How to color `matplotlib` scatterplot using a continuous value [`seaborn` color palettes?] : <p>I have a scatterplot and I want to color it based on another value (naively assigned to <code>np.random.random()</code> in this case).  </p>

<p><strong>Is there a way to use <code>seaborn</code> to map a continuous value (not directly associated with the data being plotted) for each point to a value along a continuous gradient in <code>seaborn</code>?</strong> </p>

<p>Here's my code to generate the data:</p>

<pre><code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn import decomposition
import seaborn as sns; sns.set_style("whitegrid", {'axes.grid' : False})

%matplotlib inline
np.random.seed(0)

# Iris dataset
DF_data = pd.DataFrame(load_iris().data, 
                       index = ["iris_%d" % i for i in range(load_iris().data.shape[0])],
                       columns = load_iris().feature_names)

Se_targets = pd.Series(load_iris().target, 
                       index = ["iris_%d" % i for i in range(load_iris().data.shape[0])], 
                       name = "Species")

# Scaling mean = 0, var = 1
DF_standard = pd.DataFrame(StandardScaler().fit_transform(DF_data), 
                           index = DF_data.index,
                           columns = DF_data.columns)

# Sklearn for Principal Componenet Analysis
# Dims
m = DF_standard.shape[1]
K = 2

# PCA (How I tend to set it up)
Mod_PCA = decomposition.PCA(n_components=m)
DF_PCA = pd.DataFrame(Mod_PCA.fit_transform(DF_standard), 
                      columns=["PC%d" % k for k in range(1,m + 1)]).iloc[:,:K]
# Plot
fig, ax = plt.subplots()
ax.scatter(x=DF_PCA["PC1"], y=DF_PCA["PC2"], color="k")
ax.set_title("No Coloring")
</code></pre>

<p><a href="https://i.stack.imgur.com/qh5LW.png" rel="noreferrer"><img src="https://i.stack.imgur.com/qh5LW.png" alt="enter image description here"></a></p>

<p>Ideally, I wanted to do something like this:</p>

<pre><code># Color classes
cmap = {obsv_id:np.random.random() for obsv_id in DF_PCA.index}

# Plot



fig, ax = plt.subplots()
ax.scatter(x=DF_PCA["PC1"], y=DF_PCA["PC2"], color=[cmap[obsv_id] for obsv_id in DF_PCA.index])
ax.set_title("With Coloring")

# ValueError: to_rgba: Invalid rgba arg "0.2965562650640299"
# to_rgb: Invalid rgb arg "0.2965562650640299"
# cannot convert argument to rgb sequence
</code></pre>

<p>but it didn't like the continuous value.</p>

<p>I want to use a color palette like:</p>

<pre><code>sns.palplot(sns.cubehelix_palette(8))
</code></pre>

<p><a href="https://i.stack.imgur.com/nVxYv.png" rel="noreferrer"><img src="https://i.stack.imgur.com/nVxYv.png" alt="enter image description here"></a></p>

<p>I also tried doing something like below, but it wouldn't make sense b/c it doesn't know which values I used in my <code>cmap</code> dictionary above:</p>

<pre><code>ax.scatter(x=DF_PCA["PC1"], y=DF_PCA["PC2"],cmap=sns.cubehelix_palette(as_cmap=True)
</code></pre>
