from pandas import DataFrame

data = {
    "city": ["Paris", "Paris", "Paris", "Paris",
             "London", "London", "London", "London",
             "Rome", "Rome", "Rome", "Rome"],
    "year": [2001, 2008, 2009, 2010,
             2001, 2006, 2011, 2015,
             2001, 2006, 2009, 2012],
    "pop": [2.148, 2.211, 2.234, 2.244,
            7.322, 7.657, 8.174, 8.615,
            2.547, 2.627, 2.734, 2.627]
}
census = DataFrame(data)

We start by grabbing the year that we care about:

census[census["year"] == 2001]

We can see that the smallest population was in Paris that year but let's try to extract it using pandas.

First, we get the population data:

pop = census[census["year"] == 2001]["pop"]
pop

0    2.148
4    7.322
8    2.547
Name: pop, dtype: float64

If we call min on the Series we get back the smallest value:

pop.min()

2.148

But what we actually want is the index for the row on which the smallest value was found, not the value itself. For this we can use the function idxmin:

pop.idxmin()

0

We can then take that value and pass it back into the city column to find out which city is on row 0:

census["city"][pop.idxmin()]

'Paris'

And indeed we see that the answer is Paris

Introduction to Data Analysis in Python