Python 实用宝典

Question 1

I’m using matplotlib to plot data (using plot and errorbar functions) from Python. I have to plot a set of totally separate and independent plots, and then adjust their ylim values so they can be easily visually compared.

How can I retrieve the ylim values from each plot, so that I can take the min and max of the lower and upper ylim values, respectively, and adjust the plots so they can be visually compared?

Of course, I could just analyze the data and come up with my own custom ylim values… but I’d like to use matplotlib to do that for me. Any suggestions on how to easily (and efficiently) do this?

Here’s my Python function that plots using matplotlib:

import matplotlib.pyplot as plt

def myplotfunction(title, values, errors, plot_file_name):

    # plot errorbars
    indices = range(0, len(values))
    fig = plt.figure()
    plt.errorbar(tuple(indices), tuple(values), tuple(errors), marker='.')

    # axes
    axes = plt.gca()
    axes.set_xlim([-0.5, len(values) - 0.5])
    axes.set_xlabel('My x-axis title')
    axes.set_ylabel('My y-axis title')

    # title
    plt.title(title)

    # save as file
    plt.savefig(plot_file_name)

    # close figure
    plt.close(fig)

Question 2

Just use axes.get_ylim(), it is very similar to set_ylim. From the docs:

get_ylim()

Get the y-axis range [bottom, top]

Question 3

 ymin, ymax = axes.get_ylim()

If you are using the plt api directly, you can avoid calls to the axes altogether:

def myplotfunction(title, values, errors, plot_file_name):

    # plot errorbars
    indices = range(0, len(values))
    fig = plt.figure()
    plt.errorbar(tuple(indices), tuple(values), tuple(errors), marker='.')

    plt.xlim([-0.5, len(values) - 0.5])
    plt.xlabel('My x-axis title')
    plt.ylabel('My y-axis title')

    # title
    plt.title(title)

    # save as file
    plt.savefig(plot_file_name)

   # close figure
    plt.close(fig)

Question 4

Leveraging from the good answers above and assuming you were only using plt as in

import matplotlib.pyplot as plt

then you can get all four plot limits using plt.axis() as in the following example.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5, 6, 7, 8]  # fake data
y = [1, 2, 3, 4, 3, 2, 5, 6]

plt.plot(x, y, 'k')

xmin, xmax, ymin, ymax = plt.axis()

s = 'xmin = ' + str(round(xmin, 2)) + ', ' + \
    'xmax = ' + str(xmax) + '\n' + \
    'ymin = ' + str(ymin) + ', ' + \
    'ymax = ' + str(ymax) + ' '

plt.annotate(s, (1, 5))

plt.show()

The above code should produce the following output plot.

Question 5

I am making an application in Python which collects data from a serial port and plots a graph of the collected data against arrival time. The time of arrival for the data is uncertain. I want the plot to be updated when data is received. I searched on how to do this and found two methods:

Clear the plot and re-draw the plot with all the points again.
Animate the plot by changing it after a particular interval.

I do not prefer the first one as the program runs and collects data for a long time (a day for example), and redrawing the plot will be pretty slow. The second one is also not preferable as time of arrival of data is uncertain and I want the plot to update only when the data is received.

Is there a way in which I can update the plot just by adding more points to it only when the data is received?

Question 6

Is there a way in which I can update the plot just by adding more point[s] to it…

There are a number of ways of animating data in matplotlib, depending on the version you have. Have you seen the matplotlib cookbook examples? Also, check out the more modern animation examples in the matplotlib documentation. Finally, the animation API defines a function FuncAnimation which animates a function in time. This function could just be the function you use to acquire your data.

Each method basically sets the data property of the object being drawn, so doesn’t require clearing the screen or figure. The data property can simply be extended, so you can keep the previous points and just keep adding to your line (or image or whatever you are drawing).

Given that you say that your data arrival time is uncertain your best bet is probably just to do something like:

import matplotlib.pyplot as plt
import numpy

hl, = plt.plot([], [])

def update_line(hl, new_data):
    hl.set_xdata(numpy.append(hl.get_xdata(), new_data))
    hl.set_ydata(numpy.append(hl.get_ydata(), new_data))
    plt.draw()

Then when you receive data from the serial port just call update_line.

Question 7

In order to do this without FuncAnimation (eg you want to execute other parts of the code while the plot is being produced or you want to be updating several plots at the same time), calling draw alone does not produce the plot (at least with the qt backend).

