This is the code repository for Learning Python Data Visualization [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
This video course is a beginner’s introduction to data visualization, and the techniques and libraries which can be leveraged with the Python language to achieve this. The end goal is to teach analysts and data scientists how they can visually represent complex sets of data using Python. The video course introduces visualization concepts so viewers can analyze large and small sets of data using libraries such as Matplotlib, IPython, and so on. This course primarily employs the IPython environment and matplotlib, with the following structure: •Introduce key data visualization libraries (matplotlib and so on.) and cover data importing/exporting (CSV, Excel, JSON and so on). •Introduce real-world data sets (to be visualized in the video). •Visualization types/techniques (bar chart, histogram, scatter plot, geospatial, and so on); demonstrate how to customize visualizations. •Introduce intermediate topics to create more advanced visualizations and using complex techniques, such as real-time data visualization. By the end of the course, you will be able to demonstrate visualizations with interesting, real-world data sets.
The code bundle for this video course is available at- https://github.com/PacktPublishing/Learning-Python-Data-Visualization
- Why Python is used for data analysis and using important packages
- Data analysis using Pandas
- Using different plots and how to apply these plots to different datasets
- Improving the visuals and making them look aesthetic
- Working with the Geographical data and Seaborn Packages
- Applying data visualization on large datasets
To fully benefit from the coverage included in this course, you will need:
To fully benefit from the coverage included in this course, you will need:
Basic knowledge of python
Rudimentary algebra skills
This course has the following software requirements:
This course has the following software requirements:
Python 2.7+ or Python 3.4+
Numpy 1.11+
Matplotlib 1.5+
Pandas 0.18+
Seaborn 0.8+
Statsmodels 0.5+
Basemap 1.0+
This course has been tested on the following system configuration:
OS:Debian Linux 9.4
Processor:AMD Phenom II X6 1055T 2.8GHz 6-core
Memory:16GB
Hard Disk Space:4TB