Row/Column Transpose

Question

I was wondering if there is a smarter way of doing the following code. Basically what is does is that it opens a data file with a lot of rows and columns. The columns are then sorted so each column is a vector with all the data inside.

"3.2.2 - Declare variables"
lineData    = list()

for line in File:
  splittedLine = line.split() # split
  lineData.append(splittedLine) #collect 

And here the fun begins

"3.2.3 - define desired variables from file"
col1    = "ElemNo"
col2    = "Node1"
col3    = "Node2"
col4    = "Length"
col5    = "Area"
col6    = "Inertia"
col7    = "Fnode1"
col8    = "Fnode2"
col9    = "SigmaMin"
col10   = "SigmaMax"

"3.2.3 - make each variable as a list/vector"
var ={col1:[], col2:[], col3:[], col4:[], col5:[], col6:[], col7:[], col8:[]
     ,col9:[],col10:[]}  

"3.2.3 - take the values from each row in lineData and collect them into the correct variable"
for row in lineData:
  var[col1] .append(float(row[0])      )    #[-]    ElemNo
  var[col2] .append(float(row[1])      )    #[-]    Node1
  var[col3] .append(float(row[2])      )    #[-]    Node2
  var[col4] .append(float(row[3])      )    #[mm]   Length
  var[col5] .append(float(row[4])      )    #[mm^2] Area
  var[col6] .append(float(row[5])*10**6)    #[mm^4] Inertia 
  var[col7] .append(float(row[6])      )    #[N]    Fnode1
  var[col8] .append(float(row[7])      )    #[N]    Fnode2
  var[col9] .append(float(row[8])      )    #[MPa]  SigmaMin
  var[col10].append(float(row[9])      )    #[MPa]  SigmaMax

As you see this is a rather annoying way of making each row into a variable. Any suggestions?

Answer 1

First of all don't create variables for those keys, store them in a list.

keys = ["ElemNo", "Node1", "Node2", "Length", "Area", "Inertia",
        "Fnode1", "Fnode2", "SigmaMin", "SigmaMax"]

You can use collections.defaultdict here, so no need to initialize the dictionary with those keys and empty list.

from collections import defaultdict
var = defaultdict(list)

Now, instead of storing the data in a list, you can populate the dictionary during iteration over File itself.

for line in File:
    for i, (k, v) in enumerate(zip(keys, line.split())):
        if i == 5:
            var[k].append(float(v)*10**6)
        else:
            var[k].append(float(v)) 

Answer 2

In functional approach, without refering to meaningless numeric indexes but column names, and creating a dictionary with "ElemNo" as keys instead of a dict of lists, I'd write:

columns = [
  "ElemNo", "Node1", "Node2", "Length", "Area", "Inertia",
  "Fnode1", "Fnode2", "SigmaMin", "SigmaMax",
]

def process_line(line):
    def get_value(column, value):
        if column == "Inertia":
            return float(value) * (10**6)
        else:
            return float(value)
    elem = {col: get_value(col, value) for (col, value) in zip(columns, line.split())}
    return (elem["ElemNo"], elem)

data = dict(process_line(line) for line in fd)

Answer 3

You can use zip to perform a transpose operation on a list of lists:

column_names = [
    "ElemNo", "Node1", "Node2", "Length", 
    "Area", "Inertia", "Fnode1", "Fnode2", 
    "SigmaMin", "SigmaMax"
]

columns = dict(zip(column_names, (map(float, col) for col in zip(*lineData))))
#                                        transposes lineData ^^^

columns["Inertia"] = [x * 10 ** 6 for x in columns["Inertia"]]