Python Data Analysis with NumPy and pandas

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Overview

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

 

Goals
  1. Learn to use NumPy to work with arrays and matrices of numbers.
  2. Learn to work with pandas to analyze data.
  3. Learn to work with matplotlib from within pandas.

Outline
  1. NumPy
    1. One-dimensional Arrays
    2. Multi-dimensional Arrays
    3. Getting Basic Information about an Array
    4. NumPy Arrays Compared to Python Lists
    5. Universal Functions
    6. Modifying Parts of an Array
    7. Adding a Row Vector to All Rows
    8. Random Sampling
  2. pandas
    1. Series and DataFrames
    2. Accessing Elements from a Series
    3. Series Alignment
    4. Comparing One Series with Another
    5. Element-wise Operations
    6. Creating a DataFrame from NumPy Array
    7. Creating a DataFrame from Series
    8. Creating a DataFrame from a CSVl
    9. Getting Columns and Rows
    10. Cleaning Data
    11. Combining Row and Column Selection
    12. Scalar Data: at[] and iat[]
    13. Boolean Selection
    14. Plotting with matplotlib

Class Materials

Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.

 

Class Prerequisites

Experience in the following is required for this Python class:

  • Basic Python programming experience. In particular, you should be very comfortable with:
    1. Working with strings.
    2. Working with lists, tuples and dictionaries.
    3. Loops and conditionals.
    4. Writing your own functions.

Prerequisite Courses

Courses that can help you meet these prerequisites:

  • Introduction to Python Training
  • Advanced Python Training

Course Setup
  • Live Online: Course Setup | WebEx Setup