Python|熊猫 dataframe.rpow()

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.rpow()函数用于查找数据帧和其他元素的指数幂（二元运算符rfloordiv）。此函数与执行其他 ** 数据帧基本相同，但支持替换其中一个输入中的缺失数据。

Syntax:DataFrame.rpow(other, axis=’columns’, level=None, fill_value=None) 
Parameters : 
other : Series, DataFrame, or constant 
axis : For Series input, axis to match Series index on 
level : Broadcast across a level, matching Index values on the passed MultiIndex level
fill_value : Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.
Returns : result : DataFrame 

示例 #1：使用 rpow()函数将系列的每个元素提升到列轴上数据框中的相应值。

Python3

# 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]},
                    index =["A1", "A2", "A3", "A4", "A5"])
 
# Print the dataframe
df

让我们创建系列

Python3

# importing pandas as pd
import pandas as pd
 
# Create the series
sr = pd.Series([12, 25, 64, 18], index =["A", "B", "C", "D"])
 
# Print the series
sr

让我们使用 dataframe.rpow()函数将系列中的每个元素提升到数据框中相应元素的幂。

Python3

# equivalent to sr ** df
df.rpow(sr, axis = 1)

输出 ：

示例 #2：使用 rpow()函数将数据帧中的每个元素提升到其他数据帧中相应元素的幂

Python3

# importing pandas as pd
import pandas as pd
 
# Creating the first dataframe
df1 = 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]},
                     index =["A1", "A2", "A3", "A4", "A5"])
 
# Creating the second dataframe
df2 = pd.DataFrame({"A":[10, 11, 7, 8, 5],
                    "B":[21, 5, 32, 4, 6],
                    "C":[11, 21, 23, 7, 9],
                    "D":[1, 5, 3, 8, 6]},
                     index =["A1", "A2", "A3", "A4", "A5"])
 
# Print the first dataframe
print(df1)
 
# Print the second dataframe
print(df2)

让我们执行 df2 ** df1

Python3

# raise df2 to the power of df1
df1.rpow(df2)

输出 ：