Python Data Analysis

Python Data Analysis
Python Data Analysis
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 30m | 431 MB

Data science is transforming the way that government and industry leaders look at both specific problems and the world at large. Curious about how data analysis actually works in practice? In this course, instructor Michele Vallisneri shows you how, explaining what it takes to get started with data science using Python.

Michele demonstrates how to set up your analysis environment and provides a refresher on the basics of working with data structures in Python. Then, he jumps into the big stuff: the power of arrays, indexing, and tables in NumPy and pandas—two popular third-party packages designed specifically for data analysis. He also walks through two sample big-data projects: using NumPy to identify and visualize weather patterns and using pandas to analyze the popularity of baby names over the last century. Challenges issued along the way help you practice what you’ve learned.

Note: This version of the course was updated to reflect recent changes in Python 3, NumPy, and pandas.

Table of Contents

1 Get started in data analysis with Python
2 What you need to know
3 What’s new in this update
4 Install Anaconda Python on OS X
5 Install Anaconda Python on Windows
6 Working with Jupyter Notebooks
7 Using the exercise files
8 Using Python in the cloud
9 Warmup with Python loops
10 Sequences Lists, tuples, and the slicing syntax
11 Dictionaries and sets
12 Comprehensions
13 Advanced Python containers
14 Anagrams overview
15 Loading a dictionary
16 Finding anagrams
17 Challenge Palindromes
18 Solution Palindromes
19 NumPy overview
20 Creating NumPy arrays
21 Indexing NumPy arrays
22 Doing math with NumPy arrays
23 Special arrays Records and dates
24 Overview of use case
25 Loading station and temperature data
26 Filling missing values
27 Smoothing time series
28 Weather charts
29 Challenge Weather anomalies
30 Solution Weather anomalies
31 pandas overview
32 DataFrames and Series
33 Indexing in pandas
34 Plotting
35 Overview of use case
36 Loading data sets
37 Comparing name popularity
38 Yearly top ten names
39 Challenge Unisex baby names
40 Solution Unisex baby names
41 Next steps