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Advanced Applications of the Code Interpreter (GPT)

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The code interpreter in OpenAI's GPT is a powerful tool that enables complex and interactive coding capabilities within a safe and sandboxed environment. This README provides examples of advanced applications, demonstrating how to leverage this tool for sophisticated tasks.

Table of Contents

Introduction

This document aims to showcase the advanced capabilities of the code interpreter in OpenAI's GPT. From data analysis and machine learning to web scraping and image processing, the examples provided here are intended to help users unlock the full potential of this tool.

Data Analysis and Visualization

Complex Data Analysis with Pandas and Matplotlib

Performing advanced data analysis and visualizing the results using pandas and matplotlib.

import pandas as pd
import matplotlib.pyplot as plt

# Load a complex dataset
df = pd.read_csv('/mnt/data/complex_data.csv')

# Perform data cleaning and preprocessing
df = df.dropna()
df['date'] = pd.to_datetime(df['date'])

# Analyze and visualize data
pivot_table = df.pivot_table(index='date', values='sales', aggfunc='sum')
pivot_table.plot(figsize=(10, 6), title='Sales Over Time')
plt.xlabel('Date')
plt.ylabel('Sales')
plt.grid(True)
plt.savefig('/mnt/data/sales_over_time.png')
plt.show()

Machine Learning Applications

Building a Predictive Model with Scikit-Learn

Creating a machine learning model to predict outcomes based on complex datasets.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

# Load the dataset
df = pd.read_csv('/mnt/data/ml_dataset.csv')

# Prepare the data
X = df.drop('target', axis=1)
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a Random Forest model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Make predictions and evaluate the model
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')

Advanced Data Processing

Merging Multiple DataFrames

Merging multiple dataframes to create a comprehensive dataset for analysis.

import pandas as pd

# Load multiple datasets
df1 = pd.read_csv('/mnt/data/data1.csv')
df2 = pd.read_csv('/mnt/data/data2.csv')
df3 = pd.read_csv('/mnt/data/data3.csv')

# Merge datasets
merged_df = pd.merge(df1, df2, on='common_column')
merged_df = pd.merge(merged_df, df3, on='another_common_column')

# Display the merged dataframe
print(merged_df.head())

Web Scraping

Scraping Data from Web Pages with BeautifulSoup

Using BeautifulSoup to scrape data from web pages for analysis.

maybe usefull? Python-XPath Tutorial | JavaScript-XPath Tutorial

import requests
from bs4 import BeautifulSoup

# Send a request to the webpage
url = 'https://example.com/data-page'
response = requests.get(url)

# Parse the HTML content
soup = BeautifulSoup(response.content, 'html.parser')

# Extract specific data
data = []
table = soup.find('table', {'id': 'data-table'})
for row in table.find_all('tr'):
    columns = row.find_all('td')
    row_data = [col.text for col in columns]
    data.append(row_data)

# Display the scraped data
for item in data:
    print(item)

Natural Language Processing

Sentiment Analysis with TextBlob

Performing sentiment analysis on text data using TextBlob.

from textblob import TextBlob

# Example text
text = "OpenAI's GPT is amazing. I'm so happy with the results!"

# Perform sentiment analysis
blob = TextBlob(text)
sentiment = blob.sentiment

# Display the sentiment
print(f'Sentiment: {sentiment}')

Image Processing

Advanced Image Manipulations with PIL

Performing advanced image manipulations using the Python Imaging Library (PIL).

from PIL import Image, ImageEnhance, ImageFilter

# Open an image file
img = Image.open('/mnt/data/sample_image.jpg')

# Apply enhancements and filters
enhancer = ImageEnhance.Contrast(img)
img_enhanced = enhancer.enhance(2)
img_filtered = img_enhanced.filter(ImageFilter.DETAIL)

# Save the processed image
img_filtered.save('/mnt/data/processed_image.jpg')

# Display the processed image
img_filtered.show()

Interactive Widgets

Creating Interactive Widgets with ipywidgets

Creating interactive widgets to enhance user interaction within Jupyter notebooks.

import ipywidgets as widgets
from IPython.display import display

# Create a slider widget
slider = widgets.IntSlider(value=10, min=0, max=100, step=1, description='Value:')

# Define an event handler
def on_value_change(change):
    print(f'Slider value: {change["new"]}')

# Attach the event handler to the slider
slider.observe(on_value_change, names='value')

# Display the slider
display(slider)

Troubleshooting

Here are some common issues and their solutions:

  • ImportError: No module named '...': Ensure that all required libraries are installed. Use pip install <library_name> to install any missing libraries.
  • FileNotFoundError: [Errno 2] No such file or directory: '...': Check the file path and ensure that the file is in the correct directory. Use absolute paths or ensure that the file is saved in /mnt/data.
  • PermissionError: [Errno 13] Permission denied: '...': Ensure that you have permissions to read and write in the /mnt/data directory.

If you encounter further issues, open an issue on GitHub or contact the project maintainer.

Contributing

Contributions are welcome! Please feel free to submit a pull request.

👣 other GPT stuff

Credits