Pandas use a list of values to select rows from a column   

Filtering pandas dataframe by list of a values is a common operation in data science world. You have two main ways of selecting data:

• select pandas rows by exact match from a list
• filter pandas rows by partial match from a list

Related resources:

• Video
• Notebok

Also pandas offers big variety of options to solve those problems. I'll recommend to use vectorized operations when it's possible because it's much faster:

Vectorization is the process of executing operations on entire arrays.

So let say that we have this data(Value count for a given column):

and our goal is to find are this values part of the column and create a series with it:

area_list = ['biology', 'physics', 'Computer', 'enginnering']

to get output like:

and total count:

This can be done by using this code:

import re
area_df = pd.DataFrame(dict((area, df.UndergradMajor.str.contains(area))
                             for area in area_list))

where:

• we create a new dataframe for the result
• use vectorized function str.contains in order to verify if the value is part of the column
• create a dictionary for the result of the all values

This example show a partial match. If you want to use a full match than you can use another vectorized method from pandas which is str.isin. This is how to filter rows by exact match for the values of a list:

df[df['UndergradMajor'].isin(['Mathematics or statistics', 
                              'Web development or web design'])]

This will filter the rows of the dataframe which contains exactly the values from the list.

The bonus tip for today is how to apply value_counts for the whole dataframe or several columns. This can be done by:

df.apply(pd.Series.value_counts)

the result will be:

And perform value counts for several columns:

df[['Mobile','QA']].apply(pd.Series.value_counts)

the result will be: