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Helping a company analyze sales data
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• ## .mfe-app-workspace-kj242g{position:absolute;top:-8px;}.mfe-app-workspace-11ezf91{display:inline-block;}.mfe-app-workspace-11ezf91:hover .Anchor__copyLink{visibility:visible;}Reporting on sales data

### ðŸ“– Background

You work in the accounting department of a company that sells motorcycle parts. The company operates three warehouses in a large metropolitan area.

Youâ€™ve recently learned data manipulation and plotting, and suggest helping your colleague analyze past sales data. Your colleague wants to capture sales by payment method. She also needs to know the average unit price for each product line.

### ðŸ’¾ The data

##### The sales data has the following fields:
• "date" - The date, from June to August 2021.
• "warehouse" - The company operates three warehouses: North, Central, and West.
• "client_type" - There are two types of customers: Retail and Wholesale.
• "product_line" - Type of products purchased.
• "quantity" - How many items were purchased.
• "unit_price" - Price per item sold.
• "total" - Total sale = quantity * unit_price.
• "payment" - How the client paid: Cash, Credit card, Transfer.

### ðŸ’ª Challenge

1. What are the total sales for each payment method?
2. What is the average unit price for each product line?
3. Create plots to visualize findings for questions 1 and 2.
4. [Optional] Investigate further (e.g., average purchase value by client type, total purchase value by product line, etc.)

## Imports

```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as mdates

pd.set_option('display.float_format', '{:,.2f}'.format)``````
``````df = pd.read_csv('data/sales_data.csv', parse_dates=['date'])

## Q1. What are the total sales for each payment method?

``````total_sales_per_payment = df.groupby('payment')['total'].sum().reset_index()
total_sales_per_payment.columns = ['payment_method', 'total_sales']
total_sales_per_payment = total_sales_per_payment.sort_values(by = 'total_sales', ascending = False)
total_sales_per_payment``````

## Q2. What is the average unit price for each product line?

``````avg_unit_price = df.groupby('product_line')['unit_price'].mean().reset_index()
avg_unit_price.columns = ['product_line', 'avg_unit_price']
avg_unit_price = avg_unit_price.sort_values(by = 'avg_unit_price', ascending = False)
avg_unit_price``````

## Q3. Create plots to visualize findings for questions 1 and 2.

``````fig, axs = plt.subplots(1, 2, figsize = (16, 6))

sns.barplot(data = total_sales_per_payment, x = 'payment_method', y = 'total_sales', ax = axs[0])
sns.barplot(data = avg_unit_price, x = 'product_line', y = 'avg_unit_price', ax = axs[1])

axs[0].set_title("Total sales by payment method")

axs[1].tick_params(axis = 'x', rotation = 90)
axs[1].set_title("Average unit price by product line")

plt.tight_layout()``````

## Q4. Investigate further (e.g., average purchase value by client type, total purchase value by product line, etc.)

### Q4.1 What is the average purchase value by client type?

``````avg_purchase_by_client = df.groupby('client_type')['total'].mean().sort_values(ascending = False).reset_index()
avg_purchase_by_client.columns = ['client_type', 'avg_purchase_value']
avg_purchase_by_client``````
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