Cleaning missing values

In this section, we'll go over some of the most straightforward approaches for handling missing values. This includes dropping observations where there are missing values; assigning a sample-wide summary statistic, such as the mean, to the missing values; and assigning values based on the mean value for an appropriate subset of the data:

1. Let's load the NLS data and select some of the educational data:

   import pandas as pd
   nls97 = pd.read_csv("data/nls97b.csv")
   nls97.set_index("personid", inplace=True)
   schoolrecordlist = 
     ['satverbal','satmath','gpaoverall','gpaenglish',
     'gpamath','gpascience','highestdegree',
     'highestgradecompleted']
   schoolrecord = nls97[schoolrecordlist]
   schoolrecord.shape
   (8984, 8)

2. We can use the techniques we explored in the previous section to identify missing values. schoolrecord.isnull...