I'm thinking this is extremely simple. I would like to graph the line y = 7.87. This graphs a line parallel to the y-axis, but I'm looking for something horizontal, parallel with x-axis. Any ideas?
import matplotlib.pyplot as plt
plt.axvline(x = 7.85)
plt.show()
It really is quite simple:
import matplotlib.pyplot as plt
plt.axhline(y=7.87)
plt.show()
The h and v in the function names (axhline and axvline) stand for horizontal and vertical.
Related
I want to scale Y axis so that I can see values, as code below plots cant see anything other than a thin black line. Changing plot height doesn't expand the plot.
import numpy as np
import matplotlib.pyplot as plt
data=np.random.random((4,10000))
plt.rcParams["figure.figsize"] = (20,100)
#or swap line above with one below, still no change in plot height
#fig=plt.figure(figsize=(20, 100))
plt.matshow(data)
plt.show()
One way to do this is just repeat the values then plot result, but I would have thought it possible to just scale the height of the plot?
data_repeated = np.repeat(data, repeats=1000, axis=0)
You can do it like this:
import numpy as np
import matplotlib.pyplot as plt
data=np.random.random((4, 10000))
plt.figure(figsize=(40, 10))
plt.matshow(data, fignum=1, aspect='auto')
plt.show()
Output:
I am creating shot plots for NHL games and I have succeeded in making the plot, but I would like to draw the lines that you see on a hockey rink on it. I basically just want to draw two circles and two lines on the plot like this.
Let me know if this is possible/how I could do it
Pandas plot is in fact matplotlib plot, you can assign it to variable and modify it according to your needs ( add horizontal and vertical lines or shapes, text, etc)
# plot your data, but instead diplaying it assing Figure and Axis to variables
fig, ax = df.plot()
ax.vlines(x, ymin, ymax, colors='k', linestyles='solid') # adjust to your needs
plt.show()
working code sample
import pandas as pd
import matplotlib.pyplot as plt
import seaborn
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
df = seaborn.load_dataset('tips')
ax = df.plot.scatter(x='total_bill', y='tip')
ax.vlines(x=40, ymin=0, ymax=20, colors='red')
patches = [Circle((50,10), radius=3)]
collection = PatchCollection(patches, alpha=0.4)
ax.add_collection(collection)
plt.show()
I am trying to find a way in matplotlib to draw a lineplot, except that I don't want to draw a line between points. Instead I want to draw a perpendicular line between each of my points and the x axis.
When I do a standard plot, I obtain the following :
import numpy as np
import matplotlib.pyplot as plt
data = np.array([0,1,3,2,3,1,4])
plt.plot(data)
plt.xlim([-0.2,6.2])
plt.ylim([-0.2,5])
Instead I want to obtain the following :
Any ideas how to do this ?
Thanks
There are two other options apart from stem and bar chart is the following using vlines() and LineCollection()
Option 1 -- Using vlines()
for x, y in enumerate(data):
plt.vlines(x=x, ymin=0, ymax=y, color='r')
Or in a single line without using loops
plt.vlines(x=range(data.size), ymin=0, ymax=data, color='r')
Option 2 -- Using LineCollection()
from matplotlib.collections import LineCollection
lines = [[(x, 0), (x, y)] for x, y in enumerate(data)]
linesCol = LineCollection(lines, linewidths=3, color='r')
fig, ax = plt.subplots()
ax.add_collection(linesCol)
plt.scatter(range(len(data)), data, s=0)
I am writting a very simple script, one that plot a sin using jupyter notebook (python 3). when I put:
import numpy
import matplotlib.pyplot as plt
x=np.arange(0.0,5*np.pi,0.001)
y = np.sin(x)
plt.plot(x,y)
The plot is fine.
However if :
import numpy
import matplotlib.pyplot as plt
x=np.arange(0.0,5*np.pi,0.001)
np.random.shuffle(x)
y = np.sin(x)
plt.plot(x,y)
the image is
I don't understand why shuffling the x BEFORE I ran sin does it.
thank you
Let's first simplify things a bit. We plot 4 points and annote them with the order in which they are plotted.
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
x=np.arange(4)
y = np.sin(x)
plt.plot(x,y, marker="o")
for i, (xi,yi) in enumerate(zip(x,y)):
plt.annotate(str(i), xy=(xi,yi), xytext=(0,4),
textcoords="offset points", ha="center")
plt.show()
No if we shuffle x and plot the same graph,
x=np.arange(4)
np.random.shuffle(x)
y = np.sin(x)
we see that positions of the points are still are the same, but while e.g. previously the first point was the one at (0,0), it's now the third one appearing there. Due to this randomized order, the connecting lines go zickzack.
Now if you use enough points, all those lines will add up to look like a complete surface, which is what you get in your image.
When plotting using matplotlib, I ran into an interesting issue where the y axis is scaled by a very inconvenient quantity. Here's a MWE that demonstrates the problem:
import numpy as np
import matplotlib.pyplot as plt
l = np.linspace(0.5,2,2**10)
a = (0.696*l**2)/(l**2 - 9896.2e-9**2)
plt.plot(l,a)
plt.show()
When I run this, I get a figure that looks like this picture
The y-axis clearly is scaled by a silly quantity even though the y data are all between 1 and 2.
This is similar to the question:
Axis numerical offset in matplotlib
I'm not satisfied with the answer to this question in that it makes no sense to my why I need to go the the convoluted process of changing axis settings when the data are between 1 and 2 (EDIT: between 0 and 1). Why does this happen? Why does matplotlib use such a bizarre scaling?
The data in the plot are all between 0.696000000017 and 0.696000000273. For such cases it makes sense to use some kind of offset.
If you don't want that, you can use you own formatter:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker
l = np.linspace(0.5,2,2**10)
a = (0.696*l**2)/(l**2 - 9896.2e-9**2)
plt.plot(l,a)
fmt = matplotlib.ticker.StrMethodFormatter("{x:.12f}")
plt.gca().yaxis.set_major_formatter(fmt)
plt.show()