How do I sum up lines of string based on a dictionary value?-CodePudding

I am pretty new to python and coding. I am trying to write a code that will print out the total amount of each given wire type from a list of wires. This is a side project for work. I was able to come up with a code to sum up all of the wire for a user defined wire type. Now I would like to make another code that prints out the total of each wire type in the file.

This is the code that I came up with to sum up individual wire type as selected by the user.

wtype = []
w = []
w1 = []

#opens the .TXT file

fhand = input('\nEnter Text File\n')

try:
    if (len(fhand) <= 0):
        fhand = 'test.txt'
    fh = open(fhand)
except:
    print('\nNo File Found:', fhand, '\n')
    exit()

#prints out the possible wire types

for line in fh:
    line = line.rstrip()
    wtype.append(line)  #needed for later in the code
    line2 = line.split(',')[2]
    if line2 not in w:
        w.append(line2)
    else:
        continue
d1 = dict(enumerate(w))
print(d1)

#sums up the selected wire types total length from the given .TXT file

wire = int(input('\nEnter the number that is before the wire type you need:\n'))

for key, val in d1.items():
    if key == wire:
        for x in wtype:
            x = x.split(',')
            if x[2] == val:
                w1.append(x[1])
            else:
                continue
        s = [eval(i) for i in w1]
        print('\nYour will need ', sum(s)/12, ' Feet of ', val, '.\n')

This is the test.txt file, the length is in inches and the converted to feet in the last line of the code sum(s)/12:

the column are WIRE, LENGTH, TYPE, QTY for this file.

WIRE-006A22,72,M22759/16-22-9,1
WIRE-005A22,60,M22759/16-22-9,1
WIRE-004A22,72,M22759/16-22-9,1
WIRE-003A22,72,M22759/16-20-9,1
WIRE-002A22,60,M22759/16-20-9,1
WIRE-001A22,72,M22759/16-22-9,1
WIRE-009A22,72,M22759/16-22-9,1
WIRE-008A22,60,M22759/16-22-9,1
WIRE-007A22,72,M22759/16-20-9,1
WIRE-011A22,72,M22759/16-22-9,1
WIRE-012A22,72,M22759/16-22-9,1
WIRE-014A22,72,M22759/16-20-9,1
WIRE-013A22,60,M22759/16-22-9,1
WIRE-021A22,72,M22759/16-20-9,1
WIRE-031A22,72,M22759/16-22-9,1
WIRE-032A22,72,M22759/16-20-9,1
WIRE-043A22,60,M22759/16-22-9,1
WIRE-054A22,72,M22759/16-20-9,1
WIRE-065A22,72,M22759/16-22-9,1
WIRE-076A22,60,M22759/16-22-9,1
WIRE-087A22,72,M22759/16-22-9,1
WIRE-098A22,72,M22759/16-20-9,1
WIRE-089A22,72,M22759/16-20-9,1
WIRE-078A22,72,M22759/16-20-9,1
WIRE-067A22,60,M22759/16-22-9,1
WIRE-056A22,72,M22759/16-22-9,1
WIRE-045A22,72,M22759/16-20-9,1
WIRE-034A22,60,M22759/16-22-9,1
WIRE-023A22,60,M22759/16-22-9,1
WIRE-012A22,72,M22759/16-20-9,1

The output I am looking to try and achieve is:

output: {'M22759/16-22-9': 100, 'M22759/16-20-9': 71}

and have that be expandable to all the different wire types that could be in d1

CodePudding user response：

Similar to what you did - but run through the whole file once making a dictionary while iterating.

import collections
d = collections.defaultdict(int)
with open('thefile.txt') as f:
    next(f)
    for line in f:
        wire,length,type,qty = line.strip().split(',')
        d[type]  = int(length)

for type,l in d.items():
    print(type,l)

>>>
M22759/16-22-9 1200
M22759/16-20-9 852

For feet instead of inches:

import collections
d = collections.defaultdict(float)
with open('thefile.txt') as f:
    next(f)
    for line in f:
        wire,length,type,qty = line.strip().split(',')
        d[type]  = int(length)/12

CodePudding user response：

Here is one simple way, with very little code, that uses the pandas library.

import pandas

df = pandas.read_csv("test.csv")
df_out = df.groupby("TYPE")["QTY"].sum()
print("Output:", df_out.to_dict())

# Output: {'M22759/16-20-9': 12, 'M22759/16-22-9': 18}

It assumes that the input CSV file looks like this:

WIRE,LENGTH,TYPE,QTY
WIRE-006A22,72,M22759/16-22-9,1
WIRE-005A22,60,M22759/16-22-9,1
WIRE-004A22,72,M22759/16-22-9,1
WIRE-003A22,72,M22759/16-20-9,1
...

If the CSV file has no header, then you can still use pandas. Just tell it there's no header, and then use column numbers instead of column names. For example:

import pandas

df = pandas.read_csv("test-noheader.csv", header=None)
df_out = df.groupby(2)[3].sum()
print("Output:", df_out.to_dict())

Of course you can achieve the same result fairly simply with non-pandas code but I thought it worth sharing how few lines of code this could be.

Here's a simple non-pandas version that uses the standard csv module:

import csv

output = {}

with open("test.csv") as csvfile:
    for row in csv.DictReader(csvfile):
        if row["TYPE"] in output:
            output[row["TYPE"]]  = int(row["QTY"])
        else:
            output[row["TYPE"]] = int(row["QTY"])

print("Output:", output)

And again, if the CSV file has no header:

import csv

output = {}

with open("test-noheader.csv") as csvfile:
    for row in csv.DictReader(csvfile, fieldnames=["WIRE", "LENGTH", "TYPE", "QTY"]):
        if row["TYPE"] in output:
            output[row["TYPE"]]  = int(row["QTY"])
        else:
            output[row["TYPE"]] = int(row["QTY"])

print("Output:", output)

PS your text file is actually a csv file so it's probably better to name it accordingly (e.g. test.csv).

CodePudding user response：

I would suggest using a csv file so you can properly sort data. I have something that hopefully will get you started.It doesn't include your input options but hopefully this works for you. I have some Python experience but still considered a beginner by my standards #It shows. :)

import pandas as py


data = py.read_csv('testfile.csv').sort_values(by=['col3'])


wtype = []
w = []
w1 = []

data_dict = {}
this_val = ''
for x in data.iterrows():
    wire_name = x[1][2]
    if this_val == wire_name:
        data_dict[wire_name]  = x[1][1]
    else:
        data_dict[wire_name] = x[1][1]
        this_val = wire_name
#Removed to use dict comp
data_dict = {key:int(val/12) for key,val in data_dict.items()}
#for key,val in data_dict.items():
    #data_dict[key] = int(val/12)

print(data_dict)