In the world of data science, Python’s pandas library is a go-to tool for data manipulation. A recent survey revealed that 75% of data scientists prefer Python for its aggregation capabilities. The `groupby` function in pandas allows users to group data based on specified conditions, followed by the `agg` function which applies various aggregation methods like sum, mean, or custom functions. For instance, a function named `group_and_aggregate_data` can be defined to group data by a condition and apply an aggregation function. This method simplifies data analysis by providing a straightforward way to summarize large datasets. The flexibility of Python’s pandas library in handling data aggregation is a key reason for its popularity among data professionals.
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