Sns distplot python примеры

Seaborn Distplot

Seaborn distplot lets you show a histogram with a line on it. This can be shown in all kinds of variations. We use seaborn in combination with matplotlib, the Python plotting module.

A distplot plots a univariate distribution of observations. The distplot() function combines the matplotlib hist function with the seaborn kdeplot() and rugplot() functions.

Example

Distplot example

The plot below shows a simple distribution. It creats random values with random.randn().
This will work if you manually define values too.

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

sns.set(rc={«figure.figsize»: (8, 4)}); np.random.seed(0)
x = np.random.randn(100)
ax = sns.distplot(x)
plt.show()

Distplot examples

You can show all kinds of variations of the distplot. We use the subplot() method from the pylab module to show 4 variations at once.

By changing the parameters in the distplot() method you can create totally different views. You can play around with these parameters to change color, orientation and more.

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import matplotlib.pyplot as plt
import seaborn as sns, numpy as np
from pylab import *

sns.set(rc={«figure.figsize»: (8, 4)}); np.random.seed(0)
x = np.random.randn(100)

subplot(2,2,1)
ax = sns.distplot(x)

subplot(2,2,2)
ax = sns.distplot(x, rug=False, hist=False)

subplot(2,2,3)
ax = sns.distplot(x, vertical=True)

subplot(2,2,4)
ax = sns.kdeplot(x, shade=True, color=«r»)

plt.show()

Seaborn dist

You an show a standard dataset from seaborn in histogram too.
This is qutie a large dataset so only take one column.

import matplotlib.pyplot as plt
import seaborn as sns

titanic=sns.load_dataset(‘titanic’)
age1=titanic[‘age’].dropna()
sns.distplot(age1)
plt.show()

Distplot bins

import matplotlib.pyplot as plt
import seaborn as sns

titanic=sns.load_dataset(‘titanic’)
age1=titanic[‘age’].dropna()
sns.distplot(age1,bins=30,kde=False)
plt.show()

Seaborn different plots

The example below shows some other distribution plots examples. You an activate a grid with the grid(True) method call.

import matplotlib.pyplot as plt
import seaborn as sns

titanic=sns.load_dataset(‘titanic’)
age1=titanic[‘age’].dropna()

fig,axes=plt.subplots(1,2)
sns.distplot(age1,ax=axes[0])
plt.grid(True)
sns.distplot(age1,rug=True,ax=axes[1])
plt.show()

Источник

Seaborn Distplot: A Comprehensive Guide

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What is a Seaborn Distplot?

A Distplot or distribution plot, depicts the variation in the data distribution. Seaborn Distplot represents the overall distribution of continuous data variables. The Seaborn module along with the Matplotlib module is used to depict the distplot with different variations in it. The Distplot depicts the data by a histogram and a line in combination to it.

Creating a Seaborn Distplot

Python Seaborn module contains various functions to plot the data and depict the data variations. The seaborn.distplot() function is used to plot the distplot. The distplot represents the univariate distribution of data i.e. data distribution of a variable against the density distribution. Syntax:

The seaborn.distplot() function accepts the data variable as an argument and returns the plot with the density distribution. Example 1:

import numpy as np import seaborn as sn import matplotlib.pyplot as plt data = np.random.randn(200) res = sn.distplot(data) plt.show() 

We have used the numpy.random.randn() function to generate random data values. Further, the pyplot.show() function is used show the plot. Output: Example 2:

import numpy as np import seaborn as sn import matplotlib.pyplot as plt import pandas as pd data_set = pd.read_csv("C:/mtcars.csv") data = pd.DataFrame(data_set['mpg']) res = sn.distplot(data) plt.show() 

The pandas.read_csv() function loads the dataset into the Python environment. Output:

Adding labels to the axis of DistPlot

The Seaborn Distplot can be provided with labels of the axis by converting the data values into a Pandas Series using the below syntax: Syntax:

pandas.Series(data,name='name') seaborn.distplot() 

import numpy as np import seaborn as sn import matplotlib.pyplot as plt data = np.random.randn(200) res = pd.Series(data,name="Range") plot = sn.distplot(res) plt.show() 

Output:

Seaborn DistPlot along with Kernel Density Estimate Plot

The Seaborn Distplot can also be clubbed along with the Kernel Density Estimate Plot to estimate the probability of distribution of continuous variables across various data values. Syntax:

seaborn.distplot(data,kde=True) 

The kde parameter is set to True to enable the Kernel Density Plot along with the distplot. Example:

import numpy as np import seaborn as sn import matplotlib.pyplot as plt data = np.random.randn(100) res = pd.Series(data,name="Range") plot = sn.distplot(res,kde=True) plt.show() 

Output:

Visualizing the data with Seaborn DistPlot along with Rug Plot

We can map the Seaborn Distplot along with Rug Plot to depict the distribution of data against bins with respect to the univariate data variable. The Rug Plot describes visualizes distribution of data in the form of bins. Syntax:

seaborn.distplot(data, rug=True, hist=False) 

The ‘ rug ’ parameter needs to be set to True to enable the rug plot distribution. Example:

import numpy as np import seaborn as sn import matplotlib.pyplot as plt data = np.random.randn(100) res = pd.Series(data,name="Range") plot = sn.distplot(res,rug=True,hist=False) plt.show() 

Output:

Plotting Seaborn Distplot along vertical axis

The entire Distplot can be plotted on the y axis using the below syntax: Syntax:

seaborn.distplot(data,vertical=True) 

The ‘ vertical ’ parameter needs to be set to True to plot the distplot on the y-axis. Example:

import numpy as np import seaborn as sn import matplotlib.pyplot as plt data = np.random.randn(100) plot = sn.distplot(data,vertical=True) plt.show() 

Output:

Setting a different style using seaborn.set() function

Seaborn has a number of in-built functions to add extra background features to the plots. The seaborn.set() function is used to set different background to the distribution plots. Syntax:

import numpy as np import seaborn as sn import matplotlib.pyplot as plt sn.set(style='dark',) data = np.random.randn(500) plot = sn.distplot(data) plt.show() 

Output:

Setting Custom color to Seaborn DistPlot

We can set different colors to the distplot to add to the visualization of the data using the ‘ color ’ parameter of the seaborn.distplot() function. Syntax:

seaborn.distplot(data, color='color') 

import numpy as np import seaborn as sn import matplotlib.pyplot as plt sn.set(style='dark',) data = np.random.randn(500) plot = sn.distplot(data,color='purple') plt.show() 

Output:

Conclusion

Thus, Seaborn Module along with Matplotlib module helps in the data visualization and depicts the distribution of data. I strongly recommend all the readers to read the Python Matplotlib Module to understand the basics of Data Visualization.

References

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