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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:
 python libs for all vis things
Step1:  d3.js via mpld3
example here
quickstart here
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Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
from ipywidgets import interact
import matplotlib.pyplot as plt, mpld3
import seaborn as sns
Explanation: python libs for all vis things
End of explanation
mpld3.enable_notebook()
mean, cov = [0, 1], [(1, .5), (.5, 1)]
data = np.random.multivariate_normal(mean, cov, 200)
df = pd.DataFrame(data, columns=["x", "y"])
scatter_sns = sns.jointplot(x="x", y="y", data=df)
fig = plt.gcf()
ax = plt.gca()
pts = ax.get_children()[3]
mpld3.plugins.connect(fig)
mpld3.display(fig)
t = np.arange(0.0, 1.0, 0.01)
y1 = np.sin(2*np.pi*t)
y2 = np.sin(2*2*np.pi*t)
data = pd.DataFrame({"t": t,
                     "y1": y1,
                     "y2": y2,
                     "size": np.random.randint(20,200, size=len(t))
                     })
scatter_1 = sns.lmplot("t", "y1", 
           scatter_kws={"s": data["size"]},
           robust=False, # slow if true
           data=data, size=8)
fig1 = plt.gcf()
ax1 = plt.gca()
pts1 = ax1.get_children()[3]
scatter_2 = sns.lmplot("t", "y2", 
           scatter_kws={"s": data["size"]},
           robust=False, # slow if true
           data=data, size=8)
fig2 = plt.gcf()
ax2 = plt.gca()
pts2 = ax2.get_children()[3]
#mpld3.plugins.connect(fig2)
#mpld3.display(fig2)
# Scatter points
fig, ax = plt.subplots()
np.random.seed(42)
x, y = np.random.normal(size=(2,200))
color, size = np.random.random((2,200))
ax.scatter(x, y, c=color, s = 500 * size, alpha=0.3)
ax.grid(color='lightgray', alpha=0.7)
Explanation: d3.js via mpld3
example here
quickstart here
End of explanation
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