I have this function and I want to change the size of the figure, however whatever values I put in the figsize, the figure size does not change. Can anybody suggest what's the problem? or if there is an alternative way for doing this?
def plot_comparison_barplots(df, bar_col, hue_col, scaled):
fig = plt.figure(figsize=(12,10))
fig.set_tight_layout(True)
plt.rcParams.update({'font.size': 9})
ax = sns.catplot(x=bar_col, y='count in %', data=df, hue=hue_col, kind='bar', order=scaled)
There is an easier way to implement this function, by using subplots instead of figure, as when you use the set_tight_layout() method, this automatically changes the location of the axes to make your graph look nicer, and make sure that none of the axis names are cut off. The quickest way of getting around this is to define your axes before the set_tight_layout() method, so that the figsize parameter overrides it.
Change:
fig = plt.figure(figsize=(12,10))
to
fig, ax = plt.subplots(figsize=(12,10))
You can add through Seaborn as:
sns.set(rc={'figure.figsize':(12,10)})
Related
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.
How to have no spaces between subplots in this example ?
(And keep a good visualization if I have more lines)
I've seen that what we see can change a lot with a value for figsize.
So do we have to guess the "correct" value for figsize ?
fig = plt.figure()
ax = fig.add_subplot(211)
ax.matshow([[1,2,3,4,5]])
ax = fig.add_subplot(212)
ax.matshow([[5,4,3,2,1]])
plt.subplots_adjust(wspace=0, hspace=0)
I've tried to use something different like gridspec but I have exactly the same issue.
Take a look at the Tight Layout Guide, you would use it like plt.tight_layout() with parameters to control the padding for your figures to make them fit nicely.
Pretty much what it says in the title.. most pandas examples suggest doing fig = plt.figure() before df.plot(..). But if I do that, two figures pop up after plt.show() - the first completely empty and the second with the actual pandas figure.. Any ideas why?
On a DataFrame, df.plot(..) will create a new figure, unless you provide an Axes object to the ax keyword argument.
So you are correct that the plt.figure() is not needed in this case. The plt.figure() calls in the pandas documentation should be removed, as they indeed are not needed. There is an issue about this: https://github.com/pydata/pandas/issues/8776
What you can do with the ax keyword is eg:
fig, ax = plt.subplots()
df.plot(..., ax=ax)
Note that when plotting a series, this will by default plot on the 'current' axis (plt.gca()) if you don't provide ax.
I'm puzzled by the meaning of the 'ax' keyword in the pandas scatter_matrix function:
pd.scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, diagonal='hist', marker='.', density_kwds={}, hist_kwds={}, **kwds)
The only clue given in the docstring for the ax keyword is too cryptic for me:
ax : Matplotlib axis object
I had a look in the pandas code for the scatter_matrix function, and the ax variable is incorporated in the following matplotlib subplots call:
fig, axes = plt.subplots(nrows=n, ncols=n, figsize=figsize, ax=ax,
squeeze=False)
But, for the life of me, I can't find any reference to an 'ax' keyword in matplotlib subplots!
Can anyone tell me what this ax keyword is for???
This is tricky here. When looking at the source of pandas scatter_matrix you will find this line right after the docstring:
fig, axes = _subplots(nrows=n, ncols=n, figsize=figsize, ax=ax, squeeze=False)
Hence, internally, a new figure, axes combination is created using the internal _subplots method. This is strongly related to the matplotlibs subplots command but slightly different. Here, the ax keyword is supplied as well. If you look at the corresponding source (pandas.tools.plotting._subplots) you will find these lines:
if ax is None:
fig = plt.figure(**fig_kw)
else:
fig = ax.get_figure()
fig.clear()
Hence, if you supply an axes object (e.g. created using matplotlibs subplots command), pandas scatter_matrix grabs the corresponding (matplolib) figure object and deletes its content. Afterwards a new subplots grid is created into this figure object.
All in all, the ax keyword allows to plot the scatter matrix into a given figure (even though IMHO in a slightly strange way).
In short, it targets a subplot within a grid.
If you have nrows=2 and ncols=2, for example, then ax allows you to plot on a specific axis by passing ax=axes[0,0] (top left) or ax=axes[1,1] (bottom right), etc.
When you create the subplots, you receive an axes variable. You can later plot (or subplot) with an element of that axes variable as above.
Take a look at the "Targeting different subplots" section of this page: http://pandas.pydata.org/pandas-docs/dev/visualization.html#targeting-different-subplots
I hope this helps.
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