Shuffle a Pandas Dataframe with Sci-Kit Learn’s shuffleĪnother helpful way to randomize a Pandas Dataframe is to use the machine learning library, sklearn. When we rerun this code, we now get the same result each time. Shuffled = df.sample(frac=1, random_state=1).reset_index() Let’s see how this works: # Reproducing a shuffled dataframe in Pandas with random_state= It’s also very helpful in being able to properly troubleshoot your code. This can be particularly helpful when others are reviewing and reproduce your results. Why use random_state? Being able to reproduce your results is a helpful skill in machine learning in order to better be able to understand your workflow. We can simply pass in an integer value and the shuffled dataframe will look the same each time. We’re able to reproduce our results by passing a value into the random_state= argument. When you apply the sample method to a dataframe, it returns a newly shuffled dataframe each time. One of the important aspects of data science is the ability to reproduce your results. In the next section, you’ll learn how to shuffle a Pandas Dataframe using sample, while being able to reproduce your results. Shuffled = df.sample(frac=1).reset_index() Let’s see what this looks like: # Shuffling a Pandas dataframe with. reset_index() method, which resets our index to be sorted from 0 onwards. We can see, however, that our original index values are maintained. sample() method, that the dataframe was shuffled in a random order. Let’s try this out in Pandas: # Shuffling a Pandas dataframe with. This instructs Pandas to return 100% of the dataframe. In order to do this, we apply the sample method to our dataframe and tell the method to return the entire dataframe by passing in frac=1. Because of this, we can simply specify that we want to return the entire Pandas Dataframe, in a random order. The df.sample method allows you to sample a number of rows in a Pandas Dataframe in a random order. One of the easiest ways to shuffle a Pandas Dataframe is to use the Pandas sample method. We can see that our dataframe has four columns: two containing strings and two containing numeric values. You can also use your own dataframe, but your results will, of course, vary from the ones in the tutorial. If you want to follow along with this tutorial line-by-line, feel free to copy the code below in order. In the code block below, you’ll find some Python code to generate a sample Pandas Dataframe.
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