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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 example of a corrplot
Biokit proposes nice graphs for correlation
Step1:  To avoid created another graph container
Step2:  We compare it with seaborn and this example
Discovering structure in heatmap data.
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Python Code:
%matplotlib inline
import pyensae
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import pandas
import numpy
letters = "ABCDEFGHIJKLM"[0:10]
df = pandas.DataFrame(dict(( (k, numpy.random.random(10)+ord(k)-65) for k in letters)))
df.head()
from pyensae.graphhelper import Corrplot
c = Corrplot(df)
c.plot(figsize=(12,6))
Explanation: example of a corrplot
Biokit proposes nice graphs for correlation: corrplot function in Python but it only works with Python 2.7. I took the code out and put a modified version of in pyensae.
End of explanation
fig = plt.figure(num=None, facecolor='white', figsize=(12,6))
ax = plt.subplot(1, 1, 1, aspect='equal', facecolor='white')
c = Corrplot(df)
c.plot(ax=ax)
Explanation: To avoid created another graph container:
End of explanation
import seaborn as sns
cmap = sns.diverging_palette(h_neg=210, h_pos=350, s=90, l=30, as_cmap=True, center="light")
sns.clustermap(df.corr(), figsize=(10, 10), cmap=cmap)
Explanation: We compare it with seaborn and this example
Discovering structure in heatmap data.
End of explanation
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