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In Pandas, you can employ several criteria in both groupby and transform functions in several ways. Some of the ways include:

1. Group by multiple columns: You can group by multiple columns by passing a list of column names to the groupby function. For instance, df.groupby(['column1', 'column2']).agg() will group by column1 and column2.

2. Use boolean masks: You can use boolean masks to group data based on conditions. For instance, df.groupby(df['column1'] > 0).agg() will group df based on whether column1 is greater than 0.

3. Use lambda functions: You can also define a custom function using a lambda function to group data based on more complex conditions.

4. Apply multiple transform functions: You can apply multiple transform functions to the same column by chaining them, separated by a period. For example, df['column1'].transform(func1).transform(func2) will apply func1 and func2 to column1 sequentially.

5. Create custom aggregation functions: You can define your own custom aggregation functions and pass them to the agg method. For instance, df.groupby('column1').agg({'column2': my_agg_function}) will group by column1 and apply my_agg_function to column2.