Creating a column containing the min by max of each row in Pandas can be useful in data analysis and data preprocessing. The min by max of each row is a normalized value that represents the proportion of the smallest value in a row to the largest value in that row. This can be a useful metric when analyzing datasets with varying scales and ranges, as it allows us to compare the relative magnitudes of the values within each row.

There are various methods to create a new column in a Pandas dataframe containing the minimum by maximum of each row.

##### 1. Using "apply()" function:

You can use the `apply()`

method to apply a custom function to each row of the DataFrame. You can define a function that takes the minimum value divided by the maximum value for each row, and then apply that function to the DataFrame using the `apply()`

method.

##### 2. Using the "min()" and "max()" functions:

You can also use the `min()`

and `max()`

methods to calculate the minimum and maximum values for each row, and then divide the minimum value by the maximum value to get the min by max for each row.

##### 3. Using the "numpy.minimum()" and "numpy.maximum()" functions:

You can use `numpy.minimum()`

and `numpy.maximum()`

functions in pandas to create a new column containing the minimum or maximum of two existing columns because these functions allow us to perform element-wise comparisons and calculations on NumPy arrays, which are the underlying data structure of pandas dataframes.