I have a dataframe with ~120 features that I would like to examine by year. I am plotting each feature, x = year, y = feature value within a loop. Whilst these plot successfully, the charts are illegible as they are totally squashed.
I have tried using plt.tight_layout() and adjusting the figure size using plt.rcParams['figure.figsize'] but sadly to no avail
for i in range(len(roll_df.columns)):
plt.subplot(len(roll_df.columns), 1, i+1)
name = roll_df.columns[i]
plt.plot(roll_df[name])
plt.title(name, y=0)
plt.yticks([])
plt.xticks([])
plt.tight_layout()
plt.show()
The loop runs but all plots are so squashed on the y-axis as to become illegible:
Matplotlib will not automatically adjust the size of your figure. So if you add more subplots below each other, it will split the available space instead of extending the figure. That's why your y axes are so narrow.
You could try to define the figure size beforehand, or determine the figure size based on how many subplots you have:
n_plots = roll_df.shape[1]
fig, axes = plt.subplots(n_plots, 1, figsize=(8, 4 * n_plots), tight_layout=True)
# Then your usual part, but plot on the created axes
for i in range(n_plots):
name = roll_df.columns[i]
axes[i].plot(roll_df[name])
axes[i].title(name, y=0)
axes[i].yticks([])
axes[i].xticks([])
plt.show()
Related
I need the plot legend to appear side-by-side to the plot axes, i.e. outside of the axes and non-overlapping.
The width of the axes and the legend should adjust "automatically" so that they both fill the figure w/o them to overlap or the legend to be cut, even when using tight layout. The legend should occupy a minor portion of the figure (let's say max to 1/3 of figure width so that the remaining 2/3 are dedicated to the actual plot).
Eventually, the font of the legend entries can automatically reduce to meet the requirements.
I've read a number of answers regarding legend and bbox_to_anchor in matplotlib with no luck, among which:
how to put the legend out of the plot
moving matplotlib legend outside of the axis makes it cutoff by the figure box
I tried by creating a dedicated axes in which to put the legend so that plt.tight_layout() would do its job properly but then the legend only takes a minor portion of the dedicated axes, with the result that a lot of space is wasted. Or if there isn't enough space (the figure is too small), the legend overlaps the first axes anyway.
import matplotlib.pyplot as plt
import numpy as np
# generate some data
x = np.arange(1, 100)
# create 2 side-by-side axes
fig, ax = plt.subplots(1,2)
# create a plot with a long legend
for ii in range(20):
ax[0].plot(x, x**2, label='20201110_120000')
ax[0].plot(x, x, label='20201104_110000')
# grab handles and labels from the first ax and pass it to the second
hl = ax[0].get_legend_handles_labels()
leg = ax[1].legend(*hl, ncol=2)
plt.tight_layout()
I'm open to use a package different from matplotlib.
Instead of trying to plot the legend in a separate axis, you can pass loc to legend:
# create 2 side-by-side axes
fig, ax = plt.subplots(figsize=(10,6))
# create a plot with a long legend
for ii in range(20):
ax.plot(x, x**2, label='20201110_120000')
ax.plot(x, x, label='20201104_110000')
# grab handles and labels from the first ax and pass it to the second
ax.legend(ncol=2, loc=[1,0])
plt.tight_layout()
Output:
I have 9 matplotlib subplots arranged in a grid. I'm trying to do two simple things: (1) adjust subplot spacing to reduce white space, (2) place the legend outside of the plot. Individually, both things are super easy. Together, they don't work: If I place legend outside of the subplots using bbox_to_achnor=(...), subplot spacing gets messed up and subplots_adjust(...) don't work anymore.
UPD: This works for tight spacing:
fig, axes = plt.subplots(3, 3)
plt.subplot(331)
# plot something on every subplot
plt.subplot(339)
# plot something here too
plt.subplots_adjust(wspace=0, hspace=0)
plt.tight_layout()
plt.savefig("blabla.pdf", format="pdf")
And with this code all figures become squeezed:
fig, axes = plt.subplots(3, 3)
plt.subplot(331)
# plot something on every subplot
plt.subplot(339)
# plot something here too
# add outside legend to the first plot
plt.subplot(331)
lgd = plt.legend(ncol=1, loc=2, prop={'size': 10}, bbox_to_anchor=4.2, 0.2))
plt.subplots_adjust(wspace=0, hspace=0)
plt.tight_layout()
plt.savefig("blabla.pdf", format="pdf", bbox_inches="tight", bbox_extra_artists=(lgd,))
Any ideas?
