| Name | pandasai JSON |
| Version |
2.4.1
JSON |
| download |
| home_page | None |
| Summary | Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG. |
| upload_time | 2024-12-18 09:13:29 |
| maintainer | None |
| docs_url | None |
| author | Gabriele Venturi |
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| license | MIT |
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No requirements were recorded.
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# 
[](https://pypi.org/project/pandasai/)
[](https://github.com/sinaptik-ai/pandas-ai/actions/workflows/ci.yml/badge.svg)
[](https://github.com/sinaptik-ai/pandas-ai/actions/workflows/cd.yml/badge.svg)
[](https://codecov.io/gh/sinaptik-ai/pandas-ai)
[](https://discord.gg/kF7FqH2FwS)
[](https://pepy.tech/project/pandasai) [](https://opensource.org/licenses/MIT)
[](https://colab.research.google.com/drive/1ZnO-njhL7TBOYPZaqvMvGtsjckZKrv2E?usp=sharing)
PandasAI is a Python platform that makes it easy to ask questions to your data in natural language. It helps non-technical users to interact with their data in a more natural way, and it helps technical users to save time, and effort when working with data.
# 🚀 Deploying PandasAI
PandasAI can be used in a variety of ways. You can easily use it in your Jupyter notebooks or Streamlit apps, or you can deploy it as a REST API such as with FastAPI or Flask.
If you are interested in the managed PandasAI Cloud or our self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
# 🔧 Getting started
You can find the full documentation for PandasAI [here](https://pandas-ai.readthedocs.io/en/latest/).
You can either decide to use PandasAI in your Jupyter notebooks, Streamlit apps, or use the client and server architecture from the repo.
## ☁️ Using the platform
[](https://www.youtube.com/watch?v=kh61wEy9GYM)
### 📦 Installation
PandasAI platform is uses a dockerized client-server architecture. You will need to have Docker installed in your machine.
```bash
git clone https://github.com/sinaptik-ai/pandas-ai/
cd pandas-ai
docker-compose build
```
### 🚀 Running the platform
Once you have built the platform, you can run it with:
```bash
docker-compose up
```
This will start the client and server, and you can access the client at `http://localhost:3000`.
## 📚 Using the library
### 📦 Installation
You can install the PandasAI library using pip or poetry.
With pip:
```bash
pip install pandasai
```
With poetry:
```bash
poetry add pandasai
```
### 🔍 Demo
Try out the PandasAI library yourself in your browser:
[](https://colab.research.google.com/drive/1ZnO-njhL7TBOYPZaqvMvGtsjckZKrv2E?usp=sharing)
### 💻 Usage
#### Ask questions
```python
import os
import pandas as pd
from pandasai import Agent
# Sample DataFrame
sales_by_country = pd.DataFrame({
"country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"],
"revenue": [5000, 3200, 2900, 4100, 2300, 2100, 2500, 2600, 4500, 7000]
})
# By default, unless you choose a different LLM, it will use BambooLLM.
# You can get your free API key signing up at https://pandabi.ai (you can also configure it in your .env file)
os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"
agent = Agent(sales_by_country)
agent.chat('Which are the top 5 countries by sales?')
```
```
China, United States, Japan, Germany, Australia
```
---
Or you can ask more complex questions:
```python
agent.chat(
"What is the total sales for the top 3 countries by sales?"
)
```
```
The total sales for the top 3 countries by sales is 16500.
```
#### Visualize charts
You can also ask PandasAI to generate charts for you:
```python
agent.chat(
"Plot the histogram of countries showing for each one the gd. Use different colors for each bar",
)
```
#### Multiple DataFrames
You can also pass in multiple dataframes to PandasAI and ask questions relating them.
```python
import os
import pandas as pd
from pandasai import Agent
employees_data = {
'EmployeeID': [1, 2, 3, 4, 5],
'Name': ['John', 'Emma', 'Liam', 'Olivia', 'William'],
'Department': ['HR', 'Sales', 'IT', 'Marketing', 'Finance']
}
salaries_data = {
'EmployeeID': [1, 2, 3, 4, 5],
'Salary': [5000, 6000, 4500, 7000, 5500]
}
employees_df = pd.DataFrame(employees_data)
salaries_df = pd.DataFrame(salaries_data)
# By default, unless you choose a different LLM, it will use BambooLLM.
# You can get your free API key signing up at https://pandabi.ai (you can also configure it in your .env file)
os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"
agent = Agent([employees_df, salaries_df])
agent.chat("Who gets paid the most?")
