I've created a (3,2) subplots and forced them in constrained_layout=True.
Then I wanted to disable the constrained_layout with .set_constrained_layout(False) due to performance issues, but I 'd like to keep the axes positions.
I tried to get the positions in constrained layout state, and then apply these positions to the axes after setting constrained_layout to False, following these instructions, but it doesn't work.
With the code below, I expected to obtain the following figure, but I ended up with the one below it.
What am I missing?
import matplotlib.pyplot as plt
fig, ax = plt.subplots(3,2,constrained_layout =True)
mng = plt.get_current_fig_manager()
mng.window.showMaximized()
fig.canvas.draw()
bounds = [ex.get_position().bounds for ex in fig.axes]
fig.set_constrained_layout(False)
for i in range(len(fig.axes)):
fig.axes[i].set_position(bounds[i])
fig.canvas.draw()
Is it wrong to adjust the vertical and horizontal in the subplot?
fig, ax = plt.subplots(3,2)
fig.subplots_adjust(wspace=0.3, hspace=0.3)
Related
I have a list of dataframes named merged_dfs that I am looping through to get the correlation and plot subplots of heatmap correlation matrix using seaborn.
I want to customize the colorbar tick labels, but I am having trouble figuring out how to do it with my example.
Currently, my colorbar scale values from top to bottom are
[1,0.5,0,-0.5,-1]
I want to keep these values, but change the tick labels to be
[1,0.5,0,0.5,1]
for my diverging color bar.
Here is the code and my attempt:
fig, ax = plt.subplots(nrows=6, ncols=2, figsize=(20,20))
for i, (title,merging) in enumerate (zip(new_name_data,merged_dfs)):
graph = merging.corr()
colormap = sns.diverging_palette(250, 250, as_cmap=True)
a = sns.heatmap(graph.abs(), cmap=colormap, vmin=-1,vmax=1,center=0,annot = graph, ax=ax.flat[i])
cbar = fig.colorbar(a)
cbar.set_ticklabels(["1","0.5","0","0.5","1"])
fig.delaxes(ax[5,1])
plt.show()
plt.close()
I keep getting this error:
AttributeError: 'AxesSubplot' object has no attribute 'get_array'
Several things are going wrong:
fig.colorbar(...) would create a new colorbar, by default appended to the last subplot that was created.
sns.heatmap returns an ax (indicates a subplot). This is very different to matplotlib functions, e.g. plt.imshow(), which would return the graphical element that was plotted.
You can suppress the heatmap's colorbar (cbar=False), and then create it newly with the parameters you want.
fig.colorbar(...) needs a parameter ax=... when the figure contains more than one subplot.
Instead of creating a new colorbar, you can add the colorbar parameters to sns.heatmap via cbar_kws=.... The colorbar itself can be found via ax.collections[0].colobar. (ax.collections[0] is where matplotlib stored the graphical object that contains the heatmap.)
Using an index is strongly discouraged when working with Python. It's usually more readable, easier to maintain and less error-prone to include everything into the zip command.
As now your vmin now is -1, taking the absolute value for the coloring seems to be a mistake.
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
merged_dfs = [pd.DataFrame(data=np.random.rand(5, 7), columns=[*'ABCDEFG']) for _ in range(5)]
new_name_data = [f'Dataset {i + 1}' for i in range(len(merged_dfs))]
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 7))
for title, merging, ax in zip(new_name_data, merged_dfs, axes.flat):
graph = merging.corr()
colormap = sns.diverging_palette(250, 250, as_cmap=True)
sns.heatmap(graph, cmap=colormap, vmin=-1, vmax=1, center=0, annot=True, ax=ax, cbar_kws={'ticks': ticks})
ax.collections[0].colorbar.set_ticklabels([abs(t) for t in ticks])
fig.delaxes(axes.flat[-1])
fig.tight_layout()
plt.show()
I would like to minimize white space in my figure. I have a row of sub plots where four plots share their y-axis and the last plot has a separate axis.
