of 7 runs, 100 loops each)ĭid that make our calculation 10,000x times faster than apply? What if we try the same calculation using integer operations by representing the start and end dates as epoch integers rather than DateTime objects? %%timeit SECONDS_TO_YEARS = 60*60*24*365.25*1_000_000_000 (end_dates.astype(int) - start_dates.astype(int))/SECONDS_TO_YEARS2.22 ms ± 32.6 µs per loop (mean ± std. In the code snippets above we’re using pandas date-time operations to get the number of days between two dates and divide it by a timedelta object representing the number of days in a year. Running the calculation directly on the series made this operation 2,000x faster! What if we passed the series through our function instead? %%timeit # with date series, vectorized ages = calc_age_function(start_dates, end_dates)10.3 ms ± 104 µs per loop (mean ± std. With pandas we can use arithmetic operations on Series objects which are significantly faster than for loops (usually what apply does, with exceptions which we’ll get to later). This works, though is not fast enough for us. All you need to remember is an apply method and how to write a python function. of 7 runs, 1 loop each)Īs a python newbie, apply might be an easy thing to use. Now let’s calculate the age using our function on the columns in the DataFrame using apply with a lambda function (not best) %%timeit # slow, for loop apply age_df = age_df.apply( lambda x: calc_age_function(x, x), axis=1, )20.6 s ± 63.7 ms per loop (mean ± std. If you’re an Excel user, operations such as finding the sum or the cumulative sum of all columns in an array can be done in pandas by calling the DataFrame.sum or DataFrame.cumsum methods. Pandas look familiar to new users coming from many different backgrounds.

This post is in collaboration with Sam MouradĪnd we’re not talking about these pandas (adorable), but the python library we all data scientists out there use on a daily basis to do anything data-related.

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