Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Series.agg() is used to pass a function or list of function to be applied on a series or even each element of series separately. In case of list of function, multiple results are returned by
Syntax: Series.agg(func, axis=0)
func: Function, list of function or string of function name to be called on Series.
axis:0 or ‘index’ for row wise operation and 1 or ‘columns’ for column wise operation.
Return Type: The return type depends on return type of function passed as parameter.
In this example, a lambda function is passed which simply adds 2 to each value of series. Since the function will be applied to each value of series, the return type is also series. A random series of 10 elements is generated by passing array generated using Numpy random method.
As shown in output, the function was applied to each value and 2 was added to each value of series.
Example #2: Passing List of functions
In this example, a list of some Python’s default function is passed and multiple results are returned by
agg() method into multiple variables.
As shown in output, multiple results were returned. Min, Max and Sorted array were returned into different variables result1, result2, result3 respectively.
- Python | pandas.map()
- Python | Pandas Series.mul()
- Python | Pandas Series.max()
- Python | Pandas Series.min()
- Python | Pandas Dataframe.pop()
- Python | Pandas Series.last()
- Python | Pandas Series.std()
- Python | Pandas Series.div()
- Python | Pandas Series.mean()
- Python | Pandas TimedeltaIndex.name
- Python | Pandas Series.gt()
- Python | Pandas dataframe.cov()
- Python | Pandas Series.add()
- Python | Pandas Series.sub()
- Python | Pandas DataFrame.ix[ ]
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