```
```
Olivia gets paid the most.
```
You can find more examples in the [examples](examples) directory.
## 🔒 Privacy & Security
In order to generate the Python code to run, we take some random samples from the dataframe, we randomize it (using random generation for sensitive data and shuffling for non-sensitive data) and send just the randomized head to the LLM.
If you want to enforce further your privacy you can instantiate PandasAI with `enforce_privacy = True` which will not send the head (but just column names) to the LLM.
## 📜 License
PandasAI is available under the MIT expat license, except for the `pandasai/ee` directory (which has it's [license here](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE) if applicable.
If you are interested in managed PandasAI Cloud or self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
## Resources
- [Docs](https://pandas-ai.readthedocs.io/en/latest/) for comprehensive documentation
- [Examples](examples) for example notebooks
- [Discord](https://discord.gg/kF7FqH2FwS) for discussion with the community and PandasAI team
## 🤝 Contributing
Contributions are welcome! Please check the outstanding issues and feel free to open a pull request.
For more information, please check out the [contributing guidelines](CONTRIBUTING.md).
### Thank you!
[](https://github.com/sinaptik-ai/pandas-ai/graphs/contributors)
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"description": "# \n\n[](https://pypi.org/project/pandasai/)\n[](https://github.com/sinaptik-ai/pandas-ai/actions/workflows/ci.yml/badge.svg)\n[](https://github.com/sinaptik-ai/pandas-ai/actions/workflows/cd.yml/badge.svg)\n[](https://codecov.io/gh/sinaptik-ai/pandas-ai)\n[](https://discord.gg/kF7FqH2FwS)\n[](https://pepy.tech/project/pandasai) [](https://opensource.org/licenses/MIT)\n[](https://colab.research.google.com/drive/1ZnO-njhL7TBOYPZaqvMvGtsjckZKrv2E?usp=sharing)\n\nPandasAI is a Python platform that makes it easy to ask questions to your data in natural language. It helps non-technical users to interact with their data in a more natural way, and it helps technical users to save time, and effort when working with data.\n\n# \ud83d\ude80 Deploying PandasAI\n\nPandasAI can be used in a variety of ways. You can easily use it in your Jupyter notebooks or Streamlit apps, or you can deploy it as a REST API such as with FastAPI or Flask.\n\nIf you are interested in the managed PandasAI Cloud or our self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).\n\n# \ud83d\udd27 Getting started\n\nYou can find the full documentation for PandasAI [here](https://pandas-ai.readthedocs.io/en/latest/).\n\nYou can either decide to use PandasAI in your Jupyter notebooks, Streamlit apps, or use the client and server architecture from the repo.\n\n## \u2601\ufe0f Using the platform\n\n[](https://www.youtube.com/watch?v=kh61wEy9GYM)\n\n### \ud83d\udce6 Installation\n\nPandasAI platform is uses a dockerized client-server architecture. You will need to have Docker installed in your machine.\n\n```bash\ngit clone https://github.com/sinaptik-ai/pandas-ai/\ncd pandas-ai\ndocker-compose build\n```\n\n### \ud83d\ude80 Running the platform\n\nOnce you have built the platform, you can run it with:\n\n```bash\ndocker-compose up\n```\n\nThis will start the client and server, and you can access the client at `http://localhost:3000`.\n\n## \ud83d\udcda Using the library\n\n### \ud83d\udce6 Installation\n\nYou can install the PandasAI library using pip or poetry.