Check if value is equal to 0 for mixed type column (with timedelta and floats)

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Let's say we have the following dataframe. Which in real case is an comparison of columns after melting, that's the reason there are mixed types.

df = pd.DataFrame({'value':[0.0, 0.0, pd.Timedelta(hours=1), pd.Timedelta(0)]})

             value
0                0
1                0
2  0 days 01:00:00
3  0 days 00:00:00

What I wanted to do, is to check if this is equal to 0 and based on that make a conditional column.

So first we have a to get a boolean to mark which rows are 0. Simply using eq or ==, wont work:

df['value'].eq(0)

0     True
1     True
2    False
3    False
Name: value, dtype: bool

This is because we have a Timedelta type probably, so I thought lets convert the timedelta to seconds. So I checked with:

df['value'].apply(type) == pd._libs.tslibs.timedeltas.Timedelta

0    False
1    False
2     True
3     True
Name: value, dtype: bool

Which works.

Then used, which did not work:

np.where(df['value'].apply(type) == pd._libs.tslibs.timedeltas.Timedelta, 
         df['value'].total_seconds(), 
         df['value'])

'Series' object has no attribute 'total_seconds'

Finally, this works.

df['value'].apply(lambda x: x.total_seconds() if type(x) == pd._libs.tslibs.timedeltas.Timedelta else x).eq(0)

0     True
1     True
2    False
3     True
Name: value, dtype: bool

But it's quite slow and does not look "panda like".

So my question is, is this there a faster more optimal solution?

2

There are 2 answers

0
BENY On BEST ANSWER

I will 'upgrade' the int to timedelta

pd.to_timedelta(df.value).dt.total_seconds()==0
Out[232]: 
0     True
1     True
2    False
3     True
Name: value, dtype: bool
0
ansev On

You can use:

df.eq(pd.Timedelta(0))|df.eq(0)



  value
0   True
1   True
2  False
3   True