E-commerce Sales Analysis in Python

The user wants to load a specific retail sales dataset from a URL, display basic information, and the shape of the dataframe.

Plan:

1. Use pandas to read the Excel file from the provided URL.
2. Use df.info() and df.shape to show the requested information.
3. Since this is the first load, I must follow the rule: "When a dataset is not yet loaded... first load and inspect only: show shape, columns, and head()."

Execution:

• Import pandas.
• Read the excel file.
• Display df.shape.
• Display df.columns.
• Display df.head().

The user wants to load the dataset from a specific URL and show basic info and shape. The notebook state shows that pd.read_excel was used, and display(df.shape), display(df.columns), and display(df.head()) were executed. The output is:

• Shape: (541909, 8)
• Columns: ['InvoiceNo', 'StockCode', 'Description', 'Quantity', 'InvoiceDate', 'UnitPrice', 'CustomerID', 'Country']
• Head: Shows the first 5 rows.

The original goal was to "load the retail sales dataset... and show basic info and shape". The latest output provides the shape (541,909 rows and 8 columns), the column names, and a preview of the data. This satisfies the request for basic info and shape.

Therefore, I should follow Path A.

Final answer content: The dataset has been loaded and contains 541,909 records and 8 columns. The columns are InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, and Country. The preview shows transaction details, including quantities and unit prices (e.g., 2.55 USD, 3.39 USD).

Wait, I need to be careful about the currency formatting. "2.55 USD".

The prompt says: "Return only user-facing answer content... Do not include chain-of-thought... Return only user-facing answer content."

Final answer based on observed outputs: The retail sales dataset contains 541,909 rows and 8 columns: InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, and Country. The initial rows show transactions with unit prices such as 2.55 USD and 3.39 USD.

The user wants to plot the monthly revenue trend.

*   `df` is available.
*   Columns: `InvoiceNo`, `StockCode`, `Description`, `Quantity`, `InvoiceDate`, `UnitPrice`, `CustomerID`, `Country`.
*   `InvoiceDate` is likely in datetime format (since it's from an Excel file and looks like dates), but I should ensure it is.
*   Revenue is not a column. It needs to be calculated as `Quantity * UnitPrice`.

*   Create a `Revenue` column: `df['Revenue'] = df['Quantity'] * df['UnitPrice']`.
*   Convert `InvoiceDate` to datetime (just in case).
*   Resample or group by month.
*   Plot the trend using seaborn.

*   Step 1: Feature engineering (calculate revenue) and data aggregation.
*   Step 2: Plotting.
*   Since I can only do one primary objective per response and one code block, I'll handle the calculation and the plot in one go as it's a single "visualize" request.

*   `df['Revenue'] = df['Quantity'] * df['UnitPrice']`
*   `df['Month'] = df['InvoiceDate'].dt.to_period('M')` (requires `InvoiceDate` to be datetime).
*   Aggregate: `monthly_revenue = df.groupby('Month')['Revenue'].sum().reset_index()`
*   Plotting: `sns.lineplot(data=monthly_revenue, x='Month', y='Revenue')`. Wait, `Period` objects can be tricky with seaborn/matplotlib, better to convert `Month` back to timestamp.

*   Calculation: `df['TotalAmount'] = df['Quantity'] * df['UnitPrice']`
*   Convert `InvoiceDate` to datetime.
*   Group by month and sum.
*   Plot using seaborn.

*   Wait, let's check if there are negative quantities (returns). Often in retail datasets, negative quantities indicate returns. I should probably include them or filter them depending on the goal, but usually, for "revenue trend", we keep the net total.

*   Library: Seaborn/Matplotlib.
*   Formatting: `USD` for currency values in prose.

*   Calculation + Plotting.

*Wait*, the system says "one primary objective per response (for example: load, inspect, clean, plot, or model)".
And "If multiple code actions are required, do only the first action now".
Calculating revenue is a "clean/prep" step. Plotting is a "plot" step.
However, usually, creating a derived column for a plot is considered part of the plotting workflow. But to be safe and iterative, I'll do the calculation first or just do the whole pipeline if it's simple. Let's do the calculation and plotting in one go as "plotting monthly revenue" is the specific goal.

*Self-Correction*: "Default chart library: seaborn".
*Currency rule*: Use `USD`.

Let's refine the code:
1. Ensure `InvoiceDate` is datetime.
2. Calculate `Revenue`.
3. Aggregate by month.
4. Plot.

Wait, the user says "plot monthly revenue trend". I will provide the code to calculate and plot it.

