Which Python Library is the Best One for Data Visualization?

Data visualization is a crucial step in any exploratory data analysis or report. It’s usually easy to read, and it can give us insight into the dataset in one look. There are dozens of great tools for business intelligence, such as Tableau, Google Data Studio, and PowerBI that allow us to create graphs easily. A data analyst or data scientist will often create visualizations on a Jupyter Notebook using Python. Luckily, there are dozens of great Python libraries that create great graphs. However, the million-dollar question is: which one is the best?

Whether you are a student or a professional, you should know a few options out there. There are not perfect libraries. Thus, you should know the pros and cons of each data visualization. I will go over four of the most popular Python libraries for data visualization: Matplotlib, Seaborn, Plotly Express, and Altair. To do so, I will create a simple bar plot and analyze how easy it is to use each library. For this blog, I will use a city dataset. You can find the notebook here.

Categories

This blog will analyze how easy it is to set up a barplot, how easy it is to customize the graph to make it minimally presentable, and the library documentation.

Setting up the dataset

First, let’s import all the important libraries. It’s very likely that you already have Matplotlib and Seaborn installed on your computer. However, you might not have Plotly Express and Altair. You can easily install them using pip install plotly==4.14.3 and pip install altair vega_datasets.

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import altair as alt
import plotly.express as px

Now we will import the dataset. For demonstration purposes, let’s only create a data frame with the top 15 more populated cities in the US. I will also fix the capitalization of the names of the cities. It will facilitate the editing process when we create the visualizations.

df = pd.read_csv('worldcitiespop.csv')

us = df[df['Country'] == 'us']
us['City'] = us['City'].str.title()
cities = us[['City', 'Population']].nlargest(15, ['Population'], keep=’first’)

Now we should be ready to analyze each library. Are you ready?

Category: Difficulty to Set Up and Initial Result

**Winner: **Plotly Express

**Losers: **Matplotlib, Altair, and Seaborn

In this category, all the libraries performed well. They are all easy to set up, and the results with basic editing are good enough for most of the analysis, but we need to have winners and losers, right?

Matplotlib is very easy to set up and remember the code. However, the chart doesn’t look good. It will probably do the job for data analysis, but its result for a business meeting is not great.

#data-analysis #python #data-visualization #data-science #libraries

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Clash of Python Data Visualization Libraries

Seaborn, Altair, and Plotly

Data visualization is a fundamental ingredient of data science. It helps us understand the data better by providing insights. We also use data visualization to deliver the results or findings.

Python, being the predominant choice of programming language in the data science ecosystem, offers a rich selection of data visualization libraries. In this article, we will do a practical comparison of 3 popular ones.

The libraries we will cover are Seaborn, Altair, and Plotly. The examples will consist of 3 fundamental data visualization types which are scatter plot, histogram, and line plot.

We will do the comparison by creating the same visualizations with all 3 libraries. We will be using the Melbourne housing dataset available on Kaggle for the examples.

#data-visualization #python #data-science #programming #clash of python data visualization libraries #libraries

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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Must-Know Data Science Libraries in Python

Python is the most widespread and popular programming language in data science, software development, and related fields. The simplicity of codes in Python, which helps learners avoid any confusion, is the key to this popularity. Python has constantly been developing, and it keeps getting updated for more ease in using. With 137,000 plus libraries and tools, Python has always provided its users with the solutions to problems of any complexity level. This reason makes Python the ideal language for Data Science operations. This article focuses on some of the essential and must-learn libraries in Python used heavily by Data Scientists. I have tried to cover different libraries used in various stages of a data science cycle, such as Data Mining, processing and modeling, Data Visualization.

Learn Data Science in Python from here!

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Which Python Library is the Best One for Data Visualization?

Data visualization is a crucial step in any exploratory data analysis or report. It’s usually easy to read, and it can give us insight into the dataset in one look. There are dozens of great tools for business intelligence, such as Tableau, Google Data Studio, and PowerBI that allow us to create graphs easily. A data analyst or data scientist will often create visualizations on a Jupyter Notebook using Python. Luckily, there are dozens of great Python libraries that create great graphs. However, the million-dollar question is: which one is the best?

Whether you are a student or a professional, you should know a few options out there. There are not perfect libraries. Thus, you should know the pros and cons of each data visualization. I will go over four of the most popular Python libraries for data visualization: Matplotlib, Seaborn, Plotly Express, and Altair. To do so, I will create a simple bar plot and analyze how easy it is to use each library. For this blog, I will use a city dataset. You can find the notebook here.

Categories

This blog will analyze how easy it is to set up a barplot, how easy it is to customize the graph to make it minimally presentable, and the library documentation.

Setting up the dataset

First, let’s import all the important libraries. It’s very likely that you already have Matplotlib and Seaborn installed on your computer. However, you might not have Plotly Express and Altair. You can easily install them using pip install plotly==4.14.3 and pip install altair vega_datasets.

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import altair as alt
import plotly.express as px

Now we will import the dataset. For demonstration purposes, let’s only create a data frame with the top 15 more populated cities in the US. I will also fix the capitalization of the names of the cities. It will facilitate the editing process when we create the visualizations.

df = pd.read_csv('worldcitiespop.csv')

us = df[df['Country'] == 'us']
us['City'] = us['City'].str.title()
cities = us[['City', 'Population']].nlargest(15, ['Population'], keep=’first’)

Now we should be ready to analyze each library. Are you ready?

Category: Difficulty to Set Up and Initial Result

**Winner: **Plotly Express

**Losers: **Matplotlib, Altair, and Seaborn

In this category, all the libraries performed well. They are all easy to set up, and the results with basic editing are good enough for most of the analysis, but we need to have winners and losers, right?

Matplotlib is very easy to set up and remember the code. However, the chart doesn’t look good. It will probably do the job for data analysis, but its result for a business meeting is not great.

#data-analysis #python #data-visualization #data-science #libraries