Sometimes, we want to add a column to an existing DataFrame. We might want to add new information or perform a calculation based on the data that we already have.
One way that we can add a new column is by giving a list of the same length as the existing DataFrame.
Suppose we own a hardware store called The Handy Woman and have a DataFrame containing inventory information:
| Product ID | Product Description | Cost to Manufacture | Price |
|---|---|---|---|
| 1 | 3 inch screw | 0.50 | 0.75 |
| 2 | 2 inch nail | 0.10 | 0.25 |
| 3 | hammer | 3.00 | 5.50 |
| 4 | screwdriver | 2.50 | 3.00 |
It looks like the actual quantity of each product in our warehouse is missing!
Let’s use the following code to add that information to our DataFrame.
df['Quantity'] = [100, 150, 50, 35]Our new DataFrame looks like this:
| Product ID | Product Description | Cost to Manufacture | Price | Quantity |
|---|---|---|---|---|
| 1 | 3 inch screw | 0.50 | 0.75 | 100 |
| 2 | 2 inch nail | 0.10 | 0.25 | 150 |
| 3 | hammer | 3.00 | 5.50 | 50 |
| 4 | screwdriver | 2.50 | 3.00 | 35 |
The DataFrame df contains information on products sold at a hardware store. Add a column to df called 'Sold in Bulk?', which indicates if the product is sold in bulk or individually. The final table should look like this:
| Product ID | Product Description | Cost to Manufacture | Price | Sold in Bulk? |
|---|---|---|---|---|
| 1 | 3 inch screw | 0.50 | 0.75 | Yes |
| 2 | 2 inch nail | 0.10 | 0.25 | Yes |
| 3 | hammer | 3.00 | 5.50 | No |
| 4 | screwdriver | 2.50 | 3.00 | No |