mean python_Python mean（）方法的详细指南

深藏阁楼爱情的钟 2023-02-28 08:40 155阅读 0赞

mean python

Hey, folks! In this article, we will be focusing on Python mean() function in order to perform the mean operation.

嘿伙计！ 在本文中，我们将专注于Python mean（）函数以执行均值运算。

**Mean** is the value that represents an entire set of entities. It is considered as the *central value* of a set of numbers.

**平均值**是代表整个实体集的值。 它被视为一组数字的*中心值* 。

Mean is calculated by dividing the summation of all the entity values by the number of entities. Essentially, the mean is the average of the specified values.

通过将所有实体值的总和除以实体数来计算平均值。 本质上，平均值是指定值的平均值。

Formula:

式：

`(sum of values)/total values`

Now, let us understand the working of **the mean() function** to calculate the mean.

现在，让我们了解一下**mean（）函数**计算平均值的工作原理。

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## 使用Python mean（）函数 **(****Using the Python mean() function****)** ##

The mean() function helps in calculating the mean of a set of values passed to the function.

mean（）函数有助于计算传递给该函数的一组值的平均值。

Python statistics module is used to perform all the statistical operations on the data. We need to import the statistics module using the below command:

Python统计模块用于对数据执行所有统计操作。 我们需要使用以下命令导入统计信息模块：

**Syntax**: Importing the statistics module

**语法** ：导入统计信息模块

import statistics

The statistics.mean() function accepts the data values as argument and returns the mean of the values passed to it.

statistics.mean（）函数接受数据值作为参数，并返回传递给它的值的平均值。

**Syntax:**

**句法：**

statistics.mean(data)

**Example:**

**例：**

import statistics
    data = [10,20,30,40,50]
    
    res_mean = statistics.mean(data)
    print(res_mean)

**Output:**

**输出：**

--------------------

## 带有NumPy模块的Python mean（）函数 **(****Python mean() function with NumPy module****)** ##

[Python NumPy module][] represents the set of values in the form of an array. We can calculate the mean of these array elements using numpy.mean() function.

[Python NumPy模块][Python NumPy module]以数组形式表示一组值。 我们可以使用numpy.mean（）函数计算这些数组元素的平均值。

The `numpy.mean() function` performs the same way as of the statistics.mean() function.

`numpy.mean() function`执行方式与statistics.mean（）函数相同。

**Syntax:**

**句法：**

numpy.mean(data)

**Example:**

**例：**

import numpy as np
    data = np.arange(1,10)
    res_mean = np.mean(data)
    print(res_mean)

In the above example, we have used the `numpy.arange(start,stop)` function to generate evenly spaced values in the range provided as parameters. Further, `numpy.mean() function` is used to calculate the mean of all the array elements.

在上面的示例中，我们使用了`numpy.arange(start,stop)`函数在作为参数提供的范围内生成均匀间隔的值。 此外， `numpy.mean() function`用于计算所有数组元素的均值。

**Output:**

**输出：**

5.0

--------------------

## 带有Pandas模块的Python mean（）函数 **(****Python mean() function with Pandas module****)** ##

[Python Pandas module][] deals with huge datasets in the form of DataFrames. The mean of those huge data sets can be calculated by using pandas.DataFrame.mean() function.

[Python Pandas模块][Python Pandas module]以DataFrames的形式处理庞大的数据集。 可以使用pandas.DataFrame.mean（）函数计算这些庞大数据集的平均值。

The `pandas.DataFrame.mean()` function returns the mean of those data values.

`pandas.DataFrame.mean()`函数返回这些数据值的平均值。

**Syntax:**

**句法：**

pandas.DataFrame.mean()

**Example 1:**

**范例1：**

import numpy as np
    import pandas as pd
    data = np.arange(1,10)
    df = pd.DataFrame(data)
    res_mean = df.mean()
    print(res_mean)

In the above example, we have created a **NumPy array** using `numpy.arange() function` and then converted the array values into a DataFrame using `pandas.DataFrame() function`. Further, we have calculated the mean of the DataFrame values using `pandas.DataFrame.mean() function`.

在上述例子中，我们已经使用创建的**NumPy的阵列** `numpy.arange() function` ，然后使用变换后的数组值成数据帧`pandas.DataFrame() function` 。 此外，我们使用`pandas.DataFrame.mean() function`计算了DataFrame值的平均值。

**Output:**

**输出：**

0    5.0
    dtype: float64

**Example 2:**

**范例2：**

import pandas as pd 
    data = pd.read_csv("C:/mtcars.csv")
    res_mean = data['qsec'].mean()
    print(res_mean)

**Input Dataset:**

**输入数据集：**

![MTCARS Dataset][]

**MTCARS Dataset** **MTCARS数据集**

In the above example, we have used the above mentioned dataset, and calculated the mean of all the data values present in the data column ‘**qsec**‘.

在上面的示例中，我们使用了上面提到的数据集，并计算了数据列“ **qsec** ”中所有数据值的**平均值** 。

**Output:**

**输出：**

17.848750000000003

--------------------

## 结论 **(****Conclusion****)** ##

Thus, in this article, we have understood the working of Python mean() function along with NumPy and Pandas module.

因此，在本文中，我们了解了Python mean（）函数以及NumPy和Pandas模块的工作方式。

--------------------

## 参考资料 **(****References****)** ##

[Python mean() function — Official Documentation][Python mean_ function _ Official Documentation]

[Python mean（）函数—官方文档][Python mean_ function _ Official Documentation]

> 翻译自: [https://www.journaldev.com/39570/python-mean-method][https_www.journaldev.com_39570_python-mean-method]

mean python

[Python NumPy module]: https://www.journaldev.com/15646/python-numpy-tutorial
[Python Pandas module]: https://www.journaldev.com/29055/python-pandas-module-tutorial
[MTCARS Dataset]: /images/20230209/71c20df85193409288c4282a5f059bf6.png
[Python mean_ function _ Official Documentation]: https://docs.python.org/3/library/statistics.html#statistics.mean
[https_www.journaldev.com_39570_python-mean-method]: https://www.journaldev.com/39570/python-mean-method