We Keep Coding

display ";" separated values in table format with python

I'm having the following function which picks results from a results.txt file and displays it to the user, the first row comes out nicely but the rest of the results are not well-aligned. I'm not sure what's causing this. here's the function
def show_result():
'''prints the score list'''
file_exist()
print_whole_list = []
print("Results")
print("*"*41)
result = open("results.txt","r")
res = result.readlines()
print("Name: Lap1: Lap2: Lap3: In total: Average:")
for i in res:
temp_list = i.split(";")
total = int(temp_list[1]) + int(temp_list[2]) + int(temp_list[3])
average = int(total) / 3.0
average = round(average, 2)
temp_list.insert(4, total)
temp_list.insert(5, average)
print_whole_list.extend(temp_list)
for row in print_whole_list:
print("{0:{width}}".format(row, width=10), end=' ')
result.close()
The records in the text file:
Kembos;23;43;23;
Moses;42;51;43;
Ben;43;23;21;

You can use tabulate instead to populate your data in a tabular format in python
from tabulate import tabulate
result = open("results.txt","r")
res = result.readlines()
final = []
for line in res:
temp_list = line.split(';')[:-1]
total = int(temp_list[1]) + int(temp_list[2]) + int(temp_list[3])
average = round(int(total) / 3.0, 2)
temp_list.append(total)
temp_list.append(average)
final.append(temp_list)
print(tabulate(final, headers=["Name", "Lap1", "Lap2", "Lap3", "In Total", "Average"]))
Above code will give the following output:

how to do loop over the tickers and save result on the dictionary

I’m not so sure how to do the last part which is the dictionary part and ticker part, and also
On
“”file = open("/home/ubuntu/environment/hw5/" + tickers + “.txt”)””"
This line keep showing
TypeError: must be str, not list
Any suggestion on how to fix those or make the code works ?
Here’s my code
import json
def meanReversionStrategy(prices):
total_profit = 0
first_buy = None
buy = 0
for i in range(len(prices)):
if i >= 5:
current_price = prices[i]
moving_average = (prices[i-1] + prices[i-2] + prices[i-3] + prices[i-4] +prices[i-5]) / 5
if current_price < moving_average * 0.95 and buy == 0:
buy = current_price
print("buy at: ",round (current_price,2))
if first_buy is None:
first_buy = buy
elif current_price > moving_average * 1.05 and buy != 0:
print("sell at: ", round(current_price,2))
print("trade profit: ", round(current_price - buy,2))
total_profit = current_price - buy
buy = 0
final_profit_percentage = ( total_profit / first_buy ) * 100
print("First buy: " , round(first_buy,2))
print("Total profit: " , round(total_profit, 2))
print("Percentage return: ", round(final_profit_percentage, 2),"%")
def simpleMovingAverageStrategy(prices):
i = 0
buy = 0
total_profit = 0
first_buy = 0
for p in prices:
if i >= 5:
moving_average = (prices[i-1] + prices[i-2] + prices[i-3] + prices[i-4] +
prices[i-5]) / 5
#simple moving average logic, not mean
if p > moving_average and buy == 0: #buy
print("buying at: ", p)
buy = p
if first_buy == 0:
first_buy = p
elif p < moving_average and buy != 0: #sell
print("selling at: ", p)
print("trade profit: ", p - buy)
total_profit += p - buy
buy = 0
i += 1
final_percentage = (total_profit / first_buy) * 100
print("first buy: ", first_buy)
print("total profit: ", total_profit)
print("final percentage: ", final_percentage, "%")
return total_profit, final_percentage
tickers = ["AAPL1" , "ADBE" , "BA", "CMCSA", "CSCO", "CVS", "GOOG", "TLSYY","TM"]
file = open("/home/ubuntu/environment/hw5/" + tickers + ".txt")
lines = file.readlines()
# print(lines)
prices = []
for line in lines:
prices.append(float(line))
profit, returns = simpleMovingAverageStrategy(prices)
results = {}
results["AAPL1_profit"] =profit
results["AAPL1_returns"] = returns
json.dump(results, open("/home/ubuntu/environment/hw5/results.json", "w") )
Coding Requirements
-Create a function called meanReversionStrategy which takes a list called “prices” as an argument. The function runs a mean reversion strategy, and outputs to the console the buys and sells of the strategy (like you did in HW4). The function returns the profit and final returns percentage.
-Create a function called simpleMovingAverageStrategy which takes a list called “prices” as an argument. The function runs a Simple Moving Average strategy, and outputs to the console the buys and sells of the strategy. The function returns the profit and final returns percentage.
-Create a function called saveResults which takes a dictionary as an argument. Save the dictionary to a json file called “results.json”.
loop through the list of tickers
for ticker in tickers:
-load prices from a file <ticker>.txt, and store them in the results dictionary with the
key “<ticker>_prices”
-call meanReversionStrategy(prices) and store the profit and returns in the results
dictionary with the keys “<ticker>_mr_profit” and “<ticker>_mr_returns”
-call simpleMovingAverageStrategy(prices) and store the profit and returns in the
results dictionary with the keys “<ticker>_sma_profit” and “<ticker>_sma_profit”
with the keys “ticker_mr_profit” and “ticker_mr_returns”
call saveResults(results) and save the results dictionary to a file called results.json

