# 138、pandas.Series.product方法

# 138-1、计算乘积

# 24

# 138-2、忽略NaN进行乘积计算

# 8.0

# 138-3、不忽略NaN进行乘积计算

# nan

# 138-4、使用numeric_only

# 12

139、pandas.Series.dot方法

139-1、语法

# 139、pandas.Series.dot方法

pandas.Series.dot(other)

Compute the dot product between the Series and the columns of other.

This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array.

It can also be called using self @ other.

Parameters:

other

Series, DataFrame or array-like

The other object to compute the dot product with its columns.

Returns:

scalar, Series or numpy.ndarray

Return the dot product of the Series and other if other is a Series, the Series of the dot product of Series and each rows of other if other is a DataFrame or a numpy.ndarray between the Series and each columns of the numpy array.

139-2、参数

139-2-1、other(必须)：表示另一个Series或类似对象，它应该与调用该方法的Series具有相同的索引，如果other是一个标量，则结果将是标量乘以Series中的每个值。

139-3、功能

        用于计算两个Series对象之间的点积(内积)，该方法对于数值计算非常有用，尤其是在处理矢量和数学运算时。

139-4、返回值

        返回一个标量，表示两个Series之间的点积。如果Series的索引不匹配，方法将根据索引进行对齐，并仅计算匹配的部分。

139-5、说明

        使用场景：

139-5-1、数学和统计分析：经常用于计算向量的点积，常见于机器学习和数据分析中的特征计算。

139-5-2、线性代数：在处理矩阵和线性方程组时，也可以使用这个方法进行计算。

139-6、用法

139-6-1、数据准备

无

139-6-2、代码示例

# 139、pandas.Series.dot方法

import pandas as pd

s1 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])

s2 = pd.Series([4, 5, 6], index=['a', 'b', 'c'])

dot_product = s1.dot(s2)

print(dot_product)

139-6-3、结果输出

# 139、pandas.Series.dot方法

# 32 （1*4 + 2*5 + 3*6 = 32）

140、pandas.Series.apply方法

140-1、语法

# 140、pandas.Series.apply方法

pandas.Series.apply(func, convert_dtype=_NoDefault.no_default, args=(), *, by_row='compat', **kwargs)code>

Invoke function on values of Series.

Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values.

Parameters:

funcfunction

Python function or NumPy ufunc to apply.

convert_dtypebool, default True

Try to find better dtype for elementwise function results. If False, leave as dtype=object. Note that the dtype is always preserved for some extension array dtypes, such as Categorical.

Deprecated since version 2.1.0: convert_dtype has been deprecated. Do ser.astype(object).apply() instead if you want convert_dtype=False.

argstuple

Positional arguments passed to func after the series value.

by_rowFalse or “compat”, default “compat”

If "compat" and func is a callable, func will be passed each element of the Series, like Series.map. If func is a list or dict of callables, will first try to translate each func into pandas methods. If that doesn’t work, will try call to apply again with by_row="compat" and if that fails, will call apply again with by_row=False (backward compatible). If False, the func will be passed the whole Series at once.code>

by_row has no effect when func is a string.

New in version 2.1.0.

**kwargs

Additional keyword arguments passed to func.

Returns:

Series or DataFrame

If func returns a Series object the result will be a DataFrame.

140-2、参数