I am trying to convert the columns with type 'object' to 'int' or 'string' or 'datetime' or 'float'. My dataframe has the following column types, and below 3 records from the dataframe are shown. The following runtime plot shows that there's a huge gap in performance depending on whether you passed format or not. Convert Object Type in dataframe to Int / float / String.
It turns out explicitly specifying the format is about 25x faster. In my project, for a column with 5 millions rows, the difference was huge: ~2.5 min vs 6s. There's barely any difference if the column is only date, though. Hi I want to convert a dataframe column (string) into date.I found it converted some of the dates correctly and some of them are wrong. If the column contains a time component and you know the format of the datetime/time, then passing the format explicitly would significantly speed up the conversion. df.apply(pd.to_datetime)), that would not be vectorized, and should be avoided.
On the other hand, if you call pd.to_datetime using apply on a column (e.g. If it has to be called on multiple columns, the options are either use an explicit for-loop, or pass it to apply. apply is needed here because pd.to_datetime can only be called on a single column. Passing to apply, without specifying axis, still converts values vectorially for each column. You can pass parameters to to_datetime as kwargs. Convert to daily dates df.index pd.DatetimeIndex(datadf.index) Convert to monthly dates df.index df.index.toperiod(freq'M') Convert to strings df.index df.index.strftime('Y-m') Convert to daily. If you want to convert multiple string columns to datetime, then using apply() would be useful. Below, I sequentially convert to a number of date formats, ultimately ending up with a set of daily dates at the beginning of the month.
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