Welcome!
As this is a homework question, I will not solve it for You, but here is my advice for You:
tickers is an array and as your homework description states, You must 'loop through the list of tickers' and open each of them. So how do You loop over tickers?
And reflect about the error. What do You think should be the result of "/home/ubuntu/environment/hw5/" + tickers + ".txt"?

Taking values from one file and after some calculation and a bit changes need to print into another file in python

below is c.txt
CO11 CSE C1 8
CO12 ETC C1 8
CO13 Electrical C2 12
CO14 Mech E 5
my program needs to print a course summary on screen and save that summary into a file
named cr.txt. Given the above c.txt, your program output should look like
below. The content of course_report.txt should also be the same, except the last line. Course
names in the second column use * to indicate a compulsory course and – to indicate an elective
course. The fourth column is the number of students enrolled in that course. The fifth column is the average score of the course.
CID Name Points. Enrollment. Average.
----------------------------------
CO11 * CSE 8 2 81
CO12 * ETC 8 10 71
CO13 * Electrical 12 8 61
CO14 - Mech 5 4 51
----------------------------------
poor-performing subject is CO14 with an average 51.
cr.txt generated!
below is what I've tried:
def read(self):
ctype = []
fi = open("c.txt", "r")
l = fi.readline()
while l != "":
fields = l.strip().split(" ")
self.c.append(fields)
l = fi.readline().strip()
f.close()
# print(f"{'CID'}{'Name':>20}{'Points.':>16}{'Enrollment.':>18}{'Average.':>10}")
# print("-" * 67, end="")
print()
for i in range(0, len(self.c)):
for j in range(len(self.c[i])):
obj = self.c[i][j]
print(obj.ljust(18), end="")
print()
print("-" * 67, end="")
print()

you can try use 'file.read' or 'file.readlines' after use 'open' function, if you choose 'file.readlines' you'll have to use 'for row in file.readlines()' look my example with 'file.read':
headers = ['CID', 'Name', 'Points.', 'Enrollment.', 'Average.']
compulsory_course = ['CO11', 'CO12', 'CO13']
elective_course = ['CO14']
count = 0
with open('c.txt', 'r') as file_c:
file_c.seek(0, 0)
file_string = file_c.read().replace('\n', ' ')
fields = file_string.split(' ')
with open('cr.txt', 'w') as file_cr:
for field in headers:
file_cr.write(f'{field} ')
file_cr.write('\n')
for v in fields:
if count == 4:
file_cr.write('\n')
count = 0
count += 1
if v in compulsory_course:
file_cr.write(f'{v} * ')
continue
elif v in elective_course:
file_cr.write(f'{v} - ')
continue
elif count == 3:
file_cr.write(f' ')
continue
file_cr.write(f'{v} ')

