Develop Reference

Custom Chart Formatting in Seaborn

I have a straightforward countplot in seaborn.
Code is:
ax = sns.countplot(x="days", data=df,color ='cornflowerblue')
ax.set_xticklabels(ax.get_xticklabels(),rotation=90)
ax.set(xlabel='days', ylabel='Conversions')
ax.set_title("Days to Conversion")
for p in ax.patches:
count = p.get_height()
x = p.get_x() + p.get_width()/1.25
y = p.get_height()*1.01
ax.annotate(count, (x, y),ha='right')
Which produces:
I'm trying to make the chart a bit 'prettier'. Specifically I want to raise the height of the outline so it wont cross the count numbers on first bar, and make the count numbers centered with a small space about the bar. Can't get it to work.
Guidance please.

To set the labels, in the latest matplotlib version (3.4.2), there is a new function bar_label() which takes care of positioning. In older versions you could use your code, but use x = p.get_x() + p.get_width()/2 and set ax.text(..., ha='center').
To make room for the labels, an extra margin can be added via ax.margins(y=0.1).
import matplotlib.pyplot as plt
import seaborn as sns
df = sns.load_dataset('tips')
ax = sns.countplot(x="day", data=df, color='cornflowerblue')
ax.tick_params(axis='x', labelrotation=90)
ax.bar_label(ax.containers[-1])
ax.margins(y=0.1)
plt.tight_layout()
plt.show()

Title font in subplots with axes.title.set_text

I have intention in making multiple subplot to present my results. I used subplots from matplotlib. I have a problem with text sizes. As you can see in the trivial code here. In the plt.title documentation it says title(label, fontdict=None, loc='center', pad=None, **kwargs)
import random
from matplotlib.pyplot import figure, plot, xlabel, ylabel, legend, close, subplots, title, savefig, get_current_fig_manager, show, pause, clf
x = []
for i in range(10):
x.append(random.random()*i)
y_1 = []
for i in range(10):
y_1.append(random.random()*i)
y_2 = []
for i in range(10):
y_2.append(random.random()*i)
fig, ax = subplots(1, 2, squeeze = False, figsize = (10,10))
ax[0,1].title.set_text('y_1', fontdict = {'font.size':22})
ax[0,1].plot(x,y_1)
ax[0,1].set_xlabel('x')
ax[0,1].set_ylabel('y_1')
ax[0,0].title.set_text('y_2', fontdict = {'font.size':22})
ax[0,0].plot(x,y_2)
ax[0,0].set_xlabel('x')
ax[0,0].set_ylabel('y_2')
but if I run this code I get an error TypeError: set_text() got an unexpected keyword argument 'fontdict'
am I using the wrong command.

This is really just a minor issue:
To set the title of a specific axes you should use the set_title method of the axes. Using plt.title sets the title of the current axes instance.
Basically replace your ax[0,0].title.set_text with ax[0,0].set_title and you are good to go!

You can also simply use fontsize=22 directly , as in
ax[0,1].set_title('y_1', fontsize=22)

Python : plots with different line styles having the same legend

I am using matplotlib to create few plots and have hit a roadblock with specifying the legends.
The plots have different line styles and the legend required for them should describe both or in other words a custom legend that can support both the plots.
plot(x1,y1,'o', x2,y2,'-')
I am trying to get a merged legend for both the plots.
It would be great, if someone has any pointers regarding this.
Thanks !

If your plots styles just differ in marker and linestyle (as in your example) you could create a third plot mixing those styles and just put this into the legend. A simple example could look like
import matplotlib.pyplot as plt
y1 = [1,2,3,4]
y2 = [3,2,5,1]
l1, = plt.plot(y1,'o',label=None)
l2, = plt.plot(y2,'-', color='r', label=None)
plt.plot(y1[0],'-o', color=l2.get_color(), markerfacecolor=l1.get_color(), label='My plots')
plt.legend()
plt.show()
this creates this plot

I do not know how much this helps, but this is an example of what I did in a similar situation:
import matplotlib.patches as patch
red_patch = patch.Patch(color = "red", label = 'Plot 1')
black_patch = patch.Patch(color = 'black', label = 'Plot 2')
plt.legend(handles = [red_patch, black_patch], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

Moving matplotlib legend outside of the axis makes it cutoff by the figure box

I'm familiar with the following questions:
Matplotlib savefig with a legend outside the plot
How to put the legend out of the plot
It seems that the answers in these questions have the luxury of being able to fiddle with the exact shrinking of the axis so that the legend fits.
Shrinking the axes, however, is not an ideal solution because it makes the data smaller making it actually more difficult to interpret; particularly when its complex and there are lots of things going on ... hence needing a large legend
The example of a complex legend in the documentation demonstrates the need for this because the legend in their plot actually completely obscures multiple data points.
http://matplotlib.sourceforge.net/users/legend_guide.html#legend-of-complex-plots
What I would like to be able to do is dynamically expand the size of the figure box to accommodate the expanding figure legend.
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-2*np.pi, 2*np.pi, 0.1)
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(x, np.sin(x), label='Sine')
ax.plot(x, np.cos(x), label='Cosine')
ax.plot(x, np.arctan(x), label='Inverse tan')
lgd = ax.legend(loc=9, bbox_to_anchor=(0.5,0))
ax.grid('on')
Notice how the final label 'Inverse tan' is actually outside the figure box (and looks badly cutoff - not publication quality!)
Finally, I've been told that this is normal behaviour in R and LaTeX, so I'm a little confused why this is so difficult in python... Is there a historical reason? Is Matlab equally poor on this matter?
I have the (only slightly) longer version of this code on pastebin http://pastebin.com/grVjc007

