del statement does not delete an instance, it merely deletes a name.

When you do del i, you are deleting just the name i - but the instance is still bound to some other name, so it won't be Garbage-Collected.

If you want to release memory, your dataframes has to be Garbage-Collected, i.e. delete all references to them.

If you created your dateframes dynamically to list, then removing that list will trigger Garbage Collection.

>>> lst = [pd.DataFrame(), pd.DataFrame(), pd.DataFrame()]
>>> del lst     # memory is released

If you created some variables, you have to delete them all.

>>> a, b, c = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
>>> lst = [a, b, c]
>>> del a, b, c # dfs still in list
>>> del lst     # memory release now

It's not as straightforward in Python as it is in R. The best and safest option is to manually del each dataframe by name (as well as any other references to the object, e.g. if they are also in a list). However, if this isn't an option, you can iterate through all available variables, check if they are an instance of a pandas Dataframe, and then delete them.

If your goal is to free memory, you should manually run garbage collection after references to the objects have been deleted.

The following works for me:

import gc
import pandas as pd

for i in dir():
    if isinstance(globals()[i], pd.DataFrame):
        del globals()[i]

gc.collect()

IIUC, you can do this rather than iterating through your frame with iterrows:

df = df[df.email.str.endswith('.com')]

which returns:

>>> df
             email
0    [email protected]
1    [email protected]
3  [email protected]

Or, for larger dataframes, it's sometimes faster to not use the str methods provided by pandas, but just to do it in a plain list comprehension with python's built in string methods:

df = df[[i.endswith('.com') for i in df.email]]