Calculate the future value for only one category using the IRR (Python)

import xlrd
import numpy
fileWorkspace = 'C://Users/jod/Desktop/'
wb1 = xlrd.open_workbook(fileWorkspace + 'assign2.xls')
sh1 = wb1.sheet_by_index(0)
time,amount,category = [],[],[]
for a in range(2,sh1.nrows):
time.append(int(sh1.cell(a,0).value)) # Pulling time from excel (column A)
amount.append(float(sh1.cell(a,1).value)) # Pulling amount from excel (column B)
category.append(str(sh1.cell(a,2).value)) # Pulling category from excel (column C)
#print(time)
#print(amount)
#print(category)
print('\n')
p_p2 = str(sh1.cell(0,1))
p_p1 = p_p2.replace("text:'","")
pp = p_p1.replace("'","")
print(pp) # Printing the type of pay period (Row 1, col B)
c_p2 = str(sh1.cell(1,1))
c_p1 = c_p2.replace("text:'","")
cp = c_p1.replace("'","")
print(cp) # Printing the type of compound period (Row 2, col B)
netflow = 0
outflow = 0
inflow = 0
flow = 0
cat = ["Sales", "Salvage", "Subsidy", "Redeemable", "Utility", "Labor",
"Testing", "Marketing", "Materials", "Logistics"]
if pp == "Years" and cp == "Years": # if pay period and compound period are both in years
IRR = numpy.irr(amount) * 100 # Calculates the internal rate of return (IRR)
print ("IRR:", round(IRR, 2), '%', '\n') # prints (IRR)
for i in time: # for every value in time array
if cat[5] in category: # if "Labor" for cat array is in category array or not
# calculates the present values using all the amount values (col B) instead of
# just using the ones that has "Labor" category label beside them
# Need to make every other value 0, such as beside "Redeemable" and "Salvage"
flow = amount[i] / numpy.power((1 + (IRR/100)), time[i])
if flow>0:
inflow = inflow + flow
if flow<0:
outflow = outflow + flow
print ('Present Value (P) is:', round(flow,0), '\n')
netflow = outflow + inflow
print("In year 0 or current year")
print("-------")
print ('Outflow is: ', round(outflow,0))
print ('Inflow is: ', round(inflow,0))
print ('Netflow is: ', round(netflow,0), '\n')
outflow2 = (round(outflow,0))*(1+(IRR/100))**(9)
inflow2 = (round(inflow,0))*(1+(IRR/100))**(9)
netflow2 = outflow2 + inflow2
print("In year 9")
print("-------")
print ('Outflow is: ', round(outflow2,0))
print ('Inflow is: ', round(inflow2,0))
print ('Netflow is: ', round(netflow2,0), '\n')
I have commented important lines of code for clarification.
Here is the original question:
illustrate the breakdown of major project revenues and expenses by category as a percentage of that project’s future value in year 9. The illustration must also clearly indicate the total future value of the project in year 9 as well as the IRR.
There will be a total of 10 revenue and cost categories that a project may be composed of. The categories are: Sales, salvage, subsidy, redeemable, utility, labor, testing, marketing, materials and logistics. All revenues and expenses will fall in one of these ten categories. The project pay period and compound period will be identified at the top of the Excel sheet. Pay period and compound period may be designated as any of the following: years, quarters, months.
I am getting confused because I am not able to pull the only values from beside the "Labor", "Redeemable", or "Salvage". I just don't know where I am making a mistake, or there is something that is incomplete. Below is the excel file image:
Excel File Image 2
Excel File Image 3

