I am trying to display the following values in the form of a bar chart. However, I am only getting one value displayed (619,1). Below is the code which I used in an attempt to plot the below graph:
import matplotlib.pyplot as plt
plt.style.use('ggplot')
values= [1, 2, 3, 4, 5]
a = [619, 101, 815, 1361, 178]
plt.figure(figsize=(5, 5))
plt.bar(a, values)
plt.show()
The bar width is set to a default value of 0.8 so when your x-axis has such a large range, the bars are so skinny that they disappear.
The reason for the 0.8 is that bar charts are typically used for labelled categories, which are effectively spaced by 1 along the x-axis.
So you can set the width directly. (It's also possible to calculate a width, to make this more automatic, but then you need to decide about overlaps, etc.)
plt.figure(figsize=(5, 5))
plt.xlim(0, 1450)
plt.bar(a, values, width = 50)
It seems your data might be better suited for a horizontal bar plot (but don't take this too seriously as it may not have the right meaning at all), and if you want horizontal bars, you can do so like this:
plt.barh(values, a)
Related
I've been working for a while with the matplotlib package in Python, and I know that you can do 2D graphs (usually involving two "dimensions", x and y) or 3D graphs (with functions like plot3D). However, I am unable to find documentation about giving a '3D aesthetic' to a 2D plot.
That is, giving the plot a bit of volume, some shadows, etc.
To give an example, let's say I wanted to create a donut chart in matplotlib. A first draft could be something like this:
import matplotlib.pyplot as plt
#Given an array of values 'values' and,
#optionally, an array of colors 'colors'
#and an array of labels 'labels':
ax = plt.subplot()
ax.pie(
x = values,
labels = labels,
colors = colors
)
center_circle = plt.Circle((0,0), radius = 0.5, fc = "white")
ax.add_artist(center_circle)
plt.show()
However, a quick graph with Excel can give a much more appealing result:
Looking at the documentation of plt.pie, I was not able to find anything significant, apart from the parameter shadow, which when set to True, gives an underwhelming result:
Also, I would like to add effect such as the use of bevel (like the 3d-look of the borders of each wedge of the pie) and more style things. How could I improve the look of my graph with matplotlib? Is it even possible to accomplish it with this library?
One solution might be using a different library. I am not familiar with seaborn, but I know it is also a powerful visualisation library. The same with plotly. Does any one of these libraries allow for these kind of customisations?
There are a whole bunch of options on the matplotlib website for pie charts here: https://matplotlib.org/stable/gallery/pie_and_polar_charts/index.html
Matplotlib does not have a built-in option to add a bevel to a 2D pie chart or any other types of charts directly.
But, you could do this (raised shaddow) for a 3d effect:
import matplotlib.pyplot as plt
# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.show()
which give this:
I am trying to create a barchart (overlaid on a line graph with days as the x axis instead of quarters) where the labels are end-of-quarter days. That is all fine, and generates nicely, but I am trying to set the labels so that they are lined up with the right edge of the plot and the corresponding bar's right-side is aligned with the x-tick.
A reproducible example (with just the bar chart, not the line) is:
import matplotlib.pyplot as pyplot
import pandas
import random
random.seed(2020)
dates = pandas.date_range("2016-12-31", "2017-12-31")
bar = pandas.DataFrame([.02, .01, -0.01, .05], index = ["2017-03-31", "2017-06-30", "2017-09-30", "2017-12-31"], columns = ["test"])
line = pandas.DataFrame([random.random() for r in range(len(dates))], index = dates, columns = ["test"])
fig, ax = pyplot.subplots(1, 1, figsize = (7, 3))
ax2 = fig.add_subplot(111, frame_on = False)
bar.plot(kind = "bar", ax = ax, width = 1)
line.plot(kind = "line", ax = ax2)
ax2.set_xticks([])
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
fig.tight_layout()
pyplot.show()
Which yields a plot as:
My goal is to have the right side of the 2017-12-31 column aligned with the right edge of the plot and the 2017-12-31 label at the right side as well. Further, the left side of the 2017-03-31 bar touch the left side of the plot. For the remaining bars, I would like them evenly spaced with all labels aligned with the right side of each bar, and no space in between bars. Like this example below:
Frankly, I'm at a loss. I've tried adding ha="right" to no such avail and just shifting the graphs but that leaves me with other problems and doesn't really address the problem. Even with the bars shifted, I'm still fairly constrained as to moving the tick labels and haven't found anything online that remotely addresses the problem.
