Open Source Data Visualization Tools: Top Picks for 2023

Name

Sebastian Brandt

Published on 6/20/2023

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In the modern era, data is the new gold. Companies across the globe are leveraging vast amounts of data to boost sales, enter new markets, and survive in highly demanding, competitive industries. However, the sheer volume of data available today can be overwhelming. This is where open source data visualization tools come into play, converting complex data into simple, straightforward visual forms that make it easy for anyone to understand and interpret.

Data visualization tools enable us to see the invisible patterns and precious insights that raw data conceals. They transform data into comprehensible visuals like graphs, pie charts, bar charts, histograms, infographics, scatter plots, and maps. In this article, we will explore some of the best open source data visualization tools available today, including Tableau, Plotly, Datawrapper, and a notable option in the field, PyGWalker.

Part 1: The Power of Data Visualization

Data visualization is more than just a pretty interface for your data. It's a powerful tool that can reveal hidden patterns, trends, and insights that might otherwise go unnoticed. Organizing data into more understandable formats can help business analysts make better decisions in shorter time spans, potentially boosting a company's revenue.

Moreover, data visualization is a cost-effective method to understand data compared to other more sophisticated and complex methods. It saves time and money by providing a quick and easy way to draw simple conclusions from complex data. Furthermore, it increases engagement by presenting data in an easy-to-interpret graph that catches attention.

Part 2: Common Types of Data Visualization

There are many types of data visualization, each suited to different kinds of data and different analytical needs. Here are a few of the most common ones:

1. Pie Charts: These are circular statistical graphics divided into slices to illustrate numerical proportions. They are useful for visualizing variables and their share in a category.
2. Bar Charts: These present categorical data with rectangular bars of various heights or lengths proportional to the values that they represent. They are useful for visual quantitative comparisons between multiple variables.
3. Histograms: These are graphical representations that organize a group of data points into user-specified ranges. They are useful for identifying percentiles, averages, and range outliers.
4. Scatter Plots: These use an XY plane to display values for two variables for a set of data. They are useful for visualizing strong or weak variance and displaying positive and negative correlations.
5. Tree Maps: These display hierarchical data using nested figures, usually rectangles. They are a more sophisticated way of a Pie Chart for comparing more variables.
6. Heat Maps: These use a 2D table and correlation percentages between the X and Y axis variables to show the magnitude of a phenomenon as color in two dimensions. They are useful for visualizing variance across multiple variables to display patterns in correlations.

Part 3: Highlighting Open Source Data Visualization Tools

There are numerous open source data visualization tools available today, each with its own unique features and benefits. Let's take a closer look at some of the top ones:

RawGraphs

RawGraphs is one of the best open-source data visualization tools out there. It's easy to use and efficient, offering over 30 visualization models. It provides great data security from outside intruders and allows users to save and

share their work with others.

Plotly

Plotly is a popular open-source data visualization tool that allows users to create interactive and visually appealing graphs. It supports over 40 unique chart types, including 3D charts, statistical graphs, and SVG maps. Plotly integrates well with many programming languages, including Python, R, and Julia.

Tableau Public

Tableau Public is a free software that allows anyone to connect to a spreadsheet or file and create interactive data visualizations for the web. It's a great tool for storytelling and is used by journalists, data geeks, and many others who need to visualize and share data on the web.

PyGWalker

PyGWalker is a Python library that simplifies the data analysis and visualization workflow in Jupyter Notebook. It turns your pandas dataframe into a Tableau-style user interface for visual exploration. Pronounced like "Pig Walker", it integrates Jupyter Notebook with Graphic Walker, an open-source alternative to Tableau, allowing data scientists to analyze data and visualize patterns with simple drag-and-drop operations.

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Part 4: A Closer Look at PyGWalker

PyGWalker is a notable option in the field of open-source data visualization tools. It's a Python library that integrates Jupyter Notebook with Graphic Walker, a different type of open-source alternative to Tableau. It allows data scientists to analyze data and visualize patterns with simple drag-and-drop operations.

