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Python Data Analysis with NumPy and Pandas

Duration: 2 Days
Course Price: $1,895

Overview

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.

Audience

Students who have basic Python programming experience.

Overview

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.

Audience

Students who have basic Python programming experience.

Pre-requisites

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

NumPy

Efficiency NumPy Arrays Getting Basic Information about an Array np.arange() Similar to Lists Different from Lists Universal Functions

Exercise 1: Multiplying Array Elements

Multi-dimensional Arrays

Exercise 2: 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

Exercise 3: Rolling Doubles Using

Boolean Arrays to Get New Arrays

More with NumPy Arrays 2. pandas

Series

Other Ways of Creating Series

np.nan

Accessing Elements from a Series

Exercise 4: Retrieving Data from a Series

Series Alignment

Exercise 5: 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

 

Exercise 6: 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

 

Exercise 7: Series and DataFrames

Plotting with matplotlib

Inline Plots in IPython Notebook

Line Plot

Bar Plot

Annotation

 

Exercise 8: Plotting a DataFrame Other Kinds of Plots

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