Pandas 数据框和字典的深拷贝

Deep copy of Pandas dataframes and dictionaries(Pandas 数据框和字典的深拷贝)

问题描述

I'm creating a small Pandas dataframe:

df = pd.DataFrame(data={'colA': [["a", "b", "c"]]})

I take a deepcopy of that df. I'm not using the Pandas method but general Python, right?

import copy
df_copy = copy.deepcopy(df)

A df_copy.head() gives the following:

Then I put these values into a dictionary:

mydict = df_copy.to_dict()

That dictionary looks like this:

Finally, I remove one item of the list:

mydict['colA'][0].remove("b")

I'm surprized that the values in df_copy are updated. I'm very confused that the values in the original dataframe are updated too! Both dataframes look like this now:

I understand Pandas doesn't really do deepcopy, but this wasn't a Pandas method. My questions are:

1) how can I build a dictionary from a dataframe that doesn't update the dataframe?

2) how can I take a copy of a dataframe which would be completely independent?

thanks for your help!

Cheers, Nicolas

Disclaimer

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Notice that putting mutable objects inside a DataFrame can be an antipattern so make sure that you really need it and you understand what you are doing.

Why doesn't your copy independent

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When applied on an object, copy.deepcopy is looked up for a _deepcopy_ method of that object, that is called in turn. It's added to avoid copying too much for objects. In the case of a DataFrame instance in version 0.20.0 and above - _deepcopy_ doesn`t work recursively.

Similarly, if you will use DataFrame.copy(deep=True) deep copy will copy the data, but will not do so recursively. .

How to solve the problem

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To take a truly deep copy of a DataFrame containing a list(or other python objects), so that it will be independent - you can use one of the methods below.

df_copy = pd.DataFrame(columns = df.columns, data = copy.deepcopy(df.values))

For a dictionary, you may use same trick:

mydict = pd.DataFrame(columns = df.columns, data = copy.deepcopy(df_copy.values)).to_dict()
mydict['colA'][0].remove("b")

There's also a standard hacky way of deep-copying python objects:

import pickle
df_copy = pickle.loads(pickle.dumps(df))  

Feel free to ask for any clarifications, if needed.

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