## Python Data Analysis with NumPy and Pandas Training

We offer private customized training for groups of 3 or more attendees.

### Course Description

This is a rapid introduction to NumPy, pandas and matplotlib for experienced Python programmers who are new to those libraries. Students will learn to use NumPy to work with arrays and matrices of numbers; learn to work with pandas to analyze data; and learn to work with matplotlib from within pandas.
Course Length: 2 Days
Course Tuition: \$790 (US)

#### Prerequisites

: Basic Python programming experience. In particular working with strings; working with lists, tuples and dictionaries; loops and conditionals; and writing your own functions.

#### Course Outline

 Jupyter Notebook Getting Started with Jupyter Notebook Creating Your First Jupyter notebook More Experimenting with Jupyter Notebook Getting the Class Files Markdown Magic Commands Automagic Autosave Directory Commands Bookmarking Command History Last Three Inputs and Outputs Environment Variables Loading and Running Code from Files Shell Execution More Magic Commands Getting Help    NumPy Efficiency NumPy Arrays Getting Basic Information about an Array np.arange() Similar to Lists Different from Lists Universal Functions Multiplying Array Elements Multi-dimensional Arrays Retrieving Data from an Array Modifying Parts of an Array Adding a Row Vector to All Rows More Ways to Create Arrays Getting the Number of Rows and Columns in an Array Random Sampling Rolling Doubles Using Boolean Arrays to Get New Arrays More with NumPy Arrays    pandas Series Other Ways of Creating Series np.nan Accessing Elements from a Series Retrieving Data from a Series Series Alignment Using Boolean Series to Get New Series Comparing One Series with Another Element-wise Operations and the apply() Method Series: A More Practical Example DataFrame Creating a DataFrame from a NumPy Array Creating a DataFrame using Existing Series as Rows Creating a DataFrame using Existing Series as Columns Creating a DataFrame from a CSV Exploring a DataFrame Getting Columns Exploring a DataFrame Cleaning Data Getting Rows Combining Row and Column Selection Scalar Data: at[] and iat[] Boolean Selection Using a Boolean Series to Filter a DataFrame Series and DataFrames Plotting with matplotlib Inline Plots in Jupyter Notebook Line Plot Bar Plot Annotation Plotting a DataFrame Other Kinds of Plots

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## Python Programming Uses & Stats

Python Programming is Used For:
Web Development Video Games Desktop GUI's Software Development
 Difficulty Popularity Year Created 1991
 Pros Easy to Learn: The learning curve is very mild and the language is versatile and fast to develop.   Massive Libraries: You can find a library for basically anything: from web development, through game development, to machine learning.   Do More with Less Code: You can build prototypes and test out  ideas much quicker in Python than in other language Cons Speed Limitations: It is an interpretive language and therefore much slower than compiled languages. Problems with Threading: Multi-threaded CPU-bound programs may be slower than single-threaded ones do to the Global Interpreter Lock (GIL) that allows only one thread to execute at a time. Weak on Mobile: Although, there are a number or libraries that provide a way to develop for both Android and iOS using Python currently Android and iOS don’t support Python as an official programming language.
 Python Programming Job Market
 Average Salary \$107,000 Job Count 26,856 Top Job Locations New York City Mountain View San Francisco
 Complimentary Skills to have along with Python Programming The potential for career growth, whether you are new to the industry or plan to expand your current skills, depends upon your interests:   - For knowledge in building in PC or windows, phone apps or you are looking your future in Microsoft learn C#   - For android apps and also cross platform apps then learn Java   - If you are an Apple-holic and want to build iOS and MAC apps and then choose Objective C or Swift   - Interested in game development? C++   - Data mining or statistics then go with R programming or MATLAB   - Building an operating systems? C