Autoviz: An Automated EDA Python Library

Autoviz: An Automated EDA Python Library

Exploratory Data Analysis (EDA) is a critical step in the data analysis process, where analysts explore and understand the structure, patterns, and relationships within a dataset. Traditionally, EDA involves performing various statistical and visual techniques manually, which can be time-consuming and tedious. To address this challenge, several automated EDA libraries have been developed, and one such library is Autoviz

Autoviz

Autoviz is an open-source Python library that automates the process of exploratory data analysis. It aims to provide data analysts and scientists with a quick and efficient way to visualize and understand their datasets. The library is built on top of popular data manipulation and visualization libraries such as Pandas and Matplotlib, leveraging their functionalities while simplifying the EDA process.

 

The Magic of Autoviz

Autoviz boasts several remarkable features that make it an invaluable tool for EDA:

 

a. Automated Chart Selection: Autoviz eliminates the need for manual chart selection by automatically identifying the appropriate chart types based on the data types of variables. Say goodbye to the cumbersome task of handpicking charts for numerical or categorical variables—Autoviz does it all for you!

 

b. Streamlined Execution: Imagine performing comprehensive EDA with just a single line of code! Autoviz's one-line code execution simplifies the process, making it accessible to both Python novices and seasoned analysts. Harness the power of automation to turbocharge your data analysis.

 

c. Interactive Visualizations: Autoviz goes beyond static visualizations. It leverages the Plotly library to generate interactive charts that enable users to explore data in unprecedented detail. Zoom, pan, and hover over data points to unveil hidden patterns and unlock the true potential of your datasets.

 

d. Tackling Large Datasets: Autoviz doesn't shy away from massive datasets. It handles large-scale data efficiently by intelligent sampling and generating visualizations. Even with millions of rows, Autoviz remains lightning-fast and responsive, ensuring you never compromise on performance.

 

e. Seamless Data Preprocessing: Autoviz simplifies data preprocessing by seamlessly integrating basic preprocessing capabilities. From handling missing values to outlier detection, Autoviz streamlines the workflow by incorporating preprocessing steps into the automated EDA process

EXAMPLE:

pip3 install autoviz

import pandas as pd

import pandas_profiling as pp

#load the data into a pandas dataframe

df = pd.read_csv("/Users/brendan.tierney/Downloads/Video_Games_Sales_as_at_22_Dec_2016.csv")

from autoviz import AutoViz_Class

AV = AutoViz_Class()

df2 = AV.AutoViz(filename="", dfte=df)  #for a file, fill in the filename and remove dfte parameter

This will analyze the data and create lots and lots of charts for you. 

 


CONCLUSION

As the world becomes increasingly data-driven, embracing tools like Autoviz is crucial for data professionals seeking to gain a competitive edge. The time-saving automation, intuitive visualizations, and seamless integration make Autoviz a game-changer in the field of EDA.🌟📺

 

#DataAnalysis #EDA #Autoviz #PythonLibrary #DataVisualization #Automation #DataInsights #DataScientists #DataAnalytics #ExploratoryAnalysis #DataDrivenDecisionMaking #RevolutionizingEDA #Efficiency #InteractiveVisualizations #BigData #DataPreprocessing #DataProfessionals #CompetitiveEdge #LinkedIn #Nanobi #Hunnarvi #ISME

 

Reference:

1.     https://medium.com/geekculture/autoviz-create-simple-charts-from-any-dataset-in-python-6514db8252b6

2.     https://medium.datadriveninvestor.com/autoviz-the-key-to-effortless-data-visualization-4b930b0c5ad9

 

  *Please Note: all views are personal*

-Ayushi pandey

Intern @ Hunnarvi technologies in collaboration with Nanobi Data and Analytics

ISME

  

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