Python Analysis with NumPy and Pandas New for 2017

#text-block-10 { margin-bottom:0px; text-align:left; }

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

#text-block-11 { margin-bottom:0px; text-align:left; }

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