Setting Up PyGWalker

Before using PyGWalker, make sure to install the packages through the command line using pip or conda. Here's how you can do it:

## Using pip
pip install pygwalker
 
## Using conda
conda install -c conda-forge pygwalker

Using PyGWalker in Jupyter Notebook

You can use PyGWalker without breaking your existing workflow. For example, you can call up Graphic Walker with the dataframe loaded in this way:

import pandas as pd
import pygwalker as pyg
 
## Load your dataframe
df = pd.read_csv('bikesharing_dc.csv', parse_dates='date')
 
## Call up Graphic Walker
gwalker = pyg.walk(df)

You can use PyGWalker with polars since pygwalker>=0.14.7a0:

import polars as pl
df = pl.read_csv('bikesharing_dc.csv', try_parse_dates = True)
gwalker = pyg.walk(df)

You can even try it online by visiting Google Colab or Kaggle Code.

Cool Things You Can Do with PyGWalker

With PyGWalker, you can change the mark type into others to make different charts. For example, a line chart to compare different measures. You can create a concat view by adding more than one measure into rows/columns to make a facet view of several subviews divided by the value in dimension. You can view the data frame in a table and configure the analytic types and semantic types. You can save the data exploration result to a local file.

For more detailed instructions, visit the Graphic Walker GitHub page (opens in a new tab).

Part 5: Step-by-Step Guide to Using PyGWalker

Let's delve into a step-by-step guide on how to use PyGWalker with sample codes. This guide will help you get started with this powerful open-source data visualization tool.

Step 1: Installation

First, you need to install PyGWalker. You can do this using pip or conda. Here's how:

## Using pip
pip install pygwalker
 
## Using conda
conda install -c conda-forge pygwalker

Step 2: Importing Libraries

Next, import the necessary libraries into your Jupyter Notebook. You will need pandas for data manipulation and PyGWalker for data visualization.

import pandas as pd
import pygwalker as pyg

Step 3: Loading Data

Load your data into a pandas dataframe. You can do this using pandas' read_csv function. Here's an example:

df = pd.read_csv('bikesharing_dc.csv', parse_dates='date')

Step 4: Visualizing Data with PyGWalker

Now, you can visualize your data using PyGWalker. Simply pass your dataframe to the walk function of PyGWalker.

gwalker = pyg.walk(df)

Step 5: Exploring Data

With PyGWalker, you can explore your data in a Tableau-style user interface. You can change the mark type to create different charts, add more than one measure to create a concat view, and save your data exploration results to a local file.

Part 6: Benefits of Using Open Source Data Visualization Tools

Open source data visualization tools like PyGWalker, Tableau, and Plotly offer numerous benefits. They are free to use, which makes them accessible to everyone, from students to small businesses. They are also highly customizable, allowing users to tailor their visualizations to their specific needs.

Moreover, open source tools often have a supportive and active community of users who can help troubleshoot issues, provide advice, and share new features. They also offer transparency, as users can see the source code and understand exactly how the tool works.

Conclusion

In conclusion, open source data visualization tools like PyGWalker, Tableau, and Plotly are powerful resources for anyone who needs to analyze and visualize data. They offer a range of features that can help you make sense of complex data and uncover valuable insights. Whether you're a data scientist, a business analyst, or just a curious individual, these tools can help you make the most of your data.

FAQs

Here are some frequently asked questions about open source data visualization tools:

1. What is the best open source data visualization tool? There are many excellent open source data visualization tools available, and the best one depends on your specific needs. However, PyGWalker, Tableau, and Plotly are all popular choices due to their powerful features and ease of use.

2. Which open source data visualization tool is free? Most open source data visualization tools are free to use. This includes PyGWalker, Tableau Public, and Plotly.

3. Can you use Python for open source data visualization? Yes, many open source data visualization tools, including PyGWalker and Plotly, offer Python libraries that you can use to create interactive and visually appealing graphs.