http://datacamp-community-prod.s3.amazonaws.com/ba1fe95a-8b70-4d2f-95b0-bc954e9071b0

This NumPy cheat sheet helps you to tackle the basics of this Python scientific computing library!

NumPy Cheat Sheet: Data Analysis in Python

https://www.datacamp.com/community/news/my-take-on-25-questions-to-test-a-data-scientist-on-image-processing-w-gys0alqzhh

My take on 25 Questions to test a Data Scientist on Image Processing with Interactive Code- Part 1

My take on 25 Questions to test a Data Scientist on Image Processing w

https://www.datacamp.com/community/news/my-journey-to-reinforcement-learning-part-15-simple-binary-image-t-ltzcohutw6h

My Journey to Reinforcement Learning — Part 1.5: Simple Binary Image Transformation with Q-Learning

My Journey to Reinforcement Learning — Part 1.5: Simple Binary Image T

https://www.datacamp.com/community/news/fitting-the-noise-x4vfmw1dbpm

Quick explainer I wrote about overfitting and the bias-variance tradeoff. Come learn something. Code included! Feedback welcome and wanted! 

Fitting the Noise

<p>Numpy&#39;s random module, a suite of functions based on pseudorandom number generation. Random means something that can not be predicted logically.</p>
<h2 id="-np-random-seed-function"><code>np.random.seed()</code> Function</h2>
<p>In this example, you will simulate a coin flip. You will use the function <code>np.random()</code>, which draws a number between 0 and 1 such that all numbers in this interval are equally likely to occur.</p>
<p>If the number you draw is less than 0.5, which has a 50% chance of happening, you say heads and tails otherwise. This type of result where results are either True (Heads) or False (Tails) is referred to as Bernoulli trial.</p>
<p>The pseudorandom number works by starting with an integer called a seed and then generates numbers in succession. The same seed gives the same sequence of random numbers, hence the name &quot;pseudo&quot; random number generation. If you want to have reproducible code, it is good to seed the random number generator using the <code>np.random.seed()</code> function.</p>
<p>To do the coin flips, you import <code>NumPy</code>, seed the random number generator, and then draw four random numbers. You can specify how many random numbers you want with the <code>size</code> keyword.</p>
<pre><code class="lang-Python">import numpy as np
np.random.seed(42)
random_numbers = np.random.random(size=4)
random_numbers
</code></pre>
<pre><code>array([0.3745012, 0.95071431, 0.73199394, 0.59865848])
</code></pre><p>The first number you get is less than 0.5, so it is heads while the remaining three are tails. You can show this explicitly using the less than operation, which gives you an array with boolean values, True for heads while False for tails.</p>
<pre><code class="lang-Python">heads = random_numbers &lt; 0.5
heads
</code></pre>
<pre><code>array([True, False, False, False], dtype=bool)
</code></pre><p>Finally, you can compute the number of heads by summing the array of booleans <code>heads</code>, because in numerical context, Python treats True as one and False as zero.</p>
<pre><code class="lang-Python">np.sum(heads)
</code></pre>
<pre><code>1
</code></pre><h2 id="simulating-four-random-coin-flips">Simulating Four Random Coin Flips</h2>
<p>In the following example, we want to know the probability of getting four heads if we were to repeat the four flips of the coin over and over again. We can do this with a for loop.</p>
<p>We first initialize the count to zero. We then do repeat 10,000 repeats of the four-flip trials. If a given trial had four heads, we would increase the count.</p>
<p>So what is the probability of getting all four heads? It&#39;s the number of times you got all heads, divided by the total number of trials. The result is about 0.06.</p>
<pre><code class="lang-Python">n_all_heads = 0

# Initialize number of 4-heads trials
for _ in range(10000):
         heads = np.random.random(size=4) &lt; 0.5
         n_heads = np.sum(heads)
         if n_heads == 4:
             n_all_heads += 1

n_all_heads / 10000
</code></pre>
<pre><code>0.0621
</code></pre><h2 id="interactive-example-of-generating-random-numbers">Interactive Example of Generating Random Numbers</h2>
<p>In this example, we&#39;ll generate lots of random numbers between zero and one, and then plot a histogram of the results. If the numbers are truly random, all bars in the histogram should be of (close to) equal height.</p>
<p>For this example you will need to:</p>
<ul>
<li>Seed the random number generator with <code>np.random.seed</code> using the seed <code>42</code>.</li>
<li>Initialize an empty array, random_numbers, of 100,000 entries to store the <code>random numbers</code>. Make sure you use <code>np.empty(100000)</code> to do this.</li>
<li>Write a <code>for</code> loop to draw 100,000 random numbers using <code>np.random.random()</code>, storing them in the <code>random_numbers</code> array. To do so, loop over <code>range(100000)</code>.</li>
<li>Plot a histogram of <code>random_numbers</code>. It is not necessary to label the axes in this case because we are just checking the random number generator.</li>
</ul>
<pre><code class="lang-Python"># Seed the random number generator
np.random.seed(42)

# Initialize random numbers: random_numbers
random_numbers = np.empty(100000)

# Generate random numbers by looping over range(100000)
for i in range(100000):
    random_numbers[i] = np.random.random()

# Plot a histogram
_ = plt.hist(random_numbers)

# Show the plot
plt.show()
</code></pre>
<p>When we run the above code, it produces the following result:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1597694766/nprand2_wwnymj.png" /></center>

<p><a href="https://campus.datacamp.com/courses/statistical-thinking-in-python-part-1/thinking-probabilistically-discrete-variables?ex=5">Try it for yourself</a>.</p>
<p>To learn more about random number generators and hacker statistics, please see this video from our course <a href="https://campus.datacamp.com/courses/statistical-thinking-in-python-part-1/thinking-probabilistically-discrete-variables?ex=4">Statistical Thinking in Python (Part 1)</a>.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/NX4eCKJHb5M?start=50" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<p>This content is taken from DataCamp’s <a href="https://www.datacamp.com/courses/statistical-thinking-in-python-part-1">Statistical Thinking in Python (Part 1)</a> course by Justin Bois.</p>


https://www.datacamp.com/community/tutorials/numpy-random

Random Number Generator Using Numpy Tutorial 

https://www.datacamp.com/community/tutorials/docstrings-python

Learn about Python Docstrings. Find different examples & format types of docstrings for Sphinx, Numpy and Pydoc.

See Python Docstrings. Learn about the different types of docstrings & various docstring formats like Sphinx, Numpy, and Pydoc with examples now.

community

datacamp

numpy

News (7)

My take on 25 Questions to test a Data Scientist on Image Processing w

Fitting the Noise

Tutorials (10)

Random Number Generator Using Numpy Tutorial

Docstrings in Python Tutorial

Cheat Sheets (1)

NumPy Cheat Sheet: Data Analysis in Python

Open Courses (0)

Podcast (0)

Blog Posts (0)

Tech Thoughts (0)