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
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"])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
df using Pandas’ aggregate statistics functions.
df.columnName.mean() # Average of all values in columndf.columnName.std() # Standard deviation of columndf.columnName.median() # Median value of columndf.columnName.max() # Maximum value in columndf.columnName.min() # Minimum value in columndf.columnName.count() # Number of values in columndf.columnName.nunique() # Number of unique values in columndf.columnName.unique() # List of unique values in column
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:
orderswould contain the information necessary to describe an order:
productswould contain the information to describe each product:
customerswould contain the information for each customer:
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.
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.