pandasai-0.6.4.tar.gz

# PandasAI 🐼 [](https://pypi.org/project/pandasai/) [](https://github.com/gventuri/pandas-ai/actions/workflows/ci.yml/badge.svg) [](https://github.com/gventuri/pandas-ai/actions/workflows/cd.yml/badge.svg) [](https://pandas-ai.readthedocs.io/en/latest/?badge=latest) [](https://discord.gg/kF7FqH2FwS) [](https://pepy.tech/project/pandasai) [](https://opensource.org/licenses/MIT) [](https://colab.research.google.com/drive/1rKz7TudOeCeKGHekw7JFNL4sagN9hon-?usp=sharing) PandasAI is a Python library that adds Generative AI capabilities to [pandas](https://github.com/pandas-dev/pandas), the popular data analysis and manipulation tool. It is designed to be used in conjunction with pandas, and is not a replacement for it. <!-- Add images/pandas-ai.png -->  ## 🔧 Quick install ```bash pip install pandasai ``` ## 🔍 Demo Try out PandasAI in your browser: [](https://colab.research.google.com/drive/1rKz7TudOeCeKGHekw7JFNL4sagN9hon-?usp=sharing) ## 📖 Documentation The documentation for PandasAI can be found [here](https://pandas-ai.readthedocs.io/en/latest/). ## 💻 Usage > Disclaimer: GDP data was collected from [this source](https://ourworldindata.org/grapher/gross-domestic-product?tab=table), published by World Development Indicators - World Bank (2022.05.26) and collected at National accounts data - World Bank / OECD. It relates to the year of 2020. Happiness indexes were extracted from [the World Happiness Report](https://ftnnews.com/images/stories/documents/2020/WHR20.pdf). Another useful [link](https://data.world/makeovermonday/2020w19-world-happiness-report-2020). PandasAI is designed to be used in conjunction with pandas. It makes pandas conversational, allowing you to ask questions to your data in natural language. ### Queries For example, you can ask PandasAI to find all the rows in a DataFrame where the value of a column is greater than 5, and it will return a DataFrame containing only those rows: ```python import pandas as pd from pandasai import PandasAI # Sample DataFrame df = pd.DataFrame({ "country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"], "gdp": [19294482071552, 2891615567872, 2411255037952, 3435817336832, 1745433788416, 1181205135360, 1607402389504, 1490967855104, 4380756541440, 14631844184064], "happiness_index": [6.94, 7.16, 6.66, 7.07, 6.38, 6.4, 7.23, 7.22, 5.87, 5.12] }) # Instantiate a LLM from pandasai.llm.openai import OpenAI llm = OpenAI(api_token="YOUR_API_TOKEN") pandas_ai = PandasAI(llm) pandas_ai(df, prompt='Which are the 5 happiest countries?') ``` The above code will return the following: ``` 6 Canada 7 Australia 1 United Kingdom 3 Germany 0 United States Name: country, dtype: object ``` Of course, you can also ask PandasAI to perform more complex queries. For example, you can ask PandasAI to find the sum of the GDPs of the 2 unhappiest countries: ```python pandas_ai(df, prompt='What is the sum of the GDPs of the 2 unhappiest countries?') ``` The above code will return the following: ``` 19012600725504 ``` ### Charts You can also ask PandasAI to draw a graph: ```python pandas_ai( df, "Plot the histogram of countries showing for each the gdp, using different colors for each bar", ) ```  You can save any charts generated by PandasAI by setting the `save_charts` parameter to `True` in the `PandasAI` constructor. For example, `PandasAI(llm, save_charts=True)`. Charts are saved in `./pandasai/exports/charts` . ### Multiple DataFrames Additionally, you can also pass in multiple dataframes to PandasAI and ask questions relating them. ```python import pandas as pd from pandasai import PandasAI 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) llm = OpenAI() pandas_ai = PandasAI(llm) pandas_ai([employees_df, salaries_df], "Who gets paid the most?") ``` The above code will return the following: ``` Oh, Olivia gets paid the most. ``` You can find more examples in the [examples](examples) directory. ### ⚡️ Shortcuts PandasAI also provides a number of shortcuts (beta) to make it easier to ask questions to your data. For example, you can ask PandasAI to `clean_data`, `impute_missing_values`, `generate_features`, `plot_histogram`, and many many more. ```python # Clean data pandas_ai.clean_data(df) # Impute missing values pandas_ai.impute_missing_values(df) # Generate features pandas_ai.generate_features(df) # Plot histogram pandas_ai.plot_histogram(df, column="gdp") ``` Learn more about the shortcuts [here](https://pandas-ai.readthedocs.io/en/latest/shortcuts/). ## 🔒 Privacy & Security In order to generate the Python code to run, we take the dataframe head, we randomize it (using random generation for sensitive data and shuffling for non-sensitive data) and send just the head. Also, 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. ## ⚙️ Command-Line Tool Pai is the command line tool designed to provide a convenient way to interact with PandasAI through a command line interface (CLI). In order to access the CLI tool, make sure to create a virtualenv for testing purpose and to install project dependencies in your local virtual environment using `pip` by running the following command: Read more about how to use the CLI [here](https://pandas-ai.readthedocs.io/en/latest/pai_cli/). ## 🤝 Contributing Contributions are welcome! Please check out the todos below, and feel free to open a pull request. For more information, please see the [contributing guidelines](CONTRIBUTING.md). After installing the virtual environment, please remember to install `pre-commit` to be compliant with our standards: ```bash pre-commit install ``` ## 📜 License PandasAI is licensed under the MIT License. See the LICENSE file for more details. ## Acknowledgements - This project is based on the [pandas](https://github.com/pandas-dev/pandas) library by independent contributors, but it's in no way affiliated with the pandas project. - This project is meant to be used as a tool for data exploration and analysis, and it's not meant to be used for production purposes. Please use it responsibly.