Build Quick Data Visualizations with these Three Python Libraries4 min read

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Data Visualizations
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Data Visualizations

Data Visualization is one of the most important aspects of Data Science and a prerequisite for Machine Learning before you move ahead with Data Modelling. Data Visualization is the art of presenting discrete aspects of the Data which allows the viewers to perceive the importance of the Data at hand. It also allows us to summarize lots of data with us and present the important aspects of the Dataset that we have.

Effective Data Visualization helps to effectively communicate our analysis and present information clearly and efficiently. Python offers a lot of libraries and modules for effective Data Visualization. While many of the Developers tend to use the standard Pandas for stacking the Dataframe and Matplotlib for Graphical Viewing, Python has got many Third-Party Libraries and Modules for quick Data Visualization in few lines of codes.

In this post, we will take a look at seven of the most popular Libraries and Modules which allows us to build Quick Data Visualization with Python as a Programming Libraries:

  1. Pandas Profiling

Pandas Profiling is one of the most flexible libraries for the purpose of Data Visualization with Python with just one line of code. We can just load our Dataframe in Pandas Profiling and Pandas Profiling would quickly develop a Data Visualization Report corresponding to the Dataset.

You can set up Pandas Profiling by directly installing it via pip: pip install pandas_profiling. Here is a sample code which would allow you to set up Data Visualization Projects with Pandas:

import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport

df = pd.DataFrame(
    np.random.rand(100, 5),
    columns=["a", "b", "c", "d", "e"]
)

profile = ProfileReport(df, title="Pandas Profiling Report")

2. Sweetviz

Sweetviz is another Data Visualization Library in Python that has been inspired by Pandas Profiling and provides a greater deal of extensibility and flexibility. The output in Sweetviz is stored in an externally generated HTML File and it can be used to automate the whole Exploratory Data Analysis within few clicks and seconds.

Sweetviz provides extra functionality by allowing the Developers to quickly analyze target characteristics, compare training and the testing data finally characterize other tasks in a few lines of code. Here is an example of how you can set up Sweetviz for a Diabetes dataset:

import numpy as np
import pandas as pd
import sweetviz as sv
dataset=pd.read_csv("diabetes.csv")
my_report = sv.analyze(dataset)

3. Autoviz

Autoviz is yet another Library used for Data Visualization and performing Exploratory Data Analysis. It takes up the Dataset in either CSV or JSON and it performs the Data Visualization in just one line of code. Unline Pandas Profiling or Sweetviz, Data Visualizations generated by Autoviz are extremely dense and provides greater insights on the Dataset that we have.

Here is how you can set up Autoviz for an Adult Income Dataset that has got various Columns like Workclass, Education, Income and more. Let’s try to visualize it using Autoviz:

import pandas as pd
import numpy as np
from autoviz.AutoViz_Class import AutoViz_Class
AV = AutoViz_Class()
data = pd.read_csv('adult.csv')
filename = ""
sep = ","
dft = AV.AutoViz(
    filename,
    sep,
    'income',
    data,
    header=0,
    verbose=1,
    lowess=True,
    chart_format="svg",
    max_rows_analyzed=150000,
    max_cols_analyzed=30,
)

Here is the Data Visualization generated:

Also See – Memory Profiling in Python

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