Introduction to Data Visualization with the Most Popular Python Tools and Frameworks
2021-01-16 15:30:12 |
- Linux Ubuntu 20.04
- Windows 10
- macOS Catalina
Data visualization is the discipline of using graphical elements to expose correlations, trends, and patterns, from a given data set. Graphical elements include lines, points, bars, slices, and series, which are much easier to visually digest than rows upon rows of raw data.
If you're a beginner to data science, looking to learn without a real world objective in mind, this tutorial is a good place to start. We provide you with an overview of data collection, and recommend some of the more popular Python frameworks for data processing and visualization.
This tutorial requires a basic understanding of Python data types, conditionals, functions, loops, modules, and virtual environments. We cover these topics, in depth, in our free, introductory Python tutorial series. Check those out if you want a Python from zero, crash course. But if you're looking to create charts as soon as possible, read on.
What Are Some Practical Use Cases of Data Visualization?
Once you have a solid grasp of data analysis, Python, and Python data science/visualization frameworks, you'll be well on your path to data-driven decision-making. Data visualization's key strength is to make it easy to understand massive data points, through the use of digestible visual representations. And the applications are widespread, both in the workplace and out:
- Track visitor behavior, in order to increase customer conversion rates
- Understand customer needs, in order to provide a better service and generate more sales
- Measure employee performance, tweak management practices, and boost morale and productivity
- Gauge your health, manage caloric intake, design a suitable exercise regimen, and live a healthier lifestyle
- Audit time spent on various activities, and adjust commitments to reach your goals faster
While data analysis can be a process of discovery, making conclusions and taking action is where true value lies. This is where research can translate into something tangible and make real world impact. Being able to understand and communicate that impact makes you a valuable asset to both yourself and the companies that will want to hire you.
How to Gather and Process Data Ethically
Data should not be visualized without understanding how to read, process and make conclusions from it, first. It's so easy for findings to become compromised—both intentionally and by accident or through negligence. Additionally, you have to verify that the data in front of you was gathered ethically and appropriately. For example, the way a poll question is phrased can have a huge influence on the poll taker's answer.
How to Write Unbiased Poll Questions
Learning how to gather data is not really the focus of this tutorial, but we need to go over it briefly, so you know how to identify if the data in front of you has been compromised. Here are a few examples of biased poll questions:
- How much did you enjoy this video? Positive bias; influences the respondent to answer more favorably by implying they enjoyed it.
- Should responsible, ethical citizens consume meat and animal products? Puts the respondent on the defensive by implying that meat eaters are unethical.
- How unhappy are you with the current administration's immigration policy? Negative bias.
- A recent poll indicated that the president's approval rating is 30%. Do you agree or disagree with his policy on ____? Question leads with a statistic that shows the president is unpopular with the majority, suggesting that the poll taker should be more critical.
Instead, questions should be phrased as objectively as possible, with leading words and phrases removed:
- On a scale of 1-10, with 1 being the worst and 10 being the best, please rate this video.
- Do you think people should consume meat and/or animal products?
- Please rate your agreement level with this governmental immigration policy.
- Please indicate whether you agree or disagree with this policy on ____.
This is just one example of how data collection can be compromised. We won't go over the others, because the focus of this tutorial is to show you how to visualize the data you already have in front of you.
Where to Find Datasets for Getting Started with Data Analysis and Visualization
Built in datasets are included with the Seaborn library. Kaggle is also a great resource for data science and machine learning. You also have the option to generate datasets, yourself, by populating linear arrays, multidimensional arrays, and CSV files, databases, and other storage formats.
What are Some Popular Data Visualization Frameworks?
- Line, Bar graphs
- Scatter plot and Pie charts
- Reading CSV for plotting
- Saving plot figures
- Much more
- Flexible customization of graphics
- API abstraction across visualization
- Visualizing univariate and bivariate distributions
- Distribution plots, heat maps, matrix plots, grids and regression plots
- Plotting statistical time series data
- Can also be access from a Python Notebook and through the API
- Contour plots
- 3D Charts
- Scatter plots
- Box plots
- Line charts, bar charts, and histograms
- Error bars
- Multiple axes and subplots
- Python implementation of the Grammar of Graphics of R
- Declarative graphics creation
- Tightly integrated with Pandas
- Minimal customization
- Declarative statistical visualization library
- Built on top of the Vega-lite visualization grammar
- Simple API for easily producing graphical charts
Pandas is used for working with tabular data, such as CSV, Json, and SQL database formats.
- Creating DataFrames
- Manipulating DataFrame
- Removing Rows and Columns
- Time Series
- Plotting with Pandas
Numpy is popular for its multidimensional arrays. Numpy will be used in conjunction with Matplotlib and Pandas to generate data through various numpy methods.
In this tutorial, we introduced you to data collection and data visualization. We also recommended some of the most popular Python tools, frameworks, and libraries for data visualization.