Manipulating data with Pandas
You can combine data from different sources into a single DataFrame.
Imagine that you are tasked with calculating GDP to understand the health of the US economy. You have gathered the data you need from disparate sources in different formats.
You can calculate gross domestic product using the supplied DataFrames for personal consumption expenditures, government expenditures, gross private domestic investment, and net exports. The DataFrames ge, gpdi, ne, and pce are provided.
This exercise is part of the course
Intermediate Python for Finance
Exercise instructions
- Combine the supplied source DataFrames
ge,gpdi,neandpcein that order into a single new DataFrame. - Sum the values in each row to produce the GDP per year.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Combine the source DataFrames into one
gdp = pd.____([ge, gpdi, ____, ____], axis=1)
# Add the columns and create a new column with the result
gdp['GDP'] = gdp.____(np.sum, axis=1)