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Aggregates and Multiple Tables

Pandas’ Groupby

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.

df = pd.DataFrame([
["Amy","Assignment 1",75],
["Amy","Assignment 2",35],
["Bob","Assignment 1",99],
["Bob","Assignment 2",35]
], columns=["Name", "Assignment", "Grade"])
# output of the groupby command
|Name | Grade|
| - | - |
|Amy | 55|
|Bob | 67|

Pandas DataFrame Aggregate Function

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.

df.columnName.mean() # Average of all values in column
df.columnName.std() # Standard deviation of column
df.columnName.median() # Median value of column
df.columnName.max() # Maximum value in column
df.columnName.min() # Minimum value in column
df.columnName.count() # Number of values in column
df.columnName.nunique() # Number of unique values in column
df.columnName.unique() # List of unique values in column

Efficient Data Storage with Multiple Tables

For efficient data storage, related information is often spread across multiple tables of a database.

Consider an e-commerce business that tracks the products that have been ordered from its website. Business data for the company could be split into three tables:

  • orders would contain the information necessary to describe an order: order_id, customer_id, product_id, quantity, and timestamp
  • products would contain the information to describe each product: product_id, product_description and product_price
  • customers would contain the information for each customer: customer_id, customer_name, customer_address, and customer_phone_number

This table structure prevents the storage of redundant information, given that each customer’s and product’s information is only stored once, rather than each time a customer places an order for another item.

Pandas DataFrame Inner Merge

In Pandas the .merge() function uses an inner merge by default. An inner merge can be thought of as the intersection between two (or more) DataFrames. This is similar to a Venn diagram. In other words, an inner merge only returns rows both tables have in common. Any rows in one DataFrame that are not in the other, will not be in the result.

A Venn diagram of the intersection of two sets. The RED area is the intersection. This is what we get from an INNER MERGE.

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