Using the following code, the first contour plot has grid lines. For the second plot, I have imported seaborn, but the grid lines don't show up. What do I need to add to make the grid lines show on the second plot.
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
dx=0.05
x=np.arange(0,5+dx,dx)
y=x
X,Y = np.meshgrid(x,y)
Z = np.sin(X)**10+np.cos(10+Y*Y)*np.cos(X)
nbins=10
levels=mpl.ticker.MaxNLocator(nbins=nbins).tick_values(Z.min(),Z.max())
plt.figure()
plt.contourf(x,y,Z,levels=levels)
plt.colorbar()
plt.grid('on')
import seaborn as sns
sns.set_context("notebook")
sns.set_style("whitegrid")
plt.figure()
plt.contourf(x,y,Z,levels=levels)
plt.colorbar()
plt.grid('on')
plt.show()
You either need to change either the axes.axisbelow rc parameter or the zorder of the contourf plot. So you could do
sns.set(context="notebook", style="whitegrid",
rc={"axes.axisbelow": False})
When you set up the style or
plt.contourf(x, y, Z, levels=levels, zorder=0)
When you draw the plot.
Related
from mpl_toolkits.mplot3d import Axes3D
ax.scatter(X_lda[:,0], X_lda[:,1], X_lda[:,2], alpha=0.4, c=y_train, cmap='rainbow', s=20)
plt.legend()
plt.show()
Essentially I'd like to add a legend for the scatterplot that shows the unique values in y_train and what color point it corresponds to on the plot.
The output plot:
Producing either a legend or a colorbar for a scatter is usually quite simple:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
x,y,z = (np.random.normal(size=(300,4))+np.array([0,2,4,6])).reshape(3,400)
c = np.tile([1,2,3,4], 100)
fig, ax = plt.subplots(subplot_kw=dict(projection="3d"))
sc = ax.scatter(x,y,z, alpha=0.4, c=c, cmap='rainbow', s=20)
plt.legend(*sc.legend_elements())
plt.colorbar(sc)
plt.show()
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.
I am trying to get a hexbin plot in a Seaborn Grid. I have the following code,
# Works in Jupyter with Python 2 Kernel.
%matplotlib inline
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
# Borrowed from http://stackoverflow.com/a/31385996/4099925
def hexbin(x, y, color, **kwargs):
cmap = sns.light_palette(color, as_cmap=True)
plt.hexbin(x, y, gridsize=15, cmap=cmap, extent=[min(x), max(x), min(y), max(y)], **kwargs)
g = sns.PairGrid(tips, hue='sex')
g.map_diag(plt.hist)
g.map_lower(sns.stripplot, jitter=True, alpha=0.5)
g.map_upper(hexbin)
However, that gives me the following image,
How can I fix the hexbin plots in such a way that they cover the entire surface of the graph and not just a subset of the shown plot area?
There are (at least) three problems with what you are trying to do here.
stripplot is for data where at least one axis is categorical. This is not true in this case. Seaborn guesses that the x axis is the categorical one which messes up the x axes of your subplots. From the docs for stripplot:
Draw a scatterplot where one variable is categorical.
In my suggested code below I have changed it to a simple scatter plot.
Drawing two hexbin-plots on top of eachother will only show the latter one. I added some alpha=0.5 to the hexbin arguments, but the result is far from pretty.
The extent parameter in your code adjusted the hexbin plot to x and y of each sex one at a time. But both of the hexbin plots need to be equal in size so they should use min/max of an entire series over both sexes. To achieve this I passed in the minimum and maximum values for all series to the hexbin function which can then pick and use the relevant ones.
Here is what I came up with:
# Works in Jupyter with Python 2 Kernel.
%matplotlib inline
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
# Borrowed from http://stackoverflow.com/a/31385996/4099925
def hexbin(x, y, color, max_series=None, min_series=None, **kwargs):
cmap = sns.light_palette(color, as_cmap=True)
ax = plt.gca()
xmin, xmax = min_series[x.name], max_series[x.name]
ymin, ymax = min_series[y.name], max_series[y.name]
plt.hexbin(x, y, gridsize=15, cmap=cmap, extent=[xmin, xmax, ymin, ymax], **kwargs)
g = sns.PairGrid(tips, hue='sex')
g.map_diag(plt.hist)
g.map_lower(plt.scatter, alpha=0.5)
g.map_upper(hexbin, min_series=tips.min(), max_series=tips.max(), alpha=0.5)
And here is the result:
I want to use a scatter plot to describe the relationship between X, Y and Z. Z is p-value so it is better to denote it as log values.
Following the instructions here, I can plot a logarithmic scatter plot, but the color bar seems wrong. The color bar is almost totally blue, but there should be some red! Below is the figure and my codes.
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import LogNorm
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
ax1.set_title("P-value")
Z1 = pos_spearmanr['pval']
X = pos_spearmanr['X']
Y = pos_spearmanr['Y']
im = ax1.scatter(X,
Y,
edgecolors=None,
c=Z1,
norm=LogNorm(),
cmap=plt.get_cmap('bwr'), alpha=0.2)
ax1.set_xlabel('X')
ax1.set_ylabel('Y')
ax1.set_xlim(0, 1)
ax1.set_ylim(0, 1)
cbar = fig.colorbar(im,ax=ax1)
I'm working with data that has the data has 3 plotting parameters: x,y,c. How do you create a custom color value for a scatter plot?
Extending this example I'm trying to do:
import matplotlib
import matplotlib.pyplot as plt
cm = matplotlib.cm.get_cmap('RdYlBu')
colors=[cm(1.*i/20) for i in range(20)]
xy = range(20)
plt.subplot(111)
colorlist=[colors[x/2] for x in xy] #actually some other non-linear relationship
plt.scatter(xy, xy, c=colorlist, s=35, vmin=0, vmax=20)
plt.colorbar()
plt.show()
but the result is TypeError: You must first set_array for mappable
From the matplotlib docs on scatter 1:
cmap is only used if c is an array of floats
So colorlist needs to be a list of floats rather than a list of tuples as you have it now.
plt.colorbar() wants a mappable object, like the CircleCollection that plt.scatter() returns.
vmin and vmax can then control the limits of your colorbar. Things outside vmin/vmax get the colors of the endpoints.
How does this work for you?
import matplotlib.pyplot as plt
cm = plt.cm.get_cmap('RdYlBu')
xy = range(20)
z = xy
sc = plt.scatter(xy, xy, c=z, vmin=0, vmax=20, s=35, cmap=cm)
plt.colorbar(sc)
plt.show()
Here is the OOP way of adding a colorbar:
fig, ax = plt.subplots()
im = ax.scatter(x, y, c=c)
fig.colorbar(im, ax=ax)
If you're looking to scatter by two variables and color by the third, Altair can be a great choice.
Creating the dataset
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame(40*np.random.randn(10, 3), columns=['A', 'B','C'])
Altair plot
from altair import *
Chart(df).mark_circle().encode(x='A',y='B', color='C').configure_cell(width=200, height=150)
Plot