# Python for MATLAB Users

Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.

Start Course for Free4 Hours15 Videos51 Exercises5,786 Learners4200 XP

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## Course Description

Python is a versatile programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. This course focuses on helping Matlab users learn to use Python specifically for data science. You will quickly learn how to migrate from Matlab to Python for data analysis and visualization. Learn the fundamentals of Python syntax, how to use numpy arrays to store and manipulate data. You will learn how to use matplotlib to discover trends, correlations, and patterns in real datasets, including bicycle traffic in the city of Seattle and avocado prices across the United States.

- 1
### From MATLAB to Python

**Free**This chapter gets you started moving from MATLAB to Python. You'll learn about some of the similarities and differences between MATLAB and Python, how to use methods and packages, and be introduced to the popular NumPy package.

Welcome to Python!50 xpBasic calculations100 xpForecasting an investment100 xpTypes of variables100 xpMethods and packages50 xpManipulating strings with methods100 xpUsing Python packages100 xpArrays & plotting50 xpGetting started with NumPy arrays100 xpPlotting bicycle traffic100 xpWhat predicts animal longevity?100 xp - 2
### NumPy and Matplotlib

In this chapter, you will build on your new NumPy knowledge. You will dive into NumPy arrays, the Python analog to matrices by performing mathematical operations and indexing. You will also begin to explore another important Python data structure, the list, and then round out the chapter by making customs plots of your arrays using Matplotlib.

Diving into NumPy arrays50 xpForecasting over time100 xpHow much more do organic avocados cost?100 xpIndexing NumPy arrays50 xpGetting columns from NumPy arrays50 xpBike traffic throughout the week100 xpFiltering arrays with Boolean indexing100 xpLists50 xpList exploration100 xpMaking NumPy arrays from lists100 xpOperating on lists and arrays100 xpCustomizing plots50 xpColors, linestyles, and legends100 xpEncoding data in color & size100 xpDetermine engine types in wildlife strikes100 xp - 3
### Dictionaries and pandas

This chapter introduces some powerful Python data structures: the dictionary and the pandas DataFrame. You will learn to create dictionaries by setting key-value pairs, and view then how to view and modify your dictionary. Then you will be introduced to one of the most important packages in the Pythonista's toolbox, pandas. Specifically, you'll focus on the pandas' structure, the DataFrame, which organizes tabular data in an easily accessible way. Lastly, you'll learn how to transform different data types into DataFrames to make your data easier to work with.

Dictionaries50 xpWhich keys?50 xpGet data out100 xpModifying dictionaries100 xpIntroduction to DataFrames50 xpExplore a pandas DataFrame100 xpPlot data from a DataFrame100 xpAccessing pandas DataFrames50 xpAccessing rows and columns100 xpThe many flavors of .iloc100 xpCreating pandas DataFrames50 xpFrom a CSV file100 xpFrom a dictionary of lists100 xpFrom a list of dictionaries100 xp - 4
### Control Flow and Loops

You'll finish the course by controlling your Python flow. You will learn how to iterate through different Python data structures using for loops. You will also learn about Python contingencies using if, else, and elif and the Python comparison operators (greater than, less than, etc.) that will decide which lines of your code will be executed. Lastly, you'll circle back to NumPy arrays by using Python comparison operators to filter your arrays.

Looping through data50 xpLooping through lists50 xpOverlaying multiple plots on a figure100 xpComparisons and control flow50 xpCounting different types of aircraft100 xpFinding certain rows in a DataFrame100 xpFiltering data50 xpBoolean indexing for quick stats100 xpBooleans for the win!100 xpBoolean indexing and Matplotlib fun100 xpWell done!50 xp

Datasets

FAA Wildlife StrikesFremont Bridge - hourly trafficFremont Bridge - daily trafficAnimal taxonomyHistorical avocado dataCollaborators

#### Justin Kiggins

Product Manager

While getting his Ph.D. in Neuroscience, Justin Kiggins abandoned MATLAB in favor of Python to run
experiments and analyze data. Since then, he's used Python to perform machine
learning on brain activity, automate the boring parts of scientific experiments,
create video games for mice, build web applications for storing scientific
data, and scrape avocado prices from the internet to figure out where Millenials
can best afford a house. You can find him on Twitter at
@neuromusic or on
his blog at justinkiggins.com.

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