Explain what each line in the following short program does: what is in first, second, etc.?
first = pd.read_csv('data/gapminder_all.csv', index_col='country')
second = first[first['continent'] == 'Americas']
third = second.drop('Puerto Rico')
fourth = third.drop('continent', axis = 1)
fourth.to_csv('result.csv')
first = pd.read_csv('data/gapminder_all.csv', index_col='country')
This line loads the dataset containing the GDP data from all countries into a dataframe called first. The index_col='country' parameter selects which column to use as the row labels in the dataframe.
Python
second = first[first['continent'] == 'Americas']This line makes a selection: only those rows of
first for which the ‘continent’ column matches ‘Americas’ are extracted. Notice how the Boolean expression inside the brackets, first['continent'] == 'Americas', is used to select only those rows where the expression is true. Try printing this expression! Can you print also its individual True/False elements? (hint: first assign the expression to a variable)
Python
third = second.drop('Puerto Rico')
As the syntax suggests, this line drops the row from second where the label is ‘Puerto Rico’. The resulting dataframe third has one row less than the original dataframe second.
Python
fourth = third.drop('continent', axis = 1)
Again we apply the drop function, but in this case we are dropping not a row but a whole column. To accomplish this, we need to specify also the axis parameter (we want to drop the second column which has index 1).
Python
fourth.to_csv('result.csv')
The final step is to write the data that we have been working on to a csv file. Pandas makes this easy with the to_csv() function. The only required argument to the function is the filename. Note that the file will be written in the directory from which you started the Jupyter or Python session.