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I am creating a figure in Matplotlib like this:
from matplotlib import pyplot as plt
fig = plt.figure()
plt.plot(data)
fig.suptitle('test title')
plt.xlabel('xlabel')
plt.ylabel('ylabel')
fig.savefig('test.jpg')
I want to specify font sizes for the figure title and the axis labels. I need all three to be different font sizes, so setting a global font size (mpl.rcParams['font.size']=x) is not what I want. How do I set font sizes for the figure title and the axis labels individually?
Functions dealing with text like label, title, etc. accept parameters same as matplotlib.text.Text. For the font size you can use size/fontsize:
from matplotlib import pyplot as plt
fig = plt.figure()
plt.plot(data)
fig.suptitle('test title', fontsize=20)
plt.xlabel('xlabel', fontsize=18)
plt.ylabel('ylabel', fontsize=16)
fig.savefig('test.jpg')
For globally setting title and label sizes, mpl.rcParams contains axes.titlesize and axes.labelsize. (From the page):
axes.titlesize : large # fontsize of the axes title
axes.labelsize : medium # fontsize of the x any y labels
(As far as I can see, there is no way to set x and y label sizes separately.)
And I see that axes.titlesize does not affect suptitle. I guess, you need to set that manually.
You can also do this globally via a rcParams dictionary:
import matplotlib.pylab as pylab
params = {'legend.fontsize': 'x-large',
'figure.figsize': (15, 5),
'axes.labelsize': 'x-large',
'axes.titlesize':'x-large',
'xtick.labelsize':'x-large',
'ytick.labelsize':'x-large'}
pylab.rcParams.update(params)
If you're more used to using ax objects to do your plotting, you might find the ax.xaxis.label.set_size() easier to remember, or at least easier to find using tab in an ipython terminal. It seems to need a redraw operation after to see the effect. For example:
import matplotlib.pyplot as plt
# set up a plot with dummy data
fig, ax = plt.subplots()
x = [0, 1, 2]
y = [0, 3, 9]
ax.plot(x,y)
# title and labels, setting initial sizes
fig.suptitle('test title', fontsize=12)
ax.set_xlabel('xlabel', fontsize=10)
ax.set_ylabel('ylabel', fontsize='medium') # relative to plt.rcParams['font.size']
# setting label sizes after creation
ax.xaxis.label.set_size(20)
plt.draw()
I don't know of a similar way to set the suptitle size after it's created.
To only modify the title's font (and not the font of the axis) I used this:
import matplotlib.pyplot as plt
fig = plt.Figure()
ax = fig.add_subplot(111)
ax.set_title('My Title', fontdict={'fontsize': 8, 'fontweight': 'medium'})
The fontdict accepts all kwargs from matplotlib.text.Text.
Per the official guide, use of pylab is no longer recommended. matplotlib.pyplot should be used directly instead.
Globally setting font sizes via rcParams should be done with
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 16
plt.rcParams['axes.titlesize'] = 16
# or
params = {'axes.labelsize': 16,
'axes.titlesize': 16}
plt.rcParams.update(params)
# or
import matplotlib as mpl
mpl.rc('axes', labelsize=16, titlesize=16)
# or
axes = {'labelsize': 16,
'titlesize': 16}
mpl.rc('axes', **axes)
The defaults can be restored using
plt.rcParams.update(plt.rcParamsDefault)
You can also do this by creating a style sheet in the stylelib directory under the matplotlib configuration directory (you can get your configuration directory from matplotlib.get_configdir()). The style sheet format is
axes.labelsize: 16
axes.titlesize: 16
If you have a style sheet at /path/to/mpl_configdir/stylelib/mystyle.mplstyle then you can use it via
plt.style.use('mystyle')
# or, for a single section
with plt.style.context('mystyle'):
# ...
You can also create (or modify) a matplotlibrc file which shares the format
axes.labelsize = 16
axes.titlesize = 16
Depending on which matplotlibrc file you modify these changes will be used for only the current working directory, for all working directories which do not have a matplotlibrc file, or for all working directories which do not have a matplotlibrc file and where no other matplotlibrc file has been specified. See this section of the customizing matplotlib page for more details.
A complete list of the rcParams keys can be retrieved via plt.rcParams.keys(), but for adjusting font sizes you have (italics quoted from here)
axes.labelsize - Fontsize of the x and y labels
axes.titlesize - Fontsize of the axes title
figure.titlesize - Size of the figure title (Figure.suptitle())
xtick.labelsize - Fontsize of the tick labels
ytick.labelsize - Fontsize of the tick labels
legend.fontsize - Fontsize for legends (plt.legend(), fig.legend())
legend.title_fontsize - Fontsize for legend titles, None sets to the same as the default axes. See this answer for usage example.
all of which accept string sizes {'xx-small', 'x-small', 'smaller', 'small', 'medium', 'large', 'larger', 'x-large', 'xxlarge'} or a float in pt. The string sizes are defined relative to the default font size which is specified by
font.size - the default font size for text, given in pts. 10 pt is the standard value
Additionally, the weight can be specified (though only for the default it appears) by
font.weight - The default weight of the font used by text.Text. Accepts {100, 200, 300, 400, 500, 600, 700, 800, 900} or 'normal' (400), 'bold' (700), 'lighter', and 'bolder' (relative with respect to current weight).
