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Uraz Akgül
Uraz Akgül

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Visualize Data on the Fly like Tableau-style with PyGWalker

Have you ever wished for a more intuitive and interactive way to explore your datasets?

Meet PyGWalker (called "Pig Walker" for fun) - it's about to transform your data analysis routine. The name is short for "Python binding of Graphic Walker" and it seamlessly integrates with Jupyter Notebook and other Jupyter-based environments. PyGWalker offers a Tableau-style User Interface for effortless visual exploration, making data analysis a walk in the park.

But how does it work?

First things first, you'll need to install PyGWalker. Open your terminal and run the following command:

pip install pygwalker

Once you have PyGWalker installed, you can start using it in your favorite development environment, like Visual Studio Code.

Let's dive into a quick example using PyGWalker and the popular yfinance library to visualize financial data.

In this example, we'll take a closer look at the Istanbul Stock Exchange 100 Index (XU100.IS) and Turkish Airlines (THYAO.IS) logarithmic returns by creating a scatter plot.

Import the necessary libraries:

import yfinance as yf
import pygwalker as pyg
import numpy as np
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Fetch historical stock data using yfinance:

symbols = ['XU100.IS', 'THYAO.IS']
start_date = '2021-09-16'
end_date = '2023-09-15'

data =, start=start_date, end=end_date)
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Calculate the log returns for both symbols:

log_returns = np.log(data['Adj Close'] / data['Adj Close'].shift(1))
log_returns = log_returns.reset_index(drop=True)
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Create an interactive environment with PyGWalker:

walker = pyg.walk(log_returns)
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With this simple line of code, an interactive interface will open up, allowing you to create visualizations effortlessly.

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Create a scatter plot:

In this interface, you can drag and drop to build your visualizations. For our scatter plot, set the Y-axis to THYAO.IS and the X-axis to XU100.IS. Please don't forget to click on the Aggregation button in the menu. This will remove the variable values from being aggregated.

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If you wish, you can adjust the height and width of the graph as shown below in the Layout Mode menu.

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If you click on the tab labeled Data in the top left corner, you can also view the raw data.

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With PyGWalker, data exploration becomes a playful and intuitive process. Bid farewell to manual coding, which is undeniably valuable, but in today's fast-paced world, efficiency is key. Say hello to a more interactive and enjoyable data analysis journey.

Happy exploring!

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