I want to plot a heatmap in seaborn. My code is following:
plt.rcParams['font.size'] = 13
plt.rcParams['font.weight'] = 'bold'
my_dpi=96
fig, ax = plt.subplots(figsize=(800/my_dpi, 600/my_dpi), dpi=my_dpi, facecolor='black')
rdgn = sns.diverging_palette(h_neg=130, h_pos=10, s=99, l=55, sep=3)
sns.heatmap(df, cmap=rdgn, center=0.00, annot=True, fmt ='.2%', linewidths=1.3, linecolor='black', cbar=False, ax=ax)
plt.savefig('./image/image.png', dpi=96, facecolor='black')
And the result is following:
I want the set 0 to be white, and the value >0 to be red, the values which <0 to be green. But the center in heatmap is invalid.
By the way, how to set the color unsymmetrical. Because the min value in my data is -0.34 and the maxima is 1.31. I want to set 0 to be white, -0.34 to be greenest and 1.31 to be reddest.
center would require something that can be centered. So instead of a palette, which is a list of colors, you will need a colormap. Seaborn provides the as_cmap parameter for this case,
sns.diverging_palette(..., as_cmap=True)
Alternatively, you can of course use any other matplotlib colormap, or specify your custom colormap.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data = np.linspace(-0.34, 1.31, 100).reshape(10,10)
fig, ax = plt.subplots()
rdgn = sns.diverging_palette(h_neg=130, h_pos=10, s=99, l=55, sep=3, as_cmap=True)
sns.heatmap(data, cmap=rdgn, center=0.00, annot=True, fmt ='.0%',
linewidths=1.3, linecolor='black', cbar=True, ax=ax)
plt.show()
If instead of centering the colormap you want to shift its middle point you cannot use center. But instead a matplotlib.colors.DivergingNorm.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import DivergingNorm
import seaborn as sns
data = np.linspace(-0.34, 1.31, 100).reshape(10,10)
fig, ax = plt.subplots()
rdgn = sns.diverging_palette(h_neg=130, h_pos=10, s=99, l=55, sep=3, as_cmap=True)
divnorm = DivergingNorm(vmin=data.min(), vcenter=0, vmax=data.max())
sns.heatmap(data, cmap=rdgn, norm=divnorm, annot=True, fmt ='.0%',
linewidths=1.3, linecolor='black', cbar=True, ax=ax)
plt.show()
Here, the full colormap will be squeezed in the green part and stretched in the red part.
It looks like the vmin and vmax parameters of seaborn.heatmap might help you:
sns.heatmap(df, cmap=rdgn, annot=True, fmt ='.2%', linewidths=1.3,
linecolor='black', cbar=False, ax=ax,
vmin=-0.34, vmax=1.31)
However there doesn't seem to be a way to also set the center to 0 for non-divergent color maps, so if that is a required feature then you can't use seaborn.heatmap. The best you could do would be to set vmin = -vmax which would at least make the center white.
It looks like you might have diverging data (no hard limit), in which case you could look at using one of the divergent color maps (in which case you need to use center=0 and not vmin/vmax).
Related
I would like to add to the plot below open circles surrounding each data point and set the diameter proportional to the values of a 3rd variable. Currently, this is what I tried but the circles are filled and cover the data points. Using "facecolors='none'" did not help.
z = df.z # this is the 3rd variable
s = [10*2**n for n in range(len(z))]
ax1 = sns.scatterplot(x='LEF', y='NPQ', hue="Regime", markers=["o",
"^"], s=s, facecolors='none', data=df, ax=ax1)
The following approach loops through the generated dots, and sets their edgecolors to their facecolors. Then the facecolors are set to fully transparent.
import matplotlib.pyplot as plt
import seaborn as sns
tips = sns.load_dataset('tips')
ax = sns.scatterplot(data=tips, x="total_bill", y="tip", hue="day", size="size", sizes=(10, 200))
for dots in ax.collections:
facecolors = dots.get_facecolors()
dots.set_edgecolors(facecolors.copy())
dots.set_facecolors('none')
dots.set_linewidth(2)
plt.show()
I have a parallel coordinates plot with lots of data points so I'm trying to use a continuous colour bar to represent that, which I think I have worked out. However, I haven't been able to remove the default key that is put in when creating the plot, which is very long and hinders readability. Is there a way to remove this table to make the graph much easier to read?
This is the code I'm currently using to generate the parallel coordinates plot:
parallel_coordinates(data[[' male_le','
female_le','diet','activity','obese_perc','median_income']],'median_income',colormap = 'rainbow',
alpha = 0.5)
fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
cmap = mpl.cm.rainbow
bounds = [0.00,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N,)
plt.colorbar(mpl.cm.ScalarMappable(norm = norm, cmap=cmap),cax = ax, orientation = 'horizontal',
label = 'normalised median income', alpha = 0.5)
plt.show()
Current Output:
I want my legend to be represented as a color bar, like this:
Any help would be greatly appreciated. Thanks.
You can use ax.legend_.remove() to remove the legend.
The cax parameter of plt.colorbar indicates the subplot where to put the colorbar. If you leave it out, matplotlib will create a new subplot, "stealing" space from the current subplot (subplots are often referenced to by ax in matplotlib). So, here leaving out cax (adding ax=ax isn't necessary, as here ax is the current subplot) will create the desired colorbar.
The code below uses seaborn's penguin dataset to create a standalone example.