The following works for me:

import matplotlib.pyplot as plt
plt.ion()
class DynamicUpdate():
    #Suppose we know the x range
    min_x = 0
    max_x = 10

    def on_launch(self):
        #Set up plot
        self.figure, self.ax = plt.subplots()
        self.lines, = self.ax.plot([],[], 'o')
        #Autoscale on unknown axis and known lims on the other
        self.ax.set_autoscaley_on(True)
        self.ax.set_xlim(self.min_x, self.max_x)
        #Other stuff
        self.ax.grid()
        ...

    def on_running(self, xdata, ydata):
        #Update data (with the new _and_ the old points)
        self.lines.set_xdata(xdata)
        self.lines.set_ydata(ydata)
        #Need both of these in order to rescale
        self.ax.relim()
        self.ax.autoscale_view()
        #We need to draw *and* flush
        self.figure.canvas.draw()
        self.figure.canvas.flush_events()

    #Example
    def __call__(self):
        import numpy as np
        import time
        self.on_launch()
        xdata = []
        ydata = []
        for x in np.arange(0,10,0.5):
            xdata.append(x)
            ydata.append(np.exp(-x**2)+10*np.exp(-(x-7)**2))
            self.on_running(xdata, ydata)
            time.sleep(1)
        return xdata, ydata

d = DynamicUpdate()
d()

Question 8

Here is a way which allows to remove points after a certain number of points plotted:

import matplotlib.pyplot as plt
# generate axes object
ax = plt.axes()

# set limits
plt.xlim(0,10) 
plt.ylim(0,10)

for i in range(10):        
     # add something to axes    
     ax.scatter([i], [i]) 
     ax.plot([i], [i+1], 'rx')

     # draw the plot
     plt.draw() 
     plt.pause(0.01) #is necessary for the plot to update for some reason

     # start removing points if you don't want all shown
     if i>2:
         ax.lines[0].remove()
         ax.collections[0].remove()

Question 9

I know I’m late to answer this question, but for your issue you could look into the “joystick” package. I designed it for plotting a stream of data from the serial port, but it works for any stream. It also allows for interactive text logging or image plotting (in addition to graph plotting). No need to do your own loops in a separate thread, the package takes care of it, just give the update frequency you wish. Plus the terminal remains available for monitoring commands while plotting. See http://www.github.com/ceyzeriat/joystick/ or https://pypi.python.org/pypi/joystick (use pip install joystick to install)

Just replace np.random.random() by your real data point read from the serial port in the code below:

import joystick as jk
import numpy as np
import time

class test(jk.Joystick):
    # initialize the infinite loop decorator
    _infinite_loop = jk.deco_infinite_loop()

    def _init(self, *args, **kwargs):
        """
        Function called at initialization, see the doc
        """
        self._t0 = time.time()  # initialize time
        self.xdata = np.array([self._t0])  # time x-axis
        self.ydata = np.array([0.0])  # fake data y-axis
        # create a graph frame
        self.mygraph = self.add_frame(jk.Graph(name="test", size=(500, 500), pos=(50, 50), fmt="go-", xnpts=10000, xnptsmax=10000, xylim=(None, None, 0, 1)))

    @_infinite_loop(wait_time=0.2)
    def _generate_data(self):  # function looped every 0.2 second to read or produce data
        """
        Loop starting with the simulation start, getting data and
    pushing it to the graph every 0.2 seconds
        """
        # concatenate data on the time x-axis
        self.xdata = jk.core.add_datapoint(self.xdata, time.time(), xnptsmax=self.mygraph.xnptsmax)
        # concatenate data on the fake data y-axis
        self.ydata = jk.core.add_datapoint(self.ydata, np.random.random(), xnptsmax=self.mygraph.xnptsmax)
        self.mygraph.set_xydata(t, self.ydata)

t = test()
t.start()
t.stop()

Question 10

I have an existing plot that was created with pandas like this:

df['myvar'].plot(kind='bar')

The y axis is format as float and I want to change the y axis to percentages. All of the solutions I found use ax.xyz syntax and I can only place code below the line above that creates the plot (I cannot add ax=ax to the line above.)

How can I format the y axis as percentages without changing the line above?

Here is the solution I found but requires that I redefine the plot:

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as mtick

data = [8,12,15,17,18,18.5]
perc = np.linspace(0,100,len(data))

fig = plt.figure(1, (7,4))
ax = fig.add_subplot(1,1,1)

ax.plot(perc, data)

fmt = '%.0f%%' # Format you want the ticks, e.g. '40%'
xticks = mtick.FormatStrFormatter(fmt)
ax.xaxis.set_major_formatter(xticks)

plt.show()

Link to the above solution: Pyplot: using percentage on x axis

Question 11

This is a few months late, but I have created PR#6251 with matplotlib to add a new PercentFormatter class. With this class you just need one line to reformat your axis (two if you count the import of matplotlib.ticker):

import ...
import matplotlib.ticker as mtick

ax = df['myvar'].plot(kind='bar')
ax.yaxis.set_major_formatter(mtick.PercentFormatter())

PercentFormatter() accepts three arguments, xmax, decimals, symbol. xmax allows you to set the value that corresponds to 100% on the axis. This is nice if you have data from 0.0 to 1.0 and you want to display it from 0% to 100%. Just do PercentFormatter(1.0).

