It is easy to do this kind of matching for one row, but hard to do it for multiple rows.
Luckily, Pandas can efficiently do this for the entire table. We use the
.merge() method looks for columns that are common between two DataFrames and then looks for rows where those column’s values are the same. It then combines the matching rows into a single row in a new table.
We can call the
pd.merge() method with two tables like this:
new_df = pd.merge(orders, customers)
This will match up all of the customer information to the orders that each customer made.
You are an analyst at Cool T-Shirts Inc. You are going to help them analyze some of their sales data.
There are two DataFrames defined in the file script.py:
salescontains the monthly revenue for Cool T-Shirts Inc. It has two columns:
targetscontains the goals for monthly revenue for each month. It has two columns:
Create a new DataFrame
sales_vs_targets which contains the merge of
Cool T-Shirts Inc. wants to know the months when they crushed their targets.
Select the rows from
revenue is greater than
target. Save these rows to the variable