Reshaping a Pandas DataFrame after removing negative values is a common data cleaning and preparation step in data analysis. Negative values may be errors or outliers in the data that need to be removed to avoid skewing the analysis. After removing negative values, reshaping the DataFrame can help to transform the data into a format that is more useful for analysis or visualization.
There are different ways to reshape a dataframe to the largest possible square after removing the negative values in Pandas. Here are two methods:

Using "NumPy" and "Pandas" functions:
This method uses the NumPy sqrt function to calculate the size of the square and the `iloc` function to remove any excess values that cannot fit in the square.
Example:

Using "NumPy" function:
This method uses the NumPy floor function to calculate the size of the square and the `to_numpy` function to convert the dataframe to a NumPy array before reshaping it.
Example:
All these methods remove the negative values from the dataframe using the mask function, reshape the dataframe to the largest possible square, and convert it back to a Pandas dataframe.