Welcome to your Interactive Analysis Notebook!
This notebook combines the power of Python with AI assistance. You can write code directly or describe what you want in plain English and let the AI generate the code for you.
# Sample data loading
import pandas as pd
import numpy as np
# Load sample sales data
df = pd.read_csv('sample_sales_data.csv')
df.head()
import pandas as pd
import numpy as np
# Load sample sales data
df = pd.read_csv('sample_sales_data.csv')
df.head()
| Order ID | Product | Category | Quantity | Price | Order Date |
|---|---|---|---|---|---|
| 1001 | Ultra HD TV | Electronics | 1 | 899.99 | 2023-01-15 |
| 1002 | Wireless Headphones | Electronics | 2 | 129.99 | 2023-01-16 |
| 1003 | Coffee Maker | Appliances | 1 | 49.99 | 2023-01-17 |
# Generated code: Bar chart of sales by category
import matplotlib.pyplot as plt
import seaborn as sns
# Group by category and sum the sales
sales_by_category = df.groupby('Category')['Price'].sum().reset_index()
# Create bar plot
plt.figure(figsize=(10, 6))
sns.barplot(x='Category', y='Price', data=sales_by_category)
plt.title('Total Sales by Product Category')
plt.ylabel('Total Sales ($)')
plt.xticks(rotation=45)
plt.show()
import matplotlib.pyplot as plt
import seaborn as sns
# Group by category and sum the sales
sales_by_category = df.groupby('Category')['Price'].sum().reset_index()
# Create bar plot
plt.figure(figsize=(10, 6))
sns.barplot(x='Category', y='Price', data=sales_by_category)
plt.title('Total Sales by Product Category')
plt.ylabel('Total Sales ($)')
plt.xticks(rotation=45)
plt.show()
