Aggregates in Pandas
Learn the basics of aggregate functions in Pandas, which let us calculate quantities that describe groups of data..
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Pandas’ Groupby
Pandas’ Groupby
df = pd.DataFrame([
["Amy","Assignment 1",75],
["Amy","Assignment 2",35],
["Bob","Assignment 1",99],
["Bob","Assignment 2",35]
], columns=["Name", "Assignment", "Grade"])
df.groupby('Name').Grade.mean()
# output of the groupby command
|Name | Grade|
| - | - |
|Amy | 55|
|Bob | 67|
In a pandas DataFrame, aggregate statistic functions can be applied across multiple rows by using a groupby function. In the example, the code takes all of the elements that are the same in Name and groups them, replacing the values in Grade with their mean. Instead of mean() any aggregate statistics function, like median() or max(), can be used. Note that to use the groupby() function, at least two columns must be supplied.
Aggregates in Pandas
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Aggregates in Pandas
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