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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 | shoe_type | shoe_material | shoe_color | |
|---|---|---|---|---|---|---|
| 54791 | Rebecca | Lindsay | RebeccaLindsay57@hotmail.com | clogs | faux-leather | black |
| 53450 | Emily | Joyce | EmilyJoyce25@gmail.com | ballet flats | faux-leather | navy |
| 91987 | Joyce | Waller | Joyce.Waller@gmail.com | sandals | fabric | black |
| 14437 | Justin | Erickson | Justin.Erickson@outlook.com | clogs | faux-leather | red |
| 79357 | Andrew | Banks | AB4318@gmail.com | boots | leather | brown |
| 52386 | Julie | Marsh | JulieMarsh59@gmail.com | sandals | fabric | black |
| 20487 | Thomas | Jensen | TJ5470@gmail.com | clogs | fabric | navy |
| 76971 | Janice | Hicks | Janice.Hicks@gmail.com | clogs | faux-leather | navy |
| 21586 | Gabriel | Porter | GabrielPorter24@gmail.com | 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 | shoe_type | shoe_material | shoe_color | |
|---|---|---|---|---|---|---|
| 14437 | Justin | Erickson | Justin.Erickson@outlook.com | clogs | faux-leather | red |
| 79357 | Andrew | Banks | AB4318@gmail.com | boots | leather | brown |
| 52386 | Julie | Marsh | JulieMarsh59@gmail.com | sandals | fabric | black |
| 20487 | Thomas | Jensen | TJ5470@gmail.com | 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 | shoe_type | shoe_material | shoe_color | |
|---|---|---|---|---|---|---|
| 54791 | Rebecca | Lindsay | RebeccaLindsay57@hotmail.com | clogs | faux-leather | black |
| 53450 | Emily | Joyce | EmilyJoyce25@gmail.com | ballet flats | faux-leather | navy |
| 91987 | Joyce | Waller | Joyce.Waller@gmail.com | sandals | fabric | black |
| 14437 | Justin | Erickson | Justin.Erickson@outlook.com | 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 | shoe_type | shoe_material | shoe_color | |
|---|---|---|---|---|---|---|
| 20487 | Thomas | Jensen | TJ5470@gmail.com | clogs | fabric | navy |
| 76971 | Janice | Hicks | Janice.Hicks@gmail.com | clogs | faux-leather | navy |
| 21586 | Gabriel | Porter | GabrielPorter24@gmail.com | 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.