You can select a subset of a DataFrame by using logical statements:

df[df.MyColumnName == desired_column_value]

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:

df[df.age == 30]

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 not Clara Oswald:
    df[df.name != 'Clara Oswald']



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'.


Inspect january using print.

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