Getting Started

Welcome to the world of pd-explain! (This is Itay, test…) This guide will walk you through the essential steps to get started with your data exploration and storytelling journey.

Installation

First, you’ll need to install the pd-explain library. You can easily install it using pip:

pip install pd-explain

Importing pd-explain

Import the library in your Python script or Jupyter Notebook to get started:

import pd_explain

Reading a Table

You can read your dataset into an explainable DataFrame (ExpDataFrame) using Pandas. Here’s an example of how to read a CSV file:

import pandas as pd

# Replace 'your_data.csv' with the path to your dataset
data = pd.read_csv('your_data.csv')

# Create an ExpDataFrame from the loaded data
df = ExpDataFrame(data)

Now you have your dataset loaded into an ExpDataFrame, and you’re ready to start exploring and explaining your data.

Making it Explainable

The real power of pd-explain lies in its ability to provide explanations for your data transformations. Whether you want to filter, group, or manipulate your data, pd-explain allows you to generate insightful narratives and visualizations to better understand your data.

Explore the documentation to learn more about how to use pd-explain’s features for in-depth data exploration and storytelling.

That’s it! You’re all set to begin your data exploration journey with pd-explain. Dive into the documentation to discover the full range of capabilities and features at your disposal.