There are no ylabels or ticklabels for the shared axis middle panels.
tight_layout creates a lot of white space between the the middle plots as if leaving space for tick labels and ylabels but I would rather stretch the sub plots. Is this possible?
import matplotlib.gridspec as gridspec
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
fig = plt.figure()
gs = gridspec.GridSpec(1, 5, width_ratios=[4,1,4,1,2])
ax = fig.add_subplot(gs[0])
axes = [ax] + [fig.add_subplot(gs[i], sharey=ax) for i in range(1, 4)]
axes[0].plot(np.random.randint(0,100,100))
barlist=axes[1].bar([1,2],[1,20])
axes[2].plot(np.random.randint(0,100,100))
barlist=axes[3].bar([1,2],[1,20])
axes[0].set_ylabel('data')
axes.append(fig.add_subplot(gs[4]))
axes[4].plot(np.random.randint(0,5,100))
axes[4].set_ylabel('other data')
for ax in axes[1:4]:
plt.setp(ax.get_yticklabels(), visible=False)
sns.despine();
plt.tight_layout(pad=0, w_pad=0, h_pad=0);
Setting w_pad = 0 is not changing the default settings of tight_layout. You need to set something like w_pad = -2. Which produces the following figure:
You could go further, to say -3 but then you would start to get some overlap with your last plot.
Another way could be to remove plt.tight_layout() and set the boundaries yourself using
plt.subplots_adjust(left=0.065, right=0.97, top=0.96, bottom=0.065, wspace=0.14)
Though this can be a bit of a trial and error process.
Edit
A nice looking graph can be achieved by moving the ticks and the labels of the last plot to the right hand side. This answer shows you can do this by using:
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
So for your example:
axes[4].yaxis.tick_right()
axes[4].yaxis.set_label_position("right")
In addition, you need to remove sns.despine(). Finally, there is now no need to set w_pad = -2, just use plt.tight_layout(pad=0, w_pad=0, h_pad=0)
Using this creates the following figure:
I am new to matplotlib, and I am finding it very confusing. I have spent quite a lot of time on the matplotlib tutorial website, but I still cannot really understand how to build a figure from scratch. To me, this means doing everything manually... not using the plt.plot() function, but always setting figure, axis handles.
Can anyone explain how to set up a figure from the ground up?
Right now, I have this code to generate a double y-axis plot. But my xlabels are disappearing and I dont' know why
fig, ax1 = plt.subplots()
ax1.plot(yearsTotal,timeseries_data1,'r-')
ax1.set_ylabel('Windspeed [m/s]')
ax1.tick_params('y',colors='r')
ax2 = ax1.twinx()
ax2.plot(yearsTotal,timeseries_data2,'b-')
ax2.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
ax2.set_xticklabels(ax1.xaxis.get_majorticklabels(), rotation=90)
ax2.set_ylabel('Open water duration [days]')
ax2.tick_params('y',colors='b')
plt.title('My title')
fig.tight_layout()
plt.savefig('plots/my_figure.png',bbox_inches='tight')
plt.show()
Because you are using a twinx, it makes sense to operate only on the original axes (ax1).
Further, the ticklabels are not defined at the point where you call ax1.xaxis.get_majorticklabels().
If you want to set the ticks and ticklabels manually, you can use your own data to do so (although I wouldn't know why you'd prefer this over using the automatic labeling) by specifying a list or array
ticks = np.arange(min(yearsTotal),max(yearsTotal)+1)
ax1.set_xticks(ticks)
ax1.set_xticklabels(ticks)
Since the ticklabels are the same as the tickpositions here, you may also just do
ax1.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
plt.setp(ax1.get_xticklabels(), rotation=70)
Complete example:
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
yearsTotal = np.arange(1977, 1999)
timeseries_data1 = np.cumsum(np.random.normal(size=len(yearsTotal)))+5
timeseries_data2 = np.cumsum(np.random.normal(size=len(yearsTotal)))+20
fig, ax1 = plt.subplots()
ax1.plot(yearsTotal,timeseries_data1,'r-')
ax1.set_ylabel('Windspeed [m/s]')
ax1.tick_params('y',colors='r')
ax1.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
plt.setp(ax1.get_xticklabels(), rotation=70)
ax2 = ax1.twinx()
ax2.plot(yearsTotal,timeseries_data2,'b-')
ax2.set_ylabel('Open water duration [days]')
ax2.tick_params('y',colors='b')
plt.title('My title')
fig.tight_layout()
plt.show()
Based on your code, it is not disappear, it is set (overwrite) by these two functions:
ax2.set_xticks(np.arange(min(yearsTotal),max(yearsTotal)+1))
ax2.set_xticklabels(ax1.xaxis.get_majorticklabels(), rotation=90)
set_xticks() on the axes will set the locations and set_xticklabels() will set the xtick labels with list of strings labels.