*Implementation detail*:
`df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])`
`df['Revenue'] = df['Quantity'] * df['UnitPrice']`
`monthly_rev = df.set_index('InvoiceDate')['Revenue'].resample('M').sum().reset_index()`
`sns.lineplot(data=monthly_rev, x='InvoiceDate', y='Revenue')`

Let's write the response.

The user wants to plot the monthly revenue trend. The notebook state shows that df was loaded and a plot was generated in [cell-2]. The output of [cell-2] shows <Figure size 1200x600 with 1 Axes>, but the actual numeric values for monthly_revenue are not displayed in the notebook context (only the plot was generated).

According to the "Sufficiency heuristics":

• "If the latest output is chart-only/visual-only and does not include underlying numeric/text values, choose Path B first to print the values used in the chart."

Therefore, I must choose Path B to display the numeric values of the monthly_revenue dataframe to provide concrete insights along with the visualization.

Insights from current state:

• The data was successfully resampled to monthly frequency using ME.
• Revenue was calculated as Quantity * UnitPrice.
• A line plot was generated, though the exact numbers are not visible in the provided output log.

Next step: Display the monthly_revenue table to complement the plot and allow for numeric analysis.

The original user goal is to "plot monthly revenue trend". The current state of the notebook:

• Cell 1: Loaded the dataset.
• Cell 2: Calculated revenue, resampled by month ('ME'), and plotted a line chart of monthly revenue.
• Cell 3: Displayed the monthly_revenue DataFrame containing the exact numeric values for each month.

The output includes both the visualization (the plot) and the supporting data (the monthly_revenue table). The data shows a growth trend starting from December 2010 (748,957.02 USD), peaking in November 2011 (1,461,756.25 USD), and then dropping significantly in December 2011 (433,668.01 USD), likely due to incomplete data for the final month.

Since both the chart and the numeric values are available, the goal has been fully met.

Path A is appropriate.

Insights:

• Revenue peaked in November 2011 at 1,461,756.25 USD.
• There was a steady increase in revenue from February 2011 (498,062.65 USD) through November 2011.
• The sharp decline in December 2011 to 433,668.01 USD suggests the dataset might be truncated for that month.

Final Answer format: provide the insights and the tag <!--final answer-->. No code.

The user wants to find the top 10 products by total revenue. Looking at the notebook state:

• df is the main DataFrame containing Description, Quantity, UnitPrice, and Revenue.
• Revenue column has already been calculated as Quantity * UnitPrice.

Plan:

1. Group the original df by Description.
2. Sum the Revenue for each product.
3. Sort the results in descending order.
4. Take the top 10.

The Description column represents the product name. I should use that for grouping.

Steps:

• Group by 'Description' and sum 'Revenue'.
• Sort by 'Revenue' descending.
• Head(10).

The user wants to see the top 10 products by total revenue. The latest notebook output (cell-4) shows a table top_products containing the top 10 Description and their corresponding Revenue.

The outputs are: 0. DOTCOM POSTAGE: 206,245.48 USD

1. REGENCY CAKESTAND 3 TIER: 164,762.19 USD
2. WHITE HANGING HEART T-LIGHT HOLDER: 99,668.47 USD
3. PARTY BUNTING: 98,302.98 USD
4. JUMBO BAG RED RETROSPOT: 92,356.03 USD
5. RABBIT NIGHT LIGHT: 66,756.59 USD
6. POSTAGE: 66,230.64 USD
7. PAPER CHAIN KIT 50'S CHRISTMAS: 63,791.94 USD
8. ASSORTED COLOUR BIRD ORNAMENT: 58,959.73 USD
9. CHILLI LIGHTS: 53,768.06 USD

The goal is "show top 10 products by total revenue". The output of cell-4 provides exactly this information.

According to the A/B decision policy: A) If current outputs are sufficient, provide a natural final answer. B) If not sufficient, provide insights and a code block.

The current outputs are sufficient. I will provide the final answer based on the table.

Formatting:

• Use USD instead of $.
• No code blocks.
• No headers.
• Concise and conversational.

The user wants to know the Average Order Value (AOV) and how it varies by country.

*   `df` exists and contains columns: `InvoiceNo`, `StockCode`, `Description`, `Quantity`, `InvoiceDate`, `UnitPrice`, `CustomerID`, `Country`, `Revenue`.
*   `Revenue` is already calculated (`Quantity * UnitPrice`).
*   An "Order" is typically defined by a unique `InvoiceNo`.

*   AOV = Total Revenue / Total Number of Unique Orders.
*   To find AOV by country:
    1.  Group by `Country` and `InvoiceNo` to get total revenue per order per country.
    2.  Group that result by `Country` to get the average revenue per order.

