Note that, since the function get_mean() is applied to each column, we didn’t need to explicitly pass 0 to the axis parameter since it is its default value. Here, the apply() function is used to get the average score for each of the subjects across all students. Rows represent the students whereas columns represent the subjects. In the above example, the dataframe df contains scores of students in three subjects. The result of applying the function on the dataframe: Print("\nThe result of applying the function on the dataframe:\n") For instance, you’re working with a dataframe having all numerical columns and you want to find the mean for each of those columns. Let’s say you want to apply a function to each column of a dataframe, that is, along the index axis. Apply a function to each column of the dataframe Let’s look at some of the use-cases of the apply() function through examples. The examples below illustrate the difference. And to apply the function to each row, pass 1 or 'columns' to the axis parameter. To apply the function to each column, pass 0 or 'index' to the axis parameter which is 0 by default. We pass the function to be applied and the axis along which to apply it as arguments. The following is the syntax: result = df.apply(func, axis=0) The pandas dataframe apply() function is used to apply a function along a particular axis of a dataframe. In this tutorial, we’ll look at how to apply a function to a pandas dataframe through some examples. Pandas dataframes allow you the flexibility of applying a function along a particular axis of a dataframe.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |