pandas select subset of pivot_table

Question

There are a few questions here on this topic, but none seem to be helpful in my case. Here's a dumbed down version of what I want:

This is the csv file of interest: http://pastebin.com/rP7tPDse

I'm creating the pivot table as:

piv = pd.read_csv("test.csv",delimiter = "\s+").pivot_table('z','x','y')

And this returns

y    0.0  1.0  1.3  2.0
x
0.0  1.0  5.0  NaN  4.0
1.0  3.0  4.0  NaN  6.0
1.5  NaN  NaN  7.0  NaN
2.0  3.0  5.0  NaN  7.0

I would like to find a slice of this array as a pivot_table, such as:

y    1.3  2.0
x
0.0  NaN  4.0
1.0  NaN  6.0

Based on the x and y values. I want to include the NaN's as well, to do processing on them later. Help much appreciated.

EDIT: updating the question to be more specific.

I'm looking to extract a pivot table that has values denoted by the column 'z' and indexed by 'x' and 'y', with the condition that:

• All x values between arbitrary xmin and xmax
• All y values between arbitrary ymin and ymax

From piv, as defined above, I want to do something like:

piv.loc[(piv.y <= 2.0) & 
(piv.y >= 1.3) & 
(piv.x >= 0.0) & 
(piv.x <= 1.2)]

And this would yield me the example answer, above. Also, in the actual dataset, which I did not post here, there are many more columns. 'x', 'y' and 'z' are just some of them.

Answer 1

When I copied dataframe, the columns were strings and rows were floats.
To get the columns as float

df.columns = df.columns.astype(float)

Now you can pd.IndexSlice

df.loc[pd.IndexSlice[0:1], pd.IndexSlice[1.3:2]]