How can I keep seaborn.despine from putting both of my y-scales onto the left side of my plot?
The best I've come up with so far is:
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style("white")
fig, ax = plt.subplots()
ax.plot(np.random.rand(10))
ax2 =ax.twinx()
ax2.plot(100*np.random.rand(10))
sns.despine(ax=ax, right=True, left=True)
sns.despine(ax=ax2, left=True, right=False)
But any other combination will either not despine the y-axes or put the right axis onto the left.
Output of the above: (desired output has no spines, just numbers on left and right)
I guess that's what you want then.
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style("white")
fig, ax = plt.subplots()
ax.plot(np.random.rand(10))
ax2 =ax.twinx()
ax2.plot(100*np.random.rand(10))
sns.despine(ax=ax, right=True, left=True)
sns.despine(ax=ax2, left=True, right=False)
ax2.spines['right'].set_color('white')
Related
I am trying to align the matplotlib plot with its colorbar. However, when there is a tick on the top of the colormap, the figure itself shrinks a little bit:
Is there a way to equalize this distance (blue arrows) consistently?
For generating the plot, I am using following code:
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
ax.plot(...)
divider = make_axes_locatable(plt.gca())
cax = divider.append_axes('right', '5%', pad='3%')
sm = plt.cm.ScalarMappable(cmap=plt.get_cmap('viridis'),
norm=mpl.colors.Normalize(vmin=0, vmax=60))
sm.set_array([])
fig.colorbar(sm, cax=cax)
plt.tight_layout()
plt.savefig('pic.png', dpi=500)
I am trying to highlight minimum values of each row using the same color:
For instance, the first row minimum is 0.3. I want to highlight it with blue color. Similarly, for the second row, 0.042 and so on.
Here's the code.
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
from matplotlib.patches import Rectangle
Pe = np.random.rand(5,5)
annot=True
fig, ax1 = plt.subplots(1)
ax1 = sns.heatmap(Pe, linewidth=0.5,ax=ax1,annot=annot)
You could loop through the rows, find the index of the minimum, and draw a rectangle there. Setting clip_on=False prevents that the rectangles would be clipped by the border.
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
Pe = np.random.rand(5, 5)
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 4))
sns.set_style('white')
sns.heatmap(Pe, linewidth=0.5, annot=True, ax=ax1)
for ind, row in enumerate(Pe):
min_col = np.argmin(row)
ax1.add_patch(plt.Rectangle((min_col, ind), 1, 1, fc='none', ec='skyblue', lw=5, clip_on=False))
sns.heatmap(Pe, mask=Pe != Pe.min(axis=1, keepdims=True), annot=True, lw=2, linecolor='black', clip_on=False,
cmap=ListedColormap(['skyblue']), cbar=False, ax=ax2)
plt.tight_layout()
plt.show()
PS: To create animations, the Celluloid library is a lightweight option:
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn as sns
import numpy as np
from celluloid import Camera
Pe = np.random.rand(5, 5)
fig, ax1 = plt.subplots()
camera = Camera(fig)
sns.set_style('white')
row_array = np.arange(Pe.shape[0]).reshape(-1, 1)
for row in range(Pe.shape[0]):
sns.heatmap(Pe, mask=(Pe != Pe.min(axis=1, keepdims=True)) | (row < row_array),
annot=True, lw=2, linecolor='black', clip_on=False,
cmap=ListedColormap(['skyblue']), cbar=False, ax=ax1)
camera.snap()
animation = camera.animate(interval=800)
animation.save('animation.gif')
plt.show()
For more complicated animations, matplotlib's animation API can be considered.
I usually use matplotlib, but was playing with pandas plotting and experienced unexpected behaviour. I was assuming the following would return red and green edges rather than alternating. What am I missing here?
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({"col1":[1,2,4,5,6], "col2":[4,5,1,2,3]})
def amounts(df):
fig, ax = plt.subplots(1,1, figsize=(3,4))
(df.filter(['col1','col2'])
.plot.bar(ax=ax,stacked=True, edgecolor=["red","green"],
fill=False,linewidth=2,rot=0))
ax.set_xlabel("")
plt.tight_layout()
plt.show()
amounts(df)
I think plotting each column separately and setting the bottom argument to stack the bars provides the output you desire.