The other two parameters allow you to set the number of digits after the decimal point and the symbol. They default to None and '%', respectively. decimals=None will automatically set the number of decimal points based on how much of the axes you are showing.

Update

PercentFormatter was introduced into Matplotlib proper in version 2.1.0.

Question 12

pandas dataframe plot will return the ax for you, And then you can start to manipulate the axes whatever you want.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(100,5))

# you get ax from here
ax = df.plot()
type(ax)  # matplotlib.axes._subplots.AxesSubplot

# manipulate
vals = ax.get_yticks()
ax.set_yticklabels(['{:,.2%}'.format(x) for x in vals])

enter image description here

Question 13

Jianxun‘s solution did the job for me but broke the y value indicator at the bottom left of the window.

I ended up using FuncFormatterinstead (and also stripped the uneccessary trailing zeroes as suggested here):

import pandas as pd
import numpy as np
from matplotlib.ticker import FuncFormatter

df = pd.DataFrame(np.random.randn(100,5))

ax = df.plot()
ax.yaxis.set_major_formatter(FuncFormatter(lambda y, _: '{:.0%}'.format(y)))

Generally speaking I’d recommend using FuncFormatter for label formatting: it’s reliable, and versatile.

Question 14

For those who are looking for the quick one-liner:

plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()])

Or if you are using Latex as the axis text formatter, you have to add one backslash ‘\’

plt.gca().set_yticklabels(['{:.0f}\%'.format(x*100) for x in plt.gca().get_yticks()])

Question 15

I propose an alternative method using seaborn

Working code:

import pandas as pd
import seaborn as sns
data=np.random.rand(10,2)*100
df = pd.DataFrame(data, columns=['A', 'B'])
ax= sns.lineplot(data=df, markers= True)
ax.set(xlabel='xlabel', ylabel='ylabel', title='title')
#changing ylables ticks
y_value=['{:,.2f}'.format(x) + '%' for x in ax.get_yticks()]
ax.set_yticklabels(y_value)

Question 16

I’m late to the game but I just realize this: ax can be replaced with plt.gca() for those who are not using axes and just subplots.

Echoing @Mad Physicist answer, using the package PercentFormatter it would be:

import matplotlib.ticker as mtick

plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(1))
#if you already have ticks in the 0 to 1 range. Otherwise see their answer

Question 17

How can I put text in the top left (or top right) corner of a matplotlib figure, e.g. where a top left legend would be, or on top of the plot but in the top left corner? E.g. if it’s a plt.scatter(), then something that would be within the square of the scatter, put in the top left most corner.

I’d like to do this without ideally knowing the scale of the scatterplot being plotted for example, since it will change from dataset to data set. I just want it the text to be roughly in the upper left, or roughly in the upper right. With legend type positioning it should not overlap with any scatter plot points anyway.

thanks!

Question 18

You can use text.

text(x, y, s, fontsize=12)

text coordinates can be given relative to the axis, so the position of your text will be independent of the size of the plot:

The default transform specifies that text is in data coords, alternatively, you can specify text in axis coords (0,0 is lower-left and 1,1 is upper-right). The example below places text in the center of the axes::

text(0.5, 0.5,'matplotlib',
     horizontalalignment='center',
     verticalalignment='center',
     transform = ax.transAxes)

To prevent the text to interfere with any point of your scatter is more difficult afaik. The easier method is to set y_axis (ymax in ylim((ymin,ymax))) to a value a bit higher than the max y-coordinate of your points. In this way you will always have this free space for the text.

EDIT: here you have an example:

In [17]: from pylab import figure, text, scatter, show
In [18]: f = figure()
In [19]: ax = f.add_subplot(111)
In [20]: scatter([3,5,2,6,8],[5,3,2,1,5])
Out[20]: <matplotlib.collections.CircleCollection object at 0x0000000007439A90>
In [21]: text(0.1, 0.9,'matplotlib', ha='center', va='center', transform=ax.transAxes)
Out[21]: <matplotlib.text.Text object at 0x0000000007415B38>
In [22]:

enter image description here

The ha and va parameters set the alignment of your text relative to the insertion point. ie. ha=’left’ is a good set to prevent a long text to go out of the left axis when the frame is reduced (made narrower) manually.

Question 19

One solution would be to use the plt.legend function, even if you don’t want an actual legend. You can specify the placement of the legend box by using the loc keyterm. More information can be found at this website but I’ve also included an example showing how to place a legend:

ax.scatter(xa,ya, marker='o', s=20, c="lightgreen", alpha=0.9)
ax.scatter(xb,yb, marker='o', s=20, c="dodgerblue", alpha=0.9)
ax.scatter(xc,yc marker='o', s=20, c="firebrick", alpha=1.0)
ax.scatter(xd,xd,xd, marker='o', s=20, c="goldenrod", alpha=0.9)
line1 = Line2D(range(10), range(10), marker='o', color="goldenrod")
line2 = Line2D(range(10), range(10), marker='o',color="firebrick")
line3 = Line2D(range(10), range(10), marker='o',color="lightgreen")
line4 = Line2D(range(10), range(10), marker='o',color="dodgerblue")
plt.legend((line1,line2,line3, line4),('line1','line2', 'line3', 'line4'),numpoints=1, loc=2)

Note that because loc=2, the legend is in the upper-left corner of the plot. And if the text overlaps with the plot, you can make it smaller by using legend.fontsize, which will then make the legend smaller.

