Pandas Cookbook

How to do it...

1.  Read in the college dataset, and filter for just the undergraduate race columns:

>>> college = pd.read_csv('data/college.csv', index_col='INSTNM')>>> college_ugds_ = college.filter(like='UGDS_')

2. Many of these colleges have missing values for all their race columns. We can count all the missing values for each row and sort the resulting Series from the highest to lowest. This will reveal the colleges that have missing values:

>>> college_ugds_.isnull()\                 .sum(axis=1)\                 .sort_values(ascending=False)\                 .head()INSTNMExcel Learning Center-San Antonio South         9
Philadelphia College of Osteopathic Medicine    9
Assemblies of God Theological Seminary          9
Episcopal Divinity School                       9
Phillips Graduate Institute                     9
dtype: int64

3. Now that we ...