Simple question about python logic.

[seungchan.oh]

I have a file containing following data. But the dimension can be
different.

A B C D E F G
3 4 1 5 6 2 4
7 2 4 1 6 9 3
3 4 1 5 6 2 4
7 2 4 1 6 9 3
..
..
..
..

What is the best approach to make a column vector with the name such
as A B, etc?

 

[Carl Banks]

I have a file containing following data. But the dimension can be
different.

A B C D E F G
3 4 1 5 6 2 4
7 2 4 1 6 9 3
3 4 1 5 6 2 4
7 2 4 1 6 9 3
.
.
.
.

What is the best approach to make a column vector with the name such
as A B, etc?

Use a dict. dict objects are the main way we map names to objects on
the fly. Simple example:

columns = {}
columns["A"] = [3,7,3,3]
columns["B"] = [4,2,4,2]


Filling in the columns with data from your file is left as an
exercise.


Carl Banks

 

[Tim Chase]

I have a file containing following data. But the dimension can be

different.

A B C D E F G
3 4 1 5 6 2 4
7 2 4 1 6 9 3
3 4 1 5 6 2 4
7 2 4 1 6 9 3
.
.
.
.

What is the best approach to make a column vector with the name such
as A B, etc?

There are a couple different ways to go about it depending on

1) whether just one or whether both dimensions can be different
2) whether you want to extract each column vector

Both of the following should allow for an arbitrary number of
columns:

To map everything, you can do something like

#######################################
infile = file("in.txt")
header_row = infile.next().rstrip('\n').split()
values = [[] for header in header_row]
for line in infile:
line = line.rstrip('\n').split()
for dest, value in zip(values, line):
dest.append(value)
infile.close()
results = dict(zip(header_row, values))
#######################################


which you can then use with

results['A']

However, if you just want a particular column from the file and
don't care about the rest, you can do something like

#######################################
from itertools import islice

column_c = [
line.rstrip('\n').split()[2]
for line in islice(file('in'), 1, None)
]
#######################################

where "2" is the zero-based column offset you want (in this case,
column C = 2)

-tkc

 

[Colin J. Williams]

I have a file containing following data. But the dimension can be
different.

A B C D E F G
3 4 1 5 6 2 4
7 2 4 1 6 9 3
3 4 1 5 6 2 4
7 2 4 1 6 9 3
.
.
.
.

What is the best approach to make a column vector with the name such
as A B, etc?

There are a couple different ways to go about it depending on

1) whether just one or whether both dimensions can be different
2) whether you want to extract each column vector

Both of the following should allow for an arbitrary number of
columns:

To map everything, you can do something like

#######################################
infile = file("in.txt")
header_row = infile.next().rstrip('\n').split()
values = [[] for header in header_row]
for line in infile:
line = line.rstrip('\n').split()
for dest, value in zip(values, line):
dest.append(value)
infile.close()
results = dict(zip(header_row, values))
#######################################


which you can then use with

results['A']

However, if you just want a particular column from the file and
don't care about the rest, you can do something like

#######################################
from itertools import islice

column_c = [
line.rstrip('\n').split()[2]
for line in islice(file('in'), 1, None)
]
#######################################

where "2" is the zero-based column offset you want (in this case,
column C = 2)

-tkc

Numpy appears to have this capability.

Colin W.