Changing DataFrame values using bracket or dot notation

Many of the methods we've discussed can be used to change the values in a DataFrame, as well as select slices or ranges. In the following screenshot, we can see the GDP data that we have been working with for 2015:

Figure 5.50 – The new GDP_2015 DataFrame

Now, suppose that as part of the economic analysis, we want to increase all the GDP values by 5,000. We can do this by selecting the GDP column using bracket notation on the left, and then doing the same and adding 5,000 on the right:

GDP_2015['GDP'] = GDP_2015['GDP'] + 5000

GDP_2015

This will produce the following output:

Figure 5.51 – The GDP_2015 DataFrame with every value in the GDP column increased by 5,000

Here, we can see the expected result – that is, all our GDP figures have been increased by 5,000. Thus, using bracket notation, we can choose where new data goes into...