Best practice for passing Pandas DataFrame to functions

user_13660400 0
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I am currently working with a few DataFrames and want to make my code modular. That entails passing DataFrames to functions. I am aware of the mutable nature of DataFrames and some of the 'gotchas' when passing mutable instances to functions. Is there a best practice for DataFrames to the functions? Should I make a copy within the function and then pass it back? Or should I just make changes to df within the function and return None?

Is option 1 or 2 better? Below is basic code to convey the idea:

Option 1:

def test(df):
    df['col1'] = df['col1']+1
    return None

test(df)

Option 2:

def test(main_df):
    df = main_df.copy()
    df['col1'] = df['col1']+1
    return df 

main_df = test(main_df)
pandas mutable dataframe function python
2 Answers

I think Option 1 is bad way. Why? Because is not Pure function (side effects on mutable reference arguments).

How to google for details: pure / deterministic / nondeterministic functions.

So I think the second way is better.

user_49296460

I use a lot of DataFrame.pipe to organize my code so, I'm going to say option 2. pipe takes and returns a DataFrame and you can chain multiple steps together.

def step1(main_df):
    df = main_df.copy()
    df['col1'] = df['col1']+1
    return df 

def step2(main_df):
    df = main_df.copy()
    df['col1'] = df['col1']+1
    return df 

def setp3(main_df):
    df = main_df.copy()
    df['col1'] = df['col1']+1
    return df 

main_df = (main_df.pipe(step1)
    .pipe(step2)
    .pipe(step3)
)
user_150859690
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