I add plt.subplots(2,1,2) for this figure it ends up separating this figure into a separate figure and the subplot is an empty figure.
How can I make this code into a subplot?
-Thank you in advance, I am a newbie in python.
ax1 = da.plot(rot = 90, title ='Pre-Folsom Dam Spring Recession')
ax1.set_xlabel('Water Year Day')
ax1.axhline( y = float(fSP_Mag) , xmin=0, xmax=35,color ='r', linestyle='--',zorder=0,label= 'Magnitude')
ax1.axvline(x=float(fSP_Tim), color ='r',linestyle='--', label='Timing')
ax1.legend(framealpha=1, frameon=True)
import pandas as pd
import matplotlib.pyplot as plt
data=pd.DataFrame({"col1":[1,2,3,4,5],"col2":[2,4,6,8,10]})
fig=plt.figure()
ax1=fig.add_subplot(2,1,1)
ax2=fig.add_subplot(2,1,2)
data["col1"].plot(ax=ax1)
data["col2"].plot(ax=ax2)
Create a plt.figure() and assign subplots to ax1 and ax2.Now plot the dataframe using these axes.
Reference:-
Pyplot
I am using stripplot in python where in axis I have time range from 3601 to 8600 in seconds. But from figure It's hard to see the time in x-axis. How can I add specific interval (like the y-axis in my figure) to make it visible? Here is my code to generate the figure.
import seaborn as sns; sns.set(color_codes=True)
# Visualising the plots
fig, ax = plt.subplots(figsize=(10,7))
params_anno = dict(jitter=0.25,size=8, color='#91bfdb', edgecolor='black', linewidth=1, dodge=False)
ax=sns.stripplot(x=dataset["Time"], y=dataset["Ob7"],**params_anno)
params_anno = dict(data=dataset_2, x='Time', y='Ob7',jitter=0.25)
ax=sns.stripplot(size=8,color='red', edgecolor='black',linewidth=1,**params_anno)
Here is my figure
I have seen seaborn.stripplot documentations but couldn't find any suitable example. Does anybody help?
The question is alredy 5months old, but if someone need answer.
You can try this:
from matplotlib import pyplot as plt
import matplotlib.ticker as ticker
fig,axs = plt.subplots()
axs.yaxis.set_major_locator(ticker.MultipleLocator(0.05))