This is the first release of PersonalFinance, a tool meant to make it easier to keep track of your finances. It was purposely build to work with any type of export from your bank by making use of the configuration files and the amount of mumbo jumbo is also limited to ensure that it makes perfect sense how the model categorizes your transactions. In case it doesn't work for you, please reach out to me via one of the channels found here so I can help to make it work.
It outputs an Excel file as follows:
Besides that, you don't have to continue in Excel if you are handy with Python as all created datasets can be directly accessed in Python as well. All of the datasets can be accessed through the related get functions for example:
cashflows.get_period_overview(period='yearly')Which returns:
| Yearly | Totals | Income | Investing | Charity | Government | Health and Insurance | Housing | Study | Subscriptions | Transactions | Transport | Sports | Shopping | Groceries | Food and Drinks | Holidays | Cultural | Festivals, Clubs and Concerts | Other |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014 | 149.46 | 1222.75 | 0 | 0 | 0 | -75.41 | 0 | -95.7 | -131.42 | 469.12 | -77.7 | -82.91 | -650.32 | -319.46 | -278.28 | -163.07 | 0 | 71.67 | 260.19 |
| 2015 | 789.73 | 1242.6 | 0 | 0 | -127.57 | -71.59 | -1026.65 | 1108.65 | -31.79 | 578.43 | -251.82 | -4.51 | -1286.13 | -149.76 | -218.76 | 0 | -14.48 | 0 | 1043.11 |
| 2016 | 1306.27 | 4993.12 | 0 | 0 | -39.64 | 0 | 518.6 | -2334.47 | -20.61 | -11.02 | -44.48 | -47 | -1192.55 | -193.12 | -140.6 | -281.97 | 0 | -28.3 | 128.31 |
| 2017 | -352.76 | 6258.63 | 0 | 0 | 0 | -974.74 | -1396.04 | -859.6 | -83.95 | 51.26 | -222.98 | -257.71 | -2146.88 | -680.85 | -89.78 | -883 | -53.22 | -109 | 1095.1 |
| 2018 | -1237.81 | 12989.7 | -1.04 | 0 | -356.92 | -1220.38 | -1235.84 | -2462.28 | -420.47 | 221.27 | -305.25 | -34.51 | -2057.27 | -1209.5 | -931.88 | -1042.69 | -80.68 | -93.65 | -2996.43 |
| 2019 | 8754.51 | 29320.7 | 0 | 0 | -311.95 | -1300.17 | 0 | -1288.88 | -292.23 | -1063.32 | -1130.1 | -413.42 | -3692.94 | -2098.15 | -1362.4 | -701.8 | -230.32 | -179.51 | -6501 |
| 2020 | -1170.22 | 34069.3 | -8430.84 | -250.08 | -59.7 | -1113.59 | 0 | -13.83 | -22.87 | -246.95 | -9873.4 | -331.94 | -4743.16 | -2373.74 | -1489.41 | -635.22 | -63.8 | 0 | -5591.02 |
| 2021 | 2354.07 | 34372.5 | -12231.2 | -273.87 | 888.03 | -144.25 | -52.87 | -70.02 | -210.36 | -1198.2 | -1184.15 | -30.12 | -4145.31 | -3529.78 | -2758.37 | -748.1 | -159.17 | 0 | -6170.67 |
| 2022 | 19802.3 | 93827.3 | -25007 | -274.27 | -812.78 | -1339.41 | -8110.85 | -2.74 | -785.28 | -2142.96 | -3092.08 | -87.76 | -14984.6 | -3670.8 | -6591.52 | -3657.21 | -359.38 | -75.52 | -3030.89 |
| 2023 | -8997.16 | 60268.2 | -1016.73 | -180.57 | -24546.8 | -1001.21 | -13886.2 | -8370.02 | -1601.86 | 789.25 | -1824.88 | -609.54 | -2386.02 | -2919.07 | -5753.73 | -4268.35 | -476.46 | -480.95 | -732.3 |
And the following:
cashflows.get_transactions_overview(period='weekly')Which returns:
| Weekly | Date | Name | Value | Description | Category | Keyword | Certainty |
|---|---|---|---|---|---|---|---|
| 2023-09-04/2023-09-10 | 2023-09-10 | thuisbezorgd - Omitted due to Privacy Reasons | -12.55 | thuisbezorgd - Omitted due to Privacy Reasons | Food and Drinks | thuisbezorgd | 100% |
| 2023-09-04/2023-09-10 | 2023-09-10 | Tinq - Omitted due to Privacy Reasons | -53.81 | Tinq - Omitted due to Privacy Reasons | Transport | Tinq | 100% |
| 2023-09-11/2023-09-17 | 2023-09-12 | geldmaat - Omitted due to Privacy Reasons | -18.43 | geldmaat - Omitted due to Privacy Reasons | Transactions | geldmaat | 100% |
| 2023-09-11/2023-09-17 | 2023-09-13 | asr - Omitted due to Privacy Reasons | 12.2 | asr - Omitted due to Privacy Reasons | Income | asr | 100% |
These datasets make it possible to plot the spending pattern over time for each category. This can be simply by selecting the column and using .plot() from Pandas but it also possible to create a larger overview as shown below:
import matplotlib.pyplot as plt
# Obtain the Quarterly Cashflow Overview
quarterly_cashflows = cashflows.get_period_overview(period='quarterly')
# Define the colormap
cmap = plt.get_cmap('tab20c')
# Create the figure and axes
fig, axes = plt.subplots(
nrows=2,
height_ratios=[6, 1],
figsize=(30, 10))
# Plot the data per category
quarterly_cashflows.plot.bar(
stacked=True,
colormap=cmap,
title="Quarterly Cashflow Overview",
ax=axes[0])
# Calculate the totals
totals = quarterly_cashflows.sum(axis=1)
# Plot the totals
totals.plot.bar(
color=['g' if x >= 0 else 'r' for x in totals],
ax=axes[1])
# Format the plot by rotating labels and adjusting space
plt.xticks(rotation=45)
fig.subplots_adjust(wspace=0, hspace=0)This returns the following plot:
