The specific use of pandas DataFrame truediv

Pandas2.2 DataFrame

Binary operator functions

()

()Methods are used to perform element-by-element true division operations. True division refers to the use of floating-point division. Even if both operands are integers, the result will be floating-point numbers. This method can be used for division between two DataFrames, or for division between DataFrame and a scalar. The following is a detailed description of the parameters:

• other: Can be another DataFrame, Series, Index, constant, or an array that can be broadcast to the same shape.
• axis: Specifies which axis to operate along.0or'index'Indicates operation along the line,1or'columns'Indicates operation along the column. Default is'columns'。
• level: If the index is a multi-index, you can specify which level to operate along. Default isNone。
• fill_value: If a missing value (NaN) is encountered, you can use this value to fill it. Default isNone。

Example

Suppose we have two DataFrames:

import pandas as pd

df1 = ({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

df2 = ({
    'A': [1, 1, 1],
    'B': [2, 2, 2]
})

Example 1: True division between DataFrame and DataFrame

result = (df2)
print(result)

Output:

     A    B
0  1.0  2.0
1  2.0  2.5
2  3.0  3.0

Example 2: True division between DataFrame and scalar

result = (2)
print(result)

Output:

   A  B
0  0.5  2.0
1  1.0  2.5
2  1.5  3.0

Example 3: Use fill_value to handle missing values

Assumptiondf2There is a missing value:

[0, 0] = None  # Set a value in df2 to NaNresult = (df2, fill_value=1)
print(result)

Output:

     A    B
0  1.0  2.0
1  2.0  2.5
2  3.0  3.0

In this example,df2The first element in it isNaN,usefill_value=1back,df1Corresponding elements in1Divided by1, the result is still1。

Example 4: Handling the case of dividing by zero

ifdf2There is a zero value in  , the result will beinfor-inf：

[1, 1] = 0  # Set a value in df2 to 0result = (df2)
print(result)

Output:

     A   B
0  1.0  2.0
1  2.0   inf
2  3.0  3.0

In this example,df2The second element in it is0，df1Corresponding elements in5Divided by0,turn outinf。

Example 5: Specify axis parameter

Suppose we have a DataFrame and a Series, which can be specified byaxisParameters to control the axis of division operation:

series_row = ([100, 200, 300], index=[0, 1, 2])
result_axis_0 = (series_row, axis=0)
print("\nDataFrame 1 / Series (axis=0):")
print(result_axis_0)

series_col = ([10, 20], index=['A', 'B'])
result_axis_1 = (series_col, axis=1)
print("\nDataFrame 1 / Series (axis=1):")
print(result_axis_1)

Output:

DataFrame 1 / Series (axis=0):
      A     B
0  0.01  0.02
1  0.01  0.025
2  0.01  0.02

DataFrame 1 / Series (axis=1):
    A    B
0  0.1  0.2
1  0.2  0.25
2  0.3  0.3

Summarize

()Methods provide a flexible way to perform element-by-element true division operations of a DataFrame with another object (such as DataFrame, Series, or scalar). passaxisThe parameter can specify the axis of the operation, throughlevelParameters can handle multi-level indexes, throughfill_valueParameters can be filled with missing values. This is very useful for data processing and calculations, especially when dealing with missing values ​​and multi-level indexes.

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