Sorry EMS, but I actually just got another response from the matplotlib mailling list (Thanks goes out to Benjamin Root).
The code I am looking for is adjusting the savefig call to:
fig.savefig('samplefigure', bbox_extra_artists=(lgd,), bbox_inches='tight')
#Note that the bbox_extra_artists must be an iterable
This is apparently similar to calling tight_layout, but instead you allow savefig to consider extra artists in the calculation. This did in fact resize the figure box as desired.
import matplotlib.pyplot as plt
import numpy as np
plt.gcf().clear()
x = np.arange(-2*np.pi, 2*np.pi, 0.1)
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.plot(x, np.sin(x), label='Sine')
ax.plot(x, np.cos(x), label='Cosine')
ax.plot(x, np.arctan(x), label='Inverse tan')
handles, labels = ax.get_legend_handles_labels()
lgd = ax.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5,-0.1))
text = ax.text(-0.2,1.05, "Aribitrary text", transform=ax.transAxes)
ax.set_title("Trigonometry")
ax.grid('on')
fig.savefig('samplefigure', bbox_extra_artists=(lgd,text), bbox_inches='tight')
This produces:
[edit] The intent of this question was to completely avoid the use of arbitrary coordinate placements of arbitrary text as was the traditional solution to these problems. Despite this, numerous edits recently have insisted on putting these in, often in ways that led to the code raising an error. I have now fixed the issues and tidied the arbitrary text to show how these are also considered within the bbox_extra_artists algorithm.

Added: I found something that should do the trick right away, but the rest of the code below also offers an alternative.
Use the subplots_adjust() function to move the bottom of the subplot up:
fig.subplots_adjust(bottom=0.2) # <-- Change the 0.02 to work for your plot.
Then play with the offset in the legend bbox_to_anchor part of the legend command, to get the legend box where you want it. Some combination of setting the figsize and using the subplots_adjust(bottom=...) should produce a quality plot for you.
Alternative:
I simply changed the line:
fig = plt.figure(1)
to:
fig = plt.figure(num=1, figsize=(13, 13), dpi=80, facecolor='w', edgecolor='k')
and changed
lgd = ax.legend(loc=9, bbox_to_anchor=(0.5,0))
to
lgd = ax.legend(loc=9, bbox_to_anchor=(0.5,-0.02))
and it shows up fine on my screen (a 24-inch CRT monitor).
Here figsize=(M,N) sets the figure window to be M inches by N inches. Just play with this until it looks right for you. Convert it to a more scalable image format and use GIMP to edit if necessary, or just crop with the LaTeX viewport option when including graphics.

Here is another, very manual solution. You can define the size of the axis and paddings are considered accordingly (including legend and tickmarks). Hope it is of use to somebody.
Example (axes size are the same!):
Code:
#==================================================
# Plot table
colmap = [(0,0,1) #blue
,(1,0,0) #red
,(0,1,0) #green
,(1,1,0) #yellow
,(1,0,1) #magenta
,(1,0.5,0.5) #pink
,(0.5,0.5,0.5) #gray
,(0.5,0,0) #brown
,(1,0.5,0) #orange
]
import matplotlib.pyplot as plt
import numpy as np
import collections
df = collections.OrderedDict()
df['labels'] = ['GWP100a\n[kgCO2eq]\n\nasedf\nasdf\nadfs','human\n[pts]','ressource\n[pts]']
df['all-petroleum long name'] = [3,5,2]
df['all-electric'] = [5.5, 1, 3]
df['HEV'] = [3.5, 2, 1]
df['PHEV'] = [3.5, 2, 1]
numLabels = len(df.values()[0])
numItems = len(df)-1
posX = np.arange(numLabels)+1
width = 1.0/(numItems+1)
fig = plt.figure(figsize=(2,2))
ax = fig.add_subplot(111)
for iiItem in range(1,numItems+1):
ax.bar(posX+(iiItem-1)*width, df.values()[iiItem], width, color=colmap[iiItem-1], label=df.keys()[iiItem])
ax.set(xticks=posX+width*(0.5*numItems), xticklabels=df['labels'])
#--------------------------------------------------
# Change padding and margins, insert legend
fig.tight_layout() #tight margins
leg = ax.legend(loc='upper left', bbox_to_anchor=(1.02, 1), borderaxespad=0)
plt.draw() #to know size of legend
padLeft = ax.get_position().x0 * fig.get_size_inches()[0]
padBottom = ax.get_position().y0 * fig.get_size_inches()[1]
padTop = ( 1 - ax.get_position().y0 - ax.get_position().height ) * fig.get_size_inches()[1]
padRight = ( 1 - ax.get_position().x0 - ax.get_position().width ) * fig.get_size_inches()[0]
dpi = fig.get_dpi()
padLegend = ax.get_legend().get_frame().get_width() / dpi
widthAx = 3 #inches
heightAx = 3 #inches
widthTot = widthAx+padLeft+padRight+padLegend
heightTot = heightAx+padTop+padBottom
# resize ipython window (optional)
posScreenX = 1366/2-10 #pixel
posScreenY = 0 #pixel
canvasPadding = 6 #pixel
canvasBottom = 40 #pixel
ipythonWindowSize = '{0}x{1}+{2}+{3}'.format(int(round(widthTot*dpi))+2*canvasPadding
,int(round(heightTot*dpi))+2*canvasPadding+canvasBottom
,posScreenX,posScreenY)
fig.canvas._tkcanvas.master.geometry(ipythonWindowSize)
plt.draw() #to resize ipython window. Has to be done BEFORE figure resizing!
# set figure size and ax position
fig.set_size_inches(widthTot,heightTot)
ax.set_position([padLeft/widthTot, padBottom/heightTot, widthAx/widthTot, heightAx/heightTot])
plt.draw()
plt.show()
#--------------------------------------------------
#==================================================

