I am very new to Python. I know that this has already been asked, and I apologise, but the difference in this new situation is that spaces between strings are not equal. I have a file, named coord, that contains the following space delimited strings:
1 C 6.00 0.000000000 1.342650315 0.000000000
2 C 6.00 0.000000000 -1.342650315 0.000000000
3 C 6.00 2.325538562 2.685300630 0.000000000
4 C 6.00 2.325538562 -2.685300630 0.000000000
5 C 6.00 4.651077125 1.342650315 0.000000000
6 C 6.00 4.651077125 -1.342650315 0.000000000
7 C 6.00 -2.325538562 2.685300630 0.000000000
8 C 6.00 -2.325538562 -2.685300630 0.000000000
9 C 6.00 -4.651077125 1.342650315 0.000000000
10 C 6.00 -4.651077125 -1.342650315 0.000000000
11 H 1.00 2.325538562 4.733763602 0.000000000
12 H 1.00 2.325538562 -4.733763602 0.000000000
13 H 1.00 -2.325538562 4.733763602 0.000000000
14 H 1.00 -2.325538562 -4.733763602 0.000000000
15 H 1.00 6.425098097 2.366881801 0.000000000
16 H 1.00 6.425098097 -2.366881801 0.000000000
17 H 1.00 -6.425098097 2.366881801 0.000000000
18 H 1.00 -6.425098097 -2.366881801 0.000000000
Please, note the spaces before the start of each string in the first column. So I have tried the following in order of converting it to csv:
with open('coord') as infile, open('coordv', 'w') as outfile:
outfile.write(infile.read().replace(" ", ", "))
# Unneeded columns are deleted from the csv
input = open('coordv', 'rb')
output = open('coordcsvout', 'wb')
writer = csv.writer(output)
for row in csv.reader(input):
if row:
writer.writerow(row)
input.close()
output.close()
with open("coordcsvout","rb") as source:
rdr= csv.reader( source )
with open("coordbarray","wb") as result:
wtr= csv.writer(result)
for r in rdr:
wtr.writerow( (r[5], r[6], r[7]) )
When I run the script, I get the following for the coordv in the very first part of the script, which is of course very wrong:
, 1, C, , , 6.00, , 0.000000000, , 1.342650315, , 0.000000000
, 2, C, , , 6.00, , 0.000000000, -1.342650315, , 0.000000000
, 3, C, , , 6.00, , 2.325538562, , 2.685300630, , 0.000000000
, 4, C, , , 6.00, , 2.325538562, -2.685300630, , 0.000000000
, 5, C, , , 6.00, , 4.651077125, , 1.342650315, , 0.000000000
, 6, C, , , 6.00, , 4.651077125, -1.342650315, , 0.000000000
, 7, C, , , 6.00, -2.325538562, , 2.685300630, , 0.000000000
, 8, C, , , 6.00, -2.325538562, -2.685300630, , 0.000000000
, 9, C, , , 6.00, -4.651077125, , 1.342650315, , 0.000000000
, 10, C, , , 6.00, -4.651077125, -1.342650315, , 0.000000000
, 11, H, , , 1.00, , 2.325538562, , 4.733763602, , 0.000000000
, 12, H, , , 1.00, , 2.325538562, -4.733763602, , 0.000000000
, 13, H, , , 1.00, -2.325538562, , 4.733763602, , 0.000000000
, 14, H, , , 1.00, -2.325538562, -4.733763602, , 0.000000000
, 15, H, , , 1.00, , 6.425098097, , 2.366881801, , 0.000000000
, 16, H, , , 1.00, , 6.425098097, -2.366881801, , 0.000000000
, 17, H, , , 1.00, -6.425098097, , 2.366881801, , 0.000000000
, 18, H, , , 1.00, -6.425098097, -2.366881801, , 0.000000000
I have tried different possibilities in .replace without any success, and so far I haven't found any source of information on how I could do this. What would be the best way to get a comma-separated values from this coord file? What I'm interested is in using then the csv module in python to choose columns 4:6 and finally use numpy to import them as follows:
from numpy import genfromtxt
cocmatrix = genfromtxt('input', delimiter=',')
I'd be very glad if somebody could help me with this problem.
You can use csv:
import csv
with open(ur_infile) as fin, open(ur_outfile, 'w') as fout:
o=csv.writer(fout)
for line in fin:
o.writerow(line.split())
You can use python pandas, I have written your data to data.csv
:
import pandas as pd
>>> df = pd.read_csv('data.csv',sep='\s+',header=None)
>>> df
0 1 2 3 4 5
0 1 C 6 0.000000 1.342650 0
1 2 C 6 0.000000 -1.342650 0
2 3 C 6 2.325539 2.685301 0
3 4 C 6 2.325539 -2.685301 0
4 5 C 6 4.651077 1.342650 0
5 6 C 6 4.651077 -1.342650 0
...
The great thing about this is to access the underlying numpy array you can use df.values
:
>>> type(df.values)
<type 'numpy.ndarray'>
To save the data frame with comma delimiters:
>>> df.to_csv('data_out.csv',header=None)
Pandas is a great library for managing large amounts of data, as a bonus it works well with numpy. There is also a very good chance that this will be much faster then using the csv
module.
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