如何将Python的统计模块用于基本统计计算？

Python的statistics模块提供内置统计计算功能，无需额外安装。该模块支持计算中心趋势（1.使用mean()求平均值；2.使用median()求中位数；3.使用mode()求众数），评估数据离散程度（4.用variance()和stdev()分别计算样本方差和标准差；若为总体数据则使用pvariance()和pstdev()），并支持多种数据类型如int、float、decimal.Decimal及fractions.Fraction以保持精度，但需注意多数函数要求至少一个数据点否则会抛出StatisticsError。

Python’s statistics module is a built-in library that makes it easy to perform basic statistical calculations without needing external packages like NumPy or pandas. It's perfect for quick analysis when you don’t need advanced features.

Calculating Measures of Central Tendency

One of the most common uses of the statistics module is calculating averages — mean, median, and mode.

• Mean: Use mean() to get the average value in a dataset.
• Median: Use median() to find the middle value when data is sorted.
• Mode: Use mode() to find the most frequently occurring value.

For example:

import statistics

data = [2, 4, 4, 6, 8]
print(statistics.mean(data))     # Output: 4.8
print(statistics.median(data))   # Output: 4
print(statistics.mode(data))     # Output: 4

Keep in mind, mode() works best with nominal or categorical data and will raise an error if there’s no unique mode.

Measuring Spread and Dispersion

Understanding how spread out your data is just as important as knowing its center.

• Variance: variance() gives the sample variance, which measures how far each number in the set is from the mean.
• Standard deviation: stdev() is the square root of variance and easier to interpret since it's in the same unit as the data.
• If you're working with an entire population instead of a sample, use pvariance() and pstdev().

Here’s a quick example:

data = [10, 12, 14, 16, 18]

print(statistics.variance(data))    # Output: 6.5
print(statistics.stdev(data))       # Output: ~2.55

These values help assess consistency in your data — lower values mean data points are closer to the mean.

Handling Different Data Types

The statistics module works well with several numeric types like int, float, and even decimal.Decimal or fractions.Fraction. This flexibility helps maintain precision when needed.

For instance, using Fraction:

from fractions import Fraction
import statistics

data = [Fraction(1, 2), Fraction(3, 4), Fraction(5, 8)]
avg = statistics.fmean(data)
print(avg)  # Output: Fraction(15, 32)

This comes in handy when dealing with exact arithmetic, especially in financial or scientific contexts.

Also, note that many functions expect at least one data point — otherwise, they’ll raise a StatisticsError.

That’s pretty much what you need to know to start using Python’s statistics module effectively. It’s simple, doesn’t require any extra installations, and covers most basic needs.

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