Question 20

I want to set the upper limit of the y-axis to ‘auto’, but I want to keep the lower limit of the y-axis to always be zero. I tried ‘auto’ and ‘autorange’, but those don’t seem to work. Thank you in advance.

Here is my code:

import matplotlib.pyplot as plt

def plot(results_plt,title,filename):

    ############################
    # Plot results

    # mirror result table such that each parameter forms an own data array
    plt.cla()
    #print results_plt
    XY_results = []

    XY_results = zip( *results_plt)

    plt.plot(XY_results[0], XY_results[2], marker = ".")

    plt.title('%s' % (title) )
    plt.xlabel('Input Voltage [V]')
    plt.ylabel('Input Current [mA]')

    plt.grid(True)
    plt.xlim(3.0, 4.2)  #***I want to keep these values fixed"
    plt.ylim([0, 80]) #****CHANGE**** I want to change '80' to auto, but still keep 0 as the lower limit 
    plt.savefig(path+filename+'.png')

Question 21

You can pass just left or right to set_xlim:

plt.gca().set_xlim(left=0)

For the y axis, use bottom or top:

plt.gca().set_ylim(bottom=0)

Question 22

Just set xlim for one of the limits:

plt.xlim(xmin=0)

Question 23

As aforementioned and according to the matplotlib documentation, the x-limits of a given axis ax can be set using the set_xlim method of the matplotlib.axes.Axes class.

For instance,

>>> ax.set_xlim(left_limit, right_limit)
>>> ax.set_xlim((left_limit, right_limit))
>>> ax.set_xlim(left=left_limit, right=right_limit)

One limit may be left unchanged (e.g. the left limit):

>>> ax.set_xlim((None, right_limit))
>>> ax.set_xlim(None, right_limit)
>>> ax.set_xlim(left=None, right=right_limit)
>>> ax.set_xlim(right=right_limit)

To set the x-limits of the current axis, the matplotlib.pyplot module contains the xlim function that just wraps matplotlib.pyplot.gca and matplotlib.axes.Axes.set_xlim.

def xlim(*args, **kwargs):
    ax = gca()
    if not args and not kwargs:
        return ax.get_xlim()
    ret = ax.set_xlim(*args, **kwargs)
    return ret

Similarly, for the y-limits, use matplotlib.axes.Axes.set_ylim or matplotlib.pyplot.ylim. The keyword arguments are top and bottom.

Question 24

Just add a point on @silvio ‘s: if you use axis to plot like figure, ax1 = plt.subplots(1,2,1). Then ax1.set_xlim(xmin = 0) also works!

Question 25

I have a simple line plot and need to move the y-axis ticks from the (default) left side of the plot to the right side. Any thoughts on how to do this?

Question 26

Use ax.yaxis.tick_right()

for example:

from matplotlib import pyplot as plt

f = plt.figure()
ax = f.add_subplot(111)
ax.yaxis.tick_right()
plt.plot([2,3,4,5])
plt.show()

enter image description here

Question 27

For right labels use ax.yaxis.set_label_position("right"), i.e.:

f = plt.figure()
ax = f.add_subplot(111)
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
plt.plot([2,3,4,5])
ax.set_xlabel("$x$ /mm")
ax.set_ylabel("$y$ /mm")
plt.show()

Question 28

joaquin’s answer works, but has the side effect of removing ticks from the left side of the axes. To fix this, follow up tick_right() with a call to set_ticks_position('both'). A revised example:

from matplotlib import pyplot as plt

f = plt.figure()
ax = f.add_subplot(111)
ax.yaxis.tick_right()
ax.yaxis.set_ticks_position('both')
plt.plot([2,3,4,5])
plt.show()

The result is a plot with ticks on both sides, but tick labels on the right.

enter image description here

Question 29

Just is case somebody asks (like I did), this is also possible when one uses subplot2grid. For example:

import matplotlib.pyplot as plt
plt.subplot2grid((3,2), (0,1), rowspan=3)
plt.plot([2,3,4,5])
plt.tick_params(axis='y', which='both', labelleft='off', labelright='on')
plt.show()

It will show this:

enter image description here

Question 30

I’ve got the following simple script that plots a graph:

import matplotlib.pyplot as plt
import numpy as np

T = np.array([6, 7, 8, 9, 10, 11, 12])
power = np.array([1.53E+03, 5.92E+02, 2.04E+02, 7.24E+01, 2.72E+01, 1.10E+01, 4.70E+00])

plt.plot(T,power)
plt.show()

As it is now, the line goes straight from point to point which looks ok, but could be better in my opinion. What I want is to smooth the line between the points. In Gnuplot I would have plotted with smooth cplines.

