Python|熊猫 dataframe.product()

Python是一种用于进行数据分析的出色语言，主要是因为以数据为中心的Python包的奇妙生态系统。 Pandas就是其中之一，它使导入和分析数据变得更加容易。

Pandas dataframe.product()函数返回所请求轴的产品值。它将请求轴上的所有元素相乘。默认选择索引轴。

Syntax: DataFrame.product(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Parameters :
axis : {index (0), columns (1)}
skipna : Exclude NA/null values when computing the result.
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
min_count : The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

Returns : prod : Series or DataFrame (if level specified)

示例 #1：使用product()函数查找数据框中列轴上所有元素的乘积。

# importing pandas as pd
import pandas as pd
  
# Creating the dataframe 
df = pd.DataFrame({"A":[1, 5, 3, 4, 2], 
                   "B":[3, 2, 4, 3, 4], 
                   "C":[2, 2, 7, 3, 4], 
                   "D":[4, 3, 6, 12, 7]})
  
# Print the dataframe
df

让我们使用dataframe.product()函数来查找数据框中每个元素在列轴上的乘积。

# find the product over the column axis
df.product(axis = 1)

输出 ：
示例 #2：使用product()函数查找数据框中任何轴的乘积。数据框包含NaN值。

# importing pandas as pd
import pandas as pd
  
# Creating the first dataframe 
df = pd.DataFrame({"A":[1, 5, 3, 4, 2],
                   "B":[3, None, 4, 3, 4], 
                   "C":[2, 2, 7, None, 4],
                   "D":[None, 3, 6, 12, 7]})
  
# using prod() function to raise each element
# in df1 to the power of corresponding element in df2
df.product(axis = 1, skipna = True)

输出 ：