All Automated EDA Libraries

Visualize dataset of any size with just one line of code

Exploratory Data Analysis is a process where we tend to analyze the dataset and summarize the main characteristics of the dataset often using visual methods. EDA is really important because if you are not familiar with the dataset you are working on, then you won’t be able to infer something from that data. However, EDA generally takes a lot of time.

In this article, we will work on Automating EDA using 5 libraries . It is a python library that generates beautiful, high-density visualizations to start your EDA. Let us explore it.

1. D-Tale for interactive data exploration

Installing

Like any other python library, we can install D-Tale by using the pip install command given below.

pip install dtale

Analyzing Dataset

import seaborn as sns
df=sns.load_dataset('titanic')
import dtale
dtale.show(df)

2. Exploratory Data Analysis with Pandas Profiling

Installing

Like any other python library, we can install Pandas Profiling by using the pip install command given below.

pip install pandas-profiling

Analyzing Dataset

import seaborn as sns 
df1 = sns.load_dataset('tips')
from pandas_profiling import ProfileReport
profile = ProfileReport(df1,explorative=True)
profile.to_file('output.html')

3. Sweetviz: Automated EDA in Python

Installing

Like any other python library, we can install Sweetviz by using the pip install command given below.

pip install sweetviz

Analyzing Dataset

import sweetviz as sv
report = sv.analyze(df)
report.show_html('sweet.html')

4. Autoviz: Automatically Visualize any Dataset

Installing

Like any other python library, we can install Autoviz by using the pip install command given below.

pip install autoviz

Analyzing Dataset

from autoviz.AutoViz_Class import AutoViz_Class
AV = AutoViz_Class()
df = AV.AutoViz('car_design.csv')

5. Dataprep.EdA: Accelerate your EDA

Installing

Like any other python library, we can install Dataprep by using the pip install command given below.

pip install dataprep

Analyzing Dataset

from dataprep.datasets import load_dataset
from dataprep.eda import create_report
df = load_dataset("titanic")
create_report(df)

In this article, we saw that we can visualize datasets with just one line of code and we can find the patterns in the dataset accordingly.

You can view the code and data I have used here in my GITHUB