From 0d2386870dc82f3b4f72ed92f56bfab33ded5bec Mon Sep 17 00:00:00 2001
From: medusa <newton214@gmail.com>
Date: Sat, 4 May 2024 10:38:36 +0000
Subject: [PATCH] Update work/tbx/charter/meraki_info.md

---
 work/tbx/charter/meraki_info.md | 86 ++++++++++++++++++++++++++++++++-
 1 file changed, 85 insertions(+), 1 deletion(-)

diff --git a/work/tbx/charter/meraki_info.md b/work/tbx/charter/meraki_info.md
index 17f80ad..e24342f 100644
--- a/work/tbx/charter/meraki_info.md
+++ b/work/tbx/charter/meraki_info.md
@@ -643,4 +643,88 @@ Replace `'inventory_data.csv'` with the path to your actual inventory data file.
 
 For the KPIs that require additional data (e.g., total orders, backorders, gross margin), replace the placeholder values with your actual data.
 
-When you run this script in your Jupyter or IPython notebook, it will calculate and print the values for each KPI based on your inventory data.
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+When you run this script in your Jupyter or IPython notebook, it will calculate and print the values for each KPI based on your inventory data.
+
+Certainly! Here's a step-by-step guide to set up Python and install the necessary packages and libraries for your project:
+
+1. Install Python:
+   - Download the latest version of Python from the official website: https://www.python.org/downloads/
+   - Run the installer and follow the installation wizard.
+   - Make sure to check the option to add Python to the PATH environment variable during the installation process.
+
+2. Verify Python installation:
+   - Open a terminal or command prompt.
+   - Run the following command to check the Python version:
+     ```
+     python --version
+     ```
+   - If Python is installed correctly, you should see the version number printed in the console.
+
+3. Set up a virtual environment (optional but recommended):
+   - Virtual environments help keep your project's dependencies separate from other projects.
+   - In the terminal, navigate to your project directory.
+   - Run the following command to create a new virtual environment:
+     ```
+     python -m venv myenv
+     ```
+     Replace `myenv` with your desired virtual environment name.
+   - Activate the virtual environment:
+     - On Windows:
+       ```
+       myenv\Scripts\activate
+       ```
+     - On macOS and Linux:
+       ```
+       source myenv/bin/activate
+       ```
+
+4. Install required packages and libraries:
+   - With your virtual environment activated (if using one), run the following commands to install the necessary packages:
+     ```
+     pip install pandas matplotlib seaborn
+     ```
+   - This will install the pandas library for data manipulation and analysis, matplotlib for data visualization, and seaborn for enhanced data visualization.
+
+5. Verify package installation:
+   - In the terminal, start the Python interactive shell by running:
+     ```
+     python
+     ```
+   - Import the installed packages to check if they are available:
+     ```python
+     import pandas as pd
+     import matplotlib.pyplot as plt
+     import seaborn as sns
+     ```
+   - If the imports succeed without any errors, the packages are installed correctly.
+   - Exit the Python shell by running:
+     ```
+     exit()
+     ```
+
+6. Create your Python script:
+   - Open a text editor or an integrated development environment (IDE) of your choice.
+   - Create a new file and save it with a `.py` extension (e.g., `inventory_analysis.py`) in your project directory.
+   - Copy and paste the provided Python code into the file.
+
+7. Prepare your inventory data:
+   - Ensure that your inventory data is in CSV format.
+   - Place the CSV file in the same directory as your Python script.
+   - Update the `'inventory_data.csv'` file path in the Python script to match your CSV file name.
+
+8. Run the Python script:
+   - In the terminal, navigate to the directory where your Python script is located.
+   - Run the following command to execute the script:
+     ```
+     python inventory_analysis.py
+     ```
+   - The script will load the inventory data from the CSV file, perform the analysis, and generate tables and visualizations.
+
+9. Review the output:
+   - The script will print summary tables for slow-moving inventory, fast-moving inventory, potential excess inventory, and ABC classification in the console.
+   - Visualizations, such as the ABC classification pie chart, inventory turnover ratio bar chart, and days of supply histogram, will be displayed in separate windows.
+   - Close each visualization window to proceed to the next one.
+
+That's it! You should now have Python set up with the necessary packages and libraries, and you can run the provided script to analyze your inventory data and generate tables and visualizations.
+
+If you encounter any issues along the way, make sure to double-check each step and ensure that you have the necessary permissions and a stable internet connection for package installation.
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