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Pandas is a frequently used package in Python and every aspiring Python data scientist should have some familiarity with it. However, Pandas has a lot of quirks to it which are not obvious to even advanced users. One of them is the different methods of how to apply a given function to every row of the data set. As we will see here, this can make an enormous speed difference of a factor of 1000.
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This notebook will show you some examples of pandas code. Often, new and experienced users of this library will write unnecessarily complicated or slow code when pandas has a built-in functionality to do the same task for you. This notebook is supposed to make you aware of some of the functionalities that pandas has to offer.
import pandas as pd import numpy as np import datetime Create some Data We create sample DataFrames with the champions in German men’s and women’s football from 2007 to 2014 and their average points per match.