Would it be better to create the bar chart so that it has the same index as the line chart, then set the x tick labels to be the desired dates?
Does anyone have any guidance? I've spent too much time on this problem today and it's driving me nuts.
In order to plot the bar chart tightly, you can use the autoscale function as below.
To move the tick labels, you can modify the transformations to include some offset. Below I used 0.7 but you can select it based on other sizes used in your chart.
import matplotlib.pyplot as pyplot
import pandas
import matplotlib.transforms as tr
df = pandas.DataFrame([.02, .01, -0.01, .05], index = ["2017-03-31", "2017-06-30", "2017-09-30", "2017-12-31"], columns = ["test"])
fig, ax = pyplot.subplots(1, 1, figsize = (7, 3))
df.plot(kind = "bar", ax = ax, width = 1)
pyplot.autoscale(enable=True, axis='x', tight=True) # tight layout
# for each tick label, shift 0.7 to right
for tick in ax.get_xticklabels():
tick.set_transform(tick.get_transform()+tr.ScaledTranslation(0.7, 0, fig.dpi_scale_trans))
pyplot.show()
The result looks like this.
I'm trying to plot a 3x3 subplots, the first line using imshow, and second and third are regular graphs. All the 3x3 plots share the same x-axis, so it's really important that they will be perfectly aligned (I didn't use sharey=true because I wanted the y-ticks). I was able to put the graphs legends outside the figure in the right (the legend is the same legend for the whole 3 figures in the same row) by using:
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
But I couldn't find how to put the colorbar of the first row above them, without shrinking the third plot in the first row. I was trying to use "make_axes_locatable":
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(powers_ax)
cax = divider.append_axes("right", size="5%", pad=1)
plt.colorbar(im, cax=cax)
Or "inset_axes":
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
axins = inset_axes(powers_ax, width="10%", height="100%", loc=5,
bbox_to_anchor=(0.5, 0, 1, 1), bbox_transform=powers_ax.transAxes)
plt.colorbar(im, cax=axins)
Or even this trick:
fig.colorbar(im2, ax=ax[0, :].ravel().tolist(), location='right', shrink=0.6)
But nothing gave me the result I watned.
Any ideas?
* EDIT *
As #ImportanceOfBeingErnest suggested, the solution was tweaking the first parameter in the bbox_to_anchor (from 0.5 to 1.15 in this case):
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
axins = inset_axes(powers_ax, width="5%", height="100%", loc=5,
bbox_to_anchor=(1.15, 0, 1, 1), bbox_transform=powers_ax.transAxes)
plt.colorbar(im2, cax=axins)
I wrote a Python script based on matplotlib that generates curves based on a common timeline. The number of curves sharing the same x axis in my plot can vary from 1 to 6 depending on user options.
Each of the data plotted use different y scales and require a different axis for drawing. As a result, I may need to draw up to 5 different Y axes on the right of my plot. I found the way in some other post to offset the position of the axes as I add new ones, but I still have two issues:
How to control the position of the multiple axes so that the tick labels don't overlap?
How to control the position of each axis label so that it is placed vertically at the bottom of each axis? And how to preserve this alignment as the display window is resized, zoomed-in etc...
I probably need to write some code that will first query the position of the axis and then a directive that will place the label relative to that position but I really have no idea how to do that.
