'Extracting data from a .txt file without using modules

I am taking a course in python and one of the problem sets is as follows:

Read in the contents of the file SP500.txt which has monthly data for 2016 and 2017 about the S&P 500 closing prices as well as some other financial indicators, including the “Long Term Interest Rate”, which is interest rate paid on 10-year U.S. government bonds.

Write a program that computes the average closing price (the second column, labeled SP500) and the highest long-term interest rate. Both should be computed only for the period from June 2016 through May 2017. Save the results in the variables mean_SP and max_interest.

SP500.txt:

Date,SP500,Dividend,Earnings,Consumer Price Index,Long Interest Rate,Real Price,Real Dividend,Real Earnings,PE10 1/1/2016,1918.6,43.55,86.5,236.92,2.09,2023.23,45.93,91.22,24.21 2/1/2016,1904.42,43.72,86.47,237.11,1.78,2006.62,46.06,91.11,24 3/1/2016,2021.95,43.88,86.44,238.13,1.89,2121.32,46.04,90.69,25.37 4/1/2016,2075.54,44.07,86.6,239.26,1.81,2167.27,46.02,90.43,25.92 5/1/2016,2065.55,44.27,86.76,240.23,1.81,2148.15,46.04,90.23,25.69 6/1/2016,2083.89,44.46,86.92,241.02,1.64,2160.13,46.09,90.1,25.84 7/1/2016,2148.9,44.65,87.64,240.63,1.5,2231.13,46.36,91,26.69 8/1/2016,2170.95,44.84,88.37,240.85,1.56,2251.95,46.51,91.66,26.95 9/1/2016,2157.69,45.03,89.09,241.43,1.63,2232.83,46.6,92.19,26.73 10/1/2016,2143.02,45.25,90.91,241.73,1.76,2214.89,46.77,93.96,26.53 11/1/2016,2164.99,45.48,92.73,241.35,2.14,2241.08,47.07,95.99,26.85 12/1/2016,2246.63,45.7,94.55,241.43,2.49,2324.83,47.29,97.84,27.87 1/1/2017,2275.12,45.93,96.46,242.84,2.43,2340.67,47.25,99.24,28.06 2/1/2017,2329.91,46.15,98.38,243.6,2.42,2389.52,47.33,100.89,28.66 3/1/2017,2366.82,46.38,100.29,243.8,2.48,2425.4,47.53,102.77,29.09 4/1/2017,2359.31,46.66,101.53,244.52,2.3,2410.56,47.67,103.74,28.9 5/1/2017,2395.35,46.94,102.78,244.73,2.3,2445.29,47.92,104.92,29.31 6/1/2017,2433.99,47.22,104.02,244.96,2.19,2482.48,48.16,106.09,29.75 7/1/2017,2454.1,47.54,105.04,244.79,2.32,2504.72,48.52,107.21,30 8/1/2017,2456.22,47.85,106.06,245.52,2.21,2499.4,48.69,107.92,29.91 9/1/2017,2492.84,48.17,107.08,246.82,2.2,2523.31,48.76,108.39,30.17 10/1/2017,2557,48.42,108.01,246.66,2.36,2589.89,49.05,109.4,30.92 11/1/2017,2593.61,48.68,108.95,246.67,2.35,2626.9,49.3,110.35,31.3 12/1/2017,2664.34,48.93,109.88,246.52,2.4,2700.13,49.59,111.36,32.09

My solution (correct but not optimal):

file = open("SP500.txt", "r")

content = file.readlines()

# List that will hold the range of months we need
data=[]

for line in content:
    # Get a list of values for each line
    values = line.split(',')
    # Return lines with the required dates
    for i in range(6,13):
        month_range = f"{i}/1/2016"
        if month_range == values[0]:
            data.append(values)
    # Return lines with the required dates
    for i in range(1,6):
        month_range = f"{i}/1/2017"
        if month_range == values[0]:
            data.append(values)

sum_total = 0
max_interest = 0
# Loop through the data of our required months
for entry in data:
    # Get the sum total
    sum_price += float(entry[1])
    # Find the highest interest rate in list
    if max_interest < float(entry[5]):
        max_interest = float(entry[5])

mean_SP = sum_total / len(data)

I'm self-learning these concepts and I would love to learn a better way of implementing this solution. My code seems borderline hard coding (exact date in values[0]) and I imagine it to be error prone for bigger problems. Especially the excessive looping that's being done, which seems quite exaustive for such a simple problem. Thanks in advance.

EDIT: New code (based Deepak Tripathi answer):

with open('SP500.txt') as f:
    lines = f.readlines()
lines = [line.rstrip().split(",") for line in lines]
date_index, spf_index, long_interest_rate = 0, 1, 5
start_year, end_year = 2016, 2017
start_month, end_month = 6, 5
mean_SP, max_interest = 0, -1000 # Some random negative number
total_entries = 0
for line in lines[1:]:
    date_values = line[date_index].split('/')
    if (int(date_values[2]) == start_year and int(date_values[0]) >= start_month) or (int(date_values[2]) == end_year and int(date_values[0]) <= end_month):
        total_entries += 1
        mean_SP += float(line[spf_index])
        max_interest = max(max_interest, float(line[long_interest_rate]))
mean_SP /= total_entries
print(mean_SP, max_interest)
pythonpython-3.xalgorithm


Solution 1:[1]

I think you can optimized by storing the index of columns in some variable

with open('temp.txt') as f:
    lines = f.readlines()
lines = [line.rstrip().split(",") for line in lines]
date_index, spf_index, long_interest_rate = 0, 1, 5
start_date, end_date = "01/06/2016", "31/05/2017"
mean_SP, max_interest = 0, -1000 # Some random negative number

for line in lines[1:]:
    if start_date.zfill(10) <= line[date_index] <= end_date.zfill(10):
        mean_SP += float(line[spf_index])
        max_interest = max(max_interest, float(line[long_interest_rate]))
mean_SP /= len(lines[1:])
print(mean_SP, max_interest)

Sources

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Source: Stack Overflow

Solution Source
Solution 1

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