Unpacking NumPy and Pandas
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Unpacking NumPy and Pandas

Get to grips with the most popular Python packages that make Data Analysis possible
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
14 students enrolled
Created by Packt Publishing
Last updated 7/2017
Current price: $10 Original price: $125 Discount: 92% off
5 hours left at this price!
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  • 2.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • See how to read, sort, and map various data into NumPy and Pandas
  • Find out how to create and slice data arrays using NumPy
  • Get to know how to subset your data frames using Pandas
  • Handle missing data in a Pandas DataFrame
  • Interface your Python data analysis with R’s language packages
View Curriculum
  • Should have knowledge of Python.

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning.

In this video course, you will explore two of the most important Python packages used by Data Analysts. You will start off by learning how to set up the right environment for data analysis with Python. Here, you’ll learn to install the right Python distribution, as well as work with the Jupyter notebook, and set up a database. After that you will dive into Python’s NumPy package, Python’s powerful extension with advanced mathematical functions. You will learn to create NumPy arrays, as well as employ different array methods and functions. Then, you will explore Python’s Pandas extension, where you will learn to subset your data, as well as dive into data mapping using Pandas. You’ll also learn to manage your data sets by sorting and ranking them. Finally, you will learn to index and group your data for sophisticated data analysis and manipulation.

About the Author

Curtis Miller is a graduate student at the University of Utah, seeking an Master’s in Statistics (MSTAT) and a Big Data Certificate.

In the past, Curtis has worked as a Math Tutor, and has a double major adding mathematics with an emphasis in statistics as a second major.

Curtis has studied the gender pay gap, and presented his paper or Gender Pay Disparity in Utah, which grabbed the attention of local media outlets.

He currently teaches Basic Statistics at the University of Utah. He enjoys writing and is an avid reader, and enjoys studying politics, economics, history, and psychology and sociology.

Who is the target audience?
  • If you are a Python developer and want to take your first steps into the world of data analysis, then this is the video course you have been waiting for!
Compare to Other NumPy Courses
Curriculum For This Course
19 Lectures
Let's Do This! Setting Up a Python Data Analysis Environment
6 Lectures 34:24

This video gives an overview of the entire course.

Preview 04:02

We prepare to analyze data with Python by installing Anaconda

Installing Anaconda

You'll learn to write literate Python code with Jupyter Notebooks

Exploring Jupyter Notebooks

You'll learn when and how to use alternative development environments to Jupyter

Exploring Alternatives to Jupyter

You'll learn how to manage packages and environments with conda.

Package Management with conda

We set up a MySQL database to store the data used in data analysis

Setting Up a Database
It's All in the Numbers – Diving into NumPy
5 Lectures 42:41

You'll learn about NumPydtypes, special values, ndarrays, and matrices.

Preview 09:02

How can we create an ndarray with our data? We see the NumPy functions for creating ndarrays.

Creating NumPy Arrays

We need to access data in an ndarray; here, we see how subsetting is done, so we get exactly the data we need.

Slicing Arrays in NumPy

How can we use ndarrays for computation? We see how arithmetic and linear algebra with NumPy arrays is done.

Arithmetic and Linear Algebra with Arrays

How can we use NumPy to perform calculations fast? With ufuncs and vectorization!

Employing Array Methods and Functions
Black, White, and Awesome – Working with Pandas
8 Lectures 01:01:02

What is pandas? We get an overview of the pandas functionality.

Preview 06:24

How is a Series or DataFrame structured? How can we create them? You'lllearn how Series and DataFrames work by creating some from scratch.

Exploring Series and DataFrame Objects

How do we work with a subset of data in a pandas object? Here,you'lllearn about and use slicing operations.

Subsetting Your Data

How can we perform computations using pandas objects? Here we use arithmetic, vectorized functions, and function mappings to get useful quantities.

Arithmetic, Function Application, Mapping with Pandas

What do we do with the missing data? We talk about strategies for handling missing data and how to implement them with pandas

Handling Missing Data in a Pandas DataFrame

How do we sort/rank data? We see how to use pandas objects for this task.

Managing Your Data by Sorting and Ranking

How do we use hierarchical indices? We talk about how to create hierarchical indices with pandas and how to use them for data subsetting.

Hierarchical Indexing

How can we visualize data in a pandas object? We apply pandas plotting convenience methods.

Plotting with Pandas
About the Instructor
Packt Publishing
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