I'll assume that Time and Product are columns in a DataFrame, df is an instance of DataFrame, and that other variables are scalar values:
For now, you'll have to reference the DataFrame instance:
k1 = df.loc[(df.Product == p_id) & (df.Time >= start_time) & (df.Time < end_time), ['Time', 'Product']]
The parentheses are also necessary, because of the precedence of the & operator vs. the comparison operators. The & operator is actually an overloaded bitwise operator which has the same precedence as arithmetic operators which in turn have a higher precedence than comparison operators.
In pandas 0.13 a new experimental DataFrame.query() method will be available. It's extremely similar to subset modulo the select argument:
With query() you'd do it like this:
df[['Time', 'Product']].query('Product == p_id and Month < mn and Year == yr')
Here's a simple example:
In [9]: df = DataFrame({'gender': np.random.choice(['m', 'f'], size=10), 'price': poisson(100, size=10)})
In [10]: df
Out[10]:
gender price
0 m 89
1 f 123
2 f 100
3 m 104
4 m 98
5 m 103
6 f 100
7 f 109
8 f 95
9 m 87
In [11]: df.query('gender == "m" and price < 100')
Out[11]:
gender price
0 m 89
4 m 98
9 m 87
The final query that you're interested will even be able to take advantage of chained comparisons, like this:
k1 = df[['Time', 'Product']].query('Product == p_id and start_time <= Time < end_time')