I cannot share my entire code because it is too big, but I derived it from the code in this example. I modified that example by adding one extra plot and one extra axis to more closely match what intend to do in my script.
import matplotlib.pyplot as plt
def make_patch_spines_invisible(ax):
ax.set_frame_on(True)
ax.patch.set_visible(False)
for sp in ax.spines.values():
sp.set_visible(False)
fig, host = plt.subplots()
fig.subplots_adjust(right=0.75)
par1 = host.twinx()
par2 = host.twinx()
par3 = host.twinx()
# Offset the right spine of par2. The ticks and label have already been
# placed on the right by twinx above.
par2.spines["right"].set_position(("axes", 1.2))
# Having been created by twinx, par2 has its frame off, so the line of its
# detached spine is invisible. First, activate the frame but make the patch
# and spines invisible.
make_patch_spines_invisible(par2)
# Second, show the right spine.
par2.spines["right"].set_visible(True)
par3.spines["right"].set_position(("axes", 1.4))
make_patch_spines_invisible(par3)
par3.spines["right"].set_visible(True)
p1, = host.plot([0, 1, 2], [0, 1, 2], "b-", label="Density")
p2, = par1.plot([0, 1, 2], [0, 3, 2], "r-", label="Temperature")
p3, = par2.plot([0, 1, 2], [50, 30, 15], "g-", label="Velocity")
p4, = par3.plot([0,0.5,1,1.44,2],[100, 102, 104, 108, 110], "m-", label="Acceleration")
host.set_xlim(0, 2)
host.set_ylim(0, 2)
par1.set_ylim(0, 4)
par2.set_ylim(1, 65)
host.set_xlabel("Distance")
host.set_ylabel("Density")
par1.set_ylabel("Temperature")
par2.set_ylabel("Velocity")
par3.set_ylabel("Acceleration")
host.yaxis.label.set_color(p1.get_color())
par1.yaxis.label.set_color(p2.get_color())
par2.yaxis.label.set_color(p3.get_color())
par3.yaxis.label.set_color(p4.get_color())
tkw = dict(size=4, width=1.5)
host.tick_params(axis='y', colors=p1.get_color(), **tkw)
par1.tick_params(axis='y', colors=p2.get_color(), **tkw)
par2.tick_params(axis='y', colors=p3.get_color(), **tkw)
par3.tick_params(axis='y', colors=p4.get_color(), **tkw)
host.tick_params(axis='x', **tkw)
lines = [p1, p2, p3, p4]
host.legend(lines, [l.get_label() for l in lines])
# fourth y axis is not shown unless I add this line
plt.tight_layout()
plt.show()
When I run this, I obtain the following plot:
output from above script
In this image, question 2 above means that I would want the y-axis labels 'Temperature', 'Velocity', 'Acceleration' to be drawn directly below each of the corresponding axis.
Thanks in advance for any help.
Regards,
L.
What worked for me was ImportanceOfBeingErnest's suggestion of using text (with a line like
host.text(1.2, 0, "Velocity" , ha="left", va="top", rotation=90,
transform=host.transAxes))
instead of trying to control the label position.
Like the title says, I'm (learning to) plot with MatPlotLib/PyLab, and everything turns out fine, but the graph doesn't contain the first value in the list I'm plotting, and then causes all the values to be transformed one coordinate to the left. I've made a really simple program to illustrate this, and have provided an image of the final graph.
Windows 7, using NotePad++ but it makes the same result in IDLE as well.
Thanks for the help!
-Cory
from pylab import plot, show, axis
test = [1,2,1,2,1,2,1,2,1,2,1,2]
axis([1, 12, 0, 3])
plot(test, marker='o')
show()
You've set your axis to start from 1, it should be from 0. X-values begin from 0 by default.
from pylab import plot, show, axis
test = [1,2,1,2,1,2,1,2,1,2,1,2]
axis([0, 12, 0, 3])
plot(test, marker='o')
show()
However, if you from your first test value 1 to map for x=1, then provide the x values, like
from pylab import plot, show, axis
test = [1,2,1,2,1,2,1,2,1,2,1,2]
xvals = range(1, len(test)+1)
axis([1, 12, 0, 3])
plot(xvals, test, marker='o')
show()