After revising, all cashflows are discounted at the irr. What is done is the following:
i) determineAdjustments takes the pay period (column A) and adjusts if for the year ended (if it is a monthly amount it puts it in the proper year ended) and if its monthly puts in in the month ended (no adjustment necessary). This will divide the pay period by 12 if yearly cash flows are needed (yearly compounding)
ii) IRR is calculated, and the compounding period is used to adjust the monthly IRR for monthly pay periods
iii) all expenses are discounted at the IRR and input into a list for cat_contributions['category_name'] = [discounted period 1, discounted period 2 ... ]
iv) Then the net inflows and outflows are sums of these.
I can't type up data in the spreadsheets from the images as that would take a while, but maybe tinker with this and see if you can get it to work.
from __future__ import division
import xlrd
import numpy
import os
import math
def main(xls = 'xls_name.xlsx', sh = 0):
#save script in same folder as the xls file
os.chdir( os.getcwd() )
wb = xlrd.open_workbook(xls)
sh = wb.sheet_by_index(0)
pay_period = sh.cell_value(0,1)
compounding_period = sh.cell_value(1,1)
compounding_factor, pay_factor = determineAdjustments(
pay_period, compounding_period)
number_of_periods = max( sh.col_values(0, start_rowx = 2) )
flow_per_period = [ 0*i for i in range( int( math.ceil( number_of_periods/pay_factor ) ) + 1 ) ]#list of length number of pay_periods
for r in range(2,sh.nrows):
pay_period = int( math.ceil( sh.cell_value(r,0) / pay_factor ) )
flow_per_period[pay_period] += sh.cell_value(r,1) #unadjusted cash flows
irr = calculateIRR(flow_per_period, compounding_factor)
cat_contributions = sortExpenditures(sh, irr, pay_factor)
total_cat_contributions, netflow, total_outflow, total_inflow = calculateFlows(cat_contributions)
printStats(cat_contributions, irr, compounding_factor, pay_factor,
total_cat_contributions, netflow, total_outflow, total_inflow)
return
def determineAdjustments(pay_period, compounding_period):
if compounding_period == 'years':
compounding_factor = 1
if pay_period == 'months':
pay_factor = 12
if pay_period == 'years':
pay_factor = 1
#assume no days pay periods
if compounding_period == 'months':
compounding_factor = 12
#assume no yearly payouts and that the
#all payments are in months
pay_factor = 1
return compounding_factor, pay_factor
def calculateIRR(cashflow, compounding_factor):
irr = numpy.irr(cashflow)
irr_comp = (1 + irr)**compounding_factor - 1
#seems like in first example it uses rounded irr, can do something like:
#irr_comp = round(irr_comp,4)
return irr_comp
def sortExpenditures(sh, irr, pay_factor):
#percentages and discounting occurs at the IRR caculated in the main
#function
cat = ["Sales", "Salvage", "Subsidy", "Redeemable", "Utility", "Labor",
"Testing", "Marketing", "Materials", "Logistics"]
#python dictionary to sort contributions into categories
cat_contributions = {}
for c in cat:
cat_contributions[c] = []
# create list of contributions of each list item to FV in a dictionary
for r in range(2,sh.nrows):
try:
#discounted cash flow of each expenditure
#using formula FV = expenditure/(1+i)^n
cat_contributions[sh.cell_value(r,2)].append(
sh.cell_value(r,1) / ( (1 + irr) ** (sh.cell_value(r,0)/pay_factor) )
)
except KeyError:
print "No category for type: " + sh.cell_value(r,2) +'\n'
return cat_contributions
def calculateFlows(cat_contributions):
total_outflow = 0
total_inflow = 0
total_cat_contributions = {}
for cat in cat_contributions:
total_cat_contributions[cat] = sum( cat_contributions[cat] )
if total_cat_contributions[cat] < 0:
total_outflow += total_cat_contributions[cat]
else:
total_inflow += total_cat_contributions[cat]
netflow = total_inflow + total_outflow
return total_cat_contributions, netflow, total_outflow, total_inflow
def printStats(cat_contributions, irr, compounding_factor, pay_period,
total_cat_contributions, netflow, total_outflow, total_inflow):
print "IRR: "+str(irr*100) +' %'
if compounding_factor == 1: print "Compounding: Yearly"
if compounding_factor == 12: print "Compounding: Monthly"
if pay_period == 1: "Cashflows: Year Ended"
if pay_period == 12: "Cashflows: Month Ended"
print "Future Value (Net Adjusted Cashflow): " +str(netflow)
print "Adjusted Inflows: " + str(total_inflow)
print "Adjusted Outflows: " + str(total_outflow) +'\n'
for cat in total_cat_contributions:
if total_cat_contributions[cat] != 0:
print '-----------------------------------------------------'
print cat + '\n'
print "Total Contribution to FV " + str( total_cat_contributions[cat] )
if total_cat_contributions[cat] < 0:
print "Contribution to Expenses: " + str ( abs(100 * total_cat_contributions[cat]/total_outflow) )
else:
print "Contribution to Revenues: " + str ( abs(100 * total_cat_contributions[cat]/total_inflow) ) +'\n'
main(xls='Book1.xlsx')

