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Pandas’ Groupby

Pandas DataFrame Aggregate Function

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.

Pandas DataFrame Aggregate Function

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|

Pandas’ aggregate statistics functions can be used to calculate statistics on a column of a DataFrame. For example, df.columnName.mean() computes the mean of the column columnName of dataframe df. The code block shows how to calculate statistics on the column columnName of df using Pandas’ aggregate statistics functions.

Aggregates in Pandas
Lesson 1 of 1
  1. 1
    This lesson you will learn about aggregates in Pandas. An aggregate statistic is a way of creating a single number that describes a group of numbers. Common aggregate statistics include mean,…
  2. 2
    In the previous lesson, you learned how to perform operations on each value in a column using apply. In this exercise, you will learn how to combine all of the values from a column for a single…
  3. 3
    When we have a bunch of data, we often want to calculate aggregate statistics (mean, standard deviation, median, percentiles, etc.) over certain subsets of the data. Suppose we have a grade book w…
  4. 4
    After using groupby, we often need to clean our resulting data. As we saw in the previous exercise, the groupby function creates a new Series, not a DataFrame. For our ShoeFly.com example, the in…
  5. 5
    Sometimes, the operation that you want to perform is more complicated than mean or count. In those cases, you can use the apply method and lambda functions, just like we did for individual column …
  6. 6
    Sometimes, we want to group by more than one column. We can easily do this by passing a list of column names into the groupby method. Imagine that we run a chain of stores and have data about the…
  7. 7
    When we perform a groupby across multiple columns, we often want to change how our data is stored. For instance, recall the example where we are running a chain of stores and have data about the n…
  8. 8
    This lesson introduced you to aggregates in Pandas. You learned: How to perform aggregate statistics over individual rows with the same value using groupby. How to rearrange a DataFrame into…

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