Things will supposedly work fine if you use the legend with the figure object fig. Currently you use it with the last plt object which correspond to the last subfigure 339. Using fig, you don't need a large offset of 4.2 for the bbox_to_anchor. Something like 1.1 or 1.2 should work fine
lgd = fig.legend(ncol=1, loc=2, prop={'size': 10}, bbox_to_anchor=(1.2, 0.2))
I am new to matplotlib and trying to create and save plots from pandas dataframes via a loop. Each plot should have an identical x-axis, but different y-axis lengths and labels. I have no problem creating and saving the plots with different y-axis lengths and labels, but when I create the plots, matplotlib rescales the x-axis depending on how much space is needed for the y-axis labels on the left side of the figure.
These figures are for a technical report. I plan to place one on each page of the report and I would like to have all of the x-axes take up the same amount of space on the page.
Here is an MSPaint version of what I'm getting and what I'd like to get.
Hopefully this is enough code to help. I'm sure there are lots of non-optimal parts of this.
import pandas as pd
import matplotlib.pyplot as plt
import pylab as pl
from matplotlib import collections as mc
from matplotlib.lines import Line2D
import seaborn as sns
# elements for x-axis
start = -1600
end = 2001
interval = 200 # x-axis tick interval
xticks = [x for x in range(start, end, interval)] # create x ticks
# items needed for legend construction
lw_bins = [0,10,25,50,75,90,100] # bins for line width
lw_labels = [3,6,9,12,15,18] # line widths
def make_proxy(zvalue, scalar_mappable, **kwargs):
color = 'black'
return Line2D([0, 1], [0, 1], color=color, solid_capstyle='butt', **kwargs)
# generic image ID
img_path = r'C:\\Users\\user\\chart'
img_ID = 0
for line_subset in data:
# create line collection for this run through loop
lc = mc.LineCollection(line_subset)
# create plot and set properties
sns.set(style="ticks")
sns.set_context("notebook")
fig, ax = pl.subplots(figsize=(16, len(line_subset)*0.5)) # I want the height of the figure to change based on number of labels on y-axis
# Figure width should stay the same
ax.add_collection(lc)
ax.set_xlim(left=start, right=end)
ax.set_xticks(xticks)
ax.set_ylim(0, len(line_subset)+1)
ax.margins(0.05)
sns.despine(left=True)
ax.xaxis.set_ticks_position('bottom')
ax.set_yticks(line_subset['order'])
ax.set_yticklabels(line_subset['ylabel'])
ax.tick_params(axis='y', length=0)
# legend
proxies = [make_proxy(item, lc, linewidth=item) for item in lw_labels]
ax.legend(proxies, ['0-10%', '10-25%', '25-50%', '50-75%', '75-90%', '90-100%'], bbox_to_anchor=(1.05, 1.0),
loc=2, ncol=2, labelspacing=1.25, handlelength=4.0, handletextpad=0.5, markerfirst=False,
columnspacing=1.0)
# title
ax.text(0, len(line_subset)+2, s=str(img_ID), fontsize=20)
# save as .png images
plt.savefig(r'C:\\Users\\user\\Desktop\\chart' + str(img_ID) + '.png', dpi=300, bbox_inches='tight')
Unless you use an axes of specifically defined aspect ratio (like in an imshow plot or by calling .set_aspect("equal")), the space taken by the axes should only depend on the figure size along that direction and the spacings set to the figure.
