Exploratory Data Analysis (EDA) is the process of analyzing and summarizing the main characteristics of a data set, through visual and statistical methods. It’s an important step in the data science process that helps to understand the data, identify patterns and trends, detect outliers and anomalies, and formulate hypotheses for further investigation. EDA is typically done before building a model or making predictions, and it can be done using various tools and techniques such as data visualization, summary statistics, and statistical tests.

Implementation of Exploratory Data Analysis libraries with a few lines of Python code

Table of Contents

1. Pandas-Profiling

2. SweetViz

3. AutoViz

4. DataPrep

5. D-Tale

6. dabl

7. QuickDA

8. Datatile

9. Lux

10. ExploriPy

Automated EDA (Exploratory Data Analysis) packages can perform EDA in a few lines of Python code. In this article, we will discuss 10 Automated EDA Tools that can perform EDA and generate insights about the data.

1)Pandas-Profiling

Pandas-Profiling is a Python library used for data exploration and visualization. It creates an interactive HTML report that displays various summary statistics and visualizations of a given Pandas DataFrame.

Here’s an example:

import pandas as pd
from pandas_profiling import ProfileReport
df = pd.read_csv("https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv")
profile = ProfileReport(df, title="Pandas Profiling Report")
profile.to_file("report.html")

This code will generate an interactive HTML report showing summary statistics and visualizations of the “airtravel” dataset. The report can be easily viewed in a Jupyter Notebook or exported as a standalone HTML file.

Documentation Link: https://pandasprofiling.ydata.ai/docs/master/index.html

2) SweetViz

Sweetviz is a library in Python that can be used to create exploratory data visualizations in a fast and easy way. It can be used for data profiling and comparing datasets.

Here’s an example of how you can use Sweetviz to create visualizations for a Pandas DataFrame:

import sweetviz as sv
import pandas as pd
# Load your data into a Pandas DataFrame
df = pd.read_csv("your_data.csv")
# Create an analysis report for your data
report = sv.analyze(df)
# Display the report
report.show_html()

This will create an HTML report with visualizations that provide insights into the data, including distributions of features, missing values, and correlations between features.

GitHub repository for sweetviz package

3) AutoViz

AutoViz is a library in Python that can be used to automatically generate visualizations for a given dataset. It can be used to quickly get a visual overview of the data, making it easier to perform exploratory data analysis.

Here’s an example of how you can use AutoViz to create visualizations for a Pandas DataFrame:

import autoviz as av
import pandas as pd
# Load your data into a Pandas DataFrame
df = pd.read_csv("your_data.csv")
# Automatically generate visualizations for the data
viz = av.AutoViz(df)
# Show the visualizations
viz.show()

This will generate a series of visualizations that provide insights into the data, including distributions of features, missing values, and correlations between features. The visualizations can be customized and fine-tuned as needed to best suit your analysis needs.

GitHub repository for AutoViz package

4) DataPrep

DataPrep is a library in Python that can be used for preprocessing data prior to analysis. It provides a suite of tools for cleaning, transforming, and preparing data for analysis, making it easier to work with and analyze.

Here’s an example of how you can use DataPrep to preprocess a Pandas DataFrame:

import dataprep as dp
import pandas as pd
# Load your data into a Pandas DataFrame
df = pd.read_csv("your_data.csv")
# Use the DataPrep API to preprocess the data
df = dp.DataFrame(df) \
    .dropna() \
    .rename(columns={'old_col_name': 'new_col_name'}) \
    .replace('old_value', 'new_value', columns='col_name') \
    .to_pandas()
# Save the preprocessed data to a new file
df.to_csv("preprocessed_data.csv", index=False)

This will preprocess the data by dropping any rows with missing values, renaming columns, replacing values in a specific column, and saving the preprocessed data to a new file. The DataPrep API provides a convenient and easy-to-use interface for performing common data preprocessing tasks.

GitHub repository for DataPrep package

5) D-Tale

D-Tale is a library in Python that can be used for exploratory data analysis. It provides an interactive web-based interface for exploring and visualizing data, making it easier to perform data analysis tasks.

Here’s an example of how you can use D-Tale to analyze a Pandas DataFrame:

import dtale
import pandas as pd
# Load your data into a Pandas DataFrame
df = pd.read_csv("your_data.csv")
# Start a D-Tale instance for the data
d = dtale.show(df)
# The D-Tale instance is now running in the background, you can access it in your web browser
# at the URL displayed in the output.

This will start a D-Tale instance for the data and make it accessible in your web browser. You can use the interactive web-based interface to explore and visualize the data, including features like column histograms, missing value analysis, and more.

GitHub repository for D-Tale package

6) dabl

dabl is a library in Python that can be used for exploratory data analysis and machine learning. It provides a suite of tools for quickly analyzing and visualizing data, as well as for building machine learning models.

Here’s an example of how you can use dabl to analyze a Pandas DataFrame:

import dabl
import pandas as pd
# Load your data into a Pandas DataFrame
df = pd.read_csv("your_data.csv")
# Create a dabl SimpleClassifier object for the data
clf = dabl.SimpleClassifier(random_state=0)
# Fit the SimpleClassifier to the data
clf.fit(df)
# Plot the classifier's performance
clf.plot()

This will fit a SimpleClassifier to the data and plot the classifier’s performance, including visualizations of the data and the performance of the classifier on a holdout set. dabl provides a convenient and easy-to-use interface for quickly performing exploratory data analysis and building machine learning models.

GitHub repository for dabl package

7) Datatile

Datatile is a Python library that provides a fast and flexible way to handle raster and vector data in Python. It allows you to perform tasks such as data exploration, data visualization, and image processing.

Here’s an example of how you could use Datatile to visualize a raster image:

import datatile as dt
# Load the image
img = dt.open_raster("path/to/image.tif")
# Plot the image
img.plot()

This will display the raster image using the default visualization settings. You can also customize the visualization, such as setting the color map, by passing additional arguments to the plot method.

GitHub repository for datatile package

8) QuickDA

Simple & Easy-to-use python modules to perform Quick Exploratory Data Analysis for any structured dataset!

GitHub repository for QuickDA package

9) Lux

Lux is a Python library that facilitate fast and easy data exploration by automating the visualization and data analysis process. By simply printing out a dataframe in a Jupyter notebook, Lux recommends a set of visualizations highlighting interesting trends and patterns in the dataset. Visualizations are displayed via an interactive widget that enables users to quickly browse through large collections of visualizations and make sense of their data.

GitHub repository for lux package

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10) ExploriPy

ExploriPy reduces a data analyst’s efforts significantly in the initial EDA. It is designed in a way to perform automated EDA, and statistical tests including Analysis of Variance, Chi Square Test of Independence, Weight of Evidence, Information Value and Tukey Honest Significance Difference. It provides easy interpretation on these statistical test results, based on industry standard assumptions. It expects a Pandas DataFrame, along with a list of categorical variables, as input. Output will be a presentable HTML document, with the result of analysis and statistical tests, represented through several interactive charts, and tables (with option to download as CSV). The ExploriPy package is available in the Python Package Index.

GitHub repository for ExploriPy package

Conclusion

In conclusion, Exploratory Data Analysis (EDA) is a crucial step in any data analysis project. It provides a deeper understanding of the data and its underlying patterns, which can then be leveraged to generate insights, make predictions, and drive data-driven decision making. The techniques and tools used in EDA can vary depending on the type of data and the question being asked, but the goal remains the same: to uncover the story behind the data and gain a better understanding of the underlying trends, relationships, and patterns. Whether you are a data scientist, a business analyst, or a student, EDA is a valuable skill to have and a critical component of any data-driven project.

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