I tried a very simple way, just make the figure a bit wider:
fig, ax = plt.subplots(1, 1, figsize=(a, b))
adjust a and b to a proper value such that the legend is included in the figure

Matplotlib make tick labels font size smaller

In a matplotlib figure, how can I make the font size for the tick labels using ax1.set_xticklabels() smaller?
Further, how can one rotate it from horizontal to vertical?

There is a simpler way actually. I just found:
import matplotlib.pyplot as plt
# We prepare the plot
fig, ax = plt.subplots()
# We change the fontsize of minor ticks label
ax.tick_params(axis='both', which='major', labelsize=10)
ax.tick_params(axis='both', which='minor', labelsize=8)
This only answers to the size of label part of your question though.

To specify both font size and rotation at the same time, try this:
plt.xticks(fontsize=14, rotation=90)

Please note that newer versions of MPL have a shortcut for this task. An example is shown in the other answer to this question: https://stackoverflow.com/a/11386056/42346
The code below is for illustrative purposes and may not necessarily be optimized.
import matplotlib.pyplot as plt
import numpy as np
def xticklabels_example():
fig = plt.figure()
x = np.arange(20)
y1 = np.cos(x)
y2 = (x**2)
y3 = (x**3)
yn = (y1,y2,y3)
COLORS = ('b','g','k')
for i,y in enumerate(yn):
ax = fig.add_subplot(len(yn),1,i+1)
ax.plot(x, y, ls='solid', color=COLORS[i])
if i != len(yn) - 1:
# all but last
ax.set_xticklabels( () )
else:
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(14)
# specify integer or one of preset strings, e.g.
#tick.label.set_fontsize('x-small')
tick.label.set_rotation('vertical')
fig.suptitle('Matplotlib xticklabels Example')
plt.show()
if __name__ == '__main__':
xticklabels_example()

Alternatively, you can just do:
import matplotlib as mpl
label_size = 8
mpl.rcParams['xtick.labelsize'] = label_size

Another alternative
I have two plots side by side and would like to adjust tick labels separately.
The above solutions were close however they were not working out for me. I found my solution from this matplotlib page.
ax.xaxis.set_tick_params(labelsize=20)
This did the trick and was straight to the point. For my use case, it was the plot on the right that needed to be adjusted. For the plot on the left since I was creating new tick labels I was able to adjust the font in the same process as seting the labels.
ie
ax1.set_xticklabels(ax1_x, fontsize=15)
ax1.set_yticklabels(ax1_y, fontsize=15)
thus I used for the right plot,
ax2.xaxis.set_tick_params(labelsize=24)
ax2.yaxis.set_tick_params(labelsize=24)
A minor subtlety... I know... but I hope this helps someone :)
Bonus points if anyone knows how to adjust the font size of the order of magnitude label.

plt.tick_params(axis='both', which='minor', labelsize=12)

In current versions of Matplotlib, you can do axis.set_xticklabels(labels, fontsize='small').

The following worked for me:
ax2.xaxis.set_tick_params(labelsize=7)
ax2.yaxis.set_tick_params(labelsize=7)
The advantage of the above is you do not need to provide the array of labels and works with any data on the axes.

For smaller font, I use
ax1.set_xticklabels(xticklabels, fontsize=7)
and it works!

You can also change label display parameters like fontsize with a line like this:
zed = [tick.label.set_fontsize(14) for tick in ax.yaxis.get_major_ticks()]