How to delete multiple pandas (python) dataframes from memory to save RAM?

Question

I have lot of dataframes created as part of preprocessing. Since I have limited 6GB ram, I want to delete all the unnecessary dataframes from RAM to avoid running out of memory when running GRIDSEARCHCV in scikit-learn.

1) Is there a function to list only, all the dataframes currently loaded in memory?

I tried dir() but it gives lot of other object other than dataframes.

2) I created a list of dataframes to delete

del_df=[Gender_dummies,
 capsule_trans,
 col,
 concat_df_list,
 coup_CAPSULE_dummies]

& ran

for i in del_df:
    del (i)

But its not deleting the dataframes. But deleting dataframes individially like below is deleting dataframe from memory.

del Gender_dummies
del col

Solution 1

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

Solution 2

In python automatic garbage collection deallocates the variable (pandas DataFrame are also just another object in terms of python). There are different garbage collection strategies that can be tweaked (requires significant learning).

You can manually trigger the garbage collection using

import gc
gc.collect()

But frequent calls to garbage collection is discouraged as it is a costly operation and may affect performance.

Reference

Solution 3

This will delete the dataframe and will release the RAM/memory

del [[df_1,df_2]]
gc.collect()
df_1=pd.DataFrame()
df_2=pd.DataFrame()

the data-frame will be explicitly set to null

in the above statements

Firstly, the self reference of the dataframe is deleted meaning the dataframe is no longer available to python there after all the references of the dataframe is collected by garbage collector (gc.collect()) and then explicitly set all the references to empty dataframe.

more on the working of garbage collector is well explained in https://stackify.com/python-garbage-collection/

Comments

[GeorgeOfTheRF]

K.How to release memory in python?

[GeorgeOfTheRF]

K. Why does "del Gender_dummies work" but when i try to delete dataframes in a loop its not working? for i in del_df: del (i)

[Nathan Lloyd]

Thanks for this! Automatic garbage collection after del df doesn't seem to happen if I've done df.iterrows(), but gc.collect() seems to have the desired effect.

[Saeed]

So, is this solution saying that in order to delete a number of dataframes, we have to first put them in a list and then delete the list? This sounds so inefficient. Not sure if I understood this correctly.

[pacholik]

@Saeed No. In order to delete a number of dataframes that are also in list, you have to del the list too.

[Blue]

Welcome to Stack Overflow! While this code snippet may solve the question, including an explanation really helps to improve the quality of your post. Remember that you are answering the question for readers in the future, and those people might not know the reasons for your code suggestion. Please also try not to crowd your code with explanatory comments, as this reduces the readability of both the code and the explanations!

[mcrrnz]

Awesomre, very usefull especially you work with large Pandas dataframes that could drain all your memory.

[Jaya Kommuru]

@pacholik So, if the dataframe aren't in a list, then just del of that dataframe works?

[pacholik]

@JayaKommuru Yes, exactly.