I am using the following codes to plot several data points (xi, yi)
import numpy as np
import matplotlib.pyplot as plt
xi = np.array(data_df[['col_A']])
yi = np.array(data_df[['col_B']])
plt.figure()
plt.plot(xi, yi)
x = np.linspace(0, 30, 30)
y= np.exp(x*0.16)
plt.plot(x, y)
plt.show()
I want the plot to look like this:
Thanks!
User subplots to plot more than 1 plots in 1 figure.You need to call plt.show() only once.
import numpy as np
import matplotlib.pyplot as plt
xi = np.array(data_df[['col_A']])
yi = np.array(data_df[['col_B']])
plt.figure()
plt.subplot(2,1,1)
plt.plot(xi, yi)
plt.subplot(2,1,2)
x = np.linspace(0, 30, 30)
y= np.exp(x*0.16)
plt.plot(x, y)
plt.show()
Related
Could you help me with the following script please? How to set the width of ticks in this multiplot for plotting 6 subplots?
import numpy as np
import matplotlib.pyplot as plt
from numpy import array
import matplotlib as mpl
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig, ax = plt.subplots(sharex=True)
plt.figure(figsize=(12, 9))
fig1 = plt.subplot(231)
plt.plot(x, y**2)
fig1.set_xlim(0e-13,2e-13)
fig1.set_ylim(-1.15e-14,0.01e-14)
fig2=plt.subplot(232)
plt.plot(x, y**2)
fig2.set_xlim(0e-13,2e-13)
fig2.set_ylim(-7.3e-15,7.3e-15)
fig3=plt.subplot(233)
plt.plot(x, y**2)
fig3.set_ylim(0e-13,1.2e-13)
fig3.set_xlim(0e-13,2e-13)
fig4=plt.subplot(234)
plt.plot(x, y**2)
fig4.set_xlim(-1.15e-14,0.01e-14)
fig4.set_ylim(-7.3e-15,7.3e-15)
fig5=plt.subplot(235)
plt.plot(x, y**2)
fig5.set_xlim(-7.3e-15,7.3e-15)
fig5.set_ylim(0e-13,1.2e-13)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig6=plt.subplot(236)
plt.plot(x, y**2)
fig6.set_xlim(-1.5e-14,0e-14)
fig6.set_ylim(0e-13,1.2e-13)
plt.show()
I tried:
ax.xaxis.set_tick_params(width=2)
ax.yaxis.set_tick_params(width=2)
and
for figures in [fig1, fig2, fig3, fig4, fig5, fig6]:
ax.xaxis.set_tick_params(width=2)
ax.yaxis.set_tick_params(width=2)
but nothing has changed and the width of ticks stayed the same.
First of all, the following
fig, ax = plt.subplots(sharex=True)
plt.figure(figsize=(12, 9))
creates two figures, which I guess you do not want.
Second, when you execute fig1 = plt.subplot(231), you do not create a Figure object but rather an Axes one. This call is redundant as it can be handled directly with plt.subplots().
Third, ax.xaxis.set_tick_params(width=2) has no effect in the figure you are interested in because ax refers to the axis created by fig, ax = plt.subplots(sharex=True) and not to any axis in the current figure you are drawing in, which was created by plt.figure(figsize=(12, 9)).
Have a look below for a cleaner version.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
fig, axarr = plt.subplots(nrows=2, ncols=3)
for ax in axarr.flatten():
ax.plot(x, y ** 2)
ax.tick_params(width=2)
fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.show()
How to set linewidth of axis in 3d plot in python? Is it somehow possible with mpl.rcParams?
Code:
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label='parametric curve')
ax.legend()
plt.show()
Try this:
ax.plot_surface(X, Y, Z, linewidth=1)
I can generate an error-bar plot using the code below. The graph produced by the code shows vertical lines that represent the errors in y. I would like to have horizontal lines at the tips of these errors ("error bars") and am not sure how to do so.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 10, dtype=int)
y = 2**x
yerr = np.sqrt(y)*10
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr, solid_capstyle='projecting')
ax.grid(alpha=0.5, linestyle=':')
plt.show()
plt.close(fig)
The code generates the figure below. I've played with the solid_capstyle kwarg. Is there a specific kwarg that does what I am trying to do?
And as an example of what I'd like, the figure below:
In case it's relevant, I am using matplotlib 2.2.2
The argument you are looking for is capsize= in ax.errorbar(). The default is None so the length of the cap will default to the value of matplotlib.rcParams["errorbar.capsize"]. The number you give will be the length of the cap in points:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 10, dtype=int)
y = 2**x
yerr = np.sqrt(y)*10
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr, solid_capstyle='projecting', capsize=5)
ax.grid(alpha=0.5, linestyle=':')
plt.show()
draw a graph of equation in the form of y=mx+b in python3.x
example y = 5x + 9
This is a very general question. Try to be more specific. It depends how you want to draw it.
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0., 5., 0.2)
y = 5 * x + 9
plt.plot(x, y)
plt.show()
or
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(-1., 5., 0.2)
y = 5 * x + 9
fig, ax = plt.subplots()
ax.plot(x,y)
ax.grid(True, which='both')
ax.axhline(y=0, color='k')
ax.axvline(x=0, color='k')
These are very basic drawing. You can create more sophisticated graphs, but you will have to be more specific in your question.
You can define your y(x) function and then plot it as follows:
import matplotlib.pyplot as plt
def y(x):
return [5*i+9 for i in x]
x = range(0,10)
plt.plot(x,y(x))
plt.show()
This produces follwing graph:
With turtle
You can as well get a graph with turtle with following code for example:
from turtle import Turtle, Screen
def y(x):
return 5*x+9
def plotter(turtle, x_range):
turtle.penup()
for x in x_range:
turtle.goto(x, y(x))
turtle.pendown()
screen = Screen()
screen.setworldcoordinates(0, 0, 9, 60)
turtle = Turtle(visible=False)
x = range(0,10)
plotter(turtle, x)
screen.exitonclick()
Which produces:
With the following code I create four histograms:
import numpy as np
import pandas as pd
data = pd.DataFrame(np.random.normal((1, 2, 3 , 4), size=(100, 4)))
data.hist(bins=10)
I want the histograms to look like this:
I know how to make it one graph at the time, see here
But how can I do it for multiple histograms without specifying each single one? Ideally I could use 'pd.scatter_matrix'.
Plot each histogram seperately and do the fit to each histogram as in the example you linked or take a look at the hist api example here. Essentially what should be done is
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
for ax in [ax1, ax2, ax3, ax4]:
n, bins, patches = ax.hist(**your_data_here**, 50, normed=1, facecolor='green', alpha=0.75)
bincenters = 0.5*(bins[1:]+bins[:-1])
y = mlab.normpdf( bincenters, mu, sigma)
l = ax.plot(bincenters, y, 'r--', linewidth=1)
plt.show()