Is there an easy way to do this in PyPlot? I’ve found some tutorials, but they all seem rather complex.

Question 31

You could use scipy.interpolate.spline to smooth out your data yourself:

from scipy.interpolate import spline

# 300 represents number of points to make between T.min and T.max
xnew = np.linspace(T.min(), T.max(), 300)  

power_smooth = spline(T, power, xnew)

plt.plot(xnew,power_smooth)
plt.show()

spline is deprecated in scipy 0.19.0, use BSpline class instead.

Switching from spline to BSpline isn’t a straightforward copy/paste and requires a little tweaking:

from scipy.interpolate import make_interp_spline, BSpline

# 300 represents number of points to make between T.min and T.max
xnew = np.linspace(T.min(), T.max(), 300) 

spl = make_interp_spline(T, power, k=3)  # type: BSpline
power_smooth = spl(xnew)

plt.plot(xnew, power_smooth)
plt.show()

Before:

After:

Question 32

For this example spline works well, but if the function is not smooth inherently and you want to have smoothed version you can also try:

from scipy.ndimage.filters import gaussian_filter1d

ysmoothed = gaussian_filter1d(y, sigma=2)
plt.plot(x, ysmoothed)
plt.show()

if you increase sigma you can get a more smoothed function.

Proceed with caution with this one. It modifies the original values and may not be what you want.

Question 33

I presume you mean curve-fitting and not anti-aliasing from the context of your question. PyPlot doesn’t have any built-in support for this, but you can easily implement some basic curve-fitting yourself, like the code seen here, or if you’re using GuiQwt it has a curve fitting module. (You could probably also steal the code from SciPy to do this as well).

Question 34

See the scipy.interpolate documentation for some examples.

The following example demonstrates its use, for linear and cubic spline interpolation:

>>> from scipy.interpolate import interp1d

>>> x = np.linspace(0, 10, num=11, endpoint=True)
>>> y = np.cos(-x**2/9.0)
>>> f = interp1d(x, y)
>>> f2 = interp1d(x, y, kind='cubic')

>>> xnew = np.linspace(0, 10, num=41, endpoint=True)
>>> import matplotlib.pyplot as plt
>>> plt.plot(x, y, 'o', xnew, f(xnew), '-', xnew, f2(xnew), '--')
>>> plt.legend(['data', 'linear', 'cubic'], loc='best')
>>> plt.show()

enter image description here

Question 35

Based on this question about heatmaps in matplotlib, I wanted to move the x-axis titles to the top of the plot.

import matplotlib.pyplot as plt
import numpy as np
column_labels = list('ABCD')
row_labels = list('WXYZ')
data = np.random.rand(4,4)
fig, ax = plt.subplots()
heatmap = ax.pcolor(data, cmap=plt.cm.Blues)

# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[0])+0.5, minor=False)
ax.set_yticks(np.arange(data.shape[1])+0.5, minor=False)

# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.set_label_position('top') # <-- This doesn't work!

ax.set_xticklabels(row_labels, minor=False)
ax.set_yticklabels(column_labels, minor=False)
plt.show()

However, calling matplotlib’s set_label_position (as notated above) doesn’t seem to have the desired effect. Here’s my output:

enter image description here

What am I doing wrong?

Question 36

Use

ax.xaxis.tick_top()

to place the tick marks at the top of the image. The command

ax.set_xlabel('X LABEL')    
ax.xaxis.set_label_position('top')

affects the label, not the tick marks.

import matplotlib.pyplot as plt
import numpy as np
column_labels = list('ABCD')
row_labels = list('WXYZ')
data = np.random.rand(4, 4)
fig, ax = plt.subplots()
heatmap = ax.pcolor(data, cmap=plt.cm.Blues)

# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1]) + 0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0]) + 0.5, minor=False)

# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()

ax.set_xticklabels(column_labels, minor=False)
ax.set_yticklabels(row_labels, minor=False)
plt.show()

enter image description here

Question 37

You want set_ticks_position rather than set_label_position:

ax.xaxis.set_ticks_position('top') # the rest is the same

This gives me:

enter image description here

Question 38

tick_params is very useful for setting tick properties. Labels can be moved to the top with:

    ax.tick_params(labelbottom=False,labeltop=True)

Question 39

You’ve got to do some extra massaging if you want the ticks (not labels) to show up on the top and bottom (not just the top). The only way I could do this is with a minor change to unutbu’s code:

import matplotlib.pyplot as plt
import numpy as np
column_labels = list('ABCD')
row_labels = list('WXYZ')
data = np.random.rand(4, 4)
fig, ax = plt.subplots()
heatmap = ax.pcolor(data, cmap=plt.cm.Blues)

# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1]) + 0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0]) + 0.5, minor=False)

# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()
ax.xaxis.set_ticks_position('both') # THIS IS THE ONLY CHANGE

ax.set_xticklabels(column_labels, minor=False)
ax.set_yticklabels(row_labels, minor=False)
plt.show()

Output:

enter image description here

Question 40

I am trying to plot a histogram using the matplotlib.hist() function but I am not sure how to do it.

