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Creating, Loading, and Selecting Data with Pandas
Select Rows with Logic I
You can select a subset of a DataFrame by using logical statements:
We have a large DataFrame with information about our customers. A few of the many rows look like this:
| name | address | phone | age |
|---|---|---|---|
| Martha Jones | 123 Main St. | 234-567-8910 | 28 |
| Rose Tyler | 456 Maple Ave. | 212-867-5309 | 22 |
| Donna Noble | 789 Broadway | 949-123-4567 | 35 |
| Amy Pond | 98 West End Ave. | 646-555-1234 | 29 |
| Clara Oswald | 54 Columbus Ave. | 714-225-1957 | 31 |
| … | … | … | … |
Suppose we want to select all rows where the customer’s age is 30. We would use:
In Python, == is how we test if a value is exactly equal to another value.
We can use other logical statements, such as:
- Greater Than,
>— Here, we select all rows where the customer’s age is greater than 30:df[df.age > 30] - Less Than,
<— Here, we select all rows where the customer’s age is less than 30:df[df.age < 30] - Not Equal,
!=— This snippet selects all rows where the customer’s name is notClara Oswald:df[df.name != 'Clara Oswald']
Instructions
1.
You’re going to staff the clinic for January of this year. You want to know how many visits took place in January of last year, to help you prepare.
Create variable january using a logical statement that selects the row of df where the 'month' column is 'January'.
2.
Inspect january using print.