Learn Python by Building Data Science Applications

Masking

Now, both loc and simple square brackets accept masks. Mask can be represented by a Series, a NumPy array, or a simple list of Boolean values of the same length as the number of rows in the dataframe. If given, this collection will be interpreted as a mask—essentially, an explanation of which rows to return. For example, we can use our third column, z, as a mask to filter on. Because we only have a True value in the first row, a dataframe of one row will be returned:

>>> df[df['z']]    x   y   z    new_column1   2   b   True         -1

This is a very important technique, which we'll be using all the time! Such a mask can be generated using any logic operations, for example, an equality operator. Take a look: here, we are creating a mask by checking whether ...