- A file selection dialog box opens allowing browsing to/selection of your desired CSV file.
- CSV is imported and cleaned.
- All serial numbers are printed to the terminal.
- All unique model types along with their total number of machines are printed to the terminal.
- A visualization displays a bar graph of the model data by total number of each machine.
Code and Resources Used
- Python 3.9
- Pandas
- Matplotlib
- Easygui
- Pyperclip
- From the command line, input
pip install -r requirements.txtfrom within the project directory to add required modules.
This program begins by simply opening a (system agnostic) file selection dialog box. Upon navigation to and selection of the proper DT_Export.csv file, the data is extracted to a Pandas dataframe. All serials are printed to the terminal and automatically copied to the (system agnostic) operating system's clipboard. This print serves as a hard method against which to check your serials upon pasting. All models received on the purchase order CSV are then printed to the terminal as well as their totals. Finally, a visualization of total models is displayed.
Two common occurances at my workplace are; the need for every serial number from a certain purchase order, and a quick breakdown of machines received.
- Often batches of machines will need their statuses shifted, or designated to another warehouse. This is done through a tool on the company's website, however extracting the serials in the past had to be done manually. This is time consuming.
- Each purchase order is an investment. Many orders do not exactly match the numbers and types of machines planned during purchasing. The company owners/operators are eager for clear data as fast as possible.
