how do you filter pandas dataframes by multiple columns

To filter a dataframe (df) by a single column, if we consider data with male and females we might:

    males = df[df[Gender]=='Male']

Question 1 - But what if the data spanned multiple years and i wanted to only see males for 2014?

In other languages I might do something like:

    if A = "Male" and if B = "2014" then

(except I want to do this and get a subset of the original dataframe in a new dataframe object)

Question 2. How do I do this in a loop, and create a dataframe object for each unique sets of year and gender (i.e. a df for: 2013-Male, 2013-Female, 2014-Male, and 2014-Female

    for y in year:

    for g in gender:

    df = .....

Using & operator, don't forget to wrap the sub-statements with ():

    males = df[(df[Gender]=='Male') & (df[Year]==2014)]

To store your dataframes in a dict using a for loop:

    from collections import defaultdict
    for g in ['male', 'female']:
      for y in [2013, 2014]:
        dic[g][y]=df[(df[Gender]==g) & (df[Year]==y)] #store the DataFrames to a dict of dict


A demo for your getDF:

    def getDF(dic, gender, year):
      return dic[gender][year]

    print genDF(dic, 'male', 2014)


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