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({"col1":[1,2,4,5,6], "col2":[4,5,1,2,3]})
def amounts(df):
fig, ax = plt.subplots(1,1, figsize=(3,4))
df['col1'].plot.bar(ax=ax, linewidth=2, edgecolor='green', rot=0, fill=False)
df['col2'].plot.bar(ax=ax, bottom=df['col1'], linewidth=2, edgecolor='red', rot=0, fill=False)
plt.legend()
plt.tight_layout()
plt.show()
amounts(df)
How can I position xlabel on top of the plot using something else than "tick_top" - that adds a tick to the x label, and I don't want it.
xlabel on the bottom with no tick:
code:
import numpy as np; np.random.seed(0)
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
uniform_data = np.random.rand(10, 12)
ax = sns.heatmap(uniform_data, vmin=0, vmax=1)
plt.yticks(rotation=0)
plt.show()
xlabel on top but with tick:
code:
import numpy as np; np.random.seed(0)
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
uniform_data = np.random.rand(10, 12)
ax = sns.heatmap(uniform_data, vmin=0, vmax=1)
plt.yticks(rotation=0)
ax.xaxis.tick_top() # x axis on top
ax.xaxis.set_label_position('top')
plt.show()
Try this:
plt.tick_params(axis='both', which='major', labelsize=10, labelbottom = False, bottom=False, top = False, labeltop=True)
the parameter top=False means no ticks. :)
Ok finally found.
Needs a
ax.tick_params(length=0)
I have a matplotlib plot with a colorbar attached. I want to position the colorbar so that it is horizontal, and underneath my plot.
I have almost done this via the following:
plt.colorbar(orientation="horizontal",fraction=0.07,anchor=(1.0,0.0))
But the colorbar is still overlapping with the plot slightly (and the labels of the x axis). I want to move the colorbar further down, but I can't figure out how to do it.
using padding pad
In order to move the colorbar relative to the subplot, one may use the pad argument to fig.colorbar.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, ax = plt.subplots(figsize=(4,4))
im = ax.imshow(np.random.rand(11,16))
ax.set_xlabel("x label")
fig.colorbar(im, orientation="horizontal", pad=0.2)
plt.show()
using an axes divider
One can use an instance of make_axes_locatable to divide the axes and create a new axes which is perfectly aligned to the image plot. Again, the pad argument would allow to set the space between the two axes.
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np; np.random.seed(1)
fig, ax = plt.subplots(figsize=(4,4))
im = ax.imshow(np.random.rand(11,16))
ax.set_xlabel("x label")
divider = make_axes_locatable(ax)
cax = divider.new_vertical(size="5%", pad=0.7, pack_start=True)
fig.add_axes(cax)
fig.colorbar(im, cax=cax, orientation="horizontal")
plt.show()
using subplots
One can directly create two rows of subplots, one for the image and one for the colorbar. Then, setting the height_ratios as gridspec_kw={"height_ratios":[1, 0.05]} in the figure creation, makes one of the subplots much smaller in height than the other and this small subplot can host the colorbar.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, (ax, cax) = plt.subplots(nrows=2,figsize=(4,4),
gridspec_kw={"height_ratios":[1, 0.05]})
im = ax.imshow(np.random.rand(11,16))
ax.set_xlabel("x label")
fig.colorbar(im, cax=cax, orientation="horizontal")
plt.show()
Edit: Updated for matplotlib version >= 3.
Three great ways to do this have already been shared in this answer.
The matplotlib documentation advises to use inset_locator. This would work as follows:
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np
rng = np.random.default_rng(1)
fig, ax = plt.subplots(figsize=(4,4))
im = ax.imshow(rng.random((11, 16)))
ax.set_xlabel("x label")
axins = inset_axes(ax,
width="100%",
height="5%",
loc='lower center',
borderpad=-5
)
fig.colorbar(im, cax=axins, orientation="horizontal")