If you aren't explicitly creating figure and axis objects you can set the title fontsize when you create the title with the fontdict argument.
You can set and the x and y label fontsizes separately when you create the x and y labels with the fontsize argument.
For example:
plt.title('Car Prices are Increasing', fontdict={'fontsize':20})
plt.xlabel('Year', fontsize=18)
plt.ylabel('Price', fontsize=16)
Works with seaborn and pandas plotting (when Matplotlib is the backend), too!
Others have provided answers for how to change the title size, but as for the axes tick label size, you can also use the set_tick_params method.
E.g., to make the x-axis tick label size small:
ax.xaxis.set_tick_params(labelsize='small')
or, to make the y-axis tick label large:
ax.yaxis.set_tick_params(labelsize='large')
You can also enter the labelsize as a float, or any of the following string options: 'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', or 'xx-large'.
An alternative solution to changing the font size is to change the padding. When Python saves your PNG, you can change the layout using the dialogue box that opens. The spacing between the axes, padding if you like can be altered at this stage.
Place right_ax before set_ylabel()
ax.right_ax.set_ylabel('AB scale')
libraries
import numpy as np
import matplotlib.pyplot as plt
create dataset
height = [3, 12, 5, 18, 45]
bars = ('A', 'B', 'C', 'D', 'E')
x_pos = np.arange(len(bars))
Create bars and choose color
plt.bar(x_pos, height, color = (0.5,0.1,0.5,0.6))
Add title and axis names
plt.title('My title')
plt.xlabel('categories')
plt.ylabel('values')
Create names on the x axis
plt.xticks(x_pos, bars)
Show plot
plt.show()
7 (best solution)
from numpy import*
import matplotlib.pyplot as plt
X = linspace(-pi, pi, 1000)
class Crtaj:
def nacrtaj(self,x,y):
self.x=x
self.y=y
return plt.plot (x,y,"om")
def oznaci(self):
return plt.xlabel("x-os"), plt.ylabel("y-os"), plt.grid(b=True)
6 (slightly worse solution)
from numpy import*
M = array([[3,2,3],[1,2,6]])
class AriSred(object):
def __init__(self,m):
self.m=m
def srednja(self):
redovi = len(M)
stupci = len (M[0])
lista=[]
a=0
suma=0
while a<stupci:
for i in range (0,redovi):
suma=suma+ M[i,a]
lista.append(suma)
a=a+1
suma=0
b=array(lista)
b=b/redovi
return b
OBJ = AriSred(M)
sr = OBJ.srednja()
So currently learning how to import data and work with it in matplotlib and I am having trouble even tho I have the exact code from the book.
This is what the plot looks like, but my question is how can I get it where there is no white space between the start and the end of the x-axis.
Here is the code:
import csv
from matplotlib import pyplot as plt
from datetime import datetime
# Get dates and high temperatures from file.
filename = 'sitka_weather_07-2014.csv'
with open(filename) as f:
reader = csv.reader(f)
header_row = next(reader)
#for index, column_header in enumerate(header_row):
#print(index, column_header)
dates, highs = [], []
for row in reader:
current_date = datetime.strptime(row[0], "%Y-%m-%d")
dates.append(current_date)
high = int(row[1])
highs.append(high)
# Plot data.
fig = plt.figure(dpi=128, figsize=(10,6))
plt.plot(dates, highs, c='red')
# Format plot.
plt.title("Daily high temperatures, July 2014", fontsize=24)
plt.xlabel('', fontsize=16)
fig.autofmt_xdate()
plt.ylabel("Temperature (F)", fontsize=16)
plt.tick_params(axis='both', which='major', labelsize=16)
plt.show()
There is an automatic margin set at the edges, which ensures the data to be nicely fitting within the axis spines. In this case such a margin is probably desired on the y axis. By default it is set to 0.05 in units of axis span.
To set the margin to 0 on the x axis, use
plt.margins(x=0)
or
ax.margins(x=0)
depending on the context. Also see the documentation.
In case you want to get rid of the margin in the whole script, you can use
plt.rcParams['axes.xmargin'] = 0
at the beginning of your script (same for y of course). If you want to get rid of the margin entirely and forever, you might want to change the according line in the matplotlib rc file:
axes.xmargin : 0
axes.ymargin : 0
Example
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset('tips')
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
tips.plot(ax=ax1, title='Default Margin')
tips.plot(ax=ax2, title='Margins: x=0')
ax2.margins(x=0)
Alternatively, use plt.xlim(..) or ax.set_xlim(..) to manually set the limits of the axes such that there is no white space left.
