import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib notebook
plt.style.use("seaborn-colorblind")

np.random.seed(123)

# cumsum: add value_of_i + value_of_i+1 = value_of_i+2
df = pd.DataFrame({‘A‘: np.random.randn(365).cumsum(0), 
                   ‘B‘: np.random.randn(365).cumsum(0) + 20,
                   ‘C‘: np.random.randn(365).cumsum(0) - 20}, 
                  index=pd.date_range(‘1/1/2017‘, periods=365))
# create a scatter plot of columns ‘A‘ and ‘C‘, with changing color (c) and size (s) based on column ‘B‘
df.plot.scatter(‘A‘, ‘C‘, c=‘B‘, s=df[‘B‘], colormap=‘viridis‘)
#df.plot.box();
#df.plot.hist(alpha=0.7);
#df.plot.kde();
#pd.tools.plotting.scatter_matrix(iris); Create scater plots between the different variables and 
#histograms aloing the diagonals to see the obvious patter

#pd.tools.plotting.parallel_coordinates(iris, ‘Name‘);
#visualizing high dimensional multivariate data, each variable in the data set corresponds to an equally spaced parallel vertical line

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib notebook

np.random.seed(1234)

v1 = pd.Series(np.random.normal(0,10,1000), name=‘v1‘)
v2 = pd.Series(2*v1 + np.random.normal(60,15,1000), name=‘v2‘)

# plot a kernel density estimation over a stacked barchart
plt.figure()
plt.hist([v1, v2], histtype=‘barstacked‘, normed=True);
v3 = np.concatenate((v1,v2))
sns.kdeplot(v3);

plt.figure()
# we can pass keyword arguments for each individual component of the plot
sns.distplot(v3, hist_kws={‘color‘: ‘Teal‘}, kde_kws={‘color‘: ‘Navy‘});

plt.figure()
# sns.jointplot(v1, v2, alpha=0.4);

# grid = sns.jointplot(v1, v2, alpha=0.4);
# grid.ax_joint.set_aspect(‘equal‘)

# sns.jointplot(v1, v2, kind=‘hex‘);

# set the seaborn style for all the following plots
# sns.set_style(‘white‘)
# sns.jointplot(v1, v2, kind=‘kde‘, space=0);# space is used to set the margin of the joint plot

iris = pd.read_csv(‘iris.csv‘)
sns.pairplot(iris, hue=‘Name‘, diag_kind=‘kde‘, size=2);

iris = pd.read_csv(‘iris.csv‘)
plt.figure(figsize=(8,6))
plt.subplot(121)
sns.swarmplot(‘Name‘, ‘PetalLength‘, data=http://www.mamicode.com/iris);
plt.subplot(122)
sns.violinplot(‘Name‘, ‘PetalLength‘, data=http://www.mamicode.com/iris);