I have a list

probability = [0.3602150537634409, 0.42028985507246375, 
  0.373117033603708, 0.36813186813186816, 0.32517482517482516, 
  0.4175257731958763, 0.41025641025641024, 0.39408866995073893, 
  0.4143222506393862, 0.34, 0.391025641025641, 0.3130841121495327, 
  0.35398230088495575]

and a list of names(strings).

How do I make the probability as my y-value of each bar and names as x-values?

Question 41

If you want a histogram, you don’t need to attach any ‘names’ to x-values, as on x-axis you would have data bins:

import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(42)
x = np.random.normal(size=1000)
plt.hist(x, density=True, bins=30)  # `density=False` would make counts
plt.ylabel('Probability')
plt.xlabel('Data');

You can make your histogram a bit fancier with PDF line, titles, and legend:

import scipy.stats as st
plt.hist(x, density=True, bins=30, label="Data")
mn, mx = plt.xlim()
plt.xlim(mn, mx)
kde_xs = np.linspace(mn, mx, 301)
kde = st.gaussian_kde(x)
plt.plot(kde_xs, kde.pdf(kde_xs), label="PDF")
plt.legend(loc="upper left")
plt.ylabel('Probability')
plt.xlabel('Data')
plt.title("Histogram");

However, if you have limited number of data points, like in OP, a bar plot would make more sense to represent your data (then you may attach labels to x-axis):

x = np.arange(3)
plt.bar(x, height=[1,2,3])
plt.xticks(x, ['a','b','c'])

Question 42

If you haven’t installed matplotlib yet just try the command.

> pip install matplotlib

Library import

import matplotlib.pyplot as plot

The histogram data:

plot.hist(weightList,density=1, bins=20) 
plot.axis([50, 110, 0, 0.06]) 
#axis([xmin,xmax,ymin,ymax])
plot.xlabel('Weight')
plot.ylabel('Probability')

Display histogram

plot.show()

And the output is like :

Question 43

Though the question appears to be demanding plotting a histogram using matplotlib.hist() function, it can arguably be not done using the same as the latter part of the question demands to use the given probabilities as the y-values of bars and given names(strings) as the x-values.

I’m assuming a sample list of names corresponding to given probabilities to draw the plot. A simple bar plot serves the purpose here for the given problem. The following code can be used:

import matplotlib.pyplot as plt
probability = [0.3602150537634409, 0.42028985507246375, 
  0.373117033603708, 0.36813186813186816, 0.32517482517482516, 
  0.4175257731958763, 0.41025641025641024, 0.39408866995073893, 
  0.4143222506393862, 0.34, 0.391025641025641, 0.3130841121495327, 
  0.35398230088495575]
names = ['name1', 'name2', 'name3', 'name4', 'name5', 'name6', 'name7', 'name8', 'name9',
'name10', 'name11', 'name12', 'name13'] #sample names
plt.bar(names, probability)
plt.xticks(names)
plt.yticks(probability) #This may be included or excluded as per need
plt.xlabel('Names')
plt.ylabel('Probability')

Question 44

This is a very round-about way of doing it but if you want to make a histogram where you already know the bin values but dont have the source data, you can use the np.random.randint function to generate the correct number of values within the range of each bin for the hist function to graph, for example:

import numpy as np
import matplotlib.pyplot as plt

data = [np.random.randint(0, 9, *desired y value*), np.random.randint(10, 19, *desired y value*), etc..]
plt.hist(data, histtype='stepfilled', bins=[0, 10, etc..])

as for labels you can align x ticks with bins to get something like this:

#The following will align labels to the center of each bar with bin intervals of 10
plt.xticks([5, 15, etc.. ], ['Label 1', 'Label 2', etc.. ])

Question 45

This is an old question but none of the previous answers has addressed the real issue, i.e. that fact that the problem is with the question itself.

First, if the probabilities have been already calculated, i.e. the histogram aggregated data is available in a normalized way then the probabilities should add up to 1. They obviously do not and that means that something is wrong here, either with terminology or with the data or in the way the question is asked.

Second, the fact that the labels are provided (and not intervals) would normally mean that the probabilities are of categorical response variable – and a use of a bar plot for plotting the histogram is best (or some hacking of the pyplot’s hist method), Shayan Shafiq’s answer provides the code.

