Your First Example

Let’s walk through a complete example to get you familiar with Sagely. We’ll work with pandas to demonstrate the key features.

Step 1: Setup

First, make sure you have Sagely installed and your API key configured:

import sagely
import pandas as pd

# Verify everything is working
print("Sagely is ready!")

Step 2: Basic Question

Let’s start with a simple question about pandas:

pd.sage.ask("What is a DataFrame and how do I create one?")

You should see: 1. Status updates showing what Sagely is doing 2. A comprehensive answer with code examples 3. Syntax-highlighted output

Step 3: Working with Data

Now let’s create some data and ask questions about it:

# Create a sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'City': ['New York', 'London', 'Paris', 'Tokyo'],
    'Salary': [50000, 60000, 70000, 55000]
}
df = pd.DataFrame(data)

# Ask about the DataFrame
df.sage.ask("What are the basic operations I can perform on this DataFrame?")

Step 4: Error Context

Let’s see how Sagely handles errors:

try:
    # This will cause an error
    result = df['Age'] + "years"
except TypeError:
    # Now ask about the error
    df.sage.ask("How do I convert the Age column to string and add 'years'?")

Step 5: Data Analysis

Let’s perform some analysis and ask for help:

# Calculate some statistics
mean_age = df['Age'].mean()
print(f"Average age: {mean_age}")

# Ask about more advanced analysis
df.sage.ask("How do I group by City and calculate average salary?")

Step 6: Visualization

Let’s add visualization and ask for help:

import matplotlib.pyplot as plt

# Create a simple plot
plt.figure(figsize=(8, 6))
df.plot(x='Name', y='Salary', kind='bar')
plt.title('Salary by Name')
plt.show()

# Ask about improving the visualization
plt.sage.ask("How do I make this bar chart more professional looking?")

Step 7: Advanced Operations

Let’s explore more advanced pandas operations:

# Ask about data manipulation
df.sage.ask("How do I create a new column that categorizes ages into groups?")

# Ask about data filtering
df.sage.ask("How do I filter this DataFrame to show only people over 30?")

Step 8: Best Practices

Let’s learn about best practices:

pd.sage.ask("What are the best practices for working with pandas DataFrames?")
pd.sage.ask("How do I handle missing data effectively?")

Complete Example

Here’s the complete example in one block:

import sagely
import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'City': ['New York', 'London', 'Paris', 'Tokyo'],
    'Salary': [50000, 60000, 70000, 55000]
}
df = pd.DataFrame(data)

# Basic operations
df.sage.ask("What are the basic operations I can perform on this DataFrame?")

# Data analysis
df.sage.ask("How do I group by City and calculate average salary?")

# Visualization
plt.figure(figsize=(8, 6))
df.plot(x='Name', y='Salary', kind='bar')
plt.title('Salary by Name')
plt.show()

plt.sage.ask("How do I make this bar chart more professional looking?")

# Best practices
pd.sage.ask("What are the best practices for working with pandas DataFrames?")

What You’ve Learned

In this example, you’ve experienced:

Basic Usage: How to ask questions about modules
Context Awareness: How Sagely understands your data and errors
Status Updates: Real-time feedback about what’s happening
Caching: Responses are cached for future use
Integration: How Sagely works with multiple libraries
Best Practices: Learning recommended approaches

Key Takeaways

Simple Interface: Just add .sage.ask() to any module
Contextual Help: Sagely understands your specific situation
Progressive Learning: Build on previous questions
Error Handling: Get help when things go wrong
Best Practices: Learn recommended approaches

Next Steps

Now that you’ve completed your first example, try:

Explore Other Libraries: Try numpy.sage.ask(), requests.sage.ask(), etc.
Ask Complex Questions: Combine multiple concepts in one question
Use IPython Magic: Try %sagely commands in Jupyter
Customize Settings: Explore the configuration options
Check the Examples: Look at more advanced examples in the documentation

Remember, Sagely is designed to be your coding companion. Don’t hesitate to ask questions about any aspect of the libraries you’re using!