For the plot
sns.countplot(x="HostRamSize",data=df)
I got the following graph with x-axis label mixing together, how do I avoid this? Should I change the size of the graph to solve this problem?
Having a Series ds like this
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(136)
l = "1234567890123"
categories = [ l[i:i+5]+" - "+l[i+1:i+6] for i in range(6)]
x = np.random.choice(categories, size=1000,
p=np.diff(np.array([0,0.7,2.8,6.5,8.5,9.3,10])/10.))
ds = pd.Series({"Column" : x})
there are several options to make the axis labels more readable.
Change figure size
plt.figure(figsize=(8,4)) # this creates a figure 8 inch wide, 4 inch high
sns.countplot(x="Column", data=ds)
plt.show()
Rotate the ticklabels
ax = sns.countplot(x="Column", data=ds)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
plt.show()
Decrease Fontsize
ax = sns.countplot(x="Column", data=ds)
ax.set_xticklabels(ax.get_xticklabels(), fontsize=7)
plt.tight_layout()
plt.show()
Of course any combination of those would work equally well.
Setting rcParams
The figure size and the xlabel fontsize can be set globally using rcParams
plt.rcParams["figure.figsize"] = (8, 4)
plt.rcParams["xtick.labelsize"] = 7
This might be useful to put on top of a juypter notebook such that those settings apply for any figure generated within. Unfortunately rotating the xticklabels is not possible using rcParams.
I guess it's worth noting that the same strategies would naturally also apply for seaborn barplot, matplotlib bar plot or pandas.bar.
You can rotate the x_labels and increase their font size using the xticks methods of pandas.pyplot.
For Example:
import matplotlib.pyplot as plt
plt.figure(figsize=(10,5))
chart = sns.countplot(x="HostRamSize",data=df)
plt.xticks(
rotation=45,
horizontalalignment='right',
fontweight='light',
fontsize='x-large'
)
For more such modifications you can refer this link:
Drawing from Data
If you just want to make sure xticks labels are not squeezed together, you can set a proper fig size and try fig.autofmt_xdate().
This function will automatically align and rotate the labels.
plt.figure(figsize=(15,10)) #adjust the size of plot
ax=sns.countplot(x=df['Location'],data=df,hue='label',palette='mako')
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right") #it will rotate text on x axis
plt.tight_layout()
plt.show()
you can try this code & change size & rotation according to your need.
I don't know whether it is an option for you but maybe turning the graphic could be a solution (instead of plotting on x=, do it on y=), such that:
sns.countplot(y="HostRamSize",data=df)
I'm trying to plot a figure without tickmarks or numbers on either of the axes (I use axes in the traditional sense, not the matplotlib nomenclature!). An issue I have come across is where matplotlib adjusts the x(y)ticklabels by subtracting a value N, then adds N at the end of the axis.
