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Creating, Loading, and Selecting Data with Pandas
Selecting Multiple Rows

You can also select multiple rows from a DataFrame.

Here are a few more rows from ShoeFly.com’s orders DataFrame:

id first_name last_name email shoe_type shoe_material shoe_color
54791 Rebecca Lindsay [email protected] clogs faux-leather black
53450 Emily Joyce [email protected] ballet flats faux-leather navy
91987 Joyce Waller [email protected] sandals fabric black
14437 Justin Erickson [email protected] clogs faux-leather red
79357 Andrew Banks [email protected] boots leather brown
52386 Julie Marsh [email protected] sandals fabric black
20487 Thomas Jensen [email protected] clogs fabric navy
76971 Janice Hicks [email protected] clogs faux-leather navy
21586 Gabriel Porter [email protected] clogs leather brown

Here are some different ways of selecting multiple rows:

  • orders.iloc[3:7] would select all rows starting at the 3rd row and up to but not including the 7th row (i.e., the 3rd row, 4th row, 5th row, and 6th row)

id first_name last_name email shoe_type shoe_material shoe_color
14437 Justin Erickson [email protected] clogs faux-leather red
79357 Andrew Banks [email protected] boots leather brown
52386 Julie Marsh [email protected] sandals fabric black
20487 Thomas Jensen [email protected] clogs fabric navy

  • orders.iloc[:4] would select all rows up to, but not including the 4th row (i.e., the 0th, 1st, 2nd, and 3rd rows)

id first_name last_name email shoe_type shoe_material shoe_color
54791 Rebecca Lindsay [email protected] clogs faux-leather black
53450 Emily Joyce [email protected] ballet flats faux-leather navy
91987 Joyce Waller [email protected] sandals fabric black
14437 Justin Erickson [email protected] clogs faux-leather red

  • orders.iloc[-3:] would select the rows starting at the 3rd to last row and up to and including the final row

id first_name last_name email shoe_type shoe_material shoe_color
20487 Thomas Jensen [email protected] clogs fabric navy
76971 Janice Hicks [email protected] clogs faux-leather navy
21586 Gabriel Porter [email protected] clogs leather brown

Instructions

1.

One of your doctors thinks that there are more clinic visits in the late Spring.

Write a command that will produce a DataFrame made up of the data for April, May, and June from df for all four sites (rows 3 through 6), and save it to april_may_june.

2.

Inspect april_may_june using print.

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