The problem is somewhat simple. My objective is to compute the days difference between two dates, say A and B.
These are my attempts:
df['daydiff'] = df['A']-df['B']
df['daydiff'] = ((df['A']) - (df['B'])).dt.days
df['daydiff'] = (pd.to_datetime(df['A'])-pd.to_datetime(df['B'])).dt.days
These works for me before but for some reason, I'm keep getting this error this time:
TypeError: class 'datetime.time' is not convertible to datetime
When I export the df to excel, then the date works just fine. Any thoughts?
2 Answers
Use pd.Timestamp to handle the awkward differences in your formatted times.
df['A'] = df['A'].apply(pd.Timestamp) # will handle parsing
df['B'] = df['B'].apply(pd.Timestamp) # will handle parsing
df['day_diff'] = (df['A'] - df['B']).dt.days
Of course, if you don't want to change the format of the df['A'] and df['B'] within the DataFrame that you are outputting, you can do this in a one-liner.
df['day_diff'] = (df['A'].apply(pd.Timestamp) - df['B'].apply(pd.Timestamp)).dt.days
This will give you the days between as an integer.
When I applied the solution offered by emmet02, I got TypeError: Cannot convert input [00:00:00] of type as well. It's basically saying that the dataframe contains missing timestamp values which are represented as [00:00:00], and this value is rejected by pandas.Timestamp function.
To address this, simply apply a suitable missing-value strategy to clean your data set, before using
df.apply(pd.Timestamp)
