Seaborn data visualization

use Seaborn Data visualization

Seaborn And Matplotlib

Matplotlib drawing

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

plt.figure()

x = np.linspace(0, 10, 500)
y = np.cumsum(np.random.randn(500, 6), 0)

plt.plot(x, y)
plt.legend('ABCDEF', ncol=2, loc='upper left')

#  display picture 
plt.show()

Seaborn There are many advanced drawing functions , And you can rewrite Matplotlib Default parameters , So as to use simple Matplotlib Get better results , It can be used Seaborn Of set() Method to set the style
Other codes are the same as above , Just add and import this module and use set() function

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

sns.set()
plt.figure()

x = np.linspace(0, 10, 500)
y = np.cumsum(np.random.randn(500, 6), 0)

plt.plot(x, y)
plt.legend('ABCDEF', ncol=2, loc='upper left')

#  display picture 
plt.show()

Seaborn Graphic introduction

For many graphics Matplotlib Can be realized , Three uses Seaborn Will be more convenient

Frequency histogram 、KDE And density diagram
Frequency histogram

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

sns.set()
plt.figure()

data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)
data = pd.DataFrame(data, columns=['x', 'y'])

for col in 'xy':
    plt.hist(data[col], alpha=0.5)

#  display picture 
plt.show()

use KDE Get the smooth evaluation of variable distribution , adopt sns.kdeplot Realization

for col in 'xy':
    sns.kdeplot(data[col], shade=True)

use distplot We can make the frequency histogram and KDE Combine

for col in 'xy':
    sns.distplot(data[col])

If you want to kdeplot The input is a two-dimensional data set , Then you can get a two-dimensional number visualization

sns.kdeplot(data['x'],data['y'])

use sns.jointplot You can see the joint distribution of two variables and the independent distribution of one variable at the same time , Use a white background here

with sns.axes_style('white'):
    sns.jointplot("x", "y", data, kind='kde')

with sns.axes_style('white'):
    sns.jointplot("x", "y", data, kind='hex')

Matrix diagram
The following is a matrix diagram of the relationship between the four variables

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

sns.set()
plt.figure()

iris = sns.load_dataset("iris")
print(iris.head())
''' sepal_length sepal_width petal_length petal_width species 0 5.1 3.5 1.4 0.2 setosa 1 4.9 3.0 1.4 0.2 setosa 2 4.7 3.2 1.3 0.2 setosa 3 4.6 3.1 1.5 0.2 setosa 4 5.0 3.6 1.4 0.2 setosa '''
sns.pairplot(iris, hue='species', size=2.5)

#  display picture 
plt.show()

Faceted frequency histogram

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

sns.set()
plt.figure()

tips = sns.load_dataset("tips")
print(tips)
''' total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 .. ... ... ... ... ... ... ... 239 29.03 5.92 Male No Sat Dinner 3 240 27.18 2.00 Female Yes Sat Dinner 2 241 22.67 2.00 Male Yes Sat Dinner 2 242 17.82 1.75 Male No Sat Dinner 2 243 18.78 3.00 Female No Thur Dinner 2 [244 rows x 7 columns] '''
tips['tip_pct'] = 100*tips['tip']/tips['total_bill']
grid = sns.FacetGrid(tips, row="sex", col="time", margin_titles=True)
grid.map(plt.hist, "tip_pct", bins=np.linspace(1, 40, 15))

#  display picture 
plt.show()

Factor map

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

sns.set()
plt.figure()

tips = sns.load_dataset("tips")
tips['tip_pct'] = 100*tips['tip']/tips['total_bill']

with sns.axes_style(style='ticks'):
    g = sns.factorplot("day", "total_bill", "sex", data=tips, kind="box")
    g.set_axis_labels("day", "Total Bill")

#  display picture 
plt.show()

Joint distribution

with sns.axes_style('white'):
    sns.jointplot("total_bill", "tip", data=tips, kind='hex')

with sns.axes_style('white'):
    sns.jointplot("total_bill", "tip", data=tips, kind='reg')

Bar chart ：sns.factorplot Draw a bar chart

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

sns.set()
plt.figure()

planets = sns.load_dataset('planets')
print(planets.head())
''' method number orbital_period mass distance year 0 Radial Velocity 1 269.300 7.10 77.40 2006 1 Radial Velocity 1 874.774 2.21 56.95 2008 2 Radial Velocity 1 763.000 2.60 19.84 2011 3 Radial Velocity 1 326.030 19.40 110.62 2007 4 Radial Velocity 1 516.220 10.50 119.47 2009 '''
with sns.axes_style('white'):
    g = sns.factorplot("year", data=planets, aspect=2, kind="count", color='steelblue')
    g.set_xticklabels(step=5)

#  display picture 
plt.show()

with sns.axes_style('white'):
    g = sns.factorplot("year", data=planets, aspect=4.0, kind="count", hue='method', order=range(2001, 2015))
    g.set_ylabels('Number of Planets Discovered')