PyBank-Python-Data-Structures-Analysis

In two separate Python scripts, I perform financial analysis of csv datasets. This analysis features two examples, each with a separate dataset, and each exploring different Python methods and data structures.

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Python for Finance: PyBank Example

PyBank: Code Explanation & Dataset

The purpose of this PyBank code is to demonstrate Python proficiency in working with CSV files, lists, dictionaries, loops and particulary nested data structures.

The code below is a Python script for analyzing the financial records of a fictional company. I utilize a dataset: budget_data.csv. This dataset is composed of two columns, Date and Profit/Losses. This code originally appeared in a Jupyter notebook on Github.

The script will utilze the following actions / methods:

#import required libraries 
import csv
from pathlib import Path
import numpy as np 

# set up the file path 
# importing the financial dataset 
csvpath = Path('./Resources/budget_data.csv')
csvpath

WindowsPath('Resources/budget_data.csv')

# Initialize list to hold financial date data  
dates = []

#list holding only the financial profits and losses 
profit_losses = []

# use open method to open csvpath 
with open(csvpath, 'r') as csvfile:

    # check datatype 
    print(type(csvfile))

    # set up csv.reader, specify a delimiter 
    csvreader = csv.reader(csvfile, delimiter=',')
    # check csv datatype 
    print(type(csvreader))
        
    # skip over header
    header = next(csvreader)
        
    #read in data into a list object 
    for row in csvreader:
        # date variable equal to date row values 
         
        date = row[0]
        # Append the row date value to the list of dates
        dates.append(date)
        #set profit_loss value and casting to an integer 
        profit_loss = int(row[1])
        profit_losses.append(profit_loss)      
    

<class '_io.TextIOWrapper'>
<class '_csv.reader'>
['Date', 'Profit/Losses']

#initialize Variables for total and count 

total_profit_losses = 0
count_profit_losses = 0

#use profit_losses list to find total and count 

for profit_loss in profit_losses:

    # Sum the total and count variables
    total_profit_losses += profit_loss
    count_profit_losses += 1

#for testing purposes, printing values of variables 
print(total_profit_losses)  
print(count_profit_losses)
 

38382578
86

#Find the total number of months included in the dataset.
#using the built in "len" method 
total_months = len(dates)

total_months

86

#using Numpy diff() method to compute all changes in profit/losses
#Link for reference purposes: https://numpy.org/doc/stable/reference/generated/numpy.diff.html

differences  = np.diff(profit_losses)

#the numpy array "differences" now represents all changes in profit/losses
differences

array([  116771,  -662642,  -391430,   379920,   212354,   510239,
        -428211,  -821271,   693918,   416278,  -974163,   860159,
       -1115009,  1033048,    95318,  -308093,    99052,  -521393,
         605450,   231727,   -65187,  -702716,   177975, -1065544,
        1926159,  -917805,   898730,  -334262,  -246499,   -64055,
       -1529236,  1497596,   304914,  -635801,   398319,  -183161,
         -37864,  -253689,   403655,    94168,   306877,   -83000,
         210462, -2196167,  1465222,  -956983,  1838447,  -468003,
         -64602,   206242,  -242155,  -449079,   315198,   241099,
         111540,   365942,  -219310,  -368665,   409837,   151210,
        -110244,  -341938, -1212159,   683246,   -70825,   335594,
         417334,  -272194,  -236462,   657432,  -211262,  -128237,
       -1750387,   925441,   932089,  -311434,   267252, -1876758,
        1733696,   198551,  -665765,   693229,  -734926,    77242,
         532869])

#Using max method for the greatest increase in profits over the entire period.
greatest_increase_profits = max(differences)
greatest_increase_profits


1926159

#Using min method for the greatest decrease in profits over the entire period.
greatest_decrease_profits = min(differences)
greatest_decrease_profits

-2196167

#Using the Numpy average method for the average change in profits over the entire period.
average_change = np.average(differences)
average_change

-2315.1176470588234

#this is to match the inc/decrease index with the correct month value 

#use where() method to find index of greatest increase & decrease
max_index = np.where(differences == greatest_increase_profits)
min_index = np.where(differences == greatest_decrease_profits)

#compare dates and differences array. then make new variable to hold difference
if (len(differences)) != (len(dates)): 
    length_diff = np.absolute(len(differences) - len(dates))

#to account for the header, we adjust for that difference to the index. 
adjusted_max = int(max_index[0]) + length_diff
adjusted_min = int(min_index[0]) + length_diff

#set the index for dates as either adjusted max/min with the difference added
max_date = dates[adjusted_max]
min_date = dates[adjusted_min]

#print the analysis to the terminal 

print("Financial Analysis")
print("----------------------------")
print(f"Total Months: {total_months}")
print(f"Total: ${total_profit_losses}")
print(f"Average  Change: ${round(average_change, 2)}")
print(f"Greatest Increase in Profits: {max_date} (${greatest_increase_profits})")
print(f"Greatest Decrease in Profits: {min_date} (${greatest_decrease_profits})")

Financial Analysis
----------------------------
Total Months: 86
Total: $38382578
Average  Change: $-2315.12
Greatest Increase in Profits: Feb-2012 ($1926159)
Greatest Decrease in Profits: Sep-2013 ($-2196167)

# export a text file with the results.
output_file = Path('./Resources/output.txt')

# use file.write to output to the file. 
with open(output_file, 'w') as file:
    file.write("Financial Analysis\n")
    file.write("----------------------------\n")
    file.write(f"Total Months: {total_months}\n")
    file.write(f"Total: ${total_profit_losses}\n")
    file.write(f"Average  Change: ${round(average_change, 2)}\n")
    file.write(f"Greatest Increase in Profits: {max_date} (${greatest_increase_profits})\n")
    file.write(f"Greatest Decrease in Profits: {min_date} (${greatest_decrease_profits})")

Outcome / Results

In this case the outcome is seen when you open the output.txt file and there should be the output written to the file. The output.txt file should be found in the resources folder. The file.write method was utilized and the filepath was set into the output_file variable.