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Are you interested in analyzing your data using natural language?
Learn how to do so using the Python library PandasAI.
In a world where data is crucial, understanding and analyzing it is essential.
However, traditional data analysis can be complex.
Thats where PandasAI comes in.
It simplifies data analysis by letting you talk to your data using natural language.
Pandas AI works by turning your questions into code for data analysis.
Its intended to complement pandas rather than replace them.
After completing it, youll be able to perform data analysis more easily and intuitively using natural language.
So, lets explore the fascinating world of natural language data analysis with Pandas AI!
Im using a Jupyter Notebook for this project.
But you could use Google Collab or VS Code as per your requirements.
This makes data analysis more accessible and user-friendly.
PandasAI is versatile and can work with various types of models.
These include Hugging Face models, Azure OpenAI, Google PALM, and Google VertexAI.
Each of these models brings its own strengths to the table, enhancing the capabilities of PandasAI.
Remember, to use these models, youll need the appropriate API keys.
So, check that to have your API keys handy when setting up PandasAI for your projects.
you might fetch theAPI Key, and export it as an environment variable.
to make it use OpenAI models, you should probably have an OpenAI API key.
you’re free to get onehere.
Wondering how to do this?
This way, you’re able to effectively manage your resources and avoid any surprises in your billing.
How to use Hugging Face Models?
to use HuggingFace models, you oughta have a HuggingFace API key.
you’re free to create a HuggingFace accounthereand get an API keyhere.
Weve successfully set up our environment and explored how to use both OpenAI and Hugging Face LLMs models.
Now, lets move forward with our data analysis journey.
The dataset has 12 columns and 8524 rows.
Youll get the link at the end of the article.
Youll get the link to the dataset at the end of the article.
Use OpenAIs LLM model
After loading our data.
Im going to use OpenAIs LLM model to use PandasAI
All Good!
Now, Lets attempt to use prompts.
Lets understand our dataset.
Find Missing Values
There are two ways to find missing values using pandas ai.
This shortcut will do data cleaning on the data frame.
Now, Lets fill in the missing null values.
This shortcut will impute missing values in the data frame.
Lets give a shot to find an overall sales trend over the years since its establishment.
This shortcut will plot a line chart of the data frame.
You might be wondering why there is a decline in the trend.
Its due to we dont have data from 1989 to 1994.
Finding Year of Highest Sales
Now, Lets find out which year has the highest sales.
So, the year with the highest sales is 1985.
Now, I want to find out the distribution of sales across different outlets.
This shortcut will plot a bar chart of the data frame.
This shortcut will plot a histogram of the data frame.
Average Sales for Each Item jot down
I want to compare all products with their average sales.
All sections of the pie chart appear similar because they have nearly the same sales figures.
This shortcut will plot a pie chart of the data frame.
As expected, Starchy Foods is the highest-selling item based on average sales.
It does not analyze your previous prompts to offer more accurate answers.
This enables you to have more interactive and meaningful exchanges.
The code is available onGitHub.
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