Seven Killer Memory Optimization Techniques Every Pandas User Should Know

Once we load a DataFrame into the Python environment, we typically perform a wide range of modifications on the DataFrame, don't we? These include adding new columns, renaming headers, deleting columns, altering row values, replacing NaN values, and many more. Standard Assignment intends to create a new copy of the DataFrame after transformation, leaving the original DataFrame untouched. As a result of the standard assignment, two distinct Pandas DataFrames (original and transformed) co-exist in the environment (df and df_copy above), doubling the memory utilization. In contrast to the standard assignment operations, inplace assignment operations intend to modify the original DataFrame itself without creating a new Pandas DataFrame object.