The statistics module is a built-in Python library that provides functions for calculating statistics of numeric data. These statistics include common measures of central tendency — such as mean, median, and mode — and dispersion — such as standard deviation and variance. The module also includes more specialized functions, making it a convenient choice for basic statistical calculations in Python without external dependencies.
The functions in the statistics module work with both lists and iterables of real-valued numbers. The module is particularly useful for smaller datasets where you don’t need the full capabilities of libraries like NumPy or Pandas. This ensures that Python users always have statistical tools available by default.
This article will give a tour of the Python statistics module, through a short description of the functions within, and point you in the direction of full tutorials on using each. Below you will find links to these Statology tutorials, along with a short description.
🗒️ How to Use the Python statistics.mean() Function
The statistics.mean() function calculates the arithmetic mean (average) of numeric data by summing all values and dividing by the count of numbers, accepting either a sequence of numbers or an iterator as input. It can handle integer and float values, and will raise a StatisticsError if the input sequence is empty or contains non-numeric values.
🗒️ How to Use the Python statistics.fmean() Function
The statistics.fmean() function is a faster version of mean() that forces all inputs to be converted to float values first, making it more efficient but less memory-friendly than the original. It’s particularly useful when you know your data will contain floating point numbers or when maximum performance is needed.
🗒️ How to Use the Python statistics.geometric_mean() Function
The statistics.geometric_mean() function calculates the geometric mean of numeric data, which is the nth root of the product of n numbers, and is useful for analyzing proportional growth or ratios between values. It automatically converts inputs to floats and handles positive numbers only, raising a StatisticsError if any values are zero or negative.
🗒️ How to Use the Python statistics.harmonic_mean() Function
The statistics.harmonic_mean() function calculates the harmonic mean of data, which is the reciprocal of the arithmetic mean of the reciprocals, making it particularly useful for averaging rates or speeds. It returns a float and raises a StatisticsError for zero or negative values, as the harmonic mean is only defined for positive numbers.
🗒️ How to Use the Python statistics.median() Function
The statistics.median() function calculates the middle value of numeric data by sorting all numbers and selecting the center value (or the average of the two middle values if there’s an even number of items). It works with both odd and even-length sequences, and if there are multiple copies of the middle value, it returns the first occurrence of that value.
🗒️ How to Use the Python statistics.median_low() Function
The statistics.median_low() function returns the low median of numeric data, which means when dealing with an even number of values, it returns the lower of the two middle values rather than their average. This is particularly useful when you need a pure middle value from your dataset rather than a potentially calculated average, and like other statistics functions, it works by first sorting the data.
🗒️ How to Use the Python statistics.median_high() Function
The statistics.median_high() function returns the high median of numeric data. This means when dealing with an even number of values, it returns the higher of the two middle values rather than their average. Like median_low(), this function is particularly helpful in situations where you want to err on the higher side of the middle value.
🗒️ How to Use the Python statistics.median_grouped() Function
The statistics.median_grouped() function calculates the median of grouped continuous data, assuming uniform distribution within each class interval (which defaults to 1). It’s specifically designed for analyzing binned or grouped data where values are organized into intervals rather than individual points.
🗒️ How to Use the Python statistics.mode() Function
The statistics.mode() function returns the most common value in a dataset, and if there are multiple values with the same highest frequency, it returns the first one encountered. If the dataset is empty, it raises a StatisticsError.
🗒️ How to Use the Python statistics.multimode() Function
The statistics.multimode() function returns a list of all most common values in a dataset, unlike mode() which returns just one value. It will return multiple values if there’s a tie for the highest frequency, and returns the entire dataset if all values appear equally often.
🗒️ How to Use the Python statistics.quantiles() Function
The statistics.quantiles() function divides ordered data into equal-sized segments (defaulting to quartiles) and returns a list of the cut points. However, the function can create any number of segments specified by the optional n parameter.
🗒️ How to Use the Python statistics.pstdev() Function
The statistics.pstdev() function calculates the population standard deviation, which measures the spread of a dataset from its mean by taking the square root of the population variance (calculated using n as the denominator). It differs from stdev() by calculating for an entire population rather than a sample.
🗒️ How to Use the Python statistics.pvariance() Function
The statistics.pvariance() function calculates the population variance, which is the average of squared deviations from the mean using n as the denominator. It represents the spread of an entire population’s values from their mean, rather than a sample’s variance.
🗒️ How to Use the Python statistics.stdev() Function
The statistics.stdev() function calculates the sample standard deviation by taking the square root of the sample variance (using n-1 as the denominator). It’s used when working with a sample of a larger population, rather than the entire population.
🗒️ How to Use the Python statistics.variance() Function
The statistics.variance() function calculates the sample variance, which measures the spread of values from their mean using n-1 as the denominator. Like stdev(), it’s designed for analyzing samples rather than entire populations.
🗒️ How to Use the Python statistics.covariance() Function
The statistics.covariance() function calculates the sample covariance between two inputs of equal length, measuring how the two variables change together. It represents the degree to which two variables are linearly associated, using the n-1 adjustment for sample calculations rather than population calculations.
🗒️ How to Use the Python statistics.linear_regression() Function
The statistics.linear_regression() function takes two inputs of equal length and returns a tuple of slope and intercept representing the best-fit line through the data points. The function uses the least squares method to calculate the linear regression coefficients, making it useful for basic predictive modeling and trend analysis.