This may be vague, but the following simplified example highlights the issue, with '6.18' being the offending value of N:
import matplotlib.pyplot as plt
import random
prefix = 6.18
rx = [prefix+(0.001*random.random()) for i in arange(100)]
ry = [prefix+(0.001*random.random()) for i in arange(100)]
plt.plot(rx,ry,'ko')
frame1 = plt.gca()
for xlabel_i in frame1.axes.get_xticklabels():
xlabel_i.set_visible(False)
xlabel_i.set_fontsize(0.0)
for xlabel_i in frame1.axes.get_yticklabels():
xlabel_i.set_fontsize(0.0)
xlabel_i.set_visible(False)
for tick in frame1.axes.get_xticklines():
tick.set_visible(False)
for tick in frame1.axes.get_yticklines():
tick.set_visible(False)
plt.show()
The three things I would like to know are:
How to turn off this behaviour in the first place (although in most cases it is useful, it is not always!) I have looked through matplotlib.axis.XAxis and cannot find anything appropriate
How can I make N disappear (i.e. X.set_visible(False))
Is there a better way to do the above anyway? My final plot would be 4x4 subplots in a figure, if that is relevant.
Instead of hiding each element, you can hide the whole axis:
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
Or, you can set the ticks to an empty list:
frame1.axes.get_xaxis().set_ticks([])
frame1.axes.get_yaxis().set_ticks([])
In this second option, you can still use plt.xlabel() and plt.ylabel() to add labels to the axes.
If you want to hide just the axis text keeping the grid lines:
frame1 = plt.gca()
frame1.axes.xaxis.set_ticklabels([])
frame1.axes.yaxis.set_ticklabels([])
Doing set_visible(False) or set_ticks([]) will also hide the grid lines.
If you are like me and don't always retrieve the axes, ax, when plotting the figure, then a simple solution would be to do
plt.xticks([])
plt.yticks([])
I've colour coded this figure to ease the process.
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
You can have full control over the figure using these commands, to complete the answer I've add also the control over the spines:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# X AXIS -BORDER
ax.spines['bottom'].set_visible(False)
# BLUE
ax.set_xticklabels([])
# RED
ax.set_xticks([])
# RED AND BLUE TOGETHER
ax.axes.get_xaxis().set_visible(False)
# Y AXIS -BORDER
ax.spines['left'].set_visible(False)
# YELLOW
ax.set_yticklabels([])
# GREEN
ax.set_yticks([])
# YELLOW AND GREEN TOGHETHER
ax.axes.get_yaxis().set_visible(False)
I was not actually able to render an image without borders or axis data based on any of the code snippets here (even the one accepted at the answer). After digging through some API documentation, I landed on this code to render my image
plt.axis('off')
plt.tick_params(axis='both', left=False, top=False, right=False, bottom=False, labelleft=False, labeltop=False, labelright=False, labelbottom=False)
plt.savefig('foo.png', dpi=100, bbox_inches='tight', pad_inches=0.0)
I used the tick_params call to basically shut down any extra information that might be rendered and I have a perfect graph in my output file.
Somewhat of an old thread but, this seems to be a faster method using the latest version of matplotlib:
set the major formatter for the x-axis
ax.xaxis.set_major_formatter(plt.NullFormatter())
One trick could be setting the color of tick labels as white to hide it!
plt.xticks(color='w')
plt.yticks(color='w')
or to be more generalized (#Armin Okić), you can set it as "None".
When using the object oriented API, the Axes object has two useful methods for removing the axis text, set_xticklabels() and set_xticks().
Say you create a plot using
fig, ax = plt.subplots(1)
ax.plot(x, y)
If you simply want to remove the tick labels, you could use
ax.set_xticklabels([])
or to remove the ticks completely, you could use
ax.set_xticks([])
These methods are useful for specifying exactly where you want the ticks and how you want them labeled. Passing an empty list results in no ticks, or no labels, respectively.
You could simply set xlabel to None, straight in your axis. Below an working example using seaborn
from matplotlib import pyplot as plt
import seaborn as sns
tips = sns.load_dataset("tips")
ax = sns.boxplot(x="day", y="total_bill", data=tips)
ax.set(xlabel=None)
plt.show()
Just do this in case you have subplots
fig, axs = plt.subplots(1, 2, figsize=(16, 8))
ax[0].set_yticklabels([]) # x-axis
ax[0].set_xticklabels([]) # y-axis