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import numpy as np
from pandas.plotting import parallel_coordinates
penguins = sns.load_dataset('penguins')
fig, ax = plt.subplots(figsize=(10, 4))
cmap = plt.get_cmap('rainbow')
bounds = np.arange(penguins['body_mass_g'].min(), penguins['body_mass_g'].max() + 200, 200)
norm = mpl.colors.BoundaryNorm(bounds, 256)
penguins = penguins.dropna(subset=['body_mass_g'])
parallel_coordinates(penguins[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']],
'body_mass_g', colormap=cmap, alpha=0.5, ax=ax)
ax.legend_.remove()
plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
ax=ax, orientation='horizontal', label='body mass', alpha=0.5)
plt.show()
Values in my matrix called 'energy' are close enough to each other: e.g. one value can be 500, another one 520. And i want to see the color difference on my plot more precisely. Like for the smallest value in my data it should be the very dark color and for the highest value it should be the very bright color.
I have the following code:
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
plt.imshow(energy[0:60, 0:5920], cmap='Reds')
ax.axes.set_aspect(aspect=100)
plt.grid(color='yellow')
plt.title('My plot')
plt.xlabel('Length points')
plt.ylabel('Time points(seconds)')
import matplotlib.ticker as plticker
loc = plticker.MultipleLocator(base=500)
ax.xaxis.set_major_locator(loc)
plt.show()
I get the following plot:
plot of energy
Other words i'd love to get this plot more colorful.
Thanks in advance.
You can set a custom range either through a custom colormap or adjusting the range value to show using the keywords vmin and vmax. For example:
from matplotlib.pyplot import subplots
import numpy as np
fig, ax = subplots()
h = ax.imshow(np.random.rand(10,10) * 10, vmin = 0,\
vmax = 2, cmap = 'Reds')
fig.colorbar(h)
fig.show()
Which produces the colors within 0, 2 value
Alternatively you can rescale your data or adjust your colormap, see the maplotlib docs for more info.
I've tried the other threads, but can't work out how to solve. I'm attempting to create a discrete colorbar. Much of the code appears to be working, a discrete bar does appear, but the labels are wrong and it throws the error: "No mappable was found to use for colorbar creation. First define a mappable such as an image (with imshow) or a contour set (with contourf)."
Pretty sure the error is because I'm missing an argument in plt.colorbar, but not sure what it's asking for or how to define it.
Below is what I have. Any thoughts gratefully received:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
norm = mpl.colors.BoundaryNorm(np.arange(-0.5,4), cmap.N)
ex2 = sample_data.plot.scatter(x='order_count', y='total_value',c='cluster', marker='+', ax=ax, cmap='plasma', norm=norm, s=100, edgecolor ='none', alpha=0.70)
plt.colorbar(ticks=np.linspace(0,3,4))
plt.show()
Indeed, the fist argument to colorbar should be a ScalarMappable, which would be the scatter plot PathCollection itself.
Setup
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame({"x" : np.linspace(0,1,20),
"y" : np.linspace(0,1,20),
"cluster" : np.tile(np.arange(4),5)})
cmap = mpl.colors.ListedColormap(["navy", "crimson", "limegreen", "gold"])
norm = mpl.colors.BoundaryNorm(np.arange(-0.5,4), cmap.N)
Pandas plotting
The problem is that pandas does not provide you access to this ScalarMappable directly. So one can catch it from the list of collections in the axes, which is easy if there is only one single collection present: ax.collections[0].
fig, ax = plt.subplots()
df.plot.scatter(x='x', y='y', c='cluster', marker='+', ax=ax,
cmap=cmap, norm=norm, s=100, edgecolor ='none', alpha=0.70, colorbar=False)
fig.colorbar(ax.collections[0], ticks=np.linspace(0,3,4))
plt.show()
Matplotlib plotting
One could consider using matplotlib directly to plot the scatter in which case you would directly use the return of the scatter function as argument to colorbar.
fig, ax = plt.subplots()
scatter = ax.scatter(x='x', y='y', c='cluster', marker='+', data=df,
cmap=cmap, norm=norm, s=100, edgecolor ='none', alpha=0.70)
fig.colorbar(scatter, ticks=np.linspace(0,3,4))
plt.show()
Output in both cases is identical.
import xarray as xr
import cartopy.crs as ccrs
USA_PROJ = ccrs.AlbersEqualArea(central_longitude=-97., central_latitude=38.)
g_simple = ds_by_month.t2m.plot(x='longitude',
y='latitude',
col='month',
col_wrap=6,
aspect=ds.dims['longitude'] / ds.dims['latitude'],
subplot_kws=dict(projection=USA_PROJ),
add_colorbar=False,
transform=ccrs.PlateCarree())
g_simple.add_colorbar(orientation='horizontal')
for ax in g_simple.axes.ravel():
ax.coastlines()
ax.set_extent([-121, -72, 22.5, 50])
plt.tight_layout()
plt.show()
On running the code above, I get the foll. figure:
How do I ensure that the colorbar is not overlapping the plots? the overlap happens even if I use the xarray default colorbar.
You could give the color bar its own set of axes and set the "bottom" value to negative so that it exceeds the bounding box, or otherwise set the subplots_adjust function using a keyword argument (i.e. hspace = 2 etc).
Here's an example with random data below (modified from matplotlib subplots example):
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=6, figsize=(15,5))
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
# color bar
fig.subplots_adjust(right=0.875) #also try using kwargs bottom, top, or hspace
cbar_ax = fig.add_axes([0.1, -0.1, .8, .05]) #left, bottom, width, height
fig.colorbar(im, cax=cbar_ax, orientation="horizontal")
plt.show()