Duckdb dataviz end to end data engineering project 3 3

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okay, let's dive into a comprehensive end-to-end data engineering project using duckdb, focusing on data visualization. this project will guide you through extracting data from a hypothetical source (we'll simulate it), transforming it, loading it into duckdb, and then visualizing it using a python visualization library like plotly.

**project overview: sales data analysis**

we'll simulate a dataset of sales transactions for a fictional online store. our goal is to:

1. **extract (simulate):** generate synthetic sales data including product categories, regions, dates, sales amounts, and customer ids.
2. **transform:** clean and transform the data, calculating metrics like total sales per category, sales trends over time, and regional sales performance.
3. **load:** load the transformed data into duckdb.
4. **visualize:** use python and plotly to create interactive visualizations that reveal insights from the data.

**prerequisites**

* **python:** make sure you have python 3.7+ installed.
* **duckdb:** install duckdb using `pip install duckdb`.
* **plotly:** install plotly using `pip install plotly pandas`.
* **pandas:** install pandas using `pip install pandas`.

**step 1: data extraction (simulation)**

we'll start by creating a python script to generate synthetic sales data. this will act as our data source.
**explanation:**

1. **`generate_sales_data(num_rows)`:** this function creates a pandas dataframe containing synthetic sales data.
2. **`products`, `regions`:** lists of possible product categories and regions.
3. **`np.random.choice()`:** used to randomly select from the product and region lists.
4. **`np.random.randint()`:** used to generate random customer ids and date information.
5. **`np.random.uniform()`:** used to generate sales amounts.
6. **`pd.dataframe()`:** creates a pandas dataframe from the generated data.
7. **`sales_data.to_csv()`:** saves the dataframe to a csv file named `sales_data.csv`.

**step 2: data transf ...

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