Python Statistics Module

Last modified on: October 20, 2024

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<h3>Python <code>statistics</code> Module</h3>
<p>The <code>statistics</code> module in Python provides functions to perform various statistical operations on numerical data. It includes functions for calculating averages, variance, standard deviation, and more. This module is particularly useful for analyzing and summarizing datasets in data science and mathematical applications.</p>
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<h3>Basic Statistical Functions</h3>
<p>Here are some of the most commonly used functions from the <code>statistics</code> module.</p>
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<h3><strong>1. Mean (<code>mean</code>)</strong></h3>
<p>The <code>mean()</code> function returns the arithmetic average of a list or sequence of numbers.</p>
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<pre class="language-python"><code>import statistics

data = [10, 20, 30, 40, 50]
mean_value = statistics.mean(data)
print(mean_value)  # Output: 30</code></pre>
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<h3><strong>2. Median (<code>median</code>)</strong></h3>
<p>The <code>median()</code> function returns the middle value of a dataset. If the dataset has an even number of items, the average of the two middle numbers is returned.</p>
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<pre class="language-python"><code>data = [10, 20, 30, 40, 50]
median_value = statistics.median(data)
print(median_value)  # Output: 30
</code></pre>
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<h3><strong>3. Mode (<code>mode</code>)</strong></h3>
<p>The <code>mode()</code> function returns the most common value in a dataset. If there are multiple modes (bimodal or multimodal), the function will return the first mode it encounters.</p>
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<pre class="language-python"><code>data = [1, 2, 2, 3, 4, 4, 4, 5]
mode_value = statistics.mode(data)
print(mode_value)  # Output: 4
</code></pre>
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<h3><strong>4. Variance (<code>variance</code>)</strong></h3>
<p>The <code>variance()</code> function measures how far the data points in a set are spread out from the mean. It returns the sample variance.</p>
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<pre class="language-python"><code>data = [10, 20, 30, 40, 50]
variance_value = statistics.variance(data)
print(variance_value)  # Output: 250
</code></pre>
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<h3><strong>5. Standard Deviation (<code>stdev</code>)</strong></h3>
<p>The <code>stdev()</code> function returns the standard deviation of a dataset, which is the square root of the variance. It shows the average amount of variability in the dataset.</p>
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<pre class="language-python"><code>data = [10, 20, 30, 40, 50]
stdev_value = statistics.stdev(data)
print(stdev_value)  # Output: 15.81
</code></pre>
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<h3><strong>6. Harmonic Mean (<code>harmonic_mean</code>)</strong></h3>
<p>The <code>harmonic_mean()</code> function returns the harmonic mean of a dataset. It is calculated as the reciprocal of the arithmetic mean of the reciprocals of the data.</p>
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<pre class="language-python"><code>data = [10, 20, 30]
harmonic_mean_value = statistics.harmonic_mean(data)
print(harmonic_mean_value)  # Output: 17.14
</code></pre>
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<h3><strong>7. Geometric Mean (<code>geometric_mean</code>)</strong> (Available in Python 3.8+)</h3>
<p>The <code>geometric_mean()</code> function returns the geometric mean of a dataset. It is the nth root of the product of all numbers in the dataset.</p>
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<pre class="language-python"><code>data = [10, 20, 30]
geometric_mean_value = statistics.geometric_mean(data)
print(geometric_mean_value)  # Output: 18.26
</code></pre>
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<h3><strong>8. Median Low (<code>median_low</code>)</strong></h3>
<p>The <code>median_low()</code> function returns the low median (the smaller of the two middle numbers) in the case of an even number of data points.</p>
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<pre class="language-python"><code>data = [1, 2, 3, 4, 5, 6]
median_low_value = statistics.median_low(data)
print(median_low_value)  # Output: 3
</code></pre>
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<h3><strong>9. Median High (<code>median_high</code>)</strong></h3>
<p>The <code>median_high()</code> function returns the high median (the larger of the two middle numbers) in the case of an even number of data points.</p>
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<pre class="language-python"><code>data = [1, 2, 3, 4, 5, 6]
median_high_value = statistics.median_high(data)
print(median_high_value)  # Output: 4
</code></pre>
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<h3><strong>10. Median Grouped (<code>median_grouped</code>)</strong></h3>
<p>The <code>median_grouped()</code> function returns the median of grouped continuous data. This method estimates the median assuming the data represents continuous intervals.</p>
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<pre class="language-python"><code>data = [52, 52, 53, 54, 55, 56, 57, 57, 58]
median_grouped_value = statistics.median_grouped(data)
print(median_grouped_value)  # Output: 55.5
</code></pre>
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<h3><strong>11. Population Variance (<code>pvariance</code>)</strong></h3>
<p>The <code>pvariance()</code> function returns the variance of an entire population dataset.</p>
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<pre class="language-python"><code>data = [10, 20, 30, 40, 50]
pvariance_value = statistics.pvariance(data)
print(pvariance_value)  # Output: 200
</code></pre>
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<h3><strong>12. Population Standard Deviation (<code>pstdev</code>)</strong></h3>
<p>The <code>pstdev()</code> function returns the population standard deviation of the dataset.</p>
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<pre class="language-python"><code>data = [10, 20, 30, 40, 50]
pstdev_value = statistics.pstdev(data)
print(pstdev_value)  # Output: 14.14
</code></pre>
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<h3>Example: Summary of Statistics on a Dataset</h3>
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<pre class="language-python"><code>import statistics

data = [10, 20, 30, 40, 50]

print(f"Mean: {statistics.mean(data)}")
print(f"Median: {statistics.median(data)}")
print(f"Mode: Not applicable as all values are unique")
print(f"Variance: {statistics.variance(data)}")
print(f"Standard Deviation: {statistics.stdev(data)}")
</code></pre>
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<h3>&nbsp;</h3>
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