However, see issue 1, those probabilities are not correct and using bar plot in this case as “histogram” would be wrong because it does not tell the story of univariate distribution, for some reason (perhaps the classes are overlapping and observations are counted multiple times?) and such plot should not be called a histogram in this case.

Histogram is by definition a graphical representation of the distribution of univariate variable (see https://www.itl.nist.gov/div898/handbook/eda/section3/histogra.htm , https://en.wikipedia.org/wiki/Histogram ) and is created by drawing bars of sizes representing counts or frequencies of observations in selected classes of the variable of interest. If the variable is measured on a continuous scale those classes are bins (intervals). Important part of histogram creation procedure is making a choice of how to group (or keep without grouping) the categories of responses for a categorical variable, or how to split the domain of possible values into intervals (where to put the bin boundaries) for continuous type variable. All observations should be represented, and each one only once in the plot. That means that the sum of the bar sizes should be equal to the total count of observation (or their areas in case of the variable widths, which is a less common approach). Or, if the histogram is normalised then all probabilities must add up to 1.

If the data itself is a list of “probabilities” as a response, i.e. the observations are probability values (of something) for each object of study then the best answer is simply plt.hist(probability) with maybe binning option, and use of x-labels already available is suspicious.

Then bar plot should not be used as histogram but rather simply

import matplotlib.pyplot as plt
probability = [0.3602150537634409, 0.42028985507246375, 
  0.373117033603708, 0.36813186813186816, 0.32517482517482516, 
  0.4175257731958763, 0.41025641025641024, 0.39408866995073893, 
  0.4143222506393862, 0.34, 0.391025641025641, 0.3130841121495327, 
  0.35398230088495575]
plt.hist(probability)
plt.show()

with the results

matplotlib in such case arrives by default with the following histogram values

(array([1., 1., 1., 1., 1., 2., 0., 2., 0., 4.]),
 array([0.31308411, 0.32380469, 0.33452526, 0.34524584, 0.35596641,
        0.36668698, 0.37740756, 0.38812813, 0.39884871, 0.40956928,
        0.42028986]),
 <a list of 10 Patch objects>)

the result is a tuple of arrays, the first array contains observation counts, i.e. what will be shown against the y-axis of the plot (they add up to 13, total number of observations) and the second array are the interval boundaries for x-axis.

One can check they they are equally spaced,

x = plt.hist(probability)[1]
for left, right in zip(x[:-1], x[1:]):
  print(left, right, right-left)

Or, for example for 3 bins (my judgment call for 13 observations) one would get this histogram

plt.hist(probability, bins=3)

with the plot data “behind the bars” being

The author of the question needs to clarify what is the meaning of the “probability” list of values – is the “probability” just a name of the response variable (then why are there x-labels ready for the histogram, it makes no sense), or are the list values the probabilities calculated from the data (then the fact they do not add up to 1 makes no sense).

Question 46

我想更改轴的颜色，以及我使用matplotlib和PyQt所做的绘图的刻度和值标签。

有任何想法吗？

Question 47

I’d like to Change the color of the axis, as well as ticks and value-labels for a plot I did using matplotlib an PyQt.

Any ideas?

Question 48

举一个快速的例子（使用比可能重复的问题稍微干净的方法）：

import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111)

ax.plot(range(10))
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')

ax.spines['bottom'].set_color('red')
ax.spines['top'].set_color('red')
ax.xaxis.label.set_color('red')
ax.tick_params(axis='x', colors='red')

plt.show()

替代文字

Question 49

As a quick example (using a slightly cleaner method than the potentially duplicate question):

import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111)

ax.plot(range(10))
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')

ax.spines['bottom'].set_color('red')
ax.spines['top'].set_color('red')
ax.xaxis.label.set_color('red')
ax.tick_params(axis='x', colors='red')

plt.show()

alt text

Question 50

如果您要修改多个图形或子图，则使用matplotlib上下文管理器来更改颜色可能会有所帮助，而不是分别更改每个颜色或子图。上下文管理器允许您仅针对紧随其后的缩进代码临时更改rc参数，但不影响全局rc参数。

此代码段产生两个图形，第一个图形具有修改的轴，刻度线和刻度标签颜色，第二个图形具有默认的rc参数。

import matplotlib.pyplot as plt
with plt.rc_context({'axes.edgecolor':'orange', 'xtick.color':'red', 'ytick.color':'green', 'figure.facecolor':'white'}):
    # Temporary rc parameters in effect
    fig, (ax1, ax2) = plt.subplots(1,2)
    ax1.plot(range(10))
    ax2.plot(range(10))
# Back to default rc parameters
fig, ax = plt.subplots()
ax.plot(range(10))

您可以键入plt.rcParams以查看所有可用的rc参数，并使用列表推导来搜索关键字：

# Search for all parameters containing the word 'color'
[(param, value) for param, value in plt.rcParams.items() if 'color' in param]

Question 51

If you have several figures or subplots that you want to modify, it can be helpful to use the matplotlib context manager to change the color, instead of changing each one individually. The context manager allows you to temporarily change the rc parameters only for the immediately following indented code, but does not affect the global rc parameters.

