how do you filter pandas dataframes by multiple columns

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
dic={}
for g in ['male', 'female']:
    dic[g]=defaultdict(dict)
    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)

Start from pandas 0.13, this is the most efficient way.

df.query('Gender=="Male" & Year=="2014" ')

In case somebody wonders what is the faster way to filter (the accepted answer or the one from @redreamality):

import pandas as pd
import numpy as np

length = 100_000
df = pd.DataFrame()
df['Year'] = np.random.randint(1950, 2019, size=length)
df['Gender'] = np.random.choice(['Male', 'Female'], length)

%timeit df.query('Gender=="Male" & Year=="2014" ')
%timeit df[(df['Gender']=='Male') & (df['Year']==2014)]

Results for 100,000 rows:

6.67 ms ± 557 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
5.54 ms ± 536 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Results for 10,000,000 rows:

326 ms ± 6.52 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
472 ms ± 25.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

So results depend on the size and the data. On my laptop, query() gets faster after 500k rows. Further, the string search in Year=="2014" has an unnecessary overhead (Year==2014 is faster).

For more general boolean functions that you would like to use as a filter and that depend on more than one column, you can use:

df = df[df[['col_1','col_2']].apply(lambda x: f(*x), axis=1)]

where f is a function that is applied to every pair of elements (x1, x2) from col_1 and col_2 and returns True or False depending on any condition you want on (x1, x2).