Instructor-led training

Python Data Analysis with NumPy and Pandas Training in Saratoga Springs, New York

This is a rapid introduction to NumPy, pandas and matplotlib for experienced Python programmers who are new to those libraries.

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What you'll learn

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.

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

Training locations

Python Data Analysis with NumPy and Pandas training across the U.S. and beyond.

We provide Python Data Analysis with NumPy and Pandas training onsite and live online for organizations throughout the United States and internationally.

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