Pandas dataframe.query method syntax

@x.name - @ helps .query() to understand that x is an external object (doesn't belong to the DataFrame for which the query() method was called). In this case x is a DataFrame. It could be a scalar value as well.

I hope this small demonstration will help you to understand it:

In [79]: d1
Out[79]:
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

In [80]: d2
Out[80]:
   a   x
0  1  10
1  7  11

In [81]: d1.query("a in @d2.a")
Out[81]:
   a  b  c
0  1  2  3
2  7  8  9

In [82]: d1.query("c < @d2.a")
Out[82]:
   a  b  c
1  4  5  6

Scalar x:

In [83]: x = 9

In [84]: d1.query("c == @x")
Out[84]:
   a  b  c
2  7  8  9

Everything @MaxU said is perfect!

I wanted to add some context to the specific problem that this was applied to.

find_match

This is a helper function that is used in the dataframe dfWeeks.apply. Two things to note:

1. find_match takes a single argument x. This will be a single row of dfWeeks.
   • Each row is a pd.Series object and each row will be passed through this function. This is the nature of using apply.
   • When apply passes this row to the helper function, the row has a name attribute that is equal to the index value for that row in the dataframe. In this case, I know that the index value is a pd.Timestamp and I'll use it to do the comparing I need to do.
2. find_match references dfDays which is outside the scope of find_match itself.

I didn't have to use query... I like using query. It is my opinion that it makes some code prettier. The following function, as provided by the OP, could've been written differently

def find_match(x):
    """Original"""
    match = dfDays.query('index > @x.name & price >= @x.target')
    if not match.empty:
        return match.index[0]

dfWeeks.assign(target_hit=dfWeeks.apply(find_match, 1))

find_match_alt

Or we could've done this, which may help to explain what the query string is doing above

def find_match_alt(x):
    """Alternative to OP's"""
    date_is_afterwards = dfDays.index > x.name
    price_target_is_met = dfDays.price >= x.target
    both_are_true = price_target_is_met & date_is_afterwards
    if (both_are_true).any():
        return dfDays[both_are_true].index[0]

dfWeeks.assign(target_hit=dfWeeks.apply(find_match_alt, 1))

---

Comparing these two functions should give good perspective.