Python Data Analysis with NumPy and Pandas

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Description:
This is a rapid introduction to NumPy, pandas and matplotlib for experienced Python programmers who are new to those libraries. Participants 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
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 G
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
Python Data Analysis with
NumPy and Pandas
ITCourseware, LLC Page 2
Python Data Analysis with NumPy and Pandas v 1.0.1
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