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Effective Data Manipulation with pandas

Aggregate Methods

Explore how to apply aggregate methods to pandas Series to condense data into meaningful scalar values. Learn techniques like calculating means, quantiles, counting conditions using boolean values, and performing multiple aggregations with the agg method to analyze and summarize your data efficiently.

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Aggregate methods collapse the values of a Series down to a scalar. It allows us to take detailed data and collapse it to a single value.

Aggregations

If we want to calculate the mean value of a Series, we can use the aggregation method mean:

Python 3.8
print(city_mpg.mean())

There are also a few aggregate properties. These start with is_. These are not functions but are rather the attributes of the Series class. We do not call them, and they evaluate to True or False:

Python 3.8
print(city_mpg.is_unique)
print(city_mpg.is_monotonic_increasing)

One method we need to be aware of is the quantile method. By default, it returns the 50 percent ...