Creating a daily account log from a Pandas expense file in data frame format - python-3.x

Question

I have an expense file that I am trying to read in and from this file create a daily log. A small subset of the file that extends over years is shown below, for a few days in January 2015.
Date,Checking_Debit,Checking_Addition,Savings_Debit,Savings_Addition
2015-01-07,342.1,0.0,0.0,0.0
2015-01-07,981.0,0.0,0.0,0.0
2015-01-07,3185.0,0.0,0.0,0.0
2015-01-05,55.0,0.0,0.0,0.0
2015-01-05,75.0,0.0,0.0,0.0
2015-01-03,287.0,0.0,0.0,0.0
2015-01-02,64.8,0.0,0.0,0.0
2015-01-02,75.0,0.0,0.0,75.0
2015-01-02,1280.0,0.0,0.0,0.0
2015-01-02,245.0,0.0,0.0,0.0
2015-01-01,45.0,0.0,0.0,0.0
In my code I start with the variables checking_start and savings_start that contain the start values of the checking and savings account. I would like to give the code a start date and an end date and have the code iterate through each day, see if there was an expense on that day and subtract the checking and savings debits and add the checking and savings additions. If there were no expenses on that day it should keep the accounts at the same value as the previous day. In addition, I am trying to constrain myself to Pandas data frames in the implementation. So far my code looks like this.
import pandas as pd
from date time import date
check_start = 8500.0
savings_start = 4000.0
start_date = date(2017, 1, 1)
end_date = date(2017, 1, 8)
df = pd.read_csv(file_name.csv, dtype={'Date': str, 'Checking_Debit': float,
'Checking_Addition': float,
'Savings_Debit': float,
'Savings_Addition': float})
In a Pythonic format with the Pandas module, how do I walk through from the start date to the end date, one day at a time, then see if there is an expense or expenses on those date and then subtract that from the checking and savings. At the end I should have an array for the value of the checking account on each date and the same for the savings account on that day.
The result should be arrays written into another .csv file with the following format.
Date,Checking,Savings
2017-01-07,1865.1,3925.0
2017-01-06,6373.2,3925.0
2017-01-05,6373.2,3925.0
2017-01-04,6503.2,3925.0
2017-01-03,6503.2,3925.0
2017-01-02,6790.2,3925.0
2017-01-01,8455.0,4000.0

Answer 1

Start by reading the data that you provided and identifying the date column in data with it
import pandas as pd
df = pd.read_csv(r"dat.csv", parse_dates=[0],dtype={'Checking_Debit': float,
'Checking_Addition': float,
'Savings_Debit': float,
'Savings_Addition': float})
Set Date as index for better data manipulation.
df = df.set_index("Date")
Initialize all the variables for the loop
check_start = 8500.0
savings_start = 4000.0
start_date = pd.to_datetime('2015/1/1')
end_date = pd.to_datetime('2015/1/8')
delta = pd.Timedelta('1 days') # time that needs to be added to start date
Now group the expense data w.r.t to each date
grp_df = df.groupby('Date').sum()
Now we will do while loop for create expense report for each day
expense_report = []
while start_date<=end_date:
if start_date in df.index:
savings_start += (grp_df.loc[start_date,"Savings_Addition"]-grp_df.loc[start_date,"Savings_Debit"])
check_start += (grp_df.loc[start_date,"Checking_Addition"]-grp_df.loc[start_date,"Checking_Debit"])
expense_report.append([start_date,check_start,savings_start])
elif start_date not in df.index:
expense_report.append([start_date,check_start,savings_start])
start_date += delta
convert expense_report list to pandas Dataframe
df_exp_rpt = pd.DataFrame(expense_report,columns=["Date","Checking","Savings"])
print(df_exp_rpt)
Date Checking Savings
0 2015-01-01 8455.0 4000.0
1 2015-01-02 6790.2 4075.0
2 2015-01-03 6503.2 4075.0
3 2015-01-04 6503.2 4075.0
4 2015-01-05 6373.2 4075.0
5 2015-01-06 6373.2 4075.0
6 2015-01-07 1865.1 4075.0
7 2015-01-08 1865.1 4075.0
You can save to csv by
df_exp_rpt.to_csv("filename.csv")
Note: The saving column values are 4075 instead of 3925.0 because you have 75 value in saving_addition column in your original data