If you only want to remove the margin on one side but not the other, e.g. remove the margin from the right but not from the left, you can use set_xlim() on a matplotlib axes object.
import seaborn as sns
import matplotlib.pyplot as plt
import math
max_x_value = 100
x_values = [i for i in range (1, max_x_value + 1)]
y_values = [math.log(i) for i in x_values]
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
sn.lineplot(ax=ax1, x=x_values, y=y_values)
sn.lineplot(ax=ax2, x=x_values, y=y_values)
ax2.set_xlim(-5, max_x_value) # tune the -5 to your needs
I have the following heatmap:
I've broken up the category names by each capital letter and then capitalised them. This achieves a centering effect across the labels on my x-axis by default which I'd like to replicate across my y-axis.
yticks = [re.sub("(?<=.{1})(.?)(?=[A-Z]+)", "\\1\n", label, 0, re.DOTALL).upper() for label in corr.index]
xticks = [re.sub("(?<=.{1})(.?)(?=[A-Z]+)", "\\1\n", label, 0, re.DOTALL).upper() for label in corr.columns]
fig, ax = plt.subplots(figsize=(20,15))
sns.heatmap(corr, ax=ax, annot=True, fmt="d",
cmap="Blues", annot_kws=annot_kws,
mask=mask, vmin=0, vmax=5000,
cbar_kws={"shrink": .8}, square=True,
linewidths=5)
for p in ax.texts:
myTrans = p.get_transform()
offset = mpl.transforms.ScaledTranslation(-12, 5, mpl.transforms.IdentityTransform())
p.set_transform(myTrans + offset)
plt.yticks(plt.yticks()[0], labels=yticks, rotation=0, linespacing=0.4)
plt.xticks(plt.xticks()[0], labels=xticks, rotation=0, linespacing=0.4)
where corr represents a pre-defined pandas dataframe.
I couldn't seem to find an align parameter for setting the ticks and was wondering if and how this centering could be achieved in seaborn/matplotlib?
I've adapted the seaborn correlation plot example below.
from string import ascii_letters
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="white")
# Generate a large random dataset
rs = np.random.RandomState(33)
d = pd.DataFrame(data=rs.normal(size=(100, 7)),
columns=['Donald\nDuck','Mickey\nMouse','Han\nSolo',
'Luke\nSkywalker','Yoda','Santa\nClause','Ronald\nMcDonald'])
# Compute the correlation matrix
corr = d.corr()
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
for i in ax.get_yticklabels():
i.set_ha('right')
i.set_rotation(0)
for i in ax.get_xticklabels():
i.set_ha('center')
Note the two for sequences above. These get the label and then set the horizontal alignment (You can also change the vertical alignment (set_va()).
The code above produces this:
I want to increase the grey area around the plot, but keeping the plot the same size. I've already tried changing the figure size, which ends up stretching the plot.
The axes inside the figure is positionned relative to the figure. Per default you have e.g. a fraction of 0.125 of figure width as space at the left. This means that resizing the figure, scales the axes as well.
You may calculate how much the spacings need to change such that if the figure is rescaled, the axes size remains constant. The new spacings then need to be set using fig.subplots_adjust.
import matplotlib.pyplot as plt
def set_figsize(figw,figh, fig=None):
if not fig: fig=plt.gcf()
w, h = fig.get_size_inches()
l = fig.subplotpars.left
r = fig.subplotpars.right
t = fig.subplotpars.top
b = fig.subplotpars.bottom
hor = 1.-w/float(figw)*(r-l)
ver = 1.-h/float(figh)*(t-b)
fig.subplots_adjust(left=hor/2., right=1.-hor/2., top=1.-ver/2., bottom=ver/2.)
fig, ax=plt.subplots()
ax.plot([1,3,2])
set_figsize(9,7)
plt.show()
You may then also use this function to update the subplot params when the figure window is resized.
import matplotlib.pyplot as plt
class Resizer():
def __init__(self,fig=None):
if not fig: fig=plt.gcf()
self.fig=fig
self.w, self.h = self.fig.get_size_inches()
self.l = self.fig.subplotpars.left
self.r = self.fig.subplotpars.right
self.t = self.fig.subplotpars.top
self.b = self.fig.subplotpars.bottom
def set_figsize(self, figw,figh):
hor = 1.-self.w/float(figw)*(self.r-self.l)
ver = 1.-self.h/float(figh)*(self.t-self.b)
self.fig.subplots_adjust(left=hor/2., right=1.-hor/2., top=1.-ver/2., bottom=ver/2.)
def resize(self, event):
figw = event.width/self.fig.dpi
figh = event.height/self.fig.dpi
self.set_figsize( figw,figh)
fig, ax=plt.subplots()
ax.plot([1,3,2])
r = Resizer()
cid = fig.canvas.mpl_connect("resize_event", r.resize)
plt.show()
In the window of a matplotlib figure, there's a button called 'Configure subplots' (see below picture, screenshot on Windows 10 with matplotlib version 1.5.2). Try to change the parameters 'left' and 'right'. You can also change these parameters with plt.subplots_adjust(left=..., bottom=..., right=..., top=..., wspace=..., hspace=...).
