熊猫Datetime / dt.date的意外行为

Question

I found a bug in my code that was due to the date column of my dataframe not including the hours and minutes and only including the date. I traced the cause of the issue due to running these two functions consecutively vs. running them one by one. If I run the functions one by one, there is no problem. If I run them both, my results are unexpected.

I need to run these functions consecutively, but they are not dependent on one another. I'm new to Python, so I thought this might be due to the inputs being overwritten or something (not that that would have happened in Java, as far as I know). So, I changed the functions to be as follows:

def func1(dataset):
    originalData = dataset
    # only look at one day at a time- remove extra unnecessary info
    originalData ['Date'] = pd.to_datetime(originalData ['Date'])
    print dataset, 'test1'
    originalData ['Date'] = originalData ['Date'].dt.date
    print dataset, 'test2'
    # other stuff

def func2(dataset):
    originalData2 = dataset
    # look at entire datetime
    originalData2['Date'] = pd.to_datetime(originalData2['Date'])
    print originalData2
    # other stuff     

Run like this, I lose the time in the second function.

csv = pd.read_csv(csvFileName)
func1(csv)
func2(csv)

Run like this, func2 results in my desired output:

csv = pd.read_csv(csvFileName)
func2(csv)

The wierd thing is if run func1, test1 prints out the date with datetime, while test2 prints out only the date. The dataset is being changed even though the changes are applied to originalDataset. Am I misunderstanding something? Thanks in advance.

Answer 1

If you don't want to make changes to the underlying data I'd recommend setting your data inside the function like this: originalData = dataset.copy(). This method provides a deep copy, meaning, you'll only be editing the data within the function and not overriding the underlying object.

Odd behavior, yes.

You may also run into this when taking slices of dataframes and doing transformations on them.