Python For Accountants III – The Practical Approach

Explore pandas and numpy basics, comparing lists, series, and data frames; learn to use vectorized operations and statistical functions like mean and standard deviation for data analysis in finance.

Discover how pandas reads excel data with read_excel, sheet selection, and index options. Explore data frame exploration and filtering with head, tail, info, describe, and in or equals.

Practice filtering in pandas by combining conditions with the & operator to select product and country, then aggregate revenue and units sold, and compute mean values across countries.

Master pandas pivot_table to summarize revenue by product and region, switching from mean to sum, and building multi-index pivots for deeper data insights.

Discover how pandas loc enables vlookup-like lookups by resetting the index and retrieving region, country, Eurozone revenue, and total profit for given order ids.

Build a Python cash flow projection to compare present value, discounted cash flow, and net present value across 20 years of asset income and a buy decision.

Split an Excel file into separate Excel files for each product by looping over unique product names with the Pendas library, reading into a data frame, and saving each subset.

Learn to automate auditing listed investment valuation in the balance sheet using python and pandas, enabling a one-click, minutes-long workflow for auditors.

Explore Matplotlib fundamentals, prepare data for visualization, and answer questions on revenue by region, online versus offline sales, regional trends, and profit comparisons.

Load and inspect the Excel data, ensure a year column by extracting the year from the order date, and prepare data for visualization by installing and importing Matplotlib.

Pivot regional data, aggregate total revenue by region, and visualize with a bar chart or pie chart to show Europe as the top region, followed by Sub-Saharan Africa and Asia.

Aggregate total revenue by year using a pivot table on a data frame, then plot a revenue trend from 2010 to 2017 and investigate data gaps in 2017.

Identify the top revenue country from a dataset of 185, using a pivot table with country as index, sort values ascending, and display a horizontal bar graph of revenues.

Compare online and offline sales channels' contribution to sales volumes using a data frame and pivot table, with the sales channel column as index and pie chart showing equal contribution.

Learn to create a seven-region time-series plot in one graph by extending a pivot table with an optional columns dimension, producing seven lines and a clear legend.

Create a pivot table showing total profit and visualize regional profits with a bubble chart where bubble size reflects profit, highlighting Europe and Sub-Saharan Africa.