In matplotlib how to overlay the shapefile (available in folder) as attached below at the top right position outside the plot.
The code referenced by banderkat:
import matplotlib.pyplot as plt
import Image
import numpy as np
im = Image.open('Jbc4j.jpg')
width = im.size[0]
height = im.size[1]
# We need a float array between 0-1, rather than
# a uint8 array between 0-255
im = np.array(im).astype(np.float) / 255
a = np.random.randint(0,100,100)
b = range(100)
fig = plt.figure(1,figsize=(5, 7), dpi=80, facecolor='w')
ax = fig.add_subplot(111)
ax.scatter(a,b)
fig.canvas.draw()
# With newer (1.0) versions of matplotlib, you can
# use the "zorder" kwarg to make the image overlay
# the plot, rather than hide behind it... (e.g. zorder=10)
fig.figimage(im, fig.bbox.xmax - width, fig.bbox.ymax - height, zorder=0)
# (Saving with the same dpi as the screen default to
# avoid displacing the logo image)
fig.savefig('temp.png', dpi=80)
plt.show()
Produces the following result (imaged cropped to save space).
Changing the zorder=1 will place the image on top.
Other helpful references:
How to change background color for scatter plot in matplotlib
How do you change the size of figures drawn with matplotlib?
Python/Matplotlib - Change the relative size of a subplot
In Matplotlib, what does the argument mean in fig.add_subplot(111)?
Customizing Location of Subplot Using GridSpec
You can use basemap toolkit to load and plot shapefile. Here I've plotted shapeFile in a separate axes and aligned it to top-right of other axes plot using 'subplot2grid'.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import matplotlib.gridspec as gridspec
def plotShapeFile():
# Lambert Conformal Conic map.
m = Basemap(llcrnrlon=-100.,llcrnrlat=0.,urcrnrlon=-20.,urcrnrlat=57.,
projection='lcc',lat_1=20.,lat_2=40.,lon_0=-60.,
resolution ='l',area_thresh=1000.)
# read shapefile.
shp_info = m.readshapefile('C:/basemap-1.0.6/basemap-1.0.6/examples/huralll020','hurrtracks',drawbounds=False)
# find names of storms that reached Cat 4.
names = []
for shapedict in m.hurrtracks_info:
cat = shapedict['CATEGORY']
name = shapedict['NAME']
if cat in ['H4','H5'] and name not in names:
# only use named storms.
if name != 'NOT NAMED': names.append(name)
# plot tracks of those storms.
for shapedict,shape in zip(m.hurrtracks_info,m.hurrtracks):
name = shapedict['NAME']
cat = shapedict['CATEGORY']
if name in names:
xx,yy = zip(*shape)
# show part of track where storm > Cat 4 as thick red.
if cat in ['H4','H5']:
m.plot(xx,yy,linewidth=1.5,color='r')
elif cat in ['H1','H2','H3']:
m.plot(xx,yy,color='k')
# draw coastlines, meridians and parallels.
m.drawcoastlines()
m.drawcountries()
m.drawmapboundary(fill_color='#99ffff')
m.fillcontinents(color='#cc9966',lake_color='#99ffff')
m.drawparallels(np.arange(10,70,20),labels=[1,1,0,0])
m.drawmeridians(np.arange(-100,0,20),labels=[0,0,0,1])
if __name__ == '__main__':
fig=plt.figure()
plt.subplots_adjust(wspace=0.001, hspace=0.001)
ax1=plt.subplot2grid((5,5), (0,0), colspan=4, rowspan=4)
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
fracs = [15,30,45, 10]
explode=(0, 0.05, 0, 0)
p1,t1,at1 = plt.pie(fracs, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True)
plt.title('Raining Hogs and Dogs', bbox={'facecolor':'0.8', 'pad':5})
ax2=plt.subplot2grid((5,5), (0,4), colspan=1, rowspan=1)
#draw shapeFile on the current active axes, i.e. ax2
plotShapeFile()
plt.tight_layout()
plt.show()
Below are links to references I've used:
http://sourceforge.net/projects/matplotlib/files/matplotlib-toolkits/basemap-1.0.6/
http://matplotlib.org/basemap/users/examples.html
Output: