python pandas apply
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the apply() function in the pandas library is a powerful and flexible tool for applying a function along the axis of a dataframe or series. it allows you to transform your data in a customized way, making it a crucial tool for data manipulation and analysis. in this tutorial, we'll explore the basics of the apply() function with examples.
the apply() function in pandas is used to apply a given function along the axis of a dataframe or series. it can be applied to both rows and columns, and the function can be a built-in function, a custom function, or a lambda function.
the basic syntax of the apply() function is as follows:
let's start with a simple example to understand the basic usage of the apply() function.
output:
in this example, the square() function is applied element-wise to each element in the dataframe, resulting in a new dataframe with squared values.
the apply() function can be applied to a series as well. let's see an example:
output:
here, the double() function is applied to each element in the series, doubling each value.
you can apply functions to specific columns in a dataframe by specifying the axis parameter. when axis=0, the function is applied to each column; when axis=1, it is applied to each row. let's look at an example:
output:
here, the column_sum() function is applied to each column, resulting in a series with the sum of each column.
to apply a function to each row in a dataframe, set axis=1. here's an example:
output:
in this case, the row_product() function is applied to each row, resulting in a series with the product of each row.
you can use lambda functions for quick one-liners with apply(). here's an example:
output:
in this example, the lambda function lambda x: x * 2 is applied to each element in the dataframe, doubling each value.
you can pass additional arguments to your custom functions using the args parameter. here's an example:
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