This snippet yields two figures, the first one with modified colors for the axis, ticks and ticklabels, and the second one with the default rc parameters.

import matplotlib.pyplot as plt
with plt.rc_context({'axes.edgecolor':'orange', 'xtick.color':'red', 'ytick.color':'green', 'figure.facecolor':'white'}):
    # Temporary rc parameters in effect
    fig, (ax1, ax2) = plt.subplots(1,2)
    ax1.plot(range(10))
    ax2.plot(range(10))
# Back to default rc parameters
fig, ax = plt.subplots()
ax.plot(range(10))

You can type plt.rcParams to view all available rc parameters, and use list comprehension to search for keywords:

# Search for all parameters containing the word 'color'
[(param, value) for param, value in plt.rcParams.items() if 'color' in param]

Question 52

在以前的贡献者的激励下，这是三个轴的示例。

import matplotlib.pyplot as plt

x_values1=[1,2,3,4,5]
y_values1=[1,2,2,4,1]

x_values2=[-1000,-800,-600,-400,-200]
y_values2=[10,20,39,40,50]

x_values3=[150,200,250,300,350]
y_values3=[-10,-20,-30,-40,-50]


fig=plt.figure()
ax=fig.add_subplot(111, label="1")
ax2=fig.add_subplot(111, label="2", frame_on=False)
ax3=fig.add_subplot(111, label="3", frame_on=False)

ax.plot(x_values1, y_values1, color="C0")
ax.set_xlabel("x label 1", color="C0")
ax.set_ylabel("y label 1", color="C0")
ax.tick_params(axis='x', colors="C0")
ax.tick_params(axis='y', colors="C0")

ax2.scatter(x_values2, y_values2, color="C1")
ax2.set_xlabel('x label 2', color="C1") 
ax2.xaxis.set_label_position('bottom') # set the position of the second x-axis to bottom
ax2.spines['bottom'].set_position(('outward', 36))
ax2.tick_params(axis='x', colors="C1")
ax2.set_ylabel('y label 2', color="C1")       
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position('right') 
ax2.tick_params(axis='y', colors="C1")

ax3.plot(x_values3, y_values3, color="C2")
ax3.set_xlabel('x label 3', color='C2')
ax3.xaxis.set_label_position('bottom')
ax3.spines['bottom'].set_position(('outward', 72))
ax3.tick_params(axis='x', colors='C2')
ax3.set_ylabel('y label 3', color='C2')
ax3.yaxis.tick_right()
ax3.yaxis.set_label_position('right') 
ax3.spines['right'].set_position(('outward', 36))
ax3.tick_params(axis='y', colors='C2')


plt.show()

Question 53

motivated by previous contributors, this is an example of three axes.

import matplotlib.pyplot as plt

x_values1=[1,2,3,4,5]
y_values1=[1,2,2,4,1]

x_values2=[-1000,-800,-600,-400,-200]
y_values2=[10,20,39,40,50]

x_values3=[150,200,250,300,350]
y_values3=[-10,-20,-30,-40,-50]


fig=plt.figure()
ax=fig.add_subplot(111, label="1")
ax2=fig.add_subplot(111, label="2", frame_on=False)
ax3=fig.add_subplot(111, label="3", frame_on=False)

ax.plot(x_values1, y_values1, color="C0")
ax.set_xlabel("x label 1", color="C0")
ax.set_ylabel("y label 1", color="C0")
ax.tick_params(axis='x', colors="C0")
ax.tick_params(axis='y', colors="C0")

ax2.scatter(x_values2, y_values2, color="C1")
ax2.set_xlabel('x label 2', color="C1") 
ax2.xaxis.set_label_position('bottom') # set the position of the second x-axis to bottom
ax2.spines['bottom'].set_position(('outward', 36))
ax2.tick_params(axis='x', colors="C1")
ax2.set_ylabel('y label 2', color="C1")       
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position('right') 
ax2.tick_params(axis='y', colors="C1")

ax3.plot(x_values3, y_values3, color="C2")
ax3.set_xlabel('x label 3', color='C2')
ax3.xaxis.set_label_position('bottom')
ax3.spines['bottom'].set_position(('outward', 72))
ax3.tick_params(axis='x', colors='C2')
ax3.set_ylabel('y label 3', color='C2')
ax3.yaxis.tick_right()
ax3.yaxis.set_label_position('right') 
ax3.spines['right'].set_position(('outward', 36))
ax3.tick_params(axis='y', colors='C2')


plt.show()

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图书馆进口

直方图数据：

显示直方图

和输出是这样的：

Library import

The histogram data:

Display histogram

And the output is like :

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