Lux + Pandas: Automatic recognition of analysts is slow

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The obvious Getting started

Everyone today works with tons of data. We all love you Adultery in the head with data conflict. But let's be honest, scrolling through DataFRAmes and editing charts by hand gets boring. The standard method changes: load a DataFRAME, check a few columns, run describe()then build a similar set of charts to find patterns. It works, but it's repetitive and time consuming. I noticed this during a recent project when I spent a few hours doing basic histograms, scatter plots, and correlation views just to answer simple questions about distributions and relationships.

It helped is a python library that integrates directly with pandas as well automatically generates an intuitive view as soon as you display the dataFAme. It takes no analytical thinking; Instead, it eliminates manual labor. It helps analysts, students, and data scientists quickly explore data, discover trends, and understand patterns, even before they start modeling. In this article, we'll explore how Lux works, how to set it up, and why the perfect partner for “Lazy” analysts who want smart results.

The obvious Installation

Before understanding how Lux works, let's install Lux and run it inside your Notebook (jupyter / Google Colab). You can install it using PIP like this:

!pip install lux-api
!pip install lux-widget

After that, import it as usual with pandas:

import pandas as pd
import lux

If you are using Google Colab, you must also enable the Widget Manager:

from google.colab import output
output.enable_custom_widget_manager()

Upon import, Lux automatically hooks into your Pandas objects.

The obvious Why lux?

A traditional data analysis (EDA) workflow is:

df.head()
df.describe()
sns.pairplot(df)
df[col].hist()
plt.scatter(df[x], df[y])

This is repetitive and slow. Lux changes to:

Lux examines your data and creates distributions, connections, trends, and patterns, as well as recommended charts based on data types. Basically, Lux acts as an intelligent assistant that suggests what to look for next.

The obvious How Lux works

Now, let's look at a simple example using it COVID Dataset:

url = "
df = pd.read_csv(url)
df

When a DataFRAME is displayed, Lux automatically examines its structure and generates a logical visualization underneath it.

Output:

When you click on convert pandas / Lux, you see something like this:

You can switch between tabs to view connections, distributions, temporary charts, and countries. Let's understand what each of these terms means:

• Relation: It shows how confirmed, diagnosed, and related death rates are.
• Distribution: Highlights how case values ​​are spread to identify patterns or unusual values.
• Temporary: Tracks change conditions over time, making it easy to see rises and spikes.
• : It shows where cases are most common in all countries and regions.

This happens without writing any programming code. Luck simply enhances the default dataFrame display to help you quickly explore key patterns.

The obvious Saves all Lux Vializations as HTML

Lux provides the ability to export an entire viewport to a shared HTML file:

df.save_as_html("lux_report.html")

Output:

Saved HTML to lux_report.html

Now you can download and view this file in any browser.

You can also direct lux to focus on specific relationships within your data by setting a goal.

The obvious Objective data analysis

Let's use the same dataset to understand this. Let's say you want lux to emphasize confirmed cases. You can do this as follows:

df.intent = ["Confirmed"]
df

Output:

When you set an objective, Lux focuses on the columns you care about. It then shows us charts that clearly compare the different groups. This makes it easier to see patterns. You can quickly explore ideas and understand your data clearly.

Luxe also gives you the option to send ViaViview Recommended.

The obvious Observations are sent

Lux allows you to export any visuals you find interesting directly from the widget. Simply click on the charts you want and select the export button.

Views posted externally are stored internally df.exported. To display the complete list of saved views, use:

Output:

If you want to view a specific export chart, simply access it with an index like this:

# Get the first exported visualization
vis = df.exported[0]
vis

Output:

Lux automatically converts the visibility of finding matplotlib, altair, or vega-lite code. You can also use and customize anywhere without manually writing long scripts in the pool.

# Get the equivalent plotting code
print(vis.to_matplotlib())
print(vis.to_altair())
print(vis.to_vegalite())

For example, the result of print(vis.to_matplotlib()) I will:

import matplotlib.pyplot as plt
plt.rcParams.update(
            {
                "axes.titlesize": 20,
                "axes.titleweight": "bold",
                "axes.labelweight": "bold",
                "axes.labelsize": 16,
                "legend.fontsize": 14,
                "legend.title_fontsize": 15,
                "xtick.labelsize": 13,
                "ytick.labelsize": 13,
            }
        )
import numpy as np
from math import nan
df = pd.DataFrame({'Confirmed': {0: 0.0, 1: 8062512.0, 2: 16125024.0, 3: 24187536.0, 4: 32250048.0, 5: 40312560.0, 6: 48375072.0, 7: 56437584.0, 8: 64500096.0, 9: 72562608.0}, 'Number of Records': {0: 119202.0, 1: 917.0, 2: 399.0, 3: 220.0, 4: 212.0, 5: 122.0, 6: 25.0, 7: 7.0, 8: 7.0, 9: 65.0}})
fig, ax = plt.subplots()
bars = df['Confirmed']
measurements = df['Number of Records']
ax.bar(bars, measurements, width=6718760.0)
ax.set_xlabel('Confirmed (binned)')
ax.set_ylabel('Number of Records')

fig

The obvious When Luxe is useful (and when it isn't)

Luxu helps during the early stages of exploratory data analysis, when you are trying to understand your data and uncover the first patterns. It increases the understanding of data, the speed of hypothesis building, and works very well for teaching tasks and quick work. By creating the first layer of visual discovery, Lux reduces the time spent writing basic sites and allows you to focus more on testing the details of the translation rather than dealing with programming code. With so many advantages, there are also a few limitations of Lux, which are explained as follows:

• It works best within jupyter Notebook, Google Colab, or jupyterlab.
• It is not suitable for very large datasets.
• A publication-ready visualization still requires tools such as matplotlib, ocean or altair.

The obvious Lasting

If you're learning data analysis, Lux is a great way to quickly build intuition. Good tools don't replace skills; they help you grow. Instead of wasting time on basic charts, you can jump right into understanding your data. If you face any difficulty while checking, reach out to the comment section. A few useful resources to check out are:

Kanwal Mehreen Is a machine learning engineer and technical writer with a strong interest in data science and the intersection of AI and medicine. Authored the eBook “Increasing Productivity with Chatgpt”. As a Google Event 2022 APAC host, she is a symbol of diversity and excellence in education. He has also been recognized as a teradata distinction in tech scholar, a mitacs Globalk research scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, who has created femcodes to empower women.