Find the median from a CSV File using Python

I have a CSV file named 'salaries.csv' The content of the files is as follows:
City,Job,Salary
Delhi,Doctors,500
Delhi,Lawyers,400
Delhi,Plumbers,100
London,Doctors,800
London,Lawyers,700
London,Plumbers,300
Tokyo,Doctors,900
Tokyo,Lawyers,800
Tokyo,Plumbers,400
Lawyers,Doctors,300
Lawyers,Lawyers,400
Lawyers,Plumbers,500
Hong Kong,Doctors,1800
Hong Kong,Lawyers,1100
Hong Kong,Plumbers,1000
Moscow,Doctors,300
Moscow,Lawyers,200
Moscow,Plumbers,100
Berlin,Doctors,800
Berlin,Plumbers,900
Paris,Doctors,900
Paris,Lawyers,800
Paris,Plumbers,500
Paris,Dog catchers,400
I need to print the median salary of each profession. I tried a code, which shows some error.
My code is :
from StringIO import StringIO
import sqlite3
import csv
import operator #from operator import itemgetter, attrgetter
data = open('sal.csv', 'r').read()
string = ''.join(data)
f = StringIO(string)
reader = csv.reader(f)
conn = sqlite3.connect(':memory:')
c = conn.cursor()
c.execute('''create table data (City text, Job text, Salary real)''')
conn.commit()
count = 0
for e in reader:
if count==0:
print ""
else:
e[0]=str(e[0])
e[1]=str(e[1])
e[2] = float(e[2])
c.execute("""insert into data values (?,?,?)""", e)
count=count+1
conn.commit()
labels = []
counts = []
count = 0
c.execute('''select count(Salary),Job from data group by Job''')
for row in c:
for i in row:
if count==0:
counts.append(i)
count=count+1
else:
count=0
labels.append(i)
c.execute('''select Salary,Job from data order by Job''')
count = 1
count1 = 1
temp = 0
pri = 0
lis = []
for row in c:
lis.append(row)
for cons in counts:
if cons%2 == 0:
pri = cons/2
else:
pri = (cons+1)/2
if count1 == 1:
for li in lis:
if count == pri:
print "Median is ",li
count = count + 1
count = 0
temp = pri+cons
else:
for li in lis:
if count == temp:
print "Median is",li
count = count+1
count = 0
temp = temp + pri
count1 = count1 + 1
However, it is showing some error:
IndentationError('expected an indented block', ('', 28, 2, 'if count==0:\n'))
How do I fix the error?

You can use defaultdict to put all the salaries for each profession then just get the median.
import csv
from collections import defaultdict
with open("C:/Users/jimenez/Desktop/a.csv","r") as f:
d = defaultdict(list)
reader = csv.reader(f)
reader.next()
for row in reader:
d[row[1]].append(float(row[2]))
for k,v in d.iteritems():
print "{} median is {}".format(k,sorted(v)[len(v) // 2])
print "{} average is {}".format(k,sum(v)/len(v))
Outputs
Plumbers median is 500.0
Plumbers average is 475.0
Lawyers median is 700.0
Lawyers average is 628.571428571
Dog catchers median is 400.0
Dog catchers average is 400.0
Doctors median is 800.0
Doctors average is 787.5

It is easy if you use pandas (http://pandas.pydata.org):
import pandas as pd
df = pd.read_csv('test.csv', names=['City', 'Job', 'Salary'])
df.groupby('Job').median()
# Salary
# Job
# Doctors 800
# Dog catchers 400
# Lawyers 700
# Plumbers 450
If you want the average instead of the median,
df.groupby('Job').mean()
# Salary
# Job
# Doctors 787.500000
# Dog catchers 400.000000
# Lawyers 628.571429
# Plumbers 475.000000

If your problem is computing he median, and not inserting everything in a SQL databas and scrambling it about,
it is a matter of just reading all lines, group all salaries in a list, and get the median from there - this reduces your hundred-line-magnitude script to:
import csv
professions = {}
with open("sal.csv") as data:
for city, profession, salary in csv.reader(data):
professions.setdefault(profession.strip(), []).append(int(salary.strip()))
for profession, salaries in sorted(professions.items()):
print ("{}: {}".format(profession, sorted(salaries)[len(salaries//2)] ))
(give or take "1" to get the proper median from the sorted salaries)