\n\nWith pip:\n\n```bash\npip install pandasai\n```\n\nWith poetry:\n\n```bash\npoetry add pandasai\n```\n\n### \ud83d\udd0d Demo\n\nTry out the PandasAI library yourself in your browser:\n\n[](https://colab.research.google.com/drive/1ZnO-njhL7TBOYPZaqvMvGtsjckZKrv2E?usp=sharing)\n\n### \ud83d\udcbb Usage\n\n#### Ask questions\n\n```python\nimport os\nimport pandas as pd\nfrom pandasai import Agent\n\n# Sample DataFrame\nsales_by_country = pd.DataFrame({\n \"country\": [\"United States\", \"United Kingdom\", \"France\", \"Germany\", \"Italy\", \"Spain\", \"Canada\", \"Australia\", \"Japan\", \"China\"],\n \"revenue\": [5000, 3200, 2900, 4100, 2300, 2100, 2500, 2600, 4500, 7000]\n})\n\n# By default, unless you choose a different LLM, it will use BambooLLM.\n# You can get your free API key signing up at https://pandabi.ai (you can also configure it in your .env file)\nos.environ[\"PANDASAI_API_KEY\"] = \"YOUR_API_KEY\"\n\nagent = Agent(sales_by_country)\nagent.chat('Which are the top 5 countries by sales?')\n```\n\n```\nChina, United States, Japan, Germany, Australia\n```\n\n---\n\nOr you can ask more complex questions:\n\n```python\nagent.chat(\n \"What is the total sales for the top 3 countries by sales?\"\n)\n```\n\n```\nThe total sales for the top 3 countries by sales is 16500.\n```\n\n#### Visualize charts\n\nYou can also ask PandasAI to generate charts for you:\n\n```python\nagent.chat(\n \"Plot the histogram of countries showing for each one the gd. Use different colors for each bar\",\n)\n```\n\n\n\n#### Multiple DataFrames\n\nYou can also pass in multiple dataframes to PandasAI and ask questions relating them.\n\n```python\nimport os\nimport pandas as pd\nfrom pandasai import Agent\n\nemployees_data = {\n 'EmployeeID': [1, 2, 3, 4, 5],\n 'Name': ['John', 'Emma', 'Liam', 'Olivia', 'William'],\n 'Department': ['HR', 'Sales', 'IT', 'Marketing', 'Finance']\n}\n\nsalaries_data = {\n 'EmployeeID': [1, 2, 3, 4, 5],\n 'Salary': [5000, 6000, 4500, 7000, 5500]\n}\n\nemployees_df = pd.DataFrame(employees_data)\nsalaries_df = pd.DataFrame(salaries_data)\n\n# By default, unless you choose a different LLM, it will use BambooLLM.\n# You can get your free API key signing up at https://pandabi.ai (you can also configure it in your .env file)\nos.environ[\"PANDASAI_API_KEY\"] = \"YOUR_API_KEY\"\n\nagent = Agent([employees_df, salaries_df])\nagent.chat(\"Who gets paid the most?\")\n```\n\n```\nOlivia gets paid the most.\n```\n\nYou can find more examples in the [examples](examples) directory.\n\n## \ud83d\udd12 Privacy & Security\n\nIn order to generate the Python code to run, we take some random samples from the dataframe, we randomize it (using random generation for sensitive data and shuffling for non-sensitive data) and send just the randomized head to the LLM.\n\nIf you want to enforce further your privacy you can instantiate PandasAI with `enforce_privacy = True` which will not send the head (but just column names) to the LLM.\n\n## \ud83d\udcdc License\n\nPandasAI is available under the MIT expat license, except for the `pandasai/ee` directory (which has it's [license here](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE) if applicable.\n\nIf you are interested in managed PandasAI Cloud or self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).\n\n## Resources\n\n- [Docs](https://pandas-ai.readthedocs.io/en/latest/) for comprehensive documentation\n- [Examples](examples) for example notebooks\n- [Discord](https://discord.gg/kF7FqH2FwS) for discussion with the community and PandasAI team\n\n## \ud83e\udd1d Contributing\n\nContributions are welcome! Please check the outstanding issues and feel free to open a pull request.\nFor more information, please check out the [contributing guidelines](CONTRIBUTING.md).\n\n### Thank you!\n\n[](https://github.com/sinaptik-ai/pandas-ai/graphs/contributors)\n",
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