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Python Data Cleaning and Preparation Best Practices
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In pandas, grouping data is a fundamental operation that involves splitting data into groups based on one or more keys and then performing operations within each group. Grouping is often used in data analysis to gain insights and perform aggregate calculations on subsets of data. Let’s dive deeper into grouping data and provide examples to illustrate their usage. The code for this section can be found here: https://github.com/PacktPublishing/Python-Data-Cleaning-and-Preparation-Best-Practices/blob/main/chapter06/2.groupby_full_example.py.
Grouping data with pandas using one key is a common operation for data analysis.
To group data using one key, we use the groupby() method of a DataFrame and specify the column that we want to use as the key for grouping:
grouped = df.groupby('column_name') After grouping, you typically want to perform some aggregation. Common aggregation functions include...