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		<title>The Normal Distribution</title>
		<link>https://creatronix.de/the-normal-distribution/</link>
		
		<dc:creator><![CDATA[Jörn]]></dc:creator>
		<pubDate>Wed, 25 Apr 2018 10:47:03 +0000</pubDate>
				<category><![CDATA[Data Science & SQL]]></category>
		<category><![CDATA[Python]]></category>
		<category><![CDATA[gauss]]></category>
		<category><![CDATA[gaussian]]></category>
		<category><![CDATA[normal distribution]]></category>
		<guid isPermaLink="false">http://creatronix.de/?p=1240</guid>

					<description><![CDATA[<p>Diving deeper into data science I started to brush up my knowledge about math especially statistics. The Mother of all Distributions The normal distribution was formulated by Carl Friedrich Gauß in 1809 and can be implemented in Python like the following : def normal_distribution_pdf(x, mu=0, sigma=1): sqrt_two_pi = math.sqrt(2*math.pi) return (1 / (sqrt_two_pi * sigma))&#8230;</p>
<p>The post <a href="https://creatronix.de/the-normal-distribution/">The Normal Distribution</a> appeared first on <a href="https://creatronix.de">Creatronix</a>.</p>
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										<content:encoded><![CDATA[<p>Diving deeper into data science I started to brush up my knowledge about math especially statistics.</p>
<h2>The Mother of all Distributions</h2>
<p><img fetchpriority="high" decoding="async" class="alignnone size-full wp-image-1243" src="https://creatronix.de/wp-content/uploads/2017/12/normal_distribution.png" alt="" width="493" height="469" srcset="https://creatronix.de/wp-content/uploads/2017/12/normal_distribution.png 493w, https://creatronix.de/wp-content/uploads/2017/12/normal_distribution-300x285.png 300w" sizes="(max-width: 493px) 100vw, 493px" /></p>
<p>The normal distribution was formulated by Carl Friedrich Gauß in 1809 and can be implemented in Python like the following :</p>
<div class="hcb_wrap">
<pre class="prism line-numbers lang-python" data-lang="Python"><code>def normal_distribution_pdf(x, mu=0, sigma=1): 
    sqrt_two_pi = math.sqrt(2*math.pi) 
    return (1 / (sqrt_two_pi * sigma)) * math.exp(-((x - mu) ** 2) / (2 * sigma ** 2))</code></pre>
</div>
<h3>Scipy</h3>
<p>For professional use you should packages like scipy to generate the pdf of a normal distribution</p>
<div class="hcb_wrap">
<pre class="prism line-numbers lang-python" data-lang="Python"><code>import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm

if __name__ == '__main__':
    fig, ax = plt.subplots(1, 1)
    x = np.linspace(norm.ppf(0.01), norm.ppf(0.99), 100)
    ax.plot(x, norm.pdf(x), 'r-', label='norm pdf')
    plt.savefig("pdf.png")
</code></pre>
</div>
<h3><img decoding="async" src="https://creatronix.de/wp-content/uploads/2018/04/pdf.png" alt="" class="alignnone size-full wp-image-5944" width="640" height="480" srcset="https://creatronix.de/wp-content/uploads/2018/04/pdf.png 640w, https://creatronix.de/wp-content/uploads/2018/04/pdf-300x225.png 300w" sizes="(max-width: 640px) 100vw, 640px" /></h3>
<h3>Further reading</h3>
<p><a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html">https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html</a></p>
<p>The post <a href="https://creatronix.de/the-normal-distribution/">The Normal Distribution</a> appeared first on <a href="https://creatronix.de">Creatronix</a>.</p>
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