You are therefore pretty much asking for the default behaviour and the only thing that prevents you from obtaining that is that you use bbox_inches='tight' in the savefig command.
bbox_inches='tight' will change the figure size! So don't use it and the axes will remain constant in size. `
Your figure size, defined like figsize=(16, len(line_subset)*0.5) seems to make sense according to what I understand from the question. So what remains is to make sure the axes inside the figure are the size you want them to be. You can do that by manually placing it using fig.add_axes
fig.add_axes([left, bottom, width, height])
where left, bottom, width, height are in figure coordinates ranging from 0 to 1. Or, you can adjust the spacings outside the subplot using subplots_adjust
plt.subplots_adjust(left, bottom, right, top)
To get matching x axis for the subplots (same x axis length for each subplot) , you need to share the x axis between subplots.
See the example here https://matplotlib.org/examples/pylab_examples/shared_axis_demo.html
plt.figure(figsize=(10,8))
plt.scatter(df['attacker_size'][df['year'] == 298],
# attacker size in year 298 as the y axis
df['defender_size'][df['year'] == 298],
# the marker as
marker='x',
# the color
color='b',
# the alpha
alpha=0.7,
# with size
s = 124,
# labelled this
label='Year 298')
In the above snippet of code collected from Scatterplot in Matplotlib, what is the necessity of plt.figure()?
The purpose of using plt.figure() is to create a figure object.
The whole figure is regarded as the figure object. It is necessary to explicitly use plt.figure() when we want to tweak the size of the figure and when we want to add multiple Axes objects in a single figure.
# in order to modify the size
fig = plt.figure(figsize=(12,8))
# adding multiple Axes objects
fig, ax_lst = plt.subplots(2, 2) # a figure with a 2x2 grid of Axes
Parts of a Figure
It is not always necessary because a figure is implicitly created when you create a scatter plot; however, in the case you have shown, the figure is being created explicitly using plt.figure so that the figure will be a specific size rather than the default size.
The other option would be to use gcf to get the current figure after creating the scatter plot and set the figure size retrospectively:
# Create scatter plot here
plt.gcf().set_size_inches(10, 8)
Looking to add in vertical space between plotted graphs to allow a X-Axis label to show:
Each graph needs to have space to show the day, currently the last 2 graphs are the only one's that show simply because the graphs are overlapping it.
Also curious if I could actually remove the notch labels for the X-Axis for the graphs above the one's marked Thursday/Friday, i.e. the bottom X-axis is the only one that shows. Same for the Y-Axis, but only the graphs on the left having the scale shown.
*Unfortunately I can't post an image to show this since I don't have enough rep.
Code snippet:
import mathlib.pyplot as pyplot
fig = pyplot.figure()
ax1 = fig.add_subplot(4,2,1)
ax1.set_yscale('log')
ax2 = fig.add_subplot(4,2,2, sharex=ax1, sharey=ax1)
ax3 = fig.add_subplot(4,2,3, sharex=ax2, sharey=ax2)
ax4 = fig.add_subplot(4,2,4, sharex=ax3, sharey=ax3)
ax5 = fig.add_subplot(4,2,5, sharex=ax4, sharey=ax4)
ax6 = fig.add_subplot(4,2,6, sharex=ax5, sharey=ax5)
ax7 = fig.add_subplot(4,2,7, sharex=ax6, sharey=ax6)
ax1.plot(no_dict["Saturday"],'k.-',label='Saturday')
ax1.set_xlabel('Saturday')
ax1.axis([0,24,0,10000])
pyplot.suptitle('Title')
pyplot.xlabel('Hour in 24 Hour Format')
ax2.plot(no_dict["Sunday"],'b.-',label='Sunday')
ax2.set_xlabel('Sunday')
...
Use subplots_adjust. In your case this looks good:
fig.subplots_adjust(hspace=.5)
to remove the tick labels do this:
ax1.set_xticklabels([])
Similar for the yticklabels. However, you cannot share the x-axis with the plots that do have tick labels.
To change the spacing around a certain subplot, instead of all of them, you can adjust the position of the axes of that subplot using:
bbox=plt.gca().get_position()
offset=-.03
plt.gca().set_position([bbox.x0, bbox.y0 + offset, bbox.x1-bbox.x0, bbox.y1 - bbox.y0])
If offset < 0, the subplot is moved down. If offset > 0, the subplot is moved up.
Note that the subplot will disappear if offset is so big that the new position of the subplot overlaps with another subplot.