Agreeing with RamonRamon, Pandas is definitely the way to go, and has extraordinary filtering/sub-setting capability once you get used to it. But it can be tough to first wrap your head around (or at least it was for me!), so I dug up some examples of the sub-setting you need from some of my old code. The variable itu below is a Pandas DataFrame with data on various countries over time.
# Subsetting by using True/False:
subset = itu['CntryName'] == 'Albania' # returns True/False values
itu[subset] # returns 1x144 DataFrame of only data for Albania
itu[itu['CntryName'] == 'Albania'] # one-line command, equivalent to the above two lines
# Pandas has many built-in functions like .isin() to provide params to filter on
itu[itu.cntrycode.isin(['USA','FRA'])] # returns where itu['cntrycode'] is 'USA' or 'FRA'
itu[itu.year.isin([2000,2001,2002])] # Returns all of itu for only years 2000-2002
# Advanced subsetting can include logical operations:
itu[itu.cntrycode.isin(['USA','FRA']) & itu.year.isin([2000,2001,2002])] # Both of above at same time
# Use .loc with two elements to simultaneously select by row/index & column:
itu.loc['USA','CntryName']
itu.iloc[204,0]
itu.loc[['USA','BHS'], ['CntryName', 'Year']]
itu.iloc[[204, 13], [0, 1]]
# Can do many operations at once, but this reduces "readability" of the code
itu[itu.cntrycode.isin(['USA','FRA']) &
itu.year.isin([2000,2001,2002])].loc[:, ['cntrycode','cntryname','year','mpen','fpen']]
# Finally, if you're comfortable with using map() and list comprehensions,
you can do some advanced subsetting that includes evaluations & functions
to determine what elements you want to select from the whole, such as all
countries whose name begins with "United":
criterion = itu['CntryName'].map(lambda x: x.startswith('United'))
itu[criterion]['CntryName'] # gives us UAE, UK, & US