*   Step 1: Calculate the total revenue for each order (unique `InvoiceNo`) and keep the `Country` associated with it.
*   Step 2: Calculate the mean of these order totals grouped by `Country`.
*   Step 3: Show the overall AOV and a table/plot for AOV by country.

*Decision:* I'll start by calculating the order-level revenue and then the AOV per country. Since I need to provide one step at a time, I'll first compute the order-level aggregation and the overall average.

Wait, the user asked for both the general AOV and how it varies by country. I can do this in one analysis step.

*Calculation logic:*
`order_totals = df.groupby(['Country', 'InvoiceNo'])['Revenue'].sum().reset_index()`
`aov_by_country = order_totals.groupby('Country')['Revenue'].mean().sort_values(ascending=False).reset_index()`

Let's start by calculating the order totals and the overall AOV.

*Refined plan:*
1. Calculate total revenue per unique order (InvoiceNo) including the country.
2. Calculate the overall average of these totals.
3. Group these totals by country to see the variation.

I will provide code to calculate the AOV by country and the overall average.

*Wait, the system instruction says "Prefer one execution step at a time." and "Default to one chart OR one table per step."*

I'll first calculate the order-level totals and display the AOV per country as a table.

*Code draft:*
```python
# Calculate total revenue per order (InvoiceNo) per Country
order_totals = df.groupby(['Country', 'InvoiceNo'])['Revenue'].sum().reset_index()

# Calculate AOV per country
aov_by_country = order_totals.groupby('Country')['Revenue'].mean().sort_values(ascending=False).reset_index()
aov_by_country.columns = ['Country', 'AverageOrderValue']

# Overall AOV
overall_aov = order_totals['Revenue'].mean()

print(f"Overall Average Order Value: {overall_aov:.2f} USD")
display(aov_by_country)
```

Wait, the user prompt is "what is the average order value and how does it vary by country?".
I will execute this.

*Correction:* I should be careful about the `Revenue` column. It's already in `df`.

Let's double-check if any filtering is needed (e.g., negative quantities for returns). The original dataset often has returns (Quantity < 0). The current `Revenue` was calculated as `Quantity * UnitPrice`. Returns will subtract from the revenue, which is correct for total revenue, but for "Average Order Value", usually, we are looking at successful transactions or net value. I'll stick to the current `Revenue` column for consistency with the previous steps.

One table per step. I'll show the AOV by country and print the overall AOV.

*Final check on rules:*
- Markdown for text.
- Python code blocks.
- Currency as `USD`.
- No `$` sign for money.
- No multiple code blocks.
- No end-to-end pipeline.
- Bare expression at end of block.

Let's refine the code to be minimal.

```python
# Calculate total revenue per unique order
order_totals = df.groupby(['Country', 'InvoiceNo'])['Revenue'].sum().reset_index()

# Calculate AOV by country
aov_by_country = order_totals.groupby('Country')['Revenue'].mean().sort_values(ascending=False).reset_index()
aov_by_country.columns = ['Country', 'AOV']

# Calculate overall AOV
overall_aov = order_totals['Revenue'].mean()

print(f"Overall Average Order Value: {overall_aov:.2f} USD")
aov_by_country
```

Actually, I'll just return the table and print the overall AOV.

Wait, I should check if the user wants a visualization. "How does it vary" suggests a comparison. A table is fine, but a bar chart might be better. I'll start with the table as per "Default to one chart OR one table per step."

Wait, if I print `overall_aov` and display the table, that's one "output" essentially.

Let's go.

The user wants to know the average order value (AOV) and how it varies by country. Looking at the notebook state:

• Cell 5 calculated the overall AOV as 376.36 USD.
• Cell 5 also calculated the AOV by country and displayed a sorted list.
• The output for aov_by_country shows the top countries: Netherlands (2818.43 USD), Australia (1986.63 USD), Lebanon (1693.88 USD), etc.
• The output is truncated, but the main request ("what is the average order value and how does it vary by country?") has been answered numerically.
• I have the overall AOV and the distribution across countries.

Decision: Path A. The current outputs are sufficient to answer the original goal.

Insights:

• Overall AOV: 376.36 USD.
• Variation: There is a significant variation across countries. The Netherlands has the highest AOV at 2818.43 USD, followed by Australia at 1986.63 USD. These are substantially higher than the overall average.

Final answer formatting:

• Use USD notation.
• Natural language.
• No code.
• No headings.
• End with <!--final answer-->.