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When grouping a Pandas DataFrame, when should I use transform and when should I use aggregate? How do they differ with respect to their application in practice and which one do you consider more important?
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If you want to get a single value for each group, use aggregate() (or one of its shortcuts). If you want to get a subset of the original rows, use filter() .
Pandas Series: transform() function The transform() function is used to call function on self producing a Series with transformed values and that has the same axis length as self.
transform() can take a function, a string function, a list of functions, and a dict. However, apply() is only allowed a function. apply() works with multiple Series at a time. But, transform() is only allowed to work with a single Series at a time.
What are pandas aggregate functions? Similar to SQL, pandas also supports multiple aggregate functions that perform a calculation on a set of values (grouped data) and return a single value. An aggregate is a function where the values of multiple rows are grouped together to form a single summary value.
consider the dataframe df
df = pd.DataFrame(dict(A=list('aabb'), B=[1, 2, 3, 4], C=[0, 9, 0, 9])) 
groupby is the standard use aggregater
df.groupby('A').mean() 
maybe you want these values broadcast across the whole group and return something with the same index as what you started with.
use transform
df.groupby('A').transform('mean') 
df.set_index('A').groupby(level='A').transform('mean') 
agg is used when you have specific things you want to run for different columns or more than one thing run on the same column.
df.groupby('A').agg(['mean', 'std']) 
df.groupby('A').agg(dict(B='sum', C=['mean', 'prod'])) 
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