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## Python3.x Python statistics Module In data analysis and scientific computing, statistics is a very important tool. Python provides a built-in `statistics` module, specifically for handling basic statistical calculations. This article will provide a detailed introduction to the functions and usage of the `statistics` module, helping beginners quickly master how to use this module for basic statistical analysis. The `statistics` module provides many commonly used statistical functions, such as mean, median, variance, standard deviation, etc. To use statistics functions, you must first import: import statistics View the contents of the statistics module: >>>import statistics >>>dir(statistics) ['Counter','Decimal','Fraction','NormalDist','StatisticsError','__all__','__builtins__','__cached__','__doc__','__file__','__loader__','__name__','__package__','__spec__','_coerce','_convert','_exact_ratio','_fail_neg','_find_lteq','_find_rteq','_isfinite','_normal_dist_inv_cdf','_ss','_sum','bisect_left','bisect_right','erf','exp','fabs','fmean','fsum','geometric_mean','groupby','harmonic_mean','hypot','itemgetter','log','math','mean','median','median_grouped','median_high','median_low','mode','multimode','numbers','pstdev','pvariance','quantiles','random','sqrt','stdev','tau','variance'] * * * ## Common Statistical Functions ### Mean The mean is the average value of all numbers in a dataset. The `statistics` module provides the `mean()` function to calculate the mean. ## Example data =[1,2,3,4,5] mean_value = statistics.mean(data) print("Mean:", mean_value) Output: Mean: 3 ### Median The median is the value at the middle position when a dataset is arranged in order. The `statistics` module provides the `median()` function to calculate the median. ## Example data =[1,2,3,4,5] median_value = statistics.median(data) print("Median:", median_value) Output: Median: 3 If the length of the dataset is even, the `median()` function will automatically calculate the average of the two middle numbers. ## Example data =[1,2,3,4] median_value = statistics.median(data) print("Median:", median_value) Output: Median: 2.5 ### Mode The mode is the value that appears most frequently in a dataset. The `statistics` module provides the `mode()` function to calculate the mode. ## Example data =[1,2,2,3,4] mode_value = statistics.mode(data) print("Mode:", mode_value) Output: Mode: 2 If there are no duplicate values in the dataset, the `mode()` function will throw a `StatisticsError` exception. ### Variance Variance is a measure of how spread out the values in a dataset are. The `statistics` module provides the `variance()` function to calculate variance. ## Example data =[1,2,3,4,5] variance_value = statistics.variance(data) print("Variance:", variance_value) Output: Variance: 2.5 ### Standard Deviation Standard deviation is the square root of variance, used to measure the dispersion of a dataset. The `statistics` module provides the `stdev()` function to calculate the standard deviation. ## Example data =[1,2,3,4,5] stdev_value = statistics.stdev(data) print("Standard Deviation:", stdev_value) Output: Standard Deviation: 1.5811388300841898 ### Harmonic Mean The harmonic mean is a special type of average, suitable for calculating rates and other scenarios. The `statistics` module provides the `harmonic_mean()` function to calculate the harmonic mean. ## Example data =[1,2,4] harmonic_mean_value = statistics.harmonic_mean(data) print("Harmonic Mean:", harmonic_mean_value) Output: Harmonic Mean: 1.7142857142857142 ### Geometric Mean The geometric mean is an average used for calculating growth rates or ratios. The `statistics` module provides the `geometric_mean()` function to calculate the geometric mean. ## Example data =[1,2,4] geometric_mean_value = statistics.geometric_mean(data) print("Geometric Mean:", geometric_mean_value) Output: Geometric Mean: 2.0 * * * ## Other Common Functions ### Median Low and Median High The `statistics` module also provides `median_low()` and `median_high()` functions, which are used to calculate the low median and high median of a dataset respectively. ## Example data =[1,2,3,4] median_low_value = statistics.median_low(data) median_high_value = statistics.median_high(data) print("Median Low:", median_low_value) print("Median High:", median_high_value) Output: Median Low: 2Median High: 3 ### Quantiles Quantiles are values that divide a dataset into equal parts. The `statistics` module provides the `quantiles()` function to calculate quantiles. ## Example data =[1,2,3,4,5] quantiles_value = statistics.quantiles(data, n=4) print("Quartiles:", quantiles_value) Output: Quartiles: [1.5, 3.0, 4.5] * * * ## math Module Methods | Method | Description | | --- | --- | | [statistics.harmonic_mean()](#) | Calculates the harmonic mean of the given dataset. | | [statistics.mean()](#) | Calculates the mean of the dataset | | [statistics.median()](#) | Calculates the median of the dataset | | [statistics.median_grouped()](#) | Calculates the grouped median of the given grouped dataset | | [statistics.median_high()](#) | Calculates the high median of the given dataset | | [statistics.median_low()](#) | Calculates the low median of the given dataset. | | [statistics.mode()](#) | Calculates the mode of the dataset (the value that appears most frequently) | | [statistics.pstdev()](#) | Calculates the sample standard deviation of the given dataset | | [statistics.stdev()](#) | Calculates the standard deviation of the dataset | | [statistics.pvariance()](http