Lesson 2-4: Data Distribution Statistics and Heat Map
tags: Get started with Python data visualization data visualization data analysis
This lesson will continue to introduce statistical graphs in Seaborn. It must be remembered that Seaborn is an advanced encapsulation of Matplotlib. It optimizes a lot of the ancient drawing process, so you will see a function to solve the problem.
2.4.1 Data Distribution Statistics
In statistics, studying the distribution of data is also an important task, such as whether certain data is normally distributed—some machine learning models care about the distribution of data.
In Matplotlib, you can visualize the distribution of data by drawing a histogram. In Seaborn, a function for drawing histograms is also provided.
%matplotlib inline
import seaborn as sns
import numpy as np
sns.set()
np.random.seed(0)
x = np.random.randn(100)
ax = sns.distplot(x)
Output result:
The sns.distplot function implements the histogram and draws the curve by the way-the curve actually represents KDE.
seaborn.distplot(a, bins=None, hist=True, kde=True, rug=False, fit=None, hist_kws=None, kde_kws=None, rug_kws=None, fit_kws=None, color=None, vertical=False, norm_hist=False, axlabel=None, label=None, ax=None)
Except sns.distpl
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