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MatPlotLib with Python
Rating: 4.0 out of 5(203 ratings)
13,721 students

MatPlotLib with Python

Data Visualization using Matplotlib library with Python
Created byDATAhill Solutions Srinivas Reddy
Last updated 1/2020
English

What you'll learn

  • Visualize multiple forms of both 2D and 3D graphs, like line graphs, scatter plots, bar charts, and more
  • Create live graphs
  • Multiple Plots in a Graph
  • PyPlot, Bar, Pie, Histogram, Scatter & 3D Plot
  • Customize graphs, modifying colors, lines, fonts, and more

Course content

1 section • 11 lectures • 2h 33m total length

  • Session 1. Matplotlib Introduction1:07

    Explore Matplotlib, the open source Python library for graphical representations and data visualizations; learn its core plotting functions and how to create multiple plots on a single graph.

  • Matplotlib Introduction31:23

    Explore Matplotlib with Python: import the plotting library, create line plots, bar charts, histograms, and pies, and customize axis labels, titles, colors, and figure size.

  • Session 2. PyPlot, Bar, Pie, Histogram, Scatter & 3D Plot0:57

    Explore matplotlib's PyPlot in Python, learning how to create bar, pie, histogram, scatter, and 3d plots while visualizing data distributions and normal distribution patterns.

  • PyPlot, Bar, Pie, Histogram, Scatter & 3D Plot32:58

    Create and customize bar, stacked bar, pie, histogram, scatter, and 3d plots using matplotlib in Python, with practical examples like monthly sales in Hyderabad and Bangalore.

  • Session 3. Multiple Plots in a Graph0:46

    Learn to create and organize multiple plots on a single graph using the subplot function in matplotlib, enabling effective data visualization with several subplots on one figure.

  • Multiple Plots in a Graph14:38

    Learn how to create multiple plots in one graph using subplot, specify rows, columns, and positions, and customize titles and labels for clear graphical representations.

  • Session 4. Data Visualization using Pandas0:45

    Explore data visualization with Pandas using Matplotlib-style functions to create bar charts and box plots, while examining available visualization libraries and functions.

  • Data Visualization using Pandas19:33

    Explore data visualization with pandas and matplotlib by analyzing a used cars dataset, mastering box plots, scatter plots, and distributions while loading and preparing data in a Python data frame.

  • What is Data Science25:06

    Explain what data science is, contrast traditional programming with machine learning, cover supervised and reinforcement learning, statistics’ role, and how Python programming supports real-time projects.

  • What is Machine Learning25:06

    Explore the foundations of machine learning, compare with traditional programming, and learn data collection, statistics, linear regression, supervised learning, and reinforcement learning through practical Python projects.

  • Summary1:16

    Explore macroeconomics concepts with graphing functions and data in Python, linking inputs, models, and money flow while discussing regulators and data visualization.

Requirements

  • Students should be comfortable with the basics of the Python 3 programming language
  • Python 3 should be already installed, and students should already know how open IDLE or their own favorite editor to write programs in.

Description

More and more people are realizing the vast benefits and uses of analyzing big data. However, the majority of people lack the skills and the time needed to understand this data in its original form. That's where data visualization comes in; creating easy to read, simple to understand graphs, charts and other visual representations of data. Python 3 and Matplotlib are the most easily accessible and efficient to use programs to do just this.

Learn Big Data Python

Visualize multiple forms of 2D and 3D graphs; line graphs, scatter plots, bar charts, etc.

Load and organised data from various sources for visualization

Create and customize live graphs

Add finesse and style to make your graphs visually appealing

Python Data Visualization made Easy

With over 58 lectures and 6 hours of content, this course covers almost every major chart that Matplotlib is capable of providing. Intended for students who already have a basic understanding of Python, you'll take a step-by-step approach to create line graphs, scatter plots, stack plots, pie charts, bar charts, 3D lines, 3D wire frames, 3D bar charts, 3D scatter plots, geographic maps, live updating graphs, and virtually anything else you can think of!

Starting with basic functions like labels, titles, window buttons and legends, you'll then move onto each of the most popular types of graph, covering how to import data from both a CSV and NumPy. You'll then move on to more advanced features like customized spines, styles, annotations, averages and indicators, geographical plotting with Basemap and advanced wire frames.

This course has been specially designed for students who want to learn a variety of ways to visually display python data. On completion of this course, you will not only have gained a deep understanding of the options available for visualizing data, but you'll have the know-how to create well presented, visually appealing graphs too.

Tools Used

Python 3: Python is a general purpose programming language which a focus on readability and concise code, making it a great language for new coders to learn. Learning Python gives a solid foundation for learning more advanced coding languages, and allows for a wide variety of applications.

Matplotlib: Matplotlib is a plotting library that works with the Python programming language and its numerical mathematics extension 'NumPy'. It allows the user to embed plots into applications using various general purpose tool kits (essentially, it's what turns the data into the graph).

IDLE: IDLE is an Integrated Development Environment for Python; i.e where you turn the data into the graph. Although you can use any other IDE to do so, we recommend the use of IDLE for this particular course.


1. Matplotlib Introduction

2. PyPlot, Bar, Pie, Histogram, Scatter & 3D Plot

3. Multiple Plots in a Graph

Who this course is for:

  • Beginner Python developers curious about Data Science

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